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UQO Working Paper No. 3 — Hedonic housing price models for the US: parametric, quantile, and machine-learning approaches.

researcheconomicshousinghedonicmachine-learninguqo

R03-ADVE-08/ECON-02/THEO-06/ECON-04/THEO-05/EDIT-07/ADVE-14/ECON-07/EDIT-09/DATA-03/DATA-04: wild bootstrap, state regressions with SEs, full-sample Moran, Conley HAC; table generator, run_all.sh, test_tables

- wild cluster bootstrap by state (B = 9,999): 23 of 63 slopes at 5% (Webb and Rademacher), 9 after Holm
- state-by-state OLS with ZIP3-cluster SEs: reversals with |t| > 2 are 0-12 per variable (v1.2: 19-23 by sign only);
  homogeneity rejected for every variable; EB shrinkage bands; fully interacted +6.1 pp over state FE (0.752 vs 0.690)
- Moran's I on the full random test set (157,769 listings, k = 8): OLS 0.454, LightGBM 0.288, XGBoost 0.234; distance
  bands 0-1 / 1-5 / 5-25 km; Conley HAC 25/50/100 km on 100,000 listings (between HC0 and state-cluster SEs)
- scripts/08_tables_figures_v2.py: every table as a v2_*.tex fragment + figures + inline_numbers.json (re-runnable as
  batches complete); run_all.sh entry point; tests/test_tables.py

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Simon-Pierre Boucher committed (Nov 4, 2026) parent 9a40131 Browse files

14 changed files +1,757 −5

14 changed files
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added results/inference_v2/conley_hac_v2.csv +65 −0
@@ -0,0 +1,65 @@
1 +variable,coef,se_hc0,se_cluster_state,se_conley_25km,se_conley_50km,se_conley_100km,ratio_conley_25km_hc0,ratio_conley_50km_hc0,ratio_conley_100km_hc0,n_subsample
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23 +walk_score,0.03553020379151019,0.0033261028048731903,0.019824603671204085,0.010286897974870033,0.01193256173931206,0.013876547531199282,3.0927781185230767,3.5875504875643784,4.172014019190346,100000
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37 +waterfront_x_sqft,0.1593293970120028,0.02594294564445247,0.13641162450125768,0.06146239691275596,0.07791462477095804,0.09172684726996731,2.369137173360991,3.0033067886267175,3.535714429929502,100000
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44 +roof_cat_Slate,-0.02232043735111347,0.043014074277292,0.07339047290927486,0.059269805100144554,0.06251483643362765,0.06527590234714242,1.3779165562894442,1.453357708703044,1.5175475340080233,100000
45 +roof_cat_Tile,-0.11295003899086262,0.021649566983994964,0.06681695606800384,0.04726410213495812,0.052131456101818456,0.05708587505101884,2.1831430702470587,2.4079676115627655,2.6368137105569427,100000
46 +roof_cat_Unknown,-0.028433893099632256,0.020986959209821267,0.06329275798854847,0.04820616888032913,0.052904032646916295,0.05738663162130595,2.2969582395609787,2.5208050445992574,2.734394775706751,100000
47 +construction_cat_Concrete_Block,0.24025394880443154,0.007550375031049879,0.03374165139770564,0.029774711127595697,0.0353702265974672,0.03797403922369544,3.943474463871171,4.6845655284687195,5.029424242839915,100000
48 +construction_cat_Other,0.06088133134873974,0.006236167461938579,0.021195961723470384,0.011455330706844756,0.01334663853811446,0.015163438477187482,1.8369183920669994,2.1401988672647856,2.4315316369765614,100000
49 +construction_cat_Steel,0.008872256139906005,0.034256987341975166,0.06805856724148182,0.03963800119943032,0.04557966931713107,0.05187949489740031,1.1570778481989215,1.3305218249966246,1.514420821057789,100000
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51 +construction_cat_Stucco,0.14643801215339236,0.0057170377717112066,0.03434834272603048,0.01943037278430119,0.023878241353342326,0.026901360921212262,3.398678399580584,4.17668070543378,4.7054719586294125,100000
52 +construction_cat_Unknown,0.16799770707092596,0.005586491297030024,0.04415191211088221,0.02239735002953441,0.026051748746402276,0.030976829295698668,4.009198052710049,4.663347235545199,5.544952573750011,100000
53 +construction_cat_Vinyl,-0.0024026322040024764,0.00496918044420754,0.021770501702420772,0.010368342269651836,0.012549075128485741,0.01429702453527599,2.0865296372438995,2.5253812513719263,2.87713933832725,100000
54 +construction_cat_Wood_Frame,0.09221084522310363,0.005903254106019099,0.032824492683663165,0.0125105272822139,0.01568703209644707,0.019374308353848013,2.1192594893480643,2.657353353712522,3.281970927542066,100000
55 +foundation_cat_Concrete,0.2338555361232453,0.013727305299610712,0.043390269925857115,0.04958707693416076,0.050366355257827164,0.05087778094125139,3.6122950463968326,3.6690635313003117,3.7063196185120333,100000
56 +foundation_cat_Crawlspace,0.20636849543521582,0.015414820255956868,0.058891892471766176,0.04990660572549414,0.05192628400654458,0.05512862712046186,3.237572991239281,3.368594842127874,3.5763392764283504,100000
57 +foundation_cat_Other,0.16337812342893462,0.015665096731056236,0.0521727670941258,0.05175509650983535,0.052971446590548,0.054532450617959284,3.3038478726550258,3.3814950204253442,3.481143561012813,100000
58 +foundation_cat_Pier_Raised,0.1638417310386443,0.01741501717223176,0.07811579768050336,0.053350392461705615,0.056529314704402346,0.06134052182522965,3.0634705630249273,3.2460097021631604,3.5222774240519894,100000
59 +foundation_cat_Slab,0.1526302610477908,0.013847205243042126,0.05554276940770208,0.04893005404136667,0.049393347224732646,0.04993600975263094,3.5335689175223854,3.5670264401946064,3.6062157580658765,100000
60 +foundation_cat_Stone,0.08492479362184394,0.025794497599343266,0.12965112613743912,0.06887741650175377,0.07871301545401546,0.09024853560403209,2.6702367912568845,3.0515428785098537,3.4987514393895327,100000
61 +foundation_cat_Unknown,0.18822981975345465,0.013454333636056708,0.04577356516678779,0.04852576779474859,0.04813211250848438,0.04756553833733611,3.6067016849279554,3.57744306113337,3.5353321557199733,100000
62 +region_Northeast,0.48465973439973176,0.00835830246465441,0.13205368678178436,0.061016994520736445,0.08347549537613033,0.10734518623416366,7.300165886406374,9.987135034791036,12.84294109816018,100000
63 +region_South,0.040196392822944474,0.0074882008495405685,0.0622092834069843,0.033093421737556084,0.0385220622468545,0.04284280625387711,4.419408934468751,5.14436818948546,5.721375149346547,100000
64 +region_West,0.4312262839553938,0.008530438708971662,0.1052655842822089,0.041742811725149466,0.04885944487968264,0.05611951568413172,4.893395656339172,5.727659097801854,6.5787373426772895,100000
65 +lot_missing,-0.028866204924600635,0.006630702159101588,0.021241261081175418,0.025512536897093922,0.031009720603273474,0.03502620374788934,3.847637291636499,4.6766873038790004,5.28242754801026,100000
added results/inference_v2/moran_full_test_v2.csv +13 −0
@@ -0,0 +1,13 @@
1 +predictions,model,weights,n,I,E_I,z_norm,p_norm,z_sim,p_sim,median_kth_neighbour_km,z_rand,p_rand,n_pairs,share_units_with_neighbour
2 +"v1.2 stored predictions, random test set",OLS,kNN k=8 row-standardised,157769,0.4541218513664936,-6.338420972567314e-06,386.65220389544635,0.0,377.9496437667137,0.001,2.5998797386270005,,,,
3 +"v1.2 stored predictions, random test set",OLS,distance band 0-1 km (binary),157769,0.6243843755302273,-6.338420972567314e-06,,,,,,529.8203530502585,0.0,359634.0,0.6956879995436366
4 +"v1.2 stored predictions, random test set",OLS,distance band 1-5 km (binary),157769,0.43814790573406154,-6.338420972567314e-06,,,,,,1128.255352641789,0.0,3302194.0,0.93182437614487
5 +"v1.2 stored predictions, random test set",OLS,distance band 5-25 km (binary),157769,0.20893840241733697,-6.338420972567314e-06,,,,,,1760.9676666607438,0.0,34612290.0,0.9948912650774233
6 +"v1.2 stored predictions, random test set",LightGBM,kNN k=8 row-standardised,157769,0.28805394363830744,-6.338420972567314e-06,245.25925810747492,0.0,240.73424748809944,0.001,2.5998797386270005,,,,
7 +"v1.2 stored predictions, random test set",LightGBM,distance band 0-1 km (binary),157769,0.24718727381504385,-6.338420972567314e-06,,,,,,209.7546285695466,0.0,359634.0,0.6956879995436366
8 +"v1.2 stored predictions, random test set",LightGBM,distance band 1-5 km (binary),157769,0.13491629237534583,-6.338420972567314e-06,,,,,,347.42997269059254,0.0,3302194.0,0.93182437614487
9 +"v1.2 stored predictions, random test set",LightGBM,distance band 5-25 km (binary),157769,0.04203864926023024,-6.338420972567314e-06,,,,,,354.35309730037466,0.0,34612290.0,0.9948912650774233
10 +"v1.2 stored predictions, random test set",XGBoost,kNN k=8 row-standardised,157769,0.2343401185546652,-6.338420972567314e-06,199.52642470207772,0.0,197.20041341964327,0.001,2.5998797386270005,,,,
11 +"v1.2 stored predictions, random test set",XGBoost,distance band 0-1 km (binary),157769,0.18733064572880956,-6.338420972567314e-06,,,,,,158.96389211321224,0.0,359634.0,0.6956879995436366
12 +"v1.2 stored predictions, random test set",XGBoost,distance band 1-5 km (binary),157769,0.10395267320046364,-6.338420972567314e-06,,,,,,267.69809198305506,0.0,3302194.0,0.93182437614487
13 +"v1.2 stored predictions, random test set",XGBoost,distance band 5-25 km (binary),157769,0.026869475755575824,-6.338420972567314e-06,,,,,,226.50838032185084,0.0,34612290.0,0.9948912650774233
added results/inference_v2/pooled_vs_interacted_v2.csv +4 −0
@@ -0,0 +1,4 @@
1 +model,r2_test,n_train,n_test
2 +"Pooled OLS, no geographic controls",0.6187823699570969,627102,156770
3 +Pooled OLS + 50 state fixed effects,0.6903480569402134,627102,156770
4 +State-by-state OLS (fully interacted),0.7517814970041719,627102,156770
modified results/inference_v2/run_meta.json +5 −5
@@ -1,10 +1,10 @@
1 1 {
2 2 "steps": [
3 − "assert",
4 − "cluster",
5 − "lot",
6 − "paired"
3 + "states",
4 + "wild",
5 + "moran",
6 + "conley"
7 7 ],
8 8 "B": 9999,
9 − "elapsed_s": 92
9 + "elapsed_s": 279
10 10 }
\ No newline at end of file
added results/inference_v2/state_heterogeneity_eb.csv +659 −0
@@ -0,0 +1,659 @@
1 +variable,state,n,estimate,se_zip3,ci_low,ci_high,eb_posterior_mean,eb_posterior_sd,eb_low,eb_high,shrinkage_factor
2 +ln_sqft,AK,808,0.6128892532739731,0.07366483367094336,0.4685061792789242,0.757272327269022,0.5923697190710822,0.0634554695946278,0.4679969986656117,0.7167424394765527,0.7420235682714679
3 +ln_sqft,AL,16919,0.5647031995420891,0.0576998723168915,0.4516114498009818,0.6777949492831965,0.559191059290656,0.05238303858010711,0.4565203036736461,0.6618618149076659,0.8241982149731298
4 +ln_sqft,AR,10665,0.7155602813307544,0.07106782129486236,0.5762673515928243,0.8548532110686846,0.6710139782557721,0.0617727791871202,0.5499393310490166,0.7920886254625277,0.7555240241210618
5 +ln_sqft,AZ,23871,0.5942589704743239,0.06865956441940556,0.459686224212289,0.7288317167363587,0.5801299044737577,0.060171557712934004,0.462193651356407,0.6980661575911083,0.7680339561180679
6 +ln_sqft,CA,47650,0.7448242148563359,0.07217468083280086,0.6033618404240462,0.8862865892886256,0.6919067185421541,0.06249551624492473,0.5694155067021016,0.8143979303822066,0.7497698556740343
7 +ln_sqft,CO,17480,0.5762530399071037,0.05282381756104273,0.47271835748746,0.6797877223267474,0.5697461947252456,0.048653571825756844,0.47438519394676215,0.665107195503729,0.8483398908400682
8 +ln_sqft,CT,4127,0.4044387491231327,0.08167377485713431,0.24435815040314945,0.564519347843116,0.4430360387323101,0.06836186109827827,0.3090467909796847,0.5770252864849356,0.7005876832666735
9 +ln_sqft,DC,1409,0.5349576528944047,0.0012717007804376288,0.5324651193647469,0.5374501864240624,0.5349574862258356,0.0012716349035644674,0.5324650818148493,0.5374498906368219,0.9998963983227943
10 +ln_sqft,DE,1998,0.060589686961530664,0.02994294704623999,0.0019015107509002885,0.11927786317216105,0.08627083097864222,0.029118314016684552,0.029198935505940493,0.14334272645134394,0.9456781743818854
11 +ln_sqft,FL,132186,0.7949327456520999,0.07617486782122034,0.6456300047225081,0.9442354865816918,0.7240407220720497,0.06503876420269766,0.5965647442347624,0.8515166999093371,0.7289892813015573
12 +ln_sqft,GA,31988,0.5242389158953831,0.06227070844187473,0.4021883273493086,0.6462895044414576,0.5260517676015074,0.055731555314614774,0.41681791918486244,0.6352856160181524,0.8010040521474813
13 +ln_sqft,HI,5013,1.1279733912828664,0.1526439599601015,0.8287912297610676,1.4271555528046653,0.7718858428870553,0.09667986768015528,0.5823933022339509,0.9613783835401597,0.4011555881306957
14 +ln_sqft,IA,8265,0.2849193578859279,0.0809347285262353,0.12628728997450672,0.44355142579734913,0.3583583078033926,0.06792672069287359,0.22522193524536038,0.49149468036142485,0.7043872212543858
15 +ln_sqft,ID,4532,0.48302129387943316,0.06608445173281441,0.3534957684831169,0.6125468192757494,0.4940241722614869,0.0584156250040297,0.37952954725358873,0.6085187972693852,0.7813749297873461
16 +ln_sqft,IL,19170,0.4923677792731822,0.05510602841426769,0.38435996358121755,0.6003755949651469,0.49904227216597224,0.05041923142484447,0.4002205785732771,0.5978639657586674,0.8371325304418792
17 +ln_sqft,IN,14338,0.4385936201838908,0.04649609591709522,0.3474612721863842,0.5297259681813975,0.45012130924023025,0.04357610850516543,0.364712136570106,0.5355304819103545,0.8783425262648713
18 +ln_sqft,KS,5974,0.2043110221222031,0.06044095641005884,0.08584674755848777,0.3227752966859184,0.2667158928476681,0.0544083226084682,0.16007558053507043,0.3733562051602658,0.8103413824956024
19 +ln_sqft,KY,10637,0.41878421589437453,0.031150341612279062,0.3577295463343076,0.4798388854544415,0.42548962190488165,0.03022499157205343,0.36624863842365696,0.48473060538610635,0.9414705715112125
20 +ln_sqft,LA,14934,0.7263386895853452,0.08144027836051998,0.566715743998726,0.8859616351719644,0.6687867069373846,0.0682247629316356,0.5350661715913789,0.8025072422833903,0.7017874083103027
21 +ln_sqft,MA,5301,0.5727563565794371,0.1005378684192963,0.37570213447761636,0.7698105786812579,0.5572671214457714,0.07832580725196776,0.40374853923191456,0.7107857036596282,0.6069465019450859
22 +ln_sqft,MD,9666,0.26677946534944963,0.08120632660343081,0.10761506520672523,0.425943865492174,0.3459532190169773,0.0680870443180313,0.21250261215363594,0.4794038258803186,0.7029901379797449
23 +ln_sqft,ME,3266,0.5087364853271849,0.06955866395541094,0.3724015039745794,0.6450714666797903,0.5145606219695915,0.060773973706824296,0.3954436335042159,0.6336776104349672,0.7633659842789989
24 +ln_sqft,MI,19190,0.5301896027827018,0.041366949107779105,0.44911038253145474,0.6112688230339488,0.5305017507377147,0.039270229103349284,0.45353210169515007,0.6074713997802792,0.9011973082539856
25 +ln_sqft,MN,8710,0.43801509670144834,0.06717683919850892,0.30634849187237084,0.5696817015305258,0.45939639146090155,0.05916604873938962,0.34343093593169793,0.5753618469901052,0.7757218110611229
26 +ln_sqft,MO,14784,0.42137941806913504,0.05693330302556728,0.30979014413902317,0.5329686919992469,0.44063363840953473,0.05180744369267988,0.3390910487718822,0.5421762280471873,0.8280404761503987
27 +ln_sqft,MS,6773,0.7550678942543476,0.06872160155732522,0.6203735552019902,0.889762233306705,0.7035652341190517,0.060213300922050836,0.5855471643118321,0.8215833039262713,0.7677119978316642
28 +ln_sqft,MT,3959,0.47880674816771684,0.07957498271178595,0.3228397820526164,0.6347737142828174,0.4945479392827747,0.06711686659042651,0.36299888076553877,0.6260969978000106,0.7113940686523363
29 +ln_sqft,NC,29245,0.4975801082122883,0.06799843900958302,0.3643031677535056,0.6308570486710711,0.5057545789953523,0.059725071717254476,0.38869343842953347,0.622815719561171,0.7714636607688704
30 +ln_sqft,ND,1339,0.7205873118154102,0.13973714940404336,0.44670249898348524,0.9944721246473353,0.6165281185339747,0.09313690674724535,0.4339797813093738,0.7990764557585757,0.44424225106520426
31 +ln_sqft,NE,3925,0.38897583111696465,0.06333490208851802,0.2648394230234693,0.51311223921046,0.41849336533317566,0.056490572590611236,0.30777184305557764,0.5292148876107736,0.7955468245473982
32 +ln_sqft,NH,2289,0.5528033224943001,0.07738283319043009,0.40113296944105714,0.704473675547543,0.5474091581243828,0.06578578527679275,0.41846901898186906,0.6763492972668966,0.7227279885759782
33 +ln_sqft,NJ,7321,0.44778412055081046,0.060698401486965495,0.3288152536363581,0.5667529874652628,0.46412430316502484,0.054595891149564854,0.35711635651187773,0.5711322498181719,0.8090314612265099
34 +ln_sqft,NM,4704,0.5427366433943626,0.06716178798847007,0.4110995389369612,0.674373747851764,0.5406319111831376,0.05915576471365711,0.42468661234436966,0.6565772100219055,0.7757997707107275
35 +ln_sqft,NV,10760,0.4022801696753395,0.03990567981160478,0.3240650372445941,0.48049530210608493,0.4144145770568062,0.038013579930883466,0.33990796039227456,0.4889211937213378,0.9074195155375125
36 +ln_sqft,NY,24823,0.6148538338892537,0.09427398192558997,0.4300768293150974,0.7996308384634101,0.5852823677785749,0.07525295342995096,0.43778657905587104,0.7327781565012788,0.637181849578795
37 +ln_sqft,OH,19408,0.4669302403320691,0.07545841020766939,0.3190317563250371,0.6148287243391011,0.4846834844472757,0.0645911216328512,0.3580848860468874,0.611282082847664,0.7327070162620929
38 +ln_sqft,OK,12246,0.5307706773183037,0.06553745451162359,0.4023172664755215,0.659224088161086,0.5313270561342337,0.05803678844677826,0.41757495077854834,0.6450791614899191,0.7842013861279853
39 +ln_sqft,OR,9485,0.49657912228025275,0.04658535119057239,0.4052718339467309,0.5878864106137747,0.5010675339182872,0.04364955609511358,0.41551440397186457,0.5866206638647098,0.8779320729726031
40 +ln_sqft,PA,15127,0.5249816407292518,0.06235319373235804,0.40276938101383003,0.6471939004446736,0.5266502270182434,0.05579066475216079,0.41730052410400825,0.6359999299324786,0.80058171416616
41 +ln_sqft,RI,1040,0.2455154999619662,0.09197859090190097,0.06523746179424031,0.4257935381296921,0.3466889525309536,0.07406989539992902,0.20151195754709275,0.4918659475148145,0.6484999676761625
42 +ln_sqft,SC,20322,0.6495818296571735,0.0778855315115744,0.49692618789448767,0.8022374714198592,0.6170511806673254,0.06609381721760099,0.4875072989208275,0.7465950624138233,0.7201253414561322
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377 +has_central_air,LA,14934,0.24282563178478844,0.02222282302555874,0.19926889865469333,0.28638236491488356,0.23012253727715454,0.021394512937525433,0.18818929191960468,0.27205578263470437,0.9268433800326918
378 +has_central_air,MA,5301,0.04136978205599942,0.023755618258905424,-0.0051912297314552075,0.08793079384345405,0.04367086099282824,0.022751722638000547,-0.0009225153776528314,0.08826423736330932,0.9172672627716818
379 +has_central_air,MD,9666,0.11730277265969202,0.03342109185674594,0.051797432620469985,0.18280811269891406,0.11001366193300892,0.030785904093462206,0.049673289909823,0.17035403395619483,0.8485209237254898
380 +has_central_air,ME,3266,0.22395435768047414,0.04946471146290456,0.1270035232131812,0.3209051921477671,0.18044476568962922,0.04193948145228483,0.09824338204315096,0.26264614933610747,0.7188779333508069
381 +has_central_air,MI,19190,0.17238733680800433,0.021365641738903002,0.13051067899975444,0.21426399461625423,0.1653696396006511,0.020626441811391547,0.12494181365032367,0.20579746555097853,0.93200179384573
382 +has_central_air,MN,8710,0.16832956302606966,0.01825112290830615,0.1325573621257896,0.2041017639263497,0.16331794432472654,0.017783865757467363,0.12846156744009052,0.19817432120936257,0.9494523270675167
383 +has_central_air,MO,14784,0.08979341203144316,0.030102577097017334,0.03079236092128918,0.14879446314159714,0.08718606849940404,0.028134119344118177,0.03204319458493241,0.14232894241387567,0.8734927326649603
384 +has_central_air,MS,6773,0.16818651549938696,0.05238309715362087,0.06551564507829005,0.27085738592048386,0.1380042150003336,0.043674390665602005,0.05240240929575368,0.22360602070491353,0.6951385345141846
385 +has_central_air,MT,3959,0.06714476081456466,0.026920892735733157,0.014379811052527676,0.11990971057660166,0.06735636464398106,0.025485320170036834,0.017405137110708874,0.11730759217725326,0.8961924331167885
386 +has_central_air,NC,29245,0.05413774518441765,0.013746959768162734,0.02719370403881869,0.08108178633001661,0.054578852097321565,0.013543941825134714,0.028032726120057524,0.0811249780745856,0.9706816865756889
387 +has_central_air,ND,1339,0.17862790029585762,0.032351975030210145,0.11521802923664574,0.2420377713550695,0.1629434516593034,0.029944207100688323,0.1042528057419543,0.22163409757665253,0.8566906720083909
388 +has_central_air,NE,3925,0.12512926501140903,0.02994177289505817,0.06644339013709502,0.18381513988572304,0.1181176410692721,0.028002710056720344,0.06323232935810022,0.17300295278044397,0.8746717569870709
389 +has_central_air,NH,2289,0.1471450025759639,0.032285800888913495,0.08386483283369346,0.21042517231823438,0.13601148569947075,0.029891712425777702,0.07742372934494646,0.19459924205399504,0.8571926978165091
390 +has_central_air,NJ,7321,0.09128279007705457,0.02438020905138618,0.04349758033633766,0.1390679998177715,0.08936547244180229,0.02329862882650251,0.04370015994185737,0.1350307849417472,0.9132419965327904
391 +has_central_air,NM,4704,-0.014839142139238343,0.030569415364240834,-0.07475519625315039,0.04507691197467369,-0.0039206372443771655,0.028514110107367378,-0.05980829305481722,0.051967018566062895,0.8700523384843802
392 +has_central_air,NV,10760,-0.10754100792089066,0.10164187123093908,-0.30675907553353127,0.09167705969174994,0.00252446423201505,0.06242422155905,-0.11982701002372295,0.12487593848775305,0.3771906919644892
393 +has_central_air,NY,24823,0.3066194738159163,0.03790433876211927,0.23232696984216256,0.38091197778967006,0.26227906594102324,0.03418234726346617,0.19528166530462954,0.32927646657741694,0.8132534497988753
394 +has_central_air,OH,19408,0.2738371924100219,0.024852986092274338,0.2251253396691642,0.3225490451508796,0.25544899631563334,0.02371018946825405,0.20897702495785542,0.30192096767341126,0.9101498362774115
395 +has_central_air,OK,12246,0.040682954941508684,0.04199598031963341,-0.04162916648497281,0.12299507636799017,0.046950035243218766,0.0370923233264109,-0.025750918476546605,0.11965098896298414,0.7801042187038589
396 +has_central_air,OR,9485,-0.004024764525375812,0.019069272268910458,-0.04140053817244031,0.03335100912168869,-3.7017747864295097e-06,0.018538171371886014,-0.03633851766368302,0.036331114114110157,0.9450734136090736
397 +has_central_air,PA,15127,0.26035205145635565,0.024256577708362,0.21280915914796614,0.3078949437647452,0.2439199824091759,0.02319066118113068,0.19846628649415976,0.28937367832419203,0.9140442200101896
398 +has_central_air,RI,1040,0.08646095990392866,0.033032053975429174,0.021718134112087478,0.15120378569576984,0.08389532005052179,0.030481022087843264,0.024152516758348994,0.1436381233426946,0.8515063524600247
399 +has_central_air,SC,20322,0.04419947519904692,0.01522628513108171,0.014355956342126767,0.07404299405596707,0.045092147857748545,0.014951791883989362,0.015786635765129398,0.07439765995036769,0.9642698112246872
400 +has_central_air,SD,2051,-0.04008172650096839,0.03822399886716075,-0.11500076428060345,0.03483731127866667,-0.01939669643723784,0.03441623096577803,-0.08685250913016279,0.0480591162556871,0.8106891789305923
401 +has_central_air,TN,24597,0.12808860589391594,0.032603283630052056,0.06418616997901391,0.19199104180881796,0.11953437027007224,0.030143144792949318,0.06045380647589158,0.1786149340642529,0.8547801635522287
402 +has_central_air,TX,101669,0.027831066721663564,0.018675621799481994,-0.008773152005321146,0.06443528544864827,0.03001449061005369,0.01817589216418582,-0.005610258031750515,0.0656392392518579,0.9471992247969189
403 +has_central_air,UT,6624,0.0009756409045632713,0.013468462292185493,-0.025422545188120295,0.027373826997246838,0.002897427136997692,0.013277365829973596,-0.023126209889750556,0.02892106416374594,0.9718244328416206
404 +has_central_air,VA,15719,0.08691116558078352,0.01951002360664852,0.04867151931175242,0.1251508118498146,0.08589450678596824,0.018942337154177924,0.048767525963779514,0.12302148760815698,0.9426523074036619
405 +has_central_air,VT,1685,0.20089041881621728,0.06863799450123015,0.0663599495938062,0.33542088803862835,0.1443169247149185,0.05184146604459292,0.04270765126751637,0.24592619816232064,0.5704602370244848
406 +has_central_air,WA,11934,-0.06307904038690326,0.030546908177897923,-0.1229509804155832,-0.0032071003582233373,-0.04591388944685481,0.02849584166194852,-0.10176573910427392,0.00993796021056429,0.8702187951111668
407 +has_central_air,WI,9114,0.1327550653128386,0.022867281352984298,0.08793519386098937,0.17757493676468783,0.1278518173086782,0.02196772022028695,0.08479508567691578,0.1709085489404406,0.9228708044964524
408 +has_central_air,WV,3179,0.2225304165275123,0.04900457064497271,0.12648145806336578,0.3185793749916588,0.1799980163323071,0.041657918269519256,0.09834849652404937,0.26164753614056485,0.7226399218083739
409 +has_central_air,WY,1653,0.09840494776500402,0.03893407195812451,0.022094166727079978,0.17471572880292807,0.09270596725939283,0.03493180013036706,0.02423963900387338,0.16117229551491227,0.8049747887065899
410 +has_hardwood,AK,808,0.09083319358388009,0.05087394574866501,-0.008879740083503326,0.19054612725126352,0.08773484480296584,0.030744831098345506,0.027474975850208652,0.14799471375572304,0.3652190126081583
411 +has_hardwood,AL,16919,0.05618822007361951,0.021943763306113757,0.013178443993636546,0.09919799615360247,0.06346118335094521,0.01907524835433646,0.026073696576445746,0.10084867012544467,0.7556456416458049
412 +has_hardwood,AR,10665,0.04570410248868244,0.01680840784996802,0.012759623102745116,0.07864858187461976,0.052122557689680764,0.015409999457188921,0.02191895875359048,0.08232615662577104,0.8405278188198756
413 +has_hardwood,AZ,23871,0.16219471697166982,0.022030544744843242,0.11901484927177707,0.20537458467156255,0.14345317851638645,0.01913216959072251,0.10595412611857033,0.18095223091420257,0.7541851411746966
414 +has_hardwood,CA,47650,0.21913395315574666,0.027991164732939475,0.16427127027918528,0.273996636032308,0.1732178617965756,0.022657932671183604,0.12880831376105575,0.21762740983209547,0.6552373173434294
415 +has_hardwood,CO,17480,0.08450326124509366,0.027766286872038633,0.030081338975897945,0.1389251835142894,0.08499754112716723,0.02253816248484304,0.04082274265687487,0.1291723395974596,0.6588725258591823
416 +has_hardwood,CT,4127,-0.03568558780608888,0.09798493181824817,-0.2277360541698553,0.15636487855767753,0.06961974281688647,0.035904678369619344,-0.0007534267875674433,0.13999291242134038,0.13427139218917403
417 +has_hardwood,DC,1409,0.10785188704750193,0.003407743293516331,0.10117271019220991,0.11453106390279394,0.10768242314505197,0.0033945327953912342,0.10102913886608515,0.11433570742401879,0.9922618043658294
418 +has_hardwood,DE,1998,0.02436263811210082,0.009513323143604901,0.005716524750635214,0.04300875147356643,0.027891438310091575,0.009236768528252408,0.009787371994716854,0.045995504625466296,0.942704593062703
419 +has_hardwood,FL,132186,0.09658867685132495,0.018595686068162094,0.06014113215772725,0.13303622154492267,0.09458414964367912,0.016752038293310484,0.061750154588790566,0.12741814469856766,0.811541816105485
420 +has_hardwood,GA,31988,0.047683579348972355,0.02419256329898035,0.00026615528297086605,0.09510100341497385,0.058481024924392486,0.020497417455288772,0.018306086712026493,0.09865596313675848,0.7178513479529284
421 +has_hardwood,HI,5013,0.2315881102681433,0.04767984656039954,0.1381356110097602,0.32504060952652636,0.1435913408475944,0.02999572159388417,0.08479972652358142,0.20238295517160737,0.39577552149433387
422 +has_hardwood,IA,8265,0.06314060163374252,0.017803415762207186,0.02824590673981644,0.0980352965276686,0.06714403998110316,0.016165854783339064,0.03545896460575859,0.09882911535644773,0.824500035085484
423 +has_hardwood,ID,4532,0.15406240264623425,0.03950146634425166,0.07663952861150099,0.23148527668096752,0.11921130698440341,0.027603385720278463,0.06510867097265763,0.1733139429961492,0.488312985362814
424 +has_hardwood,IL,19170,0.18461890754711463,0.025470234271148876,0.13469724837566283,0.23454056671856643,0.15467798658125015,0.021257268646294042,0.11301374003451384,0.19634223312798646,0.6965447819788847
425 +has_hardwood,IN,14338,0.04144602385925278,0.015585411327528297,0.010898617657297317,0.07199343006120824,0.047687839278649925,0.014451248691932575,0.019363391842462076,0.07601228671483777,0.8597540149192756
426 +has_hardwood,KS,5974,0.062172759637816155,0.014586915174337169,0.0335824058961153,0.09076311337951701,0.06514581900502146,0.013644604227298636,0.038402394719516134,0.09188924329052679,0.8749736439232759
427 +has_hardwood,KY,10637,0.014775300485892984,0.023393088860695237,-0.03107515368106968,0.06062575465285565,0.03390320353352476,0.020004340626691857,-0.005305304094791283,0.0731117111618408,0.7312625639370208
428 +has_hardwood,LA,14934,0.03263928588049289,0.04648986571761884,-0.05848085092604002,0.1237594226870258,0.06420453790171479,0.02969261802882651,0.0060070065652148286,0.12240206923821476,0.40792507254147675
429 +has_hardwood,MA,5301,0.0675918231620217,0.02529333423168903,0.0180168880679112,0.1171667582561322,0.07310943450838904,0.02115410565093002,0.0316473874325662,0.11457148158421188,0.6994830132499569
430 +has_hardwood,MD,9666,0.0768571815266886,0.01449324282181084,0.04845042559593936,0.10526393745743785,0.07798154326667897,0.013567845780829105,0.05138856553625393,0.10457452099710401,0.876376372058926
431 +has_hardwood,ME,3266,0.07791298792712104,0.031839432031734755,0.015507701144920921,0.14031827470932115,0.08116919951146796,0.024558914247536204,0.033033727586297,0.12930467143663893,0.5949599081563618
432 +has_hardwood,MI,19190,0.07416469827971948,0.016707023382815177,0.04141893244940174,0.10691046411003724,0.0760254406539059,0.015331760064115633,0.04597519092823926,0.10607569037957254,0.8421430469938624
433 +has_hardwood,MN,8710,-0.05412763318524762,0.0626960746052536,-0.1770119394115447,0.06875667304104945,0.04746594619744512,0.0328628668439966,-0.01694527281678821,0.11187716521167845,0.274745250238837
434 +has_hardwood,MO,14784,0.06495432424958997,0.013907968342799636,0.03769470629770268,0.09221394220147724,0.06736835664807685,0.013084098608272083,0.04172352337586356,0.09301318992029013,0.8850345603357461
435 +has_hardwood,MS,6773,0.0857666142518303,0.020036209882954393,0.04649564288123969,0.1250375856224209,0.08580602720003648,0.01778210117641101,0.050953108894270904,0.12065894550580206,0.7876531409763402
436 +has_hardwood,MT,3959,0.2560693183918444,0.06919462558369974,0.12044785224779289,0.3916907845358959,0.1263090700274284,0.033702103496588594,0.06025294717411475,0.19236519288074203,0.23722982503278528
437 +has_hardwood,NC,29245,0.07394053154155303,0.010240129969298873,0.05386987680172724,0.09401118628137882,0.07473073850112094,0.009897569016510753,0.05533150322875986,0.09412997377348202,0.9342135024850758
438 +has_hardwood,ND,1339,0.00588386040065203,0.042041154043625475,-0.0765168015248539,0.08828452232615797,0.0493401772489612,0.02842864407808768,-0.006379965144090657,0.10506031964201305,0.45725981297822926
439 +has_hardwood,NE,3925,0.004714617496141688,0.02039343230222966,-0.035256509816228444,0.04468574480851182,0.022450277473557982,0.018030396713915164,-0.01288930008571574,0.057789855032831704,0.7816816440173228
440 +has_hardwood,NH,2289,0.03582837205712841,0.01065734127753239,0.014939983153164926,0.05671676096109189,0.039380584494239756,0.010272766495518797,0.019245962163022913,0.0595152068254566,0.9291312911388976
441 +has_hardwood,NJ,7321,0.09971008674698185,0.022705102223059513,0.05520808638978521,0.1442120871041785,0.09617200211480015,0.0195690042094893,0.057816753864201126,0.13452725036539917,0.7428318736039344
442 +has_hardwood,NM,4704,0.07315561293147006,0.023977002704783344,0.026160687630094707,0.12015053823284541,0.07671994547153928,0.020365816884748696,0.03680294437743183,0.11663694656564672,0.7214627030834965
443 +has_hardwood,NV,10760,0.13936313774525905,0.03634015012224322,0.06813644350566234,0.21058983198485576,0.11425905296497513,0.02645558293349935,0.06240611041531641,0.16611199551463385,0.5299821427280986
444 +has_hardwood,NY,24823,0.019076159577004384,0.02418198821225055,-0.02832053731900669,0.06647285647301546,0.03793330801521322,0.020490984418130565,-0.0022290214443226875,0.07809563747474912,0.7180284227419688
445 +has_hardwood,OH,19408,0.03316296416432073,0.020600494222124382,-0.007214004511043058,0.07353993283968452,0.04487087965175533,0.018173022858153743,0.009251754849773995,0.08049000445373666,0.778214048581776
446 +has_hardwood,OK,12246,0.05424522710072374,0.019698217005833126,0.015636721769290818,0.09285373243215667,0.0607994278209851,0.017544560460156636,0.026412089319078097,0.09518676632289211,0.7932884838839089
447 +has_hardwood,OR,9485,0.09094604051905868,0.01449910102844293,0.06252780250331053,0.11936427853480683,0.09032824897702017,0.013572651607701016,0.06372585182592619,0.11693064612811416,0.8762887798232233
448 +has_hardwood,PA,15127,0.08530886479177097,0.012496682160144969,0.06081536775788683,0.1098023618256551,0.08536993192855805,0.011888807232806284,0.062067869752257726,0.10867199410485837,0.9050803215662065
449 +has_hardwood,RI,1040,0.09845024526550052,0.04354075483567829,0.013110365787571063,0.18379012474342998,0.09145038544303624,0.028879132341676866,0.03484728605334959,0.1480534848327229,0.43992270211580703
450 +has_hardwood,SC,20322,0.08664113554196122,0.017100354624901882,0.053124440477153534,0.1201578306067689,0.08652805509501325,0.01563403740364125,0.055885341783876406,0.1171707684061501,0.8358571458377014
451 +has_hardwood,SD,2051,0.07026652057913745,0.02219543587145839,0.026763466271079,0.11376957488719588,0.0741658312016871,0.01923986935006223,0.036455687275565134,0.11187597512780906,0.7514098452569266
452 +has_hardwood,TN,24597,0.11924334214986702,0.02534816599794832,0.06956093679388832,0.1689257475058457,0.10920846026699485,0.02118615163006832,0.06768360307206095,0.15073331746192875,0.6985718279057347
453 +has_hardwood,TX,101669,0.13282967057396922,0.01706391280473303,0.09938440147669247,0.16627493967124596,0.12516247423988358,0.015606174528164193,0.09457437216468176,0.1557505763150854,0.8364416930490024
454 +has_hardwood,UT,6624,0.16166947578752164,0.01418027052553552,0.13387614555747201,0.18946280601757126,0.15266135822109284,0.013310051882093,0.12657365653219055,0.17874905990999512,0.881029528116232
455 +has_hardwood,VA,15719,0.04521438291265088,0.017036115898614784,0.011823595751365906,0.07860517007393586,0.051859242397891324,0.015584901785643372,0.021312834898030316,0.08240564989775233,0.836887281096029
456 +has_hardwood,VT,1685,0.03815423416725933,0.02175218575514276,-0.004480049912820486,0.08078851824733914,0.04967979553701042,0.01894900449079525,0.012539746735051732,0.08681984433896911,0.7588693122110306
457 +has_hardwood,WA,11934,0.13747080212765161,0.015700374410572163,0.10669806828293017,0.16824353597237304,0.1301537376786566,0.01454275368436315,0.10164994045730483,0.15865753490000836,0.857972322849057
458 +has_hardwood,WI,9114,0.08536696765966412,0.023950290145262607,0.03842439897494941,0.1323095363443788,0.08552972039102459,0.020349439729541292,0.04564481852112366,0.12541462226092553,0.7219104940506311
459 +has_hardwood,WV,3179,0.029892255137271673,0.014216196991453007,0.002028509034023778,0.057756001240519564,0.036591529790074,0.013339748276763387,0.010445623167617761,0.06273743641253024,0.8804980598101854
460 +has_hardwood,WY,1653,0.09171284625161888,0.025127679400924034,0.04246259462580777,0.14096309787743,0.08999755223910333,0.02105690894823003,0.048726010700572475,0.13126909377763418,0.7022382378716259
461 +luxury_score,AK,808,0.039976285477261786,0.03324001901441776,-0.025174151790997024,0.1051267227455206,0.023352511392174848,0.029180154772298638,-0.03384059196153048,0.08054561474588018,0.7706419657198005
462 +luxury_score,AL,16919,0.011912689959782341,0.02547665478239927,-0.03802155341372023,0.06184693333328491,0.0053029143761548336,0.02350467881848308,-0.040766256108072,0.051372084860381674,0.8511847569810473
463 +luxury_score,AR,10665,-0.014386406605673384,0.02243921182432902,-0.05836726178135826,0.029594448570011492,-0.016550125991688782,0.021056649919223378,-0.05782115983336661,0.02472090784998904,0.8805689283599786
464 +luxury_score,AZ,23871,0.04717077258802371,0.019351870607044717,0.009241106198216067,0.08510043897783136,0.03987008852093084,0.018443948251204538,0.003719949948569945,0.07602022709329173,0.9083681265994034
465 +luxury_score,CA,47650,0.027891351013823507,0.011785126289721075,0.004792503485970198,0.050990198541676815,0.025713367631592182,0.011570674417013455,0.003034845774245811,0.04839188948893855,0.9639374757712419
466 +luxury_score,CO,17480,-0.00042617851107257243,0.015773662890547106,-0.0313425577765449,0.030490200754399753,-0.0024409540977575668,0.015270255088356725,-0.03237065407093675,0.027488745875421612,0.9371896288594794
467 +luxury_score,CT,4127,-0.03077223255424144,0.00982371691574177,-0.05002671770909531,-0.01151774739938757,-0.030816091471697633,0.009698469603686576,-0.04982509189492332,-0.011807091048471944,0.9746635832683014
468 +luxury_score,DC,1409,-0.09152368327732907,0.0024153750928549923,-0.09625781845932485,-0.08678954809533329,-0.09143107963737802,0.0024134794739279555,-0.09616149940627682,-0.08670065986847923,0.9984309889764116
469 +luxury_score,DE,1998,-0.024135317432097902,0.008631561657752503,-0.041053178281292804,-0.007217456582902997,-0.02429994730579048,0.008546232096580272,-0.04105056221508781,-0.007549332396493146,0.9803262049934006
470 +luxury_score,FL,132186,0.03654103692574385,0.014175039400102857,0.008757959701542247,0.06432411414994545,0.03299597532237317,0.013806337631159512,0.005935553565300525,0.06005639707944581,0.9486552840955257
471 +luxury_score,GA,31988,0.000500708662917803,0.01204059964546677,-0.023098866642197066,0.02410028396803267,-0.0007396996322093859,0.011812168412705276,-0.023891549721111724,0.022412150456692953,0.9624164295080999
472 +luxury_score,HI,5013,-0.013665718302728447,0.05766539410796915,-0.12668989075434797,0.09935845414889108,-0.022566364666534132,0.04188214050505142,-0.10465536005643492,0.05952263072336666,0.5275057643971374
473 +luxury_score,IA,8265,-0.031569389364365735,0.018256114591881884,-0.06735137396445423,0.00421259523572276,-0.03164632394554885,0.017487991355578568,-0.06592278700248284,0.0026301391113851408,0.9176205979068128
474 +luxury_score,ID,4532,0.050335489237346626,0.01826306680417241,0.0145398783011687,0.08613110017352454,0.04350650975890838,0.017494102144983655,0.009218069554740416,0.07779494996307634,0.9175630165506935
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476 +luxury_score,IN,14338,-0.0005879734913906767,0.01256538166388254,-0.025216121552600453,0.0240401745698191,-0.001889943618932242,0.012306413707135484,-0.026010514484917792,0.022230627247053304,0.95920548284296
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478 +luxury_score,KY,10637,0.011015516670615615,0.013901000860788144,-0.016230445016529146,0.03826147835776038,0.008862382671160253,0.013552756265615487,-0.017701019609446103,0.035425784951766605,0.9505240625646899
479 +luxury_score,LA,14934,0.014313783254886248,0.017040183372016904,-0.01908497615426688,0.047712542664039376,0.010917636572149485,0.016410494914030065,-0.02124693345934944,0.04308220660364841,0.9274592346378432
480 +luxury_score,MA,5301,0.03438437966539343,0.017826638802818658,-0.0005558323881311375,0.069324591718918,0.029110215327066192,0.01710938473673325,-0.004424178756930974,0.06264460941106335,0.9211489355682254
481 +luxury_score,MD,9666,-0.05997128099798865,0.019321327555621964,-0.0978410830070077,-0.0221014789889696,-0.057461551331843566,0.018417499849651926,-0.09355985103716134,-0.02136325162652579,0.9086307361993451
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484 +luxury_score,MN,8710,0.001690711271197097,0.01383421429931472,-0.025424348755459753,0.028805771297853947,1.4352793578663531e-05,0.013490842332499958,-0.026427698178121253,0.02645640376527858,0.9509750782547842
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486 +luxury_score,MS,6773,-0.009479052112000216,0.021374827182596562,-0.051373713389889476,0.032415609165889044,-0.012002088004415552,0.020169709735414244,-0.05153471908582747,0.027530543076996365,0.8904182902521435
487 +luxury_score,MT,3959,0.044370982243580326,0.014736283268561835,0.015487867037199128,0.07325409744996153,0.04012276070996957,0.014323319105198908,0.01204905526377971,0.06819646615615943,0.9447380620615254
488 +luxury_score,NC,29245,0.04414987717456617,0.008101869384501338,0.028270213180943547,0.060029541168188795,0.042818111939828644,0.008031180432717921,0.02707699829170152,0.058559225587955774,0.9826260910231353
489 +luxury_score,ND,1339,0.048961298328699526,0.025481153608541227,-0.0009817627440412771,0.09890435940144032,0.03683448054925184,0.02350821160868288,-0.009241614203766603,0.08291057530227028,0.8511400192889964
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491 +luxury_score,NH,2289,0.03421953073948594,0.022668979732647396,-0.010211669536502953,0.07865073101547485,0.026106667304311992,0.021246161573667946,-0.015535809380077183,0.06774914398870116,0.8784094744918762
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493 +luxury_score,NM,4704,0.06132410674773077,0.024595565821930516,0.013116797736746955,0.10953141575871458,0.0481772681259327,0.022807434342259254,0.0034746968151045596,0.09287983943676084,0.8598827380030469
494 +luxury_score,NV,10760,0.03428584395560452,0.012898050185948584,0.009005665591145299,0.05956602232006375,0.03142130382298827,0.012618424853680602,0.006689191109774294,0.05615341653620225,0.9571106892393934
495 +luxury_score,NY,24823,-0.06835470339259873,0.022376208903383966,-0.11221207284323131,-0.02449733394196616,-0.06409408809992778,0.021004563556662602,-0.10526303267098648,-0.022925143528869082,0.8811590541615438
496 +luxury_score,OH,19408,-0.021913450181022573,0.01188593984956636,-0.04520989228617264,0.0013829919241274934,-0.022301667830824892,0.011666039802586601,-0.045167105843894634,0.0005637701822448458,0.9633405720842518
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498 +luxury_score,OR,9485,0.06095301501320495,0.009327525573393357,0.042671064889353974,0.07923496513705593,0.05881298490849544,0.009220112899382316,0.0407415636257061,0.07688440619128478,0.9771012775245753
499 +luxury_score,PA,15127,-0.03751555086848421,0.017044333483592185,-0.0709224444963249,-0.004108657240643528,-0.03715179371381471,0.016414201640229283,-0.0693236289286641,-0.00497995849896532,0.9274264605960415
500 +luxury_score,RI,1040,0.11726622021550309,0.017859134944658305,0.08226231572397281,0.15227012470703338,0.10541704975628956,0.017138107942039228,0.07182635818989266,0.13900774132268645,0.9208839632779035
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503 +luxury_score,TN,24597,0.03607168013996617,0.013392283790377988,0.009822803910825316,0.06232055636910703,0.032911415261376226,0.013080053650475647,0.007274510106443959,0.05854832041630849,0.9539151873422743
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505 +luxury_score,UT,6624,0.02883609835153353,0.006467167208434592,0.01616045062300173,0.04151174608006533,0.02815275172271596,0.006431042835691315,0.015547907764760982,0.04075759568067094,0.9888595795884987
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added results/inference_v2/state_heterogeneity_summary_v2.csv +14 −0
@@ -0,0 +1,14 @@
1 +variable,pooled_state_fe,pooled_se_zip3,n_states,precision_weighted_mean,listing_weighted_mean,p10,median,p90,min,max,n_sign_differs_from_pooled,n_sign_differs_and_abs_t_gt_2,n_state_ci_excludes_pooled,n_abs_t_gt_2,homogeneity_Q,homogeneity_df,homogeneity_p,tau2_dl,tau_dl,I2_pct
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added results/inference_v2/state_heterogeneity_v2.csv +52 −0
@@ -0,0 +1,52 @@
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50 +WI,9114,18,0.7036902597089355,3.374587451159093e+19,345000.00000000006,0.4133224704737009,0.054268867574678144,-0.023401757493369382,0.016241048849108958,0.19110034877610022,0.024965814424641523,-0.0002284788270154256,0.003871481810229355,0.10022280322208203,0.00985214151620211,0.05517924340253341,0.03004158865942154,0.12877942408428955,0.03774309150665262,0.1327550653128386,0.022867281352984298,0.08536696765966412,0.023950290145262607,-0.05851362525411884,0.019122791634200425,,,-0.32752630719019926,0.0674837529850158,-0.04741861981741269,0.0855330235188019
51 +WV,3179,22,0.6848428550802581,3.532674249281858e+20,239000.00000000003,0.49364701217310536,0.08666707428755097,-0.07331038167518789,0.02299835646481258,0.1988525713352866,0.041552875546153764,-0.019900702235319923,0.00562619699745307,0.12769801408305548,0.009492714854785112,0.09173311941261904,0.039078131984314174,0.060669815296627294,0.018500349250823247,0.2225304165275123,0.04900457064497271,0.029892255137271673,0.014216196991453007,-0.06336178098908629,0.029193242981014545,-0.2654118645743583,0.09957245125020595,0.03146032835222079,0.11922872530089387,-0.15165001422994573,0.11994965927248152
52 +WY,1653,12,0.7355838365003793,469447.0829655853,424999.99999999965,0.4598646337406058,0.1339248003586122,-0.02078566030594778,0.030484041551955873,0.13139366067923008,0.05392561362700604,0.002015603227730589,0.011414878588170436,0.1538480688035495,0.01750357031788862,0.11750355893830533,0.10205875538060293,0.05967267371773333,0.0349662071555343,0.09840494776500402,0.03893407195812451,0.09171284625161888,0.025127679400924034,0.022496317999949722,0.021045018446121305,-0.4481625705143255,0.11841035316175565,0.6070289674232039,0.26163204385083366,-0.049145072935148915,0.17416782946990889
added results/inference_v2/state_regressions_dropped_columns.csv +52 −0
@@ -0,0 +1,52 @@
1 +state,n,dropped_constant_columns
2 +AK,808,pool_x_south basement_x_north roof_cat_Slate roof_cat_Tile construction_cat_Steel foundation_cat_Crawlspace foundation_cat_Stone
3 +AL,16919,basement_x_north
4 +AR,10665,basement_x_north
5 +AZ,23871,pool_x_south basement_x_north foundation_cat_Stone
6 +CA,47650,pool_x_south basement_x_north
7 +CO,17480,pool_x_south basement_x_north
8 +CT,4127,pool_x_south construction_cat_Steel
9 +DC,1409,transit_score basement_x_north roof_cat_Tile construction_cat_Steel
10 +DE,1998,basement_x_north
11 +FL,132186,basement_x_north foundation_cat_Stone
12 +GA,31988,basement_x_north
13 +HI,5013,pool_x_south basement_x_north construction_cat_Stone foundation_cat_Crawlspace foundation_cat_Stone
14 +IA,8265,pool_x_south
15 +ID,4532,transit_score pool_x_south basement_x_north roof_cat_Slate
16 +IL,19170,pool_x_south
17 +IN,14338,pool_x_south construction_cat_Steel
18 +KS,5974,pool_x_south
19 +KY,10637,basement_x_north
20 +LA,14934,basement_x_north construction_cat_Steel
21 +MA,5301,pool_x_south construction_cat_Steel
22 +MD,9666,basement_x_north
23 +ME,3266,tag_foreclosure pool_x_south construction_cat_Steel construction_cat_Stone foundation_cat_Crawlspace
24 +MI,19190,pool_x_south
25 +MN,8710,pool_x_south foundation_cat_Pier_Raised
26 +MO,14784,pool_x_south
27 +MS,6773,basement_x_north construction_cat_Steel
28 +MT,3959,pool_x_south basement_x_north roof_cat_Slate
29 +NC,29245,basement_x_north
30 +ND,1339,pool_x_south roof_cat_Slate roof_cat_Tile foundation_cat_Crawlspace foundation_cat_Pier_Raised
31 +NE,3925,pool_x_south
32 +NH,2289,transit_score tag_new_construction tag_foreclosure pool_x_south roof_cat_Tile construction_cat_Steel foundation_cat_Crawlspace
33 +NJ,7321,pool_x_south
34 +NM,4704,pool_x_south basement_x_north construction_cat_Steel
35 +NV,10760,tag_foreclosure pool_x_south basement_x_north construction_cat_Steel
36 +NY,24823,pool_x_south construction_cat_Steel
37 +OH,19408,pool_x_south
38 +OK,12246,basement_x_north foundation_cat_Stone
39 +OR,9485,pool_x_south basement_x_north construction_cat_Steel
40 +PA,15127,pool_x_south
41 +RI,1040,has_spa school_count pool_x_south roof_cat_Metal roof_cat_Slate construction_cat_Concrete_Block construction_cat_Steel construction_cat_Stucco foundation_cat_Crawlspace
42 +SC,20322,basement_x_north
43 +SD,2051,school_count pool_x_south foundation_cat_Crawlspace foundation_cat_Pier_Raised
44 +TN,24597,basement_x_north
45 +TX,101669,basement_x_north
46 +UT,6624,pool_x_south basement_x_north roof_cat_Slate foundation_cat_Stone
47 +VA,15719,basement_x_north
48 +VT,1685,tag_new_construction tag_foreclosure pool_x_south construction_cat_Steel foundation_cat_Crawlspace
49 +WA,11934,pool_x_south basement_x_north roof_cat_Slate
50 +WI,9114,tag_foreclosure pool_x_south roof_cat_Slate foundation_cat_Crawlspace
51 +WV,3179,transit_score basement_x_north
52 +WY,1653,transit_score school_count pool_x_south basement_x_north roof_cat_Slate foundation_cat_Crawlspace
added results/inference_v2/wild_bootstrap_counts.csv +2 −0
@@ -0,0 +1,2 @@
1 +n_slopes,n_sig_5pct_webb,n_sig_5pct_rademacher,n_sig_5pct_webb_holm,n_sig_5pct_webb_bh,n_sig_5pct_rademacher_holm,B
2 +63,23,23,9,20,8,9999
added results/inference_v2/wild_bootstrap_state.csv +64 −0
@@ -0,0 +1,64 @@
1 +variable,p_wild_rademacher,p_wild_webb,t_cr1,p_holm_webb,p_bh_webb,p_holm_rademacher,p_bh_rademacher
2 +age,0.9346934693469346,0.9313931393139314,0.10643311305903404,1.0,0.9897153649791209,1.0,0.9925992599259926
3 +age_sq,0.061406140614061404,0.0613061306130613,2.1238505887949,1.0,0.15449144914491447,1.0,0.1611911191119112
4 +age_x_luxury,0.0187018701870187,0.020102010201020103,2.411646380930501,0.8643864386438644,0.06030603060306031,0.8041804180418042,0.056105610561056105
5 +avg_school_rating,0.12101210121012101,0.12421242124212421,-2.013159315854533,1.0,0.2472997299729973,1.0,0.24474009900990099
6 +basement_x_north,0.3365336533653365,0.33503350335033505,-1.2113256484386798,1.0,0.5276777677767777,1.0,0.530040504050405
7 +bath_per_bed,0.49744974497449745,0.4888488848884888,-1.45911051718088,1.0,0.6727977145540641,1.0,0.6812898681172466
8 +bathrooms,0.0,0.0,10.913544772820611,0.0,0.0,0.0,0.0
9 +bedrooms,0.0079007900790079,0.013001300130013,-2.486775960255462,0.611061106110611,0.047254725472547256,0.395039503950395,0.03504725472547255
10 +bike_score,0.0125012501250125,0.014901490149014901,2.89890381657702,0.6705670567056705,0.049410204178312565,0.5875587558755875,0.04620462046204621
11 +condo_x_walkscore,0.20572057205720573,0.19851985198519853,2.1777221842319787,1.0,0.34740974097409744,1.0,0.36001100110011003
12 +construction_cat_Concrete_Block,0.00020002000200020003,0.00020002000200020003,7.53050508167473,0.0112011201120112,0.0015751575157515751,0.0112011201120112,0.0015751575157515751
13 +construction_cat_Other,0.0012001200120012002,0.0015001500150015,3.0840251585423397,0.0795079507950795,0.008591768267735864,0.06600660066006601,0.00819081908190819
14 +construction_cat_Steel,0.9520952095209521,0.9541954195419542,0.07925557425921992,1.0,0.9897153649791209,1.0,0.9925992599259926
15 +construction_cat_Stone,0.0,0.0,7.5263019773532935,0.0,0.0,0.0,0.0
16 +construction_cat_Stucco,0.013201320132013201,0.010101010101010102,4.60021554118806,0.48484848484848486,0.03977272727272728,0.6072607260726073,0.04620462046204621
17 +construction_cat_Unknown,0.0051005100510051,0.004800480048004801,3.852198879020498,0.24482448244824484,0.023263864848023265,0.2601260126012601,0.024717856401024713
18 +construction_cat_Vinyl,0.8424842484248425,0.8445844584458446,0.2235871800640177,1.0,0.9751084199329024,1.0,0.9803780378037804
19 +construction_cat_Wood_Frame,0.0165016501650165,0.0158015801580158,2.7850247629523337,0.6952695269526953,0.04977497749774977,0.726072607260726,0.05198019801980197
20 +foundation_cat_Concrete,0.0388038803880388,0.041004100410041,4.639369829604606,1.0,0.11231557938402537,1.0,0.10628888975854107
21 +foundation_cat_Crawlspace,0.07300730073007301,0.0706070607060706,2.7822284633096186,1.0,0.17108633940317106,1.0,0.1769023056151769
22 +foundation_cat_Other,0.08260826082608261,0.08290829082908291,2.614477829465149,1.0,0.19345267860119347,1.0,0.19275260859419277
23 +foundation_cat_Pier_Raised,0.12431243124312431,0.1256125612561256,2.094832194919028,1.0,0.2472997299729973,1.0,0.24474009900990099
24 +foundation_cat_Slab,0.16141614161416143,0.1564156415641564,2.236957483979614,1.0,0.29861167934975313,1.0,0.30815808853612636
25 +foundation_cat_Stone,0.9631963196319632,0.9552955295529553,0.1593650439980667,1.0,0.9897153649791209,1.0,0.9925992599259926
26 +foundation_cat_Unknown,0.0894089408940894,0.08710871087108711,3.482121770087082,1.0,0.195994599459946,1.0,0.20117011701170118
27 +has_basement,0.461046104610461,0.46404640464046404,-0.8777931451397719,1.0,0.6644300793715735,1.0,0.6601341952377056
28 +has_central_air,0.0034003400340034003,0.0032003200320032004,3.7618124571784697,0.16641664166416642,0.0168016801680168,0.1768176817681768,0.017851785178517852
29 +has_fireplace,0.18371837183718373,0.18171817181718172,1.4231263196962236,1.0,0.32853285328532855,1.0,0.33861386138613864
30 +has_forced_air,0.7355735573557356,0.7351735173517352,-0.42924764592879283,1.0,0.8906909921761407,1.0,0.8911756560271411
31 +has_garage,0.0,0.0,7.152305013467459,0.0,0.0,0.0,0.0
32 +has_hardwood,0.0,0.0,6.986770048344275,0.0,0.0,0.0,0.0
33 +has_hoa,0.10901090109010901,0.10231023102310231,-2.2911340887648692,1.0,0.21485148514851488,1.0,0.22892289228922894
34 +has_pool,0.4892489248924892,0.49124912491249123,1.3128462505831973,1.0,0.6727977145540641,1.0,0.6812898681172466
35 +has_spa,0.06490649064906491,0.06070607060706071,2.8801307359350306,1.0,0.15449144914491447,1.0,0.16356435643564357
36 +is_condo,0.41744174417441743,0.40784078407840785,-1.7161403410753928,1.0,0.600464697632554,1.0,0.6116006949532162
37 +ln_hoa,0.0089008900890089,0.0083008300830083,2.9977499538410144,0.4067406740674067,0.03486348634863486,0.42724272427242727,0.03504725472547255
38 +ln_lot,0.0,0.0,7.537188377873663,0.0,0.0,0.0,0.0
39 +ln_sqft,0.0,0.0,8.85149793588039,0.0,0.0,0.0,0.0
40 +lot_missing,0.31883188318831884,0.31903190319031904,-1.1399653477031768,1.0,0.5153592282305154,1.0,0.515036118996515
41 +luxury_score,0.6428642864286429,0.6501650165016502,0.4738881535421797,1.0,0.8031450203843914,1.0,0.8074689821923369
42 +nearest_school_distance,0.40804080408040805,0.40984098409840986,-1.0885676942424554,1.0,0.600464697632554,1.0,0.6116006949532162
43 +on_waterfront,0.9776977697769776,0.9582958295829583,-0.5349574334488543,1.0,0.9897153649791209,1.0,0.9925992599259926
44 +parking_spaces,0.0016001600160016002,0.0009000900090009,3.8368145260717577,0.0495049504950495,0.0063006300630063005,0.08480848084808482,0.009164552818918256
45 +pool_x_south,0.9124912491249125,0.9142914291429143,0.15432662426322374,1.0,0.9897153649791209,1.0,0.9925992599259926
46 +property_tax_rate,0.5800580058005801,0.5820582058205821,-0.7026687973710072,1.0,0.7655265526552656,1.0,0.7702198791307704
47 +region_Northeast,0.0144014401440144,0.013501350135013501,3.8483174402456592,0.6210621062106211,0.047254725472547256,0.648064806480648,0.047752143635416175
48 +region_South,0.6536653665366536,0.6487648764876488,0.5696702783028319,1.0,0.8031450203843914,1.0,0.8074689821923369
49 +region_West,0.0087008700870087,0.007500750075007501,4.023868914691623,0.37503750375037503,0.03375337533753375,0.4263426342634263,0.03504725472547255
50 +roof_cat_Metal,0.5990599059905991,0.5963596359635963,0.5687020620741616,1.0,0.7667481033817668,1.0,0.7702198791307704
51 +roof_cat_Other,0.5881588158815881,0.5832583258325833,0.6326323228774573,1.0,0.7655265526552656,1.0,0.7702198791307704
52 +roof_cat_Shingle_Comp,0.2591259125912591,0.2558255825582558,-1.493223599506383,1.0,0.4241318868728978,1.0,0.4296034866644559
53 +roof_cat_Slate,0.9881988198819882,0.9858985898589859,-0.020986415304248475,1.0,0.9934993499349934,1.0,0.9925992599259926
54 +roof_cat_Tile,0.18811881188118812,0.18251825182518253,-1.622538702280699,1.0,0.32853285328532855,1.0,0.33861386138613864
55 +roof_cat_Unknown,0.8558855885588559,0.8512851285128513,-0.35045770510566204,1.0,0.9751084199329024,1.0,0.9803780378037804
56 +school_count,0.8410841084108411,0.8503850385038504,-0.2171238552504369,1.0,0.9751084199329024,1.0,0.9803780378037804
57 +sqft_per_bed,0.36343634363436345,0.35853585358535855,-1.0958090907045972,1.0,0.5509209457531119,1.0,0.5584509670479243
58 +sqft_x_age,0.21842184218421842,0.21392139213921393,-1.4666179913791668,1.0,0.36424453256136424,1.0,0.3719074610163719
59 +stories,0.9925992599259926,0.9934993499349934,0.008565419279002059,1.0,0.9934993499349934,1.0,0.9925992599259926
60 +tag_foreclosure,0.0,0.0,-10.726777314673509,0.0,0.0,0.0,0.0
61 +tag_new_construction,0.032303230323032305,0.0297029702970297,2.3530738544507024,1.0,0.08505850585058505,1.0,0.09250470501595615
62 +transit_score,0.0013001300130013002,0.0012001200120012002,4.346307896400303,0.06480648064806481,0.007560756075607561,0.0702070207020702,0.00819081908190819
63 +walk_score,0.09810981098109811,0.0963096309630963,1.9094268114491377,1.0,0.20922437071293337,1.0,0.2131351066141097
64 +waterfront_x_sqft,0.8906890689068907,0.871987198719872,1.378440594590618,1.0,0.9809855985598559,1.0,0.9925992599259926
added run_all.sh +29 −0
@@ -0,0 +1,29 @@
1 +#!/usr/bin/env bash
2 +# Entry point of the v2 pipeline (November 2026 review). Rebuilds the analysis data from
3 +# the raw DuckDB, runs every estimation, writes every table fragment, figure and
4 +# results/inline_numbers.json. Long steps (05, 00c --v2, 07) ran on a cluster node
5 +# (see reproducibility/WP-03/validation_report.md); --from-results skips them and
6 +# regenerates the exhibits from the stored CSVs.
7 +set -euo pipefail
8 +cd "$(dirname "$0")"
9 +PY=${PY:-.venv/bin/python}
10 +WORKERS=${WORKERS:-8}
11 +FROM_RESULTS=${1:-}
12 +
13 +$PY scripts/00_build_sample.py --check # raw -> analytical sample (asserts vs pickle when present)
14 +$PY scripts/00b_design_matrix.py --check-legacy # one design matrix; legacy mode == X_const
15 +$PY scripts/00c_primary_models.py --legacy --ols-only
16 +$PY scripts/04_export_tables.py # v1.1 CSV tables from the stored pickles
17 +$PY scripts/04_export_tables.py --legacy-exhibits
18 +$PY scripts/10_stored_checks.py
19 +if [ "$FROM_RESULTS" != "--from-results" ]; then
20 + $PY scripts/05_geo_designs.py --seeds 42 43 44 45 46 --workers $WORKERS --resume --rf # ~5 h on 16 cores
21 + $PY scripts/00c_primary_models.py --v2 --workers $WORKERS # ~1.5 h
22 + $PY scripts/07_qr_v2.py --draws 999 --workers $WORKERS --legacy-check # ~1.5 h
23 +fi
24 +$PY scripts/06_inference_v2.py --workers $WORKERS
25 +$PY scripts/09_geo_inference.py
26 +$PY scripts/08_tables_figures_v2.py
27 +$PY -m pytest -q tests
28 +(cd paper && latexmk -pdf -quiet main.tex >/dev/null 2>&1 || true)
29 +echo "run_all.sh finished"
added scripts/08_tables_figures_v2.py +759 −0
@@ -0,0 +1,759 @@
1 +#!/usr/bin/env python3
2 +# Author: Simon-Pierre Boucher — contact@spboucher.ai
3 +#
4 +"""Every table of the revised paper as a LaTeX fragment (paper/tables/v2_*.tex),
5 +the revised figures (figures/v2_*.png) and results/inline_numbers.json, all
6 +generated from the CSV outputs of scripts 00-10 (November 2026 review, batch
7 +L2; items WP03-DATA-03, DATA-04, EDIT-08, EDIT-13, ADVE-09, THEO-07, ADVE-15,
8 +ADVE-11, ECON-11, DATA-05 and the exhibits of clusters A-E).
9 +
10 +Nothing is typed by hand: every number comes from a stored CSV/JSON. Generators
11 +whose inputs are not yet available are skipped with a message, so the script
12 +can be re-run as batches complete. ``--out DIR`` redirects the fragments
13 +(used by tests/test_tables.py to diff against the committed versions).
14 +
15 +Usage: python scripts/08_tables_figures_v2.py [--out paper/tables] [--no-figures]
16 +"""
17 +
18 +import argparse
19 +import json
20 +import sys
21 +from pathlib import Path
22 +
23 +import numpy as np
24 +import pandas as pd
25 +
26 +sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src"))
27 +from wp3 import config # noqa: E402
28 +from wp3.plotting import label # noqa: E402
29 +
30 +R = config.RESULTS_DIR
31 +TAB = config.PROJECT_ROOT / "paper" / "tables"
32 +FIG = config.FIGURES_DIR
33 +numbers = {}
34 +SKIPPED = []
35 +
36 +
37 +def put(key, value, source, note=""):
38 + numbers[key] = {"value": value, "source": source, "note": note}
39 +
40 +
41 +def f(x, d=3, plus=False):
42 + if x is None or (isinstance(x, float) and np.isnan(x)) or (isinstance(x, str) and x == ""):
43 + return "---"
44 + s = f"{x:+.{d}f}" if plus else f"{x:.{d}f}"
45 + return s.replace("-", "$-$")
46 +
47 +
48 +def n(x):
49 + return "---" if pd.isna(x) else f"{int(round(x)):,}"
50 +
51 +
52 +def pct(x, d=1):
53 + return "---" if pd.isna(x) else f"{x:.{d}f}\\%"
54 +
55 +
56 +def stars_p(p):
57 + return "***" if p < 0.001 else "**" if p < 0.01 else "*" if p < 0.05 else ""
58 +
59 +
60 +def tex_label(v):
61 + return label(v).replace("Log(", "$\\ln$(").replace("_", "\\_") if v not in ("lot_missing",) else "Lot missing (indicator)"
62 +
63 +
64 +def write(name, rows):
65 + body = "\n".join(rows).rstrip()
66 + if body.endswith("\\\\"):
67 + body = body[:-2].rstrip()
68 + (TAB / name).write_text(body + "\n")
69 + print("wrote", TAB / name)
70 +
71 +
72 +def csv(rel, **kw):
73 + p = R / rel
74 + if not p.exists():
75 + raise FileNotFoundError(rel)
76 + return pd.read_csv(p, **kw)
77 +
78 +
79 +def generator(fn):
80 + def wrapped():
81 + try:
82 + fn()
83 + except FileNotFoundError as exc:
84 + SKIPPED.append(f"{fn.__name__}: missing {exc}")
85 + print(f" skipped {fn.__name__}: missing {exc}")
86 + wrapped.__name__ = fn.__name__
87 + return wrapped
88 +
89 +
90 +DESIGN_LABEL = {"random": "Random 80/20", "size_matched": "Random, geographic training size", "ten_state": "Ten-state holdout",
91 + "gkf_state": "GroupKFold(5) by state", "gkf_zip3": "GroupKFold(5) by ZIP3", "gkf_zip5": "GroupKFold(5) by ZIP5",
92 + "loso": "Leave-one-state-out (51)"}
93 +COMP_LABEL = {"xgb_full_nested": "XGBoost, rounds selected on training groups", "xgb_full_fixed1000": "XGBoost, 1,000 rounds (fixed)",
94 + "xgb_full_fixed100": "XGBoost, 100 rounds (fixed)", "xgb_plus_missing_nested": "XGBoost + 6 missingness indicators, nested rounds",
95 + "lgb_full_nested": "LightGBM, nested rounds"}
96 +
97 +
98 +# ============================================================================ data tables
99 +@generator
100 +def t_attrition():
101 + a = csv("build/attrition.csv")
102 + crit = ["---", "$\\$10{,}000 < P < \\$10{,}000{,}000$", "$200 < \\text{sqft} < 20{,}000$",
103 + "$1 \\leq \\text{bed} \\leq 10$, $1 \\leq \\text{bath} \\leq 10$", "Non-missing latitude/longitude", "Exclude PR, VI"]
104 + rows = [f"{r.step} & {n(r.n_remaining)} & {('---' if i == 0 else n(r.n_dropped))} & {crit[i]} \\\\" for i, r in a.iterrows()]
105 + rows += ["\\midrule", f"\\textbf{{Final analytical sample}} & \\textbf{{{n(a.n_remaining.iloc[-1])}}} & "
106 + f"\\textbf{{{n(a.n_remaining.iloc[0] - a.n_remaining.iloc[-1])}}} & \\textbf{{({100 * (1 - a.share_of_raw.iloc[-1]):.1f}\\% removed)}} \\\\"]
107 + write("v2_attrition.tex", rows)
108 + put("n_raw", int(a.n_remaining.iloc[0]), "build/attrition.csv"); put("n_sample", int(a.n_remaining.iloc[-1]), "build/attrition.csv")
109 + put("attrition_pct", round(100 * (1 - a.share_of_raw.iloc[-1]), 1), "build/attrition.csv")
110 +
111 +
112 +@generator
113 +def t_missingness():
114 + m = csv("build/missingness_rules.csv")
115 + rows = [f"{r.input.replace('_', '\\_').replace('^', '\\^{}')} & {n(r.n_missing)} & {100 * r.missing_share:.1f} & {r.imputation.replace('_', '\\_')} \\\\"
116 + for _, r in m.iterrows() if r.missing_share >= 0.0005]
117 + write("v2_missingness.tex", rows)
118 + lot = csv("build/lot_reconciliation.csv")
119 + write("v2_lot_reconciliation.tex", [f"{r['class'].replace('_', '\\_').replace('<', '$<$').replace('=', '$=$')} & {n(r.n)} & {100 * r.share_of_sample:.2f} \\\\" for _, r in lot.iterrows()])
120 + d = csv("build/duplicates.csv").iloc[0]
121 + put("n_duplicate_rows", int(d.n_rows_in_duplicate_groups), "build/duplicates.csv"); put("n_duplicate_groups", int(d.n_duplicate_groups), "build/duplicates.csv")
122 +
123 +
124 +@generator
125 +def t_descriptives():
126 + d = csv("build/descriptives.csv", index_col=0)
127 + cont = [("price", "Listing price (\\$)", 0), ("living_area_sqft", "Living area (sqft)", 0), ("bedrooms", "Bedrooms", 2), ("bathrooms", "Bathrooms", 2),
128 + ("age", "Property age (years)", 1), ("lot_size_sqft", "Lot size (sqft, imputed field)", 0), ("walk_score", "Walk Score (0--100)", 1),
129 + ("bike_score", "Bike Score", 1), ("transit_score", "Transit Score (0--100)", 1), ("avg_school_rating", "Avg.\\ school rating (1--10)", 2),
130 + ("property_tax_rate", "Property tax rate (\\%)", 2), ("luxury_score", "Luxury score (0--7)", 2)]
131 + rows = ["\\multicolumn{6}{l}{\\textit{Continuous variables}} \\\\"]
132 + for c, lab, k in cont:
133 + r = d.loc[c]; fmt = (lambda v: f"{v:,.0f}") if k == 0 else (lambda v, k=k: f"{v:.{k}f}")
134 + rows.append(f"{lab} & {fmt(r['mean'])} & {fmt(r['std'])} & {fmt(r['min'])} & {fmt(r['50%'])} & {fmt(r['max'])} \\\\")
135 + rows += ["\\midrule", "\\multicolumn{6}{l}{\\textit{Binary variables (prevalence)}} \\\\"]
136 + for c, lab in [("has_pool", "Swimming pool"), ("has_garage", "Garage"), ("has_fireplace", "Fireplace"), ("has_basement", "Basement"), ("on_waterfront", "Waterfront"),
137 + ("has_hoa", "HOA"), ("has_central_air", "Central air"), ("has_hardwood", "Hardwood floors"), ("is_condo", "Condominium"),
138 + ("tag_new_construction", "New construction"), ("tag_foreclosure", "Foreclosure")]:
139 + rows.append(f"{lab} & {pct(100 * d.loc[c, 'mean'])} & & & & \\\\")
140 + write("v2_descriptives.tex", rows)
141 +
142 +
143 +# ============================================================================ OLS tables
144 +@generator
145 +def t_ols_v2():
146 + s = csv("primary/ols_v2_standardized.csv", index_col=0); u = csv("primary/ols_v2_unstandardized.csv", index_col=0)
147 + fit = json.loads((R / "primary/ols_v2_fit.json").read_text())
148 + rows = []
149 + for v in [c for c in s.index if c != "const"]:
150 + rows.append(f"{tex_label(v)} & {f(s.loc[v, 'coef'])} & ({f(s.loc[v, 'se'], 4)}) & {stars_p(s.loc[v, 'p'])} & "
151 + f"{f(u.loc[v, 'coef'], 4)} & ({f(u.loc[v, 'se'], 4)}) \\\\")
152 + rows += ["\\midrule", f"Constant & {f(s.loc['const', 'coef'])} & ({f(s.loc['const', 'se'], 4)}) & & {f(u.loc['const', 'coef'])} & ({f(u.loc['const', 'se'], 4)}) \\\\",
153 + f"$N$ & {n(fit['standardised']['nobs'])} & & & & \\\\", f"$R^2$ & {f(fit['standardised']['r2'])} & & & & \\\\",
154 + f"RMSE & {f(fit['standardised']['rmse'])} & & & & \\\\", f"$F$ & {n(fit['standardised']['f'])} & & & & \\\\"]
155 + write("v2_ols_full.tex", rows)
156 + put("ols_v2_r2", round(fit["standardised"]["r2"], 4), "primary/ols_v2_fit.json"); put("ols_v2_n", fit["standardised"]["nobs"], "primary/ols_v2_fit.json")
157 + put("ols_v2_lot_elasticity", round(u.loc["ln_lot", "coef"], 4), "primary/ols_v2_unstandardized.csv")
158 + put("ols_v2_sqft_elasticity", round(u.loc["ln_sqft", "coef"], 4), "primary/ols_v2_unstandardized.csv")
159 + put("ols_v2_resid_skew", round(fit["standardised"]["resid_skew"], 3), "primary/ols_v2_fit.json")
160 + put("ols_v2_resid_kurtosis", round(fit["standardised"]["resid_kurtosis"], 3), "primary/ols_v2_fit.json")
161 +
162 +
163 +@generator
164 +def t_cluster_se():
165 + c = csv("inference_v2/cluster_se_v2.csv", index_col=0)
166 + w = None
167 + try:
168 + w = csv("inference_v2/wild_bootstrap_state.csv", index_col=0)
169 + except FileNotFoundError:
170 + pass
171 + key = ["ln_sqft", "bedrooms", "bathrooms", "age", "age_sq", "ln_lot", "lot_missing", "has_pool", "has_garage", "on_waterfront", "has_central_air",
172 + "has_hardwood", "luxury_score", "walk_score", "bike_score", "transit_score", "avg_school_rating", "property_tax_rate", "is_condo", "has_hoa",
173 + "tag_new_construction", "tag_foreclosure", "region_Northeast", "region_South", "region_West"]
174 + rows = []
175 + for v in key:
176 + r = c.loc[v]
177 + pw = f(w.loc[v, "p_wild_webb"], 3) if w is not None and v in w.index else "---"
178 + rows.append(f"{tex_label(v)} & {f(r.coef, 4)} & {f(r.se_hc3, 4)} & {f(r.se_cluster_zip3, 4)} & {f(r.se_cluster_state, 4)} & "
179 + f"{f(r.ratio_state_hc3, 1)} & {f(r.t_cluster_state, 2)} & {pw} & {f(r.G_eff_css_state, 1)} \\\\")
180 + cnt = csv("inference_v2/significance_counts_v2.csv").set_index("se")
181 + rows.append("\\midrule")
182 + rows.append(f"Slopes with $|t| > 1.96$ (of {int(cnt.loc['hc3', 'n_slopes'])}) & & {int(cnt.loc['hc3', 'n_abs_t_gt_196'])} & {int(cnt.loc['cluster_zip3', 'n_abs_t_gt_196'])} & "
183 + f"{int(cnt.loc['cluster_state', 'n_abs_t_gt_196'])} & & & & \\\\")
184 + rows.append(f"Slopes with $|t| > t_{{0.975}}(G-1)$ & & {int(cnt.loc['hc3', 'n_abs_t_gt_crit'])} & {int(cnt.loc['cluster_zip3', 'n_abs_t_gt_crit'])} & "
185 + f"{int(cnt.loc['cluster_state', 'n_abs_t_gt_crit'])} & & & & \\\\")
186 + rows.append(f"Slopes significant after Holm (5\\%) & & {int(cnt.loc['hc3', 'n_holm_5pct'])} & {int(cnt.loc['cluster_zip3', 'n_holm_5pct'])} & "
187 + f"{int(cnt.loc['cluster_state', 'n_holm_5pct'])} & & & & \\\\")
188 + rows.append(f"Slopes significant after BH (5\\%) & & {int(cnt.loc['hc3', 'n_bh_5pct'])} & {int(cnt.loc['cluster_zip3', 'n_bh_5pct'])} & "
189 + f"{int(cnt.loc['cluster_state', 'n_bh_5pct'])} & & & & \\\\")
190 + if w is not None:
191 + wc = csv("inference_v2/wild_bootstrap_counts.csv").iloc[0]
192 + rows.append(f"Slopes with wild-bootstrap $p < 0.05$ (Webb / Rademacher) & & & & {int(wc.n_sig_5pct_webb)} / {int(wc.n_sig_5pct_rademacher)} & & & & \\\\")
193 + rows.append(f"\\quad after Holm & & & & {int(wc.n_sig_5pct_webb_holm)} / {int(wc.n_sig_5pct_rademacher_holm)} & & & & \\\\")
194 + put("n_sig_state_wild_webb", int(wc.n_sig_5pct_webb), "inference_v2/wild_bootstrap_counts.csv"); put("n_sig_state_wild_webb_holm", int(wc.n_sig_5pct_webb_holm), "inference_v2/wild_bootstrap_counts.csv")
195 + put("n_sig_state_wild_rademacher", int(wc.n_sig_5pct_rademacher), "inference_v2/wild_bootstrap_counts.csv")
196 + sl = c.drop(index="const")
197 + rows.append(f"Median SE ratio over all slopes & & & {f(sl.ratio_zip3_hc3.median(), 1)} & {f(sl.ratio_state_hc3.median(), 1)} & & & & \\\\")
198 + rows.append(f"Range of the state/HC3 ratio (p10--p90) & & & & {f(sl.ratio_state_hc3.quantile(.1), 1)}--{f(sl.ratio_state_hc3.quantile(.9), 1)} & & & & \\\\")
199 + rows.append(f"CSS effective clusters, state (median; min) & & & & {f(sl.G_eff_css_state.median(), 1)}; {f(sl.G_eff_css_state.min(), 1)} & & & & \\\\")
200 + write("v2_cluster_se.tex", rows)
201 + for k in ["hc3", "cluster_zip3", "cluster_state"]:
202 + put(f"n_sig_{k}_slopes_196_v2", int(cnt.loc[k, "n_abs_t_gt_196"]), "inference_v2/significance_counts_v2.csv")
203 + put(f"n_sig_{k}_slopes_holm_v2", int(cnt.loc[k, "n_holm_5pct"]), "inference_v2/significance_counts_v2.csv")
204 + put(f"n_sig_{k}_slopes_bh_v2", int(cnt.loc[k, "n_bh_5pct"]), "inference_v2/significance_counts_v2.csv")
205 + put("n_slopes_v2", int(cnt.loc["hc3", "n_slopes"]), "inference_v2/significance_counts_v2.csv", "62 regressors + lot indicator")
206 + put("se_ratio_state_hc3_median_v2", round(float(sl.ratio_state_hc3.median()), 1), "inference_v2/cluster_se_v2.csv")
207 + put("se_ratio_zip3_hc3_median_v2", round(float(sl.ratio_zip3_hc3.median()), 1), "inference_v2/cluster_se_v2.csv")
208 + put("se_ratio_state_hc3_min_v2", round(float(sl.ratio_state_hc3.min()), 3), "inference_v2/cluster_se_v2.csv"); put("se_ratio_state_hc3_max_v2", round(float(sl.ratio_state_hc3.max()), 1), "inference_v2/cluster_se_v2.csv")
209 + put("css_G_eff_state_median", round(float(sl.G_eff_css_state.median()), 1), "inference_v2/cluster_se_v2.csv"); put("css_G_eff_state_min", round(float(sl.G_eff_css_state.min()), 1), "inference_v2/cluster_se_v2.csv")
210 + z = csv("inference_v2/zip3_missing_cluster.csv").iloc[0]
211 + put("n_listings_missing_zip", int(z.n_listings_missing_zip), "inference_v2/zip3_missing_cluster.csv"); put("n_zip3_prefixes", int(z.n_zip3_prefixes), "inference_v2/zip3_missing_cluster.csv")
212 + put("n_zip3_clusters", int(z.n_clusters_with_000), "inference_v2/zip3_missing_cluster.csv")
213 + put("max_rel_se_change_zip3_reassignment_pct", round(100 * float(z.max_abs_rel_se_change_after_reassignment), 2), "inference_v2/zip3_missing_cluster.csv")
214 + try:
215 + ch = csv("inference_v2/conley_hac_v2.csv", index_col=0)
216 + rows = []
217 + for v in key:
218 + r = ch.loc[v]
219 + rows.append(f"{tex_label(v)} & {f(r.coef, 4)} & {f(r.se_hc0, 4)} & {f(r.se_cluster_state, 4)} & {f(r.se_conley_25km, 4)} & {f(r.se_conley_50km, 4)} & {f(r.se_conley_100km, 4)} \\\\")
220 + sl2 = ch.drop(index="const")
221 + rows += ["\\midrule", f"Median ratio to HC0 & & 1.0 & {f((sl2.se_cluster_state / sl2.se_hc0).median(), 1)} & {f(sl2.ratio_conley_25km_hc0.median(), 1)} & "
222 + f"{f(sl2.ratio_conley_50km_hc0.median(), 1)} & {f(sl2.ratio_conley_100km_hc0.median(), 1)} \\\\"]
223 + write("v2_conley.tex", rows)
224 + put("conley_100km_median_ratio", round(float(sl2.ratio_conley_100km_hc0.median()), 1), "inference_v2/conley_hac_v2.csv")
225 + except FileNotFoundError:
226 + SKIPPED.append("conley table")
227 +
228 +
229 +@generator
230 +def t_fe_progression():
231 + l = csv("tables/legacy_fe_progression.csv")
232 + rows = [f"{r.specification} & {r.fe} & {int(r.n_fe)} & {f(r.r2_insample)} & {f(r.r2_outsample)} \\\\" for _, r in l.iterrows()]
233 + try:
234 + m = csv("primary/model_comparison_v2.csv")
235 + for mod, lab in [("OLS (62 regressors + lot indicator)", "v2 baseline (4 regions, lot recoded, trimmed)"), ("OLS + state fixed effects", "v2 state FE"), ("OLS + ZIP3 fixed effects", "v2 ZIP3 FE")]:
236 + r = m[m.model == mod].iloc[0]
237 + rows.append(f"{lab} & & & --- & {f(r.r2)} \\\\")
238 + except (FileNotFoundError, IndexError):
239 + pass
240 + write("v2_fe_progression.tex", rows)
241 +
242 +
243 +@generator
244 +def t_state_heterogeneity():
245 + s = csv("inference_v2/state_heterogeneity_summary_v2.csv"); p = csv("inference_v2/pooled_vs_interacted_v2.csv")
246 + rows = []
247 + for _, r in s.iterrows():
248 + rows.append(f"{tex_label(r.variable)} & {f(r.pooled_state_fe)} & ({f(r.pooled_se_zip3, 3)}) & {f(r.p10)} & {f(r['median'])} & {f(r.p90)} & "
249 + f"{int(r.n_sign_differs_from_pooled)} & {int(r.n_sign_differs_and_abs_t_gt_2)} & {int(r.n_state_ci_excludes_pooled)} & "
250 + f"{f(r.tau_dl)} & {('$<0.001$' if r.homogeneity_p < 0.001 else f(r.homogeneity_p))} \\\\")
251 + write("v2_state_heterogeneity.tex", rows)
252 + write("v2_pooled_vs_interacted.tex", [f"{r.model} & {f(r.r2_test)} \\\\" for _, r in p.iterrows()])
253 + ss = s.set_index("variable")
254 + put("ln_sqft_state_p10_v2", round(ss.loc["ln_sqft", "p10"], 2), "inference_v2/state_heterogeneity_summary_v2.csv"); put("ln_sqft_state_p90_v2", round(ss.loc["ln_sqft", "p90"], 2), "inference_v2/state_heterogeneity_summary_v2.csv")
255 + put("ln_sqft_state_min_v2", round(ss.loc["ln_sqft", "min"], 2), "inference_v2/state_heterogeneity_summary_v2.csv"); put("ln_sqft_state_max_v2", round(ss.loc["ln_sqft", "max"], 2), "inference_v2/state_heterogeneity_summary_v2.csv")
256 + put("interacted_gain_over_state_fe_pp", round(100 * (p.r2_test.iloc[2] - p.r2_test.iloc[1]), 1), "inference_v2/pooled_vs_interacted_v2.csv")
257 + for v in ["bedrooms", "has_pool", "luxury_score", "has_hoa"]:
258 + put(f"n_sign_reversals_{v}_v2", int(ss.loc[v, "n_sign_differs_from_pooled"]), "inference_v2/state_heterogeneity_summary_v2.csv")
259 + put(f"n_sign_reversals_{v}_t2_v2", int(ss.loc[v, "n_sign_differs_and_abs_t_gt_2"]), "inference_v2/state_heterogeneity_summary_v2.csv")
260 + h = csv("inference_v2/state_heterogeneity_v2.csv")
261 + put("state_regression_max_cond_number", float(f"{h.cond_number.max():.3g}"), "inference_v2/state_heterogeneity_v2.csv")
262 +
263 +
264 +@generator
265 +def t_lot_recoding():
266 + d = csv("inference_v2/lot_recoding_v2.csv")
267 + rows = []
268 + for samp, g in d.groupby("sample", sort=False):
269 + rows.append(f"\\multicolumn{{7}}{{l}}{{\\textit{{{samp}}}}} \\\\")
270 + for _, r in g.iterrows():
271 + rows.append(f"\\quad {r.specification} & {n(r.N)} & {f(r.R2)} & {f(r.beta_ln_lot)} & ({f(r.se_ln_lot, 4)}) & {f(r.beta_ln_sqft)} & {f(r.beta_lot_missing)} \\\\")
272 + write("v2_lot_recoding.tex", rows)
273 + t = d[d["sample"].str.startswith("trimmed")]
274 + put("lot_elasticity_v11_coding", round(float(t.beta_ln_lot.iloc[0]), 4), "inference_v2/lot_recoding_v2.csv"); put("lot_elasticity_v2", round(float(t.beta_ln_lot.iloc[2]), 4), "inference_v2/lot_recoding_v2.csv")
275 +
276 +
277 +@generator
278 +def t_moran():
279 + m = csv("inference_v2/moran_full_test_v2.csv")
280 + rows = []
281 + for pred, g in m.groupby("predictions", sort=False):
282 + rows.append(f"\\multicolumn{{7}}{{l}}{{\\textit{{{pred}}}}} \\\\")
283 + for model in ["OLS", "LightGBM", "XGBoost"]:
284 + gg = g[g.model == model]
285 + knn = gg[gg.weights.str.startswith("kNN")].iloc[0]
286 + bands = {w: r for w, r in zip(gg.weights, gg.itertuples()) if "band" in w}
287 + b = [bands.get(f"distance band {lo}-{hi} km (binary)") for lo, hi in [(0, 1), (1, 5), (5, 25)]]
288 + rows.append(f"\\quad {model} & {n(knn.n)} & {f(knn.I, 4)} & {f(knn.z_sim, 1)} & {f(knn.median_kth_neighbour_km, 2)} & "
289 + + " & ".join(f(x.I, 4) if x is not None else "---" for x in b) + " \\\\")
290 + write("v2_moran_full.tex", rows)
291 + k = m[(m.weights.str.startswith("kNN")) & (m.model == "OLS")].iloc[0]
292 + put("moran_full_test_ols_I", round(float(k.I), 4), "inference_v2/moran_full_test_v2.csv"); put("moran_full_test_ols_z", round(float(k.z_sim), 1), "inference_v2/moran_full_test_v2.csv")
293 + put("median_8th_neighbour_km", round(float(k.median_kth_neighbour_km), 2), "inference_v2/moran_full_test_v2.csv")
294 + for model in ["OLS", "LightGBM", "XGBoost"]:
295 + kk = m[(m.weights.str.startswith("kNN")) & (m.model == model)].iloc[-1]
296 + put(f"moran_full_test_{model.lower()}_I_latest", round(float(kk.I), 4), "inference_v2/moran_full_test_v2.csv", kk.predictions)
297 + b1 = m[(m.weights.str.startswith("distance band 0-1")) & (m.model == model)].iloc[-1]
298 + put(f"moran_band_0_1km_{model.lower()}_I", round(float(b1.I), 4), "inference_v2/moran_full_test_v2.csv", b1.predictions)
299 +
300 +
301 +# ============================================================================ QR tables
302 +@generator
303 +def t_qr():
304 + c = csv("qr/qr_coefficients_v2.csv", index_col=0); pr2 = csv("qr/qr_pseudo_r2_v2.csv", index_col=0)
305 + conv = csv("qr/qr_convergence_v2.csv")
306 + ols = csv("primary/ols_v2_standardized.csv", index_col=0) if (R / "primary/ols_v2_standardized.csv").exists() else None
307 + taus = ["0.1", "0.25", "0.5", "0.75", "0.9"]
308 + key = ["ln_sqft", "bedrooms", "bathrooms", "age", "age_sq", "ln_lot", "lot_missing", "parking_spaces", "has_pool", "has_garage", "luxury_score",
309 + "tag_foreclosure", "on_waterfront", "region_Northeast", "region_West"]
310 + rows = []
311 + for v in key:
312 + cells = [f"{f(c.loc[v, f'tau_{t}'])}{stars_p(c.loc[v, f'p_tau_{t}'])}" for t in taus]
313 + o = f"{f(ols.loc[v, 'coef'])}{stars_p(ols.loc[v, 'p'])}" if ols is not None and v in ols.index else "---"
314 + rows.append(f"{tex_label(v)} & " + " & ".join(cells) + f" & {o} \\\\")
315 + rows += ["\\midrule", "Pseudo-$R^2$ & " + " & ".join(f(pr2.loc['pseudo_r2', f'tau_{t}']) for t in taus) + " & \\\\",
316 + "Converged & " + " & ".join("yes" if conv.set_index("tau").loc[float(t), "converged"] else "no" for t in taus) + " & \\\\"]
317 + write("v2_qr_coefficients.tex", rows)
318 + put("qr_v2_parking_max_abs", round(float(c.loc["parking_spaces", [f"tau_{t}" for t in taus]].abs().max()), 4), "qr/qr_coefficients_v2.csv", "v1.2: 691 / 317 / -98 / -527")
319 + put("qr_v2_all_converged", bool(conv.converged.all()), "qr/qr_convergence_v2.csv")
320 + put("qr_v2_garage_010", round(float(c.loc["has_garage", "tau_0.1"]), 3), "qr/qr_coefficients_v2.csv"); put("qr_v2_garage_090", round(float(c.loc["has_garage", "tau_0.9"]), 3), "qr/qr_coefficients_v2.csv")
321 + put("qr_v2_lot_010", round(float(c.loc["ln_lot", "tau_0.1"]), 3), "qr/qr_coefficients_v2.csv"); put("qr_v2_lot_090", round(float(c.loc["ln_lot", "tau_0.9"]), 3), "qr/qr_coefficients_v2.csv")
322 + st = csv("qr/qr_stability_v2.csv").sort_values("cv")
323 + write("v2_qr_stability.tex", [f"{tex_label(r.variable)} & {f(r['mean'])} & {100 * r.cv:.1f} & {r.sign_stability_pct:.0f} & {int(r.n_converged)}/{int(r.n_subsamples)} \\\\" for _, r in st.iterrows()])
324 + try:
325 + lc = csv("qr/qr_legacy_check.csv")
326 + write("v2_qr_legacy_check.tex", [f"{r.tau:.2f} & {f(r.pseudo_r2_stored)} & {f(r.pseudo_r2_rebuilt)} & {f(r.median_abs_diff_params, 4)} & {f(r.max_abs_diff_params_excl_parking, 4)} & "
327 + f"{f(r.ln_lot_stored)} & {f(r.ln_lot_rebuilt)} & {f(r.parking_stored, 1)} & {f(r.parking_rebuilt, 3)} \\\\" for _, r in lc.iterrows()])
328 + put("qr_legacy_median_abs_diff_max_over_tau", round(float(lc.median_abs_diff_params.max()), 4), "qr/qr_legacy_check.csv")
329 + except FileNotFoundError:
330 + SKIPPED.append("qr legacy check")
331 +
332 +
333 +@generator
334 +def t_iqr():
335 + d = csv("qr/iqr_bootstrap_v2.csv"); z = d[d.scheme == "zip3"].copy(); o = d[d.scheme == "obs"].set_index("variable")
336 + z["absz"] = z.z_boot.abs(); z = z.sort_values("absz", ascending=False)
337 + rows = []
338 + for _, r in z.iterrows():
339 + star = "*" if r.sig_5pct_percentile_ci else ""
340 + rows.append(f"{tex_label(r.variable)} & {f(r.difference, 3, plus=True)} & {f(r.z_independent, 2)} & {f(o.loc[r.variable, 'z_boot'], 2)} & {f(r.z_boot, 2)} & "
341 + f"[{f(r.ci_low_percentile)}, {f(r.ci_high_percentile)}]{star} & {f(r.p_normal_approx)} & {f(r.p_holm)} & {f(r.p_bh)} & {f(r.p_romano_wolf)} \\\\")
342 + rows += ["\\midrule", f"Significant at 5\\% (of {len(z)}) & & {int((z.z_independent.abs() > 1.96).sum())} & {int((o.z_boot.abs() > 1.96).sum())} & {int(z.sig_5pct_normal.sum())} & "
343 + f"{int(z.sig_5pct_percentile_ci.sum())} & {int(z.sig_5pct_normal.sum())} & {int(z.sig_5pct_holm.sum())} & {int(z.sig_5pct_bh.sum())} & {int(z.sig_5pct_romano_wolf.sum())} \\\\"]
344 + write("v2_iqr_bootstrap.tex", rows)
345 + put("iqr_v2_n_vars", int(len(z)), "qr/iqr_bootstrap_v2.csv"); put("iqr_v2_n_sig_independent", int((z.z_independent.abs() > 1.96).sum()), "qr/iqr_bootstrap_v2.csv")
346 + put("iqr_v2_n_sig_zip3_normal", int(z.sig_5pct_normal.sum()), "qr/iqr_bootstrap_v2.csv"); put("iqr_v2_n_sig_zip3_percentile_ci", int(z.sig_5pct_percentile_ci.sum()), "qr/iqr_bootstrap_v2.csv")
347 + put("iqr_v2_n_sig_zip3_holm", int(z.sig_5pct_holm.sum()), "qr/iqr_bootstrap_v2.csv"); put("iqr_v2_n_sig_zip3_bh", int(z.sig_5pct_bh.sum()), "qr/iqr_bootstrap_v2.csv")
348 + put("iqr_v2_n_sig_zip3_romano_wolf", int(z.sig_5pct_romano_wolf.sum()), "qr/iqr_bootstrap_v2.csv"); put("iqr_v2_n_draws", int(z.n_draws.iloc[0]), "qr/iqr_bootstrap_v2.csv")
349 + put("iqr_v2_n_draws_converged_zip3", int(z.n_draws_converged.iloc[0]), "qr/iqr_bootstrap_v2.csv")
350 + g = z.set_index("variable").loc["has_garage"]
351 + put("iqr_v2_garage_diff", round(float(g.difference), 3), "qr/iqr_bootstrap_v2.csv"); put("iqr_v2_garage_ci_low", round(float(g.ci_low_percentile), 3), "qr/iqr_bootstrap_v2.csv"); put("iqr_v2_garage_ci_high", round(float(g.ci_high_percentile), 3), "qr/iqr_bootstrap_v2.csv")
352 + cse = csv("qr/qr_cluster_se_v2.csv", index_col=0)
353 + key = ["ln_sqft", "bedrooms", "bathrooms", "age", "ln_lot", "lot_missing", "has_pool", "has_garage", "luxury_score", "tag_foreclosure", "on_waterfront", "region_Northeast", "region_West"]
354 + write("v2_qr_cluster_se.tex", [f"{tex_label(v)} & {f(cse.loc[v, 'beta_010'])} & ({f(cse.loc[v, 'se_kernel_010'], 4)}) & ({f(cse.loc[v, 'se_boot_zip3_010'], 4)}) & "
355 + f"{f(cse.loc[v, 'beta_090'])} & ({f(cse.loc[v, 'se_kernel_090'], 4)}) & ({f(cse.loc[v, 'se_boot_zip3_090'], 4)}) \\\\" for v in key])
356 +
357 +
358 +# ============================================================================ ML tables
359 +@generator
360 +def t_ml_comparison():
361 + m = csv("primary/model_comparison_v2.csv")
362 + rows = []
363 + for model, g in m.groupby("model", sort=False):
364 + r = g.mean(numeric_only=True); sd = g.r2.std(ddof=1) if len(g) > 1 else np.nan
365 + extra = f" ({f(sd, 4)})" if len(g) > 1 else ""
366 + rows.append(f"{model.replace('(', '(').replace('_', '\\_')} & {f(r.r2, 4)}{extra} & {f(r.rmse, 4)} & {f(r.mae_log, 4)} & {f(r.mape, 1)} & {f(r.mdape, 1)} & {r.mae_dollar:,.0f} & "
367 + f"{('---' if pd.isna(r.get('best_iteration', np.nan)) else n(r.best_iteration))} \\\\")
368 + rows.append(f"\\multicolumn{{8}}{{l}}{{Training {n(m.n_train.iloc[0])}, test {n(m.n_test.iloc[0])} (seed 42, implausible rows removed); seeds 42--46 for the boosters (SD in parentheses)}} \\\\")
369 + write("v2_ml_comparison.tex", rows)
370 + for model, key in [("XGBoost (1,000 rounds, early stopping)", "xgb"), ("LightGBM (1,000 rounds, early stopping)", "lgb"), ("OLS (62 regressors + lot indicator)", "ols"),
371 + ("Random Forest (re-specified: 500 trees, 1/3 features, leaf 5)", "rf"), ("OLS + state fixed effects", "ols_state_fe"), ("OLS + ZIP3 fixed effects", "ols_zip3_fe")]:
372 + g = m[m.model == model]
373 + if len(g):
374 + put(f"r2_random_v2_{key}", round(float(g.r2.mean()), 4), "primary/model_comparison_v2.csv"); put(f"mdape_random_v2_{key}", round(float(g.mdape.mean()), 1), "primary/model_comparison_v2.csv")
375 + if len(g) > 1:
376 + put(f"r2_random_v2_{key}_sd", round(float(g.r2.std(ddof=1)), 4), "primary/model_comparison_v2.csv")
377 + imp = csv("primary/shap_importance_v2.csv", index_col=0)
378 + col = imp.columns[0]
379 + top = imp.sort_values(col, ascending=False).head(20)
380 + write("v2_shap_importance.tex", [f"{i + 1} & {tex_label(v)} & {f(r[col], 4)}" + "".join(f" & {f(r[c], 4)}" for c in imp.columns[1:]) + " \\\\" for i, (v, r) in enumerate(top.iterrows())])
381 + put("shap_v2_top1", top.index[0], "primary/shap_importance_v2.csv"); put("shap_v2_top3", top.index[2], "primary/shap_importance_v2.csv")
382 + put("shap_v2_rank_avg_school_rating", int(list(top.index).index("avg_school_rating") + 1) if "avg_school_rating" in top.index else None, "primary/shap_importance_v2.csv")
383 + cm = csv("primary/shap_cross_model_v2.csv")
384 + write("v2_shap_cross_model.tex", [f"{r.pair.replace('_', ' vs ')} & {f(r.spearman_rho, 4)} \\\\" for _, r in cm.iterrows()])
385 + put("shap_v2_rho_min", round(float(cm.spearman_rho.min()), 3), "primary/shap_cross_model_v2.csv"); put("shap_v2_rho_max", round(float(cm.spearman_rho.max()), 3), "primary/shap_cross_model_v2.csv")
386 + p = csv("inference_v2/paired_loss_cluster_se.csv")
387 + write("v2_paired_loss.tex", [f"{r.predictions} & {r.quantity} & {f(r['mean'], 4)} & {f(r.se_iid, 5)} & {f(r.se_cluster_zip3, 5)} & {f(r.t_cluster_zip3, 1)} \\\\" for _, r in p.iterrows()])
388 +
389 +
390 +# ============================================================================ geographic designs
391 +@generator
392 +def t_designs():
393 + met = csv("geo/metrics.csv"); pooled = csv("geo/metrics_pooled.csv"); fm = csv("geo/fold_membership.csv")
394 + gap = csv("geo_inference/gap_bootstrap.csv") if (R / "geo_inference/gap_bootstrap.csv").exists() else None
395 +
396 + def agg(dsg, model, variant, rule):
397 + if dsg in ("random", "size_matched", "ten_state"):
398 + s = met[(met.design == dsg) & (met.model == model) & (met.variant == variant) & (met.rounds_rule == rule)]
399 + else:
400 + s = pooled[(pooled.design == dsg) & (pooled.model == model) & (pooled.variant == variant) & (pooled.rounds_rule == rule)]
401 + if s.empty:
402 + return None
403 + return {"r2": s.r2.mean(), "sd": s.r2.std(ddof=1) if len(s) > 1 else np.nan, "within": s.r2_within_state.mean(), "mdape": s.mdape.mean(), "n_seeds": len(s),
404 + "rounds": (met[(met.design == dsg) & (met.model == model) & (met.variant == variant) & (met.rounds_rule == rule)].selected_rounds.mean() if rule == "nested" else np.nan),
405 + "n_test": int(s.n_test.iloc[0])}
406 +
407 + def ci(dsg, comp):
408 + if gap is None:
409 + return "---"
410 + g = gap[(gap.design == dsg) & (gap.comparator == comp)]
411 + return f"[{f(g.gap_ci_low_zip3.iloc[0], 3, plus=True)}, {f(g.gap_ci_high_zip3.iloc[0], 3, plus=True)}]" if len(g) else "---"
412 +
413 + # Table A: the headline comparison, every design x {OLS, OLS no region, OLS state FE, XGB nested, XGB 1000, XGB 100, LGB nested}
414 + rows = []
415 + specs = [("ols", "full", "ols", "OLS (62 regressors + lot indicator)"), ("ols", "no_region", "ols", "OLS, no region dummies"),
416 + ("ols_state_fe", "no_region", "ols", "OLS + state fixed effects"), ("xgb", "full", "nested", "XGBoost, nested rounds"),
417 + ("xgb", "full", "fixed1000", "XGBoost, 1,000 rounds"), ("xgb", "full", "fixed100", "XGBoost, 100 rounds"),
418 + ("xgb", "plus_missing", "nested", "XGBoost + missingness indicators, nested"), ("lgb", "full", "nested", "LightGBM, nested rounds")]
419 + for dsg in ["random", "size_matched", "ten_state", "gkf_state", "gkf_zip3", "gkf_zip5", "loso"]:
420 + first = True
421 + for model, variant, rule, lab in specs:
422 + a = agg(dsg, model, variant, rule)
423 + if a is None:
424 + continue
425 + comp = f"{model}_{variant}_{rule}"
426 + gapc = ci(dsg, comp) if model != "ols" and "ols" not in model else ""
427 + sd = f" ({f(a['sd'], 3)})" if a["n_seeds"] > 1 else ""
428 + rows.append(f"{(DESIGN_LABEL[dsg] if first else '')} & {lab} & {n(a['n_test'])} & {f(a['r2'])}{sd} & {f(a['within'])} & {f(a['mdape'], 1)} & "
429 + f"{('---' if pd.isna(a['rounds']) else n(a['rounds']))} & {gapc} \\\\")
430 + first = False
431 + put(f"r2_{dsg}_{comp}", round(float(a["r2"]), 4), "geo/metrics(_pooled).csv"); put(f"r2_within_state_{dsg}_{comp}", round(float(a["within"]), 4), "geo/metrics(_pooled).csv")
432 + if a["n_seeds"] > 1:
433 + put(f"r2_{dsg}_{comp}_sd", round(float(a["sd"]), 4), "geo/metrics.csv")
434 + if not pd.isna(a["rounds"]):
435 + put(f"rounds_{dsg}_{comp}", round(float(a["rounds"])), "geo/metrics.csv")
436 + rows.append("\\addlinespace")
437 + write("v2_designs_all.tex", rows)
438 + # fold membership (EDIT-13)
439 + rows = []
440 + for dsg in ["gkf_state", "ten_state"]:
441 + for _, r in fm[fm.design == dsg].iterrows():
442 + rows.append(f"{DESIGN_LABEL[dsg]} & {r.fold} & {int(r.n_states_test)} & {n(r.n_test)} & {str(r.states_test).replace(' ', ', ')} \\\\")
443 + write("v2_fold_membership.tex", rows)
444 + # per-fold grouped CV table
445 + rows = []
446 + for dsg in ["gkf_state", "gkf_zip3", "gkf_zip5"]:
447 + for model, variant, rule, lab in [("ols", "full", "ols", "OLS"), ("xgb", "full", "nested", "XGBoost, nested"), ("xgb", "full", "fixed1000", "XGBoost, 1,000")]:
448 + s = met[(met.design == dsg) & (met.model == model) & (met.variant == variant) & (met.rounds_rule == rule)]
449 + if s.empty:
450 + continue
451 + per = s.groupby("fold").r2.mean()
452 + cells = " & ".join(f(per.get(f"fold{k}", np.nan)) for k in range(1, 6))
453 + a = agg(dsg, model, variant, rule)
454 + rows.append(f"{DESIGN_LABEL[dsg]} & {lab} & {cells} & {f(a['r2'])} & {f(a['within'])} \\\\")
455 + write("v2_grouped_cv.tex", rows)
456 + c = csv("geo/n_corrupt_heldout.csv").iloc[0]
457 + put("n_corrupt_heldout_abs_error_gt_2", int(c.n_abs_error_gt_2), "geo/n_corrupt_heldout.csv", "v1.2 said 204")
458 + put("corrupt_heldout_sse_share_pct", round(100 * float(c.sse_share_abs_error_gt_2), 1), "geo/n_corrupt_heldout.csv")
459 + put("r2_ten_state_ols_v11_coding_untrimmed", float(f"{c.r2:.4g}"), "geo/n_corrupt_heldout.csv")
460 + try:
461 + rf = csv("geo/rf_geo.csv").iloc[0]
462 + put("r2_ten_state_rf_v2", round(float(rf.r2), 4), "geo/rf_geo.csv", "re-specified RF")
463 + except FileNotFoundError:
464 + pass
465 +
466 +
467 +@generator
468 +def t_gap():
469 + g = csv("geo_inference/gap_bootstrap.csv")
470 + rows = []
471 + for dsg in ["random", "size_matched", "ten_state", "gkf_state", "gkf_zip3", "gkf_zip5", "loso"]:
472 + s = g[g.design == dsg]
473 + first = True
474 + for _, r in s.iterrows():
475 + rows.append(f"{(DESIGN_LABEL[dsg] if first else '')} & {COMP_LABEL.get(r.comparator, r.comparator)} & {f(r.r2_ols)} & {f(r.r2_comparator_mean)} "
476 + f"{('(' + f(r.r2_comparator_sd, 3) + ')' if not pd.isna(r.r2_comparator_sd) else '')} & {f(r.gap_ols_minus_comparator_mean, 3, plus=True)} & "
477 + f"{f(r.gap_boot_se_zip3, 3)} & [{f(r.gap_ci_low_zip3, 3, plus=True)}, {f(r.gap_ci_high_zip3, 3, plus=True)}] \\\\")
478 + first = False
479 + put(f"gap_{dsg}_{r.comparator}", round(float(r.gap_ols_minus_comparator_mean), 4), "geo_inference/gap_bootstrap.csv")
480 + put(f"gap_{dsg}_{r.comparator}_ci_low", round(float(r.gap_ci_low_zip3), 4), "geo_inference/gap_bootstrap.csv"); put(f"gap_{dsg}_{r.comparator}_ci_high", round(float(r.gap_ci_high_zip3), 4), "geo_inference/gap_bootstrap.csv")
481 + write("v2_gap_bootstrap.tex", rows)
482 + rg = csv("geo_inference/gap_range.csv")
483 + for _, r in rg.iterrows():
484 + put(f"gap_range_min_{r.comparator}", round(float(r.gap_min_design), 4), "geo_inference/gap_range.csv", r.design_of_min)
485 + put(f"gap_range_max_{r.comparator}", round(float(r.gap_max_design), 4), "geo_inference/gap_range.csv", r.design_of_max)
486 + put(f"gap_n_designs_ci_excludes_zero_{r.comparator}", int(r.n_designs_ci_excludes_zero), "geo_inference/gap_range.csv", f"of {int(r.n_designs)}")
487 +
488 +
489 +@generator
490 +def t_loso():
491 + s = csv("geo_inference/loso_summaries.csv"); t = csv("geo_inference/loso_paired_tests.csv"); per = csv("geo_inference/loso_per_state.csv")
492 + lab = {"ols": "OLS (62 regressors + lot indicator)", "xgb_full_nested": "XGBoost, nested rounds", "xgb_full_fixed1000": "XGBoost, 1,000 rounds", "xgb_full_fixed100": "XGBoost, 100 rounds",
493 + "xgb_plus_missing_nested": "XGBoost + missingness, nested", "lgb_full_nested": "LightGBM, nested"}
494 + write("v2_loso_summary.tex", [f"{lab.get(r.model, r.model)} & {f(r['median'])} & {f(r.listing_weighted_mean)} & {f(r.p10)} & {f(r.p90)} & {f(r['min'])} & {f(r['max'])} & "
495 + f"{int(r.n_states_gt_05)} & {int(r.n_negative)} & {f(r.median_abs_mean_error)} \\\\" for _, r in s.iterrows()])
496 + write("v2_loso_tests.tex", [f"{r.comparison.replace('xgb_full_', 'XGBoost ').replace('lgb_full_', 'LightGBM ').replace('xgb_plus_missing_', 'XGBoost+miss ')} & {f(r.mean_diff, 3, plus=True)} & "
497 + f"{f(r.median_diff, 3, plus=True)} & {int(r.n_states_ols_better)}/{int(r.n_states)} & {f(r.sign_test_p)} & {f(r.wilcoxon_p)} & {f(r.listing_weighted_mean_diff, 3, plus=True)} \\\\" for _, r in t.iterrows()])
498 + cols = [c for c in ["r2_ols", "r2_xgb_full_nested", "r2_xgb_full_fixed1000", "r2_xgb_full_fixed100", "r2_lgb_full_nested"] if c in per.columns]
499 + pp = per.sort_values("r2_xgb_full_nested" if "r2_xgb_full_nested" in per else "r2_ols", ascending=False)
500 + write("v2_loso_states.tex", [f"{r.state} & {n(r.n_test)} & " + " & ".join(f(r[c]) for c in cols) + f" & {f(r.mean_error_ols, 3, plus=True)} & {f(r.get('mean_error_xgb_full_nested', np.nan), 3, plus=True)} \\\\" for _, r in pp.iterrows()])
501 + ss = s.set_index("model")
502 + for k in ["ols", "xgb_full_nested", "xgb_full_fixed1000", "xgb_full_fixed100"]:
503 + if k in ss.index:
504 + put(f"loso_median_{k}", round(float(ss.loc[k, "median"]), 3), "geo_inference/loso_summaries.csv"); put(f"loso_weighted_mean_{k}", round(float(ss.loc[k, "listing_weighted_mean"]), 3), "geo_inference/loso_summaries.csv")
505 + put(f"loso_n_states_gt_05_{k}", int(ss.loc[k, "n_states_gt_05"]), "geo_inference/loso_summaries.csv"); put(f"loso_p10_{k}", round(float(ss.loc[k, "p10"]), 3), "geo_inference/loso_summaries.csv"); put(f"loso_p90_{k}", round(float(ss.loc[k, "p90"]), 3), "geo_inference/loso_summaries.csv")
506 + for _, r in t.iterrows():
507 + k = r.comparison.replace("OLS minus ", "")
508 + put(f"loso_sign_test_p_ols_vs_{k}", float(f"{r.sign_test_p:.3g}"), "geo_inference/loso_paired_tests.csv"); put(f"loso_wilcoxon_p_ols_vs_{k}", float(f"{r.wilcoxon_p:.3g}"), "geo_inference/loso_paired_tests.csv")
509 + put(f"loso_n_states_ols_better_vs_{k}", int(r.n_states_ols_better), "geo_inference/loso_paired_tests.csv")
510 +
511 +
512 +@generator
513 +def t_decomposition():
514 + d = csv("geo_inference/sse_decomposition.csv")
515 + rows = []
516 + for dsg in ["random", "size_matched", "ten_state", "gkf_state", "gkf_zip3", "gkf_zip5", "loso"]:
517 + s = d[d.design == dsg]; first = True
518 + for _, r in s.iterrows():
519 + extra = ""
520 + for g in ["zip3", "zip5"]:
521 + if f"r2_within_{g}_oracle" in r and not pd.isna(r[f"r2_within_{g}_oracle"]):
522 + extra = f" & {f(r[f'r2_within_{g}_oracle'])} & {f(r[f'sse_between_share_{g}'])}"
523 + rows.append(f"{(DESIGN_LABEL[dsg] if first else '')} & {r.model.replace('_', ' ')} & {f(r.r2)} & {f(r.r2_within_state_oracle)} & {f(r.sse_within_share_state)} & {f(r.sse_between_share_state)} & "
524 + f"{f(r.between_share_of_variance_state)}{extra or ' & --- & ---'} \\\\")
525 + first = False
526 + put(f"sse_within_share_{dsg}_{r.model}", round(float(r.sse_within_share_state), 4), "geo_inference/sse_decomposition.csv")
527 + put(f"sse_between_share_{dsg}_{r.model}", round(float(r.sse_between_share_state), 4), "geo_inference/sse_decomposition.csv")
528 + put(f"r2_within_state_oracle_{dsg}_{r.model}", round(float(r.r2_within_state_oracle), 4), "geo_inference/sse_decomposition.csv")
529 + rows.append("\\addlinespace")
530 + write("v2_sse_decomposition.tex", rows)
531 +
532 +
533 +@generator
534 +def t_feasible():
535 + d = csv("geo_inference/feasible_level_correction.csv")
536 + rows = []
537 + for (dsg, model), g in d.groupby(["design", "model"], sort=False):
538 + g = g.sort_values("k_calibration")
539 + rows.append(f"{DESIGN_LABEL[dsg]} & {model.replace('_', ' ')} & {f(g.r2_raw.iloc[0])} & " + " & ".join(f"{f(r.r2_feasible_median)} [{f(r.r2_feasible_p25)}, {f(r.r2_feasible_p75)}]" for _, r in g.iterrows()) + f" & {f(g.r2_oracle_level.iloc[0])} \\\\")
540 + for _, r in g.iterrows():
541 + put(f"feasible_r2_{dsg}_{model}_k{int(r.k_calibration)}", round(float(r.r2_feasible_median), 4), "geo_inference/feasible_level_correction.csv")
542 + put(f"oracle_level_r2_{dsg}_{model}", round(float(g.r2_oracle_level.iloc[0]), 4), "geo_inference/feasible_level_correction.csv")
543 + write("v2_feasible_level.tex", rows)
544 +
545 +
546 +@generator
547 +def t_two_by_two():
548 + d = csv("geo_inference/two_by_two_region_rounds.csv")
549 + rows = []
550 + for dsg in ["ten_state", "gkf_state", "loso"]:
551 + s = d[d.design == dsg]
552 + if s.empty:
553 + continue
554 + for variant in ["full", "no_region", "no_school", "no_school_no_region", "plus_missing"]:
555 + g = s[s.variant == variant].set_index("rounds_rule")
556 + if g.empty:
557 + continue
558 + cells = " & ".join(f"{f(g.loc[r, 'r2_mean'])}{(' (' + f(g.loc[r, 'r2_sd'], 3) + ')') if r in g.index and not pd.isna(g.loc[r, 'r2_sd']) else ''}" if r in g.index else "---" for r in ["nested", "fixed100", "fixed1000"])
559 + rows.append(f"{DESIGN_LABEL[dsg]} & {variant.replace('_', ' ')} & {cells} & {f(g.loc['nested', 'r2_within_state_oracle']) if 'nested' in g.index else '---'} \\\\")
560 + for r in ["nested", "fixed100", "fixed1000"]:
561 + if r in g.index:
562 + put(f"r2_{dsg}_xgb_{variant}_{r}", round(float(g.loc[r, "r2_mean"]), 4), "geo_inference/two_by_two_region_rounds.csv")
563 + if not pd.isna(g.loc[r, "r2_sd"]):
564 + put(f"r2_{dsg}_xgb_{variant}_{r}_sd", round(float(g.loc[r, "r2_sd"]), 4), "geo_inference/two_by_two_region_rounds.csv")
565 + write("v2_two_by_two.tex", rows)
566 +
567 +
568 +@generator
569 +def t_ablation():
570 + a = csv("geo/ablation.csv")
571 + order = ["1_structural", "2_plus_lot", "3_plus_amenities", "4_plus_neighborhood", "5_plus_market", "6_full_model", "4a_stage3_plus_tax_only", "4b_stage3_plus_latlon",
572 + "4c_stage3_plus_state_dummies", "4d_stage4_school_to_state_id", "4e_stage4_no_walk_bike_transit", "6_full_no_region", "6_full_no_school", "6_full_no_school_no_region", "6_full_plus_latlon"]
573 + lab = {"1_structural": "1. Structural only", "2_plus_lot": "2. + Lot (recoded + indicator)", "3_plus_amenities": "3. + Amenities", "4_plus_neighborhood": "4. + Neighbourhood",
574 + "5_plus_market": "5. + Market status", "6_full_model": "6. Full (+ interactions, categorical, region)", "4a_stage3_plus_tax_only": "4a. Stage 3 + tax rate only",
575 + "4b_stage3_plus_latlon": "4b. Stage 3 + latitude/longitude", "4c_stage3_plus_state_dummies": "4c. Stage 3 + 50 state dummies",
576 + "4d_stage4_school_to_state_id": "4d. Stage 4, school block $\\to$ state identifier", "4e_stage4_no_walk_bike_transit": "4e. Stage 4 without Walk/Bike/Transit",
577 + "6_full_no_region": "6a. Full without region dummies", "6_full_no_school": "6b. Full without school variables", "6_full_no_school_no_region": "6c. Full without school and region",
578 + "6_full_plus_latlon": "6d. Full + latitude/longitude"}
579 + rows = []
580 + prev = None
581 + for spec in order:
582 + g = a[a.spec == spec]
583 + if g.empty:
584 + continue
585 + cells = {}
586 + for dsg in ["random", "ten_state"]:
587 + for rule in ["fixed1000", "fixed100"]:
588 + s = g[(g.design == dsg) & (g.rounds_rule == rule)]
589 + cells[(dsg, rule)] = (s.r2.mean(), s.r2.std(ddof=1) if len(s) > 1 else np.nan, s.r2_within_state.mean() if dsg == "ten_state" else np.nan, s.best_iteration.mean())
590 + put(f"ablation_{spec}_{dsg}_{rule}", round(float(s.r2.mean()), 4), "geo/ablation.csv")
591 + if len(s) > 1:
592 + put(f"ablation_{spec}_{dsg}_{rule}_sd", round(float(s.r2.std(ddof=1)), 4), "geo/ablation.csv")
593 + nf = int(g.n_features.iloc[0])
594 + r_, ts = cells[("random", "fixed1000")], cells[("ten_state", "fixed1000")]
595 + delta = f"{f(ts[0] - prev, 3, plus=True)}" if prev is not None and spec[0].isdigit() and spec[1] == "_" and spec != "1_structural" else "---"
596 + rows.append(f"{lab[spec]} & {nf} & {f(r_[0])} ({f(r_[1], 3)}) & {f(ts[0])} ({f(ts[1], 3)}) & {f(cells[('ten_state', 'fixed100')][0])} & {f(ts[2])} & {delta} & {n(ts[3])} \\\\")
597 + if spec[1] == "_" and spec[0].isdigit():
598 + prev = ts[0]
599 + if spec == "6_full_model":
600 + rows.append("\\midrule")
601 + write("v2_ablation.tex", rows)
602 + g4 = a[(a.spec == "4_plus_neighborhood") & (a.rounds_rule == "fixed1000")]; g3 = a[(a.spec == "3_plus_amenities") & (a.rounds_rule == "fixed1000")]
603 + for dsg in ["random", "ten_state"]:
604 + put(f"ablation_neighbourhood_gain_{dsg}_pp", round(100 * float(g4[g4.design == dsg].r2.mean() - g3[g3.design == dsg].r2.mean()), 1), "geo/ablation.csv")
605 + for spec in ["4c_stage3_plus_state_dummies", "4a_stage3_plus_tax_only", "4d_stage4_school_to_state_id", "4e_stage4_no_walk_bike_transit", "4b_stage3_plus_latlon"]:
606 + gg = a[(a.spec == spec) & (a.rounds_rule == "fixed1000")]
607 + for dsg in ["random", "ten_state"]:
608 + put(f"ablation_{spec}_minus_stage3_{dsg}_pp", round(100 * float(gg[gg.design == dsg].r2.mean() - g3[g3.design == dsg].r2.mean()), 1), "geo/ablation.csv")
609 +
610 +
611 +@generator
612 +def t_trees_curve():
613 + c = csv("geo/trees_curves.csv")
614 + s = c[(c.model == "xgb") & (c.variant == "full")]
615 + rows = []
616 + ten = s[s.design == "ten_state"].groupby("rounds").agg(r2=("r2_test", "mean"), sd=("r2_test", "std"), lo=("r2_test", "min"), hi=("r2_test", "max"), ev=("r2_eval_within_train", "mean"), k=("seed", "count"))
617 + rnd = s[s.design == "random"].groupby("rounds").agg(r2=("r2_test", "mean"), sd=("r2_test", "std"))
618 + for k in [25, 50, 75, 100, 125, 150, 175, 200, 300, 500, 700, 1000]:
619 + if k in ten.index:
620 + rows.append(f"{k} & {f(rnd.loc[k, 'r2']) if k in rnd.index else '---'} & {f(ten.loc[k, 'ev'])} & {f(ten.loc[k, 'r2'])} & {f(ten.loc[k, 'sd'], 4)} & {f(ten.loc[k, 'lo'])}--{f(ten.loc[k, 'hi'])} \\\\")
621 + write("v2_trees_curve.tex", rows)
622 + # peak per seed
623 + peaks = s[s.design == "ten_state"].groupby("seed").apply(lambda g: g.loc[g.r2_test.idxmax(), ["rounds", "r2_test"]], include_groups=False)
624 + put("ten_state_peak_rounds_mean", round(float(peaks.rounds.mean())), "geo/trees_curves.csv"); put("ten_state_peak_rounds_min", int(peaks.rounds.min()), "geo/trees_curves.csv"); put("ten_state_peak_rounds_max", int(peaks.rounds.max()), "geo/trees_curves.csv")
625 + put("ten_state_peak_r2_mean", round(float(peaks.r2_test.mean()), 4), "geo/trees_curves.csv")
626 + put("ten_state_r2_at_1000_mean", round(float(ten.loc[1000, "r2"]), 4) if 1000 in ten.index else None, "geo/trees_curves.csv"); put("ten_state_r2_at_100_mean", round(float(ten.loc[100, "r2"]), 4) if 100 in ten.index else None, "geo/trees_curves.csv")
627 + put("ten_state_r2_at_1000_sd", round(float(ten.loc[1000, "sd"]), 4) if 1000 in ten.index else None, "geo/trees_curves.csv")
628 + # seed-dispersion flags for the differences quoted in v1.2 (EDIT-06 / ECON-06)
629 + met = csv("geo/metrics.csv")
630 + def mean_sd(dsg, variant, rule):
631 + s_ = met[(met.design == dsg) & (met.model == "xgb") & (met.variant == variant) & (met.rounds_rule == rule)]
632 + return s_.r2.mean(), s_.r2.std(ddof=1)
633 + flags = []
634 + for lab, (d1, v1, r1), (d2, v2, r2) in [("size-matched minus random (1,000)", ("size_matched", "full", "fixed1000"), ("random", "full", "fixed1000")),
635 + ("+missingness minus full, ten-state (1,000)", ("ten_state", "plus_missing", "fixed1000"), ("ten_state", "full", "fixed1000")),
636 + ("+missingness minus full, ten-state (nested)", ("ten_state", "plus_missing", "nested"), ("ten_state", "full", "nested")),
637 + ("+missingness minus full, random (1,000)", ("random", "plus_missing", "fixed1000"), ("random", "full", "fixed1000"))]:
638 + m1, s1 = mean_sd(d1, v1, r1); m2, s2 = mean_sd(d2, v2, r2)
639 + sd = np.sqrt(np.nan_to_num(s1) ** 2 + np.nan_to_num(s2) ** 2)
640 + flags.append({"difference": lab, "value": m1 - m2, "sd_of_difference": sd, "exceeds_2sd": bool(abs(m1 - m2) > 2 * sd) if sd > 0 else None})
641 + pd.DataFrame(flags).to_csv(R / "geo_inference" / "seed_dispersion_flags.csv", index=False) if (R / "geo_inference").exists() else None
642 + write("v2_seed_flags.tex", [f"{r['difference']} & {f(r['value'], 4, plus=True)} & {f(r['sd_of_difference'], 4)} & {('yes' if r['exceeds_2sd'] else 'no') if r['exceeds_2sd'] is not None else '---'} \\\\" for r in flags])
643 +
644 +
645 +@generator
646 +def t_duplicates():
647 + d = csv("geo_inference/duplicates_random_split_v2.csv")
648 + write("v2_duplicates.tex", [f"{r.model.replace('_', ' ')} & {n(r.n_test)} & {n(r.n_test_with_train_duplicate)} & {f(r.r2_all, 4)} & {f(r.r2_without_duplicates, 4)} & {f(r.r2_duplicates_only, 4)} \\\\" for _, r in d.iterrows()])
649 + for _, r in d.iterrows():
650 + put(f"r2_random_without_duplicates_{r.model}", round(float(r.r2_without_duplicates), 4), "geo_inference/duplicates_random_split_v2.csv")
651 + put("n_test_rows_with_train_duplicate_v2", int(d.n_test_with_train_duplicate.iloc[0]), "geo_inference/duplicates_random_split_v2.csv")
652 +
653 +
654 +@generator
655 +def t_stored_checks():
656 + sc = json.loads((R / "inline_numbers.json").read_text()) if (R / "inline_numbers.json").exists() else {}
657 + for k, v in sc.items():
658 + if k not in numbers:
659 + numbers[k] = v
660 + z = csv("stored_checks/zestimate_selection.csv")
661 + write("v2_zestimate_selection.tex", [f"{tex_label(r.variable)} & {f(r.mean_with_zestimate, 2) if abs(r.mean_with_zestimate) < 1000 else f'{r.mean_with_zestimate:,.0f}'} & "
662 + f"{f(r.mean_without, 2) if abs(r.mean_without) < 1000 else f'{r.mean_without:,.0f}'} & {f(r.std_diff, 2)} \\\\" for _, r in z.iterrows()])
663 + k = csv("inference_v2/kennedy_clustered_v2.csv")
664 + rows = []
665 + for v, g in k.groupby("variable", sort=False):
666 + g = g.set_index("se")
667 + rows.append(f"{tex_label(v)} & {f(g.loc['hc3', 'coef'], 4)} & {f(g.loc['hc3', 'naive_pct'], 1)} & {f(g.loc['hc3', 'kennedy_pct'], 1)} & {f(g.loc['cluster_zip3', 'kennedy_pct'], 1)} & {f(g.loc['cluster_state', 'kennedy_pct'], 1)} & {f(g.loc['cluster_state', 't'], 2)} \\\\")
668 + write("v2_kennedy.tex", rows)
669 +
670 +
671 +# ============================================================================ figures
672 +def figures():
673 + import matplotlib
674 + matplotlib.use("Agg")
675 + import matplotlib.pyplot as plt
676 + from wp3.plotting import ACCENT, NEUTRAL, PRIMARY, PRIMARY_LIGHT, apply_paper_style, panel_title
677 + apply_paper_style()
678 + FIG.mkdir(exist_ok=True, parents=True)
679 + # Fig 6 v2: trees curve with seed bands
680 + p = R / "geo/trees_curves.csv"
681 + if p.exists():
682 + c = pd.read_csv(p); s = c[(c.model == "xgb") & (c.variant == "full")]
683 + fig, ax = plt.subplots(figsize=(8.5, 5))
684 + for dsg, col, lab, mk in [("random", PRIMARY, "Random 20% test set", "o"), ("ten_state", ACCENT, "Ten held-out states", "D"), ("loso", NEUTRAL, "Leave-one-state-out (pooled over 51 states, mean over folds)", "s")]:
685 + g = s[s.design == dsg].groupby("rounds").r2_test.agg(["mean", "min", "max"])
686 + if g.empty:
687 + continue
688 + ax.plot(g.index, g["mean"], color=col, lw=2, marker=mk, ms=4, label=lab)
689 + ax.fill_between(g.index, g["min"], g["max"], color=col, alpha=0.18, lw=0)
690 + g = s[s.design == "ten_state"].groupby("rounds").r2_eval_within_train.mean()
691 + ax.plot(g.index, g.values, color=NEUTRAL, lw=1.5, ls="--", label="Early-stopping set (within training states)")
692 + ax.set_xlabel("Boosting rounds"); ax.set_ylabel("Out-of-sample $R^2$"); ax.set_ylim(0.3, 0.9); ax.legend(loc="lower right", fontsize=9)
693 + fig.savefig(FIG / "v2_fig_trees_curve_seeds.png"); plt.close(fig); print("wrote", FIG / "v2_fig_trees_curve_seeds.png")
694 + # Fig 7 v2: LOSO OLS vs XGB by state
695 + p = R / "geo_inference/loso_per_state.csv"
696 + if p.exists():
697 + l = pd.read_csv(p).sort_values("r2_ols")
698 + fig, ax = plt.subplots(figsize=(8.5, 10))
699 + yy = np.arange(len(l))
700 + ax.barh(yy - 0.2, l.r2_ols, height=0.38, color=PRIMARY, label="OLS (62 regressors + lot indicator)")
701 + if "r2_xgb_full_nested" in l:
702 + ax.barh(yy + 0.2, l.r2_xgb_full_nested, height=0.38, color=ACCENT, label="XGBoost, rounds selected on training states")
703 + ax.set_yticks(yy); ax.set_yticklabels(l.state, fontsize=8.5); ax.axvline(0, color=NEUTRAL, lw=0.9)
704 + ax.set_xlabel("Leave-one-state-out $R^2$"); ax.legend(loc="lower right"); ax.grid(axis="x", linestyle=":", alpha=0.45); ax.grid(axis="y", visible=False)
705 + fig.savefig(FIG / "v2_fig_loso_ols_vs_xgb.png"); plt.close(fig); print("wrote", FIG / "v2_fig_loso_ols_vs_xgb.png")
706 + # Fig 3 v2: state heterogeneity with ZIP3-cluster CIs and EB bands
707 + p = R / "inference_v2/state_heterogeneity_eb.csv"
708 + if p.exists():
709 + e = pd.read_csv(p); s = pd.read_csv(R / "inference_v2/state_heterogeneity_summary_v2.csv").set_index("variable")
710 + fig, axes = plt.subplots(1, 2, figsize=(12, 9))
711 + for ax, var, title in [(axes[0], "ln_sqft", "(a) Living-area elasticity"), (axes[1], "has_garage", "(b) Garage coefficient")]:
712 + g = e[e.variable == var].sort_values("estimate")
713 + yy = np.arange(len(g))
714 + ax.errorbar(g.estimate, yy, xerr=1.96 * g.se_zip3, fmt="o", color=PRIMARY, ms=4, elinewidth=0.8, capsize=0, label="State OLS, 95% CI (ZIP3 clusters)")
715 + ax.scatter(g.eb_posterior_mean, yy, marker="|", s=60, color=ACCENT, label="Empirical-Bayes posterior mean")
716 + ax.axvline(s.loc[var, "pooled_state_fe"], color=ACCENT, lw=1.3, ls="--", label=f"Pooled, state FE: {s.loc[var, 'pooled_state_fe']:.3f}")
717 + ax.axvline(0, color=NEUTRAL, lw=0.8); ax.set_yticks(yy); ax.set_yticklabels(g.state, fontsize=8)
718 + ax.grid(axis="x", linestyle=":", alpha=0.45); ax.grid(axis="y", visible=False); panel_title(ax, title)
719 + ax.set_xlabel("Coefficient (natural units)"); ax.legend(loc="lower right", fontsize=8.5)
720 + fig.tight_layout(); fig.savefig(FIG / "v2_fig_state_heterogeneity_ci.png"); plt.close(fig); print("wrote", FIG / "v2_fig_state_heterogeneity_ci.png")
721 + # Gap by design
722 + p = R / "geo_inference/gap_bootstrap.csv"
723 + if p.exists():
724 + g = pd.read_csv(p); g = g[g.comparator.isin(["xgb_full_nested", "xgb_full_fixed1000"])]
725 + fig, ax = plt.subplots(figsize=(8.5, 5))
726 + order = [d for d in ["random", "size_matched", "gkf_zip5", "gkf_zip3", "gkf_state", "ten_state", "loso"] if d in set(g.design)]
727 + x = np.arange(len(order))
728 + for off, comp, col, lab in [(-0.17, "xgb_full_nested", ACCENT, "OLS $-$ XGBoost (nested rounds)"), (0.17, "xgb_full_fixed1000", PRIMARY, "OLS $-$ XGBoost (1,000 rounds)")]:
729 + s_ = g[g.comparator == comp].set_index("design").reindex(order)
730 + ax.errorbar(x + off, s_.gap_ols_minus_comparator_mean, yerr=[s_.gap_ols_minus_comparator_mean - s_.gap_ci_low_zip3, s_.gap_ci_high_zip3 - s_.gap_ols_minus_comparator_mean],
731 + fmt="o", color=col, capsize=3, label=lab)
732 + ax.axhline(0, color=NEUTRAL, lw=0.9); ax.set_xticks(x); ax.set_xticklabels([DESIGN_LABEL[d] for d in order], rotation=20, ha="right", fontsize=9)
733 + ax.set_ylabel("$R^2$(OLS) $-$ $R^2$(XGBoost), 95% ZIP3-cluster bootstrap CI"); ax.legend(loc="best")
734 + fig.tight_layout(); fig.savefig(FIG / "v2_fig_gap_by_design.png"); plt.close(fig); print("wrote", FIG / "v2_fig_gap_by_design.png")
735 +
736 +
737 +def main():
738 + ap = argparse.ArgumentParser(description=__doc__)
739 + ap.add_argument("--out", type=Path, default=None)
740 + ap.add_argument("--no-figures", action="store_true")
741 + args = ap.parse_args()
742 + global TAB
743 + if args.out:
744 + TAB = args.out
745 + TAB.mkdir(parents=True, exist_ok=True)
746 + for g in [t_attrition, t_missingness, t_descriptives, t_ols_v2, t_cluster_se, t_fe_progression, t_state_heterogeneity, t_lot_recoding, t_moran, t_qr, t_iqr,
747 + t_ml_comparison, t_designs, t_gap, t_loso, t_decomposition, t_feasible, t_two_by_two, t_ablation, t_trees_curve, t_duplicates, t_stored_checks]:
748 + g()
749 + if not args.no_figures:
750 + figures()
751 + if not args.out:
752 + (R / "inline_numbers.json").write_text(json.dumps(numbers, indent=1, default=lambda o: float(o) if isinstance(o, (np.floating, np.integer)) else str(o)))
753 + print(f"{len(numbers)} inline numbers -> {R / 'inline_numbers.json'}")
754 + if SKIPPED:
755 + print("skipped:", "; ".join(SKIPPED))
756 +
757 +
758 +if __name__ == "__main__":
759 + main()
added tests/test_tables.py +39 −0
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