1{2 "record_id": "0b9eefecd90b818651be4c57fe476bab87958359",3 "eligible": true,4 "exclusion_reason": null,5 "study": {6 "title": "Determining Factors on Housing Price in Beijing Using Regression Analysis",7 "year": 2025,8 "doi": "10.54254/2754-1169/2024.ld19187",9 "country": "China",10 "region_city": "Beijing",11 "sample_start": null,12 "sample_end": null,13 "property_type": "condo_apartment",14 "price_type": "transaction",15 "data_source": "Lianjia Beijing housing dataset (Kaggle csv), first 3139 complete rows",16 "document_type": "conference",17 "estimators": [18 "OLS",19 "IV"20 ],21 "spatial_method": "none",22 "time_fe": false,23 "location_fe": false,24 "se_type": "robust",25 "n_obs_total": 3139,26 "ai_methods": [],27 "notes": "Student conference paper (ICFTBA 2024). Dependent variable is price per m2 (Lianjia \"price\" field). Sample period not stated (Lianjia/Kaggle Beijing data span roughly 2011-2017). Table 2 (non-robust OLS, p.4) prints SE, t, p and CI but omits the coefficient column, so it was not extracted (point estimates identical to Table 5). p-values printed exactly; no star legend. n_obs inferred from text (first 3139 rows) and F(1,3130) df."28 },29 "models": [30 {31 "model_id": "T5-c1",32 "table": "5",33 "column": "1",34 "page": 6,35 "dep_var": "price (average price per square metre, Lianjia listing/transaction data, RMB per m2)",36 "dep_transform": "per_m2",37 "estimator": "OLS",38 "n_obs": 3139,39 "r2": 0.8689,40 "se_or_t_in_parentheses": "se",41 "sample_subset": "Full sample of first 3139 complete Lianjia rows; robust SEs (Table 3 R2 refers to the non-robust OLS with identical point estimates)"42 },43 {44 "model_id": "T7-c1",45 "table": "7",46 "column": "1",47 "page": 7,48 "dep_var": "price (average price per square metre, Lianjia listing/transaction data, RMB per m2)",49 "dep_transform": "per_m2",50 "estimator": "IV",51 "n_obs": 3139,52 "r2": 0.8688,53 "se_or_t_in_parentheses": "se",54 "sample_subset": "2SLS, square instrumented by number of bathrooms and number of followers; robust SEs; z-statistics"55 }56 ],57 "coefficients": [58 {59 "model_id": "T5-c1",60 "var_name_printed": "square",61 "taxonomy_id": "living_area",62 "x_transform": "level",63 "x_unit": "m2",64 "beta": -59.74631,65 "se": 5.937107,66 "t": -10.06,67 "p": 0.0,68 "p_stars": "",69 "ci_lo": -71.38733,70 "ci_hi": -48.1053,71 "page": 6,72 "confidence": "high",73 "notes": "Total housing area"74 },75 {76 "model_id": "T5-c1",77 "var_name_printed": "livingRoom",78 "taxonomy_id": "bedrooms",79 "x_transform": "level",80 "x_unit": "count",81 "beta": 542.6135,82 "se": 291.935,83 "t": 1.86,84 "p": 0.063,85 "p_stars": "",86 "ci_lo": -29.78979,87 "ci_hi": 1115.017,88 "page": 6,89 "confidence": "medium",90 "notes": "Printed meaning \"the living room's number\"; in the Lianjia/Kaggle Beijing dataset this field (shi) is the number of bedrooms, hence mapped to bedrooms"91 },92 {93 "model_id": "T5-c1",94 "var_name_printed": "drawingRoom",95 "taxonomy_id": "other",96 "x_transform": "level",97 "x_unit": "count",98 "beta": 1993.013,99 "se": 351.7496,100 "t": 5.67,101 "p": 0.0,102 "p_stars": "",103 "ci_lo": 1303.329,104 "ci_hi": 2682.696,105 "page": 6,106 "confidence": "high",107 "notes": "Number of drawing rooms (ting, living/reception rooms)"108 },109 {110 "model_id": "T5-c1",111 "var_name_printed": "ladderRatio",112 "taxonomy_id": "other",113 "x_transform": "level",114 "x_unit": "ratio (residents per floor per elevator)",115 "beta": 2716.725,116 "se": 932.9369,117 "t": 2.91,118 "p": 0.004,119 "p_stars": "",120 "ci_lo": 887.4948,121 "ci_hi": 4545.954,122 "page": 6,123 "confidence": "high",124 "notes": "Ladder ratio: proportion between number of residents on the same floor and number of elevators"125 },126 {127 "model_id": "T5-c1",128 "var_name_printed": "elevator",129 "taxonomy_id": "accessibility",130 "x_transform": "binary",131 "x_unit": "binary",132 "beta": 843.4289,133 "se": 327.006,134 "t": 2.58,135 "p": 0.01,136 "p_stars": "",137 "ci_lo": 202.2612,138 "ci_hi": 1484.597,139 "page": 6,140 "confidence": "high",141 "notes": "=1 if building has an elevator"142 },143 {144 "model_id": "T5-c1",145 "var_name_printed": "subway",146 "taxonomy_id": "transit_distance",147 "x_transform": "binary",148 "x_unit": "binary (proximity to subway)",149 "beta": 968.7746,150 "se": 315.3396,151 "t": 3.07,152 "p": 0.002,153 "p_stars": "",154 "ci_lo": 350.4814,155 "ci_hi": 1587.068,156 "page": 6,157 "confidence": "high",158 "notes": "Proximity to subway dummy (Lianjia field)"159 },160 {161 "model_id": "T5-c1",162 "var_name_printed": "communityAverage",163 "taxonomy_id": "other",164 "x_transform": "level",165 "x_unit": "currency (RMB per m2)",166 "beta": 0.8442177,167 "se": 0.0078306,168 "t": 107.81,169 "p": 0.0,170 "p_stars": "",171 "ci_lo": 0.8288642,172 "ci_hi": 0.8595713,173 "page": 6,174 "confidence": "high",175 "notes": "Average price per m2 in the community (neighbourhood price level control)"176 },177 {178 "model_id": "T7-c1",179 "var_name_printed": "square",180 "taxonomy_id": "living_area",181 "x_transform": "level",182 "x_unit": "m2",183 "beta": -52.27434,184 "se": 11.09248,185 "t": -4.71,186 "p": 0.0,187 "p_stars": "",188 "ci_lo": -74.0152,189 "ci_hi": -30.53347,190 "page": 7,191 "confidence": "high",192 "notes": "Total housing area; z-statistic recorded in t"193 },194 {195 "model_id": "T7-c1",196 "var_name_printed": "livingRoom",197 "taxonomy_id": "bedrooms",198 "x_transform": "level",199 "x_unit": "count",200 "beta": 299.7442,201 "se": 408.9099,202 "t": 0.73,203 "p": 0.464,204 "p_stars": "",205 "ci_lo": -501.7045,206 "ci_hi": 1101.193,207 "page": 7,208 "confidence": "medium",209 "notes": "Printed meaning \"the living room's number\"; in the Lianjia/Kaggle Beijing dataset this field (shi) is the number of bedrooms, hence mapped to bedrooms; z-statistic recorded in t"210 },211 {212 "model_id": "T7-c1",213 "var_name_printed": "drawingRoom",214 "taxonomy_id": "other",215 "x_transform": "level",216 "x_unit": "count",217 "beta": 1812.467,218 "se": 407.2673,219 "t": 4.45,220 "p": 0.0,221 "p_stars": "",222 "ci_lo": 1014.238,223 "ci_hi": 2610.696,224 "page": 7,225 "confidence": "high",226 "notes": "Number of drawing rooms (ting, living/reception rooms); z-statistic recorded in t"227 },228 {229 "model_id": "T7-c1",230 "var_name_printed": "ladderRatio",231 "taxonomy_id": "other",232 "x_transform": "level",233 "x_unit": "ratio (residents per floor per elevator)",234 "beta": 2368.047,235 "se": 1112.697,236 "t": 2.13,237 "p": 0.033,238 "p_stars": "",239 "ci_lo": 187.2018,240 "ci_hi": 4548.892,241 "page": 7,242 "confidence": "high",243 "notes": "Ladder ratio: proportion between number of residents on the same floor and number of elevators; z-statistic recorded in t"244 },245 {246 "model_id": "T7-c1",247 "var_name_printed": "elevator",248 "taxonomy_id": "accessibility",249 "x_transform": "binary",250 "x_unit": "binary",251 "beta": 663.3836,252 "se": 404.546,253 "t": 1.64,254 "p": 0.101,255 "p_stars": "",256 "ci_lo": -129.512,257 "ci_hi": 1456.279,258 "page": 7,259 "confidence": "high",260 "notes": "=1 if building has an elevator; z-statistic recorded in t"261 },262 {263 "model_id": "T7-c1",264 "var_name_printed": "subway",265 "taxonomy_id": "transit_distance",266 "x_transform": "binary",267 "x_unit": "binary (proximity to subway)",268 "beta": 974.9528,269 "se": 314.9833,270 "t": 3.1,271 "p": 0.002,272 "p_stars": "",273 "ci_lo": 357.597,274 "ci_hi": 1592.309,275 "page": 7,276 "confidence": "high",277 "notes": "Proximity to subway dummy (Lianjia field); z-statistic recorded in t"278 },279 {280 "model_id": "T7-c1",281 "var_name_printed": "communityAverage",282 "taxonomy_id": "other",283 "x_transform": "level",284 "x_unit": "currency (RMB per m2)",285 "beta": 0.8450588,286 "se": 0.0079132,287 "t": 106.79,288 "p": 0.0,289 "p_stars": "",290 "ci_lo": 0.8295493,291 "ci_hi": 0.8605683,292 "page": 7,293 "confidence": "high",294 "notes": "Average price per m2 in the community (neighbourhood price level control); z-statistic recorded in t"295 }296 ]297}