{
 "record_id": "0b9eefecd90b818651be4c57fe476bab87958359",
 "eligible": true,
 "exclusion_reason": null,
 "study": {
  "title": "Determining Factors on Housing Price in Beijing Using Regression Analysis",
  "year": 2025,
  "doi": "10.54254/2754-1169/2024.ld19187",
  "country": "China",
  "region_city": "Beijing",
  "sample_start": null,
  "sample_end": null,
  "property_type": "condo_apartment",
  "price_type": "transaction",
  "data_source": "Lianjia Beijing housing dataset (Kaggle csv), first 3139 complete rows",
  "document_type": "conference",
  "estimators": [
   "OLS",
   "IV"
  ],
  "spatial_method": "none",
  "time_fe": false,
  "location_fe": false,
  "se_type": "robust",
  "n_obs_total": 3139,
  "ai_methods": [],
  "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."
 },
 "models": [
  {
   "model_id": "T5-c1",
   "table": "5",
   "column": "1",
   "page": 6,
   "dep_var": "price (average price per square metre, Lianjia listing/transaction data, RMB per m2)",
   "dep_transform": "per_m2",
   "estimator": "OLS",
   "n_obs": 3139,
   "r2": 0.8689,
   "se_or_t_in_parentheses": "se",
   "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)"
  },
  {
   "model_id": "T7-c1",
   "table": "7",
   "column": "1",
   "page": 7,
   "dep_var": "price (average price per square metre, Lianjia listing/transaction data, RMB per m2)",
   "dep_transform": "per_m2",
   "estimator": "IV",
   "n_obs": 3139,
   "r2": 0.8688,
   "se_or_t_in_parentheses": "se",
   "sample_subset": "2SLS, square instrumented by number of bathrooms and number of followers; robust SEs; z-statistics"
  }
 ],
 "coefficients": [
  {
   "model_id": "T5-c1",
   "var_name_printed": "square",
   "taxonomy_id": "living_area",
   "x_transform": "level",
   "x_unit": "m2",
   "beta": -59.74631,
   "se": 5.937107,
   "t": -10.06,
   "p": 0.0,
   "p_stars": "",
   "ci_lo": -71.38733,
   "ci_hi": -48.1053,
   "page": 6,
   "confidence": "high",
   "notes": "Total housing area"
  },
  {
   "model_id": "T5-c1",
   "var_name_printed": "livingRoom",
   "taxonomy_id": "bedrooms",
   "x_transform": "level",
   "x_unit": "count",
   "beta": 542.6135,
   "se": 291.935,
   "t": 1.86,
   "p": 0.063,
   "p_stars": "",
   "ci_lo": -29.78979,
   "ci_hi": 1115.017,
   "page": 6,
   "confidence": "medium",
   "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"
  },
  {
   "model_id": "T5-c1",
   "var_name_printed": "drawingRoom",
   "taxonomy_id": "other",
   "x_transform": "level",
   "x_unit": "count",
   "beta": 1993.013,
   "se": 351.7496,
   "t": 5.67,
   "p": 0.0,
   "p_stars": "",
   "ci_lo": 1303.329,
   "ci_hi": 2682.696,
   "page": 6,
   "confidence": "high",
   "notes": "Number of drawing rooms (ting, living/reception rooms)"
  },
  {
   "model_id": "T5-c1",
   "var_name_printed": "ladderRatio",
   "taxonomy_id": "other",
   "x_transform": "level",
   "x_unit": "ratio (residents per floor per elevator)",
   "beta": 2716.725,
   "se": 932.9369,
   "t": 2.91,
   "p": 0.004,
   "p_stars": "",
   "ci_lo": 887.4948,
   "ci_hi": 4545.954,
   "page": 6,
   "confidence": "high",
   "notes": "Ladder ratio: proportion between number of residents on the same floor and number of elevators"
  },
  {
   "model_id": "T5-c1",
   "var_name_printed": "elevator",
   "taxonomy_id": "accessibility",
   "x_transform": "binary",
   "x_unit": "binary",
   "beta": 843.4289,
   "se": 327.006,
   "t": 2.58,
   "p": 0.01,
   "p_stars": "",
   "ci_lo": 202.2612,
   "ci_hi": 1484.597,
   "page": 6,
   "confidence": "high",
   "notes": "=1 if building has an elevator"
  },
  {
   "model_id": "T5-c1",
   "var_name_printed": "subway",
   "taxonomy_id": "transit_distance",
   "x_transform": "binary",
   "x_unit": "binary (proximity to subway)",
   "beta": 968.7746,
   "se": 315.3396,
   "t": 3.07,
   "p": 0.002,
   "p_stars": "",
   "ci_lo": 350.4814,
   "ci_hi": 1587.068,
   "page": 6,
   "confidence": "high",
   "notes": "Proximity to subway dummy (Lianjia field)"
  },
  {
   "model_id": "T5-c1",
   "var_name_printed": "communityAverage",
   "taxonomy_id": "other",
   "x_transform": "level",
   "x_unit": "currency (RMB per m2)",
   "beta": 0.8442177,
   "se": 0.0078306,
   "t": 107.81,
   "p": 0.0,
   "p_stars": "",
   "ci_lo": 0.8288642,
   "ci_hi": 0.8595713,
   "page": 6,
   "confidence": "high",
   "notes": "Average price per m2 in the community (neighbourhood price level control)"
  },
  {
   "model_id": "T7-c1",
   "var_name_printed": "square",
   "taxonomy_id": "living_area",
   "x_transform": "level",
   "x_unit": "m2",
   "beta": -52.27434,
   "se": 11.09248,
   "t": -4.71,
   "p": 0.0,
   "p_stars": "",
   "ci_lo": -74.0152,
   "ci_hi": -30.53347,
   "page": 7,
   "confidence": "high",
   "notes": "Total housing area; z-statistic recorded in t"
  },
  {
   "model_id": "T7-c1",
   "var_name_printed": "livingRoom",
   "taxonomy_id": "bedrooms",
   "x_transform": "level",
   "x_unit": "count",
   "beta": 299.7442,
   "se": 408.9099,
   "t": 0.73,
   "p": 0.464,
   "p_stars": "",
   "ci_lo": -501.7045,
   "ci_hi": 1101.193,
   "page": 7,
   "confidence": "medium",
   "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"
  },
  {
   "model_id": "T7-c1",
   "var_name_printed": "drawingRoom",
   "taxonomy_id": "other",
   "x_transform": "level",
   "x_unit": "count",
   "beta": 1812.467,
   "se": 407.2673,
   "t": 4.45,
   "p": 0.0,
   "p_stars": "",
   "ci_lo": 1014.238,
   "ci_hi": 2610.696,
   "page": 7,
   "confidence": "high",
   "notes": "Number of drawing rooms (ting, living/reception rooms); z-statistic recorded in t"
  },
  {
   "model_id": "T7-c1",
   "var_name_printed": "ladderRatio",
   "taxonomy_id": "other",
   "x_transform": "level",
   "x_unit": "ratio (residents per floor per elevator)",
   "beta": 2368.047,
   "se": 1112.697,
   "t": 2.13,
   "p": 0.033,
   "p_stars": "",
   "ci_lo": 187.2018,
   "ci_hi": 4548.892,
   "page": 7,
   "confidence": "high",
   "notes": "Ladder ratio: proportion between number of residents on the same floor and number of elevators; z-statistic recorded in t"
  },
  {
   "model_id": "T7-c1",
   "var_name_printed": "elevator",
   "taxonomy_id": "accessibility",
   "x_transform": "binary",
   "x_unit": "binary",
   "beta": 663.3836,
   "se": 404.546,
   "t": 1.64,
   "p": 0.101,
   "p_stars": "",
   "ci_lo": -129.512,
   "ci_hi": 1456.279,
   "page": 7,
   "confidence": "high",
   "notes": "=1 if building has an elevator; z-statistic recorded in t"
  },
  {
   "model_id": "T7-c1",
   "var_name_printed": "subway",
   "taxonomy_id": "transit_distance",
   "x_transform": "binary",
   "x_unit": "binary (proximity to subway)",
   "beta": 974.9528,
   "se": 314.9833,
   "t": 3.1,
   "p": 0.002,
   "p_stars": "",
   "ci_lo": 357.597,
   "ci_hi": 1592.309,
   "page": 7,
   "confidence": "high",
   "notes": "Proximity to subway dummy (Lianjia field); z-statistic recorded in t"
  },
  {
   "model_id": "T7-c1",
   "var_name_printed": "communityAverage",
   "taxonomy_id": "other",
   "x_transform": "level",
   "x_unit": "currency (RMB per m2)",
   "beta": 0.8450588,
   "se": 0.0079132,
   "t": 106.79,
   "p": 0.0,
   "p_stars": "",
   "ci_lo": 0.8295493,
   "ci_hi": 0.8605683,
   "page": 7,
   "confidence": "high",
   "notes": "Average price per m2 in the community (neighbourhood price level control); z-statistic recorded in t"
  }
 ]
}