{
 "record_id": "1cd473df8f393883694de4530b0d1c667382890c",
 "eligible": true,
 "exclusion_reason": null,
 "study": {
  "title": "Exploring a multilevel approach with spatial effects to model housing price in San Jose, Costa Rica",
  "year": 2021,
  "doi": "10.1177/23998083211041122",
  "country": "Costa Rica",
  "region_city": "San Jose Metropolitan Region",
  "sample_start": null,
  "sample_end": null,
  "property_type": "single_family",
  "price_type": "listing",
  "data_source": "1,264 real estate listings of detached houses from an aggregator website (year of listings not stated in main text; covariates from 2018 and Census 2011)",
  "document_type": "journal",
  "estimators": [
   "other"
  ],
  "spatial_method": "Bayesian multilevel random intercepts (166 districts) with intrinsic CAR spatial prior (CARM); MCMC in WinBUGS",
  "time_fe": false,
  "location_fe": true,
  "se_type": "unknown",
  "n_obs_total": 1264,
  "ai_methods": [],
  "notes": "Environment and Planning B (accepted manuscript version). Dependent variable = natural log of listing price per m2 of living area (US$/m2), so living area is not a regressor. Three models with covariates (GaLM, MLRE, CARM); VPM model has no covariates and is not extracted. Estimator recorded as 'other' = Bayesian hierarchical (MCMC). Sample years of listings not stated. Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed."
 },
 "models": [
  {
   "model_id": "T3-c1",
   "table": "3",
   "column": "1",
   "page": 10,
   "dep_var": "ln housing price per m2 of living area (US$/m2), from listings",
   "dep_transform": "log",
   "estimator": "other",
   "n_obs": 1264,
   "r2": null,
   "se_or_t_in_parentheses": "none",
   "sample_subset": "Linear model without multilevel effects (Bayesian, WinBUGS); posterior means; dependent variable is log of price per m2"
  },
  {
   "model_id": "T3-c2",
   "table": "3",
   "column": "2",
   "page": 10,
   "dep_var": "ln housing price per m2 of living area (US$/m2), from listings",
   "dep_transform": "log",
   "estimator": "other",
   "n_obs": 1264,
   "r2": null,
   "se_or_t_in_parentheses": "none",
   "sample_subset": "Multilevel random-intercept model (district level); posterior means; dependent variable is log of price per m2"
  },
  {
   "model_id": "T3-c3",
   "table": "3",
   "column": "3",
   "page": 10,
   "dep_var": "ln housing price per m2 of living area (US$/m2), from listings",
   "dep_transform": "log",
   "estimator": "other",
   "n_obs": 1264,
   "r2": null,
   "se_or_t_in_parentheses": "none",
   "sample_subset": "Multilevel random intercepts with intrinsic CAR spatial effect at district level; posterior means; dependent variable is log of price per m2"
  }
 ],
 "coefficients": [
  {
   "model_id": "T3-c1",
   "var_name_printed": "beta1 (ln house plot area)",
   "taxonomy_id": "lot_size",
   "x_transform": "log",
   "x_unit": "m2",
   "beta": 0.0785,
   "se": null,
   "t": null,
   "p": null,
   "p_stars": "",
   "ci_lo": 0.0633,
   "ci_hi": 0.0936,
   "page": 10,
   "confidence": "high",
   "notes": "ln plot area (m2); lower-level covariate. Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed."
  },
  {
   "model_id": "T3-c2",
   "var_name_printed": "beta1 (ln house plot area)",
   "taxonomy_id": "lot_size",
   "x_transform": "log",
   "x_unit": "m2",
   "beta": 0.072,
   "se": null,
   "t": null,
   "p": null,
   "p_stars": "",
   "ci_lo": 0.0559,
   "ci_hi": 0.088,
   "page": 10,
   "confidence": "high",
   "notes": "ln plot area (m2); lower-level covariate. Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed."
  },
  {
   "model_id": "T3-c3",
   "var_name_printed": "beta1 (ln house plot area)",
   "taxonomy_id": "lot_size",
   "x_transform": "log",
   "x_unit": "m2",
   "beta": 0.0696,
   "se": null,
   "t": null,
   "p": null,
   "p_stars": "",
   "ci_lo": 0.0538,
   "ci_hi": 0.0854,
   "page": 10,
   "confidence": "high",
   "notes": "ln plot area (m2); lower-level covariate. Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed."
  },
  {
   "model_id": "T3-c1",
   "var_name_printed": "beta2 (ln travel time to CBD)",
   "taxonomy_id": "cbd_distance",
   "x_transform": "log",
   "x_unit": "minutes",
   "beta": 0.613,
   "se": null,
   "t": null,
   "p": null,
   "p_stars": "",
   "ci_lo": 0.426,
   "ci_hi": 0.801,
   "page": 10,
   "confidence": "high",
   "notes": "ln estimated travel time to CBD San Jose (min); quadratic in logs with beta3. Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed."
  },
  {
   "model_id": "T3-c2",
   "var_name_printed": "beta2 (ln travel time to CBD)",
   "taxonomy_id": "cbd_distance",
   "x_transform": "log",
   "x_unit": "minutes",
   "beta": 0.506,
   "se": null,
   "t": null,
   "p": null,
   "p_stars": "",
   "ci_lo": 0.264,
   "ci_hi": 0.756,
   "page": 10,
   "confidence": "high",
   "notes": "ln estimated travel time to CBD San Jose (min); quadratic in logs with beta3. Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed."
  },
  {
   "model_id": "T3-c3",
   "var_name_printed": "beta2 (ln travel time to CBD)",
   "taxonomy_id": "cbd_distance",
   "x_transform": "log",
   "x_unit": "minutes",
   "beta": 0.383,
   "se": null,
   "t": null,
   "p": null,
   "p_stars": "",
   "ci_lo": 0.118,
   "ci_hi": 0.647,
   "page": 10,
   "confidence": "high",
   "notes": "ln estimated travel time to CBD San Jose (min); quadratic in logs with beta3. Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed."
  },
  {
   "model_id": "T3-c1",
   "var_name_printed": "beta3 (ln travel time to CBD squared)",
   "taxonomy_id": "cbd_distance",
   "x_transform": "other",
   "x_unit": "minutes",
   "beta": -0.113,
   "se": null,
   "t": null,
   "p": null,
   "p_stars": "",
   "ci_lo": -0.148,
   "ci_hi": -0.0779,
   "page": 10,
   "confidence": "high",
   "notes": "Square of ln travel time to CBD. Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed."
  },
  {
   "model_id": "T3-c2",
   "var_name_printed": "beta3 (ln travel time to CBD squared)",
   "taxonomy_id": "cbd_distance",
   "x_transform": "other",
   "x_unit": "minutes",
   "beta": -0.0945,
   "se": null,
   "t": null,
   "p": null,
   "p_stars": "",
   "ci_lo": -0.142,
   "ci_hi": -0.0491,
   "page": 10,
   "confidence": "high",
   "notes": "Square of ln travel time to CBD. Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed."
  },
  {
   "model_id": "T3-c3",
   "var_name_printed": "beta3 (ln travel time to CBD squared)",
   "taxonomy_id": "cbd_distance",
   "x_transform": "other",
   "x_unit": "minutes",
   "beta": -0.0723,
   "se": null,
   "t": null,
   "p": null,
   "p_stars": "",
   "ci_lo": -0.125,
   "ci_hi": -0.0198,
   "page": 10,
   "confidence": "high",
   "notes": "Square of ln travel time to CBD. Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed."
  },
  {
   "model_id": "T3-c1",
   "var_name_printed": "beta4 (ln distance to major road)",
   "taxonomy_id": "highway_distance",
   "x_transform": "log",
   "x_unit": "m",
   "beta": -0.0111,
   "se": null,
   "t": null,
   "p": null,
   "p_stars": "",
   "ci_lo": -0.0207,
   "ci_hi": -0.00147,
   "page": 10,
   "confidence": "high",
   "notes": "ln Euclidean distance to nearest major road (m). Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed."
  },
  {
   "model_id": "T3-c2",
   "var_name_printed": "beta4 (ln distance to major road)",
   "taxonomy_id": "highway_distance",
   "x_transform": "log",
   "x_unit": "m",
   "beta": -0.012,
   "se": null,
   "t": null,
   "p": null,
   "p_stars": "",
   "ci_lo": -0.0224,
   "ci_hi": -0.00165,
   "page": 10,
   "confidence": "high",
   "notes": "ln Euclidean distance to nearest major road (m). Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed."
  },
  {
   "model_id": "T3-c3",
   "var_name_printed": "beta4 (ln distance to major road)",
   "taxonomy_id": "highway_distance",
   "x_transform": "log",
   "x_unit": "m",
   "beta": -0.0113,
   "se": null,
   "t": null,
   "p": null,
   "p_stars": "",
   "ci_lo": -0.0218,
   "ci_hi": -0.000723,
   "page": 10,
   "confidence": "high",
   "notes": "ln Euclidean distance to nearest major road (m). Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed."
  },
  {
   "model_id": "T3-c1",
   "var_name_printed": "beta5 (ln distance to bus route)",
   "taxonomy_id": "transit_distance",
   "x_transform": "log",
   "x_unit": "m",
   "beta": 0.0112,
   "se": null,
   "t": null,
   "p": null,
   "p_stars": "",
   "ci_lo": 0.00463,
   "ci_hi": 0.0178,
   "page": 10,
   "confidence": "high",
   "notes": "ln Euclidean distance to nearest bus route (m). Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed."
  },
  {
   "model_id": "T3-c2",
   "var_name_printed": "beta5 (ln distance to bus route)",
   "taxonomy_id": "transit_distance",
   "x_transform": "log",
   "x_unit": "m",
   "beta": 0.0084,
   "se": null,
   "t": null,
   "p": null,
   "p_stars": "",
   "ci_lo": 0.00183,
   "ci_hi": 0.015,
   "page": 10,
   "confidence": "high",
   "notes": "ln Euclidean distance to nearest bus route (m). Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed."
  },
  {
   "model_id": "T3-c3",
   "var_name_printed": "beta5 (ln distance to bus route)",
   "taxonomy_id": "transit_distance",
   "x_transform": "log",
   "x_unit": "m",
   "beta": 0.00806,
   "se": null,
   "t": null,
   "p": null,
   "p_stars": "",
   "ci_lo": 0.0016,
   "ci_hi": 0.0145,
   "page": 10,
   "confidence": "high",
   "notes": "ln Euclidean distance to nearest bus route (m). Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed."
  },
  {
   "model_id": "T3-c1",
   "var_name_printed": "gamma2 (ln crime rate)",
   "taxonomy_id": "crime",
   "x_transform": "log",
   "x_unit": "count",
   "beta": 0.000414,
   "se": null,
   "t": null,
   "p": null,
   "p_stars": "",
   "ci_lo": -0.00336,
   "ci_hi": 0.00419,
   "page": 10,
   "confidence": "high",
   "notes": "ln homicides per 100,000 residents in district (2018), 0.001 added; district-level covariate. Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed."
  },
  {
   "model_id": "T3-c2",
   "var_name_printed": "gamma2 (ln crime rate)",
   "taxonomy_id": "crime",
   "x_transform": "log",
   "x_unit": "count",
   "beta": -6.36e-05,
   "se": null,
   "t": null,
   "p": null,
   "p_stars": "",
   "ci_lo": -0.00642,
   "ci_hi": 0.00641,
   "page": 10,
   "confidence": "high",
   "notes": "ln homicides per 100,000 residents in district (2018), 0.001 added; district-level covariate. Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed."
  },
  {
   "model_id": "T3-c3",
   "var_name_printed": "gamma2 (ln crime rate)",
   "taxonomy_id": "crime",
   "x_transform": "log",
   "x_unit": "count",
   "beta": 0.000859,
   "se": null,
   "t": null,
   "p": null,
   "p_stars": "",
   "ci_lo": -0.00455,
   "ci_hi": 0.00622,
   "page": 10,
   "confidence": "high",
   "notes": "ln homicides per 100,000 residents in district (2018), 0.001 added; district-level covariate. Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed."
  },
  {
   "model_id": "T3-c1",
   "var_name_printed": "gamma3 (ln number of jobs in district)",
   "taxonomy_id": "employment_access",
   "x_transform": "log",
   "x_unit": "count",
   "beta": 0.0188,
   "se": null,
   "t": null,
   "p": null,
   "p_stars": "",
   "ci_lo": 0.00934,
   "ci_hi": 0.0283,
   "page": 10,
   "confidence": "high",
   "notes": "ln number of jobs in district (2018); district-level covariate. Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed."
  },
  {
   "model_id": "T3-c2",
   "var_name_printed": "gamma3 (ln number of jobs in district)",
   "taxonomy_id": "employment_access",
   "x_transform": "log",
   "x_unit": "count",
   "beta": 0.0203,
   "se": null,
   "t": null,
   "p": null,
   "p_stars": "",
   "ci_lo": 0.00406,
   "ci_hi": 0.0367,
   "page": 10,
   "confidence": "high",
   "notes": "ln number of jobs in district (2018); district-level covariate. Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed."
  },
  {
   "model_id": "T3-c3",
   "var_name_printed": "gamma3 (ln number of jobs in district)",
   "taxonomy_id": "employment_access",
   "x_transform": "log",
   "x_unit": "count",
   "beta": 0.0108,
   "se": null,
   "t": null,
   "p": null,
   "p_stars": "",
   "ci_lo": -0.00437,
   "ci_hi": 0.0261,
   "page": 10,
   "confidence": "high",
   "notes": "ln number of jobs in district (2018); district-level covariate. Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed."
  },
  {
   "model_id": "T3-c1",
   "var_name_printed": "gamma4 (ln poverty proxy)",
   "taxonomy_id": "income",
   "x_transform": "log",
   "x_unit": "count",
   "beta": -0.0195,
   "se": null,
   "t": null,
   "p": null,
   "p_stars": "",
   "ci_lo": -0.0346,
   "ci_hi": -0.00427,
   "page": 10,
   "confidence": "high",
   "notes": "ln number of households with unsatisfied housing needs (Census 2011), poverty proxy; district-level covariate; closest leaf income (sign negative = poorer). Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed."
  },
  {
   "model_id": "T3-c2",
   "var_name_printed": "gamma4 (ln poverty proxy)",
   "taxonomy_id": "income",
   "x_transform": "log",
   "x_unit": "count",
   "beta": -0.0259,
   "se": null,
   "t": null,
   "p": null,
   "p_stars": "",
   "ci_lo": -0.0542,
   "ci_hi": 0.00214,
   "page": 10,
   "confidence": "high",
   "notes": "ln number of households with unsatisfied housing needs (Census 2011), poverty proxy; district-level covariate; closest leaf income (sign negative = poorer). Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed."
  },
  {
   "model_id": "T3-c3",
   "var_name_printed": "gamma4 (ln poverty proxy)",
   "taxonomy_id": "income",
   "x_transform": "log",
   "x_unit": "count",
   "beta": -0.0117,
   "se": null,
   "t": null,
   "p": null,
   "p_stars": "",
   "ci_lo": -0.0349,
   "ci_hi": 0.0114,
   "page": 10,
   "confidence": "high",
   "notes": "ln number of households with unsatisfied housing needs (Census 2011), poverty proxy; district-level covariate; closest leaf income (sign negative = poorer). Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed."
  }
 ]
}