{
 "record_id": "fe9b5a96e7007d52e9bebe9eaea418541aa3b090",
 "eligible": false,
 "exclusion_reason": "Evolutionary Polynomial Regression (EPR) automated valuation models of log rent for five Italian cities (Milan, Turin, Rome, Naples, Catania; second half of 2019). The fitted equations are sums of multiplicative monomials of several variables raised to exponents (0, 0.5, 1, 2); no separable per-characteristic coefficients, no standard errors, t-statistics or p-values are reported (only CoD/RMSE/MAPE). No extractable hedonic coefficients.",
 "study": {
  "title": "An Automatic Tool for the Determination of Housing Rental Prices: An Analysis of the Italian Context",
  "year": 2021,
  "doi": "10.3390/su14010309",
  "country": "Italy",
  "region_city": "Milan, Turin, Rome, Naples, Catania",
  "sample_start": 2019,
  "sample_end": 2019,
  "property_type": "rental",
  "price_type": "rent",
  "data_source": "Rented residential properties, second half of 2019 (online listings)",
  "document_type": "journal",
  "estimators": [
   "ML_marginal"
  ],
  "spatial_method": "none",
  "time_fe": false,
  "location_fe": false,
  "se_type": "unknown",
  "n_obs_total": null,
  "ai_methods": [
   "evolutionary polynomial regression (EPR)"
  ],
  "notes": "Rent study; dependent variable ln(rental price); Table 2 lists polynomial expressions, Table 3 the variables selected per city. Kept qualitatively for the AI/AVM synthesis (performance metrics only)."
 },
 "models": [],
 "coefficients": []
}