1{"record_id": "1076844f99e9f35c7768a4e64f2c167ab26b5afd", "eligible": true, "short_ref": "Ezzeldin et al. (2025)", "country": "Saudi Arabia", "city": "Riyadh", "property_type": "residential units (apartments and villas)", "price_type": "listing price (log)", "n_obs": 2997,2 "sample_period": "Q3 2024", "data_source": "Aqar listing platform (sa.aqar.fm), scraped and georeferenced", "methods_compared": ["semi-log OLS hedonic regression", "artificial neural network (MLP, 5-fold CV tuning)", "random forest (500 trees)"],3 "best_method": "random forest", "metric": "R2", "best_value": 0.9065, "hedonic_value": 0.8034,4 "test_set": "holdout", "features_ai": "none",5 "incremental_ai_gain": null, "incremental_ai_metric": "", "marginal_effects": "coefficients", "notes": "70/30 split (2,097 train / 900 test), same split for all three models. Test-set metrics as printed in the abstract/Tables 3, 5, 6: RF R2 0.9065, RMSE 0.1911, MAE 0.1353; ANN R2 0.8968, RMSE 0.2008, MAE 0.1450; hedonic R2 0.8034, RMSE 0.2673, MAE 0.1970 (dependent variable is log price, so RMSE/MAE are in log units). Hedonic coefficients reported (Table 2) plus RF impurity-based feature importance; no SHAP/PDP computed for the ML models.", "confidence": "high"}6