{
 "record_id": "bc5c2c3c86f1f45909dcce75c8f09acff914dd28",
 "eligible": true,
 "short_ref": "An, Song, Jang & Ahn (2025)",
 "country": "South Korea",
 "city": "Busan",
 "property_type": "condominiums (apartments)",
 "price_type": "transaction price (log)",
 "n_obs": 61152,
 "sample_period": "2015",
 "data_source": "Song et al. (2021) hedonic dataset of condominium transactions",
 "methods_compared": [
  "OLS hedonic",
  "spatial lag regression (SLR)",
  "random forest",
  "deep neural network"
 ],
 "best_method": "random forest",
 "metric": "R2",
 "best_value": 0.941,
 "hedonic_value": null,
 "test_set": "holdout",
 "features_ai": "none",
 "incremental_ai_gain": null,
 "incremental_ai_metric": "",
 "marginal_effects": "SHAP",
 "notes": "70/30 train-test split. RF baseline RMSE 0.080 (log price) and R2 0.941 printed in appendix Table 8 (tuned RF 0.079/0.942). OLS and SLR accuracy appear only graphically in Fig. 3 (OLS R2 roughly 0.67, RMSE roughly 0.19 by eye), so hedonic_value is left null. Compares SHAP ranks with Gini importance and with OLS/SLR coefficient signs (Tables 5-6).",
 "confidence": "medium"
}