{
 "record_id": "5bcd886348a12f52aa055354a9647ba5c4fa64c4",
 "eligible": true,
 "short_ref": "Almaas, Oust & Pollestad (2026)",
 "country": "Norway",
 "city": "Oslo",
 "property_type": "condominiums",
 "price_type": "transaction price",
 "n_obs": 15702,
 "sample_period": "January 2022 - December 2023",
 "data_source": "Boligmappa AS transactions matched with 393,914 listing images; crowdsourced condition labels",
 "methods_compared": [
  "hedonic linear regression (HLR)",
  "XGBoost",
  "each without condition / with computer-vision condition / with human-labelled condition"
 ],
 "best_method": "XGBoost with human-labelled condition (XGBoost with computer-vision condition: MAPE 7.71%)",
 "metric": "MAPE",
 "best_value": 7.49,
 "hedonic_value": 12.41,
 "test_set": "holdout",
 "features_ai": "interior-photo CNN",
 "incremental_ai_gain": -0.32,
 "incremental_ai_metric": "MAPE (percentage points, XGBoost)",
 "marginal_effects": "SHAP",
 "notes": "AI feature = CNN classification of listing interior photos (room type + 1-5 condition). Table 4, stratified 70/15/15 split, test set: HLR MAPE 12.75% (no condition) -> 12.42% (CV condition) -> 12.41% (human), R2 0.672 -> 0.687 -> 0.690; XGBoost MAPE 8.03% -> 7.71% -> 7.49%, R2 0.847 -> 0.855 -> 0.863. incremental_ai_gain recorded for XGBoost (8.03 -> 7.71, -0.32 pp); for HLR -0.33 pp. Coefficients of condition in HLR (Tables 5-7) and SHAP dependence plots for XGBoost.",
 "confidence": "high"
}