1{"record_id": "0c59335ca1b171d1a7095c8fce95805f7852118d", "eligible": true, "short_ref": "Cui et al. (2024)", "country": "China", "city": "Tai'an (central district), Shandong", "property_type": "pre-owned (second-hand) housing, community-level average", "price_type": "asking price per m2 (RMB/m2, Anjuke listings, community average)", "n_obs": 1177,2 "sample_period": "2023", "data_source": "Anjuke.com listings (web crawler) + Baidu Street View imagery + Amap POI", "methods_compared": ["OLS hedonic price model (HPM)", "GWR", "MGWR"],3 "best_method": "MGWR", "metric": "R2", "best_value": 0.613, "hedonic_value": 0.346,4 "test_set": "none", "features_ai": "street-view CNN",5 "incremental_ai_gain": null, "incremental_ai_metric": "", "marginal_effects": "coefficients", "notes": "Table 1 (in-sample fit, 1,177 communities, macro-scale model): HPM R2 0.346 (adj 0.334), GWR 0.582 (0.521), MGWR 0.613 (0.560); mountain-view subsample (475 communities): HPM 0.419 (0.392), GWR 0.893 (0.841), MGWR 0.911 (0.881). AI features = street-view element indices from semantic segmentation (building/flat/mountain view indices) plus a CNN mountain-view satisfaction classifier (66.7% accuracy on an 80/20 image split) and Grad-CAM; the price models themselves are not out-of-sample tested and no with/without-AI-feature comparison is printed. Local MGWR coefficients for street-view variables are mapped (Figure 9).", "confidence": "medium"}6