1{"record_id": "0795e1f2569ab3c9b87c0451960aa54739093319", "eligible": true, "short_ref": "Amri & Tularam (2012)", "country": "Australia", "city": "Bathurst, NSW", "property_type": "houses (residential sales)", "price_type": "sale price", "n_obs": 7849,2 "sample_period": "1992-2004", "data_source": "Council/cadastre sales records GIS-matched with land value and ABS census social data", "methods_compared": ["stepwise multiple regression (MR)", "MR with social indicators (MRsoc)", "neural network (MLP)", "ANFIS neuro-fuzzy"],3 "best_method": "neural network", "metric": "R2", "best_value": 0.4536, "hedonic_value": 0.3694,4 "test_set": "k-fold", "features_ai": "none",5 "incremental_ai_gain": null, "incremental_ai_metric": "", "marginal_effects": "coefficients", "notes": "Table 2 (10-fold CV): adjusted R2 NN 0.4536, ANFIS 0.3815, MR 0.3694; MR with social data 0.42 (text). Share of predictions within 10% of actual: NN 31%, ANFIS 28%, MR 28%. Dataset lacks structural attributes (land value + location + social variables only), which the authors cite as the reason for low R2. n_obs = 7,849 GIS-matched unique sales (counts in Table 2 imply ~4,200 used in the comparison). MR coefficients reported (Eq. 2-3).", "confidence": "medium"}6