1{2 "record_id": "8674ce1937729ab9e1f1084511ae7a29ecdbc635",3 "eligible": true,4 "short_ref": "Yang, Krenz, Qiu & Li (2023)",5 "country": "United Kingdom",6 "city": "Greater London",7 "property_type": "mixed (houses and flats)",8 "price_type": "transaction price (GBP per m2)",9 "n_obs": 49603,10 "sample_period": "2017 onwards (Land Registry transactions since 2017)",11 "data_source": "HM Land Registry Price Paid Data + EPC (Chi et al. compilation); Google Street View; online perception survey",12 "methods_compared": [13 "OLS hedonic with traditional location attributes (Model 0)",14 "OLS hedonic with space-syntax location attributes at R400/R800/R2000/R6000 (Models 1-4)"15 ],16 "best_method": "OLS hedonic, Model 0 (baseline location attributes)",17 "metric": "R2",18 "best_value": 0.496,19 "hedonic_value": 0.496,20 "test_set": "none",21 "features_ai": "street-view CNN",22 "incremental_ai_gain": null,23 "incremental_ai_metric": "",24 "marginal_effects": "coefficients",25 "notes": "Hedonic OLS study, not a prediction contest: a BP neural network trained on survey ratings of Google Street View segmentations (PSPNet) predicts 8 subjective-perception scores that enter the hedonic model. Table 5: adjusted R2 (in-sample) 0.496 (Model 0), 0.482-0.494 (Models 1-4). No specification without the SP scores is reported, so no incremental gain is printed; Table 4 gives SP scores alone adj. R2 = 0.342. Standardized coefficients reported (Fig. 8).",26 "confidence": "high"27}