1{2 "record_id": "84fe25f12d7833b71a83f2c2ae3a06dfc013ffcf",3 "eligible": false,4 "exclusion_reason": "Unit of analysis is the residential estate/plot (FANG.com plot-level average selling price per residential unit, with 'average usable area in the apartment' and 'number of households' as regressors), i.e. project-level means rather than individual dwellings; sample size not printed. Flag for human review: double-log OLS for Beijing and Shanghai (Table 2, p. 13) with street-view CNN (PSPNet) green/sky/building view indices is relevant to RQ6 (AI-derived characteristics) and could be extracted if estate-level means are admitted.",5 "study": {6 "title": "Do street-level scene perceptions affect housing prices in Chinese megacities? An analysis using open access datasets and deep learning",7 "year": 2019,8 "doi": "10.1371/journal.pone.0217505",9 "country": "China",10 "region_city": "Beijing and Shanghai",11 "sample_start": 2015,12 "sample_end": 2015,13 "property_type": "condo_apartment",14 "price_type": "listing",15 "data_source": "FANG.com house purchasing records 2015 (plot/estate level), Baidu street view panoramas (Mar-Oct 2015), Baidu POI, AutoNavi road network",16 "document_type": "journal",17 "estimators": [18 "OLS"19 ],20 "spatial_method": "none",21 "time_fe": false,22 "location_fe": false,23 "se_type": "robust",24 "n_obs_total": null,25 "ai_methods": [26 "computer_vision",27 "semantic_segmentation_PSPNet",28 "street_view_indices"29 ],30 "notes": "Table 2 (p.13): double-log OLS, dependent log price in 10,000 RMB; Beijing adj R2 0.7242, Shanghai 0.7511; GVI elasticity 0.2273 (SE 0.0818) Beijing / 0.1853 (0.0471) Shanghai; SVI 0.0899 (0.0280) / 0.0466 (0.0298); BVI insignificant. Significance markers printed as symbols (1-3 glyphs = 10/5/1%). Not extracted because unit is the residential estate."31 },32 "models": [],33 "coefficients": []34}