1{2 "record_id": "23a821c97160e852a6c2dd5c6097aa79bfac7ed3",3 "eligible": false,4 "exclusion_reason": "Aggregate unit of analysis: 1498 residential price samples averaged into 653 sample-area means (Shunde District, Foshan, China, 1999/2001-2010, Yuan/m2, calibrated to 2010 level) and modelled with a flexible spatiotemporal mixed model (FSTM) built for spatial prediction. The only coefficient table (Table 1, p.15: level Yuan/m2 on distance to centre, road/market/bus/telecom/supermarket/post/bank densities, public area, shared area, 2003 population density; SE and p-values printed, units of regressors undefined) contains no dwelling characteristic coefficients and the regressors are area-level densities with unstated units. Not usable for coefficient synthesis; keep as qualitative (ML/spatiotemporal prediction) evidence.",5 "study": {6 "title": "Prediction and analysis of residential house price using a flexible spatiotemporal model",7 "year": 2022,8 "doi": "10.1080/15140326.2022.2045466",9 "country": "China",10 "region_city": "Shunde District, Foshan, Guangdong",11 "sample_start": 2001,12 "sample_end": 2010,13 "property_type": "unknown",14 "price_type": "transaction",15 "data_source": "Shunde District residential house price database / real estate transaction system and Land Bureau land price updating database; 1498 samples in 653 sample areas",16 "document_type": "journal",17 "estimators": [18 "other"19 ],20 "spatial_method": "flexible spatiotemporal mixed-effects model (FSTM, random coefficients, Gaussian Markov random fields, ML)",21 "time_fe": true,22 "location_fe": false,23 "se_type": "unknown",24 "n_obs_total": 653,25 "ai_methods": [],26 "notes": "Dependent variable: residential house price per m2 (Chinese Yuan/m2, level) averaged by sample area; year factor dummies 2002-2010 reported. Table 1 (p.15) reports coefficient, standard error and p-value. Prediction accuracy via 10-fold CV (RMSE 2087 Yuan/m2, R2 0.275)."27 },28 "models": [],29 "coefficients": []30}