1{2 "record_id": "8506f16b0710a57ed835f9583338b207fd204258",3 "eligible": true,4 "exclusion_reason": null,5 "study": {6 "title": "Analysis of Tianjin real estate market: multiple linear regression prediction method",7 "year": 2024,8 "doi": "10.62051/a4tma782",9 "country": "China",10 "region_city": "Tianjin",11 "sample_start": 2018,12 "sample_end": 2022,13 "property_type": "unknown",14 "price_type": "transaction",15 "data_source": "Tianjin real estate transaction records 2018-2022 (source not specified)",16 "document_type": "conference",17 "estimators": [18 "OLS"19 ],20 "spatial_method": "none",21 "time_fe": false,22 "location_fe": false,23 "se_type": "OLS",24 "n_obs_total": null,25 "ai_methods": [],26 "notes": "High-school student conference paper. Table 1 (p.4) reports B, SE, t and p for four regressors; dependent variable 'house price' in yuan (level; whether total price or price per m2 is not stated). Sample size, descriptive statistics and data provider are not reported; all printed values are round numbers (450/35, -200/60, -800/120, 1200/150, intercept 50000/15000, R2 0.85, F 75.00) and appear illustrative rather than estimated -- all coefficients recorded with confidence 'low' (exclude from primary synthesis; flag for human review). 'Supporting facilities' is a composite amenity score (schools, hospitals, shopping centres)."27 },28 "models": [29 {30 "model_id": "T1-c1",31 "table": "1",32 "column": "1",33 "page": 4,34 "dep_var": "house price (yuan; total vs per-m2 not stated)",35 "dep_transform": "level",36 "estimator": "OLS",37 "n_obs": null,38 "r2": 0.85,39 "se_or_t_in_parentheses": "se",40 "sample_subset": "All Tianjin transactions 2018-2022 (n not reported); R2 0.85, adjusted R2 0.83, F 75.00"41 }42 ],43 "coefficients": [44 {45 "model_id": "T1-c1",46 "var_name_printed": "Housing area (X1) (per square meter)",47 "taxonomy_id": "living_area",48 "x_transform": "level",49 "x_unit": "m2",50 "beta": 450,51 "se": 35,52 "t": 12.86,53 "p": 0.001,54 "p_stars": "",55 "ci_lo": null,56 "ci_hi": null,57 "page": 4,58 "confidence": "low",59 "notes": "Housing area in square metres; p printed as '<0.001' (upper bound recorded); values appear illustrative (round numbers, no sample size) -- do not use in primary synthesis"60 },61 {62 "model_id": "T1-c1",63 "var_name_printed": "Room age (X2) (per year)",64 "taxonomy_id": "age",65 "x_transform": "level",66 "x_unit": "years",67 "beta": -200,68 "se": 60,69 "t": -3.33,70 "p": 0.01,71 "p_stars": "",72 "ci_lo": null,73 "ci_hi": null,74 "page": 4,75 "confidence": "low",76 "notes": "Age of the house in years; p printed as '<0.01' (upper bound recorded); values appear illustrative (round numbers, no sample size) -- do not use in primary synthesis"77 },78 {79 "model_id": "T1-c1",80 "var_name_printed": "Geographical location (X3) (per kilometer)",81 "taxonomy_id": "cbd_distance",82 "x_transform": "level",83 "x_unit": "km",84 "beta": -800,85 "se": 120,86 "t": -6.67,87 "p": 0.001,88 "p_stars": "",89 "ci_lo": null,90 "ci_hi": null,91 "page": 4,92 "confidence": "low",93 "notes": "Distance from the city centre in km; p printed as '<0.001' (upper bound recorded); values appear illustrative (round numbers, no sample size) -- do not use in primary synthesis"94 },95 {96 "model_id": "T1-c1",97 "var_name_printed": "Supporting facilities (X4) (each score)",98 "taxonomy_id": "neighbourhood_amenities",99 "x_transform": "level",100 "x_unit": "score",101 "beta": 1200,102 "se": 150,103 "t": 8.0,104 "p": 0.001,105 "p_stars": "",106 "ci_lo": null,107 "ci_hi": null,108 "page": 4,109 "confidence": "low",110 "notes": "Composite score of surrounding facilities (schools, hospitals, shopping centres); p printed as '<0.001' (upper bound recorded); values appear illustrative (round numbers, no sample size) -- do not use in primary synthesis"111 }112 ]113}