1{2 "record_id": "33bb6f1b232198d68a02a325e907247d5c15da07",3 "eligible": false,4 "exclusion_reason": "No hedonic coefficient table is reported: the paper builds a rental dataset from online ads and estimates hedonic rent and price models by department/year only to derive municipal hedonic indices and rent-price ratios (Tables 5-8 are descriptive, index statistics, or ads-vs-survey comparison regressions). No printed characteristic coefficients for prices or rents in the main text (reproduction programs on OSF).",5 "study": {6 "title": "Can big data increase our knowledge of local rental markets? A dataset on the rental sector in France",7 "year": 2022,8 "doi": "10.1371/journal.pone.0260405",9 "country": "France",10 "region_city": "France (all municipalities)",11 "sample_start": 2016,12 "sample_end": 2017,13 "property_type": "mixed",14 "price_type": "rent",15 "data_source": "Webscraped online rental ads (authors' dataset) and DVF administrative transaction data (prices)",16 "document_type": "journal",17 "estimators": [18 "OLS"19 ],20 "spatial_method": "municipality / neighbourhood fixed effects",21 "time_fe": true,22 "location_fe": true,23 "se_type": "clustered",24 "n_obs_total": null,25 "ai_methods": [],26 "notes": "Hedonic models (log rent or price per m2 on surface, rooms, furnished, charges, municipality FE) estimated separately by year, department and dwelling type, but coefficients are not printed; only hedonic indices and rent-price ratios are reported. Kept for qualitative synthesis (data-source paper)."27 },28 "models": [],29 "coefficients": []30}