{
 "record_id": "33bb6f1b232198d68a02a325e907247d5c15da07",
 "eligible": false,
 "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).",
 "study": {
  "title": "Can big data increase our knowledge of local rental markets? A dataset on the rental sector in France",
  "year": 2022,
  "doi": "10.1371/journal.pone.0260405",
  "country": "France",
  "region_city": "France (all municipalities)",
  "sample_start": 2016,
  "sample_end": 2017,
  "property_type": "mixed",
  "price_type": "rent",
  "data_source": "Webscraped online rental ads (authors' dataset) and DVF administrative transaction data (prices)",
  "document_type": "journal",
  "estimators": [
   "OLS"
  ],
  "spatial_method": "municipality / neighbourhood fixed effects",
  "time_fe": true,
  "location_fe": true,
  "se_type": "clustered",
  "n_obs_total": null,
  "ai_methods": [],
  "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)."
 },
 "models": [],
 "coefficients": []
}