1{2 "record_id": "323b30d53cb25830c8397eeb95e569a5dc5a3044",3 "eligible": false,4 "exclusion_reason": "Aggregate unit of analysis: observations are OMI-zone rental quotation ranges (min-max EUR/m2 per zone x dwelling type x conservation status; 54,214 cells, 8,046 municipalities), not individual dwellings; purpose is a Country-Product-Dummy spatial rent index. Only coefficients are region dummies, dwelling type (economic apartment, detached vs well-finished apartment), conservation status (excellent vs normal) and district class (semi-central, outskirts, suburban, rural vs central) with SEs (Tables 3A-3B, pp.13-16; log rent per m2 midpoint/min/max; N=54,214 and 16,040). Flag for human review: if zone-level quotation cells are accepted, type/condition/district coefficients could be extracted.",5 "study": {6 "title": "Sub-National Spatial Price Indexes for Housing: Methodological Issues and Computation for Italy",7 "year": 2022,8 "doi": "10.2478/jos-2022-0004",9 "country": "Italy",10 "region_city": "Italy, 20 regions (all municipalities)",11 "sample_start": 2017,12 "sample_end": 2017,13 "property_type": "mixed",14 "price_type": "rent",15 "data_source": "OMI (Real Estate Market Observatory, Agenzia delle Entrate) rental quotations, first semester 2017",16 "document_type": "journal",17 "estimators": [18 "OLS",19 "other"20 ],21 "spatial_method": "none (district and homogeneous-zone dummies)",22 "time_fe": false,23 "location_fe": true,24 "se_type": "OLS",25 "n_obs_total": 54214,26 "ai_methods": [],27 "notes": "Model [2] = OLS on log midpoint of interval rent; model [3] = SUR on log min and log max (symbolic data analysis). Dependent variable in EUR per m2 per month (log). All coefficients significant at p<0.01 per table note."28 },29 "models": [],30 "coefficients": []31}