1{2 "record_id": "c30509d1b7dff13ef9c6d68570be3618b239e0cd",3 "eligible": false,4 "exclusion_reason": "Price functions are EPR-MOGA (evolutionary polynomial regression) equations: each term is a multiplicative combination of several variables raised to fractional powers (e.g. Sb^0.5*B^0.5*Vm^0.5), so no interpretable per-characteristic coefficient exists; no standard errors, t or p values are reported; only CoD and cross-validation errors (Tables 2-4, 6-7) and narrative average percentage variations from a simplified exogenous simulation (text pp. 14-16, Figures S1-S12). Not usable for coefficient synthesis; keep qualitatively (COVID-19, six Italian cities, 165 sales per city per phase, 2019H2 vs 2021H1).",5 "study": {6 "title": "An Assessment Methodology for the Evaluation of the Impacts of the COVID-19 Pandemic on the Italian Housing Market Demand",7 "year": 2021,8 "doi": "10.3390/buildings11120592",9 "country": "Italy",10 "region_city": "Turin, Milan, Florence, Rome, Naples, Catania",11 "sample_start": 2019,12 "sample_end": 2021,13 "property_type": "condo_apartment",14 "price_type": "transaction",15 "data_source": "165 residential sales per city per phase (2019H2 pre-COVID; 2021H1 during COVID), OMI trade areas",16 "document_type": "journal",17 "estimators": ["other"],18 "spatial_method": "none (OMI trade-area dummies)",19 "time_fe": false,20 "location_fe": true,21 "se_type": "unknown",22 "n_obs_total": 1980,23 "ai_methods": ["genetic algorithm (EPR-MOGA)"],24 "notes": "Dependent variable ln(total selling price). Variables: Si internal area, Sg garden area, Sb balcony/terrace area, Se condominium outdoor area (dummy), L floor level, B bathrooms, Yc construction year, C central area, Sc semi-central, P peripheral, Mg maintenance good?, Me maintenance excellent (dummy), Vm average OMI market value. Equations in Tables 2 (Phase I) and 6 (Phase II, p.16)."25 },26 "models": [],27 "coefficients": []28}29