{
 "record_id": "7f206890031d1d54ba82515e526e51ef8c34c412",
 "eligible": false,
 "exclusion_reason": "No printed characteristic coefficients: the paper fits a structured additive distributional regression (GAMLSS-type, batchwise backfitting with regional clustering) to >1 million German apartment asking prices per m2, but reports covariate effects only graphically (effect plots, county maps) and in Table 1 (out-of-sample prediction accuracy); Tables 2-3 are descriptive statistics only. Methodological/predictive study; keep qualitatively (AI/ML section).",
 "study": {
  "title": "A Parsimonious Hedonic Distributional Regression Model for Large Data with Heterogeneous Covariate Effects",
  "year": 2025,
  "doi": "10.17713/ajs.v54i2.1904",
  "country": "Germany",
  "region_city": "Germany (national, county-level clusters)",
  "sample_start": 2016,
  "sample_end": 2022,
  "property_type": "condo_apartment",
  "price_type": "listing",
  "data_source": "Large German dataset of apartment asking prices (> 1 million listings), quarters 2016-2022Q2",
  "document_type": "journal",
  "estimators": [
   "ML_marginal",
   "other"
  ],
  "spatial_method": "model-based regional clustering of county effects; structured additive distributional regression (location and scale)",
  "time_fe": false,
  "location_fe": true,
  "se_type": "unknown",
  "n_obs_total": 0,
  "ai_methods": [
   "structured additive distributional regression (GAMLSS)",
   "batchwise backfitting",
   "boosting-type variable selection",
   "model-based clustering"
  ],
  "notes": "Dependent variable log(price per square metre). n_obs_total not printed exactly (stated as over one million)."
 },
 "models": [],
 "coefficients": []
}