Modeling unobserved heterogeneity in hedonic price models
研究了在享乐价格模型中，通过加入房产随机效应和空间随机效应来处理未观测异质性，从而提升房价预测精度，对商业和住宅房产数据均有效。
Abstract This paper studies unobserved heterogeneity in hedonic price models, arising from missing property and locational characteristics. Specifically, commercial real estate is very heterogeneous, and data on detailed property characteristics are often lacking. We show that adding mutually independent property random effects to a hedonic price model results in more precise out‐of‐sample price predictions, both for commercial multifamily housing in Los Angeles and owner‐occupied single‐family housing in Heemstede, the Netherlands. The standard hedonic price model does not take advantage of the fact that some properties sell more than once. We subsequently show that adding spatial random effects leads to an additional increase in prediction accuracy. The increase is highest for properties without prior sales.