Dealing with Unobserved Heterogeneity in Hedonic Price Models

Abstract

This paper deals with unobserved heterogeneity in hedonic price models, arising from missing property and locational characteristics. In specific, 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.

Keywords: Bayesian Inference, Besag model, Commercial real estate, INLA, Leave-on-out-cross validation, Travelings Salesperson Problem

JEL Classification: R32, C01