This paper tests whether out-of-sample hedonic value predictions can be improved when a large urban housing market is divided into submarkets.

In this paper, it is examined whether the structure of constrained submarkets constructed according to three a priori classifications (property type, house value and geographical areas) differs from that of unconstrained submarkets constructed by means of principal component analysis and cluster...

A house is a bundle of land and improvements, with the weights of the two components varying both over time and across locations. We capture the land intensity or "leverage" of a property by measuring the ratio of land to total value. This is accomplished using transactions data for...

We analyze the impacts of alternative submarket definitions when predicting house prices in a mass appraisal context, using both ordinary least squares (OLS) and geostatistical techniques. For this purpose, we use over 13,000 housing transactions for Louisville, Kentucky. We use districts...

Purpose: The purpose of this paper is to investigate the accuracy and volatility of different methods for estimating and updating hedonic valuation models. Design/methodology/approach: The authors apply six estimation methods (linear least squares, robust regression, mixed-effects regression,...

In some applications of supervised machine learning, it is desirable to trade model complexity with greater interpretability for some covariates while letting other covariates remain a "black box". An important example is hedonic property valuation modeling, where machine learning techniques...