Comparing hedonic models for estimating and forecasting house prices

Abstract

In published work on hedonic house price estimation it is not uncommon to examine some of the conditions required for the estimators to have desirable properties such as minimum variance and unbiasedness, in particular spatial autocorrelation. However, other conditions that can give rise to similar difficulties with the estimates are often ignored. If these technical conditions are not met, it is sometimes because the model is misspecified in some way. This paper argues that a wider range of diagnostic statistics should be used in the specification search for a good model, in particular, but not exclusively, those concerned with predictive stability. The paper illustrates this approach by examining both in‐sample and out‐of‐sample diagnostic tests of various specifications of a hedonic house price model using data taken from the sale of over 1,600 properties in the Midlands of the UK in 1999/2000. The models investigated include various specifications of the dependent and independent variables, including models that are non‐linear in the parameters. The paper concludes that such statistics can often help in model selection and should be more widely employed.