A forecast comparison of residential housing prices by parametric versus semiparametric conditional mean estimators

The hedonic price model involves examining how the price of a commodity varies with the set of characteristics it possesses and has been used extensively in the housing market literature. The majority of the previous literature has concentrated on the parametric specifications of the hedonic price model by estimating econometric specifications such as ordinary least squares or Box-Cox models; all restrict the functional form and interaction between regressors. Recent evidence suggests that non-parametric and semiparametric techniques fit the data substantially better than the parametric specifications. Here, the parsimony of the parametric and semiparametric hedonic price models are examined by their out-of-sample forecast comparisons. The evidence presented here reveals that the semiparametric model provides the smallest out-of-sample mean square prediction error in comparison with the parametric specifications such as the ordinary least squares regression, the Box-Cox and the Wooldridge transformations. The results of this paper suggest that semiparametric regression can be successfully used for prediction and assessment of residential housing prices.