A prediction comparison of housing sales prices by parametric versus semi-parametric regressions

Abstract

This study estimates a hedonic price function using a semi-parametric regression and compares the price prediction performance with conventional parametric models. This study utilizes a large data set representing 2595 single-family residential home sales between July 2000 and June 2002 from Pitt County, North Carolina. Data from Geographic Information Systems (GIS) are incorporated to account for locational attributes of the houses. The results show that the semi-parametric regression outperforms the parametric counterparts in both in-sample and out-of-sample price predictions, indicating that the semi-parametric model can be useful for measurement and prediction of housing sales prices.