NONPARAMETRIC METHODS WITH APPLICATIONS TO HEDONIC MODELS

Current real estate statistical valuation involves the estimation of parameters within a posited specification. Such parametric estimation requires judgment concerning model (1) variables; and (2) functional form. In contrast, nonparametric regression estimation requires attention to (1) but permits greatly reduced attention to (2). Parametric estimators functionally model the parameters and variables affecting E(y\x) while nonparametric estimators directly model pdf(y, x) and hence E(y\x). This article applies the kernel nonparametric regression estimator to two different data sets and specifications. The article shows the nonparametric estimator outperforms the standard parametric estimator (OLS) across variable transformations and across data subsets differing in quality. In addition, the article reviews properties of nonparametric estimators, presents the history of nonparametric estimators in real estate, and discusses a representation of the kernel estimator as a nonparametric grid method.