Improved Estimators of Hedonic Housing Price Models

In hedonic housing price modeling, real estate researchers and practitioners are often not completely ignorant about the parameters to be estimated. Experience and expertise usually provide them with tacit understanding of the likely values of the true parameters. Under this scenario the subjective knowledge about the parameter value can be incorporated as non-sample information in the hedonic price model. In this paper, we consider a class of Generalized Stein Variance Double k-class (GSVKK) estimators which allows real estate practitioners to introduce potentially useful information about the parameter values in the estimation of hedonic pricing models. The GSVKK estimator is a generalization of a family of shrinkage estimators introduced by Ohtani and Wan (2002, Econometric Reviews). Data from the Hong Kong real estate market are used to investigate the estimators' performance empirically. Compared with the traditional Ordinary Lease Squares approach, the GSVKK estimators have smaller predictive mean squared errors and lead to more precise parameter estimates. Some results on the theoretical properties of the GSVKK estimators are also presented