Improved Estimators of Hedonic Housing Price Models

36 Pages Posted: 15 Nov 2007

See all articles by Helen X. H. Bao

Helen X. H. Bao

Department of Land Economy, University of Cambridge

Alan T. K. Wan

City University of Hong Kong (CityU) - Department of Management Sciences

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Abstract

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. This paper considers 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 into the estimation of hedonic pricing models. 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.

Keywords: Hedonic price models, shrinkage estimators, Stein rule, valuation, Hong Kong, non-sample information

JEL Classification: R31, R21, C20

Suggested Citation

Bao, Helen X. H. and Wan, Alan T. K., Improved Estimators of Hedonic Housing Price Models. Journal of Real Estate Research, Vol. 29, No. 3, 2007, Available at SSRN: https://ssrn.com/abstract=1030001

Helen X. H. Bao (Contact Author)

Department of Land Economy, University of Cambridge ( email )

19 Silver Street
Cambridge, CB3 9EP
United Kingdom

Alan T. K. Wan

City University of Hong Kong (CityU) - Department of Management Sciences ( email )

Tat Chee Avenue
Kowloon Tong
Kowloon
Hong Kong

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