Improved Estimators of Hedonic Housing Price Models

Helen Bao and Alan Wan

Journal of Real Estate Research, 2007, vol. 29, issue 3, 267-302

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.

Date: 2007
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/10835547.2007.12091199 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:rjerxx:v:29:y:2007:i:3:p:267-302

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/rjer20

DOI: 10.1080/10835547.2007.12091199

Access Statistics for this article

Journal of Real Estate Research is currently edited by William Hardin and Michael Seiler

More articles in Journal of Real Estate Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().