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Improved Estimators of Hedonic Housing Price Models

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  • Helen X. H. Bao

    ()
    (University of Cambridge 19 Silver Street Cambridge CB3 9EP, U.K)

  • Alan T. K. Wan

    ()
    (Department of Management Sciences, City University of Hong Kong Kowloon, Hong Kong)

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.

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Bibliographic Info

Article provided by American Real Estate Society in its journal journal of Real Estate Research.

Volume (Year): 29 (2007)
Issue (Month): 3 ()
Pages: 267-302

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Handle: RePEc:jre:issued:v:29:n:3:2007:p:267-302

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Postal: American Real Estate Society Clemson University School of Business & Behavioral Science Department of Finance 401 Sirrine Hall Clemson, SC 29634-1323
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  1. J. R. Knight & R. Carter Hill & C. F. Sirmans, 1992. "Biased Prediction of Housing Values," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 20(3), pages 427-456.
  2. Brownstone, David, 1990. "Bootstrapping improved estimators for linear regression models," Journal of Econometrics, Elsevier, vol. 44(1-2), pages 171-187.
  3. Chi, Xie Wen & Judge, George, 1985. "On assessing the precision of Stein's estimator," Economics Letters, Elsevier, vol. 18(2-3), pages 143-148.
  4. Wan, Alan T. K. & Zou, Guohua, 2003. "Optimal critical values of pre-tests when estimating the regression error variance: analytical findings under a general loss structure," Journal of Econometrics, Elsevier, vol. 114(1), pages 165-196, May.
  5. Alan Wan & Anoop Chaturvedi & Guohuazou Zou, 2003. "Unbiased estimation of the MSE matrices of improved estimators in linear regression," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(2), pages 173-189.
  6. Bourassa, Steven C. & Hoesli, Martin & Peng, Vincent S., 2003. "Do housing submarkets really matter?," Journal of Housing Economics, Elsevier, vol. 12(1), pages 12-28, March.
  7. Kazuhiro Ohtani & Alan Wan, 2002. "ON THE USE OF THE STEIN VARIANCE ESTIMATOR IN THE DOUBLE k-CLASS ESTIMATOR IN REGRESSION," Econometric Reviews, Taylor & Francis Journals, vol. 21(1), pages 121-134.
  8. Goodman, Allen C. & Thibodeau, Thomas G., 1998. "Housing Market Segmentation," Journal of Housing Economics, Elsevier, vol. 7(2), pages 121-143, June.
  9. Clapp, John M. & Wang, Yazhen, 2006. "Defining neighborhood boundaries: Are census tracts obsolete?," Journal of Urban Economics, Elsevier, vol. 59(2), pages 259-284, March.
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Cited by:
  1. Ahmed, S. Ejaz & Nicol, Christopher J., 2012. "An application of shrinkage estimation to the nonlinear regression model," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3309-3321.
  2. Magnus, Jan R. & Wan, Alan T.K. & Zhang, Xinyu, 2011. "Weighted average least squares estimation with nonspherical disturbances and an application to the Hong Kong housing market," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1331-1341, March.
  3. Zhang, Xinyu & Chen, Ti & Wan, Alan T.K. & Zou, Guohua, 2009. "Robustness of Stein-type estimators under a non-scalar error covariance structure," Journal of Multivariate Analysis, Elsevier, vol. 100(10), pages 2376-2388, November.

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