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Biased Prediction of Housing Values

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Author Info

  • J. R. Knight
  • R. Carter Hill
  • C. F. Sirmans

Abstract

This paper introduces the use of non-sample, prior information to the problem of predicting prices of heterogeneous products. Using data from the 1983 American Housing Survey, the predictive performance of three Stein-like empirical Bayes estimation rules are compared to the least squares estimator and the traditional biased estimation technique, ridge regression. The biased estimators improve upon the least squares mean square error of prediction risk under certain design-related conditions. We provide evidence of this for the housing market in this paper. Copyright American Real Estate and Urban Economics Association.

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File URL: http://www.blackwell-synergy.com/doi/abs/10.1111/1540-6229.00590
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Bibliographic Info

Article provided by American Real Estate and Urban Economics Association in its journal Real Estate Economics.

Volume (Year): 20 (1992)
Issue (Month): 3 ()
Pages: 427-456

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Handle: RePEc:bla:reesec:v:20:y:1992:i:3:p:427-456

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Cited by:
  1. 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.
  2. Helen X. H. Bao & Alan T. K. Wan, 2007. "Improved Estimators of Hedonic Housing Price Models," Journal of Real Estate Research, American Real Estate Society, vol. 29(3), pages 267-302.

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