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Multicollinearity and Reduced‐Form Price Equations For Residential Markets: An Evaluation of Alternative Estimation Methods

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  • George W. Gau
  • Daniel B. Kohlhepp

Abstract

One of the problems that has plagued researchers in their estimation of reduced‐form price equations for specific housing markets has been multicollinearity—the lack of statistical independence of the explanatory variables. This paper evaluates the suitability for structural analysis and prediction of stepwise regression and principal components regression as alternatives to the standard regression model in the estimation of equations with interdependent data. In general, the results of this study indicate that under conditions of multicollinearity principal components regression is the superior estimation technique.

Suggested Citation

  • George W. Gau & Daniel B. Kohlhepp, 1978. "Multicollinearity and Reduced‐Form Price Equations For Residential Markets: An Evaluation of Alternative Estimation Methods," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 6(1), pages 50-69, March.
    • Handle: RePEc:bla:reesec:v:6:y:1978:i:1:p:50-69
    DOI: 10.1111/1540-6229.00168
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    Cited by:

    1. Brent W. Ambrose, 1990. "An Analysis of the Factors Affecting Light Industrial Property Valuation," Journal of Real Estate Research, American Real Estate Society, vol. 5(3), pages 355-370.
    2. George H. Lentz & Ko Wang, 1998. "Residential Appraisal and the Lending Process: A Survey of Issues," Journal of Real Estate Research, American Real Estate Society, vol. 15(1), pages 11-40.

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