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The Use of Rank Transformation and Multiple Regression Analysis in Estimating Residential Property Values With A Small Sample

Conventional multiple regression analysis which has been used in estimating residential property values typically relies upon cardinal data. This paper argues that appraisal theory requires the appraiser to rank the comparables from best to worst and use a regression technique which can be applied to ordinal data. The rank regression procedure illustrated here was successfully used on small sample sizes, and did not violate the critical assumptions underlying conventional multiple regression. The results indicate that the rank regression technique illustrated here is more theoretically correct than conventional multiple regression and produces a better model with more accurate price estimates.

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File URL: http://pages.jh.edu/jrer/papers/pdf/past/vol01n01/v01p019.pdf
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Article provided by American Real Estate Society in its journal Journal of Real Estate Research.

Volume (Year): 1 (1986)
Issue (Month): 1 ()
Pages: 19-31

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Handle: RePEc:jre:issued:v:1:n:1:1986:p:19-31
Contact details of provider: Postal: American Real Estate Society Clemson University School of Business & Behavioral Science Department of Finance 401 Sirrine Hall Clemson, SC 29634-1323
Web page: http://www.aresnet.org/
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Order Information: Postal: Diane Quarles American Real Estate Society Manager of Member Services Clemson University Box 341323 Clemson, SC 29634-1323
Web: http://pages.jh.edu/jrer/about/get.htm Email:


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  1. Robert Carbone & Richard L. Longini, 1977. "A Feedback Model for Automated Real Estate Assessment," Management Science, INFORMS, vol. 24(3), pages 241-248, November.
  2. Peter F. Colwell & Roger E. Cannaday & Chunchi Wu, 1983. "The Analytical Foundations of Adjustment Grid Methods," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 11(1), pages 11-29.
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