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A Note on the Optimal Selection and Weighting of Comparable Properties

This paper reexamines the recommendation by Vandell (1991), Gau, Lai and Wang (1992, 1994) and Green (1994) for the use of the minimum variance and coefficient of variation criteria in the optimum selection of comparables. These authors under-emphasize the typical valuation scenario that involves extremely small samples. The analyst must rank the few available comparable properties and select the "best" to carry the most weight in the final estimate of value. Rank transformation regression is suggested as one approach that can be used to extract the buying trend. The commonly taught paired-sale analysis will remain as the industry tool until more accurate estimates of value are developed with small samples.

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

Volume (Year): 14 (1997)
Issue (Month): 2 ()
Pages: 175-182

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Handle: RePEc:jre:issued:v:14:n:2:1997:p:175-182
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
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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
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  1. George W. Gau & Tsong-Yue Lai & Ko Wang, 1992. "Optimal Comparable Selection and Weighting in Real Property Valuation: An Extension," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 20(1), pages 107-123.
  2. Kerry D. Vandell, 1991. "Optimal Comparable Selection and Weighting in Real Property Valuation," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 19(2), pages 213-239.
  3. Richard K. Green, 1994. "Optimal Comparable Weighting and Selection: A Comment," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 22(4), pages 647-654.
  4. Ben-Akiva, Moshe & Morikawa, Takayuki & Shiroishi, Fumiaki, 1992. "Analysis of the reliability of preference ranking data," Journal of Business Research, Elsevier, vol. 24(2), pages 149-164, March.
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