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Income, Value and Returns in Socially Responsible Office Properties

Author

Listed:
  • Gary Pivo

    (University of Arizona)

  • Jeffrey D. Fisher

    (Indiana University)

Abstract
This paper compares alternative methods for taking spatial dependence into account in house price prediction. We select hedonic methods that have been reported in the literature to perform relatively well in terms of ex-sample prediction accuracy. Because differences in performance may be due to differences in data, we compare the methods using a single data set. The estimation methods include simple OLS, a two-stage process incorporating nearest neighbors’ residuals in the second stage, geostatistical, and trend surface models. These models take into account submarkets by adding dummy variables or by estimating separate equations for each submarket. Based on data for approximately 13,000 transactions from Louisville, Kentucky, we conclude that a geostatistical model with disaggregated submarket variables performs best.

Suggested Citation

  • Gary Pivo & Jeffrey D. Fisher, 2010. "Income, Value and Returns in Socially Responsible Office Properties," Journal of Real Estate Research, American Real Estate Society, vol. 32(3), pages 243-270.
    • Handle: RePEc:jre:issued:v:32:n:3:2010:p:243-270
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    More about this item

    JEL classification:

    • L85 - Industrial Organization - - Industry Studies: Services - - - Real Estate Services

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