Using Geographically Weighted Regression to Detect Housing Submarkets: Modeling Large-Scale Spatial Variations in Value

Richard A Borst, William J McCluskey

    Research output: Contribution to journal › Article

    Abstract

    Many researchers and mass appraisal practitioners have established the benefit of segmenting a study area into two or more submarkets as a means of incorporating the large-scale effects of location within mass valuation models. The techniques applied for identifying locational submarkets or segments are quite varied, and often arbitrary. This article describes a segmentation technique based on the use of geographically weighted regression (GWR) which could be applied within the mass appraisal environment. The efficacy of the procedure is established by demonstrating improvements in predictive accuracy of the resultant segmented market models as compared to a baseline global unsegmented model for each of the study areas and then using the segmented markets in a series of spatially aware valuation models.
    LanguageEnglish
    Pages21-51
    JournalJournal of Property Tax Assessment and Administration
    Volume5
    Issue number1
    Publication statusPublished - 1 Feb 2008

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    spatial variation
    valuation
    modeling
    market
    scale effect
    segmentation
    appraisal
    global model

    Cite this

    Borst, Richard A ; McCluskey, William J. / Using Geographically Weighted Regression to Detect Housing Submarkets: Modeling Large-Scale Spatial Variations in Value. In: Journal of Property Tax Assessment and Administration. 2008 ; Vol. 5, No. 1. pp. 21-51.
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    abstract = "Many researchers and mass appraisal practitioners have established the benefit of segmenting a study area into two or more submarkets as a means of incorporating the large-scale effects of location within mass valuation models. The techniques applied for identifying locational submarkets or segments are quite varied, and often arbitrary. This article describes a segmentation technique based on the use of geographically weighted regression (GWR) which could be applied within the mass appraisal environment. The efficacy of the procedure is established by demonstrating improvements in predictive accuracy of the resultant segmented market models as compared to a baseline global unsegmented model for each of the study areas and then using the segmented markets in a series of spatially aware valuation models.",
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    Borst, RA & McCluskey, WJ 2008, 'Using Geographically Weighted Regression to Detect Housing Submarkets: Modeling Large-Scale Spatial Variations in Value', Journal of Property Tax Assessment and Administration, vol. 5, no. 1, pp. 21-51.

    Using Geographically Weighted Regression to Detect Housing Submarkets: Modeling Large-Scale Spatial Variations in Value. / Borst, Richard A; McCluskey, William J.

    In: Journal of Property Tax Assessment and Administration, Vol. 5, No. 1, 01.02.2008, p. 21-51.

    Research output: Contribution to journal › Article

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    Borst RA, McCluskey WJ. Using Geographically Weighted Regression to Detect Housing Submarkets: Modeling Large-Scale Spatial Variations in Value. Journal of Property Tax Assessment and Administration. 2008 Feb 1;5(1):21-51.

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