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 journalArticle

    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.
    Original languageEnglish
    Pages (from-to)21-51
    JournalJournal of Property Tax Assessment and Administration
    Volume5
    Issue number1
    Publication statusPublished - 1 Feb 2008

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