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

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Borst, Richard A and McCluskey, William J (2008) Using Geographically Weighted Regression to Detect Housing Submarkets: Modeling Large-Scale Spatial Variations in Value. Journal of Property Tax Assessment and Administration, 5 (1). pp. 21-51. [Journal article]

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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.

Item Type:Journal article
Faculties and Schools:Faculty of Art, Design and the Built Environment
Faculty of Art, Design and the Built Environment > School of the Built Environment
Research Institutes and Groups:Built Environment Research Institute
Built Environment Research Institute > Centre for Research on Property and Planning (RPP)
ID Code:10037
Deposited By:Dr William McCluskey
Deposited On:01 Feb 2010 16:26
Last Modified:15 Feb 2010 16:57

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