Time-Geographically Weighted Regressions and Residential Property Value Assessment
Jeffrey Cohen (),
Cletus Coughlin and
Jeffrey Zabel ()
The Journal of Real Estate Finance and Economics, 2020, vol. 60, issue 1, No 8, 134-154
Abstract In this study, we develop and apply a new methodology for obtaining accurate and equitable property value assessments. This methodology adds a time dimension to the Geographically Weighted Regressions (GWR) framework, which we call Time-Geographically Weighted Regressions (TGWR). That is, when generating assessed values, we consider sales that are close in time and space to the designated unit. We think this is an important improvement of GWR since this increases the number of comparable sales that can be used to generate assessed values. Furthermore, it is likely that units that sold at an earlier time but are spatially near the designated unit are likely to be closer in value than units that are sold at a similar time but farther away geographically. This is because location is such an important determinant of house value. We apply this new methodology to sales data for residential properties in 50 municipalities in Connecticut for 1994–2013 and 145 municipalities in Massachusetts for 1987–2012. This allows us to compare results over a long time period and across municipalities in two states. We find that TGWR performs better than OLS with fixed effects and leads to less regressive assessed values than OLS. In many cases, TGWR performs better than GWR that ignores the time dimension. In at least one specification, several suburban and rural towns meet the IAAO Coefficient of Dispersion cutoffs for acceptable accuracy.
Keywords: Time-geographically weighted regression; Residential property assessment; Coefficient of dispersion; Price-related differential; Percent absolute prediction error (search for similar items in EconPapers)
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Working Paper: Time-Geographically Weighted Regressions and Residential Property Value Assessment (2019)
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