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Time-Geographically Weighted Regressions and Residential Property Value Assessment

Author

Listed:
  • Cletus C. Coughlin

    (Board of Governors of the Federal Reserve System (U.S.))

  • Jeffrey Zabel
  • Jeffrey P. Cohen

    (National Bureau of Economic Research
    University of Connecticut
    University of Texas at Austin
    UT Austin)

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.

Suggested Citation

  • Cletus C. Coughlin & Jeffrey Zabel & Jeffrey P. Cohen, 2019. "Time-Geographically Weighted Regressions and Residential Property Value Assessment," Working Papers 2019-5, Federal Reserve Bank of St. Louis, revised 30 Jan 2019.
  • Handle: RePEc:fip:fedlwp:2019-005
    DOI: 10.20955/wp.2019.005
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    References listed on IDEAS

    as
    1. Daniel P. McMillen & Christian L. Redfearn, 2010. "Estimation And Hypothesis Testing For Nonparametric Hedonic House Price Functions," Journal of Regional Science, Wiley Blackwell, vol. 50(3), pages 712-733, August.
    2. A S Fotheringham & M E Charlton & C Brunsdon, 1998. "Geographically weighted regression: a natural evolution of the expansion method for spatial data analysis," Environment and Planning A, Pion Ltd, London, vol. 30(11), pages 1905-1927, November.
    3. A S Fotheringham & M E Charlton & C Brunsdon, 1998. "Geographically Weighted Regression: A Natural Evolution of the Expansion Method for Spatial Data Analysis," Environment and Planning A, , vol. 30(11), pages 1905-1927, November.
    4. repec:kap:jrefec:v:54:y:2017:i:3:d:10.1007_s11146-017-9604-5 is not listed on IDEAS
    5. W.J. McCluskey & M. McCord & P.T. Davis & M. Haran & D. McIlhatton, 2013. "Prediction accuracy in mass appraisal: a comparison of modern approaches," Journal of Property Research, Taylor & Francis Journals, vol. 30(4), pages 239-265, December.
    6. repec:kap:jrefec:v:54:y:2017:i:3:d:10.1007_s11146-016-9570-3 is not listed on IDEAS
    7. Cohen, Jeffrey P. & Osleeb, Jeffrey P. & Yang, Ke, 2014. "Semi-parametric regression models and economies of scale in the presence of an endogenous variable," Regional Science and Urban Economics, Elsevier, vol. 49(C), pages 252-261.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    property value; assessment; price-related differential; coefficient of dispersion; geographically weighted regression;

    JEL classification:

    • H71 - Public Economics - - State and Local Government; Intergovernmental Relations - - - State and Local Taxation, Subsidies, and Revenue
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • R51 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Regional Government Analysis - - - Finance in Urban and Rural Economies
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

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