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Advances in Automated Valuation Modeling pp 191–199Cite as

Further Evaluating the Impact of Kernel and Bandwidth Specifications of Geographically Weighted Regression on the Equity and Uniformity of Mass Appraisal Models

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Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 86))

Abstract

Research has consistently demonstrated that geographically weighted regression (GWR) models significantly improve upon accuracy of ordinary least squares (OLS)-based computer-assisted mass appraisal (CAMA) models by more accurately accounting for the effects of location (Fotheringham et al. 2002; LeSage 2004; Huang et al. 2010). Bidanset and Lombard (2014a, 2017) previously studied the impacts of various kernel and bandwidth combinations employed in building residual (i.e. sale price less land value) GWR CAMA models and found that the specification of each does bear significant effect on valuation equity attainment. This paper builds upon the previous research by comparing performance of weighting specifications of non-building residual (i.e. full sale price) GWR CAMA models using new data of a different geographic real estate market. We find that the exponential kernel and fixed bandwidth together achieve a superior COD for our data, and that COD does fluctuate depending on the GWR weighting specification.

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Notes

  1. 1.

    Building residual techniques subtract an a priori land value from the sale price of a valuation model (IAAO 2013). The resulting value is the theoretical price of the building (improvement) only, and the independent variables used in a building residual CAMA model are used to isolate price determinants of the physical structure(s) only (age, living area, condition, etc.).

  2. 2.

    The Akaike Information Criterion (AIC) is a goodness-of-fit measurement. AIC corrected (AICc) is a goodness-of-fit measurement that penalizes for irrelevant variables (Sugiura 1978).

  3. 3.

    Sales in the most recent month of the dataset receive a reverse month of sale value of 1. Sales in the second most recent month of the dataset receive a reverse month of sale value of 2 (and so on). A dataset consisting of three full years of sales will have 36 reverse month of sale values (1–36) (Borst 2013).

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Authors and Affiliations

  1. School of the Built Environment, Ulster University, Newtownabbey, UK

    Paul E. Bidanset, Peadar Davis & Michael McCord

  2. Old Dominion University, Norfolk, VA, USA

    John R. Lombard

  3. African Tax Institute, University of Pretoria, Pretoria, South Africa

    William J. McCluskey

Authors
  1. Paul E. Bidanset
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  2. John R. Lombard
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  3. Peadar Davis
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  4. Michael McCord
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  5. William J. McCluskey
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Corresponding author

Correspondence to Paul E. Bidanset .

Editor information

Editors and Affiliations

  1. Property Valuation & Investment DICATECh, Technical University Politecnico di Bari, Bari, Italy

    Maurizio d'Amato

  2. School of the Built Environment, Oxford Brookes University, Headington, Oxfordshire, United Kingdom

    Tom Kauko

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Bidanset, P.E., Lombard, J.R., Davis, P., McCord, M., McCluskey, W.J. (2017). Further Evaluating the Impact of Kernel and Bandwidth Specifications of Geographically Weighted Regression on the Equity and Uniformity of Mass Appraisal Models. In: d'Amato, M., Kauko, T. (eds) Advances in Automated Valuation Modeling. Studies in Systems, Decision and Control, vol 86. Springer, Cham. https://doi.org/10.1007/978-3-319-49746-4_11

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  • DOI: https://doi.org/10.1007/978-3-319-49746-4_11

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