Advances in Automated Valuation Modeling

Paul E. Bidanset, John R Lombard, Peadar Davis, Michael McCord, William J. McCluskey

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Further Evaluating the Impact of Kernel and Bandwidth Specifications of Geographically Weighted Regression on the Equity and Uniformity of Mass Appraisal Models
LanguageEnglish
Title of host publicationStudies in Systems, Decision and Control
EditorsMaurizio d'Amato, Tom Kauko
Pages191-199
Volume86
DOIs
Publication statusE-pub ahead of print - 2 Feb 2017

Fingerprint

valuation
equity
modeling
appraisal

Keywords

  • 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

Cite this

Bidanset, P. E., Lombard, J. R., Davis, P., McCord, M., & McCluskey, W. J. (2017). Advances in Automated Valuation Modeling. In M. d'Amato, & T. Kauko (Eds.), Studies in Systems, Decision and Control (Vol. 86, pp. 191-199) https://doi.org/10.1007/978-3-319-49746-4_11
Bidanset, Paul E. ; Lombard, John R ; Davis, Peadar ; McCord, Michael ; McCluskey, William J. / Advances in Automated Valuation Modeling. Studies in Systems, Decision and Control. editor / Maurizio d'Amato ; Tom Kauko. Vol. 86 2017. pp. 191-199
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title = "Advances in Automated Valuation Modeling",
abstract = "Further Evaluating the Impact of Kernel and Bandwidth Specifications of Geographically Weighted Regression on the Equity and Uniformity of Mass Appraisal Models",
keywords = "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",
author = "Bidanset, {Paul E.} and Lombard, {John R} and Peadar Davis and Michael McCord and McCluskey, {William J.}",
note = "Reference text: Anselin, L., & Griffith, D. A. (1988). Do spatial effects really matter in regression analysis? Papers in Regional Science, 65(1), 11–34. Google Scholar Ball, M. J. (1973). Recent empirical work on the determinants of relative house prices. Urban Studies, 10(2), 213—233. Google Scholar Berry, B. J., & Bednarz, R. S. (1975). A hedonic model of prices and assessments for single-family homes: Does the assessor follow the market or the market follow the assessor? Land Economics, 51(1), 21–40. CrossRefGoogle Scholar Bidanset, P. E., & Lombard, J. R. (2014a). The effect of kernel and bandwidth specification in geographically weighted regression models on the accuracy and uniformity of mass real estate appraisal. Journal of Property Tax Assessment & Administration, 11(3). Google Scholar Bidanset, P. E., & Lombard, J. R. (2014b). Evaluating spatial model accuracy in mass real estate appraisal: a comparison of geographically weighted regression and the spatial lag model. Cityscape: A Journal of Policy Development and Research, 16(3), 169. Google Scholar Bidanset, P. E., & Lombard, J. R. (2017). Optimal kernel and bandwidth specifications for geographically weighted regression. Applied Spatial Modelling and Planning. J. R. Lombard, E. Stern, G. Clarke (Ed.). Abingdon, Oxon; New York, NY: Routledge. Google Scholar Borst, R. (2013). Optimal market segmentation and temporal methods. spatio-temporal methods in mass appraisal. Fairfax, VA: International Property Tax Institute. Mason Inn Conference Center. Google Scholar Borst, R. A., & McCluskey, W. 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), 21–51. Google Scholar Brunsdon, C. (1998). Geographically weighted regression: A natural evolution of the expansion method for spatial data analysis. Environment and planning A, 30, 1905–1927. CrossRefGoogle Scholar Brunsdon, C., Fotheringham, A. S., & Charlton, M. E. (1996). Geographically weighted regression: A method for exploring spatial nonstationarity. Geographical Analysis, 28(4), 281–298. CrossRefGoogle Scholar Cho, S. H., Lambert, D. M., & Chen, Z. (2010). Geographically weighted regression bandwidth selection and spatial autocorrelation: An empirical example using Chinese agriculture data. Applied Economics Letters, 17(8), 767–772. CrossRefGoogle Scholar Cleveland, W. S., & Devlin, S. J. (1988). Locally weighted regression: An approach to regression analysis by local fitting. Journal of the American Statistical Association, 83(403), 596–610. CrossRefMATHGoogle Scholar Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically weighted regression: The analysis of spatially varying relationships. Chichester, West Sussex, England: Wiley. MATHGoogle Scholar Gloudemans, R. J. (1999). Mass appraisal of real property. Chicago, IL: International Association of Assessing Officers. Google Scholar Gloudemans, R., & Almy, R. (2011). Fundamentals of mass appraisal. Kansas City, MO: International Association of Assessing Officers. Google Scholar Gollini, I., Lu, B., Charlton, M., Brunsdon, C., & Harris, P. (2013). GWmodel: An R package for exploring spatial heterogeneity using geographically weighted models. arXiv preprint: arXiv:1306.0413. Guo, L., Ma, Z., & Zhang, L. (2008). Comparison of bandwidth selection in application of geographically weighted regression: A case study. Canadian Journal of Forest Research, 38(9), 2526–2534. CrossRefGoogle Scholar Huang, B., Wu, B., & Barry, M. (2010). Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices. International Journal of Geographical Information Science, 24(3), 383–401. CrossRefGoogle Scholar IAAO (2003). Standard on automated valuation models (AVM’s). Assessment Journal (Fall 2003), 109–154. Google Scholar International Association of Assessing Officers. (2013). Standard on ratio studies. Chicago, IL: IAAO. Google Scholar LeSage, J. P. (2004). A family of geographically weighted regression models. In Advances in Spatial Econometrics (pp. 241–264). Springer. Google Scholar Lockwood, T., & Rossini, P. (2011). Efficacy in modelling location within the mass appraisal process. Pacific Rim Property Research Journal, 17(3), 418–442. CrossRefGoogle Scholar Lu, B., Harris, P., Charlton, M., & Brunsdon, C. (2014). The GWmodel R package: Further topics for exploring spatial heterogeneity using geographically weighted models. Geo-spatial Information Science, 17(2), 85–101. CrossRefGoogle Scholar McCluskey, W. J., McCord, M., Davis, P. T., Haran, M., & McIlhatton, D. (2013). Prediction accuracy in mass appraisal: A comparison of modern approaches. Journal of Property Research, 30(4), 239–265. CrossRefGoogle Scholar McMillen, D. P. (1996). One hundred fifty years of land values in Chicago: A nonparametric approach. Journal of Urban Economics, 40(1), 100–124. CrossRefMATHGoogle Scholar McMillen, D. P., & Redfearn, C. L. (2010). Estimation and hypothesis testing for nonparametric hedonic house price functions. Journal of Regional Science, 50(3), 712–733. CrossRefGoogle Scholar Moore, J. W. (2009). A History of appraisal theory and practice looking back from IAAO’s 75th year. Journal of Property Tax Assessment & Administration, 6(3), 23. Google Scholar Moore, J. W., & Myers, J. (2010). Using geographic-attribute weighted regression for CAMA modeling. Journal of Property Tax Assessment & Administration, 7(3), 5–28. Google Scholar Sugiura, N. (1978). Further analysts of the data by Akaike’s information criterion and the finite corrections. Communications in Statistics-Theory and Methods, 7(1), 13–26.",
year = "2017",
month = "2",
day = "2",
doi = "10.1007/978-3-319-49746-4_11",
language = "English",
isbn = "978-3-319-49746-4",
volume = "86",
pages = "191--199",
editor = "Maurizio d'Amato and Tom Kauko",
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}

Bidanset, PE, Lombard, JR, Davis, P, McCord, M & McCluskey, WJ 2017, Advances in Automated Valuation Modeling. in M d'Amato & T Kauko (eds), Studies in Systems, Decision and Control. vol. 86, pp. 191-199. https://doi.org/10.1007/978-3-319-49746-4_11

Advances in Automated Valuation Modeling. / Bidanset, Paul E.; Lombard, John R; Davis, Peadar; McCord, Michael; McCluskey, William J.

Studies in Systems, Decision and Control. ed. / Maurizio d'Amato; Tom Kauko. Vol. 86 2017. p. 191-199.

Research output: Chapter in Book/Report/Conference proceedingChapter

TY - CHAP

T1 - Advances in Automated Valuation Modeling

AU - Bidanset, Paul E.

AU - Lombard, John R

AU - Davis, Peadar

AU - McCord, Michael

AU - McCluskey, William J.

N1 - Reference text: Anselin, L., & Griffith, D. A. (1988). Do spatial effects really matter in regression analysis? Papers in Regional Science, 65(1), 11–34. Google Scholar Ball, M. J. (1973). Recent empirical work on the determinants of relative house prices. Urban Studies, 10(2), 213—233. Google Scholar Berry, B. J., & Bednarz, R. S. (1975). A hedonic model of prices and assessments for single-family homes: Does the assessor follow the market or the market follow the assessor? Land Economics, 51(1), 21–40. CrossRefGoogle Scholar Bidanset, P. E., & Lombard, J. R. (2014a). The effect of kernel and bandwidth specification in geographically weighted regression models on the accuracy and uniformity of mass real estate appraisal. Journal of Property Tax Assessment & Administration, 11(3). Google Scholar Bidanset, P. E., & Lombard, J. R. (2014b). Evaluating spatial model accuracy in mass real estate appraisal: a comparison of geographically weighted regression and the spatial lag model. Cityscape: A Journal of Policy Development and Research, 16(3), 169. Google Scholar Bidanset, P. E., & Lombard, J. R. (2017). Optimal kernel and bandwidth specifications for geographically weighted regression. Applied Spatial Modelling and Planning. J. R. Lombard, E. Stern, G. Clarke (Ed.). Abingdon, Oxon; New York, NY: Routledge. Google Scholar Borst, R. (2013). Optimal market segmentation and temporal methods. spatio-temporal methods in mass appraisal. Fairfax, VA: International Property Tax Institute. Mason Inn Conference Center. Google Scholar Borst, R. A., & McCluskey, W. 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), 21–51. Google Scholar Brunsdon, C. (1998). Geographically weighted regression: A natural evolution of the expansion method for spatial data analysis. Environment and planning A, 30, 1905–1927. CrossRefGoogle Scholar Brunsdon, C., Fotheringham, A. S., & Charlton, M. E. (1996). Geographically weighted regression: A method for exploring spatial nonstationarity. Geographical Analysis, 28(4), 281–298. CrossRefGoogle Scholar Cho, S. H., Lambert, D. M., & Chen, Z. (2010). Geographically weighted regression bandwidth selection and spatial autocorrelation: An empirical example using Chinese agriculture data. Applied Economics Letters, 17(8), 767–772. CrossRefGoogle Scholar Cleveland, W. S., & Devlin, S. J. (1988). Locally weighted regression: An approach to regression analysis by local fitting. Journal of the American Statistical Association, 83(403), 596–610. CrossRefMATHGoogle Scholar Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically weighted regression: The analysis of spatially varying relationships. Chichester, West Sussex, England: Wiley. MATHGoogle Scholar Gloudemans, R. J. (1999). Mass appraisal of real property. Chicago, IL: International Association of Assessing Officers. Google Scholar Gloudemans, R., & Almy, R. (2011). Fundamentals of mass appraisal. Kansas City, MO: International Association of Assessing Officers. Google Scholar Gollini, I., Lu, B., Charlton, M., Brunsdon, C., & Harris, P. (2013). GWmodel: An R package for exploring spatial heterogeneity using geographically weighted models. arXiv preprint: arXiv:1306.0413. Guo, L., Ma, Z., & Zhang, L. (2008). Comparison of bandwidth selection in application of geographically weighted regression: A case study. Canadian Journal of Forest Research, 38(9), 2526–2534. CrossRefGoogle Scholar Huang, B., Wu, B., & Barry, M. (2010). Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices. International Journal of Geographical Information Science, 24(3), 383–401. CrossRefGoogle Scholar IAAO (2003). Standard on automated valuation models (AVM’s). Assessment Journal (Fall 2003), 109–154. Google Scholar International Association of Assessing Officers. (2013). Standard on ratio studies. Chicago, IL: IAAO. Google Scholar LeSage, J. P. (2004). A family of geographically weighted regression models. In Advances in Spatial Econometrics (pp. 241–264). Springer. Google Scholar Lockwood, T., & Rossini, P. (2011). Efficacy in modelling location within the mass appraisal process. Pacific Rim Property Research Journal, 17(3), 418–442. CrossRefGoogle Scholar Lu, B., Harris, P., Charlton, M., & Brunsdon, C. (2014). The GWmodel R package: Further topics for exploring spatial heterogeneity using geographically weighted models. Geo-spatial Information Science, 17(2), 85–101. CrossRefGoogle Scholar McCluskey, W. J., McCord, M., Davis, P. T., Haran, M., & McIlhatton, D. (2013). Prediction accuracy in mass appraisal: A comparison of modern approaches. Journal of Property Research, 30(4), 239–265. CrossRefGoogle Scholar McMillen, D. P. (1996). One hundred fifty years of land values in Chicago: A nonparametric approach. Journal of Urban Economics, 40(1), 100–124. CrossRefMATHGoogle Scholar McMillen, D. P., & Redfearn, C. L. (2010). Estimation and hypothesis testing for nonparametric hedonic house price functions. Journal of Regional Science, 50(3), 712–733. CrossRefGoogle Scholar Moore, J. W. (2009). A History of appraisal theory and practice looking back from IAAO’s 75th year. Journal of Property Tax Assessment & Administration, 6(3), 23. Google Scholar Moore, J. W., & Myers, J. (2010). Using geographic-attribute weighted regression for CAMA modeling. Journal of Property Tax Assessment & Administration, 7(3), 5–28. Google Scholar Sugiura, N. (1978). Further analysts of the data by Akaike’s information criterion and the finite corrections. Communications in Statistics-Theory and Methods, 7(1), 13–26.

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BT - Studies in Systems, Decision and Control

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Bidanset PE, Lombard JR, Davis P, McCord M, McCluskey WJ. Advances in Automated Valuation Modeling. In d'Amato M, Kauko T, editors, Studies in Systems, Decision and Control. Vol. 86. 2017. p. 191-199 https://doi.org/10.1007/978-3-319-49746-4_11