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Appraisal Using Generalized Additive Models

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  • Kelley Pace

Abstract

Many of the results from real estate empirical studies depend upon using a correct functional form for their validity. Unfortunately, common parametric statistical tools cannot easily control for the possibility of misspecification. Recently, semiparametric estimators such as generalized additive models (GAMs) have arisen which can automatically control for additive (in price) or multiplicative (in ln(price)) nonlinear relations among the independent and dependent variables. As the paper shows, GAMs can empirically outperform naive parametric and polynomial models in exsample predictive behavior. Moreover, GAMs have well-developed statistical properties and can suggest useful transformations in parametric settings.

Suggested Citation

  • Kelley Pace, 1998. "Appraisal Using Generalized Additive Models," Journal of Real Estate Research, Taylor & Francis Journals, vol. 15(1), pages 77-99, January.
  • Handle: RePEc:taf:rjerxx:v:15:y:1998:i:1:p:77-99
    DOI: 10.1080/10835547.1998.12090916
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    Cited by:

    1. Jason Robert Bailey & Davide Lauria & W. Brent Lindquist & Stefan Mittnik & Svetlozar T. Rachev, 2022. "Hedonic Models of Real Estate Prices: GAM Models; Environmental and Sex-Offender-Proximity Factors," JRFM, MDPI, vol. 15(12), pages 1-11, December.
    2. Jason R. Bailey & Davide Lauria & W. Brent Lindquist & Stefan Mittnik & Svetlozar T. Rachev, 2022. "Hedonic Models of Real Estate Prices: GAM and Environmental Factors," Papers 2210.14266, arXiv.org.
    3. Sun, Tianyu & Chand, Satish & Sharpe, Keiran, 2018. "Effect of aging on housing prices: evidence from a panel data," MPRA Paper 94418, University Library of Munich, Germany, revised 01 Mar 2019.

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