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Examining the Information Content of Residuals from Hedonic and Spatial Models Using Trees and Forests

Author

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  • R. Kelley Pace

    (E.J. Ourso College of Business Administration Louisiana State University Baton Rouge)

  • Darren Hayunga

    (University of Georgia Athens)

Abstract

Machine learning algorithms such as neural nets, support vector machines, and tree-based techniques (classification and regression trees) have shown great success in dealing with a number of complex problems (Hastie et al. 2009). However, real estate data exhibit both temporal dependence and high levels of spatial dependence (Pace et al., International Journal of Forecasting16(2), 229–246, 2000; LeSage and Pace 2009) that may make it harder to use with off-the-shelf machine learning procedures. We examine tree-based techniques (CART, boosting, and bagging) and compare these to spatiotemporal methods. We find that bagging works well and can give lower ex-sample residuals than global spatiotemporal methods, but do not perform better than local spatiotemporal methods.

Suggested Citation

  • R. Kelley Pace & Darren Hayunga, 2020. "Examining the Information Content of Residuals from Hedonic and Spatial Models Using Trees and Forests," The Journal of Real Estate Finance and Economics, Springer, vol. 60(1), pages 170-180, February.
    • Handle: RePEc:kap:jrefec:v:60:y:2020:i:1:d:10.1007_s11146-019-09724-w
    DOI: 10.1007/s11146-019-09724-w
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    References listed on IDEAS

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    1. Pace, R. Kelley & Barry, Ronald & Gilley, Otis W. & Sirmans, C. F., 2000. "A method for spatial-temporal forecasting with an application to real estate prices," International Journal of Forecasting, Elsevier, vol. 16(2), pages 229-246.
    2. Alexander N. Bogin & Jessica Shui, 2018. "Appraisal Accuracy, Automated Valuation Models, And Credit Modeling in Rural Areas," FHFA Staff Working Papers 18-03, Federal Housing Finance Agency.
    3. A. Quang Do & G. Grudnitski, 1993. "A Neural Network Analysis of the Effect of Age on Housing Values," Journal of Real Estate Research, American Real Estate Society, vol. 8(2), pages 253-264.
    4. R. Kelley Pace & Otis W. Gilley, 1998. "Generalizing the OLS and Grid Estimators," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 26(2), pages 331-347, June.
    5. McMillen, Daniel P., 1996. "One Hundred Fifty Years of Land Values in Chicago: A Nonparametric Approach," Journal of Urban Economics, Elsevier, vol. 40(1), pages 100-124, July.
    6. Kelley Pace, R. & LeSage, James P., 2008. "A spatial Hausman test," Economics Letters, Elsevier, vol. 101(3), pages 282-284, December.
    7. Nghiep Nguyen & Al Cripps, 2001. "Predicting Housing Value: A Comparison of Multiple Regression Analysis and Artificial Neural Networks," Journal of Real Estate Research, American Real Estate Society, vol. 22(3), pages 313-336.
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    Cited by:

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    2. Takahiro Yoshida & Daisuke Murakami & Hajime Seya, 2024. "Spatial Prediction of Apartment Rent using Regression-Based and Machine Learning-Based Approaches with a Large Dataset," The Journal of Real Estate Finance and Economics, Springer, vol. 69(1), pages 1-28, July.
    3. Aoife K. Hurley & James Sweeney, 2024. "Irish Property Price Estimation Using A Flexible Geo-spatial Smoothing Approach: What is the Impact of an Address?," The Journal of Real Estate Finance and Economics, Springer, vol. 68(3), pages 355-393, April.

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