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Locally Regularized Linear Regression in the Valuation of Real Estate

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  • Mariusz Kubus

Abstract

Regression methods are used for the valuation of real estate in the comparative approach. The basis for the valuation is a data set of similar properties, for which sales transactions were concluded within a short period of time. Large and standardized databases, which meet the requirements of the Polish Financial Supervision Authority, are created in Poland and used by the banks involved in mortgage lending, for example. We assume that in the case of large data sets of transactions, it is more advantageous to build local regression models than a global model. Additionally, we propose a local feature selection via regularization. The empirical research carried out on three data sets from real estate market confirmed the effectiveness of this approach. We paid special attention to the model quality assessment using cross-validation for estimation of the residual standard error.

Suggested Citation

  • Mariusz Kubus, 2016. "Locally Regularized Linear Regression in the Valuation of Real Estate," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 17(3), pages 515-524, September.
    • Handle: RePEc:csb:stintr:v:17:y:2016:i:3:p:515-524
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    References listed on IDEAS

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    1. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    2. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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    Cited by:

    1. Mach Łukasz, 2017. "The Application of Classical and Neural Regression Models for the Valuation of Residential Real Estate," Folia Oeconomica Stetinensia, Sciendo, vol. 17(1), pages 44-56, June.

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