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Impact of the Regularization of Regression Models on the Results of the Mass Valuation of Real Estate

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  • Gnat Sebastian

    (University of Szczecin, Institute of Economics and Finance, Mickiewicza 64, 71-101Szczecin, Poland)

Abstract

Research background: Mass appraisal is a process in which multiple properties are appraised simultaneously, with a uniform approach. One of the tools that can be used in this area are multiple regression models. In the valuation of real estate features are often described on an ordinal or nominal scale. Replacing them with dummy variables with an insufficient number of observations leads to multicollinearity. On the other hand, there is a risk of overfitting the model. One of the ways to eliminate or weaken these phenomena is to introduce regularization based on a model’s penalization for the high values of its weights.Purpose: The aim of the study is to verify the hypothesis whether regularized regression reduces the errors of property valuation and which of the analyzed methods is the most effective in this context.Research methodology: The article will present a study in which two ways of regularization will be applied – ridge and lasso regression, in the context of their impact on the errors of property valuation. The analyzed data set includes over 300 land properties valued by property appraisers. The key aspects of the study are the selection of optimal values of the regularization parameter and its influence on model’s errors with a different number of observations in the training sets.Results: The study showed that regularization improves valuation results and, more specifically, allows for lower average absolute percentage errors. The improvement of model effectiveness was more pronounced in the case of ridge regression. An important result is also that regularization has provided a higher accuracy of valuation compared to multiple regression models for smaller training sets.Novelty: The article confirms the effectiveness of regularization as a way to eliminate the problem of multicollinearity or overfitting of the model. The results showed that ridge regression can be an effective way of modelling the value of real estate. Especially in the case of a small amount of market data, which is an important conclusion in the context of the real estate market.

Suggested Citation

  • Gnat Sebastian, 2020. "Impact of the Regularization of Regression Models on the Results of the Mass Valuation of Real Estate," Folia Oeconomica Stetinensia, Sciendo, vol. 20(1), pages 163-176, June.
    • Handle: RePEc:vrs:foeste:v:20:y:2020:i:1:p:163-176:n:9
    DOI: 10.2478/foli-2020-0009
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    References listed on IDEAS

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    1. Jozef Zurada & Alan S. Levitan & Jian Guan, 2011. "A Comparison of Regression and Artificial Intelligence Methods in a Mass Appraisal Context," Journal of Real Estate Research, American Real Estate Society, vol. 33(3), pages 349-388.
    2. W.J. McCluskey & M. McCord & P.T. Davis & M. Haran & D. McIlhatton, 2013. "Prediction accuracy in mass appraisal: a comparison of modern approaches," Journal of Property Research, Taylor & Francis Journals, vol. 30(4), pages 239-265, December.
    3. Hans R. Isakson, 1998. "The Review of Real Estate Appraisals Using Multiple Regression Analysis," Journal of Real Estate Research, American Real Estate Society, vol. 15(2), pages 177-190.
    4. Clifford M. Hurvich & Jeffrey S. Simonoff & Chih‐Ling Tsai, 1998. "Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(2), pages 271-293.
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    More about this item

    Keywords

    property valuation; market analysis; regularization;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • R30 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - General

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