Assessment of Predictor Importance with the Example of the Real Estate Market

Kubus Mariusz ()
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Kubus Mariusz: Opole University of Technology, Faculty of Production Engineering and Logistics, Institute of Mathematics and Physics, Department of Mathematics and Applied Computer Science, Sosnkowskiego 31, 45-272 Opole, Poland

Folia Oeconomica Stetinensia, 2016, vol. 16, issue 2, 29-39

Abstract: Regression methods can be used for the valuation of real estate in the comparative approach. However, one of the problems of predictive modelling is the presence of redundant or irrelevant variables in data. Such variables can decrease the stability of models, and they can even reduce prediction accuracy. The choice of real estate’s features is largely determined by an appraiser, who is guided by his/her experience. Still, the use of statistical methods of a feature selection can lead to a more accurate valuation model. In the paper we apply regularized linear regression which belongs to embedded methods of a feature selection. For the considered data set of real estate land designated for single-family housing we obtained a model, which led to a more accurate valuation than some other popular linear models applied with or without a feature selection. To assess the model’s quality we used the leave-one-out cross-validation.

Keywords: valuation of real estate; multiple regression model; cross validation; feature selection; regularization (search for similar items in EconPapers)
JEL-codes: C52 R39 (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:foeste:v:16:y:2016:i:2:p:29-39:n:3

DOI: 10.1515/foli-2016-0023

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