Can machine learning algorithms deliver superior models for rental guides?

Oliver Trinkaus () and Göran Kauermann ()
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Oliver Trinkaus: EMA-Institut für empirische Marktanalysen
Göran Kauermann: Ludwig-Maximilians-Universität München

AStA Wirtschafts- und Sozialstatistisches Archiv, 2023, vol. 17, issue 3, No 5, 305-330

Abstract: Abstract In this paper we discuss the use and potential advantages and disadvantages of machine learning driven models in rental guides. Rental guides are a formal legal instrument in Germany for surveying rents of flats in cities and municipalities, which are today based on regression models or simple contingency tables. We discuss if and how modern and timely methods of machine learning outperform existing and established routines. We make use of data from the Munich rental guide and mainly focus on the predictive power of these models. We discuss the “black-box” character making some of these models difficult to interpret and hence challenging for applications in the rental guide context. Still, it is of interest to see how “black-box” models perform with respect to prediction error. Moreover, we study adversarial effects, i.e. we investigate robustness in the sense how corrupted data influence the performance of the prediction models. With the data at hand we show that models with promising predictive performance suffer from being more vulnerable to corruptions than classic linear models including Ridge or Lasso regularization.

Keywords: Machine learning; Statistical learning; Regression; Adversarial regression; Mietspiegel; Rental guide (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11943-023-00333-x

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