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Rental guides are a\u00a0formal 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 \u201cblack-box\u201d 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 \u201cblack-box\u201d 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.<\/jats:p>","DOI":"10.1007\/s11943-023-00333-x","type":"journal-article","created":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T14:02:11Z","timestamp":1702389731000},"page":"305-330","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Can machine learning algorithms deliver superior models for rental guides?"],"prefix":"10.1007","volume":"17","author":[{"given":"Oliver","family":"Trinkaus","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G\u00f6ran","family":"Kauermann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,12]]},"reference":[{"key":"333_CR1","unstructured":"Aigner, Oberhofer, Schmidt (1993) Eine neue methode zur erstellung eines mietspiegels am beispiel der stadt regensburg. 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This work consists of surveying rents of flats and doing statistical analyses in behalf of German cities and municipalities. He was not involved in creating the Munich rental guide. The second author was involved in the statistical analyses of the Munich rental guides in 2013, 2015, 2017, 2019, 2021, 2023. The data used for the Munich rental guide 2019, which are used in this paper, have been collected by KANTAR (TNS Infratest). These involvements of both authors could be interpreted as conflict of interest appeared to influence the work reported in this paper.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declaration of competing interest"}}]}}