From human business to machine learning—methods for automating real estate appraisals and their practical implications

Vom Vergleichswertverfahren zum maschinellen Lernen – Methoden zur automatisierten Wertermittlung von Wohnimmobilien und deren praktische Implikationen

Moritz Stang (), Bastian Krämer, Cathrine Nagl and Wolfgang Schäfers
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Moritz Stang: University of Regensburg
Bastian Krämer: University of Regensburg
Cathrine Nagl: University of Regensburg
Wolfgang Schäfers: University of Regensburg

Zeitschrift für Immobilienökonomie (German Journal of Real Estate Research), 2023, vol. 9, issue 2, No 1, 108 pages

Abstract: Abstract Until recently, in most countries, the use of Automated Valuation Models (AVMs) in the lending process was only allowed for support purposes, and not as the sole value-determining tool. However, this is currently changing, and regulators around the world are actively discussing the approval of AVMs. But the discussion is generally limited to AVMs that are based on already established methods such as an automation of the traditional sales comparison approach or linear regressions. Modern machine learning approaches are almost completely excluded from the debate. Accordingly, this study contributes to the discussion on why AVMs based on machine learning approaches should also be considered. For this purpose, an automation of the sales comparison method by using filters and similarity functions, two hedonic price functions, namely an OLS model and a GAM model, as well as a XGBoost machine learning approach, are applied to a dataset of 1.2 million residential properties across Germany. We find that the machine learning method XGBoost offers the overall best performance regarding the accuracy of estimations. Practical application shows that optimization of the established methods—OLS and GAM—is time-consuming and labor-intensive, and has significant disadvantages when being implemented on a national scale. In addition, our results show that different types of methods perform best in different regions and, thus, regulators should not only focus on one single method, but consider a multitude of them.

Keywords: Automated Valuation Models; Extreme Gradient Boosting; Housing Market; Machine Learning; Sales Comparison Method; Automated Valuation Models; Extreme Gradient Boosting; Wohnungsmarkt; Machine Learning; Vergleichswertverfahren (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1365/s41056-022-00063-1

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