From Human Business to Machine Learning – Methods for Automating Real Estate Appraisals and their Practical Implications

Moritz Stang, Bastian Krämer, Cathrine Nagl and Wolfgang Schäfers

ERES from European Real Estate Society (ERES)

Abstract: The ongoing digitalization is picking up speed in slowly changing industries such as real estate. Especially in the field of real estate valuation, which is strongly dependent on data quality and quantity, automation is able to change the appraisal process substantially. However, in most countries, only the use of Automated Valuation Models (AVMs) based on simple non-statistical methods is allowed, as the regulatory system does not yet give the green light to higher-order methods. This study provides a relevant contribution to the debate on why AVMs based on statistical and machine learning methods should be widely used in practice. Therefore, various methods for AVMs are implemented and applied to a dataset of 1.2 million observations across Germany. An automation of the traditional sales comparison method, two hedonic price functions, as well as a machine learning approach are compared with each other. The aim of this paper is to show how the methods perform in direct comparison with each other as well as in different structural regions of Germany and whether the use of modern learning-based algorithms in real estate valuation is beneficial. The results of this research have various implications regarding the different accuracy and transparency levels of the methods from a regulatory and practical perspective. Moreover, the comparison at the spatial level shows that the models perform differently in urban and rural areas. This allows conclusions to be drawn about the design of AVMs for cross-regional models.

Keywords: Automated Valuation Models; eXtreme Gradient Boosting; housing market; Machine Learning (search for similar items in EconPapers)
JEL-codes: R3 (search for similar items in EconPapers)
Date: 2022-01-01
New Economics Papers: this item is included in nep-big and nep-ure
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Persistent link: https://EconPapers.repec.org/RePEc:arz:wpaper:2022_49

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