Interpretable machine learning for real estate market analysis
Publication Date
2022-06-26Journal Title
Real Estate Economics
ISSN
1080-8620
Publisher
Wiley
Language
en
Type
Article
This Version
AO
VoR
Metadata
Show full item recordCitation
Lorenz, F., Willwersch, J., Cajias, M., & Fuerst, F. (2022). Interpretable machine learning for real estate market analysis. Real Estate Economics https://doi.org/10.1111/1540-6229.12397
Abstract
While Machine Learning (ML) excels at predictive tasks, its inferential capacity is limited due
to its complex non-parametric structure. This paper aims to elucidate the analytical behavior
of ML through Interpretable Machine Learning (IML) in a real estate context. Using a hedonic
ML approach to predict unit-level residential rents for Frankfurt, Germany, we apply a set of
model-agnostic interpretation methods to decompose the rental value drivers and plot their
trajectories over time. Living area and building age are the strongest predictors of rent,
followed by proximity to CBD and neighborhood amenities. Our approach is able to detect the
critical distances to these centers beyond which rents tend to decline more rapidly. Conversely,
close proximity to hospitality facilities as well as public transport is associated with rental
discounts. Overall, our results suggest that IML methods provide insights into algorithmic
decision-making by illustrating the relative importance of hedonic variables and their
relationship with rental prices in a dynamic perspective.
Keywords
ORIGINAL ARTICLE, ORIGINAL ARTICLES, black box, hedonic modeling, interpretable machine learning, rental estimation, residential real estate
Identifiers
reec12397
External DOI: https://doi.org/10.1111/1540-6229.12397
This record's URL: https://www.repository.cam.ac.uk/handle/1810/338527
Rights
Licence:
http://creativecommons.org/licenses/by-nc-nd/4.0/
Statistics
Total file downloads (since January 2020). For more information on metrics see the
IRUS guide.