Interpretable machine learning for real estate market analysis

Artikel

Machine Learning (ML) excels at most predictive tasks but its complex nonparametric structure renders it less useful for inference and out‐of sample predictions. This article aims to elucidate and enhance the analytical capabilities of ML in real estate through Interpretable ML (IML). Specifically, we compare a hedonic ML approach to a set of model‐agnostic interpretation methods. Our results suggest that IML methods permit a peek into the black box of algorithmic decision making by showing the web of associative relationships between variables in greater resolution. In our empirical applications, we confirm that size and age are the most important rent drivers. Further analysis reveals that certain bundles of hedonic characteristics, such as large apartments in historic buildings with balconies located in affluent neighborhoods, attract higher rents than adding up the contributions of each hedonic characteristic. Building age is shown to exhibit a U‐shaped pattern in that both the youngest and oldest buildings attract the highest rents. Besides revealing valuable distance decay functions for spatial variables, IML methods are also able to visualise how the strength and interactions of hedonic characteristics change over time, which investors could use to determine the types of assets that perform best at any given stage of the real estate investment cycle.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Real Estate Economics ; ISSN: 1540-6229 ; Volume: 51 ; Year: 2022 ; Issue: 5 ; Pages: 1178-1208 ; Hoboken, NJ: Wiley

Classification and Topics

• Classification: Wirtschaft
• Subject: black box ; hedonic modeling ; interpretable machine learning ; rental estimation ; residential real estate

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Lorenz, Felix ; Willwersch, Jonas ; Cajias, Marcelo ; Fuerst, Franz
• Event: Veröffentlichung
• (who): Wiley
• (where): Hoboken, NJ
• (when): 2022

Further information

• DOI: doi:10.1111/1540-6229.12397
• Last update: 10.03.2025, 11:42 AM CET