Predictability of Belgian residential real estate rents using tree-based ML models and IML techniques
International Journal of Housing Markets and Analysis
ISSN: 1753-8270
Article publication date: 13 April 2023
Issue publication date: 10 January 2024
Abstract
Purpose
The purpose is twofold. First, this study aims to establish that black box tree-based machine learning (ML) models have better predictive performance than a standard linear regression (LR) hedonic model for rent prediction. Second, it shows the added value of analyzing tree-based ML models with interpretable machine learning (IML) techniques.
Design/methodology/approach
Data on Belgian residential rental properties were collected. Tree-based ML models, random forest regression and eXtreme gradient boosting regression were applied to derive rent prediction models to compare predictive performance with a LR model. Interpretations of the tree-based models regarding important factors in predicting rent were made using SHapley Additive exPlanations (SHAP) feature importance (FI) plots and SHAP summary plots.
Findings
Results indicate that tree-based models perform better than a LR model for Belgian residential rent prediction. The SHAP FI plots agree that asking price, cadastral income, surface livable, number of bedrooms, number of bathrooms and variables measuring the proximity to points of interest are dominant predictors. The direction of relationships between rent and its factors is determined with SHAP summary plots. In addition to linear relationships, it emerges that nonlinear relationships exist.
Originality/value
Rent prediction using ML is relatively less studied than house price prediction. In addition, studying prediction models using IML techniques is relatively new in real estate economics. Moreover, to the best of the authors’ knowledge, this study is the first to derive insights of driving determinants of predicted rents from SHAP FI and SHAP summary plots.
Keywords
Acknowledgements
The authors would like to express their sincere gratitude to Eline Sergeant for her insightful comments and invaluable contributions to this paper. Her knowledge and collaborative spirit were essential to the development of this work. Additionally, authors would like to extend their thanks to the reviewers for their thoughtful suggestions and recommendations, which helped to improve the quality of the paper. Without their guidance and feedback, they would not have been able to achieve such a high level of quality in this work.
Citation
Lenaers, I., Boudt, K. and De Moor, L. (2024), "Predictability of Belgian residential real estate rents using tree-based ML models and IML techniques", International Journal of Housing Markets and Analysis, Vol. 17 No. 1, pp. 96-113. https://doi.org/10.1108/IJHMA-11-2022-0172
Publisher
:Emerald Publishing Limited
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