Ian Lenaers, Kris Boudt and Lieven De Moor
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…
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.
Details
Keywords
Ugur Yavas and Mahmoud M. Yasin
Looks at the findings of a survey of 115 Saudi Arabian managers who had completed their undergraduate education in the United States in relation to the informational and computing…
Abstract
Looks at the findings of a survey of 115 Saudi Arabian managers who had completed their undergraduate education in the United States in relation to the informational and computing resources and their applications in Saudi organisations. Considers the role of computers in business and highlights the lack of specialists able to train within the country. Concludes that whilst the skills to use information technology exist, they are limited by cultural resistance to change, traditional viewpoints, authoritarian leadership and bureaucracy. Advocates government encouragement and ties with developed nations to help change such attitudes.