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Though previous analyses of housing rental prices rely on hedonic pricing or ordinary least squares models, they cannot fully account for the nonlinearity and complexity inherent in the determination of market prices. We exploit machine-learning techniques to accurately understand the determinants of housing rental prices and emphasise explainable artificial intelligence (XAI) to overcome the limitations of classical machine learning with black-box features. To analyse the determinants of housing rental prices in Seoul, we apply extreme gradient boosting (XGBoost) and Tree Shapley additive explanations (TreeSHAP) models which learn and explain nonlinear features of the rental prices. XGBoost predicts rental prices more accurately than regressions and is robust to multicollinearity. TreeSHAP identifies local and global features affecting the determination of rental prices. Our analyses identify the individual impacts of various determinant variables on rental prices and underscore the multifaceted influences that these variables exert on housing affordability.
Kim et al. (Wed,) studied this question.