In some applications of supervised machine learning, it is desirable to trade model complexity with greater interpretability for some covariates while letting other covariates remain a "black box". An important example is hedonic property valuation modeling, where machine learning techniques...

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)....

Data-driven machine learning algorithms have initiated a paradigm shift in hedonic house price and rent modeling through their ability to capture highly complex and non-monotonic relationships. Their superior accuracy compared to parametric model alternatives has been demonstrated repeatedly in...

In some applications of supervised machine learning, it is desirable to trade model complexity with greater interpretability for some covariates while letting other covariates remain a "black box". An important example is hedonic property valuation modeling, where machine learning techniques...

Data-driven machine learning algorithms have initiated a paradigm shift in hedonic house price and rent modeling through their ability to capture highly complex and non-monotonic relationships. Their superior accuracy compared to parametric model alternatives has been demonstrated repeatedly in...

It is well known that risk factors influence how investment portfolios perform from a lender's perspective; therefore, a thorough risk assessment of the housing market is vital. The aim of this paper was to analyze the risks from housing apartments in different housing market segments by using...