JOURNAL OF HOUSING AND THE BUILT ENVIRONMENT, sa.Forthcoming, ss.1-30, 2025 (SSCI)
This paper investigates the existence, causes, and predictive markers of housing price bubbles in 18
OECD countries from 1870 to 2020, thus addressing a significant gap in the understanding of housing
market dynamics and their implications for global financial stability. Housing bubbles have substantial
impact on economic resilience and have historically led to severe financial crises. Employing the
Generalized Supremum Augmented Dickey-Fuller (GSADF) test, this study identifies multiple bubble
episodes in Australia, Denmark, Germany, Japan, Portugal, and the United States. Furthermore, by
using an advanced machine learning approach, Extreme Gradient Boosting (XGBoost), this study
statistically confirms the significance of interest rates, loan growth, and population growth as key
predictors of housing bubbles. The findings indicate that interest rate variables are the predominant
predictors, explaining over 60% of bubble dynamics in Australia and Japan, whereas credit growth and
demographic factors are more influential in predicting bubbles in Germany, Denmark, and the United
States. This study’s originality lies in its comprehensive integration of econometric and machine
learning methodologies, offering more accurate, data-driven detection and prediction of housing
bubbles than previous research. The study's findings underscore the necessity of coordinated monetary
and macroprudential policies, along with proactive demographic and credit market management, to
mitigate future bubble-related risks, presenting significant implications for global policymakers and
market participants.