Adaptive Modeling and Risk Strategies for Cross-Border Real Estate Investments

Abstract

In this study, we examine predictive modeling and risk management approaches tailored for cross-border real estate investments, with a focus on adapting models to stable and volatile market conditions. Drawing on an extensive dataset spanning diverse economic environments, we evaluate the performance of linear, polynomial, and logarithmic models in capturing real estate price dynamics. Findings indicate that the linear model provides reliable accuracy in stable markets (R2=0.98), aligning well with predictable, incremental trends typical of such environments. However, in markets marked by high volatility, the polynomial model outperforms, effectively capturing non-linear fluctuations with R² values of 0.89, thus providing a more robust framework for regions subject to economic and political shifts. To address currency risk and extreme loss potential, we integrate Conditional Value-at-Risk (CVaR) and Dynamic Optimal Hedge Ratio (DOHR) methodologies. These approaches collectively reduce return volatility by approximately 15% in volatile markets, enhancing stability in high-risk environments. Furthermore, the analysis underscores the strategic value of Environmental, Social, and Governance (ESG) alignment, particularly in fostering regulatory support and community acceptance, which are vital for long-term investment sustainability. Our findings suggest a tailored strategy: linear models with simplified risk management are well-suited for stable markets, while volatile markets benefit from polynomial models paired with advanced risk measures. Prioritizing ESG-compliant projects further mitigates regulatory and reputational risks. These insights provide a foundation for optimizing investment strategies across varied economic landscapes, with future work recommended to explore adaptive machine learning techniques for real-time model adjustments.