Abstract
Credit risk assessment in the real estate industry has garnered significant attention from government regulators, investors, and business scholars. However, the evaluation of credit risk in this sector poses numerous challenges, primarily due to the intricate interplay of economic cycles and political landscapes. In this study, we propose a novel method that leverages the GARCH(1,1) model in conjunction with the Genetic Algorithm (GA) to enhance the KMV model's performance. By refining the default point and equity value volatility in the KMV model, our approach offers more accurate credit risk evaluations in the real estate industry. Empirical results demonstrate the superior accuracy of our improved KMV model, providing valuable insights for early credit risk warning in the real estate sector.
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