Climate Policy Uncertainty and Housing Prices: Analyzing Bidirectional Transmission Across U.S. Metropolitan Areas

This study examines the relationship between climate policy uncertainty (CPU) and residential housing prices across U.S. metropolitan areas using the U.S. CPU index developed by Gavriilidis in 2021 and monthly S&P CoreLogic Case-Shiller Home Price Indices, covering January 1991 to May 2024. Employing a Fourier-augmented Toda–Yamamoto causality framework that accounts for both abrupt and gradual structural breaks, we document significant CPU → housing prices transmission in multiple metropolitan markets, with bidirectional transmission dynamics emerging in Los Angeles, New York, San Diego, and San Francisco, as well as at the U.S. national level. The results reveal substantial spatial heterogeneity across various market types. Coastal high-exposure markets exhibit strong CPU sensitivity, which may reflect the influence of physical climate risks and regulatory uncertainty; inland growth markets display housing prices → CPU feedback, likely operating through political economy channels; Midwest extreme-weather markets show persistent transmission despite their non-coastal locations; recession-sensitive markets become CPU-responsive following the Great Recession; and insulated markets show no significant transmission. The findings indicate that CPU operates as a priced systematic risk factor requiring integration into housing finance oversight, macroprudential frameworks, and investment strategies. These results have important implications for financial stability monitoring, mortgage credit risk assessment, and climate policy design as markets navigate transition risks in a low-carbon economy.