The Distributional Consequences of Climate Change: The Role of Housing Wealth, Expectations, and Uncertainty

Abstract

The median US household holds a majority of its wealth in a single climate-exposed asset: the home. I study the implications of this fact for the distributional consequences of climate change, using a dynamic, stochastic, heterogeneous-agent model with 1713 locations. Forward-looking equilibrium real estate prices and rents endogenously respond to climate news shocks in spatially-segmented markets. Households participate on both sides of each market. To quantify climate impacts on economic fundamentals, I harmonize recent estimates of local productivity, amenities, energy costs, and disaster damage sensitivities. I find the model’s global solution under aggregate climate uncertainty with a simple but general deep learning method introduced in a companion paper. In the calibrated model, a switch from widespread climate denial to widespread climate acceptance causes an effective transfer of housing wealth across regions of $41bn immediately and $507bn over the following century. Migration exacerbates this by amplifying housing price responses. The spatially-equalizing effects of migration only dominate 50 years post-shock. Climate uncertainty causes ongoing regressive wealth transfers through higher equilibrium rents, borne mostly by poorer households.

Presenters

Jeffrey Sun
Student, PhD, Princeton University, New Jersey, United States

Details

Presentation Type

Paper Presentation in a Themed Session

Theme

2025 Special Focus—Sustainable Development for a Dynamic Planet: Lessons, Priorities, and Solutions

KEYWORDS

Environmental, Urban, Spatial, Housing, Machine Learning, Deep Learning, Heterogeneity, Climate