In housing markets, neighborhood characteristics are a key source of information heterogeneity: sellers are usually better informed about neighborhood values than buyers are, but some sellers and buyers are better informed than their peers are. Consistent with predictions from a new framework for analyzing such markets with heterogeneous assets and differentially informed agents, we find that changes in the composition of sellers toward more informed sellers and sellers with a larger supply elasticity predict subsequent house price declines. This effect is larger for houses with more price exposure to neighborhood characteristics, and smaller for houses bought by buyers that are more informed.

In housing markets, neighborhood characteristics are a key source of information heterogeneity: sellers are usually better informed about neighborhood values than buyers are, but some sellers and buyers are better informed than their peers are. Consistent with predictions from a new framework for analyzing such markets with heterogeneous assets and differentially informed agents, we find that changes in the composition of sellers toward more informed sellers and sellers with a larger supply elasticity predict subsequent house price declines. This effect is larger for houses with more price exposure to neighborhood characteristics, and smaller for houses bought by buyers that are more informed.

We show that housing markets provide information about the appropriate discount rates for valuing investments in climate change abatement. Real estate is exposed to both consumption and climate risk and its term structure of discount rates is downward sloping, reaching 2.6% for payoffs beyond 100 years. We use a tractable asset pricing model that incorporates features of climate change to show that the term structure of discount rates for climate-hedging investments is thus upward sloping but bounded above by the risk-free rate. At horizons at which risk-free rates are unavailable, the estimated housing discount rates provide an upper bound.

We show that institutional investors are more likely to invest in firms from regions to which they have stronger social ties but find no evidence that these investments earn a differential return. Firms in regions with stronger social ties to locations with many institutional investors have higher valuations and liquidity. These effects are largest for small firms with little analyst coverage, suggesting that the investors’ behavior is explained by their increased awareness of firms in socially proximate locations. Our results highlight that the social structure of regions affects firms’ access to capital and contributes to geographic differences in economic outcomes.

We propose and implement a procedure to dynamically hedge climate change risk. We extract innovations from climate news series that we construct through textual analysis of newspapers. We then use a mimicking portfolio approach to build climate change hedge portfolios. We discipline the exercise by using third-party ESG scores of firms to model their climate risk exposures. We show that this approach yields parsimonious and industry-balanced portfolios that perform well in hedging innovations in climate news both in sample and out of sample. We discuss multiple directions for future research on financial approaches to managing climate risk.