Research Lab

We develop a new approach to understand the joint dynamics of transaction prices and trading volume in the market for commercial real estate. We start from a micro-founded model in which buyers and sellers differ in their private valuation of building characteristics, such as size, location, and quality. Consistent with the decentralized nature of the commercial real estate market, we model the probability that a seller meets a particular buyer, where the meeting probability depends on the characteristics of the buyer, the seller, and the building.

We show remote work led to large drops in lease revenues, occupancy, and market rents in the commercial office sector. We revalue New York City office buildings taking into account both the cash flow and discount rate implications of these shocks, and find a 46% decline in long run value. For all U.S. office markets combined, we find a $556.8 billion value destruction. Higher quality buildings were buffered against these trends due to a flight to quality, while lower quality office is at risk of becoming a stranded asset.

Professional house price forecast data are consistent with a rational model where agents must learn about the parameters of the house price growth process and the underlying state of the housing market. Slow learning about the long-run mean generates overreaction to forecast revisions and a modest response of forecasts to lagged realizations. Heterogeneity in signals and priors about the long-run mean helps the model account for cross-sectional dispersion in forecasts. Introducing behavioral biases helps improve the model's predictions for short-horizon overreaction and dispersion.

Residential properties with the lowest rent levels provide the highest investment returns to their owners. Using detailed rent, cost, and price data from the United States, Belgium, and The Netherlands, we show that this phenomenon holds across housing markets and time. If anything, low-rent units hedge business cycle risk. We also find no evidence for differential regulatory risk exposure. We document segmentation of investors, with large corporate landlords shying away from the low-tier segment possibly for reputational reasons.

This paper exploits strict flood insurance coverage limits and staggered flood map updates to show that mortgage lenders offload flood risk to the government through flood insurance contracts, and to under-insured households through higher down payments. Lender risk management leads delinquency rates to equalize inside and outside of flood zones. The combination of insurance requirements and credit rationing shift the composition of mortgages in flood zones towards richer and higher credit quality borrowers.

We analyze the impact of credit risk and higher interest rates on U.S. bank solvency, expanding on the work of Jiang et al. (2023). Our variation of their bank-run model demonstrates how credit losses and asset declines from higher interest rates can trigger self-fulfilling solvency runs, even when banks hold fully liquid assets. Banks with high credit losses, greater exposure to interest rate increases, low capital, and high uninsured leverage are particularly vulnerable.