This article uses a structural model to show that foreclosures played a crucial role in exacerbating the recent housing bust and to analyse foreclosure mitigation policy. We consider a dynamic search model in which foreclosures freeze the market for non-foreclosures and reduce price and sales volume by eroding lender equity, destroying the credit of potential buyers, and making buyers more selective. These effects cause price-default spirals that amplify an initial shock and help the model fit both national and cross-sectional moments better than a model without foreclosure. When calibrated to the recent bust, the model reveals that the amplification generated by foreclosures is significant: ruined credit and choosey buyers account for 25.4% of the total decline in non-distressed prices and lender losses account for an additional 22.6%. For policy, we find that principal reduction is less cost-effective than lender equity injections or introducing a single seller that holds foreclosures off the market until demand rebounds. We also show that policies that slow down the pace of foreclosures can be counterproductive.

With “2020 hindsight,” the 2000s housing cycle is not a boom–bust but a boom–bust–rebound. Using a spatial equilibrium regression in which house prices are determined by income, amenities, urbanization, and supply, we show that long-run city-level fundamentals predict not only 1997–2019 price and rent growth but also the amplitude of the boom–bust–rebound. This evidence motivates our model of a cycle rooted in fundamentals. Households learn about fundamentals by observing “dividends” but become over-optimistic in the boom due to diagnostic expectations. A bust ensues when beliefs start to correct, exacerbated by a price–foreclosure spiral that drives prices below their long-run level. The rebound follows as prices converge to a path commensurate with higher fundamental growth. The estimated model explains the boom–bust–rebound with a single shock and accounts quantitatively for the dynamics of prices, rents, and foreclosures in cities with the largest cycles. We draw implications for asset cycles more generally.