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.

ABSTRACT We investigate how the deterioration of household balance sheets affects worker productivity, and in turn economic downturns. Specifically, we compare the output of innovative workers who experienced differential declines in housing wealth during the financial crisis but were employed at the same firm and lived in the same metropolitan area. We find that, following a negative wealth shock, innovative workers become less productive and generate lower economic value for their firms. The reduction in innovative output is not driven by workers switching to less innovative firms or positions. These effects are more pronounced among workers at greater risk of financial distress.

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.

ABSTRACT How can mortgages be redesigned to reduce macrovolatility and default? We address this question using a quantitative equilibrium life‐cycle model. Designs with countercyclical payments outperform fixed payments. Among those, designs that front‐load payment reductions in recessions outperform those that spread relief over the full term. Front‐loading alleviates liquidity constraints when they bind most, reducing default and stimulating housing demand. To illustrate, a fixed‐rate mortgage (FRM) with an option to convert to adjustable‐rate mortgage, which front‐loads payment reductions relative to an FRM with an option to refinance underwater, reduces price and consumption declines six times as much and default three times as much.