House prices exhibit substantially more momentum, positive autocorrelation in price changes, than existing theories can explain. I introduce an amplification mechanism to reconcile this discrepancy. Sellers do not set a unilaterally high or low list price because they face a concave demand curve: increasing the price of an above-average-priced house rapidly reduces its sale probability, but cutting the price of a below-average-priced house only slightly improves its sale probability. The resulting strategic complementarity amplifies frictions because sellers gradually adjust their price to stay near average. I provide empirical evidence for concave demand using a quantitative search model that amplifies momentum two- to threefold.

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.

We provide new time-varying estimates of the housing wealth effect back to the 1980s. We use three identification strategies: ordinary least squares with a rich set of controls, the Saiz housing supply elasticity instrument, and a new instrument that exploits systematic differences in city-level exposure to regional house price cycles. All three identification strategies indicate that housing wealth elasticities were if anything slightly smaller in the 2000s than in earlier time periods. This implies that the important role housing played in the boom and bust of the 2000s was due to larger price movements rather than an increase in the sensitivity of consumption to house prices. Full-sample estimates based on our new instrument are smaller than recent estimates, though they remain economically important. We find no significant evidence of a boom–bust asymmetry in the housing wealth elasticity. We show that these empirical results are consistent with the behaviour of the housing wealth elasticity in a standard life-cycle model with borrowing constraints, uninsurable income risk, illiquid housing, and long-term mortgages. In our model, the housing wealth elasticity is relatively insensitive to changes in the distribution of loan-to-value (LTV) for two reasons: first, low-leverage homeowners account for a substantial and stable part of the aggregate housing wealth elasticity; second, a rightward shift in the LTV distribution increases not only the number of highly sensitive constrained agents but also the number of underwater agents whose consumption is insensitive to house prices.

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.