Can learning explain boom-bust cycles in asset prices? : an application to the US housing boom

Explaining asset price booms poses a difficult question for researchers in macroeconomics: how can large and persistent price growth be explained in the absence large and persistent variation in fundamentals? This paper argues that boom-bust behavior in asset prices can be explained by a model in which boundedly rational agents learn the process for prices. The key feature of the model is that learning operates in both the demand for assets and the supply of credit. Interactions between agents on either side of the market create complementarities in their respective beliefs, providing an additional source of propagation. In contrast, the paper shows why learning involving only one side on the market, which has been the focus of most of the literature, cannot plausibly explain persistent and large price booms. Quantitatively, the model explains recent experiences in US housing markets. A single unanticipated mortgage rate drop generates 20 quarters of price growth whilst capturing the full appreciation in US house prices in the early 2000s. The model is able to generate endogenous liberalizations in household lending conditions during price booms, consistent with US data, and replicates key volatilities of housing market variables at business cycle frequencies