Explaining the boom-bust cycle in the U.S. housing market: a reverse-engineering approach

We use a simple quantitative asset pricing model to “reverse-engineer” the sequences of stochastic shocks to housing demand and lending standards that are needed to exactly replicate the boom-bust patterns in U.S. household real estate value and mortgage debt over the period 1995 to 2012. Conditional on the observed paths for U.S. disposable income growth and the mortgage interest rate, we consider four different specifications of the model that vary according to the way that household expectations are formed (rational versus moving average forecast rules) and the maturity of the mortgage contract (one-period versus long-term). We find that the model with moving average forecast rules and long-term mortgage debt does best in plausibly matching the patterns observed in the data. Counterfactual simulations show that shifting lending standards (as measured by a loan-to-equity limit) were an important driver of the episode while movements in the mortgage interest rate were not. Our results lend support to the view that the U.S. housing boom was a classic credit-fueled bubble involving over-optimistic projections about future housing values, relaxed lending standards, and ineffective mortgage regulation.