Explaining the Boom–Bust Cycle in the U.S. Housing Market: A Reverse‐Engineering Approach

Abstract

We use a quantitative asset pricing model to “reverse‐engineer” the sequences of shocks to housing demand and lending standards needed to replicate the boom–bust patterns in U.S. housing value and mortgage debt from 1993 to 2015. Conditional on the observed paths for U.S. real consumption growth, the real mortgage interest rate, and the supply of residential fixed assets, a specification with random walk expectations outperforms one with rational expectations in plausibly matching the patterns in the data. Counterfactual simulations show that shocks to housing demand, housing supply, and lending standards were important, but movements in the mortgage interest rate were not.