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

Paolo Gelain, Kevin Lansing and Gisle Natvik

No 2015/11, Working Paper from Norges Bank

Abstract: 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. All models deliver rapid consumption growth during the boom, negative consumption growth during the Great Recession, and sluggish consumption growth during the recovery when households are deleveraging.

Keywords: Housing bubbles; Mortgage debt; Borrowing constraints; Lending standards; Macroprudential policy (search for similar items in EconPapers)
JEL-codes: D84 E32 E44 G12 O40 R31 (search for similar items in EconPapers)
Pages: 50 pages
Date: 2015-06-16
New Economics Papers: this item is included in nep-ban, nep-dge, nep-mac and nep-ure
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Citations: View citations in EconPapers (5)

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http://www.norges-bank.no/en/Published/Papers/Working-Papers/2015/112015/

Related works:
Journal Article: Explaining the Boom–Bust Cycle in the U.S. Housing Market: A Reverse‐Engineering Approach (2018) Downloads
Working Paper: Explaining the Boom-Bust Cycle in the U.S. Housing Market: A Reverse-Engineering Approach (2015) Downloads
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