​​Abstract:

We develop a theoretically-based statistical technique to identify a conservative lower bound for house prices.  Leveraging a model based upon consumer and investor incentives, we are able to explain the depth of housing market downturns at both the national and state level over a variety of market environments.  This approach performs well in several historical back tests and has strong out-of-sample predictive ability.  When back-tested, our estimation approach does not understate house price declines in any state over the 1987 to 2001 housing cycle and only understates declines in three states during the most recent financial crisis.  This latter result is particularly noteworthy given that the post-2001 estimates are performed out-of-sample. Our measure of a conservative lower bound is attractive because it (1) provides a leading indicator of the severity of future downturns and (2) allows trough estimates to dynamically adjust as markets conditions change.  This estimation technique could prove particularly helpful in measuring the credit risk associated with portfolios of mortgage assets as part of evaluating static stress tests or designing dynamic stress tests.​​

A revised version of this paper has undergone external peer-review and is published in an academic journal with open (free) access. Citation: Alexander N. Bogin, Stephen D. Bruestle, William M. Doerner. 2017. "How Low Can House Prices Go? Estimating a Conservative Lower Bound." Journal of Real Estate Finance and Economics, 54(1), 97-116. https://link.springer.com/article/10.1007%2Fs11146-015-9538-8

The research has been presented at the Federal Reserve Bank of Richmond, the American Real Estate Society annual meeting, and the American Real Estate and Urban Economics Association national conference​. Popular news coverage has included Calculated Risk, GARP, HousingWire, Inside Mortgage Finance, and RealtyTrac. It was also selected as best paper in 2015 in real estate cycles by the American Real Estate Society.