William M. Doerner

How Low Can House Prices Go? Estimating a Conservative Lower Bound

Abstract In risk management, the credit risk and required capital associated with mortgage assets is often estimated through stress testing where the house price path is an important determinant of the severity of the stress test. Specifically, the extent of credit-related losses is a function of how far house prices are above long-term trend and the extent to which they can fall below trend. Focusing on the latter, we develop a theoretically-based statistical technique to identify a conservative lower bound (CLB) for house prices. Leveraging a model based upon investor incentives, the CLB explains 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, the 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. The CLB is attractive because it (1) provides a leading indicator of the severity of future downturns and (2) allows estimates of trough to recover or decrease in magnitude as markets return to baseline conditions. This estimation technique could prove helpful in measuring the credit risk associated with portfolios of mortgage assets as part of evaluating static or designing dynamic stress tests.

Generating Historically‐Based Stress Scenarios Using Parsimonious Factorization

This paper describes an empirical approach to generate plausible, historically-based interest rate shocks, which can be applied to any market environment and can readily link to movements in other key risk factors. The approach is based upon yield curve parameterization and requires a parsimonious yet flexible factorization model.

Local House Price Dynamics: New Indices and Stylized Facts: Local House Price Dynamics

We introduce the first publicly available data set of constant‐quality house price indices for counties, ZIP codes and census tracts in the United States, at an annual frequency, over a 40‐year period. Between 1990 and 2015, house price gradients within large cities steepen, documenting a reversal of decades of increasing relative desirability of suburban locations. Real house prices are more likely to be nonstationary near the centers of large cities. Within‐city differences in house price appreciation at the ZIP code level are, on average, about half of between‐city differences, though this ratio varies depending on the time period and city size.

Overlooked Market Risk Shocks: Prepayment Uncertainty and Option-Adjusted Spreads

Assessments of market risk for economic or regulatory capital typically involve calculating a portfolio’s sensitivity to key risk factor movements. In this article, we describe how to generate shocks to prepayment rates and mortgage security option-adjusted spreads (OAS) conditional on a corresponding set of interest rate shocks. By using historical performance data from multiple model vendors, we show that prepayment rate shocks capture model misspecification but potentially fail to account for other important sources of model error. Mortgage security OAS serves as a broader measure of model error, encompassing both model misspecification and forecasting errors, as well as credit and liquidity risk. While sometimes overlooked, alternative risk factors, such as prepayment and OAS shocks, can have a pronounced effect on the valuation of institutional portfolios with mortgage securities.