AFTER THE FALL: AN EX POST CHARACTERIZATION OF HOUSING PRICE DECLINES ACROSS METROPOLITAN AREAS

The value of the U.S. housing stock fell $4.4 trillion dollars from 2006 to the first quarter of 2009, (1) leading to considerable turmoil in financial markets across the world. This fall is widely believed to be one of the primary contributing factors to the subsequent financial crises and recession. Economic commentators, particularly those focused on the macro economy and the health of financial institutions, tend to treat this fall in housing prices as a nationwide phenomenon. (2) Casual inspection of the magnitude of the change in housing prices across metropolitan areas suggests changes are anything, but uniform. Across 358 areas the magnitude of the fall varies from essentially 0 to over 60%. Figure 1 illustrates this variation in a map of the Office of Management and Budget metropolitan statistical areas (MSAs). In this article, we investigate whether it is possible to predict differences in percentage change in housing prices across metropolitan areas given a set of predictors available before the fall. [FIGURE 1 OMITTED] Our analysis is primarily descriptive. Although the variables used are predetermined in the sense of being ex ante, the analysis is of course ex post. Although no claims of causality are being made, the statistical relationships we document give insight into what went wrong. In this sense, our article is much less ambitious than several working papers currently in circulation or recently published (e.g., Glaeser and Gyourko 2007; Glaeser, Gyourko, and Saiz 2008; Himmelberg, Mayer, and Sinai 2005) that attempt to explain the entire dynamics of housing price changes. Our contribution is to present a set of stylized facts focusing on differences in recent housing price declines across U.S. metropolitan areas that need to be explained and to put forward a few initial hypotheses. Remarkably, over 70% of the variation in housing price declines across metropolitan areas can be accounted for by a small number of variables that were readily available before the fall, including previous appreciation rates, building trends, the prevalence of subprime lending, and changes in median income levels. A non-parametric analysis suggests that exceeding particular thresholds for some key predictor variables is associated with much larger price drops. The relationships we find are also consistent when our exercise is repeated with the data prior to 2000. The repetition of these relationships across the decades of available data suggests a predictive relationship of local housing variables and the size of subsequent price declines. It would be possible to incorporate these metropolitan level indicators into mortgage lending policy and portfolio risk assessment. Perhaps most importantly, our regression model suggests that lenders drew the wrong implications from increases in home prices between 2000 and 2005. Instead of indicating decreased risk, greater price appreciation is associated with increases in the magnitude of subsequent price declines. We also show that local housing market variables are better predictors of declines than regional location, which provides intuition into why common approaches taken to diversify risks in bundling mortgages were unsuccessful. Our article is related to two literatures. The first describes and models housing price dynamics. Although we do not model price movements, this strand of research informs our examination of relationships between the magnitude of recent price declines and predetermined market factors in both the choice of factors to explore and the discussion of hypotheses for the observed relationships. Market-level price trends historically exhibit short-run positive serial correlation and long-run mean reversion (e.g., Case and Shiller 1989). Capozza et al. (2002) estimate variability by market conditions in this mean reversion and serial correlation for 62 metropolitan areas and estimate adjustment rates to fundamental values based on 1979 to 1995 data. …