Conditional Insight, Unconditional Disaster
By Bryan Caplan
I just read Foote, Gerardi, and Willen’s subprime facts manifesto. Twice. In the process, I learned more about the subprime crisis than I learned in the last five years put together. If you’re going to read one piece on this topic, read this one. Quick version:
1. A simple model where insiders and outsiders grossly overestimated housing price appreciation elegantly explains all the key facts.
2. The competing models – especially stories that allege an “inside job” – contradict basic facts.
I could easily write twenty posts about this paper. The authors draw on an amazing range of evidence. For now, though, let me just direct your attention to the single most striking table. In 2005, Lehman Brothers made the following conditional forecasts about the performance of subprime-backed bonds:
Using data supplied by issuers and lenders, as well as quantitative tools designed to exploit this information efficiently, investors were able to predict with a fair degree of accuracy how mortgages and related securities would perform under various macroeconomic scenarios. Table 2, taken from a Lehman Brothers analyst report published in August 2005, shows predicted losses for a pool of subprime loans originated in the second half of 2005 under different assumptions for U.S. house prices (Mago and Shu 2005). The top three house price scenarios, which range from “base” to “aggressive,” predict losses of between 1 and 6 percent. Such losses had been typical of previous subprime deals and implied that investments even in lower-rated tranches of subprime deals would be profitable. The report also considers two adverse scenarios for house prices, one labeled “pessimistic” and the other labeled “meltdown.” These two scenarios assume near-term annualized growth in house prices of 0 and -5 percent, respectively. For those scenarios, losses are dramatically worse. The pessimistic scenario generates an 11.1 percent loss while the meltdown scenario generates a 17.1 percent loss…
Lehman analysts were not alone in understanding the strong relationship between house prices and losses on subprime loans. As Gerardi et al. (2008) show, analysts at other banks reached similar conclusions and were similarly accurate in their forecasts conditional on house price appreciation outcomes. (footnotes omitted)
The authors remark:
The analysis underscores investors’ knowledge about the sensitivity of subprime loans to adverse movements in housing prices, and it refutes the idea that investors did not or could not determine how risky these loans were.
Despite its foreboding name, the “meltdown” scenario was actually optimistic with respect to the observed fall in housing prices that began in 2006. The current forecast for losses on deals in the ABX 2006-1 index, which largely contains loans originated in the second half of 2005, is about 22 percent (Jozoff et al. 2012). This is consistent with the relationship between losses and house prices implied by the table. The bottom line is that analysts working in real time had little trouble figuring out how much subprime investors would lose if house prices fell.
If the conditional forecasts were so prescient, why were unconditional forecasts so ridiculous?
The answer to why investors purchased subprime securities is contained in the third column of the same Lehman analysis cited above, which lists the probabilities that were assigned to each of the various house price scenarios. It indicates that the adverse price scenarios received very little weight. In particular, the meltdown scenario–the only scenario generating losses that threatened repayment of any AAA-rated tranche–was assigned only a 5 percent probability. The more benign pessimistic scenario received only a 15 percent probability. By contrast, the top two price scenarios, each of which assumes at least 8 percent annual growth in house prices over the next several years, receive probabilities that sum to 30 percent. In other words, the authors of the Lehman report were bullish about subprime investments not because they believed that borrowers had some “moral obligation” to repay mortgages, or because they didn’t realize that the lenders had not fully verified borrower incomes. The authors were not concerned about losses because they thought that house prices would continue to rise, and that steady increases in the value of the collateral backing the loans would cover any losses generated by borrowers who would not or could not repay.
The hardest question is simply why such optimism arose in the first place. It contradicted all historical experience:
Relative to historical experience, even the baseline forecast was optimistic, while the two stronger scenarios were almost euphoric. A widely circulated calculation by Shiller (2005) showed that real house price appreciation over the period from 1890 to 2004 was less than 1 percent per year. A cursory look at the FHFA national price index gives slightly higher real house price appreciation–more than 1 percent–from 1975 to 2000, but still offers nothing to justify 5 percent nominal annual price appreciation, let alone 8 or 11 percent.
As far as I remember, I neglected to blog any housing price forecast when prices were rising. But the forecast I shared with friends from 2005 on was always “Prices have plateaued.” Relative to the facts, I was clearly optimistic. But I was more pessimistic than Lehman’s Meltdown Scenario! If I’d only understood the implications of my bearish housing price forecast, I would have made a lot of money from the crisis. Oh well.
Many will look at the Lehman forecasts and confidently declare, “What fools they were.” My reaction, though, is bewilderment. How can experts nail the hard question of “What will happen to our investments given housing prices?” yet botch the easy question of “Will unprecedented increases in nominal housing prices continue indefinitely?” I wish I knew.