Real Estate Research
Real Estate Research provides analysis of topical research and current issues in the fields of housing and real estate economics. Authors for the blog include the Atlanta Fed's Kristopher Gerardi, Carl Hudson, and analysts, as well as the Boston Fed's Christopher Foote and Paul Willen.
April 20, 2015
Income Growth, Credit Growth, and Lending Standards: Revisiting the Evidence
Almost a decade has passed since the peak of the housing boom, and a handful of economics papers have emerged as fundamental influences on the way that economists think about the boom—and the ensuing bust. One example is a paper by Atif Mian and Amir Sufi that appeared in the Quarterly Journal of Economics in 2009 (MS2009 hereafter). A key part of this paper is an analysis of income growth and mortgage-credit growth in individual U.S. ZIP codes. The authors find that from 2002 to 2005, ZIP codes with relatively low growth in incomes experienced high growth in mortgage credit; that is, income growth and credit growth were negatively correlated during this period.
Economists often cite this negative correlation as evidence of improper lending practices during the housing boom. The thinking is that prudent lenders would have generated a positive correlation between area-level growth in income and mortgage credit, because borrowers in ZIP codes with high income growth would be in the best position to repay their loans. A negative correlation suggests that lenders instead channeled credit to borrowers who couldn't repay.
Some of the MS2009 results are now being reexamined in a new paper by Manuel Adelino, Antoinette Schoar, and Felipe Severino (A2S hereafter). The A2S paper argues that the statistical evidence in MS2009 is not robust and that using borrower-level data, rather than data aggregated up to the ZIP-code level, is the best way to investigate lending patterns. The A2S paper has already received a lot of attention, which has centered primarily on the quality of the alternative individual-level data that A2S sometimes employ.1 To understand the relevant issues in this debate, it's helpful to go back to MS2009's original statistical work that uses data aggregated to the ZIP-code level to get a sense of what it does and doesn't show.
Chart 1 summarizes the central MS2009 result. We generated this chart from information we found in either MS2009 or its supplementary online appendix. The dark blue bars depict the coefficients from separate regressions of ZIP-code level growth in new purchase mortgages on growth in ZIP-code level incomes.2 (These regressions also include county fixed effects, which we discuss further below.) Each regression corresponds to a different sample period. The first regression projects ZIP-level changes in credit between 1991 and 1998 on ZIP-level changes in income between these two years. The second uses growth between 1998 and 2001, and so on.3 During the three earliest periods, ZIP-level income growth enters positively in the regressions, but in 2002–04 and 2004–05, the coefficients become negative. A key claim of MS2009 is that this flip signals an important and unwelcome change in the behavior of lenders. Moreover, the abstract points out that the negative coefficients are anomalous: "2002 to 2005 is the only period in the past eighteen years in which income and mortgage credit growth are negatively correlated."
There are, however, at least three reasons to doubt that the MS2009 coefficients tell us anything about lending standards. First of all, the coefficients for the 2005–06 and 2006–07 regressions are positive—for the latter period, strongly so. By MS2009's logic, these positive coefficients indicate that lending standards improved after 2005, but in fact loans made in 2006 and 2007 were among the worst-performing loans in modern U.S. history. Chart 2 depicts the share of active loans that are 90-plus days delinquent or in foreclosure as a share of currently active loans, using data from Black Knight Financial Services. To be sure, loans made in 2005 did not perform well during the housing crisis, but the performance of loans made in 2006 and 2007 was even worse.4 This poor performance is not consistent with the improvement in lending standards implied by MS2009's methodology.
A second reason that sign changes among the MS2009 coefficients may not be informative is that these coefficients are not really comparable. The 1991–98 regression is based on growth in income and credit across seven years, while later regressions are based on growth over shorter intervals. This difference in time horizon matters, because area-level income and credit no doubt fluctuate from year to year while they also trend over longer periods. A "high-frequency" correlation calculated from year-to-year growth rates may therefore turn out to be very different from a "low-frequency" correlation calculated by comparing growth rates across more-distant years. One thing we can't do is think of a low-frequency correlation as an "average" of high-frequency correlations. Note that MS2009 also run a regression with growth rates calculated over the entire 2002–05 period, obtaining a coefficient of -0.662. This estimate, not pictured in our graph, is much larger in absolute value than either of the coefficients generated in the subperiods 2002–04 and 2004–05, which are pictured.
A third and perhaps more fundamental problem with the MS2009 exercise is that the authors do not report correlations between income growth and credit growth but rather regression coefficients.5 And while a correlation coefficient of 0.5 indicates that income growth and credit growth move closely together, a regression coefficient of the same magnitude could be generated with much less comovement. MS2009 supply the data needed to convert their regression coefficients into correlation coefficients, and we depict those correlations as green bars in chart 1.6 Most of the correlations are near 0.1 in absolute value or smaller. To calculate how much comovement these correlations imply, recall that the R-squared of a regression of one variable on another is equal to the square of their correlation coefficient. A correlation coefficient of 0.1 therefore indicates that a regression of credit growth deviated from county-level means on similarly transformed income growth would have an R-squared in the neighborhood of 1 percent. The reported R-squareds from the MS2009 regressions are much larger, but that is because the authors ran their regressions without demeaning the data first, letting the county fixed effects do the demeaning automatically. While this is standard practice, this specification forces the reported R-squared to encompass the explanatory power of the fixed effects. The correlation coefficients that we have calculated indicate that the explanatory power of within-county income growth for within-county credit growth is extremely low.7 Consequently, changes in the sign of this correlation are not very informative.
How do these arguments relate to A2S's paper? Part of that paper provides further evidence that the negative coefficients in the MS2009 regressions do not tell us much about lending standards. For example, A2S extend a point acknowledged in MS2009: expanding the sample of ZIP codes used for the regressions weakens the evidence of a negative correlation. The baseline income-credit regressions in MS2009 use less than 10 percent of the ZIP codes in the United States (approximately 3,000 out of more than 40,000 total U.S. ZIP codes). Omitted from the main sample are ZIP codes that do not have price-index data or that lack credit-bureau data.8 MS2009 acknowledge that if one relaxes the restriction related to house-price data, the negative correlations weaken. Our chart 1 conveys this information with the correlation coefficients depicted in red, which are even closer to zero. A2S go farther to show that if the data set also includes ZIP codes that lack credit-bureau data, the negative correlation and regression coefficients become positive.
But perhaps a deeper contribution of A2S is to remind the researchers that outstanding questions about the housing boom should be attacked with individual-level data. No one doubts that credit expanded during the boom, especially to subprime borrowers. But how much of the aggregate increase in credit went to subprime borrowers, and how did factors like income, credit scores, and expected house-price appreciation affect both borrowing and lending decisions? Even under the best of circumstances, it is hard to study these questions with aggregate data, as MS2009 did. People who take out new-purchase mortgages typically move across ZIP-code boundaries. Their incomes and credit scores may be different than those of the people who lived in their new neighborhoods one, two, or seven years before. A2S therefore argue for the use of HMDA individual-level income data so that credit allocation can be studied at the individual level. This use has been criticized by Mian and Sufi, who believe that fraud undermines the quality of the individual-level income data that appear in HMDA records. We should take these criticisms seriously. But the debate over whether lending standards are best studied with aggregate or individual-level data should take place with the understanding that aggregate data on incomes and credit may not be as informative as previously believed.
2 Data on new-purchase mortgage originations come from records generated by the Home Mortgage Disclosure Act (HMDA). Average income at the ZIP-code level is tabulated in the selected years by the Internal Revenue Service.
3 Growth rates used in the regressions are annualized. The uneven lengths of the sample periods are necessitated by the sporadic availability of the IRS income data, especially early on. The 1991 data are no longer available because IRS officials have concerns about their quality.
4 Chart 2 includes data for both prime and subprime loans. The representativeness of the Black Knight/LPS data improves markedly in 2005, so LPS loans originated before that year may not be representative of the universe of mortgages made at the same time. For other evidence specific to the performance of subprime loans made in 2006 and 2007, see Figure 2 of Christopher Mayer, Karen Pence, and Shane M. Sherlund, "The Rise in Mortgage Defaults," Journal of Economic Perspectives (2009), and Figure 1 of Yuliya Demyanyk and Otto Van Hemert, "Understanding the Subprime Mortgage Crisis," Review of Financial Studies (2009). For data on the performance of GSE loans made in 2006 and 2007, see Figure 8 of W. Scott Frame, Kristopher Gerardi, and Paul S. Willen, "The Failure of Supervisory Stress Testing: Fannie Mae, Freddie Mac, and OFHEO," Atlanta Fed Working Paper (2015).
5 MS2009 often refer to their regression coefficients as "correlations" in the text as well as in the relevant tables and figures, but these statistics are indeed regression coefficients. Note that in the fourth table of the supplemental online appendix, one of the "correlations" exceeds 1, which is impossible for an actual correlation coefficient.
6 Because a regression coefficient from a univariate regression is Cov(X,Y)/Var(X), multiplying this coefficient times StdDev(X)/StdDev(Y) gives Cov(X,Y)/StdDev(X)*StdDev(Y), which is the correlation coefficient. Here, the Y variable is ZIP-code–level credit growth, demeaned from county-level averages, while X is similarly demeaned income growth. As measures of the standard deviations, we use the within-county standard deviations displayed in Table I of MS2009. Specifically, we use the within-county standard deviation of "mortgage origination for home purchase annual growth" calculated over the 1996–02 and 2002–05 periods (0.067 and 0.15, respectively) and the within-county standard deviation of "income annualized growth" over the 1991–98, 1998–2002, 2002–05, and 2005–06 periods (0.022, 0.017, 0.031, and 0.04, respectively). Unfortunately, the time periods over which the standard deviations were calculated do not line up exactly with the time periods over which the regression coefficients were calculated, so our conversion to correlation coefficients is an approximation.
7 It is true that the regression coefficients in the MS2009 coefficients often have large t-statistics, so one may argue that ZIP-level income growth has sometimes been a statistically significant determinant of ZIP-level credit growth. But the low correlation coefficients indicate that income growth has never been economically significant determinant of credit allocation within counties. It is therefore hard to know what is driving the income-credit correlation featured in MS2009, or what may be causing its sign to fluctuate.
8 Though house prices and credit bureau data are not required to calculate a correlation between income growth and mortgage-credit growth, the authors use house prices and credit bureau data in other parts of their paper.
April 25, 2014
Two Views of the Involvement of Credit Rating Agencies in the Mortgage Crisis
A lot of people have blamed credit rating agencies (CRAs) for helping to cause the mortgage crisis. The report of the Financial Crisis Inquiry Commission (FCIC) labelled CRAs as "key enablers of the crisis," because the exploding mortgage-backed bonds that caused so much trouble could not have been sold without stamps of approval from the CRAs. Commentators often link CRA failings to the fact that they are paid by the issuers of the securities they rate, with the implication that CRAs are thus given incentive to award good ratings to securities that do not deserve them. Indeed, two recent articles by academic economists on this topic come to the same conclusion: financial markets would work better if we scrapped the issuer-pays model in favor of some other way to pay CRAs for their evaluations. But the two articles disagree on why this is so, and understanding the source of this disagreement sheds some harsh light on claims that CRAs should be even partly blamed for the financial crisis in the first place.
Grade inflation in the student-pays model
The first article is a Wall Street Journal op-ed piece by Princeton economist Alan Blinder. Blinder likens the awarding of credit ratings to mortgage-backed securities to his own awarding of letter grades to his Princeton students. "Suppose I proposed to grade my students by a 'student pays' model," Blinder suggests. Such a setup would encourage him to give easy As in hopes of attracting more students and higher pay, and the information in the grades would suffer as a result. "Yet that description comes pretty close to mimicking the way we pay rating agencies," Blinder writes. "Looking back, is it any wonder that so many securities were blessed with undeserved triple-A ratings?"
One interpretation of Blinder's analogy is that college grading works better than securities rating because universities have not adopted the student-pays model. That argument will seem curious to many college instructors, because this model approximates their own compensation arrangements pretty well. Students may not write checks to professors, but they (or their parents) write checks to colleges, who then pay the professors. Instructors whose grades are overly harsh in relation to other courses are likely to see their class sizes dwindle, to the dismay of department chairs facing hard budget constraints. Even if an instructor has no problem attracting students, she may not want grading disparities among courses to distort student decisions on what to study, so she might ease up in her own grading as well. Given the incentives of professors, it is not surprising that grade inflation is debated at many universities, even the very best ones. A December 2013 article in Harvard University's student newspaper, the Crimson, described a faculty meeting at which a professor bemoaned the fact that the most frequently awarded grade at Harvard College is an A-minus. A university dean corrected him: "The median grade in Harvard College is indeed an A-minus," the dean is quoted as saying. "The most frequently awarded grade in Harvard College is actually a straight A." (Disclosure: Harvard's grading policy is of personal interest to two authors of this blog post, who teach intermediate macroeconomics courses there in their spare time.)
Rational employers, rational investors
If the student-pays model leads to grade inflation, then don't we have even more ammunition that the bad incentives inherent in the investor-pays model for CRAs is partly responsible for the mortgage crisis? Not necessarily. For bad CRA incentives to have caused the crisis, two things must be true: one, the incentives must have caused inflated ratings, and two, the investors had to believe the inflated ratings. The second step in this causal chain is open to question. If the investors knew that the issuer-pays model gave incent to the rating agencies to inflate ratings, then rational investors would have taken that information into account when making investment decisions.
The college-grading analogy is again useful here. Consider an employer who is thinking about hiring a recent graduate who received a B-minus in a course that is highly relevant to what the firm does. How should the employer use this information? One option would be for the employer to look up how the student's official university documents define a B-minus—the documents are likely to define a grade in the B range as indicating a better-than-average understanding of the material. But a rational employer who knows the incentives facing American professors would also know that instructors are given cause to inflate grades. The firm could thus surmise that an applicant on the border between a B and a C may actually have a lower-than-average mastery of the subject. In the same way, rational mortgage investors who knew that CRAs had incentive to inflate ratings would have taken those ratings with a grain of salt when evaluating mortgage-backed investments.
Investor rationality plays a prominent role in a second recent piece on CRA incentives, a formal paper by the economists Anil Kashyap and Natalia Kovrijnykh (KK). Because this article is part of the academic economics literature, the authors adopt the fundamental assumption that all actors in the model are rational. As we might expect from our analogy of the job applicant, the rationality assumption makes a big difference when analyzing CRA payment regimes. Consider a situation in which CRAs are paid by the issues of securities, as they are today. Further assume that CRAs receive more money for good ratings than for bad ones. Rational investors in the KK model would realize the ratings are likely to be inflated under this set of incentives and would deflate the ratings accordingly. But if the CRAs are unable to fool investors who know both the CRAs' preferences and their opportunities, then the CRAs might as well tell the truth. KK therefore constrain their attention to equilibria where rating agencies are always truthful.
The revelation principle
In assuming truth-telling, KK are following a long tradition in the modeling of imperfect information. In fact, the assumption that actors with private information tell the truth shows up so often in models of imperfect information that it has a special name: the revelation principle. This principle is useful for modelers because it allows them to focus on equilibria in which the agent with private information has no reason to lie. To be clear, in this situation, the revelation principle does not mean that rating agencies never lie. Rather, it states that any equilibrium in which rating agencies lie is equivalent to one in which they tell the truth. The lying doesn't affect the actions of investors who know the incentives and opportunities of the CRAs, just as inflation of our B-minus student's grade does not lead the employer into an inappropriate hire. Because lying does not encourage agents to take inappropriate actions, it can safely be ignored when thinking through the fundamental aspects of the problem.
The appropriateness of the revelation principle in this context hinges on the ability of mortgage investors to analyze CRA incentives and opportunities and thereby back out the truth. Is this realistic? Ironically, the critics of CRAs provide evidence in support of this view. When Barney Frank alleged that CRA incentives led them to inflate ratings, he was doing exactly the sort of reverse engineering that lies behind the revelation principle. And if legislators could figure out that rating agencies had distorted incentives, why couldn't investors, who were putting up their own money? Indeed, investors should have had much better information about agency incentives than Barney Frank. It turns out that financial intermediaries lost enormous sums on the mortgage-related securities that they purchased and held on their balance sheets (more details on this in the next post). At the same time, they were also large issuers of these securities. Who would know better about the potential for corruption of rating agencies than the financial intermediaries that supposedly corrupted them?
Of course, if the KK model holds that rating agencies always tell the truth, then the model cannot rationalize arguments that CRAs helped cause the crisis by misleading investors. Indeed, the revelation principle makes it hard to rescue any story about untruthful CRAs. What if credit rating agencies had private information about their incentives, in addition to private information about their effort and the quality of the securities that they rated? Setting aside the fact that the issuer-pays model of credit ratings was common knowledge in the market, this change to the model has no effect on its outcome. Here again, the revelation principle would imply that CRAs truthfully reveal the private information about their incentives. For investors to be misled, they cannot simply be confused about incentives. Rather, they must believe that the CRAs' incentives were better aligned than they actually were. In our view, that is unlikely.
CRA payment arrangements
We began this post by noting that both of the recent articles on CRA incentives argued against the issuer-pays model. How can KK make this argument if investors in their model are not fooled? The reason involves some subtle implications of exactly how CRAs are paid in different states of the world. In all contracts in KK's issuer-pays regime, CRA pay is contingent on the outcome of the security. That means that if an AAA-rated security defaults, the CRA gets paid less than if the security pays off. To induce effort by the CRA, the spread between the payoffs must be large (that is, the CRA must be paid a lot more when the AAA security is successful compared to when it defaults). Because of limited liability, the CRA's compensation is bounded below by zero when a bond defaults—that is, investors can't demand payment from the CRAs in the default state—so high-powered incentives, which require high average pay, imply that compensation to the CRA in the good state has to be very high. As a result, paying the CRA for high effort can be prohibitively expensive for the issuer, causing the issuer to settle for low-powered incentives instead and thus receiving low effort from the CRA. Even in the low-effort equilibrium, however, CRAs increase the information set of investors and are socially useful.
Going farther, KK show that having the investor rather than the issuer pay the CRA solves the limited-liability problem and thereby raises social welfare. Particularly surprising about this finding is that the investor-pays model is not only good for society, but it is also good for the CRAs! The reason once again involves the revelation principle. In equilibrium, everyone knows both the amount and usefulness of the effort expended by the CRAs in evaluating securities. The larger the CRA's social benefit, the more the CRA gets paid. If KK's model is accurate, then CRAs themselves may lead the way to a better social outcome by encouraging the adoption of the investor-pays model.
While KK's paper includes many specific lessons about potential CRA payment arrangements, the bottom line to emerge from a comparison of the Blinder op-ed and the KK model involves their differing assumptions regarding investor rationality. The KK model illustrates how the revelation principle, which follows from investor rationality, works against the argument that CRAs helped cause the crisis by misleading investors. As long as investors understand the basic structure of the market, then standard models of asymmetric information—of which the KK model is an example— do not predict that investors will experience large and unexpected losses.
You can read the Harvard Crimson article on the magazine's website.
Chris Foote, senior economist and policy adviser at the Federal Reserve Bank of Boston,
Kris Gerardi, financial economist and associate policy adviser at the Federal Reserve Bank of Atlanta, and
By Paul Willen, senior economist and policy adviser at the Federal Reserve Bank of Boston
February 19, 2014
Asymmetric Information and the Financial Crisis
In describing the $13 billion settlement reached between JPMorgan and the Department of Justice last November, Attorney General Eric Holder said,
Without a doubt, the conduct uncovered in this investigation helped sow the seeds of the mortgage meltdown. JPMorgan was not the only financial institution during this period to knowingly bundle toxic loans and sell them to unsuspecting investors, but that is no excuse for the firm's behavior.
What Holder describes sounds like a textbook example of what economists call asymmetric information: JPMorgan knew something about the loans it was selling (that they were toxic) that they didn't reveal to investors. Specifically, the government alleged that JPMorgan reported facts to the investors that turned out to be wrong. For example, JPMorgan may have said that it made only 10 percent of the loans in a pool to investors (as opposed to owner-occupants) when the actual percentage was 20 percent. So it would seem as if economic theory, which has a lot to say about asymmetric information, should help us understand the crisis. Indeed, to many, asymmetric information and "bad incentives" are the leading explanations of the financial crisis. For example, a Reuters article that described the settlement made the following claim:
The behavior that the largest U.S. bank admitted to, authorities said, is at the heart of what inflated the housing bubble: lenders making bad mortgages and selling them to investors who thought they were relatively safe. When the loans started turning bad, investors lost faith in the banking system, and a housing crisis turned into a financial crisis.
In future posts, we will consider this seemingly intuitive idea, and argue that the economic theory of asymmetric information, in fact, provides very little aid in understanding the central questions of the crisis.
Let's focus on Holder's quote. The standard theory of asymmetric information implies that JPMorgan's misrepresentations could not cause significant losses to investors. That may seem surprising. Many may think that either we don't understand the economics of asymmetric information or it's just another example of the naïveté of economists regarding how the real world actually works. While there is certainly no shortage of examples of economists holding naïve opinions about the real world, in this case, we will argue that we are correctly characterizing the economist's view and that it is based on a common-sense argument.
Let's start with the economics. Let's assume that JPMorgan is selling a pool of loans, about which it knows the true quality, to a group of buyers who can't observe the true quality. What does economic theory say will happen?
A. Investors will overpay for the assets and lose money.
B. Investors will underpay for the assets and make money.
C. Investors will infer the true quality of the loans and pay accordingly.
The answer is C. To many, that may sound shocking, but the basic logic is simple: investors know that they cannot observe the true quality of the loans and they know that JPMorgan has an incentive to dump bad loans in the pool. Thus, they correctly infer that JPMorgan will dump bad loans in the pool. In other words, investors form correct beliefs about the quality of a loan,1 despite not being able to observe quality directly.2
"Knowingly bundl[ing] toxic loans" may be unethical or even illegal, but according to the economic theory of asymmetric information, it shouldn't cause unexpected financial losses to investors. The key to understanding the gap between Holder and economics is the word "unsuspecting." Economists assume that all market participants are inherently suspicious. Market participants understand that the people with whom they are doing business have an incentive to cheat them if those people know more about the products that they are selling.
Are economists naïve to think that market participants can figure out the incentives of their adversaries? We would argue that common sense says people are pretty suspicious. Take, for example, real estate agents. A cursory search on the internet yields the following table of "translations" of real estate listings:
Loaded with Potential: means loaded with problems the seller didn't want to tackle.
Cute: means they couldn't think of any other possible way to describe it.
Great Bones: means you're going to have to gut it and rebuild.
Wooded/Shaded Lot: means surrounded by trees and leaves on the ground.
Charming: means they couldn't think of a more appropriate word.
Needs a Little TLC: means it needs about $45,000 dollars or more in renovations and repairs.
Won't Last Long at This Price: means the price is so low it will compel you to see it but it will take a miracle for you to want to buy it.
No Disclosures: means you're going to have to find out all the problems with the home on your own.
Most people read this and chuckle, but no one is surprised that real estate agents stretch the truth. After all, it's their job to convince you to buy. And, in general, people view salespeople as among the least ethical of all occupations, only slightly above members of Congress. Perhaps the most egregious example of this, and in fact the example that motivated the seminal paper on the economics of asymmetric information, is used-car salespeople. Do used-car salespeople try to misrepresent the quality of the cars that they are trying to sell? Most people would likely answer this question with a resounding "Yes, of course." Does this cause injury to most used-car buyers? Not so much. Since the general public recognizes that "used-car salesman" is basically American slang for a fraudster, nobody really believes what they say.
In subsequent posts, we will answer questions about the crisis that turn on asymmetric information problems:
- Theory says investors should have guessed the quality of the loans. Did they?
- If investors knew the quality of the loans they were buying, why did JPMorgan pay $13 billion to settle accusations that it misrepresented the quality of the loans it was selling?
- Can't policymakers fix some of these incentive problems? Doesn't forcing issuers such as JPMorgan to retain a portion of the securities they issue align incentives and mitigate the asymmetric information problem?
- If asymmetric information didn't cause investor losses, does that mean it doesn't affect economic outcomes? (Spoiler: The answer is an emphatic no.)
- What about rating agencies? Didn't they know that deals were bad but lie to investors and say they were good?
By Paul Willen, senior economist and policy adviser at the Federal Reserve Bank of Boston, and
Kris Gerardi, associate economist and policy adviser at the Federal Reserve Bank of Atlanta.
1 In some situations, investors will hold beliefs that may be wrong on an individual asset-by-asset basis, but that are right on average. For example, they might not know which loans are the most likely to default, but their beliefs about the performance of the pool of loans will be, on average, right.
2 More generally, the revelation principle says that in any equilibrium of an asymmetric information game, we can confine our attention to equilibria in which all private information is fully revealed. For example, in Akerlof's (1970) example of equilibrium in the used car market, the seller knows whether the car is a peach or a lemon but only the lemons trade. Everyone knows which car is good (the one that the dealer doesn't sell), but the buyer doesn't buy it because he knows that the dealer would have an incentive to substitute a bad car.
January 22, 2014
Wall Street and the Housing Bubble
The conventional wisdom on the 2008 financial crisis is that finance industry insiders on Wall Street deceived naïve, uninformed mortgage borrowers into taking out unaffordable mortgages and mortgage-backed security (MBS) investors into purchasing securities backed by bad loans—mortgages and securities that had not been properly vetted and that would eventually default. This theory is on display front and center in the Academy Award-winning documentary Inside Job, and it has motivated new regulations aimed at realigning incentives among Wall Street insiders and their customers. (One such rule is the risk retention requirement in the Dodd-Frank Act, which we will discuss in some detail in a future post.)
We've written in support of an alternative hypothesis for the financial crisis—specifically, that overly optimistic views about house prices, not poorly designed incentives on Wall Street, are the better explanation for the crisis (for an example, see this 2012 paper). This alternative theory holds that investors lost money not because they were deceived by financial market insiders, but because they were instead misled by their own belief that housing-related investments could not lose money because house prices were sure to keep rising.
A new paper makes an important empirical contribution to this debate by inferring the beliefs of Wall Street insiders during the height of the bubble. The paper, titled "Wall Street and the Housing Bubble," performs a clever analysis of personal housing-related transactions (like home purchases) made by individuals who worked in the mortgage securitization business during the peak of the housing boom. The behavior of these mortgage insiders is compared with that of a control group of people who worked for similar institutions in the finance industry but did not have any obvious connection to the mortgage market. What the analysis finds should be an eye-opener for believers in the inside-job explanation of the crisis. There is no evidence that mortgage insiders believed there was a housing bubble in the 2004–06 period. In fact, mortgage insiders were actually more aggressive in increasing their personal exposure to housing at the peak of the boom. The increase in insider exposure contradicts the claim that insiders sold securities backed by loans that they knew would eventually go bad when the housing bubble burst.
The authors construct a random sample from the list of attendees of the 2006 American Securitization Forum, which is a large industry conference featuring employees of most of the major U.S. investment and commercial banks (as well as hedge funds and other boutique firms). The sample is mainly comprised of vice presidents, managing directors, and other nonexecutives in mid-managerial positions whose jobs focused on the structuring and trading of MBS. The authors refer to this group as "securitization agents." As a comparison group, they use a random sample of Wall Street equity analysts who covered firms that were in the S&P 500 in 2006 but did not have a strong connection to the housing market (in other words, the sample includes no homebuilders). These equity analysts worked for similar financial institutions, had similar skill sets, and likely experienced similar income shocks (in the form of bonuses during the boom) but did not have any experience in the securitization business and thus did not have access to any insider information. (As a second control group, the authors use a random sample of lawyers who did not specialize in real estate law.) The names of the securitization agents and the equity analysts are then matched to a database of publicly available information on property transactions. The final data set contains information on the number of housing transactions, the sale price of each transaction, some mortgage characteristics, and income at the time of origination for each individual in the sample spanning the period 2000–10.
Armed with this unique data set, the authors then implement a number of empirical tests to determine whether the securitization agents' beliefs about the likelihood of a housing crash differed from the beliefs of the control groups. The first test considers whether the securitization agents timed the housing market cycle better than the comparison groups by reducing their exposure to the market at the peak of the bubble (2004–06) by either selling their homes outright or downsizing. The second test is slightly weaker in that it simply tests whether the securitization agents were more cautious in their housing transactions by avoiding home purchases at the peak of the bubble to a greater extent than the control groups. The third test looks at whether the average return on housing transactions during the entire sample period was higher for the securitization agents. The final test considers a prediction of the permanent income hypothesis: if securitization agents were armed with superior knowledge of the impending collapse of the housing bubble, then through reductions in their expectations of permanent income, they should have decreased the size of their housing purchases relative to their current incomes by a greater amount than the comparison groups.
The results of these empirical tests show very little evidence to support the inside-job theory of the financial crisis. The authors conclude that there is "little systematic evidence that the average securitization exhibited awareness through their home transactions of problems in overall house markets and anticipated a broad-based crash earlier than others." If anything, the authors are being a little timid in their interpretation as the empirical results clearly show that securitization agents were significantly more aggressive in their housing transactions during the bubble period, which suggests that they held even more optimistic expectations of housing prices dynamics than did the control groups.
This is an important paper because it sheds light on one of the most striking aspects of the financial crisis, which the inside-job theory is unable to reconcile: the financial institutions involved in the creation of the subprime MBS and collateralized debt obligations (CDO)—the true "insiders," if you will—lost enormous amounts of money on those securities. The table clearly supports this observation. The firms that lost the most money from mortgage-related credit losses were the same investment and commercial banks that are being accused of profiting off of naïve investors by selling securities comprised of loans that they knew would eventually go bad. The table shows that these firms lost enormous sums of money, and the paper provides a simple answer to explain why: like the rest of the market, agents working at those firms believed that housing prices would continue to rise so that even the riskiest mortgages would continue to perform well.
Kris Gerardi, financial economist and associate policy adviser at the Federal Reserve Bank of Atlanta, with
Chris Foote, senior economist and policy adviser at the Federal Reserve Bank of Boston.
November 17, 2011
Taking on the conventional wisdom about fixed rate mortgages
The long-term fixed rate mortgage (FRM) is a central part of the mortgage landscape in America. According to recent data, the FRM accounts for 81 percent of all outstanding mortgages and 85 percent of new originations.1 Why is it so common? The conventional wisdom is that the FRM is a great product created during the Great Depression to bring some stability to the housing market. Homeowners were defaulting in record numbers, the story goes, because their adjustable rate mortgages (ARMs) adjusted upward and caused payment shocks they could not absorb.
In a Senate Committee on Banking, Housing, and Urban Affairs hearing on October 20, some experts presented testimony that followed this conventional wisdom. As John Fenton, president and CEO, Affinity Federal Credit Union, who testified on behalf of the National Association of Federal Credit Unions, laid out in his written testimony:
Prior to the introduction of the 30-year FRM, U.S. homeowners were at the mercy of adjustable interest rates. After making payments on a loan at a fluctuating rate for a certain period, the borrower would be liable for the repayment of the remainder of the loan (balloon payment). Before the innovation of the 30-year FRM, borrowers could also be subject to the "call in" of the loan, meaning the lender could demand an immediate payment of the full remainder. The 30-year FRM was an innovative measure for the banking industry, with lasting significance that enabled mass home ownership through its predictability.
Of course, this picture of the 30-year FRM as bringing stability to the housing market has profound implications for recent history. Many critics attribute the problems in the mortgage market that started in 2007 to the proliferation of ARMs. According to the narrative, lenders, after 70 years of stability and success with FRMs, started experimenting with ARMs again in the 2000s, exposing borrowers to payment shocks that inevitably led to defaults and the housing crisis. Indeed, one of the other panelists at the hearing, Janis Bowdler, senior policy analyst for the National Council of La Raza, argued in her written testimony that "when the toxic mortgages began to reset and brokers and lenders could no longer maintain their refinance schemes, a recession ushered in record-high foreclosure rates."
I argue, on the other hand—both in my testimony at the hearing and in this post—that the narrative of the fixed rate mortgage as an inherently safe product invented during the Depression that would have mitigated the subprime crisis because it
eliminated payment shocks does not fit the facts.
Parsing the myths around the fixed rate mortgage
First, the FRM has been around far longer than most people realize. Most people attribute the FRM's introduction to the Federal Housing Administration (FHA) in the 1930s.2 But it was the building and loan societies (B&Ls), later known as savings and loans, that created them, and they created them a full hundred years earlier. Starting with the very first B&L—the Oxford Provident Building Society in Frankfort, Pennsylvania, in 1831—the FRM accounted for almost every mortgage B&Ls originated. By the time of the Depression, B&Ls were not a niche player in the U.S. housing market. They were, rather, the largest single source of funding for residential mortgages, and the FRM was central to their business model.
As Table 2 of my testimony shows, B&Ls made about 40 percent of new residential mortgage originations in 1929 and 95 percent of those loans were long-term, fixed-rate, fully amortized mortgages. Importantly, B&Ls suffered mightily during the Depression, so the facts simply do not support the idea that the widespread use of FRMs would have prevented the housing crisis of the 1930s.
Source: Grebler, Blank and Winnick (1956)
Note: Market percentage is dollar-weighted. Building and loan societies were the main source of funds for residential mortgages and almost exclusively used long-term, fixed-rate, fully amortizing instruments.
To be sure, at 15–20 years, the terms on the FRMs the FHA insured were somewhat longer than those of pre-Depression FRMs, which typically had 10–15 year maturities.3 The 30-year FRM did not emerge into widespread use until later. It must be stressed that none of the arguments that Fenton made hinge on the length of the contract. Furthermore, the argument that Bowdler made in her testimony—that by delaying amortization, a 30-year maturity lowers the monthly payment as compared to a loan with shorter maturity—applies as much to ARMs as it does to FRMs.
But even though the ARMs may not have caused the Depression, FRM supporters might ask, didn't the payment shocks from the exotic ARMs cause the most recent crisis? Again, the data say no. Table 1 of my Senate testimony shows that payment shocks actually played little role in the crisis.
Of the large sample of borrowers who lost their homes, only 12 percent had a payment amount at the time they defaulted that exceeded the amount of the first scheduled monthly payment on the loan. The reason there were so few is that almost 60 percent of the borrowers who lost their homes had, in fact, FRMs. But even the defaulters who did have ARMs typically had either the same or a lower payment amount due to policy-related cuts in short-term interest rates.
To be absolutely clear here, my discussion so far focuses entirely on the question of whether the design of the FRM is inherently safe and eliminates a major cause of foreclosures. The data say it does not, but that does not necessarily mean that the FRM does not have benefits. As I discussed in my testimony, all else being equal, ARMs do default more than FRMs, but since defaults occur even when the payments stay the same or fall, the higher rate is most likely connected to the type of borrower who chooses an ARM, not to the design of the mortgage itself.
The difficulty of measuring the systemic value of fixed rate mortgages
One common response to my claim that the payment shocks from ARMs did not cause the crisis is that ARMs caused the bubble and thus indirectly caused the foreclosure crisis. However, it is important to understand that this argument, which suggests that the FRM has some systemic benefit, is fundamentally different from the argument that the FRM is inherently safe. This difference is as significant as that between arguing that airbags reduce fatalities by preventing traumatic injuries and arguing that they somehow prevent car accidents.
Measuring the systemic contribution of the FRM is exceedingly difficult because the use of different mortgage products is endogenous. Theory predicts that home buyers in places where house price appreciation is high would try to get the biggest mortgage possible, conditional on their income, something that an ARM typically facilitates. When the yield-curve has a positive slope (in most cases) and short-term interest rates are lower than long-term interest rates, ARMs loans offer lower initial payments compared to FRMs. Thus, it is very difficult to disentangle the causal effect of the housing boom on mortgage choice from the effect of mortgage choice on the housing boom.
In addition, there is evidence from overseas that suggests that the FRM is not essential for price stability. As Anthony B. Sanders, professor of finance at the George Mason School of Management, points out in his written testimony, FRMs are rare outside the United States. A theory of the stabilizing properties of FRMs would have to explain why Canadian borrowers emerged more or less unscathed from the global property bubble of the 2000s, despite almost exclusively using ARMs.
By Paul Willen, senior economist and policy adviser at the Boston Fed (with Boston Fed economist Christopher Foote and Atlanta Fed economist Kristopher Gerardi)
1 First liens in LPS data for May 2011.
August 10, 2010
Part 2: A closer look at Michael Lewis's "The Big Short"
In the first part of our discussion of The Big Short, we argued that the bet against subprime mortgages that the book title refers to was not a sure thing, as the book's protagonists claimed, but a highly risky bet that just happened to turn out well. In this post, we focus on the logic of the "sure thing" claim, which is that the subprime bears were exploiting the ignorance of the subprime bulls. The idea that subprime bulls were ignorant is central to the thesis of the book, because it explains both why investors made such huge errors and why it was possible for the subprime bears to exploit, with little risk, the collapse of the mortgage market.
Lewis argues that the ignorance of the subprime bulls resulted from a combination of laziness and obfuscation by issuers of the securities they were buying. We argue, however, that the evidence, including some in the book itself, shows this claim to be patently incorrect. Issuers provided staggering amounts of information about mortgage securities and there was a whole industry of analysts on Wall Street who pored over that data and published literally thousands of reports.
The question, then, is this: if there was so much research going on and so much information available, why did so few investors get it right? The answer comes back to the same issue we discussed in the previous post: house prices. Investors bought subprime bonds not because they were too stupid or lazy to do research, or because issuers prevented them from getting relevant information, or because the securities were so complex that they couldn't figure out that a subprime borrower was a risky proposition. Subprime bulls bought the bonds because careful research based on vast amounts of loan-level data using state-of-the-art models (which, as we will show, was and still is largely correct) showed that if house prices continued to behave as they had for the previous ten years, the bonds would perform well. The research also showed that if house prices collapsed, investors would lose big, but, after ten years of solid appreciation in house prices, researchers viewed a big fall as unlikely.
Lewis's portrayal of those who lost money on subprime as bumbling and ignorant and those who made money as prescient is wrong and it is not a mere detail, it is the heart of the book. Lewis writes:
…a smaller number of people—more than ten, fewer than twenty—made a straightforward bet against the entire multi-trillion-dollar subprime mortgage market and, by extension, the global financial system. In and of itself it was a remarkable fact: The catastrophe was foreseeable, yet only a handful noticed. (p. 105)
What we argue here is that to "foresee" the crisis, one had to explore something to which the subprime bears paid little attention: the evolution of house prices. Whether the fall in house prices that ultimately caused all subprime bonds to default was itself foreseeable is a question that we will return to in subsequent posts, but even the most outspoken subprime bear, Michael Burry, would have a hard time explaining how the main focus of his research—"reading dozens of prospectuses [of subprime mortgage bonds] and scour[ing] hundreds more"—gave any special insight into the dynamics of house prices.
Was the market opaque?
Lewis argues that the issuers of mortgage-related securities "had a special talent for obscuring what needed to be clarified." But to outsiders, specialist terminology often sounds deliberately obscure: why do doctors say the results are "negative" when an X-ray shows good news? The typical buyer in the mortgage marketplace was a specialist, and those who weren't specialists were spending hundreds of millions of dollars and could afford to, and usually did, hire experts to explain to them what was going on.
In the end, Lewis's examples mostly demonstrate his ignorance of the market, not anybody's deliberate attempts to deceive investors. For example, he claims that "a bond backed entirely by subprime mortgages, for example, wasn't called a subprime mortgage bond. It was called an ABS, or asset-backed security" (p. 127). As Lewis himself says, ABS is a class of securities that included "bonds backed by credit card loans, auto loans and other, wackier collateral…" (p. 95n). As such, these securities historically had been characterized by default rates that were literally orders of magnitude bigger than those on mortgage-backed securities (MBS), which were composed of prime residential mortgages. Thus, to the typical buyer of securities, the ABS designation did precisely the opposite of what Lewis claims—it actually drew attention to the credit risk inherent in subprime mortgages.
Another major error along these lines concerns Lewis's discussion of the Alt-A market. Lewis writes that:
Alt-A was just what they called crappy mortgage loans for which they hadn't even bothered to acquire the proper documents—to verify the borrower's income, say. "A" was the designation attached to the most creditworthy borrowers; Alt-A, which stood for "Alternative A-paper," meant an alternative to the most creditworthy, which of course sounds a lot more fishy once it is put that way. (p. 127)
This is just wrong. Alt-A loans were made to borrowers with impeccable credit who, for various and perfectly valid reasons (self-employment being the most common one) could not document income in the standard way. If a borrower had several years' worth of income in the bank, no other debt, and a credit score that indicated that he or she had not missed a payment on anything for years, lenders would rationally overlook his or her inability to provide a letter from an employer documenting income. By calling the loans Alt-A and not A, the lender drew attention to the fact that the loan did not have traditional documentation. The historical credit performance of Alt-A loans was very, very close to that of prime loans and vastly better than that of subprime, so to call Alt-A loans subprime would be completely misleading. As far as investors were concerned, the main difference between Alt-A and A paper involved prepayment risk: Alt-A loans prepaid less and thus were more valuable to investors.1
Were the "shorters" the only people to do serious research on subprime mortgages?
Another central claim of the book is that Wall Street analysts did not seriously research the market. The following passage suggests that until March of 2007, researchers on Wall Street did not pay attention to the details of the pools of loans they were trading.
On March 19 his salesman at Citigroup sent [Michael Burry], for the first time, serious analysis on a pool of mortgages. The mortgages were not subprime but Alt-A.* Still, the guy was trying to explain how much of the pool consisted of interest-only loans, what percentage was owner-occupied, and so on—the way a person might do who actually was thinking about the creditworthiness of the borrowers. "When I was analyzing these back in 2005," Burry wrote in an e-mail, sounding like Stanley watching tourists march through the jungle on a path he had himself hacked, "there was nothing even remotely close to this sort of analysis coming out of brokerage houses. I glommed onto ‘silent seconds'* as an indicator of a stretched buyer and made it a high-value criterion in my selection process, but at the time no one trading derivatives had any idea what I was talking about and no one thought they mattered." (p. 194)
In fact, researchers had done exactly that sort of detailed analysis for years. This paper provides a detailed discussion of the state of mortgage research in the years 2003–2006, reviewing a relatively small sample of the contemporary literature, which still amounts to dozens of reports. Burry's claim to be the only person doing standard credit analysis–"In doing so, [Burry] likely also became the only investor to do the sort of old-fashioned bank credit analysis on the home loans that should have been done before they were made (p. 50)"—is fatuous.
In fact, some of the quotes in the book suggest that the subprime bears, and not the bulls, were the ones who had little understanding of the details. Lewis writes:
As early as 2004, if you looked at the numbers, you could clearly see the decline in lending standards. In Burry's view, standards had not just fallen but hit bottom. The bottom even had a name: the interest-only negative-amortizing adjustable-rate subprime mortgage. (p. 28)
A loan cannot simultaneously be "interest only" and "negative amortizing." Interest only means you pay only the interest every month and "negative amortizing" means you pay less than the interest so that the principal balance of the loan actually increases over time. Unlike the distinction between ABS and MBS, the distinction between these two terms is easy to understand, even for a nonspecialist, and a self-proclaimed expert like Michael Burry should have understood it. But a careful student of prospectuses like Michael Burry should also have a hard time digging up a subprime negatively amortizing loan. "Option-ARMs," largely the only loans that allowed negative amortization in the United States, rarely ever appeared in subprime deals; they were generally considered Alt-A or prime and most were held in the portfolios of banks and never securitized.
In fact, Lewis erodes his own case by providing compelling evidence that other investors were trying to exploit subtle differences between pools and must have done exactly the sort of detailed loan-level analysis that Burry claims was not going on:
A smaller group used credit default swaps to make what often turned out to be spectacularly disastrous gambles on the relative value of subprime mortgage bonds—buying one subprime mortgage bond while simultaneously selling another. They would bet, for instance, that bonds with large numbers of loans made in California would underperform bonds with very little of California in them. Or that the upper triple-A-rated floor of some subprime mortgage bond would outperform the lower, triple-B-rated, floor. Or that bonds issued by Lehman Brothers or Goldman Sachs (both notorious for packaging America's worst home loans) would underperform bonds packaged by J.P. Morgan or Wells Fargo (which actually seemed to care a bit about which loans it packaged into bonds). (p. 105)
The fact that investors who had done such detailed research made "spectacularly disastrous gambles" refutes the idea that the success of the subprime bulls reflected their willingness to do research.
Why did the subprime bulls believe in the market?
If so many investors did so much research, why didn't they bet against subprime? Lewis hears and reports the right answer over and over again. They didn't believe that house prices were going to fall. On page 89, he quotes one participant: "For the bonds to default, he now said, U.S. house prices had to fall, and Joe Cassano didn't believe house prices could ever fall everywhere in the country at once."
On page 157, he quotes another:
"We asked everyone the same two questions," said Vinny. "What is your assumption about home prices, and what is your assumption about loan losses." Both rating agencies said they expected home prices to rise and loan losses to be around 5 percent—which, if true, meant that even the lowest-rated, triple-B, subprime mortgage bonds crafted from them were money-good.
To me, the most compelling piece of evidence about what the subprime bulls got wrong, the smoking gun that makes sense of what happened, is the following table from a Lehman Brothers report from August of 2005 titled "HEL Bond Profile across HPA Scenarios."
|Source: Lehman Brothers 2005|
Lehman Brothers analysts used a default model to predict losses for deals made up of mortgages originated in the second half of 2005 under different scenarios for house prices.
There are two key things to notice in the table. The first is the researchers predict catastrophic losses for the "meltdown" scenario of 5 percent annual house price declines. A 17 percent loss means that anything below a AAA-rated bond was essentially wiped out. Because the collateralized debt obligations (CDO) were composed of BBB-rated bonds from these deals, the meltdown scenario implies complete default on the CDOs. The actual price fall that took place was roughly twice as bad as the meltdown—annual declines of 10 percent rather than 5 percent—but the predictions of the model were largely correct: the deals based on these loans should rack up losses of about 23 percent. Thus, this table completely and utterly invalidates the argument that researchers at the top investment banks did no research and were completely ignorant of what they were buying or selling and had no idea that there was any possible scenario in which the bonds might lose.
The second thing to notice about the table is in the last column. The researchers assigned the meltdown scenario a 5 percent probability—a better outcome than the one that actually obtained. More importantly, they assigned 80 percent probability to house price appreciation of 5 percent or more, scenarios where the losses were sufficiently small that even the BBB-rated bonds were "money-good," scenarios in which the heroes of The Big Short would have seen their bets expire worthless.
In a sense, the subprime bears, the heroes of The Big Short, profited from their own ignorance. Their basic thesis was that making loans to people with poor credit histories was dumb and massive losses were inevitable under any circumstances. But what subprime bears failed to understand was that making unsecured loans to borrowers with poor credit histories generally leads to large credit losses—it's called payday lending—but making loans secured by an asset with a rising price is a low-risk business. The subprime bear logic that making mortgages to borrowers with problematic credit histories was guaranteed to fail would have generated massive losses between 1995 and 2004, as actual outcomes resembled scenarios 1, 2, and 3 from the Lehman Brothers' report chart year in and year out. It was their good fortune, not their astuteness, to make the bets in 2006.
By Paul Willen, research economist and policy adviser at the Boston Fed (with Boston Fed economist Christopher Foote and Atlanta Fed economist Kris Gerardi)
July 06, 2010
The Big View of Michael Lewis's "The Big Short"
Author's note: This is the first of two posts on The Big Short. This one addresses the overall theme of the book. The next will focus on the book's details—in particular, the question of whether issuers obfuscated or even deliberately misled investors about subprime mortgage securities.
In The Big Short (Norton, W. W. & Company, 2010), Michael Lewis provides a narrative of the subprime mortgage crisis through the stories of a set of unconnected investors, including Michael Burry of Scion Capital, Steve Eisman of Frontpoint, and Jamie Mai and Charlie Ledley of Cornwall Capital, all of whom made a common bet against subprime mortgage bonds and won big. The book is a treasure trove of anecdotes about the crisis and deserves the wide audience it has received. But, in terms of reforming Wall Street or preventing another crisis, The Big Short—the title refers to the controversial Wall Street practice of short selling—could do more harm than good because it perpetuates the idea that it is possible to make large amounts of money in financial markets while taking little or no risk.
A reader might get the impression that the protagonists of The Big Short went to the roulette table knowing exactly where the ball would land. But they actually took a huge gamble when they bet against subprime bonds in 2006. In fact, had they tried their bet in 2005, The Big Short would not have been written.
Composition of pre- and post-2005 mortgages were not dissimilar
To understand the extent of the risk that characterized the bets these investors made, one needs to realize that the high levels of defaults on the loans in the deals that the investors bet against were not inevitable and were, in fact, unprecedented. The difference in performance between subprime loans originated before 2005 and after 2005 is like night and day. Loans originated before 2005 were only half as likely to default as the loans in the pools that Burry and his cohorts invested in. More importantly, while none of the BBB-rated bonds in the deals that originated in 2004 and 2005 defaulted, virtually all did for the deals that The Big Short investors traded on.
What accounts for the differences in performance between pre-2005 and post-2005 loans? None of the variables that Burry or any of the traders in The Big Short focus on. For example, while it's true that 35 percent of subprime loans originated in 2005 and 2006 had reduced documentation, that percentage is only marginally higher than the 30 percent with reduced documentation before 2005. Yes, it's true that 78 percent of the subprime loans originated after 2005 had "teaser rates" that would expire two or three years after origination—but 67 percent of the loans originated before 2005 had the same feature. Sure, 73 percent of the loans originated after 2005 had prepayment penalties, but that was down from the 74 percent that had them before 2004. Plus, the average FICO score had actually risen to 615 from 607.
House prices are the difference
So if the composition of mortgages did not change dramatically between 2004 and 2006, what explains the completely different outcomes? The answer is house prices. House prices are central to mortgage performance. When they are rising, few mortgages default because borrowers who can't make their payments can profitably sell to avoid foreclosure. Lewis's statement that a "person with a FICO score of 550 was virtually certain to default and should never have been lent money in the first place" (p. 100) is misleading. In fact, in the pre-2005 pools in which the average FICO score was 607, fewer than 5 percent of borrowers missed a payment in the first year of the loan.
The point here is that the timing of the bet was crucial. Simply betting against deals because they contained loans that were incompletely documented or because the FICO scores were low would have been a losing strategy in 2001 or 2002 or 2003 or 2004 or 2005. Nor was there anything inevitable about the timing of the fall in house prices. By 2003, standard measures of the relationship of house prices to income or to rents already showed overvaluation, and yet house prices continued to rise and even accelerate for the next three years.
In short, the success of the traders in The Big Short was not based on logic and skill but on their willingness to gamble that house prices would fall dramatically in 2006. It's not clear that they understood how much their bet depended on the evolution of house prices.1
Subprime bulls had an extraordinarily successful run
There is a kind of irony here in that in writing The Big Short, Michael Lewis falls for precisely the same logic that created the subprime crisis in the first place. The logic is that investors who make money are smart and investors who lose money are dumb. The problem is that someone writing in 2005 could and did tell an identical story about the subprime bulls.2 Then the smart people were the investors in subprime bonds who made huge returns because the high interest rates on the loans more than compensated them for the surprisingly small credit losses. The dumb ones were the suckers who invested in prime mortgages. What Lewis forgets is that in 2006, the subprime bulls were coming off a string of successful investments no less impressive than that of the heroes of his book. These subprime bulls were the smart ones at that time.
This dissonance is perfectly illustrated in one of the high points of the book when Lewis tells the story of Howie Hubler, a trader at Morgan Stanley:
Some people enjoyed Hubler, some people didn't, but, by early 2004, what others thought didn't really matter anymore, because for nearly a decade Howie Hubler had made money trading bonds for Morgan Stanley (p. 200).
Lewis understands the dangers of Hubler's logic:
Hubler and his traders thought they were smart guys put on earth to exploit the market's stupid inefficiencies. Instead, they simply contributed more inefficiency (p. 215).
Hubler's subprime bets end up going grievously wrong and he ends up causing the biggest single trading loss, $9 billion, in Wall Street history. Yet in many ways, the heroes of the book have a lot in common with Howie Hubler. Like Hubler, they took big bets. Like Hubler, they thought they were exploiting the stupidity of others. And like Hubler, they made a lot of money. Hubler ended up losing big, which may eventually happen to the stars of The Big Short.
The lesson of the crisis really is that one should be skeptical of any trader or fund manager promising high returns without risk. But for many who read The Big Short, the book will only make them look harder for that big score.
2 See "Making sense of the subprime crisis," by Kristopher Gerardi, Andreas Lehnert, Shane Sherlund, and Paul Willen, Brookings Papers on Economic Activity, Fall 2008: 69–145.
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- Construction Spending Update
- Teachers Teaching Teachers: The Role of Networks in Financial Decisions
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- Keeping an Eye on the Housing Market
- Do Millennials Prefer to Live Closer to the City Center?
- The Multifamily Market: Is a Hot Market Overheating?
- Are Millennials Responsible for the Decline in First-Time Home Purchases? Part 2
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