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 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.
August 01, 2013
Government Policy and the Crisis: The Case of the Community Reinvestment Act
Commentators on both the right and left seem to agree on one aspect of the recent mortgage crisis: government policy was at the heart of it. But they disagree on which particular government policy is at fault. The theory from the left is that financial deregulation allowed mortgage lenders and securitizers to exploit both mortgage borrowers and the investors in mortgage-backed securities. On the right, the thinking is that the government instituted policies and programs that were designed to increase credit availability and expand homeownership—policies that induced lenders to make massively risky loans.
To test these theories, researchers must identify a specific change in government policy and then explain the effects this policy change should have. They must then turn to the data to show that the predicted effects occurred soon after the government policy was instituted. A new paper by Sumit Agarwal, Efraim Benmelech, Nittai Bergman, and Amit Seru weighs some evidence related to one government policy that has long been controversial in conservative policy circles: the Community Reinvestment Act (CRA). In particular, the authors claim that the CRA played a role in the mortgage crisis by encouraging banks to make risky loans. How does this research project hold up?
History of the CRA
Before we discuss the details, here is some background on the CRA, which was enacted in 1977. In a 2003 retrospective on the law, William C. Apgar and Mark Duda noted that the CRA "was built on the simple proposition that deposit-taking banking organizations have a special obligation to serve the credit needs of the communities in which they maintain branches" (Apgar and Duda 2003, 169). The act instructed regulators to conduct periodic CRA examinations to make sure that banks were meeting the credit needs of their deposit bases. To enforce compliance, regulators had to take a banking institution's CRA record into account whenever the institution applied to consolidate with some other institution or to expand its operations with new branches (Apgar and Duda 2003, 172).
What economic effect should we expect the CRA to have? For banks, the act changes the economics of mortgage lending. In effect, it adds an extra "shadow return" to each CRA-eligible loan, over and above the loan's usual financial return. For example, the risk-adjusted return on a particular mortgage loan may be 5 percent without the CRA, but this return could rise to 6 percent after the bank factors in the benefit of the loan to its CRA compliance—and by extension, to its ability to perform a profitable merger or open a profitable branch. Simple economic theory implies that after the CRA, banks should make more and riskier loans in CRA-eligible locations, all else being equal.
The Agarwal et al. paper provides evidence that the CRA did indeed lead to more lending and also to riskier lending. The authors argue that in the three quarters before and the three quarters following a CRA examination, the average lender would make more loans and riskier loans in CRA-eligible areas.
In principle, the evidence in the paper seems consistent with the theory: government policy to encourage more lending encouraged more lending. However, other researchers have raised strong objections to the paper's empirical design. Most notably, the University of North Carolina Center (UNC) for Community Capital published a paper that claims to rebut the evidence put forth in the Agarwal et al. study, asserting that the study's entire identification strategy is invalid and therefore the results are spurious. In our reading of the paper, we found three significant issues that make us skeptical of the authors' interpretation that the CRA played a significant role in the crisis by increasing the amount of risky lending during the housing boom.
First issue: Time periods do not correspond
As the authors of the UNC paper note, the six-quarter window that Agarwal et al. use to identify the causal impact of CRA examinations "rarely corresponds to the actual period that is covered by the CRA exam." Instead, the CRA examiners typically analyze loans originated well before the actual exam date. To illustrate the issue, the UNC paper looks at a CRA exam of JPMorgan Chase that occurred in June 2011. The authors, who obtained their information from the public record of the exam (the CRA Performance Evaluation), find that the exam covered mortgage originations from January 2007 through December 2010. In contrast, the Agarwal et al. six-quarter window would have run from October 2010 to March 2012, implying an overlap of only one quarter. And even that one quarter of overlap is unlikely—the authors point out that the CRA examiner evaluated only JPMorgan's market share of lending through 2009, as the 2010 data generated to comply with the Home Mortgage Disclosure Act (HMDA) were unavailable at the time of the exam. The implication is that JPMorgan would have had no incentive to increase CRA-eligible mortgage originations in the three quarters before the examination period, since the CRA examiner was not going to consider those loans anyway.
Another relevant example (obtained from correspondence with economists from the Federal Reserve Board of Governors) is the June 2006 exam of Citibank. That exam used 2004 HMDA data for the market share analysis and used data through 2005 to compute the bank's distribution of loans to low- and moderate-income borrowers or neighborhoods. Thus, there is almost no overlap between the data used by the CRA examiners and the window employed by Agarwal et al. If the UNC paper is correct in its assertion that CRA examiners often consider loans that are outside of the six-quarter window used by Agarwal et al., then their claim that institutions were ramping up their CRA-eligible lending in order to pass their CRA examinations is flawed.
Second issue: CRA treatment effects possibly overestimated
Agarwal et al. find an increase in lending resulting from the CRA in non-CRA-eligible census tracts for both high- and low-income households. Specifically, they stratify their sample based on income terciles and find that origination rates to borrowers in the bottom-income tercile in non-CRA-eligible tracts increased by 6 percentage points around the initiation of CRA exams. This result supports their interpretation because banks would obtain CRA credit for loans to these borrowers. However, the results also show that origination rates for borrowers in the highest-income tercile in the non-CRA-eligible tracts increased by almost 4 percentage points around the initiation of CRA exams. Since these loans did not count toward fulfilling CRA obligations, the effect cannot be interpreted as a CRA treatment effect. Rather, a reasonable interpretation of this estimate is that it is picking up an unobserved factor that happens to be correlated with the timing of the CRA examinations (that is, a spurious correlation). If this is the case, then the true CRA treatment effect in CRA-ineligible tracts is really the difference between the increase in origination rates for the borrowers in the bottom income tercile and the borrowers in the top income tercile, which is an economically small 2 percentage points. Furthermore, by lending to high-income borrowers in non-CRA-eligible tracts, banks would tilt the distribution of their lending away from areas targeted by the CRA, which would end up hurting them in a CRA exam. Thus, it's difficult to imagine a scenario in which banks would target these borrowers for CRA-related purposes.
Issue 3: Securitization an unlikely explanation for effects
Agarwal et al. argue that they find significant CRA effects on lending in the 2004–06 and 2007–09 periods but not during the 1999–2003 period, and they find significant CRA effects on default rates only in the 2004–06 period. The authors' explanation for this pattern is that 2004–06 was the period in which the securitization of mortgage loans peaked, and "banks are more likely to originate loans to risky borrowers around CRA examinations when they have an avenue to securitize and pass these loans to private investors after the exam" (p. 21).
There are at least three problems with this line of reasoning. First, private securitization markets shut down in the 2007–09 period, so they couldn't possibly explain the increase in lending in CRA-eligible tracts during that period. The GSEs were very active in securitizing mortgages during this period—but they were also very active in the early 2000s, so agency securitization doesn't seem like an adequate explanation either.
Second, while it is true that securitization could alter the risk-return tradeoff for mortgage lending—it does so by allowing mortgage originators to offload their credit risk by selling their loans into mortgage-backed securities—securitization would make this offloading possible and appealing to many mortgage originators, not just CRA lenders. The result could easily be a decline in mortgage lending by depository institutions in CRA-eligible areas rather than an increase, thanks to increased competition from nondepository institutions. In fact, we would argue that the empirical evidence supports this interpretation more than Agarwal et al.'s interpretation. The late 1990s and early 2000s saw the emergence of nondepository institutions that specialized in originating subprime mortgages and selling them to securitizers. These aggressive subprime lenders were typically not subject to CRA requirements, a fact that is consistent with the shrinking footprint of CRA institutions, which we discuss in more detail below. According to Bhutta and Canner (2009), only about 6 percent of subprime loans made in 2005 and 2006 were made to CRA-targeted populations by CRA-regulated lenders. In effect, one of the consequences of the dramatic rise in private-label securitization volume was that it created lots of competition among the riskier segments of the mortgage market. This situation likely resulted in less lending by banks in CRA-eligible areas rather than more.
Finally, perhaps a more fundamental reason to doubt that securitization explains the timing of the paper's effects is that securitization has been around a long time. Laws needed to be changed before securitization could take off, but these legal changes occurred in the 1980s. So if the CRA and securitization together formed a lethal combination for the mortgage market, then why did the crisis occur in the late 2000s rather than the late 1980s?
Even if CRA encouraged risk, would it really say much about government policy?
With these caveats in mind, what would a finding that the CRA encouraged risky lending really tell us? In our opinion, a finding that the CRA encouraged risky lending would probably tell us little about the role of government in the financial crisis.
The focus needs to be on quantitative magnitudes. The question of whether or not the CRA led to an increase in risky lending of any size may not be that interesting because it is hard to imagine a world in which the CRA would not have done so. Economists begin with the premise that banks are profit-maximizing entities, so they should make all the loans that increase their expected profits. If a loan is not made, then that is because the bank must have judged the loan to reduce expected profits rather than raise them. As we described above, the CRA increases the risk-adjusted return for certain loans, so that some of the loans a bank deems unprofitable in the absence of the CRA (because of risk-adjusted returns that were too low) become profitable with CRA. Because these are marginal loans in risk-adjusted returns, then risk must be increasing.
If we start with the assumption that the CRA leads to more risky lending, the more interesting question is how much risky lending is encouraged. As it happens, the quantitative magnitudes of the estimates in the Agarwal et al. study are quite small. For example, if we assume that the appropriate CRA treatment effect should only be measured using the difference in the increase in lending between CRA-eligible census tracts and CRA-ineligible tracts, then magnitudes are trivial. Specifically, the paper finds that the CRA increased origination rates in CRA-eligible census tracts relative to CRA-ineligible census tracts by somewhere between 1 and 3 percentage points, depending on the specific quarter around the initiation of the CRA exam. When one considers that the average origination rate in the Agarwal et al. sample is 72 percent, and only 15 percent of loan originations in the sample came from CRA-eligible tracts, this is an extremely small effect. The effect becomes even smaller if you adjust the baseline estimates to take into account the likely simultaneity bias that we discussed above in the subsection titled "Issue 2."
The CRA passed long before the crisis
In concluding, we should point out that the CRA went into effect in 1977, 30 years before the financial crisis. If the CRA did shift the risk-return tradeoff for mortgage lending, then why didn't risky lending take off in 1978 rather than 2003? Moreover, the footprint of CRA-regulated institutions in the mortgage market has shrunk dramatically since the law passed. Figure 1 (taken from Foote, Gerardi, and Willen ) shows that nondepository mortgage companies—which generally are not covered by the CRA—accounted for only 15 percent of mortgage lending when the CRA was passed in 1977. By the late 1990s, however, these non-CRA entities had grown to nearly 60 percent of the mortgage market. If the CRA is so toxic to the mortgage market, then it is puzzling why the act had no effect soon after its enactment, when it covered 85 percent of the mortgage market, yet led to an explosion of risky lending 25 years later, when it covered only 40 percent of the market.
Indeed, any attempt to link the recent crisis to government policies aimed at expanding mortgage credit and homeownership faces an uphill struggle. The basic problem is that the federal government has been deeply involved in housing and mortgage markets since at least the end of World War II. In particular, the Federal Housing Authority (FHA) and Veterans Administration (VA) loan programs began at about that time and were explicitly designed to extend homeownership to underserved populations. As figure 2 (also from Foote, Gerardi, and Willen, 2012) shows, the FHA and VA pioneered no and low down payment loans in the 1950s and 1960s. And as figure 3 shows, FHA loans accounted for 40 percent of loans outstanding in the 1970s and had default rates that were an economically massive 100 percent higher than non-FHA loans. In their size and their effect on housing markets, the FHA and VA were literally orders of magnitude more important than the CRA. Did government lead to risky lending? Yes! But it did so 30 years before CRA and 60 years before the recent financial crisis.
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
July 01, 2013
Misrepresentation, or a Failure in Due Diligence? Another Argument
In the last post we wrote together, we discussed a paper on the role of misrepresentation in mortgage securitization by Tomasz Piskorski, Amit Seru, and James Witkin (2013, henceforth PSW).1 That paper argues that the people who created mortgage-backed securities (MBS) during the housing boom did not always tell the truth about the mortgages backing these bonds. Today, we discuss a second paper on misrepresentation, this one by John M. Griffin and Gonzalo Maturama (2013, henceforth GM).2 The two papers have a similar research approach, and the two sets of authors interpret their results in the same way—namely, in support of the hypothesis that misrepresentation was an important cause of the mortgage crisis. We offer an alternative interpretation.
We believe that the evidence shows that investors were not fooled and that deception had little or no effect on investor forecasts of defaults. Consequently, deception played little or no role in causing the crisis (see the post on PSW for details). We do think, however, that some results in the GM paper have significant implications for our understanding of the crisis, although GM does not focus on these particular results.
We argue that one can interpret their evidence on misreporting as a measure of due diligence on the part of lenders. Many—including most notably the New York Attorney General's office in a lawsuit against JP Morgan—allege that the dismal performance of securitized mortgages made after 2005 relative to those made before 2005 reflects a precipitous drop in due diligence among lenders starting in that year. But GM's paper implies that there was no such decline. In fact, for most measures of due diligence, there is almost no time series variation over the housing cycle at all.
Before we discuss the paper's implications for underwriting standards, it is important to outline GM's basic research approach with regards to misrepresentation. As with PSW, GM's fundamental idea is to compare two sets of loan-level mortgage records to see if the people marketing MBS misrepresented what they were selling. Specifically, GM compare information about mortgages supplied by MBS trustees with public records data from deed registries, as well as data on estimated house prices from an automated valuation model (AVM). PSW, by contrast, compare MBS trustees' data with information from a credit bureau. In general, GM's choice to use public records data as the comparison data set is probably more functional.
While PSW refer to their credit bureau data as "actual" data, it is well known that credit bureau data also contain errors, a fact that complicates any study of misrepresentation. For example, PSW often find that the credit bureau reports a second lien for a particular mortgage borrower while the MBS trustees report no such lien. The implication in such instances is that the MBS trustees misrepresented the loan. But PSW must also acknowledge that the reverse discrepancy turns out to be equally likely. Just as often, second liens appear in MBS data and not in the supposedly pristine data from the credit bureau. No data set is perfect, but GM's public records data is no doubt much cleaner than the credit bureau data. For a purchase mortgage, the records filed at a deed registry are not only important legal documents, they are also recorded on or very close to the day that the mortgage is originated. As a result, the public records data come closer to being "actual" data than data from a credit bureau.
GM measure four types of "misreporting" with their data: 1) unreported second liens; 2) investors incorrectly reported as owner-occupants; 3) unreported "flipping," in which the collateral had been sold previously; and 4) overvaluation of the property, which is defined to occur when the AVM reports a valuation that is more than 10 percent below the appraised house value appearing on the loan application. To us, neither 3 nor 4 seem like reasonable definitions of misreporting. For point 3, issuers never reported anything about whether the house was flipped. This issue turns to be a moot point, however, as Figure 1 from GM (reproduced below) shows that flipping almost never occurred. Regarding point 4, it's not surprising that AVMs often report substantially different numbers than flesh-and-blood appraisers do, for the same reason that two people guessing the number of jelly beans in a jar are likely to disagree. Estimating the right value exactly is not easy, even for people (and automated computer models) with the best of intentions.
More consequential are GM's findings relating to misrepresentations of the types identified in points 1 and 2. Here, GM's findings are essentially the same as PSW's, though GM report much higher rates of misrepresentation than do PSW. However, GM acknowledges that the difference stems almost entirely from their decision to ignore refinance loans. According to Table IA.VIII in GM's appendix, refinances have dramatically lower misrepresentation rates. But just as the central findings of GM are similar to those in PSW, so is our critique. The historical evidence indicates that investors were properly skeptical of the data provided by MBS issuers. Moreover, deception did not prevent investors from making accurate forecasts about default rates among securitized loans. We direct the reader to our post on PSW for more details.
Though we do not believe that GM can persuasively link misrepresentation of MBS data to massive investor losses, an alternative interpretation of their data has the potential to shed light on the mortgage crisis. One way to interpret the level of misreporting—in particular, for occupancy—is as a measure of due diligence on the part of lenders. Neither PSW nor GM suggest that for any particular loan, the MBS issuer knew that the borrower was an investor and did not plan to occupy the property. Instead, these authors claim that someone along the securitization chain failed to do the necessary due diligence to determine if the borrowers who claimed to be owner-occupiers were in fact investors. This due diligence was certainly possible. A sufficiently motivated loan officer could have done exactly what GM did: match loan files with public records to figure out that a potential borrower did not intend to live in the house he was buying.3 As a result, we would expect that when due diligence goes down, occupancy misreporting would go up.
Obtaining a proxy measure of due diligence is useful, because many commentators have argued that the poor performance of subprime loans made after 2005 as compared to loans made before 2005 (see Figure 3 from Foote, Gerardi, and Willen, 2012) resulted from a precipitous drop in due diligence. For example, in the recent complaint against JP Morgan, the New York Attorney General's office writes that:
[Subprime lenders], as early as February 2005, began to reduce the amount of due diligence conducted "in order to make us more competitive on bids with larger sub-prime sellers."
So what does GM's proxy measure of due diligence show? With respect to occupancy, there is little or no change in the incidence of occupancy misreporting in 2005. Indeed, looking across the entire sample, we see that occupancy misreporting rose smoothly from about 11 percent in 2002 to a peak of about 13 percent in 2006. In other words, at the peak of the boom, the incidence of sloppy underwriting was almost the same as it was four years earlier. In fact, all four series reported by GM show the same pattern or lack thereof. With the exception of the first quarter of 2006, second-lien misreporting was uniformly lower during what commentator Yves Smith refers to as the "toxic phase of subprime" lending than it was in 2004 and 2003 when loans performed dramatically better.
By Paul Willen, senior economist and policy adviser at the Federal Reserve Bank of Boston, with help from
Chris Foote, senior economist and policy adviser at the Federal Reserve Bank of Boston, and
Kris Gerardi, financial economist and associate policy adviser at the Federal Reserve Bank of Atlanta
1 Piskorski, Tomasz; Amit Seru; and James Witkin. "Asset Quality Misrepresentation by Financial Intermediaries: Evidence from RMBS Market" (February 12, 2013). Columbia Business School Research Paper No. 13-7. Available at SSRN: ssrn.com/abstract=2215422 or http://dx.doi.org/10.2139/ssrn.2215422
3 For example, the loan officer could use the public records to determine if a potential buyer owned multiple properties, or if the buyer recently put another property in a spouse's name.
June 19, 2013
Asset-Quality Misrepresentation as a Factor in the Financial Crisis
A provocative new paper by Tomasz Piskorski, Amit Seru, and James Witkin (2013, henceforth PSW) presents evidence in support of a popular theory of the financial crisis. That theory is that issuers of mortgage-backed securities (MBS) misrepresented the credit quality of the loans backing those bonds. Without the proper information about what they were buying, MBS investors lost so much money that the entire financial system nearly buckled as a result. A closer look at the evidence, however, shows that any deception in the marketing of MBS had little or no effect on investor decisions, so it is unlikely that MBS misrepresentation played a significant role in the financial crisis.
The basic approach of the PSW paper is to compare two sets of loan-level mortgage datasets in a search for issuer misrepresentation. One set of records is provided by the MBS issuers themselves (formally known as trustees), while the other is a set of loan-level records from a credit bureau. PSW assumes that if they find an MBS trustee reported one fact about a given loan while the credit bureau data shows something different, then the MBS trustee misrepresented the loan. For example, assume that a trustee reported that the borrower on a particular loan was an owner-occupier, not an absentee investor. PSW consulted the credit bureau data to see whether the bureau reported a borrower living at the same address as the property. If this was not the case, then PSW say the loan was misrepresented by the MBS trustee as a loan for an occupier when in reality it was a loan for an investor (and thus much riskier than advertised).
Loans backed by investor properties were likelier to default
The headline finding is that misrepresentation was present. According to PSW, about 7 percent of investor loans were misrepresented as backed by owner-occupied homes. PSW also find that loans backed by investor-owned properties were much more likely to default, and that MBS issuers systematically failed to report the existence of second liens.
The link between misrepresentation about MBS and the mortgage crisis may at first glance seem obvious. MBS investors constructed their forecasts of loan defaults using the loan characteristics reported by MBS trustees. When defaults turned out to be higher than the investors expected—because the MBS trustees had misrepresented the loan characteristics—massive investor losses and a financial crisis resulted.
Yet the historical record reveals something puzzling. Despite the ostensible misrepresentation by trustees, investor forecasts of MBS performance were exceptionally accurate. The table comes from a Lehman Brothers analyst report from 2005 and provides forecasts of the performance of securitized subprime loans under varying scenarios for house prices. The bottom row of this table gives the "meltdown" scenario: three years of house-price declines at an annual rate of 5 percent. Under this scenario, Lehman researchers expected subprime deals to lose about 17 percent. In reality, prices fell 10 percent per year—double the rate in the meltdown scenario—yet the actual losses on subprime deals from that vintage are expected to come in around 23 percent.
Source: Lehman Brothers, "HEL Bond Profile across HPA Scenarios," in U.S. ABS Weekly Outlook, August 15, 2005.
This table shows that investors knew that subprime investments would turn sour if housing prices fell. The "meltdown" scenario for housing prices implies cumulative losses of 17.1 percent on subprime-backed bonds. Such losses would be large enough to wipe out all but the highest-rated tranches of most subprime deals. The table also shows that investors placed small probabilities on these adverse price scenarios, a fact that explains why they were so willing to buy these bonds.
If issuers were lying about the quality of the loans, then why did the investors produce such reasonable forecasts of loan defaults?
There are two closely related answers. The first comes from information economics: rational investors were properly skeptical of any information they couldn't verify, so they rationally assumed that there was some misrepresentation going on. The logic here is exactly that of the celebrated "lemons" model of George Akerlof, who won a Nobel Prize for his insight about the effect of asymmetric information on markets. Assume, for example, that used-car buyers know that used-car sellers have private information about the quality of the cars that they bring to the market. As a result, potential buyers assume that they will be offered only low-quality used cars (lemons), so these buyers offer appropriately low prices for all cars in the used-car market. The same skepticism was likely to be present in the market for mortgages. In our case, pooling across mortgage loans and securitization deals means that investors will sometimes overestimate the share of misrepresented loans and sometimes underestimate it, but on average they get it right.
The second, related, answer to the question of why misrepresentation doesn't matter is that this lack of trust leads investors to base their forecasts on the historical performance of the loans. In other words, investors do not construct a theoretical model of how often an idealized owner-occupant should default. Rather, the investors simply measure the previous default probabilities of loans that were represented as going to owner-occupants. As long as misrepresentation doesn't significantly change over time, the forecasts based on the investors' reduced-form statistical models would not have underpredicted defaults. (Indeed, the table above shows that this method seems to have worked pretty well.) Of course, one might worry that the misrepresentation problem did get worse over time. However, although the authors claim in the abstract that the problem got worse, the results in the paper show that differences between the level of misreporting in 2005 and 2006 and 2007 were minimal. Moreover, in many cases, the differences have the wrong sign.
Looking elsewhere for an explanation
At the end of the day, the PWS paper—like many others written since the crisis—tries to explain a fact that isn't really a fact. Investors didn't really think of securitized subprime loans as less risky than they actually were. The documentary evidence repeatedly shows that investors understood the risk inherent in purchasing MBS that were backed by loans to people with bad credit histories. As the table shows, analysts expected 17.1 percent losses in the meltdown scenario. If we assume a 50-percent loss rate for each default, then overall losses of 17.1-percent imply that 34.2 percent of the loans—more than a third—would go to foreclosure. With the benefit of hindsight, we can see that the real problem for investors was not that they didn't think subprime borrowers would default if house prices fell. Rather, they didn't think house prices would fall in the first place.
Finally, it is important to stress that the quantitative effects of the level of misrepresentation found in the paper are economically insignificant. Remember that the harm of misrepresentation for an investor arises because misrepresentation leads investors to under-forecast defaults. Quantitatively, the largest finding of misrepresentation reported by PSW is that 15 percent of purchase mortgages had some form of misrepresentation, and that the misrepresented loans were 1.6 times more likely to default. So, if an investor assumes that none of the loans were misrepresented, then a simple calculation shows that actual defaults are likely to come in about 1.09 times higher than forecast:
(0.85 x 1) + (0.15 x 1.6) = 1.09
This calculation implies that if a naïve investor forecasted, say, 8 percent defaults for a pool of subprime loans, then the true number would actually be 8 percent x 1.09 = 8.72 percent. But even this figure overstates the effect of misrepresentation, because it assumes that the investors had access to a "pure" data set that was uncontaminated by misreporting. In any case, for subprime loans originated in 2006, actual defaults came in about 40 percentage points over what was expected. In other words, even if we assume investors were completely naïve, misrepresentation can, at most, explain 0.72 percentage points out of 40.
We feel researchers should look elsewhere for an explanation for why investors lost so much money during the housing crisis. A good place to start is the belief by all actors in the drama— borrowers, Wall Street intermediaries, and investors—that housing prices could only go up.
By Paul Willen, senior economist and policy adviser at the Federal Reserve Bank of Boston, with help from
Chris Foote, senior economist and policy adviser at the Federal Reserve Bank of Boston, and
Kris Gerardi, financial economist and associate policy adviser at the Federal Reserve Bank of Atlanta
March 17, 2010
Demand for subprime credit or higher housing prices: Solving the conundrum of which came first
The process of securitizing mortgages has received a lot of negative attention during the financial crisis. An oft-made claim is that by collateralizing risky subprime mortgages, securitization drove an unprecedented expansion of mortgage credit to borrowers with bad credit histories. This increase in available credit fed the housing bubble, which ultimately burst after the expansion of subprime credit had run its course. The implication is that without Wall Street's insatiable appetite for mortgage-backed securities (MBS), less bad credit would have been extended, housing prices would not have soared so high, and the subsequent housing bust would not have been so bad.
On the surface, a link between securitization of subprime mortgages and the housing bubble seems both intuitive and plausible. After all, the vast majority of subprime mortgages were sold by originators to private institutions, not the government-sponsored housing-finance agencies like Freddie Mac and Fannie Mae. These private institutions operated in the secondary mortgage market, where loans are securitized and sold to investors around the world. Figure 1A in Mayer and Pence (2008) provides a nice illustration of this pattern.
Higher housing prices, not MBS, may have encouraged subprime lending
But the arrow of causality may not run from the expansion of securitized subprime credit to higher housing prices. Rather, expectations of higher housing prices may have encouraged more lending to subprime borrowers, whose loans were subsequently securitized. (Any loan is a good loan if prices are rising, because the collateral that backs the loan is getting more valuable over time.) Both interpretations are equally validated by aggregate data. To answer the chicken-or-egg question of what comes first in the logical chain—higher housing prices or increased subprime securitization—you need to perform a more disaggregated analysis.
In a recent paper, Taylor Nadauld of Ohio State and Shane Sherlund of the Federal Reserve Board of Governors attempt to solve this identification problem. Their analysis is based on a change in regulations that could have affected the level of securitization but was plausibly unrelated to either housing prices or the demand for subprime credit. Consequently, by examining the effects of this regulatory change, the authors can ask whether changes in securitization had true, causal effects on the amount of credit extended to subprime borrowers.
Did capital requirements reduction increase the demand for MBS?
The regulatory change at the heart of the paper involves the required capital levels at five broker-dealers: Bear Stearns, Morgan Stanley, Goldman Sachs, Lehman Brothers, and Merrill Lynch. By some accounts, the rule reduced capital requirements at these five institutions by up to 40 percent. Specifically, in April 2004, the Securities and Exchange Commission amended a series of rules that had the effect of reducing capital requirements for the five broker-dealers (hereafter referred to as consolidated supervised entities, or CSEs). The change was made in response to the European Union's Conglomerates Directive that required U.S. broker-dealer affiliates to show proof that their consolidated holding companies were subject to supervision by a U.S. regulator. The rule change established an alternative method of calculating capital requirements for the CSEs, which were not already subject to consolidated capital regulation from a regulatory authority. Basically, the CSEs could use their own internal risk-based models to calculate a capital adequacy measure consistent with those put forth by the Basel Committee on Banking Supervision. (For further detail, see the Nadauld and Sherlund paper.)
The authors hypothesize that the reduction in capital requirements increased the institutions' demand for purchasing subprime mortgages from the primary market for the purpose of securitizing them either to sell to other investors or to hold themselves. That is, lower capital requirements either increased their demand to invest in subprime MBS themselves or increased their capacity as intermediaries to securitize the mortgages and sell to other investors.
Authors use two-stage strategy to support findings
The authors use a two-stage econometric strategy to identify the impact of this exogenous increase in the CSEs' demand to purchase subprime mortgages to securitize, which would have raised the supply of credit to subprime borrowers. In the first stage of their analysis, they verify that the regulatory event did indeed raise secondary-market purchases at the affected institutions. In particular, they ask whether securitization activity among the five institutions increased by more than the securitization activity of institutions that were not affected by the regulatory event. The answer is yes. According to data on privately securitized mortgages from FirstAmerican LoanPerformance, the CSE banks securitized about 32 percent more loans on average than did their non-CSE counterparts after 2003.
In the second stage, the authors ask whether the increase in CSE securitization is linked to an increase in subprime credit supply. For this step, they obtain ZIP-code–level data on mortgage originations and securitization activity from Home Mortgage Disclosure Act data and then merge these data with the LoanPerformance data. Essentially, in this stage, the authors ask whether ZIP codes that experienced higher CSE securitization activity (relative to non-CSE securitization activity) also experienced higher levels of subprime mortgage originations. The answer again is yes. The authors interpret their findings as evidence that increased demand for the CSEs to securitize mortgages resulted in increased access to subprime mortgage credit at the household level.
Analysis may need refinement
In our opinion, this paper is one of the few that has come up with a reasonable way to identify the effect of the secondary mortgage market on the ability of households to obtain mortgages. But the paper needs to address two issues in order to offer a more convincing analysis.
The first issue concerns the authors' measure of subprime credit supply. They use the ratio of subprime mortgages originated to total housing units in a given ZIP code. But this is a measure of mortgage credit issued in equilibrium. Many factors could create cross-sectional variation in this variable (across ZIP codes) that have nothing to do with differences in access to credit. For example, differences in homeownership rates, the fraction of homeowners with a mortgage, and wealth and income differences could all affect the quantity of mortgage lending in a ZIP code without explaining differences in access to credit. Of course, the authors try their best to control for such factors in their estimates, but ultimately it is impossible to control for all of them.
The second substantive issue concerns the link between the regulatory event and demand and supply for MBS in the secondary market. The authors argue that the regulatory event could have affected the secondary mortgage market through two channels. First, they argue, relaxing capital requirements may have increased the CSE banks' demand for highly rated subprime MBS. We know for certain that the five CSE institutions were heavily involved in the supply of MBS to other investors, but we also think that these institutions were investors as well. It shouldn't be too hard for the authors to find evidence of this connection, but what would be even more convincing, and perhaps more difficult, would be to review whether the CSE banks substantially increased their holdings of subprime MBS after the regulatory event.
The second potential channel involved relaxing the constraints associated with the supply of subprime MBS. In this case, capital is needed to warehouse mortgages during the process of creating securities. In addition, most deals required over-collateralization, which usually meant that the issuer would take the first-loss position. If these constraints were binding for these institutions before the regulatory event (that is, the secondary market had pent-up demand for subprime MBS), then the relief on capital requirements after the event may have resulted in increased supply. This hypothesis seems a little far-fetched to us, not to mention virtually impossible to test in the data, so the authors may be better off focusing on the demand-side effects.
Note: The authors were given an opportunity to respond to this blog posting. As of this publishing, the author has not commented.
- Investigating the Trend in Office Renovations
- Commercial Construction Update: Third-Quarter 2016
- Construction Lending Update: Have the Banks Finally Opened the Spigots?
- Construction Spending Update
- Teachers Teaching Teachers: The Role of Networks in Financial Decisions
- The Pass-Through of Monetary Policy
- 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
- February 2017
- November 2016
- June 2016
- May 2016
- April 2016
- November 2015
- September 2015
- August 2015
- July 2015
- May 2015
- Affordable housing goals
- Credit conditions
- Expansion of mortgage credit
- Federal Housing Authority
- Financial crisis
- Foreclosure contagion
- Foreclosure laws
- Government-sponsored enterprises
- Homebuyer tax credit
- House price indexes
- Household formations
- Housing boom
- Housing crisis
- Housing demand
- Housing prices
- Income segregation
- Individual Development Account
- Loan modifications
- Monetary policy
- Mortgage crisis
- Mortgage default
- Mortgage interest tax deduction
- Mortgage supply
- Multifamily housing
- Negative equity
- Positive demand shock
- Positive externalities
- Rental homes
- Subprime MBS
- Subprime mortgages
- Supply elasticity
- Upward mobility
- Urban growth