Real Estate Research provided analysis of topical research and current issues in the fields of housing and real estate economics. Authors for the blog included the Atlanta Fed's Jessica Dill, Kristopher Gerardi, Carl Hudson, and analysts, as well as the Boston Fed's Christopher Foote and Paul Willen.
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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 1, 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 1, 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.
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- Governmentsponsored 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