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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 Jessica Dill, Kristopher Gerardi, Carl Hudson, and analysts, as well as the Boston Fed's Christopher Foote and Paul Willen.

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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.

Photo of Paul WillenBy Paul Willen, senior economist and policy adviser at the Federal Reserve Bank of Boston, with help from


Photo of Chris FooteChris Foote, senior economist and policy adviser at the Federal Reserve Bank of Boston, and


Photo of Kris GerardiKris 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: or

2 Griffin, John M. and Gonzalo Maturana. "Who Facilitated Misreporting in Securitized Loans?" (April 20, 2013). Available at

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.

Distribution of Returns: Shiller Home Price Index

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.

Photo of Paul WillenBy Paul Willen, senior economist and policy adviser at the Federal Reserve Bank of Boston, with help from

Photo of Chris Foote Chris Foote, senior economist and policy adviser at the Federal Reserve Bank of Boston, and

Photo of Kris Gerardi 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.

By Kris Gerardi, research economist and assistant policy adviser at the Atlanta Fed (with Boston Fed economists Christopher Foote and Paul Willen)

Note: The authors were given an opportunity to respond to this blog posting. As of this publishing, the author has not commented.