Real Estate Research

April 26, 2012

Can home loan modification through the 60/40 Plan really save the housing sector?

In a recent article in the Federal Reserve Bank of St. Louis Review, Manuel Santos, a professor at the University of Miami, claims to offer a simple solution to "save the housing sector." Called the "60/40 Plan," his proposal is the centerpiece of a business called 60/40 The Plan Inc. Santos’s article is, in our opinion, written less like an academic article and more like promotional material.

The developer of the 60/40 Plan, Gustavo Diaz, is seeking a patent for the proposal. Unfortunately for the stressed mortgage market, his idea is simply a specific variant of a long-standing mortgage-servicing practice known as "principal forbearance." In general, principal forbearance occurs when the mortgage lender grants a temporary reduction of a borrower’s monthly mortgage payment, often reducing the payment by a significant fraction, with the stipulation that the borrower repay this benefit, with interest, at a later date.

Principal forbearance is a loss-mitigation tool that mortgage lenders and servicers have been using for decades. In fact, Fannie Mae and Freddie Mac are currently using this technique as a loss mitigation tool and alternative to principal forgiveness (which Federal Housing Finance Agency Acting Director Edward DeMarco discussed here). Private mortgage lenders have also widely used principal forbearance, especially in the first few years of the recent foreclosure crisis.

As articulated in Diaz’s 60/40 Plan, principal forbearance simply splits a distressed borrower’s current principal balance into two parts: a 60 percent share that will fully amortize over 30 years and be subject to interest payments at market rates, and a 40 percent share that is treated as a zero-interest balloon loan due at the time of sale.

Of course, in practice, the optimal shares and other terms of a principal forbearance program should be, and often are in practice, based on a given household’s financial situation. One size does not fit all. Professor Santos advocates the 60/40 Plan in large part because it is, in the language of economists, "incentive compatible." What this means is that borrowers who need assistance with their mortgage payments will find the program helpful and borrowers who do not need assistance will not find the program very appealing and thus will have little incentive to pretend to be a borrower in need of help in order to qualify for the program.

He writes: "It is important to understand that the 60/40 Plan builds on financial postulates and incentive compatible mechanisms that can be firmly implemented. It is designed as a first-best contract between the homeowner and the lender by holding onto some basic principles of incentive theory."

We agree completely with this sentiment. In fact, one of us wrote an article almost five years ago that advocated a policy of principal forbearance over principal forgiveness for exactly these reasons. Thus, the 60/40 Plan is not a novel concept, as Professor Santos seems to believe. But even more problematic, principal forbearance, as we have come to realize over the past few years, is not a panacea for the housing market for several reasons. First, it is really only helpful and appealing to borrowers that have temporary cash-flow problems who do not wish to move. This is because under the 60/40 Plan and principal forbearance in general, a borrower remains in a position of negative equity, which makes it virtually impossible to sell, since the borrower would need to come up with the amount of negative equity in cash to repay the entire principal balance of the mortgage at closing. For example, in the numerical example that Professor Santos works through to illustrate how the 60/40 Plan would work in practice, the borrower remains in a position of negative equity for 15 years. Thus, if a cash-strapped borrower needs to move immediately, or even a few years down the road, default (or re-default) is very likely.

Second, carrying 40 percent of the mortgage at a zero (or below market) interest rate imposes significant costs on the lender or investor. (These costs are viewed as being offset by savings from avoiding foreclosure.) Nevertheless, principal forbearance is not always going to be a positive net-present-value proposition; this depends on the share being protected (40 percent is quite high), the amortization schedule (30 years is very long), the discount rate, and the re-default rate. Indeed, Professor Santos seemingly assumes no re-default despite the fact that under the plan a borrower would remain in negative equity for a very long time, as we discussed above.

Third, most distressed mortgages are not held by depository institutions as whole loans. Fannie Mae and Freddie Mac have been able to selectively employ principal forbearance because they make investors whole in terms of the original promised principal and interest payments. This is not true for private-label securitizations, and there have been ongoing disagreements between investors and servicers as to optimal loss-mitigation strategies. (And there is no reason to think this proposal would not be similarly controversial.) The 60/40 Plan also seemingly ignores the significant complications posed by existing second liens and mortgage insurance policies.

Finally, Professor Santos claims that the 40 percent zero-coupon balloon shares—typically nonrecourse loans to severely distressed homeowners—will have a deep secondary market to pull liquidity back into the housing market. This seems far-fetched given that these assets have little or no yield and will have high default rates with no recourse. However, reading further, it appears that the proposal assumes a Federal Deposit Insurance Corporation (FDIC) insurance wrap for these assets to facilitate their sale. The cost of this insurance would likely be expensive and require a controversial new program, with premiums expected to cover losses or a congressional appropriation. However, it also ignores the fact that FDIC-insured depository institutions only hold about 25 percent of all mortgages.

Principal forbearance can be a useful loss-mitigation tool, although its value depends on economic circumstances. The 60/40 Plan that Professor Santos advocates is an example of principal forbearance and not a novel concept. Moreover, the 60/40 Plan does not consider a number of important institutional factors that have hampered loss-mitigation activities since the onset of the mortgage foreclosure crisis. Simply put, the 60/40 Plan will not save the housing market.

By Scott Frame, financial economist and senior policy adviser, and

Kris Gerardi, financial economist and associate policy adviser at the Federal Reserve Bank of Atlanta

April 26, 2012 in Foreclosure contagion, Loan modifications, Mortgage crisis, Mortgage default | Permalink

April 05, 2012

Debunking a popular myth about mortgage lending

In their research paper "The New Deal and the Origins of the Modern American Real Estate Loan Contract in the Building and Loan Industry," Jonathan Rose and Kenneth Snowden discuss financial innovation in the mortgage market in the 1930s. The main focus of the paper is the switch among building and loan societies (B&L) from amortization-by-share-accumulation to amortization-by-direct-reduction. To the typical reader—even one interested in the mortgage market—the topic sounds, to put it gently, quite esoteric. But I think this is an excellent paper and highly relevant to anyone interested in the financial crisis.

The authors systematically debunk a highly popular story about the history of mortgage lending in the United States. Rather than explain the story, I will quote Robert Kuttner, who exposited it in The American Prospect in July 2008:

Before the Roosevelt era, virtually all mortgages were short term loans of five years or less, typically interest-only, with the principal due and payable at the end. If the homeowner could not roll over the loan, he lost the house. As foreclosures skyrocketed, the New Deal invented the modern, long-term, self-amortizing mortgage.

Kuttner is not an economist, but most economists are equally fond of the story. As Nobel Prize winner Franco Modigliani wrote, in a book coauthored with industry expert Frank Fabozzi: "Until [the Depression], mortgages were not fully amortized, as they are now..., but were balloon instruments in which the principal was not amortized, or only partially amortized at maturity, leaving the debtor with the problem of refinancing the balance."

Modigliani is not alone, as many economists who discuss the history of the mortgage market repeat some version of the story.¹ In fact, it appears that the only historical fact that most economists know about the mortgage market is that the federal government invented the amortizing mortgage during the Great Depression.

The myth of the balloon mortgage
What Rose and Snowden document is that B&Ls, which started lending money to borrowers in the 1830s, had never offered balloon products and had always demanded full amortization from their borrowers. B&Ls were the main source of residential finance on the eve of the Depression, so Kuttner and Modigliani's reading of history is clearly missing something important. I will discuss the sources of their misconception below, but first let me discuss what else Rose and Snowden address in the paper.

Rose and Snowden show that a major and economically interesting change did occur during the 1930s, but it wasn't the switch from balloon mortgages to fully amortizing loans. Rather, it was a change in the way that amortization was done. In the 19th century, the typical B&L mortgage, known as a "share-accumulation" contract, was a combination of a perpetual interest-only loan and a forced-saving scheme. When the accumulated forced saving equaled the balance of the loan, the savings were used to pay off the loan. To the borrower, the share-accumulation contract appeared much like a fully amortized mortgage today, with the borrower making a constant monthly payment until the loan was paid off. There was a difference, however, because the forced saving was not explicitly used to pay down the loan, but rather was invested in shares in the B&L.

In general, these shares were valued at par and paid dividends, which were invested in additional shares in the B&L. The loan was considered to be paid off when the borrower's accumulated investment (deposits plus accumulated dividends) reached the original mortgage balance.

Because of the role that dividends played in the amortization schedule, share accumulation did expose the borrower to some risk. If interest rates fell or credit losses were large, dividends might fall and, if credit losses were severe enough, shares might trade below par. The result was that the timing of the payoff of the loan was random, although until the Depression, it was almost always around 11 or 12 years. Starting in the 1870s, some lenders started allowing borrowers to apply the forced saving directly to the balance of the loan to reduce it. B&Ls slowly adopted the new design, called "direct reduction." Rose and Snowden document that right before the Depression, most B&Ls still used the share-accumulation system, but by the end, virtually all used the direct-reduction system.

Switch to direct-reduction contract not the result of government policy
Rose and Snowden argue that the failure of the share-accumulation model during the Depression, and not government policy, led to its demise. As mentioned above, borrowers were exposed to credit risk through their investment in B&L shares. During the Depression many B&Ls failed, undoing a lot of the amortization that borrowers had done. Rose and Snowden show that the rejection of the share-accumulation system across states was highly correlated with the number of local B&L failures during the Depression.

It is important to stress here that the switch from share accumulation to direct reduction is not what either Kuttner or the dozens of economists have in mind when they discuss financial innovation during the Depression. The share-accumulation mortgage was the antithesis of a balloon mortgage. The loan never came due, and even when the borrower lost money on the forced-saving scheme, as they did during the Depression, the borrower could keep the loan current by making the interest and forced-saving payment. The failure of large numbers of B&Ls during the Depression shows that short-term balloon payments weren't the main reason for foreclosures.

Commercial banks were small players in Depression-era loans
The historical basis for the story about the role of the government in the expansion of the fully amortized mortgage has to do with commercial banks, comparatively small players in the mortgage market. Commercial banks limited their offering to short-term, nonamortizing balloon instruments, but the banks accounted for only about 10 percent of mortgage lending in the United States in 1929. Their unwillingness to make long-term, fully amortized loans did not result from a failure of imagination or a lack of understanding of household finances but rather from legislation and regulation that forbade them from making long-term loans secured by real estate.²

The Homeowners Loan Corporation, set up by Congress in 1933 and the Federal Housing Administration, which opened its doors shortly thereafter, did insist on the direct-reduction design for all loans originated under their auspices, but Rose and Snowden argue that this had little effect on the B&Ls, which were rapidly moving in that direction anyway. Ironically, to allow commercial banks to do FHA loans, Congress had to amend the National Banking Act. In other words, the adoption of direct-reduction mortgages by commercial banks did not result from the encouragement of policymakers but rather from the cessation of discouragement.³

"Financial innovation" is incremental, not spontaneous
I am particularly pleased to see this paper, as I have been making this point in speeches,⁴ blog posts and congressional testimony for many years, albeit with much more limited evidence. In the interest of full disclosure, I must confess that I myself had been seduced by the legend of the invention of the amortized mortgage during the Depression, and for many years used it as an example in macroeconomics lectures. It was only when I started researching the history of the mortgage market in 2005 that I looked at the data and found that it wasn't true. Let me say that virtually no economist who repeats the story cites any data or even cites a study that uses data.

Debunking a popular story will get the most attention, but I believe Rose and Snowden have a deeper, more important point to make. That point is that financial innovation does not emerge as a bolt from the blue but typically reflects the accretion of small changes over long periods of time. Rose and Snowden describe that the emergence of the direct reduction mortgage as the dominant contract in the United States in the 1930s was the result of 100 years of incremental innovation. B&Ls, first created in England in the 18th century, came to the United States in the 1830s and were temporary associations in which a group of households would agree to contribute to a pool to provide loans to one another until all the members of the association had homes. Over the next 40 years, B&Ls morphed into permanent institutions but still retained many cooperative features. The direct reduction contract, imported from England like the original B&L idea, appeared in the 1870s in Ohio, which had a particularly innovative B&L industry.

Rose and Snowden argue that the incremental character of financial innovation is similar to that of nonfinancial innovation. They write that:

Rosenberg (1982) provides a useful conceptual framework for explaining the trajectory of innovations in the B&L industry. He emphasizes that the unit of innovation is rarely a single invention; instead, major productivity improvements are driven by the accumulation of incremental changes that follow a path shaped by compatibility with existing practices.

For researchers working on financial innovation, Rose and Snowden's paper illustrates the importance of careful and thorough historical analysis of institutions. Many researchers writing about the crisis that started five years ago make little effort to document institutional facts and instead base theories on speculation and hearsay. In recent years, researchers have argued that until the boom of the 2000s, adjustable rate mortgages, negative amortization, and down payments of less than 20 percent were rare or limited to sophisticated borrowers. Careful analysis of the historical records shows that these loan features were no rarer before the boom than fully amortized loans were before the Depression. Another problematic claim is the popular idea that, during the 2000s, the mortgage market transitioned from the originate-to-hold model, whereby lenders hold mortgages on their books, to the originate-to-distribute model, whereby they sell the loans to investors. Again, the data shows that the originate-to-distribute model was widely used throughout the postwar era and emerged as the dominant model of lending in the U.S. in the 1980s.

In conclusion, it is remarkable that despite the paucity of data and the fact that anyone alive with direct knowledge of pre-Depression era lending is a centenarian, Rose and Snowden know far more about mortgage markets in 1925 than many economists doing research on the mortgage market today do about mortgage markets in 2005.

By Paul Willen, senior economist and policy adviser at the Boston Fed (with Boston Fed economist Christopher Foote and Atlanta Fed economist Kristopher Gerardi)

April 5, 2012 in Mortgage crisis, Mortgage supply | Permalink

November 17, 2011

Taking on the conventional wisdom about fixed rate mortgages

The long-term fixed rate mortgage (FRM) is a central part of the mortgage landscape in America. According to recent data, the FRM accounts for 81 percent of all outstanding mortgages and 85 percent of new originations.¹ Why is it so common? The conventional wisdom is that the FRM is a great product created during the Great Depression to bring some stability to the housing market. Homeowners were defaulting in record numbers, the story goes, because their adjustable rate mortgages (ARMs) adjusted upward and caused payment shocks they could not absorb.

In a Senate Committee on Banking, Housing, and Urban Affairs hearing on October 20, some experts presented testimony that followed this conventional wisdom. As John Fenton, president and CEO, Affinity Federal Credit Union, who testified on behalf of the National Association of Federal Credit Unions, laid out in his written testimony:

Prior to the introduction of the 30-year FRM, U.S. homeowners were at the mercy of adjustable interest rates. After making payments on a loan at a fluctuating rate for a certain period, the borrower would be liable for the repayment of the remainder of the loan (balloon payment). Before the innovation of the 30-year FRM, borrowers could also be subject to the "call in" of the loan, meaning the lender could demand an immediate payment of the full remainder. The 30-year FRM was an innovative measure for the banking industry, with lasting significance that enabled mass home ownership through its predictability.

Of course, this picture of the 30-year FRM as bringing stability to the housing market has profound implications for recent history. Many critics attribute the problems in the mortgage market that started in 2007 to the proliferation of ARMs. According to the narrative, lenders, after 70 years of stability and success with FRMs, started experimenting with ARMs again in the 2000s, exposing borrowers to payment shocks that inevitably led to defaults and the housing crisis. Indeed, one of the other panelists at the hearing, Janis Bowdler, senior policy analyst for the National Council of La Raza, argued in her written testimony that "when the toxic mortgages began to reset and brokers and lenders could no longer maintain their refinance schemes, a recession ushered in record-high foreclosure rates."

I argue, on the other hand—both in my testimony at the hearing and in this post—that the narrative of the fixed rate mortgage as an inherently safe product invented during the Depression that would have mitigated the subprime crisis because it

eliminated payment shocks does not fit the facts.

Parsing the myths around the fixed rate mortgage
First, the FRM has been around far longer than most people realize. Most people attribute the FRM's introduction to the Federal Housing Administration (FHA) in the 1930s.² But it was the building and loan societies (B&Ls), later known as savings and loans, that created them, and they created them a full hundred years earlier. Starting with the very first B&L—the Oxford Provident Building Society in Frankfort, Pennsylvania, in 1831—the FRM accounted for almost every mortgage B&Ls originated. By the time of the Depression, B&Ls were not a niche player in the U.S. housing market. They were, rather, the largest single source of funding for residential mortgages, and the FRM was central to their business model.

As Table 2 of my testimony shows, B&Ls made about 40 percent of new residential mortgage originations in 1929 and 95 percent of those loans were long-term, fixed-rate, fully amortized mortgages. Importantly, B&Ls suffered mightily during the Depression, so the facts simply do not support the idea that the widespread use of FRMs would have prevented the housing crisis of the 1930s.

Source: Grebler, Blank and Winnick (1956)
Note: Market percentage is dollar-weighted. Building and loan societies were the main source of funds for residential mortgages and almost exclusively used long-term, fixed-rate, fully amortizing instruments.

To be sure, at 15–20 years, the terms on the FRMs the FHA insured were somewhat longer than those of pre-Depression FRMs, which typically had 10–15 year maturities.³ The 30-year FRM did not emerge into widespread use until later. It must be stressed that none of the arguments that Fenton made hinge on the length of the contract. Furthermore, the argument that Bowdler made in her testimony—that by delaying amortization, a 30-year maturity lowers the monthly payment as compared to a loan with shorter maturity—applies as much to ARMs as it does to FRMs.

But even though the ARMs may not have caused the Depression, FRM supporters might ask, didn't the payment shocks from the exotic ARMs cause the most recent crisis? Again, the data say no. Table 1 of my Senate testimony shows that payment shocks actually played little role in the crisis.

Source: Lender Processing Services and author's calculations.
Note: Sample is all first-lien mortgages originated after 2005 on which lenders initiated foreclosure proceedings from 2007 to 2010.

Of the large sample of borrowers who lost their homes, only 12 percent had a payment amount at the time they defaulted that exceeded the amount of the first scheduled monthly payment on the loan. The reason there were so few is that almost 60 percent of the borrowers who lost their homes had, in fact, FRMs. But even the defaulters who did have ARMs typically had either the same or a lower payment amount due to policy-related cuts in short-term interest rates.

To be absolutely clear here, my discussion so far focuses entirely on the question of whether the design of the FRM is inherently safe and eliminates a major cause of foreclosures. The data say it does not, but that does not necessarily mean that the FRM does not have benefits. As I discussed in my testimony, all else being equal, ARMs do default more than FRMs, but since defaults occur even when the payments stay the same or fall, the higher rate is most likely connected to the type of borrower who chooses an ARM, not to the design of the mortgage itself.

The difficulty of measuring the systemic value of fixed rate mortgages
One common response to my claim that the payment shocks from ARMs did not cause the crisis is that ARMs caused the bubble and thus indirectly caused the foreclosure crisis. However, it is important to understand that this argument, which suggests that the FRM has some systemic benefit, is fundamentally different from the argument that the FRM is inherently safe. This difference is as significant as that between arguing that airbags reduce fatalities by preventing traumatic injuries and arguing that they somehow prevent car accidents.

Measuring the systemic contribution of the FRM is exceedingly difficult because the use of different mortgage products is endogenous. Theory predicts that home buyers in places where house price appreciation is high would try to get the biggest mortgage possible, conditional on their income, something that an ARM typically facilitates. When the yield-curve has a positive slope (in most cases) and short-term interest rates are lower than long-term interest rates, ARMs loans offer lower initial payments compared to FRMs. Thus, it is very difficult to disentangle the causal effect of the housing boom on mortgage choice from the effect of mortgage choice on the housing boom.

In addition, there is evidence from overseas that suggests that the FRM is not essential for price stability. As Anthony B. Sanders, professor of finance at the George Mason School of Management, points out in his written testimony, FRMs are rare outside the United States. A theory of the stabilizing properties of FRMs would have to explain why Canadian borrowers emerged more or less unscathed from the global property bubble of the 2000s, despite almost exclusively using ARMs.

By Paul Willen, senior economist and policy adviser at the Boston Fed (with Boston Fed economist Christopher Foote and Atlanta Fed economist Kristopher Gerardi)

November 17, 2011 in Homeownership, Housing prices, Mortgage crisis, Mortgage default, Subprime mortgages | Permalink

October 04, 2011

The uncertain case against mortgage securitization

The opinions, analysis, and conclusions set forth are those of the authors and do not indicate concurrence by members of the Board of Governors of the Federal Reserve System or by other members of the staff.

Did mortgage securitization cause the mortgage crisis? One popular story goes like this: banks that originated mortgage loans and then sold them to securitizers didn't care whether the loans would be repaid. After all, since they sold the loans, they weren't on the hook for the defaults. Without any "skin in the game," those banks felt free to make worse and worse loans until...kaboom! The story is an appealing one and, since the beginning of the crisis, it has gained popularity among academics, journalists, and policymakers. It has even influenced financial reform. The only problem? The story might be wrong.

In this post we report on the latest round in an ongoing academic debate over this issue. We recently released two papers, available here and here, in which we argue that the evidence against securitization that many have found most damning has in fact been misinterpreted. Rather than being a settled issue, we believe securitization's role in the crisis remains an open and pressing question.

The question is an empirical one
Before we dive into the weeds, let us point out why the logic of the above story need not hold. The problem posed by securitization—that selling risk leads to excessive risk-taking—is not new. It is an example of the age-old incentive problem of moral hazard. Economists usually believe that moral hazard causes otherwise-profitable trade to not occur, or that it leads to the development of monitoring and incentive mechanisms to overcome the problem.

In the case of mortgage securitization, such mechanisms had been in place, and a high level of trade had been achieved, for a long time. Mortgage securitization was not invented in 2004. To the contrary, it has been a feature of the housing finance landscape for decades, without apparent incident. As far back as 1993, nearly two-thirds (65.3 percent) of mortgage volume was securitized, about the same fraction as was securitized in

2006 (67.6 percent) on the eve of the crisis. In order to address potential moral hazard, securitizers such as Fannie Mae and Freddie Mac (the government sponsored enterprises, or GSEs) long ago instituted regular audits, "putback" clauses forcing lenders to repurchase nonperforming or improperly originated loans, and other procedures designed to force banks to lend responsibly. Were such mechanisms successful? Perhaps, perhaps not. It is an empirical question, and so our understanding will rest heavily on the evidence.

The case against securitization
Benjamin Keys, Tanmoy Mukherjee, Amit Seru, and Vikrant Vig released an empirical paper in 2008 (revised in 2010) titled "Did Securitization Lead to Lax Screening? Evidence from Subprime Loans" (henceforth, KMSV) that pointed the finger squarely at securitization. The paper won several awards and, when it was published in the Quarterly Journal of Economics in 2010, it became that journal's most-cited paper that year by more than a factor of two. In other words, it was a very well-received and influential paper.

And for good reason. KMSV employs a clever method to try to answer the question of securitization's role in the crisis. Banks often rely on borrowers' credit (FICO) scores to make lending decisions, using particular score thresholds to make determinations. Below 620, for example, it is hard to get a loan. KMSV argues that securitizers also use FICO score thresholds when deciding which loans to buy from banks. Loan applicants just to the left of the threshold (FICO of 619) are very similar to those just to the right (FICO of 621), but they differ in the chance that their bank will be able to sell their loan to securitizers. Will the bank treat them differently as a result? This seems to have the makings of an excellent natural experiment.

Figures 1 and 2, taken from KMSV, illustrate the heart of their findings. Using a data set of only private-label securitized loans, the top panel plots the number of loans at each FICO score. There is a large jump at 620, which, KMSV argues, is evidence that it was easier to securitize loans above 620. The bottom panel shows default rates for each FICO score. Though we would expect default to smoothly decrease as FICO increases, there is a significant jump up in default at exactly the same 620 threshold. It appears that because securitization is easier to the right of the 620 cutoff, banks made worse loans. This seems prima facie evidence in favor of the theory that mortgage securitization led to moral hazard and bad loans.

Reexamining the evidence
But what is really going on here? In September 2009, the Boston Fed published a paper we wrote (original version here, updated version here) arguing for a very different interpretation of this evidence. In fact, we argue that this evidence actually supports the opposing hypothesis that securitizers were to some extent able to regulate originators' lending practices.

The data set used in KMSV only tells part of the story because it contains only privately securitized loans. We see a jump in the number of these loans at 620, but we know nothing about what is happening to the number of nonsecuritized loans at this cutoff. The relevant measure of ease of securitization is not the number of securitized loans, but the chance that a given loan is securitized—in other words, the securitization rate.

We used a different data set that includes both securitized and nonsecuritized loans, allowing us to calculate the securitization rate. Figures 3 and 4 come from the latest version of our paper.

Like KMSV, we find a clear jump up in the default rate at 620, as shown in the bottom panel. However, the chance a loan is securitized actually goes down slightly at 620, as shown in the top panel. How can this be? It turns out that above the 620 cutoff banks make more of all loans, securitized and nonsecuritized alike. This general increase in the lending rate drives the increase in the number of securitized loans that was found in KMSV, even though the securitization rate itself does not increase. With no increase in the probability of securitization, it is hard to argue that the jump in defaults at 620 is occurring because easier securitization motivates banks to lend more freely.

The real story behind the jumps in default
So why are banks changing their behavior around certain FICO cutoffs? To answer this question, we must go back to the mid-1990s and the introduction of FICO into mortgage underwriting. In 1995, Freddie Mac began to require mortgage lenders to use FICO scores and, in doing so, established a set of FICO tiers that persists to this day. Freddie directed lenders to give greater scrutiny to loan applicants with scores in the lower tiers. The threshold separating worse-quality applicants from better applicants was 620. The next threshold was 660. Fannie Mae followed suit with similar directives, and these rules of thumb quickly spread throughout the mortgage market, in part aided by their inclusion in automated underwriting software.

Importantly, the GSEs did not establish these FICO cutoffs as rules about what loans they would or would not securitize—they continued to securitize loans on either side of the thresholds, as before. These cutoffs were recommendations to lenders about how to improve underwriting quality by focusing their energy on vetting the most risky applicants, and they became de facto industry standards for underwriting all loans. Far from being evidence that securitization led to bad loans, the cutoffs are evidence of the success securitizers like Fannie and Freddie have had in directing lenders how to lend.

With this in mind, the data begin to make sense. Lenders, following the industry standard originally promulgated by the GSEs, take greater care extending credit to borrowers below 620 (and 660). They scrutinize applicants with scores below 620 more carefully and are less likely to approve them than applicants above 620, resulting in a jump-up in the total number of loans at the threshold. However, because of the greater scrutiny, the loans that are made below 620 are of higher average quality than the loans that are made above 620. This causes the jump-up in the default rate at the threshold.

Figures 5 and 6 show that this pattern also exists among loans that are kept in portfolio and never securitized. The change in lending standards causes these loans, as well as securitized loans, to jump in number and drop in quality at 620 (and 660). However, as figure 3 shows, the securitization rate doesn't change because securitized and nonsecuritized loans increase proportionately. The FICO cutoffs are used by lenders because they are general industry standards, not because the securitization rate changes. This means the cutoffs cannot provide evidence that securitization led to loose lending.

KMSV's rejoinder
But the debate does not end there. In April 2010, Keys, Mukherjee, Seru, and Vig released a working paper (KMSV2), currently forthcoming in the Review of Financial Studies, that responded to the issues we raised. According to the paper, the mortgage market is segmented into two completely separate markets: 1) a "prime" market, in which only the GSEs buy loans, and 2) a "subprime" market, in which only private-label securitizers buy loans. KMSV2 argues that only private-label securitizers follow the 620 rule and, by pooling these two types of loans in our analysis, we obscured the jump in the securitization rate in that market.

The latest round in the debate
We went back to the drawing board to investigate these claims. We detail our findings in a new paper, available here. In the paper, we demonstrate that the pattern of jumps in default—without jumps in securitization—is not simply an artifact of pooling, but rather exists for many subsamples that do not pool GSE and private-label securitized loans. For example, we find the pattern among jumbo loans (by law an exclusively private-label market), among loans bought by the GSEs, and among loans originated in the period 2008–9 after the private-label market shut down. Furthermore, as figure 7 shows, the private-label market boomed in 2004 and disappeared around 2008, while the size of the jump in the number of loans at 620 continued to grow through 2010, demonstrating that use of the threshold was not tied to the private market.

What's more, KMSV's response fails to address the fundamental problem we identified with their research design: following the mandate of the GSEs, lenders independently use a 620 FICO rule of thumb in screening borrowers. Even if some subset of securitizers had used 620 as a securitization cutoff, one would not be able to tell what part of the jump in defaults is caused by an increase in securitization, and what part is simply due to the lender rule of thumb. Consequently, the jump in defaults at 620 cannot tell us whether securitization led to a moral hazard problem in screening.

To put this in more technical jargon, KMSV use the 620 cutoff as an instrument for securitization to investigate the effect of securitization on lender screening. But the guidance from the GSEs that caused lenders to adopt the 620 rule of thumb in the first place means that the exclusion restriction for the instrument is not satisfied—the 620 cutoff affects lender screening through a channel other than any change in securitization.

We also found that the GSE and private-label markets were not truly separate. In addition to qualitative sources describing them as actively competing for subprime loans, we find that 18 percent of the loans in our sample were at one time owned by a GSE and at another time owned by a private-label securitizer—a lower bound on the fraction of loans at risk of being sold to both. Because the markets were not separate, the data must be pooled.

Conclusion
Our findings, of course, do not settle the question of whether securitization caused the crisis. Rather, they show that the cutoff rule evidence does not resolve the question in the affirmative but instead points a bit in the opposite direction. Credit score cutoffs demonstrate that large securitizers like Fannie Mae and Freddie Mac were able to successfully impose their desired underwriting standards on banks. We hope our work causes researchers and policymakers to reevaluate their views on mortgage securitization and leads eventually to a conclusive answer.

By Ryan Bubb, assistant professor at the New York University School of Law, and Alex Kaufman, economist with the Board of Governors of the Federal Reserve System

October 4, 2011 in Government-sponsored enterprises, GSE, Mortgage crisis, Mortgage default | Permalink

February 14, 2011

New study claims to solve the econometric problem of the link between foreclosure and house prices

Many policymakers are now concerned about how the next wave of foreclosures will affect the housing market. Analysts have cited a large "shadow inventory" of homes, referring to the mass of delinquent mortgages that have yet to make their way through the foreclosure process. When these foreclosures occur, they could raise the number of homes for sale and put downward pressure on house prices. They could also impose negative externalities to other homes in the same neighborhoods, sending house prices even lower. (We recently blogged about the so-called contagion effects of foreclosures on surrounding properties.)

These potential effects seem intuitive, but measuring them is not easy. The main problem is what economists call "simultaneity." Foreclosures lead to an increased supply of homes for sale, which can lower prices—but lower prices also increase the probability that borrowers have negative equity, which can lead to foreclosure. Thus, there is simultaneous causality: foreclosures can reduce prices, and lower prices can cause the negative equity that leads to foreclosure. As a result, simply showing a correlation between foreclosures and falling house prices is not sufficient to measure—or even establish—a causal effect of foreclosures on prices.

A new study by Atif Mian, Amir Sufi, and Francesco Trebbi claims to have solved this econometric problem. Their paper reports a substantial causal impact of foreclosures on not only house prices, but also residential investment and automobile purchases. However, the authors make a major data error that, in our opinion, invalidates a large part of their analysis. In addition, there are important conceptual issues that raise deep questions about their identification strategy, even if it is possible to correct the data error.

Can simultaneity be solved by classifying states as judicial, nonjudicial?
The authors attack the simultaneity problem with a classic method: they use differences in state laws as an instrumental variable. The essential idea is that states vary randomly as to whether they are judicial or nonjudicial. Judicial states are typically characterized by longer foreclosure durations, since the mortgage servicer must navigate through the legal system to get court approval, which usually entails a significant amount of time (see Pennington-Cross 2010 for a nice discussion). If the judicial/nonjudicial classification is random with respect to the health of state-level housing markets, then state laws will generate random variation in the number of foreclosures across states. Under these assumptions, using the classification as an instrument yields consistent estimates of the effect of foreclosures on house prices.

Of course, the classification of states into judicial and nonjudicial groups may not be random. It turns out that there is a strong regional component to this classification. Figure 3 in the Mian-Sufi-Trebbi paper shows that states in the Northeast and Midwest tend to be judicial, while the states in the South and West are mostly nonjudicial. It's no secret that problems in the U.S. housing market also have a strong regional character, with housing markets in Arizona, California, Florida, and Nevada (all located in the South and West) in particularly bad shape.

One way to check for the possibility of confounding effects across the two classifications of states is to compare their observable variables. The authors do this, and then claim that "states with a judicial foreclosure requirement are remarkably similar to other states in all attributes of interest except the propensity to foreclose" (p.3). But eyeballing their Figure 3 should give a reader pause. Nevada and Arizona, which are nonjudicial states, include the number one and two MSAs for new construction and for house price appreciation in the two years prior to the collapse of the mortgage market.¹

Cross-state differences challenge regressions
Regional patterns in both state laws and housing markets cause problems for the authors' identification strategy. If we find that foreclosures tend to be more frequent in the nonjudicial states, this might be because foreclosing on delinquent homeowners is easier in those states, as the authors' identification strategy assumes. But high foreclosure rates in the nonjudicial states could also stem from negative shocks to housing demand in the parts of the country where the nonjudicial states happen to be located. Consequently, if we find that housing prices are lower and foreclosure rates are higher in nonjudicial states, then we can't be sure what's causing what. The high foreclosure rates could be causing the falling prices, as the authors' claim. But it could also be true that low regional demand and falling prices in the South and West are causing the high foreclosure rate—the very possibility that the authors were hoping to rule out.

The authors recognize that unobserved cross-state differences make the state-level experimental approach problematic so they propose an alternative set of regressions that are not subject to such criticism. In addition to estimating the first set of regressions—which, in the manner described above, uses all the states in the country—they estimate a second set that includes only ZIP codes adjacent to borders between judicial and nonjudicial states. The idea is that while unobserved heterogeneity across states could potentially invalidate the first set of regressions, this heterogeneity is less likely to be a problem in the second. In other words, the housing market in Arizona may differ markedly from the housing market in Maine and not just because Arizona is a nonjudicial state while Maine is judicial.

However, the ZIP codes just north of the Massachusetts-Rhode Island border are likely to have similar housing markets to the ZIP codes that are just south of this border. So, if the border ZIP codes in Massachusetts, which the authors label a judicial state, are experiencing higher foreclosures than the border ZIP codes in Rhode Island, a nonjudicial state, then differences in the two state's laws—and not unobserved differences in demand— are probably the reason why. And if the state laws are generating random variation in foreclosures, then the authors claim that this variation can be used to get a clean estimate of the causal effect of foreclosures on housing prices.

Problems in the data: Massachusetts, Wisconsin are misclassified
The authors find similar results in both sets of regressions. This similarity gives them some confidence that they have truly pinned down the direct effect of foreclosures on other economic outcomes. But here's where the data error comes in: the authors make a mistake in classifying at least two states as judicial or nonjudicial, which has major implications for their results. Specifically, they misclassify Massachusetts as judicial and Wisconsin as nonjudicial.² Most sources, including the National Consumer Law Center (NCLC), reverse those classifications.

(For readers interested in the gory details, we show that for Massachusetts, there is no question that the NCLC is right.)

While the misclassification of two out of 50 states may seem minor, it turns out that Wisconsin and Massachusetts dominate the samples for the "border discontinuity" regressions. As the table shows, depending on the sample, using the alternative classification from the NCLC invalidates between 58 and 78 percent of the ZIP codes the authors use. Consider the sample that uses ZIP codes in 5-mile bands around state borders. Because it uses homes closest to state borders, this sample is least susceptible to unobservable differences between geographic areas, although we argue below that even 5-mile bands are inadequate to obtain clean identification. In this sample, classifying Massachusetts—correctly—as nonjudicial eliminates 70 percent of the comparisons.³

One response to this criticism would be to reclassify the states correctly and then reestimate both sets of regressions. The problem for the border regressions is that Massachusetts's and Wisconsin's borders with judicial and nonjudicial states respectively are sparsely populated and do not meet the authors' criteria for inclusion in the border sample. For example, farms and weekend homes comprise most of the properties in border ZIP codes between western Massachusetts and southern Vermont.

Misclassification proves detrimental to the identification strategy
As the authors have written the paper, they claim to find big differences in ZIP-code-level outcomes based on the judicial/nonjudicial classification. However, they use regressions with the wrong classification for most of the comparisons. If the identification strategy worked as the authors had hoped, their regressions would have implied that there are no important differences on either side of most judicial/nonjudicial borders because these borders in fact separated states with similar laws. However, because the regressions instead reported significant differences, some other important sources of heterogeneity across the state lines must exist—and if the authors can't control for heterogeneity across, say, the Massachusetts–Rhode Island border, the reader can't be expected to have confidence in their ability to control for unobserved differences between Massachusetts and Nevada.

Another way of putting this is that the authors have inadvertently performed and failed a falsification, or placebo, test on their data. They estimated their regressions on a sample of borders that are, for the most part, not characterized by differences in foreclosure laws, at least in terms of the judicial/nonjudicial classification, and found large effects where they should have found none. In our opinion, this is very strong evidence against their claim that judicial/nonjudicial foreclosure laws are a valid instrument for foreclosure rates. Even if the authors correctly reclassify the states and reestimate the IV regressions for the border sample, this failed falsification test still sheds doubt on the entire empirical strategy.

In addition to this primary critique, we also found some other important drawbacks in the analysis. For readers that are interested in learning more about these issues, here is a detailed discussion.

We remain unconvinced by the authors' claim that exogenous increases in foreclosures substantially reduce housing prices. This issue, of the link between foreclosure and house prices, is of first-order importance to policymakers, who struggle not only with the foreclosure problem itself but also with the potential effects of foreclosures on the economic recovery. However, the authors' research strategy is unlikely to be helpful in addressing these problems given the deep conceptual issues it did not deal with and the poor data on which it is based.

Kris Gerardi
Research economist and assistant policy adviser at the Federal Reserve Bank of Atlanta

 

Paul Willen
Research economist and policy adviser at the Boston Fed

February 14, 2011 in Foreclosure contagion, Foreclosure laws, Housing prices, Mortgage crisis | Permalink

December 21, 2010

Revisiting real estate revisionism: Concessionary mortgage modifications during the Depression

During the current foreclosure crisis, lenders have seemed far more willing to foreclose on delinquent borrowers rather than offer them loan modifications. Some commentators have argued that this was not always the case. They claim that loan modifications are infrequent today because so many loans have been securitized, and thus are not owned by any one person or firm. They also say that the modern securitization process reduces loan modifications because securitization separates the entity that makes the modification decision—that is, the mortgage servicer—from the entities that gain the most if a foreclosure is avoided—that is, the mortgage investors. As we pointed out in our last post, Yale economist John Geanakoplos and Boston University law professor Susan Koniak argued in a March 2008 New York Times op-ed that the uncomplicated relationship between banks and borrowers in the good old days allowed the banks to work out modifications when their borrowers ran into trouble.

The Congressional Oversight Panel, created by Congress in October 2008 to "review the current state of financial markets and the regulatory system," expressed a similar belief in a March 2009 report on the state of the U.S. housing market:

For decades, lenders in this circumstance [that is, with troubled borrowers] could negotiate with can-pay borrowers to maximize the value of the loan for the lender (100 percent of the market value) and for the homeowner (a sustainable mortgage that lets the family stay in the home). Because the lender held the mortgage and bore all the loss if the family couldn't pay, it had every incentive to work something out if a repayment was possible.

Even in the good old days, lenders reluctant to restructure
Such claims, however, have usually been made with little or no reference to supporting research. Fortunately, a recent paper by Andra Ghent of Baruch College exploits a new data set to shed considerable light on this topic. Her findings argue against the idea that lender reluctance to modify is a recent phenomenon.

Ghent uses a data set from the National Bureau of Economic Research (NBER) that covers mortgages from 1920 to 1939, a period that encompasses the massive housing turmoil of the Great Depression. The data set consists of "mortgage experience cards," which the NBER collected in the 1940s from mortgage lenders in the New York metropolitan area. On the cards are the answers to short questionnaires about the characteristics of individual mortgage loans (see page 5 of Ghent's paper for an example). The cards also contain explicit information about any loan modifications, including the date of the modification and whether it was principal reduction, interest-rate reduction, change to the amortization schedule, or something else. The cards include loans from three types of mortgage lenders: life insurance companies, savings and loans, and commercial banks.¹

Ghent finds few modifications in these cards, and these few were not particularly generous. Using a fairly conservative definition of what constitutes a concessionary modification, Ghent finds that approximately 5 percent of loans originated between 1920 and 1939 were modified, while 14 percent were terminated by foreclosure or a deed-in-lieu of foreclosure (the latter occurs when the owner surrenders the house to the lender without going through the foreclosure process). Of the loans that received a concessionary modification, about 40 percent received an interest rate reduction, which Ghent defines as an interest rate cut of at least 25 basis points (relative to origination) resulting in a new rate that is at least two standard deviations below the average interest rate on newly originated loans. The average rate reduction was only 78 basis points below the prevailing interest rate of new originations, suggesting that interest rate cuts were not particularly generous.

Another 40 percent of the modified loans received reductions in their amortization schedules, which would have likely decreased the required mortgage payments. However, Ghent points out that most of these extended amortizations occurred before 1930. In the period 1930–32, when house prices fell and unemployment rose the most, this type of modification was rare.

Principal balance reductions—and increases
Ghent also finds that less than 2 percent of all loans received principal balance increases. She argues that such increases may correspond to instances of forbearance. Forbearance occurs when a lender reduces the required mortgage payment for a short period. At the end of the period, the lender adds the arrears back to the loan balance. We have a minor quibble on this point: today, forbearance is not considered a permanent concessionary modification when the lender does not have to write down any debt.

What about principal reductions? Perhaps the most surprising finding is that the data set shows no instances of principal reduction in the New York City metropolitan area and only a handful of instances in a broader sample that includes the entire states of Connecticut, New Jersey, and New York over a similar period. To us, this low number of principal reductions is compelling evidence that even Depression-era lenders were averse to renegotiating with troubled borrowers, just as lenders are today.

Balloon mortgages sank some borrowers
Another interesting finding concerns the refinancing decisions of lenders. Short-term balloon mortgages were more common in the 1920s and 1930s than they are today, and various scholars have linked the high foreclosure rate of the Depression to the unwillingness of lenders to refinance these mortgages when they came due. In fact, lender reluctance to refinance maturing mortgages is often used to explain the existence of the Home Owners' Loan Corporation (HOLC), a government organization set up in the early 1930s to refinance troubled mortgages. Ghent revisits this hypothesis with her data, measuring the frequency at which short-term balloon mortgages ended in foreclosure. She finds that balloon mortgages that were about to expire did indeed experience increased rates of foreclosure (see Ghent's table 4). However, this relationship only exists during the years when HOLC was purchasing a great many loans (1933–35). In other years, balloon mortgages were no more likely to end in foreclosure than other loans.

To us, this finding suggests a "HOLC effect." While HOLC was actively buying loans, private lenders may have refused to roll them over so that the borrowers would qualify for a HOLC refinance. If they did, then the lenders would be paid close to par for the loans by the government (see our previous post about the generosity of the HOLC program). In particular, the lenders received what were effectively government bonds in return for their mortgage. While these bonds carried lower interest rates, they carried vastly less credit risk as well.

To explain her findings, Ghent points to information problems between borrowers and lenders. In particular, lenders may not have known which borrowers were likely to truly need modifications, nor did they know with certainty which borrowers were likely to re-default if a modification were offered. Note that these information problems must have been quite severe. The national unemployment rate hit 10.8 percent in November 1930 and stayed in double digits for more than a decade. In this environment, a borrower asking for a modification was quite likely to really need one. The fact that lenders made few modifications suggests some strong intrinsic hurdles to renegotiation when information between borrowers and lenders is less than perfect.

Old problems, new analysis
The crucial policy question is what the Depression-era reluctance of lenders to renegotiate teaches us about today's foreclosure crisis. Ghent surmises that the information problems are less of an issue in the current environment, but we disagree. Even with better data and screening technology, today's lenders face significant information problems when deciding on modifications. Moreover, Ghent's paper is also informative on the role of securitization in reducing modifications. Even when individual lenders owned entire loans, modifications were rare.

All told, Ghent's paper is full of solid analysis on a topical subject. And while she doesn't quite go this far, we believe that her findings not only confirm the importance of information problems, but also they may bury the notion that securitization is the primary obstacle to renegotiation in the current foreclosure crisis.

Kris Gerardi
Research economist and assistant policy adviser at the Federal Reserve Bank of Atlanta

 

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

 

December 21, 2010 in Loan modifications, Mortgage crisis, Mortgage default | Permalink

August 10, 2010

Part 2: A closer look at Michael Lewis's "The Big Short"

In the first part of our discussion of The Big Short, we argued that the bet against subprime mortgages that the book title refers to was not a sure thing, as the book's protagonists claimed, but a highly risky bet that just happened to turn out well. In this post, we focus on the logic of the "sure thing" claim, which is that the subprime bears were exploiting the ignorance of the subprime bulls. The idea that subprime bulls were ignorant is central to the thesis of the book, because it explains both why investors made such huge errors and why it was possible for the subprime bears to exploit, with little risk, the collapse of the mortgage market.

Lewis argues that the ignorance of the subprime bulls resulted from a combination of laziness and obfuscation by issuers of the securities they were buying. We argue, however, that the evidence, including some in the book itself, shows this claim to be patently incorrect. Issuers provided staggering amounts of information about mortgage securities and there was a whole industry of analysts on Wall Street who pored over that data and published literally thousands of reports.

The question, then, is this: if there was so much research going on and so much information available, why did so few investors get it right? The answer comes back to the same issue we discussed in the previous post: house prices. Investors bought subprime bonds not because they were too stupid or lazy to do research, or because issuers prevented them from getting relevant information, or because the securities were so complex that they couldn't figure out that a subprime borrower was a risky proposition. Subprime bulls bought the bonds because careful research based on vast amounts of loan-level data using state-of-the-art models (which, as we will show, was and still is largely correct) showed that if house prices continued to behave as they had for the previous ten years, the bonds would perform well. The research also showed that if house prices collapsed, investors would lose big, but, after ten years of solid appreciation in house prices, researchers viewed a big fall as unlikely.

Lewis's portrayal of those who lost money on subprime as bumbling and ignorant and those who made money as prescient is wrong and it is not a mere detail, it is the heart of the book. Lewis writes:

…a smaller number of people—more than ten, fewer than twenty—made a straightforward bet against the entire multi-trillion-dollar subprime mortgage market and, by extension, the global financial system. In and of itself it was a remarkable fact: The catastrophe was foreseeable, yet only a handful noticed. (p. 105)

What we argue here is that to "foresee" the crisis, one had to explore something to which the subprime bears paid little attention: the evolution of house prices. Whether the fall in house prices that ultimately caused all subprime bonds to default was itself foreseeable is a question that we will return to in subsequent posts, but even the most outspoken subprime bear, Michael Burry, would have a hard time explaining how the main focus of his research—"reading dozens of prospectuses [of subprime mortgage bonds] and scour[ing] hundreds more"—gave any special insight into the dynamics of house prices.

Was the market opaque?
Lewis argues that the issuers of mortgage-related securities "had a special talent for obscuring what needed to be clarified." But to outsiders, specialist terminology often sounds deliberately obscure: why do doctors say the results are "negative" when an X-ray shows good news? The typical buyer in the mortgage marketplace was a specialist, and those who weren't specialists were spending hundreds of millions of dollars and could afford to, and usually did, hire experts to explain to them what was going on.

In the end, Lewis's examples mostly demonstrate his ignorance of the market, not anybody's deliberate attempts to deceive investors. For example, he claims that "a bond backed entirely by subprime mortgages, for example, wasn't called a subprime mortgage bond. It was called an ABS, or asset-backed security" (p. 127). As Lewis himself says, ABS is a class of securities that included "bonds backed by credit card loans, auto loans and other, wackier collateral…" (p. 95n). As such, these securities historically had been characterized by default rates that were literally orders of magnitude bigger than those on mortgage-backed securities (MBS), which were composed of prime residential mortgages. Thus, to the typical buyer of securities, the ABS designation did precisely the opposite of what Lewis claims—it actually drew attention to the credit risk inherent in subprime mortgages.

Another major error along these lines concerns Lewis's discussion of the Alt-A market. Lewis writes that:

Alt-A was just what they called crappy mortgage loans for which they hadn't even bothered to acquire the proper documents—to verify the borrower's income, say. "A" was the designation attached to the most creditworthy borrowers; Alt-A, which stood for "Alternative A-paper," meant an alternative to the most creditworthy, which of course sounds a lot more fishy once it is put that way. (p. 127)

This is just wrong. Alt-A loans were made to borrowers with impeccable credit who, for various and perfectly valid reasons (self-employment being the most common one) could not document income in the standard way. If a borrower had several years' worth of income in the bank, no other debt, and a credit score that indicated that he or she had not missed a payment on anything for years, lenders would rationally overlook his or her inability to provide a letter from an employer documenting income. By calling the loans Alt-A and not A, the lender drew attention to the fact that the loan did not have traditional documentation. The historical credit performance of Alt-A loans was very, very close to that of prime loans and vastly better than that of subprime, so to call Alt-A loans subprime would be completely misleading. As far as investors were concerned, the main difference between Alt-A and A paper involved prepayment risk: Alt-A loans prepaid less and thus were more valuable to investors.¹

Were the "shorters" the only people to do serious research on subprime mortgages?
Another central claim of the book is that Wall Street analysts did not seriously research the market. The following passage suggests that until March of 2007, researchers on Wall Street did not pay attention to the details of the pools of loans they were trading.

On March 19 his salesman at Citigroup sent [Michael Burry], for the first time, serious analysis on a pool of mortgages. The mortgages were not subprime but Alt-A.* Still, the guy was trying to explain how much of the pool consisted of interest-only loans, what percentage was owner-occupied, and so on—the way a person might do who actually was thinking about the creditworthiness of the borrowers. "When I was analyzing these back in 2005," Burry wrote in an e-mail, sounding like Stanley watching tourists march through the jungle on a path he had himself hacked, "there was nothing even remotely close to this sort of analysis coming out of brokerage houses. I glommed onto ‘silent seconds'* as an indicator of a stretched buyer and made it a high-value criterion in my selection process, but at the time no one trading derivatives had any idea what I was talking about and no one thought they mattered." (p. 194)

In fact, researchers had done exactly that sort of detailed analysis for years. This paper provides a detailed discussion of the state of mortgage research in the years 2003–2006, reviewing a relatively small sample of the contemporary literature, which still amounts to dozens of reports. Burry's claim to be the only person doing standard credit analysis–"In doing so, [Burry] likely also became the only investor to do the sort of old-fashioned bank credit analysis on the home loans that should have been done before they were made (p. 50)"—is fatuous.

In fact, some of the quotes in the book suggest that the subprime bears, and not the bulls, were the ones who had little understanding of the details. Lewis writes:

As early as 2004, if you looked at the numbers, you could clearly see the decline in lending standards. In Burry's view, standards had not just fallen but hit bottom. The bottom even had a name: the interest-only negative-amortizing adjustable-rate subprime mortgage. (p. 28)

A loan cannot simultaneously be "interest only" and "negative amortizing." Interest only means you pay only the interest every month and "negative amortizing" means you pay less than the interest so that the principal balance of the loan actually increases over time. Unlike the distinction between ABS and MBS, the distinction between these two terms is easy to understand, even for a nonspecialist, and a self-proclaimed expert like Michael Burry should have understood it. But a careful student of prospectuses like Michael Burry should also have a hard time digging up a subprime negatively amortizing loan. "Option-ARMs," largely the only loans that allowed negative amortization in the United States, rarely ever appeared in subprime deals; they were generally considered Alt-A or prime and most were held in the portfolios of banks and never securitized.

In fact, Lewis erodes his own case by providing compelling evidence that other investors were trying to exploit subtle differences between pools and must have done exactly the sort of detailed loan-level analysis that Burry claims was not going on:

A smaller group used credit default swaps to make what often turned out to be spectacularly disastrous gambles on the relative value of subprime mortgage bonds—buying one subprime mortgage bond while simultaneously selling another. They would bet, for instance, that bonds with large numbers of loans made in California would underperform bonds with very little of California in them. Or that the upper triple-A-rated floor of some subprime mortgage bond would outperform the lower, triple-B-rated, floor. Or that bonds issued by Lehman Brothers or Goldman Sachs (both notorious for packaging America's worst home loans) would underperform bonds packaged by J.P. Morgan or Wells Fargo (which actually seemed to care a bit about which loans it packaged into bonds). (p. 105)

The fact that investors who had done such detailed research made "spectacularly disastrous gambles" refutes the idea that the success of the subprime bulls reflected their willingness to do research.

Why did the subprime bulls believe in the market?
If so many investors did so much research, why didn't they bet against subprime? Lewis hears and reports the right answer over and over again. They didn't believe that house prices were going to fall. On page 89, he quotes one participant: "For the bonds to default, he now said, U.S. house prices had to fall, and Joe Cassano didn't believe house prices could ever fall everywhere in the country at once."

On page 157, he quotes another:

"We asked everyone the same two questions," said Vinny. "What is your assumption about home prices, and what is your assumption about loan losses." Both rating agencies said they expected home prices to rise and loan losses to be around 5 percent—which, if true, meant that even the lowest-rated, triple-B, subprime mortgage bonds crafted from them were money-good.

To me, the most compelling piece of evidence about what the subprime bulls got wrong, the smoking gun that makes sense of what happened, is the following table from a Lehman Brothers report from August of 2005 titled "HEL Bond Profile across HPA Scenarios."

Scenario # Name Scenario Loss Probability 1 Aggressive 11% HPA over the life of the pool 1.4% 15% 2 8% HPA for the life of the pool 3.2% 15% 3 Base HPA slows to 5% by end-2005 5.6% 50% 4 Pessimistic 0% HPA for the next 3 years, 5% thereafter 11.1% 15% 5 Meltdown -5% for the next 3 years, 5% thereafter 17.1% 5%

Source: Lehman Brothers 2005

Lehman Brothers analysts used a default model to predict losses for deals made up of mortgages originated in the second half of 2005 under different scenarios for house prices.

There are two key things to notice in the table. The first is the researchers predict catastrophic losses for the "meltdown" scenario of 5 percent annual house price declines. A 17 percent loss means that anything below a AAA-rated bond was essentially wiped out. Because the collateralized debt obligations (CDO) were composed of BBB-rated bonds from these deals, the meltdown scenario implies complete default on the CDOs. The actual price fall that took place was roughly twice as bad as the meltdown—annual declines of 10 percent rather than 5 percent—but the predictions of the model were largely correct: the deals based on these loans should rack up losses of about 23 percent. Thus, this table completely and utterly invalidates the argument that researchers at the top investment banks did no research and were completely ignorant of what they were buying or selling and had no idea that there was any possible scenario in which the bonds might lose.

The second thing to notice about the table is in the last column. The researchers assigned the meltdown scenario a 5 percent probability—a better outcome than the one that actually obtained. More importantly, they assigned 80 percent probability to house price appreciation of 5 percent or more, scenarios where the losses were sufficiently small that even the BBB-rated bonds were "money-good," scenarios in which the heroes of The Big Short would have seen their bets expire worthless.

In a sense, the subprime bears, the heroes of The Big Short, profited from their own ignorance. Their basic thesis was that making loans to people with poor credit histories was dumb and massive losses were inevitable under any circumstances. But what subprime bears failed to understand was that making unsecured loans to borrowers with poor credit histories generally leads to large credit losses—it's called payday lending—but making loans secured by an asset with a rising price is a low-risk business. The subprime bear logic that making mortgages to borrowers with problematic credit histories was guaranteed to fail would have generated massive losses between 1995 and 2004, as actual outcomes resembled scenarios 1, 2, and 3 from the Lehman Brothers' report chart year in and year out. It was their good fortune, not their astuteness, to make the bets in 2006.

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

August 10, 2010 in Housing prices, Mortgage crisis, Subprime mortgages | Permalink

July 06, 2010

The Big View of Michael Lewis's "The Big Short"

Author's note: This is the first of two posts on The Big Short. This one addresses the overall theme of the book. The next will focus on the book's details—in particular, the question of whether issuers obfuscated or even deliberately misled investors about subprime mortgage securities.

In The Big Short (Norton, W. W. & Company, 2010), Michael Lewis provides a narrative of the subprime mortgage crisis through the stories of a set of unconnected investors, including Michael Burry of Scion Capital, Steve Eisman of Frontpoint, and Jamie Mai and Charlie Ledley of Cornwall Capital, all of whom made a common bet against subprime mortgage bonds and won big. The book is a treasure trove of anecdotes about the crisis and deserves the wide audience it has received. But, in terms of reforming Wall Street or preventing another crisis, The Big Short—the title refers to the controversial Wall Street practice of short selling—could do more harm than good because it perpetuates the idea that it is possible to make large amounts of money in financial markets while taking little or no risk.

A reader might get the impression that the protagonists of The Big Short went to the roulette table knowing exactly where the ball would land. But they actually took a huge gamble when they bet against subprime bonds in 2006. In fact, had they tried their bet in 2005, The Big Short would not have been written.

Composition of pre- and post-2005 mortgages were not dissimilar
To understand the extent of the risk that characterized the bets these investors made, one needs to realize that the high levels of defaults on the loans in the deals that the investors bet against were not inevitable and were, in fact, unprecedented. The difference in performance between subprime loans originated before 2005 and after 2005 is like night and day. Loans originated before 2005 were only half as likely to default as the loans in the pools that Burry and his cohorts invested in. More importantly, while none of the BBB-rated bonds in the deals that originated in 2004 and 2005 defaulted, virtually all did for the deals that The Big Short investors traded on.

What accounts for the differences in performance between pre-2005 and post-2005 loans? None of the variables that Burry or any of the traders in The Big Short focus on. For example, while it's true that 35 percent of subprime loans originated in 2005 and 2006 had reduced documentation, that percentage is only marginally higher than the 30 percent with reduced documentation before 2005. Yes, it's true that 78 percent of the subprime loans originated after 2005 had "teaser rates" that would expire two or three years after origination—but 67 percent of the loans originated before 2005 had the same feature. Sure, 73 percent of the loans originated after 2005 had prepayment penalties, but that was down from the 74 percent that had them before 2004. Plus, the average FICO score had actually risen to 615 from 607.

House prices are the difference
So if the composition of mortgages did not change dramatically between 2004 and 2006, what explains the completely different outcomes? The answer is house prices. House prices are central to mortgage performance. When they are rising, few mortgages default because borrowers who can't make their payments can profitably sell to avoid foreclosure. Lewis's statement that a "person with a FICO score of 550 was virtually certain to default and should never have been lent money in the first place" (p. 100) is misleading. In fact, in the pre-2005 pools in which the average FICO score was 607, fewer than 5 percent of borrowers missed a payment in the first year of the loan.

The point here is that the timing of the bet was crucial. Simply betting against deals because they contained loans that were incompletely documented or because the FICO scores were low would have been a losing strategy in 2001 or 2002 or 2003 or 2004 or 2005. Nor was there anything inevitable about the timing of the fall in house prices. By 2003, standard measures of the relationship of house prices to income or to rents already showed overvaluation, and yet house prices continued to rise and even accelerate for the next three years.

In short, the success of the traders in The Big Short was not based on logic and skill but on their willingness to gamble that house prices would fall dramatically in 2006. It's not clear that they understood how much their bet depended on the evolution of house prices.¹

Subprime bulls had an extraordinarily successful run
There is a kind of irony here in that in writing The Big Short, Michael Lewis falls for precisely the same logic that created the subprime crisis in the first place. The logic is that investors who make money are smart and investors who lose money are dumb. The problem is that someone writing in 2005 could and did tell an identical story about the subprime bulls.² Then the smart people were the investors in subprime bonds who made huge returns because the high interest rates on the loans more than compensated them for the surprisingly small credit losses. The dumb ones were the suckers who invested in prime mortgages. What Lewis forgets is that in 2006, the subprime bulls were coming off a string of successful investments no less impressive than that of the heroes of his book. These subprime bulls were the smart ones at that time.

This dissonance is perfectly illustrated in one of the high points of the book when Lewis tells the story of Howie Hubler, a trader at Morgan Stanley:

Some people enjoyed Hubler, some people didn't, but, by early 2004, what others thought didn't really matter anymore, because for nearly a decade Howie Hubler had made money trading bonds for Morgan Stanley (p. 200).

Lewis understands the dangers of Hubler's logic:

Hubler and his traders thought they were smart guys put on earth to exploit the market's stupid inefficiencies. Instead, they simply contributed more inefficiency (p. 215).

Hubler's subprime bets end up going grievously wrong and he ends up causing the biggest single trading loss, $9 billion, in Wall Street history. Yet in many ways, the heroes of the book have a lot in common with Howie Hubler. Like Hubler, they took big bets. Like Hubler, they thought they were exploiting the stupidity of others. And like Hubler, they made a lot of money. Hubler ended up losing big, which may eventually happen to the stars of The Big Short.

The lesson of the crisis really is that one should be skeptical of any trader or fund manager promising high returns without risk. But for many who read The Big Short, the book will only make them look harder for that big score.

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

July 6, 2010 in Housing prices, Mortgage crisis, Subprime mortgages | Permalink

April 21, 2010

What role (if any) did the federal government and the GSEs have in the housing boom?

The government-sponsored enterprises (GSEs) have recently come under fire for possibly contributing to the housing and foreclosure crisis that has plagued our economy over the past few years. The GSEs are particularly appealing targets because of their controversial place at the forefront of the U.S. housing finance system over the past half century or so. (See background.) One argument focuses on the government's role in mandating that Fannie and Freddie extend mortgage credit in areas where they otherwise would not have, thus "forcing" the GSEs to make loans to borrowers likelier to default because of insufficient income, poor credit histories, or both. Namely, the GSE Affordable Housing Goals that Congress stipulated in the 1992 GSE Act requires Fannie and Freddie to use specific fractions of their loan purchase activity for certain underserved segments of the population.

The complicated issue of market factors versus Affordable Housing Goals
While this argument certainly sounds plausible, actually determining whether the federal government, through its regulation of GSE activities, had a causal effect on the mortgage boom and housing bubble is an extremely difficult task. GSE decisions about the number and type of loans to purchase and the locations of these purchases are almost surely influenced by market factors such as house prices, so it's quite possible that these market factors and not government mandates were what influenced Fannie and Freddie to expand their purchases into the underserved areas. Thus, in order to shed any light on this issue, one must first come up with a strategy to disentangle the effect of GSE activity on market variables due to government mandates from the effect of those variables on GSE activity.

In a recent paper, Federal Reserve Board economist Neil Bhutta has taken a stab at solving one aspect of this difficult issue: identifying the effect of government legislation on GSE activity. Bhutta's study, like several previous studies in the literature, exploits a discontinuity in the data caused by one of the GSE Affordable Housing Goals that was stipulated by Congress in the 1992 GSE Act. However, unlike the previous studies, Bhutta uses a more robust estimation technique and superior data coverage to arrive at a very different conclusion: He finds that one of the Affordable Housing Goals—the Underserved Areas Goal (UAG)—positively affected GSE loan purchase activity without crowding out other market participants such as the Federal Housing Administration (FHA) and private subprime mortgage lenders. In our opinion, Bhutta's analysis is very careful, and has provided an important first step toward answering the question of how much the GSEs contributed to the mortgage boom that preceded the crisis.

According to the UAG, Fannie and Freddie must purchase a certain fraction of their loans in low-income or minority census tracts. To be specific, a loan qualifies for the UAG if it is originated in a tract with median family income less than or equal to 90 percent of median family income in the metropolitan statistical area (MSA), or in a tract with minority population share of at least 30 percent and median family income less than or equal to 120 percent of the MSA median. Figure 1 in the Bhutta paper shows the UAG began at just over 20 percent in the mid-1990s and increased over time to almost 40 percent in 2006. Bhutta, and other authors who have studied this issue, hypothesize that to the extent that the UAG is binding, it will increase GSE loan purchases in the qualifying census tracts. This, in turn, may increase the supply of mortgage credit to low-income and minority households, depending on whether or not increased GSE purchases "crowd out" other lenders such as FHA and private, subprime originators.

Bhutta’s paper draws different conclusion
The previous literature on this topic found little evidence of much of an effect. An et al. (2007) and An and Bostic (2008) previously studied the link between the UAG and housing market outcomes using a two-stage econometric approach and data from the 1990s. Their first stage results showed only weak evidence of a causal link between the UAG and GSE purchase activity. In fact, An et al. (2007) found lower GSE market shares in census tracts that qualified for the UAG. Gabriel and Rosenthal (2008) also studied the link between the UAG and GSE purchase activity using data through 2000, and found no statistically significant effect. So then what exactly distinguishes Bhutta's paper from these previous studies, and why does he come to a different conclusion?

First, and most importantly, Bhutta uses a different and, we would argue, superior econometric approach. He employs a regression discontinuity strategy that solves a serious misspecification issue in the studies mentioned above. Those studies essentially compared GSE purchase activity in qualifying census tracts to non-qualifying tracts, and controlled for a host of potentially important census tract variables like demographic trends and housing market characteristics.

However, the studies did not control for the fact that GSE purchase activity is correlated with the variable that the UAG is based on—the ratio of census tract median income to MSA median income, which we will refer to as the "assignment variable." Since the correlation is positive (higher median income, on average, makes an area more attractive to a potential lender), not controlling for it will lead to a negative bias in the estimated effect of the UAG on purchase activity. For example, holding all else constant, the GSEs are more likely to purchase a higher volume of loans in a census tract with an assignment ratio of 110 percent compared to a qualifying tract with a ratio of, say, 90 percent.

Bhutta controls for this effect in a couple of different ways. In one specification, he focuses on tracts that have assignment ratios within 5 percentage points of the qualifying ratio, and explicitly controls for the correlation between the assignment ratio and GSE purchase volume assuming a linear correlation structure. In a second specification, he uses census tracts with assignment ratios within 2 percentage points of the qualifying ratio, and does not control for the correlation (note that most of the prior studies used a window of 10 percentage points without controlling for the correlation).

The second difference is the data coverage used by Bhutta compared to the previous studies. Unlike those studies, Bhutta uses data through the mid-2000s, which is important because much of the increase in the UAG came after the year 2000. Thus, it is possible that the UAG was not binding before 2000, which would also explain the different conclusions.

When all is said and done, Bhutta finds a statistically significant, positive effect of the UAG on GSE purchases, but the magnitude of the effect is quite modest, at around 4 percent. That is, all else being equal, the GSEs purchase approximately 4 percent more loans in census tracts with assignment ratios just below the qualifying ratio versus tracts just above the ratio. In addition, he finds no evidence of crowding out, which suggests that the UAG may exert a causal impact on access to mortgage credit (as long as it is not a simple compositional effect whereby the GSEs are simply offsetting their increased purchases in qualifying tracts by lowering their purchases in non-qualifying census tracts).

Findings, though accurate, may not paint complete picture
While we believe the Bhutta paper is a very careful and rigorous analysis, there is some reason to suspect that its findings are a lower bound for the impact of the UAG. Bhutta uses Home Mortgage Disclosure Act (HMDA) data for his analysis, which is likely to under measure the number of loans purchased by the GSEs. HMDA identifies only mortgages purchased in the same calendar year of origination, so it is possible, if not likely, that a non-trivial fraction of GSE purchases take place with a significant lag from the date of origination. Bhutta attempts to address this issue by looking at the sample of loans eligible to be purchased by the GSEs separately, rather than considering only the loans that are actually purchased. However, the difficulty of determining which loans are eligible and which are not likely creates a significant amount of measurement error in this exercise.

A second, possibly more serious source of mismeasurement comes from the fact that the HMDA data don't contain information on the loans backing mortgage-backed securities that the GSEs purchased for their retained portfolios. These were not a trivial fraction of their total purchase activity and, if accounted for, could significantly add to the estimated effect of the UAG on GSE secondary market activity. Unfortunately, one would need to use loan-level GSE data, which is virtually impossible to obtain, at least up to now.

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

April 21, 2010 in Affordable housing goals, GSE, Mortgage crisis | Permalink