Real Estate Research provides analysis of topical research and current issues in the fields of housing and real estate economics. Authors for the blog include the Atlanta Fed's Kristopher Gerardi, Carl Hudson, and analysts, as well as the Boston Fed's Christopher Foote and Paul Willen.
November 24, 2015
The Pass-Through of Monetary Policy
In the wake of the Great Recession, the Federal Reserve instituted three rounds of large-scale asset purchases (LSAPs) in 2008, 2010 and 2012, more commonly known as "quantitative easing 1" (QE1), "QE2" and "QE3." The objective of these interventions was to keep interest rates low in an attempt to stimulate household consumption and business investment.1
In the United States, housing is the single largest asset on the household balance sheet, accounting for 73 percent of nonfinancial assets for the average U.S. household and an even higher share for homeowners.2 Mortgage payments represent the largest class of household debt obligation.
Evidence of the effectiveness of the asset purchase programs on real economic activity has until recently been limited due to the lack of data and a credible identification strategy (by which we mean a way to separate the causal impact of the LSAPs on the economy from other government programs and market factors that were occurring at the same time). When we chart a timeline of the three LSAPs against the primary mortgage rate, we can see that the primary mortgage market rate effectively dropped below 6 percent when the Fed began buying $600 billion in mortgage-backed securities during QE1. Indeed, the rate dropped following each of the subsequent LSAPs.
Since 2009, a number of papers have been published that evaluate the effectiveness of the policy interventions through different transmission channels. One such paper (Keys, Piskorski, Seru, and Yao, 2014) reports on borrowers with adjustable-rate mortgages (ARMs) who automatically receive the benefits of lower interest rates with no frictions or transaction costs, unlike borrowers with fixed-rate mortgages (FRMs) who must refinance in order to take advantage of lower interest rates. The paper provides new evidence on the effectiveness of the LSAPs.
Our strategy is to compare the change in the household balance sheets of 7/1 ARM borrowers to those of 5/1 ARM borrowers, using credit bureau data linked to mortgages. These two ARMs are the most popular ARM products among prime borrowers with very similar credit quality and risk preferences, yet they differ only in years 6 and 7, when the 5/1 ARM is eligible for a rate reset and the 7/1 is still locked (that is, the rate is still fixed). This creates a natural experiment that allows us to isolate other factors that might affect the mortgage rate.
By controlling for borrower characteristics and economic environments, we estimate that mortgage rates in the treatment group (5/1 ARMs) dropped in the first year by 1.14 percentage points, from 5.1 percent, and that payments dropped by $150 per month, or about a 20 percent reduction on average. The average borrower had a cumulative two-year savings of $3,456.3 We also subsequently found that borrowers spent 18 percent of the money saved on paying off credit card balances and that there was an 11 percent increase in new car purchases for the group. As a result, the leverage of U.S. households' dropped considerably from its peak during the financial crisis.4
We also find significant heterogeneity for these effects across different populations. Less creditworthy and more liquidity-constrained borrowers appear to have benefited the most from LSAPs as they experienced significantly greater reductions in mortgage rates and payments and larger improvements in mortgage and credit card performance. In terms of how they spent the extra liquidity received, highly leveraged borrowers (high credit utilization) spent 40 to 50 percent of the extra liquidity received during the first year, or $814 out of $1,740, to repay their revolving debts, then spent 20 percent of the extra liquidity received during the second year. Borrowers in the top quartile of credit utilization rates allocated about 70 percent of the extra liquidity toward repaying their credit card debt. We found similar effects among borrowers in the bottom quartile of credit scores. (The low-wealth borrowers with low credit utilization experienced a much larger increase in auto debt or new car purchases.) In other words, the LSAP programs effectively stimulated household investment and consumption.
We also find, as a result of the estimated effects at the micro level, a significant impact on local (nontradable) employment growth, consumer spending, and house price recovery in regions that were more exposed to ARMs. For example, a 10 percentage point increase in the ARM share, which is associated with about a 20-basis-point average reduction in ZIP code mortgage rates, is associated with about a 0.25 percentage point increase in quarterly home price growth, or about 1 percent annual appreciation, a very meaningful increase.
By Vincent Yao, visiting scholar at the Federal Reserve Bank of Atlanta and associate professor in the Real Estate Department in the J. Mack Robinson College of Business at Georgia State University.
Di Maggio, Marco; Amir Kermani; and Rodney Ramcharan. 2014. "Monetary Pass-Through: Household Consumption and Voluntary Deleveraging," Working Paper.
Hancock, Diana and Wayne Passmore. 2014. "How the Federal Reserve's Large-Scale Asset Purchases (LSAPs) Influence Mortgage-Backed Securities (MBS) Yields and U.S. Mortgage Rates," Finance and Economics Discussion Series 2014–12. Board of Governors of the Federal Reserve System.
Keys, Benjamin J.; Tomasz Piskorski; Amit Seru; and Vincent Yao. 2014. "Mortgage Rates, Household Balance Sheets, and the Real Economy," NBER Working Paper No. 20561.
1 The LSAPs involved purchases of long-term securities issued by the U.S. Treasury, agency debts, and agency mortgage-backed securities (MBS). They ultimately affected the yields of the MBS as well as the mortgage rates offered to borrowers in the primary mortgage market through several potential transmission channels: (1) the signaling of the Fed's commitment to keeping rates low, (2) a portfolio rebalance between assets and deposits and among different durations, and (3) increasing the liquidity value of MBS (Hancock and Passmore, 2014).
2 Survey for Consumer Finance, Federal Reserve Board of Governors, 2013.
3 Di Maggio, Kermani. and Ramcharan (2014) found much bigger savings for subprime and Alt-A borrowers based on a similar approach.
4 It is notable that in the United States the majority of prime borrowers take out fixed rate mortgages while most subprime borrowers take out adjustable rate mortgages.
April 20, 2015
Income Growth, Credit Growth, and Lending Standards: Revisiting the Evidence
Almost a decade has passed since the peak of the housing boom, and a handful of economics papers have emerged as fundamental influences on the way that economists think about the boom—and the ensuing bust. One example is a paper by Atif Mian and Amir Sufi that appeared in the Quarterly Journal of Economics in 2009 (MS2009 hereafter). A key part of this paper is an analysis of income growth and mortgage-credit growth in individual U.S. ZIP codes. The authors find that from 2002 to 2005, ZIP codes with relatively low growth in incomes experienced high growth in mortgage credit; that is, income growth and credit growth were negatively correlated during this period.
Economists often cite this negative correlation as evidence of improper lending practices during the housing boom. The thinking is that prudent lenders would have generated a positive correlation between area-level growth in income and mortgage credit, because borrowers in ZIP codes with high income growth would be in the best position to repay their loans. A negative correlation suggests that lenders instead channeled credit to borrowers who couldn't repay.
Some of the MS2009 results are now being reexamined in a new paper by Manuel Adelino, Antoinette Schoar, and Felipe Severino (A2S hereafter). The A2S paper argues that the statistical evidence in MS2009 is not robust and that using borrower-level data, rather than data aggregated up to the ZIP-code level, is the best way to investigate lending patterns. The A2S paper has already received a lot of attention, which has centered primarily on the quality of the alternative individual-level data that A2S sometimes employ.1 To understand the relevant issues in this debate, it's helpful to go back to MS2009's original statistical work that uses data aggregated to the ZIP-code level to get a sense of what it does and doesn't show.
Chart 1 summarizes the central MS2009 result. We generated this chart from information we found in either MS2009 or its supplementary online appendix. The dark blue bars depict the coefficients from separate regressions of ZIP-code level growth in new purchase mortgages on growth in ZIP-code level incomes.2 (These regressions also include county fixed effects, which we discuss further below.) Each regression corresponds to a different sample period. The first regression projects ZIP-level changes in credit between 1991 and 1998 on ZIP-level changes in income between these two years. The second uses growth between 1998 and 2001, and so on.3 During the three earliest periods, ZIP-level income growth enters positively in the regressions, but in 2002–04 and 2004–05, the coefficients become negative. A key claim of MS2009 is that this flip signals an important and unwelcome change in the behavior of lenders. Moreover, the abstract points out that the negative coefficients are anomalous: "2002 to 2005 is the only period in the past eighteen years in which income and mortgage credit growth are negatively correlated."
There are, however, at least three reasons to doubt that the MS2009 coefficients tell us anything about lending standards. First of all, the coefficients for the 2005–06 and 2006–07 regressions are positive—for the latter period, strongly so. By MS2009's logic, these positive coefficients indicate that lending standards improved after 2005, but in fact loans made in 2006 and 2007 were among the worst-performing loans in modern U.S. history. Chart 2 depicts the share of active loans that are 90-plus days delinquent or in foreclosure as a share of currently active loans, using data from Black Knight Financial Services. To be sure, loans made in 2005 did not perform well during the housing crisis, but the performance of loans made in 2006 and 2007 was even worse.4 This poor performance is not consistent with the improvement in lending standards implied by MS2009's methodology.
A second reason that sign changes among the MS2009 coefficients may not be informative is that these coefficients are not really comparable. The 1991–98 regression is based on growth in income and credit across seven years, while later regressions are based on growth over shorter intervals. This difference in time horizon matters, because area-level income and credit no doubt fluctuate from year to year while they also trend over longer periods. A "high-frequency" correlation calculated from year-to-year growth rates may therefore turn out to be very different from a "low-frequency" correlation calculated by comparing growth rates across more-distant years. One thing we can't do is think of a low-frequency correlation as an "average" of high-frequency correlations. Note that MS2009 also run a regression with growth rates calculated over the entire 2002–05 period, obtaining a coefficient of -0.662. This estimate, not pictured in our graph, is much larger in absolute value than either of the coefficients generated in the subperiods 2002–04 and 2004–05, which are pictured.
A third and perhaps more fundamental problem with the MS2009 exercise is that the authors do not report correlations between income growth and credit growth but rather regression coefficients.5 And while a correlation coefficient of 0.5 indicates that income growth and credit growth move closely together, a regression coefficient of the same magnitude could be generated with much less comovement. MS2009 supply the data needed to convert their regression coefficients into correlation coefficients, and we depict those correlations as green bars in chart 1.6 Most of the correlations are near 0.1 in absolute value or smaller. To calculate how much comovement these correlations imply, recall that the R-squared of a regression of one variable on another is equal to the square of their correlation coefficient. A correlation coefficient of 0.1 therefore indicates that a regression of credit growth deviated from county-level means on similarly transformed income growth would have an R-squared in the neighborhood of 1 percent. The reported R-squareds from the MS2009 regressions are much larger, but that is because the authors ran their regressions without demeaning the data first, letting the county fixed effects do the demeaning automatically. While this is standard practice, this specification forces the reported R-squared to encompass the explanatory power of the fixed effects. The correlation coefficients that we have calculated indicate that the explanatory power of within-county income growth for within-county credit growth is extremely low.7 Consequently, changes in the sign of this correlation are not very informative.
How do these arguments relate to A2S's paper? Part of that paper provides further evidence that the negative coefficients in the MS2009 regressions do not tell us much about lending standards. For example, A2S extend a point acknowledged in MS2009: expanding the sample of ZIP codes used for the regressions weakens the evidence of a negative correlation. The baseline income-credit regressions in MS2009 use less than 10 percent of the ZIP codes in the United States (approximately 3,000 out of more than 40,000 total U.S. ZIP codes). Omitted from the main sample are ZIP codes that do not have price-index data or that lack credit-bureau data.8 MS2009 acknowledge that if one relaxes the restriction related to house-price data, the negative correlations weaken. Our chart 1 conveys this information with the correlation coefficients depicted in red, which are even closer to zero. A2S go farther to show that if the data set also includes ZIP codes that lack credit-bureau data, the negative correlation and regression coefficients become positive.
But perhaps a deeper contribution of A2S is to remind the researchers that outstanding questions about the housing boom should be attacked with individual-level data. No one doubts that credit expanded during the boom, especially to subprime borrowers. But how much of the aggregate increase in credit went to subprime borrowers, and how did factors like income, credit scores, and expected house-price appreciation affect both borrowing and lending decisions? Even under the best of circumstances, it is hard to study these questions with aggregate data, as MS2009 did. People who take out new-purchase mortgages typically move across ZIP-code boundaries. Their incomes and credit scores may be different than those of the people who lived in their new neighborhoods one, two, or seven years before. A2S therefore argue for the use of HMDA individual-level income data so that credit allocation can be studied at the individual level. This use has been criticized by Mian and Sufi, who believe that fraud undermines the quality of the individual-level income data that appear in HMDA records. We should take these criticisms seriously. But the debate over whether lending standards are best studied with aggregate or individual-level data should take place with the understanding that aggregate data on incomes and credit may not be as informative as previously believed.
2 Data on new-purchase mortgage originations come from records generated by the Home Mortgage Disclosure Act (HMDA). Average income at the ZIP-code level is tabulated in the selected years by the Internal Revenue Service.
3 Growth rates used in the regressions are annualized. The uneven lengths of the sample periods are necessitated by the sporadic availability of the IRS income data, especially early on. The 1991 data are no longer available because IRS officials have concerns about their quality.
4 Chart 2 includes data for both prime and subprime loans. The representativeness of the Black Knight/LPS data improves markedly in 2005, so LPS loans originated before that year may not be representative of the universe of mortgages made at the same time. For other evidence specific to the performance of subprime loans made in 2006 and 2007, see Figure 2 of Christopher Mayer, Karen Pence, and Shane M. Sherlund, "The Rise in Mortgage Defaults," Journal of Economic Perspectives (2009), and Figure 1 of Yuliya Demyanyk and Otto Van Hemert, "Understanding the Subprime Mortgage Crisis," Review of Financial Studies (2009). For data on the performance of GSE loans made in 2006 and 2007, see Figure 8 of W. Scott Frame, Kristopher Gerardi, and Paul S. Willen, "The Failure of Supervisory Stress Testing: Fannie Mae, Freddie Mac, and OFHEO," Atlanta Fed Working Paper (2015).
5 MS2009 often refer to their regression coefficients as "correlations" in the text as well as in the relevant tables and figures, but these statistics are indeed regression coefficients. Note that in the fourth table of the supplemental online appendix, one of the "correlations" exceeds 1, which is impossible for an actual correlation coefficient.
6 Because a regression coefficient from a univariate regression is Cov(X,Y)/Var(X), multiplying this coefficient times StdDev(X)/StdDev(Y) gives Cov(X,Y)/StdDev(X)*StdDev(Y), which is the correlation coefficient. Here, the Y variable is ZIP-code–level credit growth, demeaned from county-level averages, while X is similarly demeaned income growth. As measures of the standard deviations, we use the within-county standard deviations displayed in Table I of MS2009. Specifically, we use the within-county standard deviation of "mortgage origination for home purchase annual growth" calculated over the 1996–02 and 2002–05 periods (0.067 and 0.15, respectively) and the within-county standard deviation of "income annualized growth" over the 1991–98, 1998–2002, 2002–05, and 2005–06 periods (0.022, 0.017, 0.031, and 0.04, respectively). Unfortunately, the time periods over which the standard deviations were calculated do not line up exactly with the time periods over which the regression coefficients were calculated, so our conversion to correlation coefficients is an approximation.
7 It is true that the regression coefficients in the MS2009 coefficients often have large t-statistics, so one may argue that ZIP-level income growth has sometimes been a statistically significant determinant of ZIP-level credit growth. But the low correlation coefficients indicate that income growth has never been economically significant determinant of credit allocation within counties. It is therefore hard to know what is driving the income-credit correlation featured in MS2009, or what may be causing its sign to fluctuate.
8 Though house prices and credit bureau data are not required to calculate a correlation between income growth and mortgage-credit growth, the authors use house prices and credit bureau data in other parts of their paper.
January 14, 2015
The Effectiveness of Restrictions of Mortgage Equity Withdrawal in Curtailing Default: The Case of Texas
As an economist who has studied the causes of the recent mortgage default and foreclosure crisis, I am often asked how to design policies that will minimize the likelihood of another crisis. My typical response to such a question is that one of the most effective ways of lowering mortgage defaults would be to limit borrower leverage by either increasing down payment requirements at the time of purchase or limiting home equity withdrawal subsequent to purchase.
The reason behind my belief is twofold. First, economic theory tells us that being in a situation of negative equity (where the remaining balance of the mortgage is greater than the market value of the property) is a necessary condition for default and foreclosure. Homeowners with positive equity will almost always have a financial incentive to sell their homes instead of suffering through the foreclosure process, while borrowers who are “under water” have a difficult time refinancing or selling (since they would need to have enough cash at closing to cover the difference between the outstanding balance of the mortgage and the sale price/appraisal of the house) and have less of a financial incentive to continue paying the mortgage. Second, numerous empirical studies in the literature have confirmed the theory by documenting a strong positive correlation between the extent of negative equity and the propensity to default on one’s mortgage.
New evidence on preventing defaults
An important new paper by Anil Kumar, an economist at the Federal Reserve Bank of Dallas, provides new evidence that shows just how effective restricting leverage can be in preventing mortgage defaults. His paper confirms many of the findings in previous studies that have shown a positive relationship between negative equity and default. However, it goes a step further by using plausibly random variation in home equity positions created by a government policy that placed explicit restrictions on home equity withdrawal.
Kumar's paper is a significant contribution to the literature because it seems to overcome a serious identification issue that has plagued most empirical studies on the topic. The major challenge is that a homeowner can partially control his or her equity position through decisions about initial down payments on purchase mortgages and decisions about cash-out refinancing and home equity loans or lines of credit subsequent to purchase. As a result, it's unclear whether homeowners with more negative equity are more likely to default because of their worse equity positions or because of other reasons (unobserved by the researcher) that happen to be correlated with the decision to put less money down at purchase or to extract more equity over time.
Both theory and empirical evidence tell us that more impatient individuals tend to borrow more and are more likely to default on their debts. Thus, it might simply be the case that more impatient borrowers who are less likely to repay any type of debt choose to put less money down and extract more equity over time, creating the observed correlation between negative equity and the propensity to default. To put it in the language of econometrics, there are both selection and treatment effects that could be driving the correlation that we see in the data, and the policy implications of restricting borrower leverage are likely very different depending on which cause is more important.
Do home equity restrictions cause lower default rate?
The paper focuses on a policy enacted in the state of Texas that placed severe restrictions on the extent of home equity withdrawal. The Texas constitution, enacted in 1876, actually prohibited home equity withdrawal. The prohibition was eventually lifted in 1997 and the restrictions were further relaxed in 2003, but even in the post-2003 period, Texas law placed serious limits on equity withdrawal, which remain in effect today.1 Subsequent to purchase, a borrower cannot take out more than 50 percent of the appraised value of the home, nor exceed 80 percent of total loan-to-value (LTV). For example, if a borrower owned a home worth $200,000 and had an outstanding mortgage balance of $140,000, the borrower would be allowed to take out only $20,000 in a cash-out refinance. It is important to note that this LTV restriction does not bind at the time of purchase, so a homebuyer in Texas could take out a zero-down-payment loan, and thus begin the homeownership tenure with an LTV ratio of 100 percent (we will come back to this issue later).
Here's a nice quote in the April 4, 2010, issue of the Washington Post crediting the cash-out restriction for Texas weathering the foreclosure crisis better than many areas of the country.
But there is a broader secret to Texas's success, and Washington reformers ought to be paying very close attention. If there's one thing that Congress can do to help protect borrowers from the worst lending excesses that fueled the mortgage and financial crises, it's to follow the Lone Star State's lead and put the brakes on "cash-out" refinancing and home-equity lending.
At first glance, the data suggest that such a sentiment may be correct. In the figure below, we display subprime mortgage serious delinquency rates (defined as loans that are at least 90 days delinquent) for Texas and its neighbors (Arkansas, Louisiana, New Mexico, and Oklahoma). We focus on the subprime segment of the market because these are the borrowers who are more likely to be credit-constrained and thus more likely to extract home equity at any given time. It is apparent from the figure that Texas had the lowest subprime mortgage delinquency rates over most of the sample period. While the paper uses a slightly different data set, a similar pattern holds (see Figure 1 in the paper). The figure is certainly compelling and suggests that the home equity withdrawal restrictions in Texas had an important effect on default behavior, but a simple comparison of aggregate default rates across states really doesn’t tell us whether the policy had a causal impact on behavior. There could be other differences between Texas and its neighboring states that are driving the differences in default rates. For example, house price volatility over the course of the boom and bust was significantly lower in Texas compared to the rest of the country, which could also explain the differences in default rates that we see in the figure.
The paper uses a relatively sophisticated econometric technique called "regression discontinuity" to try to isolate the causal impact of the Texas policy on mortgage default rates. We won't get into the gory details of the methodology in this post, so for anyone who wants more details, this paper provides a nice general overview of the technique. Essentially, the regression discontinuity approach implemented in the paper compares default rates over the 1999–2011 period in Texas counties and non-Texas counties close to the Texas borders with Louisiana, New Mexico, Arkansas, and Oklahoma while controlling for potential (nonlinear) trends in default rates that occur as a function of distance on each side of the Texas border. The paper also controls for other differences across counties that are likely correlated with mortgage default rates (such as average house price appreciation, average credit score, and more). The idea is to precisely identify a discontinuity in default rates at the Texas border caused by the restrictions on home equity withdrawal in Texas. This strikes us as a pretty convincing identification strategy, especially in light of the fact that information on actual home equity withdrawal is not available in the data set used in the paper.
The estimation results of the regression discontinuity specification show that the equity restriction policy in Texas lowered overall mortgage default rates over the 13-year period by 0.4 to 1.8 percentage points depending on assumptions about sample restrictions (including counties within 25, 50, 75, or 100 miles of the border) and functional form assumptions for the “control function” (that is, whether distance to the border is assumed to be a linear, quadratic, or cubic polynomial). At first glance, this may not seem like a large effect, but keep in mind that the average mortgage default rate over the entire sample period was only slightly above 3 percentage points in Texas and 4 percentage points in the neighboring states. The paper also restricts the sample to subprime mortgages only and finds significantly larger effects (2 to 4 percentage points), which makes sense. We expect subprime mortgage borrowers to be affected more by the equity restriction since they are more likely to withdraw home equity.2 The paper implements a battery of robustness checks to make sure that the results aren’t overly sensitive to functional form assumptions and adds controls for other types of state-level policy differences. Based on the results of those tests, the findings appear to be quite stable.
But is it a good policy?
So the paper appears to confirm what previous research on the relationship between equity and mortgage default has found, although it uses methods that aren’t quite as clean as the regression discontinuity approach employed in this analysis. However, it doesn’t mean that such a law change is necessarily good policy. While it seems to be effective in reducing defaults, it may also have some real costs. The most obvious one is the decrease in the volume of low-cost secured credit that many borrowers used to improve their circumstances during the housing boom. An unintended consequence of the policy might have been to push financially distressed households into higher-cost credit markets like credit cards or payday loans. A second drawback of the policy may have been that it increased homeowner leverage at the time of purchase. As there were no restrictions on LTV ratios at the time of purchase, many homebuyers may have decided to make lower down payments, knowing that their access to equity would be restricted in the future. It’s also possible that this may have resulted in a larger volume of subprime mortgage lending in Texas. Households with relatively high credit scores who could have obtained a prime mortgage with significant down payments (say, 20 percent), may have turned to the subprime segment of the market, where they could obtain loans with low down payments but with much more onerous contract terms.
While it’s not clear whether the actual Texas policy of restricting home equity extraction is welfare-improving, it might seem from the research that restricting borrower leverage is an effective way to reduce mortgage default rates. But limiting borrower leverage is very unpopular. In fact, it probably isn’t too much of an exaggeration to say that the vast majority of market participants are adamantly opposed to such policies. After all, it is perhaps the only policy upon which both the Center for Responsible Lending (CRL) and the Mortgage Bankers Association (MBA) share the same negative view.3 Thus, while such policies have been adopted in other countries, don’t expect to see them adopted in the United States any time soon.4 To the contrary, policy is more likely to go in the opposite direction as evidenced by the Federal Housing Finance Agency’s announcement to relax down payment requirements for Fannie Mae and Freddie Mac.
By Kris Gerardi, financial economist and associate policy adviser at the Federal Reserve Bank of Atlanta
1 Before 1998, both home equity lending (loans and lines of credit) and cash-out refinancing were explicitly prohibited in Texas. A 1997 constitutional amendment relaxed this ban by allowing for closed-end home equity loans and cash-out refinancing as long as the combined LTV ratio did not exceed 80 percent of the appraised value (among a few other limitations that are discussed in the paper). In 2003, another constitutional amendment passed that further allowed home equity lines of credit for up to 50 percent of the property’s appraised value, although still subject to a cap on the combined LTV ratio of 80 percent.
2 The effects are actually smaller for the subprime sample when compared to the average default rate over the entire sample period, since the average rate is significantly higher in the subprime segment of the market (10 percent subprime default rate compared to the 3 percent overall default rate in Texas).
April 25, 2014
Two Views of the Involvement of Credit Rating Agencies in the Mortgage Crisis
A lot of people have blamed credit rating agencies (CRAs) for helping to cause the mortgage crisis. The report of the Financial Crisis Inquiry Commission (FCIC) labelled CRAs as "key enablers of the crisis," because the exploding mortgage-backed bonds that caused so much trouble could not have been sold without stamps of approval from the CRAs. Commentators often link CRA failings to the fact that they are paid by the issuers of the securities they rate, with the implication that CRAs are thus given incentive to award good ratings to securities that do not deserve them. Indeed, two recent articles by academic economists on this topic come to the same conclusion: financial markets would work better if we scrapped the issuer-pays model in favor of some other way to pay CRAs for their evaluations. But the two articles disagree on why this is so, and understanding the source of this disagreement sheds some harsh light on claims that CRAs should be even partly blamed for the financial crisis in the first place.
Grade inflation in the student-pays model
The first article is a Wall Street Journal op-ed piece by Princeton economist Alan Blinder. Blinder likens the awarding of credit ratings to mortgage-backed securities to his own awarding of letter grades to his Princeton students. "Suppose I proposed to grade my students by a 'student pays' model," Blinder suggests. Such a setup would encourage him to give easy As in hopes of attracting more students and higher pay, and the information in the grades would suffer as a result. "Yet that description comes pretty close to mimicking the way we pay rating agencies," Blinder writes. "Looking back, is it any wonder that so many securities were blessed with undeserved triple-A ratings?"
One interpretation of Blinder's analogy is that college grading works better than securities rating because universities have not adopted the student-pays model. That argument will seem curious to many college instructors, because this model approximates their own compensation arrangements pretty well. Students may not write checks to professors, but they (or their parents) write checks to colleges, who then pay the professors. Instructors whose grades are overly harsh in relation to other courses are likely to see their class sizes dwindle, to the dismay of department chairs facing hard budget constraints. Even if an instructor has no problem attracting students, she may not want grading disparities among courses to distort student decisions on what to study, so she might ease up in her own grading as well. Given the incentives of professors, it is not surprising that grade inflation is debated at many universities, even the very best ones. A December 2013 article in Harvard University's student newspaper, the Crimson, described a faculty meeting at which a professor bemoaned the fact that the most frequently awarded grade at Harvard College is an A-minus. A university dean corrected him: "The median grade in Harvard College is indeed an A-minus," the dean is quoted as saying. "The most frequently awarded grade in Harvard College is actually a straight A." (Disclosure: Harvard's grading policy is of personal interest to two authors of this blog post, who teach intermediate macroeconomics courses there in their spare time.)
Rational employers, rational investors
If the student-pays model leads to grade inflation, then don't we have even more ammunition that the bad incentives inherent in the investor-pays model for CRAs is partly responsible for the mortgage crisis? Not necessarily. For bad CRA incentives to have caused the crisis, two things must be true: one, the incentives must have caused inflated ratings, and two, the investors had to believe the inflated ratings. The second step in this causal chain is open to question. If the investors knew that the issuer-pays model gave incent to the rating agencies to inflate ratings, then rational investors would have taken that information into account when making investment decisions.
The college-grading analogy is again useful here. Consider an employer who is thinking about hiring a recent graduate who received a B-minus in a course that is highly relevant to what the firm does. How should the employer use this information? One option would be for the employer to look up how the student's official university documents define a B-minus—the documents are likely to define a grade in the B range as indicating a better-than-average understanding of the material. But a rational employer who knows the incentives facing American professors would also know that instructors are given cause to inflate grades. The firm could thus surmise that an applicant on the border between a B and a C may actually have a lower-than-average mastery of the subject. In the same way, rational mortgage investors who knew that CRAs had incentive to inflate ratings would have taken those ratings with a grain of salt when evaluating mortgage-backed investments.
Investor rationality plays a prominent role in a second recent piece on CRA incentives, a formal paper by the economists Anil Kashyap and Natalia Kovrijnykh (KK). Because this article is part of the academic economics literature, the authors adopt the fundamental assumption that all actors in the model are rational. As we might expect from our analogy of the job applicant, the rationality assumption makes a big difference when analyzing CRA payment regimes. Consider a situation in which CRAs are paid by the issues of securities, as they are today. Further assume that CRAs receive more money for good ratings than for bad ones. Rational investors in the KK model would realize the ratings are likely to be inflated under this set of incentives and would deflate the ratings accordingly. But if the CRAs are unable to fool investors who know both the CRAs' preferences and their opportunities, then the CRAs might as well tell the truth. KK therefore constrain their attention to equilibria where rating agencies are always truthful.
The revelation principle
In assuming truth-telling, KK are following a long tradition in the modeling of imperfect information. In fact, the assumption that actors with private information tell the truth shows up so often in models of imperfect information that it has a special name: the revelation principle. This principle is useful for modelers because it allows them to focus on equilibria in which the agent with private information has no reason to lie. To be clear, in this situation, the revelation principle does not mean that rating agencies never lie. Rather, it states that any equilibrium in which rating agencies lie is equivalent to one in which they tell the truth. The lying doesn't affect the actions of investors who know the incentives and opportunities of the CRAs, just as inflation of our B-minus student's grade does not lead the employer into an inappropriate hire. Because lying does not encourage agents to take inappropriate actions, it can safely be ignored when thinking through the fundamental aspects of the problem.
The appropriateness of the revelation principle in this context hinges on the ability of mortgage investors to analyze CRA incentives and opportunities and thereby back out the truth. Is this realistic? Ironically, the critics of CRAs provide evidence in support of this view. When Barney Frank alleged that CRA incentives led them to inflate ratings, he was doing exactly the sort of reverse engineering that lies behind the revelation principle. And if legislators could figure out that rating agencies had distorted incentives, why couldn't investors, who were putting up their own money? Indeed, investors should have had much better information about agency incentives than Barney Frank. It turns out that financial intermediaries lost enormous sums on the mortgage-related securities that they purchased and held on their balance sheets (more details on this in the next post). At the same time, they were also large issuers of these securities. Who would know better about the potential for corruption of rating agencies than the financial intermediaries that supposedly corrupted them?
Of course, if the KK model holds that rating agencies always tell the truth, then the model cannot rationalize arguments that CRAs helped cause the crisis by misleading investors. Indeed, the revelation principle makes it hard to rescue any story about untruthful CRAs. What if credit rating agencies had private information about their incentives, in addition to private information about their effort and the quality of the securities that they rated? Setting aside the fact that the issuer-pays model of credit ratings was common knowledge in the market, this change to the model has no effect on its outcome. Here again, the revelation principle would imply that CRAs truthfully reveal the private information about their incentives. For investors to be misled, they cannot simply be confused about incentives. Rather, they must believe that the CRAs' incentives were better aligned than they actually were. In our view, that is unlikely.
CRA payment arrangements
We began this post by noting that both of the recent articles on CRA incentives argued against the issuer-pays model. How can KK make this argument if investors in their model are not fooled? The reason involves some subtle implications of exactly how CRAs are paid in different states of the world. In all contracts in KK's issuer-pays regime, CRA pay is contingent on the outcome of the security. That means that if an AAA-rated security defaults, the CRA gets paid less than if the security pays off. To induce effort by the CRA, the spread between the payoffs must be large (that is, the CRA must be paid a lot more when the AAA security is successful compared to when it defaults). Because of limited liability, the CRA's compensation is bounded below by zero when a bond defaults—that is, investors can't demand payment from the CRAs in the default state—so high-powered incentives, which require high average pay, imply that compensation to the CRA in the good state has to be very high. As a result, paying the CRA for high effort can be prohibitively expensive for the issuer, causing the issuer to settle for low-powered incentives instead and thus receiving low effort from the CRA. Even in the low-effort equilibrium, however, CRAs increase the information set of investors and are socially useful.
Going farther, KK show that having the investor rather than the issuer pay the CRA solves the limited-liability problem and thereby raises social welfare. Particularly surprising about this finding is that the investor-pays model is not only good for society, but it is also good for the CRAs! The reason once again involves the revelation principle. In equilibrium, everyone knows both the amount and usefulness of the effort expended by the CRAs in evaluating securities. The larger the CRA's social benefit, the more the CRA gets paid. If KK's model is accurate, then CRAs themselves may lead the way to a better social outcome by encouraging the adoption of the investor-pays model.
While KK's paper includes many specific lessons about potential CRA payment arrangements, the bottom line to emerge from a comparison of the Blinder op-ed and the KK model involves their differing assumptions regarding investor rationality. The KK model illustrates how the revelation principle, which follows from investor rationality, works against the argument that CRAs helped cause the crisis by misleading investors. As long as investors understand the basic structure of the market, then standard models of asymmetric information—of which the KK model is an example— do not predict that investors will experience large and unexpected losses.
You can read the Harvard Crimson article on the magazine's website.
Chris Foote, senior economist and policy adviser at the Federal Reserve Bank of Boston,
Kris Gerardi, financial economist and associate policy adviser at the Federal Reserve Bank of Atlanta, and
By Paul Willen, senior economist and policy adviser at the Federal Reserve Bank of Boston
February 19, 2014
Asymmetric Information and the Financial Crisis
In describing the $13 billion settlement reached between JPMorgan and the Department of Justice last November, Attorney General Eric Holder said,
Without a doubt, the conduct uncovered in this investigation helped sow the seeds of the mortgage meltdown. JPMorgan was not the only financial institution during this period to knowingly bundle toxic loans and sell them to unsuspecting investors, but that is no excuse for the firm's behavior.
What Holder describes sounds like a textbook example of what economists call asymmetric information: JPMorgan knew something about the loans it was selling (that they were toxic) that they didn't reveal to investors. Specifically, the government alleged that JPMorgan reported facts to the investors that turned out to be wrong. For example, JPMorgan may have said that it made only 10 percent of the loans in a pool to investors (as opposed to owner-occupants) when the actual percentage was 20 percent. So it would seem as if economic theory, which has a lot to say about asymmetric information, should help us understand the crisis. Indeed, to many, asymmetric information and "bad incentives" are the leading explanations of the financial crisis. For example, a Reuters article that described the settlement made the following claim:
The behavior that the largest U.S. bank admitted to, authorities said, is at the heart of what inflated the housing bubble: lenders making bad mortgages and selling them to investors who thought they were relatively safe. When the loans started turning bad, investors lost faith in the banking system, and a housing crisis turned into a financial crisis.
In future posts, we will consider this seemingly intuitive idea, and argue that the economic theory of asymmetric information, in fact, provides very little aid in understanding the central questions of the crisis.
Let's focus on Holder's quote. The standard theory of asymmetric information implies that JPMorgan's misrepresentations could not cause significant losses to investors. That may seem surprising. Many may think that either we don't understand the economics of asymmetric information or it's just another example of the naïveté of economists regarding how the real world actually works. While there is certainly no shortage of examples of economists holding naïve opinions about the real world, in this case, we will argue that we are correctly characterizing the economist's view and that it is based on a common-sense argument.
Let's start with the economics. Let's assume that JPMorgan is selling a pool of loans, about which it knows the true quality, to a group of buyers who can't observe the true quality. What does economic theory say will happen?
A. Investors will overpay for the assets and lose money.
B. Investors will underpay for the assets and make money.
C. Investors will infer the true quality of the loans and pay accordingly.
The answer is C. To many, that may sound shocking, but the basic logic is simple: investors know that they cannot observe the true quality of the loans and they know that JPMorgan has an incentive to dump bad loans in the pool. Thus, they correctly infer that JPMorgan will dump bad loans in the pool. In other words, investors form correct beliefs about the quality of a loan,1 despite not being able to observe quality directly.2
"Knowingly bundl[ing] toxic loans" may be unethical or even illegal, but according to the economic theory of asymmetric information, it shouldn't cause unexpected financial losses to investors. The key to understanding the gap between Holder and economics is the word "unsuspecting." Economists assume that all market participants are inherently suspicious. Market participants understand that the people with whom they are doing business have an incentive to cheat them if those people know more about the products that they are selling.
Are economists naïve to think that market participants can figure out the incentives of their adversaries? We would argue that common sense says people are pretty suspicious. Take, for example, real estate agents. A cursory search on the internet yields the following table of "translations" of real estate listings:
Loaded with Potential: means loaded with problems the seller didn't want to tackle.
Cute: means they couldn't think of any other possible way to describe it.
Great Bones: means you're going to have to gut it and rebuild.
Wooded/Shaded Lot: means surrounded by trees and leaves on the ground.
Charming: means they couldn't think of a more appropriate word.
Needs a Little TLC: means it needs about $45,000 dollars or more in renovations and repairs.
Won't Last Long at This Price: means the price is so low it will compel you to see it but it will take a miracle for you to want to buy it.
No Disclosures: means you're going to have to find out all the problems with the home on your own.
Most people read this and chuckle, but no one is surprised that real estate agents stretch the truth. After all, it's their job to convince you to buy. And, in general, people view salespeople as among the least ethical of all occupations, only slightly above members of Congress. Perhaps the most egregious example of this, and in fact the example that motivated the seminal paper on the economics of asymmetric information, is used-car salespeople. Do used-car salespeople try to misrepresent the quality of the cars that they are trying to sell? Most people would likely answer this question with a resounding "Yes, of course." Does this cause injury to most used-car buyers? Not so much. Since the general public recognizes that "used-car salesman" is basically American slang for a fraudster, nobody really believes what they say.
In subsequent posts, we will answer questions about the crisis that turn on asymmetric information problems:
- Theory says investors should have guessed the quality of the loans. Did they?
- If investors knew the quality of the loans they were buying, why did JPMorgan pay $13 billion to settle accusations that it misrepresented the quality of the loans it was selling?
- Can't policymakers fix some of these incentive problems? Doesn't forcing issuers such as JPMorgan to retain a portion of the securities they issue align incentives and mitigate the asymmetric information problem?
- If asymmetric information didn't cause investor losses, does that mean it doesn't affect economic outcomes? (Spoiler: The answer is an emphatic no.)
- What about rating agencies? Didn't they know that deals were bad but lie to investors and say they were good?
By Paul Willen, senior economist and policy adviser at the Federal Reserve Bank of Boston, and
Kris Gerardi, associate economist and policy adviser at the Federal Reserve Bank of Atlanta.
1 In some situations, investors will hold beliefs that may be wrong on an individual asset-by-asset basis, but that are right on average. For example, they might not know which loans are the most likely to default, but their beliefs about the performance of the pool of loans will be, on average, right.
2 More generally, the revelation principle says that in any equilibrium of an asymmetric information game, we can confine our attention to equilibria in which all private information is fully revealed. For example, in Akerlof's (1970) example of equilibrium in the used car market, the seller knows whether the car is a peach or a lemon but only the lemons trade. Everyone knows which car is good (the one that the dealer doesn't sell), but the buyer doesn't buy it because he knows that the dealer would have an incentive to substitute a bad car.
January 22, 2014
Wall Street and the Housing Bubble
The conventional wisdom on the 2008 financial crisis is that finance industry insiders on Wall Street deceived naïve, uninformed mortgage borrowers into taking out unaffordable mortgages and mortgage-backed security (MBS) investors into purchasing securities backed by bad loans—mortgages and securities that had not been properly vetted and that would eventually default. This theory is on display front and center in the Academy Award-winning documentary Inside Job, and it has motivated new regulations aimed at realigning incentives among Wall Street insiders and their customers. (One such rule is the risk retention requirement in the Dodd-Frank Act, which we will discuss in some detail in a future post.)
We've written in support of an alternative hypothesis for the financial crisis—specifically, that overly optimistic views about house prices, not poorly designed incentives on Wall Street, are the better explanation for the crisis (for an example, see this 2012 paper). This alternative theory holds that investors lost money not because they were deceived by financial market insiders, but because they were instead misled by their own belief that housing-related investments could not lose money because house prices were sure to keep rising.
A new paper makes an important empirical contribution to this debate by inferring the beliefs of Wall Street insiders during the height of the bubble. The paper, titled "Wall Street and the Housing Bubble," performs a clever analysis of personal housing-related transactions (like home purchases) made by individuals who worked in the mortgage securitization business during the peak of the housing boom. The behavior of these mortgage insiders is compared with that of a control group of people who worked for similar institutions in the finance industry but did not have any obvious connection to the mortgage market. What the analysis finds should be an eye-opener for believers in the inside-job explanation of the crisis. There is no evidence that mortgage insiders believed there was a housing bubble in the 2004–06 period. In fact, mortgage insiders were actually more aggressive in increasing their personal exposure to housing at the peak of the boom. The increase in insider exposure contradicts the claim that insiders sold securities backed by loans that they knew would eventually go bad when the housing bubble burst.
The authors construct a random sample from the list of attendees of the 2006 American Securitization Forum, which is a large industry conference featuring employees of most of the major U.S. investment and commercial banks (as well as hedge funds and other boutique firms). The sample is mainly comprised of vice presidents, managing directors, and other nonexecutives in mid-managerial positions whose jobs focused on the structuring and trading of MBS. The authors refer to this group as "securitization agents." As a comparison group, they use a random sample of Wall Street equity analysts who covered firms that were in the S&P 500 in 2006 but did not have a strong connection to the housing market (in other words, the sample includes no homebuilders). These equity analysts worked for similar financial institutions, had similar skill sets, and likely experienced similar income shocks (in the form of bonuses during the boom) but did not have any experience in the securitization business and thus did not have access to any insider information. (As a second control group, the authors use a random sample of lawyers who did not specialize in real estate law.) The names of the securitization agents and the equity analysts are then matched to a database of publicly available information on property transactions. The final data set contains information on the number of housing transactions, the sale price of each transaction, some mortgage characteristics, and income at the time of origination for each individual in the sample spanning the period 2000–10.
Armed with this unique data set, the authors then implement a number of empirical tests to determine whether the securitization agents' beliefs about the likelihood of a housing crash differed from the beliefs of the control groups. The first test considers whether the securitization agents timed the housing market cycle better than the comparison groups by reducing their exposure to the market at the peak of the bubble (2004–06) by either selling their homes outright or downsizing. The second test is slightly weaker in that it simply tests whether the securitization agents were more cautious in their housing transactions by avoiding home purchases at the peak of the bubble to a greater extent than the control groups. The third test looks at whether the average return on housing transactions during the entire sample period was higher for the securitization agents. The final test considers a prediction of the permanent income hypothesis: if securitization agents were armed with superior knowledge of the impending collapse of the housing bubble, then through reductions in their expectations of permanent income, they should have decreased the size of their housing purchases relative to their current incomes by a greater amount than the comparison groups.
The results of these empirical tests show very little evidence to support the inside-job theory of the financial crisis. The authors conclude that there is "little systematic evidence that the average securitization exhibited awareness through their home transactions of problems in overall house markets and anticipated a broad-based crash earlier than others." If anything, the authors are being a little timid in their interpretation as the empirical results clearly show that securitization agents were significantly more aggressive in their housing transactions during the bubble period, which suggests that they held even more optimistic expectations of housing prices dynamics than did the control groups.
This is an important paper because it sheds light on one of the most striking aspects of the financial crisis, which the inside-job theory is unable to reconcile: the financial institutions involved in the creation of the subprime MBS and collateralized debt obligations (CDO)—the true "insiders," if you will—lost enormous amounts of money on those securities. The table clearly supports this observation. The firms that lost the most money from mortgage-related credit losses were the same investment and commercial banks that are being accused of profiting off of naïve investors by selling securities comprised of loans that they knew would eventually go bad. The table shows that these firms lost enormous sums of money, and the paper provides a simple answer to explain why: like the rest of the market, agents working at those firms believed that housing prices would continue to rise so that even the riskiest mortgages would continue to perform well.
Kris Gerardi, financial economist and associate policy adviser at the Federal Reserve Bank of Atlanta, with
Chris Foote, senior economist and policy adviser at the Federal Reserve Bank of Boston.
August 01, 2013
Government Policy and the Crisis: The Case of the Community Reinvestment Act
Commentators on both the right and left seem to agree on one aspect of the recent mortgage crisis: government policy was at the heart of it. But they disagree on which particular government policy is at fault. The theory from the left is that financial deregulation allowed mortgage lenders and securitizers to exploit both mortgage borrowers and the investors in mortgage-backed securities. On the right, the thinking is that the government instituted policies and programs that were designed to increase credit availability and expand homeownership—policies that induced lenders to make massively risky loans.
To test these theories, researchers must identify a specific change in government policy and then explain the effects this policy change should have. They must then turn to the data to show that the predicted effects occurred soon after the government policy was instituted. A new paper by Sumit Agarwal, Efraim Benmelech, Nittai Bergman, and Amit Seru weighs some evidence related to one government policy that has long been controversial in conservative policy circles: the Community Reinvestment Act (CRA). In particular, the authors claim that the CRA played a role in the mortgage crisis by encouraging banks to make risky loans. How does this research project hold up?
History of the CRA
Before we discuss the details, here is some background on the CRA, which was enacted in 1977. In a 2003 retrospective on the law, William C. Apgar and Mark Duda noted that the CRA "was built on the simple proposition that deposit-taking banking organizations have a special obligation to serve the credit needs of the communities in which they maintain branches" (Apgar and Duda 2003, 169). The act instructed regulators to conduct periodic CRA examinations to make sure that banks were meeting the credit needs of their deposit bases. To enforce compliance, regulators had to take a banking institution's CRA record into account whenever the institution applied to consolidate with some other institution or to expand its operations with new branches (Apgar and Duda 2003, 172).
What economic effect should we expect the CRA to have? For banks, the act changes the economics of mortgage lending. In effect, it adds an extra "shadow return" to each CRA-eligible loan, over and above the loan's usual financial return. For example, the risk-adjusted return on a particular mortgage loan may be 5 percent without the CRA, but this return could rise to 6 percent after the bank factors in the benefit of the loan to its CRA compliance—and by extension, to its ability to perform a profitable merger or open a profitable branch. Simple economic theory implies that after the CRA, banks should make more and riskier loans in CRA-eligible locations, all else being equal.
The Agarwal et al. paper provides evidence that the CRA did indeed lead to more lending and also to riskier lending. The authors argue that in the three quarters before and the three quarters following a CRA examination, the average lender would make more loans and riskier loans in CRA-eligible areas.
In principle, the evidence in the paper seems consistent with the theory: government policy to encourage more lending encouraged more lending. However, other researchers have raised strong objections to the paper's empirical design. Most notably, the University of North Carolina Center (UNC) for Community Capital published a paper that claims to rebut the evidence put forth in the Agarwal et al. study, asserting that the study's entire identification strategy is invalid and therefore the results are spurious. In our reading of the paper, we found three significant issues that make us skeptical of the authors' interpretation that the CRA played a significant role in the crisis by increasing the amount of risky lending during the housing boom.
First issue: Time periods do not correspond
As the authors of the UNC paper note, the six-quarter window that Agarwal et al. use to identify the causal impact of CRA examinations "rarely corresponds to the actual period that is covered by the CRA exam." Instead, the CRA examiners typically analyze loans originated well before the actual exam date. To illustrate the issue, the UNC paper looks at a CRA exam of JPMorgan Chase that occurred in June 2011. The authors, who obtained their information from the public record of the exam (the CRA Performance Evaluation), find that the exam covered mortgage originations from January 2007 through December 2010. In contrast, the Agarwal et al. six-quarter window would have run from October 2010 to March 2012, implying an overlap of only one quarter. And even that one quarter of overlap is unlikely—the authors point out that the CRA examiner evaluated only JPMorgan's market share of lending through 2009, as the 2010 data generated to comply with the Home Mortgage Disclosure Act (HMDA) were unavailable at the time of the exam. The implication is that JPMorgan would have had no incentive to increase CRA-eligible mortgage originations in the three quarters before the examination period, since the CRA examiner was not going to consider those loans anyway.
Another relevant example (obtained from correspondence with economists from the Federal Reserve Board of Governors) is the June 2006 exam of Citibank. That exam used 2004 HMDA data for the market share analysis and used data through 2005 to compute the bank's distribution of loans to low- and moderate-income borrowers or neighborhoods. Thus, there is almost no overlap between the data used by the CRA examiners and the window employed by Agarwal et al. If the UNC paper is correct in its assertion that CRA examiners often consider loans that are outside of the six-quarter window used by Agarwal et al., then their claim that institutions were ramping up their CRA-eligible lending in order to pass their CRA examinations is flawed.
Second issue: CRA treatment effects possibly overestimated
Agarwal et al. find an increase in lending resulting from the CRA in non-CRA-eligible census tracts for both high- and low-income households. Specifically, they stratify their sample based on income terciles and find that origination rates to borrowers in the bottom-income tercile in non-CRA-eligible tracts increased by 6 percentage points around the initiation of CRA exams. This result supports their interpretation because banks would obtain CRA credit for loans to these borrowers. However, the results also show that origination rates for borrowers in the highest-income tercile in the non-CRA-eligible tracts increased by almost 4 percentage points around the initiation of CRA exams. Since these loans did not count toward fulfilling CRA obligations, the effect cannot be interpreted as a CRA treatment effect. Rather, a reasonable interpretation of this estimate is that it is picking up an unobserved factor that happens to be correlated with the timing of the CRA examinations (that is, a spurious correlation). If this is the case, then the true CRA treatment effect in CRA-ineligible tracts is really the difference between the increase in origination rates for the borrowers in the bottom income tercile and the borrowers in the top income tercile, which is an economically small 2 percentage points. Furthermore, by lending to high-income borrowers in non-CRA-eligible tracts, banks would tilt the distribution of their lending away from areas targeted by the CRA, which would end up hurting them in a CRA exam. Thus, it's difficult to imagine a scenario in which banks would target these borrowers for CRA-related purposes.
Issue 3: Securitization an unlikely explanation for effects
Agarwal et al. argue that they find significant CRA effects on lending in the 2004–06 and 2007–09 periods but not during the 1999–2003 period, and they find significant CRA effects on default rates only in the 2004–06 period. The authors' explanation for this pattern is that 2004–06 was the period in which the securitization of mortgage loans peaked, and "banks are more likely to originate loans to risky borrowers around CRA examinations when they have an avenue to securitize and pass these loans to private investors after the exam" (p. 21).
There are at least three problems with this line of reasoning. First, private securitization markets shut down in the 2007–09 period, so they couldn't possibly explain the increase in lending in CRA-eligible tracts during that period. The GSEs were very active in securitizing mortgages during this period—but they were also very active in the early 2000s, so agency securitization doesn't seem like an adequate explanation either.
Second, while it is true that securitization could alter the risk-return tradeoff for mortgage lending—it does so by allowing mortgage originators to offload their credit risk by selling their loans into mortgage-backed securities—securitization would make this offloading possible and appealing to many mortgage originators, not just CRA lenders. The result could easily be a decline in mortgage lending by depository institutions in CRA-eligible areas rather than an increase, thanks to increased competition from nondepository institutions. In fact, we would argue that the empirical evidence supports this interpretation more than Agarwal et al.'s interpretation. The late 1990s and early 2000s saw the emergence of nondepository institutions that specialized in originating subprime mortgages and selling them to securitizers. These aggressive subprime lenders were typically not subject to CRA requirements, a fact that is consistent with the shrinking footprint of CRA institutions, which we discuss in more detail below. According to Bhutta and Canner (2009), only about 6 percent of subprime loans made in 2005 and 2006 were made to CRA-targeted populations by CRA-regulated lenders. In effect, one of the consequences of the dramatic rise in private-label securitization volume was that it created lots of competition among the riskier segments of the mortgage market. This situation likely resulted in less lending by banks in CRA-eligible areas rather than more.
Finally, perhaps a more fundamental reason to doubt that securitization explains the timing of the paper's effects is that securitization has been around a long time. Laws needed to be changed before securitization could take off, but these legal changes occurred in the 1980s. So if the CRA and securitization together formed a lethal combination for the mortgage market, then why did the crisis occur in the late 2000s rather than the late 1980s?
Even if CRA encouraged risk, would it really say much about government policy?
With these caveats in mind, what would a finding that the CRA encouraged risky lending really tell us? In our opinion, a finding that the CRA encouraged risky lending would probably tell us little about the role of government in the financial crisis.
The focus needs to be on quantitative magnitudes. The question of whether or not the CRA led to an increase in risky lending of any size may not be that interesting because it is hard to imagine a world in which the CRA would not have done so. Economists begin with the premise that banks are profit-maximizing entities, so they should make all the loans that increase their expected profits. If a loan is not made, then that is because the bank must have judged the loan to reduce expected profits rather than raise them. As we described above, the CRA increases the risk-adjusted return for certain loans, so that some of the loans a bank deems unprofitable in the absence of the CRA (because of risk-adjusted returns that were too low) become profitable with CRA. Because these are marginal loans in risk-adjusted returns, then risk must be increasing.
If we start with the assumption that the CRA leads to more risky lending, the more interesting question is how much risky lending is encouraged. As it happens, the quantitative magnitudes of the estimates in the Agarwal et al. study are quite small. For example, if we assume that the appropriate CRA treatment effect should only be measured using the difference in the increase in lending between CRA-eligible census tracts and CRA-ineligible tracts, then magnitudes are trivial. Specifically, the paper finds that the CRA increased origination rates in CRA-eligible census tracts relative to CRA-ineligible census tracts by somewhere between 1 and 3 percentage points, depending on the specific quarter around the initiation of the CRA exam. When one considers that the average origination rate in the Agarwal et al. sample is 72 percent, and only 15 percent of loan originations in the sample came from CRA-eligible tracts, this is an extremely small effect. The effect becomes even smaller if you adjust the baseline estimates to take into account the likely simultaneity bias that we discussed above in the subsection titled "Issue 2."
The CRA passed long before the crisis
In concluding, we should point out that the CRA went into effect in 1977, 30 years before the financial crisis. If the CRA did shift the risk-return tradeoff for mortgage lending, then why didn't risky lending take off in 1978 rather than 2003? Moreover, the footprint of CRA-regulated institutions in the mortgage market has shrunk dramatically since the law passed. Figure 1 (taken from Foote, Gerardi, and Willen ) shows that nondepository mortgage companies—which generally are not covered by the CRA—accounted for only 15 percent of mortgage lending when the CRA was passed in 1977. By the late 1990s, however, these non-CRA entities had grown to nearly 60 percent of the mortgage market. If the CRA is so toxic to the mortgage market, then it is puzzling why the act had no effect soon after its enactment, when it covered 85 percent of the mortgage market, yet led to an explosion of risky lending 25 years later, when it covered only 40 percent of the market.
Indeed, any attempt to link the recent crisis to government policies aimed at expanding mortgage credit and homeownership faces an uphill struggle. The basic problem is that the federal government has been deeply involved in housing and mortgage markets since at least the end of World War II. In particular, the Federal Housing Authority (FHA) and Veterans Administration (VA) loan programs began at about that time and were explicitly designed to extend homeownership to underserved populations. As figure 2 (also from Foote, Gerardi, and Willen, 2012) shows, the FHA and VA pioneered no and low down payment loans in the 1950s and 1960s. And as figure 3 shows, FHA loans accounted for 40 percent of loans outstanding in the 1970s and had default rates that were an economically massive 100 percent higher than non-FHA loans. In their size and their effect on housing markets, the FHA and VA were literally orders of magnitude more important than the CRA. Did government lead to risky lending? Yes! But it did so 30 years before CRA and 60 years before the recent financial crisis.
Chris Foote, senior economist and policy adviser at the Federal Reserve Bank of Boston,
Kris Gerardi, financial economist and associate policy adviser at the Federal Reserve Bank of Atlanta, and
By Paul Willen, senior economist and policy adviser at the Federal Reserve Bank of Boston
July 01, 2013
Misrepresentation, or a Failure in Due Diligence? Another Argument
In the last post we wrote together, we discussed a paper on the role of misrepresentation in mortgage securitization by Tomasz Piskorski, Amit Seru, and James Witkin (2013, henceforth PSW).1 That paper argues that the people who created mortgage-backed securities (MBS) during the housing boom did not always tell the truth about the mortgages backing these bonds. Today, we discuss a second paper on misrepresentation, this one by John M. Griffin and Gonzalo Maturama (2013, henceforth GM).2 The two papers have a similar research approach, and the two sets of authors interpret their results in the same way—namely, in support of the hypothesis that misrepresentation was an important cause of the mortgage crisis. We offer an alternative interpretation.
We believe that the evidence shows that investors were not fooled and that deception had little or no effect on investor forecasts of defaults. Consequently, deception played little or no role in causing the crisis (see the post on PSW for details). We do think, however, that some results in the GM paper have significant implications for our understanding of the crisis, although GM does not focus on these particular results.
We argue that one can interpret their evidence on misreporting as a measure of due diligence on the part of lenders. Many—including most notably the New York Attorney General's office in a lawsuit against JP Morgan—allege that the dismal performance of securitized mortgages made after 2005 relative to those made before 2005 reflects a precipitous drop in due diligence among lenders starting in that year. But GM's paper implies that there was no such decline. In fact, for most measures of due diligence, there is almost no time series variation over the housing cycle at all.
Before we discuss the paper's implications for underwriting standards, it is important to outline GM's basic research approach with regards to misrepresentation. As with PSW, GM's fundamental idea is to compare two sets of loan-level mortgage records to see if the people marketing MBS misrepresented what they were selling. Specifically, GM compare information about mortgages supplied by MBS trustees with public records data from deed registries, as well as data on estimated house prices from an automated valuation model (AVM). PSW, by contrast, compare MBS trustees' data with information from a credit bureau. In general, GM's choice to use public records data as the comparison data set is probably more functional.
While PSW refer to their credit bureau data as "actual" data, it is well known that credit bureau data also contain errors, a fact that complicates any study of misrepresentation. For example, PSW often find that the credit bureau reports a second lien for a particular mortgage borrower while the MBS trustees report no such lien. The implication in such instances is that the MBS trustees misrepresented the loan. But PSW must also acknowledge that the reverse discrepancy turns out to be equally likely. Just as often, second liens appear in MBS data and not in the supposedly pristine data from the credit bureau. No data set is perfect, but GM's public records data is no doubt much cleaner than the credit bureau data. For a purchase mortgage, the records filed at a deed registry are not only important legal documents, they are also recorded on or very close to the day that the mortgage is originated. As a result, the public records data come closer to being "actual" data than data from a credit bureau.
GM measure four types of "misreporting" with their data: 1) unreported second liens; 2) investors incorrectly reported as owner-occupants; 3) unreported "flipping," in which the collateral had been sold previously; and 4) overvaluation of the property, which is defined to occur when the AVM reports a valuation that is more than 10 percent below the appraised house value appearing on the loan application. To us, neither 3 nor 4 seem like reasonable definitions of misreporting. For point 3, issuers never reported anything about whether the house was flipped. This issue turns to be a moot point, however, as Figure 1 from GM (reproduced below) shows that flipping almost never occurred. Regarding point 4, it's not surprising that AVMs often report substantially different numbers than flesh-and-blood appraisers do, for the same reason that two people guessing the number of jelly beans in a jar are likely to disagree. Estimating the right value exactly is not easy, even for people (and automated computer models) with the best of intentions.
More consequential are GM's findings relating to misrepresentations of the types identified in points 1 and 2. Here, GM's findings are essentially the same as PSW's, though GM report much higher rates of misrepresentation than do PSW. However, GM acknowledges that the difference stems almost entirely from their decision to ignore refinance loans. According to Table IA.VIII in GM's appendix, refinances have dramatically lower misrepresentation rates. But just as the central findings of GM are similar to those in PSW, so is our critique. The historical evidence indicates that investors were properly skeptical of the data provided by MBS issuers. Moreover, deception did not prevent investors from making accurate forecasts about default rates among securitized loans. We direct the reader to our post on PSW for more details.
Though we do not believe that GM can persuasively link misrepresentation of MBS data to massive investor losses, an alternative interpretation of their data has the potential to shed light on the mortgage crisis. One way to interpret the level of misreporting—in particular, for occupancy—is as a measure of due diligence on the part of lenders. Neither PSW nor GM suggest that for any particular loan, the MBS issuer knew that the borrower was an investor and did not plan to occupy the property. Instead, these authors claim that someone along the securitization chain failed to do the necessary due diligence to determine if the borrowers who claimed to be owner-occupiers were in fact investors. This due diligence was certainly possible. A sufficiently motivated loan officer could have done exactly what GM did: match loan files with public records to figure out that a potential borrower did not intend to live in the house he was buying.3 As a result, we would expect that when due diligence goes down, occupancy misreporting would go up.
Obtaining a proxy measure of due diligence is useful, because many commentators have argued that the poor performance of subprime loans made after 2005 as compared to loans made before 2005 (see Figure 3 from Foote, Gerardi, and Willen, 2012) resulted from a precipitous drop in due diligence. For example, in the recent complaint against JP Morgan, the New York Attorney General's office writes that:
[Subprime lenders], as early as February 2005, began to reduce the amount of due diligence conducted "in order to make us more competitive on bids with larger sub-prime sellers."
So what does GM's proxy measure of due diligence show? With respect to occupancy, there is little or no change in the incidence of occupancy misreporting in 2005. Indeed, looking across the entire sample, we see that occupancy misreporting rose smoothly from about 11 percent in 2002 to a peak of about 13 percent in 2006. In other words, at the peak of the boom, the incidence of sloppy underwriting was almost the same as it was four years earlier. In fact, all four series reported by GM show the same pattern or lack thereof. With the exception of the first quarter of 2006, second-lien misreporting was uniformly lower during what commentator Yves Smith refers to as the "toxic phase of subprime" lending than it was in 2004 and 2003 when loans performed dramatically better.
By Paul Willen, senior economist and policy adviser at the Federal Reserve Bank of Boston, with help from
Chris Foote, senior economist and policy adviser at the Federal Reserve Bank of Boston, and
Kris Gerardi, financial economist and associate policy adviser at the Federal Reserve Bank of Atlanta
1 Piskorski, Tomasz; Amit Seru; and James Witkin. "Asset Quality Misrepresentation by Financial Intermediaries: Evidence from RMBS Market" (February 12, 2013). Columbia Business School Research Paper No. 13-7. Available at SSRN: ssrn.com/abstract=2215422 or http://dx.doi.org/10.2139/ssrn.2215422
3 For example, the loan officer could use the public records to determine if a potential buyer owned multiple properties, or if the buyer recently put another property in a spouse's name.
June 19, 2013
Asset-Quality Misrepresentation as a Factor in the Financial Crisis
A provocative new paper by Tomasz Piskorski, Amit Seru, and James Witkin (2013, henceforth PSW) presents evidence in support of a popular theory of the financial crisis. That theory is that issuers of mortgage-backed securities (MBS) misrepresented the credit quality of the loans backing those bonds. Without the proper information about what they were buying, MBS investors lost so much money that the entire financial system nearly buckled as a result. A closer look at the evidence, however, shows that any deception in the marketing of MBS had little or no effect on investor decisions, so it is unlikely that MBS misrepresentation played a significant role in the financial crisis.
The basic approach of the PSW paper is to compare two sets of loan-level mortgage datasets in a search for issuer misrepresentation. One set of records is provided by the MBS issuers themselves (formally known as trustees), while the other is a set of loan-level records from a credit bureau. PSW assumes that if they find an MBS trustee reported one fact about a given loan while the credit bureau data shows something different, then the MBS trustee misrepresented the loan. For example, assume that a trustee reported that the borrower on a particular loan was an owner-occupier, not an absentee investor. PSW consulted the credit bureau data to see whether the bureau reported a borrower living at the same address as the property. If this was not the case, then PSW say the loan was misrepresented by the MBS trustee as a loan for an occupier when in reality it was a loan for an investor (and thus much riskier than advertised).
Loans backed by investor properties were likelier to default
The headline finding is that misrepresentation was present. According to PSW, about 7 percent of investor loans were misrepresented as backed by owner-occupied homes. PSW also find that loans backed by investor-owned properties were much more likely to default, and that MBS issuers systematically failed to report the existence of second liens.
The link between misrepresentation about MBS and the mortgage crisis may at first glance seem obvious. MBS investors constructed their forecasts of loan defaults using the loan characteristics reported by MBS trustees. When defaults turned out to be higher than the investors expected—because the MBS trustees had misrepresented the loan characteristics—massive investor losses and a financial crisis resulted.
Yet the historical record reveals something puzzling. Despite the ostensible misrepresentation by trustees, investor forecasts of MBS performance were exceptionally accurate. The table comes from a Lehman Brothers analyst report from 2005 and provides forecasts of the performance of securitized subprime loans under varying scenarios for house prices. The bottom row of this table gives the "meltdown" scenario: three years of house-price declines at an annual rate of 5 percent. Under this scenario, Lehman researchers expected subprime deals to lose about 17 percent. In reality, prices fell 10 percent per year—double the rate in the meltdown scenario—yet the actual losses on subprime deals from that vintage are expected to come in around 23 percent.
Source: Lehman Brothers, "HEL Bond Profile across HPA Scenarios," in U.S. ABS Weekly Outlook, August 15, 2005.
This table shows that investors knew that subprime investments would turn sour if housing prices fell. The "meltdown" scenario for housing prices implies cumulative losses of 17.1 percent on subprime-backed bonds. Such losses would be large enough to wipe out all but the highest-rated tranches of most subprime deals. The table also shows that investors placed small probabilities on these adverse price scenarios, a fact that explains why they were so willing to buy these bonds.
If issuers were lying about the quality of the loans, then why did the investors produce such reasonable forecasts of loan defaults?
There are two closely related answers. The first comes from information economics: rational investors were properly skeptical of any information they couldn't verify, so they rationally assumed that there was some misrepresentation going on. The logic here is exactly that of the celebrated "lemons" model of George Akerlof, who won a Nobel Prize for his insight about the effect of asymmetric information on markets. Assume, for example, that used-car buyers know that used-car sellers have private information about the quality of the cars that they bring to the market. As a result, potential buyers assume that they will be offered only low-quality used cars (lemons), so these buyers offer appropriately low prices for all cars in the used-car market. The same skepticism was likely to be present in the market for mortgages. In our case, pooling across mortgage loans and securitization deals means that investors will sometimes overestimate the share of misrepresented loans and sometimes underestimate it, but on average they get it right.
The second, related, answer to the question of why misrepresentation doesn't matter is that this lack of trust leads investors to base their forecasts on the historical performance of the loans. In other words, investors do not construct a theoretical model of how often an idealized owner-occupant should default. Rather, the investors simply measure the previous default probabilities of loans that were represented as going to owner-occupants. As long as misrepresentation doesn't significantly change over time, the forecasts based on the investors' reduced-form statistical models would not have underpredicted defaults. (Indeed, the table above shows that this method seems to have worked pretty well.) Of course, one might worry that the misrepresentation problem did get worse over time. However, although the authors claim in the abstract that the problem got worse, the results in the paper show that differences between the level of misreporting in 2005 and 2006 and 2007 were minimal. Moreover, in many cases, the differences have the wrong sign.
Looking elsewhere for an explanation
At the end of the day, the PWS paper—like many others written since the crisis—tries to explain a fact that isn't really a fact. Investors didn't really think of securitized subprime loans as less risky than they actually were. The documentary evidence repeatedly shows that investors understood the risk inherent in purchasing MBS that were backed by loans to people with bad credit histories. As the table shows, analysts expected 17.1 percent losses in the meltdown scenario. If we assume a 50-percent loss rate for each default, then overall losses of 17.1-percent imply that 34.2 percent of the loans—more than a third—would go to foreclosure. With the benefit of hindsight, we can see that the real problem for investors was not that they didn't think subprime borrowers would default if house prices fell. Rather, they didn't think house prices would fall in the first place.
Finally, it is important to stress that the quantitative effects of the level of misrepresentation found in the paper are economically insignificant. Remember that the harm of misrepresentation for an investor arises because misrepresentation leads investors to under-forecast defaults. Quantitatively, the largest finding of misrepresentation reported by PSW is that 15 percent of purchase mortgages had some form of misrepresentation, and that the misrepresented loans were 1.6 times more likely to default. So, if an investor assumes that none of the loans were misrepresented, then a simple calculation shows that actual defaults are likely to come in about 1.09 times higher than forecast:
(0.85 x 1) + (0.15 x 1.6) = 1.09
This calculation implies that if a naïve investor forecasted, say, 8 percent defaults for a pool of subprime loans, then the true number would actually be 8 percent x 1.09 = 8.72 percent. But even this figure overstates the effect of misrepresentation, because it assumes that the investors had access to a "pure" data set that was uncontaminated by misreporting. In any case, for subprime loans originated in 2006, actual defaults came in about 40 percentage points over what was expected. In other words, even if we assume investors were completely naïve, misrepresentation can, at most, explain 0.72 percentage points out of 40.
We feel researchers should look elsewhere for an explanation for why investors lost so much money during the housing crisis. A good place to start is the belief by all actors in the drama— borrowers, Wall Street intermediaries, and investors—that housing prices could only go up.
By Paul Willen, senior economist and policy adviser at the Federal Reserve Bank of Boston, with help from
Chris Foote, senior economist and policy adviser at the Federal Reserve Bank of Boston, and
Kris Gerardi, financial economist and associate policy adviser at the Federal Reserve Bank of Atlanta
April 26, 2012
Can home loan modification through the 60/40 Plan really save the housing sector?
padding-top: 5px !important; margin-bottom: 0px !important; padding-bottom: 2000px !important; }
margin-bottom: 0px !important; padding-bottom: 0px !important; }
margin-bottom: 0px !important; padding-bottom: 0px !important; }
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
- Is the Share of Real Estate Sales to Investors Increasing?
- Investigating the Trend in Office Renovations
- Commercial Construction Update: Third-Quarter 2016
- Construction Lending Update: Have the Banks Finally Opened the Spigots?
- Construction Spending Update
- Teachers Teaching Teachers: The Role of Networks in Financial Decisions
- The Pass-Through of Monetary Policy
- Keeping an Eye on the Housing Market
- Do Millennials Prefer to Live Closer to the City Center?
- The Multifamily Market: Is a Hot Market Overheating?
- April 2017
- February 2017
- November 2016
- June 2016
- May 2016
- April 2016
- November 2015
- September 2015
- August 2015
- July 2015
- Affordable housing goals
- Credit conditions
- Expansion of mortgage credit
- Federal Housing Authority
- Financial crisis
- Foreclosure contagion
- Foreclosure laws
- Government-sponsored enterprises
- Homebuyer tax credit
- House price indexes
- Household formations
- Housing boom
- Housing crisis
- Housing demand
- Housing prices
- Income segregation
- Individual Development Account
- Loan modifications
- Monetary policy
- Mortgage crisis
- Mortgage default
- Mortgage interest tax deduction
- Mortgage supply
- Multifamily housing
- Negative equity
- Positive demand shock
- Positive externalities
- Rental homes
- Subprime MBS
- Subprime mortgages
- Supply elasticity
- Upward mobility
- Urban growth