November 17, 2011
Taking on the conventional wisdom about fixed rate mortgages
The long-term fixed rate mortgage (FRM) is a central part of the mortgage landscape in America. According to recent data, the FRM accounts for 81 percent of all outstanding mortgages and 85 percent of new originations.1 Why is it so common? The conventional wisdom is that the FRM is a great product created during the Great Depression to bring some stability to the housing market. Homeowners were defaulting in record numbers, the story goes, because their adjustable rate mortgages (ARMs) adjusted upward and caused payment shocks they could not absorb.
In a Senate Committee on Banking, Housing, and Urban Affairs hearing on October 20, some experts presented testimony that followed this conventional wisdom. As John Fenton, president and CEO, Affinity Federal Credit Union, who testified on behalf of the National Association of Federal Credit Unions, laid out in his written testimony:
Prior to the introduction of the 30-year FRM, U.S. homeowners were at the mercy of adjustable interest rates. After making payments on a loan at a fluctuating rate for a certain period, the borrower would be liable for the repayment of the remainder of the loan (balloon payment). Before the innovation of the 30-year FRM, borrowers could also be subject to the "call in" of the loan, meaning the lender could demand an immediate payment of the full remainder. The 30-year FRM was an innovative measure for the banking industry, with lasting significance that enabled mass home ownership through its predictability.
Of course, this picture of the 30-year FRM as bringing stability to the housing market has profound implications for recent history. Many critics attribute the problems in the mortgage market that started in 2007 to the proliferation of ARMs. According to the narrative, lenders, after 70 years of stability and success with FRMs, started experimenting with ARMs again in the 2000s, exposing borrowers to payment shocks that inevitably led to defaults and the housing crisis. Indeed, one of the other panelists at the hearing, Janis Bowdler, senior policy analyst for the National Council of La Raza, argued in her written testimony that "when the toxic mortgages began to reset and brokers and lenders could no longer maintain their refinance schemes, a recession ushered in record-high foreclosure rates."
I argue, on the other hand—both in my testimony at the hearing and in this post—that the narrative of the fixed rate mortgage as an inherently safe product invented during the Depression that would have mitigated the subprime crisis because it
eliminated payment shocks does not fit the facts.
Parsing the myths around the fixed rate mortgage
First, the FRM has been around far longer than most people realize. Most people attribute the FRM's introduction to the Federal Housing Administration (FHA) in the 1930s.2 But it was the building and loan societies (B&Ls), later known as savings and loans, that created them, and they created them a full hundred years earlier. Starting with the very first B&L—the Oxford Provident Building Society in Frankfort, Pennsylvania, in 1831—the FRM accounted for almost every mortgage B&Ls originated. By the time of the Depression, B&Ls were not a niche player in the U.S. housing market. They were, rather, the largest single source of funding for residential mortgages, and the FRM was central to their business model.
As Table 2 of my testimony shows, B&Ls made about 40 percent of new residential mortgage originations in 1929 and 95 percent of those loans were long-term, fixed-rate, fully amortized mortgages. Importantly, B&Ls suffered mightily during the Depression, so the facts simply do not support the idea that the widespread use of FRMs would have prevented the housing crisis of the 1930s.
Source: Grebler, Blank and Winnick (1956)
Note: Market percentage is dollar-weighted. Building and loan societies were the main source of funds for residential mortgages and almost exclusively used long-term, fixed-rate, fully amortizing instruments.
To be sure, at 15–20 years, the terms on the FRMs the FHA insured were somewhat longer than those of pre-Depression FRMs, which typically had 10–15 year maturities.3 The 30-year FRM did not emerge into widespread use until later. It must be stressed that none of the arguments that Fenton made hinge on the length of the contract. Furthermore, the argument that Bowdler made in her testimony—that by delaying amortization, a 30-year maturity lowers the monthly payment as compared to a loan with shorter maturity—applies as much to ARMs as it does to FRMs.
But even though the ARMs may not have caused the Depression, FRM supporters might ask, didn't the payment shocks from the exotic ARMs cause the most recent crisis? Again, the data say no. Table 1 of my Senate testimony shows that payment shocks actually played little role in the crisis.
Source: Lender Processing Services and author's calculations.
Note: Sample is all first-lien mortgages originated after 2005 on which lenders initiated foreclosure proceedings from 2007 to 2010.
Of the large sample of borrowers who lost their homes, only 12 percent had a payment amount at the time they defaulted that exceeded the amount of the first scheduled monthly payment on the loan. The reason there were so few is that almost 60 percent of the borrowers who lost their homes had, in fact, FRMs. But even the defaulters who did have ARMs typically had either the same or a lower payment amount due to policy-related cuts in short-term interest rates.
To be absolutely clear here, my discussion so far focuses entirely on the question of whether the design of the FRM is inherently safe and eliminates a major cause of foreclosures. The data say it does not, but that does not necessarily mean that the FRM does not have benefits. As I discussed in my testimony, all else being equal, ARMs do default more than FRMs, but since defaults occur even when the payments stay the same or fall, the higher rate is most likely connected to the type of borrower who chooses an ARM, not to the design of the mortgage itself.
The difficulty of measuring the systemic value of fixed rate mortgages
One common response to my claim that the payment shocks from ARMs did not cause the crisis is that ARMs caused the bubble and thus indirectly caused the foreclosure crisis. However, it is important to understand that this argument, which suggests that the FRM has some systemic benefit, is fundamentally different from the argument that the FRM is inherently safe. This difference is as significant as that between arguing that airbags reduce fatalities by preventing traumatic injuries and arguing that they somehow prevent car accidents.
Measuring the systemic contribution of the FRM is exceedingly difficult because the use of different mortgage products is endogenous. Theory predicts that home buyers in places where house price appreciation is high would try to get the biggest mortgage possible, conditional on their income, something that an ARM typically facilitates. When the yield-curve has a positive slope (in most cases) and short-term interest rates are lower than long-term interest rates, ARMs loans offer lower initial payments compared to FRMs. Thus, it is very difficult to disentangle the causal effect of the housing boom on mortgage choice from the effect of mortgage choice on the housing boom.
In addition, there is evidence from overseas that suggests that the FRM is not essential for price stability. As Anthony B. Sanders, professor of finance at the George Mason School of Management, points out in his written testimony, FRMs are rare outside the United States. A theory of the stabilizing properties of FRMs would have to explain why Canadian borrowers emerged more or less unscathed from the global property bubble of the 2000s, despite almost exclusively using ARMs.
By Paul Willen, senior economist and policy adviser at the Boston Fed (with Boston Fed economist Christopher Foote and Atlanta Fed economist Kristopher Gerardi)
1 First liens in LPS data for May 2011.
3 See the discussion in chapter XV of Leo Grebler, David M. Blank, and Louis Winnick (Princeton, NJ: Princeton University Press, 1956), 218–235; available on the website of the National Bureau of Economic Research.
November 17, 2011 in Homeownership, Housing prices, Mortgage crisis, Mortgage default, Subprime mortgages | Permalink
June 20, 2011
The housing wealth effect revisited
A lot of economic researchers have struggled to answer what seems like a simple question: how much does consumption rise when housing prices go up? Many economists believe that, by increasing the wealth of homeowners, rising house prices during the housing boom had a substantial positive effect on consumption (see this speech by former Fed Chairman Alan Greenspan). Along the same lines, the dramatic decline in home values during the Great Recession is thought by many to be a drag on consumption today.
Conventional wisdom among market participants, and to some extent academics, is that the marginal propensity to consume (MPC) out of housing wealth is somewhere between 3 and 5 cents per dollar for households in the United States (see this paper by Bostic, Gabriel, and Painter for a nice survey of the literature on housing wealth effects). In other words, on average, every $1 increase in housing wealth results in a 3–5 cent increase in consumption. However, many researchers are highly skeptical of these numbers for a number of reasons (see, for example, this 2009 critique). First, a basic life-cycle model that incorporates Friedman's permanent income hypothesis (PIH) would predict that modest changes in housing wealth (both anticipated and unanticipated) should result in changes in consumption that are smaller than the 3–5 cent estimate.
Second, most research that finds big MPCs out of housing wealth estimate these with aggregate consumption and wealth data (see, for example, this 2005 study by Case, Quigley, and Shiller). Aggregate regressions like these are highly susceptible to an omitted variables critique; it may be that both consumption and housing prices are driven by some other variable not accounted for in the regressions. One possibility is that as expected future income in a particular area goes up, the anticipated increase would cause residents in this area to consume more—because their future incomes are higher—at the same time house prices are rising—because more people would want to live in that particular area and enjoy these higher incomes. Ideally, one would use micro-level consumption and housing data to control for these confounding variables. Unfortunately, high-quality panel data on both housing values and household consumption have been hard to find.
An innovative new paper by Jie Gan of the Hong Kong University of Science and Technology recently published in the Review of Financial Studies may have partly solved these data issues. The paper uses property-transaction and credit-card data from Hong Kong to estimate the causal effect of changes in housing wealth on individual-level consumption behavior. For her mortgage and housing data, Gan uses a data set similar in quality and scope to those now being used in the United States (including some papers that we have written). Specifically, Gan obtained detailed mortgage data and some demographic information from a large Hong Kong bank for the period 1988–2004. She also obtained government data on the universe of all housing and mortgage transactions in Hong Kong from the early 1990s to the mid-2000s, which allowed her to construct district-level house price indices. For her consumption data, Gan used monthly credit card statements from the largest credit card issuers in Hong Kong, available for the early 2000s. While credit card spending is certainly not a perfect measure of consumption, Gan argues that credit
cards account for more than 20 percent of consumer spending in Hong Kong and are used to purchase a diverse, representative set of products.
Merging these three data sets yields a panel of about 12,000 homeowners along with monthly information on credit expenditures and home values during the 2000–2 period. Gan then estimates a regression of quarterly consumption growth, measured with the individual-level credit-card data, on quarterly growth in lagged housing values, measured at the district level, and a set of individual fixed effects that control for time-invariant individual characteristics. The regression also includes a set of interaction variables between quarterly time dummies and occupational categories. These variables control for changes in income growth that may be correlated with both house price movements and consumption growth.
Gan finds that the elasticity of consumption growth to house price growth is about 0.17, which implies that a 10 percent increase in house price growth leads to a 1.7 percent increase in consumption growth. While this is a large effect, it translates into a slightly lower MPC out of housing wealth than the 3–5 cent effect common in previous studies. The reason is that the estimated sensitivity of consumption growth to house price growth that Gan estimates must be multiplied by the consumption-to-housing wealth ratio in Hong Kong in order to construct an estimated MPC out of housing wealth. The consumption-to-housing-wealth ratio is relatively small in Hong Kong (11.5 percent) because housing is extremely expensive. Consequently, the MPC out of housing wealth in Hong Kong is about 2 cents per dollar. While 2 cents seems low, especially given previous empirical evidence, Gan argues that this seemingly low MPC should not be construed as evidence that changes in housing wealth have only a small effect on economic activity. To the contrary, because housing is so expensive in Hong Kong, a small MPC implies substantial impacts of changes in house prices on the real economy.1
Given these large and statistically significant results, an obvious question is whether they truly correspond to causal effects, or whether omitted variables might still be causing problems. The answer is probably mixed. On one hand, Gan's data is a significant improvement over previous studies on multiple dimensions. The micro-level credit-card panel data allow her to use individual-level fixed effects, something not possible when using aggregate consumption data. As Gan points out, a fixed-effects specification controls for unobserved household heterogeneity that could lead to biased estimates. Other studies have used micro-level consumption data from the Panel Study of Income Dynamics (PSID), but that data set only measures expenditures on food and has been found to contain substantial measurement error (see Runkle 1991 for evidence of substantial measurement error in the PSID measure of food consumption). Furthermore, the district-level house price indexes used to calculate estimates of home values are more disaggregated than the state-level and even MSA-level house price indexes that previous studies have used. As a result, Gan's price indexes probably suffer less from measurement error than the price indexes in other work.
But even with these significant improvements in data quality, Gan may not have solved the endogeneity issue. As Gan writes in her paper, "[s]ince housing prices are available only at the aggregate level or at the level of the metropolitan statistical areas (MSAs), the observed consumption sensitivities may be driven by economic- or MSA-wide shocks that simultaneously affect housing prices and consumption." In other words, from an econometric standpoint, a regression of consumption growth at the individual level on house price growth at the district level is only identified from time-series changes in average consumption growth at the district level and house price growth at the district level.2 That means that other, unobserved district-level variables could potentially be driving the correlation between consumption growth and house price growth. As noted above, one potential omitted variable is expectation of higher future income at the district level.
Gan downplays this issue by noting that many Hong Kong residents tend to work and reside in different districts. As a result, district-level shocks are less likely to simultaneously influence both housing prices and consumption. But as an empirical matter, it is not clear from the paper how common it is to work and reside in separate districts. And from a theoretical perspective, it is not clear whether this argument effectively rules out the simultaneity issue. If a significant fraction of individuals who work in one district reside in the same outside district, then a shock to employment in a district could cause prices and consumption to co-move in the outside district. In addition, there are other types of district-level shocks that could create simultaneity regardless of the commuting patterns of its residents. For example, a public works project that improved existing infrastructure or that developed new park space (or created some other desirable public good) in a district would be expected to increase the attractiveness of the district—thus increasing housing demand, which would raise prices and possibly also average district-wide consumption, by changing the income/wealth composition of its population. To completely solve the simultaneity issue, one would need time-series variation in home values at the individual level. Unfortunately, that data is simply not available at this point—in Hong Kong or anywhere else.
Innovative tests of the housing wealth channel
Still, while Gan's data may not completely solve the simultaneity issue, some of her additional empirical tests go a long way toward alleviating these concerns. The first is a check on whether the consumption–housing wealth relationship is stronger for people who own multiple homes. A pure housing wealth effect would predict that the consumption behavior of individuals with higher levels of housing wealth would be more sensitive to changes in wealth. This is exactly what Gan finds in her data.
In a series of other tests, Gan tries to distinguish between the role of credit constraints and a precautionary savings motive in generating the positive housing-wealth effect. The credit constraint story is that increasing housing wealth relaxes borrowing constraints for individuals, which results in higher consumption. This effect is expected to be relevant only for households that are borrowing-constrained. The precautionary savings motive refers to the tendency for risk-averse individuals to accumulate wealth in order to self-insure against negative future income or wealth shocks. If individuals consider housing equity to be a component of precautionary savings, then an increase in housing wealth might increase consumption by reducing other components of precautionary savings. For example, if a household is saving a certain percentage of each paycheck for precautionary motives, then an increase in housing equity might be considered to be a viable substitute, and the household might be expected to decrease the percentage saved of each paycheck, and thus increase consumption.
Gan focuses on households that refinance as a first test in distinguishing between these two effects. She argues that borrowing-constrained households would need to refinance in order to access any equity increases, while precautionary savers would not need to refinance, since they could increase consumption by decreasing other forms of saving. Thus, if the relaxation of borrowing constraints is driving the positive elasticity of consumption growth to changes in housing wealth, then the elasticity estimate should be larger among households that refinance.3 Gan finds evidence of both effects: the estimated elasticity is significantly higher for households that refinance but is still positive and statistically significant for households that do not.
In a second test, she identifies households that are likely borrowing-constrained based on their use of credit card lines and separately estimates regressions for households that are close to their credit limit and those that are far from their limit. If the credit-constraint channel is present, the elasticity should be higher for the households that are close to their limit, while the precautionary savings channel, in contrast, predicts that the elasticity should be higher for less-constrained households and thus households that are far from their credit limit. The results of this exercise provide support for the precautionary savings motive, as less-leveraged households have a stronger consumption elasticity than more-leveraged households.
Gan performs a few more clever tests to try to distinguish between the credit-constraint and precautionary-savings channels. She finds strong evidence in favor of the precautionary savings channel and little evidence of an important role for credit constraints. It appears that in Hong Kong, households use housing wealth as an important component of an overall self-insurance strategy and view increases in housing wealth as a substitute for other types of savings and thus an opportunity to increase consumption. This is a very interesting and important finding on its own, but we also view it as strong evidence that Gan has truly identified a causal relationship between housing prices and consumption. The reason is that if simultaneity bias is truly responsible for the positive estimate of the consumption elasticity, then we wouldn't expect the estimate to be sensitive to different samples. The fact that it is, and in ways that are consistent with theory, suggests that Gan has really identified the impact of housing wealth on consumption behavior.
One final caveat is that Gan focuses solely on homeowners, leaving renters out of her analysis. We would expect renters to be hurt by increases in housing values. Thus, while Gan finds a significant positive effect of housing wealth on consumption for the population of homeowners in Hong Kong, we interpret her results as an upper bound of the effect of housing wealth on aggregate consumption in Hong Kong.
Kris Gerardi
Research economist and assistant policy adviser at the Federal Reserve Bank of Atlanta
1 I believe that what Gan means here, although it's not completely clear, is that since the housing stock in Hong Kong is so highly valued, a 1 percent change in house prices translates into a lot of additional consumption, even with a marginal propensity to consume out of housing wealth of 2 cents.
2 The reason is that any time the variation in a right-hand side variable is at a more aggregated level than the dependent variable, the coefficient estimate associated with that right-hand side variable is identified from the variation in both variables at the more aggregated level.
3 Another potential way that a borrowing-constrained household could access increases in housing equity is through home equity lines of credit (HELOC). HELOCs do not require households to refinance their mortgage. This possibility is not discussed in the paper, perhaps because HELOCs are not quite as popular in Hong Kong as they are in the United States.
June 20, 2011 in Housing boom, Housing prices | Permalink
April 18, 2011
What effect does negative equity have on mobility?
A debate has broken out in the housing literature over the effect of negative equity on geographic mobility. The key question is whether homeowners with negative equity—those who are "under water"—are more or less likely to move relative to homeowners with positive equity. In a paper published in the Journal of Urban Economics last year (available on the New York Fed website), Fernando Ferreira, Joseph Gyourko, and Joseph Tracy (hereafter FGT leaving out these categories) argue that underwater owners are far less mobile. Using data from 1985 to 2005, they find that negative equity reduces the two-year mobility rate of the average American household by approximately 50 percent. This is a very large effect and, if true, FGT's findings have important policy implications for both the housing market and the labor market today. For example, the economist and Nobel laureate Joseph Stiglitz, in testimony to the Joint Economic Committee of Congress on December 10, 2009, stated:
But the weak housing market will contribute to high unemployment and lower productivity in another way: a distinguishing feature of America's labor market is its high mobility. But if individuals' mortgages are underwater or if home equity is significantly eroded, they will be unable to reinvest in a new home.
The fear is that if people with negative equity can't move to new jobs, then the job-matching efficiency of the U.S. labor market will suffer, putting upward pressure on the unemployment rate. This type of "house lock" is exactly what the economy doesn't need as it emerges from the recent housing crisis and recession.
However, recent research by Sam Schulhofer-Wohl, an economist from the Minneapolis Fed, casts doubt on FGT's conclusions, as well as the economic intuition in Stiglitz's testimony. Schulhofer-Whol replicated the FGT analysis using the same data set (the American Housing Survey, or AHS) over the same sample period. But he found the exact opposite result: negative equity significantly increases geographic mobility.
What is the source of the discrepancy?
The difference in results stems from what at first blush seems like a small discrepancy in how the two papers identify household moves in the AHS. Here are the details: the AHS is conducted every two years by the U.S. Census Bureau as a panel survey of homes. That means that AHS interviewers go to the same homes every two years to record who lives there (among other pieces of information). For a home that is owner-occupied in one survey year, there are four possibilities regarding its status two years later. First, the home could still be owner-occupied by the same household as before. Second, the home could be owner-occupied by a different household. Third, it could be occupied by a different household that rents the home but doesn't own it. Finally, the home could be vacant.
In their paper, FGT treated the first category as a non-move and the second category as a move. FGT threw out of their analysis any observations that fell into the third and fourth categories.1 Dropping these last two categories, rather than coding them as moves, introduces significant bias into FGT's results. As Schulhofer-Wohl notes, it effectively assumes that households in negative equity positions are no more likely to rent out their homes, or leave them vacant when they move, than are households with positive equity. But it is relatively straightforward to show that this assumption is not borne out in the data. Specifically, Schulhofer-Wohl finds that positive-equity households who move sell their houses to new owner-occupiers two-thirds of the time. The other two possibilities (renting out the home or leaving it vacant) combine to occur only one-third of the time. In contrast, among negative-equity households who move, sales to new owner-occupant households occur half of the time, with the other two possibilities occurring the other half. Thus, by dropping the last two categories of transitions, FGT are artificially increasing the mobility rate of positive equity households relative to negative equity households.
Schulhofer-Wohl recodes the moving variable so that instances in which an owner-occupied home is rented or vacated also count as moves. He then re-estimates FGT's regressions. The coding change reverses the estimated relationship between negative equity and mobility. The new estimates show that negative equity raises the probability of moving by 10 to 18 percent, relative to the overall probability of moving in the AHS data. This of course is in marked contrast to FGT's results, where negative equity was found to significantly decrease the probability of moving.
What does theory tell us?
When thinking about what economic theory might say about the relationship between negative equity and mobility, it is important to distinguish how equity might affect selling versus how equity might affect moving. FGT write that their results suggest a role for what behavioral economists call "loss aversion." In this context, loss aversion can occur when owners are reluctant to turn paper losses into real ones by selling a home that has fallen in price. But, as Schulhofer-Wohl's analysis makes clear, it is possible and even common for households to move to different houses without selling their old ones. That means that loss aversion potentially affects the probability of selling a home without affecting the probability of moving.
Of course, while moving and selling are theoretically distinct, they often occur together in practice. One reason for the tight relationship between moving and selling involves liquidity constraints. Even short-distance moves entail nontrivial transaction costs, so households that do not have liquid wealth may not be able to move without selling their home. As a result, to the extent that negative equity decreases the probability of selling (via loss aversion), it may also decrease the probability of moving.
Besides loss aversion, there are at least two other channels through which liquidity constraints are relevant for the way that negative equity affects homeowner mobility. By definition, underwater households cannot retire their mortgages by selling their houses. Liquidity-constrained households that are also under water do not have the cash to make up the difference between the outstanding mortgage balance and sale price. As a result, negative equity could reduce selling (and, by extension, moving). On the other hand, liquidity-constrained households are more likely to simply default on their mortgages. Thus, negative equity might increase the probability of moving, though the moves that it facilitates are accompanied by foreclosures and not sales. Note that this "default channel" between negative equity and mobility depends importantly on expectations of future housing prices. Negative-equity households who do not think housing prices will rise any time soon are more likely to default on their mortgages, and thus move, than households who think that higher prices and restored housing equity are just around the corner.
The offsetting implications of liquidity constraints on mobility mean that theory doesn't provide a clean prediction for how negative equity should affect mobility. The question boils down to which implication is dominant in the data. The findings from the Schulhofer-Wohl paper suggest that the default channel may be relatively large, so concern about negative equity impeding homeowner mobility may be overblown.
Are these studies relevant to the current environment?
The sample period for both papers we have discussed ended in 2005. While we certainly believe that the issue addressed by both papers is very important, and that the Schulhofer-Wohl analysis corrects an important omission in the FGT study, we would offer a cautionary note to those who would extrapolate the findings of these studies to the current environment. The period 1985–2005 was a boom time in housing markets for most areas of the country. One way to see this is by noting the low number of negative equity observations in both the FGT and Schulhofer-Wohl papers. The majority of negative equity observations in the AHS data is likely from only a couple of areas in the country and from a narrow time period (most likely from the East and West coasts in the late 1980s and early 1990s). These places and time periods may be unrepresentative of the average negative-equity owner today.
Even more importantly, there were very few foreclosures from 1985 to 2005 relative to the past several years. This paucity of foreclosures was probably due not only to the low number of negative-equity households, but also to the low probability of foreclosure conditional on having negative equity. Recall that if housing prices are generally rising, households with negative equity will try hard to hang on to their homes and reap the benefits of future price appreciation, even if they are liquidity-constrained. It's probably safe to say that price expectations are lower today than they were in 1985–2005. Because low price expectations increase defaults, and because defaults and foreclosures increase the mobility of negative-equity owners through the default channel, it might be the case that the current effect of negative-equity on mobility is not only positive, but also even larger than the positive estimates in Schulhofer-Wohl's paper.
Kris Gerardi
Research economist and assistant policy adviser at the Federal Reserve Bank of Atlanta
1 This coding choice is not divulged in the FGT paper. The authors confirmed in private correspondence that it was a conscious decision to omit these categories and not a coding error, and that they are currently working on a revision of their original work that will address this issue.
April 18, 2011 in Housing prices, Mortgage default, Negative equity | Permalink
February 14, 2011
New study claims to solve the econometric problem of the link between foreclosure and house prices
Many policymakers are now concerned about how the next wave of foreclosures will affect the housing market. Analysts have cited a large "shadow inventory" of homes, referring to the mass of delinquent mortgages that have yet to make their way through the foreclosure process. When these foreclosures occur, they could raise the number of homes for sale and put downward pressure on house prices. They could also impose negative externalities to other homes in the same neighborhoods, sending house prices even lower. (We recently blogged about the so-called contagion effects of foreclosures on surrounding properties.)
These potential effects seem intuitive, but measuring them is not easy. The main problem is what economists call "simultaneity." Foreclosures lead to an increased supply of homes for sale, which can lower prices—but lower prices also increase the probability that borrowers have negative equity, which can lead to foreclosure. Thus, there is simultaneous causality: foreclosures can reduce prices, and lower prices can cause the negative equity that leads to foreclosure. As a result, simply showing a correlation between foreclosures and falling house prices is not sufficient to measure—or even establish—a causal effect of foreclosures on prices.
A new study by Atif Mian, Amir Sufi, and Francesco Trebbi claims to have solved this econometric problem. Their paper reports a substantial causal impact of foreclosures on not only house prices, but also residential investment and automobile purchases. However, the authors make a major data error that, in our opinion, invalidates a large part of their analysis. In addition, there are important conceptual issues that raise deep questions about their identification strategy, even if it is possible to correct the data error.
Can simultaneity be solved by classifying states as judicial, nonjudicial?
The authors attack the simultaneity problem with a classic method: they use differences in state laws as an instrumental variable. The essential idea is that states vary randomly as to whether they are judicial or nonjudicial. Judicial states are typically characterized by longer foreclosure durations, since the mortgage servicer must navigate through the legal system to get court approval, which usually entails a significant amount of time (see Pennington-Cross 2010 for a nice discussion). If the judicial/nonjudicial classification is random with respect to the health of state-level housing markets, then state laws will generate random variation in the number of foreclosures across states. Under these assumptions, using the classification as an instrument yields consistent estimates of the effect of foreclosures on house prices.
Of course, the classification of states into judicial and nonjudicial groups may not be random. It turns out that there is a strong regional component to this classification. Figure 3 in the Mian-Sufi-Trebbi paper shows that states in the Northeast and Midwest tend to be judicial, while the states in the South and West are mostly nonjudicial. It's no secret that problems in the U.S. housing market also have a strong regional character, with housing markets in Arizona, California, Florida, and Nevada (all located in the South and West) in particularly bad shape.
One way to check for the possibility of confounding effects across the two classifications of states is to compare their observable variables. The authors do this, and then claim that "states with a judicial foreclosure requirement are remarkably similar to other states in all attributes of interest except the propensity to foreclose" (p.3). But eyeballing their Figure 3 should give a reader pause. Nevada and Arizona, which are nonjudicial states, include the number one and two MSAs for new construction and for house price appreciation in the two years prior to the collapse of the mortgage market.1
Cross-state differences challenge regressions
Regional patterns in both state laws and housing markets cause problems for the authors' identification strategy. If we find that foreclosures tend to be more frequent in the nonjudicial states, this might be because foreclosing on delinquent homeowners is easier in those states, as the authors' identification strategy assumes. But high foreclosure rates in the nonjudicial states could also stem from negative shocks to housing demand in the parts of the country where the nonjudicial states happen to be located. Consequently, if we find that housing prices are lower and foreclosure rates are higher in nonjudicial states, then we can't be sure what's causing what. The high foreclosure rates could be causing the falling prices, as the authors' claim. But it could also be true that low regional demand and falling prices in the South and West are causing the high foreclosure rate—the very possibility that the authors were hoping to rule out.
The authors recognize that unobserved cross-state differences make the state-level experimental approach problematic so they propose an alternative set of regressions that are not subject to such criticism. In addition to estimating the first set of regressions—which, in the manner described above, uses all the states in the country—they estimate a second set that includes only ZIP codes adjacent to borders between judicial and nonjudicial states. The idea is that while unobserved heterogeneity across states could potentially invalidate the first set of regressions, this heterogeneity is less likely to be a problem in the second. In other words, the housing market in Arizona may differ markedly from the housing market in Maine and not just because Arizona is a nonjudicial state while Maine is judicial.
However, the ZIP codes just north of the Massachusetts-Rhode Island border are likely to have similar housing markets to the ZIP codes that are just south of this border. So, if the border ZIP codes in Massachusetts, which the authors label a judicial state, are experiencing higher foreclosures than the border ZIP codes in Rhode Island, a nonjudicial state, then differences in the two state's laws—and not unobserved differences in demand— are probably the reason why. And if the state laws are generating random variation in foreclosures, then the authors claim that this variation can be used to get a clean estimate of the causal effect of foreclosures on housing prices.
Problems in the data: Massachusetts, Wisconsin are misclassified
The authors find similar results in both sets of regressions. This similarity gives them some confidence that they have truly pinned down the direct effect of foreclosures on other economic outcomes. But here's where the data error comes in: the authors make a mistake in classifying at least two states as judicial or nonjudicial, which has major implications for their results. Specifically, they misclassify Massachusetts as judicial and Wisconsin as nonjudicial.2 Most sources, including the National Consumer Law Center (NCLC), reverse those classifications.
(For readers interested in the gory details, we show that for Massachusetts, there is no question that the NCLC is right.)
While the misclassification of two out of 50 states may seem minor, it turns out that Wisconsin and Massachusetts dominate the samples for the "border discontinuity" regressions. As the table shows, depending on the sample, using the alternative classification from the NCLC invalidates between 58 and 78 percent of the ZIP codes the authors use. Consider the sample that uses ZIP codes in 5-mile bands around state borders. Because it uses homes closest to state borders, this sample is least susceptible to unobservable differences between geographic areas, although we argue below that even 5-mile bands are inadequate to obtain clean identification. In this sample, classifying Massachusetts—correctly—as nonjudicial eliminates 70 percent of the comparisons.3
One response to this criticism would be to reclassify the states correctly and then reestimate both sets of regressions. The problem for the border regressions is that Massachusetts's and Wisconsin's borders with judicial and nonjudicial states respectively are sparsely populated and do not meet the authors' criteria for inclusion in the border sample. For example, farms and weekend homes comprise most of the properties in border ZIP codes between western Massachusetts and southern Vermont.
Misclassification proves detrimental to the identification strategy
As the authors have written the paper, they claim to find big differences in ZIP-code-level outcomes based on the judicial/nonjudicial classification. However, they use regressions with the wrong classification for most of the comparisons. If the identification strategy worked as the authors had hoped, their regressions would have implied that there are no important differences on either side of most judicial/nonjudicial borders because these borders in fact separated states with similar laws. However, because the regressions instead reported significant differences, some other important sources of heterogeneity across the state lines must exist—and if the authors can't control for heterogeneity across, say, the Massachusetts–Rhode Island border, the reader can't be expected to have confidence in their ability to control for unobserved differences between Massachusetts and Nevada.
Another way of putting this is that the authors have inadvertently performed and failed a falsification, or placebo, test on their data. They estimated their regressions on a sample of borders that are, for the most part, not characterized by differences in foreclosure laws, at least in terms of the judicial/nonjudicial classification, and found large effects where they should have found none. In our opinion, this is very strong evidence against their claim that judicial/nonjudicial foreclosure laws are a valid instrument for foreclosure rates. Even if the authors correctly reclassify the states and reestimate the IV regressions for the border sample, this failed falsification test still sheds doubt on the entire empirical strategy.
In addition to this primary critique, we also found some other important drawbacks in the analysis. For readers that are interested in learning more about these issues, here is a detailed discussion.
We remain unconvinced by the authors' claim that exogenous increases in foreclosures substantially reduce housing prices. This issue, of the link between foreclosure and house prices, is of first-order importance to policymakers, who struggle not only with the foreclosure problem itself but also with the potential effects of foreclosures on the economic recovery. However, the authors' research strategy is unlikely to be helpful in addressing these problems given the deep conceptual issues it did not deal with and the poor data on which it is based.
Kris Gerardi
Research economist and assistant policy adviser at the Federal Reserve Bank of Atlanta
Paul Willen
Research economist and policy adviser at the Boston Fed
1 Moreover, one of the main stylized demographic facts about the United States in the last 50 years has been the spread of population south and west across the country. Indeed, for the past 25 years, population has consistently and steadily grown twice as fast in the states the authors identify as nonjudicial compared to the states they identify as judicial.
2 Arguably, the authors misclassify as many as six states: the two listed plus Maryland, Nebraska, New Mexico, and Iowa. However, as we explain below, it's the misclassification of Massachusetts and Wisconsin that dramatically affects their results.
3 The authors are aware that there are alternative classifications but view the discrepancies as minimal, relegating the following comment to a footnote: "The only states that differ across these three classifications are Massachusetts, Nebraska, Oklahoma, Rhode Island, and Wisconsin." It is unclear whether they were aware that two of those states accounted for most of their border sample and that their border sample specification was not robust to the alternatives.
February 14, 2011 in Foreclosure contagion, Foreclosure laws, Housing prices, Mortgage crisis | Permalink
November 15, 2010
Mortgage relief in the Great Depression
Bemoaning the unwillingness of lenders to renegotiate loans in the current mortgage crisis, critics often point to the "old days" when, they argue, foreclosures were a rarity because of a different institutional setup. John Geanakoplos and Susan Koniak took this view in an op-ed they wrote for The New York Times.
In the old days, a mortgage loan involved only two parties, a borrower and a bank. If the borrower ran into difficulty, it was in the bank's interest to ease the homeowner's burden and adjust the terms of the loan. When housing prices fell drastically, bankers renegotiated, helping to stabilize the market.
Luigi Zingales uses almost the same language to make the same argument in an article in The Economist's Voice.
In the old days, when the mortgage was granted by your local bank, there was a simple solution to this tremendous inefficiency. The bank forgave part of your mortgage….
But what evidence do we have to back up these claims? The authors do not provide any direct evidence, do not provide a source for the evidence, and are not clear about what exactly they mean by "the old days." Until recently, there was little hard evidence on the subject. However, the simple existence of foreclosure crises in the past—in New England in the 1990s, for example, and across the nation during the Depression—is, at least on the surface, evidence that large numbers of mortgages escaped the seemingly win-win solution of modification even in the past.
In the last two years, two researchers, Andra Ghent of Baruch College and Jonathan D. Rose, an economist with the Federal Reserve System's Board of Governors, have gone to the records from the Depression, in one possible definition of "the old days," to see if, indeed, lenders renegotiated on a wide scale. Looking at the Depression gives us a good opportunity to test the theory that our current set of institutions are the problem because the institutional setup was different during the Depression—and if there was ever a profitable opportunity to modify loans, it was the period from 1932 to 1939. The extent of the crisis at the time minimized the information problems that we argue prevent profitable modifications now. Even if some borrowers who received modifications then could have afforded to repay their loans or more than their modified loans, there were so many deeply delinquent borrowers that the gains on the rest should have made up for it.
Comparing the 1930s Home Owners Loan Corporation to today's programs
In this post, we focus on the paper by Rose, who explores the Home Owners Loan Corporation (HOLC), a federal program aimed at transitioning troubled borrowers into new loans. (We will focus on Ghent's paper in the next post.) Many of today's critics have held up HOLC as an example of enlightened government policy; it was the model of the Hope for Homeowners (H4H) program enacted by Congress in 2008. Rose argues in his paper that, contrary to popular belief, HOLC actually did not offer particularly good terms to borrowers and instead focused mostly on assisting banks.
The last time that national housing prices crashed as low as they have over the past three to four years was during the Depression. As with today's crash, the 1930s fall coincided with a rash of mortgage defaults and foreclosures. According to Wheelock (2008), by 1933, 13.3 of every 1,000 mortgages in the United States was in foreclosure, and by the beginning of 1934, almost half of all outstanding urban home mortgages were delinquent. To stem the rising tide of foreclosures, the Roosevelt Administration created HOLC in 1933. Over the following three years, HOLC purchased and refinanced more than 1 million delinquent home loans. Although 1 million loans may not seem like that many, keep in mind that the U.S. mortgage market was significantly smaller 80 years ago and that current government programs have permanentlymodified or refinanced far fewer than 1 million loans during this crisis.
HOLC was a voluntary program that aimed to prevent foreclosures by refinancing troubled borrowers into mortgages that were more affordable. The program accepted applications from borrowers from June 1933 to November 1934, and then again from May to June 1935. Rather than paying cash for the mortgages, HOLC exchanged its own bonds for the lender's claim on the underlying house. The tax-exempt bonds were essentially equivalent to U.S. Treasury securities and thus could be considered very low-risk assets, especially relative to mortgage debt at the time. After HOLC purchased the loan from the lender, it would issue a new 15-year, fully amortizing mortgage at an interest rate of between 4.5 and 5 percent. The HOLC loans contained no prepayment penalties and had an interest-only option for the first three years. Thus, in most cases, a HOLC refinance gave the borrower a more affordable mortgage by lowering the interest rate and stretching out payments. Like modification programs today, HOLC tried to help borrowers in financial duress, discouraging applications from borrowers who just wanted a lower interest rate or borrowers for whom a refinance from a private lender was a viable alternative.
Did HOLC inflate home appraisals to encourage lenders to modify loans?
Until Rose did the research for his paper, a lack of data—other than a handful of aggregate statistics—prevented us from knowing much about HOLC activities. However, Rose was able to obtain loan-level data for a sample of HOLC loans from New York, New Jersey, and Connecticut.1 His goal was to analyze how HOLC encouraged lenders to part with their loans—after all, although HOLC bonds were much less risky than the mortgage debt that lenders held on their portfolios at the time, the bonds also carried lower interest rates. Thus, some 1930s lenders could have decided to take their chances with their old mortgages and refuse participation in the government's program. One key factor affecting the lender's decision was the amount of mortgage debt that HOLC was willing to refinance, which because of a combination of law and HOLC policy, was only 80 percent of the value of the property as estimated by a HOLC appraisal. If the amount of the new HOLC mortgage was lower than the old mortgage, then a participating lender would receive a "haircut" on the loan and the borrower would receive a principal reduction in addition to a lower interest rate and longer maturity schedule.2
Rose's main finding is that HOLC seems to have recognized that placing a low value on the house would make it more likely that the lender would have to offer a principal reduction, so that a low appraisal would reduce the chance that the lender would participate in the program. As a result, Rose argues, HOLC tended to place high values on properties in its appraisal process. This practice was good for lenders, who, in many cases, were paid in full for their mortgages. But high appraisals were bad for borrowers, because they made principal reductions less likely.3
A strength of the Rose paper is a careful explanation of how HOLC appraisals came to be relatively high. The HOLC appraisal formula consisted of three components. The first was the estimated present market value of the property, as in today's appraisals. The second was the estimated cost of purchasing the lot and constructing a similar structure. The third component was capitalizing the estimated monthly rental value of the property over the past ten years over a ten-year period assuming no discount rate. HOLC averaged these three measures to determine the final appraised value.
Because of the dramatic decline in housing values at the beginning of the Depression, the second and third measures were typically higher than the first one, the market-based measure, which resulted in appraisals being higher than market values on average. According to Rose's data, which consists of loan applications that HOLC accepted and mortgages that they refinanced, the appraisals exceeded the market-value estimates almost 74 percent of the time and equaled the market-value estimates approximately 8 percent of the time. The value that came out of this process was not necessarily the actual value the organization used, however. HOLC performed two additional reviews (at the district and then the state level) on each application to guard against any obvious errors. These two reviews were highly subjective. HOLC's policy was that these reviews could lower the final appraisal without bound but could raise the appraisal only by 10 percent. According to Rose's analysis, the final appraisal exceeded the market value estimate in 58.5 percent and equaled it in 10.6 percent of the cases, showing that the review process was proactive in adjusting the values that came out of the three-component appraisal formula. Even more compelling is the fact that almost one-third of the HOLC refinances had amounts that exceeded 80 percent of the estimated market value of the property, while HOLC regulations meant that none had amounts that exceeded 80 percent of the final appraisal.
Inflated appraisals helped keep Depression-era banks solvent, at the expense of homeowners
We view these findings as convincing evidence that HOLC was inflating appraisals in order to increase lender participation rather than directly reducing principal or trying to make lenders take write-downs. Rose takes this reasoning a step further and concludes that the inflated appraisals were motivated by the desire to keep banks and other lending institutions solvent, at the expense of mortgage borrowers. While he cannot offer a straightforward way to confirm this interpretation, Rose does offer some tantalizing contemporaneous quotations to support it. For example, he includes this quote from one of the HOLC loan examiners:
There seems to be a deliberate effort made by the Connecticut officials to make high appraisals with the purpose of holding up real estate values. We have had this suspicion confirmed in a recent interview with the State Counsel, Mr. Tierney. This gentleman, during a call in our office last month, stated that they believed it necessary, to prevent depreciation of realty value as much as possible so as to maintain the soundness of the banks and other financial institutions which had made mortgage loans during the past 5 years, to make high appraisals. His opinion was that many of these financial institutions would be today in an unsound condition if their mortgage loans were appraised on a basis of today's realty values. This statement is illuminating when appraisals by our Connecticut offices are being analyzed. (p. 19)
One potential problem with Rose's interpretation is that it assumes HOLC didn't negotiate to the fullest possible extent with lenders. That may be true, but it's also possible that lenders were unwilling to substantially write down loans, which would have forced HOLC to maximize lender participation by paying high prices.
What does the HOLC experience teach us about the current foreclosure situation?
Lenders today still seem reluctant to modify large numbers of troubled loans. In the Depression, HOLC solved the problem of lender reluctance with high appraisals and by essentially transferring a large amount of mortgage credit risk from the private sector to the public sector. By contrast, in today's Home Affordable Mortgage Modification (HAMP) program, government payments encourage a modification only when the modification is determined to be a win-win proposition for both the borrower and the lender. The small number of modifications to date may suggest that the number of win-win modifications is low. In other words, just as in the Depression, today's lenders may be willing to take their chances with existing mortgages rather than offer generous concessionary modifications to borrowers.
We find the HOLC policy of refusing to directly reduce mortgage principal to be potentially informative to the current modification debate in another way. Principal reductions appear to have been as rare in the 1930s as they are today (more on this in our post about the Ghent paper). Many have blamed securitization by private institutions for this pattern today, but if securitization were the real culprit, how do we explain a similar lack of principal reduction in a period when securitization was basically nonexistent?
By Chris Foote, senior economist and policy adviser at the Federal Reserve Bank of Boston, and Kris Gerardi, research economist and assistant policy adviser at the Federal Reserve Bank of Atlanta
1 While these three states are not necessarily representative of the entire country, they were among the worst hit by the foreclosure crisis of the 1930s.
2 HOLC could purchase the loan at full value from the lender and extend the borrower a principal reduction, but Rose shows that HOLC never did this.
3 Of course, even borrowers who did not receive principal reductions were helped because they could swap their short-term balloon mortgages with longer-term HOLC loans. A longer amortization period tends to lower monthly payments, which make homeownership more affordable. Borrowers who could not roll over balloon mortgages when they came due no doubt found HOLC mortgages particularly helpful in preventing foreclosure.
November 15, 2010 in Housing prices, Loan modifications, Mortgage default | Permalink
September 16, 2010
Answering the bloggers
In this post, we depart from our usual practice of addressing research by other authors and discuss a recent paper of our own: "Reasonable people did disagree: Optimism and pessimism about the U.S. housing market before the crash." We first review the main points of the paper, and then touch on some of the comments and criticisms that have been raised about it in various blogs.
Optimists, pessimists, and agnostics: Revisiting the three housing-boom camps
The paper basically asks a simple question: What did professional economists think about the housing boom while it was in full swing? Did most of them warn that the boom was really a bubble, sustained only by its own momentum and destined to crash someday? Or did most professional economists think that the run-up in housing values was entirely justified by fundamental factors, like low interest rates or higher rents? Our paper mainly discusses, rather than evaluates, the housing literature at this time. As a result, the paper is really more of a literature review rather than formal economic research. But we think that this type of review is helpful if people think that a disruptive gyration in asset prices could happen again.
By looking at the research papers and opinion pieces of professional economists, we concluded that these economists could be separated into three camps. All three camps agreed that house prices had risen dramatically—that was obvious from the data—but they differed on why. The "optimist" camp argued that there were good reasons why house prices had gone up, while the "pessimists" argued that there were no good reasons. A third group, whom we label "agnostics," refused to take a position one way or the other. Remarkably, most economists fell into this third camp. We argue that one reason for the popularity of the agnostic position is that economists generally believe that asset prices reflect fundamental factors unless there is a lot of strong evidence to the contrary. After all, asset prices are set in free markets, by people using their own money. Who are economists to say that the prices that people have agreed upon are wrong?
The strongest forms of this view comprise the various flavors of efficient-market theory, which argues that asset prices are always correct in some sense. While most economists would not go that far, they would still demand a lot of evidence before they would label some asset-price increase a bubble. This tendency to view asset prices as reflecting all available information makes economists generally reluctant to predict where asset prices are headed, or to say that some asset price is unsustainable. As we write in the paper:
In a sense, this reluctance to commit should not surprise anyone familiar with modern asset-pricing theory. The "Fundamental Theorem of Asset Pricing" implies that the evolution of asset prices is, to a first approximation, unpredictable. If housing was so obviously overvalued, as the pessimists suggested, then investors stood to make huge profits by betting against housing. By doing so, investors would have ensured that house prices would have fallen immediately. Regardless of whether the theory of the unpredictability of asset prices is correct, the theory is part of the basic training of almost every economist. Consequently, any economist who suggests to his or her peers that an asset is over- or undervalued faces a heavy burden of proof.
The optimists were reasonable people
Among bloggers, the paper has generated a mixed reaction. Naturally, we think the people that like the paper are absolutely correct in their assessments. But more seriously, we also think that our critics fail to appreciate the major point we were trying to make. In fact, some of their arguments actually provide further support for our position.
First off, many critics have said that housing-market optimists were right-wing ideologues who were deluded by efficient-market theory into thinking that asset bubbles were impossible. This story simply doesn't square with the facts. Two of the most prominent optimists were Chris Mayer and Todd Sinai. Both are professors at top business schools (Columbia and Wharton respectively), and both serve as co-heads of the Real Estate program at the National Bureau of Economic Research, a research body that includes economists of all ideological stripes. Even more importantly, one of Chris Mayer's seminal real-estate papers discusses "loss aversion" among potential house sellers. Loss aversion is a behavioral theory that blends psychology and economics, so it is not exactly high on the right-wing ideological research agenda, which is firmly rooted in the free market, rational decision-making tradition. By virtually any definition, Chris Mayer and Todd Sinai are reasonable people. Even so, based on their joint research, Mayer identified people who were predicting a collapse in house prices at the end of 2005 as "Chicken Littles."
The second issue has to do with economics as a science. Paul Krugman argues that "the evidence just screamed bubble." He points to the following picture, which shows real housing prices from the late 1970s through 2005.
This picture might convince the lay person that housing prices were headed for a fall. But the point of our paper is that convincing economists of this fact would have required much more work. Despite what some influential commentators might have us believe, large and persistent increases in real house prices have occurred in the U.S. without subsequent crashes. For example, Robert Shiller has found that U.S. housing prices increased by 60 percent between 1942 and 1947, adjusted for inflation. (This increase basically reversed a long-lasting period of low housing prices that occurred during the Depression.) After 1947, house prices were basically flat until the close of the 20th century. Nobody refers to the 1940s period as a house price bubble, because housing prices remained high for a very long time.
Another example of a long-lasting swing in housing prices is the boom–bust cycle along the U.S. coasts during the late 1980s and early 1990s. Krugman's post states that the late-'80s price rise in southern California depicted in this figure was a bubble, but that claim is debatable. Even after their recent crash, California housing prices are approximately 20 percent higher than they were during their late-1980s peak.
The responsibility of policymakers to create a robust financial system
What is most puzzling to us is the statement by some bloggers that we are essentially trying to exonerate the economics profession for failing to miss the housing bubble. Exactly the opposite is true. What we try to argue in the paper is that the economics profession simply doesn't have the tools to achieve real-time consensus on whether a bubble exists in this or that asset market. One response to this problem is for economists to relax their views on the correctness of asset prices. Undoubtedly, the recent housing cycle will nudge a lot of economists away from efficient-markets theory and the fundamental unpredictability of asset prices. For policymakers, we would argue the main lesson of our literature review is that they should not depend on economists to achieve consensus on whether some asset price reflects a potentially disruptive bubble. Rather, policymakers should construct a financial system that is robust enough to withstand steep drops in asset prices. If our review of the housing literature is any indication, there will be little warning of the next asset-market crash in the peer-reviewed economics literature.
By Kristopher Gerardi, research economist and assistant policy adviser at the Atlanta Fed, and Christopher Foote and Paul Willen, research economists and policy advisers at the Boston Fed
September 16, 2010 in Housing boom, Housing prices | Permalink
August 10, 2010
Part 2: A closer look at Michael Lewis's "The Big Short"
In the first part of our discussion of The Big Short, we argued that the bet against subprime mortgages that the book title refers to was not a sure thing, as the book's protagonists claimed, but a highly risky bet that just happened to turn out well. In this post, we focus on the logic of the "sure thing" claim, which is that the subprime bears were exploiting the ignorance of the subprime bulls. The idea that subprime bulls were ignorant is central to the thesis of the book, because it explains both why investors made such huge errors and why it was possible for the subprime bears to exploit, with little risk, the collapse of the mortgage market.
Lewis argues that the ignorance of the subprime bulls resulted from a combination of laziness and obfuscation by issuers of the securities they were buying. We argue, however, that the evidence, including some in the book itself, shows this claim to be patently incorrect. Issuers provided staggering amounts of information about mortgage securities and there was a whole industry of analysts on Wall Street who pored over that data and published literally thousands of reports.
The question, then, is this: if there was so much research going on and so much information available, why did so few investors get it right? The answer comes back to the same issue we discussed in the previous post: house prices. Investors bought subprime bonds not because they were too stupid or lazy to do research, or because issuers prevented them from getting relevant information, or because the securities were so complex that they couldn't figure out that a subprime borrower was a risky proposition. Subprime bulls bought the bonds because careful research based on vast amounts of loan-level data using state-of-the-art models (which, as we will show, was and still is largely correct) showed that if house prices continued to behave as they had for the previous ten years, the bonds would perform well. The research also showed that if house prices collapsed, investors would lose big, but, after ten years of solid appreciation in house prices, researchers viewed a big fall as unlikely.
Lewis's portrayal of those who lost money on subprime as bumbling and ignorant and those who made money as prescient is wrong and it is not a mere detail, it is the heart of the book. Lewis writes:
…a smaller number of people—more than ten, fewer than twenty—made a straightforward bet against the entire multi-trillion-dollar subprime mortgage market and, by extension, the global financial system. In and of itself it was a remarkable fact: The catastrophe was foreseeable, yet only a handful noticed. (p. 105)
What we argue here is that to "foresee" the crisis, one had to explore something to which the subprime bears paid little attention: the evolution of house prices. Whether the fall in house prices that ultimately caused all subprime bonds to default was itself foreseeable is a question that we will return to in subsequent posts, but even the most outspoken subprime bear, Michael Burry, would have a hard time explaining how the main focus of his research—"reading dozens of prospectuses [of subprime mortgage bonds] and scour[ing] hundreds more"—gave any special insight into the dynamics of house prices.
Was the market opaque?
Lewis argues that the issuers of mortgage-related securities "had a special talent for obscuring what needed to be clarified." But to outsiders, specialist terminology often sounds deliberately obscure: why do doctors say the results are "negative" when an X-ray shows good news? The typical buyer in the mortgage marketplace was a specialist, and those who weren't specialists were spending hundreds of millions of dollars and could afford to, and usually did, hire experts to explain to them what was going on.
In the end, Lewis's examples mostly demonstrate his ignorance of the market, not anybody's deliberate attempts to deceive investors. For example, he claims that "a bond backed entirely by subprime mortgages, for example, wasn't called a subprime mortgage bond. It was called an ABS, or asset-backed security" (p. 127). As Lewis himself says, ABS is a class of securities that included "bonds backed by credit card loans, auto loans and other, wackier collateral…" (p. 95n). As such, these securities historically had been characterized by default rates that were literally orders of magnitude bigger than those on mortgage-backed securities (MBS), which were composed of prime residential mortgages. Thus, to the typical buyer of securities, the ABS designation did precisely the opposite of what Lewis claims—it actually drew attention to the credit risk inherent in subprime mortgages.
Another major error along these lines concerns Lewis's discussion of the Alt-A market. Lewis writes that:
Alt-A was just what they called crappy mortgage loans for which they hadn't even bothered to acquire the proper documents—to verify the borrower's income, say. "A" was the designation attached to the most creditworthy borrowers; Alt-A, which stood for "Alternative A-paper," meant an alternative to the most creditworthy, which of course sounds a lot more fishy once it is put that way. (p. 127)
This is just wrong. Alt-A loans were made to borrowers with impeccable credit who, for various and perfectly valid reasons (self-employment being the most common one) could not document income in the standard way. If a borrower had several years' worth of income in the bank, no other debt, and a credit score that indicated that he or she had not missed a payment on anything for years, lenders would rationally overlook his or her inability to provide a letter from an employer documenting income. By calling the loans Alt-A and not A, the lender drew attention to the fact that the loan did not have traditional documentation. The historical credit performance of Alt-A loans was very, very close to that of prime loans and vastly better than that of subprime, so to call Alt-A loans subprime would be completely misleading. As far as investors were concerned, the main difference between Alt-A and A paper involved prepayment risk: Alt-A loans prepaid less and thus were more valuable to investors.1
Were the "shorters" the only people to do serious research on subprime mortgages?
Another central claim of the book is that Wall Street analysts did not seriously research the market. The following passage suggests that until March of 2007, researchers on Wall Street did not pay attention to the details of the pools of loans they were trading.
On March 19 his salesman at Citigroup sent [Michael Burry], for the first time, serious analysis on a pool of mortgages. The mortgages were not subprime but Alt-A.* Still, the guy was trying to explain how much of the pool consisted of interest-only loans, what percentage was owner-occupied, and so on—the way a person might do who actually was thinking about the creditworthiness of the borrowers. "When I was analyzing these back in 2005," Burry wrote in an e-mail, sounding like Stanley watching tourists march through the jungle on a path he had himself hacked, "there was nothing even remotely close to this sort of analysis coming out of brokerage houses. I glommed onto ‘silent seconds'* as an indicator of a stretched buyer and made it a high-value criterion in my selection process, but at the time no one trading derivatives had any idea what I was talking about and no one thought they mattered." (p. 194)
In fact, researchers had done exactly that sort of detailed analysis for years. This paper provides a detailed discussion of the state of mortgage research in the years 2003–2006, reviewing a relatively small sample of the contemporary literature, which still amounts to dozens of reports. Burry's claim to be the only person doing standard credit analysis–"In doing so, [Burry] likely also became the only investor to do the sort of old-fashioned bank credit analysis on the home loans that should have been done before they were made (p. 50)"—is fatuous.
In fact, some of the quotes in the book suggest that the subprime bears, and not the bulls, were the ones who had little understanding of the details. Lewis writes:
As early as 2004, if you looked at the numbers, you could clearly see the decline in lending standards. In Burry's view, standards had not just fallen but hit bottom. The bottom even had a name: the interest-only negative-amortizing adjustable-rate subprime mortgage. (p. 28)
A loan cannot simultaneously be "interest only" and "negative amortizing." Interest only means you pay only the interest every month and "negative amortizing" means you pay less than the interest so that the principal balance of the loan actually increases over time. Unlike the distinction between ABS and MBS, the distinction between these two terms is easy to understand, even for a nonspecialist, and a self-proclaimed expert like Michael Burry should have understood it. But a careful student of prospectuses like Michael Burry should also have a hard time digging up a subprime negatively amortizing loan. "Option-ARMs," largely the only loans that allowed negative amortization in the United States, rarely ever appeared in subprime deals; they were generally considered Alt-A or prime and most were held in the portfolios of banks and never securitized.
In fact, Lewis erodes his own case by providing compelling evidence that other investors were trying to exploit subtle differences between pools and must have done exactly the sort of detailed loan-level analysis that Burry claims was not going on:
A smaller group used credit default swaps to make what often turned out to be spectacularly disastrous gambles on the relative value of subprime mortgage bonds—buying one subprime mortgage bond while simultaneously selling another. They would bet, for instance, that bonds with large numbers of loans made in California would underperform bonds with very little of California in them. Or that the upper triple-A-rated floor of some subprime mortgage bond would outperform the lower, triple-B-rated, floor. Or that bonds issued by Lehman Brothers or Goldman Sachs (both notorious for packaging America's worst home loans) would underperform bonds packaged by J.P. Morgan or Wells Fargo (which actually seemed to care a bit about which loans it packaged into bonds). (p. 105)
The fact that investors who had done such detailed research made "spectacularly disastrous gambles" refutes the idea that the success of the subprime bulls reflected their willingness to do research.
Why did the subprime bulls believe in the market?
If so many investors did so much research, why didn't they bet against subprime? Lewis hears and reports the right answer over and over again. They didn't believe that house prices were going to fall. On page 89, he quotes one participant: "For the bonds to default, he now said, U.S. house prices had to fall, and Joe Cassano didn't believe house prices could ever fall everywhere in the country at once."
On page 157, he quotes another:
"We asked everyone the same two questions," said Vinny. "What is your assumption about home prices, and what is your assumption about loan losses." Both rating agencies said they expected home prices to rise and loan losses to be around 5 percent—which, if true, meant that even the lowest-rated, triple-B, subprime mortgage bonds crafted from them were money-good.
To me, the most compelling piece of evidence about what the subprime bulls got wrong, the smoking gun that makes sense of what happened, is the following table from a Lehman Brothers report from August of 2005 titled "HEL Bond Profile across HPA Scenarios."
| ||||||||||||||||||||||||||||||
| Source: Lehman Brothers 2005 |
Lehman Brothers analysts used a default model to predict losses for deals made up of mortgages originated in the second half of 2005 under different scenarios for house prices.
There are two key things to notice in the table. The first is the researchers predict catastrophic losses for the "meltdown" scenario of 5 percent annual house price declines. A 17 percent loss means that anything below a AAA-rated bond was essentially wiped out. Because the collateralized debt obligations (CDO) were composed of BBB-rated bonds from these deals, the meltdown scenario implies complete default on the CDOs. The actual price fall that took place was roughly twice as bad as the meltdown—annual declines of 10 percent rather than 5 percent—but the predictions of the model were largely correct: the deals based on these loans should rack up losses of about 23 percent. Thus, this table completely and utterly invalidates the argument that researchers at the top investment banks did no research and were completely ignorant of what they were buying or selling and had no idea that there was any possible scenario in which the bonds might lose.
The second thing to notice about the table is in the last column. The researchers assigned the meltdown scenario a 5 percent probability—a better outcome than the one that actually obtained. More importantly, they assigned 80 percent probability to house price appreciation of 5 percent or more, scenarios where the losses were sufficiently small that even the BBB-rated bonds were "money-good," scenarios in which the heroes of The Big Short would have seen their bets expire worthless.
In a sense, the subprime bears, the heroes of The Big Short, profited from their own ignorance. Their basic thesis was that making loans to people with poor credit histories was dumb and massive losses were inevitable under any circumstances. But what subprime bears failed to understand was that making unsecured loans to borrowers with poor credit histories generally leads to large credit losses—it's called payday lending—but making loans secured by an asset with a rising price is a low-risk business. The subprime bear logic that making mortgages to borrowers with problematic credit histories was guaranteed to fail would have generated massive losses between 1995 and 2004, as actual outcomes resembled scenarios 1, 2, and 3 from the Lehman Brothers' report chart year in and year out. It was their good fortune, not their astuteness, to make the bets in 2006.
By Paul Willen, research economist and policy adviser at the Boston Fed (with Boston Fed economist Christopher Foote and Atlanta Fed economist Kris Gerardi)
August 10, 2010 in Housing prices, Mortgage crisis, Subprime mortgages | Permalink
July 06, 2010
The Big View of Michael Lewis's "The Big Short"
Author's note: This is the first of two posts on The Big Short. This one addresses the overall theme of the book. The next will focus on the book's details—in particular, the question of whether issuers obfuscated or even deliberately misled investors about subprime mortgage securities.
In The Big Short (Norton, W. W. & Company, 2010), Michael Lewis provides a narrative of the subprime mortgage crisis through the stories of a set of unconnected investors, including Michael Burry of Scion Capital, Steve Eisman of Frontpoint, and Jamie Mai and Charlie Ledley of Cornwall Capital, all of whom made a common bet against subprime mortgage bonds and won big. The book is a treasure trove of anecdotes about the crisis and deserves the wide audience it has received. But, in terms of reforming Wall Street or preventing another crisis, The Big Short—the title refers to the controversial Wall Street practice of short selling—could do more harm than good because it perpetuates the idea that it is possible to make large amounts of money in financial markets while taking little or no risk.
A reader might get the impression that the protagonists of The Big Short went to the roulette table knowing exactly where the ball would land. But they actually took a huge gamble when they bet against subprime bonds in 2006. In fact, had they tried their bet in 2005, The Big Short would not have been written.
Composition of pre- and post-2005 mortgages were not dissimilar
To understand the extent of the risk that characterized the bets these investors made, one needs to realize that the high levels of defaults on the loans in the deals that the investors bet against were not inevitable and were, in fact, unprecedented. The difference in performance between subprime loans originated before 2005 and after 2005 is like night and day. Loans originated before 2005 were only
half as likely to default as the loans in the pools that Burry and his cohorts invested in. More importantly, while none of the BBB-rated bonds in the deals that originated in 2004 and 2005 defaulted, virtually all did for the deals that The Big Short investors traded on.
What accounts for the differences in performance between pre-2005 and post-2005 loans? None of the variables that Burry or any of the traders in The Big Short focus on. For example, while it's true that 35 percent of subprime loans originated in 2005 and 2006 had reduced documentation, that percentage is only marginally higher than the 30 percent with reduced documentation before 2005. Yes, it's true that 78 percent of the subprime loans originated after 2005 had "teaser rates" that would expire two or three years after origination—but 67 percent of the loans originated before 2005 had the same feature. Sure, 73 percent of the loans originated after 2005 had prepayment penalties, but that was down from the 74 percent that had them before 2004. Plus, the average FICO score had actually risen to 615 from 607.
House prices are the difference
So if the composition of mortgages did not change dramatically between 2004 and 2006, what explains the completely different outcomes? The answer is house prices. House prices are central to mortgage performance. When they are rising, few mortgages default because borrowers who can't make their payments can profitably sell to avoid foreclosure. Lewis's statement that a "person with a FICO score of 550 was virtually certain to default and should never have been lent money in the first place" (p. 100) is misleading. In fact, in the pre-2005 pools in which the average FICO score was 607, fewer than 5 percent of borrowers missed a payment in the first year of the loan.
The point here is that the timing of the bet was crucial. Simply betting against deals because they contained loans that were incompletely documented or because the FICO scores were low would have been a losing strategy in 2001 or 2002 or 2003 or 2004 or 2005. Nor was there anything inevitable about the timing of the fall in house prices. By 2003, standard measures of the relationship of house prices to income or to rents already showed overvaluation, and yet house prices continued to rise and even accelerate for the next three years.
In short, the success of the traders in The Big Short was not based on logic and skill but on their willingness to gamble that house prices would fall dramatically in 2006. It's not clear that they understood how much their bet depended on the evolution of house prices.1
Subprime bulls had an extraordinarily successful run
There is a kind of irony here in that in writing The Big Short, Michael Lewis falls for precisely the same logic that created the subprime crisis in the first place. The logic is that investors who make money are smart and investors who lose money are dumb. The problem is that someone writing in 2005 could and did tell an identical story about the subprime bulls.2 Then the smart people were the investors in subprime bonds who made huge returns because the high interest rates on the loans more than compensated them for the surprisingly small credit losses. The dumb ones were the suckers who invested in prime mortgages. What Lewis forgets is that in 2006, the subprime bulls were coming off a string of successful investments no less impressive than that of the heroes of his book. These subprime bulls were the smart ones at that time.
This dissonance is perfectly illustrated in one of the high points of the book when Lewis tells the story of Howie Hubler, a trader at Morgan Stanley:
Some people enjoyed Hubler, some people didn't, but, by early 2004, what others thought didn't really matter anymore, because for nearly a decade Howie Hubler had made money trading bonds for Morgan Stanley (p. 200).
Lewis understands the dangers of Hubler's logic:
Hubler and his traders thought they were smart guys put on earth to exploit the market's stupid inefficiencies. Instead, they simply contributed more inefficiency (p. 215).
Hubler's subprime bets end up going grievously wrong and he ends up causing the biggest single trading loss, $9 billion, in Wall Street history. Yet in many ways, the heroes of the book have a lot in common with Howie Hubler. Like Hubler, they took big bets. Like Hubler, they thought they were exploiting the stupidity of others. And like Hubler, they made a lot of money. Hubler ended up losing big, which may eventually happen to the stars of The Big Short.
The lesson of the crisis really is that one should be skeptical of any trader or fund manager promising high returns without risk. But for many who read The Big Short, the book will only make them look harder for that big score.
By Paul Willen, research economist and policy adviser at the Boston Fed (with Boston Fed economist Christopher Foote and Atlanta Fed economist Kris Gerardi)
2 See "Making sense of the subprime crisis," by Kristopher Gerardi, Andreas Lehnert, Shane Sherlund, and Paul Willen, Brookings Papers on Economic Activity, Fall 2008: 69–145.
July 6, 2010 in Housing prices, Mortgage crisis, Subprime mortgages | Permalink
June 18, 2010
Explaining local supply elasticities: Quantifying the importance of space limitations in housing prices
It's an old joke among real estate professionals: the price of a house depends on three factors—location, location, and location. A half-million dollars will buy a sprawling estate in Wichita but only a modest apartment in New York. Economists have long suspected that geographic space limitations have a lot to do with this discrepancy. The logic goes that houses are cheap in Wichita because there is plenty of surrounding space on which to build new homes, but Manhattan Island isn't getting any larger. Unfortunately, it has been difficult to precisely quantify the importance of space limitations in housing prices, due to data limitations as well as a large number of potentially confounding factors that also matter for housing markets.
An exciting new paper by Albert Saiz of the University of Pennsylvania's Wharton School makes a significant advance in this area by using detailed geographic data to show how both space limitations and local development policies affect housing prices. This paper will be a big help to those who study the geographic pattern of urban development in the United States. It will also be widely cited in future studies of local development policy. But, as we argue below, one must be careful when using the Saiz results to infer anything about the rise-and-fall in housing prices during the recent housing bubble.
The main empirical contribution of the Saiz paper is to calculate, for each major city, the amount of land that cannot be used to build houses because of geographical constraints. Lying next to a major body of water such as an ocean or one of the Great Lakes clearly limits a city's ability to build, and figuring out which cities are affected is trivial. In fact, a "coastal dummy variable" has long been used in models of housing prices. But new construction can also be limited by inland waterways, such as wetlands or lakes. It is also tough to build on steeply sloped terrain, as in the foothills of mountains. To measure the importance of these latter two factors on a city-by-city basis, Saiz uses Geographic Information System (GIS) techniques and highly detailed topographical data from the United States Geological Survey. Specifically, for all the land within 50 kilometers of each large city's center, Saiz measures the geographic characteristics of finely disaggregated parcels (for example, 30-meter-by-30-meter squares). He then adds up the prevalence of water or steep slopes across individual parcels to get overall, city-specific measures of geographically determined space constraints. According to this method, the most constrained city in the country is Ventura (CA), where almost 80 percent of the land within 50 kilometers of the city center is undevelopable. Miami, Fort Lauderdale, and New Orleans are close behind, with about 75 percent of their land essentially off limits to residential construction. At the other end of the spectrum lie cities like Wichita (KS), Indianapolis (IN), Dayton-Springfield (OH), and McAllen-Edinburg-Mission (TX), where less than 2 percent of the land is undevelopable.
Saiz then confronts his city-specific measure of space constraints with the data. He finds that the fraction of undevelopable land is correlated with house price levels, house price growth from 1970 to 2000, average income levels, the extent of tourism, and a measure of creativity (measured by the number patents awarded to residents of the city). The index is not correlated with the size of cities, or with the share of city residents who have a bachelor's degree or work in manufacturing. Many of these correlations are consistent with the detailed theoretical model that Saiz builds to explain how space constraints should matter for a number of city-specific variables. 1
A question that Saiz explores in depth is how space constraints matter for the way a city adjusts to a positive demand shock, which causes more people to want to live in that city. Cities that are space-constrained have a tough time accommodating a positive shock with a burst of new construction. In formal terms, the supply curve for new homes in space-constrained cities will be inelastic, or close to vertical. As a result, a positive shift in housing demand will result mostly in higher housing prices, not more construction.
These predictions are borne out by the data. Using regression models, Saiz finds that local supply elasticities are determined both by his space-constraint measure and by an index of local building regulations, which was also developed at Wharton.2 Interestingly, the local-regulation index is itself strongly correlated with Saiz's space-constraint index, as space-constrained cities tend to have stricter regulatory limits on new construction. This correlation provides compelling evidence for something that many housing economists have long suspected—local voters seek to protect the values of expensive homes by preventing new homes from being built.3 This finding may be puzzling to some, as it may be hard to imagine why land-constrained cities would need to implement further restrictions on new construction. However, some new development, perhaps via dense apartment buildings, is usually possible. Note that unlike a lot of correlations in economics, we can be reasonably sure that the direction of causality runs from space constraints to local regulations, not the other way around. After all, it is hard to create a new mountain, lake, or ocean through the political process.
The Saiz paper is forthcoming in a top economics journal, and its results are already being used by housing economists. We draw from it ourselves in a paper that investigates why so many economists missed the housing bubble.4 But we caution that one should not push the Saiz results too far. The Saiz paper concerns the slopes of housing supply curves in different cities. As a result, it says nothing about shifts in housing demand that might have occurred during the housing boom. For example, the Saiz results would predict that, during the housing boom, prices in high-supply-elasticity cities like Wichita would rise less than prices in low-elasticity cities like Boston. Sure enough, this is what we find in the data. However, this finding does not prove that the boom was caused by some uniform, nationwide increase in housing demand (arising, for example, from easier subprime lending, or from lower interest rates). It is true we would expect a uniform demand increase to have a small effect on Wichita's prices and a big effect on Boston's prices. But because Wichita has a flat supply curve, its house prices will be stable no matter what happens to demand there. To determine whether Wichita and Boston saw similar increases in demand, one would have to look not only at prices but also at quantities (that is, new construction). Researchers should therefore be careful when using the Saiz results to study the housing boom—a point we hope to revisit in future posts.
By Chris Foote, senior economist and policy adviser at the Boston Fed (with Atlanta Fed economist Kris Gerardi and Boston Fed economist Paul Willen)
__________________________________________________________________________________________________________________________1 We will refer interested readers to the paper for the model's details, but it assumes that people can move freely between cities, so that the utility of people is equalized across cities. The model also assumes all the jobs in a city are located in a central business district, to which each city resident must commute.
2 Specifically, to measure the extent of land-use restrictions in a metropolitan area, Saiz uses the Wharton Residential Urban Land Regulation Index (WRI) that was created by Gyourko, Saiz, and Summers (2007).
3 Because the land-regulation index is endogenous, Saiz also provides some instrumental variables (IV) regressions that depend on city-level variation in general political attitudes toward regulation to identify the specific effect of housing regulations on housing-supply elasticities. The IV results are also consistent with a role for both space constraints and regulations in determining housing-supply elasticities.
4 Specifically, we show that housing prices rose in places like Phoenix and Las Vegas, which, according to the Saiz results, should have had very elastic housing prices (flat supply curves). Thus, we argue that some other factor besides building constraints must be invoked to explain the rapid run-up in prices for cities like these.
June 18, 2010 in Housing boom, Housing prices, Positive demand shock, Supply elasticity | Permalink
