Real Estate Research
Real Estate Research provides analysis of topical research and current issues in the fields of housing and real estate economics. Authors for the blog include the Atlanta Fed's Kristopher Gerardi, Carl Hudson, and analysts, as well as the Boston Fed's Christopher Foote and Paul Willen.
March 05, 2014
Government Involvement in Residential Mortgage Markets
With the federal funds rate effectively at the zero lower bound, the Federal Reserve has used unconventional forms of monetary policy. Specifically, the central bank has issued forward guidance about the policy path and purchased large amounts of U.S. Treasury bonds and agency mortgage-backed securities (MBS) in an effort to lower long-term interest rates. In the case of agency MBS purchases, a goal was to stimulate the housing market by lowering mortgage rates. Two papers presented at the recent Atlanta Fed/University of North Carolina—Charlotte conference, "Government Involvement in Residential Mortgage Markets," examine the extent to which the Federal Reserve has been successful.
Unanticipated announcements of new large-scale asset purchase programs (LSAPs), or changes in these programs, should have an immediate impact on interest rates under the assumption that the total stock of purchases is what matters. On the other hand, the flow of purchases may independently influence markets through portfolio rebalancing—that is, investors reacting to the removal of duration and convexity from the market—and liquidity effects—that is, ease of reselling assets in the future. Diana Hancock and Wayne Passmore conduct an empirical analysis of the differing effects of the LSAPs in their paper, "How the Federal Reserve's Large-Scale Asset Purchases Influence MBS Yields and Mortgage Rates." Using weekly data from July 2000 to June 2013, the authors estimate a model of MBS yields that controls for market expectations about future interest rates and find that the Federal Reserve's market share of MBSs and Treasuries are negatively related to MBS yields. Under their model, the Fed's holdings of MBSs has lowered MBS yields by 54 basis points and the Treasury holdings have pushed down the MBS yields another 70 basis points. This finding is consistent with portfolio rebalancing and liquidity effects being important determinants of MBS yields.
The finding is important because it suggests that agency MBS yields and mortgage rates will rise when the Federal Reserve reduces its MBS purchases—even if the Fed successfully signals that it intends to keep rates low for an extended time. On the margin, this could serve to dampen housing market activity, including refinancing. Since the beginning of the third phase of quantitative easing (QE3), the Fed's MBS market share has grown from around 17 percent to nearly 24 percent. Given the estimate that each percentage point increase in market share pushes MBS yields down by 2.3 basis points, reducing the Fed’s MBS market share back to a pre-QE3 level would push MBS yields up by around 16 basis points, which is unlikely to be economically meaningful.
While the cost of mortgage refinancing is borne by MBS investors, most of the policy attention is placed on the benefit to borrowers through an increase in their disposable income. In cases where borrowers are underwater and having difficulty making mortgage payments, refinancing can ease borrowers’ financial distress. In "The Effect of Mortgage Payment Reduction on Default: Evidence from the Home Affordable Refinance Program," Jun Zhu, Jared Janowiak, Lu Ji, Kadiri Karamon, and Douglas McManus estimate that during the 2009 to 2012 period, a 10 percent reduction in monthly mortgage payments for participants in Freddie Mac’s Home Affordable Refinance Program (HARP) resulted in a 12 percent reduction in the monthly default hazard for 30-year fixed rate conventional-conforming mortgages. This likely helped slow the flow of mortgages entering the foreclosure pipeline and gave neighborhoods time to stabilize.
Government involvement in residential mortgage markets takes many forms (see the conference website for papers that examine other forms of intervention). Taken together, the papers discussed here provide evidence that the Federal Reserve's LSAPs and Freddie Mac's HARP did put downward pressure on longer-term interest rates and facilitated refinancing activity that helped to support housing and mortgage markets. The tapering of the MBS LSAPs should not be a cause for concern. The Fed’s strong forward guidance combined with the slow, judicious pace of the taper imply that stagnation of the housing market is unlikely.
By Carl Hudson, Director, Center for Real Estate Analytics in the Atlanta Fed's research department, and
October 04, 2011
The uncertain case against mortgage securitization
The opinions, analysis, and conclusions set forth are those of the authors and do not indicate concurrence by members of the Board of Governors of the Federal Reserve System or by other members of the staff.
Did mortgage securitization cause the mortgage crisis? One popular story goes like this: banks that originated mortgage loans and then sold them to securitizers didn't care whether the loans would be repaid. After all, since they sold the loans, they weren't on the hook for the defaults. Without any "skin in the game," those banks felt free to make worse and worse loans until...kaboom! The story is an appealing one and, since the beginning of the crisis, it has gained popularity among academics, journalists, and policymakers. It has even influenced financial reform. The only problem? The story might be wrong.
In this post we report on the latest round in an ongoing academic debate over this issue. We recently released two papers, available here and here, in which we argue that the evidence against securitization that many have found most damning has in fact been misinterpreted. Rather than being a settled issue, we believe securitization's role in the crisis remains an open and pressing question.
The question is an empirical one
Before we dive into the weeds, let us point out why the logic of the above story need not hold. The problem posed by securitization—that selling risk leads to excessive risk-taking—is not new. It is an example of the age-old incentive problem of moral hazard. Economists usually believe that moral hazard causes otherwise-profitable trade to not occur, or that it leads to the development of monitoring and incentive mechanisms to overcome the problem.
In the case of mortgage securitization, such mechanisms had been in place, and a high level of trade had been achieved, for a long time. Mortgage securitization was not invented in 2004. To the contrary, it has been a feature of the housing finance landscape for decades, without apparent incident. As far back as 1993, nearly two-thirds (65.3 percent) of mortgage volume was securitized, about the same fraction as was securitized in
2006 (67.6 percent) on the eve of the crisis. In order to address potential moral hazard, securitizers such as Fannie Mae and Freddie Mac (the government sponsored enterprises, or GSEs) long ago instituted regular audits, "putback" clauses forcing lenders to repurchase nonperforming or improperly originated loans, and other procedures designed to force banks to lend responsibly. Were such mechanisms successful? Perhaps, perhaps not. It is an empirical question, and so our understanding will rest heavily on the evidence.
The case against securitization
Benjamin Keys, Tanmoy Mukherjee, Amit Seru, and Vikrant Vig released an empirical paper in 2008 (revised in 2010) titled "Did Securitization Lead to Lax Screening? Evidence from Subprime Loans" (henceforth, KMSV) that pointed the finger squarely at securitization. The paper won several awards and, when it was published in the Quarterly Journal of Economics in 2010, it became that journal's most-cited paper that year by more than a factor of two. In other words, it was a very well-received and influential paper.
And for good reason. KMSV employs a clever method to try to answer the question of securitization's role in the crisis. Banks often rely on borrowers' credit (FICO) scores to make lending decisions, using particular score thresholds to make determinations. Below 620, for example, it is hard to get a loan. KMSV argues that securitizers also use FICO score thresholds when deciding which loans to buy from banks. Loan applicants just to the left of the threshold (FICO of 619) are very similar to those just to the right (FICO of 621), but they differ in the chance that their bank will be able to sell their loan to securitizers. Will the bank treat them differently as a result? This seems to have the makings of an excellent natural experiment.
Figures 1 and 2, taken from KMSV, illustrate the heart of their findings. Using a data set of only private-label securitized loans, the top panel plots the number of loans at each FICO score. There is a large jump at 620, which, KMSV argues, is evidence that it was easier to securitize loans above 620. The bottom panel shows default rates for each FICO score. Though we would expect default to smoothly decrease as FICO increases, there is a significant jump up in default at exactly the same 620 threshold. It appears that because securitization is easier to the right of the 620 cutoff, banks made worse loans. This seems prima facie evidence in favor of the theory that mortgage securitization led to moral hazard and bad loans.
Reexamining the evidence
But what is really going on here? In September 2009, the Boston Fed published a paper we wrote (original version here, updated version here) arguing for a very different interpretation of this evidence. In fact, we argue that this evidence actually supports the opposing hypothesis that securitizers were to some extent able to regulate originators' lending practices.
The data set used in KMSV only tells part of the story because it contains only privately securitized loans. We see a jump in the number of these loans at 620, but we know nothing about what is happening to the number of nonsecuritized loans at this cutoff. The relevant measure of ease of securitization is not the number of securitized loans, but the chance that a given loan is securitized—in other words, the securitization rate.
We used a different data set that includes both securitized and nonsecuritized loans, allowing us to calculate the securitization rate. Figures 3 and 4 come from the latest version of our paper.
Like KMSV, we find a clear jump up in the default rate at 620, as shown in the bottom panel. However, the chance a loan is securitized actually goes down slightly at 620, as shown in the top panel. How can this be? It turns out that above the 620 cutoff banks make more of all loans, securitized and nonsecuritized alike. This general increase in the lending rate drives the increase in the number of securitized loans that was found in KMSV, even though the securitization rate itself does not increase. With no increase in the probability of securitization, it is hard to argue that the jump in defaults at 620 is occurring because easier securitization motivates banks to lend more freely.
The real story behind the jumps in default
So why are banks changing their behavior around certain FICO cutoffs? To answer this question, we must go back to the mid-1990s and the introduction of FICO into mortgage underwriting. In 1995, Freddie Mac began to require mortgage lenders to use FICO scores and, in doing so, established a set of FICO tiers that persists to this day. Freddie directed lenders to give greater scrutiny to loan applicants with scores in the lower tiers. The threshold separating worse-quality applicants from better applicants was 620. The next threshold was 660. Fannie Mae followed suit with similar directives, and these rules of thumb quickly spread throughout the mortgage market, in part aided by their inclusion in automated underwriting software.
Importantly, the GSEs did not establish these FICO cutoffs as rules about what loans they would or would not securitize—they continued to securitize loans on either side of the thresholds, as before. These cutoffs were recommendations to lenders about how to improve underwriting quality by focusing their energy on vetting the most risky applicants, and they became de facto industry standards for underwriting all loans. Far from being evidence that securitization led to bad loans, the cutoffs are evidence of the success securitizers like Fannie and Freddie have had in directing lenders how to lend.
With this in mind, the data begin to make sense. Lenders, following the industry standard originally promulgated by the GSEs, take greater care extending credit to borrowers below 620 (and 660). They scrutinize applicants with scores below 620 more carefully and are less likely to approve them than applicants above 620, resulting in a jump-up in the total number of loans at the threshold. However, because of the greater scrutiny, the loans that are made below 620 are of higher average quality than the loans that are made above 620. This causes the jump-up in the default rate at the threshold.
Figures 5 and 6 show that this pattern also exists among loans that are kept in portfolio and never securitized. The change in lending standards causes these loans, as well as securitized loans, to jump in number and drop in quality at 620 (and 660). However, as figure 3 shows, the securitization rate doesn't change because securitized and nonsecuritized loans increase proportionately. The FICO cutoffs are used by lenders because they are general industry standards, not because the securitization rate changes. This means the cutoffs cannot provide evidence that securitization led to loose lending.
But the debate does not end there. In April 2010, Keys, Mukherjee, Seru, and Vig released a working paper (KMSV2), currently forthcoming in the Review of Financial Studies, that responded to the issues we raised. According to the paper, the mortgage market is segmented into two completely separate markets: 1) a "prime" market, in which only the GSEs buy loans, and 2) a "subprime" market, in which only private-label securitizers buy loans. KMSV2 argues that only private-label securitizers follow the 620 rule and, by pooling these two types of loans in our analysis, we obscured the jump in the securitization rate in that market.
The latest round in the debate
We went back to the drawing board to investigate these claims. We detail our findings in a new paper, available here. In the paper, we demonstrate that the pattern of jumps in default—without jumps in securitization—is not simply an artifact of pooling, but rather exists for many subsamples that do not pool GSE and private-label securitized loans. For example, we find the pattern among jumbo loans (by law an exclusively private-label market), among loans bought by the GSEs, and among loans originated in the period 2008–9 after the private-label market shut down. Furthermore, as figure 7 shows, the private-label market boomed in 2004 and disappeared around 2008, while the size of the jump in the number of loans at 620 continued to grow through 2010, demonstrating that use of the threshold was not tied to the private market.
What's more, KMSV's response fails to address the fundamental problem we identified with their research design: following the mandate of the GSEs, lenders independently use a 620 FICO rule of thumb in screening borrowers. Even if some subset of securitizers had used 620 as a securitization cutoff, one would not be able to tell what part of the jump in defaults is caused by an increase in securitization, and what part is simply due to the lender rule of thumb. Consequently, the jump in defaults at 620 cannot tell us whether securitization led to a moral hazard problem in screening.
To put this in more technical jargon, KMSV use the 620 cutoff as an instrument for securitization to investigate the effect of securitization on lender screening. But the guidance from the GSEs that caused lenders to adopt the 620 rule of thumb in the first place means that the exclusion restriction for the instrument is not satisfied—the 620 cutoff affects lender screening through a channel other than any change in securitization.
We also found that the GSE and private-label markets were not truly separate. In addition to qualitative sources describing them as actively competing for subprime loans, we find that 18 percent of the loans in our sample were at one time owned by a GSE and at another time owned by a private-label securitizer—a lower bound on the fraction of loans at risk of being sold to both. Because the markets were not separate, the data must be pooled.
Our findings, of course, do not settle the question of whether securitization caused the crisis. Rather, they show that the cutoff rule evidence does not resolve the question in the affirmative but instead points a bit in the opposite direction. Credit score cutoffs demonstrate that large securitizers like Fannie Mae and Freddie Mac were able to successfully impose their desired underwriting standards on banks. We hope our work causes researchers and policymakers to reevaluate their views on mortgage securitization and leads eventually to a conclusive answer.
By Ryan Bubb, assistant professor at the New York University School of Law, and Alex Kaufman, economist with the Board of Governors of the Federal Reserve System
October 20, 2010
Securitized mortgage loan or not, lenders are not restructuring
In a new paper, Agarwal, Amromin, Ben-David, Chomsisengphet, and Evanoff (2010) finally put to rest the widespread belief that securitization massively exacerbated the foreclosure crisis by preventing lenders from renegotiating loans. The authors show that the data do not support the argument articulated by Paul Krugman and Robin Wells in the New York Review of Books:
In a housing market that is now depressed throughout the economy, mortgage holders and troubled borrowers would both be better off if they were able to renegotiate their loans and avoid foreclosure. But when mortgages have been sliced and diced into pools and then sold off internationally so that no investor holds more than a fraction of any one mortgage, such negotiations are impossible.
This post is the first in a three-part series in which we discuss recent studies, including that of Agarwal et al. (2010), providing evidence that the low modification rate has not resulted from an excess of securitized loans, what we call the "institutional view." These studies show, rather, that the low rate comes from lenders having imperfect information. This view—the "information view"—holds that lenders cannot determine whether a delinquent borrower will default even if the lender makes concessions.
While Agarwal et al. (2010) find that lenders fail to renegotiate 93 percent of seriously delinquent securitized mortgages, they also find that the figure drops only to 90 percent for portfolio loans without the supposed problems generated by securitization. Whether that 3-percentage-point difference really reflects securitization frictions is disputable, as we discuss below. But since most renegotiated mortgages fail anyway, it means that the elimination of securitization frictions would at most have reduced the number of foreclosures by less than 2 percent. The authors clearly show that Krugman and Wells and others who argue that securitization frictions were generating millions of unnecessary foreclosures are way, way off base. Securitization may or may not inhibit renegotiation, but most troubled borrowers cannot blame it for their situation, since their lender probably would not have helped them even if the lender owned the loan free and clear.
The trouble with imperfect information
We mention above the two schools of thought about why lenders are reluctant to renegotiate. Proponents of the institutional view argue that securitization creates perverse incentives for mortgage servicers, the agents that collect monthly mortgage payments from borrowers and who are given the responsibility for renegotiating troubled loans. In short, the institutionalists argue that servicers gain little from successful loan modifications, even though the ultimate owners of the mortgages (that is, the investors in the MBS) gain a lot. They claim that so few modifications take place because the incentives of mortgage servicers and investors were not properly aligned when the MBS was created.
The information view, on the other hand, holds that lenders face a difficult decision whenever they are confronted with a delinquent borrower, and they cannot easily predict which of three groups a delinquent borrower belongs to. One group of delinquent borrowers will "cure" on their own, becoming current on their loans without a modification. Another group will wind up defaulting even if they are given a modification. A third group will default without a modification but will remain current if their loans are modified. In other words, only modifications in the third group are profitable for lenders.
Unfortunately, lenders don't have the perfect information needed to place each borrower in the appropriate group. Lenders' profit-maximizing strategy may well make them stingy with modifications in general. Low modification rates mean that many borrowers in the third group will lose homes that could have been saved with a modification. But the low rates also mean that the lender does not incur losses by awarding modifications to borrowers in the first two groups.
Note that those who hold the information view argue that securitization is not an important issue because both MBS investors and owners of whole mortgages face the same information problems when deciding whether a modification is worthwhile.
Compelling evidence for the information view
Agarwal et al. (2010) do not explicitly aim to distinguish between the institutional and information views, but they do provide what we believe to be compelling evidence in favor of the information view. The researchers used a comprehensive database of troubled mortgages, known as the Mortgage Metrics database, to assess loss mitigation efforts by mortgage servicers in all of 2008 and the first five months of 2009. The Mortgage Metrics database contains detailed information on exactly how servicers handled delinquent loans for a wide range of institutions. (Other data sets force researchers to infer whether a modification was made from auxiliary information such as the interest rate or remaining maturity of the loan.) For example, the authors were able to measure how likely servicers were to offer borrowers repayment plans, in which arrears are tacked on to the remaining balance of the loan, as compared with offering concessionary modifications, such as interest-rate cuts or principal reductions. They were also able to determine whether lenders initiated and completed foreclosures or allowed borrowers such exit strategies as deeds-in-lieu-of-foreclosure, and whether lenders did nothing at all, waiting to see if troubled borrowers eventually cure on their own.
Most importantly, the database contains an extensive list of attributes of the troubled loans, which permitted the authors to look at the relationship between the likelihood of modification and such loan-level attributes as the borrower's credit history, and whether the loan was held in an MBS or in a lender's individual portfolio.
As we noted above, lenders sometimes offer delinquent borrowers repayment plans, giving them the chance to repay the loan under the original terms of the mortgage. Significantly, the repayment plan requires borrowers to pay back any arrears, usually with interest. Lenders may also offer troubled borrowers forbearance, which means the borrowers pay lower payments for some time and then make up the arrears at the end of the forbearance period. These two types of mortgage help are temporary measures aimed to help the troubled borrower through a difficult period. By contrast, loan modifications are specific, permanent changes to the terms of a mortgage after origination.
Among other things, the Agarwal et al. (2010) paper invalidates the argument that a focus on modifications is too narrow, proposed by Piskorski, Seru, and Vig (2010) and Mayer (2010: 18), and that other methods, like repayment plans and forbearance, were important forms of loan renegotiation. Table 2 in the paper shows quite definitively that loan modifications accounted for the vast majority of the resolutions of troubled loans that did not involve foreclosure proceedings during the crisis period.
"One message is quite clear: Lenders rarely renegotiate"
The paper has three additional major findings, one of them that loan modifications are indeed rare. According to Table 1.A, fewer than 10 percent of borrowers received a loan modification in the first six months after becoming 60-days delinquent (missing two mortgage payments). In other words, 90 percent of borrowers who became delinquent received no substantive assistance from the lender. This finding mirrors Adelino, Gerardi, and Willen (2010), who calculated the frequency of modification using a different data set over a slightly different time period. The Adelino, Gerardi, and Willen (2010) paper also reports a modification frequency under 10 percent, and concludes, "No matter which definition of renegotiation we use, one message is quite clear: lenders rarely renegotiate."
Another finding of the Agarwal et al. (2010) paper sheds some light on the debate between institutional and information explanations for the infrequency of modifications. Although securitization seems to have had some effect on the likelihood of modification in their data, the effects are economically small and difficult to interpret. Table 3 shows that loans securitized either by the government-sponsored enterprises (GSE), such as Fannie Mae and Freddie Mac, or by private institutions, which often handled subprime or jumbo loans, were between 3 and 6 percent less likely to receive a modification than were whole loans held in the portfolios of banks.
In some sense, this finding is evidence for the institutional school, since loans in MBSs were less likely to receive modifications. But while the 3- to 6-percentage-point difference is large relative to the overall modification rate, it is still small relative to the total number of troubled loans. Essentially, servicers do nothing to help 90 percent of delinquent private-label borrowers, compared to 87 percent of portfolio loans. Even if we assume that the entire 3-percentage-point difference between portfolio and private-label loans is a treatment effect related to problematic incentives in private securitization contracts (pooling and servicing agreements), it is still just 3 percent of delinquent mortgages. Moreover, given the extremely high redefault rates that have characterized modifications during this period, this difference translates into a reduction in foreclosure frequency of less than 2 percent. In other words, under this (extreme) assumption, if we solved all of the issues with private securitization contracts, we could prevent 2 percent of the foreclosures.
No evidence for causal link between securitization and modification
Even this measure of the effect of poor institutional incentives may be too big. There are at least two good reasons to doubt a truly causal relationship between private securitization contracts and the frequency of renegotiation. The first reason is that, as the Agarwal et al. (2010) paper finds, loans securitized by the GSEs were actually much less likely to receive a modification than even the privately securitized loans. The conventional wisdom on the link between securitization and renegotiation (see Piskorski, Seru, and Vig 2010) pointed the finger at specific details in private securitization contracts that failed to align the incentives of servicers and investors. But this story applies only to privately securitized loans, not to agency loans. None of the institutional "facts" that the Piskorski, Seru, and Vig (2010) paper proposes apply to the GSEs, since the GSEs retain all of the credit risk when they securitize a loan. When a GSE loan becomes delinquent, it effectively turns into a portfolio loan. The GSEs have full discretion to modify any loan at any time for any reason and stand to enjoy all of the benefits. Agarwal et al. (2010) point out that the "precarious financial position of the GSEs in 2008 prior to their conservatorship may have made it difficult for them to engage in modifications and the attendant loss recognition," but this argument applies to only half of the period under study. After conservatorship started in September of 2008, capital was no longer a concern for the GSEs.
The second reason to doubt a causal link between securitization and modification is that the financial crisis triggered by the failure of Lehman Brothers and the ensuing heavy intervention by the federal government make it problematic to view behavior after September 2008 as "market-based approaches to stem mounting mortgage losses" (Agarwal et al. 2010, 1) By October 2008, the Troubled Asset Relief Program (TARP) had become law, and the government effectively owned stakes in many of the major commercial and investment banks. These banks also happened to be the largest mortgage servicers. In fact, TARP explicitly linked the provision of assistance to banks on their willingness to assist borrowers. That JP Morgan announced in February 2009 a foreclosure moratorium in a letter to Congressman Barney Frank, the head of the congressional committee tasked with overhauling regulation of their industry, illustrates the political considerations in dealing with troubled mortgages. Thus, by the end of 2008, political considerations played a central role in any calculation of the relative merits of renegotiating or foreclosing on a loan. For this reason, Adelino, Gerardi, and Willen (2010) focus on the period prior to September 2008. They find that, while the overall likelihood of modification is roughly the same, the difference in modification activity attributable to private-label mortgages is much smaller: only 1 percentage point.
Finally, Agarwal et al. (2010) show that information asymmetries matter, which is the third main finding of the paper. A key impediment to renegotiation is the self-cure risk, or the possibility that a delinquent borrower will resume repayment and eventually cover the balance of the loan in full. Any concession the lender made to such a borrower would thus be wasted. The authors show evidence of precisely this mechanism at work, finding
...much lower rates of modification for troubled borrowers with higher FICO scores and lower LTV ratios, which is the group with ex ante greatest likelihood of self-curing their delinquency.
The growing literature disputing the institutional argument
In showing that information, not institutions, is at the heart of the renegotiation issue, the authors build on an increasingly large body of evidence, which includes the Adelino, Gerardi, and Willen (2010) paper mentioned above. They are further supported by Ghent (2010) and Rose (2010), who both debunk the myth that the absence of securitization facilitated widespread renegotiation during the Depression (more on this topic in upcoming posts). In fact, Wechsler (1984)1 shows that many of today's anti-deficiency laws, which limit the ability of lenders to pursue borrowers for the difference between the loan balance and the amount recovered in foreclosure, originated as a policy response to the particularly harsh treatment of defaulting mortgagors during the Depression. Moreover, Hunt (2010) exhaustively studied securitization contracts and found little to support for the claim that private securitization explicitly distorts the incentives of servicers of securitized loans as compared to portfolio loans, writing that:
Certain general standards are extremely common [in private securitization contracts]: Servicers typically must...act in the interests of investors, and service loans in the same manner as they service their own loans.
Finally, we note the work of Mayer, Morrison, Piskorski, and Gupta (2010), which perfectly illustrates the difficulty in identifying borrowers who are truly in financial distress and thus suitable for a loan modification. The authors find that the announcement of a generous loan modification program caused borrowers to default on their mortgages.
It is our hope that the Agarwal et al. (2010) paper will put an end to three years of misguided public policy. The appeal of renegotiations was that they appeared to allow policymakers to prevent foreclosures at little cost to investors, lenders, or taxpayers and without unfairly helping anyone. The reality is that preventing foreclosures costs money, and it's time we had a debate about how or whether we want to spend money rather than trying to convince ourselves that we can prevent millions of foreclosures by tweaking the incentives of financial intermediaries.
May 21, 2010
Are there really social benefits of homeownership?
Over the past half century or so, the U.S. government has supported homeownership with numerous policies. For example, it created the government-sponsored enterprises (GSEs) to develop a stable secondary mortgage market so that households could obtain the necessary financing, with particular focus on less advantaged households. The federal government has also chosen to subsidize homeownership through the tax code, implementing tax deductions for mortgage interest and property taxes. The result of such policies has been the upward trend in the homeownership rate from about 62 percent in 1960 to a peak of 69 percent in 2004. (The foreclosure crisis has resulted in a decrease in the homeownership rate to about 67 percent as of the first quarter of 2010.)1
One of the main rationales for the government's pro-homeownership stance is the social benefit that homeownership is believed to produce. Basically, many perceive that homeownership gives individuals a stronger incentive to improve their neighborhood and community. A fairly extensive literature on this subject has purported to confirm such beliefs. Evidence supports the notion that homeowners participate more in the political system than renters (DiPasquale and Glaeser 1999) and are more likely to become involved in community activism in general (Rohe and Stegman 1994). Alba, Logan, and Bellair (1994) and Glaeser and Sacerdote (1999) have found a negative correlation between homeownership and the incidence of crime. Other researchers have found some evidence that homeowners take better care of their homes than renters do (Mayer 1981).
Other factors may drive both homeownership and community activism
But there is a very difficult econometric problem that many of these studies either do not address at all or do not address completely: the possible existence of unobserved characteristics that are correlated with both homeownership and the tendency to participate in community activism. That is, the types of people who are likely to become homeowners may also be the same people who are more likely to participate in their community. If this is the case, then these studies are mistakenly identifying homeownership as a causal factor of these social outcomes and falsely concluding that homeownership yields positive social benefits. To avoid this econometric issue and truly identify the causal effect of homeownership on participation in these various activities, we need to find some way to create random variation in homeownership decisions that is not correlated with any unobserved characteristics of individuals.
Matching savings program facilitates study of homeownership's social benefits
A new study by Gary V. Engelhardt, Michael D. Erikson, William G. Gale, and Gregory B. Mills in the Journal of Urban Economics attempts to accomplish such a task. The authors performed a field experiment with low-income renters in Tulsa, Oklahoma, from 1998 to 2003 that subsidized saving for a home purchase through what is called an Individual Development Account (IDA). They started with a pool of individual renters interested in such a program and then randomly picked a sample of them to participate. (Participation after selection was optional.) The program matched participants' saving specifically for a future home purchase at a 2:1 rate for annual deposits of up to $750 for three consecutive years. Thus, counting both deposits and matched funds, at the end of the three years, a participant could accumulate up to $6,750. This may not sound like that much, but it is a non-trivial fraction (about 11 percent) of the median house value in Tulsa for a similar low-income population of homeowners during the same period. Indeed, the IDAs appear to have encouraged homeownership: the authors find that after four years, the individuals who were given the option to participate in the program (the treatment group) had a homeownership rate of 7–11 percentage points higher than the individuals not given the option (the control group).
The authors use participation in the IDA—more specifically, the ability to participate, which was randomly assigned—as an instrument for homeownership. They collected information for their sample of renters and homeowners on these attributes: the extent of interior and exterior home maintenance expenditures; political involvement (propensity to vote, amount of support in time and money given to political candidates, tendency to write to or call public representatives); neighborhood involvement (volunteering and fundraising for a church, school, or other neighborhood organization; time spent working on neighborhood projects; and time spent participating in community associations); and time spent giving to other community members (providing childcare or care for another adult, watching someone else's home or pet, and making calls or writing/reading letters for someone else). Thus, their empirical strategy is a two-stage regression in which the first stage uses IDA participation to instrument for the probability of becoming a homeowner, and the second stage regresses the various measures of social involvement on the component of the variation in homeownership decisions that is due to the IDA experiment.
Their first finding is that when they don't instrument for homeownership decisions, they find very large social benefits, which is consistent with the previous literature. For example, becoming a homeowner increases the probability of having called or written a public representative by more than 17 percentage points and of voting in an election by almost 24 percentage points! They also find that becoming a homeowner seems to significantly increase the amount of exterior home maintenance by 13 percentage points.
Controlling for ownership finds negative relationship, underscores need for more research
But the more interesting finding is that when they do instrument for homeownership, all these positive effects disappear. In fact, in some cases the estimated effects become negative. For example, becoming a homeowner makes one less likely to volunteer or help raise money for a church, school, or neighborhood organization, and makes one less likely to become involved in local politics. The evidence regarding maintenance isn't quite as definitive. The estimated effect of homeownership on the likelihood of performing exterior maintenance is not precisely measured (the point estimate is positive but is not statistically significantly different from zero). The estimated effect of homeownership on the likelihood of performing interior maintenance is positive and statistically significant, but interior maintenance is really an internal benefit of homeownership rather than a social benefit (what you do inside of your home does not really affect the value of your neighbors' homes).
In our opinion, this is a really nice piece of work, on a very topical subject. It emphasizes the need to revisit some of the findings of the early literature on the social benefits of homeownership, as many of the positive effects found in that literature appear to be the result of spurious correlation—unobserved characteristics that influence the likelihood of an individual both to become a homeowner and to participate in his or her community to a greater extent. The study has some issues, which the authors themselves point out, with the design and implementation of the IDA experiment that might not make it completely representative of the entire U.S. population. The experiment was performed on low-income, employed individuals in Tulsa, where housing prices are relatively low. In addition, by simply signing up for the program, the individuals were likely signaling that they were more motivated to save (and thus more patient) than others. The authors also point out another potential problem, which is the possible conflation of homeownership and wealth effects resulting from the IDA experiment design. The matching funds increased individuals' wealth in addition to making them more likely to become homeowners. If increased wealth has an effect on the various social outcomes studied in the paper, then IDA participation would be capturing both homeownership effects and wealth effects. In any event, at the very least, the paper is a nice starting point for future research on this important topic!
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