Asymmetric Information in Loan Renegotiation: The Importance of Originator-Servicer Affiliation

Transcription

1 Asymmetric Information in Loan Renegotiation: The Importance of Originator-Servicer Affiliation James N. Conklin Moussa Diop Walter D Lima November 28, 2016 Abstract We present evidence that affiliation between the debt renegotiator and the originator represents a mechanism to reduce asymmetric information inherent in debt renegotiation. We hypothesize that affiliation affords servicers lower-cost access to borrower information, thus improving their ability to implement efficient debt restructuring. Consistent with this, we show that affiliation affects the likelihood, form, and effectiveness of modifications in a large sample of delinquent securitized mortgages. In a significant departure from the recent literature, we show that the additional information available through affiliation is hard in nature. As banks disintegrate origination and servicing, information critical for debt renegotiation will be lost. JEL Classifications: G21, R2, R3 Keywords: Information Asymmetry, Mortgage Default, Debt Renegotiation, Servicing, Securitization, Mortgage Redefault, Non-agency MBS We thank Itzak Ben-David, Scott Frame, Benjamin J. Keys, Michael LaCour-Little, Adam Levitin, Erwan Quintin, Abdullah Yavas, and participants at the American Real Estate and Urban Economics Association National meeting for helpful comments. We also thank Dennis McWeeny for outstanding research assistance. All errors and omissions are our own. Funding support for the data used in this study was from University of Wisconsin-Madison Graduate School. Terry School of Business, University of Georgia, jnc152@uga.edu Wisconsin School of Business, University of Wisconsin-Madison, moussa.diop@wisc.edu. Mendoza College of Business, University of Notre Dame, wdlima@nd.edu.

2 1 Introduction Evidence is mounting that informational problems in mortgage markets played a significant role in the financial crisis of 2007 to Much of the academic research has focused on the rise of securitization and its effect on information production and use in loan origination (Mian and Sufi (2009), Keys et al. (2009), Keys et al. (2010), Purnanandam (2011), Keys et al. (2012), and Demiroglu and James (2012)). In contrast, relatively little research has focused on asymmetric information in mortgage servicing. Because mortgage servicers are responsible for loss mitigation efforts on delinquent loans (including debt renegotiation 1 and foreclosure), servicers play a crucial role in mortgage markets during economic downturns. But servicers debt renegotiation efforts are plagued by asymmetric information: borrowers have an informational advantage over servicers regarding their prospects of repayment. The mechanisms available to reduce this asymmetric information problem in mortgage renegotiation are not well understood. We attempt to fill this gap in the literature. In the traditional model of vertically integrated lending, the mortgage originator and the servicer are the same entity. However, in the securitization model of lending, there may be no link between the servicer and the originator. In this paper we argue that severing the link between the originator and the servicer a common practice in securitization reduces the information available to the mortgage servicer, consequently impairing its ability to evaluate and implement effective debt renegotiation strategies. In other words, affiliation between the servicer and the originator acts as a mechanism to reduce the asymmetric information inherent in debt renegotiation. In contrast to recent papers that stress the importance of softinformation collection (or lack thereof) by originators (Keys et al. (2010), Keys et al. (2009), Purnanandam (2011), Demiroglu and James (2012), and Rajan et al. (2015)), the information hypothesis that we discuss in Section 3 argues that hard information collected by the originator is relevant to loss mitigation, and that this information is more easily transmitted to and used by affiliated servicers. 1 Debt renegotiation is typically called loan modification in mortgage markets. We will use these two terms interchangeably throughout this paper. 1

3 To test the information hypothesis, we first examine whether servicer-originator affiliation affects the likelihood of debt renegotiation. Using a large sample of non-agency securitized mortgage loans, 2 we find that servicer-originator affiliated loans are 16% more likely to be modified (relative to the mean) after controlling for contract and borrower characteristics, house price changes, general economic conditions, and servicer fixed effects. The rich set of control variables included in our regressions reduces concerns of omitted variable bias, and the inclusion of servicer fixed effects exploits within servicer variation in modification rates between affiliated and non-affiliated loans. To assuage concerns that endogeneity of servicer-originator affiliation is biasing our results, we estimate a 2SLS model and a bivariate probit model. For each loan in our sample, we construct an instrument that measures the originator s share of mortgages serviced by affiliated entities over the three months prior to that mortgage s origination month. If a large share of the originator s loans over the past few months have been sent to affiliated servicers, there is a high probability that the next loan originated will also go to an affiliated servicer. However, the share of the originator s business that went to an affiliated entity before origination should not directly impact the servicer s subsequent modification decision on an individual loan. Our results remain unchanged after instrumenting for affiliation: servicer-originator affiliation is positively related to the probability that a seriously delinquent loan is modified. This relationship also holds in a robustness check using propensity score matching to control for the possibility of selection on observables. After demonstrating that servicer-originator affiliation affects the likelihood of debt renegotiation, we turn to whether differential access to information for affiliated servicers explains this result. If affiliated servicers have lower cost access to information relevant to the modification decision, then this information should affect both the type of modification offered and the effectiveness of the modification. Our empirical results confirm that modification type varies with servicer-originator affiliation. More importantly, mortgages modified by affiliated servicers are 2 Focusing only on securitized loans ensures that the cash flow rights of the mortgages are sold to mortgage backed security (MBS) investors regardless of whether the originator and the servicer are affiliated entities. Thus, any principal-agent issues between the MBS investors (the principal) and the servicers (the agent) should be independent of originator-servicer affiliation. 2

4 less likely to redefault after modification. Thus, by reducing information asymmetry, affiliation allows the servicer to renegotiate mortgages more efficiently. The mortgage servicing industry experienced considerable consolidation in We exploit variation in servicer and originator fates to provide further support for the information hypothesis. We also consider whether affiliation with another intermediary (the MBS deal sponsor) explains our result. The deal sponsor purchases originated loans and pools them for securitization. The sponsor may be an unrelated party, or it may be affiliated to the originator, the servicer, or both entities. Recent evidence suggests that sponsors may be more likely to obtain private information when loans are originated by an affiliated entity (Demiroglu and James (2012) and Adelino et al. (2014)). Critical to our information hypotheses is the idea that servicers have lower-cost access to information on borrowers when the information collector (the originator) is an affiliated entity. Thus, the information hypothesis predicts that originator-servicer affiliation, rather than originator-sponsor or sponsor-servicer affiliation, should be related to debt renegotiation. Consistent with this prediction, we find that neither originator-sponsor nor servicer-sponsor affiliation is significantly related to the likelihood of mortgage modification after controlling for servicer-originator affiliation. More importantly, servicer-originator affiliation remains positively related to mortgage modification after controlling for the other types of affiliation, suggesting that private information is more likely to be acquired by the servicer when the collector of loan information (the originator) is an affiliated entity. The availability of total debt-to-income (DTI) ratio of borrowers at origination provides further support that affiliated servicers have more information (or lower cost access to information) on borrowers than unaffiliated servicers. There is a huge difference in the proportion of affiliated loans that have this information (59%) relative to loans serviced by unaffiliated entities (2%). This differential access to information is not due to missing DTI on low- and no-doc loans since the share of low- and no-doc loans is similar across affiliated and unaffiliated servicers. Without conditioning on servicer-originator affiliation, access to DTI information increases the likelihood of mortgage modification. However, when we control for originatorservicer affiliation, availability of DTI information is no longer significantly related to the 3

5 probability of modification. This suggests that DTI provides some of the information transmitted through affiliation. Finally, we investigate whether the informational advantage of affiliated servicers is a result of hard or soft information. There is a large distance between the information collector (the originator) and the servicer, both in terms of time and organizational form: the debt renegotiation decision often occurs months or years after origination, and even when the servicer is affiliated with the originator, servicing is handled in a separate department from where origination takes place. The large distance between the information collector and the servicer suggests that soft information is not likely to be transferred between them (Agarwal and Hauswald (2010) and Petersen (2004)). To provide empirical support of this assertion, we examine whether the effect of affiliation on modification varies across low- and full-income documentation mortgages. A common empirical strategy used in the recent mortgage literature consists of demonstrating that a significant relationship is confined to low-doc loans, then arguing that soft-information drives the results because soft information is more important on low-doc loans (Keys et al. (2010), Keys et al. (2012), and Demiroglu and James (2012)). In our study, the positive relationship between servicer-originator affiliation and the likelihood of modification is important for both low- and full-doc loans. Since our result is not confined to low-doc loans, this strengthens our argument that hard information relevant to debt renegotiation is more easily transmitted between the originator and an affiliated servicer. The fact that DTI information clearly a piece of hard information is so much more readily available to affiliated servicers also supports our conclusion. A potential alternative explanation for our results is that loan quality drives the relationship between servicer-originator affiliation and debt renegotiation. Demiroglu and James (2012) argue that originators screen more intensively on loans where the sponsor or the servicer is likely to be an affiliated entity. Thus, affiliated loans are of higher average quality along dimensions unobservable to the econometrician, and may represent better candidates for modification. There are several reasons we believe this is not a major concern for our study. First, since we focus only on delinquent mortgages, our analysis is based on loans that are revealed to be lowquality ex post. Furthermore, our finding obtains when we restrict our sample to severely (150+ 4

6 days) delinquent loans, the lowest quality loans ex post. Most importantly, Demiroglu and James (2012) argue that affiliation between the originator and the deal sponsor proxies for the quality of a loan, and that the market is aware of the relationship between originator-sponsor affiliation and loan quality. If unobserved loan quality is driving our results, and the market is aware of this quality, then we would expect both originator-servicer and originator-sponsor affiliation to be related to loss mitigation. However, we find that only originator-servicer affiliation is significantly related to debt renegotiation. This paper adds to the broad literature on asymmetric information in debt renegotiation discussed in Section 2. Our results imply that vertical integration between the original lender and the party responsible for the debt renegotiation decision alleviates asymmetric information problems in debt restructuring. We also add to the literature examining asymmetric information in mortgage markets and its role in the recent financial crisis. Although several papers examine informational problems related to securitization and origination (Mian and Sufi (2009), Keys et al. (2009), Keys et al. (2010), Purnanandam (2011), Keys et al. (2012), and Demiroglu and James (2012))), relatively little research has examined asymmetric information and its impact on debt renegotiation. 3 Our results show that affiliation between the servicer and the information collector (the originator) reduces asymmetric information that prevents efficient mortgage modifications. Our paper has important policy implications as well. High levels of mortgage defaults combined with low rates of mortgage modifications prompted regulatory changes in the wake of the recent mortgage crisis. Several regulatory changes, including the Secure and Fair Enforcement for Mortgage Licensing Act of 2008 (SAFE Act) and the Ability to Repay rule, target lax screening in the loan origination/underwriting process. Although these policies may increase information collection in loan screening, they do not address the asymmetric information problem in debt renegotiation examined in this paper. Also, many of the large vertically integrated banks that traditionally handled origination and mortgage servicing have curtailed these activities in recent years, at least in part due to higher compliance and regulatory capital costs relative to non-banks (Karan and Goodman (2016)). The results in our paper suggest 3 Adelino et al. (2013) and Mayer et al. (2014) are notable exceptions. 5

7 that this could heighten asymmetric information problems that may constrain mortgage debt restructuring in the future. 2 Asymmetric Information and Debt Renegotiation Information asymmetry about borrower risk is an important consideration in lending and debt renegotiation (Giammarino (1989)). As Leland and Pyle (1977) note: [b]orrowers cannot be expected to be entirely straightforward about their characteristics, nor entrepreneurs about their projects, since there may be substantial rewards for exaggerating positive qualities. Lenders attempt to mitigate this information asymmetry through costly production of information pertaining to the borrower s prospects of repayment prior to extending credit (Sharpe (1990)). Lenders also gather information from ongoing lending relationships to monitor existing loans and to determine whether to provide additional financing to the debtor (Fama (1985)). Moreover, the information collected by the lender can reduce asymmetric information in the event of debt renegotiation. 4 As far as firm financing is concerned, Haugen and Senbet (1978) argue that as long as it is costly for creditors to collect payments on a defaulted debt, they should offer to reduce or modify the debt claim to avoid the large costs associated with bankruptcy (Haugen and Senbet (1978) and Wruck (1990)). However, assuming the managers of the firm are better informed than the debt holders about the value of the firm s assets, it will be difficult for debt and equity holders to agree on a workout when the firm is in distress (Giammarino (1989) and Wang et al. (2002)). Consequently, any factors likely to reduce information asymmetry between managers and debt holders about the value of the firm should increase the probability of successful debt renegotiation in the event of default. Chan et al. (1986) further add that the ability of banks to benefit from existing lending relationships depends on the reusability of borrower-specific information. 5 The additional information provided through the ongoing relationship gives the bank an informational advantage relative to competing banks in the provision of additional financing to the existing customer. 4 Building closer ties with lenders also benefits borrowers by giving them access to more financing (Petersen and Rajan (1994)). 5 According to Chan et al. (1986), for information to be reusuable it must be durable, not fast decaying, and the lender must be in a position to capitalize from it later by maintaining a business relationship with the borrower. 6

8 The accumulation of more borrower-specific information should also reduce asymmetric information in future debt renegotiations. When the link between a lender and a borrower weakens or is severed (e.g., through securitization or sale of the debt) the reusability of archived borrower information diminishes according to Chan et al. (1986). Thus the new debt holder (or its agent) faces the challenge of having to gather new information on the borrower unless the information collected by the original lender can be easily accessed. According to this theory, banks should be in a better informational position to renegotiate debts originated in-house relative to loans purchased from other lenders. One might argue that the information asymmetry emphasized by Giammarino (1989) is less severe in mortgage lending since both the debtor the property owner and the lender should have similar information about the value of the collateral (Wang et al. (2002)). 6 Furthermore, the coordination problem raised by Gertner and Scharfstein (1991) is less acute since most real estate loans involve one lender. According to this line of reasoning lenders should be more likely to renegotiate mortgage loans relative to corporate debt. However, a distinction must be drawn between commercial loans, whose repayment is generally tied the income generated by the property, and residential mortgages, whose repayment depends on borrowers personal income. Even though information asymmetry about the value of the collateral may not be a major issue in real estate lending, lenders are at an informational disadvantage (relative to the borrower) regarding the borrower s prospects of repayment. Adelino et al. (2013) argue that this type of information asymmetry helps to explain low levels of debt renegotiation observed in residential mortgage markets. 7 In this paper we argue that information asymmetry regarding a borrower s prospects of repayment is mitigated on securitized loans through affiliation between the servicer and the information collector (the originator) and that this has implications for debt restructuring. 6 Both the lender and the borrower rely on independent appraisals of property values based on similar information about the market, the attributes of the property and recent comparable sales. 7 In addition to information asymmetry, moral hazard is an important consideration affecting a lender s decision about whether to modify a loan. Riddiough and Wyatt (1994) note that many mortgage lenders take a hard line in dealing with defaults and are reluctant to renegotiate debt because of this moral hazard. Mayer et al. (2014) provide empirical evidence that a lax modification policy can induce borrowers to default when they have the ability to pay. 7

9 Mortgage debt restructuring has received considerable attention in recent years in the academic literature. 8 Much of this research has focused on investigating frictions that potentially prevent debt renegotiation even when it is (supposedly) in the best interests of both borrowers and investors. Eggert (2007) discusses several of these frictions for securitized mortgages, including agency issues related to the mortgage servicer, 9 securitization contracts that limit servicer discretion, and conflicting interests between investors in different tranches of securitizations. Several papers argue that securitization itself prevents efficient debt renegotiation (Piskorski et al. (2010), Agarwal et al. (2011), Kruger (2014), and Adelino et al. (2013)) and some have specifically mentioned information asymmetry as a key hindrance to debt renegotiation (Adelino et al. (2013)). In this study we examine the relationship between servicers access to information about borrowers and debt renegotiation that has largely been ignored in the existing literature. In a parallel study, Le (2016) investigates the relationship between servicer-originator affiliation and the effectiveness of mortgage modifications using a different data source for securitized mortgages (Blackbox Logic). Consistent with our results, Le (2016) finds that redefault rates are significantly lower on affiliated loans. Whereas Le (2016) focuses primarily on the quality of the modification, we spend considerable time on the relationship between servicer-originator affiliation and the likelihood of modification. The papers also differ in their emphasis on the relevant type of information (e.g., hard or soft) and methods to control for endogeneity in affiliation. Also, Le (2016) does not control for other types of affiliation (e.g., originator-sponsor and sponsor-servicer). Despite their differences, the papers can be viewed as complementary in that they both highlight the importance of servicer access to information for debt renegotiation. 8 Levitin and Twomey (2011) provide an overview of the institutional details of the mortgage servicing industry and the economics of loss mitigation for securitized mortgages. 9 Since servicers typically do not own the mortgage asset they control, a classic principal-agent problem exits. Servicers generally act to maximize the value of their servicing asset, rather than the net present value of the mortgage asset for investors. Misaligned incentives can lead servicers to foreclose when debt restructuring is optimal, or alternatively, to modify the mortgage when foreclosure is optimal. Levitin and Twomey (2011) and Thompson (2011) argue that servicers incentives are skewed towards foreclosure. 8

10 3 Information hypothesis of mortgage servicing During the Great Recession much of the policy debate focused on mortgage modifications, particularly on the idea that frictions in the mortgage market prevented mortgage modifications even when modifications were in the best interests of both borrowers and MBS investors. 10 Adelino et al. (2013) refer to this as the institutional theory for low levels of modification. For example, servicer incentives may favor foreclosure as a loss mitigation strategy (Levitin and Twomey (2011), Thompson (2011), Eggert (2007), Mayer et al. (2009), and Kruger (2014)). Also, pooling and servicing agreements (PSA) that lay out the duties and responsibilities of the mortgage servicer in a securitization may limit the number and types of modifications the deal servicer may perform (Levitin and Twomey (2011), Eggert (2007), and Mayer et al. (2009)). In addition to the institutional theory, recent evidence suggests that information asymmetry also inhibits debt renegotiation. Adelino et al. (2013) argue that borrowers have an informational advantage over servicers regarding their prospects of repayment, which can lead to an inefficiently high level of renegotiations if servicers modify delinquent loans that would have self-cured on their own. Additionally, servicers may inefficiently delay foreclosure by modifying loans that will redefault. Adelino et al. (2013) show that both self-cures on delinquent mortgages and redefaults on modified loans are quite common, potentially explaining the observed low levels of loan modifications. In addition, servicers need to consider the potential for moral hazard. If it is costly or difficult to determine who truly needs a modification, a relatively liberal modification policy by a servicer may induce borrowers to strategically default in order to receive a mortgage modification. The potential for moral hazard is particularly acute if the servicer is at an informational disadvantage with respect to the borrower s prospects of repayment. Taken together, the results of Adelino et al. (2013) and Mayer et al. (2014) suggest that information problems play a key role in the servicing of delinquent mortgages. This also implies that the availability of additional information to servicers can reduce these problems and affect servicers debt renegotiation strategies. 10 Throughout the paper we will refer to investors in a MBS as one group. In reality, in each MBS deal there are multiple tranches, and the the interests of investors in different tranches may not be aligned. 9

11 Although information is critical to effective mortgage servicing, the information available to servicers on the loans they service is not standardized. The loan boarding process illustrates this point. During loan underwriting, the originator collects information relevant to the credit and pricing decisions. Although some of the information may be standardized, such as the information captured on the Uniform Residential Loan Application, additional information collected varies across originators. After origination, each loan is boarded onto the servicer s mortgage servicing platform software. During the boarding process, a subset of the information collected by the originator is transferred from the originator s system to the servicer s system. For example, the information boarded typically includes borrower contact information, loan information (e.g., loan amount, maturity date, interest rate, loan term, loan type, adjustablerate reset parameters) and lien information (Spoto (2014)). Additional borrower and loan information required or available for loan boarding varies across servicers and originators. For example, one mortgage servicer lists 14 key fields used in mortgage boarding, but also states [W]e ll be happy to take as many as 148 fields of primary loan data. 11 Discussions with industry participants further confirm that the amount of information transferred between the originator and the servicer varies significantly. Because information transfer between the originators and servicers is not standardized, information available to servicers to aid in mortgage loss-mitigation efforts also varies both across and within servicers. 12 The information collected by the originator is necessary for implementing an effective loss mitigation strategy in the case of borrower default. If this information is incomplete or incorrect, loss mitigation may not be possible. Obviously, if the borrower s phone number is missing or incorrect, contacting the borrower to discuss renegotiation options would prove difficult. More subtly, servicers typically base loss-mitigation efforts, such as calling frequency and options to present to homeowners, at least in part on information collected by the originator such as the borrower s product type and prior credit rating. 13 Again, missing or incomplete information may prevent the servicer from determining and implementing the most effective 11 Available at 12 Within servicer variation in boarded information is likely if the servicer works with multiple loan originators. Technically, we do not fully observe the information available to servicers; we have access only to information reported by the servicers to the MBS trustees. 13 Servicers may also independently produce their own information for loss mitigation purposes. 10

12 loss mitigation strategy. It is important to note that some information transferred from the originator to the servicer becomes stale over time. However, this does not mean that the information provided is not valuable to the servicer s modification decision. To fix ideas, consider two borrowers: a borrower with a high FICO score at origination and a borrower with a low FICO score at origination. Now, suppose that both borrowers default on their mortgage. Even though both borrowers have low current FICO scores, the FICO scores at origination may still be valuable to the mortgage servicer s modification decision. The borrower with the higher initial FICO score is likely to be a better candidate for modification since he previously demonstrated a propensity to pay his debts. As this example suggests, even stale information can affect the servicer s loss mitigation strategy. Consistent with this idea, in a recent trade publication a senior executive at a mortgage servicer stated: Loan boarding is the single most crucial process to get right to avoid servicing pitfalls...that can adversely impact potential loss-mitigation activities (Spoto (2014)). In this paper, we hypothesize that affiliation between the servicer and the loan originator reduces the information asymmetry inherent in debt renegotiation. In order to determine the most appropriate loss-mitigation strategy, the servicer must engage in costly information production. However, if some or all of this information was originally produced by an affiliated originator, obtaining this information from the affiliated entity rather than engaging in new information production reduces costs. Even if a servicer can obtain the same information from an unaffiliated originator, costs of information transmission across unaffiliated entities is likely higher than the costs of transmission within affiliated entities. Our information hypothesis posits that the additional information available at lower cost from an affiliated originator will allow the servicer to make a better modification decision. We must also be clear on the type of information that is likely to be transferred from originators to affiliated servicers. Information gathered in credit markets generally falls into two categories: hard and soft. Although hard and soft information are difficult to distinguish, 14 hard information can usually be captured on a credit application and reduced to a numerical score (Petersen (2004)). Examples in the mortgage market include the borrower s credit score, 14 In fact, the hardness of information is probably best thought of as a continuum (Petersen (2004)). 11

13 income, or loan-to-value ratio. Soft information, on the other hand, is difficult to reduce to a number, and is not readily transferable to or interpretable by parties other than the original information collector. Even within the same firm, soft information may be difficult to credibly communicate across agents (Stein (2002)), especially as the distance between the agents increases. In our context, there is a large distance between the original collector of the information (the originator) and the mortgage servicer, both in terms of time and organizational structure. Debt renegotiation occurs months or years after origination, and even when the servicer is affiliated with the originator, servicing is handled in a separate department from where origination takes place. The large distance between the servicer and originator suggests that affiliated originators are more likely to have low cost access to hard (rather than soft) information. The case of unreported second liens provides further intuition behind our information hypothesis. By increasing the total mortgage payments while reducing the borrower s equity position in the home, second mortgages can significantly affect the risk of default on the primary mortgage. Two recent studies carefully document that second liens were often not reported in mortgage securitizations (Piskorski et al. (2015) and Griffin and Maturana (2016)). Moreover, Griffin and Maturana (2016) provide evidence that much of the misreporting occurred between the originator and the MBS sponsor. In other words, originators knew about second mortgages but did not transmit this information to MBS sponsors. 15 If the MBS sponsor does not have this information, then a servicer unaffiliated with the originator is also unlikely to know about the second lien. However, if the servicer and the originator are the same entity (or affiliates), then the servicer will be aware of the presence of a second lien (hard information). Knowledge of the second lien, which an affiliated servicer is more likely to have, will affect loss mitigation strategy because a second mortgage can make it difficult or impossible for a servicer to renegotiate the terms on first-lien mortgage debt (Mayer et al. (2009), McCoy (2013), and Agarwal et al. (2014)). In other words, asymmetric information inherent in debt renegotiation is reduced through servicer-originator affiliation. 15 A growing body of literature examines the role of fraud in the recent financial crisis (Jiang et al. (2014), Agarwal et al. (2015), Ambrose et al. (2016), Ben-David (2011), and Mian and Sufi (2015)). Our information hypothesis, however, does not depend on fraudulent behavior by the originator. 12

14 4 Data 4.1 Mortgage Data We use ABSNet Loan, a comprehensive non-agency mortgage origination, performance, and securitization database compiled by Lewtan, a Moody s Analytics company. Lewtan sources, normalizes, and analyzes vast amounts of non-agency mortgage data reported by MBS trustees and servicers to provide granular information on a broad array of mortgage loan and deal attributes under the ABSNet Loan brand. This database contains more than 22 million loans collateralizing roughly 7,000 MBS deals. 16 Using monthly loan performance updates, we identify loans that were seriously delinquent in December We then track first-time modifications over the next 12 months. 17 Our initial sample consists of 818,879 loans collateralizing MBS deals issued from 2002 to 2007 that have current servicer information at the end of 2007 and that are at least 60 days delinquent at that date. After dropping second liens, HELOCs, loans where the subject property state is missing, loans originated in Puerto Rico and Guam, and loans originated before 2000, 18 our sample contains 674,540 observations. Next, we exclude loans with missing originator information (160,104). After further cleaning of the data to deal with potential reporting errors, our final sample consists of 501,673 loans collateralizing 2,347 non-agency MBS deals. 19 These loans were originated by 1,086 institutions and managed by 37 servicers at the end of Table 1 lists the 20 largest originators and servicers, which together account for 85% and 95% of our sample, respectively. The mortgage servicing industry experienced considerable 16 Lewtan markets its services primarily to non-agency MBS investors for valuation purposes. The bulk of the loans in the ABSNet database were originated after In addition to ABSNet Loan, the company provides periodic collateral valuations it markets as ABSNet HomeVal. 17 Lewtan monitors changes in loan terms monthly in a separate data file within ABSNet Loan. The company receives monthly loan modification updates directly from servicers and trustees, but it also independently checks modifications against changes in loan characteristics recorded in the monthly loan snapshots for confirmation. Unreported modifications identified by the company are then recorded and flagged as implied modifications. We classify multiple reports of modifications within the same month as one modification. 18 We excluded loans originated before 2000 in order to focus on the last housing boom. 19 To limit the potential for reporting errors, we dropped 3,025 loans with a balance less than $25,000, 195 loans with original property appraised values missing or less than $25,000, 798 loans with original LTV below 25% or above 150%, 535 loans with less than 60 months of remaining life at the end of 2007, 141 loans with initial maturity of more than 600 months, 6,097 observations with missing loan characteristics included in equation (1), and 1,972 loans from servicers with less than 500 loans under management. 13

15 consolidation in Table 2 shows the fates of the 20 largest servicers in our sample. 20 Also, a number of originators were no longer operating by the end of This has implications under the information hypothesis. We will return to this idea in Section 6.1 where we use variation in servicer and originator fates to provide support for the information hypothesis. Panel A of Table 3 shows that the majority of loans in our sample were more than 90 days delinquent at the onset of the study. As expected, our sample disproportionately contains loans originated during the peak of the housing bubble in 2005 and By loan purpose, it breaks down into 49.9% purchase loans, 47.2% refinance loans, and 2.9% unclassified. The data also confirm the increased use of ARM financing and other exotic mortgage products as the housing boom gained steam. Seventy six percent of the loans went into subprime MBS deals with another 23% collateralizing Alt-A deals. 21 To avoid contamination from various government programs aimed at curbing foreclosures during the recession following the housing market bust, our primary analysis covers 12 months: January through December We identify 34,884 and 66,659 first-time modifications within the first half of 2008 and all of 2008, respectively. Panel B of Table 3 tallies the types of modifications implemented by servicers over each period. The data show that modifications generally address multiple loan attributes. For example, a rate reduction may be combined with capitalization of past-due amounts and a maturity adjustment. Typically, we cannot accurately identify the individual contract alterations involved in multiple-attribute modifications. Consistent with previous studies, the data also show that capitalization of past due amounts is a common debt renegotiation strategy while principal forgiveness is rarely used. 20 In a bank failure, the mortgage servicing assets are typically transferred to another servicing company. This is analogous to a servicing firm being acquired. 21 The subprime, Alt-A, and prime labels refer to deals, not the individual loans. They provide a general description of the characteristics of the collateral backing the MBS bonds. 22 Expanding the study period complicates matters because modifications may have come about as a result of federal programs, such as the Home Affordable Modification Program (HAMP) enacted in March 2009 to provide relief to distressed homeowners. Moreover, it is not clear if or how these programs would differentially impact affiliated versus unaffiliated servicers. For this reason, in this paper we focus on modifications in 2008, but our findings remain unchanged when we expand the study period through

16 4.2 Servicer Affiliation and Descriptive Statistics Our primary independent variable of interest is a dummy that equals one if the servicer is affiliated to the initial information collector (the originator). To construct this variable we match the originator of each loan to the current servicer in December 2007 and then manually check whether the two entities are related. 23 Table 4 shows that loans from affiliated originators represent 63% of our sample. This reflects the fact that large financial institutions generally maintain an integrated mortgage origination and securitization business in order to capture the entire value chain. Average loan characteristics show that our sample is relatively representative of the non-agency mortgage sector. Average loan to value (LTV) and combined LTV (CLTV) are 82% and 88%, respectively. FICO scores range from 345 to 850, with an average of 626. Sixty six percent of the loans restrict prepayments, 80% are ARMs, 24% are interest-only (IO), and 7% of loans involve negative amortization. Table 4 compares the average characteristics of affiliated and unaffiliated loans. There are economically and statistically significant differences between the two groups in servicers unconditional propensity to modify loans over 6 and 12 months, with unaffiliated loans 1.5% and 0.7% more likely to be modified over 6 and 12 months, respectively. The rate of redefault of modified affiliated loans is slightly higher. However, in Section 5 we find that conditional estimation of both modification and redefault rates yield different results. The two groups are relatively similar in terms of borrower income documentation. Their similarity also extends to average loan maturity, LTV, CLTV, credit score, loan amount, and shares of IO and owneroccupied loans. On the other hand, affiliated loans have higher proportions of refinancing (5.2%), negative amortization (5.8%), and single-family homes (9.6%). In contrast, affiliated loans have fewer ARMs (4.8%) and prepayment penalties (3.7%). The difference in average pricing of roughly 83 basis points is also significant, highlighting the need to include pricing variables in our empirical models. 23 We recognize that servicer-originator affiliation is dynamic, and the further our study period extends the noisier our measure becomes. We believe this is not a major concern for our study for two reasons. First, if our measure is noisy, it should bias us away from finding a significant relationship between affiliation and modification. Second, we limit our study period to a relatively short period of time after we measure affiliation (6 months and 12 months), thus reducing the possibility that our proxy for affiliation is stale. 15

17 It is difficult to form any definite conclusion regarding the effect of affiliation on modifications from these unconditional differences in loan characteristics. However, the proportion of loans with missing total debt-to-income (DTI) information offers a striking difference between affiliated and unaffiliated loans. The computation of total DTI requires knowledge of the borrower s full balance sheet at origination, which will be valuable during subsequent debt renegotiation. Total DTI information (which is hard in nature) is available for only 2% of unaffiliated loans. In contrast, DTI information is available to 59% of the affiliated servicers, which is consistent with the information hypothesis. In Section 6, we will exploit differential access to DTI information to further explore the channel through which affiliation affects loan loss mitigation. 5 Empirical Results 5.1 Modification Decision To investigate if servicers loss mitigation decisions depend on whether a loan was originated by an affiliated entity, we estimate a model of the following form: Pr(Modified ijk ) = α + β Affiliation kj + X iγ + Z iδ + Year i + Servicer k + ξ i, (1) The dependent variable Pr(Modified ijk ) is the probability that mortgage i, originated by entity j, is modified in 2008, conditional upon being 60 or more days delinquent in December Affiliation kj is an indicator that equals one if the servicer of the loan is affiliated with the information collector (the mortgage originator j ). X i is a vector of loan characteristics that includes the delinquency status at the end of 2007, income documentation indicators, an ARM indicator, a purchase (versus refinance) indicator, the loan amount, the interest rate, the 24 The study sample includes securitized mortgage loans that were 60 or more days delinquent at the end of We track modifications through the end of 2008 to avoid contamination from government programs. We chose the end of 2007 as the onset of the likelihood of modification for all of our loans because this date corresponds to a time when modifications were being discussed intensely as a potential solution to high rates of foreclosure (FDIC (2007)). Also, using remittance reports from 2007 and 2008 for twenty-six pools of subprime mortgages originated in 2005 and 2006, White (2009) shows modification rates were relatively modest up until December of

18 borrower s credit score, and the initial combined loan to value ratio (CLTV). Z i is a vector of controls for housing market and general economic conditions, including changes in house prices, monthly inflation, mortgage interest rates, area income, area unemployment, 25 and an estimate of the current loan to value ratio (LTV). 26 We also include loan origination year fixed effects (Year i ) to control for economic conditions at origination and changes in underwriting standards, and servicer fixed effects (Servicer k ) to control for time-invariant servicer idiosyncrasies, such as heterogeneity in their propensity to modify problem loans. For convenience, we will primarily assume a linear probability model (LPM) and use OLS to estimate the model s parameters, α, β, γ, and δ. 27 β is our primary estimate of interest: β 0 suggests that information from affiliated originators affects the probability of debt renegotiation. Table 7 reports the results from OLS estimation of the probability of modification over 12 months for our sample. We estimate several variants of equation (1) to pinpoint the role of affiliation in loan modification. Consistent with the information hypothesis, servicer-originator affiliation is significantly related to the likelihood of modification. The economic magnitude of the relationship is large as well. The model specification in column 1 shows that affiliation increases the probability of modification by 2.8% in absolute terms with the control variables behaving as expected. Relative to the average rate of modification, this represents a 21% increase in the likelihood of modification. 28 Evidence suggests that significant heterogeneity exists in loss mitigation strategies across servicers (Stegman et al. (2007), Agarwal et al. (2011), Agarwal et al. (2012), and Collins et al. (2016)). 29 To control for heterogeneity in servicing strategies, we include servicer fixed effects in our estimation reported in column 2. This strategy exploits within servicer variation in affiliation. In this specification, loans from affiliated originators are on average 2.1% more 25 Time-varying covariates in Z i are measured at the last date before an event. In equation (1) the event is modification, so they are measured the month prior to modification. For loans that are not modified, they are calculated at the end of our sample period. In Section 5.5 redefault is the event. 26 Current LTV= balance t value t, where balance t is the current outstanding balance of the mortgage and value t is the updated value of the house after adjusting for changes in the FHFA MSA-level house price index. 27 This choice of estimation method does not drive our empirical findings. Our results are robust to the use of a logit model, a Cox proportional hazard model, and a competing risks hazard model. 28 This is calculated as the marginal effect divided by the 12-month mean modification rate in our sample 2.8%/13.35%. 29 This heterogeneity in modification strategies could be due to technology (i.e., cost structure), size, expertise, or simply business strategy. 17

19 likely to be modified over 12 months, which represents a 15.7% increase relative to the mean. Column 2 is our preferred specification, which we refer to as the base model in the text. In addition to the time-varying geographic controls included in columns 1 and 2, column 3 includes geographic fixed effects. The positive relationship between affiliation and modification is identical to column 2 in sign, significance, and economic magnitude. The type of modification chosen by a servicer may have a large impact on the borrower s welfare and propensity to redefault. Capitalization of arrearages (a relatively common modification type in our sample) simply adds any past-due amounts (including interest and fees) to the outstanding loan balance. With capitalization, there is no real concession to the borrowers. Thus, this type of modification is often perceived as kicking the can down the road with no real long-term benefit to the borrower. To test whether affiliated servicers are more likely to perform borrower-friendly modifications, we exclude the modifications that entailed capital arrearages and report the results in column 4 of Table 7. Affiliation is associated with a 1.1% absolute increase in the probability of a borrower-friendly modification, still representing a 12.1% increase relative to the mean modification rate. 30 Modification is only one of several potential outcomes after a borrower reaches serious delinquency. Alternatively, the loan can be liquidated following foreclosure or bankruptcy, paid off partially or in full through a sale or refinancing, or the mortgage can remain active. To account for these alternative outcomes, we re-estimate the likelihood of modification using a multinomial logit specification. Table 5 lists the marginal effects of the variables included in equation (1) for each of the alternative outcomes. Consistent with our previous finding, these multinomial logit results indicate that servicer-originator affiliation significantly increases the likelihood of modification relative to the other outcomes. The magnitude of the effect is even slightly higher compared to the baseline linear probability estimation (2.4% vs. 2.1%). This increased probability of modification of affiliated loans is associated with fewer loan liquidations, which may benefit borrowers % is the mean 12 month modification rate after excluding capitalization arrearage modifications. The 6-month results in Table A.1 of the appendix confirm these results over the shorter observation window. 31 The estimation of a Cox competing-risk model following Fine and Gray (1999) yields similar results (unreported). 18