1. No category

Disclosure Regulation on Mortgage Securitization and
Subprime Loan Performance
Lantian Liang
Harold H. Zhang
Feng Zhao
Xiaofei Zhao∗
March 24, 2015
Abstract
In 2006, the US Securities and Exchange Commission adopted Regulation AB (Reg
AB) that mandates the disclosure of originators with 20% or more of the pool assets
in mortgage backed securities. Using data on non-agency mortgage backed securities
(MBS), we uncover a discrete jump in the fraction of mortgage deals consisting of origination stakes just below the disclosure threshold (low stake) after Reg AB. These deals
have significantly larger losses than those without low stake when issued after Reg AB.
Further, our analysis on loan level data provides evidence that securitized loans show
higher delinquency when their originators increase the use of low origination stakes
after Reg AB. Our findings suggest that mortgage securitizers attempt to circumvent
the disclosure requirement under Reg AB when riskier loans are securitized.
Keywords: Regulation AB; Disclosure Threshold; Securitized Loan Performance
∗
We thank participants at seminars at Fordham University and the US Securities and Exchange Commission for helpful comments. The authors are from the Naveen Jindal School of Management, University
of Texas at Dallas, 800 West Campbell Road, Richardson, Texas, 75080, email: [email protected],
[email protected], [email protected], [email protected]
1.
Introduction
The regulation reform on disclosure in the non-agency mortgage backed securities (MBS)
market remains a virtually unexplored area in the aftermath of the 2007 to 2008 subprime
mortgage crisis. To meet the insatiable demand from global investors reaching for higher
yields, the entire supply chain of mortgage securitization has increasingly expanded lending
to riskier borrowers. Compelled by the rapid growth of the loan securitization market and the
lack of explicit regulations directed towards the distinguishing features of this market, the US
Securities and Exchange Commission introduced Regulation AB (Reg AB) in January 2006.
Despite the creation of Reg AB, no study has examined its effects on the loan securitization
market. Our investigation represents the first such attempt.
In this study, we focus on the impact of disclosure regulation under Reg AB on the
residential mortgage securitization market. Securitized residential mortgages accounted for a
large fraction of the new issuance of securitized loans in the period leading up to the financial
crisis.1 The research attributes the financial crisis to the sharp increase in the defaults of
mortgage loans.2 Collecting detailed deal and loan level data on securitized residential
mortgages, we conduct an in-depth investigation and provide a quantitative assessment of
the impact that the disclosure mandate in Reg AB has had on the composition of mortgage
deal originations and securitized loan performance.
To identify the impact, we use the cross-sectional variation in mortgage deal originations
composition just below the disclosure threshold and examine different performance reactions
to the regulation. This is motivated by different disclosure requirements under Reg AB on
loan originators based on the percentage of loans included in a mortgage deal from each
originator. Specifically, Reg AB Item 1110 requires as an initial level of disclosure the
1
According to former International Monetary Fund chief economist Simon Johnson, the “total volume of
private mortgage-backed securities (excluding those issued by Ginnie Mae, Fannie Mae and Freddie Mac)
grew from $11 billion in 1984 to over $200 billion in 1994 to close to $3 trillion in 2007.”
2
See, Mian and Sufi (2009), Nadauld and Sherlund (2013), Keys, Mukherjee, Seru, and Vig (2010), Keys,
Seru, and Vig (2012), Purnanandam (2011), among others.
1
identification of any originator or group of affiliated originators that originates, or expects to
originate, 10% or more of the pooled assets. If the originator originates or expects to originate
20% or more of the pool assets, then the regulation further requires disclosure of information
regarding the size and composition of the originator’s portfolio as well as information material
to an analysis of the performance of the pool assets, such as the originator’s credit-granting
or underwriting criteria for the asset types being securitized.
We collect information on the originators from mortgage deal prospectus supplements of
privately securitized residential mortgages between 2003 and 2007 from Bloomberg. For each
mortgage deal, its prospectus offers information on the performance-related characteristics
such as the FICO score, loan-to-value ratio, and the collateral’s pool size. More important for
our analysis, it provides information on the composition of the mortgage loans from different
originators. To link individual loans to a particular originator in a mortgage deal, we use the
First American Corelogic LoanPerformance database. The Corelogic data provide the name
of the original lender for each loan. We collect identity and affiliation information for the
original lender of each loan to determine if the original lender is one of the originators for the
mortgage deal or is affiliated with one of these originators. Using this information, we assign
individual loans to the originators listed in the prospectus supplements. Then we perform
a loan level analysis on the implication of the use of the origination stakes just below the
disclosure threshold on a loan’s performance under Reg AB. For mortgage deal performance,
we examine the cumulative net loss that is defined as the sum of all of the losses of the deal’s
principal suffered up to a specific date divided by the total original balance of all of the
mortgages. For the individual loan’s performance, we use the delinquency within 24 months
of the loan’s origination, a standard measure used in an analysis of a loan’s performance.
Our investigation demonstrates a significant impact of the regulatory disclosure mandate
on the use of origination stakes just below the disclosure threshold in mortgage deals. This
in turn has an important implication for the performance of the securitized mortgages.
First, we find that the percentage of deals with an origination stake from an originator
2
and its affiliates that is less than 20% of the collateral pool—the threshold for mandatory
disclosure—increased significantly after Reg AB (we refer to a low origination stake in a
mortgage deal as a low stake or LS, deals with at least one low stake as LS deals, and
deals without a low stake as non-LS deals, hereafter). In particular, the percentage of LS
deals more than doubled after Reg AB relative to before Reg AB. Second, LS deals have
larger cumulative net losses than the non-LS deals, and the performance difference becomes
significant only after Reg AB. Further, by introducing variables that represent originators
who increased the use of low stakes we find that in deals with the increased low stakes
usage, the originators have larger cumulative net losses. Our findings are robust to the
deal’s cumulative net losses measured at different dates after Reg AB. Our analysis on loan
level data provides further support that securitized loans show worse performance when their
originators increase the use of low stakes after Reg AB. It is worth noting that the increased
use of low stakes does not necessarily have any implication for the mortgage’s performance if
it is used to simply avoid the SEC compliance costs. Rather, it is more likely driven by the
intention to avoid scrutiny and to potentially withhold some adverse information on riskier
loans. The latter implies that the worse performance of the securitized mortgages is because
of the increased use of low stakes by originators.
Our paper offers the first empirical investigation on the impact of regulations on mortgage
securitization directed at the practices of financial institutions participating in this market.
It contributes to two strands of research. First, it adds to the growing literature that is
exploring the MBS market and represents the first study on the regulation aspect of the MBS
market.3 Second, our paper contributes to the literature on the economic consequences of
financial reporting and disclosure regulation.4 In particular, our study relates to the papers
trying to understand the unintended consequences of regulation changes, such as the “going
3
For studies on MBS, see e.g., Mian and Sufi (2009), Nadauld and Sherlund (2013), Keys, Mukherjee,
Seru, and Vig (2010), Keys, Seru, and Vig (2012), Purnanandam (2011), among others.
4
Many recent papers on disclosure regulation mainly focus on the impact of regulation changes under
Regulation Fair Disclosure (Reg FD) and Sarbanes-Oxley Act (SOX). See Leuz and Wysocki (2008) for a
comprehensive review of the related studies; Granja (2013) examines the effect of disclosure regulation in
the commercial banking industry, among others.
3
dark activities” after SOX.5 Our findings shed light on the effect of mandatory disclosure on
financial institutions and its implication for the performance of securitized assets issued by
these financial institutions. While early studies argue that firms disclose bad news to avoid
lawsuits in the future (Skinner (1994)), more recent evidence suggests that firms that disclose
more also have more frequent litigation (Skinner (1997)). Kothari, Shu, and Wysocki (2009)
argue that firms withhold bad news up to a certain threshold. The authors provide evidence
by using different magnitudes of the stock market’s reactions to negative and positive news.
Our evidence adds to this literature in a new and important setting and supports the view
that understanding the firms’ potential responses and avoidance strategies is crucial when
evaluating the costs and benefits of disclosure regulation and also when designing the rules
in the first place (Leuz and Wysocki (2008)).
The rest of the paper is organized as follows. Section 2 describes the information disclosure in Reg AB. Section 3 describes data and provides summary statistics. In Section 4,
we present and discuss our empirical findings at the deal level. In Section 5, we provide the
findings on the loan level analysis. And, Section 6 concludes.
2.
Regulation AB
The Securities and Exchange Commission defines asset-backed securities (ABS) as securities
that are backed by a discrete pool of self-liquidating financial assets. The ABS market has
experienced rapid growth in the last two decades. One source estimates that the annual
issuance of US public non-agency ABS grew from $46.8 billion in 1990 to $416 billion in
2003.6 Another source estimates that the new issuance for 2003 was at $800 billion.7 Prior
to the introduction of Reg AB, there were few SEC initiatives directly related to ABS. In this
5
For example, Gao, Wu, and Zimmerman (2009) provide evidence on the unintended consequences of
Sarbanes-Oxley Act exemptions for small companies (i.e., firms with a public float of less than $75 million).
They find that the size-based exemptions provide incentives for firms to stay small by curbing growth to
avoid crossing the compliance threshold. Leuz (2007) and Leuz, Triantis, and Wang (2008) show that “going
dark” is associated with SOX-related events.
6
See Bank One Capital Markets, Inc., 2004 Structured Debt Yearbook.
7
See Asset Securitization Report (pub. by Thomson Media Inc).
4
section, we describe the most relevant item of Reg AB on originator information disclosure
for mortgage securitization and discuss the implications of the use of low origination stakes
for the securitized loans’ performance.
Asset-backed securitization is a financing technique in which financial assets are pooled
and converted into instruments that can be offered and sold more freely in the capital
markets. In a basic securitization structure, a financial institution known as “sponsor”
constructs a pool of financial assets, such as mortgage loans, which can be self-originated
or acquired directly or indirectly through an affiliate. Securities that are backed by a pool
of financial assets are then sold to investors by investment banks known as underwriters.
Payment on the ABS depends primarily on the cash flows generated by the assets in the
underlying pool, and the other rights designed to assure timely payment such as guarantees
called credit enhancements.
Asset-backed securities and their issuers differ from corporate securities and operating
companies in that “there is generally no business or management to describe in offering
these securities. Instead, information about the transaction structure and the quality of
the asset pool and servicing is often what is most important to investors.”8 According
to the SEC, prior to Reg AB, many of its existing disclosure and reporting requirements,
which were designed primarily for corporate issuers, did not elicit the information that is
relevant for most ABS transactions. Regulation AB, which became effective in January
2006, thus represents a comprehensive treatment of ABS under the Securities Act of 1933
and the Securities Exchange Act of 1934. It consolidates and codifies the SEC’s positions
and industry practice which the SEC has done through no-action letters and the filing review
process over time.
Regulation AB covers four primary regulatory areas: Securities Act registration, disclosure, communications during the offering process, and the ongoing reporting under the
8
See Securities and Exchange Commission Asset-Backed Securities Proposed rule Release NOS. 33-8419;
34-49644.
5
Securities Exchange Act.9 The new rules on disclosure represent the most dramatic changes
in the ABS markets. Prior to Reg AB, there was no disclosure items specifically tailored
to ABS. While eliminating unnecessary boilerplate and de-emphasizing unnecessary legal
recitations on terms, Reg AB requires that issuers disclose information material to an ABS
transaction such as the background, experience, performance, and roles of various transaction parties; including the sponsor, the servicing entity, and the trustee. It also requires,
for the first time, that certain statistical information on a “static pool” basis be provided if
material to the transaction to aid in an investor’s analysis of current and prior performance.
Specifically on loan originators, Reg AB Item 1110 requires as an initial level of disclosure
the identification of any originator or group of affiliated originators that originates, or expects
to originate, 10% or more of the pool assets. If the originator originates or expects to
originate 20% or more of the pool assets, then the regulation further requires disclosure of
information regarding the size and composition of the originator’s origination portfolio as
well as information material to an analysis of the performance of the pool assets, such as
the originator’s credit-granting or underwriting criteria for the asset types being securitized.
Thus, loan originators of 20% or more of the collateral pool represent an important disclosure
threshold that did not exist prior to Reg AB. Our empirical investigation on the implication
of the disclosure mandate in Reg AB for the securitized loans’ performance focuses on the
change in the percentage of loans originated by lenders surrounding the disclosure threshold
and the associated cross-sectional variations in the loan performance of these originators.
Prior to Reg AB, the SEC’s position on an ABS issuance was done through no-action
letters. These positions and industry practice are consolidated and codified under Reg AB.
The disclosure mandate on originators under Reg AB thus subject MBS issuers to more
scrutiny and higher litigation risk. Consequently, after Reg AB, riskier loans could be placed
in a mortgage deal at a stake below the threshold to avoid mandatory information disclosure.
Therefore, we expect the number of LS deals to increase after Reg AB. This gives us the
9
See
Securities
and
Exchange
Commission
http://www.sec.gov/rules/final/33-8518.pdf.
6
Regulation
AB
Final
Rule
33-8518.
following testable hypothesis.
Hypothesis 1: All else being equal, the fraction of low stake deals is higher after Reg AB
than before Reg AB.
There are two main motives behind using low origination stakes. The first is to reduce
the SEC compliance costs. The Reg AB imposes higher compliance costs on originators
contributing more than 20% of the collateral assets to a deal. However, low stakes should
have no impact on the deal’s or the loan’s performance under this motive because most
originators participate in both LS and non-LS deals. The second possible motive is to avoid
disclosure of information on riskier loans that constitute a low stake in a deal. Before Reg AB,
loan stakes did not have to be under 20% of the deal to avoid information disclosure because
no mandatory disclosure threshold existed. After Reg AB, if riskier loans are included in
a deal, they have to be kept under the 20% threshold to avoid scrutiny by investors and
regulators. In other words, if avoiding information disclosure is the primary motive, we
expect the performance to be worse for deals with the increased use of low stakes after Reg
AB. This expectation leads to our second hypothesis.
Hypothesis 2: All else being equal, the increased use of low stakes is associated with worse
performance in securitized mortgages after Reg AB than before Reg AB.
3.
Data description and summary statistics
Our data come primarily from two sources: Bloomberg and First American Corelogic LoanPerformance. We collect information on deal characteristics from Bloomberg. Bloomberg
provides information on the mortgage originator and underwriter extracted from the deal
prospectus supplements filed with EDGAR.10 Our sample consists of privately securitized
mortgage deals that were issued between 2003 and 2007, the period immediately preceding
the financial crisis. Each deal in our database has detailed information on its characteristics
at issuance. In the meantime, our loan level data consist of information on privately securi10
We use publicly issued non-agency mortgage deals due to data availability.
7
tized mortgages constructed by Corelogic LoanPerformance. Corelogic provides information
on loan origination dates, the mortgage loan pools, the identities of the securitizers, the MBS
where the loans are placed, and on the borrowers and loan characteristics. We also construct
variables from various sources on regional housing and economic conditions.
Bloomberg reports the identities of the originators and the percentage of dollar principal
that each of them originates for the deal. Not every deal provides origination information,
thus we focus on a sample of 2,248 deals for which the origination information is available
for our investigation. From the detailed origination information, we identify deals that have
origination stakes in 10-20% or below 20% of the pool assets from an originator and its
affiliates. Considering the disclosure requirements of Reg AB, we use 10-20% as the main
measure of a low origination stake and use below 20% as an alternative measure.11 We also
calculate the aggregate percentage of low stake loans for each deal.
Our deal level performance measure is the cumulative net loss rate measured as the sum
of all of the losses of principal suffered up to September 2014 divided by the total original
balance of all of the mortgages. As a robustness check, we also use the cumulative net loss rate
measured as the sum of all of the losses of principal suffered up to December 2012. We use
the deal’s characteristics as control variables that comprise deal original collateral balance,
an indicator for high issuer reputation following Griffin, Lowery, and Saretto (2014), the
number of tranches, share of loans that have limited or no documentation in the collateral,
weighted average FICO score, weighted average loan-to-value (LTV) ratio, percentage of
adjustable rate mortgages in the deal, an indicator for the presence of negative amortization,
percentage of purchase loans (as opposed to refinancing), percentage of loans for single family
houses, percentage of loans for owner-occupied houses, percentage of loans for equity take
out, percentage of loans for refinance, and percentage of second lien loans.
To construct a sample for the loan level analysis, we first identify the link between
each securitized loan and its originator in a deal with multiple originators. The Corelogic
11
Under Reg AB, originators contributing less than 10% to the collateral pool do not have to reveal their
identities. This explicitly precludes using below 10% as a separate threshold in the analysis.
8
database provides the name of the original lender for each loan whether it is a direct lender
or a mortgage broker. We collect identity and affiliation information for the original lender
of each loan to determine if the original lender is one of the mortgage deal’s originators
or is affiliated with one of the deal originators. When such a link can be made, we assign
individual loans to the originators listed in the prospectus supplements. When the original
lenders cannot be linked to any of the originators as is often the case with the loans acquired
by the originators, we set the originator’s information for these loans as missing and exclude
them in our loan level analysis. We merge the deal level originator variables with the loan
level data by originator name and deal number. The definitions for all of the variables at
both the deal and the loan levels are described in the appendix.
We begin our investigation with the deal level analysis. Table 1 reports the summary
statistics for the deal level variables. For our full sample, the average deal cumulative net
loss is 13.1% with a standard deviation of 12.4%. The deals with 10-20% (less than 20%)
stakes from an originator and its affiliates are 18% (23%) of the sample. For the full sample,
low stake loans account for 4.8% (5.7% for less than 20% stakes) of a collateral pool with
a standard deviation of 13% (14% for less than 20% stakes). The highest percentage of
aggregate low stake loans is 100%. In other words, in the extreme case, a deal could consist
entirely of low stake loans. For deals with 10-20% (less than 20%) low stakes, the percent of
low stake loans are on average 25.8% (24.5%) of the pool assets.
Table 1 about here
Table 2 reports the correlation coefficients on the main variables of interest at the deal
level. The cumulative net loss is significantly and positively correlated with the presence of
low stakes and the aggregate percentage of low stake loans in these deals. The results are
very similar for both measures of low stakes: the percentage of loans in a deal within 10-20%
or the percentage of loans in a deal below 20%. Consistent with the findings in the research,
the deal’s cumulative net loss is negatively correlated with the average FICO score, which
suggests that the high credit worthiness of the borrower is associated with lower defaults.
9
However, the deal’s cumulative net loss is positively correlated with the average loan-tovalue ratio, percentage of adjustable rate mortgages, the presence of negative amortization
loans, percentage of purchase loans, and the percentage of loans with a second lien due to
the higher default risk associated with these characteristics. Our correlation estimate also
suggests that the deal’s cumulative net loss is negatively correlated with the percentage of
single family home loans.
Table 2 about here
4.
The change in the use of low stakes and its implication for deal performance
We start our empirical analysis by examining the impact of Reg AB on the use of low
origination stakes. We then focus on investigating the implication of the change in the use
of low stakes for the performance of securitized mortgages at the deal level.
4.1.
The change in the use of low stakes under Reg AB
We refer to a low stake as a group of loans from an originator and its affiliates in the collateral
pool backing an MBS deal that is below the threshold necessitating mandatory disclosure
by SEC under Reg AB. To test our hypotheses on the impact of Reg AB, we define a low
stake as a group of loans from an originator and its affiliates that accounts for 10-20% of a
collateral pool. As a robustness check, we also use an alternative definition for a group of
loans that accounts for less than 20% of the collateral pool.
In Figure 1, we plot the number and the percentage of the deals with low stakes in our
sample period. The top panels present the plots for deals with 10-20% stakes before and
after Reg AB. Both the number and the percentage of deals with low stakes show similar
pattern surrounding Reg AB. Specifically, the number of deals with low stakes experience
10
a sharp increase from 121 before Reg AB (before 2006) to 303 after Reg AB (after 2006).
In percentage terms, the increase is more than double from around 11% before Reg AB to
27% after Reg AB. Moreover, the bottom panels show that the percentage of deals with low
stakes were relatively stable before Reg AB and the sharp jump occurred right after Reg AB
became effective and then remained high.
Figure 1 about here
The increase in the percentage of low stakes in a deal before and after Reg AB is statistically significant. We apply logistic regressions to evaluate this change by controlling for other
factors that might affect the use of low stakes in deals by using the following specification.
LS Deal = f (β × Post Reg AB + Deal and Macro controls + Fixed effects).
where LS Deal is a dummy variable that represents the presence of low stakes in a deal.
Because changes in the house prices and the macroeconomic environment might have an impact on the mortgage’s performance, we include additional control variables in our analysis.
We calculate the pre-deal run-up in house prices for the representative geographic area using
the house price index for the corresponding state reported by the Federal Housing Finance
Agency (FHFA). Specifically, we compute the weighted average change in the house prices
that is associated with a deal during the four quarters preceding the quarter the deal is
issued in.
Table 3 reports the estimation results. In column (1), the dependent variable is a dummy
variable equal to one if a deal has at least one 10-20% stake from an originator and its
affiliates and equals zero otherwise. The result shows a very significant increase in the
probability (more than tripled) that a deal involves at least one 10-20% low stake after Reg
AB (e1.279 = 3.59). We find a similar result in column (2) when we use the alternative
dependent variable to capture the presence of at least one low stake that is less than 20% of
the collateral pool.
11
Table 3 about here
To demonstrate that the change in the use of low origination stakes occurs around the
20% threshold, we examine the difference between the percentage of mortgage deals with
stakes just below 20%, at [10,20)%, [15,20)%, and [18,20)% and the percentage of mortgage
deals with stakes just above 20%, at [20,30]%, [20,25]%, and [20,22]%, respectively, before
and after Reg AB. We next apply a difference-in-difference test to the differentials in the
percentages of the deals with just below 20% stakes and the percentages of deals with just
above 20% stakes. We observe a differential of 6.6% for [10,20)% versus [20,30]%, 5.8% for
[15,20)% versus [20,25]%, and 2.8% for [18,20)% versus [20,22]%. Our test results show that
the increase in the percentage of deals with just below 20% stakes relative to those just above
20% stakes is statistically significant at the 1% test level for all three comparison brackets.
We also evaluate the increase in the use of low stakes quantitatively, controlling for the
other factors that might affect the use of low stakes as well as the lead underwriter fixed
effect. For each deal, we create a dummy variable to represent the presence of low stakes in
a bracket just below 20% and another dummy variable representing the presence of stakes
in a bracket just above 20%. The difference between these two dummy variables is denoted
as diffA20B where [A,20) is the bracket just below 20% and [20,B) is the bracket just above
20%. The combinations of {A,B} in our analysis include {10,30}, {15,25}, and {18,22}.
Table 4 reports the results for the OLS estimation (panel A) and the ordered logit (panel B)
regression analysis.
Controlling for the deal’s characteristics, the issuer’s reputation, and macroeconomic
variables, our OLS estimation shows that the change in the fraction of deals with stakes just
above the threshold of 20% to just below the threshold increased by 15% from [20,30]% to
[10,20)%, 8% from [20,25]% to [15,20)%, and 4% from [20,22]% to [18,20)%, respectively,
from before Reg AB to after Reg AB. Given that the average fractions of LS deals in the
[10,20)%, [15,20)%, and the [18,20)% brackets are 11%, 5.7%, and 2.1%, respectively, before
Reg AB, our estimates show that the percentage of deals with stakes just below the threshold
12
increased by 136% for [10,20)%, 140% for [15,20)%, and 190% for [18,20)%, respectively,
after Reg AB. The ordered logit regression analysis produces qualitatively similar results.
For instance, the log-odds ratio of mortgage deals with stakes just above the threshold to
just below the threshold is higher by 89% for [20,30]% to [10,20)%, 65% for [20,25]% to
[15,20)%, and 57% for [22,22]% to [18,20)%, respectively, after Reg AB.
Table 4 about here
4.2.
The implication of the change in the use of low stakes on deal
performance
Now that we have documented a significant increase in the use of low stakes after Reg AB,
we explore its implications for securitized mortgage performance. On one hand, we expect
the use of low stakes to have no implication for performance if it is primarily driven by
the motive of reducing the SEC compliance cost. On the other hand, the use of low stakes
might be associated with a deal’s worse performance if it is used by the originators to avoid
disclosure of riskier loans.
To discern these two implications, we regress the deal’s cumulative net loss on variables
that capture the presence of low stakes and their interactions with a post-Reg AB dummy
variable. The inclusion of the interaction term allows us to assess whether the increased use
of low stakes has an incremental effect after Reg AB rather than before Reg AB. Specifically,
we use the following specification for our regression analysis:
Cumulative net loss = α + β1 × Post Reg AB + β2 × LS Deal
+β3 × Post Reg AB × LS Deal
+Deal and Macro controls + Fixed effects.
where LS Deal represents the presence of low stakes in mortgage deals. In addition to the LS
deal measure defined above, we also use a continuous variable that captures the aggregate
13
percentage of low stake loans in a deal. We do so for low origination stakes at the 1020% level of a collateral pool and for low origination stakes below 20% of a collateral pool,
respectively. We include house price change which we compute as the weighted average
change in the house price associated with a deal from the quarter that the deal is issued in
to the third quarter of 2014.
The results are reported in Table 5. Columns (1) to (4) present the findings for the
10-20% stakes. It is clear that prior to Reg AB, the disclosure threshold has no significant
implication for a deal’s performance. However, after Reg AB, the LS deals have significantly
worse performance. Specifically, the estimate in column (2) indicates that LS deals have 2.38
percentage points higher deal cumulative net loss. This represents 18% of the average cumulative net loss in our full sample period (2.38/13.12). When using the aggregate percentage
of LS loans as the measure of low origination stakes, our estimate shows that a one standard
deviation increase in this aggregate percentage of LS loans is associated with a 1.03% higher
cumulative net loss. This represents an 8% average cumulative net loss for our full sample
(1.03/13.12). Our results are robust if we redefine a low stake as less than 20% (see columns
(5) to (8)).
Table 5 about here
To provide a placebo test for the effect of low stakes on the mortgage deal’s cumulative
net loss, we conduct a regression analysis that includes both the presence of 10-20% stakes
and 20-30% stakes, a bracket just above the threshold. Table 6 reports the results of our
analysis. For the dummy variable that represents the presence of low stake loans and the
continuous variable that represents the percentage of low stake loans in a collateral pool, we
find that the 20-30% stakes have no significant effect on the deal’s cumulative net loss. On
the other hand, the 10-20% stakes are associated with a significantly larger cumulative net
loss. More important, the larger cumulative net loss is concentrated in the deals with the
10-20% stakes after Reg AB. This finding highlights the implication of 10-20% stakes, an
amount just below the disclosure threshold, post-Reg AB for deal performance.
14
Table 6 about here
4.3.
Deal performance and increased use of low stakes
Next, we investigate the implication of the increased use of low stakes on the mortgage deal’s
performance. For this analysis, we introduce a dummy variable to represent the increased
use of low stakes for each originator before and after Reg AB. Specifically, for each originator,
we count the number of deals in which the originator participated and compute the change
in the percentage of its LS deals before and after Reg AB. In particular, we identify the
originators who increase their use of low stakes after Reg AB and refer to these originators
as increased low stake (ILS) originators. For each deal, we define the dummy variable as
equal to one if at least one of the originators is an ILS originator and refer to such deals as
ILS deals, and equals zero otherwise.
post
pre
as the percentage of low stake
Notationaly, for originator k, we define Lk and Lk
deals this originator and its affiliates originate before and after Reg AB, respectively, and
post
pre
∆LSk = (Lk
− Lk ) as the change in the percentage of its low stake deals before and
after Reg AB. For mortgage deal j, we define the dummy variable ILS as follows:
ILS = I(max ∆LSk > 0),
k∈Oj
where I(·) is an indicator function, and the Oj represents the set of originators for deal
j. The ILS originators have ∆LS greater than zero, and ILS deals have at least one ILS
originator. Similarly, we define a dummy variable ILS<20% to represent the increased use of
low stakes when low stake is defined as below 20% of the collateral pool.
As an alternative, we identify the originators as ILSH originators if they have an above
average increase in the use of low stakes, and deals with at least one ILSH originator are
referred to as ILSH deals. We define the dummy variable ILSH<20% to represent a larger
than average increase in the low stake usage when the low stake is defined as below 20%
15
of the collateral pool. For our sample of 149 originators, the average increase in the use
of low stakes is 2.0% for 10-20% loans and 6.4% for below 20% loans after Reg AB.12 We
use the following specification for our analysis on the implication for the mortgage deal’s
performance associated with the increased use of low stakes.
Cumulative net loss = α + β × ILS
+Deal and Macro controls + Fixed effects.
Table 7 reports the results of our analysis. Our estimate shows that deals with increased
low stake usage (the ILS deals) are associated with a 1.68% higher cumulative net loss than
the non-ILS deals (column (1)). A similar result is found when we use the dummy variable
ILS<20% (column (2)). When using the alternative measure ILSH, we find that ILSH deals
are associated with a 1.94% higher cumulative net loss than other deals (column (3)). A
similar effect is also found when we use the dummy variable ILSH<20% (column (4)).
Table 7 about here
The cumulative net loss measure can be lagged and affected by the vintage effect. To
demonstrate that our findings are robust to the deal’s cumulative net loss measured at
different dates, we construct an alternative measure of cumulative net loss at December 2012
that is scaled by the original collateral balance. The results based on this cumulative net loss
variable are reported in Table 8, Table 9, and Table 10. Consistent with our earlier findings,
the implication of the increased use of low stakes for the mortgage deal’s performance remains
significant and qualitatively similar. This finding indicates that the use of low stakes in
mortgage deals is not specific to the cumulative net loss measured at different dates. It
reflects the response of MBS securitization to changes in the regulation, and this change has
important implications for mortgage deal performance.
12
The ILS originators participated in more deals after Reg AB than the non-ILS originators. This is why
there is a sharp increase in LS deals after Reg AB.
16
Table 8, Table 9, and Table 10 about here
4.4.
The implication of the change in low stakes for deal yield
spreads and credit enhancement
One question is whether the higher cumulative net loss of mortgage deals with increased low
stake usage is reflected in the deal’s initial yield spreads and credit enhancement. This is
relevant for how investors evaluate the implication of the disclosure mandate for credit risk
protection and deal pricing. For credit enhancement, we focus on the subordination that is
measured as the percentage of the face value of trust securities not rated AAA by Moody’s or
Standard & Poor’s at the deal’s close. For deal yields, we use the initial average yield spread
for all of the securities issued by the trustee of the mortgage deals. This is the difference
between the average yield of all of the securities issued by the trustee weighted by the face
value of the securities and the yield on the 10-year Treasury bond. The former is calculated
using the standards of the Bond Market Association and reported by Bloomberg. We use
the same specifications for our yield spread and credit enhancement as for the cumulative
net loss.
Table 11 reports the results of our regression analysis. Panel A shows that LS deals do
not offer significantly different yields from the non-LS deals. The Reg AB does not change
this result, which is true for both 10-20% stakes (column (1)) and below 20% stakes (column
(2)). Furthermore, the presence of low stakes has a significant impact on credit enhancement
measured by mortgage deal subordination. Under both measures of LS deals, the presence of
low stake deals is associated with a higher average subordination before Reg AB and a lower
subordination after Reg AB (columns (3) and (4)). This shows that LS deals actually offer
less credit enhancement after Reg AB than before, in contrast to their worse performance
after Reg AB. Combining the findings on deal yield spreads and subordination provides
evidence that suggests investors might not have impounded the higher risk associated with
the use of low stakes into the mortgage deal’s yields and credit enhancement after Reg AB.
17
Table 11 about here
Panel B reports the results of the analysis on whether the increased use of low stakes
after Reg AB versus before Reg AB is reflected in the deal’s yields and credit enhancement.
We find no evidence that the presence of ILS or ILSH is reflected in the yield spreads or the
subordination.
Overall, our results on Reg AB have two important implications. First, the disclosure
threshold has increased the use of low stakes in mortgage deals after Reg AB. Second, the
increase in the use of low stakes is associated with higher deal cumulative net loss. These
findings suggest that the increased used of low stakes are likely driven by the need to avoid
scrutiny and to potentially withhold some adverse information on riskier loans.
5.
Loan performance and the change in the use of low
stakes
In this subsection, we investigate the implication of the change in the use of low stakes for
the mortgage’s performance at the loan level. We first directly test whether the change in
low stake usage (∆LS) by originators is associated with a different quality in the loans that
they originated.13 A consistent result from this analysis will reinforce our deal level findings
since loan level analysis enables us to directly compare loans from originators with different
low stake usage before and after Reg AB. Furthermore, we investigate whether the effect of
∆LS on the loan’s quality is stronger for low origination stakes after Reg AB.
It is possible that loans in different stake sizes have different quality. We control this
characteristic by including stake sizes in our regression. Following the definition in Table 7,
we compute the change in the percentage of LS deals before and after Reg AB, that is,
∆LS = (Lpost − Lpre ) for the originator of the loan under consideration. Merging the
13
We use ∆LS for each originator in the loan level regressions because we no longer need to aggregate
∆LS at the deal level.
18
deal level information on the originator with the loan level data and excluding missing
observations, we have more than three and a half million loans in 1,603 deals. Following the
standard practice in the literature, we use the securitized loan’s delinquency, defined as 60
days or more past due within 24 months of the origination as the performance measure in
the loan level analysis.
To control for the macroeconomic environment in the loan level data, we compute the
appreciation in house prices over the 24 months after origination by using the house price
index for the borrower’s metropolitan statistical area (MSA) reported by the Federal Housing
Financing Agency (FHFA). We also compute the change in the state-level unemployment
rate over the 24 months after origination using data reported by the Bureau of Economic
Analysis and collect the median household income in 1999 for the borrower’s zip code as
reported by the US Census Bureau in 2000. Additionally, we include the credit spread and
the 10-year Treasury yield as macro control variables. To control for the different qualities
of loans securitized at different time periods, we include deal’s issuing semester fixed effect.
The fixed effect mitigates both the vintage effect and other macroeconomic changes in the
sample period not captured by our macro control variables.
In Table 12, we report the summary statistics for the loan level variables for the full
sample and subsamples of loans in the 10-20% and 20-30% stakes respectively. We use loans
in the 20-30% stakes for comparison due to their comparable stake size. We observe that
the sample averages for these variables are close between the whole sample and subsamples,
and even closer between the two subsamples.
Table 12 about here
We take two steps in our loan level analysis. In the first step, we examine whether
the loans from those originators who increase their low stake participation after Reg AB
are more likely to be in delinquency. We expect that the loans from the originators with
larger increases in the use of low stakes are riskier than the loans by other originators. This
expectation suggests that the loan delinquency will increase in ∆LS. In the second step,
19
we investigate whether the effect of ∆LS on loan delinquency is particularly strong for the
10-20% stakes after Reg AB. We use loans at the 20-30% stake level as a control group
because of their close proximity to the 10-20% stake level. In the regression specification,
we interact ∆LS with a dummy variable that indicates whether the stake size is 10-20%
and expect the interaction term to be significantly positive if the 10-20% stake size is being
used to avoid disclosure on riskier loans as opposed to the 20-30% stake size. We conduct
this analysis separately for the before and after Reg AB subsamples of loans and expect the
iteration term to be significantly positive after Reg AB and insignificant before Reg AB.
Table 13 reports the marginal effects from the probit regression for the whole sample with
Stake size (column (1)), the whole sample with both Stake size and ∆LS (column (2)), the
subsample of loans in the 10-20% stakes (column (3)), and the subsample of loans in the 2030% stakes (column (4)). Our estimation shows that for the full sample the loans from larger
stake sizes have lower delinquency. This finding makes it necessary to control for the stake
size in our subsequent analysis. As expected, ∆LS is positively associated with delinquency
when controlling for the stake size and various loan level controls. The economic magnitude
is significant in that the loans from an originator who increases the use of low stakes by 10%
have a 0.5% higher delinquency rate. For the subsample of loans in the 10-20% stakes, we
observe the same effect for ∆LS. On the other hand, we find no such effect for loans in the
20-30% stakes, demonstrating a striking contrast around the 20% threshold.
Table 13 about here
Next we explicitly test whether the effect of the increase in the low stake usage is particularly strong after Reg AB. This test will provide evidence on the 10-20% stake size being
used to avoid scrutiny after Reg AB. Table 14 presents the results of a probit regression
on loans from the 10-30% stake group for the pre-Reg AB subsample (2003-2005) and the
post-Reg AB subsample (2006-2007). Our estimation for the pre-Reg AB subsample shows
that the variable ∆LS is positively associated with loan’s delinquency, yet statistically insignificant for the pre-Reg AB period. Also, there is no significant difference in the effect
20
of ∆LS between the 10-20% stake group and the 20-30% stake group, which is consistent
with our expectation that there should not be a jump at the 20% threshold before Reg AB.
In contrast, our estimation for the post-Reg AB subsample shows that the effect of ∆LS is
much stronger for loans in the 10-20% stakes than those in the 20-30% stakes. However, ∆LS
is negatively associated with delinquency for loans in the 20-30% stakes. This is consistent
with the explanation that originators use the 10-20% stake size to avoid disclosure on riskier
loans.
Table 14 about here
6.
Conclusion
How to design and implement effective regulation has received widespread attention following the 2007 to 2008 subprime mortgage crisis. Very little is known about the impact of
regulation on the non-agency mortgage backed securities (MBS) market implemented during the height of the housing boom right before the crisis. Even less is known about the
effects of these regulations on the market participants and its resulting economic impact. In
this paper we fill this void.
One of the most important aspects of Reg AB is the disclosure mandate on lenders who
contribute loans to collateral pools backing MBS. Specifically those originators who contribute more than 20% of the loans in a collateral pool are required to provide detailed
information material to the investors’ analysis of the collateral assets. The purpose of this
requirement is to encourage transparency and therefore accountability. Using data on mortgage deals constructed before and after Reg AB, we find that the disclosure requirement
might have been circumvented in the cases where riskier loans were included in the mortgage pool. Our loan level analysis provides supporting evidence on our deal level findings.
Overall, our study on how these regulations change the market participants’ behavior and
the ensuing economic impact can shed light on future research and the policy-making directed
21
at the asset-backed securities markets. Coincidentally, the recently adopted Regulation AB
II has tightened the disclosure requirement on originators that originate less than 10% of
the pool assets.14 There are also other aspects of these regulations such as communication
in the offering process and on-going reporting that could potentially change these markets;
we leave those topics for future research.
14
Regulation AB II adopted on August 27, 2014, requires that if the cumulative amount of pool assets
originated by parties other than the sponsor or its affiliates is more than 10% of the total pool assets, then
any originator that originates less than 10% of the pool assets also must be identified in the prospectus.
22
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reputation: Do reputable underwriters produce better securities?, Review of Financial
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23
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24
Appendix: Variable definitions
Deal and macro variables:
• Cumulative net loss: Historical percentages of cumulative loss on the underlying loans comprising the
entire collateral that backs the deal, measured as of September 2014
• LS Deal 10-20% (d): Equals 1 if a deal has (an) originator(s) that originate(s) a percentage of loans
between 10% and 20% and 0 otherwise
• LS Deal < 20% (d): Equals 1 if a deal has (an) originator(s) that originate(s) loans below 20% and 0
otherwise
• Total percentage of 10-20%: Total percentage of loans that are in stakes between 10% and 20%
• Total percentage of < 20%: Total percentage of loans that are in stakes below 20%
• Original collateral balance (in billions): The original balance of the underlying loans comprising the
entire collateral
• High reputation: Equals 1 if the deal has an underwriter whose IPO reputation score is greater than
or equal to 8 (from Professor Jay Ritter’s website) and 0 otherwise. This measure follows from Griffin,
Lowery, and Saretto (2014)
• No. of tranches: Number of securities in a deal
• Low documentation: Dummy variable indicating underlying loans with limited, as distinguished from
full, documentation
• FICO: Weighted average original credit score of the underlying loans
• LTV: Original loan to value percentage of the loan
• Adjustable rate mortgage: The percentage of the adjustable rate mortgage loans
• Negative amortization: Equals 1 if the deal consists of mortgages with negative amortization features
and 0 otherwise
• Purchase loans: The percentage of the Loan Purpose (the reason for the loan) for Purchase
• Single family: The percentage of Single Family Mortgaged Properties, the type of properties against
which the loans were written
• Owner occupied: The percentage of the Occupancy (the purpose of the property) for Owner Occupied
• Equity take out: The percentage of the Loan Purpose (the reason for the loan) for Equity Take Out
• Refinance: The percent of the Loan Purpose (the reason for the loan) for Refinance
• Second lien: The percentage of the loans comprising the collateral that are second liens
• House prices change: We compute the average house price changes from the issue’s quarter to the third
quarter of 2014 using the state level Federal Housing Finance Agency’s (FHFA) seasonally adjusted
quarterly house price index. The weighted average for each deal is taken over the top 5 states by their
mortgage balances assuming the remaining 45 states have equal representation
25
• House price run-up: We use the same data and method as in “House prices change” to calculate the
weighted average price change associated with a deal during the 4 quarters preceding the quarter the
deal was closed
• Credit spread: The spread between BBA and AAA corporate bond yields in the month of the issue
• 10-Year Treasury: 10-year treasury yield in the month of issue
Loan level variables:
• Delinquency: Equals 1 if the loan payment is 60 days past due within the 24 months of origination
and 0 otherwise
• FICO: Fair, Isaac and Company (FICO) credit score at origination standardized with the sample
mean and variance
• Full DOC: Dummy variable equal to 1 if the borrower has complete documentation on income and
assets
• CLTV: Combined loan to value ratio for the first lien loan at origination. The ratio includes a second
lien when it exists. The LTV ratio is in decimal (e.g., a 20% down payment = 0.80 LTV ratio)
• Investor: Dummy variable equal to 1 if the borrower does not owner-occupy the property
• DTI: Back-end debt-to-income ratio, defined as the total monthly mortgage payment to monthly
gross income at origination, in percent. The back-end DTI differs from the front-end DTI in that
the back-end DTI includes mortgage insurance, homeowners insurance, property tax, and any other
continuing home ownership expenses
• Miss DTI: Dummy variable equal to 1 if DTI is missing. Demyanyk and Van Hemert (2011) interpret
a Miss DTI as a negative signal about borrower quality
• Cash-Out: Dummy variable equal to 1 if the purpose of the loan is for a cash-out refinance where the
balance of the loan is increased to raise cash. As noted by Pennington-Cross and Chomsisengphet
(2007), the most common reasons for a cash-out refinance are to consolidate debt and to improve
property
• PrePayPen: Dummy variable equal to 1 when the loan has a prepayment penalty and/or is an option
ARM or negative amortization loan. These loan features make refinancing less likely in default
• Initial Rate: The initial mortgage interest rate in percent
• Margin: Margin (in percent) for an adjustable-rate or hybrid loan over an interest rate index, applicable after the first interest rate reset. For example, a 2/28 hybrid adjustable-rate loan has a low
(teaser) fixed rate for the first 2 years, followed by a variable rate based on the 6-month LIBOR plus
a margin that is fixed for the life of the loan
• Rate Reset: Time period (in months) before the interest rate in an adjustable-rate loan starts to
adjust. Hybrid adjustable rate loans have initial fixed interest rates of 24 or 36 months, while pure
adjustable rate loans have shorter first interest rate reset periods
• Loan Amt.: Size of the loan at origination in dollars
26
• ARM: Dummy variable equal to 1 if the loan is an adjustable rate mortgage and the first interest rate
reset period is less than or equal to 1 year from the date of origination
• Balloon: Dummy variable equal to 1 for a fixed rate or adjustable rate loan where the payments
are lower over the life of the loan leaving a balloon payment at maturity. For example, a fixed rate
mortgage that amortizes over 40 years, but matures in 30 years, leaves a balloon payment after 30
years
• Hybrid2: Dummy variable equal to 1 for an adjustable rate loan with the initial monthly payment
fixed for the first two years. This is typically referred to as a 2/28 hybrid ARM, with the interest
rate over the remaining 28 years of the loan equal to the value of an interest rate index (i.e., 6-month
LIBOR) measured at the time of adjustment, plus a margin that is fixed for the life of the loan. The
initial fixed rate is called a “teaser” interest rate because it is lower than what a borrower would pay
for a 30-year fixed rate mortgage
• Hybrid3: Dummy variable equal to 1 for a 3/27 hybrid ARM (i.e., the initial interest rate is fixed for
3 years)
• Int. Only: Dummy variable equal to 1 if the loan has an interest only feature. For example, a 30-year
fixed rate or adjustable rate loan might permit the borrower to only pay interest for the first 60
months of the loan, but then the borrower must make payments in order to repay the loan in the final
25 years
• Local Income: Zip Code level median income in 1999 from the U.S. Census Bureau 2000
• Unemployment: State-level change in the unemployment rate from loan origination to 24 months
thereafter, reported by the Bureau of Economic Analysis
• Price Appr.: MSA-level house price index appreciation (in decimal) from loan origination to 24 months
thereafter, reported by the office of Federal Housing Enterprise Oversight (OFHEO)
27
Table 1: Summary statistics
This table presents the summary statistics on the deal and macro variables defined in the
appendix. The statistics reported are the Mean, St. Dev. (standard deviation), the k th percentile
(Pk for k = 5, 25, 50, 75, 95) of each variable. We use (d) to denote that the variable is a dummy
variable. We also use (%) if the variable is in percentage.
Variable
Cumulative net loss
LS Deal 10-20%(d)
LS Deal < 20%(d)
Total % of 10%-20%
Total % of <20%
Original collateral balance ($B)
High reputation (d)
No. of tranches
Low documentation (d)
FICO
LTV
Adjustable rate mortgage (%)
Negative amortization (d)
Purchase loans (%)
Single family (%)
Owner occupied (%)
Equity take out (%)
Refinance (%)
Second lien (%)
House prices change
House prices run-up
Credit spread
10 Year Treasury
Mean
13.12
0.18
0.23
4.78
5.65
0.82
0.78
20.3
0.47
692.25
73.97
60.33
0.08
44.05
68.56
87.74
36.02
18.92
0.62
-8.3
7.47
0.88
4.5
St. Dev.
12.36
0.39
0.42
12.87
14.05
0.52
0.42
10.47
0.5
48.85
5.47
38.96
0.27
14.23
11.59
8.73
14.81
13.33
1.75
10.95
5.32
0.11
0.35
28
P5
0.1
0
0
0
0
0.24
0
10
0
609
65
0
0
19.01
54.68
71.36
13.32
3.01
0
-21.01
-2.15
0.68
3.98
P25
2.5
0
0
0
0
0.43
1
15
0
639
71
0
0
36.3
62.96
85.66
26.65
10.37
0
-15.77
2.73
0.82
4.22
P50
8.49
0
0
0
0
0.71
1
18
0
710
74.18
71.2
0
43.35
68.39
88.32
35.26
18.97
0
-11.95
9.06
0.9
4.54
P75
22.78
0
0
0
0
1.02
1
22
1
734
77
100
0
53.32
73.8
93.66
44.04
21.41
0
-3.06
11.62
0.92
4.72
P95
36.93
1
1
30.5
34.11
1.87
1
38
1
746
82
100
1
68.38
88.85
96.98
63.46
48.47
4.61
16.86
14.11
1.11
5.1
29
Cumulative net loss
LS Deal 10-20%(d)
LS Deal < 20%(d)
Total % of 10%-20%
Total % of <20%
Original collateral balance ($B)
High reputation (d)
No. of tranches
Low documentation (d)
FICO
LTV
Adjustable rate mortgage (%)
Negative amortization (d)
Purchase loans (%)
Single family (%)
Owner occupied (%)
Equity take out (%)
Refinance (%)
Second lien (%)
1.00
0.14***
0.14***
0.10***
0.10***
0.17***
-0.03
-0.06***
0.03
-0.55***
0.56***
0.26***
0.11***
0.00
-0.00
0.04*
0.36***
-0.39***
0.50***
Cum. net loss
1.00
0.87***
0.79***
0.78***
-0.06***
-0.06***
-0.00
0.02
-0.03
0.04
-0.02
-0.04*
0.00
-0.08***
-0.06***
0.06***
-0.06***
-0.01
Has LS 10-20%(d)
1.00
0.69***
0.75***
-0.05**
-0.03
0.01
0.03
-0.02
0.04*
-0.01
-0.04*
-0.02
-0.11***
-0.07***
0.07***
-0.05**
-0.03
Has LS < 20%(d)
1.00
0.95***
-0.06***
-0.05**
-0.01
0.01
-0.02
0.02
0.01
-0.02
-0.00
-0.06***
-0.03
0.04**
-0.04**
-0.02
Total % of 10%-20%
1.00
-0.05**
-0.04*
-0.00
-0.00
-0.01
0.01
0.02
-0.03
-0.01
-0.07***
-0.03
0.04*
-0.04
-0.02
Total % of <20%
This table presents the correlation coefficients between the main variables of interest and the other explanatory variables. All of the
variables are defined in the appendix. Statistical significance levels of 1%, 5%, and 10% are indicated with ***, **, and * respectively.
Table 2: Correlation matrix
Table 3: Determinants of the use of low stakes
This table presents the results of analyzing the determinants of the use of low stakes. All of
the variables are defined in the appendix. The LS Deal 10-20% (d) and LS Deal <20% (d) are
regressed on other explanatory variables using logit regressions. The standard errors clustered
by issue semester are reported in the parentheses below each coefficient estimate. Statistical
significance levels of 1%, 5%, and 10% are indicated with ***, **, and * respectively.
Post Reg AB
Original collateral balance
High reputation (d)
No. of tranches
Low documentation (d)
FICO
LTV
Adjustable rate mortgage (%)
Negative amortization (d)
Purchase loans (%)
Single family (%)
Owner occupied (%)
Equity take out (%)
Refinance (%)
Second lien (%)
House prices run-up
Credit spread
10 Year Treasury
Lead-underwriter FE
Pseudo R2
Observations
LS Deal 10-20%(d)
LS Deal < 20%(d)
1.279***
(0.239)
-0.425***
(0.146)
-0.108
(0.269)
-0.003
(0.007)
-0.214
(0.147)
-0.003
(0.002)
-0.003
(0.016)
-0.001
(0.002)
-0.951***
(0.282)
0.018
(0.017)
-0.016***
(0.006)
-0.013*
(0.008)
0.026
(0.018)
0.021
(0.018)
-0.090***
(0.033)
0.032*
(0.016)
1.121
(0.685)
0.095
(0.268)
Yes
0.082
2248
1.159***
(0.226)
-0.324**
(0.130)
0.135
(0.246)
0.000
(0.006)
-0.236*
(0.135)
-0.002
(0.002)
0.000
(0.015)
-0.000
(0.002)
-1.047***
(0.260)
0.027
(0.017)
-0.023***
(0.005)
-0.014*
(0.007)
0.039**
(0.017)
0.034*
(0.018)
-0.097***
(0.032)
0.003
(0.015)
0.396
(0.617)
-0.010
(0.250)
Yes
0.090
2248
30
Table 4: Difference in origination stakes in brackets below and above 20%
This table presents the results of analyzing the difference between the percentage of deals with
origination stakes in the bracket just below 20% and the percentage of deals with origination
stakes in the bracket just above 20%. For each deal, we create dummy variables to represent the
existence of origination stakes in a bracket just below 20% and just above 20%. The difference
between these two dummy variables is denoted as diffA20B where [A,20) is the bracket just
below 20% and [20,B) is the bracket just above 20%. The combinations of {A,B} in our analysis
include {10,30}, {15,25}, and {18,22}. Panel A reports the results of regressing this difference
on the Post Reg AB dummy variable and other control variables using OLS regressions. Panel
B reports the corresponding results using ordered logistic regressions. The control variables are
the same as in Table 3. The standard errors clustered by issue semester are reported in the
parentheses below each coefficient estimate. Statistical significance levels of 1%, 5%, and 10%
are indicated with ***, **, and * respectively.
Panel A: OLS regressions
Post Reg AB
Control variables
Lead-underwriter FE
Adjusted R2
Observations
diff102030
(1)
0.08***
(0.02)
No
Yes
0.012
2248
diff102030
(2)
0.15***
(0.03)
Yes
Yes
0.018
2248
diff152025
(3)
0.05***
(0.01)
No
Yes
0.007
2248
diff152025
(4)
0.08***
(0.02)
Yes
Yes
0.008
2248
diff182022
(5)
0.03***
(0.01)
No
Yes
0.007
2248
diff182022
(6)
0.04***
(0.01)
Yes
Yes
0.007
2248
diff102030
(2)
0.89***
(0.16)
Yes
Yes
0.0315
2248
diff152025
(3)
0.47***
(0.12)
No
Yes
0.0191
2248
diff152025
(4)
0.65***
(0.20)
Yes
Yes
0.0279
2248
diff182022
(5)
0.47***
(0.10)
No
Yes
0.0271
2248
diff182022
(6)
0.57***
(0.17)
Yes
Yes
0.0386
2248
Panel B: Ordered logit regressions
Post Reg AB
Control variables
Lead-underwriter FE
Pseudo R2
Observations
diff102030
(1)
0.48***
(0.13)
No
Yes
0.0209
2248
31
32
Control variables
Lead-underwriter and issue semester FE
Adjusted R2
Observations
Post Reg AB × Total % of <20%
Total % of <20%
Post Reg AB × LS Deal < 20%(d)
LS Deal < 20%(d)
Post Reg AB × Total % of 10-20%
Total % of 10-20%
Post Reg AB × LS Deal 10-20%(d)
LS Deal 10-20%(d)
Yes
Yes
0.789
2105
1.04*
(0.51)
(1)
Yes
Yes
0.790
2105
-0.53
(0.45)
2.38**
(0.89)
(2)
Yes
Yes
0.789
2105
0.02
(0.02)
(3)
Yes
Yes
0.790
2105
-0.01
(0.01)
0.08**
(0.03)
(4)
Yes
Yes
0.789
2105
0.86
(0.49)
(5)
Yes
Yes
0.790
2105
-0.38
(0.39)
1.95**
(0.78)
(6)
Yes
Yes
0.789
2105
0.02
(0.01)
(7)
-0.01
(0.01)
0.07***
(0.02)
Yes
Yes
0.790
2105
(8)
We estimate linear regressions to examine the relation between the use of low stakes and the cumulative net loss as
of September 2014 for deals completed between 2003 and 2007. All of the variables are defined in the appendix. The
standard errors clustered by issue semester are reported in the parentheses below each coefficient estimate. Statistical
significance levels of 1%, 5%, and 10% are indicated with ***, **, and * respectively.
Table 5: The use of low stakes and cumulative net loss
Table 6: Origination brackets [10,20), [20,30), and cumulative net loss
This table reports the results of analyzing the impact of [20,30) origination stakes on deal
performance, compared to the impact of [10,20) origination stakes. All of the variables are
defined in the appendix. The standard errors clustered by issue semester are reported in the
parentheses below each coefficient estimate. Statistical significance levels of 1%, 5%, and 10%
are indicated with ***, **, and * respectively.
LS Deal 10-20%(d)
(1)
(2)
1.03**
(0.45)
-0.53
(0.47)
2.38**
(0.74)
0.01
(0.42)
0.00
(1.14)
Post Reg AB × LS Deal 10-20%(d)
LS Deal 20-30%(d)
0.06
(0.56)
Post Reg AB × LS Deal 20-30%(d)
Total % of 10-20%
(3)
(4)
0.02
(0.02)
-0.01
(0.01)
0.08***
(0.02)
-0.00
(0.01)
0.00
(0.03)
Yes
Yes
0.790
2105
Post Reg AB × Total % of 10-20%
Total % of 20-30%
0.00
(0.01)
Post Reg AB × Total % of 20-30%
Control variables
Lead-underwriter and issue semester FE
Adjusted R2
Observations
33
Yes
Yes
0.789
2105
Yes
Yes
0.790
2105
Yes
Yes
0.789
2105
Table 7: A cross-sectional analysis of the use of low stakes and cumulative net loss
We identify originators who increase the use of low stakes (10-20% or below 20%) from before
Reg AB to after Reg AB and analyze the performance of the deals with the presence of these
originators. For each deal, we define the dummy variable ILS that equals 1 for originators with
increased low stake usage and zero otherwise. We also define a dummy variable ILSH if the
increased use of low stakes is above the average increase of low stake usage by all originators.
Similarly defined dummy variables are based on a below 20% threshold. All of the other variables
are defined in the appendix. The standard errors clustered by issue semester are reported in the
parentheses below each coefficient estimate. Statistical significance levels of 1%, 5%, and 10%
are indicated with ***, **, and * respectively.
(1)
1.68***
(0.48)
ILS (d)
ILS<20%
(2)
(3)
(4)
1.46***
(0.43)
ILSH (d)
1.94***
(0.49)
ILSH<20%
Control variables
Lead-underwriter and issue semester FE
Adjusted R2
Observations
34
Yes
Yes
0.788
2038
Yes
Yes
0.788
2038
Yes
Yes
0.790
2038
1.77***
(0.42)
Yes
Yes
0.789
2038
35
Control variables
Lead-underwriter and issue semester FE
Adjusted R2
Observations
Post Reg AB × Total % of <20%
Total % of <20%
Post Reg AB × LS Deal < 20%(d)
LS Deal < 20%(d)
Post Reg AB × Total % of 10-20%
Total % of 10-20%
Post Reg AB × LS Deal 10-20%(d)
LS Deal 10-20%(d)
Yes
Yes
0.775
2105
0.68*
(0.36)
(1)
Yes
Yes
0.776
2105
-0.53
(0.43)
1.83**
(0.77)
(2)
Yes
Yes
0.775
2105
0.02
(0.01)
(3)
Yes
Yes
0.777
2105
-0.01
(0.01)
0.07***
(0.02)
(4)
Yes
Yes
0.775
2105
0.61*
(0.33)
(5)
Yes
Yes
0.776
2105
-0.40
(0.37)
1.58**
(0.63)
(6)
Yes
Yes
0.775
2105
0.02
(0.01)
(7)
-0.01
(0.01)
0.07***
(0.02)
Yes
Yes
0.777
2105
(8)
We estimate linear regressions to examine the relation between the use of low stakes and cumulative net loss as of
December 2012 for deals completed between 2003 and 2007. All of the variables are defined in the appendix. The
standard errors clustered by issue semester are reported in the parentheses below each coefficient estimate. Statistical
significance levels of 1%, 5%, and 10% are indicated with ***, **, and * respectively.
Table 8: Robustness check: The use of low stakes and cumulative net loss 2012
Table 9: Robustness check: Origination brackets [10,20), [20,30), and cumulative net
loss 2012
This table reports the results of analyzing the impact of [20,30) stakes on deal performance,
compared to the impact of [10,20) stakes. All of the variables are defined in the appendix. The
standard errors clustered by issue semester are reported in the parentheses below each coefficient
estimate. Statistical significance levels of 1%, 5%, and 10% are indicated with ***, **, and *
respectively.
LS Deal 10-20%(d)
(1)
(2)
0.80*
(0.36)
-0.50
(0.42)
2.01**
(0.67)
-0.17
(0.33)
-0.60
(0.69)
Post Reg AB × LS Deal 10-20%(d)
LS Deal 20-30%(d)
-0.49
(0.36)
Post Reg AB × LS Deal 20-30%(d)
Total % of 10-20%
(3)
(4)
0.02
(0.01)
-0.01
(0.01)
0.08***
(0.02)
-0.00
(0.01)
-0.02
(0.03)
Yes
Yes
0.777
2105
Post Reg AB × Total % of 10-20%
Total % of 20-30%
-0.01
(0.01)
Post Reg AB × Total % of 20-30%
Control variables
Lead-underwriter and issue semester FE
Adjusted R2
Observations
36
Yes
Yes
0.775
2105
Yes
Yes
0.776
2105
Yes
Yes
0.775
2105
Table 10: Robustness check: The use of low stakes and cumulative net loss 2012
We identify originators who increase the use of low stakes (10-20% or below 20%) from before
Reg AB to after Reg AB and analyze the performance of the deals with the presence of these
originators. For each deal, we define the dummy variable ILS that equals 1 if there is the presence
of originators with increased low stake usage in a collateral pool, and zero otherwise. We also
define a dummy variable ILSH if the increased use of low stakes is above the average increase
of low stake usage by all of the originators. Similarly defined dummy variables are based on
a below 20% threshold. All of the other variables are defined in the appendix. The standard
errors clustered by issue semester are reported in the parentheses below each coefficient estimate.
Statistical significance levels of 1%, 5%, and 10% are indicated with ***, **, and * respectively.
(1)
1.33**
(0.53)
ILS (d)
ILS<20%
(2)
(3)
(4)
0.94**
(0.32)
ILSH (d)
1.51***
(0.40)
ILSH<20%
Control variables
Lead-underwriter and issue semester FE
Adjusted R2
Observations
37
Yes
Yes
0.774
2038
Yes
Yes
0.773
2038
Yes
Yes
0.775
2038
1.38***
(0.32)
Yes
Yes
0.775
2038
Table 11: The use of low stakes, yields and credit enhancement
This table reports the results of analyzing the impact of the use of low stakes on the deal’s initial yields and
credit enhancement. For deal yields, we use the initial average yield spread of all of the securities issued by the
trust of mortgage deals. This is the difference between the average yield of all of the securities issued by the trust
weighted by the face value of the securities and the yield on the 10-year Treasury bond. Credit enhancement is
the subordination measured as the percentage of the face value of trust securities not rated AAA by Moody’s or
Standard & Poor’s at the deal’s close. The originator variables ILS (d) and ILSH (d) are defined in Table 7. All
of the other variables are defined in the appendix. The t-statistics based on standard errors clustered by issue
semester are reported in the parentheses below each coefficient estimate. Statistical significance levels of 1%, 5%,
and 10% are indicated with ***, **, and * respectively.
Panel A: Low stakes, initial yield and subordination
LS Deal 10-20%(d)
Post Reg AB × Has LS 10-20%(d)
LS Deal < 20%(d)
Post Reg AB × Has LS < 20%(d)
Control variables
Lead-underwriter and issue semester FE
Adjusted R2
Observations
Initial yield
(1)
(2)
0.02
(0.12)
0.08
(0.15)
-0.06
(0.11)
0.15
(0.15)
Yes
Yes
Yes
Yes
0.622
0.622
2248
2248
Subordination
(3)
(4)
1.23***
(0.30)
-1.59***
(0.40)
1.02***
(0.29)
-1.39***
(0.35)
Yes
Yes
Yes
Yes
0.823
0.823
2153
2153
Panel B: Increased use of low stakes, initial yield, and subordination
ILS (d)
(1)
-0.07*
(0.03)
ILS<20% (d)
Initial yield
(2)
(3)
(5)
-0.51
(0.29)
0.07
(0.05)
Subordination
(6)
(7)
0.04
(0.04)
ILSH<20% (d)
Yes
Yes
0.623
2157
(8)
-0.08
(0.17)
ILSH (d)
Control variables
Lead-underwriter and issue semester FE
Adjusted R2
Observations
(4)
Yes
Yes
0.623
2157
Yes
Yes
0.623
2157
38
-0.58*
(0.31)
0.03
(0.04)
Yes
Yes
0.623
2157
Yes
Yes
0.834
2063
Yes
Yes
0.834
2063
Yes
Yes
0.834
2063
-0.27
(0.29)
Yes
Yes
0.834
2063
Table 12: Summary statistics for loans
This table reports the mean values for the loan-level variables. We report these numbers for all
of the loans for which we can identify the originators at the deal level, as well as for the loans
whose originators contributed loans to deals in the brackets of [10,20)% and [20,30)%.
Variables
Delinquency
FICO
Full Doc
CLTV
Investor
DTI
Miss DTI
Cash-Out
PrePayPen
Initial Rate
Margin
Rate Reset
Loan Amt.
ARM
Balloon
Hybrid2
Hybrid3
Int. Only
Local Income
Unemployment
Price Appr.
All loans
0.23
638
0.59
81.70
0.08
39.21
0.18
0.12
0.64
7.10
5.19
27.77
232,299
0.07
0.08
0.45
0.15
0.17
47,772
0.10
0.09
Originator’s share in a deal
[10,20)%
[20,30)%
0.25
0.23
654
645
0.50
0.52
82.20
81.40
0.10
0.10
38.48
38.55
0.15
0.15
0.13
0.12
0.58
0.62
7.05
6.93
4.70
4.97
34.36
33.83
257,756
248,703
0.06
0.07
0.07
0.03
0.35
0.39
0.29
0.27
0.30
0.22
48,485
48,252
0.26
0.16
0.08
0.09
39
Table 13: The use of low stakes and loan performance
This table reports the results of analyzing the implication of the increased use of low stakes in
mortgage deals on the performance of individual loans in the groups surrounding the disclosure
threshold. We regress the loan Delinquency status on the origination change variable and other
loan-level variables using probit regressions. The ∆LS is defined as the change from before Reg
AB to after Reg AB in the fraction of 10-20% deals for each originator (same for all loans from
the same originator). The Stake size is the share of the originator (same for all loans in the
same deal and from the same originator) in each mortgage deal. All of the other variables are
defined in the appendix. The standard errors clustered by issue semester are reported in the
parentheses below each coefficient estimate. Statistical significance levels of 1%, 5%, and 10%
are indicated with ***, **, and * respectively.
All loans
All loans
[10,20)% loans
[20,30)% loans
-0.04***
(0.00)
-0.09***
(0.00)
-0.06***
(0.00)
0.06***
(0.00)
0.05***
(0.00)
0.02***
(0.00)
0.03***
(0.01)
0.00
(0.00)
0.05***
(0.00)
0.03***
(0.00)
0.03***
(0.00)
-0.02***
(0.00)
0.02***
(0.00)
0.05***
(0.02)
-0.03***
(0.01)
-0.09***
(0.00)
-0.06***
(0.00)
0.06***
(0.00)
0.05***
(0.00)
0.02***
(0.00)
0.03***
(0.01)
0.00
(0.00)
0.05***
(0.00)
0.03***
(0.00)
0.03***
(0.00)
-0.02***
(0.00)
0.02***
(0.00)
0.05**
(0.02)
-0.05
(0.15)
-0.10***
(0.00)
-0.07***
(0.01)
0.07***
(0.00)
0.04***
(0.01)
0.02***
(0.00)
0.06***
(0.01)
-0.01*
(0.00)
0.06***
(0.00)
0.03***
(0.01)
0.03***
(0.01)
-0.01
(0.01)
0.02***
(0.00)
-0.02
(0.03)
0.04
(0.15)
-0.10***
(0.00)
-0.06***
(0.00)
0.07***
(0.00)
0.06***
(0.01)
0.02***
(0.00)
0.07***
(0.02)
0.00
(0.01)
0.05***
(0.00)
0.03***
(0.01)
0.02***
(0.01)
-0.02***
(0.00)
0.02***
(0.00)
∆LS
Stake size
FICO
Full Doc
CLTV
Investor
DTI
Miss DTI
Cash-Out
PrePayPen
Initial Rate
Margin
Rate Reset
Loan Amt.
Continued on Next Page. . .
40
Table 13 – Continued
ARM
Balloon
Hybrid2
Hybrid3
Int. Only
Local Income
Unemployment
Price Appr.
Deal and issue semester FE
Pseudo-R2
N
All loans
All loans
[10,20)% loans
[20,30)% loans
0.02**
(0.01)
0.04***
(0.01)
0.01
(0.01)
0.01
(0.01)
0.02***
(0.00)
-0.02***
(0.00)
-0.17***
(0.01)
-0.19***
(0.00)
0.02**
(0.01)
0.03***
(0.01)
0.01
(0.01)
0.00
(0.01)
0.02***
(0.00)
-0.02***
(0.00)
-0.17***
(0.01)
-0.19***
(0.00)
0.00
(0.03)
0.01
(0.02)
-0.01
(0.02)
-0.01
(0.02)
0.01
(0.01)
-0.02***
(0.00)
-0.23***
(0.02)
-0.20***
(0.01)
0.03
(0.03)
0.03
(0.02)
0.03*
(0.02)
0.03*
(0.02)
0.01
(0.01)
-0.02***
(0.00)
-0.16***
(0.03)
-0.20***
(0.01)
Yes
0.240
3531107
Yes
0.240
3531107
Yes
0.290
99108
Yes
0.239
150317
41
Table 14: Loan performance in the brackets of [10,20) and [20,30)
This table reports the results of analyzing the implication of increased low stake usage on
individual loan performance in pre- and post-Reg AB periods. The ∆LS and Stake size are
defined in Table 13. For each deal, LS Deal is a dummy variable that equals 1 if the deal has an
originator that originated a 10-20% low stake (same for all loans in the same deal and from the
same originator) and 0 otherwise. All of the other variables are defined in the appendix. The
standard errors clustered by issue semester are reported in the parentheses below each coefficient
estimate. Statistical significance levels of 1%, 5%, and 10% are indicated with ***, **, and *
respectively.
∆LS
∆LS × LS Deal
LS Deal
Stake size
FICO
Full Doc
CLTV
Investor
DTI
Miss DTI
Cash-Out
PrePayPen
Initial Rate
Margin
Rate Reset
Continued on Next Page. . .
Pre-Reg AB
[10,30)% loans
Post-Reg AB
[10,30)% loans
0.03
(0.02)
-0.02
(0.03)
0.02**
(0.01)
0.08
(0.08)
-0.06***
(0.00)
-0.03***
(0.00)
0.02***
(0.00)
0.02***
(0.01)
0.01***
(0.00)
0.03***
(0.01)
-0.01***
(0.00)
0.03***
(0.00)
0.01**
(0.00)
0.02***
(0.00)
-0.02***
-0.24***
(0.07)
0.28***
(0.08)
-0.02
(0.03)
-0.17
(0.22)
-0.14***
(0.01)
-0.13***
(0.01)
0.11***
(0.01)
0.06***
(0.01)
0.04***
(0.01)
0.13***
(0.02)
0.01*
(0.01)
0.09***
(0.01)
-0.01
(0.01)
0.05***
(0.01)
-0.03**
42
Table 14 – Continued
Loan Amt.
ARM
Balloon
Hybrid2
Hybrid3
Int. Only
Local Income
Unemployment
Price Appr.
Deal and issue semester FE
Pseudo-R2
N
(0.00)
(0.01)
Pre-Reg AB
[10,30)% loans
Post-Reg AB
[10,30)% loans
0.02***
(0.00)
-0.04***
(0.01)
-0.01
(0.01)
0.01
(0.01)
0.01
(0.01)
-0.01**
(0.00)
-0.01***
(0.00)
0.06***
(0.01)
-0.13***
(0.00)
0.06***
(0.01)
-0.04
(0.05)
0.06**
(0.03)
0.01
(0.03)
0.02
(0.03)
0.05***
(0.02)
-0.05***
(0.00)
-0.38***
(0.01)
-0.19***
(0.01)
Yes
0.313
139316
Yes
0.326
109181
43
Figure 1: The use of low stakes before and after Reg AB
The bar plots in this figure represent the difference between the number (and percentage) of deals
with originators in the [10,20)% and the number (and percentage) of deals without originators
in this range. The top panel compares the corresponding measures before Reg AB (pre 2006)
with after Reg AB (post 2006). The bottom panel plots these measures on an annual basis from
2003 to 2007.
1000
89.2
73.1
824
303
26.9
121
10.8
Pre 2006
Post 2006
Has Orig 10-20%
Pre 2006
No Orig 10-20%
Post 2006
Has Orig 10-20%
502
85.3
90.4
89.8
No Orig 10-20%
70.5
76.5
450
374
330
188
168
29.5
115
29
35
2003
2004
14.7
57
2005
Has Orig 10-20%
2006
2007
2003
No Orig 10-20%
9.6
10.2
2004
2005
Has Orig 10-20%
44
23.5
2006
2007
No Orig 10-20%