An Empirical Study of
Subordination Levels in Commercial Mortgage Backed Securities
by
Romina Padhi
Bachelor of Arts in Economics, University of Mumbai, 1997
Masters in Business Administration, University of Mumbai, 2000
Submitted to the Department of Architecture
in Partial Fulfillment of the Requirement for the Degree of Master of Science
in Real Estate Development
at the
Massachusetts Institute of Technology
September 2005
© 2005 Romina Padhi. All Rights Reserved.
The author hereby grants MIT permission to reproduce and to distribute publicly paper and
electronic copies of this thesis document in whole or in part.
Signature of Author
………………………………………………………………………
Department of Architecture
August 5, 2005
Certified by
………………………………………………………………………
Brian Anthony Ciochetti
Professor of the Practice of Real Estate, Thesis Supervisor
Accepted by
………………………………………………………………………
David Geltner
Chairman, Interdepartmental Degree Program in Real Estate Development
An Empirical Study of
Subordination Levels in Commercial Mortgage Backed Securities
by
Romina Padhi
Submitted to the Department of Architecture
on August 5, 2005 in Partial Fulfillment of the Requirements
for the Degree of Master of Science in Real Estate Development
ABSTRACT
The CMBS market has been in existence since the mid 1980s; however, it was during the mid
1990s that the market began to grow. A combination of favorable interest rate environment, entry
of new players in the market and the amount of demand for commercial real estate assets, led to
a record US CMBS issuance in 2004, with the 2005 outlook being even better. However, the
subordination or credit enhancement level of these securities has been on a downward trend since
1995. The thesis attempts to analyze the risk factors such as loan to value ratio, debt service
coverage ratio, floating versus fixed rate, large and conduit deal types, as well as diversification
factors (property type and geographic location), and their impact on subordination levels.
Finally, market forces such as spreads on CMBS are also analyzed for their influence on
subordination levels.
For the analysis, data were collected on 430 commercial mortgage backed securities issued from
1995 through mid 2005. The information was obtained from Trepp, which tracks all the
commercial mortgage backed securities issued in the market. The trend in subordination levels of
each of the tranches or bond classes was analyzed over the period of study and a quantitative
regression analysis was performed to analyze the influence of the above mentioned factors on the
subordination levels.
The results indicate that the loan to value ratio, interest rate type (fixed versus floating) and the
deal type (conduit and large loans) have a significant impact on the subordination levels. Also,
certain other market factors, including the spread differential between CMBS and corporate
bonds, strong property market performance, increased liquidity and increased number of
investors may also have influence the subordination levels of these securities.
Thesis Supervisor: Brian Anthony Ciochetti
Title: Professor of the Practice of Real Estate
2
Acknowledgement
I would like to thank Don Belanger of CSFB for his help in compiling the data used for the
regression analysis. His insights and valuable comments are appreciated. I would like to thank
my advisor, Tony Ciochetti, who has been a guiding factor for this thesis. His wisdom and inputs
on various aspects of the thesis were truly inspirational.
3
TABLE OF CONTENTS
ABSTARCT ………………………………………………………………………
2
ACKNOWLEDGEMENT ………………………………………………………..
3
Chapter 1 – INTRODUCTION
1.1
Scope of Research ………………………………….………………………
5
1.2
History and Background of CMBS …………….…………………………..
6
1.3
CMBS and Subordination Levels ………………….………………………
8
1.4
CMBS Rating Methodology ……………………………………………….
12
1.5
Summary …………………………………………………………………...
16
Chapter 2 – LITERATURE REVIEW
2.1
Mortgage Default and Loss Severity Research …………………………….
17
2.2
Real Estate Diversification Research ……………………………………...
25
2.3
Summary ……………………………………………………………………
28
Chapter 3 – DATA AND METHODOLOGY
3.1
The Data ……………………………………………………………………
30
3.2
Methodology ………………………………………………………………..
33
Chapter 4 – DATA ANALYSIS
4.1
Subordination Levels ……………………………………………………….
36
4.2
Discussion of Regression Results …………………………………………..
38
4.3
Other Factors affecting Subordination Levels ……………………………...
42
4.4
Summary ……………………………………………………………………
45
Chapter 5 – CONCLUSION ………………………………………………………..
48
BIBLIOGRAPHY …………………………………………………………………..
50
APPENDIX …………………………………………………………………………
53
4
Chapter 1
INTRODUCTION
1.1 Scope of Research
While the US commercial mortgage backed securities (CMBS) market has been in existence
since the mid 1980s, it has emerged as an important sector since 1996 and has grown
tremendously since then; with US issuance growing from $26 billion in 1996 to $93 billion in
2004. The CMBS sector has developed a broad investor base and a liquid secondary market.
These securities have provided an attractive higher yield alternative to traditional corporate
bonds, asset backed securities, and residential mortgage backed securities. From the investor’s
perspective, CMBS are desirable as a security because risk is spread among different investors
and tranches, and these assets are more liquid than owning a whole loan. The popularity of
CMBS is also a function of the unique risk / return characteristics that these complex securities
offer, particularly the lower rated tranches that exhibit both debt and equity like characteristics.
With the development of the market, the structure of CMBS transactions has become
increasingly important. The primary structure of CMBS involves a senior – subordinate structure
that gives cash flow priority to the senior classes and shifts the default risk down the structure to
the lower classes. These classes satisfy the demand of various classes of investors. The lower
bond classes provide credit support to the higher rated tranches; this is referred to as the
subordination or the credit enhancement level, which is determined for each bond class. A lot of
research has been done on the pricing of these securities, how prepayments affects these
securities and on the default of such securities. However, there exists minimal literature on the
subordination levels in CMBS, which is a structural aspect of the transaction and plays an
important role in determining how defaults and losses affect each of the bond classes.
Subordination levels have fallen dramatically from the levels of 1995 / 1996 when they used to
be in the range of 30% - 35% for the highest rated class (AAA) to as low as 12% - 15% in 2005.
This thesis examines the trend in subordination levels of CMBS from 1995 to 2005 and the
impact of specific risk and diversification factors as well as some deal specific characteristics
(such as floating rate versus fixed, or large loans) and some market factors (yield spreads
5
between CMBS and corporate bonds) on subordination levels. This is an interesting area as there
hardly exists any research in this area, though there are a few papers in the working. The analysis
is based on a sample of CMBS deals or transactions from 1995 to mid-2005. The data, sourced
from Trepp LLC, presents a summary of every deal issued including the features of the bond
class, the details of the loans in the underlying pool, and the credit characteristics of the
transaction. This thesis examines specific factors such as loan to value ratio, debt service
coverage ratio, property type diversity, geographic location diversity, floating rate, and large
loans, that may provide an explanation for this trend.
The balance of this chapter provides background information on the CMBS market, the structure
of the security and the subordination levels, how these credit enhancement levels are determined
by rating agencies. Chapter 2 presents a literature review of the relevant research. Chapter 3
presents a description of the data as well as the methodology used in analyzing the data. Chapter
4 discusses the results of the data analysis. The thesis will close with the interpretation and
implications of the results.
1.2 History and Background of CMBS
The CMBS market has grown dramatically from its modest beginnings in the mid 1980s.
Issuance, liquidity and the number of investors have all increased dramatically since and reached
record levels in 2004, with 2005 expected to be an even better year. Last year the global CMBS
issuance crossed the $100 billion mark for the first time in the decade plus history of the market.
This year, it is expected to cross the $200 billion mark (US Issuance = $72 billion in the first half
of 2005). The following graph depicts the explosion taking place in the US CMBS market.
According to the Commercial Mortgage Alert1, loan demand continues to be fueled by low
interest rates and rising property values. In fact, borrowers are currently financing securitized
mortgages, indicating that the benefit of taking out larger loans with lower coupons exceeds the
defeasance costs associated with prepayments. Global capitalization of CMBS currently exceeds
$500 billion.
1
Commercial Mortgage Alert, July 1, 2005
6
Annual US CMBS Issuance
$100.00
$93.11
$90.00
$77.85
$74.33
$80.00
$72.11
$67.15
$ Billion
$70.00
$56.57
$60.00
$50.00
$36.79
$40.00
$26.37
$30.00
$20.00
$52.07
$46.89
$15.75
$10.00
2004
1H 2005*
Year
2003
2002
2001
2000
1999
1998
1997
1996
1995
$-
Source: Commercial Mortgage Alert, July 2005
* CMBS issuance for 1st half of 2005 (January to June)
During the 1980s, a strong economy, the deregulation of the financial services industry, and
preferential tax treatment led to an explosion in the level of capital flows into the commercial
real estate markets. The real estate boom in the 1980s led to extreme overbuilding, which finally
caused the bubble to burst in the 1990s.
During the 1980s, primary sources of real estate funding included tax shelter syndicates, savings
institutions, commercial banks, and life insurance companies. The Tax Reform of 1986 and the
major devaluation of commercial property values in the early 1990s resulted in sizeable losses
and led to a major retrenchment of lending activity by the traditional sources of financing.
The largest factor contributing to the development of the CMBS market was the creation of the
Resolution Trust Corporation (RTC). The RTC was created by the Congress to bail out the ailing
thrift industry. The RTC would liquidate the assets, mostly mortgages, it acquired from insolvent
thrifts as quickly and efficiently as possible. To “monetize” its investment, the RTC issued
approximately $15 billion multifamily and mixed property CMBS.
7
Issuance has grown tremendously since 1993 and contribution from the RTC has fallen since.
Witnessing the success of RTC in CMBS, many insurance companies, pension funds and
commercial banks began to use the CMBS market as a source of restructuring their balance
sheets. Non-RTC issuance began to build in the mid-to-late 1990s and could be classified into
two broad categories – securitization of existing loan portfolios and securitization of new loans
originated for the purpose of contributing to the underlying pool. Existing portfolio securitization
consisted of both performing and non-performing loans, and thus, provided an exit strategy not
just for the RTC but also for some insurance companies and pension funds that used it to liquefy
their portfolios. The CMBS market was used as a means of liquidity for disposing the unwanted
assets, to receive better regulatory treatment for holding securities in lieu of whole loans, or to
raise capital for underwriting more loans.
The total of commercial mortgages outstanding measured approximately 14.4% of gross
domestic product, well above the long term average of 11.2%. A study2 on conduit diversity
indicates that diversity has increased slightly. However, the largest deals were not necessarily the
most diverse. Some transactions have become large by adding more loans (more diversity), while
others have become large by adding larger loans (less diversity).
1.3 CMBS Structure and Subordination Levels
A CMBS transaction is formed when an issuer places commercial mortgages into a trust, which
then issues classes of bonds backed by the interest and principal of the underlying pool of
mortgages. The basic building block of the CMBS structure is a mortgage that was written to
finance a commercial purchase of property or to refinance a prior commercial mortgage. Each of
the bond classes in a CMBS issue reflects a different risk – return profile. The objective of
creating such a structure is to shift the investment risk from the highest rated class down to the
lowest ranked class.
There are many types of CMBS. A CMBS transaction can be backed by a pool of mortgages
(conduit) or by a single asset. However, when underwriting a single property, the risk of
2
Moody’s Special Report, US CMBS 1Q 2005: Another warning light on the Credit Dashboard, April 28, 2005
8
unanticipated events that could affect the property’s cash flow is high. This is in contrast to
pooled transactions, in which the diversification of properties and leases mitigates this risk.
CMBS is very different from residential mortgage backed securities (RMBS) as there are certain
aspects of the underlying pool that require different considerations in creating CMBS versus
RMBS. First, prepayment terms differ; while residential mortgages permit prepayment without
penalty, commercial mortgages usually require penalties or defeasance for prepayment. Second,
in CMBS, even an imminent default has consequences as the special servicer (defined on Page
11) has wide discretion in modifications in such circumstances. Finally, a major distinction
between CMBS and RMBS deals is the role of the junior bond class buyers. In CMBS, no deal
occurs without first finding a buyer for the subordinate classes. This provides an extra layer of
security for the senior buyer, particularly because the buyers of the junior classes tend to be
knowledgeable real estate investors.
The majority of CMBS transactions utilize a senior / subordinate structure, whereby the cash
flow generated by the pool of underlying commercial mortgages is used to create distinct classes
of securities. In such a structure, the lower priority classes provide credit enhancement for the
senior securities. For example, credit support for Class A is the sum of classes B, C and D, and
the credit support for class B is the sum of classes C and D. The credit support for a class must
be fully extinguished before any default loss can affect the class. Thus, with subordination, the
greater the credit support the higher rating the class receives. The most junior class has no credit
support and is usually unrated. Traditionally, the lower rated (below BBB) and unrated classes
are either privately placed or retained by the issuer, although this is slowly changing as the
market matures and become comfortable with the characteristics of CMBS.
Shown below is a simple diagrammatic representation of the senior / subordinate structure of a
CMBS issue (based on the 2004 subordination levels). Credit support is the buffer or cushion
from losses that each tranche requires for a specific rating category. For example, in the above
structure 19.22% credit support at the ‘AAA’ level, the pool would have to suffer 19.22% of the
loan balance before the ‘AAA’ certificates would be impacted. Similarly, the pool would have to
9
suffer losses that exceed 2.66% of the loan balance before the ‘BB’ certificates would be
impacted.
Table 1
Class
(% Subordination)
AAA
(19.22%)
AA
(12.23%)
A
(8.39%)
BBB
(4.77%)
BB
(2.66%)
B
(1.55%)
UR (unrated)
(0%)
Source: Data from Trepp LLC (based on average 2004 deal structures)
The CMBS tranches retire sequentially beginning with the highest rated bond paying off first.
Thus, any return of principal caused by amortization, prepayment or default is used to repay the
highest rated tranche. Interest on principal outstanding will be paid to all the tranches, based on
their outstanding face or nominal value, or is accrued for the lower rated classes and used to pay
down the senior classes more quickly. The coupon rate of the security is always less than or
equal to the lowest interest rate on any individual mortgage in the collateralized pool. This
ensures that there is enough interest available to pay all the investors.
10
An important structural feature of CMBS transactions is the presence of the master and special
servicers. Master servicers manage the routine, day-to-day administration functions required by
all structured securities or collateralized transactions, while the special servicers are used to
handle delinquent loans and workout situations.3 In case of a delinquency that results in
insufficient cash to make all scheduled payments, the transaction’s servicer advances both
interest and principal. This process continues till such time that the amounts are deemed
unrecoverable. Often, the servicer’s interests are aligned with that of the CMBS investors as
servicers now invest tin the non-rated and subordinate tranches of the deals they service. Losses
arising from loan defaults will be charged against the principal balance of the lowest rated
tranche and move upwards. The total loss charged includes the amount previously advanced as
well as the actual loss incurred in the sale of the loan’s underlying property.
A unique feature of the senior / subordinate structure is that the credit enhancement can grow
over time. As principal is paid to the senior classes first, if no losses occur, these classes will pay
down faster than the mezzanine4 or B-pieces5. This results in increasing the amount of
subordinate classes as a percentage of the entire deal and thus, provides a higher credit
enhancement of the remaining senior classes.
Rating agencies play an important role in the CMBS market. The role of a rating agency is
primarily to provide a third – party opinion on the quality of each bond class in the CMBS
structure as well as the requisite amount of credit enhancement or subordination level to achieve
the desired rating level. In essence, rating agencies help in deciding the appropriate structure for
the deal. Subordination levels are set to attain AAA credit rating on the senior class. This is the
highest rating possible, signifying that the bonds are deemed to have minimal credit risk. This
makes the AAA tranche most attractive to investors. According to Standard and Poor’s, the
credit rating assigned to a CMBS transaction is “an opinion on the ability of the collateral to pay
interest on a timely basis and to repay principal by the rated final distribution date, according to
3
Frank Fabozzi, The Handbook of Commercial Mortgage Backed Securities
Mezzanine Bonds: Non-senior securities receiving investment grade ratings
5
B-piece: Non-investment grade bonds
4
11
the terms of the transaction. The rating does not reflect the impact of prepayment or any other
factors that may affect investors’ yields”.6
The credit risk is assessed by determining the probability of default and the severity of loss given
default occurs. The credit risk captures the uncertainty in the timing and the magnitude of cash
flow receipts created by a borrower’s default option. Since default generally results from a low
collateral asset value, the risk lies in the volatility of the value of the security’s collateral i.e. the
underlying real estate. Thus, the loan to value ratio as well as the debt service coverage ratio of
the underlying pool of loans, among many other parameters, is also evaluated. If the target level
of such parameters is not attained, the bond gets a lower rating. It is also important to estimate
losses in the event a loan defaults and is foreclosed or restructured. The severity of loss should
equal the property sale proceeds plus the property revenue, less principal owed upon default,
foregone interest and expenses.
1.4 CMBS Rating Methodology
According to a report by Moody’s on rating of conduit transactions7, the credit enhancement
needed to achieve a rating level for a proposed securitization typically depends on the expected
frequency, severity, and timing of future losses. An estimation of frequency and severity of
losses is usually based on a statistical analysis of historical performance data for assets like
residential mortgages, which can be homogeneous in character and, for which historical data is
available. However, commercial mortgages are not uniform in character, and relevant historical
loss information is limited. As a result, Moody’s analyzes the fundamental real estate credit risk
of each asset to estimate the frequency and severity of losses within the legal and structural
framework of structured finance. Moody’s reviews the portfolio diversification aspects of a pool,
which have an impact on the volatility of expected loss for the pool, in turn affecting the level of
credit enhancement needed for the rated bonds. In the case of non-recourse lending, which is
typical of U.S. conduits, the default probability is assumed to be highly dependent on the debtservice coverage ratio (DSCR), and the loan to value ratio (LTV) associated with the underlying
mortgage loan. DSCR is the main driver of frequency of loss, while LTV is the main determining
6
7
Standard & Poor’s Structured Finance CMBS Property Evaluation Criteria
Moody’s Approach to Rating US Conduit Transactions, September 2000
12
factor for the expected severity of loss. Moody’s believes that the stability of cash flows and
asset values of the major property types, ranked in order from best to worst, is as follows:
multifamily, anchored retail, industrial, unanchored retail, office, and hotel.
Moody’s also considers several portfolio characteristics, in addition to those of the underlying
collateral. One such characteristic is the portfolio diversity; which could be by property type,
geographic location, economic diversity and loan concentration. Moody’s views portfolios with
multiple property types and geographic locations as more stable. Different property types have
different risk profiles and market dynamics. Thus, property type diversification mitigates the
expected losses in a pool, whether a pool of loans or a loan secured by a pool of properties.
Geographic diversification helps mitigate the risk of single market declines, and serves to
smooth the variability around an expected loss. It helps offset the impact to a pool from a
regional downturn.
For rating large loan CMBS transactions, the focus is on the credit characteristics of the asset, the
structural features of the transaction, and the impact of diversification in a cross-collateralized
pool situation. Moody’s considers the going-in and the balloon loan to value ratio and the actual
and stressed debt service coverage ratios, in addition to the structural and legal issues. Large
loans are typically pooled with other loans to reduce concentration risk. If a deal has eight to ten
loans, the diversification benefit can be significant. Even though the expected loss for an
underlying single loan does not change, the volatility of the expected loss for the pool is reduced
due to higher diversity.
Moody’s rating approach compares the credit risk inherent in the underlying asset with the credit
protection offered by the structure. The credit risk of the underlying asset is determined primarily
by two factors: the frequency of default, which is largely driven by DSCR and the LTV of the
underlying loan, which impacts the severity of loss in the event of default. The structure’s credit
enhancement is quantified by the maximum loss of value on the asset the securities are able to
withstand under various stress scenarios without causing an increase in the expected loss for
various rating levels.
13
Diversity is usually beneficial for the senior classes of bonds but may hurt the lowest rated class,
all else being equal. Also, as more loans are pooled, there is a greater likelihood of the pool
experiencing a defaulted loan. Since these transactions are tranched the increased likelihood of
default is disproportionately concentrated in the junior most classes.
To determine the appropriate credit enhancement, S&P8 first adjusts the cash flow of the
underlying pool of properties to derive an estimate of the stabilized net cash flow that the
property can be expected to sustain over the life of the securitized transaction. Sustainable net
cash flow is derived by making adjustments to the property’s current revenues (typically trailing
12 months) and current expenses to produce the net operating income. These adjusted cash flows
are used as the basis for modeling credit support for the transaction. The loss model measures the
default frequency for each loan and the severity of loss that can be expected as a result of a
default.
The amount of recommended credit support is a function of the aggregate characteristics of the
loan pool and will depend on the projected losses for each loan during various economic stress
environments. Since the ‘AAA’ rating category is highest, the ‘AAA’ loss assumptions and
related credit support should be sufficient to survive the worst possible economic stress.
Conversely, the assumptions and the resulting credit support for a ‘B’ rating are less severe.
Fitch’s model9 to determine the appropriate subordination levels for any given pool consists of
three main components – default probability, loss severity, and pool composition factors. Fitch
begins by calculating the debt service coverage ratio and loan to value ratio assuming an “AA”
stress environment reflective of the real estate environment of the early 1990s. The default
probability and loss severity assumptions, based on the DSCR and LTV for each loan, are
adjusted for certain property and loan features to determine the credit enhancement based on
individual loan characteristics. Finally, the composition of the pool is analyzed to identify any
concentration risks, and the structure and the parties to the transaction are evaluated and
incorporated into the ratings.
8
9
Based on Standard and Poor’s CMBS Property Evaluation Criteria
Fitch Ratings Commercial Mortgage Criteria Report – Rating Performing Loan Pools
14
Table 2
Fitch Model Overview
Default Probability
X
Loss Severity
=
Base Subordination
Base Subordination
X
Pool Composition
Factors
=
Final Subordination
The default frequency of a specific loan is a function of its respective DSCR and LTV. The loss
severity is the sum of the principal balance that may be lost during the liquidation and
foreclosure period, accrued interest on the defaulted mortgage balance, as well as legal and other
costs associated with the foreclosure and liquidation of the asset.
Thus, the credit enhancement is a function of the default probability and loss severity, in addition
to other pool composition factors.
Subordination Level = ƒ (default probability, loss severity, pool composition factors)
The foreclosure frequency at each rating category is multiplied by the loss severity at each rating
category to provide the required credit support at each rating category. When determining the
losses associated with any loan, the analysis incorporates the amount of amortization that has
occurred by the assumed default date; the presence of additional debt; the quality of the real
estate; the property type; the loan structure; whether the property is located in a judicial
foreclosure10 or power-of-sale state11; and the availability of credit for the related asset type, or
other factors that may affect liquidity and credit and thereby heighten or reduce the risks of
potential losses associated with any loan. Thus, while the default frequency of two loans within
the same property type with the same DSCR and LTV is the same, the losses associated with
each of these two loans may differ, based on their individual characteristics as outlined above.
Other factors are also considered in deriving credit support or subordination levels, which may
affect the final levels that are recommended for the pool. For instance, environmental and
property condition, seismic reports, underwriting standards and the lending environment in
10
Judicial foreclosure necessitates that the court order the foreclosure and supervise both the process and ultimate
distribution of funds.
11
This is associated with a deed of trust mortgage instrument that allows an independent party (attorney or trustee)
to conduct the sale in the event of foreclosure.
15
which the loans were originated are also factored into the recommendations. Additionally, the
absence of reserves, cash management, and other structural features may adversely affect the
overall sizing of the transaction.
1.4 Summary
With the CMBS market outperforming expectations, the outlook still continues to be positive for
the future. Market factors such as low interest rates, the entry of an increasing number of
investors and the tightening spreads continue to make CMBS a competitive alternate to whole
loans and other investments.
The objective of this thesis is to investigate the risk and diversification factors as well as other
pool characteristics such as pool type, interest rate type and market factors such as the spread
differential between CMBS and corporate bonds that affect subordination, and if they are
significant in explaining the change in subordination levels of CMBS.
16
Chapter 2
LITERATURE REVIEW
Since subordination levels are determined by the probability of default and loss severity of the
loans, it is important to look at factors that impact these very aspects. Thus, the relevant research
includes studies on the default and loss severity characteristics of commercial mortgages. The
default and loss severity study of mortgages is relevant as subordination levels are based on the
probability of loss (due to default) of the mortgages in the underlying pool and the severity of
loss, once a loan defaults. There is extensive literature on defaults of commercial mortgages and
a few on the loss severity of commercial mortgages. Mortgage defaults have an impact on the
credit risk of the mortgages, which my affect the subordination levels of the CMBS transaction.
Also, as an attempt is being made to understand any diversification benefits that may affect
subordination, studies on diversification benefits in real estate are also discussed. However, due
to the relatively adolescent nature of the CMBS market, there have been few studies directly on
the topic of subordination.
2.1 Mortgage Default and Loss Severity Research
Default risk affects CMBS in different ways. Subordinate classes absorb losses first and they are
generally most vulnerable to default. Interest only bond holders are subject to risk in that any
decrease in the notional principal balance results in the loss of interest income without any
compensating prepayment penalties. Mezzanine class investors are generally protected against
any principal losses but need to understand their exposure to extension risk, which is the risk that
balloon loans will not be able to pay off their principal balance at maturity. Finally, even the
senior class investors are exposed to default risk. CMBS with lower quality collateral have
greater cash flow risk.
One of the most important aspects in assessing default risk in a CMBS transaction is the debt
service coverage ratio (DSCR), which is the ratio of the available income from the property to its
required debt service. The closer the DSCR is to one, the higher is the chance that the property
income may fail to cover the mortgage payments. Some of this is alleviated by reserves which
cover periods when income is interrupted or expenses are unusually high.
17
Another important aspect is the loan to value ratio (LTV), which is the ratio between the loan
balance and the value of the property. In theory, the LTV should be more important than DSCR
because a borrower should default only if the property value is less than that of the loan. Even if
the DSCR falls below one, the borrower should be able to sell the property for more than the
value of the loan and will not default. While LTV is an indication of the borrower’s incentive to
support the debts service, the DSCR is an indicator of the property’s ability to do so.
There have been several studies that have examined the relationship between default of
commercial mortgages and the LTV and DSCR. A brief summary of findings and conclusions of
the studies are discussed below.
The incidence of default rises with the LTV12; thus, holding all other factors constant, the
probability of default for a loan increases as the LTV increases. However, the increases are not
equal. For example, an increase in original LTV from 60% to 70% resulted in a 50% increase in
the probability of default from 0.54% to 0.80%. An increase in original LTV from 50% to 60%
increased the probability of default by 0.18%; and an increase in LTV from 80% to 90% drove
up the probability of default by 0.58%.
Unlike the LTV, the probability of default decreases with corresponding increases in DSCR.
However, studies have shown that the relationship between DSCR and probability of default is
weaker than the relationship between LTV and probability of default. One explanation for the
above is that borrowers have an incentive to negotiate the payment rescheduling and the debt
restructuring with the lenders, but the incentive wanes quickly when the LTV is greater than
100%.
Vandell, Barnes, Hartzell, Kraft and Wendt (1993) were the first to use loan level commercial
mortgage data from a life insurance company and the results confirm the effect of loan terms and
property value trends affecting default. Ciochetti, Deng, Gao and Yao (2002) used a similar data
12
Frank Fabozzi, The Handbook of Mortgage Backed Securities
18
set and applied the competing risk framework developed by Deng, Quigley and Van Order
(1996, 2000) in the residential mortgage market to commercial mortgages.
According to a study of defaults and loss severity in CMBS by Fitch Investors Service (1996),
commercial mortgages with relatively little excess cash flow after servicing loan payments are
more likely to default. DSCR is among the most significant variables in commercial mortgage
default and loss severity. According to Pamela Dillon, Fitch found that loans with low DSCR
had relatively higher annual rates of default. The study found significantly different average
default rates and loss severity experience among different bands of DSCR. At one end, loans
with DSCR below 0.5 showed annual default rates of 7.1% and average loss of 55.3% while the
default rate for loans with a DSCR of 2.1 was just 1.8% with a loss of 12.8%. However, when
the property’s income falls below the debt service, borrowers temporarily support the payments
if the property is viewed as having a long term ability to pay the debt service.
In another study of CMBS monitored by Fitch, Dillon and Belanger found significantly higher
defaults and loss severities for floating rate compared with fixed rate loans, despite a period of
“relatively stable and low interest rates”. The average annual default rate in the 1996 study was
6.7% for balloon loans, 2.4% for fully amortizing loans. The severity of loss was 31.1% for fixed
rate loans and 41.2% for floating rate loans, with no information on whether these were balloon
or non-balloon loans.
A study by FITCH on default rate of commercial mortgages (1991 to 1997) by property type
reported that loans secured by warehouses had the lowest annual default rate at 2.5%, followed
by multifamily properties at 3.9%. The default rates for these property types were considerably
lower than all other property types. The reason could be attributed to the fungibility and low
capital cost of warehouse space and the very slow obsolescence and stability of multifamily
properties. The default rate for the lodging sector was 4.2%, while office and retail properties
had the highest default rates at 4.8% and 4.9%. Office properties have high capital costs relating
to tenant improvement, leasing commissions, replacement reserves, etc, which may cause added
stress on the property during times of significant tenant roll. The high retail default rates could be
attributed to a significant percentage of unanchored retail and retail strip centers, which are more
19
volatile and subject to risk than malls and anchored community centers that were prevalent in the
thrift loan portfolios. The loss by property type also varies with multifamily properties at 46%.
This could be due to the fact that many of the loss observations were high leverage loans from
RTC/thrift transactions, several of which were concentrated in real estate depressed areas such as
California. These rates have changed since then but do give an insight into the importance of
diversification in a CMBS pool of mortgages. The study also revealed that loans with low
DSCRs, floating rate loans, and balloon loans had relatively higher annual rates of default.
A study by Ciochetti13 (1997) showed that state foreclosure laws appeared to impact the
performance of the assets, measured by net loss recovery. The data consisted of loans originated
by fourteen life insurance companies between 1986 and 1995. The net loss recovery over the
period averages 69%. The study finds significant evidence that the jurisdictional foreclosure
method is strongly related to not only mortgage loss recovery, but the level of foreclosure cost
and imputed interest as well. Loans foreclosed judicially have average recoveries that are 11%
lower than loans foreclosed through power-of-sale. Additionally, judicially foreclosed loans have
foreclosure costs nearly double, and total foreclosure costs nearly 50% greater, than for nonjudicially foreclosed loans.
According to Fitch14, another variable that may explain loss severity is the method of loan
resolution in each state. Foreclosures handled judicially take longer, on average, and result in
higher losses than non-judicial foreclosures, which take place in power-of-sale states. The
method of loan resolution had significant impact on loss severity, but very little discernible
impact on average annual default. Power-of-sale states had an average annual default rate of
4.2%, while judicial states had an average annual default rate of 4.4%, with the results not be
statistically significant.
Archer et al. (2001) point out that the downside of applying contingent claims modeling to
commercial mortgages is that variables such as LTV and DSCR are endogenous to the loan
origination process. Lenders often decide on the LTV and DSCR based on the risk associated
13
14
Brian A Ciochetti, Loss Characteristics of Commercial Mortgage Foreclosures, Real Estate Finance, Spring 97
Faboozi, Frank, The Handbook of Commercial Mortgage Backed Securities
20
with a particular loan as opposed to applying a single LTV or DSCR across all loans. If this is
the case, it is difficult to observe an empirical relationship between default and LTV and DSCR.
The results of the study by Ambrose and Sanders (2003) confirm that default and prepayment are
directly affected by changes in the economic environment, specifically by the future expectations
of interest rate. Also, mortgages with higher LTV at origination are more likely to prepay, but
the study doesn’t find a statistical relationship between LTV and the hazard of default.
Researchers, Rod Dubitsky and Kumar Neelakantan (2001) found that current LTV, rather than
original LTV, is a much more significant indicator of loss severity. The data suggest that when
adjusted for other factors, higher current LTV at payoff experience a higher loss severity.
Dubitsky credited the fact that low original LTV loans tend to prepay in full out of delinquency
as one of the reasons for the lack of significance of original LTV in explaining loss severity. In
summation, CSFB says that other things being equal, pools with lower average current loan
balance, greater seasoning, and higher current LTV loans would be expected to have higher loss
severity.
A study by Ciochetti et al (2003) used the proportional hazards model to understand commercial
mortgage defaults. The results of the study indicate that contemporaneous loan to value ratios are
of less importance than contemporaneous debt service coverage ratios in explaining commercial
mortgage defaults, though both these factors are of considerable significance in explaining the
quarter-to-quarter variations in default rates. Both the LTV and DSCR may change over time –
the first due to change in investors’ risk preferences and the latter due to decrease in rents or
increase in vacancy rates. The results from the model do confirm that the contemporaneous
DSCR is negatively correlated with default, while the contemporaneous LTV has a positive nonlinear relationship with commercial mortgage default. The study also evidences a greater
increase in default with a decrease in DSCR than with an increase in the LTV; thus implying that
the ability to meet the debt service obligations is the most relevant measure of commercial
mortgage default.
An analysis on loss severity by Pendergast and Jerkins of RBS Greenwich Capital (2003)
analysis the performance of 33,666 loans with an original balance of $185 billion securitized in
21
fixed rate conduit and fusion CMBS transactions issued from 1995 through 2001. They examine
the impact of property types, year of origination (vintage), loan size, loan to value ratio, and loan
liquidation on CMBS loss severity. They mention that the most compelling result of the study is
that the loan size matters – larger loans ($20 - $30 million) that have been liquidated during the
period of the study have lower loss severities than the smaller loans ($2 - $3 million) that have
been liquidated.
To study the impact of loan to value ratio on loss severity, the 194 loans that were liquidated
were also broken into various LTV buckets. According to the study, the 70% to 80% LTV bucket
experienced the highest percentage of liquidated loans at 44%. However, it should also be noted
that 50% of all the loans in the study fall in the LTV bucket of 70% to 80%. Loans with higher
original LTV, in general, experienced higher loss severities, though there were some exceptions.
Loans in the 60% - 70% LTV range experienced a weighted average loss severity of 41.2% but
the loans with an LTV in the >90% range experienced a weighted average loss severity of 74%.
At the same time, loans in the <60% range had a relatively high loss severity of 50%. The reason
for such exceptions is that the study is based on original LTV, as recent appraisals were not
available to determine the LTV just prior to disposition of the loan. However, LTV does seem to
impact the loss severity to a certain extent.
A subsequent study by Titus and Betancourt (2003) researched losses incurred on liquidated
loans in non-agency CMBS transactions from 1993 through 2002. The results of the study
indicate a cumulative loss severity of 40.11% over the nine year period. Also, the degree of loss
severity is directly related to the condition of the property markets. Loss severities increased in
the more recent years of study as the property market fundamentals had deteriorated. An
econometric analysis was performed to examine factors affecting loss severity. The results show
a statistically significant relationship between loss severity and the length of time to liquidate an
asset. However, the relationship between the loans in the study and the original debt service
coverage ratio and the original loan to value ratio were found to be statistically insignificant.
This result is consistent with earlier studies that suggest contemporaneous LTV and
contemporaneous DSCR as having greater explanatory power for delinquency and default.
22
The study by Chen and Deng (2004) states that the loan’s current equity share (calculated as one
minus LTV) has a dominant effect on the probability of the underlying collateral value dropping
below the critical value and hence, to default. The study also expects the net operating cash flows
(as evidenced by DSCR) to have a negative relationship with foreclosure. The study reveals that
the two loan specific financial variables, LTV and DSCR, are not significant, with LTV having
the correct sign and DSCR having the incorrect sign. Chen and Deng attribute this to some errors
in variable problem. Since both variables are largely estimated from market level indices, they
may not reflect the true financial conditions of the properties. They state that both LTV and
DSCR in their model are imperfect (noisy) measures of financial characteristics of an individual
property.
In their analysis, there appears to be big differences between what they refer to as “bad” loans
and “good” loans in terms of average LTV and market occupancy rates. The differences in
DSCR are also as expected, but not as pronounced as in LTV. Higher LTV leads to more
defaults, which is what the option pricing theory suggests and appears to conform to the findings
of Chen and Deng.
The pool level weighted average LTV and DSCR is analyzed for the data, over time. However, it
should be kept in mind that the weighted average statistics can be deceiving. For instance, two
deals may have the DSCR below 1.00 and one of the deals has loans with LTVs above 100%.
The lower quality of these pools is usually recognized by the rating agencies in the form of
higher subordination. Since loan in conduit pools are not cross-collateralized, defaults are
expected in such pools. While the AAA tranche may not experience losses due to adequate
subordination, there still exists a great average life uncertainty associated with such pools. This is
because recoveries from foreclosures are first paid to the senior classes. Thus, it becomes
difficult to predict when the AAA bond holder receives the principal. Thus, investors in the AAA
class from a low quality pool need extra spread to compensate for the average life uncertainty.
A Moody’s report (2004) analyzes the relationship between loan delinquency and leverage.
Delinquencies are found to be higher among loans with higher leverage, and the slope of the
increase is non-linear, rising sharply and by multiples at certain levels. The same holds true with
23
respect to the DSCR. While, even among the best cohorts, delinquency is never zero, loans with
a DSCR greater than 1.70 and LTV lower than 50% have very low delinquency rates (less than
1%). High leverage loans (greater than 90%) begin to separate from the pack as early as the first
full year of life, experiencing delinquency at two to three times the rate of the lower LTV loans.
The predictive power of initial DSCR varies with time. In the initial years, it matters a lot when
coverage is low. However, by seven to eight years into the life of the loan, the initial DSCR is
not strongly consistent with performance.
A recent study by Downing and Wallace (2005), based on Monte Carlo simulations of expected
defaults, indicate that the optimal subordination levels lie below those currently seen in the
market. One interpretation of this result is that in the absence of a track record of performance of
securitized commercial mortgages, subordination levels in the early years of CMBS were
conservative and high.
Several studies by been conducted by Esaki on commercial mortgage defaults. The first study
included loans from 1972 through 1997; the subsequent study added three more years of data
through 2000. The latest study (2005) includes loans through 2002. The addition of the new
loans alters the previous conclusion (1972 – 2000) slightly. In the past, all investment grade bond
classes of CMBS were well protected from the magnitude of losses experienced by the life
insurance company loans. The second study revealed that with the reduction in subordination
levels, some of the BBB classes would be vulnerable to a downturn of the magnitude of the late
1980s and the early 1990s. The latest study suggests that a greater proportion of the BBB classes
are now vulnerable to a downturn of the magnitude on the late 1980s and early 1990s. The loss
on the worst cohort, 1986, exceeds the average BBB subordination level on conduit and fusion
CMBS transactions being issued today, and may also result in default of some A tranches.
However, it is still below the lowest credit support levels for the AAA bond classes. Since BBB
subordinations are about 5%, most investment grade CMBS are still protected against the
average loss of origination cohorts of the last 30 years.
The results show an average lifetime cumulative default rate (based on loan balances) for cohorts
with at least ten years of seasoning decreased from 20.5% to 19.6% over 2000 to 2002. The 1986
24
cohort of originations continues to be the worst cohort in the past 30 years with nearly 33% of
the total loan balance eventually defaulting. The average loss severity on liquidated loans was
approximately 33%. The loans (as in the earlier study) are geographically well diversified, with
the largest in the West (23%) and Northeast (22%). Highest default rates were in the South
Central Region (25%) with the lowest in the West (10%).
According to a Moody’s study15, across all asset types, default rates increase progressively as
contemporaneous DSCR declines. Above a DSCR of 1.30, little variance in default rate occurs;
default would occur only under extraordinary circumstances. Default rates begin to accelerate at
a DSCR in the range of 1.20 to 1.30, a baseline commonly used for loan origination; implying
that the probability of default is not zero even if the loan has a currently adequate DSCR. As the
DSCR decreases, the probability of default progressively increases. The study states that default
probability almost doubles when the contemporaneous DSCR from 1.0 to 0.80 to 0.90. These
levels are likely to indicate a profound change in the property level conditions.
Thus, a review of the above literature indicates that the debt service coverage ratio and the loan
to value ratio just prior to the loan defaulting are important indicators of default probability.
Additionally, the average loan size also seems to have an effect on the default and loss severity
of commercial mortgages. These factors will be studied and analyzed in the thesis.
2.2 Real Estate Diversification Research
The two sources of risk relating to the underlying pool of loans are the prepayment risk and the
default or delinquency risk. Diversification of the underlying collateral is one of the ways to
mitigate default risk. CMBS with better diversification in the underlying pool of loans have less
exposure to economic events that affect particular regions or property types.
CMBS investors focus on a number of portfolio issues, especially those involving the
composition of the total collateral pool. The benefits of the CMBS structure derive from an
ability to make the real estate investments without risks associated with the direct mortgage or
equity placements (i.e. diversification by property type, geographic location, loan size, borrower,
15
Moody’s Special Report, US CMBS: DSCR Migration and Contemporaneous Probability of Default, June 7, 2005
25
etc.). This study focuses on the two aspects of diversification namely property type and
geographic location.
One of the factors considered when analyzing the risk of CMBS deal is the diversification of the
underlying pool of mortgages across space. The rationale for this “spatial diversification” is that
the default risk of the underlying mortgages is reduced if the loans are made on properties in
different regions of the country. Most benefits come from geographic diversification, since
regional recessions are more common than national recessions, and national recessions tend to
affect regions differently. The diversified portfolio of the loans can spread the risk across many
economies as opposed to having the entire portfolio of loans subject to an idiosyncratic risk
factor. For instance, the impact of a collapse in the Houston real estate market (which may lead
to higher defaults of loans) will be lessened if the commercial property markets remain strong in
New York, Seattle and Chicago. According to FITCH, geographic diversity within a pool is a
positive attribute that lessens some of the risk of regional economic and business cycle stress.
In a study of defaults by region by Ciochetti (2003), the Southwest (mostly Texas) accounts for
most of the foreclosed loans between 1986 and 1991. The Northeast moved in the opposite
direction – only six loans had been foreclosed between 1986 and 1989. However, this number
peaked in 1992 due to rising costs of living, and the economic weakness in the early 1990s.
Thus, the presence of geographic diversification is important in a CMBS pool.
In addition to spatial diversification, CMBS pools can be diversified across property types.
Rating agencies tend to assign a lower credit enhancement or subordination level to deals that
contain diversification across property types since a pool that is diversified across residential,
office, hotel, retail and industrial will most likely avoid the potential of a national glut in any one
of the sectors. Also, it is not possible to predict with certainty as to which property type will
enjoy the best performance going forward, and it is better to be adequately diversified. Different
property types vary widely in their default performance reflecting degrees of volatility in each
property type’s income and value. The property types in order of most to least stable are regional
malls, multifamily, anchored community shopping center, industrial / warehouse, unanchored
community shopping center, office and finally hotel.
26
A study by Grissom et al. (1987) studies the effects of portfolio diversification on the reduction
of unsystematic real estate investment risk by examining calculated returns of actual real estate
assets in two geographically different markets. Ex post data were collected from 170 different
income properties in two Texas markets between 1975 and 1983: Houston (40 assets) and Austin
(130). To test for the effects of portfolio diversification, 4 types of assets in each market are
identified: apartments, offices, industrial properties, and shopping centers. The empirical results
indicate that the variance of returns of real estate portfolios decreases rapidly with diversification
into a larger number of assets, across property types, and across markets. The average reduction
of total unsystematic risk is 92.5% for diversification across markets and 97.8% for
diversification across assets. The study concludes that diversification may be even more effective
for real estate assets than for common stocks.
A study by Laura Quigg (1997) indicates that the benefits of property type diversification are not
as great as geographic diversification since the performance of different property type trends
tends to be more highly correlated than that of properties in different locations. Multifamily is
diversified in that tenants comprise of different economic and demographic backgrounds, so a
100% multifamily can still be considered diverse. For other property types, there is a benefit to
mixing property types, because it gives a variety in tenant base as well as other factors that affect
property performance.
A study by Taylor and Fuchs (2004) assessed the impact of geographical dispersion on risk in
multifamily mortgages by generating different random portfolios for a given number of
apartment loan assets. All assets are granted equal weight and all have similar loan
characteristics, i.e., DSCR of 1.25 and LTV of 80%, 10-year maturity and extended lockout
provisions. The portfolio’s degree of diversification is subject to the randomness of the metros
selected for inclusion.
The Diversification Ratio (DR), defined as the ratio of unexpected loss for a diversified portfolio
over the unexpected losses for the same portfolio given no credit for diversification (i.e. ignoring
potential benefits flowing from diversification), is then calculated for each portfolio. For
27
example, a DR of .6 would indicate that unexpected losses for a particular portfolio are reduced
40% through diversification. The results show that as the number of assets or metros grow the
DR declines more or less monotonically with marked reduction in the dispersion of observed
DRs. Incremental additions (of assets) to the portfolio initially reap large diversification benefits,
but there is a substantial marginal utility to each incremental addition. The simulations used in
the study appear to indicate that the vast majority of diversification benefit is obtained by
portfolios consisting of 14-15 assets. Additional assets continue to yield benefits, but at a
declining rate. The risk mitigation benefits of maintaining a diversified portfolio are very
attractive. Simulations show unexpected loss calculations for a diversified portfolio are 55% of
those of the same portfolio assuming no diversification benefits.
The study also extends this analysis to geographically focused portfolios such as census regions,
and results show that loan portfolios concentrated in any particular region also reap
diversification effects. From highest diversification benefits to lowest, the regional results are:
the South (.48); Midwest (.51); Northeast (.60); and West (.64). Some of these portfolios offer
relatively more diversification than a randomly selected national portfolio, some less. The
difference among regional DRs including the number of metros represented in the region, the
overall market condition or credit performance of those metros and/or the observed correlation of
the real estate markets within each region.
A review of the above studies indicates that diversification in a pool of mortgages helps mitigate
risk associated, and thus an optimum diversification is always beneficial. Thus, the thesis will
also attempt to look at the impact of pool level diversification on subordination levels.
2.3 Summary
The above referenced research tends to indicate that the current / contemporaneous loan to value
ratio and the current / contemporaneous debt service coverage ratio provide significant
explanatory power for defaults and loss severity. Also, the average loan size (large versus small
loans) seems to have an impact on default and hence needs to be considered for their possible
impact on subordination levels. And finally, the impact of pool diversification will also be
analyzed.
28
However, in addition to the above variables, other market forces may be playing an important
role in determining the subordination levels. Thus, the thesis will also attempt to analyze factors
such as spread differential between corporate bonds and CMBS, and the additional liquidity and
flow of investors into the CMBS market that may have affected the subordination levels.
29
Chapter 3
DATA AND METHODOLOGY
The objective of this chapter is to discuss the data and variables used in analyzing the trend in the
subordination levels over time. The subordination levels of all the rated tranches of a CMBS
transaction have been considered in the analysis.
3.1 The Data
Data were collected on commercial mortgage backed securities issued between 1995 and mid2005. This reflects a decade of transactions in the CMBS market, thus enabling an analysis of the
credit enhancement levels for a reasonable time frame. However, the universe of data has been
narrowed to exclude FNMA, Freddie Mac and other Government Sponsored Enterprise (GSE)
securities, due to the implied government support embedded in the subordination levels of such
deals. Similarly, there are certain deals that have an implicit support of other government
agencies, and the deals do not reflect any required subordination level. Such deals have also not
been considered in the analysis. Thus, the final data set consists of 430 commercial mortgage
backed securities issued in the past decade.
Prior to collecting the data, the data fields that were thought to be most relevant in explaining the
subordination levels were established. This includes the risk factors – loan to value ratio, the debt
service coverage ratio, interest rate type (floating rate versus fixed rate deals), deal type (conduit,
large loans); and diversification factors – property type diversification and geographic location
diversification. Additionally, the spreads between corporate bonds and CMBS, and the increased
liquidity and demand for CMBS will be examined. The dependent variable is the subordination
level of each bond class in the transaction.
The data for this thesis was collected from Trepp LLC. Trepp, LLC, is the leading provider of
CMBS and commercial real estate information, analytics and technology to the investment
management industry. Trepp meets the needs of both the primary and secondary market with a
host of innovative products and services designed specifically for CMBS and commercial
mortgage participants. A summary of every deal issued is available through the Trepp database.
30
It provides information summary information of the bond classes i.e. coupon rate and type,
current rating, original rating, etc. It also provides information on the pool characteristics in
terms of concentration of specific property types, of geographic locations, or metropolitan
statistical area (MSA), etc. Other additional information include details of the top loans in the
pool, details of delinquent loans in the pool, if any, top tenants in the properties in the underlying
mortgages, information on defeased loans, if any and the collateral summary.
Some of the descriptive statistics of the data are discussed below. From Table 3, the steady
increase in the number of deals issued is evident, indicating the growth in the CMBS market.
Table 4, further shows the classification of deals as per the deal type. The category “others”
includes deals that are classified as “miscellaneous” (typically loans on golf courses, car garages,
etc.) and credit tenant loans (as the name suggests, properties with a credit tenant makes up most
of the underlying pool of mortgages).
Table 4
Table 3
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
Total
# of deals
15
24
37
45
44
36
41
38
52
66
32
430
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
Total
Conduit
8
19
25
36
36
31
36
33
40
55
28
347
Large Loan
2
2
5
2
2
0
1
2
10
10
4
40
Deal Type
Seasoned Franchise Loan
4
0
3
0
3
0
0
4
1
2
1
2
2
0
1
0
1
1
0
0
0
0
16
9
Others
1
0
4
3
3
2
2
2
0
1
0
18
In terms of fixed versus floating rate deals, there are a total of 396 fixed rate CMBS deals and 34
floating rate deals in the entire data set. The year wise break-up is given below. This shows an
increasing number of floating rate deals in the recent years. This could be due to the low interest
rate environment.
31
Table 5
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
Fixed
15
22
36
45
44
36
40
36
41
55
26
Floating
0
2
1
0
0
0
1
2
11
11
6
The following table shows the trend in the weighted average loan to value ratio and the weighted
average debt service coverage ratio of the CMBS transactions from 1995 through mid 2005.
Table 6
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
Avg. WALTV Avg. WADSCR
65.62
1.45
66.89
1.46
67.43
1.47
68.33
1.51
68.76
1.45
67.28
1.40
67.82
1.44
66.91
1.81
62.51
2.73
64.65
2.26
65.48
1.84
Out of the 430 CMBS transactions considered, 354 were diversified while 76 were nondiversified. Additionally, of the 32 CMBS deals that had a AAA subordination level of more
than 35%, 19 were not diversified (Exhibit 1). The basis used for diversification is that the deal
should consist of at least three property types with not more that 50% concentration in any one
property type.
The information used specifically for the analysis of subordination includes, first and foremost,
the subordination levels of every bond class of every deal. Additionally, information on the
32
weighted average loan to value ratio and the weighted average debt service coverage ratio is used
for the analysis. The deal type and the coupon type (fixed or floating) are also used.
3.2 Methodology
The objective of this thesis is to analyze the trend in subordination levels for all bond classes
over the past decade. The underlying hypothesis is that the reason for lower subordination levels
in recent years is that the loans in the underlying pool may have become less risky. Risk
attributes of a loan could be considered as a function of the loan to value ratio and the debt
service coverage ratio.
Subordination Level = ƒ (Risk factors)
= ƒ (loan to value ratio, debt service coverage ratio, interest rate
type, deal type)
Another hypothesis is that the underlying pools of loans have become more diversified over the
years, hence reducing the risk in the transaction. Standard linear regression has been used to
analyze the data with subordination levels as the dependent variable and the variables described
below as the independent or explanatory variables. Based on the review of the literature and the
factors that affect default probability and loss severity, the specific variables that used for
analysis of the subordination levels are described below.
The variable used for loan to value ratio is the WALTV. This is the weighted average loan to
value ratio of the pool of mortgages just prior to being securitized. The expected result for this
variable is that a positive relationship exists between the loan to value ratio and the subordination
levels. This is because, a higher loan to value ratio implies a higher risk of the loan, which would
then impact the bond classes. Thus, a higher subordination level would be required for the
different tranches to account for the higher risk in the underlying pool of loans.
Subordination level = α + β (weighted average loan to value ratio)
The variable used for debt service coverage ratio is WADSCR. This is the weighted average debt
service coverage ratio of the pool of mortgages in the transaction prior to being securitized. The
expected relationship between subordination levels and debt service coverage ratio is an inverse
33
relationship. The higher the debt service coverage ratio, the lower the risk associated with the
loans, and thus, lower is the subordination or credit support levels required for a specific bond
class.
Subordination level = α - β (weighted average debt service coverage ratio)
To analyze the diversity in the pool of mortgages in each deal, both property type and geographic
diversification are considered. The model has been set up such that if a specific deal has
mortgages on more than three property types with no more than a 50% concentration in any one
property type, and is in at least four large cities across the United States, then the underlying pool
can be referred to as diverse. A dummy variable, DDiverse, has been created such that if a
specific CMBS transaction is diverse, the dummy variable attributed is 1 else it is 0. The
expected relationship between diversity and subordination levels is also expected to be an
inverse relationship. The greater the diversity is a pool, the lower the risk due to the spread
across property types and geographies and thus, lower is the required subordination level.
Subordination level = α - β (diversity)
Conduit deals may have a lower subordination with respect to other deal types, such as franchise
loans16, or miscellaneous loans (which may include properties like golf courses or garages). A
dummy variable, DConduit, has been created to check if, in fact, conduit deals would indicate a
lower subordination level as opposed to the other deal types. The intuition for such a relationship
to exist is that conduit deals typically include loans on regular properties such as office,
multifamily, etc. as opposed to more exotic and maybe, riskier properties such golf courses, etc.
Thus is a specific deal is a conduit deal, then the dummy variable is 1 else it is 0. The expected
relationship is a negative or inverse relationship, because conduit deals imply lower risk and
thus, lower subordination levels.
Subordination level = α - β (Conduit deal)
Another variable that may affect subordination levels is the nature of the coupon i.e. fixed or
floating. A floating rate deal, just like any floating rate mortgage, is assumed to have a higher
16
An example of a franchise loan is a retail property that is owned by a auto dealership of say, Toyota. They may
also include properties that are restaurants or gas stations.
34
risk than a fixed rate deal. This is because, there is a degree of uncertainty associated with the
movement in future interest rates that might impact the performance of the mortgages, and thus,
their mortgage backed securities adversely. Such risk may be mitigated by a higher subordination
structure for a deal. To examine this effect, a dummy variable DFloating, has been created such
that deals that have a floating rate have a dummy variable of 1 and fixed rate deals have a
dummy variable of 0. The expected relationship is that subordination levels should be higher for
the floating rate deals.
Subordination level = α + β (floating rate)
Apart from the independent variables specified above pertaining to the risk associated with the
loans, there may be other factors that may affect the subordination level for a particular deal in a
particular year. For instance, since the data is time series, in that subordination levels have been
considered over the past decade, time needs to be controlled for. This is because, arguably, other
factors specific to the real estate cycle, or investment climate for other investments such as
stocks and bonds, interest rates, etc may have also impacted the subordination levels. Thus, a
dummy variable has been created for the year to control for the influence of time on the
subordination levels. The dummy variables for the years are DNUM(yr) i.e. DNUM96, and so
on. The analysis is on the CMBS transactions from 1995 through 2005. The year 1995 is the
omitted variable; and if a deal pertains to a specific year, the dummy variable is 1 else it is 0. For
instance, for the year 2003, all deals (data points) in the year 2003 have a dummy variable equal
to 1 and all deals in the years other than 2003 have a dummy variable equal to 0.
Similarly, since the thesis analyzes the change in subordination levels of all the bond classes, a
dummy variable is created for the bond classes. All bond classes are compared to the AAA bond
class which is the omitted variable in the regression. The dummy variable, D(tranche), with the
values of 0 / 1 are used for the analysis.
The following chapter discusses the results and the interpretation of the regression results. The
spread differential between CMBS and corporate bonds is not used in the regression but is
analyzed in the following chapter.
35
Chapter 4
DATA ANALYSIS
The objective of the thesis is look at possible explanations for falling subordination levels in
CMBS. The hypothesis is that the risk of the loans in the mortgage pools has gone down, thus
giving room for lower subordination levels. Specifically, the proxy used to indicate if a loan is
risky or not is the loan to value ratio and the debt service coverage ratio. These variables have
been considered as they emerged as two important factors after a review of the default and loss
severity literature on commercial mortgages. Pool level diversification has also been considered
as increased diversification helps to mitigate risk. The relationship between the subordination
levels and all the independent variables explained in Chapter 3 is discussed below.
4.1 Subordination Levels
The first task in the analysis was to examine the subordination levels over the past decade for all
the tranches in the commercial mortgage backed securities issued in this period. The average
subordination level was calculated for each of the bond classes.
A significant change in the structure of the AAA tranches has taken place since late 2004. CMBS
issuers have started carving the senior triple-A tranches of many offerings into “super-senior”
and, more recently, “super-duper” tranches that have higher credit support levels than the ratings
agencies have reached for traditional senior tranches (sample deal in Exhibit 2). Thus, the AAA
tranche of the CMBS deal is further split into “super-duper AAA”, “super-senior AAA” and the
“regular AAA tranches”. The regular AAA tranches have a subordination level in the range of
11% to 15%. The super-duper AAA tranche still have subordination levels in the range of 30%.
Many in the industry attribute this to the growing influence of crossover investors17 from the
corporate bond market who are less comfortable with today’s lower subordination levels.
Since the “regular AAA” tranche represents the credit risk in the transaction (and the underlying
pool of mortgages), to be consistent in comparison of the subordination levels of the AAA
17
Such investors typically do not have an in-depth understanding of the real estate market, and are drawn by the
higher yields offered by the commercial mortgage backed securities. Many of these investors are also foreign
investors.
36
tranches of the earlier years in the study, subordination levels of the “regular AAA” tranche are
considered for 2005 and some of the deals in 2004.
Table 7 summarizes the trend in subordination levels for all bond classes from 1995 through
mid-2005. Table 8 provides a comparison of the subordination levels (including the percentage
decrease) in1995 versus those in 2005.
Table 7
AAA
AA
A
BBB
BB
B
1995
33.50
26.82
21.23
15.00
6.68
3.08
1996
33.25
27.22
20.57
15.59
7.79
4.14
AVERAGE SUBORDINATION LEVEL
1997
1998
1999
2000
2001
2002
30.81 28.78 26.68 22.81 21.72 20.86
24.55 23.38 21.48 18.70 17.90 16.62
18.32 17.78 16.68 14.48 13.86 12.44
12.96 12.38 11.38 10.03
9.24
8.28
6.51
6.30
5.74
5.14
4.76
4.28
3.95
3.24
2.79
2.47
2.45
2.10
2003
19.68
14.65
10.27
6.20
3.66
2.11
2004
16.34
12.23
8.39
4.77
2.66
1.55
2005
13.16
10.12
7.18
4.08
2.36
1.54
Table 8
Rating
1995
2005 Difference % Change
AAA
33.50
13.16
17.17
-61%
AA
26.82
10.12
14.59
-62%
A
21.23
7.18
12.84
-66%
BBB
15.00
4.08
10.24
-73%
BB
6.68
2.36
4.01
-65%
B
3.08
1.54
1.53
-50%
It is evident from the above that subordination levels for all the bond classes have fallen
dramatically from1995 to 2005. The subordination level of the AAA tranche has fallen from
33.50% in 1995 to 18.94% in 2005. It is also interesting to note that the subordination level of
the BBB tranche, the lowest investment grade tranche, has fallen by the highest percentage, 73%,
from a level of 15% to a current level of 4.08%.
The following graphs further elucidate the falling trend in the credit enhancement levels of the
commercial mortgage backed securities.
37
Class A Subordination Levels
40.00
35.00
33.50 33.25
30.81
28.78
Subordination Level
30.00
26.68
26.82 27.22
25.00
24.55
22.81
23.38
21.72
20.86
21.48
20.00
18.70
21.23 20.57
18.32 17.78
15.00
19.68
16.34
17.90
16.62
14.65
16.68
14.48 13.86
10.00
13.16
12.23
10.12
12.44
10.27
8.39
5.00
7.18
1995
1996
1997
1998
1999
2000
AAA
2001
2002
2003
2004
AA
2005
A
Class B Subordination Levels
18.00
16.00
15.00
15.59
12.96
Subordination Level
14.00
12.38
11.38
12.00
10.03
9.24
10.00
8.28
8.00
7.79
6.68
6.20
6.51
6.00
6.30
5.74
4.77
5.14
4.76
4.00
4.14
2.00
4.08
4.28
3.66
3.95
3.24
3.08
2.66
2.79
2.47
2.45 2.10
-
2.11
1.55
2.36
1.54
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
BBB
BB
B
4.2 Discussion of the Regression Results
The next step in the analysis was to analyze the subordination levels with respect to the loan to
value ratio with an expected positive relationship implying a positive co-efficient for the loan to
38
value ratio in the regression result. Similarly, the relationship with the debt service coverage ratio
is expected to be an inverse relationship.
A summary of the results of the regression analysis is presented in the following table. The
regression model has an R square of 0.9326 adjusted R square of 0.9320. This implies that the
model explains 93% of the variability in the subordination levels. The detailed regression output
is shown in Exhibit 3.
Variable
Regression Co-efficient
T-Statistic
WALTV
0.3917
43.27
WADSCR
2.3044
12.67
DAA
-5.2178
-18.81
DA
-9.7982
-35.22
DBBB
-14.2459
-51.36
DBB
-18.2441
-62.16
DB
-20.3075
-67.86
DConduit
-1.7266
-4.92
DNUM96
0.8139
1.20
DNUM97
-0.5939
-0.89
DNUM98
-1.7039
-2.63
DNUM99
-3.2126
-4.85
DNUM00
-4.2889
-6.43
DNUM01
-5.05515
-7.67
DNUM02
-6.4861
-9.76
DNUM03
-8.3123
-12.63
DNUM04
-9.6065
-14.87
DNUM05
-9.8814
-14.87
DFloating
2.4858
4.55
DDiverse
-0.3821
-1.33
39
The regression results seem to suggest a positive relationship between the subordination levels
and the weighted average loan to value ratio. This indicates that as the loan to value ratio
increases, the subordination level of the bond classes is higher – for a unit increase in the loan to
value ratio, the subordination level increases by 0.39. The loan to value ratio is statistically
significant at the 95% confidence level in explaining the subordination levels.
Surprisingly, there seems to exist a positive relationship between the debt service coverage ratio
and the subordination levels i.e. with a unit increase in debt service coverage ratio, the required
subordination increases by 2.30 (as per the statistical result). What is even more interesting is the
fact that the variable is actually statistically significant in explaining the subordination level. This
relationship as explained by the model may be ignored, as it also does not make intuitive sense.
It should be noted that some of the research on the loan to value ratio and the debt service
coverage ratio indicate that the latter is a better indicator of default. By that measure, the debt
service coverage ratio should have had a negative co-efficient in the regression. This result of the
regression could be attributed to the fact that the data on debt service coverage ratio, in its
current form as collected, may not be appropriate and further research needs to be done.
Another reason that may be responsible for a positive co-efficient for the debt service coverage
ratio is the manner in which it may have been calculated. The data collected from Trepp
represents the DSCR as calculated by the underwriters, and different underwriters make different
assumptions to calculate the DSCR. The DSCR for some of the deals may be based on a cash
flow figure accounting for reserves and other deductions, while other deals may be using the net
operating income to calculate DSCR. This may cause a discrepancy in the DSCR reported across
the deals. It may be more appropriate to use the rating agencies stressed DSCR (page 14
describes how S&P arrives at the net income to finally calculate DSCR). Also, the fact that the
weighted average DSCR is being used instead of the full range of ratios for all the properties,
which is more appropriate, may be a plausible explanation for the not so intuitive regression
results. Finally, changing interest rates and cap rates over the period of study, which may
influence the DSCR, have not been considered in this study.
40
As far as the effect of pool diversity on subordination levels is concerned, the regression yielded
an insignificant (t-stat = -1.33) relationship between diversity and subordination levels. At the
same time, it should be noted that most of the deals with very high subordination levels are not
diversified (Exhibit1). However, the co-efficient of the variable seems to suggest that as diversity
increases, the subordination levels fall. It should be noted that most of the CMBS deals
considered in the data are diverse. If not so much across property types, they are well diversified
as per geographic locations. Also, over the years, the composition of the diversity has changed.
While the earlier deals constituted more multifamily, hotel and healthcare, the deals in the latter
years mostly include office, retail and multifamily. Thus, there has existed a certain level of
diversity in these transactions.
As expected, the subordination levels are lower by about 1.72 if the deals are conduit as opposed
to less common deals such as miscellaneous or franchise loan deals. Also, floating rate deals
have a higher subordination level than the fixed rate deals. The subordination level seems to be
higher by 2.48 if the CMBS transactions are floating rate deals. Both these variables are
statistically significant in explaining the subordination levels.
As far the dummy variables for the years are concerned, they seem to indicate that the
subordination levels for all the bond classes have fallen with respect to the subordination levels
of 1995. The variables are statistically significant for all the years except for 1996 and 1997
(where results indicate that the subordination levels increase in 1996 while those in 1997
dropped with respect to the 1995 levels).
With respect to the AAA subordination levels, subordination levels for all other bond classes are
lower. This is an expected result as the CMBS bonds are structured so that the tranches reflect
the priority of payments and AAA being the highest rated tranche. As one moves down the deal
structure, the subordination levels keep falling with the lowest tranche (B) having the lowest
subordination level. All the variables are statistically significant. This indicates that the market
structures the commercial mortgage backed securities efficiently.
41
Thus, based on the regression results above, loan to value ratio, conduit deals, interest rate type
seem to affect the subordination levels of the bond classes in the CMBS.
4.3 Market factors affecting subordination levels
It is evident from the above results that some of the factors affecting subordination include the
loan to value ratio, the deal type (which depends on the type of loans in the underlying pool) and
the interest rate type. However, in addition to the factors tested in the model above there may be
other market factors that have played an important role in pushing down subordination levels to
where they are currently.
One of the explanations for the falling subordination levels is the increasing demand by investors
for these kinds of securities. The public real estate debt market (primarily CMBS), is further
integrating with the broader fixed income market, forcing greater conformity on the real estate
industry. The historical performance of CMBS in recent years makes a strong case that risk has
been overpriced i.e. spreads have been too wide and subordination levels have been too high for
the realized risk reflected in the low delinquency rates.
Typically, the yield on the investment grade CMBS tranches is quoted with respect to the spread
on the swaps, while the yields on the non-investment grade CMBS tranches are quoted with
respect to the spread on the US treasury. A swap spread is defined as the difference between the
negotiated and fixed rate of a swap. The spread is determined by characteristics of market supply
and creditor worthiness. The swap rate (the fixed rate) may be seen as the price for obtaining the
floating rate payment. Hence the swap rate is the price which makes demand equal to supply in
the plain vanilla market. Assuming a downward (upward) sloping demand curve (supply curve),
an increase in demand for floating payments would raise the swap rate. An increase in supply of
floating payments would lower the rate. For instance: in an environment of unusually high
interest rates, economic agents may expect interest rates to decline, wanting to pay a floating
rates instead of a fixed. The supply of floating payments would rise and the swap rate would fall,
ceteris paribus.
42
The following two graphs show the yield spreads over the 10 year US treasury for all the CMBS
bond classes on a monthly basis for the period August 1996 to July 2005. They indicate the
tightening of spreads in the CMBS market.
Investment Grade Spreads
350
300
250
200
150
100
50
AAA
AA
A
Feb-05
Aug-04
Feb-04
Aug-03
Feb-03
Aug-02
Feb-02
Aug-01
Feb-01
Aug-00
Feb-00
Aug-99
Feb-99
Aug-98
Feb-98
Aug-97
Feb-97
Aug-96
0
BBB
Source: Moody’s Investor Service
Non-Investment Grade Spreads
1200
1000
800
600
400
200
BB
Feb-05
Aug-04
Feb-04
Aug-03
Feb-03
Aug-02
Feb-02
Aug-01
Feb-01
Aug-00
Feb-00
Aug-99
Feb-99
Aug-98
Feb-98
Aug-97
Feb-97
Aug-96
0
B
Source: Moody’s Investor Service
In this study, to enable an apples-to-apples comparison of the spreads on CMBS and corporate
bonds, the treasury spread is used. The following graphs show the trend in the yield spreads of
43
the AAA/AA18, A and BBB tranches of corporate bonds and CMBS. The spreads are with
respect to the 10 year treasury.
Corporate Bonds
"AAA/AA" Yield Spreads
CMBS
200
180
160
140
120
100
80
60
40
20
Apr-05
Dec-04
Apr-04
"A" Yield Spreads
Aug-04
Dec-03
Aug-03
Apr-03
Dec-02
Aug-02
Apr-02
Dec-01
Aug-01
Apr-01
Dec-00
Apr-00
Aug-00
Dec-99
Aug-99
Apr-99
Dec-98
Aug-98
Apr-98
Dec-97
Aug-97
Apr-97
Dec-96
Aug-96
0
Corporate Bonds
CMBS
300
250
200
150
100
50
18
Feb-05
Aug-04
Feb-04
Aug-03
Feb-03
Aug-02
Feb-02
Aug-01
Feb-01
Aug-00
Feb-00
Aug-99
Feb-99
Aug-98
Feb-98
Aug-97
Feb-97
(50)
Aug-96
-
Since the data for corporate bonds is a blended AAA/AA spread, the same has been calculated for the CMBS
spreads.
44
"BBB" Yield Spreads
Corporate Bonds
CMBS
350
300
250
200
150
100
50
Feb-05
Aug-04
Feb-04
Aug-03
Feb-03
Aug-02
Feb-02
Aug-01
Feb-01
Aug-00
Feb-00
Aug-99
Feb-99
Aug-98
Feb-98
Aug-97
Feb-97
Aug-96
0
Source: Citigroup Capital Markets
The spread differential between CMBS and corporate bonds indicates that commercial mortgage
backed securities offer higher yields (as evident from the higher yield spreads of CMBS over the
treasury), thus attracting investors. According to an industry source, most of the CMBS deals in
today’s environment and market are oversubscribed by 50%. This may also explain the
increasing number of corporate bond investors looking at the CMBS market for higher returns.
This market trend implies a tremendous demand for CMBS, thus resulting in lowering of
subordination levels.
4.4 Summary
Thus, while the basic risk and diversification factors play some role in explaining the variability
in subordination levels, the spread differential between CMBS and corporate bonds,
compounded with the favorable interest rate environment and great property market performance
seem to be affecting subordination levels. Various reasons have been cited for this trend.
45
One school of thought on the lower subordination levels is that, the subordination levels are
probably not low and are just right. The reasoning behind such an argument is that the initial
years of CMBS followed the debacle in the real estate markets of the late 1980s and the early
1990s. Most of the deals then included distressed properties and other such assets and thus,
subordination levels were much higher then. Since then, the performance of the real estate
markets has improved considerably due to low interest rates, rising property values, etc. Thus,
since property level performance has increased, the underlying pools of mortgages are
performing well, thus, justifying the lower subordination level for the securities issued today. At
the same time, an industry source said that these are probably as low as the subordination levels
can get.
Other reports cite the “great performance” of the property markets. The robust capital inflows
from the public and private debt markets, the current liquidity in the real estate capital markets,
improving property market fundamentals, an expanding economy and generally lackluster
investment opportunities elsewhere make real estate a lucrative investment option. Data from the
real estate debt markets seem to show no signs of trouble. Delinquencies among seasoned CMBS
(those aged one year or more) fell slightly to 1.78% in February, according to Morgan Stanley,
and are expected to decline to about 1.30% by year-end. Loan delinquencies in life insurers’
commercial mortgage portfolios have also continued their downward trend. According to the
American Council of Life Insurers (ACLI), loan delinquencies at year-end 2004 were barely
measurable at just 0.08%. That said, delinquency and default data are, by definition, backwardlooking. And since loans rarely encounter problems in the first year or two, today’s low rates
may not say very much about how the loans originated now will perform in the future.
A recent report by Fitch19 addresses the concerns of lower subordination levels in CMBS. The
market overall has benefited from the low interest rates and increasing values and CMBS
transactions have benefited from improved loan and sponsor quality. The study states that the
earliest CMBS transactions were conservative in many respects to attract investors. Early
transactions consisted on distressed assets, often with little information on the collateral. Loans
19
Fitch Special Report, CMBS Subordination Levels: Too High, Too Low or Just Right, 2004
46
were not being originated for securitization, but rather securitization was undertaken as a means
to clear balance sheets. Since then, CMBS performance has exceeded everybody’s expectations.
Commercial real estate has also been systemized and disciplined through the capital markets.
Flow of information has improved and detailed information of each property and its sponsor is
available at origination. The lower subordination levels required by rating agencies are based on
the outstanding performance of CMBS as well as intensive tracking of the bonds and the
underlying collateral. Other factors such as low interest rates, inclusion of large loans in conduit
structures, and improved collateral and sponsorship quality, have contributed to today’s lower
levels.
Thus, it can be argued, that while the more fundamental variables have an impact on in the
subordination levels of the commercial mortgage backed securities, there are other market forces
that impact subordination levels as well. For lack of time for the thesis, the other market forces
could not be examined, and have potential for future research.
47
Chapter 5
CONCLUSION
The objective of the thesis is to understand the determination of subordination levels in
structuring commercial mortgage backed securities. Over the past decade, the subordination or
credit enhancement levels of each of the bond classes have been falling. Through a quantitative
analysis of 430 securities issued from 1995 through 2005, an attempt has been made to analyze
the trend in subordination levels as well as to identify various factors that have contributed to this
trend.
The essence of subordination levels assigned to the various bond classes or tranches of the
security is to ensure that the highest rated tranches are protected in the event of defaults and
losses on the underlying pool of mortgages. Subordination is a means of mitigating the credit risk
that exists in such a bond structure. The fundamental questions underlying the research of
subordination levels are: have the underlying pools of loans become less risky so as to support
the lower subordination levels or has the pool level diversity increased so as to justify the
subordination levels?
To answer the first question, loan to value ratio and debt service coverage ratio have been used to
determine if the loan can be classified as risky or not. Additionally, the interest rate type and the
nature of the deals (conduit, etc.) are also used as proxies for “risk” in the underlying pool of
mortgages. Since subordination levels are determined by loan defaults and loss severity, the
literature review makes an attempt to look at previous studies that have identified the variables
that affect default and losses of commercial mortgages. Most of the research confirms the
predictability of the contemporaneous debt service coverage ratio and the contemporaneous loan
to value ratio as important variables. Additionally, the literature review also focuses on
diversification benefits arising from both property type as well as geographic locations in real
estate.
Based on the factors that seem to influence probability of default and loss severity, and hence
subordination levels, data were collected and analyzed. The results seem to indicate that over the
48
years, subordination levels have indeed dropped since 1995. Subordination levels seem to be
affected by the loan to value ratio and other factors such as deal and interest rate type. However,
more broadly, the average loan to value ratio has remained in the range of 60% to 70% while the
subordination trends have declined. Thus, there seem to be other market forces that have
influenced this downward trend.
The thesis also looks at the difference in spreads between corporate bonds and commercial
mortgage backed securities. The CMBS market has outperformed expectations and there has
been an influx of investors into this area. The spread differential of the commercial mortgage
backed securities over the corporate bonds seems to be luring the corporate bond investors to the
CMBS market. This increasing demand for CMBS may be driving down the subordination
levels.
Another reason could be the time period for comparison of subordination levels. The CMBS
market is a relatively nascent compared to its counterpart investments such as bonds and stocks.
Also, the CMBS market emerged as a result of the real estate bust of the late 1980s and the early
1990s. The initial issues included loans on distressed assets, and other such loans which may
have resulted in the higher subordination levels. Property market fundamentals have improved
since, and this may be a reason for the lower subordination levels seen in today’s market. It may
be likely that the ratings in the early years of CMBS were conservative, following the real estate
cycle, and the current subordination levels are a reflection of the true inherent credit risk in the
securities.
Due to the time constraints of the thesis, the impact of the market forces on subordination levels
could not be tested. Going forward, future research on the change in property market
fundamentals, the composition of the underlying pools of mortgages (if the deals in the early and
mid 1990s consisted of distressed loans) may throw some light on the reasons for this trend.
49
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Default Probability: Evidence from Special Serviced CMBS Loans, February 2004
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Fabozzi, Frank , The Handbook of Mortgage Backed Securities
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Securities, 1999
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52
Exhibit 1
Diversity of bonds with a High AAA Subordination Level
Sr No Year
1 1995
2 1995
3 1995
4 1996
5 1997
6 2001
7 2003
8 2003
9 2003
10 2003
11 2003
12 2003
13 2004
14 2004
2004
15 2004
16 2004
17 2004
18 2005
19 2005
20 1995
21 1995
22 1996
23 1996
24 1996
25 1998
26 2002
27 2003
28 2003
29 2004
30 2004
31 2004
32 2004
Deal CUSIP
CSFB 1995-M1
CSFB 1995-WF1
PSSF 1995-MCF2
SBM7 1996-C1
MSC 1997-ALIC
JPMCC 2001-A
COMM 2003-FL9
CSFB 2003-TF2A
CSFB 2003-TFLA
HVBMC 2003-FL1A
JPMCC 2003-FL1A
MSC 2003-XLF
BSCMS 2004-BBA3
BSCMS 2004-HS2A
BSCMS 2004-HS2A
CGCMT 2004-FL1
COMM 2004-HTL1
LBFRC 2004-LLFA
COMM 2005-F10A
CSFB 2005-TFLA
DLJMA 1995-CF2
NASC 1995-MD3
DLJMA 1996-CF1
PMLIC 6-PML
SASC 1996-CFL
CCAO 3A
BASST 2002-X1
BALL 2003-BBA2
GSMS 2003-FL6A
CSFB 2004-TF2A
CSFB 2004-TFLA
GCCFC 2004-FL2A
MSC 2004-XLF
Deal Type
Miscellaneous
Conduit
Conduit
Conduit
Seasoned
Miscellaneous
Large Loan
Large Loan
Large Loan
Seasoned
Large Loan
Large Loan
Large Loan
Large Loan
Large Loan
Large Loan
Large Loan
Large Loan
Large Loan
Large Loan
Conduit
Large Loan
Conduit
Seasoned
Seasoned
Miscellaneous
Miscellaneous
Large Loan
Large Loan
Large Loan
Large Loan
Large Loan
Large Loan
Fixed / Floating
Fixed
Fixed
Fixed
Fixed
Fixed
Fixed
Floating
Floating
Floating
Floating
Floating
Floating
Floating
Floating
Floating
Floating
Floating
Floating
Floating
Floating
Fixed
Fixed
Fixed
Fixed
Fixed
Fixed
Fixed
Floating
Floating
Floating
Floating
Floating
Floating
53
Rating
AAA
AAA
AAA
AAA
AAA
AAA
AAA
AAA
AAA
AAA
AAA
AAA
AAA
AAA
AAA
AAA
AAA
AAA
AAA
AAA
AAA
AAA
AAA
AAA
AAA
AAA
AAA
AAA
AAA
AAA
AAA
AAA
AAA
Subordination
35.01
35
37
38
43
49.25
41.88
41.98
43.29
48.62
38
48.79
47.27
56.32
41.76
61.22
40.3
41.51
44.88
42.7
35.99
35
37.02
35
41
37
39.75
42.89
41.27
41.1
42.59
42.1
42.86
Diversified (y/n)
n
n
n
n
n
n
n
n
n
n
n
n
n
n
n
n
n
n
n
n
y
y
y
y
y
y
y
y
y
y
y
y
y
Exhibit 2
Sample of a Multiple AAA Tranche Deal
Source: Trepp
Class
A1
A1P
ADP
A2
A3
ABA
ABB
A4
A4A
A4B
A1A
AJ
B
C
D
E
F
G
H
J
K
L
M
N
O
P
XP*
XC*
Total
Balance
Original
100,000,000
50,000,000
166,616,000
349,848,000
288,705,000
207,259,000
29,609,000
500,000,000
1,171,595,000
167,371,000
169,634,000
300,060,000
65,013,000
35,007,000
75,015,000
40,008,000
55,011,000
45,009,000
40,008,000
20,004,000
20,004,000
20,004,000
10,002,000
10,002,000
10,002,000
55,011,486
3,831,315,000
4,000,797,487
4,000,797,486
GS Mortgage Securities Corp. II 2005-GG4 (GSMS 2005-GG4)
BOND SUMMARY
Credit
Enhancement Original Ratings Original Ratings Original Ratings
Securitized
MDY
S&P
FII
Coupon
Coupon Type
4.369
Fixed Rate
20
Aaa
AAA
AAA
5.285
Fixed Rate
20
Aaa
AAA
AAA
3.452
Fixed Rate
20
Aaa
AAA
AAA
4.475
Fixed Rate
20
Aaa
AAA
AAA
4.607
Fixed Rate
20
Aaa
AAA
AAA
4.68
Fixed Rate
20
Aaa
AAA
AAA
4.756
Fixed Rate
20
Aaa
AAA
AAA
4.761
Fixed Rate
20
Aaa
AAA
AAA
4.751
Fixed Rate
20
Aaa
AAA
AAA
4.732
Fixed Rate
20
Aaa
AAA
AAA
4.744
Fixed Rate
20
Aaa
AAA
AAA
4.782
Fixed Rate
12.5
Aaa
AAA
AAA
4.841
Fixed Rate
10.88
Aa2
AA
AA
4.89
Fixed Rate
10
Aa3
AAAA4.939
Fixed Rate
8.13
A2
A
A
5.078
Fixed Rate
7.13
A3
AA5.415
Fixed Rate
5.75
Baa1
BBB+
BBB+
WAC/PT-4bp
4.63
Baa2
BBB
BBB
WAC/PT-0bp
3.63
Baa3
BBBBBB4.462
Fixed Rate
3.13
Ba1
BB+
BB+
4.462
Fixed Rate
2.63
Ba2
BB
BB
4.462
Fixed Rate
2.13
Ba3
BBBB4.462
Fixed Rate
1.88
B1
B+
B+
4.462
Fixed Rate
1.63
B2
B
B
4.462
Fixed Rate
1.38
B3
BB4.462
Fixed Rate
0
NR
NR
NR
IO, Other Non-Fixed
Aaa
AAA
AAA
IO, Other Non-Fixed
Aaa
AAA
AAA
* - Notional Balance
54
Exhibit 3
Regression Output
Source
SS
df
MS
Model
Residual
521776.97
37688.55
20.00
2267.00
26088.85
16.62
Total
559465.53
2287.00
244.63
subordinat~n Coef.
waltv
wadscr
daa
da
dbbb
dbb
db
dconduit
dnum96
dnum97
dnum98
dnum99
dnum00
dnum01
dnum02
dnum03
dnum04
dnum05
dfloating
ddiverse
Std. Err.
0.3917
2.3044
-5.2179
-9.7982
-14.2459
-18.2441
-20.3075
-1.7266
0.8140
-0.5940
-1.7040
-3.2127
-4.2890
-5.0552
-6.4861
-8.3123
-9.6066
-9.8814
2.4858
-0.3821
0.0091
0.1818
0.2774
0.2782
0.2774
0.2935
0.2992
0.3507
0.6811
0.6705
0.6471
0.6624
0.6674
0.6590
0.6645
0.6581
0.6460
0.6646
0.5460
0.2874
t
Number of obs
F( 20, 2267)
Prob > F
R-squared
Adj R-squared
Root MSE
P>t
43.2700
12.6700
-18.8100
-35.2200
-51.3600
-62.1600
-67.8600
-4.9200
1.2000
-0.8900
-2.6300
-4.8500
-6.4300
-7.6700
-9.7600
-12.6300
-14.8700
-14.8700
4.5500
-1.3300
55
[95% Conf.
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.2320
0.3760
0.0090
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.1840
0.3740
1.9479
-5.7618
-10.3438
-14.7899
-18.8196
-20.8944
-2.4144
-0.5216
-1.9089
-2.9730
-4.5116
-5.5978
-6.3474
-7.7893
-9.6029
-10.8734
-11.1847
1.4150
-0.9456
2287
1569.27
0.0000
0.9326
0.9320
4.0774
Interval]
0.4095
2.6610
-4.6739
-9.2526
-13.7020
-17.6685
-19.7207
-1.0389
2.1496
0.7210
-0.4350
-1.9138
-2.9801
-3.7629
-5.1829
-7.0218
-8.3397
-8.5781
3.5566
0.1814