Default Study of Commercial Mortgages in CMBS pools
An Empirical Analysis of Defaults and Loss Severity
By
ROHIT SRIVASTAVA
B.Arch, Bachelor in Architecture, Manipal Institute of Technology, 1993
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
Septeber
003
September 2003
Copyright 2003 Rohit Srivastava
All Rights Reserved
ASSACHUSETTS INSTITUTE
OF TECHNOLOGY
LIBRARIES
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.
A
Signature of
A utho r..........................
... :.............................................................
Department of Architecture
August 04, 2003
Certified by
William C.Wheaton
Professor of Economics
Thesis Advisor
Accepted by
David Geltner
Chairman, Interdepartmental Degree Program in Real Estate Development
ROTCH
DEFAULT STUDY OF COMMERCIAL MORTGAGES IN CMBS POOLS
AN EMPIRICAL ANALYSIS OF DEFAULTS AND LOSS SEVERITY
by
ROHIT SRIVASTAVA
Submitted to the Department of Architecture
On August 4, 2003 in Partial Fulfillment of the Requirement for
the Degree of Master of Science in Real Estate Development
ABSTRACT
The commercial mortgage backed securities ("CMBS") market has become a major
source of real estate financing over the last 10-12 years. The growth of this market has
been accompanied by strong real estate fundamentals. Consequently the collateral in the
CMBS pools has not seen a major real estate recession till now. The loss assumptions
used by market participants is based on the experience of banks and insurance companies
("Portfolio Lenders"). As the underwriting criteria and the collateral quality in the CMBS
market is markedly different than that of the Portfolio Lenders', it can be assumed that
the loss experience is going to be different as well. The loss experience for a portfolio is
determined by two main factors, the frequency of defaults (or delinquencies) in the pool
and the actual severity of loss on the defaulted loans. Each defaulted loan in a pool
represents loss severity and eventual yield degradation. As Real Estate is a lagging
indicator, historically, every economic downturn has been followed by a surge in default
rates. The recession of 2001-03, which appears to be no different from its predecessors, is
also indicative of possible rising commercial mortgage default rates. With further
alleviating political risks and no signs of economic recovery in sight, concerns run high
investors regarding rising delinquencies and default rates. It is time again, to evaluate the
potential yield degradation for truer reflection of the credit risk in the pricing of the
CMBS.
In an effort to empirically quantify the credit risk and the economic cost of the losses on a
portfolio basis, this study examines 801 CMBS deals with 78,680 commercial mortgage
loans originated from 1960-2003, tracking them through to June'2003. The effort is
focused on evaluating performance of this portfolio, which is mainly representative of
mortgages originated by "conduit" lenders. These loans originated mostly with the
intention of immediately selling through securitization rather than holding on balance
sheet are truly representative of the majority of today's CMBS collateral. The study
further examines the possibility of different variables like Debt Service Coverage Ratio,
Loan to value, Metropolitan Statistical Areas location and economic growth, impacting
the performance of commercial mortgages. This evaluation is based on empirical analysis
of this data collected by Intex.
The methodology adopted entails a review of the defaulted loans based on factors such as
origination cohorts, geographical distribution of defaulting properties, loan sizes,
seasoning period, timing of default, number of loans by originator and year, severity of
loss, foreclosure process and defaults by property types. Analysis of the different
variables helps provide insight into the relationships and importance of these factors in
the eventual economic loss or the impact on the imputed yield.
Thesis Advisor: William C.Wheaton
Title: Professor of Economics
ACKNOWLEDGEMENT
I would like to thank George Pappadopoulos of Property Portfolio Research and Davis
Cable of Wachovia Securities for trusting me with a project of such magnitude.
I would like to thank Kathy Mixon and Bruce Miller of Wachovia Securities for their
support, comments and insights.
I would like to thank my advisor Prof. William Wheaton for his thoughts and
recommendations.
I would like to thank Sameer Nayar for being truly inspirational and being there for me at
all times.
Last and not the least, I would like to thank Shweta for the constant support that kept me
going.
Table of Contents
Abstract
Acknowledgements
Table of Contents
2
4
5
Objective and the importance of a default study
Summary of findings
6
9
Chapter 1: Introduction
Chapter 2: Literature Review
CMBS: Overview & Market Development
Commercial Mortgage Default Risk
Default risk in CMBS
13
15
21
Chapter 3: Data & Methodology
Data Overview
Description of variables
Assumptions
Methodology
27
28
29
31
Chapter 4: Observations and Results of Descriptive Statistics
Default by Origination Cohort
Default by Loan Size
Default by Property Type
Default by Originator
Default by State
Average Timing of default
36
40
43
47
49
52
Chapter 5: Loss Severity and Final results
Conclusions
55
56
References
APPENDIX-I
APPENDIX -II
57
Chapter 1
Introduction
Objective and the importance of a default study
The past decade has seen the emergence of commercial mortgage backed securities
(CMBS) as a much preferred investment product. This has been due to its innovative
structural characteristics fulfilling the heterogeneous appetite of investors for risk and
subsequently proportionate returns. What began as a structural innovation of the capital
markets for covering the S & L debacle of 1980's has grown today into a $470 Billion
industry. Factors further contributing to its explosive growth have also been historically
low interest rates, continued drought at the stock market and the preference for CMBS
relative to other fixed income investments.
The hybrid nature of CMBS, combining
fundamental real estate features with structural features and protections of a diverse pool
of assets, makes this product appealing to fixed income investors.
Mortgages on income producing properties serve as the underlying collateral, which
governs the payment characteristics of these securities. Any event of default in the pool
of mortgages is indicative of an economic loss leading to disruption of cash flows to the
holders of these securities based on the subordination levels. Given the vividly etched
memories of defaults during the real estate downturn of the late 1980's, investors look
upon any event of default with concern due to the eventual potential yield degradation.
As historically observed, each economic recession has been followed by a surge in
delinquencies and eventual foreclosures. Real estate sector has been typically observed to
display a distinct lag in the event of an economic downturn. Rising vacancies and
declining rents are representative of an anticipated rise in delinquencies. Given the
present recessionary conditions, geopolitical uncertainties marked by alleviating political
risks, and historically high unemployment rates, fears run high of the onslaught of
delinquencies and bankruptcies. As the lending community braces itself for the worst, the
first signs of above normal delinquency rates are beginning to show up.
Studies by Snyderman (1991 and 1994) and Snyderman and Esaki (1999 and 2000) were
the seminal attempts to evaluate the economic costs of defaults in a pool of 16,500 ACLI
loans. However this data on life insurance company loans is not truly representative of
the collateral used for structuring CMBS pools. The industry wide acceptance of these
studies has been representative of the insufficiency of data to compare CMBS
delinquencies and evaluate the potential losses and yield degradation in a CMBS pool of
mortgages.
However, now with availability of data on CMBS pools from data companies like Intex,
Trepp and Charter, this study looks to examine the loss severity on defaulted and
foreclosed loans for a given population of 78,680 loans in 801 CMBS deals originated
from 1960-2003. The pool that is composed of mostly "conduit loans" is truly
representative of CMBS collateral used for securitization today. Given the availability of
relevant data "it is now time to compare apples to apples".
In the recent past numerous attempts have been made to empirically and qualitatively
evaluate the default incidence using data on conduit loans and determine the lifetime
default rates given the prevalent market conditions. However each effort fell short of
identifying the true economic cost of default on a portfolio basis for a given CMBS pool
of mortgages. The reason was the limited focus and scope of these studies or the possible
insufficiency of data on losses in the portfolio.
Unlike its predecessors, this study would look to identify various descriptive statistics of
default incidence based on origination cohort, property type volatility, regional bias,
originators underwriting lapses and loan size contribution. Further to this, the eventual
aim is to identify empirically the loss severity due to defaulted loans on a portfolio basis
and the impact of these losses on the various CMBS structures. These losses across the
portfolio are representative of eventual yield loss.
Given the informationally efficient nature of the financial markets, periodic empirical
identification of expected losses also helps benchmark and price risk in commercial
mortgage more efficiently. Hence to determine expected losses across a more
representative loan population of CMBS collateral would provide a more accurate
assessment and eventual pricing of risk for CMBS securities.
Under present broad and pervasive uncertainty that continues to hold back a complete
economic recovery, the importance of such an effort cannot be over-emphasized. Given
the unprecedented growth of the CMBS industry, the need for a greater understanding of
the factors affecting default risk for commercial mortgages is critical. This should further
enhance the transparency of loss experience in the CMBS market and allow investors to
accurately price credit risk for CMBS transactions.
Summary of findings:
Based on the descriptive analysis conducted on the CMBS loans in the portfolio, the
following findings (by category) were found to be of importance:
Even with contingent claims on the actual birth of the CMBS industry in 1991-92,
-
the actual surge of securitization and the more substantive growth in the
originations began in 1996-97 and peaked in 1998. This was the period when the
conduits aggressively grew their originations. The factors responsible for this was
the void which had formed with the more traditional lenders in commercial real
estate and life insurance companies having exhausted their assets which could be
securitized.
Also conducive economic environment with falling interest rates
and capital market acceptance of CMBS as an investment product contributed
significantly to the explosive growth of the real estate securitization
Based on the definition of default and the assumption to include any loans with
-
any past delinquency experience to be in default, it was found that nearly 54.2%
of the loans to have defaulted, were restructured or experienced some remedial
actions to become current again or were paid down.
ORIGINATION COHORTS
-
Surprisingly, 1995 was the cohort with the highest lifetime default rate of 7.95%
(by principal balance) with over 50% of the defaults occurring within 3 years of
origination. This is still mild compared to the 1986 cohort lifetime default rate of
31.5% in the Snyderman study. The 1997 cohort displayed the highest lifetime
default rates by loan count of nearly 6%.
-
The 2001 cohort displayed a substantial contribution in the loan population by
dollar amount, but experienced very low lifetime default rates. This can be
attributed to not only the underwriting characteristics but also the lack of
seasoning for these loans.
-
Consistent with the high lifetime default rates by principal and loan count, loans
of 1995 and 1997 cohorts had the highest recorded loss severity of 58.48% and
53.43% on the loans liquidated or restructured.
LOAN SIZES:
Loans with principal balances between $2Million and $4Million are twice more
-
likely to default, with a higher loss severity than loans under $1Million. This
could be attributed to the ownership characteristics of the properties that serve as
collateral for these loans. In most cases with mortgages under $lMillion,
properties tend to be owner occupied with recourse terms in the event of a default.
It was found that larger loans are less likely to default, possibly due to better due
-
diligence, lower leverage and conservative underwriting.
-
Another observation was the high rate of originations for large loans of over $20
Million after 2000. This can be explained by the overall acceptability and growing
confidence of the capital markets in the CMBS as an investment vehicle for
investing in commercial real estate.
-
Larger loans (20MM-50MM) experienced lower loss severity upon liquidation
than the smaller loans. This is due to the relatively unchanged foreclosure
expenses for small or large loans. Also the quality and size of the collateral
contribute to the eventual liquidation price with larger properties fetching better
proceeds from foreclosure.
PROPERTY TYPE
-
Consistent with the present state of the economy and its impact on various
property types, Healthcare and Hotels continued to display high lifetime default
rates by both principal balance and loan count. This was appropriately reflected in
the higher levels of loss severity too.
-
Retail is observed to have a large number of loans in default but is not similarly
reflected by a large defaulted principal balance. This could be explained by a high
number of smaller size defaulted loans on un-anchored retail properties.
-
Healthcare loans exhibited higher lifetime default rates by balance due to the
tough regulatory environment that the sector has gone through and higher
leverage. The average LTV on these loans was 71.2% with a high of 106% and
low of 30%.
ORIGINATORS
As suggested by the active contribution of the "conduits" in the development of
-
the CMBS industry, commercial conduit divisions of investment banks were
responsible for nearly 62% of the originations in the loan population. Column
and Merrill Lynch displayed having the highest lifetime default rates amongst the
20 largest originators by loan count and principal balance. These high default
rates were also indicative of their high exposure to under performing property
types and regional concentration in the loans originated.
STATE
-
California, New York, Texas, Florida, New Jersey and Illinois continued to be the
primary markets with over 44% of the loan universe by balance being originated
on properties in these States. However among these states, which are considered
as primary markets, Florida displayed highest lifetime default rate of 5.16% by
loan count. Other states, which have been mostly characterized as secondary and
tertiary markets, recorded higher lifetime default rates. Some of these states were
Tennessee, Louisiana and Kentucky.
-
New York, Illinois and California were the primary markets, which experienced
low loss severity on loans liquidated. Exceptions to this are Florida and Texas,
which had loss severity on liquidated loans of 44.64% and 57.7%. This could
possibly be explained by the difference in the judicial process for foreclosure in
these states.
AVERAGE TIMING OF DEFAULT
-
Unlike Snyderman's observation of average timing of default, a distinct shift in
the probability of a loan defaulting in the early years is seen in this study. Default
incidence rises to peak in the
3 rd
year from origination and then falls over the
remaining life of the cohort. This is consistent with the belief regarding the
inherent difference in underwriting methodology between conduit loans and
ACLI loans. Other reasons for this significant shift could be the lack of sufficient
seasoning of the loan pools.
LOSS SEVERITY
-
The average cumulative loss severity on a portfolio basis for the loans that were
liquidated or recorded any losses and were not foreclosed upon is calculated to be
33.78%. These losses are representative of the loss in principal, legal and
foreclosure costs, servicer advances and any interest payments lost during the
liquidation or delinquency period.
-
By origination cohort, 1995 displayed the highest loss severity of 58.41%. This
being consistent with high lifetime default rates experienced by the same cohort.
Interestingly, this linear relationship between higher default rates and higher loss
severity was observed across other categories of descriptive too, such as State and
property type.
-
Healthcare properties with highest lifetime default rates also suffered the highest
average loss severities of 81.06% on the liquidated and/or defaulted loans.
-
Amongst the states, Texas, North Carolina, Kentucky and Arizona experienced
loss severities in the high 50's. These were all states with Power of sale
foreclosures laws, which is indicative of substantially lesser time for the
foreclosure proceedings to be completed in. Given the legal structure to foreclose
in a timely manner, these states still have their share of high loss severities due to
substantial exposure to under performing asset types like hotels and healthcare
and smaller secondary and tertiary local real estate markets.
-
Loans with sizes within the category of $2Million and $4Million experienced the
highest average loss severity of 55.2%. This is due to lack of higher quality assets
collateralizing these mortgages and the costs involved in the foreclosure
proceedings, which tend to remain the same irrespective of the loans sizes.
Chapter 2
Literature Review
Commercial Mortgage Backed Securities: Overview and Market Development
The last 12 years have been witness to the phenomenal growth and capital market
acceptance of commercial mortgage backed securities (CMBS) as a preferred investment
product. Its market capitalization has increased to over $470 Billion within this period.
This financial innovation of the capital markets has relied on the use of mortgages on
commercial and multifamily real estate as a collateral for issuing debt instruments to
investors with varying risk/return profiles. These instruments, while similar in nature to
residential mortgage backed securities, have several fundamental differences.
The development of commercial mortgage securities can be traced to mortgage backed
bonds issued in the early 1920's. However, the contemporary market for commercial
mortgage securities arguably first developed in 1983. The initial issuance was restricted
to Eurobond financings or private placements, so as to avoid the regulations and
requirements of the Securities and Exchange Commission (SEC). During this period, the
primary source of commercial real estate funding were tax shelter syndicates, savings
institutions, commercial banks and life insurance companies. The Tax Reform Act of
1986 withdrew many real estate tax benefits and eliminated the tax shelter syndicates as a
major source of funds. Further, the severe devaluation of commercial property in the
early 1990's resulted in sizeable losses amongst thrifts, banks and insurance companies.
This led to a major retrenchment of lending activity by these traditional sources of
commercial real estate financing.
It was not until 1991, that CMBS once again emerged as an investment vehicle available
to those seeking to invest in commercial real estate, or, more appropriately, in distressed
debt. In the early 1990's, the Resolution Trust Corporation (RTC) was created by the
Congress to facilitate the bailout of the ailing thrift industry. The legislation providing for
the creation of the RTC was contained in the Financial Institutions Reform, Recovery and
Enforcement Act of 1989 (FIRREA). The mandate handed down from Congress was for
the RTC to liquidate assets it acquired from insolvent thrifts as quickly and efficiently as
possible. A large portion of the assets inherited by the RTC from the thrifts consisted of
commercial mortgage loans. Hence in a bid to monetize these portfolios, pools of such
mortgages were formed and securities issued. The RTC took advantage of the strong
demand in the market, issuing more than $17 Billion of securities between 1991 and
1995. By the end of 1995, when the RTC completed its liquidation effort and ceased
issuing CMBS, the strength of the demand for the RTC issues had piqued the interest of
the private markets as well.
Traditional commercial real estate lenders and Insurance companies returned to the
market as buyers of CMBS, and began to use CMBS to increase the liquidity of their
portfolios and to recapitalize their equity bases. This disinvestments strategy further
aligned with new risk based capital requirements as applied to regulated financial
institutions. The foundations of these rules were in the Basel Agreement of 1988. The
concept of risk-based capital addressed the credit risk involved on the assets held. In the
new regulatory environment, yields alone were no longer the best way to determine the
investments in assets. Hence for these institutions to hold investment grade CMBS in
their portfolios, rather than whole loans seemed to be the most appropriate investment
strategy. Securitization by these institutions surged in 1993, peaked in 1996, and then
declined as they began to exhaust the pool of seasoned and securitizable loans in their
portfolios.
This was the time when commercial conduits emerged to fill the void. The operations of
these institutions were focused towards specifically originating loans on commercial real
estate and subsequent securitization into CMBS pools. These loans originated with the
intent of securitization came to be known as "conduit loans". Today with a continued
surge of CMBS issuance, conduit loans or the mortgages on commercial income
producing properties continue to form the bulk of the collateral for these securities. Given
the continued growth of the CMBS industry in dollar amount and investor acceptance,
appropriate credit analyses of these deals is important. This is the role that is ably
performed by the rating agencies.
The national rating agencies (Standard & Poor's, Moody's Investors Service and Fitch
Investors Service) have served as vital intermediaries providing the investors with much
required assessment on the creditworthiness of each CMBS issuance. The rating analysis
and valuation of these securities obligates evaluation of financial statistics, underwriting
standards, seasoning levels, mortgage and property types, tenancy agreements and
proficiency of the servicer.
Depending on the pool size, rating agencies perform detailed analysis and site visits on
significant number of properties in the pool or a statistical sample. Larger loans are
assigned a shadow rating. Once the loan credit quality and servicer quality have been
assessed, an expected loss for the pool of loans is calculated. This sizing of the loss
expectation for any pool is eventually based on the probability of loss (the probability
that any particular loan will go into default, be foreclosed upon and liquidated.) and Loss
severity (The loss realized upon liquidation of that loan.) Hence a realistic consideration
of default probability is of significant importance in the rating process. Other than this,
various other structural, legal and property level considerations are evaluated to
determine the eventual credit support levels and the size of the subordinate first loss
piece, cash reserve or further credit enhancement provisions.
Commercial Mortgage Default Risk:
The main risk for commercial mortgage backed securities (CMBS) has been credit risk.
Other risks such, as prepayment have no yield implications on these securities. This is
very much unlike residential mortgage backed securities (RMBS), where prepayment risk
is the biggest concern. This insulation from prepayment risk can be attributed to lockout
structures or high prepayment and yield maintenance charges. Hence the risk of default of
the collaterized mortgages remains the main concern for these securities.
Default in commercial mortgages is the event marked by borrower failing to service the
debt payments for a given loan or a pool of mortgages not meeting its required debt
service over a predefined period, after which the loan is considered in default. The
economic cost of default can be incurred from not only delayed interest payments, but
also loss in principal balance upon the liquidation or foreclosure and other legal fees
required to bring about these proceedings.
The attempt to quantify the risk of default in commercial mortgages has been the focus of
numerous financial theorists. Titman and Torous (1989) explain commercial mortgage
interest rate premiums over the risk free rate using a contingent -claims model to default.
They relate the interest rate premium to the value of the option the lender gives the
borrower to be relieved of the debt by transferring the property's ownership to the lender
by default.
Corcoran (1989) uses an approach at correlating the commercial mortgage attributes to
the comparable corporate bond markets. This is achieved by using the property
capitalization rates and comparing them against corporate earnings/ price ratios to
determine commercial mortgage risk premiums priced for in the coupon.
These studies were some of the pioneering efforts to study the determinants of
commercial mortgage defaults with attempts on using alternative benchmarks to
subsequently define meaningful modeling of default risk. However they did not focus on
the historical losses from defaults.
Vandell et al (1989 and 1990) discusses mortgage defaults more directly, but does not
make a normative estimate of the yield impact of defaults. The first study focused on the
correlation of underwriting characteristics and the event of default on individual loans.
This was achieved by using a proportional hazards model estimation technique to
determine the correlation. The data used for the analysis contained a portfolio of 2,899
loans held in portfolio by a major life insurance company beginning with originations in
1962 and concluding in the third quarter of 1989. The second study (1990) dwelled upon
the theory of equity and default, modeled with a dispersion of property values resulting in
an event of default going forward in time.
One of the obstacles, for undertaking further efforts on estimation of default risk for
commercial mortgages had been the lack of availability of historical default, delinquency
and loss severity data. In the 1990's American Council of Life Insurance (ACLI) was the
only referenced source of information on default and delinquency data on the portfolios
of life insurance companies. However this data had several shortcomings due to its lack
of sophistication in recording.
Snyderman -1991, 1994; Snyderman, L'Heureux, Esaki - 1999; Esaki -2001
It was only in 1991, when Snyderman used data on 7205 loans on non-farm, nonresidential, commercial and multifamily properties above $1 million to calculate the
historical loan loss experience on these pools of mortgages. These loans were from seven
life insurance companies originated between 1972 and 1984. The study tracked them
individually from its origination date through payoff, default or end of 1989. The review
focused on a calculation of default risk as a function of loan age and the correlation
between default and the year of origination of the commercial mortgage. This study was
further updated with addition of more loans to the existing pools in 1994, 1999 and
finally in 2000 by Easki. Exhibit 1 further elaborates the results of this study, with a
comparative statement listing the change in these findings across the four studies.
Based on the initial examination, Snydennan found the average lifetime default rate of
the given portfolio to be 12.1%. This suggested that one of every eight loans in the
portfolio defaulted. This was however further updated to 13.8% and finally 16.4% in the
Esaki study (2000).
Further, Snyderman found the average default rate by years since origination to climb and
peak in the
3 rd
year with a default percentage of 1.6%. This however was finally updated
in the Esaki study to a climb starting in the 3rd year and peaking in the 7th year at 2.84%
by loan amount as shown in Table-1.
TABLE-i
Comparative Statement of Studies on ACLI loans
Snyderman Study#3
1999
Esaki Study#4
2001
Descriptions
Snyderman Study#1
1991
Snyderman Study#2
1994
No. of Loans
705Iloanfs (7oIginators)
10955 loans (8 originators)
15109 loans (85riginators)
16595 loans (8 Orginators)
Historic average yield
cost of defaults
31-52 basis points
50 basis points (average)
corresponding evluation of
impact of defaults on bond
structures (AAA to B)
with decrease insubordination
some BBB structure would be
winerable
-dependent on the assumptions undertaken for unforeclosed loans
197 2-1984
thnu' 1989
Origination period
Tracking time
1972-1986
thru' 1991
1972-1992
thru' 1997
1972-2000
thru' 2000
ACLI
ACL
ACLI
N.A.
N.A.
over $IM illion arereported inthis study
*commercial loans
Data Source
Property Type
ACLl
Non-farm, nonresidential com. 40-50% Office; 15-25% Retail.
10% Industrial; 10-20% Apt.
Mul ti-family.
10-15% other ( Hotel, Resort)
Average Loan size
A'g. aggregate life time
rate of default
$3Million
$3Million
$4Million
75% below $8Mill.
$12 Million
75% below $8Mill.
*12.1%
*13.8%( *18.3%)
16.20%
18 1%(min.of 10yr.of sea.)
16.40%
18.4%(min.of 10yr.of sea.)
37.70%
31.00%
of defauts asafunction of loan age
yettodefault, hence basethe projection on the observed pattern
asyounger loans
*biased
for the bias
updated
Aerage Severty of Loss
32.00%
36.00%
(1992-97 - 44%)
Loss on defaulted loans
not Ibreclosed
Default by Originator
Cumulative Default rate
by yr. of origination(highest)
Not known
Not known
N.A.
Statistical info. provided
Not known
Not known
N.A
28% (1986)
32%(1986)
No
Yes
Yes
9.3%(1977)
CMBS Invest. Grade reiew
No
No. of Liquidated loans
(entering foreclosure)
N.
46%(1972-1991)
60%(1992-1997)
59%(1992-2000)
Loan years with highest
default possibilities
1-3 years
1.60%
2-5 years
1.80%
3-7 years
2.00%
3-7 years
2.00%
N.A.
NA
South-Central - 27%
South-Central - 26%
*no details on the amortzing schedule of the loans
Geographical Distribution of:
Default rates (cumulatiely)
EXHIBIT-1: Average Timing of Default (by Loan Count): Snyderman Results
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
Yrs from Origination
Snyderman calculated the average loss severity on foreclosed loans, to be 32% across the
pool. However the number of foreclosed loans remained to be only 41% of all defaulted
loans. The remaining 59% loans, remained in delinquent state, became current again or
were paid off and had assumed loss severity of half of what was experienced on the
foreclosed loans. This simplified assumption was primarily made due to unavailability of
data on the loss severity and on eventual status of these remaining defaulted but unforeclosed loans.
The historical yield impact of defaults on commercial mortgage portfolio was measured
at 31 to 52 basis points. In the most recent Esaki study, this measure has been further
realigned to benchmark the performance of the portfolio considering the worst
origination cohort since 1972 and applying the average loss severity to determine the
impact of defaults on current bond structure. Esaki et al found that given the average loss
severity of 34% on the worst origination cohort of 1986 with 32% cumulative lifetime
default rate, the 1986 cohort would stand to loose 11% of its balance. This is well below
the current credit support for AAA CMBS. In 2001, AAA CMBS credit support was
mostly in the range of 15% to 25%. This however would change for BBB tranche, which
has been observed to have only 6% to 10% credit support. Given an event with 11% loss
in the balance, BBB tranche would stand to take losses on its principal.
Snyderman further reviewed the correlation between the changes in property values
affecting the probability of default. His findings revealed a significant affect of this event
given the movement in property values. The regression equation derived was
Default rate = 12.5%-0. 312Av
Where Av is the five-year cumulative property value change. This equation indicates that
given no appreciation, 21.5% of the mortgages are likely to default.
This attempt on quantifying the historical loss severity and economic cost of losses on
given pool of commercial mortgages was welcomed by the industry with wide
acceptance. CMBS industry participants have continued to use these findings as
benchmarks with appropriate modifications.
However, there still remain certain caveats to this study. This study uses loans originated
by 8 Insurance companies, hence to use these results for the performance evaluation of
conduit loans would be in effect equivalent to comparing apples with oranges. Conduit
loans stand to be structurally different from the life insurance company loans used for the
study. Life insurance company loans have been historically subject to conservative
underwriting due to their retention on the balance sheet. Conversely conduit loans are
originated for immediate securitization in pools of mortgages and are readily traded on
the secondary mortgage markets. But due to the infancy of the conduit loan and CMBS
markets, sufficient data on the losses was yet to be recorded.
Another relevant consideration would be the seasoning of the conduit loans. These are
loans, which have been predominantly originated and securitized in the last seven years.
They have yet to be subject to sufficient market stress conditions unlike the ACLI loans,
which have withstood 3 recessionary periods. This is appropriately displayed in the
substantial difference between the lifetime default rates.
A simplified assumption made by Snyderman has been on the loss severity of the unforeclosed loans. No data was available on the economic loss incurred on the defaulted
mortgage loans that subsequent to being delinquent became current or were paid off.
Hence given the loan was not foreclosed, the expected loss would be less than what is
experienced by the foreclosed loans. Hence an assumption on the loss severity for being
half of what was experienced on foreclosed loans was made. The eventual average loss
severity of the portfolio was stated to be 27% as against 36% that was recorded on the
other half of the loans, which were foreclosed.
Default risk in CMBS.
Given the success of the CMBS industry in the late 1990's, conduit loans have become
the bulk of the collateral for these securities. The findings in the Snyderman study are
still the much-referred economic loss evaluation benchmarks for pricing and structuring
these securities. However with new time series data on the performance of conduit loans
available with 3 leading data providers Charter, Trepp and Intex, the industry can look
upon further research on the losses as experienced in the CMBS collateral pools. It is
time to finally be able to compare apples for apples.
In the last few years, multitude of CMBS industry participants and the rating agencies
have undertaken such loss and default studies using data on conduit loans. Each study
however has been unique so as to focus on key aspects of default incidence on
geographical basis, correlation of key underwriting variables to default risk, performance
of various property types and the correlation of prevalent economic conditions in the real
estate property markets and the default incidence.
Fitch Ratings: 2001 CMBS Conduit Loan Default Study
Fitch commercial mortgage-backed securities (CMBS) conduit default study (2001)
analyzed 24,338 loans in 167 Fitch-rated transactions with an aggregate principal balance
of roughly $141 billion. These were newly originated loans in standard conduits, large
loan transactions, and fusion deals issued from 1993-2000. Default of a loan was defined
to be an event of 60 days or more past its debt payment or/and may have been transferred
to a special servicer due to an event of bankruptcy of the tenant or operator. Based on
Fitch's analysis of this loan population, 248 loans defaulted during that period, resulting
in a cumulative default rate of 1.02%. The study went further by identifying the status on
the defaulted loans and the losses incurred on each loan. It reported the return of 22% of
loans, back to current status after discussions with special servicer. These defaults had
been predominantly due to short-term cash flow problems. Of the remaining, 11% were
paid in full with the borrower refinancing the mortgage had stopped making payments
while the refinancing was being completed. Another 34% continued to remain delinquent
without any foreclosure proceedings and 14% were being foreclosed upon.
REO category assets were properties that needed to be leased or stabilized prior to
disposition. These were properties, which were mismanaged or had limited demand as a
property type. According to Fitch, 12% of the default pool consisted of such properties.
Further, 8% of the defaulted loans went through disposition with varying time periods for
liquidation based on the use of either DPO (Discounted Payoff) negotiated with the
borrower (taking an average disposition period of 12.9 months) or loans foreclosed
(taking an average disposition period of 21.8 months).
A further analysis of the pool and frequency of defaults by property types yielded results
which reflected the impact of the prevalent economic environment on the performance of
the various property types.
The final analysis for the eventual loss severity of the pool yielded an average loss
percentage of outstanding balance to be 20%, with a range of losses being between 1.2%88.6%. Also determined was the importance of the time to resolve each of these troubled
loans. In general the longer the time for resolving the loan disposition, the larger were the
losses.
The loss in the study were calculated as:
Gross Proceeds - Liquidation Costs - UPB at Default
Unpaid Principal Balance (UPB) at Default
Though comprehensive, this study had certain caveats. The period of 1993-2000, was a
time when the real estate markets were still in an up cycle and property values still high
in most markets. Hence the dispositions were mostly successful and were able to achieve
substantial recovery, thereby reducing the average loss severity. Towards the beginning
of 2001, the property markets were beginning to loose momentum with demand falling
and vacancy beginning to rise. Hence for loans that were in delinquency with bankruptcy
proceedings underway, had substantial exposure of going into foreclosure in unfavorable
conditions. Also the extended period spent in the bankruptcy proceedings implied the
advances being made by the servicer during this period. This would adversely affect the
net disposition proceeds and thereby raising the losses. The average loss severity of 20%
just did not seem to be convincing and truly representative of times ahead.
Fitch Ratings: 2002 CMBS Conduit Loan Default Study
Fitch realized this shortcoming and further updated this study in 2002. By now the
market fundamentals had clearly indicated the onslaught of a recession. The
delinquencies were on the rise and the losses being recognized were substantial. However
the conditions still remained far from what was experienced in the real estate downturn of
the early 90's.
In this study, Fitch analyzed approximately 28,000 loans in 195 Fitch rated transactions
with an aggregate principal balance of roughly $168 billion. This study added more than
4,000 loans to the previous 24,000 loans used in the earlier study. As expected, the
cumulative default rate climbed from 1.02% in the previous study to 1.8% in 2001-year
end. This rise clearly displayed the weakening conditions in the US economy during
2001.
The study evaluated the correlation of loan sizes and default incidence to determine that
loans with larger sizes were beginning to exhibit greater possibility of default and further
impacting CMBS transactions. The average loan size of a defaulted loan was found to be
5.4million, in line with the average loan size in CMBS deals.
Another significant result of this study was the identification of the highest vintage
default rates, which were seen to be in 1996, 1997 and 1998 transactions, ranging from
3.11%-3.55%. These were also the years with some of the highest issuance by loan
numbers. Hotel and Healthcare continued to be the property types, which displayed all
time high default rates. Hotel industry was clearly affected by the aftermath of the
Sept. 1 1 th with a substantial surge in defaults. However it was Healthcare, which exhibited
the highest default rates by principal. Other than these property types, the performance
evaluation of the other too was performed to exhibit the affect of the real estate downturn.
Though comprehensive, emphasis of this study shifted from an effort to quantify
economic losses on the pool to more descriptive statistics on the default incidence by
property type and the seasoning effect. Most of the findings provided the evolving
perspective with considerable changes in the property markets within a short span of a
year. However this study failed to precisely indicate the loss severity on the loans
foreclosed upon or the others that remained delinquent. Due to its comprehensive nature,
the descriptive statistics employed for the investigation were not able to completely
uncover any distinct insights on the default incidence.
Moodys Default Study: CMBS Delinquency Report Card: From A to Incomplete
Moody's Investors Services undertook an exhaustive effort on evaluating the incidence of
default among conduit loans. This study recognized the importance of using appropriate
and relevant data for studying delinquencies in CMBS deals. Hence data on 40,000
conduit loans with a total current balance of approximately $240 billion were used for the
study.
The definition of default was once again taken to be an event of 60 or more days past due
on its debt payments. A loan to have experienced an event of bankruptcy, becoming REO
or going into foreclosure proceedings during the life of the loan was also considered an
event of default.
Similar to the Snyderman study, this effort too focused on analyzing delinquency at the
loan level in search for patterns, but focused on analyzing and reporting cumulative
delinquency unlike the Snyderman study, which used cumulative by cohort. Some of the
analytical categories used for investigating the delinquency patterns were by State, size of
city, economic sector, property type per MSA, size of the loan and fixed vs. floating loan.
An important understanding that emerged from this study was the predominant role of
event risk, borrower issues and corporate credit weakness in the incidence of default.
These loans were not seasoned enough to have undergone any distinct market stress
conditions, hence the delinquencies remained low due to issues that were idiosyncratic or
property level in nature. Also the incidence pattern observed was the "lumpy" clustering
of delinquencies among certain groups within a category rather than being evenly
distributed in the given category.
Some of the other significant findings of this study were:
The areas of high concentration of loans within any category, did not necessarily
-
exhibit higher default incidence.
-
Smaller towns that were not large enough to be an MSA had larger number of
defaults. This pattern prevailed across all property types.
-
Florida had the highest default rates by loan balance and number of loans. Hotels
and healthcare though a problem, however were not the only property type to
contribute significantly in the state.
-
Based on a broad evaluation of the CMBS pool, certain states like California and
Nevada and International gateway cities (New York, San Francisco, Washington
DC) were over represented relative to their share of employment.
-
A performance evaluation of various property types was undertaken to find the
Hotel sector continuing to be affected by the economic downturn and traces of
stress on other property types too.
-
Based on the descriptive statistics of loan sizes and its correlation to default
incidence, it was found that largest and smallest loans demonstrated the strongest
performance. This was justifiable due to the structural nature of these loans.
Larger loans tend to be conservatively underwritten with lower leverage levels.
While the small loans are generally on owner occupied properties on recourse
basis, minimizing the chances of default.
-
Floating loans were found to have larger delinquency rates by number. This could
also be attributable to the market risk of interest rate fluctuations.
-
The study also addressed the shortcoming of data insufficiency and small count of
defaulted loan universe for further research.
The study served to provide an updated commentary on the delinquency status in the
industry with few significant findings across certain descriptive statistics. This study
though intensive in covering significant aspects and categories of the CMBS pool, lacked
once again the empirical exploration of the loss severity on the liquidated loans. Also no
specific attempt was made towards identifying the liquidation status on the defaulted loan
pool. The limited nature of this study could have been due to the lack of consistent data
reporting on delinquent loans or the eventual focus it expected to achieve.
Further to this, numerous other default studies undertaken by various industry
participants have kept an updated outlook on the movement of delinquency rates.
However, substantially different data samples in terms of size and source used, are unable
to provide a transparent and industry wide accepted default reporting standard.
Chapter 3
DATA & METHODOLOGY: DATA OVERVIEW
To evaluate default incidence, the study uses data on individual records of 78,680 loans
with a total original principal balance of approximately $470 Billion included in 801
CMBS deals. This was made available from Intex, a leading provider of data on defaults,
cash flows and other data fields covering the time series observations, performance and
underwriting attributes of each individual loan record. Intex gathers this information
based on monthly servicing company remittance reports. Given the scope of this study,
the variables used, relate to determining the performance and the underwriting attributes
of each loan to have defaulted. The data contains loans with originations from1959 to
June 2003. Exhibit-2 presents details on the various variables used for the evaluation of
default incidence.
Unlike the ACLI data on 16,500 loans used in the Snyderman and Esaki default study,
this CMBS database of 78,680 loans is larger and has a broader representation by
originators, and across numerous property types. Also a more detailed review of property
performance is represented by the time series data on each individual loan, enabling to
track the changes in the loan performance and subsequent default incidence over the life
of each loan.
Caveat: All loans included in the universe do not tend to homogenously represent legible
information on each variable. Hence for certain descriptive statistics the operational
universe might be slightly smaller because of data inconsistency. This has been
appropriately addressed in the summary of each result, which uses the relevant variable.
This is to avoid any bias in the resultant statistical observations.
EXHIBIT-2
Description of Variables used for Descriptive Statistics
SYMBOL
DATA FIELDS
CMBS Deal Name
DNAME
Loan ID in CMBS deal
LOANID
Loan origination date
ORGDATE
Loan maturity date
MATDATE
Loan paid down date
PDDATE
Original Loan Balance
ORIGBAL
LTV at origination
ORGLTV
City
CITY
State
STATE
Region
REGION
MSA
MSA
Originator
ORIGINAT
Property type
PROPTY
Tenant1
TENT1
Tenant2
TENT2
Tenant3
TENT3
Special Servicer
SPECIALSER
Current Balance
CURRBAL
Age
AGE
Maturity Term
MATTERM
Amortization Term
AMORTERM
Remaining Term
REMTERM
Coupon
Gross
GCOUPON
Report Date
REPDATE
Losses
LOSS
LOSSLASTBAL Loss Last Balance
DELINSTATUS Delinquency Status
CMSA Status
CMSASTAT
Current LTV
CURRLTV
DSCR
DSCR1
DSCR As Of
DSCR1DATE
Appraised Value
APPVAL
APPVALDATE Appraised Value As Of
Is Bankrupt
ISBKRPT
FIRSTDEFDATE First Default
REO/Foreclosure Date
REOFCDATE
ARM Index
ARMINDEX
EXPLANATION
States the CMBS deal name the loan is securitized in
An identifying variable in a given CMBS pool of loans
Date of loan origination
Date of maturity of the loan
Date of complete payoff of the any remaining balances
Loan Amount paid to the borrower at the time of origination
LTV at the time of origination
City the property is located in
State the property is located in
Broad description of the area where property is located.
MSA the property is located in
Origination entity(by name)
Property Type classification
Tenant Name#1
Tenant Name#2
Tenant Name#3
Special Servicer for the CMBS deal
Current Balance as per the last Report Date
Age of the loan in months
Maturity period of the loan
Amortization term of the loan
Remaining period of the loan
Interest rate payable
Last date reported
Total losses on the loan (net of interest, principal loss, legal fees)
Current balance of the loan at liquidation (amortized Amount)
Status of the loan
Status of the loan as per CMSA reporting schedule
LTV as per the report date (amortized balance)
DSCR as reported on specified date (not original DSCR)
Date of report of DSCR
Appraisal Value of the property
Last date of the appraisal
Bankruptcy Status of the loan
First date a default event was recorded
Date the property goes into foreclosure/REO
Adjustable Rate Mortgage (Floaters) details
ASSUMPTIONS
Assumptions for each default study undertaken have been made based on the limitations
on the data availability for specific fields and the outlook of the industry on the issue in
question. Snyderman et al defined default as 90+ days delinquency event, while Moody's
and Fitch further updated this assumption to 60 days or more. Based on the focus of this
study, which is to evaluate the loss severity on the given loan universe, following are the
assumptions made:
1. Default will be an event when a loan is 60 days or more past due on it debt service
payment and is subsequently transferred to the Special Servicer.
2. All defaulted loans that became current have been considered to be in default.
3. CMSA status exhibit below further summarizes the various categories under
which the loan could be adjudged in default based on the assumptions for this
study
EXHIBIT-2B: CMSA Reporting Status on defaults
0
A
B
1
2
3
4
5
7
9
Current
Grace Period
< 30 days
30-59 days
60-89 days
90+ days
Performing, past balloon date
Non-performing, past balloon date
Foreclosed
IREO
Current
Current
Current
Current
Default
Default
Current
Default
Default
Default
Status
Status
Status
Status
Status
Status
Status
Status
Status
Status
4. All fixed and floating rate loans have been included in the final default count.
Floating rate loans are benchmarked to the prevalent interest rates and are
exposed to the systematic or market risk. They have been included to provide a
broader representation of the different loans within the pool and to further
increase the loan count for more statistically significant results.
5. To provide a more accurate representation of the default incidence in the loan
universe, the lifetime default rates are calculated using the number of loans in
default and the outstanding principal balance.
6. In accordance with Snyderman et all, the loss severity on defaulted commercial
mortgage is determined based on the following:
Severity of Loss = Property Sale proceeds + property revenue - principal owed
upon default - Foregone interest- Expenses
Though consistent with the above-mentioned severity of loss calculation, the data
used different variable allocation of the above mentioned data fields. This loss
figure has been represented as an aggregated expense along with a loss in the
current balance. This point is further elaborated in the documentation of the
results.
METHODOLOGY
Methodology: Default Incidence
The objective of this thesis study has been to evaluate the default incidence using various
descriptive statistics based on origination cohort, loan sizes, property type, vintage or
seasoning effects, origination entities and regions. The methodology uses the total
originations by number and principal balance comparing them against the incidence of
defaults to determine the lifetime default rates by loan count and principal balance. Given
that commercial mortgage defaults do not occur smoothly over their lifetime, hence each
loan is analyzed over its lifetime. This further helps to provide an interpretation of key
real estate market fundamentals and their correlation to the default incidence.
In having done this, the eventual emphasis is to determine the average loss severity and
yield cost of defaults on the given pool of mortgages across various primary markets.
Emphasis would also be on measuring the performance change and probability of default
of the given mortgage with seasoning based on the historical default incidence pattern. To
achieve this, the various descriptive statistics are used to determine the imputed lifetime
default rate by the following property and loan characteristics:
-
Origination Cohort
-
Property Type
-
Loan Size
-
Region
-
State
-
Originators
The results are to be further analyzed for insights on the typical characteristics of defaults
based on the historical performance of the commercial mortgage industry over the period
of the origination of the given loans in the pool.
Furthermore, the historical default patterns are observed to determine the average timing
of default by loan count and principal balance. This descriptive elaborates upon the
probability of an event of default happening in the life of the loan. The results are to be
then further evaluated against the findings in the Snyderman study. This is to determine
any distinct structural differences between the Life insurance company loans and
"conduit loans" used for this study
Methodology: Loss Severity
To determine the average loss severity on the defaulted loans in the loan universe, the
foreclosed and defaulted loans were to be isolated and further evaluated. The losses were
representative of loss in principal, foregone interest payments, legal fees for foreclosure
etc. These loss experiences were then stratified across various key characteristics like
state, originators, origination cohort, loan sizes, property type and special servicer.
Based on the assumption for Loss Severity, it would be important to have legible entries
in the data set providing information on the actual losses incurred due to any default
incidence. The total universe of 78680 loans though large, however was limited in
providing explicit loss information on defaulted and foreclosed loans. Given a total
default count of 2374 loans, we had legible loss information on only 473 loans with no or
little information on the remaining 1901 loans. The losses on the 473 loans were not
necessarily representative of losses due to foreclosure only, but covered a wide spectrum
of default stages such as Real Estate Owned (REO), Bankrupt, 60 day or 90+ day
delinquency.
Given the presence of defaulted loans in the pool in various stages of default, a further
stratification of loss severity by default stages (eg. REO, Bankrupt etc.) was calculated.
This individual loss experience was to be further used for determining the loss severity
status of other unresolved loans.
Upon further investigation of the unresolved loans, it was also found that remittance
reports submitted to the trustee or master servicer used a comprehensive CMSA reporting
code for liquidations, prepayments and loans that were in various stages of workouts.
(See Exhibit-2C). This additional CMSA information on the 1901 loans provided a more
descriptive status on these loans, helping to make more reasonable assumptions on the
loss severities experienced in this pool.
EXHIBIT-2C
CMSA Reporting Status on Liquidation/Prepayments
1
2
3
Partial Liquidation
Payoff prior to maturity
Disposition
4
Repurchase
Full payoff at Maturity
Discounted payoff
Liquidation
Payoff with penalty
Payoff with yield maintenance
Curtailment with penalty
5
6
7
8
9
10
CMSA Reporting Status on Workouts
0
1
2
3
Not Applicable
Modification
Foreclosure
4
Extension
5
Note Sale
6
DPO
7
REO
8
Resolved
9
Pending return to master
10
Deed in lieu of Foreclosure
11
Full Payoff
12
Reps and Warranties
13
Other or TBD
Bankruptcy
All loans under the CMSA prepayment/liquidation code of 4,5,8,9 and CMSA workout
code 11 were assumed to have experienced 0% loss severity. Given, these codes implied
the payoff of the loans without any stated losses to the lender; it was appropriate to make
this assumption. This assumption further helped resolve the loss status on 138 loans with
current balance of $500,274,211.
As per Snyderman et al (1991, 1994), a simplified assumption had been made on the pool
of defaulted loans with no loss data. The loss severity for these unresolved loans was
assumed to be half of what was experienced by the foreclosed loans. The rationale
justifying this simplified assumption was the expectation of lower losses on unforeclosed loans. However considering the objective of our study we found it relevant not
to use this assumption as these losses were representative of a large varying number of
skipped interest payments, discounted payoffs, partial liquidation or curtailment
Given the measurement of losses experienced by the 473 defaulted loans in various
categories, these typical losses were assumed to provide similar losses for the unresolved
loans too.
Based on the available loss information on the 473 loans, the average loss severity across
this limited pool was found to be 45.66%. This is truly representative of the losses due to
lost interest payments, loss in principal, legal and other foreclosure fees etc. This figure
implies a loss of 45.66% on the outstanding principal balance of the 473 loans at the time
the losses were acknowledged. Further stratification of these losses across various stages
of default are illustrated in Exhibit-2D
EXHIBIT-2D
LOSS SEVERITY OF 473 RESOLVED LOANS
Categories
# of Loans
Loss Severity
Portfolio Wt. Cumulative Loss
Severity
4
121
16.30%
36.82%
0.008
0.256
0.14%
9.42%
61
38.06%
0.129
4.91%
Real Estate Owned
Bankrupt
Other
196
88
3
58.31%
36.99%
24.18%
0.414
0.186
0.006
24.16%
6.88%
0.15%
TOTAL
473
60 Days
90+ Days
Foreclosure
45.66%
Of the 1901 unresolved loans, 138 were assumed to have a 0% loss severity. These loans
had been reported as per the CMSA Prepayment/Liquidation and Workout code by 4, 5,
8, 9 and 11 category. (Please refer to Exhibit-2E)
EXHIBIT-2E
Loans with Entries on CMSA Prepayment/Liquidation/Workout Code
Prepayment/Liquidation Code
Loans
Workout Code
4
5
8
9
7
55
19
20
11
Loans
The remaining 1763 unresolved loans were similarly stratified across various stages of
default and assumed to have experienced same loss severities for each stage as recorded
for the 473 loans with actual loss records. (See Exhibit-2F)
EXHIBIT-2F
LOSS SEVERITY OF 1763 UNRESOLVED LOANS
Categories
# of Loans
60 Days
Loss Severity Portfolio Wt. Cumulative Loss
Severity
348
16.30%
0.197
3.22%
90+ Days
Foreclosure
Real Estate Owned
Bankrupt
Other
836
157
224
170
28
TOTAL
1763
36.82%
38.06%
58.31%
36.99%
24.18%
0.474
0.089
0.127
0.096
0.016
17.46%
3.39%
7.41%
3.57%
0.38%
35.43%
Chapter 4
Observations and Results of Descriptive Statistics:
Review of the Loan Population:
The defined date for reporting the status of all loans in the population is assumed to be
June 30, 2003. Upon stratifying the population across various loan and default
characteristics the following observations and deductions were made on each category:
Default by Origination year
As seen in Exhibit-3, the majority of the population by loan count and principal
balance consists of loans originated after 1992, with a surge in originations beginning
in 1995 and peaking in 1998
EXHIBIT-3: Total Loans by Oriination Cohort
Count
$ InMM
1
O riginalBalance
-- 4- LoanCount
90.000
16000
80,000
14000
70,000
-12000
60,000
10000
50,000
8000
40,000
6000
30,000
4000
20,000
2000
-
10,000
0,
-
NC,4 0,
t
(0
t-
0
0
NC1 C)
LO
r-
00
O0)
N
N
-
N
M
0
Yr. of Origination
-
This surge was indicative of the capital market acceptance of CMBS as an investment
product. Other factors contributing to this origination binge were the continued
downward movement of interest rates from 1995 onwards with the exception of 1999
and 2000(see Exhibit-4), formation of a liquid secondary market and the significant
contribution of the rating agencies for providing prudent credit analysis of these
securities.
-
Another trend highlighted in Exhibit-3 is the combination of a fall in the loan count
accompanied by a rise in the principal balance for origination cohorts of 1998 and
onwards. This signifies the origination of larger loans representing the growing
confidence of the capital markets in commercial real estate debt.
EXHIBIT-4: Interest Rates by Year
Short Term rates
%/,-+
-U-Long term rates
9
8.
7.
6
5
4
3
2
1
990
991
1992
993
1994
995
996
997
998
2000
2001
2002
2003
YEAR
Interest Rates
-
999
As per the assumptions and the definition of default for this study, a lifetime
default rate by loan count and principal balance was observed to be 3.02% and
2.85%. That is, three of every hundred mortgages defaulted in the loan
population. Given the lack of sufficient exposure to market stress conditions, the
default rates in this pool are far lower than those observed in the Snyderman's
study of ACLI loans. ACLI loans were exposed to three distinct periods of
economic stress followed by a significant fall in property values, leading to all
time high default rates of 12.1% to 16.4%.
-
As of 3 0
th
June'2003, there were 1017 defaulted loans. However to better evaluate
the performance of these loans it was critical to study them longitudinally across
their lifetime. Hence, the default count was updated to include all loans to have
ever faced stints of delinquency. This implied that of the 2374 loans that defaulted
in the pool, 1357 loans or 54.2% of the defaulted population, were resolved and
came back to being current or were paid down.
-
As seen in Exhibit-5, 1995 cohort displayed the highest lifetime default rate by
principal balance of 7.95%, with 50% of the loans in the cohort defaulting within
first 3 years from origination. This phenomenon represents the possible lapses in
the underwriting standards for evaluating the quality of the collateral or
aggressive underwriting. This was also the period, which marked the return of the
traditional commercial real estate lenders. Furthermore, given the appetite of these
institutions for Class A properties, the conduit originators were left the remaining
lower quality class B and C properties. These properties, which lacked sound
fundamentals eventually contributed generously to the surge of defaults within a
few years from origination.
EXHIBIT-5: Lifetime Default Rate by Oriination Cohort (Principal Bal. & Count)
%
0 Lifetime
9.00%
Def.by
Count
7.95%
8.00%
E Lifetime
Def.by
7.00%
5.99%
Amount
6.00%
23%
4.62%
5.00%4
4.01
4.00%
w10% 2.95%
250%
3.00%
2.20%
3.23%
37%
2.18%2.05%
1.4-
2.00%
1.00%
0.95%
--1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
10.1%
2002
Yr. of Origination
-
The low lifetime default rates as observed in the most recent cohorts (2001 &
2002) represents lack of sufficient seasoning. These newly originated loans have
yet to experience a real estate downturn. However with the continued economic
uncertainty and deteriorating real estate fundamentals, these figures should be
expected to rise.
-
As seen in Exhibit-6, the loss severity as expected is the highest for loans
originated
in 1995 and 1997, further consolidating claims on possible
underwriting lapses during this period. These were loans which defaulted within 3
to 5 years from origination, which was a period marked by rising property prices.
Higher severity in such markets can be attributed to lack of sufficient quality of
the asset being liquidated. This observation also implies upon possible correlation
of high lifetime default rates and loss severity for the given cohort.
EXHIBIT-6: Loss Severity by Origination Cohort
% Loss Severity
70.00%
e
1992
1993
1994
1995
1996
1997
1998
1999
2001
2000
Yr. of Origination
Defaults by Loan Size
As observed in Exhibit-7, nearly 80% of the loan population is composed of loans
with principal balance under $8 Million. This is representative of the typical sizes of
conduit loans originated for the CMBS pools.
EXHIBIT-7: Originations by Loan Sizes (Principal Bal. & Count)
-
Count
25,000 -
-
-
-
-
-
-
-
-
-
-
--
-
-
----
*---
--
Loan Count
OrIgInalBalance
- -- 80.000
-70.000
o tOA
20,000-
80,000
50,000
15,00
15,000
0 40,744
0K
40.31
40,000
2 35A17
10,000
k 312
0
22,000
-20,000
5,000 -
a
urs
a. 2751
10,000
Ms
<IM
1M-2M
2M-4M
4M-8M
-=
25
9
200M-500M 500M8M-12M 12M-16M 16M-20M 20M-50M 5OM-100M100M-200M
1300MM
71
0
MORE
LoanfSzes
Upon further evaluation of the population in this category, the following observations
and deductions were made:
-
Loans with principal balances between $2Million and $4Million are twice more
likely to default, and with a higher loss severity than loans under $1Million. This
could be attributed to the ownership characteristics of the properties that serve as
collateral for these loans. In most cases with mortgages under $lMillion,
properties tend to be owner occupied with recourse terms in the event of a default.
Conversely, loans between $2Million and $4Million are on larger properties with
possible non-recourse terms which explains the higher default incidence in this
category.
-
Also as illustrated in Exhibit-8, the high lifetime default rates tend to cluster
around the $2Million to $8Million categories. A gradual fall in the lifetime
default rates with increase in the size of the loans is representative of better due
diligence and use of more conservative underwriting standards for larger loans.
EXHIBIT-8: Lifetime Default Rates by Loan Size (Principal Bal. & Count)
0 Lifetime Def.by Balance
i1 Lifetime Def.by count
4.50%
4.00%3
3.55%
3.44%
3.50%
30322%
03.00%
2.2%
240%
2.50%
.00%
%
2.00%
1.50%
080%
1.00%
0.50%
000%
<1M
1M-2M
2M-4M
4M-8M
8M-12M
12M-5M
IM-20M
20M-50M
50M-1)0M IDOM-200M200M-500M
500M-
0.00%
MORE
loan size
-
Another interesting observation was the increase in the origination of loans over
$20Million after year 2000. This spurt accounted for nearly 60% of all the loans
originated in this loan size category. This is once again representative of growing
confidence of the capital markets in commercial mortgage backed securities as an
investment vehicle for investing in commercial real estate.
-
A distinct fall in lifetime default rates for loans over $50Million is again
illustrative of better underwriting standards and effective due diligence.
-
Consistent with the high lifetime default rate, the $2Million to $4Million loan size
category displayed the highest loss severity of 55.26%. (See Exbhit-9). The
incident of highest loss severity of 148.58% on an individual loan was
experienced in the same category.
EXHIBIT-9: Loss Severity by Loan Size
<1M
1M-2M
2M-4M
4M-8M
8M-12M
12M-16M
16M-20M
20M-50M
50M-1OOM
1OOM-200M
200M-500M
500M-1300MM
MORE
T' AL473
85
132
136
72
21
15
5
7
0
0
0
0
0
31,434,622
188,545,036
371,377,616
398,644,188
201,045,262
202,258,031
82,855,000
176,952,355
0
0
0
0
0
40.92%
36.35%
55.26%
52.31%
109.51%
144.68%
148.58%
122.15%
47.30%
44.20%
51.30%
18.80%
0.00%
0.00%
0.00%
0.00%
0.00%
101.49%
76.02%
73.56%
77.83%
0.00%
0.00%
0.00%
0.00%
0.00%
14,653,112,110i
Contrary to the conventional wisdom on larger loans, it can be seen that smaller
loans tend to display higher loss severity than larger ones. This is suggestive of
better liquidation proceeds due to the higher quality of asset in the case of larger
loan sizes. Another factor at play is the foreclosure costs, which effectively
remains unchanged for smaller size loans.
Default by Property Type
EXHIBIT-10: Originations by Property Type
$ in MM
El Principal Bal
.- 0
Loan Count
25000
140000,
120000
20000
100000
15000
80000
60000
10000
40000
5000
20000
0
V"
Healthcare
I
Hotel
I-U
I
Industrial
9il
,
lWCA
Manufactured Multifamily
Housing
,
M
Office
,
,
Other
=0
Retall
,
miln
Self Storage
The loan universe was found to have a substantially diversified mix of property types.
Upon evaluation of default incidence in the loan population, the following
observations were notable:
-
Multifamily contributed to the pool in loan count with a 30% share, while Retail
stood second but out numbered others in principal balance. This is also
attributable to the typically larger size loans originated for retail. On a cumulative
basis, the core property types, retail, office, multifamily and industrial contributed
significantly with a 70% share in the universe.
-
The average loan sizes for Hotel/Retail/Healthcare and Offices stood to be much
higher than on multifamily loans. This is observed in the defaults too, where the
number of loans in default distinctly stands high for Multifamily but is not
proportionately represented by the principal balance in default.
-
Hotel and Healthcare continued to contribute significantly with substantially high
lifetime default rates of 11.9% and 15.23%. (See Exhibit-1 1). This being mainly
attributable to the fallout after the September
11 th
for the Hotel sector and
operator bankruptcy issues for Healthcare industry. These are the only sectors in
the population; to have been subject to un-diversifiable systematic and event
oriented risk conditions, rather than only property level risks that is idiosyncratic
in nature.
The empirical determination of the market risk component of the overall risk and
its contribution of the given lifetime default rates for the affected property types is
beyond the scope of this study, however provides a prudent and thoughtful
question that could be the focus of further research.
EXHIBIT-11: Lifetime Default Rates by Property Type (Principal Bal. & Count)
*
a Def. Rate by Loan Count
---
16.00%
1-
-
----------
---
a Def. Rate by Prin.Bal.
-
- - --
- - ---
-
14.00%
12.00%
n90
10.00%
8.00%
6.00%
3.60%
4.00%2
242
244%
0%
.%
2.00%
0.79
0.00%
Healthcare
Hotel
Industrial
Manufactured
Housing
Multifamily
Office
Other
Retail
Self Storage
Property Type
Retail, surprisingly displays the largest number of defaults by number; however
this does not get reflected with higher principal balance in default. This indicates
the presence of large number small-sized loans on un-anchored properties. (See
Exhibit-12)
EXHIBIT-12: Defaults by Property Type (by Principal Bal. & Count)
Count
..
700 ---....
---
---------
EE Default - Loan Count
~~~~--
-~~~-..................-
600
-
-a-
Default - Princ. Bal
in $MM
3..........
3000
........
5e8
,-2500
500
/
400
2000
/451
.1500
/
300
256N
1000
200
147
1500
1 0
0
Healthcare
Hotel
Industrial
Manufactured
Housing
Multifamily
Nos. of Defaulted Loans by Property Types
-
Office
Other
Retail
Self Storage
Property Type
An interesting observation was also on the increase in the number of retail
defaults in the last one year. This displays the increasing preponderance of market
stress on property types that have held up well through this recession. The
consumer has continued to contribute to the recovery, however given the jobless
characteristic of the recovery; the retail sector is now beginning to experience its
share of market stress that is being appropriately reflected by the rising number of
defaults.
-
In the real estate industry, healthcare has been always characterized as a non-core
property type with exposure of policy risks. Contrary to this belief large loans
with substantial leverage were originated on healthcare properties. The average
LTV of the loans originated was 71.2%, with a high of 106% and low of 30%.
These excesses and oversight of the originators on the Wall Street for over
leveraging these transactions across non-core property types is reflected in the
high lifetime default rates by balance.
EXHIBIT-13: Loss Severity by Property Type
%80.000/
70.00%
60.00%
50.00%
40.00%
30.000/0
20.00%10.00%-
Healthcare
Hotel
Industrial
M anuactue
M ultiferrily
Office
Other
Retall
Self Storage
Property Type
As illustrated in Exhibit-13, Healthcare as expected, continued to dominate the
loss severity experiences with an average of 72.81%. The alarmingly high loss
severity is also attributable to lack of competitive acquirers for specialized
distress properties in the case of healthcare. Hotel followed the trail with an
average loss severity of 55.24% on all foreclosed loans. Manufactured housing is
highlighted due to the use of a small sample set for this analysis. Interestingly,
once again we can observe the positive correlation between high lifetime default
rate and high loss severity. This was previously observed amongst origination
cohorts too.
Defaults by Originator
This study further looks to evaluate the contribution of certain prominent industry
participants. The focus however has been to assess their individual performance based on
the propensity of default experience in their portfolios. Upon initial statistical
stratification, conduit operations of Investment banks accounted for nearly 62% of the
defaulted loans (by Principal balance) in the population with the highest lifetime default
rates. This intrinsically identifies the aggressive underwriting characteristics of these
institutions. These loans originated mostly with the intention of immediately selling
through securitization rather than holding on balance sheet are truly representative of the
majority of today's CMBS collateral. Finance companies and Commercial banks were the
next in line to contribute significantly to the default pool. Expectantly, it was seen that
Life Insurance companies displayed the lowest lifetime default rates. This is consistent
with the industry belief on the conservative underwriting standards used by the life
insurance companies for originating loans to be retained on the balance sheet.
Certain observations on the specific Originators were:
Column and Merrill Lynch had the highest lifetime default rates amongst the 20
-
largest originators by principal balance and loan count. (See Exhibit-14). This is
representative of substantial exposure of these originators to under performing
property types like hotel, healthcare etc. Also regional concentration and adverse
selection of class B and C properties as collateral were the factors responsible for
high lifetime default rates.
EXHIBIT-14: Lifetime Default Rate by Originators (Principal Bal. & Count)
% Lifetime def.byCount
1 Lifetim def.by P rincipal BIa
e
7.00%861
.26%
6.00%
5.34%
54A3%
5.00% 3.97%
4.00%
362%
3-14%A
3.07%
3.00%
2.00%
00%1
.1%
12.
1.00% -
0.00%
COLUMNLEHMAN GACC
PRUDENTIAL
MIDLAND
MERRILL
GOLDMANECORE CITICORP VELLB BEAR OREENWCH
GECAPITALDEPOOTOR
MORGAN
NOMURAMVAC W1CHCAIA
OF JPMORGA-N
BANIC
FARGO
LYNCH BACHB
AMERICACHAM
Lifetime default rate by Originators
STANLEY
SIEARNS
Originator
-
Upon further realization of these losses by means of foreclosure, it was again
Column, which experienced the highest loss severities. (See Exhibit- 15).
EXHIBIT-15: Loss Severity by Originator
Count
50.00%.
50.00%
60.00%--
-
-
-
-
-
-
-
---
-
7
-
-----
s7.24%
57-79%
59.82%
7
52.07%49.98%
50.00%
.
--
- -
-
d46
--
5
3
. 4%r
300.00%
40.31%
40.34%
41.61%
40.00%35.15%
30.00%
20.00%
10.00%
-
-
14.71%
--
-
I-
-
0.00%
Originator
Default by State
Evaluation of the default incidence patterns on State basis displayed a wide variation. The
States with most originations did not necessarily contribute significantly to the default
pool. Given the primary market status, California, New York, Texas, Florida and Illinois,
accounted for over 40% of the loan population in principal balance (See Exhibit-16).
EXHIBIT-16: Originations by State (by Principal Bal. & Count)
1
n
70,000 . - --
-
-
-
- --
- -
- ~
- -~~----
~ - ~ -
Principal Balance
Loan Count
-
~
-
--
14000
sgo
12000
60,000
50,000
-10000
.8000
40,000
30,0006000
25,121
4000
20,000
13,440
00
1275
10,000~,89
......
,...2
-.
CA
N
1313
TX
FL
NJ
9
L
VA
MA
PA
AP12
_'7.830
-2000
9,5
MD
GA
IVM
AZ
OH
NC
State
However Florida was an exception to this, and displayed alarmingly high lifetime
-
default rates by loan count of 5.16%. The other States to have exceeded their
share of defaults were Ohio, Louisiana and North Carolina (see Exhibit- 17) These
States were representative of secondary and tertiary markets with soft local
economies.
-
Contrary to the observance of high number of defaults in California, the lifetime
default rates remained to be lowest in the pool. New York experienced a similar
fate and was another out-performer. This was attributable to the primary market
status and substantially higher number of originations.
EXHIBIT-17: Lifetime Default Rate by State (Principal Bal & Count)
%LifetimeDef byCount
LifetimeDef byPrncipal
7.00% -
6.00% 51%
476%
5.00% -
43
4.00%
3
3.59%
434%
33%
3.71%
3.46%
7
2.6%
3.00%
2.55%
2.3
2.00%
1.00%
0.00%-
CA
TX
NY
FL
MA
AZ
IL
GA
OH
NJ
WA
VA
PA
MI
State
Lifetime Default rate by Count & Balance
Georgia, Illinois and Texas were amongst the other well-represented States in the
pool by loan count and principal balance to exhibit high lifetime default rates.
Given the prevalent adverse market conditions since 2001, these markets are now
starting to contribute significantly to the default pool.
Georgia in particular
displayed high lifetime default rate by loan count of 4.76%. This significant
contribution was emphasized due to a large group of loans on Hotel properties
representing over 30% of the complete pool of defaulted loans by loan count and
principal. Retail and multifamily further contributed with 43% on an aggregate
basis.
However for Texas, it was multifamily which contributed most
significantly in the default pool with a representation of over 36% in the defaulted
pool by principal balance and 41% by loan count.
Upon further evaluation of defaults in other State's based on the exposure to
various property types, no distinct relationship could be determined. Each State
displayed a distinct weakness in performance for particular property type. This
effect further consolidates the belief on the diversity of local economies and their
varying affect on property markets.
50
Kansas, Michigan, Utah, Louisiana and New Mexico experienced the highest loss
severities on loans, which were foreclosed upon. This tends to reaffirm the
probability of higher losses for properties located in secondary and tertiary
markets. Also an important consideration is the overall state of the property
markets at the time of liquidation, which has been the period of 2000 and 2003 for
these States. Given deteriorating real estate fundamentals and prevalent market
stress across the most regional markets during this period, the high loss severities
were imminent.
Furthermore, New York, Illinois and California, which are identified as the
primary markets, displayed low levels of loss severities on loans, liquidated.
Exceptions to this are Florida and Texas, which again experienced significant fall
in property values given rising vacancies and softening rents across most property
types. (See Exhibit-18)
EXHIBIT-18: Loss Severity by State
Count
--
-.-
--
70.00%
57.70%
56.14%
60.00%
50.00%
44.64%
40.00%
35.82%
32.39%
32.39%
33.59%
3448%
26.92%
30.00%
9
20.00%
10.00%
0.00%
-
GA
MA
NC
IL
NY
FL
CA
TX
state
Average Timing of Default
As per the Snyderman and Esaki default study on ACLI loans, the timing of default was
determined by averaging the default incidence across all cohorts. This was observed to
have risen over the first 3 years after origination to about 2% maintaining itself at the
same level through year 7, and then falling over the remaining life of the cohort.
In a bid to determine the default pattern for our population across cohorts, the results
displayed a change in the default pattern for conduit loans as compared to the ACLI
loans. (See Exhibit-19)
EXHIBIT-19: Average Timing of Default (by Loan Count & PrincipalBal.)
0 Conduit
Loan default cunme
a ACLI loans default curve
%
2.500%-
2.000%-
1.500%
1.000%-
0.500%
2
1
3
5
4
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26 27
28
29
30
Yrs from Originaton
Average Timing of Default(by Loan Count)
0aConduit Loan default cunve
a ACLI Loan default cure
%
3.000%-
2.500%-
2.000%
1.500%
1.000%
0.000%
1
2
3
4
5
6
7
1i..
8
9
10
11 12
13 14
Average Timing of Default (Principal Amount)
Observations on Average timing of defaults:
15
16
17 18
19 20
21
22
23 24
25
.i.i.....
26
27
28 29
30
Yrs. from Origination
As observed by the pattern of default incidence in the given conduit pool, a loan is
most likely to default in the first 3 years from its origination. This is consistent
with conventional wisdom on the conduit loans as being structurally different
from ACLI loans. These loans mainly originated for the purpose of securitization
might not employ the stringent underwriting standards as observed in the case of
life insurance company loans. Hence given the change in property fundamentals
these loans are more likely to default in the initial years from origination.
As can be observed in Exhibit-19, the tall bars represent the ACLI loans used by
Snyderman, which display higher default rates outperforming the default
performance of the loan population used for this study. This could be attributed
to the lack of sufficient seasoning of the conduit loans in the pool used for the
study. These loans have been mostly originated in the last 6years without any
experience of market stress or recessionary conditions. On the contrary, ACLI
loans used for the Snyderman study experienced the real estate downturns of the
70's, early 80's and the early 90's. These loans had sufficient seasoning and
exposure to market stress conditions to display higher default rates.
EXHIBIT-20: Lifetime Default Rate by Cohorts (by Yr. from Oriination)
19931
-1995
+ 1992
........
1994
3.50%3.00%
2.50%
2.00%
-
-
1.50%.
1.00%.
1995
0.50%1994
0.00%L
0,
0
1
2
1
=-.
111 .-
3
4
Lifetime Defaults In Cohorts(by)yr. from Orig.)
1993
5
6
7
1992
.-
- .
8
9
10
11
Yr. from Origination
The default curve for the conduit loans tends to mask the individual pattern of
defaults in a cohort. When observed on a disaggregated basis (See Exhibit-20),
each cohorts default incidence patterns is dependent on the condition of the
commercial real estate markets and the events that transpired to affect the loan
originations. As can be seen in the 1995 cohort, the initial 3 years experienced a
surge of defaults. This however is unlike the cohorts of 1992 that experienced first
incidents of defaults only in the fifth year from origination.
Chapter 5
Loss Severity and Final Results
Hence using the results from the calculations given in Exhibit-2D/2E/2F we were able to
determine a weighted average loss severity on the 2374 resolved and unresolved
defaulted loans based on the assumption of similar losses for specific default stages. This
was calculated to be 35.41% as illustrated in Exhibit-21
EXHIBIT-21
Loss Severity Portfolio Wt. Cum.Loss Severity
473 Resolved Loans
138 Loans with CMSA status
1763 Unresolved loans
45.66%
0%
35.43%
0.199241786
0.058129739
0.742628475
9.10%
0.00%
26.31%
35.41%
Furthermore to evaluate the impact of the average loss severity of 35.41% across various
CMBS bond structures, we used the highest lifetime default rate of 7.95% of 1995 cohort.
It can be seen that only 2.82% of the principal in the pool is lost given the above
assumptions on lifetime default rate. Given BBB sub-ordination levels of 6%-10% as
observed in recent CMBS issues, the expected loss in the pools would not have an impact
on the investment grade BBB. Upon further modifying to use the lifetime default rate of
32% of the worst performing cohort of 1986 from the Snyderman study, we impute an
expected economic loss of 11.33%. This however presents a threatening possibility for
losses to the BBB but with no impact to the AAA tranche with 15%-25% credit support
levels.
CONCLUSIONS
Given the continued economic slump and political uncertainties, the decade long strong
performance of CMBS now stands threatened. The recent surge in delinquencies and
eventual high losses upon foreclosures has been representative of declining property
markets. But given the structural nature of CMBS in slicing and dicing the risk and
return, the investment grade still remains protected. Based on our analysis of the
economic cost of default and foreclosure in the conduit loan universe, we can determine
the extent of impact on the various bond structures. It can be seen that AAA to BBB
investment grade CMBS would remain unaffected given the lifetime default rates
observed in 1990's. However in the event of continued deteriorating real estate market
fundamentals as observed in the late 1980's and early 1990's, we could expect for losses
to BBB grade tranche using lifetime default rates as observed in 1986.
Furthermore this study has helped in identifying some important findings, which provide
better understanding of default incidence and risk evaluation in commercial mortgages.
The default experience over the last 10 years has demonstrated a strong performance
when compared to the onslaught during the downturn of late 1980's. However as
determined in the study, this is not indicative of high collateral quality but is attributable
to lack of sufficient seasoning for the most recent cohorts. The cohorts of 2000-2002 are
still not seasoned enough to truly represent the defaults expected given continued
conditions of market stress. The future economic performance in 2003 and 2004 will be
instrumental in addressing these maturity concerns.
References:
Mark Snyderman, " Commercial Mortgages: Default Occurrence and Estimated Yield
Impact." The Journalof Portfolio Management. Fall 1991, p. 82 -8 7 .
Mark Snyderman, "Update on Commercial Mortgage Defaults." Real Estate Finance
Journal.Summer 1994, p. 2 2 -32 .
Howard Esaki, Steven L'Heureux, and Mark Snyderman, " Commercial Mortgage
Defaults: An Update." Real Estate Finance.Spring 1999.
Patrick J. Corcoran, " Commercial mortgages: Measuring risk and return". The Journal
ofPortfolio Management, Winter 1989, p. 6 9 -7 4 .
Kerry D. Vandell, Walter Barnes, David Hartzell, Dennis Kraft and William Wendt,"
Commercial Mortgage Defaults: Proportional Hazards Estimation Using Individual Loan
Histories" AREUEA Journalvol. 21, 1993, p.4 5 1 -4 8 0
Brian A Ciochetti and Timothy J.Riddiough, " Foreclosure Loss and the Foreclosure
Process: An Examination of Commercial Mortgage Performance." Monograph 1997.
Sheridan Titman and Walter N. Torous. "Valuing Commercial Mortgages: An Empirical
Investigation of the Contingent -Claims Approach to Pricing Risky Debt ". The Journal
ofFinance, June 1989, Vol. XLIV, No.2, p-3 4 5 - 3 7 3
Sally Gordon and Christine Dziadul, Moody's Investors Service. "CMBS Delinquency
Report Card: From A to Incomplete". December 2002
Erin Stafford and Mary Metz, Fitch Ratings. "2002 CMBS Conduit Loan Default Study".
August 2002
Mary MacNeill and Noel Cain, Fitch Ratings. "Dissecting Defaults and Losses, Part-II:
2001 CMBS Conduit Loan Default Study". August 2001
Frank J. Fabozzi and David P. Jacob, " The Handbook of Commercial Mortgage Backed
Securities - Second Edition". 1999, Frank J. Fabozzi Associates, New Hope,
Pennsylvania
Richard Parkus, Duetsche Bank "Analyzing Default Risk in Conduit CMBS". October
2002
Realpoint Research. "CMBS Loss Severity Study" GMAC Institutional Advisors, May
2003
John R. Barrie. "A Study of Default Risk for Small Commercial Real Estate Loans and
its Impact and Implications for Securitization". Cambridge, Massachusetts, September
1994
Laura Quigg. "Default Risk in CMBS Bond Classes". Trends in Commercial Mortgage
Backed Securities, October 1997
APPENDIX-I: Exhibit-I
Loan Origination & Defaults by Cohorts
ORIGO
ATE
Total # of
#of
# of
on
Original Balance defauked Original Balance
on(#of loans)
loans
1
1
1
1
2
loans
4,966,861
2,890,300
356,800
130,000
3,113,147
0
0
0
0
0
%ofdefaulted %ofdefaulted
%of defauked
%of defaulted
loans
loans
0
0
0
0
0
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
3
balance)
(byoriginal
numbers)
loans)(byloan
(#of defaulted
2,145,860
7.32%
136,200,722 7.41%
180,156,848
2.35%
75.60%
0.00%
58,882,039
6,925,945
4.65%
0.00%
47
68,476,893
2,082,500
2.13%
3.04%
146,897,623
28,196,431
2.74%
19.19%
603,489,589
57,990,023
8.50%
9.61%
5.76%
0.00%
0.00%
0.00%
2
4,020,000
0
0
0.00%
0.00%
0.00%
9
16,533,398
12
16,753,439
0
0
0.00%
0.00%
7
12,990,157
0
0
0.00%
0.00%
9
32,409,573
0
0
0.00%
0.00%
11
22,941,063
0
0
0.00%
0.00%
33
39,573,324
3
7,342,390
9.09%
18.55%
25
91,112,873
1
310,000
4.00%
0.34%
29
69,531,912
0
0.00%
0.00%
33
41,678,527
2
3,425,000
6.06%
8.22%
77
93,450,752
4
5,416,000
5.19%
5.80%
236
194,885,356
2
5,800,000
0.85%
2.98%
173
173,485,106
4
6,668,443
2.31%
104,221,915
1
2,125,000
1.39%
0
loans
91,313,082
%of defaulted %of defauled
loans
loans
43
0
0.00%
on(#ofloans)
on
Balance
Original
27
0
0
1981
41
#of
Balance defauled
Original
0.00%
15,557,889
72
loans
(byoriginal balance)
numbers)
loans)(byloan
(#of defaulted
5
0
ORIGD
ATE Total #of
73
247
11.76%
453
1,006,473,945
57,954,664 7.06%
454
1,002,323,750
31,601,955
4.41%
3.15%
386
1,102,503,565
57,870,165
5.18%
525%
356
1,845,591,974
27,268,550
421%
1.48%
1258
2,836,455,977
45,499,352
1.43%
1.60%
1640
10,563,629,425
53,211,864
2.20%
0.50%
3491
9,914,349,548
203,015,835
2.18%
2.05%
6115
13,772,805,511
344,588,038 1.47%
5033
13,653,943,034
2.50%
1,084,918,18
1
4.77%
7.95%
7178 23,260,214,305
1,075,079,839
4.01%
4.62%
45,841,256,591
2,397,527,694
5.99%
5.23%
14442 80,565,479,422
2,495,775,347
3.49%
3.10%
6581 48,038,256,715
1,136,680,974
2.95%
2.37%
3.84%
6158
51,097,012,757
1,328,101,094
3.23%
2.60%
2.04%
6664
73,911,341,864
463,385,19 8
0.95%
0.63%
5142
52,338,635,556
3421
35,367,900,819
78680
46827 3,4
8691
5
0.19%
54,810,061
0
2374
11,121,917,121
0.00%
0.10%
0.00%
APPENDIX-I: Exhibit-2
Loan Origination& Defaults by Loan Size
RANGE
# of loans
Original Balance
# of
defaulted
Original Balance on
% of defaulted
loans
% of defaulted
loans
on (# of loans)
loans
(# of defaulted loans)
(byloan numbers)
(byoriginal balance)
<1M
22,643
8,586,442,217
453
195,764,529
2.00%
2.28%
1M-2M
13,452
19,626,874.913
477
700,738,385
3.55%
3.57%
2M-4M
17,126
48,755,067,963
653
1,844,426,268
3.81%
3.78%
4M-8M
13,460
74,707,111,852
463
2,534,011,856
3.44%
3.39%
8M-12M
4,846
46,743,648,771
156
1,485,618,371
3.22%
3.18%
12M-16M
2,270
31,061.690,159
68
932,442,475
3.00%
3.00%
16M-20M
1,298
22,956,148,007
34
594,313,206
2.62%
2.59%
20M-50M
2,371
69,658,122,686
57
1,609,339,538
2.40%
2.31%
SOM-100M
735
49,796,987,900
9
538,781,775
1.22%
1.08%
100M-200M
334
46,316,057,421
3
460.480,722
0.90%
0.99%
200M-500M
125
35,877,977,391
1
226,000,000
0.80%
0.63%
19
12,750,990.717
0
0
0.00%
0.00%
1
1,370,873,184
0
0
0.00%
0.00%
500M-1300MM
MORE
Loan Origination& Defaults by Property Types
PROPTYPE
# of loans
Original Balance
on (# of loans)
# of
defaulted
loans
% of defaulted
% of defaulted
loans
loans
Original Balanceon
(by
originalbalance)
(#of defaultedloans) (byloannumbers)
13.469%
Healthcare
1235
147
1,036,304,412
11.90%
Hotel
2961
38,655,122,507
451
2,764,859,303
15.23%
7.153%
Industrial
6332
24,599,909,203
153
737,390,711
2.42%
2.998%
Manufactured Housing
1831
7,330,829,987
31
72,660,521
1.69%
0.991%
23273
90,589,606,519
568
1,735,408,151
2.44%
1.916%
1.405,073,776
2.64%
1.383%
Multifamily
7,694,019,548
101,580,728,958
256
Other
15486
67,790,262,275
168
616,828,444
1.08%
0.910%
Retail
Self Storage
16342
1524
125,023,568,197
4,943,946,030
588
12
2,723,841,171
3.60%
0.79%
2.179%
0.598%
Office
9696
29,550,640
APPENDIX-I: Exhibit-3
Loan Origination& Defaults by Originator
ORIGINATOR
COLUMN
LEHMAN
BANKOFAMERICA
CHASE
JPMORGAN
# of
loans
OriginalBalance
# of
defaulted
OriginalBalance
of
% of defaulted
loans
% of defaulted
loans
on (# of loans)
loans
loansindefault
(by loan
numbers)
(byoriginal
balance)
5062 42,577,666,655.00
317
2,125,976,670
6.26%
4.99%
I ofloans
Balance
Onginal
of
defaulted Oriinal
Balance
on( ofloans)
loans
(byloan
numbers)(byoriral balance)
loans
indefaut
LASALLE
2,572,659,245.00
8
52,945,000
1.68%
2.06%
AMRESCO
2,518,579,705.00
29
198,988,000
5.39%
7.90%
CONTI
2,505,725,496.88
73
217,555,165
8.33%
8.68%
2419 32,041,758,719.00
76
374,263,300
3.14%
1.17%
1011 22,191,179,038.00
31
243,191,886
3.07%
1.10%
JOHN
HANCOCK
2,180,511,930.00
4
22,647,302
1.49%
1.04%
5693 22,024,630,881.00
88
289,323,581
1.55%
1.31%
CDC
1,992,281.986.00
3
128,049,853
8.33%
6.43%
LIFE
CONFEDERATION
1,926,602,427.00
19
98,998,162
3.41%
5.14%
TIAA
1,582,622,104.00
0
0.00%
0.88%
ARTESIA
1,458,552,932.00
5
18,198,556
1.15%
1.25%
ONE
BANC
1,413,345,943.00
39
45,879,835
322%
3.25%
ISTAR
1.346.221,925.00
0.00%
LEASE
CAPITAL
1,276,913,158.00
0.00%
0.88%
2404 20,732,450,021.00
87
640,002,762
3.62%
3.09%
NOMURA
1734 20,262,301.777.00
89
935,819,352
5.13%
4.62%
GMAC
2394 19,649,035,880.00
95
769,912.240
3.97%
3.92%
2861 18,417,918,212.00
149
504,399,199
5.21%
2.74%
WACHOVIA
%ofdefaueted
%ofdefaulted
loans
loans
Isf
ORIGINATOR
0
1301 15,031,984,412.00
40
231,935,053
3.07%
1.54%
AETNA
1,245,990,625.00
1
5,685,529
123%
0.46%
GE CAPITAL
2481 13,605,507,280.00
38
211,460,890
1.53%
1.55%
NCB
1,230,957,173.00
2
6,650,000
0.31%
0.54%
DEPOSITOR
126 13,422,005,701.00
0
0.00%
0.00%
MUTUAL
WASHINGTON
1,094,197,675.00
0
0.00%
0.00%
108
6.61%
5.02%
DAIWA
1,090,138,789.00
29
83,947,500
6.86%
7.70%
1634 11,624,541,065.00
PROViDENT
1,078,871,032.00
88
248,899,54
23.78%
23.07%
MASON
LEGG
821,228,735.00
21
39,862,345
14.79%
4.85%
CRIIMI
MAE
8D5,255,252.00
3
18,925,000
1.59%
2.35%
BRIDGER
799,551,537.00
6
24,570,877
2.78%
3.07%
PENN
MUTUAL
781,563,016.0
4
9,611,444
1.85%
1.23%
PACIFIC
SOUTHERN
778,891,339.00
97
30,655,759
4.44%
3.94%
GENERAL
AMERICAN
77,496,440.00
2
10,494,727
0.97%
1.35%
754,100,001.88
0
0.88%
0.88%
748,762,548.0
7
3.50%
4.47%
MORGANSTANLEY
MERRILLLYNCH
GOLDMANSACHS
731 10,629,814,420.00
SECORE
115
CITICORP
WELLSFARGO
10,389,840,102.00
1512 9,505,460,873.00
1949 8,496,911,968.00
583,117,897
22
100,832,500
3.01%
0.95%
3
227,282,732
2.61%
2.19%
73
15
260,610,116
73,795,425
4.83%
0.77%
2.74%
0.87%
BEARSTEARNS
913 8,175,025,904.00
17
78,490,000
1.86%
0.96%
GREENWICH
910 7,248,099,114.00
20
81,433,000
2.20%
1.12%
CCA
DYNEX
MIDLAND
PRUDENTIAL
80
193,005,522
5.34%
2.95%
5,710,581,806.00
25
175,811,806
3.47%
3.08%
5,221,075,646.00
47
172,972,545
1498 6,552,463,740.00
720
UBS
797
PRINCIPAL
742 4,651,148,590.00
2
7,171,372
KEYBANK
941
4,622,119,923.00
20
70,144,375
534 3,903,956,888.00
15
44,298,750
200
33,506,831
I'
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APPENDIX-I Exhibit-5
Average Timing ofDefault-Loan Count
0
0
0
0
0
0
0
0
0
5
4
3
2
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
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16
15
14
13
12
11
10
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7
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0
0
0
0
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0
0
0
0
0
0
0
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0
0
0
0
0
0
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0
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0
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0
0
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12
2
3
0
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0
0
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0
0
0
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10
0
0
1
2
2
2
0
0
0
0
0
0
0
0
1
7
5
1
3
3
0
0
0
0
0
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3
3
7
3
2
1
1
0
0
0
0
0
0
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6
3
2
1
3
0
0
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5
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2
0
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6
1
0
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7
5
12
2
4
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19
20
16
5
5
3
0
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17
17
11
10
17
9
5
0
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89
23
15
27
21
46
10
0
13
20
54
30
81
53
20
0
25
103
0
130
110
50
16
s
139
144
91
79
52
12
2
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41
4
31
23
4
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0
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0
a
0
30
29
28
27
26
20
24
23
22
21
20
0
0
0
0
APPENDIX-I: Exhibit-6
Average Timing of Default-Loan Count-%
0
4.
0.0%
0%
.
4
0o0%
19n
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020%
0.00%
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Exhibit-7
APPENDIX-I
Averare Timing of Default-Principalbalance
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APPENDIX-I Exhibit-8
Average Timing of Default-Principalbalance-%
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APPENDIX-II: Exhibit-I
138.492
89.8916
554.347
61.67%
733,092
268,188
36.58%
1,617.360
868,901
53.72%
15,433,991
8,452,243
54.76%
5,276,577
1,104,012
24.564,264
6,684,723
4,030,190
20.92%
16.41%
3,968,291
59.36%
5,140,587
1,427,440
1,492,757
19,586,260
256,502
4,136,998
27.77%
17.18%
398,584
2,125,000
1980
72
1
104.221,915
1985
47
1
146,897,623
918,750
198
73
2
603,489,589
1,880,007
1987
247
8
1,006.473,945
16,964,478
1988
453
6
1,002,323,750
1989
454
7
1,102,503,565
6,092,900
26,757,469
1990
1991
386
3
1,845,591,974
7,535,997
356
4
2,836,455,977
1992
1993
1258
9
10,563,629,425
5,730,877
1,708,688
1640
11
9,914,349,548
1994
1995
3491
6115
15
22
13,772,805,511
13,653,943,034
1996
1997
5033
7178
55
137
23,260.214,305
45,841,258,591
210,906,325
185,364,670
393,928,038
1998
8691
14442
137
32
80,565,479,422
48,038.256,715
523.635,232
359,507,823
473,038,778
6581
16
51,097,012,757
6158
6
73.911.341.864
1999
2000
2001
23,086,415
76,104,873
96,692,433
132,440,405
73,050,641
53,155,000
12,739,134
50,54-4,130
66,076,916
86,529,331
124,375,294
66,840,402
206,181,142
205,335,062
45,435,293
71,723,935
52,643,902
49.07%
no
24
83180145
Ou,oow,u0u
383721110
211.948,677
55.24%
21
48171605
16,611,794
34.48%
2
2619370
Multifamily
96
194556686
60,047,453
30.86%
Office
47
175720727
74,473,802
42.38%
Other
32
75419443
24,781,288
32.86%
Retail
133
535529654
224,899,024
42.00%
2
1902928
695,766
36.56%
Industrial
Manufacture
Self Storage
1,944,446
57.35%
43.41%
25,830,826
116
r19.0
36.06%
36.53%
52.77%
Loss Severity by PropertyTves
Hotel
58.41%
37,846.564
Q
Healthcare
21.12%
19.28%
74.23%
APPENDIX-II: Exhibit-2
Loss Severity by Originator
Loss Severity by State
DC
0.0
1
DE
9.30%
1
ARIES
BANC ONE
4.40%
1
MT
58.60%
1
BANK OF AMERICA
BANK OF BOSTON
VT
36.70%
1
BAYBANK
100.00%
1
BEAR STEARNS
AR
46.40%
2
IA
52.45%
2
RI
6.00%
2
CO
18.60%
3
ID
17.90%
3
MD
40.37%
MN
HI
WV
50.980,000
3.200,000
428.612
40.1*06jo~
2.912,190
415,623
554.825
87,433
190
21.00%
40.31%
2,725.508
2,452,329
25.171,037
1.779,643
6,007,780
5,320.760
2.499,282
35.15%
12,425,000
11,883.938
4.514,856
33.00%
BOSTON CAPITAL
2.360.000
2.273,282
27.010
1.20%
CENTRAL PARK
2,135.000
2,013,158
CIBC
1,660,000
1,617.382
57,301.942
830.324
51.30%
23,226,622
59.82%
241.863,286
35,821,254
141,022.296
14,997,575
65.09%
38,672,073
22,679,973
25,821.997
63.72%
45.17%
CITICORP
COLUMN
61,158,508
64.796,316
269,381,695
CONFEDERATION LIFE
36,838.300
3
CONTI
41-040,813
43.43%
3
DAIWA
AL
6.48%
4
DYNEX
24,304.000
8,384,263
IN
58.60%
4
FINOVA
KS
71.33%
4
GACC
OK
62.30%
4
OR
60.23%
4
ME
43.96%
5
MI
81.46%
5
HANOVER
NV
23.18%
5
IMPAC
WA
38.18%
5
WI
52.56%
UT
57.729,005
39.00%
0.00%
33.80%
7.548.447
14,775,580
1.320,936
2,359,325
4.960,000
2.138.517
901,594
17.50%
42.20%
1,810.726
989.761
35.60%
15,625,051
14.788.969
7,740,658
49.16%
104.816,562
20.515.000
99.939.798
18.691.741
32.696,438
11,087.401
41.61%
53.58%
11,740,000
7,018,307
2,033.777
46.83%
6,771.100
6.366.617
1,267.241
25.45%
641.000
615.033
373.361
60.70%
ING
8,314,000
7.587.551
4,340.175
54.30%
5
JOHN HANCOCK
3.776.810
3.615.569
1,876,000
51.90%
80.20%
6
JPMORGAN CHASE
92,949,762
88,268.637
35,665.199
42.07%
6
KEYBANK
21,584,375
13,967,930
8,360,872
40.34%
70.31%
VA
LASALLE
4.280,857
1,507.665
35.20%
78.61%
7
4,400.000
LA
7,853.831
6.985,304
3.879,117
56.00%
SC
54.66%
7
LEGG MASON
33.949.836
CT
34.30%
49.98%
0.00%
MS
MO
GE CAPITAL
GMAC
GOLDMAN SACHS
GREENWICH
63,377,002
LEHMAN
67.305,000
9
LIBERTY
8,100,000
7,353,034
47.19%
9
LOVE
60.87%
10
MANUFACTURERS LIFE
5,160.000
11,900.000
10.751.952
TN
58.38%
10
MBIC
AZ
54.06%
13
MERRILL LYNCH
96,500,621
87,014.248
40,580.108
35.91%
13
MIDLAND
24.063.541
17,497,948
47,973,323
28,081,155
57.79%
NM
75.56%
13
26,785,000
54,161,450
57.24%
KY
898,916
554.347
61.70%
41.22%
13
MUTUAL BENEFIT LIFE
2,125.000
PA
2,630,980
2.342,856
89.00%
NJ
14
NA - ACQUIRED FROM RTC
NA - VARIOUS SEASONED LOAN SOURCES
2,846,273
52.43%
56.365,485
48,330,659
6,462,282
25.99%
OH
33.29%
16
NA
15,277,019
11.110,45
4,840.009
45.95%
NA
54.34%
17
NOMURA
66,237,767
58.72%
GA
35.82%
20
OPERF
MA
33.59%
20
PENN MUTUAL
NC
56.14%
20
PROVIDENT
32.39%
21
NY
34.48%
24
PRUDENTIAL
RESOURCE
FL
44.64%
35
RFG FINANCIAL
SOUTHERN PACIFIC
CA
26.92%
42
UBS
24.331.905
23,084.433
12,554,465
50.03%
TX
57.70%
59
WACHOVIA
74.916.051
69,505.421
17.075.468
14.71%
IL
MORGAN STANLEY
1.840,000
165.539,086
4.187.194
1,624,057
147.860.517
1.168,586
27.90%
1,816,142
16.90%
1.140.002
69.65%
47.64%
507.269
449.626
405,236
90.10%
6.430,692
5,396,325
1.593,076
58.37%
34,319.205
9.787.419
40,890.809
26.58%
37,811.009
86.433.580
1,900.000
6.700,000
6,044,061
83.470.196
1.848,732
6.156,595
4.884,543
-33
1,648,732
52.07%
100.00%6
4.552,180
73.90%
1,599.824
47.46%
APPENDIX-II: Exhibit-3
Loss Severity by SpecialServicer
Loss Severity by Loan Size
1M-2M
2M-4M
85
31,434,622
40.92%
109.51%
132
136
188,545.036
371,377,616
36.35%
55.26%
144.68%
148.58%
CAPMARKSERVICES
1
1
2.4810
2,497,500
2,342347
2,415,077
85,670
423,486
25.90%
17.50%
8,384,263
7,548,447
1.320,936
17.50%
4M-8M
72
398,644.188
52.31%
122.15%
DYNEXCOMMERCIAL
1
21
201.045,262
47.30%
101.49%
First UnionNationalBonk
1
1,235,000
1,218,679
277,095
22.70%
8M-12M
12M-16M
15
202.258,031
44.20%
76.02%
ASSET MANAGEMENTCOMPANY
HANFORD/HEALY
1
3,154,000
874,163
838,748
95.90%
16M-20M
5
82,855.000
51.30%
73.56%
KEYCORPREAL ESTATECAPITALMARKETS
1
15,665,000
15,490,707
5,945,451
38.40%
20M-SOM
SM-100M
7
18.80%
77.83%
11.590,000
11.444,577
3,712,190
32.40%
0.00%
0.00%
LennarPartners/ PrudentialAsset Res
OCWENFEDERALBANK / JE ROBERTCOMPANY
1
0
176,952,355
0
1
2,200,000
2,081,278
27,070
1.30%
IOOM-200M
0
0
0.00%
0.00%
ALLIEDCAPITALCORPORATION
2
5,600,000
4,594,597
2,395,543
55.15%
20OM-500M
0
0
0.00%
0.00%
AMRESCOSERVICES
2
22,175.236
21.114,755
8,444,699
38.25%
50OM-130OMM
M RE0
0
0
0
0.00%
0.00%
0.00%
0.00%
FIRSTCITYSERVICINGCORP
2
1.370,919
1,107,823
530,192
42.00%
WELLSFARGO BANK
2
4,061,658
3,512,167
1,729,831
46.25%
PACIFICLIFE INSURANCECOMPANY
5
65,658,427
54,668,843
15,863.359
38.54%
GE CAPITALREALTYGROUP
6
44,490,580
41.784,012
19,476,840
60.90%
LEND LEASEASSETMANAGEMENT
7
43,850,000
40,977.492
18,630.915
45.17%
Lennar Partners
7
15,524,517
14,989,177
4,181,615
35.91%
KEY COMMERCIALMORTGAGE
9
30,081,875
18,026,182
9,988,288
66.98%
JE ROBERTCOMPANY
11
54,724,311
49,301,725
15,727,365
28.95%
AMRESCOMANAGEMENT
15
49,706,701
43,691,540
14,580,917
46.16%
ARCAPSPECIALSERVICING
15
55,631,000
53,978,201
36,920,991
55.61%
CRIIMI MAE
17
62,331.138
56.766,605
28,676,084
45.94%
OCWENFEDERALBANK
29
16,725,703
15.167,679
3.420.946
34.06%
MIDLANDLOAN SERVICES
39
91,838,868
74,689,806
42,220,097
44.67%
ORIX REALESTATE CAPITALMARKETS
51
175,719,551
160.229,218
81.704,863
51.01%
GMACCOMMERCIALMORTGAGECORP
52
272,684.455
257.564,778
99,509,800
23.70%
BANCONE MORTGAGECAPITALMARKETS
65
195.133.262
175,061,205
66,381,981
2449%
123
359,268,506
330,489,115
186,435,367
52.76%
LENNAR PARTNERS