Analysis of Delinquencies in CRE Debt: An Approach to Understanding Risk and Pricing
by
Ramakrishna Kalicharan Kaza
Bachelor of Tech., Civil Engineering, 2005, Jawaharlal Nehru Technological University
Submitted to the Program in Real Estate Development in Conjunction with the Center for Real Estate in Partial Fulfillment of the Requirements for the Degree of Master of Science in
Real Estate Development
at the
Massachusetts Institute of Technology
February, 2019
©2019 Ramakrishna Kalicharan Kaza All rights reserved
The author hereby grants to MIT permission to reproduce and to distribute publicly paper and electronic copies of this thesis document in whole or in part in any medium now known or
hereafter created.
Signature of Author: ____________________________________________
MIT Center for Real Estate January 11, 2019
Certified by: ____________________________________________ Alex van de Minne
Research Scientist, Center for Real Estate Thesis Supervisor
Accepted by: ____________________________________________
Professor Dennis Frenchman Class of 1922 Professor of Urban Design and Planning
Director, Center for Real Estate School of Architecture and Planning
Page 2 of 74
~ page intentionally left blank ~
Page 3 of 74
Analysis of Delinquencies in CRE Debt: An Approach to Understanding Risk and Pricing
by
Ramakrishna Kalicharan Kaza
Submitted to the Program in Real Estate Development in Conjunction with the Center for Real Estate on January 11, 2019 in Partial Fulfillment of the Requirements for the
Degree Master of Science in Real Estate Development.
ABSTRACT Commercial Real Estate (CRE) debt has been the most staple form of capital for the real estate industry in the US for decades. However, a lot has changed in the CRE debt landscape since the Global Financial Crisis. With growing regulatory constraints for traditional lenders, and massive amounts of global capital chasing private market investments, private debt funds are emerging as a reliable alternate source of debt for the industry. With the current market cycle showing signs of an end-cycle environment, private debt funds/strategies have become even more popular with investors for their potential to enhance returns on a risk-adjusted basis. The intent of most such funds is to achieve “equity-like” returns at “lower risk”. Returns are for everyone to see – Risk is what is difficult to capture. The main objective of this study is to unravel the risk characteristics of CRE debt, and to empirically quantify the risk involved in CRE lending. A credit risk approach is adopted to do an extensive analysis of delinquencies in a portfolio of 65,533 CRE loans originated over 23 years; all loans are/have been a part of CMBS collateral, and their entire performance track record is sourced from Trepp LLC. The study uses the three pillars of the credit risk approach – Incidence of Default, Loss Severity, and Time to Default, and applies them to five distinct segments of the portfolio - Aggregate, Market Cycle, Asset Class, Origination Cohort, and Loan-to-Value Ratio, to come up with a comprehensive set of insights and measurements on risk. The study finds that CRE loans get incrementally risky as Loan-to-Value ratios get higher; quantitative metrics are derived to highlight the relative differences in risk. It also finds that loans originated in the later years of a market cycle tend to be much riskier than those originated earlier in a cycle. The analysis brings out many more nuanced trends and metrics that come together to portray a complete risk profile of CRE debt. The study concludes with a summary of the high-level takeaways, and a brief discussion on how the derived metrics can feed into pricing decisions. Thesis Supervisor: Alex van de Minne Title: Research Scientist, Center for Real Estate
Page 4 of 74
Acknowledgements
It has been a truly enriching experience being a part of an exceptional MIT MSRED
cohort; I would like to thank my classmates for all the learning, sharing and caring. I would
also like to thank all my professors at the Center for Real Estate for intellectually challenging
us right through our time here, and for exposing us to the very cutting edge of real estate
thought and practice. A big thanks to all the administrative staff at the Center for ensuring
that we all felt at home and had a hassle-free learning experience – a shout out to Tricia
Nesti!
I would like to thank Trepp LLC for sponsoring the data for the study. A special
mention to Erin Liberatore for helping me through the process, and Julie Yu for patiently
clarifying all the queries that I had on the data. I would like to thank Steve Weikal from the
Center for making the introduction with Trepp.
I would like to thank Alex van de Minne for being a fantastic supervisor - for
encouraging me to learn R and guiding me with valuable inputs over the course of the study. I
would like to thank Prof. David Geltner for being my sounding board; it was a privilege to
have a luminary in the field to discuss ideas with.
I would like to thank Dan Adkinson for sowing the initial seeds of the research idea
and for giving me direction. On the same note, I would also like to thank Prof. Michael
Giliberto from Columbia Business School for sharing his thoughts and advice on the topic.
Finally, I would like to thank my wife Aarohi, my parents, and all my friends who
have always put immense belief in my potential and supported me through good and
challenging times.
Page 5 of 74
Table of Contents
Abstract ............................................................................................................................... 3
Acknowledgements .............................................................................................................. 4
1. Introduction ..................................................................................................................... 7
2. The CRE Debt Market .................................................................................................. 10
2.1 CRE Debt Sources ..................................................................................................... 10
2.2 Regulation & Constraints ........................................................................................... 12
2.3 Rise of Private Debt Funds......................................................................................... 13
2.4 Evolving Landscape ................................................................................................... 15
3. Key Definitions & Concepts .......................................................................................... 16
3.1 Incidence of Default ................................................................................................... 16
3.2 Incidence of Active Workout ..................................................................................... 17
3.3 Incidence of Loss ....................................................................................................... 18
3.4 Loss Severity ............................................................................................................. 18
3.5 Time to Default .......................................................................................................... 19
4. Overview of Data ........................................................................................................... 20
4.1 Source of Data ........................................................................................................... 20
4.2 Data Timeline ............................................................................................................ 20
4.3 Loan Types ................................................................................................................ 21
4.4 Geographical Spread .................................................................................................. 21
4.5 Asset Class Distribution ............................................................................................. 22
4.6 Loan-to-Value Summary ............................................................................................ 23
5. Data Analysis & Findings ............................................................................................. 26
5.1 Aggregate Incidence Analysis .................................................................................... 26
5.2 Market Cycles & Loan Seasoning .............................................................................. 27
5.3 Incidence Analysis – Market Cycles .......................................................................... 30
5.4 Incidence Analysis – Asset Classes ............................................................................ 33
5.5 Incidence Analysis – Origination Cohorts .................................................................. 34
5.6 Incidence Analysis – Asset Classes & Origination Cohorts ........................................ 35
5.7 Aggregate Incidence Analysis – LTV Segments ......................................................... 40
5.8 Asset Class Incidence Analysis – LTV Segments ....................................................... 43
5.9 Incidence Analysis – LTV Segments & Origination Cohorts ...................................... 45
5.10 Aggregate Loss Severity .......................................................................................... 47
Page 6 of 74
5.11 Aggregate Loss Severity – Origination Cohorts ........................................................ 47
5.12 Loss Severity – Asset Classes .................................................................................. 48
5.13 Loss Severity – LTV Segments ................................................................................ 50
5.14 Aggregate Time to Default ....................................................................................... 50
5.15 Time to Default – Asset Classes ............................................................................... 52
5.16 Time to Default – Origination Cohorts ..................................................................... 52
5.17 Time to Default – LTV Segments ............................................................................ 53
6. Conclusion ..................................................................................................................... 54
References.......................................................................................................................... 58
Appendix (Exhibits 1 – 15) ................................................................................................ 59
Page 7 of 74
1. Introduction
Commercial Real Estate (CRE) debt1 is perhaps the most ubiquitous source of capital
for the real estate industry in the US. Based on the nature of the transaction/holding, CRE
debt could represent anywhere between 40% to 80% of the capital stack associated with a
real estate asset; it is usually the largest slice of the capital stack. Considering the aggregate
value of commercial real estate in the US, and the sheer volume of transactions that happen in
the space, CRE debt stands out as a significant segment at a macro level too. As of the end of
year 2017, the total outstanding CRE debt2 in the US stood at USD 4.07 trillion – around
38% of the size of the total outstanding home mortgages at the same time, and around 21% of
the size of the country’s GDP for the year.
As we mark the tenth anniversary of the GFC, it is an opportune time to evaluate
where we are in the real estate cycle now, and where we are going next. A general consensus
in the CRE industry3 seems to be that the early, high growth stage is already behind us - that
markets are currently in a mature stage, with most indicators/trends pointing towards a late-
cycle environment. From the lows of the GFC, CRE asset valuations have already grown by
over 55%4, capitalization rates are at an all-time low with nearly no room for further
compression, interest rates are rising, and the yield curve is flattening. However, there are
two principal factors that are continuing to drive the markets and keeping them buoyant – a
healthy US economy that is fostering growth in the CRE space markets5, and historically high
amounts of liquidity/capital that is supporting the CRE asset market6. But with late-cycle
signs already around the corner, institutional investors in real estate have started to re-
calibrate their portfolios in favor of strategies that can potentially provide better risk-adjusted
returns in a stagnant/declining market. CRE debt, especially the high-yield varieties, seems to
be among the most popular strategies that investment managers these days are offering to
1 CRE debt is an industry term that is usually used to categorize loans that have been advanced to income producing real estate assets that are owned/operated solely for commercial purposes (therefore excludes residential mortgages and includes multi-family apartment buildings). However, the categorization tends to get a little muddy as some lenders/agencies tend to also include other forms of related lending (like construction finance, bridge finance, loans to self-occupied commercial property etc.) under this umbrella of CRE debt. Therefore, aggregate numbers reported for the segment by different agencies might not be fully consistent with each other. 2 As reported in the segments of the “Flow of funds, Balance sheets, and Integrated Macroeconomic Accounts” report released by the Federal Reserve in September 2018. 3 Gathered from market/research reports, industry discussions, and projected market trends. 4 Based on NCREIF Capital Value Index. 5 Space markets are about occupancy, rents, leasing etc. – markets for actual use of real estate as space. 6 Asset market is about asset values, cap rates etc. – market for real estate asset transactions.
Page 8 of 74
institutional investors to address their portfolio re-calibration needs. Their pitch – in an end-
cycle environment, CRE debt has the potential to achieve “equity-like” returns with arguably
“lower risk”. Returns are for everyone to see – Risk is what is truly difficult to capture.
The purpose of this study is to unravel the risk characteristics of CRE debt, and to
quantify the risk involved in CRE lending – and while doing so, the idea has been to keep the
analysis/methodology simple enough for investors and other industry participants to be able
to use the approach and the results intuitively. There are different levels at which risk can be
studied – loan level, portfolio level, strategy level etc. However, the building blocks of the
CRE debt industry are CRE loans; the understanding of risk at this fundamental level can be
extrapolated and carried forward into higher levels. The study therefore focuses on the
performance of CRE loans, analyzes delinquencies with a credit loss approach, and comes up
with a comprehensive set of findings based on three pillars – Incidence of Default, Loss
Severity, and Time to Default.
The study analyzes the performance of 65,533 CRE loans originated over 23 years
(1995 to 2017), capturing two market cycles. The dataset is entirely provided by Trepp LLC,
a database and analytics provider for securitized mortgages, and all the loans are a part of
CMBS pools issued from the year 1998. The dataset constitutes CRE loans (from issuance
year 1998 onwards) from 10 top metropolitan areas in the US, across all asset classes.
In analyzing the risk characteristics, and in coming up with quantitative measures
related to the three pillars, the dataset is analyzed from five distinct perspectives – Aggregate
level, Market Cycle level, Asset Class level, Origination Cohort level, and LTV level. Each
of them brings out a different aspect of the risk profile of CRE debt.
The aggregate level analysis shows the overall performance/risk characteristics of all
the loans in the complete dataset/subset. For example, one of the findings of the study
was that around 9.44% of all loans (originated from 1995 to 2017) went into Default
at least once in their lifetime. For about 71.35% of such Default loans, an active
workout strategy was used. For around 84.61% of loans that required an active
workout strategy, the lender (or trust) ended up with a Realized Loss.
A market cycle-based analysis shows how performance/risk differed in both the
cycles. For example, it was found that for loans originated during Market Cycle 1
(1995 – 2007), around 15.58% of the them went into Default at least once in their
lifetime. However, in the case of loans originated during Market Cycle 2 (2008 –
Page 9 of 74
2017), a meagre 0.54% of them went into a Default at least once in their lifetime.
Does this mean CRE loans in the current market cycle are far less risky? How much
of this can be attributed to actual credit performance, and how much to other factors?
Detailed analysis and discussion on such findings are presented.
An asset class-based analysis compares the performance/risk of individual asset
classes. For example, one of the findings of the study was that Lodging and Multi-
family assets had the lowest contribution of Maturity Defaults (around 33%) to their
total Default loans. For all other asset classes, the contribution was 45% to 55%.
An analysis based on origination cohorts shows the evolution of risk over a market
cycle. For example, it was found that for loans that originated in the year 2000, the
average Loss was on an average around 30.6% of the original loan amount. However,
the severity of the Loss grew structurally as the market cycle progressed; for loans
originated in 2007, it was 45.8%.
An LTV based analysis shows how risk varies in CRE loans as one moves up the
LTV spectrum. For example, it was found that for loans in the 50% - 55% LTV
segment, only around 4.73% went on to Default at least once in their lifetime;
however, the same for loans in the 80% - 85% LTV segment was found to be 27.99%.
By presenting and discussing delinquency patterns from different perspectives, the
study aims to: 1) cover all the important variables that affect the risk of a CRE loan, and 2)
bring out the relative differences in priority of these risks by using intuitive, empirically
derived metrics. By being sensitive, both conceptually and quantitatively, to the relative
differences in risk across these perspectives, investors will have a holistic understanding of
risk in CRE debt. This could help them in adopting the right strategy for pricing loans and
generating the required risk-adjusted return.
The study, going forward, is presented in 4 separate sections: Chapter 2 provides a
brief introduction to the CRE debt market, its composition, and evolution over time; Chapter
3 introduces the important concepts that have been used in the study and describes how some
of the key metrics have been derived; Chapter 4 gives an overview of the data that has been
used for the study; Chapter 5 presents a comprehensive analysis of the data and a
simultaneous discussion on the findings; Chapter 6 ties everything up together with
concluding remarks and a brief discussion on pricing.
Page 10 of 74
2. The CRE Debt Market
As of the end of the year 2017, the total outstanding CRE debt in the US stood at
USD 4.07 trillion. Owing to the buoyant real estate markets in the US, the demand for debt
continues to be strong. Considering the sheer size of the existing market (target for
refinances), and the demand for fresh capital, it is not conceivable that just one or two sources
would be able to cater to the entire quantum or variety of debt. It is therefore not surprising
that the CRE debt space is supported by a pretty sophisticated marketplace with specialized
sources offering capital to specific segments of the demand. Some of the most important
sources are: banks (being used here as a catch-all term for all US chartered depository
institutions), government sponsored enterprises (GSEs – like Freddie Mac, Fannie Mae.),
Commercial Mortgage Backed Securities (CMBS) & credit REITS, and life insurance
companies. Each of them approaches commercial mortgage lending from a slightly different
perspective, and each is swayed by a different set of influences and priorities.
2.1 CRE Debt Sources
With around USD 2.14 trillion7 (around 53%) of outstanding CRE debt assets at the
end of 2017, banks have clearly been the largest source of capital to the space. However,
owing to incremental regulation post the GFC, banks have been finding it difficult to cater to
the demand with the same ease as they earlier used to. However, they continue to be the
largest source of CRE debt even today.
With over USD 600 billion8 (around 15%) of the outstanding CRE Debt assets, GSEs
have been the second largest lenders in the segment. GSEs are government-related entities
that hold and / or insure mortgages backed by multifamily and health care properties. During
the financial crisis, as investors shied away from other real estate-related investments, the
government guarantee on Fannie Mae, Freddie Mac and FHA-backed loans and securities
continued to attract investors. As the market has rebounded, these GSEs and FHA carried on
with the momentum and remained as large source of credit for the multifamily market.
7 Calculated from the “Flow of funds, Balance sheets, and Integrated Macroeconomic Accounts” report released by the Federal Reserve in September 2018. 8 Calculated from the “Flow of funds, Balance sheets, and Integrated Macroeconomic Accounts” report released by the Federal Reserve in September 2018.
Page 11 of 74
With around USD 550 billion9 (around 14%) of the outstanding CRE Debt assets,
CMBS (along with credit REITs) have been very close to the size of the GSEs in the
segment. The CMBS market connects local commercial real estate owners to international
fixed-income investors — providing investors with a range of asset- backed fixed-income
investment options and providing property owners access to the broader capital markets.
CMBS are securities backed by pools of CRE mortgages. Investors in the securities are
repaid as property owners make their mortgage payments and / or pay-off their mortgages.
The bonds are often structured to allow different investors to take on different levels of risk
and to receive different returns based on the risks they assume. At the peak of the market in
2007, nearly $230 billion of CMBS were issued. With the onset of the financial crisis,
issuances fell to essentially a near - zero in 2009. The CMBS market has ever since seen
multiple changes in regulation, which made it difficult for growth to reach pre-GFC levels.
However, the resurrected model, fondly known as CMBS 2.0, has been making its presence
felt, albeit at a much slower pace.
With around USD 470 billion10 (around 12%) of the outstanding CRE Debt assets, life
insurance companies are major players in the CRE debt market and have a long track record
of making commercial and multifamily mortgages. Life companies use mortgages as a means
of investing the premiums they receive from insurance contracts and annuities; the long-term
nature of their commercial and multifamily mortgage assets is well aligned with the duration
of their insurance and other liabilities. Absent significant market or regulatory changes, they
have, and continue to be a reliable source of mortgage debt for commercial and multifamily
properties.
All other sources – private debt funds, pension funds, state/federal lending, non-
financial corporate business lenders etc. account to the remaining, nearly 6%11, of the
outstanding CRE debt assets. However, some of these players (specifically private debt
funds) have become very active in the market in the last few years and are quickly ramping
up their market share.
9 Calculated from the “Flow of funds, Balance sheets, and Integrated Macroeconomic Accounts” report released by the Federal Reserve in September 2018. 10 Calculated from the “Flow of funds, Balance sheets, and Integrated Macroeconomic Accounts” report released by the Federal Reserve in September 2018. 11 Calculated from the “Flow of funds, Balance sheets, and Integrated Macroeconomic Accounts” report released by the Federal Reserve in September 2018.
Page 12 of 74
2.2 Regulation and Constraints
The GFC brought to the fore several risks that the financial system was susceptible to;
and, elevated exposure to commercial real estate was identified as one. The years following
the GFC, regulators and the government acted together to make significant changes to the
regulatory environment related to CRE lending. As these changes began to take effect, some
of the traditional sources of CRE debt found themselves increasingly constrained in their
ability to provide capital to the space. Let us look at a few significant regulatory changes and
understand their effects.
Basel III Framework
Basel III is a regulatory framework that was approved by the Federal Reserve back in
2013 and was intended to strengthen the regulation, supervision and risk management of the
banking sector. Generally, the new regulations require banking organizations to maintain
higher capital levels and limit the definition of what is included in the calculation of capital.
While the regulations went into effect in January 2015, the banking industry is still
interpreting the new regulations and determining what the impact will be on their real estate
lending strategies.
One key aspect of the regulations, specifically for real estate lending, is the
classification of certain loans into the High Volatility Commercial Real Estate (HVCRE)
category, calling for higher risk weightages and capital requirements. In May 2018, the
government passed a bill to dilute some of these requirements. As these regulations are
continually evolving and banks work to understand the rules, different interpretations are
leading to different implementations. All in all, the change in capital requirements has created
many constraints to commercial banks, which by far have been the largest source for CRE
debt.
CCAR stress test
Through the Comprehensive Capital Analysis and Review (CCAR), the Federal
Reserve has unprecedented information on and analysis of large banks’ holdings, including
commercial real estate portfolios. The analysis affects banks’ capital requirements and
incorporates a series of scenarios, including a severe stress scenario in which commercial real
estate prices drop 30 percent in the span of two years. This has been a major constraint for
commercial banks with their CRE lending portfolios.
Page 13 of 74
Dodd – Frank & Risk Retention
The retention rule that comes from the Dodd – Frank reforms came into effect from
December 2016. The new rules require CMBS issuers to retain at least a 5 percent share of
any security they issue, as determined by its fair value at the time of issuance. The
requirement can be met by holding a share of the junior tranche, which is called horizontal
retention, a portion of each tranche, known as vertical retention, or a mixture of the two.
Issuers may not directly or indirectly hedge or transfer the risk of the retained share.
However, they may sell off all or part of the junior tranche of their requirement to investors
who are experts at evaluating commercial real estate, known as B-piece buyers. Issuers
remain responsible for compliance with the risk retention rule as well as monitoring the B-
piece buyers’ compliance with the rule. The adoption of the risk retention rules significantly
changes the way CMBS issuers and bond subscribers underwrite and price risk. In some
ways, this could affect the volume of issuances in the market. Therefore, CMBS as a source
of funds could remain subdued until markets adapt and accept these changes.
Fundamental Review of Trading Book
Released as a Consultative Document in January 2016, the Fundamental Review of
the Trading Book (FRTB) proposes to overhaul the way capital requirements are calculated
for the wholesale trading activities of banks. Based on the proposed rules, going forward, it is
expected that there will be a significant increase in capital requirements for banks when they
invest in CMBS loans. With banks finding investments in CMBS uneconomical, there could
be a drop in large drop in liquidity in the CMBS market, which further would affect the
volume of CMBS loan originations.
2.3 Rise of Private Debt Funds
With traditional sources becoming increasingly conservative, and regulatory
constraints weighing in on banks and CMBS, private debt funds started gaining traction over
the last few years. Owing to an extended period of historically low interest rates, and an
abundance of global capital chasing not-too-many investment opportunities, most investment
managers in real estate started adding a debt vehicle/fund to their exiting platforms. With a
healthy growth in the number of funds and the quantum of capital raised for CRE debt
strategies in the US (See Chart 1), private debt funds are clearly emerging as a reliable,
Page 14 of 74
alternate source for CRE debt. Just in two years (2017 and 2018), around 7512 new funds with
over USD 42 billion13 of aggregate capital were raised for US market debt strategies.
US 10-year treasury rates since the low of 1.37%14 in July 2016 have increased at a
consistent pace to a high of 3.23%15 in November 2018. One would expect that cap rates
would have also moved in line with the interest rates; but, an abundance of capital flowing
into CRE has ensured that cap rates did not go up – instead, cap rate spreads contracted. The
market expectation is that cap rates, in a best-case scenario, will either remain where they are
today, or will start inching higher – the market sees very little or no chance that cap rates will
continue with their downward trend that they have been on until now. Investors have begun
to read this as an end-cycle environment where, on a going forward basis, returns from CRE
would predominantly be derived from operating income rather than property appreciation; in
fact, there could be a fall in property valuations if cap rates were to rise. So, with limited
upside potential for equity investments, investors are reallocating capital to specialized debt
strategies which can yield equity-like returns with a (arguably) higher degree of capital
protection. So, in the current environment, debt strategies have become more popular than
“Core” and “Core-Plus” strategies and are fast catching up with “Value Added” strategies in
12 As per data collected by Preqin. 13 As per data collected by Preqin. 14 As per data reported by FRED Economic Data. 15 As per data reported by FRED Economic Data.
Page 15 of 74
terms of fund-raising (See Chart 2). This further ensures that specialized debt funds would
continue to be a growing source for CRE debt in the years to come.
2.4 Evolving Landscape
With traditional providers of CRE debt confining themselves to simple and
conservative lending, private debt funds have quickly gained ground in the structured and
higher risk space. Unlike banks and life insurance companies, these debt funds are not
regulated - as long as they don’t stray too far away from their stated strategy and the
promised returns, they can always be flexible in their ability to price and take risks based on
market forces. With historically high levels of investments flowing into these debt funds
however, there is a growing concern that the excess liquidity in the space could lead to a
price-war, and thereby credit risk could increasingly get underpriced. Another area of concern
is that a good number of these debt funds have not had the first-hand experience of being in
the CRE debt business – they are mostly new or acquired debt platforms in a larger
investment management firm. Unlike banks and life insurance companies, that have sizable
existing debt portfolios that they have observed over the years to understand the credit risk of
CRE loans, most of the new debt funds will be discovering the risk characteristics of CRE
debt with the portfolios that they are starting to build now. We are currently in a very
interesting phase in the cycle where the CRE debt space is evolving rapidly – whether it is for
the good or bad, only the next downturn will tell.
Page 16 of 74
3. Key Definitions & Concepts
The focus of this study is to analyze delinquencies in CRE loans, bring out any
pertinent trends, and create a framework of metrics that can help in understanding the risk
characteristics of CRE debt better. So, choosing the right metrics and defining them in a way
that is both intuitive as well as fundamentally sound is the first step. The study will use
simplified versions of metrics that are often used by the industry in credit risk models. The
whole idea of using simplified versions is that, the framework will be easily understood by
everyone, and so, can be used in a sort of a back-of-the-envelope manner in evaluating and
comparing risk. This chapter presents five fundamental metrics that form the framework
through which the delinquencies have further been analyzed.
3.1 Incidence of Default
Put simply, a Default is any failure on behalf of the borrower in making the contracted
payments to the lender – whether interest, principle, or any other. While there is a whole
another set of defaults that are technical (or non-monetary) in nature, they will not be directly
relevant to the purpose of this study. All defaults are not the same; financial institutions
typically use a grading methodology to categorize defaults based on severity. Usually the
methodology is based on the number of days a scheduled payment is overdue. So, defaults
maybe categorized under the following typical buckets, in an increasing order of severity:
below 30 days, 30 to 59 days, 60 to 89 days, 90 days and above. Usually, loans that are 90
days and more overdue are categorized as Non-Performing Loans (NPLs). Based on the
regulatory framework under which the lending institution operates, NPLs require greater
capital requirements and have a significant negative effect on the lender’s profitability, credit
rating, and other operations.
For this study, any loan that has over its lifetime (or within the available track record,
for loans that have not reached maturity) been categorized as being delinquent for 60 days or
more or has been categorized as being delinquent due to the non-payment of any scheduled
balloon payment at the time of maturity, has been treated as a Default. A Default of the first
kind happens due to non-payment of scheduled interest/amortization within the tenure of the
loan; such Defaults have further been classified as Term Defaults. A Default of the second
kind happens when there is a scheduled balloon payment at the time of the maturity of the
Page 17 of 74
loan, and there is a failure to make such a payment; such Defaults have further been classified
as Maturity Defaults.
Probability of Default (PoD) is the probability that a subject loan will default in a
given horizon of time. PoD is a very important factor in modelling credit losses associated
with loans and portfolios. Banks, financial institutions, and credit rating agencies use
advanced statistical and econometric methods to model annual/cumulative PoDs for CRE
loans. The focus of the current study is not to build a credit loss model, but to highlight
relative differences in risk across different segments using historic, empirical data. So, a
diluted form of PoD that we can call as Incidence of Default (IoD) - a simple relative
frequency measure, has been used for this study. IoD for any group of loans is simply, the
number of Default loans over the total number of loans in the group, expressed as a
percentage.
Incidence of Default of many categories/groups have been used in the study. For
example, IoD at an aggregate level for the entire set of loans can be calculated, IoD at an
asset class level, say Multi-family, can be calculated, IoD for loans that have been originated
in a particular year can be calculated; IoD for loans falling into different LTV segments can
be calculated etc.
3.2 Incidence of Active Workout
When loans default, and borrowers don’t have the ability/willingness to cure the
default in time, lenders typically have multiple ways to deal with the situation – usually, they
choose their tools based on how severe the default is, and what the potential downside is.
Depending on these, a lender may choose to offer the defaulting borrower several workout
strategies – dilution of cashflow covenants, payment moratorium, loan terms modification,
forbearance arrangement etc. However, if everything fails, as a downside hedge, the lender
will take a step further to actively strategize a disposition/liquidation of the loan or the
underlying asset. This again can happen in several ways, based on the specific situation –
through foreclosures, by repossessing the real estate asset, by sale of the loan etc.
For this study, any loan that has over its lifetime (or within the available track record,
for loans that have not reached maturity) been actively pursued with any one of the following
workout strategies – Modification, Foreclosure, Bankruptcy, Extension, Note Sale,
Discounted Payoff, Real Estate Owned, Deed in Lieu of Foreclosure, has been flagged off as
a loan with Active Workout. Based on whether the workout strategy was successful or not,
Page 18 of 74
and whether the default has been resolved or not, these loans would definitely be susceptible
to move further into a Loss.
Incidence of Active Workouts (IoAW) is a relative frequency measure – IoAW for
any group of loans is simply, the number of loans flagged off with Active Workout over the
total number of loans in the group, expressed as a percentage. Just like IoD, IoAW has also
been calculated for different categories/groups of loans.
3.3 Incidence of Loss
The process of workouts and later disposition/liquidation takes a long time to play
out; so, a defaulting loan during this time remains unresolved if it is not cured by the
borrower. However, once the process is completed, the default is resolved by adjusting the
liquidation proceeds towards the accrued balance of the loan. If the proceeds are lower than
the accrued balance, a Loss/Realized Loss is recorded.
Incidence of Loss (IoL), as it is used in the context of this study, is a simple relative
frequency - IoL for any group of loans is simply, the number of loans with a Realized Loss
over the total number of loans in the group, expressed as a percentage. Again, just like IoD
and IoAW, IoL has been calculated for different groups/categories of loans.
Comparing IoD and IoL would give an idea on how defaults graduated further into realizing
losses for a particular group. Loans in Default that have not progressed into a Loss have
either been cured by the borrowers or are still in the process of being resolved. Therefore, it is
important to note that IoL is a measure of the relative frequency of loans that have realized a
loss; there will be many more loans where resolution is in progress and losses have not yet
been realized – IoAW would capture many such loans.
3.4 Loss Severity
Realized Loss is the non-recoverable amount from the sale/liquidation of a defaulting
loan. It is equal to the outstanding principal balance of the loan, plus all unpaid scheduled
interest, plus all fees associated with the sale process, minus the amount received from
liquidation. Loss Severity is usually the Realized Loss represented as a fraction of either the
Securitized balance of the loan, or the Outstanding balance of the loan.
For the purpose of this study, Loss Severity for a loan has been treated as the
Realized Loss of the loan over the original loan amount/principal, expressed as a percentage.
Page 19 of 74
Loss Severity for a group of loans is a simple average of the Loss Severities of all the
constituent loans.
3.5 Time to Default
It is not just enough that the Incidence of Default and the Severity of Loss are known.
The third dimension is when, over the lifetime of a loan that Default happens. If a Default
occurs early on in the lifetime of the loan, the Realized Loss associated with the Default will
be so much more than what it would have been if the Default occurred later in the lifetime;
the longer the time the loan takes to Default, lower is the outstanding balance of the loan, and
therefore, lower is the Realized Loss. Also, the lender would have also earned the scheduled
interest over the time. Therefore, Time to Default is an important third dimension to credit
loss models.
For the purpose of this study, Time to Default is the time (months or years) lapsed
between the date of origination of the loan, and the first time the loan has gone into a Default.
Time to Default for a group of loans is the simple average of the Time to Default of the
constituent loans.
Page 20 of 74
4. Overview of Data
In the previous chapter, the key metrics that form the framework for the analysis were
introduced. This chapter gives an overview of the dataset on which the framework will be
used for further analysis.
4.1 Source of Data
The entire dataset for this study is provided by Trepp LLC (Trepp), one of the largest
commercially available databases for securitized mortgages. The Trepp Data Feed is
essentially categorized into different files (loan level, deal level, property level, bond level
etc.), each of which captures an exhaustive amount of historical/time-series data pertaining to
CMBS issuances. The files consist of both, static and dynamic data fields updated either
through proprietary sources of Trepp, or from other reliable sources like Annex A's16,
Servicer Set-Up files17, CREFC Loan and Property Periodic Files18 etc.
The study primarily uses loan level files of the Trepp Data Feed that capture
comprehensive data related to the underlying loans/collateral of the CMBS structure. The
data constitutes over 500 fields recorded for every loan and updated monthly based on the
distribution dates of the CMBS pool. The entire monthly history of all the loans in the dataset
was used for the analysis.
4.2 Data Timeline
The data cut for the study comprises all U.S. historic public CMBS issuances from
January 1, 199819 through June 30, 2018. As this was a study on the performance of
underlying loans, and not CMBS bonds, emphasis was given more to the origination years of
the underlying loans rather than the issuance years of the CMBS. Overall, the dataset
constitutes 65,533 CRE loans originated way prior to 1998, through to 2018. The initial
issuances in the 1990s had portfolios of loans that were originated way before the issuance.
Therefore, the year 1995 was selected as the cutoff start year of origination for loans to be
considered for the study; the year 2017 was considered as the cutoff end year of origination
16 A standardized form that captures collateral details and is included as an attachment to the disclosure document given to investors during the offering of a CMBS issuance. 17 Updates by master servicer, special servicer etc. 18 Updates a part of the CRE Finance Council’s Investor Reporting Package. 19 Following the establishment of the Resolution Trust Corporation (RTC), the first rated issuance of CMBS was in January 1992. However, issuance volumes remained low during the initial few years. However, from 1998 onwards, CMBS started taking off as a mainstream source of CRE debt.
Page 21 of 74
for loans to be considered for the study. Running the data through these filters, the final data
set for the study came to be consisting of 61,376 CRE loans. See Chart 3 for understanding
the spread of these loans based on origination years.
4.3 Loan Types
Being collateral security to CMBS deals, all the loans in the data set are CRE loans
advanced to income generating commercial real estate assets. The data set does not include
construction finance loans, or other kinds of loan structures that do not depend on the income
generating nature of the underlying asset for repayment. The Trepp database has a data field
called “loanpurpose”; however, the field is either not populated in all cases or as there is no
standardization in the input values, there is just too much variety in what is being reported.
However, some of the more prominent purposes reported under this field are – refinance,
acquisition, construction loan take-out, recapitalization etc.
4.4 Geographical Spread
Based on the relative importance of a city and its real estate market, 10 Metropolitan
Statistical Areas were selected for the sample: New York – Newark – Jersey City, Boston –
Cambridge – Newton, Chicago – Naperville – Elgin, Washington – Arlington – Alexandria,
Houston – The Woodlands – Sugar Land, Philadelphia – Camden – Wilmington, San
Francisco – Oakland – Hayward, Los Angeles – Long Beach – Anaheim, Dallas – Fort Worth
Page 22 of 74
– Arlington, Seattle – Tacoma – Bellevue. See Chart 4 for the geographical distribution of
the loans.
4.5 Asset Class Distribution
Trepp data is classified under 13 different asset classes, all of which are present in the
study sample. However, the major asset classes (Multi-family, Retail, Office, Industrial,
Lodging, and Mixed-use) have been classified independently for the study. All other asset
classes (Healthcare, Warehousing, Mobile Home Park, Self-storage, Co-operative housing
etc.) have been grouped under “Other”. See Chart 5 for the asset class – wise distribution of
loans.
Page 23 of 74
Looking at the distribution above, one might get a feeling that CMBS loan
originations during these years were heavily biased towards Multi-family as an asset class. To
get a better sense of the distribution, one needs to take a step further and understand the
evolution of the distribution over time (See Chart 6).
It can be clearly seen from the evolution that the distribution of based on asset class
completely changed post the GFC – 2008 being the year of inflection. Let’s look at the year
2006, a year that witnessed an all-time high (5306 loans) of loan originations for the subject
dataset. Multi-family loans accounted to just 32% of this volume; Office and Retail together
contributed 40% of the volume. Fast forward to 2015, a year that witnessed the highest loan
origination volume post the GFC (4161 loans). Multi-family loans contributed to 68% of the
overall volume; retail and office put together accounted to a meagre 15% of the origination
volume. This drastic shift in the make-up of the CMBS origination market reflects a
significant change – either in the risk appetite of CMBS bond investors; in the relative
prospects/performance of different asset classes; in how regulatory measures are
differentially affecting different asset classes; in all the three, or in perhaps something
completely different. Whatever the case be, it can be a very interesting research topic by
itself.
4.6 Loan-to-Value Summary
Considering that one of the main focus of the study is to understand how LTV of CRE
loans has been influencing their performance, this is a good time to get a summary of the
LTVs and any related trends for the subject dataset. For the purpose of the summary, simple
average of LTVs has been chosen as against a loan amount-weighted average of LTVs. The
Page 24 of 74
average LTV20 of all the 61,376 CRE loans in the data set is 62.15%. See Chart 7 under
understand the movement of the average LTV over the timeline of origination years.
It can be observed that LTVs have been moving in tandem with the overall economy
and the credit cycle in the U.S. For example, they rose consistently during the boom phase
that led up to the GFC and peaked in the year 2007. After a drastic fall, they started to
recover again from 2011.To get an even better sense of the trends in LTV, we could
understand them asset class-wise. See Chart 8 for a relative comparison of how LTVs of
each asset class moved over the origination years.
20 For the purpose of this study, LTV of a loan has been derived from the “securltv” field of the Trepp Data Feed. The LTV considered is the LTV at the time of securitization of the loan, and not at the time of origination of the loan. However, considering that the time lapsed between origination and securitization is not too long, and that most of the initial payments would predominantly be towards servicing just the interest portion, there is not any significant difference between the LTVs at origination and securitization.
Page 25 of 74
Office and Industrial in 2008 and 2009 are outliers and not very significant as the deal
volume for the asset classes was very low. As in Chart 6, Retail and Office were the asset
classes securing the highest LTVs prior to the GFC; however, post the GFC, Multi-family has
clearly been the asset class getting the highest LTVs.
Page 26 of 74
5. Data Analysis & Findings
Based on the five fundamental concepts/measures – Incidence of Default, Incidence
of Active Workout, Incidence of Loss, Loss Severity, and Time to Default, the performance
of loans was analyzed at the aggregate, origination year, asset class, and LTV levels. This
section presents the analysis along with the key findings.
5.1 Aggregate Incidence Analysis
From the initial data set of 61376 loans, only 60,085 loans21 were used further for the
analysis. On an aggregate basis (all asset classes, LTV ranges, and origination years from
1995 to 2017), the performance of the loans can be captured in the following, simple
snapshot.
The aggregate IoD for the data set stood at 9.44%. Around 42.27% of the Defaults
were Maturity Defaults.
The aggregate IoAW stood at 6.74%. Looking at it another way, around 71.35% of
the loans that went into a Default required the lender to adopt an Active Workout
Strategy.
The aggregate IoL stood at 5.70%. That is, 60.37% of the loans that Defaulted, or
84.61% of the loans that went into a workout, actually ended up with a Loss.
While the numbers above may look fine at a quick glance, they don’t tell the complete
story. There are two very important factors that need to be given their due. First – real estate
21 Loans with LTV above 85% were excluded.
Page 27 of 74
asset/credit market cycles; second – loan seasoning. We discuss the relevance of both factors
before moving further with the analysis.
5.2 Market Cycles & Loan Seasoning
Let us understand the asset market and credit market cycles that were prevalent during
the timeline of the dataset. Chart 10 shows an evolution of relative trends between loan
origination volume (an indicator for credit availability) and real estate asset valuations
(NCREIF capital value index). Just be visual observation, one can find a very strong
correlation between both, albeit some lag. Essentially, as property valuations grew, credit
markets became active and provided further capital for the asset market to grow – in effect,
forming a virtuous cycle of valuation growth. And, as valuations peaked, credit markets
pulled back, fueling a downward trend in asset valuations. While there is never-ending debate
on the direction and extent of causality, it can be said with fair degree of certainty that the
asset price and credit market cycles generally move in tandem.
Market Cycle 1: Years 1995-2007
The start of the timeline (year 1995) was a year of inflection for the real estate asset
markets in the US. The NCREIF capital value (appreciation) index peaked in the last quarter
of 1989 – thereon, until the last quarter of 1995, the index witnessed a continuous fall that
saw a cumulative erosion of around 32% in its value. First quarter of 1996 witnessed the
reversal of a downward trend that lasted for 24 continuous quarters. By starting the timeline
of the dataset in year 1995, we are effectively starting at the bottom of a previous cycle, and
at the very beginning of a new growth cycle.
Page 28 of 74
Despite the Asian Financial Crisis that happened around 1997, property valuations in
the US witnessed a continuous and steady growth. Then came the Dotcom crash that only put
a pause (or a slight downward correction) on the growth for around 8 quarters in 2001 and
2002. Asset markets quickly reverted to the overarching growth momentum. Fueled by free-
flowing credit markets, real estate valuations witnessed a meteoric rise and peaked in the first
quarter of 2008. Thereafter, with the GFC playing out, valuations started to plummet.
So, years 1995 to 2007 represent one whole market cycle – a rise from a previous
bottom, up to the peak preceding the subsequent fall. Over the period, the NCREIF capital
value index grew by about 69% in value. By studying aggregate indicators of performance
for loans originated between 1995 to 2007, one would get a sense of how CRE loans have
fared on an average over an entire market cycle. Another approach would be to perhaps study
how loan performance varied as you moved across the market cycle – i.e., early cycle loans
versus late cycle loans.
Market Cycle 2: Years 2008-Present
In the history of economics and finance, the year 2008 holds a special place – it is the
year that marks the GFC, an event that brought a rather abrupt end to many things. Real
estate was right in the middle of the contagion. For nearly two years, CRE valuations were on
a free fall – from the beginning of 2008 to 2010, the NCREIF capital value index lost about
32% 22of value before beginning to start the post-GFC rise.
With valuations correcting drastically, interest rates at historic lows, and billions of
dollars of capital sitting on the sidelines, the CRE asset market started a healthy recovery
from 2010. With the overall economy improving, real estate space markets started seeing
strong growth – tenants started expanding, occupancies started to fall, and rents started
growing; overall, there was an uptrend in the operating incomes of CRE. Coupled with that,
the capital markets were flushed with liquidity, because of which capitalization rates started
approaching historic lows. Together, the effect was that CRE valuations started to grow at a
22 It is very interesting to note that the NCREIF capital value index fell by about the same degree (32% from cycle peaks) both, during the earlier downturn (1990-1995), as well as during the GFC fall (2008-2010). So, for everyone who says that the GFC was a one-off event – nothing like it has happened in the past and nothing similar will happen in the future, this is a data point that suggests the contrary. If NCREIF capital value index can be considered representative of the universe of institutional grade real estate, data shows that CRE valuations have indeed dropped to a similar degree as the GFC earlier! Yes – what perhaps made the GFC different was the sheer swiftness of the fall.
Page 29 of 74
very healthy pace. Compared to the lows in 2010, the NCREIF capital value index gained
around 55% in value until the second quarter of 2018.
The overall economy of the US still remains healthy and is growing well. This could
translate into continued growth in the real estate space market, which further could mean that
income level performance of CRE could continue to do well. However, with a reversal in the
interest rates cycle since 2016, there is a growing feeling that there is no further room for
capitalization rates to go down. In fact, the real concern is if/when capitalization rates start
going up, would it start affecting CRE valuations negatively? So, most industry players
recognize that we are currently in a late-cycle environment. We could perhaps be in the
formative years of the next downturn. So, by studying aggregate indicators of performance
for loans originated between 2008 to 2017, we will not get the picture of a complete real
estate/credit cycle – perhaps only of its early stages. There is enough evidence, including in
the later part of this analysis, that late cycle loans tend to have significantly higher default
rates. So, the market cycle characteristics of the data set needs to be kept in mind when
consuming the analysis.
Loan Seasoning
Loan Seasoning is perhaps the most important factor that differentiates the data sets
pertaining to the two different market cycles discussed above. The data set related to Market
Cycle 1 predominantly comprises loans that are fully seasoned; that is, most of these loans
have complete track records till their maturity dates – so, both term/maturity default risks are
almost fully played out.
In the case of the data set related to Market Cycle 2, term defaults are not fully played
out, and nearly all of the loans are nowhere near their maturity dates to understand any trends
in maturity defaults. So, in general, all the five measurements of delinquency will be much
lower in comparison to the corresponding measurements of the Market Cycle 1 data set.
Therefore, no direct comparisons can be made between the measurements of the two data
sets. With the knowledge of these nuances and caveats, let us now proceed with the analysis.
Page 30 of 74
5.3 Incidence Analysis – Market Cycles
Market Cycle 1
The data set constitutes a total of 36,529 loans. On an aggregate basis (all asset
classes, LTV ranges, and origination years from 1995 to 2007), the performance of the loans
can be captured in the following, simple snapshot.
The aggregate IoD for the data set stood at 15.58%. Around 42.69% of the defaults
were maturity defaults.
The aggregate IoAW stood at 11.18%. Looking at it another way, around 71.76% of
the loans that went into a default required the lender to adopt a workout strategy.
The aggregate IoL stood at 9.56%. That is, 61.39% of the loans that defaulted, or
85.54% of the loans that went into a workout, actually ended up with a loss.
Market Cycle 2
The data set constitutes a total of 24,847 loans. On an aggregate basis (all asset
classes, LTV ranges, and origination years from 2008 to 2017), the performance of the loans
can be captured in the following, simple snapshot.
Page 31 of 74
Table 1: Incidence Funnel - Market Cycle 2 Total Loans 4,507 Defaults 133
Active Workouts 72 Realized Losses 24
The aggregate IoD for the data set stood at 0.54%. Around 24.81% of the Defaults
were Maturity Defaults.
The aggregate IoAW stood at 0.29%. Looking at it another way, around 54.14% of
the loans that went into a Default required the lender/trust to adopt an Active Workout
Strategy.
The aggregate IoL stood at 0.10%. That is, 18.05% of the loans that Defaulted, or
33.33% of the loans that went into a workout, actually ended up with a Loss.
Point-in-Time Comparison
When one compares the aggregate incidences of Market Cycle 1 & Market Cycle 2,
there is a substantial difference. Combining both cycles together to study delinquencies is
therefore not an ideal approach. Loans in Market Cycle 2 seem to be doing extremely well;
considering that we are currently in the same cycle, is it a better dataset to rely on? How
much effect does seasoning (or the lack of it) have on these incidences, and how much of it is
because of better underwriting standards/markets/credit quality? In order to get some answers
to these questions, we need to make a rough comparison between the data sets pertaining to
both the cycles. To do that, we need to bring them both onto the same platform.
The first step would be to carve out equally seasoned portfolio of loans from both the
data sets. Following were the steps taken:
During both market cycles, the year during which property valuations started to move
up was first identified. For Market Cycle 1, it is 1996, and for Market Cycle 2, it
happens to be 2010 – both based of NCREIF capital value index.
For Market Cycle 2, from the year 2010, there were 7 full years of origination cohorts
available with a track record timeline of 8 years and 6 months (until 30 June 2018).
So, to make comparison possible, three portfolios were created – Portfolios 1 & 2
from the Market Cycle 1 dataset, and Portfolio 3 from the Market Cycle 2 dataset.
Page 32 of 74
Portfolio 1 consists of all loans originated from 1996 to 2003 (7 years). The default
risk of this portfolio is (almost) fully played out as we have the track records till
maturity. Therefore, this portfolio is nearly a fully seasoned one.
Portfolio 2 is Portfolio 1, minus the payment history of all the loans from July 2004.
Effectively, it is as if someone was studying Portfolio 1 on 30 June 2004 - 7 full years
of origination cohorts available with a track record timeline of 8 years and 6 months.
Portfolio 3 consists of all loans originated from 2010 to 2017, with a track record
timeline until 30 June 2018 (8 years and 6 months).
For all three portfolios, incidences were run again (See Table 2).
Table 2: Point-in-Time Incidence Comparison Portfolio 1 Portfolio 2 Portfolio 3 Incidence of Default 10.88% 3.42% 0.48% Maturity Defaults 45.08% 11.23% 20.18% Incidence of Active Workouts 6.69% 2.11% 0.25% Incidence of Loss 6.22% 0.50% 0.07%
Comparing Portfolio 1 and Portfolio 2 gives us a sense of how important seasoning is.
If someone in July 2004 were to use the performance of Portfolio 1 loans to understand credit
risk, or to build a forward-looking credit loss model, they would be using the Portfolio 2
numbers - IoD of 3.42% and IoL of 0.5%. But the portfolio ended up with an IoD of 10.88%
and IoL of 6.22%. This goes to show that using unseasoned portfolios for understanding
credit risk might not be a good idea. So, going forward in this study, Market Cycle 1 will
be the primary dataset for the analysis.
The other interesting comparison that we can make is between the incidences of
Portfolio 2 and Portfolio 3 – now that both the market cycle datasets have been rendered with
similar levels of seasoning. It can be observed that loans originated in Market Cycle 2 are
clearly performing better than the ones in Market Cycle 1. This could be attributed to many
factors pertaining to Market Cycle 2 – generally conservative underwriting due to increased
regulation post the GFC, healthy recovery in overall economy and CRE space markets,
prolonged period of progressively decreasing capitalization rates, surplus liquidity chasing
real estate assets etc. However, there is one caveat in the comparison: the performance of
Portfolio 2 takes into account the effects of the Dotcom Crash – a negative shock that
occurred in the earlier phases of Market Cycle 1. The current cycle however has been a
steady rise all through, devoid of any intermittent shocks or pauses. Perhaps we will see some
Page 33 of 74
such events playing out in the later part of the current cycle – but that’s up for anybody’s
guess.
5.4 Incidence Analysis – Asset Classes
Loans in the Market Cycle 1 dataset (35,578 numbers) were classified primarily into
six different asset classes – Multi-family, Retail, Industrial, Office, Lodging, and Mixed-use.
All other asset classes - Healthcare, Warehousing, Mobile Home Park, Self-storage, Co-
operative housing etc. have been grouped under the “Other” category. The incidence analysis
for these categories of asset classes is captured in Chart 12.
Lodging, Office and Mixed use, in the same order, come out as the top three asset
classes where Incidence of Default has been the highest. However, it is observed that
while maturity defaults (MD) contribute to around 45% of the Defaults in Office and
Mixed-use categories, they account to only around 33% of the Defaults in Lodging.
Perhaps, cashflow fluctuations in the operations of Lodging assets is much higher
than other asset classes; therefore, there are much higher incidences of Term Defaults.
Keeping Others aside, Multi-family and Industrial are the two asset classes with the
lowest Incidence of Default. However, while Defaults in Industrial come equally from
Term as well as Maturity Defaults, they are skewed more in favor of Term Defaults in
the case of Multi-family.
Not only did Lodging top the IoAW measure, it also witnessed the highest conversion
from Default to Active Workouts - around 90% of the Default loans required lenders
to adopt an Active Workout strategy. With a conversion of around 83%, Office was
Page 34 of 74
the second highest. For most other asset classes, the conversion ranged between 65% -
75%.
Lodging and Office together occupy the top position for Incidence of Loss – around
14% of all loans in these asset classes realized a loss. One interesting observation
about Lodging is that, the asset class had the lowest conversion from the loans in
Active Workouts to loans that have a Realized Loss – around 68%, while other asset
classes are in the range of 80% - 90%. If this conversion was on par with other asset
classes, the Incidence of Loss for Lodging would have been much higher. Industrial
and Multi-family, that also had the lowest IoD, witnessed the lowest Incidence of
Loss.
5.5 Incidence Analysis – Origination Cohorts
It is not sufficient that we understand incidences at an aggregate level or at an asset
class level; both these approaches do little to capture the progression of the market cycle. To
get a sense of how incidences varied with the progression of the market cycle, we have to
study them in cohorts classified by the origination year. The idea is that, loans in different
years will be subject to different origination environments, and therefore will capture the
essence of the market cycle and the prevalent environment (primarily the characteristics of
the asset and credit markets) of that year. So, let us look at the trends in aggregate incidences
for cohorts of loans based on origination years (See Chart 13).
Page 35 of 74
IoD in the first few years of the cycle remained subdued, under 10%. However, from
1998 onwards, a sudden surge in IoD is observed. A part of this can be attributed to
the fact that loans originated during these years came to maturity right during, or after
the GFC; with the credit markets practically shut, refinancing them would have been
difficult. Therefore, maturity defaults (MD) for these cohorts would have been higher;
in fact, there is a simultaneous surge in maturity defaults during these years.
Cohorts from 2001 & 2002 witnessed slightly lower levels of IoD. Perhaps, with the
Dotcom Crash playing out during the time, lenders would have been conservative
than usual in their underwriting norms.
The real trend of our interest emerged from 2003. With the credit cycle loosening,
lenders began to get aggressive in the CRE space. All three incidences – IoD,
IoAW23, and IoL started to shoot upwards. Cohorts from the end-cycle years, 2006
and 2007, witnessed IoDs of well over 20% and IoLs of over 15%.
The other interesting observation is the course reversal of the maturity defaults trend.
From a high of around 62% in 2003, the share of maturity defaults began to fall
quickly. By the 2007 cohort, maturity defaults contributed to just about 35% of the
overall defaults. This is a sign of loosening credit standards and aggressive
underwriting.
5.6 Incidence Analysis – Asset Classes & Origination Cohorts
Now that we have seen how delinquencies crept into the credit cycle as it progressed,
let us further break the aggregate level down to the six primary asset classes (Multi-family,
Retail, Industrial, Office, Lodging, and Mixed-use) and observe their relative behavior over
the origination cohorts.
23 IoAW is expected to be greater than IoL as a loan would generally have to be in a workout period before it goes into a loss. While that is the case in most cases, there could be some exceptions (like in 1999 and 2001). This could have occurred if the servicer updation of delinquency and workout codes was not up to standards. It could have also happened if the realized loss was marginal, and the loan technically did not go into a default/workout. Whatever be the case, numbers and ratios have been used as they have been reported in Trepp Data Feed.
Page 36 of 74
A quick glance at Chart 14, it can be found that Lodging is the most idiosyncratic
asset class of all. While IoDs of most asset classes for cohorts in the earlier half of the
cycle stayed low and close to the aggregate average, IoDs for Lodging started off very
high, and declined substantially toward the middle of the cycle (2001-2004). This
could have happened because, unlike all other asset classes, Lodging exerienced a
mid-cycle crash from 2001 to 2003 – to a small extent due to the Dotcom Crash, but
predominantly because of the effects of the 9/11. All three performance metrics –
Occupany, Average Daily Rate, and Revenue Per Available Room, took a plunge and
reached lows that were comparable to those of the GFC. So, origination cohorts prior
to this period would have been affected by this crash, while loans originated during
this period would have been done with very conservative underwriting. However,
market memory does not last long - the IoD started growing again from the 2004
cohort, and swiftly regained its top position by the end of the cycle.
Office is another interesting asset class. For a long time, the IoD of the asset class
stayed pretty close to the aggregate IoD. However, the asset class seems to have been
the biggest beneficiary of the loose underwriting norms prevailing during the end
years of the cycle. From around 13% for the 2003 cohort, the IoD went up to around
Page 37 of 74
38% for the 2007 cohort. Maturity Defaults contributed to around 63% of the total
Defaults for the 2003 cohort; this fell drastically to around 39% for the 2007 cohort.
Retail and Industrial exhibited a degree of correlation. As IoD in retail started to gain
momentum towards the later parts of the cycle, so did the IoD for Industrial. From
being close to the aggregate average, IoD in Retail took off during the later parts of
the cycle. Interesetingly, while IoDs of all asset classes fell sharply for the 2008
cohort, IoD of Retail remained close to the highs of the 2007 cohort.
Mixed-use exhibited a very different trend. IoD started off low for the initial few
cohorts. However, it were much above the averarge for cohorts from 1999 to 2002,
and later declined for cohorts from 2003 to 2005. However, cohorts of 2006 and 2007
saw a much higher IoD.
Multi-family was the only asset class with an IoD that was consistently less than or
just above the aggregate IoD – cohorts of 2002 and 2004 were an exception as they
witnessed intermittent spikes. However, a more interesting fact is that the IoD, after
spiking to around 23% for the 2004 cohort, year-on-year declined to reach 17% for
the 2007 cohort – exactly opposite to the direction that all other asset classes moved.
There could be two explanations to this – first, Multi-family was more resilient in its
performance than other asset classes during the downturn; second, during the late-
cycle years, credit underwriting norms were loosened much more in favour of other
asset classes than Multi-family. Either or both could have had an effect. Perhaps, this
is also a reason that can explain the progressive shift in asset class preference towards
Multi-family in Market Cycle – 224.
Now that we have analyzed IoDs, let us go ahead and see how conversions (see Chart 15),
from Default to Realized Loss, behaved over the origination cohorts.
24 Refer to the earlier discussion that we had in Chapter 2 – See Chart 6.
Page 38 of 74
The aggregate conversions (Default to Realized Loss) were under 50% for the cohorts
that were early in the cycle. However, they quickly moved up to hit a peak for the
2001 cohort for which, both Lodging and Office had 100% conversion. After a fall for
the cohorts in the mid-cycle, they went on to consolidate in the 60% - 70% range for
the late cycle years.
Overall, Mixed-use, Industrial, and Retail, in that order, had much lower conversions
in most cohorts. While IoD has been high in these asset classes, it seems that Defaults
in these asset classes could be managed better, without realizing too many Loss.
Office was consistently above the aggregate conversion for most of the cohorts,
followed by Lodging. Both these asset classes not only led in terms of IoD, but also in
conversion to Losses.
Let us now look at how Maturity Defaults contributed to the total Defaults in each of the
cohort (See Chart 16).
Page 39 of 74
The fall in the percentage of Maturity Defaults across all asset classes in cohorts that
came towards the end of the cycle is a clear indication that underwriting in these years
was not very tight – either cashflow headroom/buffer was progressively diluted, or the
extent of correction in the real estate space market (occupancy and rents) going
forward was not fully anticipated and factored in.
Multi-family and Lodging together stand out as asset classes where Maturity Defaults
contributed least to total Defaults. But both are to be seen in different light. Multi-
family is an asset class with the lowest IoD, whereas Lodging is an asset class with
the highest IoD. So, for Lodging, a larger potion of Term Defaults might indicate
higher operational/cashflow fluctuations at the asset level. On the other hand, for
Multi-family, Term Defaults form a large portion of a much smaller pie of total
Defaults. It could be that in Multi-family, getting a refinance at the time of maturity is
not as difficult as the other asset classes; so, most defaults that are there in the asset
class show up as Term Defaults.
It is interesting to note that Retail cohorts, in the early years of the cycle largely
witnessed more of Term Defaults. However, as the cycle progressed, the later cohorts
started to witness more of Maturity Defaults. Most of the cohorts from 2000 onwards
would have had their maturities after the GFC, at a time when e-commerce picked up
and malls were no longer doing well. Refinancing these assets could have been a
difficult proposition.
Page 40 of 74
5.7 Aggregate Incidence Analysis – LTV Segments
Loan – To – Value ratio is an often-used metric in credit underwriting. On the face of
it, it is a very simple fraction – the quantum of debt over the appraised value of the
underlying asset. However, it is one of the pivotal factors that can affect the performance of a
loan. It is a well understood principle in lending that – greater the LTV, greater is the risk of
default. While everyone is in general agreement with it, quantifying how credit risk varies
with respect to LTV is a challenge – it is difficult to always find sizable/comparable pools of
loans across the LTV spectrum with complete track records. However, the current dataset
(Market Cycle 1) allows us to do exactly this, and verify empirically to what extent the
principle holds good for CRE loans.
For this, the loans were classified into different categories of LTVs: up to 50%,
greater than/equal to 50% and less than 55%, greater than/equal to 55% and less than 60%,
greater than/equal to 60% and less than 65%, greater than/equal to 65% and less than 70%,
greater than/equal to 70% and less than 75%, greater than/equal to 75% and less than 80%.
While there are more loans in the dataset that are above the indicated range, they are too few
in number – such groups may reflect more of idiosyncratic risk than the LTV risk. So, all
such loans were dropped from the analysis; effectively, 35,473 loans from Market Cycle 1
were used for this analysis. To understand the aggregate distribution of loans based on these
categories, see Chart 17.
To understand the distribution of loans based on asset class, see Chart 18.
Page 41 of 74
To understand the aggregate distribution of loans based on origination cohorts, see Chart 19.
Page 42 of 74
Clearly, the largest portion of the loans lie in the 70% to 80% LTV segment – around
44%. There is also a good portion of loans with LTVs less than 50%. Overall, there is
good representation from all segments for us to do a comparative study of risk.
While all asset classes are present in all segments, Office, Retail, and Lodging have
larger presence in the higher LTV segments. Most of the Other asset classes find their
place in the lower than 50% segment.
There seems to be an expansion in the 70% to 75% segment during a few years from
1998. As the cycle matured, the 75% to 80%, and 80% to 85% segments expanded.
Now, let us look at the incidences of each of these LTV segments.
The risk of Default and Loss varied significantly between different LTV segments.
Aggregate IoD for loans under 50% LTV stood at 4.73%, while the same for loans in
the 80% to 85% segment stood at 27.99% - nearly six times greater! Aggregate IoL
for loans under 50% LTV stood at 1.79%, while the same for loans in the 80% to 85%
segment stood at 18.79% - over ten times greater!
The risk of Default has a steep growth between the lower two LTV segments. This
need to be understood with a grain of salt. The first segment constitutes loans from all
LTVs up to 50% - so, even loans with much lower LTVs are a part of this pool.
However, all other segments have loans that are strictly rangebound. So, it is expected
that there will be a big increase in incidences between the first two segments.
Page 43 of 74
IoD growth trend increased its slope from 65% LTV onwards. The story seems to be
the same for IoAW as well as IoL. This goes to show that delinquencies grew at an
incremental rate from an LTV of 65%.
Conversions, from Default to Realized Loss, also grew significantly; from around
38% to over 67% as the LTV went up. This goes to show that at higher LTVs,
prevention of default is a more effective strategy than getting into workouts. The fact
that the quantum of equity is so small at higher LTVs, borrowers tend to have a higher
propensity to lose interest in the asset when valuations fall.
The other interesting observation is that there is a steady decrease in Maturity
Defaults as one goes higher in the LTV segments. This only shows that at higher
LTVs, it is very important to have enough cashflow headroom.
5.8 Asset Class Incidence Analysis – LTV Segments
Let us further break down the loan pool and observe the IoD trends among different
asset classes over the LTV segments (See Chart 21).
From the chart, we can see that while all asset classes saw a rise in IoD as they moved
up the LTV segments, a few of them were much riskier than the others at higher
LTVs. For example, Office, Mixed-use, and Lodging clearly saw the slopes of their
IoD trends getting much steeper than other asset classes for higher LTV segments.
Page 44 of 74
Multi-family witnessed its largest jump (around 54%) in IoD in the 75% to 80% LTV
segment, and the second highest jump (around 36%) in the 70% to 75% LTV. So, IoD
for this asset class grew at an accelerated pace as LTVs crossed 70%.
Industrial witnessed its highest jump (around 48%) in IoD for a transition over the
65% LTV mark.
Office, Mixed-use, and Lodging saw substantial increases in IoD for most LTV
segments starting from 60%.
Now, let us take a look at the conversion ratio (Default to Realized Loss) trends for different
asset classes over the LTV segments (See Chart 22).
Also, the trend of Maturity Defaults contribution across asset classes (See Chart 23).
Page 45 of 74
Four out of the six assets classes - Multi-family, Mixed-use, Industrial, and Office
witnessed steep increases in conversions from Default to Realized Loss after crossing
the 65% LTV mark.
It is interesting to note that Maturity Defaults have always constituted to less that 50%
of the overall defaults in all segments for Multi-family.
Office and Lodging had the biggest differences in the Default mix of different
segments of LTV. Both had very high Maturity Defaults at low LTVs, but the
proportion quickly went down for higher LTV segments.
5.9 Incidence Analysis – LTV Segments & Origination Cohorts
Having analyzed the trends in LTV segments on an aggregate, and an asset class
basis, let us now try to find if there are any observable trends in the performance of loans in
the LTV segments grouped together in cohorts of the same origination year (See Chart 24).
Page 46 of 74
While IoDs of all LTV segments grew substantially towards the late-cycle cohorts,
the biggest spikes are observed in the top three LTV segments – loans above 70%
LTV – these segments performed increasingly worse-off than the lower LTV
segments as the cycle matured.
There are two spikes in IoD in the higher LTV segments (above 70%) – one for
cohorts from 1998 to 2001, and the other for cohorts from 2004 to 2007. As these are
loans originated just prior to the Dotcom Crash, the spike could have been caused by
the brief pause/correction in the asset valuations and performance. However, it is
interesting to observe that lower LTV segments were not affected as much and IoDs
remained relatively smooth. This could be an indication of how sensitive higher LTV
loans are to fluctuations in asset performance and market disturbances.
Page 47 of 74
5.10 Aggregate Loss Severity
Having analyzed incidences for the dataset (Market Cycle 1), let us now move onto
Loss Severities. The aggregate Loss Severity of the dataset (all LTVs, asset classes, and
origination years) is observed to be 36.07%. That is, when a loan realized a Loss, on an
average the quantum of such a loss as a percentage of the original loan amount was around
36%. However, there is a very high degree of spread on both sides of this central tendency –
the standard deviation of Loss Severity is 32.14%. To get a better sense of the distribution,
see Chart 25.
It can be seen that a large proportion of the loans have Loss Severities that are under
10%. Infact, some of them have very nominal Realized Losses. If Loss Severity of less than
1% can be considered nominal and such loans can be ignored, the revised Loss Severity
figure would be 42.62%. We can consider this as our base figure, and proceed with the rest of
the Loss Severity analysis ignoring loans with Loss Severity of less than 1%.
5.11 Aggregate Loss Severity – Origination Cohorts
Let us now look at how Loss Severities evolved over different origination cohorts
over the market cycle (See Chart 26). Similar trend lines of IoD and IoL have also been
plotted to facilitate an easy comparison.
Page 48 of 74
It can be observed that Loss Severity, just like IoD and IoL, structurally moved to
higher ranges for cohorts in the later parts of the cycle. So, loans originated in these years had
a sort of a double whammy risk – not only were they more likely to default, but also if they
did, the Realized Losses were relatively larger than those experienced by loans originated in
the earlier stages of the cycle.
5.12 Loss Severity – Asset Classes
Now, let us look at Loss Severity across asset classes, both at an aggregate as well as
Origination cohort basis (See Charts 27 & 28).
Page 49 of 74
On a cumulative basis, it is observed that Office had the highest, and Industrial had
the lowest Lost Severities among all asset classes. Otherwise, most asset classes had
Loss Severities generally in the late-thirties to mid-forties range.
Loss Severities for Office and Retail were on a consistent upward trend for cohorts
starting 2000. Even Multi-family witnessed a strong upward trend in Loss Severity in
cohorts that were in the late-cycle.
Mixed-use and Lodging did not exhibit any strong trends in Loss Severity. Partly, this
is because the losses were fewer in number – any big, or a small loss for a year has an
effect to skew the average a lot.
Page 50 of 74
5.13 Loss Severity – LTV Segments
Having analyzed Loss Severity for different cohorts and asset classes, let us now see
if there are any Loss Severity trends that can be observed for various LTV segments (See
Chart 29).
There seems to be no real impact of LTV on Loss Severities up to the 65% LTV
mark. From thereon, Loss Severity seems to be growing consistently. Which means, higher
LTV loans not only had high incidences, but also high Loss Severities.
5.14 Aggregate Time to Default
We have so far analyzed Incidences and Loss Severity; together, they can give an idea
of what is known as Expected Loss for a loan. However, it is not just enough to get a sense of
the Expected Loss - the timing of that loss vis-à-vis the term of the loan matters too. So, let us
now move on to analyzing the Time to Default for the dataset.
The first level of analysis would be at an aggregate level (All LTVs, asset classes,
origination cohorts)25 – a distribution of default loans based on the time that these loans took
to Default from their date of origination (See Chart 30).
25 It is to be noted that loans in the dataset have varying maturities too. The use of the term Default is for both, Term as well as Maturity Defaults.
Page 51 of 74
Nearly 37% of the loans defaulted in the first 5 years of their life; another 35% over
the next 5 years of their life. There is a tower of Defaults between year 10 & 11 – most of this
can be attributed to Maturity Defaults. Considering that there are loans with varying terms in
the dataset, there were defaults even afterwards; however, the extent is very small.
Having observed the Time to Default profile at the aggregate level, let us now look at
how these Defaults (of Market Cycle 1 loans) played out on a timeline from 2005 to 2018
(See Chart 31).
It can be seen that Defaults peaked in the years after the GFC. There was also a mid-
cycle rise in defaults (from 2001 to 2003) that can perhaps be attributed to the Dotcom Crash.
Page 52 of 74
5.15 Time to Default – Asset Classes
Breaking down the aggregate level, let us get a sense of how individual asset classes
behaved (See Chart 32).
It can be observed that Multi – family and Lodging witnessed Defaults much earlier in
their lifetimes compared to other asset classes. It is interetsing also to note here that both
these asset classes also had the lowest contributions from Maturity Defaults – around 33%
(See Chart 12). Considering this, it is not surprising that their average time for default is
shorter. But, overall, most asset classes were in a close range.
5.16 Time to Default – Origination Cohorts
Let us now see if there were any trends in the evolution of Time to Default over
different origination cohorts (See Chart 33). For the sake of comparison, the trend of
Maturity Defaults has also been plotted on the same chart.
Page 53 of 74
It can be observed that origination cohorts that witnessed higher Maturity Defaults
also on an average defaulted much later. Both trends peaked in the year 2003 and dropped
quickly thereon. It is clear that for loans originated during the end-cycle, defaults occurred
much earlier in their lifetimes.
5.17 Time to Default – LTV Segments
Finally, let us see how Time to Default varies across LTV segments and observe if
there are any trends to note. For the sake of comparison, trends in IoD and Loss Severity have
been added to the chart (See Chart 34).
Unlike IoD and Loss Severity which moved up substantially for higher LTV
segments, the same is not observed in the case of Time to Default. Months to Default is lower
for the bottom two LTV segments (LTV up to 55%) – however, it remains flat up to the 75%
LTV mark, and thereafter slightly reduces for the upper two LTV segments. Overall, we can
say that Time to Default was not very affected by the LTV segment of the loan.
Page 54 of 74
6. Conclusion
By analyzing the dataset from different standpoints based on five principal metrics –
Incidence of Default, Incidence of Active Workouts, Incidence of Loss, Loss Severity, and
Time to Default, and by using five distinct cross-sections – Aggregate, Market Cycle, Asset
Class, Origination Cohort, and LTV, the study throws light on several important risk
characteristics of CRE debt. Summarized below are some of the high-level observations:
A good majority (over 70% in this analysis) of the loans that Default remain uncured
and require Active Workout Strategies. The takeaway from this is that CRE lenders
need to have good asset management and servicing capabilities as they grow.
However, it is also found that a large portion (over 80% in this analysis) of loans that
require Active Workout Strategies end up in a Loss. The other takeaway is that good
asset management/servicing capabilities are not a substitute to quality underwriting.
There is a significant difference in the risk characteristics of loans originated early on
in a market cycle and loans originated towards the end of a market cycle. All three
pillars of credit risk (Incidence of Default/Loss, Loss Severity, and Time to Default)
progressively deteriorate towards the end of a market cycle.
Risk metrics of a portfolio of loans are best understood in relation to the seasoning of
the portfolio. As it was found in the analysis, there was a big disparity in the metrics
for loans in Market Cycle 1 and Market Cycle 2. Not understanding the importance of
seasoning would have led to the misguided inference that the low Incidence of
Default/Loss in Market Cycle 2 is completely attributable to the quality of credit.
Every asset class has got a distinct credit risk profile; and each one is sensitive to
varying degrees to market cycle and LTV differences. For example, as found in the
analysis, Office may become incrementally risky above an LTV level of 60%,
whereas Multi-family risk spikes post the 70% LTV mark; or risk in Office may spike
towards the end of the market cycle, while the risk in Multi-family may remain more-
or-less the same.
Both, the risk of Default, and Loss Severity go up incrementally with the LTV of the
loan. However, the same has not been observed in the case of Time to Default.
Page 55 of 74
A Discussion on CRE Debt Pricing
The approach and the metrics used in the study, while primarily meant for
understanding the risk characteristics of CRE debt, can also be used as rough determinants
for pricing CRE loans. They provide a simple and a fundamental framework to think about
pricing in a sort of a back-of-the-envelope way.
The three pillars of credit risk that have been used in the study – Incidence of Default,
Severity of Loss, and Time to Default, used together, can tell a lot about the yield
degradation26 potential of a loan. The ability to asses the potential for yield degradation can
be very useful in determining the risk premiums that can be attached to a loan. Let us
consider a couple of scenarios and understand this better.
Scenario 1
A private debt fund is exploring two options of lending a loan against a CRE asset
that is valued at USD 10 million. The first option is to just be the senior lender and take an
exposure up to 60% LTV ratio – with another lender providing another layer of capital up to
77% LTV. The second option is to do a stretch loan up to 77% of the LTV. How can the
difference in risk between these two options be evaluated?
For Option - 1:
Loan amount = USD 6,000,000
Applicable IoD27 = 11.50%
Applicable LS28 = 36.02%
Expected Loss = 11.50% * 36.02% * 6,000,000 = USD 248, 538
Applicable Time to Default29 = 78 months
26 As per the text book “Commercial Real Estate Analysis and Investments” (Geltner, Miller, Clayton, Eichholtz), Yield Degradation is the effect of credit losses on the Realized Yield as compared to the Contractual Yield of a loan. 27 IoL is conceptually the correct metric to use. However, as we have counted only loans with a Realized Loss in calculating IoL, the possibility of loans under resolution going into a Realized Loss is not factored in. So, to be conservative, IoD has been used. Aggregate IoD for the 55%-60% LTV segment in the Aggregate incidence table (Appendix: Exhibit – 1) has been used; alternatively, IoD of a year that is closest in terms of the market cycle to the current year can also be used for a more nuanced working. 28 Aggregate Loss Severity for 55%-60% LTV segment (Chart 29). 29 Aggregate Time to Default (Chart 32).
Page 56 of 74
For Option - 2:
Loan Amount = USD 7,700,000
Applicable IoD30 = 24.95%
Applicable Loss Severity31 = 46.40%
Expected Loss = 24.95% * 46.40% * 7,700,000 = USD 891,414
Applicable Time to Default32 = 78 months
The Expected Loss as a percentage of the loan amount for Option-1 is 4.14%, whereas
the same for Option – 2 is 11.57%. Considering that the Default is expected to happen at 78
months in both cases, there will be no difference in the impact of default timing on the yield.
Therefore, the yield degradation can be compared between both options, and Option – 2 can
be priced with a correspondingly higher premium.
Scenario 2
The fund just realized that it has a third option of just doing the 60% - 77% LTV piece
as a mezzanine loan. How does this compare to the earlier two options?
Mezzanine Loan Amount = USD 1,700,000
Senior Loan Amount = USD 6,000,000
Total Debt Capital = USD 7,700,000
One of the points to note is that the metrics derived in the study are pertaining to
CMBS collateral loans. An LTV segment of 75% - 80% refers to loans that have an LTV in
that range, and not the 75% - 80% piece in itself. But in this option, we are applying the
metrics to only a small piece of the total debt. Let us see how that differs.
30 Aggregate IoD for the 75%-80% LTV segment in the Aggregate incidence table (Appendix: Exhibit – 1) has been used; alternatively, IoD of a year that is closest in terms of the market cycle to the current year can also be used for a more nuanced working. 31 Aggregate Loss Severity for 75%-80% LTV segment (Chart 29). 32 Aggregate Time to Default (Chart 32).
Page 57 of 74
Applicable IoD33 = 24.95%
Applicable Loss Severity = 46.40%34
Expected Loss = 24.95% * 46.40% * 7,700,000 = USD 891,414
Applicable Time to Default35 = 78 months
Loss Severity as a metric has been derived for a whole loan, and not a tranche.
Therefore, the Expected Loss calculation is made on a Total Debt Capital basis. However, the
application of the Expected Loss is based on the priority of the debt capital structure. So, the
entire outstanding balance of the senior loan if first paid off, and if anything is remaining, it is
paid to the mezzanine lender. The outstanding balances at the time of Default for both, senior
loan as well as the mezzanine loan, can be projected as we know the expected Time to
Default. Once the Expected Loss is appropriated based on the priority of loans, the effective
Expected Loss and yield degradation for just the mezzanine portion can be calculated. Due to
the difference in priority, the effective Expected Loss as a percentage of the mezzanine loan
amount will be way higher than in Option – 2; it is not uncommon for the entire mezzanine
portion to be wiped off.
We have seen with the above examples of how these metrics can be used intuitively
for getting a rough idea on debt pricing. We discussed an example with LTVs, but the metrics
can be applied to any kind of relative risk pricing situation.
33 Applicable IoD will not be different from Option – 2 in the first scenario as in both cases, the total debt capital has an LTV of 77%. 34 Loss Severity will be applicable on the total debt capital – not on the mezzanine loan amount. 35 Aggregate Time to Default (Chart 32).
Page 58 of 74
References
Geltner, D., Miller, N, Clayton, J., & Eichholtz, P. (2014). Commercial Real Estate Analysis and Investments (3rd Edition). OnCourse Learning. Pagliari, J. L. (2017). High-Yield Lending: It’s Good Until It’s Not. The Journal of Portfolio Management, 43(6), 138–161. Woodwell, J. (2016). Sources of Commercial and Multifamily Mortgage Financing in 2016 (MBA Research Datanote) (p. 13). Mortgage Bankers Association. Woodwell, J. (2018). Commercial / Multifamily Real Estate Finance (CREF) Markets — 2018 (p. 24). Mortgage Bankers Association. Board of Governors of the Federal Reserve System (US). (1945, October 1). All Sectors; Debt Securities and Loans; Liability, Level. Retrieved December 19, 2018, from https://fred.stlouisfed.org/series/TCMDO. Berman, A. R. (2007). Risks and Realities of Mezzanine Loans. Missouri Law Review, 72, 39. Flow of Funds, Balance Sheets, and Integrated Macroeconomic Accounts. (2018, September 20). Federal Reserve Statistical Release. Vandell, Kerry D., “On the Assessment of Default Risk in Commercial Mortgage Lending,” AREUEA Journal, September 1984, 270-296. Grant. (n.d.). Real-Estate Debt Funds Amass Record War Chest - WSJ. Retrieved December 17, 2018, from https://www.wsj.com/articles/real-estate-debt-funds-amass-record-war-chest-1540296001. Srivatsava, R. (2003). Default Study of Commercial Mortgages in CMBS pools: An Empirical Analysis of Defaults and Loss Severity. Massachusetts Institute of Technology, Cambridge, MA. Diamond, K. E. (2014). Commercial Real Estate CLOs: Features That Make Them an Attractive Asset Class. The Journal of Structured Finance, 20(3), 36–41. Halpern, M., & Li, K. (2018). CMBS Loss Severities - Q4 2017 Update (No. 1114374). Moody’s Investor Service. Beur, M., Duncan, L., & Frerich, L. (2014). The 2014 CMBS Default and Loss Study (p. 19). Wells Fargo Securities LLC. Fiorilla, P. (2018). At Long Last, the CRE Market Has a Mezzanine-Loan Index. CRE Finance World, (Winter 2018), 37–38. Rubock, D. (2007). US CMBS and CRE CDO: Moody’s Approach to Rating Commercial Real Estate Mezzanine Loans (No. SF95585) (p. 10). Moody’s Investor Service.
Page 59 of 74
Appendix: Table of Contents
Exhibit 1: Incidences – Aggregate – Market Cycle 1 (1995 – 2007) .................................... 60
Exhibit 2: Incidences – Aggregate – Market Cycle 2 (2008 – 2017) .................................... 61
Exhibit 3: Incidences – 1995 Origination Cohort ................................................................. 62
Exhibit 4: Incidences – 1996 Origination Cohort ................................................................. 63
Exhibit 5: Incidences – 1997 Origination Cohort ................................................................. 64
Exhibit 6: Incidences – 1998 Origination Cohort ................................................................. 65
Exhibit 7: Incidences – 1999 Origination Cohort ................................................................. 66
Exhibit 8: Incidences – 2000 Origination Cohort ................................................................. 67
Exhibit 9: Incidences – 2001 Origination Cohort ................................................................. 68
Exhibit 10: Incidences – 2002 Origination Cohort ............................................................... 69
Exhibit 11: Incidences – 2003 Origination Cohort ............................................................... 70
Exhibit 12: Incidences – 2004 Origination Cohort ............................................................... 71
Exhibit 13: Incidences – 2005 Origination Cohort ............................................................... 72
Exhibit 14: Incidences – 2006 Origination Cohort ............................................................... 73
Exhibit 15: Incidences – 2007 Origination Cohort ............................................................... 74
Page 60 of 74
Exhibit 1: Incidences – Aggregate – Market Cycle 1 (1995 – 2007)
Asset.Class LTV.Range Total.loans Loans.in.default MD Active.Workouts Realized.Loss IoD MD Contribution IoAW IoLMulti_family < 50 1684 40 20 23 18 2.38% 50.00% 1.37% 1.07%Multi_family >= 50 & < 55 495 28 14 10 10 5.66% 50.00% 2.02% 2.02%Multi_family >= 55 & < 60 682 58 22 37 20 8.50% 37.93% 5.43% 2.93%Multi_family >= 60 & < 65 931 89 41 37 33 9.56% 46.07% 3.97% 3.54%Multi_family >= 65 & < 70 1347 150 58 83 73 11.14% 38.67% 6.16% 5.42%Multi_family >= 70 & < 75 2178 331 90 225 201 15.20% 27.19% 10.33% 9.23%Multi_family >= 75 & < 80 2724 636 189 474 456 23.35% 29.72% 17.40% 16.74%Multi_family >= 80 & < 85 402 88 35 54 53 21.89% 39.77% 13.43% 13.18%Multi_family >= 85 & < 90 29 6 0 6 4 20.69% 0.00% 20.69% 13.79%
10472 1426 469 949 868 13.62% 32.89% 9.06% 8.29%Retail < 50 788 73 35 13 29 9.26% 47.95% 1.65% 3.68%Retail >= 50 & < 55 440 43 26 27 18 9.77% 60.47% 6.14% 4.09%Retail >= 55 & < 60 673 102 57 50 42 15.16% 55.88% 7.43% 6.24%Retail >= 60 & < 65 940 114 56 81 59 12.13% 49.12% 8.62% 6.28%Retail >= 65 & < 70 1368 253 115 163 149 18.49% 45.45% 11.92% 10.89%Retail >= 70 & < 75 2084 364 162 236 226 17.47% 44.51% 11.32% 10.84%Retail >= 75 & < 80 1845 416 182 332 270 22.55% 43.75% 17.99% 14.63%Retail >= 80 & < 85 239 59 28 45 37 24.69% 47.46% 18.83% 15.48%Retail >= 85 & < 90 41 4 2 3 4 9.76% 50.00% 7.32% 9.76%
8418 1428 663 950 834 16.96% 46.43% 11.29% 9.91%Industrial < 50 428 29 16 15 13 6.78% 55.17% 3.50% 3.04%Industrial >= 50 & < 55 227 17 11 9 6 7.49% 64.71% 3.96% 2.64%Industrial >= 55 & < 60 302 26 13 18 16 8.61% 50.00% 5.96% 5.30%Industrial >= 60 & < 65 407 39 25 30 18 9.58% 64.10% 7.37% 4.42%Industrial >= 65 & < 70 558 79 40 51 45 14.16% 50.63% 9.14% 8.06%Industrial >= 70 & < 75 805 134 63 112 79 16.65% 47.01% 13.91% 9.81%Industrial >= 75 & < 80 355 77 40 55 47 21.69% 51.95% 15.49% 13.24%Industrial >= 80 & < 85 42 13 5 12 12 30.95% 38.46% 28.57% 28.57%Industrial >= 85 & < 90 4 1 0 1 1 25.00% 0.00% 25.00% 25.00%
3128 415 213 303 237 13.27% 51.33% 9.69% 7.58%Office < 50 903 58 44 44 27 6.42% 75.86% 4.87% 2.99%Office >= 50 & < 55 380 29 22 17 12 7.63% 75.86% 4.47% 3.16%Office >= 55 & < 60 535 60 31 43 41 11.21% 51.67% 8.04% 7.66%Office >= 60 & < 65 770 113 55 84 60 14.68% 48.67% 10.91% 7.79%Office >= 65 & < 70 1148 220 104 173 149 19.16% 47.27% 15.07% 12.98%Office >= 70 & < 75 1804 396 177 309 292 21.95% 44.70% 17.13% 16.19%Office >= 75 & < 80 1378 471 190 424 352 34.18% 40.34% 30.77% 25.54%Office >= 80 & < 85 243 101 31 103 73 41.56% 30.69% 42.39% 30.04%Office >= 85 & < 90 15 2 2 3 0 13.33% 100.00% 20.00% 0.00%
7176 1450 656 1200 1006 20.21% 45.24% 16.72% 14.02%Mixed_Use < 50 292 22 15 6 7 7.53% 68.18% 2.05% 2.40%Mixed_Use >= 50 & < 55 113 11 7 6 6 9.73% 63.64% 5.31% 5.31%Mixed_Use >= 55 & < 60 159 21 8 11 9 13.21% 38.10% 6.92% 5.66%Mixed_Use >= 60 & < 65 172 35 22 19 14 20.35% 62.86% 11.05% 8.14%Mixed_Use >= 65 & < 70 255 57 26 39 35 22.35% 45.61% 15.29% 13.73%Mixed_Use >= 70 & < 75 373 83 27 67 64 22.25% 32.53% 17.96% 17.16%Mixed_Use >= 75 & < 80 193 62 26 43 35 32.12% 41.94% 22.28% 18.13%Mixed_Use >= 80 & < 85 31 15 6 11 9 48.39% 40.00% 35.48% 29.03%Mixed_Use >= 85 & < 90 3 1 1 1 1 33.33% 100.00% 33.33% 33.33%
1591 307 138 203 180 19.30% 44.95% 12.76% 11.31%Lodging < 50 202 18 14 22 5 8.91% 77.78% 10.89% 2.48%Lodging >= 50 & < 55 82 12 5 8 5 14.63% 41.67% 9.76% 6.10%Lodging >= 55 & < 60 118 18 5 15 6 15.25% 27.78% 12.71% 5.08%Lodging >= 60 & < 65 190 43 11 38 28 22.63% 25.58% 20.00% 14.74%Lodging >= 65 & < 70 295 90 28 76 59 30.51% 31.11% 25.76% 20.00%Lodging >= 70 & < 75 212 57 12 55 38 26.89% 21.05% 25.94% 17.92%Lodging >= 75 & < 80 82 23 12 20 17 28.05% 52.17% 24.39% 20.73%Lodging >= 80 & < 85 15 12 4 11 10 80.00% 33.33% 73.33% 66.67%Lodging >= 85 & < 90 3 0 0 1 0 0.00% #N/A 33.33% 0.00%
1199 273 91 246 168 22.77% 33.33% 20.52% 14.01%Other < 50 1855 51 34 13 11 2.75% 66.67% 0.70% 0.59%Other >= 50 & < 55 114 11 5 5 4 9.65% 45.45% 4.39% 3.51%Other >= 55 & < 60 174 19 10 11 10 10.92% 52.63% 6.32% 5.75%Other >= 60 & < 65 227 19 11 9 6 8.37% 57.89% 3.96% 2.64%Other >= 65 & < 70 328 46 25 24 16 14.02% 54.35% 7.32% 4.88%Other >= 70 & < 75 443 43 26 22 23 9.71% 60.47% 4.97% 5.19%Other >= 75 & < 80 361 46 20 38 34 12.74% 43.48% 10.53% 9.42%Other >= 80 & < 85 82 7 5 3 4 8.54% 71.43% 3.66% 4.88%Other >= 85 & < 90 10 1 0 1 1 10.00% 0.00% 10.00% 10.00%
3594 243 136 126 109 6.76% 55.97% 3.51% 3.03%Aggregate < 50 6152 291 178 136 110 4.73% 61.17% 2.21% 1.79%Aggregate >= 50 & < 55 1851 151 90 82 61 8.16% 59.60% 4.43% 3.30%Aggregate >= 55 & < 60 2643 304 146 185 144 11.50% 48.03% 7.00% 5.45%Aggregate >= 60 & < 65 3637 452 221 298 218 12.43% 48.89% 8.19% 5.99%Aggregate >= 65 & < 70 5299 895 396 609 526 16.89% 44.25% 11.49% 9.93%Aggregate >= 70 & < 75 7899 1408 557 1026 923 17.83% 39.56% 12.99% 11.69%Aggregate >= 75 & < 80 6938 1731 659 1386 1211 24.95% 38.07% 19.98% 17.45%Aggregate >= 80 & < 85 1054 295 114 239 198 27.99% 38.64% 22.68% 18.79%Aggregate >= 85 & < 90 105 15 5 16 11 14.29% 33.33% 15.24% 10.48%
35578 5542 2366 3977 3402 15.58% 42.69% 11.18% 9.56%
Page 61 of 74
Exhibit 2: Incidences – Aggregate – Market Cycle 2 (2008 – 2017)
Asset.Class LTV.Range Total.loans Loans.in.default MD Active.Workouts Realized.Loss IoD MD Contribution IoAW IoLMulti_family < 50 2135 1 1 0 0 0.05% 100.00% 0.00% 0.00%Multi_family >= 50 & < 55 1200 0 0 1 0 0.00% 0.00% 0.08% 0.00%Multi_family >= 55 & < 60 1594 2 0 1 0 0.13% 0.00% 0.06% 0.00%Multi_family >= 60 & < 65 2658 4 1 2 0 0.15% 25.00% 0.08% 0.00%Multi_family >= 65 & < 70 3148 7 1 5 0 0.22% 14.29% 0.16% 0.00%Multi_family >= 70 & < 75 3447 15 1 9 2 0.44% 6.67% 0.26% 0.06%Multi_family >= 75 & < 80 2878 7 0 1 0 0.24% 0.00% 0.03% 0.00%Multi_family >= 80 & < 85 609 3 2 2 2 0.49% 66.67% 0.33% 0.33%Multi_family >= 85 & < 90 21 0 0 0 0 0.00% 0.00% 0.00% 0.00%
17690 39 6 21 4 0.22% 15.38% 0.12% 0.02%Retail < 50 178 0 0 0 0 0.00% 0.00% 0.00% 0.00%Retail >= 50 & < 55 151 3 2 1 3 1.99% 66.67% 0.66% 1.99%Retail >= 55 & < 60 197 3 3 1 0 1.52% 100.00% 0.51% 0.00%Retail >= 60 & < 65 297 4 1 2 2 1.35% 25.00% 0.67% 0.67%Retail >= 65 & < 70 348 4 3 2 1 1.15% 75.00% 0.57% 0.29%Retail >= 70 & < 75 392 8 0 5 1 2.04% 0.00% 1.28% 0.26%Retail >= 75 & < 80 51 1 0 1 1 1.96% 0.00% 1.96% 1.96%Retail >= 80 & < 85 1 0 0 0 0 0.00% 0.00% 0.00% 0.00%Retail >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
1615 23 9 12 8 1.42% 39.13% 0.74% 0.50%Industrial < 50 38 0 0 0 0 0.00% 0.00% 0.00% 0.00%Industrial >= 50 & < 55 24 2 2 0 0 8.33% 100.00% 0.00% 0.00%Industrial >= 55 & < 60 37 2 0 2 1 5.41% 0.00% 5.41% 2.70%Industrial >= 60 & < 65 38 0 0 0 0 0.00% 0.00% 0.00% 0.00%Industrial >= 65 & < 70 67 2 2 1 1 2.99% 100.00% 1.49% 1.49%Industrial >= 70 & < 75 64 1 0 1 0 1.56% 0.00% 1.56% 0.00%Industrial >= 75 & < 80 9 1 0 0 0 11.11% 0.00% 0.00% 0.00%Industrial >= 80 & < 85 3 2 0 0 1 66.67% 0.00% 0.00% 33.33%Industrial >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
280 10 4 4 3 3.57% 40.00% 1.43% 1.07%Office < 50 284 3 2 0 1 1.06% 66.67% 0.00% 0.35%Office >= 50 & < 55 144 2 2 1 1 1.39% 100.00% 0.69% 0.69%Office >= 55 & < 60 162 0 0 2 0 0.00% 0.00% 1.23% 0.00%Office >= 60 & < 65 248 4 1 2 0 1.61% 25.00% 0.81% 0.00%Office >= 65 & < 70 324 9 2 5 3 2.78% 22.22% 1.54% 0.93%Office >= 70 & < 75 307 10 1 4 1 3.26% 10.00% 1.30% 0.33%Office >= 75 & < 80 66 0 0 0 0 0.00% 0.00% 0.00% 0.00%Office >= 80 & < 85 3 0 0 0 0 0.00% 0.00% 0.00% 0.00%Office >= 85 & < 90 2 0 0 0 0 0.00% 0.00% 0.00% 0.00%
1540 28 8 14 6 1.82% 28.57% 0.91% 0.39%Mixed_Use < 50 146 1 0 0 0 0.68% 0.00% 0.00% 0.00%Mixed_Use >= 50 & < 55 67 2 1 0 0 2.99% 50.00% 0.00% 0.00%Mixed_Use >= 55 & < 60 68 3 2 1 0 4.41% 66.67% 1.47% 0.00%Mixed_Use >= 60 & < 65 100 4 2 1 1 4.00% 50.00% 1.00% 1.00%Mixed_Use >= 65 & < 70 92 1 0 0 0 1.09% 0.00% 0.00% 0.00%Mixed_Use >= 70 & < 75 83 0 0 0 0 0.00% 0.00% 0.00% 0.00%Mixed_Use >= 75 & < 80 10 0 0 1 0 0.00% 0.00% 10.00% 0.00%Mixed_Use >= 80 & < 85 1 0 0 0 0 0.00% 0.00% 0.00% 0.00%Mixed_Use >= 85 & < 90 2 1 1 1 0 50.00% 100.00% 50.00% 0.00%
569 12 6 4 1 2.11% 50.00% 0.70% 0.18%Lodging < 50 89 1 0 3 1 1.12% 0.00% 3.37% 1.12%Lodging >= 50 & < 55 67 1 0 1 0 1.49% 0.00% 1.49% 0.00%Lodging >= 55 & < 60 107 2 0 2 0 1.87% 0.00% 1.87% 0.00%Lodging >= 60 & < 65 137 3 0 2 1 2.19% 0.00% 1.46% 0.73%Lodging >= 65 & < 70 163 7 0 5 0 4.29% 0.00% 3.07% 0.00%Lodging >= 70 & < 75 52 4 0 2 0 7.69% 0.00% 3.85% 0.00%Lodging >= 75 & < 80 2 0 0 0 0 0.00% 0.00% 0.00% 0.00%Lodging >= 80 & < 85 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%Lodging >= 85 & < 90 1 0 0 0 0 0.00% 0.00% 0.00% 0.00%
618 18 0 15 2 2.91% 0.00% 2.43% 0.32%Other < 50 1523 1 0 0 0 0.07% 0.00% 0.00% 0.00%Other >= 50 & < 55 67 1 0 1 0 1.49% 0.00% 1.49% 0.00%Other >= 55 & < 60 97 0 0 0 0 0.00% 0.00% 0.00% 0.00%Other >= 60 & < 65 134 0 0 0 0 0.00% 0.00% 0.00% 0.00%Other >= 65 & < 70 165 0 0 1 0 0.00% 0.00% 0.61% 0.00%Other >= 70 & < 75 163 1 0 0 0 0.61% 0.00% 0.00% 0.00%Other >= 75 & < 80 45 0 0 0 0 0.00% 0.00% 0.00% 0.00%Other >= 80 & < 85 1 0 0 0 0 0.00% 0.00% 0.00% 0.00%Other >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
2195 3 0 2 0 0.14% 0.00% 0.09% 0.00%Aggregate < 50 4393 7 3 3 2 0.16% 42.86% 0.07% 0.05%Aggregate >= 50 & < 55 1720 11 7 5 4 0.64% 63.64% 0.29% 0.23%Aggregate >= 55 & < 60 2262 12 5 9 1 0.53% 41.67% 0.40% 0.04%Aggregate >= 60 & < 65 3612 19 5 9 4 0.53% 26.32% 0.25% 0.11%Aggregate >= 65 & < 70 4307 30 8 19 5 0.70% 26.67% 0.44% 0.12%Aggregate >= 70 & < 75 4508 39 2 21 4 0.87% 5.13% 0.47% 0.09%Aggregate >= 75 & < 80 3061 9 0 3 1 0.29% 0.00% 0.10% 0.03%Aggregate >= 80 & < 85 618 5 2 2 3 0.81% 40.00% 0.32% 0.49%Aggregate >= 85 & < 90 26 1 1 1 0 3.85% 100.00% 3.85% 0.00%
24507 133 33 72 24 0.54% 24.81% 0.29% 0.10%
Page 62 of 74
Exhibit 3: Incidences – 1995 Origination Cohort
Asset.Class LTV.Range Total.loans Loans.in.default MD Active.Workouts Realized.Loss IoD MD Contribution IoAW IoLMulti_family < 50 56 2 1 0 1 3.57% 50.00% 0.00% 1.79%Multi_family >= 50 & < 55 9 2 2 0 0 22.22% 100.00% 0.00% 0.00%Multi_family >= 55 & < 60 15 1 1 0 0 6.67% 100.00% 0.00% 0.00%Multi_family >= 60 & < 65 15 3 3 0 0 20.00% 100.00% 0.00% 0.00%Multi_family >= 65 & < 70 25 1 1 0 0 4.00% 100.00% 0.00% 0.00%Multi_family >= 70 & < 75 65 3 0 2 1 4.62% 0.00% 3.08% 1.54%Multi_family >= 75 & < 80 18 2 0 0 0 11.11% 0.00% 0.00% 0.00%Multi_family >= 80 & < 85 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%Multi_family >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
203 14 8 2 2 6.90% 57.14% 0.99% 0.99%Retail < 50 17 0 0 0 0 0.00% 0.00% 0.00% 0.00%Retail >= 50 & < 55 10 0 0 0 0 0.00% 0.00% 0.00% 0.00%Retail >= 55 & < 60 12 0 0 0 0 0.00% 0.00% 0.00% 0.00%Retail >= 60 & < 65 10 2 2 0 0 20.00% 100.00% 0.00% 0.00%Retail >= 65 & < 70 25 3 3 0 0 12.00% 100.00% 0.00% 0.00%Retail >= 70 & < 75 25 5 3 2 0 20.00% 60.00% 8.00% 0.00%Retail >= 75 & < 80 4 1 0 2 1 25.00% 0.00% 50.00% 25.00%Retail >= 80 & < 85 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%Retail >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
103 11 8 4 1 10.68% 72.73% 3.88% 0.97%Industrial < 50 12 0 0 0 0 0.00% 0.00% 0.00% 0.00%Industrial >= 50 & < 55 4 1 1 0 0 25.00% 100.00% 0.00% 0.00%Industrial >= 55 & < 60 5 0 0 0 0 0.00% 0.00% 0.00% 0.00%Industrial >= 60 & < 65 7 0 0 0 0 0.00% 0.00% 0.00% 0.00%Industrial >= 65 & < 70 13 1 1 0 0 7.69% 100.00% 0.00% 0.00%Industrial >= 70 & < 75 7 1 1 0 0 14.29% 100.00% 0.00% 0.00%Industrial >= 75 & < 80 1 1 0 0 1 100.00% 0.00% 0.00% 100.00%Industrial >= 80 & < 85 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%Industrial >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
49 4 3 0 1 8.16% 75.00% 0.00% 2.04%Office < 50 17 2 2 0 0 11.76% 100.00% 0.00% 0.00%Office >= 50 & < 55 7 0 0 0 0 0.00% 0.00% 0.00% 0.00%Office >= 55 & < 60 5 0 0 0 0 0.00% 0.00% 0.00% 0.00%Office >= 60 & < 65 9 1 1 0 0 11.11% 100.00% 0.00% 0.00%Office >= 65 & < 70 9 2 2 0 0 22.22% 100.00% 0.00% 0.00%Office >= 70 & < 75 3 0 0 0 0 0.00% 0.00% 0.00% 0.00%Office >= 75 & < 80 4 0 0 0 0 0.00% 0.00% 0.00% 0.00%Office >= 80 & < 85 1 0 0 0 0 0.00% 0.00% 0.00% 0.00%Office >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
55 5 5 0 0 9.09% 100.00% 0.00% 0.00%Mixed_Use < 50 5 0 0 0 0 0.00% 0.00% 0.00% 0.00%Mixed_Use >= 50 & < 55 4 1 1 0 0 25.00% 100.00% 0.00% 0.00%Mixed_Use >= 55 & < 60 4 0 0 0 0 0.00% 0.00% 0.00% 0.00%Mixed_Use >= 60 & < 65 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%Mixed_Use >= 65 & < 70 2 0 0 0 0 0.00% 0.00% 0.00% 0.00%Mixed_Use >= 70 & < 75 2 0 0 0 0 0.00% 0.00% 0.00% 0.00%Mixed_Use >= 75 & < 80 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%Mixed_Use >= 80 & < 85 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%Mixed_Use >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
17 1 1 0 0 5.88% 100.00% 0.00% 0.00%Lodging < 50 2 0 0 0 0 0.00% 0.00% 0.00% 0.00%Lodging >= 50 & < 55 1 1 0 1 0 100.00% 0.00% 100.00% 0.00%Lodging >= 55 & < 60 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%Lodging >= 60 & < 65 2 1 0 1 1 50.00% 0.00% 50.00% 50.00%Lodging >= 65 & < 70 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%Lodging >= 70 & < 75 1 1 0 1 1 100.00% 0.00% 100.00% 100.00%Lodging >= 75 & < 80 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%Lodging >= 80 & < 85 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%Lodging >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
6 3 0 3 2 50.00% 0.00% 50.00% 33.33%Other < 50 8 2 1 0 1 25.00% 50.00% 0.00% 12.50%Other >= 50 & < 55 4 1 0 1 1 25.00% 0.00% 25.00% 25.00%Other >= 55 & < 60 8 1 0 4 2 12.50% 0.00% 50.00% 25.00%Other >= 60 & < 65 3 1 1 0 0 33.33% 100.00% 0.00% 0.00%Other >= 65 & < 70 2 1 0 1 1 50.00% 0.00% 50.00% 50.00%Other >= 70 & < 75 3 1 1 0 0 33.33% 100.00% 0.00% 0.00%Other >= 75 & < 80 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%Other >= 80 & < 85 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%Other >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
28 7 3 6 5 25.00% 42.86% 21.43% 17.86%Aggregate < 50 117 6 4 0 2 5.13% 66.67% 0.00% 1.71%Aggregate >= 50 & < 55 39 6 4 2 1 15.38% 66.67% 5.13% 2.56%Aggregate >= 55 & < 60 49 2 1 4 2 4.08% 50.00% 8.16% 4.08%Aggregate >= 60 & < 65 46 8 7 1 1 17.39% 87.50% 2.17% 2.17%Aggregate >= 65 & < 70 76 8 7 1 1 10.53% 87.50% 1.32% 1.32%Aggregate >= 70 & < 75 106 11 5 5 2 10.38% 45.45% 4.72% 1.89%Aggregate >= 75 & < 80 27 4 0 2 2 14.81% 0.00% 7.41% 7.41%Aggregate >= 80 & < 85 1 0 0 0 0 0.00% 0.00% 0.00% 0.00%Aggregate >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
461 45 28 15 11 9.76% 62.22% 3.25% 2.39%
Page 63 of 74
Exhibit 4: Incidences – 1996 Origination Cohort
Asset.Class LTV.Range Total.loans Loans.in.default MD Active.Workouts Realized.Loss IoD MD Contribution IoAW IoLMulti_family < 50 161 5 4 1 1 3.11% 80.00% 0.62% 0.62%Multi_family >= 50 & < 55 14 2 1 1 0 14.29% 50.00% 7.14% 0.00%Multi_family >= 55 & < 60 42 4 2 1 0 9.52% 50.00% 2.38% 0.00%Multi_family >= 60 & < 65 61 10 4 3 0 16.39% 40.00% 4.92% 0.00%Multi_family >= 65 & < 70 123 19 13 4 1 15.45% 68.42% 3.25% 0.81%Multi_family >= 70 & < 75 136 14 4 4 6 10.29% 28.57% 2.94% 4.41%Multi_family >= 75 & < 80 63 4 0 3 1 6.35% 0.00% 4.76% 1.59%Multi_family >= 80 & < 85 7 0 0 0 0 0.00% 0.00% 0.00% 0.00%Multi_family >= 85 & < 90 2 0 0 0 0 0.00% 0.00% 0.00% 0.00%
609 58 28 17 9 9.52% 48.28% 2.79% 1.48%Retail < 50 39 5 2 0 2 12.82% 40.00% 0.00% 5.13%Retail >= 50 & < 55 19 4 0 4 1 21.05% 0.00% 21.05% 5.26%Retail >= 55 & < 60 24 6 1 4 0 25.00% 16.67% 16.67% 0.00%Retail >= 60 & < 65 39 0 0 1 0 0.00% 0.00% 2.56% 0.00%Retail >= 65 & < 70 49 5 0 3 2 10.20% 0.00% 6.12% 4.08%Retail >= 70 & < 75 47 4 0 1 1 8.51% 0.00% 2.13% 2.13%Retail >= 75 & < 80 9 0 0 0 0 0.00% 0.00% 0.00% 0.00%Retail >= 80 & < 85 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%Retail >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
226 24 3 13 6 10.62% 12.50% 5.75% 2.65%Industrial < 50 27 1 0 0 0 3.70% 0.00% 0.00% 0.00%Industrial >= 50 & < 55 16 0 0 0 0 0.00% 0.00% 0.00% 0.00%Industrial >= 55 & < 60 23 0 0 0 0 0.00% 0.00% 0.00% 0.00%Industrial >= 60 & < 65 21 0 0 0 0 0.00% 0.00% 0.00% 0.00%Industrial >= 65 & < 70 31 1 1 2 0 3.23% 100.00% 6.45% 0.00%Industrial >= 70 & < 75 27 1 0 0 0 3.70% 0.00% 0.00% 0.00%Industrial >= 75 & < 80 2 0 0 0 0 0.00% 0.00% 0.00% 0.00%Industrial >= 80 & < 85 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%Industrial >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
147 3 1 2 0 2.04% 33.33% 1.36% 0.00%Office < 50 36 0 0 0 0 0.00% 0.00% 0.00% 0.00%Office >= 50 & < 55 10 1 1 0 0 10.00% 100.00% 0.00% 0.00%Office >= 55 & < 60 19 1 1 0 0 5.26% 100.00% 0.00% 0.00%Office >= 60 & < 65 30 2 2 0 0 6.67% 100.00% 0.00% 0.00%Office >= 65 & < 70 32 4 1 3 1 12.50% 25.00% 9.38% 3.13%Office >= 70 & < 75 31 4 2 1 2 12.90% 50.00% 3.23% 6.45%Office >= 75 & < 80 5 1 1 0 0 20.00% 100.00% 0.00% 0.00%Office >= 80 & < 85 2 0 0 0 0 0.00% 0.00% 0.00% 0.00%Office >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
165 13 8 4 3 7.88% 61.54% 2.42% 1.82%Mixed_Use < 50 8 2 2 0 0 25.00% 100.00% 0.00% 0.00%Mixed_Use >= 50 & < 55 3 0 0 0 0 0.00% 0.00% 0.00% 0.00%Mixed_Use >= 55 & < 60 3 1 1 0 0 33.33% 100.00% 0.00% 0.00%Mixed_Use >= 60 & < 65 11 1 1 0 0 9.09% 100.00% 0.00% 0.00%Mixed_Use >= 65 & < 70 10 0 0 0 0 0.00% 0.00% 0.00% 0.00%Mixed_Use >= 70 & < 75 2 0 0 0 0 0.00% 0.00% 0.00% 0.00%Mixed_Use >= 75 & < 80 1 0 0 0 0 0.00% 0.00% 0.00% 0.00%Mixed_Use >= 80 & < 85 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%Mixed_Use >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
38 4 4 0 0 10.53% 100.00% 0.00% 0.00%Lodging < 50 2 0 0 0 0 0.00% 0.00% 0.00% 0.00%Lodging >= 50 & < 55 2 0 0 0 0 0.00% 0.00% 0.00% 0.00%Lodging >= 55 & < 60 3 2 0 2 0 66.67% 0.00% 66.67% 0.00%Lodging >= 60 & < 65 12 3 0 2 2 25.00% 0.00% 16.67% 16.67%Lodging >= 65 & < 70 10 6 0 6 6 60.00% 0.00% 60.00% 60.00%Lodging >= 70 & < 75 2 0 0 0 0 0.00% 0.00% 0.00% 0.00%Lodging >= 75 & < 80 1 0 0 0 0 0.00% 0.00% 0.00% 0.00%Lodging >= 80 & < 85 1 0 0 0 0 0.00% 0.00% 0.00% 0.00%Lodging >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
33 11 0 10 8 33.33% 0.00% 30.30% 24.24%Other < 50 62 1 0 0 0 1.61% 0.00% 0.00% 0.00%Other >= 50 & < 55 4 0 0 0 0 0.00% 0.00% 0.00% 0.00%Other >= 55 & < 60 7 2 0 1 2 28.57% 0.00% 14.29% 28.57%Other >= 60 & < 65 9 0 0 0 0 0.00% 0.00% 0.00% 0.00%Other >= 65 & < 70 8 3 2 2 0 37.50% 66.67% 25.00% 0.00%Other >= 70 & < 75 12 0 0 0 0 0.00% 0.00% 0.00% 0.00%Other >= 75 & < 80 12 0 0 0 0 0.00% 0.00% 0.00% 0.00%Other >= 80 & < 85 1 0 0 0 0 0.00% 0.00% 0.00% 0.00%Other >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
115 6 2 3 2 5.22% 33.33% 2.61% 1.74%Aggregate < 50 335 14 8 1 3 4.18% 57.14% 0.30% 0.90%Aggregate >= 50 & < 55 68 7 2 5 1 10.29% 28.57% 7.35% 1.47%Aggregate >= 55 & < 60 121 16 5 8 2 13.22% 31.25% 6.61% 1.65%Aggregate >= 60 & < 65 183 16 7 6 2 8.74% 43.75% 3.28% 1.09%Aggregate >= 65 & < 70 263 38 17 20 10 14.45% 44.74% 7.60% 3.80%Aggregate >= 70 & < 75 257 23 6 6 9 8.95% 26.09% 2.33% 3.50%Aggregate >= 75 & < 80 93 5 1 3 1 5.38% 20.00% 3.23% 1.08%Aggregate >= 80 & < 85 11 0 0 0 0 0.00% 0.00% 0.00% 0.00%Aggregate >= 85 & < 90 2 0 0 0 0 0.00% 0.00% 0.00% 0.00%
1333 119 46 49 28 8.93% 38.66% 3.68% 2.10%
Page 64 of 74
Exhibit 5: Incidences – 1997 Origination Cohort
Asset.Class LTV.Range Total.loans Loans.in.default MD Active.Workouts Realized.Loss IoD MD Contribution IoAW IoLMulti_family < 50 151 0 0 0 0 0.00% 0.00% 0.00% 0.00%Multi_family >= 50 & < 55 22 2 2 0 0 9.09% 100.00% 0.00% 0.00%Multi_family >= 55 & < 60 37 6 3 0 0 16.22% 50.00% 0.00% 0.00%Multi_family >= 60 & < 65 88 7 3 0 1 7.95% 42.86% 0.00% 1.14%Multi_family >= 65 & < 70 119 11 3 3 3 9.24% 27.27% 2.52% 2.52%Multi_family >= 70 & < 75 242 23 9 13 6 9.50% 39.13% 5.37% 2.48%Multi_family >= 75 & < 80 291 21 4 16 14 7.22% 19.05% 5.50% 4.81%Multi_family >= 80 & < 85 43 4 0 2 2 9.30% 0.00% 4.65% 4.65%Multi_family >= 85 & < 90 4 0 0 0 0 0.00% 0.00% 0.00% 0.00%
997 74 24 34 26 7.42% 32.43% 3.41% 2.61%Retail < 50 49 7 1 2 1 14.29% 14.29% 4.08% 2.04%Retail >= 50 & < 55 30 2 0 3 0 6.67% 0.00% 10.00% 0.00%Retail >= 55 & < 60 49 6 0 2 0 12.24% 0.00% 4.08% 0.00%Retail >= 60 & < 65 70 3 0 1 1 4.29% 0.00% 1.43% 1.43%Retail >= 65 & < 70 107 12 1 7 4 11.21% 8.33% 6.54% 3.74%Retail >= 70 & < 75 187 17 6 10 8 9.09% 35.29% 5.35% 4.28%Retail >= 75 & < 80 84 5 1 3 2 5.95% 20.00% 3.57% 2.38%Retail >= 80 & < 85 7 0 0 0 0 0.00% 0.00% 0.00% 0.00%Retail >= 85 & < 90 3 0 0 0 0 0.00% 0.00% 0.00% 0.00%
586 52 9 28 16 8.87% 17.31% 4.78% 2.73%Industrial < 50 48 0 0 0 1 0.00% 0.00% 0.00% 2.08%Industrial >= 50 & < 55 24 2 1 1 0 8.33% 50.00% 4.17% 0.00%Industrial >= 55 & < 60 40 0 0 1 1 0.00% 0.00% 2.50% 2.50%Industrial >= 60 & < 65 46 2 0 2 1 4.35% 0.00% 4.35% 2.17%Industrial >= 65 & < 70 51 4 1 4 3 7.84% 25.00% 7.84% 5.88%Industrial >= 70 & < 75 75 2 0 2 2 2.67% 0.00% 2.67% 2.67%Industrial >= 75 & < 80 22 2 1 0 0 9.09% 50.00% 0.00% 0.00%Industrial >= 80 & < 85 3 1 0 2 2 33.33% 0.00% 66.67% 66.67%Industrial >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
309 13 3 12 10 4.21% 23.08% 3.88% 3.24%Office < 50 52 1 1 1 0 1.92% 100.00% 1.92% 0.00%Office >= 50 & < 55 21 1 0 1 1 4.76% 0.00% 4.76% 4.76%Office >= 55 & < 60 31 1 0 1 1 3.23% 0.00% 3.23% 3.23%Office >= 60 & < 65 76 6 2 3 4 7.89% 33.33% 3.95% 5.26%Office >= 65 & < 70 85 6 1 2 3 7.06% 16.67% 2.35% 3.53%Office >= 70 & < 75 118 9 1 6 6 7.63% 11.11% 5.08% 5.08%Office >= 75 & < 80 27 2 0 2 2 7.41% 0.00% 7.41% 7.41%Office >= 80 & < 85 5 0 0 0 0 0.00% 0.00% 0.00% 0.00%Office >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
415 26 5 16 17 6.27% 19.23% 3.86% 4.10%Mixed_Use < 50 17 2 1 0 0 11.76% 50.00% 0.00% 0.00%Mixed_Use >= 50 & < 55 6 0 0 0 0 0.00% 0.00% 0.00% 0.00%Mixed_Use >= 55 & < 60 9 1 0 0 0 11.11% 0.00% 0.00% 0.00%Mixed_Use >= 60 & < 65 8 0 0 0 0 0.00% 0.00% 0.00% 0.00%Mixed_Use >= 65 & < 70 16 2 1 1 0 12.50% 50.00% 6.25% 0.00%Mixed_Use >= 70 & < 75 16 2 1 1 0 12.50% 50.00% 6.25% 0.00%Mixed_Use >= 75 & < 80 10 2 1 2 1 20.00% 50.00% 20.00% 10.00%Mixed_Use >= 80 & < 85 1 0 0 0 0 0.00% 0.00% 0.00% 0.00%Mixed_Use >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
83 9 4 4 1 10.84% 44.44% 4.82% 1.20%Lodging < 50 12 2 1 1 0 16.67% 50.00% 8.33% 0.00%Lodging >= 50 & < 55 9 1 0 0 0 11.11% 0.00% 0.00% 0.00%Lodging >= 55 & < 60 13 6 0 5 2 46.15% 0.00% 38.46% 15.38%Lodging >= 60 & < 65 19 3 0 4 2 15.79% 0.00% 21.05% 10.53%Lodging >= 65 & < 70 32 10 0 8 5 31.25% 0.00% 25.00% 15.63%Lodging >= 70 & < 75 17 8 1 7 4 47.06% 12.50% 41.18% 23.53%Lodging >= 75 & < 80 1 0 0 0 0 0.00% 0.00% 0.00% 0.00%Lodging >= 80 & < 85 1 1 0 1 1 100.00% 0.00% 100.00% 100.00%Lodging >= 85 & < 90 1 0 0 1 0 0.00% 0.00% 100.00% 0.00%
105 31 2 27 14 29.52% 6.45% 25.71% 13.33%Other < 50 86 3 1 0 1 3.49% 33.33% 0.00% 1.16%Other >= 50 & < 55 14 0 0 0 0 0.00% 0.00% 0.00% 0.00%Other >= 55 & < 60 17 1 0 1 1 5.88% 0.00% 5.88% 5.88%Other >= 60 & < 65 25 4 1 1 1 16.00% 25.00% 4.00% 4.00%Other >= 65 & < 70 29 3 1 3 2 10.34% 33.33% 10.34% 6.90%Other >= 70 & < 75 45 5 1 5 5 11.11% 20.00% 11.11% 11.11%Other >= 75 & < 80 27 5 1 6 5 18.52% 20.00% 22.22% 18.52%Other >= 80 & < 85 12 2 2 0 1 16.67% 100.00% 0.00% 8.33%Other >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
255 23 7 16 16 9.02% 30.43% 6.27% 6.27%Aggregate < 50 415 15 5 4 3 3.61% 33.33% 0.96% 0.72%Aggregate >= 50 & < 55 126 8 3 5 1 6.35% 37.50% 3.97% 0.79%Aggregate >= 55 & < 60 196 21 3 10 5 10.71% 14.29% 5.10% 2.55%Aggregate >= 60 & < 65 332 25 6 11 10 7.53% 24.00% 3.31% 3.01%Aggregate >= 65 & < 70 439 48 8 28 20 10.93% 16.67% 6.38% 4.56%Aggregate >= 70 & < 75 700 66 19 44 31 9.43% 28.79% 6.29% 4.43%Aggregate >= 75 & < 80 462 37 8 29 24 8.01% 21.62% 6.28% 5.19%Aggregate >= 80 & < 85 72 8 2 5 6 11.11% 25.00% 6.94% 8.33%Aggregate >= 85 & < 90 8 0 0 1 0 0.00% 0.00% 12.50% 0.00%
2750 228 54 137 100 8.29% 23.68% 4.98% 3.64%
Page 65 of 74
Exhibit 6: Incidences – 1998 Origination Cohort
Asset.Class LTV.Range Total.loans Loans.in.default MD Active.Workouts Realized.Loss IoD MD Contribution IoAW IoLMulti_family < 50 246 4 3 1 0 1.63% 75.00% 0.41% 0.00%Multi_family >= 50 & < 55 49 2 1 2 0 4.08% 50.00% 4.08% 0.00%Multi_family >= 55 & < 60 65 3 2 0 0 4.62% 66.67% 0.00% 0.00%Multi_family >= 60 & < 65 137 9 5 4 2 6.57% 55.56% 2.92% 1.46%Multi_family >= 65 & < 70 182 19 9 12 8 10.44% 47.37% 6.59% 4.40%Multi_family >= 70 & < 75 348 20 6 13 9 5.75% 30.00% 3.74% 2.59%Multi_family >= 75 & < 80 409 26 9 19 14 6.36% 34.62% 4.65% 3.42%Multi_family >= 80 & < 85 67 9 1 5 3 13.43% 11.11% 7.46% 4.48%Multi_family >= 85 & < 90 5 2 0 2 1 40.00% 0.00% 40.00% 20.00%
1508 94 36 58 37 6.23% 38.30% 3.85% 2.45%Retail < 50 86 15 3 1 8 17.44% 20.00% 1.16% 9.30%Retail >= 50 & < 55 49 0 0 1 0 0.00% 0.00% 2.04% 0.00%Retail >= 55 & < 60 80 10 4 5 4 12.50% 40.00% 6.25% 5.00%Retail >= 60 & < 65 141 8 1 5 0 5.67% 12.50% 3.55% 0.00%Retail >= 65 & < 70 175 30 10 8 14 17.14% 33.33% 4.57% 8.00%Retail >= 70 & < 75 307 21 8 13 13 6.84% 38.10% 4.23% 4.23%Retail >= 75 & < 80 175 11 2 10 9 6.29% 18.18% 5.71% 5.14%Retail >= 80 & < 85 29 1 0 1 1 3.45% 0.00% 3.45% 3.45%Retail >= 85 & < 90 6 0 0 0 0 0.00% 0.00% 0.00% 0.00%
1048 96 28 44 49 9.16% 29.17% 4.20% 4.68%Industrial < 50 73 3 1 2 1 4.11% 33.33% 2.74% 1.37%Industrial >= 50 & < 55 31 0 0 0 0 0.00% 0.00% 0.00% 0.00%Industrial >= 55 & < 60 42 3 0 1 0 7.14% 0.00% 2.38% 0.00%Industrial >= 60 & < 65 69 9 6 3 1 13.04% 66.67% 4.35% 1.45%Industrial >= 65 & < 70 88 7 3 4 2 7.95% 42.86% 4.55% 2.27%Industrial >= 70 & < 75 151 15 7 10 4 9.93% 46.67% 6.62% 2.65%Industrial >= 75 & < 80 45 2 0 1 1 4.44% 0.00% 2.22% 2.22%Industrial >= 80 & < 85 5 0 0 0 0 0.00% 0.00% 0.00% 0.00%Industrial >= 85 & < 90 1 0 0 0 0 0.00% 0.00% 0.00% 0.00%
505 39 17 21 9 7.72% 43.59% 4.16% 1.78%Office < 50 87 4 3 1 3 4.60% 75.00% 1.15% 3.45%Office >= 50 & < 55 43 3 2 1 2 6.98% 66.67% 2.33% 4.65%Office >= 55 & < 60 46 1 0 2 1 2.17% 0.00% 4.35% 2.17%Office >= 60 & < 65 100 9 3 5 3 9.00% 33.33% 5.00% 3.00%Office >= 65 & < 70 142 11 4 7 5 7.75% 36.36% 4.93% 3.52%Office >= 70 & < 75 278 27 7 23 22 9.71% 25.93% 8.27% 7.91%Office >= 75 & < 80 76 10 3 10 10 13.16% 30.00% 13.16% 13.16%Office >= 80 & < 85 12 1 1 1 0 8.33% 100.00% 8.33% 0.00%Office >= 85 & < 90 1 0 0 0 0 0.00% 0.00% 0.00% 0.00%
785 66 23 50 46 8.41% 34.85% 6.37% 5.86%Mixed_Use < 50 20 3 2 0 0 15.00% 66.67% 0.00% 0.00%Mixed_Use >= 50 & < 55 9 1 1 1 0 11.11% 100.00% 11.11% 0.00%Mixed_Use >= 55 & < 60 13 1 0 0 0 7.69% 0.00% 0.00% 0.00%Mixed_Use >= 60 & < 65 15 2 1 0 1 13.33% 50.00% 0.00% 6.67%Mixed_Use >= 65 & < 70 25 1 0 2 0 4.00% 0.00% 8.00% 0.00%Mixed_Use >= 70 & < 75 39 1 0 1 1 2.56% 0.00% 2.56% 2.56%Mixed_Use >= 75 & < 80 13 2 1 1 2 15.38% 50.00% 7.69% 15.38%Mixed_Use >= 80 & < 85 1 0 0 0 0 0.00% 0.00% 0.00% 0.00%Mixed_Use >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
135 11 5 5 4 8.15% 45.45% 3.70% 2.96%Lodging < 50 19 1 0 3 1 5.26% 0.00% 15.79% 5.26%Lodging >= 50 & < 55 14 3 2 2 2 21.43% 66.67% 14.29% 14.29%Lodging >= 55 & < 60 23 3 1 2 0 13.04% 33.33% 8.70% 0.00%Lodging >= 60 & < 65 26 6 0 5 2 23.08% 0.00% 19.23% 7.69%Lodging >= 65 & < 70 36 7 1 3 3 19.44% 14.29% 8.33% 8.33%Lodging >= 70 & < 75 32 1 0 4 2 3.13% 0.00% 12.50% 6.25%Lodging >= 75 & < 80 2 1 0 1 1 50.00% 0.00% 50.00% 50.00%Lodging >= 80 & < 85 1 0 0 0 0 0.00% 0.00% 0.00% 0.00%Lodging >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
153 22 4 20 11 14.38% 18.18% 13.07% 7.19%Other < 50 157 5 4 1 1 3.18% 80.00% 0.64% 0.64%Other >= 50 & < 55 21 3 1 2 1 14.29% 33.33% 9.52% 4.76%Other >= 55 & < 60 21 3 1 2 3 14.29% 33.33% 9.52% 14.29%Other >= 60 & < 65 37 1 0 1 1 2.70% 0.00% 2.70% 2.70%Other >= 65 & < 70 52 5 1 1 1 9.62% 20.00% 1.92% 1.92%Other >= 70 & < 75 63 4 3 1 1 6.35% 75.00% 1.59% 1.59%Other >= 75 & < 80 50 6 1 7 5 12.00% 16.67% 14.00% 10.00%Other >= 80 & < 85 7 0 0 0 0 0.00% 0.00% 0.00% 0.00%Other >= 85 & < 90 4 1 0 1 1 25.00% 0.00% 25.00% 25.00%
412 28 11 16 14 6.80% 39.29% 3.88% 3.40%Aggregate < 50 688 35 16 9 14 5.09% 45.71% 1.31% 2.03%Aggregate >= 50 & < 55 216 12 7 9 5 5.56% 58.33% 4.17% 2.31%Aggregate >= 55 & < 60 290 24 8 12 8 8.28% 33.33% 4.14% 2.76%Aggregate >= 60 & < 65 525 44 16 23 10 8.38% 36.36% 4.38% 1.90%Aggregate >= 65 & < 70 700 80 28 37 33 11.43% 35.00% 5.29% 4.71%Aggregate >= 70 & < 75 1218 89 31 65 52 7.31% 34.83% 5.34% 4.27%Aggregate >= 75 & < 80 770 58 16 49 42 7.53% 27.59% 6.36% 5.45%Aggregate >= 80 & < 85 122 11 2 7 4 9.02% 18.18% 5.74% 3.28%Aggregate >= 85 & < 90 17 3 0 3 2 17.65% 0.00% 17.65% 11.76%
4546 356 ## 214 170 7.83% 34.83% 4.71% 3.74%
Page 66 of 74
Exhibit 7: Incidences – 1999 Origination Cohort
Asset.Class LTV.Range Total.loans Loans.in.default MD Active.Workouts Realized.Loss IoD MD Contribution IoAW IoLMulti_family < 50 132 0 0 1 0 0.00% 0.00% 0.76% 0.00%Multi_family >= 50 & < 55 24 1 1 0 1 4.17% 100.00% 0.00% 4.17%Multi_family >= 55 & < 60 26 1 1 0 1 3.85% 100.00% 0.00% 3.85%Multi_family >= 60 & < 65 48 4 2 2 1 8.33% 50.00% 4.17% 2.08%Multi_family >= 65 & < 70 90 11 5 5 5 12.22% 45.45% 5.56% 5.56%Multi_family >= 70 & < 75 144 25 13 12 14 17.36% 52.00% 8.33% 9.72%Multi_family >= 75 & < 80 216 48 25 32 32 22.22% 52.08% 14.81% 14.81%Multi_family >= 80 & < 85 15 2 0 2 2 13.33% 0.00% 13.33% 13.33%Multi_family >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
695 92 47 54 56 13.24% 51.09% 7.77% 8.06%Retail < 50 52 7 0 2 2 13.46% 0.00% 3.85% 3.85%Retail >= 50 & < 55 22 3 1 0 0 13.64% 33.33% 0.00% 0.00%Retail >= 55 & < 60 47 6 3 1 2 12.77% 50.00% 2.13% 4.26%Retail >= 60 & < 65 54 4 2 4 2 7.41% 50.00% 7.41% 3.70%Retail >= 65 & < 70 96 17 7 9 8 17.71% 41.18% 9.38% 8.33%Retail >= 70 & < 75 162 38 9 7 21 23.46% 23.68% 4.32% 12.96%Retail >= 75 & < 80 83 15 10 14 16 18.07% 66.67% 16.87% 19.28%Retail >= 80 & < 85 8 1 1 1 0 12.50% 100.00% 12.50% 0.00%Retail >= 85 & < 90 7 0 0 0 0 0.00% 0.00% 0.00% 0.00%
531 91 33 38 51 17.14% 36.26% 7.16% 9.60%Industrial < 50 20 4 3 2 2 20.00% 75.00% 10.00% 10.00%Industrial >= 50 & < 55 9 1 1 1 1 11.11% 100.00% 11.11% 11.11%Industrial >= 55 & < 60 24 1 0 2 0 4.17% 0.00% 8.33% 0.00%Industrial >= 60 & < 65 26 1 1 1 4 3.85% 100.00% 3.85% 15.38%Industrial >= 65 & < 70 61 8 4 6 4 13.11% 50.00% 9.84% 6.56%Industrial >= 70 & < 75 82 17 4 17 14 20.73% 23.53% 20.73% 17.07%Industrial >= 75 & < 80 19 4 1 4 2 21.05% 25.00% 21.05% 10.53%Industrial >= 80 & < 85 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%Industrial >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
241 36 14 33 27 14.94% 38.89% 13.69% 11.20%Office < 50 63 3 2 0 3 4.76% 66.67% 0.00% 4.76%Office >= 50 & < 55 27 0 0 0 0 0.00% 0.00% 0.00% 0.00%Office >= 55 & < 60 38 2 0 2 1 5.26% 0.00% 5.26% 2.63%Office >= 60 & < 65 48 4 2 2 3 8.33% 50.00% 4.17% 6.25%Office >= 65 & < 70 95 16 5 12 9 16.84% 31.25% 12.63% 9.47%Office >= 70 & < 75 112 17 13 9 11 15.18% 76.47% 8.04% 9.82%Office >= 75 & < 80 37 11 4 12 10 29.73% 36.36% 32.43% 27.03%Office >= 80 & < 85 1 0 0 0 0 0.00% 0.00% 0.00% 0.00%Office >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
421 53 26 37 37 12.59% 49.06% 8.79% 8.79%Mixed_Use < 50 18 3 3 0 1 16.67% 100.00% 0.00% 5.56%Mixed_Use >= 50 & < 55 10 0 0 0 0 0.00% 0.00% 0.00% 0.00%Mixed_Use >= 55 & < 60 6 2 1 1 0 33.33% 50.00% 16.67% 0.00%Mixed_Use >= 60 & < 65 10 1 1 0 0 10.00% 100.00% 0.00% 0.00%Mixed_Use >= 65 & < 70 11 0 0 0 0 0.00% 0.00% 0.00% 0.00%Mixed_Use >= 70 & < 75 16 8 5 0 3 50.00% 62.50% 0.00% 18.75%Mixed_Use >= 75 & < 80 9 0 0 0 0 0.00% 0.00% 0.00% 0.00%Mixed_Use >= 80 & < 85 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%Mixed_Use >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
80 14 10 1 4 17.50% 71.43% 1.25% 5.00%Lodging < 50 14 0 0 0 0 0.00% 0.00% 0.00% 0.00%Lodging >= 50 & < 55 6 1 0 1 1 16.67% 0.00% 16.67% 16.67%Lodging >= 55 & < 60 5 1 0 1 1 20.00% 0.00% 20.00% 20.00%Lodging >= 60 & < 65 16 6 1 6 5 37.50% 16.67% 37.50% 31.25%Lodging >= 65 & < 70 8 4 3 3 1 50.00% 75.00% 37.50% 12.50%Lodging >= 70 & < 75 5 1 0 1 1 20.00% 0.00% 20.00% 20.00%Lodging >= 75 & < 80 1 0 0 0 0 0.00% 0.00% 0.00% 0.00%Lodging >= 80 & < 85 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%Lodging >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
55 13 4 12 9 23.64% 30.77% 21.82% 16.36%Other < 50 87 1 0 0 0 1.15% 0.00% 0.00% 0.00%Other >= 50 & < 55 7 0 0 0 0 0.00% 0.00% 0.00% 0.00%Other >= 55 & < 60 11 0 0 0 0 0.00% 0.00% 0.00% 0.00%Other >= 60 & < 65 18 3 3 1 0 16.67% 100.00% 5.56% 0.00%Other >= 65 & < 70 25 3 1 2 2 12.00% 33.33% 8.00% 8.00%Other >= 70 & < 75 27 3 3 1 1 11.11% 100.00% 3.70% 3.70%Other >= 75 & < 80 9 1 1 0 0 11.11% 100.00% 0.00% 0.00%Other >= 80 & < 85 2 1 1 1 1 50.00% 100.00% 50.00% 50.00%Other >= 85 & < 90 1 0 0 0 0 0.00% 0.00% 0.00% 0.00%
187 12 9 5 4 6.42% 75.00% 2.67% 2.14%Aggregate < 50 386 18 8 5 8 4.66% 44.44% 1.30% 2.07%Aggregate >= 50 & < 55 105 6 3 2 3 5.71% 50.00% 1.90% 2.86%Aggregate >= 55 & < 60 157 13 5 7 5 8.28% 38.46% 4.46% 3.18%Aggregate >= 60 & < 65 220 23 12 16 15 10.45% 52.17% 7.27% 6.82%Aggregate >= 65 & < 70 386 59 25 37 29 15.28% 42.37% 9.59% 7.51%Aggregate >= 70 & < 75 548 109 47 47 65 19.89% 43.12% 8.58% 11.86%Aggregate >= 75 & < 80 374 79 41 62 60 21.12% 51.90% 16.58% 16.04%Aggregate >= 80 & < 85 26 4 2 4 3 15.38% 50.00% 15.38% 11.54%Aggregate >= 85 & < 90 8 0 0 0 0 0.00% 0.00% 0.00% 0.00%
2210 311 143 180 188 14.07% 45.98% 8.14% 8.51%
Page 67 of 74
Exhibit 8: Incidences – 2000 Origination Cohort
Asset.Class LTV.Range Total.loans Loans.in.default MD Active.Workouts Realized.Loss IoD MD Contribution IoAW IoLMulti_family < 50 94 2 1 0 1 2.13% 50.00% 0.00% 1.06%Multi_family >= 50 & < 55 46 5 4 1 1 10.87% 80.00% 2.17% 2.17%Multi_family >= 55 & < 60 45 1 1 0 0 2.22% 100.00% 0.00% 0.00%Multi_family >= 60 & < 65 97 6 3 1 1 6.19% 50.00% 1.03% 1.03%Multi_family >= 65 & < 70 144 12 4 8 6 8.33% 33.33% 5.56% 4.17%Multi_family >= 70 & < 75 217 32 5 21 17 14.75% 15.63% 9.68% 7.83%Multi_family >= 75 & < 80 214 61 18 45 42 28.50% 29.51% 21.03% 19.63%Multi_family >= 80 & < 85 11 3 0 3 2 27.27% 0.00% 27.27% 18.18%Multi_family >= 85 & < 90 2 0 0 0 0 0.00% 0.00% 0.00% 0.00%
870 122 36 79 70 14.02% 29.51% 9.08% 8.05%Retail < 50 57 1 0 0 1 1.75% 0.00% 0.00% 1.75%Retail >= 50 & < 55 18 1 1 0 0 5.56% 100.00% 0.00% 0.00%Retail >= 55 & < 60 34 5 1 1 0 14.71% 20.00% 2.94% 0.00%Retail >= 60 & < 65 53 6 5 2 4 11.32% 83.33% 3.77% 7.55%Retail >= 65 & < 70 81 15 12 9 10 18.52% 80.00% 11.11% 12.35%Retail >= 70 & < 75 132 21 12 19 16 15.91% 57.14% 14.39% 12.12%Retail >= 75 & < 80 73 15 7 13 14 20.55% 46.67% 17.81% 19.18%Retail >= 80 & < 85 3 0 0 0 0 0.00% 0.00% 0.00% 0.00%Retail >= 85 & < 90 11 0 0 0 0 0.00% 0.00% 0.00% 0.00%
462 64 38 44 45 13.85% 59.38% 9.52% 9.74%Industrial < 50 50 5 3 2 1 10.00% 60.00% 4.00% 2.00%Industrial >= 50 & < 55 18 0 0 0 0 0.00% 0.00% 0.00% 0.00%Industrial >= 55 & < 60 25 2 2 0 1 8.00% 100.00% 0.00% 4.00%Industrial >= 60 & < 65 34 1 1 0 1 2.94% 100.00% 0.00% 2.94%Industrial >= 65 & < 70 46 7 6 5 4 15.22% 85.71% 10.87% 8.70%Industrial >= 70 & < 75 73 19 14 15 12 26.03% 73.68% 20.55% 16.44%Industrial >= 75 & < 80 17 7 5 6 6 41.18% 71.43% 35.29% 35.29%Industrial >= 80 & < 85 2 0 0 0 0 0.00% 0.00% 0.00% 0.00%Industrial >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
265 41 31 28 25 15.47% 75.61% 10.57% 9.43%Office < 50 73 6 5 2 4 8.22% 83.33% 2.74% 5.48%Office >= 50 & < 55 41 2 2 0 2 4.88% 100.00% 0.00% 4.88%Office >= 55 & < 60 50 5 3 3 3 10.00% 60.00% 6.00% 6.00%Office >= 60 & < 65 73 5 3 9 5 6.85% 60.00% 12.33% 6.85%Office >= 65 & < 70 80 15 5 12 13 18.75% 33.33% 15.00% 16.25%Office >= 70 & < 75 152 33 11 25 31 21.71% 33.33% 16.45% 20.39%Office >= 75 & < 80 36 11 7 7 9 30.56% 63.64% 19.44% 25.00%Office >= 80 & < 85 1 0 0 0 0 0.00% 0.00% 0.00% 0.00%Office >= 85 & < 90 2 0 0 0 0 0.00% 0.00% 0.00% 0.00%
508 77 36 58 67 15.16% 46.75% 11.42% 13.19%Mixed_Use < 50 12 2 1 1 1 16.67% 50.00% 8.33% 8.33%Mixed_Use >= 50 & < 55 5 1 0 0 0 20.00% 0.00% 0.00% 0.00%Mixed_Use >= 55 & < 60 8 2 2 0 0 25.00% 100.00% 0.00% 0.00%Mixed_Use >= 60 & < 65 10 2 1 1 0 20.00% 50.00% 10.00% 0.00%Mixed_Use >= 65 & < 70 13 3 1 2 1 23.08% 33.33% 15.38% 7.69%Mixed_Use >= 70 & < 75 6 2 1 1 1 33.33% 50.00% 16.67% 16.67%Mixed_Use >= 75 & < 80 2 0 0 0 0 0.00% 0.00% 0.00% 0.00%Mixed_Use >= 80 & < 85 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%Mixed_Use >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
56 12 6 5 3 21.43% 50.00% 8.93% 5.36%Lodging < 50 6 0 0 0 0 0.00% 0.00% 0.00% 0.00%Lodging >= 50 & < 55 4 0 0 0 0 0.00% 0.00% 0.00% 0.00%Lodging >= 55 & < 60 5 1 0 1 1 20.00% 0.00% 20.00% 20.00%Lodging >= 60 & < 65 4 1 0 1 1 25.00% 0.00% 25.00% 25.00%Lodging >= 65 & < 70 16 2 1 5 5 12.50% 50.00% 31.25% 31.25%Lodging >= 70 & < 75 5 1 0 2 1 20.00% 0.00% 40.00% 20.00%Lodging >= 75 & < 80 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%Lodging >= 80 & < 85 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%Lodging >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
40 5 1 9 8 12.50% 20.00% 22.50% 20.00%Other < 50 83 3 1 0 0 3.61% 33.33% 0.00% 0.00%Other >= 50 & < 55 5 0 0 0 0 0.00% 0.00% 0.00% 0.00%Other >= 55 & < 60 12 0 0 0 0 0.00% 0.00% 0.00% 0.00%Other >= 60 & < 65 17 1 0 1 1 5.88% 0.00% 5.88% 5.88%Other >= 65 & < 70 12 1 1 1 1 8.33% 100.00% 8.33% 8.33%Other >= 70 & < 75 12 2 1 3 2 16.67% 50.00% 25.00% 16.67%Other >= 75 & < 80 9 2 2 2 2 22.22% 100.00% 22.22% 22.22%Other >= 80 & < 85 1 1 1 0 0 100.00% 100.00% 0.00% 0.00%Other >= 85 & < 90 3 0 0 0 0 0.00% 0.00% 0.00% 0.00%
154 10 6 7 6 6.49% 60.00% 4.55% 3.90%Aggregate < 50 375 19 11 5 8 5.07% 57.89% 1.33% 2.13%Aggregate >= 50 & < 55 137 9 7 1 3 6.57% 77.78% 0.73% 2.19%Aggregate >= 55 & < 60 179 16 9 5 5 8.94% 56.25% 2.79% 2.79%Aggregate >= 60 & < 65 288 22 13 15 13 7.64% 59.09% 5.21% 4.51%Aggregate >= 65 & < 70 392 55 30 42 40 14.03% 54.55% 10.71% 10.20%Aggregate >= 70 & < 75 597 110 44 86 80 18.43% 40.00% 14.41% 13.40%Aggregate >= 75 & < 80 351 96 39 73 73 27.35% 40.63% 20.80% 20.80%Aggregate >= 80 & < 85 18 4 1 3 2 22.22% 25.00% 16.67% 11.11%Aggregate >= 85 & < 90 18 0 0 0 0 0.00% 0.00% 0.00% 0.00%
2355 331 154 230 224 14.06% 46.53% 9.77% 9.51%
Page 68 of 74
Exhibit 9: Incidences – 2001 Origination Cohort
Asset.Class LTV.Range Total.loans Loans.in.default MD Active.Workouts Realized.Loss IoD MD Contribution IoAW IoLMulti_family < 50 57 0 0 0 0 0.00% 0.00% 0.00% 0.00%Multi_family >= 50 & < 55 25 1 1 0 0 4.00% 100.00% 0.00% 0.00%Multi_family >= 55 & < 60 40 0 0 0 0 0.00% 0.00% 0.00% 0.00%Multi_family >= 60 & < 65 53 3 3 3 4 5.66% 100.00% 5.66% 7.55%Multi_family >= 65 & < 70 85 9 5 4 7 10.59% 55.56% 4.71% 8.24%Multi_family >= 70 & < 75 123 16 4 12 13 13.01% 25.00% 9.76% 10.57%Multi_family >= 75 & < 80 207 56 25 46 51 27.05% 44.64% 22.22% 24.64%Multi_family >= 80 & < 85 14 7 0 10 8 50.00% 0.00% 71.43% 57.14%Multi_family >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
604 92 38 75 83 15.23% 41.30% 12.42% 13.74%Retail < 50 41 4 2 0 2 9.76% 50.00% 0.00% 4.88%Retail >= 50 & < 55 11 0 0 0 0 0.00% 0.00% 0.00% 0.00%Retail >= 55 & < 60 20 4 4 2 3 20.00% 100.00% 10.00% 15.00%Retail >= 60 & < 65 29 2 1 4 3 6.90% 50.00% 13.79% 10.34%Retail >= 65 & < 70 50 7 4 8 7 14.00% 57.14% 16.00% 14.00%Retail >= 70 & < 75 133 12 10 6 15 9.02% 83.33% 4.51% 11.28%Retail >= 75 & < 80 112 12 8 8 7 10.71% 66.67% 7.14% 6.25%Retail >= 80 & < 85 3 0 0 0 0 0.00% 0.00% 0.00% 0.00%Retail >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
399 41 29 28 37 10.28% 70.73% 7.02% 9.27%Industrial < 50 26 2 2 1 1 7.69% 100.00% 3.85% 3.85%Industrial >= 50 & < 55 15 2 2 1 0 13.33% 100.00% 6.67% 0.00%Industrial >= 55 & < 60 12 3 2 2 2 25.00% 66.67% 16.67% 16.67%Industrial >= 60 & < 65 21 1 1 1 0 4.76% 100.00% 4.76% 0.00%Industrial >= 65 & < 70 32 5 3 3 2 15.63% 60.00% 9.38% 6.25%Industrial >= 70 & < 75 64 12 7 10 7 18.75% 58.33% 15.63% 10.94%Industrial >= 75 & < 80 30 2 2 2 1 6.67% 100.00% 6.67% 3.33%Industrial >= 80 & < 85 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%Industrial >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
200 27 19 20 13 13.50% 70.37% 10.00% 6.50%Office < 50 57 3 2 3 2 5.26% 66.67% 5.26% 3.51%Office >= 50 & < 55 29 1 0 0 0 3.45% 0.00% 0.00% 0.00%Office >= 55 & < 60 48 5 3 1 2 10.42% 60.00% 2.08% 4.17%Office >= 60 & < 65 44 2 1 4 4 4.55% 50.00% 9.09% 9.09%Office >= 65 & < 70 75 13 4 16 17 17.33% 30.77% 21.33% 22.67%Office >= 70 & < 75 140 32 19 28 30 22.86% 59.38% 20.00% 21.43%Office >= 75 & < 80 61 12 4 11 13 19.67% 33.33% 18.03% 21.31%Office >= 80 & < 85 4 0 0 1 1 0.00% 0.00% 25.00% 25.00%Office >= 85 & < 90 1 0 0 0 0 0.00% 0.00% 0.00% 0.00%
459 68 33 64 69 14.81% 48.53% 13.94% 15.03%Mixed_Use < 50 1 0 0 0 0 0.00% 0.00% 0.00% 0.00%Mixed_Use >= 50 & < 55 3 0 0 0 0 0.00% 0.00% 0.00% 0.00%Mixed_Use >= 55 & < 60 3 0 0 0 0 0.00% 0.00% 0.00% 0.00%Mixed_Use >= 60 & < 65 1 0 0 0 0 0.00% 0.00% 0.00% 0.00%Mixed_Use >= 65 & < 70 7 0 0 0 0 0.00% 0.00% 0.00% 0.00%Mixed_Use >= 70 & < 75 20 8 5 6 5 40.00% 62.50% 30.00% 25.00%Mixed_Use >= 75 & < 80 2 0 0 0 0 0.00% 0.00% 0.00% 0.00%Mixed_Use >= 80 & < 85 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%Mixed_Use >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
37 8 5 6 5 21.62% 62.50% 16.22% 13.51%Lodging < 50 9 0 0 1 0 0.00% 0.00% 11.11% 0.00%Lodging >= 50 & < 55 2 1 0 0 0 50.00% 0.00% 0.00% 0.00%Lodging >= 55 & < 60 8 0 0 0 0 0.00% 0.00% 0.00% 0.00%Lodging >= 60 & < 65 8 1 1 1 1 12.50% 100.00% 12.50% 12.50%Lodging >= 65 & < 70 8 2 0 2 3 25.00% 0.00% 25.00% 37.50%Lodging >= 70 & < 75 3 0 0 1 1 0.00% 0.00% 33.33% 33.33%Lodging >= 75 & < 80 1 0 0 0 0 0.00% 0.00% 0.00% 0.00%Lodging >= 80 & < 85 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%Lodging >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
39 4 1 5 5 10.26% 25.00% 12.82% 12.82%Other < 50 167 3 2 2 0 1.80% 66.67% 1.20% 0.00%Other >= 50 & < 55 6 0 0 0 0 0.00% 0.00% 0.00% 0.00%Other >= 55 & < 60 7 1 1 0 0 14.29% 100.00% 0.00% 0.00%Other >= 60 & < 65 9 1 1 0 0 11.11% 100.00% 0.00% 0.00%Other >= 65 & < 70 14 0 0 0 0 0.00% 0.00% 0.00% 0.00%Other >= 70 & < 75 15 1 0 1 1 6.67% 0.00% 6.67% 6.67%Other >= 75 & < 80 17 3 3 1 1 17.65% 100.00% 5.88% 5.88%Other >= 80 & < 85 1 0 0 0 0 0.00% 0.00% 0.00% 0.00%Other >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
236 9 7 4 2 3.81% 77.78% 1.69% 0.85%Aggregate < 50 358 12 8 7 5 3.35% 66.67% 1.96% 1.40%Aggregate >= 50 & < 55 91 5 3 1 0 5.49% 60.00% 1.10% 0.00%Aggregate >= 55 & < 60 138 13 10 5 7 9.42% 76.92% 3.62% 5.07%Aggregate >= 60 & < 65 165 10 8 13 12 6.06% 80.00% 7.88% 7.27%Aggregate >= 65 & < 70 271 36 16 33 36 13.28% 44.44% 12.18% 13.28%Aggregate >= 70 & < 75 498 81 45 64 72 16.27% 55.56% 12.85% 14.46%Aggregate >= 75 & < 80 430 85 42 68 73 19.77% 49.41% 15.81% 16.98%Aggregate >= 80 & < 85 22 7 0 11 9 31.82% 0.00% 50.00% 40.91%Aggregate >= 85 & < 90 1 0 0 0 0 0.00% 0.00% 0.00% 0.00%
1974 249 132 202 214 12.61% 53.01% 10.23% 10.84%
Page 69 of 74
Exhibit 10: Incidences – 2002 Origination Cohort
Asset.Class LTV.Range Total.loans Loans.in.default MD Active.Workouts Realized.Loss IoD MD Contribution IoAW IoLMulti_family < 50 51 1 1 0 1 1.96% 100.00% 0.00% 1.96%Multi_family >= 50 & < 55 10 0 0 0 0 0.00% 0.00% 0.00% 0.00%Multi_family >= 55 & < 60 20 3 1 2 1 15.00% 33.33% 10.00% 5.00%Multi_family >= 60 & < 65 18 0 0 0 0 0.00% 0.00% 0.00% 0.00%Multi_family >= 65 & < 70 29 2 0 2 2 6.90% 0.00% 6.90% 6.90%Multi_family >= 70 & < 75 60 16 5 13 11 26.67% 31.25% 21.67% 18.33%Multi_family >= 75 & < 80 123 36 13 29 24 29.27% 36.11% 23.58% 19.51%Multi_family >= 80 & < 85 7 1 1 0 0 14.29% 100.00% 0.00% 0.00%Multi_family >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
318 59 21 46 39 18.55% 35.59% 14.47% 12.26%Retail < 50 34 1 0 2 2 2.94% 0.00% 5.88% 5.88%Retail >= 50 & < 55 14 1 1 0 0 7.14% 100.00% 0.00% 0.00%Retail >= 55 & < 60 19 1 1 0 1 5.26% 100.00% 0.00% 5.26%Retail >= 60 & < 65 36 7 6 2 2 19.44% 85.71% 5.56% 5.56%Retail >= 65 & < 70 54 2 1 2 1 3.70% 50.00% 3.70% 1.85%Retail >= 70 & < 75 102 15 11 9 6 14.71% 73.33% 8.82% 5.88%Retail >= 75 & < 80 123 12 8 7 6 9.76% 66.67% 5.69% 4.88%Retail >= 80 & < 85 6 1 1 0 0 16.67% 100.00% 0.00% 0.00%Retail >= 85 & < 90 1 0 0 0 0 0.00% 0.00% 0.00% 0.00%
389 40 29 22 18 10.28% 72.50% 5.66% 4.63%Industrial < 50 16 0 0 0 0 0.00% 0.00% 0.00% 0.00%Industrial >= 50 & < 55 13 0 0 0 0 0.00% 0.00% 0.00% 0.00%Industrial >= 55 & < 60 16 2 2 0 2 12.50% 100.00% 0.00% 12.50%Industrial >= 60 & < 65 23 2 2 1 1 8.70% 100.00% 4.35% 4.35%Industrial >= 65 & < 70 23 1 1 0 0 4.35% 100.00% 0.00% 0.00%Industrial >= 70 & < 75 51 5 3 1 1 9.80% 60.00% 1.96% 1.96%Industrial >= 75 & < 80 21 2 1 2 3 9.52% 50.00% 9.52% 14.29%Industrial >= 80 & < 85 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%Industrial >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
163 12 9 4 7 7.36% 75.00% 2.45% 4.29%Office < 50 65 4 2 3 3 6.15% 50.00% 4.62% 4.62%Office >= 50 & < 55 30 3 3 1 0 10.00% 100.00% 3.33% 0.00%Office >= 55 & < 60 27 1 0 1 1 3.70% 0.00% 3.70% 3.70%Office >= 60 & < 65 47 10 5 3 6 21.28% 50.00% 6.38% 12.77%Office >= 65 & < 70 63 8 4 4 4 12.70% 50.00% 6.35% 6.35%Office >= 70 & < 75 109 21 10 18 17 19.27% 47.62% 16.51% 15.60%Office >= 75 & < 80 69 15 5 15 15 21.74% 33.33% 21.74% 21.74%Office >= 80 & < 85 5 1 0 0 1 20.00% 0.00% 0.00% 20.00%Office >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
415 63 29 45 47 15.18% 46.03% 10.84% 11.33%Mixed_Use < 50 7 0 0 0 0 0.00% 0.00% 0.00% 0.00%Mixed_Use >= 50 & < 55 3 0 0 0 0 0.00% 0.00% 0.00% 0.00%Mixed_Use >= 55 & < 60 3 0 0 0 0 0.00% 0.00% 0.00% 0.00%Mixed_Use >= 60 & < 65 4 0 0 0 0 0.00% 0.00% 0.00% 0.00%Mixed_Use >= 65 & < 70 6 3 1 1 3 50.00% 33.33% 16.67% 50.00%Mixed_Use >= 70 & < 75 10 1 0 1 1 10.00% 0.00% 10.00% 10.00%Mixed_Use >= 75 & < 80 4 1 1 0 0 25.00% 100.00% 0.00% 0.00%Mixed_Use >= 80 & < 85 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%Mixed_Use >= 85 & < 90 1 1 1 1 1 100.00% 100.00% 100.00% 100.00%
38 6 3 3 5 15.79% 50.00% 7.89% 13.16%Lodging < 50 9 2 1 1 0 22.22% 50.00% 11.11% 0.00%Lodging >= 50 & < 55 2 0 0 0 0 0.00% 0.00% 0.00% 0.00%Lodging >= 55 & < 60 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%Lodging >= 60 & < 65 1 0 0 0 0 0.00% 0.00% 0.00% 0.00%Lodging >= 65 & < 70 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%Lodging >= 70 & < 75 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%Lodging >= 75 & < 80 1 0 0 1 0 0.00% 0.00% 100.00% 0.00%Lodging >= 80 & < 85 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%Lodging >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
13 2 1 2 0 15.38% 50.00% 15.38% 0.00%Other < 50 240 7 4 1 0 2.92% 57.14% 0.42% 0.00%Other >= 50 & < 55 6 0 0 0 0 0.00% 0.00% 0.00% 0.00%Other >= 55 & < 60 7 0 0 0 0 0.00% 0.00% 0.00% 0.00%Other >= 60 & < 65 18 0 0 0 0 0.00% 0.00% 0.00% 0.00%Other >= 65 & < 70 15 4 4 2 0 26.67% 100.00% 13.33% 0.00%Other >= 70 & < 75 28 5 3 1 2 17.86% 60.00% 3.57% 7.14%Other >= 75 & < 80 4 0 0 0 0 0.00% 0.00% 0.00% 0.00%Other >= 80 & < 85 1 1 1 0 1 100.00% 100.00% 0.00% 100.00%Other >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
319 17 12 4 3 5.33% 70.59% 1.25% 0.94%Aggregate < 50 422 15 8 7 6 3.55% 53.33% 1.66% 1.42%Aggregate >= 50 & < 55 78 4 4 1 0 5.13% 100.00% 1.28% 0.00%Aggregate >= 55 & < 60 92 7 4 3 5 7.61% 57.14% 3.26% 5.43%Aggregate >= 60 & < 65 147 19 13 6 9 12.93% 68.42% 4.08% 6.12%Aggregate >= 65 & < 70 190 20 11 11 10 10.53% 55.00% 5.79% 5.26%Aggregate >= 70 & < 75 360 63 32 43 38 17.50% 50.79% 11.94% 10.56%Aggregate >= 75 & < 80 345 66 28 54 48 19.13% 42.42% 15.65% 13.91%Aggregate >= 80 & < 85 19 4 3 0 2 21.05% 75.00% 0.00% 10.53%Aggregate >= 85 & < 90 2 1 1 1 1 50.00% 100.00% 50.00% 50.00%
1655 199 104 126 119 12.02% 52.26% 7.61% 7.19%
Page 70 of 74
Exhibit 11: Incidences – 2003 Origination Cohort
Asset.Class LTV.Range Total.loans Loans.in.default MD Active.Workouts Realized.Loss IoD MD Contribution IoAW IoLMulti_family < 50 155 1 1 0 0 0.65% 100.00% 0.00% 0.00%Multi_family >= 50 & < 55 32 0 0 0 0 0.00% 0.00% 0.00% 0.00%Multi_family >= 55 & < 60 29 2 1 1 1 6.90% 50.00% 3.45% 3.45%Multi_family >= 60 & < 65 29 1 0 0 1 3.45% 0.00% 0.00% 3.45%Multi_family >= 65 & < 70 67 5 2 3 4 7.46% 40.00% 4.48% 5.97%Multi_family >= 70 & < 75 90 19 11 6 6 21.11% 57.89% 6.67% 6.67%Multi_family >= 75 & < 80 179 51 16 34 35 28.49% 31.37% 18.99% 19.55%Multi_family >= 80 & < 85 22 4 3 2 3 18.18% 75.00% 9.09% 13.64%Multi_family >= 85 & < 90 2 0 0 0 0 0.00% 0.00% 0.00% 0.00%
605 83 34 46 50 13.72% 40.96% 7.60% 8.26%Retail < 50 50 1 1 1 0 2.00% 100.00% 2.00% 0.00%Retail >= 50 & < 55 37 5 4 2 3 13.51% 80.00% 5.41% 8.11%Retail >= 55 & < 60 60 6 6 3 3 10.00% 100.00% 5.00% 5.00%Retail >= 60 & < 65 65 4 2 2 2 6.15% 50.00% 3.08% 3.08%Retail >= 65 & < 70 89 19 13 8 11 21.35% 68.42% 8.99% 12.36%Retail >= 70 & < 75 162 26 13 13 15 16.05% 50.00% 8.02% 9.26%Retail >= 75 & < 80 150 22 17 9 10 14.67% 77.27% 6.00% 6.67%Retail >= 80 & < 85 7 3 3 0 0 42.86% 100.00% 0.00% 0.00%Retail >= 85 & < 90 1 0 0 0 0 0.00% 0.00% 0.00% 0.00%
621 86 59 38 44 13.85% 68.60% 6.12% 7.09%Industrial < 50 19 1 1 0 0 5.26% 100.00% 0.00% 0.00%Industrial >= 50 & < 55 16 1 0 1 0 6.25% 0.00% 6.25% 0.00%Industrial >= 55 & < 60 14 1 1 0 0 7.14% 100.00% 0.00% 0.00%Industrial >= 60 & < 65 16 3 3 3 2 18.75% 100.00% 18.75% 12.50%Industrial >= 65 & < 70 41 10 6 7 8 24.39% 60.00% 17.07% 19.51%Industrial >= 70 & < 75 41 5 4 4 3 12.20% 80.00% 9.76% 7.32%Industrial >= 75 & < 80 20 5 4 0 1 25.00% 80.00% 0.00% 5.00%Industrial >= 80 & < 85 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%Industrial >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
167 26 19 15 14 15.57% 73.08% 8.98% 8.38%Office < 50 60 1 1 0 0 1.67% 100.00% 0.00% 0.00%Office >= 50 & < 55 32 1 1 1 0 3.13% 100.00% 3.13% 0.00%Office >= 55 & < 60 41 4 2 2 5 9.76% 50.00% 4.88% 12.20%Office >= 60 & < 65 59 8 7 4 5 13.56% 87.50% 6.78% 8.47%Office >= 65 & < 70 72 9 9 4 3 12.50% 100.00% 5.56% 4.17%Office >= 70 & < 75 140 21 13 13 10 15.00% 61.90% 9.29% 7.14%Office >= 75 & < 80 114 24 10 15 14 21.05% 41.67% 13.16% 12.28%Office >= 80 & < 85 7 2 1 2 2 28.57% 50.00% 28.57% 28.57%Office >= 85 & < 90 1 0 0 0 0 0.00% 0.00% 0.00% 0.00%
526 70 44 41 39 13.31% 62.86% 7.79% 7.41%Mixed_Use < 50 36 1 1 0 0 2.78% 100.00% 0.00% 0.00%Mixed_Use >= 50 & < 55 12 2 2 1 2 16.67% 100.00% 8.33% 16.67%Mixed_Use >= 55 & < 60 7 0 0 0 0 0.00% 0.00% 0.00% 0.00%Mixed_Use >= 60 & < 65 5 1 1 0 0 20.00% 100.00% 0.00% 0.00%Mixed_Use >= 65 & < 70 10 2 2 1 0 20.00% 100.00% 10.00% 0.00%Mixed_Use >= 70 & < 75 30 1 1 0 0 3.33% 100.00% 0.00% 0.00%Mixed_Use >= 75 & < 80 8 1 1 0 0 12.50% 100.00% 0.00% 0.00%Mixed_Use >= 80 & < 85 1 1 0 1 0 100.00% 0.00% 100.00% 0.00%Mixed_Use >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
109 9 8 3 2 8.26% 88.89% 2.75% 1.83%Lodging < 50 11 0 0 0 0 0.00% 0.00% 0.00% 0.00%Lodging >= 50 & < 55 3 0 0 1 0 0.00% 0.00% 33.33% 0.00%Lodging >= 55 & < 60 1 0 0 0 0 0.00% 0.00% 0.00% 0.00%Lodging >= 60 & < 65 8 0 0 0 0 0.00% 0.00% 0.00% 0.00%Lodging >= 65 & < 70 3 0 0 0 0 0.00% 0.00% 0.00% 0.00%Lodging >= 70 & < 75 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%Lodging >= 75 & < 80 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%Lodging >= 80 & < 85 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%Lodging >= 85 & < 90 1 0 0 0 0 0.00% 0.00% 0.00% 0.00%
27 0 0 1 0 0.00% #DIV/0! 3.70% 0.00%Other < 50 252 9 7 2 2 3.57% 77.78% 0.79% 0.79%Other >= 50 & < 55 7 0 0 0 0 0.00% 0.00% 0.00% 0.00%Other >= 55 & < 60 12 2 1 0 0 16.67% 50.00% 0.00% 0.00%Other >= 60 & < 65 17 2 2 0 0 11.76% 100.00% 0.00% 0.00%Other >= 65 & < 70 29 4 4 1 1 13.79% 100.00% 3.45% 3.45%Other >= 70 & < 75 33 4 4 1 0 12.12% 100.00% 3.03% 0.00%Other >= 75 & < 80 27 6 5 2 2 22.22% 83.33% 7.41% 7.41%Other >= 80 & < 85 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%Other >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
377 27 23 6 5 7.16% 85.19% 1.59% 1.33%Aggregate < 50 583 14 12 3 2 2.40% 85.71% 0.51% 0.34%Aggregate >= 50 & < 55 139 9 7 6 5 6.47% 77.78% 4.32% 3.60%Aggregate >= 55 & < 60 164 15 11 6 9 9.15% 73.33% 3.66% 5.49%Aggregate >= 60 & < 65 199 19 15 9 10 9.55% 78.95% 4.52% 5.03%Aggregate >= 65 & < 70 311 49 36 24 27 15.76% 73.47% 7.72% 8.68%Aggregate >= 70 & < 75 496 76 46 37 34 15.32% 60.53% 7.46% 6.85%Aggregate >= 75 & < 80 498 109 53 60 62 21.89% 48.62% 12.05% 12.45%Aggregate >= 80 & < 85 37 10 7 5 5 27.03% 70.00% 13.51% 13.51%Aggregate >= 85 & < 90 5 0 0 0 0 0.00% 0.00% 0.00% 0.00%
2432 301 187 150 154 12.38% 62.13% 6.17% 6.33%
Page 71 of 74
Exhibit 12: Incidences – 2004 Origination Cohort
Asset.Class LTV.Range Total.loans Loans.in.default MD Active.Workouts Realized.Loss IoD MD Contribution IoAW IoLMulti_family < 50 52 3 3 2 1 5.77% 100.00% 3.85% 1.92%Multi_family >= 50 & < 55 18 1 1 0 1 5.56% 100.00% 0.00% 5.56%Multi_family >= 55 & < 60 30 3 2 0 2 10.00% 66.67% 0.00% 6.67%Multi_family >= 60 & < 65 27 3 1 1 2 11.11% 33.33% 3.70% 7.41%Multi_family >= 65 & < 70 41 4 2 2 2 9.76% 50.00% 4.88% 4.88%Multi_family >= 70 & < 75 85 22 9 15 20 25.88% 40.91% 17.65% 23.53%Multi_family >= 75 & < 80 178 58 23 50 49 32.58% 39.66% 28.09% 27.53%Multi_family >= 80 & < 85 27 9 3 7 8 33.33% 33.33% 25.93% 29.63%Multi_family >= 85 & < 90 2 1 0 1 1 50.00% 0.00% 50.00% 50.00%
460 104 44 78 86 22.61% 42.31% 16.96% 18.70%Retail < 50 54 5 3 1 2 9.26% 60.00% 1.85% 3.70%Retail >= 50 & < 55 64 3 3 2 2 4.69% 100.00% 3.13% 3.13%Retail >= 55 & < 60 52 6 5 3 3 11.54% 83.33% 5.77% 5.77%Retail >= 60 & < 65 83 12 9 5 5 14.46% 75.00% 6.02% 6.02%Retail >= 65 & < 70 117 26 14 19 15 22.22% 53.85% 16.24% 12.82%Retail >= 70 & < 75 170 29 12 26 22 17.06% 41.38% 15.29% 12.94%Retail >= 75 & < 80 199 48 25 31 25 24.12% 52.08% 15.58% 12.56%Retail >= 80 & < 85 24 4 2 3 3 16.67% 50.00% 12.50% 12.50%Retail >= 85 & < 90 1 0 0 0 0 0.00% 0.00% 0.00% 0.00%
764 133 73 90 77 17.41% 54.89% 11.78% 10.08%Industrial < 50 16 0 0 1 0 0.00% 0.00% 6.25% 0.00%Industrial >= 50 & < 55 11 0 0 0 0 0.00% 0.00% 0.00% 0.00%Industrial >= 55 & < 60 15 0 0 0 0 0.00% 0.00% 0.00% 0.00%Industrial >= 60 & < 65 19 1 1 3 1 5.26% 100.00% 15.79% 5.26%Industrial >= 65 & < 70 25 3 1 1 0 12.00% 33.33% 4.00% 0.00%Industrial >= 70 & < 75 39 6 1 6 6 15.38% 16.67% 15.38% 15.38%Industrial >= 75 & < 80 30 8 6 4 4 26.67% 75.00% 13.33% 13.33%Industrial >= 80 & < 85 3 1 1 0 0 33.33% 100.00% 0.00% 0.00%Industrial >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
158 19 10 15 11 12.03% 52.63% 9.49% 6.96%Office < 50 85 2 2 4 1 2.35% 100.00% 4.71% 1.18%Office >= 50 & < 55 28 3 3 3 1 10.71% 100.00% 10.71% 3.57%Office >= 55 & < 60 57 1 1 0 0 1.75% 100.00% 0.00% 0.00%Office >= 60 & < 65 51 6 4 3 0 11.76% 66.67% 5.88% 0.00%Office >= 65 & < 70 99 19 10 15 13 19.19% 52.63% 15.15% 13.13%Office >= 70 & < 75 153 36 17 26 24 23.53% 47.22% 16.99% 15.69%Office >= 75 & < 80 165 33 16 31 24 20.00% 48.48% 18.79% 14.55%Office >= 80 & < 85 21 3 1 3 1 14.29% 33.33% 14.29% 4.76%Office >= 85 & < 90 1 0 0 0 0 0.00% 0.00% 0.00% 0.00%
660 103 54 85 64 15.61% 52.43% 12.88% 9.70%Mixed_Use < 50 22 0 0 0 0 0.00% 0.00% 0.00% 0.00%Mixed_Use >= 50 & < 55 6 0 0 0 0 0.00% 0.00% 0.00% 0.00%Mixed_Use >= 55 & < 60 15 1 1 1 1 6.67% 100.00% 6.67% 6.67%Mixed_Use >= 60 & < 65 13 2 1 1 0 15.38% 50.00% 7.69% 0.00%Mixed_Use >= 65 & < 70 21 5 3 5 2 23.81% 60.00% 23.81% 9.52%Mixed_Use >= 70 & < 75 21 4 2 1 2 19.05% 50.00% 4.76% 9.52%Mixed_Use >= 75 & < 80 22 3 1 3 1 13.64% 33.33% 13.64% 4.55%Mixed_Use >= 80 & < 85 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%Mixed_Use >= 85 & < 90 1 0 0 0 0 0.00% 0.00% 0.00% 0.00%
121 15 8 11 6 12.40% 53.33% 9.09% 4.96%Lodging < 50 18 2 2 2 0 11.11% 100.00% 11.11% 0.00%Lodging >= 50 & < 55 4 1 0 0 0 25.00% 0.00% 0.00% 0.00%Lodging >= 55 & < 60 10 0 0 0 0 0.00% 0.00% 0.00% 0.00%Lodging >= 60 & < 65 11 1 0 1 0 9.09% 0.00% 9.09% 0.00%Lodging >= 65 & < 70 15 0 0 0 0 0.00% 0.00% 0.00% 0.00%Lodging >= 70 & < 75 8 1 1 1 1 12.50% 100.00% 12.50% 12.50%Lodging >= 75 & < 80 7 2 2 0 0 28.57% 100.00% 0.00% 0.00%Lodging >= 80 & < 85 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%Lodging >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
73 7 5 4 1 9.59% 71.43% 5.48% 1.37%Other < 50 219 2 1 2 0 0.91% 50.00% 0.91% 0.00%Other >= 50 & < 55 7 3 3 0 0 42.86% 100.00% 0.00% 0.00%Other >= 55 & < 60 12 2 2 1 1 16.67% 100.00% 8.33% 8.33%Other >= 60 & < 65 12 1 1 1 1 8.33% 100.00% 8.33% 8.33%Other >= 65 & < 70 26 4 3 1 1 15.38% 75.00% 3.85% 3.85%Other >= 70 & < 75 44 3 2 0 1 6.82% 66.67% 0.00% 2.27%Other >= 75 & < 80 36 4 1 3 3 11.11% 25.00% 8.33% 8.33%Other >= 80 & < 85 18 0 0 0 0 0.00% 0.00% 0.00% 0.00%Other >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
374 19 13 8 7 5.08% 68.42% 2.14% 1.87%Aggregate < 50 466 14 11 12 4 3.00% 78.57% 2.58% 0.86%Aggregate >= 50 & < 55 138 11 10 5 4 7.97% 90.91% 3.62% 2.90%Aggregate >= 55 & < 60 191 13 11 5 7 6.81% 84.62% 2.62% 3.66%Aggregate >= 60 & < 65 216 26 17 15 9 12.04% 65.38% 6.94% 4.17%Aggregate >= 65 & < 70 344 61 33 43 33 17.73% 54.10% 12.50% 9.59%Aggregate >= 70 & < 75 520 101 44 75 76 19.42% 43.56% 14.42% 14.62%Aggregate >= 75 & < 80 637 156 74 122 106 24.49% 47.44% 19.15% 16.64%Aggregate >= 80 & < 85 93 17 7 13 12 18.28% 41.18% 13.98% 12.90%Aggregate >= 85 & < 90 5 1 0 1 1 20.00% 0.00% 20.00% 20.00%
2610 400 207 291 252 15.33% 51.75% 11.15% 9.66%
Page 72 of 74
Exhibit 13: Incidences – 2005 Origination Cohort
Asset.Class LTV.Range Total.loans Loans.in.default MD Active.Workouts Realized.Loss IoD MD Contribution IoAW IoLMulti_family < 50 103 5 5 2 0 4.85% 100.00% 1.94% 0.00%Multi_family >= 50 & < 55 44 1 0 1 1 2.27% 0.00% 2.27% 2.27%Multi_family >= 55 & < 60 46 2 1 1 1 4.35% 50.00% 2.17% 2.17%Multi_family >= 60 & < 65 46 8 6 0 0 17.39% 75.00% 0.00% 0.00%Multi_family >= 65 & < 70 119 15 8 8 8 12.61% 53.33% 6.72% 6.72%Multi_family >= 70 & < 75 179 25 12 15 14 13.97% 48.00% 8.38% 7.82%Multi_family >= 75 & < 80 347 97 29 64 56 27.95% 29.90% 18.44% 16.14%Multi_family >= 80 & < 85 78 22 13 11 12 28.21% 59.09% 14.10% 15.38%Multi_family >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
962 175 74 102 92 18.19% 42.29% 10.60% 9.56%Retail < 50 99 11 10 1 2 11.11% 90.91% 1.01% 2.02%Retail >= 50 & < 55 58 6 6 1 1 10.34% 100.00% 1.72% 1.72%Retail >= 55 & < 60 89 19 9 13 14 21.35% 47.37% 14.61% 15.73%Retail >= 60 & < 65 103 12 7 4 5 11.65% 58.33% 3.88% 4.85%Retail >= 65 & < 70 166 27 16 21 17 16.27% 59.26% 12.65% 10.24%Retail >= 70 & < 75 227 50 22 36 36 22.03% 44.00% 15.86% 15.86%Retail >= 75 & < 80 303 83 36 61 52 27.39% 43.37% 20.13% 17.16%Retail >= 80 & < 85 54 16 8 11 13 29.63% 50.00% 20.37% 24.07%Retail >= 85 & < 90 4 1 1 0 1 25.00% 100.00% 0.00% 25.00%
1103 225 115 148 141 20.40% 51.11% 13.42% 12.78%Industrial < 50 34 2 1 1 2 5.88% 50.00% 2.94% 5.88%Industrial >= 50 & < 55 19 2 2 1 1 10.53% 100.00% 5.26% 5.26%Industrial >= 55 & < 60 19 3 1 1 1 15.79% 33.33% 5.26% 5.26%Industrial >= 60 & < 65 22 1 0 0 0 4.55% 0.00% 0.00% 0.00%Industrial >= 65 & < 70 34 10 4 4 4 29.41% 40.00% 11.76% 11.76%Industrial >= 70 & < 75 56 14 6 11 6 25.00% 42.86% 19.64% 10.71%Industrial >= 75 & < 80 38 13 6 8 6 34.21% 46.15% 21.05% 15.79%Industrial >= 80 & < 85 9 3 2 3 3 33.33% 66.67% 33.33% 33.33%Industrial >= 85 & < 90 1 0 0 0 0 0.00% 0.00% 0.00% 0.00%
232 48 22 29 23 20.69% 45.83% 12.50% 9.91%Office < 50 89 7 6 6 3 7.87% 85.71% 6.74% 3.37%Office >= 50 & < 55 34 2 2 1 1 5.88% 100.00% 2.94% 2.94%Office >= 55 & < 60 54 11 8 8 3 20.37% 72.73% 14.81% 5.56%Office >= 60 & < 65 74 14 3 11 11 18.92% 21.43% 14.86% 14.86%Office >= 65 & < 70 116 28 14 17 18 24.14% 50.00% 14.66% 15.52%Office >= 70 & < 75 229 70 34 54 47 30.57% 48.57% 23.58% 20.52%Office >= 75 & < 80 277 100 53 84 77 36.10% 53.00% 30.32% 27.80%Office >= 80 & < 85 53 19 7 14 14 35.85% 36.84% 26.42% 26.42%Office >= 85 & < 90 2 1 1 1 0 50.00% 100.00% 50.00% 0.00%
928 252 128 196 174 27.16% 50.79% 21.12% 18.75%Mixed_Use < 50 23 2 2 0 1 8.70% 100.00% 0.00% 4.35%Mixed_Use >= 50 & < 55 14 3 0 2 2 21.43% 0.00% 14.29% 14.29%Mixed_Use >= 55 & < 60 22 2 1 1 3 9.09% 50.00% 4.55% 13.64%Mixed_Use >= 60 & < 65 19 5 4 3 3 26.32% 80.00% 15.79% 15.79%Mixed_Use >= 65 & < 70 21 4 2 3 4 19.05% 50.00% 14.29% 19.05%Mixed_Use >= 70 & < 75 43 5 4 3 4 11.63% 80.00% 6.98% 9.30%Mixed_Use >= 75 & < 80 33 10 3 6 4 30.30% 30.00% 18.18% 12.12%Mixed_Use >= 80 & < 85 7 2 2 1 1 28.57% 100.00% 14.29% 14.29%Mixed_Use >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
182 33 18 19 22 18.13% 54.55% 10.44% 12.09%Lodging < 50 33 2 2 2 2 6.06% 100.00% 6.06% 6.06%Lodging >= 50 & < 55 12 0 0 0 1 0.00% 0.00% 0.00% 8.33%Lodging >= 55 & < 60 12 1 0 1 1 8.33% 0.00% 8.33% 8.33%Lodging >= 60 & < 65 29 5 5 2 1 17.24% 100.00% 6.90% 3.45%Lodging >= 65 & < 70 49 14 8 10 8 28.57% 57.14% 20.41% 16.33%Lodging >= 70 & < 75 24 2 0 1 1 8.33% 0.00% 4.17% 4.17%Lodging >= 75 & < 80 11 3 2 1 1 27.27% 66.67% 9.09% 9.09%Lodging >= 80 & < 85 1 1 0 1 1 100.00% 0.00% 100.00% 100.00%Lodging >= 85 & < 90 1 0 0 0 0 0.00% 0.00% 0.00% 0.00%
172 28 17 18 16 16.28% 60.71% 10.47% 9.30%Other < 50 232 3 3 1 0 1.29% 100.00% 0.43% 0.00%Other >= 50 & < 55 9 1 0 1 1 11.11% 0.00% 11.11% 11.11%Other >= 55 & < 60 23 4 3 1 0 17.39% 75.00% 4.35% 0.00%Other >= 60 & < 65 16 1 1 0 1 6.25% 100.00% 0.00% 6.25%Other >= 65 & < 70 39 6 4 3 2 15.38% 66.67% 7.69% 5.13%Other >= 70 & < 75 61 4 3 2 4 6.56% 75.00% 3.28% 6.56%Other >= 75 & < 80 60 7 3 9 7 11.67% 42.86% 15.00% 11.67%Other >= 80 & < 85 11 0 0 0 0 0.00% 0.00% 0.00% 0.00%Other >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
451 26 17 17 15 5.76% 65.38% 3.77% 3.33%Aggregate < 50 613 32 29 13 10 5.22% 90.63% 2.12% 1.63%Aggregate >= 50 & < 55 190 15 10 7 8 7.89% 66.67% 3.68% 4.21%Aggregate >= 55 & < 60 265 42 23 26 23 15.85% 54.76% 9.81% 8.68%Aggregate >= 60 & < 65 309 46 26 20 21 14.89% 56.52% 6.47% 6.80%Aggregate >= 65 & < 70 544 104 56 66 61 19.12% 53.85% 12.13% 11.21%Aggregate >= 70 & < 75 819 170 81 122 112 20.76% 47.65% 14.90% 13.68%Aggregate >= 75 & < 80 1069 313 132 233 203 29.28% 42.17% 21.80% 18.99%Aggregate >= 80 & < 85 213 63 32 41 44 29.58% 50.79% 19.25% 20.66%Aggregate >= 85 & < 90 8 2 2 1 1 25.00% 100.00% 12.50% 12.50%
4030 787 391 529 483 19.53% 49.68% 13.13% 11.99%
Page 73 of 74
Exhibit 14: Incidences – 2006 Origination Cohort
Asset.Class LTV.Range Total.loans Loans.in.default MD Active.Workouts Realized.Loss IoD MD Contribution IoAW IoLMulti_family < 50 321 12 1 13 12 3.74% 8.33% 4.05% 3.74%Multi_family >= 50 & < 55 123 8 1 2 4 6.50% 12.50% 1.63% 3.25%Multi_family >= 55 & < 60 179 23 4 25 12 12.85% 17.39% 13.97% 6.70%Multi_family >= 60 & < 65 179 15 3 13 10 8.38% 20.00% 7.26% 5.59%Multi_family >= 65 & < 70 183 26 5 18 14 14.21% 19.23% 9.84% 7.65%Multi_family >= 70 & < 75 344 78 6 70 62 22.67% 7.69% 20.35% 18.02%Multi_family >= 75 & < 80 303 119 13 88 92 39.27% 10.92% 29.04% 30.36%Multi_family >= 80 & < 85 42 14 5 8 9 33.33% 35.71% 19.05% 21.43%Multi_family >= 85 & < 90 1 0 0 0 0 0.00% 0.00% 0.00% 0.00%
1675 295 38 237 215 17.61% 12.88% 14.15% 12.84%Retail < 50 113 7 5 3 4 6.19% 71.43% 2.65% 3.54%Retail >= 50 & < 55 61 10 6 10 8 16.39% 60.00% 16.39% 13.11%Retail >= 55 & < 60 105 23 19 9 8 21.90% 82.61% 8.57% 7.62%Retail >= 60 & < 65 145 33 11 34 27 22.76% 33.33% 23.45% 18.62%Retail >= 65 & < 70 202 53 22 44 39 26.24% 41.51% 21.78% 19.31%Retail >= 70 & < 75 235 73 38 50 39 31.06% 52.05% 21.28% 16.60%Retail >= 75 & < 80 260 85 33 78 54 32.69% 38.82% 30.00% 20.77%Retail >= 80 & < 85 37 10 4 7 4 27.03% 40.00% 18.92% 10.81%Retail >= 85 & < 90 4 2 0 2 2 50.00% 0.00% 50.00% 50.00%
1162 296 138 237 185 25.47% 46.62% 20.40% 15.92%Industrial < 50 52 5 3 2 4 9.62% 60.00% 3.85% 7.69%Industrial >= 50 & < 55 29 3 2 1 2 10.34% 66.67% 3.45% 6.90%Industrial >= 55 & < 60 44 6 3 8 7 13.64% 50.00% 18.18% 15.91%Industrial >= 60 & < 65 59 9 3 9 5 15.25% 33.33% 15.25% 8.47%Industrial >= 65 & < 70 71 11 3 8 10 15.49% 27.27% 11.27% 14.08%Industrial >= 70 & < 75 83 18 7 21 13 21.69% 38.89% 25.30% 15.66%Industrial >= 75 & < 80 55 15 6 13 13 27.27% 40.00% 23.64% 23.64%Industrial >= 80 & < 85 6 3 1 3 2 50.00% 33.33% 50.00% 33.33%Industrial >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
399 70 28 65 56 17.54% 40.00% 16.29% 14.04%Office < 50 123 15 9 15 5 12.20% 60.00% 12.20% 4.07%Office >= 50 & < 55 43 5 2 4 3 11.63% 40.00% 9.30% 6.98%Office >= 55 & < 60 52 10 6 8 11 19.23% 60.00% 15.38% 21.15%Office >= 60 & < 65 72 17 9 15 10 23.61% 52.94% 20.83% 13.89%Office >= 65 & < 70 153 50 30 44 30 32.68% 60.00% 28.76% 19.61%Office >= 70 & < 75 199 70 25 58 54 35.18% 35.71% 29.15% 27.14%Office >= 75 & < 80 273 133 45 119 105 48.72% 33.83% 43.59% 38.46%Office >= 80 & < 85 50 24 11 29 19 48.00% 45.83% 58.00% 38.00%Office >= 85 & < 90 5 0 0 1 0 0.00% 0.00% 20.00% 0.00%
970 324 137 293 237 33.40% 42.28% 30.21% 24.43%Mixed_Use < 50 67 3 0 3 2 4.48% 0.00% 4.48% 2.99%Mixed_Use >= 50 & < 55 23 1 1 1 1 4.35% 100.00% 4.35% 4.35%Mixed_Use >= 55 & < 60 34 5 0 3 3 14.71% 0.00% 8.82% 8.82%Mixed_Use >= 60 & < 65 42 9 8 5 2 21.43% 88.89% 11.90% 4.76%Mixed_Use >= 65 & < 70 61 18 8 10 9 29.51% 44.44% 16.39% 14.75%Mixed_Use >= 70 & < 75 85 23 3 25 23 27.06% 13.04% 29.41% 27.06%Mixed_Use >= 75 & < 80 43 21 7 21 16 48.84% 33.33% 48.84% 37.21%Mixed_Use >= 80 & < 85 12 6 3 4 3 50.00% 50.00% 33.33% 25.00%Mixed_Use >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
367 86 30 72 59 23.43% 34.88% 19.62% 16.08%Lodging < 50 45 6 5 10 2 13.33% 83.33% 22.22% 4.44%Lodging >= 50 & < 55 12 2 1 2 1 16.67% 50.00% 16.67% 8.33%Lodging >= 55 & < 60 20 1 1 2 0 5.00% 100.00% 10.00% 0.00%Lodging >= 60 & < 65 23 4 1 4 3 17.39% 25.00% 17.39% 13.04%Lodging >= 65 & < 70 72 23 8 22 16 31.94% 34.78% 30.56% 22.22%Lodging >= 70 & < 75 63 17 0 17 12 26.98% 0.00% 26.98% 19.05%Lodging >= 75 & < 80 24 5 4 7 5 20.83% 80.00% 29.17% 20.83%Lodging >= 80 & < 85 3 3 2 3 1 100.00% 66.67% 100.00% 33.33%Lodging >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
262 61 22 67 40 23.28% 36.07% 25.57% 15.27%Other < 50 188 7 7 1 3 3.72% 100.00% 0.53% 1.60%Other >= 50 & < 55 16 0 0 0 0 0.00% 0.00% 0.00% 0.00%Other >= 55 & < 60 22 3 2 1 1 13.64% 66.67% 4.55% 4.55%Other >= 60 & < 65 23 2 0 2 1 8.70% 0.00% 8.70% 4.35%Other >= 65 & < 70 47 4 3 2 1 8.51% 75.00% 4.26% 2.13%Other >= 70 & < 75 53 5 2 4 3 9.43% 40.00% 7.55% 5.66%Other >= 75 & < 80 51 4 1 3 4 7.84% 25.00% 5.88% 7.84%Other >= 80 & < 85 16 2 0 2 1 12.50% 0.00% 12.50% 6.25%Other >= 85 & < 90 1 0 0 0 0 0.00% 0.00% 0.00% 0.00%
417 27 15 15 14 6.47% 55.56% 3.60% 3.36%Aggregate < 50 909 55 30 47 32 6.05% 54.55% 5.17% 3.52%Aggregate >= 50 & < 55 307 29 13 20 19 9.45% 44.83% 6.51% 6.19%Aggregate >= 55 & < 60 456 71 35 56 42 15.57% 49.30% 12.28% 9.21%Aggregate >= 60 & < 65 543 89 35 82 58 16.39% 39.33% 15.10% 10.68%Aggregate >= 65 & < 70 789 185 79 148 119 23.45% 42.70% 18.76% 15.08%Aggregate >= 70 & < 75 1062 284 81 245 206 26.74% 28.52% 23.07% 19.40%Aggregate >= 75 & < 80 1009 382 109 329 289 37.86% 28.53% 32.61% 28.64%Aggregate >= 80 & < 85 166 62 26 56 39 37.35% 41.94% 33.73% 23.49%Aggregate >= 85 & < 90 11 2 0 3 2 18.18% 0.00% 27.27% 18.18%
5252 1159 408 986 806 22.07% 35.20% 18.77% 15.35%
Page 74 of 74
Exhibit 15: Incidences – 2007 Origination Cohort
Asset.Class LTV.Range Total.loans Loans.in.default MD Active.Workouts Realized.Loss IoD MD Contribution IoAW IoLMulti_family < 50 105 5 0 3 1 4.76% 0.00% 2.86% 0.95%Multi_family >= 50 & < 55 79 3 0 3 2 3.80% 0.00% 3.80% 2.53%Multi_family >= 55 & < 60 108 9 3 7 2 8.33% 33.33% 6.48% 1.85%Multi_family >= 60 & < 65 133 20 8 10 11 15.04% 40.00% 7.52% 8.27%Multi_family >= 65 & < 70 140 16 1 14 13 11.43% 6.25% 10.00% 9.29%Multi_family >= 70 & < 75 145 38 6 29 22 26.21% 15.79% 20.00% 15.17%Multi_family >= 75 & < 80 176 57 14 48 46 32.39% 24.56% 27.27% 26.14%Multi_family >= 80 & < 85 69 13 9 4 4 18.84% 69.23% 5.80% 5.80%Multi_family >= 85 & < 90 11 3 0 3 2 27.27% 0.00% 27.27% 18.18%
966 164 41 121 103 16.98% 25.00% 12.53% 10.66%Retail < 50 97 9 8 0 3 9.28% 88.89% 0.00% 3.09%Retail >= 50 & < 55 47 8 4 4 3 17.02% 50.00% 8.51% 6.38%Retail >= 55 & < 60 82 10 4 7 4 12.20% 40.00% 8.54% 4.88%Retail >= 60 & < 65 112 21 10 17 8 18.75% 47.62% 15.18% 7.14%Retail >= 65 & < 70 157 37 12 25 21 23.57% 32.43% 15.92% 13.38%Retail >= 70 & < 75 195 53 18 44 34 27.18% 33.96% 22.56% 17.44%Retail >= 75 & < 80 270 107 35 96 74 39.63% 32.71% 35.56% 27.41%Retail >= 80 & < 85 61 23 9 22 16 37.70% 39.13% 36.07% 26.23%Retail >= 85 & < 90 3 1 1 1 1 33.33% 100.00% 33.33% 33.33%
1024 269 101 216 164 26.27% 37.55% 21.09% 16.02%Industrial < 50 35 6 2 4 1 17.14% 33.33% 11.43% 2.86%Industrial >= 50 & < 55 22 5 2 3 2 22.73% 40.00% 13.64% 9.09%Industrial >= 55 & < 60 23 5 2 3 2 21.74% 40.00% 13.04% 8.70%Industrial >= 60 & < 65 44 9 7 7 2 20.45% 77.78% 15.91% 4.55%Industrial >= 65 & < 70 42 11 6 7 8 26.19% 54.55% 16.67% 19.05%Industrial >= 70 & < 75 56 19 9 15 11 33.93% 47.37% 26.79% 19.64%Industrial >= 75 & < 80 55 16 8 15 9 29.09% 50.00% 27.27% 16.36%Industrial >= 80 & < 85 14 5 1 4 5 35.71% 20.00% 28.57% 35.71%Industrial >= 85 & < 90 2 1 0 1 1 50.00% 0.00% 50.00% 50.00%
293 77 37 59 41 26.28% 48.05% 20.14% 13.99%Office < 50 96 10 9 9 3 10.42% 90.00% 9.38% 3.13%Office >= 50 & < 55 35 7 6 5 2 20.00% 85.71% 14.29% 5.71%Office >= 55 & < 60 67 18 7 15 13 26.87% 38.89% 22.39% 19.40%Office >= 60 & < 65 87 29 13 25 9 33.33% 44.83% 28.74% 10.34%Office >= 65 & < 70 127 39 15 37 33 30.71% 38.46% 29.13% 25.98%Office >= 70 & < 75 140 56 25 48 38 40.00% 44.64% 34.29% 27.14%Office >= 75 & < 80 234 119 42 118 73 50.85% 35.29% 50.43% 31.20%Office >= 80 & < 85 81 51 10 53 35 62.96% 19.61% 65.43% 43.21%Office >= 85 & < 90 2 1 1 1 0 50.00% 100.00% 50.00% 0.00%
869 330 128 311 206 37.97% 38.79% 35.79% 23.71%Mixed_Use < 50 56 4 3 2 2 7.14% 75.00% 3.57% 3.57%Mixed_Use >= 50 & < 55 15 2 2 1 1 13.33% 100.00% 6.67% 6.67%Mixed_Use >= 55 & < 60 32 6 2 5 2 18.75% 33.33% 15.63% 6.25%Mixed_Use >= 60 & < 65 34 12 4 9 8 35.29% 33.33% 26.47% 23.53%Mixed_Use >= 65 & < 70 52 19 8 14 16 36.54% 42.11% 26.92% 30.77%Mixed_Use >= 70 & < 75 83 28 5 28 24 33.73% 17.86% 33.73% 28.92%Mixed_Use >= 75 & < 80 46 22 11 10 11 47.83% 50.00% 21.74% 23.91%Mixed_Use >= 80 & < 85 9 6 1 5 5 66.67% 16.67% 55.56% 55.56%Mixed_Use >= 85 & < 90 1 0 0 0 0 0.00% 0.00% 0.00% 0.00%
328 99 36 74 69 30.18% 36.36% 22.56% 21.04%Lodging < 50 22 3 3 2 0 13.64% 100.00% 9.09% 0.00%Lodging >= 50 & < 55 11 2 2 1 0 18.18% 100.00% 9.09% 0.00%Lodging >= 55 & < 60 18 3 3 1 1 16.67% 100.00% 5.56% 5.56%Lodging >= 60 & < 65 31 12 3 11 10 38.71% 25.00% 35.48% 32.26%Lodging >= 65 & < 70 46 22 7 17 12 47.83% 31.82% 36.96% 26.09%Lodging >= 70 & < 75 52 25 10 20 14 48.08% 40.00% 38.46% 26.92%Lodging >= 75 & < 80 33 12 4 10 10 36.36% 33.33% 30.30% 30.30%Lodging >= 80 & < 85 8 7 2 6 7 87.50% 28.57% 75.00% 87.50%Lodging >= 85 & < 90 0 0 0 0 0 0.00% 0.00% 0.00% 0.00%
221 86 34 68 54 38.91% 39.53% 30.77% 24.43%Other < 50 74 5 3 3 3 6.76% 60.00% 4.05% 4.05%Other >= 50 & < 55 8 3 1 1 1 37.50% 33.33% 12.50% 12.50%Other >= 55 & < 60 15 0 0 0 0 0.00% 0.00% 0.00% 0.00%Other >= 60 & < 65 23 2 1 2 0 8.70% 50.00% 8.70% 0.00%Other >= 65 & < 70 30 8 1 5 4 26.67% 12.50% 16.67% 13.33%Other >= 70 & < 75 47 6 3 3 3 12.77% 50.00% 6.38% 6.38%Other >= 75 & < 80 59 8 2 5 5 13.56% 25.00% 8.47% 8.47%Other >= 80 & < 85 12 0 0 0 0 0.00% 0.00% 0.00% 0.00%Other >= 85 & < 90 1 0 0 0 0 0.00% 0.00% 0.00% 0.00%
269 32 11 19 16 11.90% 34.38% 7.06% 5.95%Aggregate < 50 485 42 28 23 13 8.66% 66.67% 4.74% 2.68%Aggregate >= 50 & < 55 217 30 17 18 11 13.82% 56.67% 8.29% 5.07%Aggregate >= 55 & < 60 345 51 21 38 24 14.78% 41.18% 11.01% 6.96%Aggregate >= 60 & < 65 464 105 46 81 48 22.63% 43.81% 17.46% 10.34%Aggregate >= 65 & < 70 594 152 50 119 107 25.59% 32.89% 20.03% 18.01%Aggregate >= 70 & < 75 718 225 76 187 146 31.34% 33.78% 26.04% 20.33%Aggregate >= 75 & < 80 873 341 116 302 228 39.06% 34.02% 34.59% 26.12%Aggregate >= 80 & < 85 254 105 32 94 72 41.34% 30.48% 37.01% 28.35%Aggregate >= 85 & < 90 20 6 2 6 4 30.00% 33.33% 30.00% 20.00%
3970 1057 388 868 653 26.62% 36.71% 21.86% 16.45%
Top Related