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

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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

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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.

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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

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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

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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.

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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 –

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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.

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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.

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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.

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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.

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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,

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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.

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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.

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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

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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,

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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.

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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.

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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.

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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

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– 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.

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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

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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

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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

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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).

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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.

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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

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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.

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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).

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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.

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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.

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To understand the aggregate distribution of loans based on origination cohorts, see Chart 19.

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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.

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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.

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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).

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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).

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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.

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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.

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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).

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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.

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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.

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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.

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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.

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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.

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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.

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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).

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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).

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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).

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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.

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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

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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%