Analysis of delinquencies in CRE debt : an approach to understanding risk and pricing

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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

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.

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' MIT Center for Real Estate January 11, 2019

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Research Scientist, Center for Real Estate T4esisupervisor

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Class of 1922 P or Urban Design and Planning Director, Center for Real Estate

School of Architecture and Planning IMASSACHUSETTS INSTITUTE

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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

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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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1. Introduction

Commercial Real Estate (CRE) debt' 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 market'. 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

' 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.

' Gathered from market/research reports, industry discussions, and projected market trends. 4 Based on NCREIF Capital Value Index.

s Space markets are about occupancy, rents, leasing etc. - markets for actual use of real estate as space.

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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

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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 trillion (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

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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 billion'0 (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%, 1, 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.

" 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 billion" of aggregate capital were raised for US market debt strategies.

Chart 1: Private Debt Funds -Capital Raise Trends

40 35 30 25 20 15 10 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 (11 NI)

Source: Preqin iAggregate Capital Raised (bii USD) -&-No. of Funds

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.}

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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.

200 180 160 140 120 100 80 60 40 20 0 Source:Pr

Core Core-Plus Debt Distressed Opportunistic Value Added

eqin Aggregate Target Capital (U SD billion) -o-No. of funds

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; loD 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

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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 Files}18 _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.

* 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.

Chart 3: Number of Loans Based on Origination Year

6000 5000 4000 3000 2000 1000

Source: Trepp Daa/Study Sample

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

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- Arlington, Seattle - Tacoma - Bellevue. See Chart 4 for the geographical distribution of

the loans.

Chart 4: Geographic Distribution of Loans

%8% 11% 9% 21-4% * Boston MSA Houston MSA * Philadelphia W

\ashi nLTon DC NIS A

* Chicago MSA " Los Angeles MSA a San Frnancisco MSA

SDallas M SA

* New York M S-A

* Seattle MSA

Somrce: Trepp )t u.li i r S! i mpic

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.

Chart 5: Loan Distribution Based on Asset Class

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14% 47% " Multi Family * Lodging 6% 17%

* Retail v Industrial * Office

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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).

Chart 6: Origination Year Based Evolution of Asset Class Distribution

6000 5000 4000 3000 2000 1000 0 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 a Multi Family a Retail * Industrial N Office s Lodging N Mixed use N Others

Sotrce: Trepp/Studv Sample

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.

Chart 7: Average LIV Trend Over Origination Years

68.00 66.00 64.00 62.00 60.00 58.00 C-56.0( 54.0u 52.00

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 201 1.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.

Chart 8: Asset Class LTVs Over Origination Years

83.0 81.0 0 79.0 77.0 75.0 0 73.0 71.0 69.0 0 67.0 0 0 0 65.0 0

8

63.0 0 00.0

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6. 0 00 ₀ 59. *o o 0 0 0 57.0 * o 0 0* 51.0 0 0 49.0 0 47.0 45.0 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 OMulti Family ORetail Olndustrial (Office OLodging OMixed use OAverage

Source: Trepp/Study Sample

20 For the purpose of this study, LTV of a loan has been derived from the "securlty" 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.

Chart 9: lncidcncc Funncl - Aggrcgate Level

Total Loans 60,085

Default 5,675

Active Workouts 14,049

Realized Losses 3,426

" 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

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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.

Chart 10: Loan Originution \ olume \. Asset Price Mos ement

6000 250 5000 ₂₀₀ 4000 150 3000 100 2000 10000 50 00 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Loan Originations -o-NCREIF Capital Value Index

Source: Trepp/NCREIF

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% 22_{0f 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

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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.

Chart I 1: Incidcncc Funnel -Market Cycle I

Total Loans 35,578

Defaults 5,542

Active Workouts 3,977

Realized Losses 3,402

" 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 ycle 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

Analysis of delinquencies in CRE debt : an approach to understanding risk and pricing

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LIBRARIES

Acknowledgements

Table of Contents

1. Introduction

2. The CRE Debt Market

3. Key Definitions & Concepts

4. Overview of Data

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Data Analysis & Findings