Cap Rate Spread

An Article Submitted to

Real Estate EconomicsManuscript 2077

The Cap Rate Spread: A New Metricfor Commercial Underwriting

Philip A. Seagraves∗ Jonathan A. Wiley†

∗University of Wisconsin, Whitewater, [email protected]†Georgia State University, [email protected]

Copyright c©2013 by the authors. All rights reserved. No part of this publication may be re-produced, stored in a retrieval system, or transmitted, in any form or by any means, electronic,mechanical, photocopying, recording, or otherwise, without the prior written permission of thepublisher, bepress, which has been given certain exclusive rights by the author.

The Cap Rate Spread: A New Metric forCommercial Underwriting∗

Philip A. Seagraves and Jonathan A. Wiley

Abstract

This study introduces the cap rate spread as a novel metric for underwriting commercial mort-gages. Cap rate spread is the difference between the cap rate and the fixed coupon rate. The spreadpredicts performance risk in a sample of 24,951 CMBS loans during 1993-2011. We demonstratethat the cap rate spread includes crucial information about performance risk. The results arise fromthe role of the cap rate spread in generating positive or negative leveraged returns to equity in sit-uations where additional equity is required. Incorporating simplistic cap rate spread requirementsin commercial underwriting is expected to reduce loan performance risk.

KEYWORDS: Default risk, underwriting, commercial real estate

∗The authors would like to recognize Jeffrey Warwick, who suggested that this topic may beworthy of research investigation, and thank him for all of his contributions to this study.

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THE CAP RATE SPREAD:

A NEW METRIC FOR COMMERCIAL UNDERWRITING

Abstract

This study introduces the cap rate spread as a novel metric for underwriting

commercial mortgages. Cap rate spread is the difference between the cap

rate and the fixed coupon rate. The spread predicts performance risk in a

sample of 24,951 CMBS loans during 1993-2011. We demonstrate that the

cap rate spread includes crucial information about performance risk. The

results arise from the role of the cap rate spread in generating positive or

negative leveraged returns to equity in situations where additional equity is

required. Incorporating simplistic cap rate spread requirements in

commercial underwriting is expected to reduce loan performance risk.

Keywords: Default risk; underwriting; commercial real estate

1Seagraves and Wiley: The Cap Rate Spread

Produced by The Berkeley Electronic Press, 2013

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Introduction

Standard underwriting models for approving and pricing debt on commercial loans rely upon

classic measures such as the size of the loan relative to the value of the property (loan-to-value

ratio, LTV) and the ratio of net income from the property to the annual debt payment obligation

(debt service coverage ratio, DSCR). Yet, the 30-day delinquency rate on commercial mortgages

has recently skyrocketed, including on commercial mortgage-backed securities (CMBS) loans

that rose from less than 0.5% in June 2008 to consistently greater than 8% since June 2010.1

While a portion of these CMBS delinquencies were originated with excessive leverage and thin

debt coverage, many distressed commercial loans would have otherwise passed traditionally

conservative underwriting standards. This suggests that conventional underwriting methods

should be given a critical evaluation. In this study, we propose the cap rate spread as a new

metric for underwriting which introduces information that is available at the time of origination,

but not fully reflected in standalone LTV or DSCR measures.

LTV is widely used to determine loan risk and one of the most commonly cited indicators that

are used to characterize individual loans and the broader lending environment. As the loan

amount approaches or exceeds the value of the underlying asset, equity vanishes and default risk

increases. DSCR is another important measure which provides a basic assessment of the

borrower’s ability to remain current on debt payments in the event that operating cash flows

from the underlying property are impaired. With a higher DSCR at origination, the debt service

has a greater cushion against the impact from reduced rental income or increased expenses. The

key limitation to LTV and DSCR criteria, when considered in isolation, is their failure to relate

1 From PREA’s Compendium of Statistics (October 2012), accessed at: http://www.prea.org/research/compendium.pdf on October 16, 2012.

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cash flows to asset values. This is true, even though incentives for equity investors are driven by

yields that are defined by cash flows relative to asset values.

LTV is a ratio of asset values only and includes the loan amount (i.e., the lender’s asset) in the

numerator, and the property value in the denominator. DSCR is a ratio of cash flows only and

includes net operating cash flows from the property in the numerator, and debt cash flows in the

denominator. Recognizable measures already exist which relate cash flows to asset values for

the property and for the debt, individually. For the property, the cap rate is defined as net

operating income divided by the initial property value and represents the unlevered income

return in commercial real estate. For the debt, the coupon rate is an annualized yield generated

from interest payments relative to the initial loan amount. In order for the use of financial

leverage to be beneficial for equity investors, there must be a positive spread between the

unlevered IRR on total capital and the mortgage coupon rate (ignoring points). The cap rate

spread is related to the outcome of positive vs. negative financial leverage, and the cap rate

spread is defined as the difference between the cap rate and the coupon rate.2 In this study, we

investigate whether the cap rate spread has an impact on commercial loan performance and we

expect that low cap rate spreads will be associated with an increased incidence of nonperforming

mortgages.

2 Absent real NOI growth, the cap rate equals the unlevered IRR on a property investment only when the going-in cap rate equals the exit cap rate (ignoring transaction costs). When cap rates fall or rise during the holding period, capital gains will be affected either positively or negatively. The income component represents approximately 75% of total cumulative returns from investment in commercial real estate for the NCREIF NPI index during 1993-2011 (per author’s calculation). Uncertainty surrounding future cap rate fluctuations limits precision in generating accurate IRR expectations. In this study, we undertake a simplifying approach by considering the going-in cap rate only, which is observable at the time of underwriting, and we do not impose any expectations upon the future direction or magnitude of possible changes in cap rates.



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We empirically evaluate the role of the cap rate spread by using a sample of 24,951 commercial

real estate mortgages originated between 1993 and 2011 that are collected from the Bloomberg

CMBS database. The sample considered in this study includes fixed-rate, interest-only “balloon”

loans so that the cap rate spread directly measures the difference between unlevered income

flows from the property and the cash flows committed to debt service. In the case of interest-

only balloon loans, the coupon rate (ignoring points up front) is exactly the mortgage constant on

the note. The Bloomberg dataset also includes partially-amortizing, fully-amortizing and

adjustable-rate commercial mortgages, but these are not considered in our analysis.3 Fixed-rate

balloon loans represent the most common type of loan in the CMBS market.4 With a long-term

interest rate locked in and a principal balance that is non-decreasing over time, fixed-rate balloon

loans are exposed to considerable performance risk and this sample appears representative of the

overall default risk in the CMBS market.5

Empirical estimations are provided for the probability of nonperforming loans as a function of

cap rate spreads in combination with a set of related variables that are developed from existing

research on the topic of CMBS defaults. The functionality of the cap rate spread in conjunction

with LTV and DSCR criteria is expanded on. The analysis in this study provides both a

3 For amortizing loans, the debt service would also include a principal component to the mortgage payment and these loans are not included in this study, even though they are included in the Bloomberg dataset. An added empirical concern stems from the variance in amortization schedules among commercial mortgages, and the inability to discern these differences in the Bloomberg data. With adjustable-rate mortgages (also not considered in this study), the denominator of the cap rate spread is not a constant coupon rate, but varies over the life of the loan according to the interest rate environment. The historic record of any changes in the coupon rate for adjustable-rate mortgages does not appear in the Bloomberg database.

4 At the point of data collection in September 2011, zero-amortization balloon loans represent 63% of all amortization types in the Bloomberg database and fixed-rate loans represent 94.5% of CMBS loans available in Bloomberg.

5 At the point of data collection in September 2011, 21% all domestic CMBS loans are nonperforming, while 20% of balloon loans are nonperforming and 21% of fixed-rate loans are nonperforming.

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simplistic application for implementing the cap rate spread and a matrix which demonstrates the

effectiveness of joint criteria across a range of underwriting standards. In doing so, we are able

to highlight the conditions under which the application of cap rate spread criteria can be expected

to provide the greatest contribution to improving underwriting decisions.

The remainder of this study is organized as follows. The Background section provides an

overview of related literature on commercial loan default risk, which supports the empirical

approach. An economic framework which demonstrates the relevance of the cap rate spread in

nonperformance risk is developed in the Model section. The Data & Empirical Method section

details the Bloomberg CMBS data and outlines the relevant empirical method that is used to

evaluate this data. The Results section discusses the key empirical findings in our research and

also provides applications for using the cap rate spread in underwriting, with alternative

outcomes based on the data collected for this study. Final remarks for the conclusions that can

be drawn from this research are provided in the Conclusions section.

Background

The earliest CMBS loans recorded in Bloomberg date to the 1960s, although the early-1990s

defined a period with increased acceptance for residential and commercial mortgage-backed

securities, including the introduction of loans originated specifically to be packaged and

securitized. Growth in of the CMBS market during the 1990s has been linked to legislative

changes including the Financial Institutes Reform, Recovery and Enforcement Act (FIRREA),

and the resulting Resolution Trust Corporation (RTC). By the end of Q2 2011, the size of the



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CMBS market is reported to include a total $520.2 billion in debt, representing approximately

23% of all commercial real estate loans in the U.S.6

Commercial mortgage defaults had a serious impact on insurance companies and pension funds

throughout the 1980s and gained increasing attention in the academic literature, including

research into the factors contributing to default, options valuation models for prepayment and

default, and mortgage risk pricing. Beginning with Vandell (1984), existing models for

residential default are claimed to be inadequate for commercial underwriting due to the income-

producing nature of commercial real estate and the differing sensitivities to economic conditions

that are inherent in commercial debt instruments. Vandell (1984) suggests that simple ratio tests

such as LTV and DSCR would not provide adequate information to fully assess default risk and

that additional information should be incorporated in the decision including property and

locational factors along with economic factors. In particular, Vandell (1984) points out that LTV

and DSCR measures may be overly simplistic in the sense that they fail to relate cash flows and

equity in a framework where both are volatile over time. This notion from Vandell (1984) is the

genesis of motivation for our research that has ultimately led to an examination of the cap rate

spread as a key metric which establishes a connection between cash flows and asset values with

potential utility for underwriting commercial mortgage risk.

Early research on the CMBS sector specifically includes that of Kau, Keenan, Muller, and

Epperson (1987), where two default scenarios are identified. The first occurs when the value of

the underlying collateral is less than the outstanding loan balance (i.e., zero equity). The second

6 Calculation from ‘Table L.220 Commercial Mortgages’ Flow of Funds Accounts of the United States, Second Quarter 2011, published by the U.S. Federal Reserve, accessed at http://www.federalreserve.gov/releases/z1/20110916/z1.pdf on May 15, 2013.

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occurs even with positive equity. Under the first scenario and assuming that transaction costs for

default are zero, the risk of default increases as the underlying asset value becomes less than the

loan balance, but default does not occur immediately because of the value of the default option.

In order for default to occur in the second scenario, there must be a sizeable margin between

market interest rates and contract interest rates during the prepayment lockout period; otherwise

prepayment or refinancing would occur as the alternative to default. Another possible aspect to

the default decision involves the relationship between income returns and the cost of debt since

commercial real estate returns experience fluctuations as market conditions unfold over time. In

order for financial leverage to be beneficial there must be some positive spread between the

unlevered returns and the cost of debt, which is related to the cap rate spread.

Vandell (1993) argues that default is not costless for borrowers since few borrowers are observed

to default even when borrowers are underwater at LTVs in excess of 110%. Not precluding the

possibility that some loans with high LTVs could be restructured, Vandell (1993) still finds 75-

85% of loans in the zero-equity category are retained by borrowers. With a focus on regional

variation and the manner in which current interest rates affect the market value of the loans, the

Vandell (1993) study highlights the influence of unique geographic markets and dynamic interest

rate conditions on the incidence of commercial mortgage default.

Goldberg and Capone (1998, 2002) advance a theory that equity and cash flow measures, if used

alone, would provide biased estimates of default probabilities on commercial mortgages.

Relying on LTV alone would tend to overestimate the probability of default, while reliance on

DSCR would tend to underestimate the probability of default. Goldberg and Capone (1998,



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2002) suggest that their double-trigger model would more accurately estimate the probability of

default than other approaches because a combination of zero-equity and negative net cash flow

would likely push borrowers to default, whereas borrowers who face only one of these

conditions may be less likely to default.

Ciochetti, Deng, Lee, Shilling, and Yao (2003) employ a proportional hazard model with

competing risks to test for factors contributing to default and prepayment. In addition to

traditional ratios in underwriting, the authors estimate contemporaneous DSCR and LTV on a

quarterly basis using changes in the NCREIF property appreciation and the NCREIF income

yield to approximate what would have happened if values and incomes were affected in the same

way as properties in the NCREIF index. Ciochetti, Deng, Lee, Shilling, and Yao (2003) find that

contemporaneous DSCR is significant and negatively related to the probability of default.

Similarly, as contemporaneous LTV rose, so did the probability of default, though in a nonlinear

fashion.

Grovenstein, Harding, Sirmans, Thebpanya, and Turnbull (2005) investigate the seemingly

inconsistent options theory prediction for the impact of LTV on default, versus the notion that

LTV along with DSCR are endogenous to loan pricing. Mortgage underwriters simultaneously

adjust LTV and DSCR along with other contract terms of the loans, such as the interest rate.

With these influences jointly determined, predicted default probabilities and the standard

underwriting ratios can become disconnected.

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Titman and Tsyplakov (2010) examine the characteristics of commercial mortgage originators

and find that loans originated by recent stock price losers tend to default more often than loans

from other firms. They find that broad financial market performance is an important factor, with

differences among originators expanding during market downturns. Firms set short-term profit

goals at the expense of reputation. Short-term profits include origination fees, and reputation is

impacted as a result of lowered underwriting standards and riskier loans originated which were

generally disclosed to investors and priced. Ratings agencies assign lower scores to mortgage

pools containing loans from underwriters whose stock price have seen recent declines and are

more likely to engage in risky underwriting behavior. More recent studies along the same lines

include Deng, Gabriel, and Sanders (2011) and Furfine (2011).

The real estate literature provides ample evidence that LTV and DSCR are important factors in

measuring risk among commercial real estate loans.7 In addition, guidance to the empirical

analysis is provided for the role of a number of important factors including recommended

variables for loan size, Treasury yields, stock market returns, along with controls for geographic

location, property type and originator. Beginning with theoretical research on default and the use

of hazard models to predict performance outcomes, to more recent studies which utilize

increasing years of historical data, research on commercial loan default risk does not appear to

have considered the potential of the cap rate spread as an underwriting metric.

7 Additional sources for CMBS and commercial mortgage default research include Grovenstein, Harding, Sirmans, Thebpanya, and Turnbull (2005), Christopoulos, Jarrow and Yildirim (2008), Yildirim (2008), Kau, Keenan, and Yildirim (2009), Corcoran (2009), Chen and Deng (2010), An, Deng, and Sanders (2010), An and Sanders (2010), and Seslen and Wheaton (2010).



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The contribution of this study is the evaluation of the cap rate spread as a valuable metric for

evaluating commercial loan performance risk, not previously considered in the existing

literature. The theoretic model introduces cost of capital in the distance-to-default framework to

demonstrate that performance risk includes a term for the cap rate spread in addition to the

“double trigger” conditions. We illustrate that the interaction term for LTV and DSCR

(previously conceived) and “double trigger” default probabilities are naïve with respect to the

interest rate level, while the cap rate spread accurately reflects the margin for the equity investor.

Relevance of the cap rate spread, in combination with LTV and DSCR measures, is confirmed

with cross-sectional analysis using a large sample of CMBS loans. We simulate the performance

risk mitigated through simplistic cap rate spread criteria using counterfactual evidence.

Model

A model is presented where property value and cash flow are defined in a consistent manner with

existing distance-to-default literature, and use this framework to derive the cap rate process. We

introduce a basic cost of capital decision to the “double trigger” setting and show that the default

probability based on a negative cap rate spread criteria exceeds the “double trigger” default

probability. The combined “double trigger” and nonnegative cap rate spread probability and

associated distance to default are provided. We demonstrate that the “double trigger” default

probability and LTV / DSCR interaction term are both naïve with respect the interest rate level,

while the cap rate spread is proportionately responsive.

The property value �� and net cash flow ��, or net operating income, from a commercial real

estate investment follow geometric Brownian motions such that at time �

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�� = �d� + �d�� 1�

and

�� = �d� + �d��. �2�

d�� and d�� are standardized Wiener processes. We assume that the asset value �� and cash

flows �� are correlated with coefficient � > 0, and have the same percentage drift �.8 A

representative borrower purchases the asset using leverage with loan amount �. �� is the

property value at the time of purchase, and the loan-to-value ratio is � ��⁄ . The commercial

mortgage instrument is non-amortizing with a fixed interest rate �, resulting in the constant

periodic interest payment �. �� is the net cash flow at the time of purchase, and the debt service

coverage ratio is �� ⁄ .

In Goldberg and Capone (2002), the “double trigger” conditions for default include the

combination of negative equity, �� ≤ �, and inadequate cash flow to cover debt service, �� ≤ �.

The joint density function Ф��, ��; �� is a bivariate normal distribution, as demonstrated in the

construct of Kim (2011). The distance to negative equity is

"� = #$�% �&⁄ �'()'*+ �⁄ ,�*√� . �3�

The conditional probability of negative equity, given ��, is

Pr�� ≤ �|�� = Ф 234'5678'5+ 9. �4�

The joint probability of negative equity and negative cash flow is a cumulative bivariate normal

distribution

8 The assumption that cash flow and asset value experience the same percentage drift is analogous to the constant dividend growth model used in corporate finance, where the dividend growth rate is equivalent to the capital gains, or percentage change in the stock price.



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Pr�� ≤ �, �� ≤ �� = ; Ф 234'5678'5+ 9 <��d��3='> = Ф��"�, "�; ��, �5�

where "� is the distance to negative net cash flow, given as

"� = #$�@ �&⁄ �'()'A+ �⁄ ,�A√� . �6�

The probability depicted in equation �5� represents the “double trigger” scenario, where default

occurs when the asset has both non-positive equity and non-positive net cash flow. Vandell

(1995) points out that with positive cash flow, the representative borrower has little opportunity

for gain from default, even when equity is non-positive. Conversely, when an asset has positive

equity but net cash flow is negative, it is possible that the owner can either access funds to cover

the negative cash flow until the property recovers, or sell the property. The theoretical construct

in the present study considers that within the scenario when there is negative cash flow but

positive equity, access to capital may become constrained – depending specifically on the cap

rate spread. If the cap rate spread is too low, then capital will be unavailable to compensate for

the negative cash flow, which causes the probability of nonperformance to increase.

The cap rate is revealed in each period as C� ≡ ��/��. By Ito’s lemma, C� follows the process

�FF = �� − ��d� + �d�� − �d�� = HdI�, �7�

where I� is a standard Brownian motion, and H� = �� + �� − 2��. For investment in

individual assets, the cap rate equates to the weighted cost of capital KF such that

KF = LKM + �1 − L�KN, �8�

where L is the leverage ratio, KM is the cost of debt, and KN is the equity cost of capital.

Rearranging terms provides the required return on equity as,

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KN = PQ'RPS�8'R� . �9�

Let UV denote the risk premium for the cost of capital components. If the debt risk premium, UM,

remains constant over a relevant range in leverage ratios, L ∈ XL̅, LZ, then

UN = UM + [ 88'R\ �UF − UM�. �10�

That is, the equity risk premium equals the debt risk premium plus the product of the leverage

ratio and the cap rate spread, �UF − UM� ≅ �C� − ��.

When the cap rate spread is zero or negative, the consequence is negative cash flow leverage and

it becomes increasingly impractical for equity investors to contribute additional capital. Adding

this to the “double trigger” scenario, the probability of nonperformance will be

Pr�^_`abUc_Ude`f� = Pr�� ≤ �, �� ≤ �� + Pr�� > �, �� ≤ �, C� ≤ ��, �11�

which includes the scenario that occurs when there is positive equity, but non-positive cash flow

and a negative cap rate spread. The probability of the additional component to equation �11� is

Pr�� > �, �� ≤ �, C� ≤ �� = ; h1 − Ф 234'5678'5+ 9i Ф 23Q'56

78'5+ 9 <��d��3='> , �12�

where "Fis the distance to a negative cap rate spread, written as

"F = #$�M F&⁄ �jk++ �

l√� . �13�

The joint probability Pr�� > �, C� ≤ �� in equation �12� is nonzero if and only if "F ≥ "�. A

negative cap rate spread at origination �� > C�� causes the joint probability in equation �12� to

become positive, even with loan-to-value ratio of less than one �� < ��, since "F > 0 > "�

under these conditions. Based on the construct, the probability of nonperformance for the



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commercial real estate mortgage includes a term for the cap rate spread, and is greater than the

probability that would be estimated when only the “double trigger” scenario is considered.

To illustrate the model, assume � = 0, � = 0.1, � = 0.1, and � = 0.5, for a property

underwritten with LTV of � ��⁄ = 0.95 and DSCR of �� ⁄ = 1.10 at the time of origination.

The LTV*DSCR interaction term is �� ⁄ �∗�� ⁄ � = 1.045. For these parameters, the

distance to negative equity is "� = −0.463 and the distance to negative net cash flow is

"� = −0.903, based on �3� and �6�. From the cumulative bivariate normal distribution, the

joint probability of the “double trigger” conditions is Ф��"�, "�; �� = 0.1124; shown in Kim

(2011). Consider an initial property value of �� = $1, then the loan amount is � = $0.95. The

debt service depends on the interest rate. If the cost of debt is KM = 8%, then annual debt

service for an interest-only loan will be � = $0.076, initial net cash flow would need to be

�� = $0.0836 in order to satisfy DSCR of 1.10, and the cap rate spread is ��/�� − KM� =0.36%. Alternatively, suppose the cost of debt is KM = 2%, then debt service is � = $0.019,

initial net cash flow must be �� = $0.0209, and the cap rate spread is ��/�� − KM� = 0.09%.

Thus, when interest rates are one-quarter of the previous level (2% vs. 8%), the cap rate spread

will also be one-quarter of its prior value (0.09% vs. 0.36%), ceteris paribus. However, the

LTV*DSCR interaction term is unchanged at 1.045, and the “double trigger” probability of

default remains at Ф��"�, "� ; �� = 0.1124. This result occurs because both the LTV*DSCR

interaction term and the “double trigger” conditions are naïve with respect to the cost of debt.

Yet, the cost of debt is important because it impacts the margin for the equity investor – which is

directly measured by the cap rate spread. Low interest rates create thinner margins which are

more vulnerable to not satisfying the cost of capital criteria.

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Data & Empirical Method

This research provides empirical analysis for an extensive sample of CMBS data, from

Bloomberg machines. The collection includes 24,951 individual loans that were originated and

securitized between January 1, 1993 and September 15, 2011. In order to be included in the

collection, each loan is required to be active (i.e., not yet retired) as of September 15, 2011,

which is the final date of the data collection. The loan data is complemented by property-level

data, also extracted from the Bloomberg CMBS database, which provides net operating incomes,

property values at origination, and locational attributes for the underlying commercial properties.

The sample considered in this study includes only domestic loans with valid observations for net

operating income and loan status that have nonnegative original loan balances, coupon rates of at

least 3%, initial LTVs between 0 and 1, and DSCR values between 0.9 and 5. This treatment of

outliers and extreme ratios is similar to the approach taken by Archer, Elmer, Harrison, and Ling

(2002).9 In addition, only fixed-rate, balloon loans are considered, which represents the majority

of commercial loans in the CMBS market.10

Table 1 provides summary statistics for the relevant variables from this sample, including

minimum/maximum values, sample means, and standard deviations. The average loan in the

sample originated 6.4 years prior to the point of data collection on September 15, 2011, so that

9 To eliminate outliers, Archer, Elmer, Harrison, and Ling (2002) set cutoff criteria that required the contract rate to be between 5-20% and higher than the 10-year constant maturity risk-free rate, an original LTV of less than 100%, and a DSCR between 0.9-5. The sample period for this research includes years during which rates below 5% were not uncommon, with 4,706 U.S. loans (6.2% of the total) carrying coupon rates below 5%. Allowing for the influence of these lower-rate loans, a cutoff level of 3% is used, which eliminated only 5 loans. Following the 20% cutoff point for the maximum rate eliminated no loans from the database, as the highest rate in the sample is 15%.

10 The cap rate spread is unlikely to be as relevant for variable-rate mortgages, since a reduction in income returns may be concurrent with a reduction in the cost of borrowing.



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the sample reflects underwriting standards centered around 2005. CMBS loan size varies

considerably with an average loan of $8.176 million and standard deviation of $15.73 million.

The sample includes several very large loans such as the Equity Office Portfolio at $6.87 billion,

the Hilton Worldwide Portfolio at $2.87 billion, and the Extended Stay Hotel Portfolio at $2.0

billion; although the highest amount attributable to an individual property is $536.2 million. The

natural log of Loan size is included in the empirical analysis to represent performance risks and

differences in underwriting behavior that are attributable to project scale, following Childs, Ott,

and Riddiough (1996).

LTV is the ratio of the original loan balance to the value of the property at origination. Lenders

typically require no greater than 75-85% for loan approval, although it was not uncommon

during the mid-2000s for loans to originate with much higher LTVs, in some cases approaching

100% or greater. LTV ratios at the securitization cutoff date in our sample range from 2.9% to

98.8% and average 67.7% with standard deviation of 12.1%. 90% of observations have LTV

between 43.3% and 79.8% and less than 1% of LTVs are greater than 81%.

DSCR is the ratio of the annual net operating income from the property to the annual debt service

requirement, which is interest-only for our sample. As a general rule, lenders often require

minimum DSCR between 1.10-1.25 for loan approval. With extreme values excluded from our

sample, the range for DSCR is from 0.93 to 5 with the average DSCR at 1.476 (i.e., net operating

income covering debt service by 47.6%). LTV and DSCR measures in Bloomberg represent the

total debt and cash flow burdens at origination, including all first-position and subordinate debt.

Senior is an indicator variable for debt with first-position liens, and Junior indicates subordinate

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debt. The Senior and Junior variables are included in the estimation to evaluate whether

subordination level has a significant impact on performance risk.11

Our analysis considers the cost of debt as a Coupon spread, measured relative to 10-year

Treasuries, and the Cap rate spread relative to the coupon rate. The relevance of coupon rates as

a form of risk pricing and the application as spreads relative to Treasuries is highlighted by

Vandell (1993), Childs, Ott, and Riddiough (1996), Ambrose and Sanders (2003), and

Grovenstein, Harding, Sirmans, Thebpanya, and Turnbull (2005), among others. 10-year

Treasuries yield 4.41% on average during the sample and the average Coupon spread on the

Treasury is 1.66%, taken together to represent the total coupon rate which is 6.07% on the

average loan. The average Cap rate spread is 2.38% over the coupon rate, so the average

calculated cap rate is 8.45%. The cap rate is calculated from the Bloomberg data as net

operating income, divided by the loan balance, divided by LTV.12 The cap rate represents the

unlevered income component of returns from investing in commercial real estate. Cap rate

spread is the difference between the going-in unlevered income returns and the locked-in cost of

debt, which is the coupon rate on the fixed-rate balloon loans considered in this study.

S&P return is included to consider changes in the financial risk structure over time, following

the connection established by Titman and Tsyplakov (2010). The average 12-month change in

the S&P 500 index during the sample, weighted by the volume of loan originations, is 9.13%.

11 One issue is that Senior and Junior classifications have missing values for a relatively large portion of the sample (9,371 observations). To verify that this missing information does not significantly bias our results, we run the analysis for only the 13,288 Senior debt observations and find that the results are consistent with those presented in this study, which are based on the larger sample.

12 In some cases, LTV takes on a very low value, such as the minimum LTV at 2.9%, leading to inflated cap rate spreads. While the maximum Cap rate spread is 392.1%, the 99th percentile is 7.174% and deleting the outliers above this threshold does not qualitatively change the results in this study.



THE CAP RATE SPREAD

18

This measure is intended to capture the effects of the broad U.S. stock market on CMBS loan

underwriting behavior.13

Figure 1 provides the snapshot of CMBS origination activity during the sample period.

Originations are relatively low during the 1990s and steadily gain pace during the 2000s until

mid-2007 when the CMBS issuance channel had all but closed. CMBS volume is calculated from

our sample, rather than an alternative source, in order to reflect the issuance activity for the

specific debt instrument of interest. Originations range from 0 to 537 at peak in December 2006,

and the average grouping is 65 commercial loans per origination date. Also in Figure 1 is the 12-

month rolling average for cap rates, coupon rates and cap rate spreads on new originations. In

early-2002, cap rates in the sample begin to rise while coupon rates are falling, causing

disconnect between the two measures. After which, cap rate spreads on CMBS originations

encounter a sharp decline during the peak of securitization, steadily descending from mid-2003

thru late-2006.

The dependent variable of interest for the empirical analysis in this study is Nonperforming. On

average, 20.2% of loans are classified as Nonperforming, a measure which encompasses

delinquencies, foreclosures, grace periods, late, non-performing, and REO loan statuses as

identified in the Bloomberg loan data. The Nonperforming variable exhibits considerable

heterogeneity within the sample, with fluctuating incidence according to property type,

geography, and originator. Table 2 provides a summary of loan performance by property type

13 Overall, S&P return during underwriting appears unrelated to nonperforming outcomes. A number of alternative measures have been attempted as substitutes, including the volatility of S&P return, long-horizon returns, measured spreads relative to Treasuries, and expected returns based on autoregressive momentum. However, these alternative variables also appear to not have a significant relation with nonperforming outcomes after controlling for other effects.

18

THE CAP RATE SPREAD

19

for the loans included in this study, along with LTV, DSCR and Cap rate spread as the

potentially contributing factors. The nonperforming status is more common for loans backed by

hotel and warehouse properties, and somewhat less common for healthcare and self-service

storage collateral. The potential for performance risk to vary according to commercial property

type is from Ciochetti, Deng, Lee, Shilling, and Yao (2003). An analogous set of statistics

according to the state where the property is located are provided in the Appendix. Individual

state indicator variables are included in the empirical analysis to control for differing economic

conditions, regulatory environments, and regional variation in commercial real estate cycles

following Vandell (1993) and Archer, Elmer, Harrison, and Ling (2002).

Summary statistics of loan performance are not provided for the 169 unique originators in the

interest of brevity, yet indicators variables are included to control for differences attributable to

individual originators underscored in the research by Titman and Tsyplakov (2010) and Black,

Chu, Cohen, and Nichols (2011).14 31 originators account for 80% of the loans securitized, with

LaSalle Bank National Association on top of the list with 3,767 loans originated. At the

extremes, 11 originators have a nonperforming status in excess of 50% while 38 originators do

not have any nonperforming loans. The top 10 originators loans have a similar rate of

nonperforming loans to the overall sample average of 21.1%, with a range from 8.5% for

Washington Mutual to 29.2% for Bank of America, which combined with CountryWide’s 29.3%

in 2008. Another highly visible merger was Wachovia with 11.8% nonperforming acquired by

Wells Fargo which had 28.1% nonperforming loans. Even though several individual loans

14 The distribution of loan performance by originator can be made available by the authors upon request from the interested reader.



THE CAP RATE SPREAD

20

originate with LTVs in excess of 80%, all originators in the sample hold average LTVs of less

than 80%.

The major advantage of the Bloomberg data is the large number of observations available and

breadth of coverage including loans in each of the 50 United States and Washington, D.C., and

spanning a wide range of income-producing property types with the potential for broad

implications to commercial underwriting. However, the Bloomberg data is not without

limitations. One issue is that the Bloomberg data is reported as current and collection at a single

point in time exposes the sample to both left- and right-censoring. Some debt has already been

extinguished at the time of collection and is not included in our sample, while other observations

remain in the sample and appear current although these loans may become nonperforming in the

future. A common empirical procedure used to accommodate issues with censored data involves

the use of a hazard model, which requires information about the date that any loans in the sample

switched from current status to Nonperforming at least. An issue that places limits on the

feasibility of a hazard model is that loan status dates are not consistently available in Bloomberg

queries.

As the alternative, we use a probit model for selection and estimate the probability that a loan

will be nonperforming at the point of data collection, with vintage indicator variables controlling

for loan seasoning fixed-effects and survivorship relative to the quarter of origination.15 It is

noteworthy considering the censored nature of our data, that nonperforming outcomes have

changed little since mid-2011. In Pension Real Estate Association’s (PREA) Compendium of

15 A very similar option is to use a logit model, which yields qualitatively identical empirical results to all models estimated in this study.

20

THE CAP RATE SPREAD

21

Statistics (2012), CMBS defaults are noticed to have skyrocketed from August 2008, when the

delinquency percentage was less than 0.5%, until they plateau in August 2010, when the

delinquency rate reached 8.5% and represented roughly $60 billion in commercial debt.16 The

magnitude of CMBS delinquencies has changed very little since mid-2010, including thru

September 2011 – the point of data collection – and beyond September 2012 (one year after the

data collection), where delinquencies remain at roughly 8.5% of the CMBS market and close to

$60 billion in commercial loans. In addition to little change in delinquency rates after our

collection ends, a portion of the CMBS debt had been extinguished prior to September 2011,

although only a very small amount were related to default.

The empirical approach seeks to estimate the probability of Nonperforming as a function of

property and loan characteristics. An issue is that DSCR and LTV, the two traditional metrics for

evaluating underwriting risk, exhibit strong negative correlation at -0.681 for the 24,951

observations in our sample. Multicollinearity may increase estimation error for the

coefficients.17 By comparison, the Cap rate spread has relatively low correlations with LTV and

DSCR, suggesting that the Cap rate spread is a potentially useful candidate in combination with

these other criteria.18 To evaluate whether the additional information gained from the cap rate

spread is relevant, the probability of nonperformance is specified as:

Pr�^_`abUc_Ude`f = 1� = Φ�I’u� �14�

16 PREA’s Compendium of Statistics (October 2012) is accessed at: http://www.prea.org/research/compendium.pdf on October 16, 2012.

17 In unreported analysis, we estimate LTV and DSCR in separate models and find results consistent with those that are reported in this study, when LTV and DSCR are included in the same equation.

18 The correlation between Cap rate spread and DSCR is -0.0136 and the correlation between Cap rate spread and LTV is 0.1055.



THE CAP RATE SPREAD

22

where Φ�·� is the cumulative distribution function of the standard Normal distribution, ^�0,1�.

A linear specification for I’u is given as:

I’u = u� + u8 · ln��_y`yd_z`�� + u� · �_za_`{aUby" + u| · 10-~byU�Uby{zU~ + u� ·�&�Ub�zU`{ + u� · ��_�zdb + u� · ��K + u� · �� + u� · �yaUy�b{aUby" +∑ uV · ��y�be`"e�y�_UV��V�� + ∑ u� · �U_abU�~�~abe`"e�y�_U�� + ∑ u� ·8��8�e`�yfbe`"e�y�_U� + ∑ u� · �Uefe`y�_Ue`"e�y�_U�|8��8�| + �. �15�

The determinants of nonperformance include the natural log of Loan size, LTV and DSCR as

underwriting factors, 10-year Treasury and Coupon spreads as the cost of debt, S&P return to

reflect returns on risky assets, CMBS volume to account for securitization activity, along with e =

50 state indicators, � = 12 property type indicators, � = 71 vintage indicators for the quarter of

origination, and d = 168 originator indicators to control for fixed effects impacting loan

performance that vary geographically, by property type, by seasoning and dependent on the

individual loan originator. The inclusion of property type indicator variables is similar to the

approach taken by Ciochetti, Deng, Lee, Shilling, and Yao (2003).

The testable hypothesis is that Cap rate spread at origination for fixed-rate, balloon loans has a

significant impact on the probability that a CMBS loan is Nonperforming. Specifically, we

expect that this relationship will be negative, with lower cap rate spreads more likely to be

associated with nonperformance outcomes. This expectation is tested using a probit model for

equation �14� with parameters I’u from �15�. Results from the empirical estimations are

discussed in the next section.

Results

22

THE CAP RATE SPREAD

23

The probit estimations of equation �14� for the full sample are provided in Table 3. Four

columns report estimations that include the DSCR only, LTV only, LTV and DSCR together, and

LTV, DSCR plus the Cap rate spread, as detailed in �15�. The central finding of our research is

that the estimated coefficient for Cap rate spread is negative and significant in the fourth

column, and that this variable should be included in the estimation for nonperformance –

confirmed by the significance of the Likelihood-ratio statistic. When controlling for all other

information included, a low cap rate spread at origination significantly increases the probability

that the loan will be nonperforming. Other determinants of nonperformance behave as expected.

Loans underwritten with high LTVs, low DSCRs and high coupon spreads are significantly more

likely to be nonperforming.

Another option for the empirical approach is to structure the coupon rate as endogenous risk

pricing by estimating the coupon rate in the first-stage as a function of a unique set of variables.19

The predicted values from the first-stage are incorporated in the numerator of the Coupon spread

and in the denominator of the Cap rate spread. The Coupon spread and Cap rate spread

variables generated from this procedure are then introduced to the second-stage, which involves

a re-estimation of equation �14� using parameters �15�. The results from the two-stage

estimation are qualitatively similar with Cap rate spread negative and significant, although the

results are not presented here in an effort to preserve a straightforward interpretation of our

results.20

19 Specifically, the first-stage least-squares estimation for the coupon rate includes 10-year Treasury, DSCR, LTV and Loan size, along with state, property type, originator and quarterly indicator variables.

20 The estimated coefficient [standard error] for Cap rate spread based on the endogenous specification for the full sample is -0.41277 [0.125306], compared to the exogenously specified estimate of -0.41275 [0.125305]. Thus, the



THE CAP RATE SPREAD

24

An additional concern is whether the cap rate spread provides utility only when traditional

underwriting criteria have not been applied effectively. To consider this, we trim the sample to

exclude loans that would not have been underwritten had traditional underwriting standards of

75% maximum LTV and 1.1 minimum DSCR been applied.21 The resulting subsample includes

18,043 loans and eliminates 48 originators. Table 4 presents the results for the probit estimations

of equation �14� using parameters �15� that are performed on the trimmed subsample of 18,043

observations. The results are consistent with those in Table 3. Cap rate spread continues to

have a negative and significant impact on the probability of nonperforming loans in both

estimations, and the estimated coefficients increase after traditional underwriting standards have

been applied. This finding suggests that the cap rate spread does not only have utility for

identifying subprime CMBS deals that would have never been originated had traditional criteria

been applied. Instead, the results suggest that the information provided by the cap rate spread is

increasingly useful after traditional LTV and DSCR criteria have been applied.

Bloomberg has provided some limited data on loan status history in the form of an 18-character

string of letters, with each letter representing loan performance status for the most recent 18-

month horizon. Based on the character display, we are able to further classify the loans

according to nonperforming status dates that occur within the last six months, within seven to 17

months prior, or when the nonperforming status precedes the observation date by at least 18

months. To evaluate whether the nonperforming status date affects the results, equation �14� is

two estimation methods provide consistent estimators for the Cap rate spread variable, yet the exogenous specification is preferred because it produces a more efficient estimator.

21 A number of other maximum LTV/minimum DSCR criteria combinations are also attempted, including 80% LTV/1.25 DSCR, and the empirical results remain qualitatively consistent with those reported in this study.

24

THE CAP RATE SPREAD

25

estimated for reduced samples that compare only nonperforming loans in each status date

category to the performing loans. Results for the DSCR, LTV, and Cap rate spread coefficients

are presented in Table 5, and the Cap rate spread variable remains significant when estimated

for the reduced samples where nonperforming status date can be identified. Status date

classifications are only defined for observations with continuous nonperforming status, so that

observations with mixed nonperforming and performing statuses are not evaluated in this

subsample analysis. Our inability to document the original date of nonperforming status for all

loans represents a limitation of this study. The amount of time elapsed increases the potential

changes in property value subsequent to loan origination, which are partially addressed through

the use of quarterly indicator variables for the vintage of loan origination.

Figure 2 illustrates the counterfactual outcome from introducing a simplistic 1% minimum cap

rate spread as an addition to a traditional 75% LTV and 1.1 DSCR criteria. The solid black line

in Figure 2 represents the time series of all quarterly CMBS originations in our sample that were

nonperforming in September 2011, which increases steadily during the period from 2002-2006.

The dotted black line represents only nonperforming loans that would have been originated had

traditional 75% LTV and 1.1 DSCR cutoffs been applied, cutting the total count of

nonperforming loans by nearly one-third. The addition of a cap rate spread criteria would have

reduced the volume of nonperforming loans that already pass the 75% LTV / 1.1 DSCR cutoffs

by another 34%, so that the amount of nonperforming loans if the three criteria had been jointly

applied would be less than one-half the total volume of CMBS loans originated that became

nonperforming by September 2011. Of particular interest is that the cap rate spread constraint

becomes the most effective in absolute terms during the 2005-2007 period, which coincides with



THE CAP RATE SPREAD

26

the same period when the highest number of loans were originated that subsequently became

nonperforming.

In Figure 2, the 1% cap rate spread criteria used is set arbitrarily for the purpose of illustration.

The complication that results from crudely increasing constraints to underwriting is that fewer

loans overall would ultimately be underwritten, which restricts access to capital and prevents

some of the current loans that may never fall into the nonperforming category. In practice,

lenders are dispersed in their appetites for risk and pursuit of yields, and a uniform set of

underwriting criteria would not apply to all lenders. In order to understand and price

performance risk, lenders consider the volume of business that would be lost as a result of

increased underwriting restrictions relative to the improvement in performance risk for the

portfolio of loans.

Table 6 provides summary statistics for the number of loans and incidence of nonperforming

loans in the sample, with underwriting criteria applied in increments. In the top left corner are

the values for the full sample, including 24,951 loans and 20.2% are nonperforming without any

LTV, DSCR or cap rate spread criteria applied. Across the columns, a DSCR restriction of 1.1 is

added and LTV restrictions increase from a maximum of 90% to 60% (i.e., 40% equity would be

required). Down the rows, the cap rate spread criteria are applied, beginning with a minimum

Cap rate spread of 0%, which simply requires the cap rate to be greater than the coupon rate, and

increases in increments of 50 basis points to a 3% minimum cap rate spread criteria. As shown

in the final column, the cap rate spread can be very effective at reducing risk, with performance

risk on 60% LTV / 1.1 DSCR loans falling from 16.1% to 14.7% with a 0% cap rate spread

26

THE CAP RATE SPREAD

27

hurdle, and all the way to 10.5% of loans nonperforming with a restrictive 3% cap rate spread.

The cap rate spread is noticeably less effective at marginally reducing performance risk when

there are low LTV restrictions, as in the 90% and 80% LTV columns. The cap rate spread

relates cash flows to the asset values, but this is less important in the default decision when the

borrower has relatively little equity tied to the asset.

While low cap rate spreads are associated with increased incidence of nonperformance, some

underwriters purposefully apply less restrictive criteria and are able to offset potential losses on

nonperforming loans by charging higher coupon rates at origination. Table 7 provides summary

statistics for Coupon spread at origination in each category for Cap rate spread. Within each

category, performing and nonperforming loans are similarly priced; suggesting that performance

risk attributed to the cap rate spread was unobserved and not priced based on other information

available at the time of underwriting. Comparing across categories, Coupon spread is lowest in

categories where Cap rate spread is low – where the performance risk is concentrated. Thus,

lenders did not effectively price performance risk, which could have been priced if information

about the cap rate spread were incorporated in underwriting.

Figure 3 illustrates the improvement in avoiding nonperforming loans as the cap rate spread is

incrementally applied to loans which already satisfy the 1.1 DSCR, combined with a 60%, 70%

or 80% LTV criteria. The avoidance rate that occurs once the cap rate spread criteria has been

applied is calculated as the increase in the number of nonperforming loans that would not have

been originated, divided by the total number of loans that would not have been originated,



THE CAP RATE SPREAD

28

measured relative to the case when no cap rate spread criteria is involved.22 Improvement in the

avoidance rate is calculated as the percentage difference between the avoidance rate and the

share of nonperforming loans that occurred when there are no restrictions on the cap rate spread

criteria.23

Figure 3 highlights a couple of practical considerations from the findings in this study. First, the

cap rate spread tightens underwriting, and its application reduces the total number of loan

originations. Introducing a basic nonnegative cap rate spread constraint eliminates 28.5% of deal

volume in the 60% LTV / 1.1 DSCR category. Second, the cap rate spread criteria behaves

similar to LTV and DSCR criteria in the sense that it is most efficient at avoiding nonperforming

loans when low thresholds are applied. As the level of restriction on the cap rate spread

increases, the ratio of performing loans to nonperforming loans also rises. There are diminishing

marginal returns to increasing underwriting restrictions even though a basic level of restriction

can significantly enhance the ability to screen loans with higher performance risk. Third, the cap

rate spread is likely to be more useful when other underwriting criteria have been applied, and in

particular as the borrower is required to contribute a greater amount of equity to the asset. The

cap rate spread makes only a limited contribution to underwriting decisions based on subprime

standards.

Conclusion

22 For example, in the 60% LTV distribution, adding a minimum cap rate spread of 0% reduces the number of nonperforming loans in the sample by 0.161*4,807 – 0.147*3,439 = 269 loans. The total loans cut are 4,807 – 3,439 = 1,368, or 28.5% of cumulative volume (shown in the bottom panel of Figure 3). The avoidance rate is then 269 divided by 1,368, equal to 19.7%. 23 When no cap rate spread criteria is applied, the share of loans meeting 60% LTV / 1.1 DSCR that are nonperforming is 16.1%. Introducing a 0% minimum cap rate spread results in an avoidance rate of 19.7%. The improvement is 0.197 divided by 0.161 minus 1, equal to 22%. A 0% minimum cap rate spread improves the success at avoiding nonperforming loans that already meet the 60% LTV / 1.1 DSCR criteria by 22%.

28

THE CAP RATE SPREAD

29

Traditional measures such as LTV and DSCR are the standard in commercial mortgage

underwriting. Despite the continued reliance upon these trusted measures, performance risk on

commercial loans skyrocketed during the financial crisis of the late-2000s. This study considers

a new metric for performance risk in commercial underwriting: the cap rate spread. The cap rate

is the unlevered rate of return on the commercial property. The cap rate spread is the difference

between the cap rate and the fixed coupon rate on a loan. While DSCR relates net operating

income to debt service, the measurement is in absolute terms and disconnected from the asset

values. As an alternative, the cap rate spread relates cash flows to equity relative to cash flows

that are sent to debt with both measured as yields relative to the underlying assets. A borrower

may have the ability to cover debt service, but consideration for the net returns to the equity

partnership could jeopardize their incentives to remain current. Leverage increases returns to

equity as long as unlevered returns are greater than the cost of debt, but leverage is also double-

edged.

The cap rate spread at origination is found to have a significant impact on the probability of

nonperforming loans. A low cap rate spread increases performance risk on commercial

mortgages. This result holds when controlling for a number of other factors, including LTV and

DSCR measures at origination, Treasury yields and coupon spreads, CMBS origination volume

and the financial market risk structure, along with controls for originator identity, vintage of

origination, property type, and the state where the property is located. Eliminating subprime

loans from the analysis only strengthens the connection between performance risk and the cap

rate spread because borrowers who have an increased equity position also have increased

exposure to the consequences of the cap rate spread.



THE CAP RATE SPREAD

30

A counterfactual numerical analysis is provided, demonstrating that a minimum cap rate spread

of 1%, when added to traditional cutoffs of 75% LTV maximum and 1.1 DSCR minimum, would

have reduced the amount of nonperforming loans by more than one-third; particularly during

periods when originations that ultimately became nonperforming were at peak volume.

Outcomes generated from a spectrum of cap rate spread criteria are provided and the

observations reveal that increasing the cap rate spread threshold reduces performance risk and is

increasingly effective when borrowers contribute larger portions of equity to the asset.

Increasing barriers to underwriting generally reduces business volume for lenders and the

efficiency of avoiding nonperforming loans relative to total loans is a key concern. We find that

there are diminishing marginal returns to avoiding nonperforming loans as the cap rate spread

criteria is applied more restrictively. The greatest improvement in performance risk avoidance

occurs with relatively low cap rate spread criteria and for loans that require nontrivial equity

positions of the borrower.

30

THE CAP RATE SPREAD

31

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THE CAP RATE SPREAD

33

Table 1. Summary Statistics, CMBS Loans

Notes: This table presents summary statistics for the 24,951 CMBS loan observations considered in this study. The columns, from left-to-right, include the variable name, the sample mean, standard deviation (Std dev), minimum (Min) and maximum (Max) values. Loan amount is the loan size at origination, in $Millions. Coupon spread equals the difference between the loan coupon rate and the 10-year Treasury rate. 10-year Treasury is the yield on 10-year U.S. T-bonds. S&P return equals the 12-month percentage change in the S&P 500 index. CMBS volume equals the number of CMBS loans issued on the same securitization date. Senior is an indicator variable for first-position debt. Junior is an indicator variable for subordinate debt. DSCR, debt service coverage ratio, equals property net operating income divided by debt service at origination. LTV, loan-to-value ratio, equals the Loan amount divided by the appraised property value at origination. Cap rate spread equals the difference between the property cap rate and the loan coupon rate. Nonperforming equals 1 if the current loan status is “delinquent”, “foreclosure”, “grace”, “late”, “non-performing”, or “REO”, and 0 otherwise.

Variable Mean Std dev Min Max

Loan amount $8.18 $15.73 $0.07 $536.2

Coupon spread 1.66% 0.9% -1.1% 11.6%

10-year Treasury 4.41% 0.6% 2.0% 7.9%

S&P return 9.13% 18.3% -48.2% 61.7%

CMBS volume 65.0 86.1 0 537

Senior 0.53 0.50 0 1

Junior 0.09 0.29 0 1

DSCR 1.48 0.39 0.93 5

LTV 67.7% 12.1% 2.9% 98.8%

Cap rate spread 2.38% 10.4% -9.9% 392.1%

Nonperforming 0.20 0.40 0 1



THE CAP RATE SPREAD

34

Table 2. Loan Performance, by Property Type

Property Type N Nonperforming LTV DSCR Cap rate spread

Healthcare 30 0.10 64.0% 1.70 2.17%

Hotels: Full-service 476 0.26 64.3% 1.63 4.61%

Hotels: Limited-service 1,167 0.24 66.6% 1.58 3.08%

Industrial 1,781 0.20 66.4% 1.45 1.75%

Mixed use 672 0.19 65.9% 1.44 1.98%

Mobile homes 727 0.18 67.5% 1.51 1.92%

Multifamily 5,643 0.22 67.4% 1.52 2.38%

Office 3,745 0.22 68.6% 1.44 2.26%

Retail: Anchored 3,397 0.20 68.7% 1.44 2.14%

Retail: Unanchored 5,378 0.19 68.7% 1.43 1.73%

Self-service storage 1,457 0.15 65.9% 1.54 5.81%

Warehouse 194 0.24 65.7% 1.46 1.59%

Other 284 0.16 65.5% 1.47 1.78%

Notes: This table reports summary statistics for the 24,951 CMBS loan observations considered in this study, by property type. The columns, from left-to-right, include the property type, number of observations (N), average percent Nonperforming, average LTV, average DSCR and average Cap rate spread. All variables are defined in the notes to Table 1.

34

THE CAP RATE SPREAD

35

Table 3. Second-stage, Probability of Nonperformance

Variable Coefficient (Wald χ2)

Coefficient (Wald χ2)



Constant -7.39 -8.37 -7.95 -8.28 (0.0) (0.0) (0.0) (0.0) Loan amount 0.08*** 0.07*** 0.07*** 0.08*** (43.3) (36.4) (36.3) (37.7) Coupon spread 0.11*** 0.13*** 0.12*** 0.15*** (72.2) (96.4) (81.5) (85.6) 10-year Treasury 0.03 0.05 0.04 0.06 (0.2) (0.9) (0.5) (1.3) S&P returns 0.17 0.17 0.17 0.17 (0.5) (0.4) (0.4) (0.4) CMBS volume -0.00 -0.00 -0.00 -0.00 (0.2) (0.3) (0.3) (0.1) Senior -0.16*** -0.16*** -0.16*** -0.15*** (23.5) (21.8) (22.3) (21.0) Junior -0.22*** -0.22*** -0.22*** -0.22*** (26.6) (26.7) (26.6) (26.8) DSCR -0.27*** -0.13*** -0.10** (62.3) (9.2) (4.7) LTV 0.84*** 0.59*** 0.71*** (77.1) (21.7) (29.0) Cap rate spread -0.40*** (10.2)

State indicators: 50 50 50 50 Property type indicators: 12 12 12 12 Vintage indicators: 71 71 71 71 Originator indicators: 168 168 168 168

Pseudo-R2: 11.0% 11.1% 11.1% 11.2% Observations: 24,951 24,951 24,951 24,951

Likelihood-ratio test: 11.357***

Notes: This table reports the probit estimations for equation �14�, using parameters �15�. The first column presents results with DSCR only �u� = u� = 0�, the second

column is with LTV only �u� = u� = 0�, the third is with DSCR and LTV �u� = 0�, and the fourth includes the Cap rate spread to complete the set of regressors. The dependent variable in each estimation is Nonperforming. The estimations are based on the full sample of 24,951 observations. The columns report the variable name (Variable), the estimated coefficient (Coefficient), and the Wald chi-square statistic in

parentheses (Wald Χ2). The Likelihood-ratio statistic�~�8�� equals the difference in the 2logL measures from the estimations sequentially. All variables are defined in the notes to Table 1. *** and ** indicate statistical significance at the 1% and 5% levels, respectively.



THE CAP RATE SPREAD

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Table 4. Probability of Nonperformance, Conditional on 75% LTV / 1.1 DSCR Criteria

Variable Coefficient (Wald χ2)




Constant -6.97 -8.14 -7.52 -7.72 (0.0) (0.0) (0.0) (0.0) Loan amount 0.06*** 0.06*** 0.06*** 0.06*** (16.4) (14.7) (14.6) (15.5) Coupon spread 0.13*** 0.18*** 0.15*** 0.16*** (33.2) (64.0) (40.4) (44.2) 10-year Treasury 0.05 0.09 0.07 0.08 (0.5) (1.8) (1.0) (1.3) S&P returns 0.31 0.32 0.31 0.30 (1.0) (1.0) (1.0) (0.9) CMBS volume -0.00 -0.00 -0.00 -0.00 (0.5) (0.4) (0.4) (0.3) Senior -0.18*** -0.17*** -0.17*** -0.17*** (18.3) (16.7) (17.4) (17.4) Junior -0.23*** -0.23*** -0.23*** -0.23*** (21.1) (21.0) (21.0) (20.8) DSCR -0.24*** -0.15*** -0.12** (46.7) (9.1) (5.2) LTV 0.80*** 0.46*** 0.57*** (47.1) (7.9) (11.0) Cap rate spread -0.89** (4.3)

State indicators: 50 50 50 50 Property type indicators: 12 12 12 12 Vintage indicators: 71 71 71 71 Originator indicators: 168 168 168 168

Pseudo-R2: 11.7% 11.7% 11.8% 11.8% Observations: 18,043 18,043 18,043 18,043

Likelihood-ratio test: 6.509**

Notes: This table reports the probit estimations for equation �14�, using parameters �15�. The first column presents results with DSCR only �u� = u� = 0�, the second

column is with LTV only �u� = u� = 0�, the third is with DSCR and LTV �u� = 0�, and the fourth includes the Cap rate spread to complete the set of regressors. The dependent variable in each estimation is Nonperforming. The estimations include a reduced sample of 18,043 observations where loans originated with LTVs greater than 75% and DSCRs less than 1.1 are not included. The columns report the variable name (Variable), the estimated coefficient (Coefficient), and the Wald chi-square statistic in

parentheses (Wald Χ2). The Likelihood-ratio statistic�~�8�� equals the difference in the 2logL measures from the estimations sequentially. All variables are defined in the notes to Table 1. *** and ** indicate statistical significance at the 1% and 5% levels, respectively.

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Table 5. Probability of Nonperformance, by Loan Status Date

Status date Nonperforming DSCR LTV Cap rate spread Observations Pseudo-R2:

1-6 months 597 -0.31** 1.12*** -0.62* 20,497 19.4%

7-17 months 1,012 -0.27*** 0.41* -0.79** 20,912 13.3%

18 months or more 928 -0.03 1.30*** -0.29* 20,828 17.8%

Notes: This table presents results from three probit estimations for equation �14�, using parameters �15�, using reduced samples for nonperforming loans according to status date. The dependent variable in each estimation is Nonperforming. The reduced samples include 19,900 performing loans along with loans that enter nonperforming status within six months of the data collection, within seven to 17 months, and 18 months or greater. Condensed results for each status date category are presented on each row, including the number of nonperforming loans included, the estimated coefficient and significance for DSCR, LTV and Cap rate spread, the total number of observations and the pseudo-R2. ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively.



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Table 6. Joint Criteria: Cap Rate Spread, LTV & DSCR

Max. LTV: None 90% 80% 75% 70% 65% 60%

Min. cap rate spread: Min. DSCR: None 1.1 1.1 1.1 1.1 1.1 1.1

None Nonperforming 20.2% 20.2% 20.1% 18.9% 17.6% 16.7% 16.1%

Loan count 24,951 24,802 24,542 18,043 11,474 7,378 4,807

0.0% Nonperforming 20.2% 20.2% 20.1% 18.7% 17.1% 15.7% 14.7%

Loan count 22,959 22,880 22,637 16,169 9,674 5,734 3,439

0.5% Nonperforming 20.4% 20.4% 20.3% 18.9% 17.2% 15.6% 14.7%

Loan count 21,280 21,216 20,976 14,575 8,360 4,888 2,937

1.0% Nonperforming 20.5% 20.5% 20.3% 18.7% 16.8% 15.3% 14.5%

Loan count 18,261 18,217 17,987 12,033 6,726 3,901 2,339

1.5% Nonperforming 20.4% 20.4% 20.2% 18.6% 16.7% 15.0% 13.7%

Loan count 14,032 13,998 13,799 9,080 5,012 2,906 1,720

2.0% Nonperforming 20.0% 20.0% 19.9% 18.5% 15.6% 13.3% 12.3%

Loan count 9,775 9,750 9,597 6,395 3,501 2,026 1,207

2.5% Nonperforming 19.9% 19.9% 19.7% 18.6% 15.5% 12.5% 11.2%

Loan count 6,098 6,078 5,960 4,095 2,274 1,316 767

3.0% Nonperforming 19.4% 19.4% 19.1% 17.7% 14.8% 12.7% 10.5%

Loan count 3,401 3,385 3,295 2,399 1,396 845 504

Notes: This table reports summary statistics for the percent of nonperforming loans and the total loan count as commercial underwriting criteria are applied. In the first column, there are no restrictions for LTV and DSCR, and the remaining columns display the subsample that would result when loans with less than 1.1 DSCR are removed and maximum LTV restrictions are applied, from left-to-right, of 90%, 80%, 75%, 70%, 65% and 60%. The rows represent, in addition to the restrictions applied across the columns, the outcome from a minimum cap rate spread restriction, from top-to-bottom, of none, 0.0%, 0.5%, 1.0%, 1.5%, 2.0%, 2.5% and 3%.

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Table 7. Loan Pricing, by Cap Rate Spread Restrictions

Nonperforming

1 0

Difference in means

Mean Mean

Cap rate spread Coupon spread Coupon spread

0.0% to 0.5% 1.48% 1.41% 1.5

0.5% to 1.0% 1.41% 1.40% 0.1

1.0% to 1.5% 1.49% 1.49% 0.2

1.5% to 2.0% 1.66% 1.62% 1.7*

2.0% to 2.5% 1.78% 1.73% 1.9*

2.5% to 3.0% 1.81% 1.81% 0.2

Notes: This table present the mean Coupon spread in each category for Cap rate spread at origination. The first column defines the Cap rate spread categories. The second and third columns reports the mean Coupon spread for nonperforming and performing observations within each Cap rate spread category, respectively. The fourth reports the t-test for difference in mean Coupon spreads between performing and nonperforming loans. * indicates a statistically significant difference in means at the 10% level.



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Figure 1. Cap Rate Spreads & CMBS Originations, 1993-2011

Notes: This figure displays the monthly volume of CMBS originations for the 24,951 fixed-rate balloon loans included in our sample, during 1993-2011. CMBS originations are represented by the solid bars along the bottom of the figure, with values identified on the left-axis. The figure also displays solid-black, solid-grey and dashed-grey lines to represent the 12-month rolling average for the cap rate spread, coupon rate and cap rate variables at origination calculated for our sample, respectively. Values for cap rates, coupon rates and cap rate spreads are on the right-axis.

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Figure 2. Counterfactual Performance with Cap Rate Spread Criteria, 1993-2011

Notes: This figure present the total volume of nonperforming loans in the sample, presented according to the quarter of origination during 1993-2011. The solid black line represents the nonperforming loans in the full sample. The dashed black line represents the subsample of nonperforming loans that were originated with no greater than 75% LTV and no less than 1.1 DSCR. The grey line represents the subsample of nonperforming loans that would have been originated, subject to the constraint of a 1% minimum cap rate spread criteria in addition to the 75% maximum LTV and 1.1 minimum DSCR constraints.

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Figure 3. Improvement in Avoiding Nonperforming Loans

Notes: The upper panel presents a graph for the improvement in avoiding nonperforming loans, based on the minimum cap rate spread constraint. Improvement in the avoidance rate equals the reduction in the share of nonperforming loans originated relative to the share of nonperforming loans included with no cap rate spread criteria involved. The lower panel presents a chart for the cumulative volume denied, measured as a percentage relative to the total volume without cap rate restrictions. Absent cap rate restrictions, there are 4,807 loans that satisfy the 60% LTV/1.1 DSCR criteria, 11,474 that satisfy 70% LTV/1.1DSCR, and 24,542 that satisfy 80% LTV/1.1 DSCR.

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42

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Appendix. Loan Performance, by Property Location

State N Nonperforming LTV DSCR Cap rate spread State N Nonperforming LTV DSCR Cap rate spread

AK 62 0.23 67.7% 1.52 2.15% MT 18 0.17 67.0% 1.51 2.42%

AL 300 0.20 72.1% 1.40 3.39% NC 825 0.29 71.1% 1.43 2.74%

AR 126 0.17 71.1% 1.36 4.13% ND 71 0.20 72.0% 1.40 2.02%

AZ 667 0.27 67.9% 1.45 2.00% NE 125 0.21 71.2% 1.43 2.08%

CA 4,087 0.16 62.6% 1.51 1.36% NH 87 0.20 69.4% 1.47 4.92%

CO 451 0.26 68.0% 1.46 2.23% NJ 707 0.21 67.1% 1.51 2.21%

CT 259 0.19 68.3% 1.47 2.52% NM 137 0.17 68.0% 1.47 1.92%

DC 84 0.18 64.5% 1.50 1.35% NV 426 0.28 67.2% 1.48 1.63%

DE 86 0.16 68.2% 1.48 2.06% NY 1,633 0.16 59.6% 1.80 1.47%

FL 1,741 0.24 68.3% 1.47 2.42% OH 866 0.20 71.7% 1.41 3.30%

GA 878 0.23 70.1% 1.43 3.17% OK 233 0.20 72.5% 1.40 4.29%

HI 31 0.13 68.2% 1.47 1.77% OR 273 0.16 66.2% 1.44 2.89%

IA 107 0.32 70.9% 1.39 2.00% PA 853 0.17 70.6% 1.45 2.75%

ID 107 0.26 66.0% 1.50 2.94% RI 38 0.18 72.5% 1.34 1.57%

IL 680 0.20 69.5% 1.43 2.37% SC 366 0.19 71.1% 1.41 4.18%

IN 407 0.20 71.4% 1.39 1.88% SD 34 0.18 72.0% 1.38 6.14%

KS 164 0.21 71.0% 1.42 3.27% TN 380 0.21 71.1% 1.41 2.44%

KY 224 0.21 71.6% 1.43 2.48% TX 2,498 0.21 70.5% 1.41 2.93%

LA 277 0.18 71.0% 1.43 5.98% UT 248 0.19 68.9% 1.41 2.20%

MA 315 0.14 68.3% 1.47 2.19% VA 860 0.18 69.0% 1.47 3.09%

MD 625 0.16 67.3% 1.49 2.01% VT 17 0.18 71.4% 1.44 2.39%

ME 61 0.08 71.8% 1.42 2.15% WA 668 0.20 65.3% 1.44 1.31%

MI 687 0.30 69.6% 1.47 2.60% WI 342 0.21 68.8% 1.41 2.19%

MN 343 0.25 69.8% 1.42 1.86% WV 69 0.26 70.4% 1.42 2.30%

MO 243 0.18 72.4% 1.40 4.53% WY 21 0.10 71.3% 1.34 1.39%

MS 144 0.26 70.6% 1.42 3.47%

Notes: This table reports summary statistics for the 24,951 CMBS loan observations considered in this study, by State. The columns, from left-to-right, include the State where the property is located, number of observations (N), average percent Nonperforming, average LTV, average DSCR and average Cap rate spread. All variables are defined in the notes to Table 1.



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Cap Rate Spread