Originator Performance, CMBS Structures and Yield

Spreads of Commercial Mortgages∗

Sheridan Titman

McCombs School of Business

Department of Finance

University of Texas at Austin

Austin, TX 78712-1179.

Sergey Tsyplakov

Moore School of Business

Department of Finance

University of South Carolina

Columbia, SC 29208

Current Draft: May 28, 2008

∗Authors’ e-mail addresses are titman@mail.utexas.edu and sergey@moore.sc.edu respectively. We

would like to thank seminar participants at the University of Texas at Austin, UCLA, the University of South

Carolina, and the 2007 Summer Real Estate Symposium, and, especially, Jefferson Duarte, Tim Koch, Steve

Mann, Eric Powers and Donghang Zhang. We also thank Renjie Wen for excellent research assistance.

Originator Performance, CMBS Structures and Yield Spreads of Commercial

Mortgages

Abstract

This paper examines information and incentive problems that can exist in the market

for conduit mortgages, which are commercial mortgages placed in pools that are repackaged

and sold as CMBS. We find that conduit mortgages that are originated by institutions

with negative stock price performance in the quarters just prior to the origination date

tend to have higher credit spreads and default more than other mortgages with similar

characteristics. This evidence is consistent with reputation models that suggest that poorly

performing originators have less incentive to expend resources evaluating the credit quality

of prospective borrowers. We also find that the originator/performance effect is stronger

when the originator of the mortgage is also the lead underwriter of the CMBS and that the

time between the origination date and the CMBS offering is shorter for originators that are

stock price losers.

1 Introduction

Securitization involves pooling cash flow claims from financial instruments to form publicly

traded securities. Financial instruments that are commonly securitized include residential

and commercial mortgages and commercial and consumer loans, which historically, were

both originated and held by banks, savings and loans and insurance companies.

With the introduction of securitization, the evaluation and risk bearing functions of

financial institutions can be split. The institution that initially underwrites the loan, and

evaluates the borrower, need not be the same institution that ultimately bears the risks

associated with holding the loan. The advantage of separating these functions is that the

loan originations are best performed by institutions with good local knowledge, while risk is

most efficiently borne by large internationally diversified portfolios. Offsetting this advantage

are potential incentive/information problems that exist when an originator with better local

knowledge sells mortgages to less informed investors. In particular, there may be a tendency,

on the part of the originator, to expend too little effort in the evaluation of individual loans.

Due to the repeated nature of the securitization business, such a tendency is not expected to

be severe during normal circumstances because originators of individual loans are expected

to be concerned about their reputation for originating high quality loans. However, there

may be a tendency to reduce origination expenses when the originator is doing poorly and

is more concerned about its short-run profitability than its reputation.

This paper examines these incentive/reputation issues within the context of what are

called conduit mortgages, which are commercial mortgages that were originated with the in-

tention of placing them in Commercial Mortgage Backed Securities (CMBS). The commercial

mortgage backed securities market was introduced in the early 1990s and grew very rapidly,

perhaps because of the advantages associated with liquidity and better risk sharing;1 by

2006 there were more than $600 billion in CMBS bonds outstanding.2 However, the CMBS

1See Riddiough and Polleys (1999), Riddiough (2002), and Ambrose and Sanders (2003).2In 2005 more than $165 billion in CMBS was issued in the United States, which represented more than

20% of the overall commercial mortgage market. These numbers are quoted from a January 8, 2006 press

release of the Commercial Mortgage Securities Association (CMSA).

1

market slowed considerably in the latter half of 2007, in part, reflecting concerns about the

origination/underwriting process induced by residential sub-prime mortgage problems.3

To understand the information issues that arise in the origination of commercial mort-

gages it is useful to contrast the commercial mortgage market with the market for residential

mortgages. In contrast to commercial mortgages, which are not always securitized, most resi-

dential mortgages are now packaged into mortgage backed securities. One difference between

these markets is that for residential mortgages originators tend to use what are known as

credit scoring systems, which are purely mechanical systems that map quantitative informa-

tion about the borrower and the property into a credit score that determines whether the

loan is offered. By using a credit scoring system banks ignore soft information (i.e., infor-

mation that cannot easily be quantified), which plays an important role in the evaluation of

commercial mortgages. In addition to evaluating hard facts like loan to value and coverage

ratios, the originator of commercial mortgages evaluates the quality of the property, its al-

ternative uses,4 and the incentives and reliability of the owners. This type of information

is likely to be costly to quantify, which means that the ultimate mortgage investors, who

see only the hard information, must to a large extent rely on the originator’s judgment and

reputation. A reputable originator, who is expected to carefully investigate the mortgages

and exercise good judgment, may be able to sell mortgages for a premium relative to less

reputable originators who are likely to expend less resources evaluating the mortgages.

There is a large theoretical literature that describes the tradeoffs between the short-

term benefits of exploiting one’s reputation, e.g., selling mortgages without bearing the

investigation costs, and the long-term costs of operating in the future without the benefit of

a favorable reputation.5 These models suggest that in situations where the originator has a

long horizon, i.e., uses a very low discount rate to evaluate this tradeoff, there is a tendency to

make choices that are likely to help its reputation. However, a more short-sighted originator

may make choices, like reducing investigation costs, that help its current earnings at the

3See, for example, Prudential Real Estate Investors Quarterly Report for October 2007.4Benmelech, Garmaise, and Moskowitz (2005) presents evidence that the redeployability of commercial

real estate affects mortgage spreads.5Klein and Leffler (1981), Shapiro (1983), and Allen (1984) provide models where buyers cannot observe

product quality prior to purchase. In these multi-period models, sellers forgo the short-term cost savings

associated with reducing quality in order to maintain their reputations and ability to credibly sell high

quality goods in the future.

2

expense of its reputation and future earnings.6

Our empirical analysis of these issues is based on the idea that an originator’s past stock

price performance is an indicator of its incentive to make short-sighted choices that help its

current earnings at the expense of its reputation. As a result, relative to the hard information

that is available to CMBS investors, the mortgages originated by poorly performing institu-

tions may be somewhat riskier. There are a number of reasons why this may be the case.

First, poorly performing originators may be more likely to approve mortgages without the

appropriate due diligence, thereby letting some relatively risky mortgages pass through their

screens. Second, on the margin, a poorly performing originator may be less willing to forego

origination fees by rejecting mortgage applications when its investigation reveals potentially

unfavorable information about the borrower or the property. Third, it is plausible that

poorly performing originators are under pressure to sell riskier mortgages and place them

in CMBS deals even though these mortgages were not intended initially to be securitized.

Finally, a poorly performing originator may attract less credit worthy mortgagors, who are

aware of their weaker screening standards. As a result, if the buyers of conduit mortgages are

aware of these tendencies they will require higher credit spreads, conditional on observable

characteristics, on the mortgages they acquire from poorly performing originators.7

Consistent with this argument we find that originators that experienced large stock price

declines in prior quarters tend to originate mortgages with wider spreads, even after con-

trolling for a variety of property and mortgage characteristics, as well as originator fixed

effects and monthly time effects, which control for the fact that some institutions specialize

in riskier mortgages and that economy-wide shocks change the riskiness of real estate from

month to month. However, while this evidence supports the reputation hypothesis, there

are other potential explanations. In particular, it is possible that the direction of causality

is the reverse of what we suggest. Specifically, it could be the case that some originating

institutions experience lower stock returns because of their focus on geographical regions or

6Maksimovic and Titman (1991) develop a model, based on Myers (1977), where the firm’s cost of raising

capital is higher when it is financially distressed. Because of its higher discount rate, the firm has less

incentive to improve product quality that cannot be initially observed. One can generate similar results

within the context of a model where the manager has shorter horizon following poor performance because

of career concerns.7See, for example, Titman and Trueman (1986) and Khanna, Noe, and Sonti (2006) which provide models

where better quality underwriters attract better quality issuers.

3

property types which have become particularly risky (relative to the entire market), and thus

originate mortgages with higher relative spreads that reflect the higher relative risk. It is

also possible that there is a reputation channel that is very different than what we propose.

For example, Hubbard, Kuttner and Palia (2002) suggest that financial institutions that are

doing poorly tend to be less concerned about their long-term reputation and exploit their

borrowers by charging higher rates on their commercial loans.

To distinguish between these alternative hypotheses we run additional cross-sectional

test. We find that the difference in spreads between stock price losers and non-losers is

wider during periods when whole financial industry experiences market downturns. This

evidence is consistent with the idea that scrutiny of origination quality can weaken during

market downturns which allows potentially distressed institutions to shift their origination

practices, and less consistent with hypothesis that stock prices decline in anticipation of

risk increase of certain regions of real estate markets. Our most direct evidence that the

mortgages of stock price losers are riskier comes from an analysis of the default histories of

the mortgages. We find that the mortgages originated by stock price losers default more

often, suggesting that the observed higher spreads reflect higher default risk rather than the

exploitation of borrowers. In addition, we find that the time lag between when a mortgage

is originated and the selling date of the CMBS deal that includes the mortgage is shorter

for mortgages originated by originators who had poorer stock price performance. This last

finding suggests that poorly performing originators may be anxious to sell the mortgages

they originate before negative information about the mortgages is revealed. Finally, we

provide evidence that suggests that the ratings agencies act as though the past stock returns

of the originators provide information about a mortgage’s risk. Specifically, we find that the

ratings agencies require higher subordination levels (a lower percent of the issued bonds are

rated AAA) for CMBS issues that include more mortgages originated by underperforming

originators.

We also examine the loan to value ratios of the mortgages, which may be influenced by

the originator’s past stock returns under both of the alternative explanations. For example,

if we believe stock price losers have riskier clients, or that they exploit them more, then we

might expect to observe lower loan to value ratios on the mortgages they originate. However,

we find no significant relation between loan to value ratios and the originator’s past stock

returns.

4

Finally, we uncover evidence that provides support for the idea that the degree of outside

scrutiny plays an important role in the determination of credit spreads. Specifically, in our

data set, about a third of the mortgages are originated by the same institution that later

becomes the lead underwriter of the CMBS in which the mortgage is sold. When this is the

case, the mortgage may be scrutinized less before it becomes a part of a CMBS and ultimately

sold to investors. We find that in these cases the past stock returns of the originator have a

stronger influence on mortgage spreads.

Although our focus is on the CMBS market, our analysis relates to the broader litera-

ture that considers the relation between the reputations of financial intermediaries and the

pricing of equity and debt issues. This literature includes theoretical papers that examine

how the reputation of underwriters relate to the quality of the corporate securities they

issue,8 as well as an empirical literature that explores the cross-sectional relation between

various measures of underwriter reputation and security pricing.9 However, in contrast to

this literature, which views the reputation or prestige of the underwriting institution as a

permanent attribute,10 our focus is on how the credibility of an institution can change over

time. Indeed, our empirical tests include originator fixed effects and thus control for differ-

ences in their “average” reputations, and focus on how the credibility of originators change

when their stock return is lower.

The organization of this paper is as follows: next analyzes the CMBS securitization

process. Section 3 describes the data. Our main results, including robustness checks, are

discussed in Section 4. Additional tests are presented in Section 5. Section 6 concludes the

paper.

8For example, in Pennacchi (1988), a bank’s ability to sell loans to investors depends on investors’

perceptions of the bank’s ability to monitor loans they sell. In addition, in Chemmanur and Fulghieri

(1994), the reputation of a financial intermediary mitigates the moral hazard problem associated with the

incentive to lower the underwriting standards.9Beatty and Ritter (1986) and Carter and Manaster (1990) find that the prestige of investment banks

is associated with the pricing of IPOs. Michaely and Shaw (1994) document that IPOs underwritten by

reputable investment banks experience less underpricing and also show that they perform better in the long

run. Livingston and Miller (2000) and Fang (2005) finds that debt issued by more prestigious underwriters

have lower yields.10Carter and Manaster (1990) measure prestige of investment banks its place in the hierarchy of bank names

in "tombstone announcements." Michaely and Shaw (1994) and Fang (2005) use a measure of investment

bank reputation based on bank’s relative size and/or market share.

5

2 The CMBS Securitization Process

In this section we describe institutional details of the process through which a typical CMBS

security is created. The process starts with a financial institution (the originator) that pro-

vides a mortgage that is collateralized by commercial property. If the mortgage is originated

with the intention of including it in a CMBS, it is called a conduit loan and the originator

has to follow certain loan origination guidelines in assessing the mortgage risk. The origi-

nator has to assess a variety of risk factors related to property type, location, the physical

quality of the property, lease structure, the quality of the management team and the quality

of the tenants.11 Some of these risk factors, such as property type, size of the property and

the current lease structure are either observable or relatively easy to assess. However, in

addition to this hard information, they must make more ambiguous assessments of softer

information, such as local competition and the quality of the property and its tenants. This

soft information can be expensive to obtain and may be difficult to verify.

When the mortgage terms are finalized the originator transfers (or sells) the mortgage

along with other mortgages to a CMBS underwriter. Normally there is a time lag between

when the mortgage is originated and when it is transferred to a CMBS underwriter. The

underwriter screens the mortgages and creates a pool of mortgages from different origina-

tors. Using this pool as collateral the underwriter issues bonds with different seniorities

(or tranches) that are sold to investors.12 Often the underwriter includes the mortgages it

originated in the pool, in which case, there is no third party that screens the mortgages.

After the pool composition is finalized, but before the securities are issued, the credit rat-

ing agencies analyze the portfolio of mortgages in the pool, considering the above mentioned

characteristics of the mortgage as well as portfolio characteristics, like the property type

composition and geographical diversification. In many cases the credit agencies reexamine

the quality of the mortgage origination process and conduct individual site inspections.13

Based on their assessment of the risk factors, rating agencies determine the subordination

levels for each credit rating. For example, AAA subordination is the percentage value of the

total CMBS pool that are in securities rated below AAA. Since a higher subordination level

reduces the default risk of the bonds, subordination levels for each rating category will be

11See Fabozzi et al (2000).12See Riddiough (2002) for a description of the underwriters role in the CMBS markets.13See Fabozzi and Jacob (1998).

6

higher when the pool of mortgages is deemed to be riskier.

3 Data Overview

Our data set, which was provided by Standard & Poors, includes information on over 19,000

commercial mortgages that were originated between 1992 and 2002. As reported in Panel 1

of Table 1, very few mortgages were originated prior to 1995, so we limit our study to the

1995 to 2002 period. Most of the mortgages in our sample are conduit mortgages, issued

specifically for inclusion in a commercial mortgage backed security (CMBS).14 The data set

includes detailed information on characteristics of the mortgaged properties along with the

mortgage contract specifications and information on mortgage originators and CMBS deals.

Our data set also includes the dates when the mortgage is originated as well as the dates

when the CMBS that includes the mortgage is issued. The time between these dates, i.e.,

the "time lag," average 159 days with a standard deviation of 141 days. The time lag for

some mortgages is several years.

For most mortgages we have information on the CMBS deal to which the mortgage was

subsequently sold. Specifically, we have data on 94 CMBS deals that include a total of

14,420 mortgages.15 ,16 For these deals we have information on the lead underwriters, which

structure the securities and sell them to the CMBS investors. The lead underwriters are

often the originator of the largest number of mortgages in a given deal, but may also be a

third party, i.e., not an originator of any mortgage in the deal. As we show in the summary

statistics, originators that also serve as underwriters of CMBS deals tend to include more

than 50% of their own mortgages in those deals.

3.1 Property Characteristics

The data set also includes information on the mortgaged properties. The information we

use includes the property type and the appraised property value as well as its annual net

14Our sample also includes a small number of mortgages that were included in CMBS pools more than 2

years after origination.15For more than 4,000 mortgages we cannot identify the CMBS deals to which the mortgages were sold.16Typically, the CMBS deals include between 100 and 200 mortgages. The largest number of mortgages

in a deal is 591 and one deal has only one mortgage.

7

operating income at the time of the origination.17 Summary statistics describing the proper-

ties are presented in Panel 3 of Table 1. The property types include multi-family apartment

complexes, unanchored retail, anchored retail, medical offices, industrial, warehouse, mo-

bile home parks, office buildings, properties of mixed use, limited service hotels, full service

hotels, and self storage. The data set also includes information about occupancy of the

property and the year it was built or renovated. These two observations are missing for

about 15% of the mortgages in the data set.

3.2 Mortgage Characteristics

The data includes the following financial information for individual mortgages: origination

date; mortgage rate; loan to value ratio; whether the mortgage is non-amortizing, amortizing,

or semi-amortizing; and the maturity of the mortgage. The loan to value ratio (LTV), which

is generally between 60% and 80%, is measured as the loan amount divided by the appraised

value of the property. Non-amortizing mortgages represent approximately two-thirds of the

mortgages with the rest being amortizing and semi-amortizing mortgages, where the loan

amortization rate is defined as 1−Principal Value at MaturityInitial Principal Value (See Panel 3 of Table 1). The majorityof the mortgages have 10 year maturities and, due to prepayment penalties, are effectively

not prepayable.

3.3 Originator Characteristics

The mortgages in our final sample were originated by 50 different institutions. Mortgage

originators include large commercial banks, investment banks, insurance companies, and

financing arms of large companies (e.g. GMAC). In Panel 4 of Table 1, we list the 30

largest originators that have at least 100 mortgages in the data set. Mortgages originated

by these institutions constitute about 97% of the mortgages in the data set. We also include

information on average spread, LTV ratios and delinquency rates across the originators. As

reported in the summary statistics, seven originators issued more than 1,000 mortgages each.

Mortgage LTV ratios do not vary significantly across originators, but the delinquency rates

17The Net Operating Income (NOI) is defined as gross annual revenue less maintenance and other oper-

ational expenses before taxes and depreciation for the 12 month period prior to the mortgage origination

date.

8

do vary across originators.

The originator characteristic that we are most interested in is their cumulative stock

returns in periods prior to the mortgages they originate. Panel 5 of Table 1, reports cu-

mulative stock returns (and standard deviations) across originators measured during two

look-back windows of three and six month prior to mortgage origination. Given that our

sample periods includes the Asian currency crises, the Russian default, as well as the period

after 9/11, there is a time-series variation as well as cross-sectional variation in these stock

returns.

4 Main Regression Analysis

4.1 Cross-Sectional Regression Specification

As we mentioned in the introduction, our goal is to estimate the extent to which the orig-

inators’ past performance influences the characteristics of the mortgages they originate. In

particular, we will examine the relation between originators’ past stock returns and the

spreads on the mortgages they originate. In these tests we define mortgage spreads as the

difference between the mortgage rate and the rate on Treasury bonds with the same maturity

as the mortgage, observed on the mortgage origination date.

The first regression we estimate, described below, regresses mortgage spreads on several

indicator (Dummy) variables describing the originators’ past stock returns, along with control

variables that correspond to property and mortgage characteristics.

Spread = intercept+ α(dummy=1 if originator is "stock price loser")

+X

βi(property characteristics variables)

+X

γi(mortgage characteristics variables)

+ (property type dummy variables)

+ (originator dummy variable)

+ (origination time dummy variables) + .

(1)

We call the indicator variables for poorly performing originators "stock price loser" dum-

mies and estimate a series of regressions with two different stock price loser dummies, defined

as follows:

9

1) Dummy variable=1, if the originator’s stock return is less than -10% during the 3

months prior to the mortgage origination date [-3mo, 0] and zero otherwise.

2) Dummy variable=1, if the originator’s stock return is less than -10% during the 6

months prior to the mortgage origination date [-6mo, 0] and zero otherwise.

We choose negative 10% return as a cutoff because this return is approximaty one stan-

dard deviation from avergae. See Panel 5 of Table 1. Panel 6, which reports the stock return

characteristics of originators, reveals that between 10% and 11% of the mortgages in our

sample were originated by institutions that realized stock price declines of at least 15% in

the prior periods. There is significant time-series variation across quarters in the fraction of

originators that are "stock price losers." For example, more than 40% of the mortgages were

originated by stock price losers (prior 3mo window) in both the fourth quarter of 1998 (the

period after the Russian default) and the fourth quarter of 2001 (the period after 9/11). In

contrast, in the first and second quarters of 1996, no mortgages were originated by stock

price losers.18

We include control variables that describe characteristics that are likely to proxy for the

riskiness of the properties. These variables include the NOI/Value ratio, which proxies for

the expected growth of net operating income,19 and the value of the property to capture size

effects.20 We also include property type dummies to control for differences in the risk for

each type of property. In addition we include variables that describe the mortgage terms,

which include the LTV ratio, the loan amortization rate, and the mortgage maturity. The

loan amortization rate and the mortgage maturity measure how fast the loan is paid off.

To control for possible differences across originators we use originator fixed effects, and to

18Alternative measures for the periods when originators perform poorly may include periods with low ROE,

EPS ratios or capitalization ratios as well as periods of debt ratings reduction. However, these alternative

indicators may be less suitable given the heterogeneity of originators. The concern is that we cannot directly

compare financial ratios across originators that include not only banks and financial institutions, but also

insurance companies and arms of large corporations (GMAC). Using periods with large stock return losses

as an indicator for poor performance is not subject to such a concern.19Properties with larger NOI/Value ratios are likely to have higher payouts and lower NOI growth in the

future. As shown in Titman, Tompaidis and Tsyplakov (2004), NOI/Value should be positively related to

credit spreads.20First, there may be economies of scale associated with the transaction costs of providing a mortgage.

Second, more reliable individuals may be acquiring the larger properties. Finally, the larger properties may

have less risky cash flows because they may be more diversified and the owners may have more market power.

10

control for macro changes in interest rates and risk we include time dummies that correspond

to the different origination months.

4.2 Cross-sectional Results

4.2.1 The Relation Between Originators’ Prior Stock Returns and Mortgage

Spreads

The estimates of these regressions, which are reported in Table 2, are consistent with our

hypothesis that poorly performing institutions originate riskier mortgages. Panel 1 of this

table reports regressions that include monthly dummies and Panel 2 reports the regressions

that include macro-economic variables and annual dummies. In both cases the coefficient of

the stock price dummy variables are positive and statistically significant, which reveals that

after controlling for mortgage and property characteristics, as well as originator and time

fixed effects, originators that experienced poorer stock price returns in prior quarters tend

to issue mortgages with wider spreads. The coefficient of the "stock price loser" dummy is

7 and 8 b.p., for look-back window of 3 and 6 months respectively.21 If one believes that the

increase in average industry credit spreads that are associated with larger fraction of stock

price losers is due to the originators providing less analysis on the mortgages they initiate

21In an alternative specification, instead of using monthly time dummies, we introduce variables that

control for changes in marco-economic credit environment and changes in real estate fundamentals. The

variables we include are shown to account for changes in credit spreads of commercial mortgages (e.g. Maris

and Segal. (2002) Titman,Tompaidis and Tsyplakov (2005)) including: spread of AAA-rated corporate

bonds; slope of the treasury curve, which is measured as Yield of 10-year Treasury minus Yield of 1-year

maturity Treasury; cumulative return of NCREIF for prior four quarters, average rate of commercial real

estate write-offs for prior four quarters; average delinquency rate of commercial real estate for prior four

quarters . In this specification, the latter three macro-economic variables are available on quarterly basis

only, therefore we include time fixed effects that correspond to different origination years. The estimated

coefficient for cumulative stock returns are statistically significant and slightly more negative than in the

regression that includes monthly time dummies. Regression results also reveal that mortgage spreads overall

are negatively related to 10-Year maturity Treasury rate which is consistent with theoretical predictions.

Mortgage spreads decline with the slope of the Treasury curve, and increase with spread of AAA-rated

corporate bonds. Also, the mortgage spreads are wider after periods when in a preceding four quarters there

is a higher delinquency rate of commercial real estate and when the commercial real estate performs poorly

as measured by the cumulative return of NCREIF index. The results of this regression are available upon

request.

11

relative to the periods when originators perform well, then the regression with monthly

dummies will likely understate the total effect of stock price performance on credit spreads.

As we will show in the next section, the economic impact of past stock performance is

stronger for periods when financial industry is suffering a downturn.

We should stress that the results should be interpreted as cross-sectional results that

institutions with at least 15% declines in stock price originate riskier mortgages relative to

better performing institutions during the same month period. An alternative interpretation

of the regression results is that poor stock price performance of the institutions is simply

driven by expectations of a decline in market fundamentals and an increase in risk for

commercial properties, which, in turn, may explain why spreads are wider after periods

with stock price declines. This interpretation of our regression results is less plausible for

two reasons. First, we control for monthly fixed effects. Second, in our data set, most

originators are large financial institutions for which origination of commercial mortgages is

only a relatively small part of their businesses, therefor we don’t expect that their stock

prices should be significantly driven by changes in real estate fundamentals.

4.2.2 Estimates of Other Control Variables

The estimated coefficient of the property control variables are consistent with our expecta-

tions. In particular, we find that mortgages on riskier property types have larger spreads,

e.g., multi-family apartment complexes and mobile home parks have the smallest spreads and

hotels and medical offices the largest. In addition, the coefficient of the NOI/Property Value

ratio is significantly positive, which is consistent with theory that suggests that a higher

expected growth rate in operating income leads to narrower spreads. We also find that the

coefficient of the logarithm of property value is significant and negative, which is consistent

with larger properties being less risky as well as with economies of scale in origination and

default costs.

In contrast, the estimated coefficients for the mortgage characteristics are somewhat

inconsistent with theory. For example, the estimated coefficients of the LTV variable are weak

and statistically insignificant in the regression with macro-economic variables. Similarly, the

coefficients of the loan amortization rate are small. It is likely that the weak relation between

spreads and mortgage characteristics is due to the fact that these are endogenous variables; to

obtain mortgages on riskier properties, originators are likely to require lower LTV and shorter

12

duration. This, of course, can create a bias in our estimates of the mortgage characteristics

coefficients, but more importantly, it can create a bias in the coefficient of past stock returns if

there is a correlation between mortgage characteristics and past stock returns. For example,

if mortgages originated by institutions with relatively worse stock returns have high LTV

ratios, the coefficient on the past stock returns will be biased upwards, because the LTV

coefficient does not appropriately capture the effect of LTV on spreads. In Section 4.5 we

examine how past stock returns influence mortgage LTV choice to help us gauge the extent

to which this is a problem.

4.3 Are Mortgage Spreads Wider for Worse Performing Origina-

tors During Industry Downturns?

The specification of our previous regression assumes that the cross-sectional relation between

mortgage spreads and the fact that the mortgage is originated by a "stock price loser" are the

same in each month. In this section we explore the possibility that the effect is stronger in

those months in which the entire financial industry is doing relatively poorly. Our intuition

is that the incentive to originate higher risk mortgages arises because of pressures on the

originator when it is doing poorly, and when the potential scrutiny of origination quality

is reduced, which means that we are more likely to see stronger results in months in which

financial institutions, as a group, are doing quite poorly. We expect that during these periods

an overall scrutiny of the origination quality is weaker which would allow a "stock price loser"

to temporarily shift its origination standards.

To examine this possibility we identify periods in which the financial industry experi-

enced economic distress and investigate whether the relation between mortgage spreads and

originator performance is stronger in those periods. We identify distress periods based on

the return of the S&P1500 Diversified Financials Price Index (S&P1500 Div), which tracks

the performance of a broad range of US financial institutions. Specifically, in the last column

of Table 4 we report the cumulative return for the prior 3 months of S&P1500 Div index

for our sample period of 1996-2002. We select the negative cumulative stock return over

3-month period, in order to capture longer-term downward periods in the index. During

1996-2002, the cumulative 3-month return of the index is negative for 27 months out of 84

months, and there were 5118 mortgages in the data set originated during these months. We

13

view these months as periods when financial industry suffered a downturn. Interestingly,

these 27 months with negative index return appear to occur in clusters and tend to overlap

with period generally known as Russian default and LTCM crises in late 1998 as well as

a period of 9/11 aftermath and Enron collapse, but not with the period of Asian currency

crisis. In Table 4 These three periods are identified by "*" and "**" and "***"in the table,

respectively.

To test the hypothesis that the difference in spreads between worse performing insti-

tutions and better performing institutions widens during distress periods we introduce a

market downturn dummy that equals 1 if the cumulative return for the prior 3 months of

S&P1500 Div index is negative, and zero otherwise. We then split the "stock price loser"

dummy into two dummy variables: for periods when market downturn dummy=1, and when

market downturn dummy=0. These two variables allow us to disentangle the effect of "stock

price losses" on mortgage spreads in periods of industry downturns.

Spread = intercept

+α(dummy=1 if originator is "stock price loser" and market downturn dummy=1)

+θ(dummy=1 if originator is "stock price loser" and market downturn dummy=0)

+ βi(property characteristics variables)

+X

γi(mortgage characteristics variables)

+ (property type dummy variables)

+ (originator dummy variable)

+ (origination time dummy variables) + .

(2)

Table 3, which reports these regression estimates, reveals that the both coefficients for "Stock

Price Loser" dummies are positive and statistically significant. The estimated values are

higher for periods of market downturns, which indicates that an institution’s past stock

losses influences the spreads on the mortgages it initiates more when financial institutions

in general are doing poorly. For example, the difference in mortgage spreads originated by

institution whose stock return is less than negative 15% for past 6 month is 13 b.p. This

implies that it is during times when the entire lending industry suffer, a "stock price loser"

originates more risky mortgages when compared to a "stock price loser" during the times

14

when the industry does not experience poor performance. In other words, loosing 15%

or more during market downturns results in a larger incremental spread relative to non-

losers if compared with losing 15% or more during normal periods. Perhaps, during market

downturns the competition between lenders weakens leading to even lesser quality of the

origination process and due diligence.

4.4 Cross-Sectional Estimates in Each Month

The regressions in the previous sections assumed that the residuals were independently

distributed. Although the regressions include monthly time dummies as well as originator

dummies, it is likely that there are sources of correlation between the residuals that are

not adequately accounted for by our controls. For example, the residuals of the mortgages

originated by the same institution in the same period may be correlated. To address this

issue, we report estimates of regressions that are identical to the regressions reported in the

last section except that they do not include originator fixed effects and are run in each of

the individual sample month.22 By running these regressions in individual months we can

gauge the level of significance by the extent to which the cross-sectional relation between

the stock returns of the originator and spreads holds across monthly periods.

Table 4 reports these regressions for each of the 84 months between 1996-2002. The

average coefficient estimate of the "stock price loser" dummy variable varies between 0.08

and 0.11 indicating that the credit spreads are 8-11 b.p. wider for mortgages originated by

poorly performing institutions. For look-back window of 6 months, the estimated coefficient

for the dummy variable takes positive values for 44 months out of 60 (for 14 months there are

no "stock price losers"). The Fama-MacBeth t-statistics is 2.77 and 3.15 implying significance

at 1% level for these two specifications. This monthly analysis supports the hypothesis that

worst performing institutions originate riskier mortgages.

4.5 Do Poorly Performing Institutions Originate Mortgages With

Higher LTV Ratios?

In this section we examine whether the mortgages originated by stock price losers have

different loan to value ratios (LTVs) than those originated by stock price winners. We

22Alternatively, we can replicate the regression on full sample with a control for error clustering.

15

examine this issue for two reasons: First, as we mentioned in the introduction, we are

concerned about the possibility that originators perform poorly because of shocks that make

their clients riskier. If this were the case, we would expect the poorly performing originators

to require higher LTVs on the mortgages they originate. The second concern is that since

LTV is endogenous, the estimates of its coefficients are biased towards zero, which means

that the regressions do not adequately control for the effect of this characteristic. As a

result, it is possible that the wider spreads of mortgages originated by stock price losers

arise because mortgages that are originated by these institutions have higher LTV ratios.

To address these issues, we examine whether the relation between LTV and the originators

past stock returns by running the following regression:

LTV = intercept+ α(dummy=1 if originator is "stock price loser")

+X

βi(property characteristics variables)

+ (property type dummy variables)

+ (originator dummy variable)

+ (origination time dummy variables) + .

(3)

As we show in Table 5, which reports this regression, the coefficients for the dummy

variables for stock price losers are not significant. In addition, unreported results indicate

that poorly performing originators do not issue mortgages with longer maturities or shift

towards risky property types (such as hotels). These results indicate that our result that

poorly performing institutions originate mortgages with wider spreads is not likely to be due

to biased estimates of endogenous variables that describe mortgage terms. In addition, given

that LTV ratios are not related to the originators’ past stock returns, we think it is unlikely

the risks of the originated mortgages are related to the originators’ past stock returns.

5 Robustness Checks

In order to verify the robustness of the main result we redefine the indicator for poorly

performing originator in two different ways and reestimate the spread regression (Table 2).

First, we assume that the originator is a "stock price loser" if its stock return declines by

at least 20% during the 3 and 6 months prior to the mortgage origination. Compared to

16

the base case with the negative 15% cutoff, the fraction of mortgages that are originated

by institutions that lost at least 20% of stock value declines. In our data set, between 8%

and 9% of the mortgages are originated by institutions that lost at least 20% of their stock

values in the prior one or two quarters.

The results of the spread regressions that include these new dummy variables are reported

in Table 6. Similar to the results in Table 2, the stock price dummy variable is positive and

significant. For example, the coefficient of the "stock price loser" in the prior 6 months has

an estimated value of 10 b.p.

6 Alternative Explanations and Additional Tests

The evidence in previous sections indicate that originators that experienced poor stock re-

turns in prior quarters tend to originate mortgages with wider spreads. However, as we

discussed in the introduction, there are a number of potential explanations for this finding.

The explanation that is our focus is that originators who are doing poorly have an incentive

to expend fewer resources on their conduit mortgages, and are thus likely to originate riskier

mortgages. Alternatively, the higher spreads may be due to poorly performing financial

institutions exploiting their borrowers by charging higher interest rates. Another possibility

is that the negative correlation between originator stock returns and credit spreads arise

because originator stock prices perform poorly when the market anticipates that real estate

markets become riskier. Since we have monthly time effects that control for changes in mar-

ket wide risk, this hypothesis is not likely to be applicable to large institutions that originate

mortgages to all segments of the market, but it could apply to originators which tend to

focus in either certain geographical regions or on particular property types. For example, if

an originator specializes in originating mortgages in California, its stock price will decline

and the mortgages it originates will have higher spreads when California real estate becomes

riskier.

In this section we present a variety of additional tests that are designed to help us dis-

tinguish between these competing hypotheses. Most of our tests are designed to determine

whether stock price losers originate riskier mortgages. While these tests allow us to distin-

guish the weak underwriting hypothesis and the hypothesis that stock price losers exploit

their borrowers, evidence of higher risk is also consistent with the idea that stock prices fall

17

in anticipation of higher risk. To distinguish between the weak underwriting hypothesis and

the later hypothesis we provide indirect evidence of sloppier underwriting and less oversight

by originators when they are stock price losers.

6.1 Are the Results Stronger for Underwriters of CMBS Deals?

In our data set, 6,330 mortgages out of 18,526 were originated by the same institution that

issued the CMBS that included the mortgages. When this is the case, there is no third entity

scrutinizing the mortgage before it is ultimately sold as part of a CMBS to investors. To

test whether the originator has a greater incentive to include higher yielding mortgages into

CMBS deals they underwrite, we introduce an "underwriter" dummy, which takes a value of

1 for mortgages originated by the institution that later becomes the lead underwriter of the

CMBS in which the mortgage is sold. Our reputation hypothesis suggests that this added

scrutiny will influence mortgage spreads during periods in which the originator experiences

poor stock price performance. In contrast, the competing hypotheses make no predictions

about the relevance of third party scrutiny.

The second column in Table 7 reports regression results that indicate that uncondi-

tionally, mortgages that are originated and issued in CMBS pools by the same institution

have about 2 b.p. higher spreads. To test whether this difference increases when the orig-

inator/issuer has poor stock performance relative to other prior to CMBS origination we

split the "stock price loser" dummy variable into two dummy variables: "stock price loser—

underwriter" and "stock price loser — not underwriter." As we report in Table 7, both

variables are positive and statistically significant for most of the specifications, but the effect

is stronger when the stock price loser is also the CMBS underwriter. 23 For example, the

estimated coefficients for the "stock price loser—underwriter" dummy variables for the look-

back window of 3 and 6 months suggests that CMBS underwriters that lost at least 15% of

their stock prices include mortgages that have spreads 10-13 b.p. wider.

23We also run similar regressions that include fixed effects for the CMBS deal. These unreported regressions

indicate that when the CMBS underwriter is doing poorly, its own mortgages that are included in the deals

have spreads between 10-20 b.p. wider than other mortgages in the pool. Conversely, when the underwriter

is not doing poorly, its own mortgages included in the pool have yields about 10-19 b.p. lower than other

mortgages in the same pool.

18

6.2 The Time Lag Between Mortgage Origination and CMBS Is-

suance

Originators sometimes refer to what they call "shelf risk," which is the risk that an event

will take place that lowers the value of a mortgage before the mortgage is packaged and

sold to the ultimate investors. If mortgages initiated by a stock price loser have higher

unobserved risk attributes, we would expect them to have greater shelf risk, which should in

turn give the originator a greater incentive to go through the packaging and issuing process

as quickly as possible.24 Hence, an analysis of the time span between mortgage origination

and CMBS issuance can provide further indirect evidence that stock price losers originate

riskier mortgages.

To analyze whether the originators with poor past performance tend to sell their mort-

gages faster, we measure the time span between when the mortgage is originated and when

it is included in a CMBS issue.25 We call this variable the "time lag,"which is measured as

the time between the cutoff date of the deal and the mortgage origination date. The cutoff

date is the date when the composition of the CMBS deal is first fixed, typically the first day

of the month in which the deal is sold.26 For our mortgages, the average “time lag” is 155

days with a standard deviation of 132 days. In the regression the time lag is measured in

days. To capture a possible relationship between the originators’ poor performance and the

24Another possibility is that poorly performing originators are less able to take risk or may want to sell

the mortgages earlier to raise cash.25Due to the data limitations stemming from the fact that for some mortgages we don’t have information

about which CMBS pool the mortgage is sold to, our sample size is somewhat smaller.26Sometimes new loans are added after the cutoff date, which can result in a negative time lag We deleted

small number of mortgages with a negative time lag along with a small number of mortgages that have "time

lags" longer than 1000 days from this regression.

19

time lag, we run the following regression:

Time Lag = intercept+ s(Spread)

+ α(dummy=1 is originator is "stock loser" )

+X

βi(property characteristics variables)

+X

γi(mortgage characteristics variables)

+ (property type dummy variables)

+ (originator dummy variable)

+ f(CMBS deal dummy) + .

(4)

The right hand side variables in this regression are the same as in previous regressions except

that in addition to the property type and originator dummies, this regression includes CMBS

deal dummies that control for the possibility that some CMBS deals are slower to come to

market. In addition, we include mortgage spread as an explanatory variable to control for

the possibility that higher risk mortgages are sold off more quickly. As one can see in Table

8, the mortgages originated by “stock price losers” tend to be sold in CMBS deals about

27-36 days quicker than other mortgages in the same deal.

This evidence is consistent with the idea that originators with lower stock returns are

more concerned about the shelf risk of their mortgages, perhaps, because they are concerned

about unfavorable information being revealed prior to the sale. An alternative interpretation

is that stock price losers sell their mortgages to CMBS deals faster because they need to raise

cash faster.27 However, our estimates also indicates that mortgages with higher spreads are

sold to CMBS deals faster than mortgages with lower spreads. Moreover, larger mortgages

and mortgages with higher LTV ratios tend to be securitized faster, while mortgages with

higher loan amortization rates and mortgages with higher NOI/(Property Value) ratios tend

to be held by institutions longer before being securitized. These findings tend to support

the idea that originators sell mortgages more quickly when they are viewed as riskier.

27Unreported tests indicate that, in general, the originators that are CMBS underwriters do not sell their

mortgage to the CMBS deals faster. In addition, there is no evidence that "stock price losers" who are also

underwriters have shorter time lags relative to other "originator-losers".

20

6.3 Are Mortgages Originated by Stock Price Losers More Likely

to Subsequently Default?

According to reputation hypothesis if originators with poor past stock returns originate

riskier mortgages, then the mortgages are likely to default more often, even after controlling

for mortgage and property characteristics. This will be the case when the higher risk is

because of poor underwriting as well as because the poor stock returns predict weak funda-

mentals. Ideally to examine default probability, we would like to have data on the mortgage

histories up until their maturities. Unfortunately, most of the mortgages in our data set

have not matured, so the complete default analysis is not possible. However, we have infor-

mation about whether mortgages in the data set are marked as "performing", "REO",28 "in

foreclosure" and "delinquent" at the time the data set was created.29

There are a total of 158 mortgages that are marked as either "in foreclosure" or as ROE,

and at least one month in delinquency. There are only 25 mortgages that are already in ROE

or in foreclosure. While there are differences between these three types, given our limited

data we view them all as defaulted mortgages and define a Dummy variable "Defaulted,"

which equals one if the status of the mortgage is either "in foreclosure", "ROE", or the

mortgage is at least one month in delinquency. Using Defaulted as the dependent variable,

we estimate the following probit regressions:

Probit "Defaulted" = intercept+ α(dummy=1 if originator is "stock price loser")

+X

βi(property characteristics variables)

+X

γi(mortgage characteristics variables)

+ (property type dummy variables)

+ (origination year dummy variable) + .

(5)

Table 9, which report the results of this regression, reveals a positive relationship (at least

at the 10% significance level) between whether the originator is a "stock price loser" and

whether the mortgage defaults.30 These results hold even when we include the spread as

28REO is the property acquired by the Servicer on behalf of the Trust through Foreclosure or deed-in-lieu

of foreclosure on a defaulted loan.29Archer, Elmer, Harrison, and, Ling (2002) classify mortgages as having defaulted if they were late by at

least 90 days.30We, however, have to interpret this result with some caution because our data includes only 158 defaults

21

an explanatory variable, which suggests that the originator’s past stock returns capture

"unobserved risk factors" that are not incorporated into the mortgage spreads. If poorly

performing institutions simply exploit their borrowers by charging higher borrowing rates,

in such case we should not expect an increase in default probabilities for those loans. A

negative relationship between stock returns and default probability is not supportive of this

hypothesis, but rather suggests that mortgages originated by institutions with poor stock

performance are riskier which is supportive of the reputation hypothesis.

6.4 The Subordination Levels of the CMBS Deals

The final question we address is the extent to which credit agencies account for the credibility

of the originator when they rate CMBS deals. To do this we examine the AAA subordination

levels of the CMBS deals, which is the percentage value of the total CMBS deal that are

in securities rated below AAA. For example, as reported in Table 10, in our data set, the

average AAA subordination level is 27%, which means that, if $1 Billion in CMBS securities

are issued in a deal, $730 million are senior securities that are rated AAA, and $270 million

are junior in priority to the AAA securities and have lower ratings. Clearly, the default risk

of the AAA securities are lower, ceteris paribus, if the subordination level is higher. Hence,

if the rating agencies believe the mortgages in a pool are riskier, they will require a higher

AAA subordination level. This test would allow us to distinguish between the hypotheses

of whether poorly performing originators originate riskier mortgages or simply overcharge

borrowers? In a latter case we should not observe an increase in subordination level.

Our data set, which contains information about the AAA subordination levels of 94

CMBS deals, allows us to test the extent to which the ratings agencies account for the credi-

bility of the originator when determining the subordination level. Specifically, we regress the

AAA-subordination level on various proxies that include the weighted-average characteris-

tics of the mortgages included in the CMBS pool, along with the variable that calculates

weighted-average fraction of stock price losers that sell mortgages to the deal where weights

out of 18,526 observations.

22

are relative sizes of mortgages in the deal

Deal’s AAA-Subordination =

intercept+ θ(Weighted-Average Mortgage Spread )

+ α(Weighted-Average Fraction of Originators that are "Stock Price Losers" )

+ β(Weighted-Average LTV)

+ γX(Weighted-Average Fraction of five Property Types in the Deal)

+ φ(Deal Size)

+ ϕ(Number of Mortgages in the Deal)

+ (Deal Origination Year dummy) + .

(6)

Table 11 reports the results of this regression. The coefficient estimates for the weighted-

average fraction of originators that are "stock price losers" in the pool are positive and

statistically significant at the 5% level. These tests suggest that rating agencies require

higher levels of subordination for CMBS deals that include more mortgages originated by

underperforming originators. This finding is consistent with reputation hypothesis and not

consistent with "overcharge" hypothesis.

It should also be noted that the estimates for year dummies decline over time, reflecting a

general decline in subordination levels of CMBS deals since 1996.31 Also, the results suggest

that credit ratings increase with the number of mortgages included in the deal and declines

with the weighted-average LTV. Interestingly, the weighted average spreads of the mortgages

do not significantly affect subordination levels.

7 Conclusion

This paper provides indirect evidence of incentive/information problems that can arise when

mortgages are originated with the intention of selling them to investors as part of CMBS

pools. Specifically, we find that the credit spreads (i.e., the spread between the mortgage

rate and a Treasury with the same maturity) of mortgages that are included in CMBSs are

31Riddiough (2002) suggests that the higher subordination levels in the earlier years was due to an initial

lack of familiarity with CMBS products.

23

larger when the mortgage originator experiences poor stock price performance in the recent

past. Moreover, mortgages that are originated in these situations are more likely to default.

This evidence, which indicates that institutions originate riskier mortgages when they are

doing poorly, is consistent with reputation models that suggest that firms that are doing

poorly expend fewer resources on product quality.

The issues that we address are likely to be equally applicable to commercial loans, which

are also bundled and sold as securities. However, identifying the effects documented in

this paper might be more challenging in a study of commercial loans, since corporate loan

contracts tend to be somewhat more complicated and less standardized than commercial

mortgages. Nevertheless, as we mentioned in the introduction, Hubbard, Kuttner and Palia

(2002) consider the relation between yield spreads and the financial condition of the lender

and also find that spreads are larger when the financial condition of the lender is worse.

However, they suggest that the larger spreads arise because lenders with low capitalization

ratios expropriate their borrowers. In other words, rather than exploiting their reputation

with investors, the banks exploit their relation with their clients.

Its likely that the channel suggested by Hubbard, Kuttner and Palia (2002) is more

applicable in their setting, since the corporate loans in their sample are held by the bank,

while the channel suggested by us is more applicable in our setting, since relationships

between borrowers and originators matter very little for mortgages that ultimately become

part of CMBS pools. However, it is possible that the incentive to initiate riskier commercial

loans when a bank’s financial condition deteriorates is also likely to arise even when the

loans are not sold (because there is still a trade-off between higher profits today versus the

risk of default and lower profits in the future). Perhaps, future research can separately

examine loans that are sold and loans that are held by the originating bank to more directly

examine how the origination process is influenced by the separation between the investors

and originators of debt.

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27

Table 1

Summary Statistics of the Commercial Mortgages in the Data Set

Panel 1: Distribution of Commercial Mortgage Originations Across Time Periods Year of Origination # of Mortgages

1992 20 1993 72 1994 199 1995 409 1996 1082 1997 2844 1998 5558 1999 3764 2000 1970 2001 1988 2002 1320

Panel 2: Distribution Across Property Types for Mortgages Originated in 1996-2002

Property Type # of Mortgages Multi-family Properties 5324 Retail Unanchored 3902 Retail Anchored 2544 Medical Office 250 Industrial 1307 Warehouse 122 Mobile Home Park 500 Office 2464 Limited Service Hotel 843 Full Service Hospitality 197 Self Storage 595 Mixed Use and Other Types 478

Table 1 –Continued

Panel 3: Mortgage and Property Characteristics at Origination Date for Mortgages Originated in 1996-2002 This table reports the mortgage rate spread over Treasury rate (i.e., the difference between the rate on the mortgage and the rate on Treasury bonds with the same maturity as the mortgage) observed on the mortgage origination date. The loan amortization rate is defined as 1-(Principal Value at Maturity)/(Initial Principal Value). NOI/Value ratio is the ratio of Net Operating Income divided by the property value at origination date. Loan-to-Value ratio (LTV) is the ratio of the face value of debt divided by the property value at the origination date. Mean St. Dev Minimum Maximum

Spread over Treasury,% 2.23 0.73 0.19 6.14 Property Value in $M 8.19 17.59 0.05 695.00 Loan to Value (LTV) 0.70 0.11 0.04 1.00 NOI/(Property Value) 0.10 0.03 0.00 0.58

Loan Amortization Rate 0.29 0.31 0.00 1.00 Mortgage Maturity, years 11.29 3.63 0.54 30.08

Table 1 –Continued Panel 4. Top 30 Originators for Mortgages Originated in 1996-2002 This table reports the top 30 Originators along with the characteristics of the mortgages they originate.

Name of the Originator

# of Mortgages

Spread, %

LTV

Delinquency rate

Percent of the originated mortgages included in CMBS deals that the originator underwrites

Bank of America 1869 1.90 0.70 0.54% 88% First Union 1631 2.17 0.70 2.15% 9% FFCA 1411 3.71 0.76 0.71% 0% Lehman Brothers 1379 1.97 0.71 0.58% 100% GE Capital 1300 2.15 0.72 0.23% 0% GMAC 1030 1.95 0.70 0.19% 0% Merrill Lynch 1029 2.05 0.70 1.75% 83% Chase Manhattan 907 2.12 0.70 0.23% 10% Bear Stearns 774 2.05 0.62 0.52% 70% Wells Fargo 554 2.17 0.59 0.18% 0% Salomon 610 2.14 0.71 0.33% 83% Morgan Stanley 590 2.05 0.68 0.54% 71% Citicorp 518 1.63 0.71 0.97% 24% Morgan Guaranty Trust 501 2.06 0.69 0.80% 53% Captec 438 3.61 0.56 7.08% 0% National Realty Funding LC 425 2.07 0.68 0.47% 0% ContiFinancial 404 2.34 0.68 0.99% 0% KeyBank 389 2.33 0.71 0.26% 0% German American Capital 353 2.00 0.71 0.00% 0% Wachovia Bank 258 2.25 0.70 0.39% 0% Paine Webber 274 2.24 0.72 0.00% 50% Amresco 231 2.02 0.70 1.30% 0% Goldman Sachs 280 2.15 0.73 0.71% 40% PNC 193 2.28 0.75 1.04% 0% Midland 170 2.34 0.66 1.03% 0% Impac 163 2.38 0.69 0.61% 0% CRIIMI MAE 125 1.52 0.67 0.00% 0% Credit Suisse 105 2.44 0.70 0.91% 100% Banc One 74 2.18 0.69 1.35% 0% Prime Capital Funding 68 1.88 0.64 0.00% 0% 20 other originators 25 1.98 0.67 0.00% 0%

Table 1 --Continued Panel 5. Stock Returns of Mortgage Originators This table reports average cumulative returns of the originators for the look-back window of 3, 6, 9 and 12 months prior to mortgage origination. For example, the period of (-12mo, 0) means the period of 12 calendar months prior to mortgage origination date, where 0 is the mortgage origination date.

Prior period of

Average Cumulative returns

Standard Deviation

(-3mo, 0) 4.2% 17% (-6mo, 0) 8.32% 22%

Table 1 --Continued Panel 6. “Stock Price” Losers This table reports fraction of mortgages originated by institutions that lost at least 15% in periods of three, six, nine and twelve months prior to the mortgage origination dates.

Prior periods of

Fraction of mortgages originated by institutions that

lost at least 15% in prior periods

(-3mo, 0) 11% (-6mo, 0) 10%

Table 2 Results of Cross Sectional Regressions for Credit Spreads of Mortgages Originated Between 1996 and 2002

The table presents the results of the following regression: Spread=intercept +α(Dummy =1 if the originator lost at least 15% of its stock value in the prior periods)+∑βi(property characteristics variables)+∑γi(mortgage

characteristics variables)+(property type dummy variables)+(originator dummy variable) +(origination time dummy variables)+ε, where Spread is the difference between the rate on the mortgage and the rate on Treasury bonds with the same maturity as the mortgage, observed on the mortgage origination date, and measured in percentage points. The loan amortization rate is defined as 1-(Principal Value at Maturity)/(Initial Principal Value). NOI/Value ratio is the ratio of Net Operating Income divided by the property value at the origination date. Loan-to-Value ratio (LTV) is the ratio of the face value of debt divided by the property value at the origination date.

Number of Obs.=18526 Coeff t-stat Coeff t-stat Const 2.26 32.89 2.30 34.79 Dummy =1 if cumulative stock return of mortgage originator is less than -15% for period (-6mo, 0) 0.08 7.15 is less than -15% for period (-3mo, 0) 0.07 6.43 Mortgage and Property Characteristics Log(Property Value in $M) -0.14 -37.79 -0.14 -37.71 LTV 0.09 2.66 0.09 2.69 NOI/Value -0.05 -0.37 -0.06 -0.42 Loan Amortization Rate -0.11 -4.82 -0.11 -4.84 Mortgage Maturity -0.004 -2.527 -0.004 -2.556 Property Type Dummies (vs. Mixed Use Type) Multifamily -0.35 -17.47 -0.35 -17.54 Retail Unanchored -0.03 -1.67 -0.03 -1.65 Retail Anchored -0.13 -6.31 -0.13 -6.36 Medical Office 0.16 4.98 0.16 5.07 Industrial -0.09 -3.92 -0.09 -3.94 Warehouse -0.08 -2.26 -0.09 -2.32 Mobile Home Park -0.25 -10.18 -0.25 -10.16 Office -0.08 -3.86 -0.08 -3.84 Limited Service Hotel 0.34 14.31 0.34 14.32 Full Service Hospitality 0.35 10.78 0.35 10.87 Self Storage 0.05 1.89 0.05 1.82 Originator Dummy Yes Yes Time Dummy Month Month Adjusted R-squared 0.75 0.75

Table 3 The regression specification is similar to one in Table 2:

Spread=intercept +α(market downturn dummy=0 )( Dummy =1 if the originator lost at least 15% of its stock value in the prior periods)+ 2(market downturn dummy=1 )×( Dummy =1 if the originator lost at least 15% of its stock value in the prior periods) + ∑βi(property characteristics variables)+∑γi(mortgage characteristics variables)+(property type dummy variables)+(originator dummy variable) +(origination time dummy variables)+ε,

where Spread is the difference between the rate on the mortgage and the rate on Treasury bonds with the same maturity as the mortgage, observed on the mortgage origination date, and measured in percentage points. t-statistics for the variables is also reported. Market downturn dummy =1 for each month when cumulative return for the prior 3 months of S&P1500 Diversified Financials price index was negative, and zero otherwise. (See Table last column in Table 4.) There are 27 months when this index experienced negative returns. During the selected months there are 5118 mortgages were originated in the data set. The remaining explanatory variables are the same as in the regression described in Table 2. For brevity we don’t report the estimates for other variables.

Number of Obs.=18526 Coeff t-stat Coeff t-stat Const 2.30 33.35 2.30 34.83 Dummy =1 if cumulative stock return of mortgage originator is less than -15% for period (-6mo, 0) during no market downturn 0.03 2.08 is less than -15% for period (-6mo, 0) during market downturn 0.13 8.03 is less than -15% for period (-3mo, 0) during no market downturn 0.05 2.88 is less than -15% for period (-3mo, 0) during market downturn 0.09 5.88 Mortgage and Property Characteristics Yes Yes Property Type Dummies (vs. Mixed Use Type) Yes Yes Originator Dummy Yes Yes Time Dummy Month Month Adjusted R-squared 0.75 0.75

Table 4 Estimated coefficients for the “Stock Price Loser Dummy” variable in the Spread regression run separately for each individual origination month

The table reports estimated coefficients for the Dummy Variable=1 if stock return of the originator declined by at least 15% in the spread regression run separately for individual month of mortgage origination. The regression is as follows:

Spread=intercept +α(cumulative stock return of mortgage originator is less than 15% over look-back window)+∑βi(property characteristics variables)+∑γi(mortgage characteristics variables)+(property type dummy variables)+ε,

where Spread is the difference between the rate on the mortgage and the rate on Treasury bonds with the same maturity as the mortgage, observed on the mortgage origination date, and measured in percentage points. The variables of property and mortgage characteristics are the same as in the regression presented in Table 2. “-“ denotes months in which no “stock price losers” participated. For brevity we don’t report the estimates for the other variables. The Fama-McBeth t-statistic is reported in the last row.

Estimate for Cumulative For Dummy Variable=1 if stock return of the

originator declined by at least 15% for the period of Cumulative return for the prior 3 months of S&P1500

Diversified Financials -- price index YEAR MONTH [-6mo, 0] [-3mo, 0]

1996 1 - -0.03 0.01 2 - 0.35 0.13 3 - - 0.11 4 - - 0.14 5 - - 0.01 6 - - -0.01 7 0.09 - -0.01 8 -0.18 -0.18 0.03 9 0.12 0.07 0.01 10 - - 0.06 11 -0.03 -0.03 0.12 12 - 0.11 0.24

1997 1 - -0.16 0.13 2 - - 0.18 3 - - 0.11 4 0.11 0.00 0.02 5 0.12 0.12 0.02 6 - - 0.05 7 - - 0.26 8 - - 0.27 9 - 1.98 0.11 10 0.48 - 0.14 11* 0.27 - 0.04 12* 0.34 0.35 0.18

1998 1* 0.14 0.29 0.09 2* 0.12 0.49 0.13 3* -0.05 - 0.12 4 -0.14 0.18 0.15 5 0.12 0.12 0.13 6 0.22 0.03 0.06 7 -0.12 0.16 0.09 8** -0.08 -0.09 0.02 9** -0.06 -0.04 -0.17 10** 0.16 0.16 -0.27 11** 0.15 0.20 -0.08 12** -0.80 0.26 0.24

Table 4—continued

Estimate for Cumulative For Dummy Variable=1 if stock return of the

originator declined by at least 15% for the period of Cumulative return for the prior 3 months of S&P1500

Diversified Financials – price index YEAR MONTH [-6mo, 0] [-3mo, 0]

1999 1** - 0.05 0.36 2 0.17 -0.08 0.20 3 -0.05 -0.07 0.13 4 0.02 -0.20 0.16 5 - 0.06 0.20 6 0.22 -0.03 0.06 7 -0.47 -0.31 0.07 8 0.22 - -0.11 9 -0.35 -0.27 -0.04 10 0.06 0.03 -0.15 11 0.06 -0.04 0.09 12 0.31 0.24 0.10

2000 1 0.33 0.04 0.14 2 0.06 0.14 0.00 3 0.10 0.08 -0.03 4 0.32 0.25 0.15 5 0.23 0.25 0.07 6 0.06 0.34 0.14 7 -0.16 0.21 -0.02 8 -0.19 -0.12 0.13 9 - -0.22 0.20 10 0.54 0.21 0.23 11 0.03 0.02 0.07 12 -0.03 0.03 -0.10

2001 1 0.21 0.69 -0.04 2 - -0.06 0.06 3 0.12 0.01 0.01 4 0.03 0.17 -0.14 5 -0.12 0.03 -0.10 6 0.14 0.12 0.05 7 0.56 0.17 0.11 8 -0.01 -0.05 -0.01 9**** 0.04 0.05 -0.13 10**** 0.05 0.01 -0.21 11**** 0.24 0.09 -0.15 12**** - 0.08 -0.03

2002 1**** - 0.01 0.14 2**** 0.16 0.14 0.04 3**** 0.80 0.45 -0.01 4**** - - 0.01 5**** - 0.14 -0.01 6**** 0.02 0.02 -0.03 7**** 0.01 0.10 -0.16 8**** 0.12 0.22 -0.21 9**** 0.04 0.19 -0.13

10**** 0.06 0.17 -0.16 11**** 0.13 -0.03 0.05 12**** - -0.14 0.04

Average

0.08

0.11

Fama -McBeth Statistics 2.77

3.15

Table 5 Loan-to-Value Regressions

The table reports the results of cross sectional regressions of the LTV of the mortgages on mortgage and property characteristics along with dummies for the past returns of the originator. The regression we estimate is as follows:

LTV=intercept +α(Dummy =1 if the originator lost at least 15% of its stock value in the prior periods) +∑βi(property characteristics variables)+(property type dummy variables)+(originator dummy variables)+(origination time dummy variables)+ε,

where Loan-to-Value ratio (LTV) is the ratio of the face value of debt divided by property value at origination. NOI/Value ratio is the ratio of Net Income divided by the property value.

Number of Obs.=18526 Coeff t-stat Coeff t-stat Const 0.69 50.59 0.69 50.94 Dummy =1 if cumulative stock return of mortgage originator is less than -15% for period (-6mo, 0) 0.003 1.020 is less than -15% for period (-3mo, 0) 0.001 0.281 Mortgage and Property Characteristics log(Property Value in $M) -0.01 -6.42 -0.01 -6.41 NOI/Value 0.59 12.88 0.58 12.87 Property Type Dummies (vs. Mixed Use Type) Multifamily 0.05 10.10 0.05 10.11 Retail Unanchored 0.00 0.35 0.00 0.37 Retail Anchored 0.03 6.77 0.03 6.78 Medical Office -0.02 -2.26 -0.02 -2.24 Industrial 0.01 1.09 0.01 1.10 Warehouse 0.02 1.81 0.02 1.81 Mobile Home Park 0.00 0.31 0.00 0.33 Office 0.00 -0.62 0.00 -0.60 Limited Service Hotel -0.06 -9.89 -0.06 -9.88 Full Service Hospitality -0.08 -8.05 -0.08 -8.04 Self Storage -0.03 -4.75 -0.03 -4.75 Originator Dummy Yes Yes

Time Dummy Month Month

Adjusted R-squared 0.23 0.23

Table 6 Robustness Check: Alternative Indicator Variables for Stock Performance of Originator

Table reports the results of cross-sectional spread regressions that include indicator variables for originators that experienced a negative stock return of at least 20% prior to the mortgage origination dates. Specifically, we estimate the following regression: Spread=intercept + α(Dummy =1 if the originator lost at least 20% of its stock value in the prior periods) + ∑βi(property characteristics variables)+∑γi(mortgage characteristics variables)+(property type dummy variables)+(originator dummy variable) +(origination time dummy variables)+ε, where Spread is the difference between the rate on the mortgage and the rate on Treasury bonds with the same maturity as the mortgage, observed on the mortgage origination date, and measured in percentage points. The remaining explanatory variables are the same as in the regression described in Table 2. For brevity we don’t report the estimates for other variables. Number of Obs.=18526 Coeff t-stat Coeff t-stat Dummy=1 if for stock returns of the mortgage originator is less than -20% for period (-6mo, 0) 0.10 7.8 is less than -20% for period (-3mo, 0) 0.07 4.9 Mortgage and Property Characteristics Yes Yes Property Type Dummies (vs. Mixed Use Type) Yes Yes Originator Dummy Yes Yes Time Dummy Month Month Adjusted R-squared 0.75 0.75

Table 7

Spread Regression for Underwriters/Non-underwriters of CMBS deals The table report the estimates of the dummy variables for originators that are “stock price losers”. In the regression the dummy variable is split into two dummy variables: "stock price looser--underwriter" and "stock price looser -- not underwriter":

Spread = intercept + a*(dummy=1 if the originator is CMBS underwriter and its stock declined by at least 15% prior to origination)+ b*(dummy=1 if the originator is NOT CMBS underwriter and its stock declined by at least 15% prior to origination) +a*( variables of property and mortgage characteristics) +f*(Dummy For Mortgage Originator) +g*(Dummy for Mortgage Origination Year or Quarter)+e,

where Spread is the difference between the rate on the mortgage and the rate on Treasury bonds with the same maturity as the mortgage, observed on the mortgage origination date, and measured in percentage points. The "underwriter" dummy=1 for the mortgages issued by the originator who later becomes the lead underwriter of the CMBS in which the mortgage is sold, and zero otherwise. The loan amortization rate is defined as 1-(Principal Value at Maturity)/(Initial Principal Value). NOI/Value ratio is the ratio of Net Operating Income divided by the property value at origination date. Loan-to-Value ratio (LTV) is the ratio of the face value of debt divided by property value at origination.

Number of Obs.=18526 Coeff t-stat Coeff t-stat Coeff t-stat Dummy=1 if originator that is also CMBS underwriter 0.023 2.28 Dummy=1 if originator is CMBS underwriter and its cumulative stock return is less than -15% for period (-6mo, 0) 0.13 7.12 is less than -15% for period (-3mo, 0) 0.10 4.91 Dummy=1 if originator that is NOT CMBS underwriter and its cumulative stock return is less than -15% for period (-6mo, 0) 0.05 4.28 is less than -15% for period (-3mo, 0) 0.06 4.92 Mortgage and Property Characteristics Yes Yes Yes Property Type Dummies (vs. Mixed Use Type) Yes Yes Yes Originator Dummy Yes Yes Yes Time Dummy Month Month Month Adjusted R-squared 0.75 0.75 0.75

Table 8 “Time Lag” Regressions

This table reports the results of the following Tobit regression: Time Lag (in Days)=intercept + s*(Spread)+α (Dummy =1 if the originator lost at least 15% of its stock value in the prior periods)+∑βi(property characteristics

variables)+∑γi(mortgage characteristics variables)+(property type dummy variables)+(originator dummy variable) +(origination time dummy variables)+ε,

where the Time Lag is the time span (measured in days) between when the mortgage is originated and when the CMBS composition is finalized. The loan amortization rate is defined as 1-(Principal Value at Maturity)/(Initial Principal Value). NOI/Value ratio is the ratio of Net Operating Income divided by the property value at origination date. Loan-to-Value ratio (LTV) is the ratio of the face value of debt divided by property value at origination. Dummies for each of the CMBS deals are included.

Number of Obs.=14420 Coeff t-stat Coeff t-stat Dummy=1 if originator’s cumulative stock return is less than -15% for period (-6mo, 0) -36.42 -9.86 is less than -15% for period (-3mo, 0) -