IRES2011-028
IRES Working Paper Series
Hold-up versus Benefits in
Relationship Banking:
A Natural Experiment Using REIT
Organizational Form
Yongheng Deng
Maggie (Rong) Hu
Anand Srinivasan
May 2011
Hold-up versus Benefits in Relationship Banking:
A Natural Experiment Using REIT Organizational Form
Yongheng Deng
Institute of Real Estate Studies and
Department of Finance, NUS Business School
National University of Singapore.
e-mail: [email protected]
Maggie (Rong) Hu
Department of Finance, NUS Business School and
Institute of Real Estate Studies
National University of Singapore.
e-mail: [email protected]
Anand Srinivasan
Department of Finance, NUS Business School
National University of Singapore
e-mail: [email protected]
May 15, 2011
Hold-up versus Benefits in Relationship Banking:
A Natural Experiment Using REIT Organizational Form
May 15, 2011
Abstract
We use different organizational forms of REITs (internally-advised versus externally-advised) as
a natural experiment to devise a clean test of the impact of hold up versus benefits in relationship
banking. Due to regulatory reasons, externally advised REITs have lower information opacity and
consequently are less subject to hold up effect. Contrary to hold up and consistent with benefits
accruing to borrowers, we find that the relatively more opaque internally advised REITs derive
greater benefits from lending relationship for both price (loan rate), and non price terms of loan
contracts (collateral, covenants and loan size). Further, relationship banks of internally advised
REITs have a higher likelihood of securing repeat business from such REITs providing further
evidence of the benefits of relationship lending.
Key words: Lending relationship; hold-up effect; Real Estate Investment Trust; organizational
form; loan contract terms
JEL classification: G20, G32, L22, L23
1. Introduction
There has been a long debate in the literature on the costs and benefits of lending
relationships. Theoretically, papers such as Boot and Thakor (1993) predict that relationships
should result in better loan contract terms over time to borrowers while papers such as Sharpe
(1990), Rajan (1992) and Von-Thadden (2004) predict that borrowers should become more
locked in to their banks as the lending relationship matures. Empirically, a similar dichotomy is
observed. While Berger and Udell (1995) and Bharath et al (2010) find benefits of relationship
lending to borrowers in terms of lower loan rates and collateral, other work (Santos and
Winton,2008; Ioannidou and Ongena, 2011) find evidence of hold-up problems being an
important effect of lending relationships.
While earlier work on relationship lending that focuses on small business borrowers
which have fewer alternate sources of financing and therefore susceptible to hold-up problem, the
fact that hold-up effect impacts publicly traded firms with several alternate sources of financing is
puzzling. The approach generally taken in the literature is to take some proxy for hold-up effect
and then to examine variation in bank behaviour under different scenarios. The proxies that have
been used to measure the firm’s susceptibility to hold up include presence of ratings (Santos and
Winton, 2008), size and analyst coverage (Bharath et al, 2011) and distance between a borrower
and lender (Dass and Massa, 2011), and duration of lending relationships (Peterson and Rajan
(1994) and Berger and Udell (1995)).
The problem with several of these proxies is that they can be varied or controlled by the
borrower firm and the lender bank in response to capital and product market conditions, hence
might not be exogenous to dependent variables used to study hold up, which is usually the loan
rate charged in several studies. For example, the duration of a relationship is clearly a variable
that the firm and the bank choose jointly. If the duration of a relationship is correlated with
factors that determine loan rate, for example, a firm that maintains a long relationship has
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unobservably higher credit risk, one would see firms having higher loan rates with longer
duration of relationships, something interpreted as evidence in favour of hold up. A similar
argument can be made for other proxies of hold up. Consequently, documented effects of hold up
and benefits of lending relationship may suffer from biases.
This paper seeks to employ an alternative proxy for hold up and/or benefits of lending
relationships that remains relatively stable over time and is not subject to the control of the firm
except in the very long term. In particular, we use the differences in organizational form between
internal and external REITs as a key identifying factor. As argued later, the difference in
organizational form should result in external REITs being different from internal REITs along
one important dimension – greater transparency for external REITs. Empirical evidence for this is
provided in Deng et al (2011), who demonstrate that external REITs are more transparent along a
number of dimensions such as analyst forecast accuracy, bid ask spread, and earnings volatility.
An important focus of the above paper is on loan contract terms and specifically it demonstrates
that the external REITs have a lower loan rate and lower likelihood of collateral, after controlling
for factors known to impact these two variables.
The above paper implies that loan contract terms reflect the higher level of transparency
of external REITs. Here, we focus on the incremental difference between relationship lending to
internal and external REITs, again focusing on loan contract terms. Specifically, the loan contract
terms we focus on are the loan rate (spread over a benchmark, usually LIBOR), a dummy variable
on whether or not the loan is collateralized, the number of covenants imposed by the lender, and
loan availability, which we define as the size of the given loan to the total assets of the lender.
In particular, our empirical work is in the spirit of a difference in difference approach,
focusing on difference in relationship loans across internal and external REITs. After controlling
for factors that impact these loan contract terms, our main results are that the external REITs
derive lower benefits from lending relationship. In particular, we find that external REITs receive
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a lower discount in loan rate, requirement of collateral, and number of covenants, when
borrowing from their relationship bank relative to internal REITs which derive larger benefits
from such relationship lending. This is consistent with the fact that lower information opacity
firms derive lower benefits from relationship lending, consistent for example, with models of
relationship lending such as Boot and Thakor (1993), and inconsistent with hold up type models,
which imply that more informationally opaque firms should derive lower benefits from
relationship lending due to the relationship bank appropriating most of the benefits from the long
term relationship formation.
Specifically, internal REITs derive a relationship discount of around 50 basis points
relative to the external REITs which do not receive any such discount. Given that the average
spread is around 170 basis points for the entire sample, this implies that engaging a relationship
bank lowers the fees by close to 30% for an internal REIT, which is an economically large
discount. Second, the likelihood of pledging collateral when borrowing from a relationship bank
is 1.2% lower for internal REITs relative to external REITs. Our results continue to hold after
accounting for the endogeneity of lending relationships using the distance between the bank and
borrower to instrument for the likelihood of relationship formation.
Second, we also examine the likelihood of repeat business for the relationship banks of
internal and external REITs. Bharath et al (2007) document that one of the important benefits of
relationship lending from the bank’s perspective is that the relationship bank is much more likely
to secure future lending business from the borrowing firm. If external REITs derive fewer
relationship lending benefits, this implies that they should be less likely to repeat the same banks
in future lending transactions. To test this, we use a model of bank choice following the method
developed Ljungqvist et al (2006) for underwriter choice. For each loan, we develop a model of
borrower’s choice of the bank for a given loan transaction based on market share of the bank in
the prior year, past lending relationships and other control variables. The critical variable of
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interest is the interaction of relationships with the external REIT status. We find that this
interaction has a negative effect on the likelihood of future retention, implying that external
REITs are less likely to retain their relationship banks in future loan business.
To summarize, we find that lending relationships matter less, both for external REITs as
well as their lending banks. There is no evidence for hold-up effect in our sample; rather all the
evidence points in favour of the less transparent internal REITs deriving large benefits of
relationship lending.
The paper proceeds as follows: In Section 2, we provide some institutional background
about internal and external REITs, and develop hypotheses to be tested in this paper. In Section 3,
we provide details on the data collection and sample construction. In Section 4 and 5, we conduct
univariate and multivariate tests of the hypotheses. Lastly in Section 6, we conclude with main
findings and directions for future research.
2.1 Institutional Background on REITs
Chan, Erikson and Wang (2003) provide a detailed analysis of the REIT industry as well
as differences between internal and external REITs. This section draws on heavily insights from
their text. A REIT is defined as a corporation that invests principally in real estate and/or
mortgages and elects a special tax treatment as a REIT. They are essentially closed-end
investment companies that provide a passive medium for investors to invest in income producing
real estate properties and income.
Prior to 1986, REITs were designed to be passive investment vehicles for public investors
to tap into real estate market. As a result, they were prohibited from actively trading their
properties in the open market or directly managing them. Further, they were required to either
employ outside property management firms or lease their properties. The tax reform act of 1986
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allowed REITs to manage their own portfolios as well as allowing them to develop their own
properties.
REITs that continued with the old charter of management by external advisors are called
external REITs, whereas those that integrate the advising function within the organization are
called internal REITs. Beginning in 1987, many REITs switched from their previous externally
advised form and hired internal professional management, becoming internally advised.
Anecdotal evidence suggests that internal REITs pursued more aggressive growth strategies via
the acquisition and development of properties.1
The coexistence of two REITs forms is puzzling since the internally-advised REITs are
believed to be more efficient than the old styled externally-advised REITs. Several leading REIT
experts in the industry predicted that the self-advised and self-managed types of REITs, i.e.,
internal REITs, would dominate the industry (Linneman, 1997). This view was driven by the
belief that internally advised REITs, similar to operating companies, would be able to improve
profits by expanding revenues or controlling expenses. Capozza and Seguin (1998) found that
during the period from 1985 to 1992, internally-advised REITs outperform externally-advised
REITs by more than 7%, and also that the externally-advised REITs typically use more debt and
pay higher interest rates on debt than do the internally-advised variety.
The above arguments are convincing, the basic premise being that agency conflicts
between the external advisors and the REIT firms themselves, would cause this organizational
form to underperform, ultimately leading to its demise. However, we observe few conversions
from external to internal advisors after 1996. As we will see, internally advised REITs still
comprise of around 20% of the listed REITs.
1
See, for example, Capozza and Seguin (2000), Ambrose and Linneman (2001) and Chan, Erikson and
Wang (2003).
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As mentioned in the introduction, Deng et al (2011) postulate that internally advised
REITs, while possibly subject to greater agency costs, also have some advantages. In particular,
the study shows that the external management structure allows a greater degree of transparency of
the operations of the REIT to the market place. Consistent with this hypothesis, this paper shows
that lenders are less likely to require collateral or covenants for external REITs. Lastly, the above
paper also shows that external REITs have lower dispersion of analyst forecasts, lower bid ask
spreads and lower earnings volatility, all consistent with greater information being available to
the market place.
2.2 Hypotheses
The above suggests that there are fundamental differences in the two organizational
forms which lead to different types of benefits for the two types of REITs. In particular, external
REITs are less subject to problems of information asymmetry as well as risk shifting. We will
examine the impact of these on loan contract terms in the context of relationship banking.
2.2.1 REIT Organizational Form, Lending Relationships and Loan Rate
Theoretically, lending relationships have been posited to reduce information asymmetry
and moral hazard problems between borrowers and lenders (Boot and Thakor, 1994; Boot and
Thakor, 2000; Bhattacharya and Chiesa, 1995). Further, a long run relationship between
borrowers and lenders can also reduce moral hazard problems between lenders and other lenders
(in the case of syndicated loans, Sufi, 2007; Bharath et al, 2011). On the other hand, a lending
relationship may also result in a borrower being locked in to its borrowers (Sharpe (1990, Rajan
(1992)).
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To the extent that relationship benefits empirically dominate and these benefits are passed
on to borrowers in terms of more favourable loan contract terms, one should expect that firms
having stronger lending relationships with their lenders are to get better loan contract terms, in
terms of lower loan rates. Further, since the relationship benefits that accrue to the banks and
borrowers are directly proportional to the degree of informational frictions with respect to the
outside capital market (Boot, 2000), one should expect that relationship lending is most beneficial
to firms with higher degree of informational asymmetry with regard to outside investors.
On the other hand, if the hold-up effect dominates, this should mean that firms with
greater degree of information asymmetry should be subject to greater hold-up costs. Thus, they
should be less subject to hold up. This implies that internal REITs should be subject to a greater
degree of hold up, if it exists in the sample.
Hypothesis 1: If relationship benefits dominate, external REITs should get a lower discount in
terms of loan rate from relationship lenders relative to internal REITs. If hold-up effects
dominate, external REITs should get a greater discount in terms of loan rate from their
relationship lenders relative to internal REITs.
2.2.2 REIT Organizational Form, Lending Relationships and Non Price Terms
Next, we examine the impact of lending relationship on non-price terms. Specifically, we
examine focus on non-price terms – the requirement of collateral, the number of covenants
required, and the loan availability.
2.2.2.1 Impact on Collateral
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First, we discuss the determinants of collateral. Among the prominent theories of
collateral requirement, Stultz and Johnson (1985) imply that collateral is demanded by lenders for
high moral hazard borrowers to mitigate the effects of borrower moral hazard whereas signalling
theories of collateral such as Besanko and Thakor (1987) imply that collateral is used as
signalling device by low risk borrowers. Empirically, several papers such as Berger and Udell
(1990) and Jiminez and Saurina (2006) find evidence in favour of moral hazard relative to
signalling. To the extent that borrower moral hazard is important, relationship banks can mitigate
such borrower moral hazard as they are continually monitoring the borrower, and can use the
threat of cutting off or calling loans to mitigate such concerns. Therefore, relationship banks
should have a lower requirement of collateral, and external REITs, which have a greater
transparency and therefore lower chances of risk shifting, should derive lower benefits from
collateral requirement. This leads to the next hypothesis.
Hypothesis 2: If relationship benefits dominate, internal REITs should have a greater likelihood
of reduction in collateral requirement from relationship lenders relative to external REITs. If
hold-up effects dominate, external REITs should have a greater likelihood of reduction in
collateral requirement from their relationship lenders relative to internal REITs.
2.2.2.2 Impact on Covenants
Next, we examine the impact of organizational form on covenant requirement. While
there are many theories on the impact of relationship benefits on the loan rate, relatively fewer
papers deal with covenant restrictions. Classic papers such as Smith and Warner (1979) examine
the role of covenants largely in the context of public debt contracts where there is little
monitoring by dispersed bondholders. Theoretically, Berlin and Mester (1992) and Rajan and
Winton (1995) are among the few that have a theoretical treatment of covenants in the context of
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bank lending. Berlin and Mester (1992) demonstrate that covenants are optimal for higher risk
borrowers even in the context of bank lending. To the extent that the relationships mitigate risk
shifting incentives, this should imply that internal REITs taking relationship loans should have
fewer covenants relative to external REITs taking relationship loans, where the benchmarks are
the same type of REITs taking non-relationship loans.
Likewise, Rajan and Winton (1995) demonstrate that covenants give incentives for banks
to monitor and under some circumstances. To the extent that relationship banks are monitoring
lenders actively, the need for covenants reduces, and therefore, one should expect relationship
banks to have lower covenants. On the other hand, if hold up dominates, the relationship banks
should take advantage of their locked in customers by imposing several covenants which would
enable them to renegotiate the terms of the loans easily in the event of covenant violation. This
should imply that external REITs should have a greater reduction of covenants from their
relationship banks relative to internal REITs. This leads to our next hypothesis.
Hypothesis 3: If relationship benefits dominate, internal REITs should have lower number of
covenants when taking loans from relationship lenders relative to external REITs. If hold-up
effects dominate, external REITs should obtain a lower number of covenants from their
relationship lenders relative to internal REITs.
2.2.2.3 Impact on Loan Size
There are few formal models of credit rationing in terms of size of the loan, although
there is a large literature of financial constraints starting with Fazzari, Hubbard and Peterson
(1988) that suggests that firms face significant costs of external financing. If firms do face such
costs, then this implies that firms should prefer larger size of loans. For individual loans, Evans
9
and Jovanic (1989) find strong empirical evidence for loan size rationing. Theoretically, a model
by Schreft and Villamil (1992) derives quantity rationing by banks as the optimal response in the
presence of information asymmetry. In their model, all borrowers except the largest are rationed
in terms of the quantity of the loan. A more recent model by Zeng (2007) also develops a role for
size in the context of lending. They demonstrate that firms would prefer larger size of debt, even
in the presence of financial intermediaries and credit rationing. To the extent that relationship
banking can mitigate informational frictions, relationship banks should be willing to offer larger
size loans to their borrowers. Again, with benefits to relationship lending, internal REITs should
derive larger benefits in terms of loan size whereas with hold up, the opposite should be true.
Hypothesis 4: If relationship benefits dominate, internal REITs should obtain larger size of loan
from relationship lenders relative to the incremental effect of relationship lending on size of the
loan for external REITs. If hold-up effects dominate, the opposite should be true.
2.2.3 Relationship Benefits from the Perspective of Banks
Next, we examine the differential impact of these benefits from the banks’ perspective.
To the extent that the benefits of lending relationship are different for internal and external REITs,
this also has implications for differences in the likelihood of repeat business across these two
organizational forms. In particular, Bharath et al (2007) document that an important benefit of
maintaining lending relationships from a bank’s perspective is a large increase in the likelihood of
repeat business. If relationship benefits dominate, then internal REITs should derive greater
benefits of repeat business and consequently, as more likely to use their relationship banks for
future loan transactions. On the other hand, if hold up dominates, the reverse should be true. This
leads to the following hypothesis.
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Hypothesis 5: If relationship benefits dominate, external REITs are less likely to retain the
relationship lender for future loan transactions relative to internal REITs. If hold-up problems
dominate, external REITs are more likely to retain their advisors for future loan transactions
relative to internally advised REITs.
The predictions from the above hypotheses are summarized in a tabular form below:
Relationship Benefits
Hold-up Effects
Lower for external REITs
Higher for external REITs
Lower for external REITs
Higher for external REITs
Number of covenants required from
relationship banks
Lower for external REITs
Higher for external REITs
Loan size from relationship banks
Lower for external REITs
Higher for external REITs
Lower for external REITs
Higher for external REITs.
Price of the loan
Discount in Loan rate from relationship
banks
Non price terms
Likelihood of Collateral Requirement from
relationship banks
Relationships from the bank’s
perspective
Future likelihood of repeat business for
relationship banks
Of course, there is a possibility that both effects exactly offset each other in which case we should
observe no effect.
3. Data and Sample Selection
Our sample includes all U.S. REITs for which data are available in Loan Pricing
Corporation (LPC) and COMPUSTAT. Data on individual loan facilities is obtained from the
DealScan database maintained by the LPC. LPC has been collecting information on loans to large
11
U.S. corporations primarily through self-reporting by lenders, SEC filings, and its staff reporters.
While the LPC database provides comprehensive information on loan contract terms (LIBOR
spread, maturity, collateral, etc.), it does not provide much information on borrowers. We
manually match the borrowers in the LPC database with the merged CRSP and COMPUSTAT
database using a text-matching algorithm outlined in Engelberg and Sankaraguruswamy (2007).
The output from the algorithm is verified by hand matching. For those REITs in the LPC database
that provide no matches using the algorithm, again we hand match directly to CRSP and
COMPUSTAT. The final sample at the end of this matching results in a sample of 228 REITs.
We exclude all loans rated ‘D’ which indicates that the borrower had defaulted. As such, such
loans are typically DIP superpriority loans and therefore an analysis of such loans would
confound the impact of relationship lending with the impact of the bargaining and negotiations
that are typical in US bankruptcies.
To get the advisor status of each REIT firm, we searched manually from LexisNexis and
SEC filings for the company business description. A REIT is recorded as internally advised status
if it is found to be internally advised or internally managed in its SEC filings or related news
articles, and similar for externally advised REITs. Out of the 228 REITs, we are able to classify
150 as internal REITs and 40 as external REITs. We are unable to classify the remaining 38
REITs and consequently exclude these from our analysis. Using the same sources, we find that
our sample consists of 192 equity REITs, 17 mortgage REITs, 3 hybrid REITs and 6 REITs
which we are unable to classify into these categories.
We then use COMPUSTAT to extract data on accounting variables for the given
company. To ensure that we only use accounting information that is publicly available at the time
of the loan we employ the following procedure: For those loans made in calendar year t, if the
loan activation date is 6 months or later than the fiscal year ending month in calendar year t, we
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use the data of that fiscal year. If the loan activation date is less than 6 months after the fiscal year
ending month, we use the data from the fiscal year ending in calendar year t-1.
3.1 Construction of Lending Relationship Measures
Since lending relationships form a central variable around which the empirical analysis is
based on, we provide a detailed explanation of the construction of the two relationship measures
here. We construct the relationship measures for a particular loan by searching all the previous
loans (over the previous 5-year window) of that borrower as recorded in the LPC database. We
note the identity of all the lead banks on these prior loans and if at least one of the lead banks for
loan We had been a lead lender in the past we classify loan as a relationship loan. Since the
identification of the “lead” bank (or banks) for a particular loan facility is the basic building block
of classifying a loan as relationship or non-relationship, we define this below.
While the LPC database contains a field that describes the lender’s role, it does not have
a uniform and consistent methodology to classify which bank is the lead bank. It includes a
number of descriptions such as “arranger”, “administrative agent”, “agent”, or “lead manager”
that roughly correspond to the lead bank status of the lender. To ensure that we do not mislabel
the lead bank we follow a simple rule. Any bank(s) that is (are) not described as a “participant” is
(are) treated as a lead bank. All relationship measures are constructed using any bank retained in
a lead role as defined above.2
For every facility, we construct two alternative measures of relationship strength by
looking back and searching the past borrowing record of the borrower. We search the previous 5
years by starting from the activation date of that loan facility. The relationship strength measures
are denoted by REL(Amount) and REL(Number), and both of them are continuous variables
2
This is also the measure used in Bharath et al (2007). We also used alternative definitions of the lead bank
that restrict the set of roles to a set of 5 major roles based on the share of the loan retained. Our results are
robust to definition of this alternative definition of lead banks.
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ranged between 0 and 1. Specifically, for lender ‘m’ lending to borrower ‘i’ the two continuous
relationship strength measures are calculated as
REL( Amount ) m ,i =
$ Amount o f loans by bank m to borrower i in the last 5 years
Total $ am ount of loans by borrower i in last 5 years
REL( Number ) m , i =
Number of loans by bank m to b orrower i in the las t 5 years
Total numb er of loans by borrower i in last 5 years
When there are no loans in the past five years, the relationship measures are set to
missing. For a given loan, if multiple lead banks are retained, then the values used are those for
the bank with the highest relationship measure. Our sample period spans from 1987 to 2009. Over
this period there were extensive mergers and acquisitions activities in the U.S. banking sector. To
ensure that mergers do not impact the construction of the relationship measure, we construct a
chronology of banking mergers/acquisitions using the Federal Reserve’s National Information
Center database and complemented it by hand matching it with the data from the SDC mergers
and acquisition database, LexisNexis, and the Hoover’s corporate histories database. This allows
us to trace lending relationships through time even if the original relationship lender disappears
due to a merger or an acquisition.
Further, the LPC database has numerous transactions where subsidiaries of banks were
involved. We use the above databases to find the ultimate parent of the given lender. Our
matching procedure is conservative in that we assign a match only if we are reasonably sure of
the ultimate parent. This, as well as possible M&A that we missed collecting data on, implies that
relationship loans may be classified as non-relationship loans (for example, for a subsidiary that
was not an independent bank, but classified as one), but the reverse would not be true. This
would bias against finding any effect of relationship in our sample.
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3.2 Measures of Loan Contract Terms and Benefits to Banks
Following Drucker and Puri (2005), we use the LPC reported “All-in-Spread-Drawn” (AISD) as
the measure of the cost for a loan. AISD is the coupon spread over LIBOR on the drawn amount
plus the annual fee. We also use collateral requirement as a measure of the cost of loan, since
exemption of collateral requirement designate a certain level of benefit that the lenders enjoy.
Collected from the LPC database, the dummy variable “collateral” equals 1 if the loan facility
was secured and 0 otherwise. Since the LPC database has a missing value for the secured field for
a large number of observations, we assume that observations that have a missing value for the
collateral are uncollateralized. 3 The above two variables are used as the principal measure of
borrowing firms benefits or costs. Due to the relatively long time period, we convert all dollar
values into year 2000 dollar values using the consumer price index .
From the lender or bank’s perspective, we use the likelihood of repeat business as the
principal dimension of benefit. We construct this measure along the lines of Ljungqvist et al
(2006) models of underwriter choice where for each loan, a choice set of 20 bank-loans pairs is
created. The potential set of 20 banks that is assumed to compete for a given loan is constructed
based on the list of top 20 banks in the previous year in terms of market share.
4. Univariate Analysis
In this section, we present summary statistics on the data sample and perform univariate
tests of the hypotheses developed in Section 3. Table 1 provides the summary statistics of loan
characteristics such as loan facility amount and spread, as well as borrower characteristics such as
total assets and leverage ratio, for the two different REIT types. Panel A shows the results for the
full sample, and Panels B and C show the results for the internal and external REITs subsamples
3
This is consistent with prior literature in this field such as Bharath et al (2011).
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respectively. There are about 3 times more loans taken by internal REITs relative to external
REITs, roughly consistent with the fractions of these in the sample as well.
Comparing Panels B and C, we find that external REITs are typically smaller in size than
internal REITs as in Capozza and Segiun (2000). Despite the smaller size, internal REITs are
associated with higher loans rates and collateral requirements and lower maturity of loans. Table
2 provides the distribution of loans by year, lending relationship and advisor status. The number
of loans drops sharply in years 2008 and 2009 consistent with a sharp decrease in loan
syndications in these years due to the credit crisis.4
In table 3, the univariate test of key loan characteristics and borrower characteristics are
conducted and results are tabulated. In Panel A, results are presented for the overall sample. Panel
B shows the results for external REITs and panel C for internal REITs. The overall average loan
spread is 172.66 basis points, while the average loan spared for internally advised REITs is
174.30 basis points, and 167.30 basis points for externally-advised REITs. From the simple
summary statistics, we can see that the average loan spread is much higher for internally advised
REITs than their external peers, which is consistent with the main findings in Deng et al (2001).
This actually provides an explanation for the external advisor puzzle in the REIT literature which
has been studied by many other researchers in the real estate finance field such as Capozza and
Seguin (2000) (see also Chan, Erickson & Wang, 2003; Ambrose and Linneman, 2001).
In table 4, we provide univariate tests for the difference in key price and non-price loan
contract terms between relationship loans and non-relationship loans. Panel A suggests that the
loan spread is significantly lower for those loans having prior lending relationship than those
without prior lending relationship. The average spread for loans without lending relationship is
about 195.48 basis points, while for those with lending relationship the average spread is about
4
In a press release, Thomson Reuters LPC reported that loan issuance in the U.S. for 2008 came in at only
$763.98 billion, which is down 55% from 2007 and that there was contraction in all industry sectors. See
“U.S. Loan Market Review: 2008 ends with lending grinding to a halt” New York, December 30, 2008
(Thomson Reuters LPC).
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165.21 basis points, a difference of more than 15% of the loan rate. Thus, relationship lending
appears to result in significant economic benefits to borrowing firms. The facility size is also
much higher for relationship loans. Lastly, relationship loans have significantly lower
requirement for collateral. This is consistent with the existing literature such as Berger and Udell
(1995) and Bharath et al (2011). Thus, at least in the overall sample, relationship lending benefits
appear to overweigh any hold-up costs.
However, the number of covenants required for relationship loans is actually higher than
for non-relationship loans, both for internal and external REITs, inconsistent with the hypothesis
that relationship banks would impose fewer restrictions on their borrowers. However, on an
overall basis, the evidence favours the hypothesis that net benefits of relationship lending accrue
to borrowers.
However, some of these differences in loan contract terms across relationship and nonrelationship loans may be driven by differences in borrower characteristics across these two sets
of firms. To investigate this, we provide statistics on borrower characteristics across of
relationship and non-relationship loans. We find that those borrowers that borrow from their
relationship bank tend to be much larger and have lower leverage. The market to book ratio is
also higher for relationship borrowers. Thus, some of the differences between relationship and
non-relationship loan contract terms may be driven by differences in borrower characteristics.
Next, we stratify the relationship loans into those made to external REITs and those made
to internal REITs. The results are shown in Table 4, panel B (for external REITs) and Table 4,
Panel C (for internal REITs). Surprisingly, we find little difference between relationship and nonrelationship loans for external REITs in terms of loan rate, loan size and collateral requirement.
The loan spread, facility size, maturity are not significantly different between relationship loans
and non-relationship loans. The differences in terms of borrower characteristics are also quite
small and mostly insignificant.
17
In contrast, there are significant differences between relationship and non-relationship
loans for internal REITs. That is to say, conditional on being an internal REIT, having prior
lending relationship provides great benefit in terms of loan rate, collateral, etc. The AISD is
significantly less for relationship loans than non-relationship loans by about 15bp, and the
collateral requirement is reduced by about 50%. This result supports the hypothesis that external
REITs enjoy lower benefits relative to internal REITs.
Overall, the results of the univariate analysis imply that external REITs have a lower loan
rate and collateral relative to internal REITs. Further, in an overall basis, relationship lending is
beneficial to borrowers resulting in a lower interest rate and collateral. When the results are
stratified by REIT type, the benefits of relationship lending are concentrated on internal REITs
whereas external REITs do not derive any benefits of relationship lending, at least in terms of
loan rate and collateral requirement.
5. Multivariate Analysis
The results in the previous section demonstrated that broad support for Hypothesis 1 and
for relationship lending benefits accruing primarily to internal REITs (Relationship lending
dominates relative to hold up). However, to the extent that there are differences in borrower
characteristics between relationship and non-relationship loans and between internal and external
REIT loans, the differences in loan rate and collateral may be reflecting these differences rather
than any fundamental differences due to organizational form. To account for these, this section
conducts multivariate analysis to account for these differences to examine if the differences
continue to persist after differences in borrower characteristics are accounted for.
5.1 Lending Relationships and Loan Rate
18
The results in Section 4 suggest that loan contract terms reflect the greater transparency
of external REITs. In this sub-section, we proceed to test if the benefits of lending relationships
differ systematically across internal and external REITs. To test this, we use empirical
specifications for the loan rate as follows:
, ∗ ,
∗ , ∗ ! " # $,,# 1
#
Where , is the spread charged for the kth loan by firm i. $,,# are the controls variables for
this loan, which includes firm specific controls, loan specific controls, an external REIT dummy,
as well as time dummies to control for possible macroeconomics effects at the time of loan
origination. A detailed definition of all the control variables used is provided in Appendix A.
Recall from hypotheses 1 that benefits of relationship lending should be lower for
external REITs if relationship benefits dominate and higher for external REITs if hold up costs
dominate. Given the empirical specification, the coefficient β1 measures the effect of relationship
on internal REITs and the coefficient β2 measures incremental effect of relationship on external
REITs relative to internal REITs. If hold up effects dominates, one should expect β1 to be positive
(reflecting aggregate hold-up effects) and β2 to be negative (reflecting lower hold up for external
REITs). On the other hand, if relationship benefits dominate, one should expect a negative sign
for β1 (reflecting aggregate benefits) and positive sign for β2 reflecting lower benefits from
relationship lending for external REITs.
Of course, one may observe β1 to be insignificantly different from zero, implying that
neither hold up nor benefits dominates for internal REITs, but at the same time, observe a positive
or negative sign for β2, reflecting differences in relationship lending benefits or costs of hold up
for external REITs relative to internal REITs. Thus, the sign of the interaction term is the main
variable of interest in the empirical specifications.
19
Table 5 shows the results of this regression. In model 1, we find a -40 basis point effect
of REL(Number) which is the relationship strength measure based on number of loans and in
model 4, we find a -34 basis point effect of REL(Amount) which is the relationship strength
measure based on total amount of loans. These numbers are quite large given that the average
loan spread is around 172 basis points for the whole sample. Thus, engaging in relationship loans
results in a discount of close to 20% relative to the average, something that has a large
significance in economic terms. Thus, for the overall sample, relationship benefits dominate the
possible hold-up costs for REITs.
Other variables have interpretations similar to the prior literature. For example, firms
with higher leverage are charged with higher loan spreads. Firms that are larger in size as
measured by total assets have a lower loan spread. Collateral is also used as a dependent variable
in the loan spread regression consistent with the specification in many prior studies. Empirically,
a comprehensive study by Jiminez et al (2006) finds strong support for the moral hazard in being
the principal determinant of the collateral decision. The positive coefficient on the collateral in
Table 5 is consistent with the above.
Market to book ratio does not have any predicted sign in the loan rate. On one hand, high
market to book firms may be firms with more intangible assets, and therefore, more risky. On the
other hand, low market to book is often interpreted as a proxy for distress, and therefore, may
have a positive impact on loan rate. As such, given that this study focuses only on REITs, where
the assets should mostly be tangible, market to book is more likely a proxy for distress risk. The
negative coefficient on market to book is more consistent with the latter explanation, although
there is no agreement in the literature as to what the impact of market to book on loan rates
should be.
We also include maturity of the loan as a potential control variable. Theoretically,
Flannery (1986) implies that loan maturity should decrease with the risk of the borrower while
20
Diamond (1991) implies that the there should be a non-monotonic relationship of between
borrower risk and loan maturity, with medium risk borrowers choosing long term loans whereas
high and low risk borrowers should have shorter maturity loans. Thus, the net impact of maturity
on the loan rate is uncertain. We find no effect on the aggregate level.
To account for year specific effects (for example, an increase in the risk premium), we
include year specific dummies in all regression specifications subsequently. To account for credit
risk not captured in the existing variables, we include dummy variables for the rating of the
borrower at the time of loan origination with non rated firms treated as a separate category. In
addition, because different types of loans may have different credit risk profiles (for example,
term loans may differ from lines of credit), we include a loan type dummy to control for these
differences.
In models 2 and 5 of table 5, we add external status dummy to both the models, and we
find both of the two variables (relationship strength and external status) continue to be significant
as before with similar coefficient estimates. Lastly, in models 3 and 6, we interact both measures
of relationship strength with the external status dummy, with REL (Number) in specification 3
and REL (Amount) in specification 6. We find a strong positive coefficient of the interaction term.
This suggests that external REITs have much lower relationship benefits relative to internal
REITs. In fact, the net benefit from relationship lending appears to be zero for external REITs, at
least based on the loan rate.
5.2 Endogeneity of Relationship Formation
The previous table suggests large economic effects of maintaining relationships for
REITs. However, these results could be biased because of potential endogeneity of the
relationship variable. In particular, firms that form relationships may have unobservably lower
21
credit risk, for example, if banks were cherry picking borrowers with lower credit risk to form
long term relationships. On the other hand, the reverse could also be true, with firms that have
unobservably higher credit risk choosing to form stronger relationships. Under either scenario, the
effect of the relationship lending variables used (REL(Amount) or REL(Number)) may be biased.
Some evidence for such self-selection in terms of observable variables that impact credit
risk is found in Table 4. If one examines the differences in relationship and non-relationship loans,
it can be seen that relationship loans are taken by larger firms with lower leverage (likely with
lower credit risk). Thus, the observable effects of lower rates for relationship loans may be due to
lower credit risk. While the specification includes ratings of the firm, which should control for
such effect, nevertheless, we investigate if an alternative approach based on instrumental
variables impacts the results.
To implement this approach, we need to have an instrument that is correlated with the
likelihood of relationship formation, but otherwise should not impact loan rates directly. Several
papers argue that distance may be important in the formation of lending relationships (Peterson
and Rajan, 2002; Degryse and Ongena, 2005). To compute the distance, we follow approach in
Dass and Massa (2011) by using the spherical distance between the headquarters of the REIT and
the headquarters of the lead bank as the measure of distance.5 Since the loans made are typically
quite large, these decisions would likely be made at the headquarters level. As such, Dass and
Massa (2011) use this distance as the proxy for lending relationship directly (without computing
the relationship strength as we do) and find significant effects using this variable.
First, before proceeding to estimate the IV estimation, we conduct the Durbin and the
Hausman-Wu test for whether the relationship strength variables we use are indeed endogenous.
For both REL(Number) and REL (Amount), both tests reject null hypothesis of that these
5
For non-US banks, we use the US headquarters of the bank to compute distance. In most cases, this was
New York. In cases where we were unable to determine the US headquarters location, we assumed that
New York was the headquarters. For loans with multiple lead banks, we use the minimum distance among
all the banks as the distance measure.
22
variables are exogenous at the 1% level of significance for the specifications in Table 5. This
implies that we do have to account for the potential endogeneity of these relationship variables in
the specification in Table 5.
Next, we proceed to estimate the first stage regression using the log of the distance
between the headquarters of the bank retained in the given loan deal as the instrument that
determines the likelihood of retention of a given bank. In unreported results, this first stage
regression confirms the finding of several prior papers that lower distance leads to a greater
likelihood of relationship formation. The results of the second stage regression shows in Table 6
show similar results as in Table 5. However, the magnitude of relationship lending jumps
significantly in the IV estimation. This is similar to other studies of relationship lending (Berger
at al, 2005) such as where estimates from IV estimation are much larger than that from OLS
estimation. More importantly, the interaction of relationships and external dummy continue to be
positive and significant implying that relationship lending results in lower benefits for external
REITs relative to internal REITs. Incidentally, the difference in relationship benefits for internal
and external REITs is still around 30 basis points, similar to the OLS specification.
Overall, the results of this subsection imply that external REITs derive lower benefits
from relationship lending relative to internal REITs consistent with benefits accruing to the more
informationally opaque internal REITs.
5.3 Lending Relationships and Non-Price Terms of Loan Contracts
The previous subsection documents the broad support for the fact that external REITs
have lower benefits of relationship lending in terms of loan rates. This sub-section investigates if
similar results exist in other terms of the loan contracts – namely collateral, covenants, and loan
size. To test this, we use empirical specifications for the loan rate as follows:
23
&!!, &'!(, )!*+, ∗ ,
∗ , ∗ ! " # $,,# 2
#
Where &!!, is a dummy variable for collateral requirement for the kth loan by firm *,
&'!(, is the number of covenant requirement in the loan contract for the kth loan by firm *,
and )!*+, is the loan amount scaled by the total assets of the borrower firm for the kth
loan by firm *. $,,# are the controls variables for this loan, which includes firm specific controls,
loan specific controls, an external REIT dummy, as well as time dummies to control for possible
macroeconomics effects at the time of loan origination. A detailed definition of all the control
variables used is provided in Appendix A.
5.3.1 Effect on Collateral
Given the earlier endogeneity of relationships, we test whether the relationship variables
are endogenous in a simple probit specification. The χ2 statistic for the Wald test for exogeneity
has a value of 3.76, which corresponds to a p value of 0.0524. Thus, at conventional levels of
significance, the relationship variable is endogenous.
Therefore, using the same approach as that for loan rates, we estimate an instrumental
variables regression using the distance between the borrower and lender as the instrument for
relationship formation. Specifications 3 and 6, which include the interaction of relationship
coefficient with the external dummy are most relevant here. While relationships have a strong
negative impact on collateral (a 1% change in the relationship coefficient leads to a 1.8%
reduction in the likelihood of collateral requirement), the interaction between external and
relationships, while positive as predicted if relationship benefits are present, is marginally
insignificant in one specification and significantly positive in another specification.
24
Since it is well known that the marginal effects in a logistic or probit regression is not
equal to the coefficient estimate unlike in an OLS specification, we report the marginal effects of
all variables rather than the coefficient estimate themselves. Other variables are as expected –
larger size firms have lower likelihood of collateral consistent with lower risk shifting incentives.
Higher leverage firms have a higher likelihood of collateral.
Market to book has an ambiguous prediction. On one hand, higher market to book firms
may have more growth options and therefore have a greater chance of asset substitution. Such
firms may require collateral to curb risk shifting incentives. On the other hand, lower market to
book may reflect larger credit risk. Based on the results in the loan rate regression, we expect the
latter factor to prevail. In fact, market to book has a negative marginal effect on the collateral,
consistent with the notion that high market to book firms have lower credit risk and lower risk
shifting incentives.
5.3.2 Effect on Covenants and Loan Size
We test for the endogeneity of relationships using a specification similar to equation (1)
for the number of covenants in imposed as well as the loan size scaled by total assets. In both
cases, the χ2 statistic for the Durbin test, and the F statistic for the Wu-Hausman test imply a
rejection of the exogeneity of the relationship variables at the 1% level of significance. Therefore,
following an approach similar to before, we employ an instrumental variable approach to account
for this endogeneity. The Anderson Rubin Wald test for weak instruments is rejected at all
conventional levels of significance with p value much lower than 1%, implying that distance is a
valid instrument that is correlated with relationship formation, but not does not have an otherwise
direct impact on the dependent variables.
25
The results for these regressions use a specification similar to equation 1. In both cases,
the interaction of relationships and the external dummy is positive, while relationships themselves
have a negative impact on covenants and loan amount.
5.4 Lending Relationships and Future Likelihood of Retention of Relationship Banks
The previous two subsections examined the benefits and costs of relationship lending
from the perspective of borrowing firms and documented that external REITs have lower benefits
of relationship lending relative to internal REITs. Since one important benefit of relationship
lending from the bank’s perspective is the likelihood of repeat business from the same borrowing
firm, it follows that firms that do not get a large benefit from relationship lending should be less
likely to direct repeat business to their relationship banks. This implies that relationships should
have a lower impact on the future likelihood of business for external REITs relative to internal
REITs.
To test this, we use an approach along the lines of Ljungqvist, Marston and Wilhelm
(2006) in the context of underwriter choice, and Bharath et al (2007) in the context of lender
choice. In particular, for each loan transaction, we have a choice model for lead bank. For each
loan, this choice set consists of banks that were ranked within the top 20 in the year prior to the
current loan. For this set of top 20 banks, all banks that were retained in the lead role for the given
loan is tabulated and used as the dependent variable labelled RETAIN. This variable takes a value
of 1 for all banks retained in a lead position in the given loan transaction and 0 for the remaining
banks in the choice set of top 20. Further, the relationship strength between the borrower and each
26
of the top 20 banks is computed based on the past 5 year loans of the given borrower. Thus, for
each loan transaction, in general, there will be 20 observations in the sample.6
To ensure that the variable RETAIN takes a value of 1 for at least one bank for each loan
transaction, we only use those loan transactions where one of the lead banks was ranked in the
top-20 banks by market share in the prior year was retained as a lead bank. This allows us to
retain 73% by number of our original sample. Our data set consists of over 37,000 loan-bank
pairs which is the unit of observation in our regression model below. The empirical model is
given as follows:
!*,#, -!.(/!#
+ !*(/*0,#, 1!.. 2 !*(/*0,#, ∗ !345
As mentioned before, Retaini,j,k takes a value of 1 if borrower ‘i’ retains bank ‘j’ in transaction ‘k’
and zero otherwise. The controls used as determinants of the retention decision include bank k’s
market share in the year prior to the current loan (Market sharek). Relationshipi,j,k, is computed for
each borrower ‘i’ with each bank j in the top 20 in the prior year as of the starting date for the
current loan transaction k. The sign of β2 will give an estimate of the relative importance of
relationships in the overall sample and the sign of β3 will give an estimate of the relative
importance of relationships for internal and external REITs in the retention decision.
Table 9 displays the results of this regression. Model 1 shows that lagged market share of
the bank is highly significant both economically and statistically. Models 2 and 4 add relationship
strength computed using dollar value (REL(Amount)) and number of loans (REL (Number)). In
both cases, relationships are highly significant confirming earlier results of bank choice for
industrial firms documented by Bharath et al (2007). Lastly, in models 3 and 5, the negative
6
In several cases, because there were not 20 banks in the market share tables in the prior year. So, the total
number observations in this estimation will be lower than the number of sample loans multiplied by 20.
27
impact of interaction suggests that external REITs are less likely to retain their relationship bank
relative to internal REITs.
6. Conclusion
The pros and cons of lending relationship have been studies extensively by various
researchers. This paper analyses the hold-up problem in a unique setting by examining lending
relationship and the cost of bank loans of two different REIT forms, i.e., internal versus external.
The coexistence of the two REIT forms provides an ideal setting for testing the impact of
potential information opacity problems and the effect on the cost of bank loans.
The empirical evidence we have identified in this study provides support for the
hypothesis that lending relationship provides less benefit for externally advised REITs. Since the
externally advised REITs are less informationally opaque, the lenders have less potential to
generate useful proprietary information from the lending relationship, which leads to fewer
discounts on relationship loans for external advised REITs.
28
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31
Appendix A – Definition of Variables
AISD is the “All In Spread-Drawn”, which is the all-inclusive cost of a drawn loan to the
borrower. This equals the coupon spread over LIBOR on the drawn amount plus the annual fee
and is reported in basis points.
Loan Amount is the dollar amount of loan facility in millions.
Maturity is length in months between facility activation date and maturity date.
Collateral is a dummy variable indicating whether the borrower needs to pledge collateral to the
lender in the loan contract.
&'!( is the number of covenant requirement in the loan contract.
Total Assets is the book value of assets of the borrower in millions as reported in the
COMPUSTAT.
Market to book is the ratio of (Book value of assets - Book value of equity + market value of
equity) divided by book value of assets.
Facility Size is the dollar amount of loan facility in millions.
REL(Number) is the ratio of the number of loans taken from the lead bank(s) to total number of
loans taken by the firm in the last 5 years before the current loan.
REL(Amount) is the ratio of dollar value of deals with the lead bank(s) to total dollar value of
loans borrowed by the firm in the last 5 years before the current loan). For a facility with multiple
lead banks, the maximum REL(Number) or REL(Amount) value among all the lead banks is used.
Log(Maturity) is logarithm of the length of the loan measured in months between facility
activation date and maturity date.
Log(Loan size) is logarithm of the loan facility size in millions of real year 2000 dollars.
External is a dummy variable that equals one if the REIT is externally-advised and zero otherwise.
External*REL(Number) is the interaction term between External and REL(Number).
External*REL(Amount) is the interaction term between External and REL(Amount).
Log(Assets) is the natural log of book value of assets in real year 2000 dollars of the borrower as
reported in the COMPUSTAT.
Leverage is the ratio of book value of total debt to book value of assets.
32
Retained is a measure of future repeat loan transaction from the same lender. It is a dummy
variable which equals 1 if any one of the borrower’s top 20 lenders in the previous year is
retained by the borrower for the current loan, and 0 otherwise.
33
Table 1 Summary Statistics for Key Loan and Borrower Characteristics
AISD is the “All In Spread-Drawn”, which is the all-inclusive cost of a drawn loan to the
borrower. This equals the coupon spread over LIBOR on the drawn amount plus the annual fee
and is reported in basis points. Loan Amount is the dollar amount of loan facility in millions.
Maturity is length in months between facility activation date and maturity date. Collateral is a
dummy variable indicating whether the borrower needs to pledge collateral to the lender in the
loan contract. Total Assets is the book value of assets of the borrower in millions as reported in
the COMPUSTAT. Market to book is the ratio of (Book value of assets - Book value of equity +
market value of equity) divided by book value of assets. All values are winsorized at the 1% and
99% level.
Panel A: Overall sample
Part 1: Loan Characteristics
Mean Std Dev Min
25%
Median
75%
Max
90.00
140.00
225.00
388.00
376.78
10.00 113.83
250.00
473.00
2000.00
34.72
14.26
5.00
29.00
36.00
36.00
95.00
3102
0.20
0.40
0
0
0
0
1
3107
4.29
3.92
0
0
5
8
11
Median
75%
Max
Variable
N
AISD
2890
172.66
105.70
45.00
Loan Amount ($ Mil)
3100
361.14
Maturity (Months)
2051
Collateral
Covenants
Part 2: Borrower Characteristics
Variable
N
Mean
Std Dev
Min
2648
3002.20
3509.79
19.15 831.10 1750.81 3941.15
Leverage
2613
0.50
0.14
0.04
0.42
0.49
0.57
0.86
Market to Book
2608
1.30
0.27
0.82
1.11
1.26
1.46
2.24
Borrower Assets
($ Mil)
25%
18794.25
34
Panel B: Internal REITs
Std
Mean
Dev
Min
25%
Part 1: Loan Characteristics
Median
75%
Max
Variable
N
AISD
2212
174.30
103.32
45.00 100.00
150.00
225.00
388.00
Loan Amount ($ Mil)
2398
352.31
370.06
10.00 110.00
235.00
450.00
2000
Maturity (Months)
1569
34.49
14.06
5.00
29.00
36.00
36.00
95.00
Collateral
2398
0.22
0.41
0
0
0
0
1
Covenants
2396
4.15
4.02
0
0
5
8
11
Part 2: Borrower Characteristics
Borrower Assets ($ Mil)
1987 3365.70 3898.88 19.15 853.06 2011.91 4425.78
18794.25
Leverage
1959
0.51
0.14
0.04
0.43
0.49
0.57
0.86
Market to Book
1957
1.29
0.27
0.86
1.11
1.25
1.43
2.24
Std
Mean
Dev
Min
25%
Part 1: Loan Characteristics
Median
75%
Max
Panel C: External REITs
Variable
N
AISD
678
167.30
113.00
45.00
85.00
120.00
200.00
388.00
Loan Amount ($ Mil)
702
391.33
397.68
10.00 125.00
300.00
500.00
2000.00
Maturity (Months)
482
35.47
14.90
5.00
28.00
36.00
39.00
95.00
Collateral
704
0.15
0.36
0
0
0
0
1
Covenants
704
4.79
3.50
0
0
6
7
10
Part 2: Borrower Characteristics
Borrower Assets ($ Mil)
661 1901.50 1438.57 76.33
57.65
1607.96 2688.61
5679.31
Leverage
654
0.47
0.14
0.04
0.38
0.48
0.56
0.86
Market to Book
651
1.32
0.29
0.76
1.09
1.33
1.55
2.24
35
Table 2: Distribution of Loans by Year, Relationship and Advisor Status
This table provides the distribution of loans by year, lending relationship and advisor status. The
sample period is from 1987 to 2009.
Year
Total
All Loans
Internal
External
Rel
Non-Rel
Rel
Non-Rel
Total
Rel
Non-Rel
Total
0
0
0
0
2
2
2
0
4
2
1
5
15
20
28
58
62
40
50
23
60
50
35
92
108
37
5
5
704
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
3
5
1
12
5
2
20
77
88
134
221
327
134
286
126
165
160
257
345
367
243
93
31
0
1
1
0
2
0
5
26
46
79
172
208
101
246
101
145
140
217
244
268
167
48
24
3
4
0
12
3
2
15
51
42
55
49
119
33
40
25
20
20
40
101
99
76
45
7
0
1
1
0
0
0
3
18
32
70
116
149
67
202
80
95
91
187
166
177
135
44
20
1
2
0
8
3
1
12
44
36
36
47
116
27
34
23
10
19
35
87
82
71
44
6
1
3
1
8
3
1
15
62
68
106
163
265
94
236
103
105
110
222
253
259
206
88
26
2
8
14
9
56
59
34
44
21
50
49
30
78
91
32
4
4
2
2
0
4
0
1
3
7
6
19
2
3
6
6
2
10
1
5
14
17
5
1
1
Total
3102
2241
861
1654
744
2398
587
117
36
Table 3: Univariate tests: Internal versus external REITS
The univariate test of key loan characteristics and borrower characteristics are reported in this
table. Panel A reports the result for the overall sample. Panel B shows the results for external
REITs and panel C for internal REITs. AISD is the “All In Spread-Drawn”, which is the allinclusive cost of a drawn loan to the borrower. This equals the coupon spread over LIBOR on the
drawn amount plus the annual fee and is reported in basis points. Facility Size is the dollar
amount of loan facility in millions. Maturity is length in months between facility activation date
and maturity date. Collateral is a dummy variable indicating whether the borrower needs to
pledge collateral to the lender in the loan contract. Total Assets is the book value of assets of the
borrower in millions as reported in the COMPUSTAT. Market to book is the ratio of (Book value
of assets - Book value of equity + market value of equity) divided by book value of assets. All
values are winsorized at the 1% and 99% level.
Panel A: Loan Characteristics
Number
AISD
Loan Size
Maturity
Collateral
Covenants
2890
3100
2051
3109
3100
Internal
External
Mean
Median
Mean
Median
174.3
358.0
35.6
0.2
4.15
150.0
235.0
36.0
0
5
167.3
397.0
36.0
0.15
4.78
120.0
300.0
36.0
0
6
t- statistic
Z- statistic for
Wilcoxon Sum Test
1.44
-2.32**
-0.44
4.14***
-4.08***
3.99***
-2.79***
-2.18**
3.83***
-3.25***
t- statistic
Z- statistic for
Wilcoxon Sum Test
14.0033***
5.8466***
-1.7527*
5.998***
4.111***
-2.605***
Panel B: Borrower Characteristics
Number
Total Assets
Leverage
Market to book
2648
2613
2608
Internal
External
Mean
Median
Mean
Median
3381.28
0.51
1.30
2011.91
0.49
1.25
1909.46
0.47
1.32
1607.96
0.48
1.33
37
Table 4: Relationship versus Non-Relationship Loans
The table below provides the descriptive statistics for the sample of loan facilities by whether the
REIT has a lending relationship. The statistics are reported separately for loans taken from
relationship lenders and loans taken from non-relationship lenders. For any particular loan facility,
we classify a loan as from relationship lender if any of the lead lenders for that loan facility had
been a lead lender on any loans to that borrower in the 5 years preceding the loan facility. AISD
is the “All In Spread-Drawn”, which is the all-inclusive cost of a drawn loan to the borrower.
This equals the coupon spread over LIBOR on the drawn amount plus the annual fee and is
reported in basis points. Facility Size is the dollar amount of loan facility in millions. Maturity is
length in months between facility activation date and maturity date. Collateral is a dummy
variable indicating whether the borrower needs to pledge collateral to the lender in the loan
contract. Total Assets is the book value of assets of the borrower in millions as reported in the
COMPUSTAT. Leverage is the ratio of book value of total debt to book value of total assets.
Market to book is the ratio of (Book value of assets - Book value of equity + market value of
equity) divided by book value of assets. All values are winsorized at the 1% and 99% level.
Panel A: Overall Sample
Loan Characteristics
No Relationship
N
Mean Median
Relationship
Mean Median
t-stats
z-stats
AISD
2890
195.48
175.00
165.21
125.00
7.10***
9.82***
Facility Size ($ mil)
Maturity (months)
Collateral
Covenants
3100
2051
3109
2582
247.29
37.13
0.33
2.78
150.00
36.00
.00
0
404.53
33.98
0.15
4.59
300.00
36.00
.00
5
-11.48***
3.39***
10.03***
-8.64***
-15.72**
0.61
11.04***
-7.55***
Total Assets ($ mil)
Leverage
Market to Book
Borrower Characteristics
2648 2217.96 835.46 3293.98 2053.84
2613
0.53
0.51
0.486
.49
2608
1.26
1.18
1.317
1.28
-7.36***
5.46***
-5.30
-14.34***
6.26***
-6.02***
38
Panel B: External REITs
Loan Characteristics
N
AISD
Facility Size ($ mil)
Maturity (months)
Collateral
Covenants
625
641
433
643
641
No Relationship
Relationship
Mean
Median
Mean
Median
t-stats
z-stats
181.71
402.00
41.79
0.27
4.06
135.00
204.00
36.00
0
5
163.52
417.00
35.11
0.12
5.07
110
300.00
36.00
0
6
1.03
-0.22
1.32
2.53**
-2.05**
1.11
-1.62*
0.42
3.32***
-1.91*
1710.11
0.49
1.36
-0.31
1.43
-0.43
-0.66
1.85*
-0.86
Borrower Characteristics
Total Assets ($ mil)
Leverage
Market to Book
603
597
597
1956.89
0.50
1.31
1713.99
0.51
1.31
2027.87
0.472
1.328
Panel C: Internal REITs
Loan Characteristics
N
AISD
Facility Size ($ mil)
Maturity (months)
Collateral
Covenants
Total Assets ($ mil)
Leverage
Market to Book
No Relationship
Relationship
Mean
Median
Mean
Median
t-stats
z-stats
1822
1939
1248
1941
1939
181.47
251.00
39.39
0.31
2.54
150
150
36
0
0
165.82
400.00
33.57
0.17
4.43
130
295
36
0
5
2.28**
-6.31***
2.70***
5.02***
-8.13***
3.45***
-10.49***
2.01**
5.80***
-6.99***
1592
1571
1577
Borrower Characteristics
2706.67 1337.31 3801.53 2430.88
0.52
0.50
0.49
0.49
1.33
1.30
1.31
1.27
-4.03***
2.71***
0.77
-6.66***
3.74***
0.68
39
Table 5: Lending Relationships and Loan Rate
The dependant variable AISD is the coupon spread over LIBOR on the drawn amount plus the
annual fee in basis points. External is a dummy variable that takes a value of 1 if the REIT is an
externally advised REIT and 0 otherwise. REL(Number) is the ratio of the number of loans taken
from the lead bank(s) to total number of loans taken by the firm in the last 5 years before the
current loan, and REL(Amount) is the ratio of dollar value of deals with the lead bank(s) to total
dollar value of loans borrowed by the firm in the last 5 years before the current loan). Numbers in
the parentheses are standard errors corrected for heteroscedasticity. (*** Significant at one
percent level, ** Significant at five percent level ,* Significant at ten percent level). See
Appendix A for a detailed definition of all variables.
VARIABLES
(1)
(2)
(3)
(4)
(5)
(6)
AISD
AISD
AISD
AISD
AISD
AISD
-16.88**
-54.89***
-18.24***
-53.30***
(6.63)
(13.91)
(6.66)
(14.71)
-40.06***
-38.10***
-52.36***
(8.58)
(8.46)
(9.82)
-34.37***
-33.01***
-45.87***
(8.66)
(8.55)
(10.27)
External
REL(Number)
External*REL(Number)
51.57***
(16.83)
REL(Amount)
External*REL(Amount)
44.52***
(16.70)
Log(maturity)
-0.11
0.82
1.25
0.2
1.14
1.61
(5.70)
(5.69)
(5.74)
(5.70)
(5.70)
(5.74)
-8.89**
-8.57**
-8.47**
-8.50**
-8.14**
-7.79*
(4.11)
(4.08)
(4.04)
(4.19)
(4.15)
(4.14)
-15.41***
-18.49***
-18.08***
-16.03***
-19.34***
-19.18***
(4.08)
(4.16)
(4.12)
(4.06)
(4.13)
(4.1)
84.18***
85.03***
82.17***
93.62***
93.85***
92.42***
(23.23)
(23.41)
(23.18)
(23.02)
(23.23)
(23.19)
39.36***
37.47***
39.04***
39.08***
37.02***
37.88***
(7.33)
(7.36)
(7.38)
(7.33)
(7.36)
(7.37)
-54.94***
-58.41***
-60.02***
-58.12***
-61.64***
-63.66***
(10.78)
(10.84)
(10.64)
(10.76)
(10.82)
(10.61)
363.78***
389.91***
426.88***
356.95***
384.92***
415.35***
(63.02)
(64.94)
(63.96)
(64.11)
(65.79)
(65.25)
Observations
1404
1404
1404
1404
1404
1404
Adj. R-squared
0.19
0.20
0.20
0.19
0.19
0.20
Log(loan size)
Log(assets)
Leverage
Collateral
Market to Book
Constant
40
Table 6: Endogeneity of Relationships – Impact on Loan Rate using IV Estimation
The dependant variable for the second stage regressions, AISD is the coupon spread over LIBOR
on the drawn amount plus the annual fee in basis points. External is a dummy variable that takes a
value of 1 if the REIT is an externally advised REIT and 0 otherwise. REL(Number) is the ratio of
the number of loans taken from the lead bank(s) to total number of loans taken by the firm in the
last 5 years before the current loan, and REL(Amount) is the ratio of dollar value of deals with the
lead bank(s) to total dollar value of loans borrowed by the firm in the last 5 years before the
current loan). Numbers in the parentheses are standard errors corrected for heteroscedasticity.
Dummy variables for year, loan type, and ratings are included in all specifications. ***
Significant at one percent level, ** Significant at five percent level ,* Significant at ten percent
level. See Appendix A for a detailed definition of all variables.
VARIABLES
(1)
AISD
External
8.20
(13.304)
REL (number)
-386.90***
(112.682)
External * REL (number)
(2)
AISD
(3)
AISD
(4)
AISD
-270.52***
(70.486)
-3.41
(11.168)
-335.66***
(91.621)
-394.02***
(109.257)
80.38***
(28.191)
368.47***
(102.016)
REL (amount)
-385.26***
(108.064)
External * REL (amount)
Log (maturity)
Log (loan size)
Log (total assets)
Leverage
Collateral
Market to book
Constant
Observations
R-squared
Adj. R-squared
(5)
AISD
(6)
AISD
-322.17***
(87.893)
-341.65***
(92.016)
-436.88***
(125.204)
62.71***
(22.918)
400.84***
(114.714)
-30.34**
(13.438)
17.97
(11.024)
-25.61**
(11.266)
10.98
(8.645)
-18.68*
(10.708)
11.60
(8.993)
-31.81**
(13.211)
27.50**
(13.102)
-27.85**
(11.433)
20.23*
(10.787)
-21.47*
(11.491)
24.36*
(12.487)
-13.12*
(7.362)
-122.96
(77.434)
16.30
(13.220)
13.70
(29.114)
17.44
(164.101)
-3.27
(7.557)
-82.70
(61.577)
30.06***
(10.915)
6.35
(25.240)
72.38
(132.816)
-11.07
(6.791)
-88.56
(64.218)
34.15***
(11.201)
-16.64
(21.454)
378.72***
(94.838)
-22.09***
(6.796)
-54.51
(57.928)
7.52
(14.337)
-11.98
(21.825)
-103.67
(189.150)
-11.24*
(6.539)
-29.59
(48.776)
20.61*
(11.556)
-13.36
(20.180)
-54.92
(161.466)
-19.54***
(6.514)
-42.35
(55.675)
21.64*
(12.435)
-38.24**
(17.509)
254.11**
(119.182)
1,401
-0.92
-0.96
1,401
-0.54
-0.57
1,401
-0.59
-0.63
1,401
-0.85
-0.89
1,401
-0.55
-0.59
1,401
-0.75
-0.79
41
Table 7: Lending Relationships and Collateral
The dependant variable Collateral is a dummy variable that equals 1 if a loan facility is secured
by collateral and 0 otherwise. We measure lending relationship strength in 2 different ways:
REL(Number) is the ratio of the number of deals with the lead bank(s) to the total number of
loans borrowed by the firm in the last 5 years before the current loan, and REL(Amount) is ratio
of dollar value of deals with the lead bank(s) to total dollar value of loans borrowed by the firm in
the last 5 years before the current loan). All the control variables used in Table 6 are used, but not
reported to conserve space. See Appendix A for a detailed definition of all variables.
Panel A – Instrumental Variables Estimation
(1)
(2)
(3)
(4)
Collateral Collateral Collateral Collateral
VARIABLES
External
-0.10
(0.134)
-1.74***
(0.616)
REL (number)
REL (number) * External
-1.65***
(0.637)
0.13
(0.294)
-1.04**
(0.461)
-1.81***
(0.696)
1.26
(0.769)
REL (amount)
(5)
Collateral
(6)
Collateral
-0.15
(0.119)
-1.17**
(0.522)
-1.71***
(0.624)
-1.61**
(0.647)
0.06
REL (amount) * External
-1.83**
(0.723)
1.30*
Panel B: Marginal Effect on Collateral Requirement (Specification 3)
Variable
∆collateral/∆x
Std. Err.
z
P>|z|
[
95% C.I. ]
X
REL(Amount)
-1.813
0.696
-2.600
0.009
-3.177
-0.448
0.675
External
-1.042
0.461
-2.260
0.024
-1.945
-0.139
0.279
External*REL(Amount)
1.258
0.769
1.640
0.102
-0.248
2.765
0.206
Log (Loan Size)
0.023
0.076
0.300
0.763
-0.126
0.171
19.190
Leverage
0.358
0.669
0.540
0.593
-0.953
1.670
0.480
Log (Asset)
-0.488
0.115
-4.250
0.000
-0.713
-0.263
7.488
Market to Book
-0.616
0.150
-4.120
0.000
-0.909
-0.323
1.274
Log (Maturity)
-0.271
0.082
-3.290
0.001
-0.432
-0.109
3.446
Panel C: Marginal Effect on Collateral Requirement (Specification 6)
Variable
REL(Number)
∆collateral/∆x
-1.830
Std. Err.
0.723
z
-2.530
P>|z|
0.011
[ 95% C.I. ]
-3.248 -0.412
X
0.734
External
-1.173
0.522
-2.250
0.025
-2.196
-0.150
0.279
External*REL(Number)
Log (Loan Size)
1.299
0.784
1.660
0.098
-0.237
2.835
0.218
0.060
0.088
0.690
0.493
-0.112
0.233
19.190
Leverage
0.617
0.608
1.020
0.310
-0.574
1.809
0.480
Log (Asset)
-0.517
0.106
-4.900
0.000
-0.725
-0.310
7.488
Market to Book
-0.669
0.160
-4.190
0.000
-0.983
-0.356
1.274
Log (Maturity)
-0.273
0.086
-3.180
0.001
-0.441
-0.105
3.446
42
Table 8 – Lending Relationships and other non price terms
In panel A, the dependent variable is the total number of covenants imposed in the loan. In panel
B, the dependent variable is the loan size scaled by the total assets of the borrower. We measure
lending relationship strength in 2 different ways: REL(Number) is the ratio of the number of deals
with the lead bank(s) to the total number of loans borrowed by the firm in the last 5 years before
the current loan, and REL(Amount) is ratio of dollar value of deals with the lead bank(s) to total
dollar value of loans borrowed by the firm in the last 5 years before the current loan). All the
control variables used in Table 6 are used, but not reported to conserve space. See Appendix A
for a detailed definition of all variables.
Panel A: Dependent Variable Covenants
(1)
(2)
VARIABLES
External
0.82*
(0.435)
-10.51**
(4.556)
REL (number)
REL (number) * External
-5.59*
(2.852)
-10.55**
(4.470)
8.51**
(4.143)
REL (amount)
(3)
(4)
0.52
(0.350)
-7.09**
(3.355)
-10.46**
(4.304)
-11.54**
(4.887)
9.63**
(4.410)
REL (amount) * External
VARIABLES
Panel B: Dependent Variable Loan to Asset Ratio
(1)
(2)
(3)
External
REL (number)
REL (number) * External
REL (amount)
REL (amount) * External
-0.01
(0.014)
0.25***
(0.081)
0.15***
(0.054)
0.26***
(0.083)
-0.22***
(0.082)
(4)
-0.01
(0.013)
0.16***
(0.059)
0.24***
(0.073)
0.27***
(0.085)
-0.21***
(0.082)
43
Table 9: Lending Relationships and future likelihood of retention
The dependent variable retained is a measure of future repeat loan transaction from the same
lender. It is a dummy variable which equals 1 if any one of the borrower’s top 20 lenders in the
previous year is retained by the borrower for the current loan, and 0 otherwise. REL(Number) is
the ratio of the number of deals with the lead bank(s) to total number of loans taken by the firm in
the last 5 years before the current loan, and REL(Amount) is the ratio of dollar value of deals with
the lead bank(s) to total dollar value of loans taken by the firm in the last 5 years before the
current loan. External is the dummy variable with 1 indicating whether a RETI is externally
advised and 0 otherwise. Market share is a measure of the market share of the lender bank in
terms of loan volume, and is calculated as the ratio of the total deal value for each lender year to
the total deal value in that year. Column 1 is the baseline model which tests the effect of lender’s
market share in the loan market on the lender retention rate. Column 2 and column 4 test the
lending relationship strength on lender retention rate in terms of Amount of loans REL(Amount)
and Number of loans REL(Number) respectively. Column 3 and 5 test the specific effect of the
interaction of external status and relationship strength measures, also in terms of Amount of loans
REL(Amount) and Number of loans REL(Number) respectively.
VARIABLES
Market share
(1)
Retained
(2)
Retained
(3)
Retained
(4)
Retained
(5)
Retained
3.186***
(0.258)
4.878***
(0.426)
0.635***
(0.023)
4.892***
(0.427)
0.675***
(0.024)
-0.132***
(0.020)
4.772***
(0.430)
4.783***
(0.431)
0.434***
(0.021)
REL(Amount)
External*REL(Amount)
REL(Number)
-1.116***
(0.014)
-1.652***
(0.027)
-1.656***
(0.028)
-1.466***
(0.025)
0.468***
(0.022)
-0.110***
(0.022)
-1.469***
(0.025)
44832
0.01
37090
0.04
37049
0.05
37090
0.03
37049
0.03
External*REL(Number)
Constant
Observations
Adj. R-squared
Marginal Effect on Future Repeat Loan Transaction (Specification 3)
Variable
∆collateral/∆x
Std. Err.
z
P>|z|
[
95% C.I. ]
X
Market Share
1.207
0.105
11.520
0.000
1.002
1.413
0.045
REL(Amount)
0.167
0.006
28.610
0.000
0.155
0.178
0.718
External*REL(Amount)
-0.033
0.005
-6.440
0.000
-0.042
-0.023
0.200
Marginal Effect on Future Repeat Loan Transaction (Specification 5)
Variable
∆collateral/∆x
Std. Err.
z
P>|z|
[
95% C.I. ]
X
Market Share
1.197
0.107
11.160
0.000
0.987
1.407
0.045
REL(Number)
0.117
0.005
21.480
0.000
0.106
0.128
0.664
External*REL(Number)
-0.028
0.005
-5.110
0.000
-0.038
-0.017
0.189
44