Journal of Monetary Economics 25 (1990) 21-42. North-Holland
COLLATERAL, LOAN QUALITY, AND BANK RISK*
Allen N. BERGER
Board of Governors of the Federal Reserve System, Washington, DC 205.51, USA
Gregory F. UDELL
New York University, New York, NY 101W4, USA
Received April 1989, final version received October 1989
Most commercial loans are made on a secured basis, yet little is known about the relationship
between collateral and credit risk. Several theoretical studies find that when borrowers have
private information about risk, the lowest-risk borrowers tend to pledge collateral. In contrast,
conventional wisdom holds that when risk is observable, the highest-risk borrowers tend to pledge
collateral. An additional issue is whether secured loans (as opposed to secured borrowers) tend to
be safer or riskier than unsecured loans. Empirical evidence presented here strongly suggests that
collateral is most often associated with riskier borrowers, riskier loans, and riskier banks.
1. Introduction
Collateral plays an important role in U.S. domestic bank lending, as
evidenced by the fact that nearly 70% of all commercial and industrial loans
are currently made on a secured basis. Not surprisingly, the role of collateral
has received considerable attention in the theoretical literature on financial
contracting. An important issue discussed in this literature is the relationship
between collateral and borrower quality with a number of studies finding that
safer borrowers are more likely to pledge collateral [e.g., Besanko and Thakor
(1987a), Chan and Kanatas (1985)]. However, this view is not generally
consistent with conventional wisdom in banking which holds that riskier
borrowers are more likely to pledge collateral [e.g., Morsman (1986)]. An
essential difference between most of the theoretical models and conventional
wisdom is that the former usually concentrate on private information about
risk known only to borrowers, while the latter concentrates on observed risk.
*The work on this paper was completed while Udell was a visiting economist at the Federal
Reserve Board. The opinions expressed do not necessarily reflect those of the Board of Governors
or its staff. The authors thank an anonymous referee, as well as Jim Booth, Mark Flannery, Mike
Goldberg, Dave Humphrey, George Kanatas, Mike Kuehlwein, Doug McManus, Charles Meiburg,
Neil Murphy, Jim O’Brien, Pat Parkinson, Wayne Passmore, Steve Rhoades, Rich Rosen, Tony
Saunders, Steve Sharpe, Anjan Thakor, Peter Tinsley, John Wolken, and numerous seminar
participants for helpful comments and Peter Zemsky and Oscar Bamhardt for invaluable research
assistance.
0304-3932/90/$3.5001990,
Elsevier Science Publishers B.V. (North-Holland)
22
A. N. Berger and G. F. Udell, Collateral,
loan quality,
and bunk risk
Despite the significance of this issue, it has largely escaped empirical scrutiny.
This paper attempts to fill this gap by examining the empirical relationship
between collateral and borrower credit risk.
A related issue and one of potentially greater policy interest is whether
secured loans (as opposed to secured borrowers) are riskier than unsecured
loans. Ceteris paribus, collateral decreases the riskiness of a given loan, since it
gives the lender a specific claim on an asset without diminishing its general
claim against the borrower [e.g., Barro (1976) Stiglitz and Weiss (1981)]. If
borrowers who pledge collateral are safer than borrowers who do not, then
secured loans are necessarily safer than unsecured loans. However, if borrowers who pledge collateral are riskier than borrowers who do not, then secured
loans may be either safer or riskier on average than unsecured loans. If the
credit-enhancing
value of recourse against collateral more than offsets a
sorting effect which associates riskier borrowers with secured loans, then
secured loans would be safer than loans to borrowers who only borrow on an
unsecured basis. The converse would hold if the value of recourse less than
fully offsets the sorting effect.
The potential empirical relationships between collateral and risk can be
formally stated in three alternative hypotheses:
Hl:
Safer borrowers more often pledge collateral, which necessarily implies
that secured loans are less risky than unsecured loans.
H2: Riskier borrowers more often pledge collateral, but recourse against
collateral more than fully offsets the difference in borrower risk, so that
secured loans are safer than loans to borrowers who borrow only on an
unsecured basis.
H3: Riskier borrowers more often pledge collateral, and recourse against
collateral less than fully offsets the difference in borrower risk, so that
secured loans are riskier than loans to borrowers who borrow only on an
unsecured basis.
To some extent, all three of these hypotheses likely apply to some groups of
borrowers and loans. The purpose of the empirical analysis is to determine
which of these tends to occur most often. An important feature of the
empirical analysis is the disentanglement of borrower risk from loan risk. The
key identifying restriction is that certain nonperformance characteristics of the
borrower (e.g., loan payments past due) temporally precede any offsetting
effect of recourse against collateral.
We also address the related issue of collateral and risk at the bank level.
Banks often differ significantly with respect to lending philosophy, including
the types of contract terms they tend to offer. For example, some banks
emphasize asset-based lending which is collateral-oriented, while other banks
A. N. Berger and G.F. Udeil, Collateral, loan quality, and bank risk
23
prefer commercial lending only on an unsecured basis. To the extent that bank
risk is strongly associated with secured lending, policy implications may be
involved. For instance, it may be appropriate to consider collateral when
allocating scarce bank supervisory resources.
The data set used here, the Federal Reserve’s ‘Survey of Terms of Bank
Lending’ (STBL), is particularly well-suited for this analysis. It includes
contract information on over 1,000,000 domestic commercial loans made from
1977 to 1988 by a stratified sample of 460 commercial banks. The extended
observation period also permits observations to be made about changes in
loan contract features over time.
Section 2 examines the theoretical and institutional literature which relates
collateral to credit risk. Section 3 uses cross-section data on individual loans to
estimate differences in risk premia between secured and unsecured loans as ex
ante indicators of risk. Section 4 employs pooled time-series cross-section data
on ex post measures of borrower risk and loan risk (nonperformance status
and net charge-offs, respectively) to analyze differences associated with the use
of collateral. Overall, the data strongly suggests that collateral is associated on
average with riskier borrowers, riskier loans, and riskier banks, implying that
Hypothesis H3 applies most often. Section 5 concludes.
2. Collateral and risk in the literature
Conventional wisdom in the banking community associates the use of
collateral with observably riskier borrowers. As part of every pre-loan credit
analysis, commercial lenders assess the riskiness of prospective borrowers and
base the collateral requirements at least in part on this assessment.’ Hempel,
Coleman, and Simonson (1986, p. 391) observe that large prime borrowers are
more likely to get unsecured financing ‘because [they] tend to have stronger
equity support in their capital structures, more stable cash flows, and more
certain investment opportunities’. In the case of seasonal loan facilities,
Morsman (1986, p. 5) notes that banks are ‘normally secured by a perfected
security interest in accounts receivable, inventory, and equipment. [However,]
exceptions can occur with well-capitalized companies with no other types of
debt and a history of seasonal payout’. In other words, observably risky
borrowers are required to pledge collateral, while observably safe borrowers
are not. We refer to this as the sorting-by-observed-risk paradigm.
Morsman (1986) also notes that some types of lending to high-risk borrowers are virtually always conducted on a secured basis. Permanent capital
lending, often referred to as ‘asset-based lending’, generally involves extending
‘See Altman (1985).
24
A. N. Berger and G. F. Udell, Collateml, loun quality, and hank risk
credit to particularly
high-risk/high-leverage
middle-market
companies.
He
further notes that companies requiring this type of credit typically fall into one
of these categories: companies with ‘rapidly expanding sales that outstrip the
repayment
owner’s equity position’, ‘new businesses that cannot demonstrate
capability’,
companies
that have experienced
‘depletion
of working capital
through purchases of noncurrent
assets, treasury stock, and acquisitions’,
or
companies
that cannot repay some other credit obligation
due to ‘some
setback such as unexpected
losses or the inability
to sell assets’ (p. 13).
Morsman
concludes
that permanent
capital lending is the ‘riskiest form of
lending a bank can undertake’ and these loans are only made ‘on proof of
adequate collateral’ (p. 13).
There is some limited theoretical support for the sorting-by-observed-risk
paradigm. In Boot, Thakor, and Udell(1988),
an exogenous quality dimension
of the borrower’s project is observable
to both borrower and lender, while
borrower effort is privately known. They find that under certain conditions,
collateral is pledged by borrowers with observably higher risk.
A requirement
of the sorting-by-observed-risk
paradigm is that banks have
information
that allows them to distinguish among borrowers on the basis of
risk, but is silent on the question of loan risk. Thus, this paradigm implies that
riskier borrowers pledge collateral, but is consistent with either Hypothesis H2
(riskier borrowers, but safer loans) or Hypothesis H3 (riskier borrowers and
riskier loans). The extant literature
on sorting-by-observed-risk
does not
indicate which of these two hypotheses is likely to dominate empirically.
In contrast to the sorting-by-observed-risk
paradigm, much of the theoretical literature
focuses on private information
known only to borrowers and
draws a different conclusion about the relationship
between borrower risk and
collateral. Besanko and Thakor (1987a) find that in a market where lenders are
at an informational
disadvantage
with respect to borrower default probabilities, collateral may mitigate a credit-rationing
problem. In equilibrium,
low-risk
borrowers pledge more collateral than high-risk borrowers. In another paper,
Besanko and Thakor (1987b) find a similar positive relationship
between
collateral and borrower risk. This latter paper examines loan contracting under
asymmetric
information
when the pricing menu has a number of dimensions,
including loan quantity, interest rate, collateral, and potential rationing. Chan
and Kanatas (1985) and Bester (1985) find that collateral can produce sorting
across borrower types when collateralization
is costly. Both papers find that
low-risk borrowers
pledge more collateral than high-risk borrowers because
collateral-associated
costs produce different marginal rates of substitution.
We
refer to the view represented
by these four papers which find a positive
relationship
between collateral and borrower risk as the sorting-by-privateinformation paradigm. This paradigm is consistent with Hypothesis Hl (safer
borrowers and safer loans) and is predicated on the assumption
that banks are
A. N. Berger und G.E Udell, Collateral, loan quality, and bank risk
25
not able to distinguish adequately among borrowers on the basis of the risk
because of informational asymmetry.*
Much of the literature on collateral has focused on ‘outside collateral’,
where the owners pledge assets not owned by the firm. However, several
papers have considered issues related to ‘inside collateral’, including Smith
and Warner (1979b), Stulz and Johnson (1985), and Swary and Udell (1988).
‘Inside collateral’ refers to assets owned by the borrowing firm that are
pledged to a particular lender. Smith and Warner (1979b) argue that inside
collateral may be useful in solving asset-substitution problems initially raised
by Jensen and Meckling (1976). The empirical implications of Smith and
Warner for the collateral-risk relationship depend upon how asset-substitution-related monitoring costs are related to risk.
Stulz and Johnson (1985) analyze the properties of secured and unsecured
debt using a contingent-claims approach. Within this framework they also
analyze the role of secured debt in solving Myers’ (1977) underinvestment
problem. Low-risk firms are unlikely to issue secured debt because they are
unlikely to have an underinvestment problem. However, firms with a potential
underinvestment problem may issue secured debt under certain conditions.
For the latter group, Stulz and Johnson (1985) demonstrate that the lower the
variance of the return on the collateral asset relative to the variance of the
return on the noncollateral asset, the more likely that collateral can solve
the underinvestment
problem. The empirical implications of Stulz and
Johnson, however, are ambiguous with respect to the average riskiness of
secured borrowers relative to those who only borrow on an unsecured basis.
Swary and Udell (1988) offer another motivation for inside collateral. They
suggest that secured debt may be useful in enforcing optimal firm closure (i.e.,
bankruptcy). The magnitude of the closure problem in their model is positively
associated with firm risk. As a result, observably riskier firms are more likely
to pledge collateral, consistent with the sorting-by-observed-risk paradigm.
Before discussing the empirical evidence, it should be noted that the two
main paradigms presented here are not intended to be exhaustive. Under
different assumptions, models may be developed which imply alternative
relationships between observed or unobserved risk and collateral [e.g., Stiglitz
and Weiss (1981,1986)]. It is also recognized that many factors other than risk,
such as borrowers’ access to tangible assets which can be pledged, have
‘Under the sorting-by-private-information paradigm discussed here, borrower risk is unobservable and safer borrowers pledge more collateral. However, it is also possible to develop models
based on private information in which riskier borrowers pledge more collateral. In Stiglitz and
Weiss (1981.1986). collateral may be positively associated with (unobservable) risk because of
adverse selection effects. These efl’ects are created by the greater ability of wealthier risk-averse
borrowers to pledge collateral in a model where neither borrower wealth nor choice of project is
observable.
26
A.N.
Berger und G.F. Udell, Collaterul,
loan quality,
and bank risk
important effects on collateral decisions. However, the sorting-by-observed-risk
and sorting-by-private-information
paradigms represent the major strands of
thought in the current collateral debate.
The paucity of empirical evidence on the relationship between risk and
collateral stems largely from the fact that information about the contract
terms of bank loans is generally private. While information about the composition of bank assets and liabilities is accessible from a variety of public
sources including the Call Report, these sources do not provide any information about many specific contract features such as collateral. As a result, even
the extensive empirical literature on bank failure and risk has not included
collateral as an explanatory variable [e.g., Avery, Belton, and Goldberg (1988)
Avery, Hanweck, and Kwast (1985) Lane, Looney, and Wansley (1986), and
West (1985)]. This makes the data set used here, the ‘Survey of Terms of Bank
Lending’, a unique source of information.
The only previous empirical studies of collateral and risk of which we are
aware are Orgler (1970) and Hester (1979). Orgler compiled a data base on
individual loans from bank examination files and distinguished ‘good’ from
‘bad’ loans on the basis of whether the loans were ultimately ‘criticized by the
bank examiners. He regressed a good-bad dummy variable on a securedunsecured dummy variable and several control variables and found secured
loans to be riskier, but the coefficient was significant only at the 10% level.
Hester used data from a 1972 survey which included loan contract terms and
limited information on the borrowers. He regressed a secured-unsecured
dummy variable on six accounting ratios that proxy risk, a Dun and Bradstreet credit-rating dummy, and numerous control variables. The coefficients
were generally, but not uniformly, consistent with the hypothesis that riskier
borrowers pledge collateral.3
Unfortunately, Orgler’s and Hester’s data sets may not be very useful for
analysis of the current relationships between risk and collateral. First, the
banking environment has changed substantially over the two decades since
these data sets were collected. For example, asset-based lending, often considered to be the riskiest form of secured lending, was rarely engaged in by banks
at that time. Second, the samples (particularly Orgler’s) were rather limited.
Orgler’s sample contained 300 loans to small borrowers with total assets less
than $12 million. Hester’s sample contained more and larger loans, but the
book accounting ratios used may have limited capacity to proxy current risk.
Finally, while these studies were able to examine the relative risk of secured
and unsecured borrowers, their data sets did not allow them to examine the
relative risks of secured and unsecured loans since no information was
3A purely econometric
difficulty with both studies is that they used OLS regressions with a
discrete,
two-valued
dependent
variable. This procedure
likely generates
a heteroskedasticity
problem, which could bias the r-statistics and potentially alter the significance results.
A. N. Berger und G.E lIdelI, Colkareml, laan quality, akd bank risk
21
available on the ultimate losses or charge-offs on the loans. That is, these data
could not be used to distinguish between Hypotheses H2 and H3, which
require separate information about borrower risk and loan risk (the latter
being inclusive of the credit enhancing value of recourse to collateral).
3. Cross-section
data and results
The empirical analysis tests whether secured borrowers and secured loans
are riskier or safer, respectively, than unsecured borrowers and unsecured
loans. In the cross-section, we relate an ex ante measure of loan risk to
collateral, while in the pooled time-series cross-section analysis in the following section, we use some ex post measures of both loan and borrower risk. The
cross-section dependent variable is the loan risk premium, the difference
between the loan rate and a risk-free rate of the same duration. Under either
the sorting-by-private-information
paradigm or the sorting-by-observed-risk
paradigm, the risk premium should incorporate ex ante evaluations of net loan
risk. Note that the dependent variable as measured could also include a
premium for additional collateral-related monitoring costs associated with
some forms of secured lending, particularly asset-based lending.
The primary data source for the cross-section is the Federal Reserve’s
Survey of Terms of Bank Lending, which contains information on over one
million business loans. Each quarter from 1977 to the first half of 1988,
approximately 340 banks listed the individual characteristics of every domestic
commercial and industrial (C&I) loan and construction and land development
loan made during one or more days of the first week of the second month of
the quarter. The sample includes the 48 largest banks in the nation in terms of
C&I lending plus 292 other banks chosen to represent the strata of smaller
banks. Banks that withdrew from the sample were replaced with banks of
similar size and other characteristics. In all, 460 different banks are represented in the sample.
Table 1 describes the data used in the cross-section regressions in which the
loan risk premium is regressed on measures of collateral and several control
variables. Sample means and numbers of observations are given for ,%ur
individual cross-sections and for the entire data set. The cross-section dates
were chosen to include observations near the beginning and end of the sample
and near the peak and trough of the interest-rate cycle. By the nature of the
survey, which selects cross-sections of business loans from all sizes of banks,
the ranges of the variables are quite broad. For instance, the calculated risk
premium ranges from less than - 10 percent to over 15 percent, while loan
maturity ranges from 1 day to 80 years and loan size from one thousand to
several hundred million dollars. As shown, the data exclude loans for which
the calculated risk premium was less than - 1 percent (about 1 percent of all
loans). These may represent insider or tied loans, for which the stated interest
28
A. N. hger
md G. F. LidelI. Collateml. loun quuliy. und bank risk
Number of observations
CONSTRUCT-RESECURED
CONSTRUCT-NONRES
CONSTRUCT-MULTIFAM
CONSTRUCT-SINGFAM
FOREIGN
FEDFUND
PRIME
PARTICIPATION
DEMANDNOTE
OFFSHORE
COMMITMENT-TOTAL
COMMITMENT-INFORMAL
Equals one if the loan is made under an informal
commitment (available beginning August 1986).
Equals one if the loan is made under either type of
commitment.
Equals one if the loan has no stated maturity.
Equals one if the loan is booked offshore (available
beginning August 1986).
Equals one if the loan is part of a participation
(available beginning August 1982).
Equals one if the loan rate is based on the prime
rate (available beginning August 1986).
Equals one if the loan rate is based on the federal
funds or other domestic rate (available beginning
August 1986).
Equals one if the loan rate is based on a foreign
money market rate (available beginning August
1986).
Equals one for a single-family construction and
development loan.
Equals one for a multi-family construction and
development loan.
Equals one for a nonresidential construction and
development loan.
Equals one for a construction and development loan
secured by real estate.
-
-
4.91%
6.88%
1.127.479
2.14%
7.08%
24,824
3.06%
6.28%
24,080
2.33%
5.43%
23,658
3.21%
7.36%
28,348
2.67%
0.38%
0.86%
0.70%
0.68%
6.49%
4.58%
0.98%
5.34%
83.35%
3.69%
26.07%
0.27%
52.88%
18.89%
3.24%
0.64%
-
-
83.72%
3.41%
-
-
2.68%
2.47%
3.93%
17.85%
-
20.10%
-
22.28%
-
54.09%
0.20%
-
38.96%
48.01%
53.30%
73.10%
1.11%
-
-
18.61%
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A. N. Berger and G. F. Udell, Cottatend, loan quality, and bank risk
31
of a nonrisk term premium incorporated in the dependent variable or another
scale economy in lending. The COMMITMENT
variables are included to
account for differences in payment terms, loan risk, and borrower selection differences
between
commitment
and noncommitment
loans.
DEMANDNOTE
accounts for differences in risk created by the bank’s option
to call a loan and any sorting effects related to this option. Most of the
remaining control variables, OFFSHORE,
PARTICIPATION,
PRIME,
FEDFUND, FOREIGN, and the CONSTRUCT measures, are included because they are exogenous factors that give information about the type of
borrower or loan that may be related to risk and correlated with collateral.
The last variable listed, CONSTRUCT-RESECURED,
represents the difference in collateral effect for a construction and land development loan that is
secured by real estate. Finally, each of the regressions contains a dummy
variable for each bank having loans in the sample. This is to control for any
systematic differences in pricing across banks due to other pricing elements,
such as up-front fees, compensating balances, coincident services, etc., as well
as any potential differences in market power across banks.7
Before turning to the regression results, there are a number of bivariate
relationships between loan collateral and the other variables that are of
interest in describing what types of loans are secured. First, over the entire
1977 to 1988 sample, secured loans are much smaller than unsecured loans
and tend to have somewhat longer duration, averaging about $253,000 and 173
days versus $982,000 and 119 days for unsecured loans. Because of the
difference in size, secured loans represent only 27 percent of the total dollar
volume of loan flow, although 58 percent of all loans in the sample were
secured. Secured loans also tend to be made more often on a floating-rate
basis and are more often made under commitment. Finally, secured loans tend
to have higher measured risk premia than unsecured loans.
Table 2 gives the results from the four cross-section regressions of the loan
risk premia on the collateral and control variables with the dependent variable
measured in basis points for expository ease. The estimates clearly indicate
that for floating-rate loans an extra premium is charged when the loan is
secured. The coefficients of COLLATERAL-FLOATING
suggest that the
addition of collateral to these loans is indicative of a 28 to 47 basis point
increase in risk premia, with all four coefficients significantly different from
zero at the 1 percent level. The coefficients of COLLATERAL-FIXED
do not
paint such a clear picture. The coefficients shown for the latter two quarters
indicate no significant relationship between collateral and risk premia, although the 1987 coefficient becomes positive and sign&ant when fixed-rate
‘FIerger and Hannan (1989) showed that local banking-market concentration substantially
affected the rates paid on bank deposits. Such a relationship may exist on the loan side of the
balance sheet as well.
regressions
of observations
Coefficient
- 14.35
3.84
- 0.30
7.71
~ 28.80
- 10.89
~ 0.73
0.37
- 0.96
0.68
-
0.87
29.20
20.82
56.10
39.69
19.68
19.41
f-statistic
1987
0.33”
28,348
- 28.84h
52.01’
- 1.17
22.98’
- 109.97h
- 75.06h
~ 7.78
4.57
- 9.71
7.05
2.51
46.X5h
63.35h
- 20.70h
- 21.27h
~ 37.15h
-47.12h
-
_a
Table 2
- 1.92
0.78
~ 0.87
3.03
- 21.52
11.14
~ 8.89
32.66h
0.22”
23,658
0.62
~ 25.01
3.07
~-51.8gh
8.69’
3.12
0.07
16.55
~ 7.58
- 66.37
1.67
0.22
35.71h
- 20.56’
~ 31.72h
1.05
_a
Coefficient
August
(dependent
of variance
variable
explained
10.86
48.30h
45.13h
17.41‘
- 45.90h
~ 122.89’
12.30h
28.77’
- 15.89h
~ 22.10h
- 34.57h
_a
Coefficient
0.19”
24,824
1.34
3.50
5.63
2.14
- 24.28
~ 24.61
6.09
10.08
- 5.74
~ 46.14
- 37.76
t-statistic
May 1977
in basis points).
crfter these intercepts.
~ 4.49
- 0.49
~ 0.29
1.85
._
~ 1.53
~~35.07
~ 16.28
13.08
27.75
- 28.59
~ 58.1X
t-statistic
1981
0.24”
24,080
~ 58.35h
-- 7.89
- 3.48
22.80
_.
- 3.89
-- 221.25h
~ 57.15h
38.00h
91.84h
- 17.12h
~ 65.83h
il
Coefficient
variables
_.
f-statistic
and control
May 1983
loan risk premia on collateral
November
of individual
“Intercepts
were included for each bank in the sample, and the R’s reflect the proportion
hSigniticantly
different from zero at the 1% level. two-sided.
‘Significantly
different from zero at the 5% level, two-sided.
RI
Number
INTERCEPT
COLLATERAL-FIXED
COLL.ATERAL.-FLOATING
FLOA TING
L>NSIZE
LNDURATION
COMMITMENT-FORMAI.
COMMITMENT-INFORMAL
COMMITMENT-TOTA
I.
DEMANDNOTE
OFFSHORE
PA RTICIPA TION
PRIME
FEDFUND
FOREIGN
CONSTRUCT-SINGFA
M
CONSTRUCT-MlJL.TIFA
M
CONSTRUCT-NONRES
CONSTRUCT-RESECL’RED
Variable
Cross-section
A. N. Berger and G.F. Udeli, Collateral, loan quality, and bank risk
33
loan data are used in a separate regression (not shown). The earlier two
quarters have significant coefficients, but the signs are opposite of each other.
The negative 1981 result may reflect an interaction with the peak of the
aggregate interest-rate cycle, in which implicit interest-rate insurance or concessionary terms were more frequently given to fixed-rate, secured borrowers.
Consistent with this interpretation, when the procedure of deleting loans with
risk premia less than - 1 percent was relaxed to - 10 percent (adding about
1500 loans), the coefficient dropped to - 130 basis points.8 The positive 1977
result for COLLA TERAL-FIXED may be explained by the fact that two-thirds
of all loans were fixed-rate loans in 1977, as opposed to a minority in the other
periods (table 1). It appears that some types of relatively risky, secured loans
that would in the 1980s be booked on a floating-rate basis were booked as
fixed-rate loans in the 1970s.
Overall, the cross-section results suggest that the sorting-by-observed-risk
paradigm occurs more frequently than the sorting-by-private-information
paradigm, although the results may only hold for floating-rate loans. When
collateral is aggregated into a single explanatory variable (COLLATERALTOTAL in table l), the coefficient is still positive and significant at the
1 percent level (not shown). These results are also robust with respect to
(i) adding interactions of collateral with all the control variables, (ii) dropping
the control variables with statistically insignificant coefficients, (iii) adding a
control variable for overnight loans, (iv) running separate regressions for fixedand floating-rate samples (with the exception that collateral becomes significantly positive for fixed-rate loans in 1987) and (v) using slightly different
definitions of the risk premium.
Turning to the control variables, larger and longer-term loans generally had
lower risk premia, suggesting that larger or longer-term borrowers may be
safer or that there are lending scale economies. Loans under commitment had
significantly lower risk premia, ceteris paribus, consistent with the findings of
Avery and Berger (1989) who found that riskier borrowers tend not to receive
commitment contracts. Offshore loans had higher-risk premia, suggesting that
either extra risk or expense is involved in foreign loans. Loan rates based on
short-term money-market quotes were lower than those based on prime rates,
ceteris paribus, perhaps because the former proxy direct access to capital
markets by the borrowers. The floating- versus fixed-rate, demand note,
“Murphy (1984) found a similar result for August 1979 STBL data when testing the relationship
between loan rates and loan size. Collateral was included as a control variable and had a
statistically significant negative coefficient in a loan-rate regression, which may reflect a negative
relationship between fixed-rate loan premia and collateral when open market rates are relatively
high. However, a number of methodological differences make it difficult to draw conclusions
about this coefficient. For instance, Murphy implicitly assumed a linear term structure by not
subtracting the risk-free rate of the same duration from the loan rate. In addition, he included
many fewer control variables than are present here and did not differentiate coefficients by fixedand Boating-rate.
34
A.N.
Rerger und G. F. Udell. Colluteml,
loun quulity,
und hunk risk
participation
and the construction
and land development
variables were generally insignificant
or inconsistent.
The exception is that construction
and land
development
loans secured by real estate have slightly higher risk premia,
consistent
with our main results.
4. Pooled time-series
The individual
cross-section data and results
loan data in the previous section were useful for revealing ex
of loan risk and their relationships
with collateral.
The
purpose of this section is to attempt to corroborate and extend those results by
examining
the actual performance of borrowers and loans on an ex past basis.
We do so by examining the net charge-offs (charge-offs minus recoveries) as a
measure of loan risk and several nonperforming
characteristics
(past due,
nonaccrual,
renegotiated
status) as measures of borrower risk. Ideally, the
analysis should take place on individual
loans, but the required data are not
reported.
Instead,
we use the semi-annual
Call Report data on all C&I
charge-offs and nonperformance
characteristics
and relate them to the proportions of secured loans issued at different points in the past on a bank-by-bank
basis. While these data do have some disadvantages
relative to the ex ante
individual
loan data in the previous section, they also have an advantage in
that the potential problems of collateral-related
monitoring
costs or other fees
affecting the loan rate are not factors here.
However, the most important
advantage of using ex post data is that it
allows us to distinguish between the risk of secured borrowers and the risk of
secured loans. Recourse against collateral reduces loan risk and net charge-offs.
However, this recourse does not necessarily help avoid a nonperforming
status
(such as loan payments past due) for the borrower, which occurs temporally
prior to charge-off. Therefore, the nonperforming
status data can help distinguish the risk of secured borrowers from the risk of their secured loans. For
instance, if secured borrowers are relatively risky on average, but their secured
loans tend to be relatively safe because of the value of recourse against
collateral (Hypothesis
H2), then secured loans should exhibit low charge-offs
but high proportions
of past dues and other nonperformance
characteristics.
Table 3 describes
the data used in the pooled time-series
cross-section
regressions
of the bank loan performance
data on present and past secured
loans
and other control
variables.
The first dependent
variable,
C&Z
CHARGE-OFFS/C&Z
LOANS, is the net charge-off ratio on C&I loans for
the semi-annual
Call Report period. This is taken to be the ex post measure of
the credit risk of the C&I loan portfolio. The next four dependent
variables,
past due 30 to 89 days, past due at least 90 days, nonaccrual,
and renegotiated
are the nonperforming
loan measures that distinguish
how well borrowers
repay their loans prior to charge-off. The two exogenous collateral variables
are much the same as in the cross-section
regressions.
The flow data on
individual
loans, however, had to be transformed
into congruence
with the
ante evaluations
35
A. N. Rerger und G. F. Udell, Colluterul, bun quali!v, and bank risk
Table 3
Individual bank data used in pooled time-series cross-section regressions.
Sample
means
Number of
observations
1.04%
2,861
2.48%
2,889
1.30%
2,889
4.88%
2.671
1.28%
2,671
Dependent variables
C&I CHARGE-OFFS/
C&l
LOANS
CL I PAST
C&l
C&I
C&I
C&I
DUE 30-89/
LOANS
PAST
DUE
r 90/
LOANS
NONACCRUAL/
C&z I LOANS
C&I
C&I
RENEGOTIATED/
LOANS
Ratio of net charge-offs to loans
for all C&I loans over a semiannual Call Report period for
an individual bank.
Ratio of past due 30-89 days to
loans for C&I loans at the time
of a semi-annual Call Report for
an individual bank.
Ratio of past due 90 or more days
to loans for C&I loans at the
time of a semi-annual Call Report for an individual bank.
Ratio of nonaccrual to loans for
C&I loans at the time of a
semi-annual Call Report for an
individual bank.
Ratio of renegotiated to loans for
C&I loans at the time of a
semi-ammal Call Report for an
individual bank.
Exogenous variables
COLLATERAL-FIXED-i
i=l ,...,12
COLLA TERA L- FLOA TING-i
i=1,...,12
FLOATING,
LNSIZE.
LNDURA TION,
COMMITMENT-TOTAL,
DEMANDNOTE,
CONSTRUCT-SINGFAM.
CONSTRUCT-MULTIFAM.
CONSTRUCT-NONRES
LNTOTALASSETS
Weighted sum of
COLLA TERA L-FIXED dummy
variable (table 1). with the
weight on each loan being its
duration times size, taken from
the ith immediately previous
STBL survey.
Weighted sum of
_a
COLLATERAL-FLOATING
dummy variable (table 1). with
the weight on each loan being its
duration times size, taken from
the i th immediately previous
STBL survey.
Weighted sum of the corresponding variables (table l), with the
weight on each loan being its
duration times sire.
Natural logarithm of the bank’s
total assets at the time of the
semiannual Call Report.
--a
“These variables are available throughout the sample, but their sample means differ across
regressions according to which observations are available for the dependent variable.
36
A. N. Berger ond G. F. Udell, Collateral, loun quality, and bank risk
Call Report balance sheet data which are compiled on a stock basis. The data
for each loan were weighted by loan size and duration in order to represent the
loan’s contribution to the bank’s future portfolio.’ The 12 quarterly lags
represent the previous 3 years of loans issued, and are taken to represent
adequately all the loans that contribute to current portfolio performance. The
control variables include all those from the cross-section that were available
throughout the sample except for CONSTRUCT-RESECURED.
The performance variables for real estate loans were not disaggregated in the Call
Report, so the construction and land development loans secured by real estate
were excluded from the pooled sample.” The log of total bank assets,
LNTOTALASSETS,
was included to account for the possibility of segmented
markets in which different sized banks have access to different types of
borrowers. This data sample primarily reflects the last few years of the sample
due to problems of availability of the dependent variables.”
Note that the signs of the effects of collateral on nonperforming loans and
charge-offs can distinguish among the three empirical hypotheses set forth in
the introduction to the paper. If both types of measures have negative signs,
then both secured borrowers and their secured loans are safer than other loans
on average (Hl). If nonperforming loans have a positive sign on collateral and
charge-offs have a negative sign on collateral, then secured borrowers and/or
their projects are inherently riskier, but the potential recourse against collateral makes their secured loans safer for the bank (H2). If both nonperforming
loans and charge-offs have positive signs on collateral, then both secured
borrowers and their loans are riskier (H3).‘*
Table 4 shows the pooled time-series cross-section regression results, with
the dependent variables again measured in basis points for expository ease.
The individual lagged collateral coefficients may be imprecisely measured
because of collinearity and because there is some discretion over the exact
timing of when loans enter the different nonperformance and charge-off
categories. The more relevant notion is the long-term or summary effects of
collateral represented by the sums of the 12 lags, COLLAT-FIXED
TOTAL
and COLLAT-FLOAT
TOTAL. These estimates suggest that fixed-rate secured borrowers and their loans have substantially poorer than average
‘For example, a $2 million loan with 3-years duration
representation
as a $1 million loan with l-year duration.
will have 6 times the average
“By the accounting
rules governing the Call Report, only the construction
ment loans that are not secured by real estate are included under C&I loans.
portfolio
and land develop-
“The charge-off data were available disaggregated
at the C&I loan level for all banks starting
in 1984, but were available earlier for large banks. The other nonperforming
categories were
available
starting at the end of 1982, and small banks did not start reporting nonaccrual
and
renegotiated
until 1985. Also, observations
from 1977 through the first half of 1979 were deleted
because of the need for lagged collateral data.
121f nonperforming
loans have a negative sign and charge-offs have a positive sign, then there is
an inconsistency,
since recourse against collateral can only reduce a bank’s credit risk.
A. N. Berger nnd G. F. Udell, Collateral, loan quality, and bank risk
37
performance, with the COLLAT-FIXED TOTAL estimates being positive for
all 5 equations and statistically significant at the 1 percent level in 4 of 5
equations. The charge-off estimate (285.75) suggests that a fixed-rate loan
being secured increases its probability of charge-off over an unsecured fixedrate loan (the base category) by more than twice the average charge-off rate
(104 basis points in table 3). Three of the four nonperforming categories also
predict an increase in problem loans of more than the mean. However, the
effect on renegotiated loans is smaller and is not statistically significant. As a
whole, the evidence on fixed-rate loans is strongly consistent with the hypothesis that secured borrowers are riskier on average than unsecured borrowers
and that recourse against collateral by the bank is not sufficient to offset this
risk (Hypothesis H3). The evidence on floating-rate loans is also consistent
with higher risk for secured borrowers and loans (H3), but less so than for the
fixed-rate loans. All 5 of the COLLAT-FLOAT
TOTAL estimates are again
positive, but they are mostly smaller than the fixed-rate effects, and only the
charge-off and nonaccrual effects are statistically significant.13
These results are fairly robust. When fixed-rate and floating-rate secured
loans are combined, the summary effects have positive signs in all 5 cases and
3 are significant at the 1 percent level. Similarly, when dummy variables are
added for each time period and when the lagged effects are limited to 2 years
instead of 3 years, the fixed-rate summary effects are positive and statistically
significant in the same 4 cases as reported, and the floating-rate effects remain
generally positive, but short of significance.‘4~‘5
Finally, the control variables are generally less significant and consistent
than in the cross-section, perhaps owing to the drastically fewer numbers of
observations. Loan size, duration, and demand notes are again generally
associated with less risk, as is bank size (total assets). Commitments are
13Note that while the Hypotheses Hl, H2, and H3 compare the secured borrowers and their
loans with unsecured borrowers and their loans, the data on unsecured loans of necessity also
contains some unsecured loans to borrowers who also have secured loans. Tbis may attenuate the
measured effect of collateral. However, this problem is not of qualitative significance here, since
Hypothesis H3 is clearly dominant in the results.
14The only robustness check in which all statistical significance was eliminated was when
dummy variables for each bank in the sample were added. Test power may be lacking in these
regressions because not many banks had substantial variation in their secured loan proportions
over this relatively short time interval.
151t is somewhat surprising that for fixed-rate loans, collateral has such strong relationships with
the ex posr risk measures and only a weak relationship with the ex ante risk premium. One
potential explanation is that, for fixed-rate loans, rates may vary substantially with other contract
terms, such as up-front fees, compensating balances, etc. Consistent with this explanation,
fixed-rate risk premia have substantially higher variance than floating-rate risk premia. Moreover,
regressions of ex post bank performance measures on lagged values of banks’ ex ante average risk
premia often yield negative, statistically significant coefficients for fixed-rate premia, suggesting
that these are poor indicators of risk. However, while floating-rate premia do a somewhat better
job of predicting ex posr performance, these premia also perform rather poorly.
0.09
2,867
0.55
-1.66
~ 1.45
1.95
- 2.90
~ 0.75
0.11
- 1.38
- 4.40
20.88
~ 10.09
- 13.37
41.79
~ 118.26”
- 180.65
22.92
~ 123.95
- 32.25”
0.14
2,889
1.14
0.76
- 1.20
1.19
-1.24
0.76
0.81
0.79
1.12
1.63
34.95
23.31
- 36.81
36.72
~ 37.80
23.53
24.53
24.55
33.22
73.97
“Significantly different from zero at the 1% level, two-sided.
hSignificantly different from zero at the 5% level, two-sided
R?
Number of observations
0.44
~ 0.62
- 2.09
0.81
- 1.87
- 0.66
- 0.38
0.19
29.20
17.87
~ 4.29
- 21.88h
19.32
- 89.26
- 241.76
- 108.26
21.37
- 42.65”
FL_OA TING
LNSIZE
LNDURA TION
COMMITMENT-TOTAL
DEMANDNOTE
CONSTRUCT-SINGFA
CONSTRUCT-MULTIFAM
CONSTRUCT-NONRES
LNTOTALASSETS
M
- 1.13
- 0.16
~0.36
0.56
~ 0.79
1.55
1.10
1.14
0.76
1.99
- 38.32
- 5.36
~ 12.16
19.06
~26.52
53.15
36.68
4.59
24.74
95.36h
COLL<ATERAL-FLOATING-4
COLL.ATERAL-FLOATING-.(
COLL‘ATERA L-FLOATING-6
COLLATERAL-FLOATING-7
COLLATERAL-FLOATING-K
COLLATERAL-FLOATING-9
COLLATERAL-FLOATING-10
COLLATERAL-FLOATING-II
COLLATERAL-FLOATING-I_?
COLLAT-FLOAT
TOTAL
27.89
- 7.60
- 36.35”
25.51
- 76.15
- 126.89
- 219.02
- 102.71
~ 44.44”
- 36.41
- 36.62
- 14.51
25.76
- 63.62h
52.01
21.67
12.27
44.58
13.70
0.24
2.889
0.71
- 1.21
- 3.84
1.16
- 1.81
- 0.51
~ 1.01
- 1.11
~ 5.88
- 1.16
- 1.16
- 0.46
0.81
- 2.03
1.63
0.70
0.39
1.46
0.29
- 1.03
0.20
- 1.17
2.42
- 2.19
- 0.97
0.09
- 0.54
- 3.97
- 145.45
4.60
~ 39.65
189.33h
~ 331.15h
- 837.36
110.19
~ 186.58
- 109.74”
0.08
2.671
-0.54
0.80
- 0.53
1.46
- 0.75
1.13
0.30
2.13
0.28
2.05
- 60.22
91.01
- 59.80
166.37
- 82.81
128.28
32.49
240.09h
30.63
340.05h
- 365.68”
- 19.52
98.44”
109.79
212.50
~ 559.84
- 136.12
~ 215.79
- 84.97”
- 30.44
111.70
- 9.52
132.91
40.40
46.87
68.31
170.36
- 57.55
169.11
0.04
2,671
- 2.76
- 0.92
3.10
1.50
1.50
~ 0.69
- 0.12
~ 0.66
- 3.27
- 0.29
1.05
- 0.09
1.24
0.39
0.44
0.66
1.60
- 0.57
1.08
COLLATERAL-FLOATING-I
COLLATERAL-FLOATING-?
COLLA TERA L-FLOATING-3
INTERCEPT
COLLATERAL-FIXED-I
COLLATERAL-FIXED--I
COLLA TERA L-FIXED-3
COLLATERAL-FIXED-I
COLLATERAL-FIXED-S
COLLATERAL-FIXED-6
COLLA TERA L-FIXED- 7
COLLA TERA L- FIXED-8
COLLATERAL-FIXED-9
COLLATERAL-FIXED-10
COLLATERAL-FIXED-II
COLLATERAL-FIXED-l.?
COLLA T-FIXED TOTAL
Variable
t-statistic
6.33
0.38
2.21
0.07
0.79
- 0.24
0.57
0.19
1.16
0.33
0.31
1.04
-0.11
4.31
0.21
0.33
0.62
Coefficient
677.01a
18.92
93.62b
3.02
34.44
- 10.06
24.56
7.65
49.84
13.28
13.07
41.71
-4.30
285.75”
7.89
10.82
20.17
C&I CHARGE-OFFS/
C&I LOANS
- 18.56
- 23.36
- 10.32
696.52a
43.90
79.02b
43.34
12.95
- 32.39
70.86
45.37
27.08
23.87
71.16b
18.03
2.04
405.22’
Coefficient
0.70
- 2.84
10.71
785.81”
29.61
- 2.44
20.41
- 9.95
- 5.86
53.58
17.07
- 15.13
43.45
27.20
13.47
- 5.49
165.91”
1.66
1.05
2.14
1.19
0.35
- 0.89
1.90
1.25
0.74
0.67
1.96
0.52
0.06
7.24
-0.54
- 0.77
- 0.34
Coefficient
4.71
- 0.33
1.30
-0.17
1.80
0.06
1.35
-0.78
1.83
1.07
0.14
1.56
0.67
5.40
1613.47”
- 54.19
185.85
- 23.67
257.66
8.42
194.97
- 109.61
259.80
147.83
19.26
211.48
90.42
1188.22’
- 29.15
-48.15
- 68.69
8.38
0.69
-0.06
0.55
- 0.26
-0.16
1.40
0.46
-0.40
1.19
0.73
0.38
-0.16
2.81
0.02
- 0.09
0.34
- 0.24
- 0.43
- 0.62
r-statistic
Coefficient
f-statistic
C&I PAST DUE > 9O/ C&I NONACCRUAL/
C&I LOANS
C&I LOANS
r-statistic
C&I PAST DUE 30-89/
C&i I LOANS
t-statistic
5.01
- 2.61
0.28
- 1.69
1.65
0.58
1.11
-1.05
0.76
0.90
-0.12
0.75
1.06
0.75
- 0.65
- 0.67
- 1.51
Coefficient
1672.27a
- 403.04”
37.03
- 223.87
222.64
78.03
150.09
- 138.18
102.14
116.76
- 15.83
95.92
133.29
154.97
- 15.51
- 70.03
- 158.38
C&I RENEGOTIATED/
C&I LOANS
Pooled time-series cross-section regressions of bank performance on collateral and control variables (dependent variables in basis points).
Table 4
40
A. N. Berger und G. F. (/dell, Collateral,
loan quality,
and hank risk
associated with slightly higher risk, contrary to the cross-section
results. The
construction
variables again are not useful indicators of risk.
In sum, the data used here consistently
suggest that collateral is associated
with higher credit risk. First, the data suggest that borrowers who pledge
collateral are riskier on average than borrowers who do not. The evidence for
this is that banks with higher proportions
of secured lending tend to have
more borrowers with nonperforming
loans (past due, nonaccrual, renegotiated).
Second, the data suggest that secured loans are riskier than unsecured loans,
that is, that the value of recourse against collateral does not fully offset the
higher risk of secured borrowers. The evidence for this is the higher risk
premia on individual
secured loans and the higher charge-off rates for banks
with higher proportions
of secured loans in their portfolios. Finally, the data
suggest that banks which tend to specialize in secured lending are riskier, as
evidenced by the aforementioned
relationship between charge-off rates and the
secured loan proportions.
5. Conclusion
This paper has analyzed the empirical relationship
between collateral and
credit risk. We have distinguished
among several types of related risk: the risk
of the borrower, the risk of the loan, and the risk of the bank. The evidence
suggests that for all three types, there is a positive relationship
between
collateral
and risk: riskier than average firms tend to borrow on a secured
basis, the average secured loan tends to be riskier than the average unsecured
loan, and banks which make a higher fraction of unsecured loans tend to have
riskier portfolios.‘6
One aspect of these findings is not particularly
surprising - in supporting
the sorting-by-observed-risk
paradigm,
the results are consistent
with the
notion that banks are capable of producing information
about borrower risk.
Perhaps more interesting,
however, is the implication
that banks use this
information
systematically
to design contracts which require that higher-risk
borrowers pledge collateral. While this tends to confirm conventional
wisdom
in the banking community,
it is not the result predicted by the majority of
theoretical
studies.
Although
the evidence
suggests that the sorting-byobserved-risk
paradigm
is empirically
dominant,
it does not rule out the
possibility
that the sorting-by-private-information
paradigm
also applies in
some cases. It also does not rule out an alternative
explanation
in which
collateral is positively associated with unobservable
risk. Nevertheless,
of the
I6 Neither our ex unte nor our ex post risk measures disentangle diversifiable from nondiversifiable loan risk. To draw conclusions about bank risk, we assume that at least some of the risk of
C&I loans are not diversifiable.
A. N. Berger und G. F. Udeii, Colluterul, loun quality, and bank risk
41
two main theories presented in the literature, the data suggest that the
sorting-by-observed-risk paradigm is empirically dominant.
On the issue of whether secured loans and banks which emphasize secured
lending tend to be relatively risky or safe, both the conventional wisdom and
the theoretical literature have generally been silent. In addition, the empirical
literature on bank risk and failure prediction has not tested collateral as an
explanatory variable. ” Consequently, our new finding that secured loans and
banks which issue them are riskier than average - with the implication that
recourse against collateral does not fully offset the higher risk of secured
borrowers - offers new insight into the lending process.
Finally, as a possible policy implication, these results would seem to suggest
that it may be appropriate to consider collateral in supervisory or regulatory
decisions. For instance, banks with very high proportions of secured loans
could be examined more frequently or supervised more carefully. However, it
is also possible that penalizing banks on the basis of collateral would reduce
loan-contracting efficiency and (paradoxically) increase bank risk. This could
occur if banks are discouraged from taking collateral on a substantial proportion of loans which would otherwise be made on a secured basis.
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