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 borrow- ers 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 borrow- ers 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

owner’s equity position’, ‘new businesses that cannot demonstrate repayment

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 theoreti- cal 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 probabili- ties, 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-private- information 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-substitu- tion-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 unobserv- able 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 compo- sition of bank assets and liabilities is accessible from a variety of public sources including the Call Report, these sources do not provide any informa- tion 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 secured- unsecured 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 Brad- street 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 consid- ered 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 follow- ing 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 repre- sented 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

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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 select- ion 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 be- cause 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 differ- ence 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, al- though 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.

Tab

le

2

Cro

ss-s

ectio

n re

gres

sion

s of

ind

ivid

ual

loan

ri

sk

prem

ia

on

colla

tera

l an

d co

ntro

l va

riab

les

(dep

ende

nt

vari

able

in

bas

is

poin

ts).

Nov

embe

r 19

87

May

19

83

Aug

ust

1981

M

ay

1977

Var

iabl

e C

oeff

icie

nt

_.

f-st

atis

tic

Coe

ffic

ient

f-

stat

istic

C

oeff

icie

nt

t-st

atis

tic

Coe

ffic

ient

t-

stat

istic

INT

ER

CE

PT

_a

_a

il

_a

CO

LL

AT

ER

AL

-FIX

ED

2.

51

0.87

0.

22

0.07

~

57.1

5h

~ 16

.28

12.3

0h

6.09

C

OL

L.A

TE

RA

L.-

FL

OA

TIN

G

46.X

5h

29.2

0 35

.71h

16

.55

38.0

0h

13.0

8 28

.77’

10

.08

FL

OA

TIN

G

63.3

5h

20.8

2 -

20.5

6’

~ 7.

58

91.8

4h

27.7

5 -

15.8

9h

- 5.

74

L>N

SIZ

E

- 20

.70h

-

56.1

0 ~

31.7

2h

- 66

.37

- 17

.12h

-

28.5

9 ~

22.1

0h

~ 46

.14

LN

DU

RA

TIO

N

- 21

.27h

-

39.6

9 1.

05

1.67

~

65.8

3h

~ 58

.1X

-

34.5

7h

- 37

.76

CO

MM

ITM

EN

T-F

OR

MA

I. ~

37.1

5h

- 19

.68

CO

MM

ITM

EN

T-I

NF

OR

MA

L

-47.

12h

-

19.4

1 C

OM

MIT

ME

NT

-TO

TA

I.

- ~-

51.8

gh

~

25.0

1 -

3.89

~

1.53

-

45.9

0h

- 24

.28

DE

MA

ND

NO

TE

-

28.8

4h

- 14

.35

8.69

’ 3.

07

-- 2

21.2

5h

~~ 35

.07

~ 12

2.89

’ ~

24.6

1 O

FF

SH

OR

E

52.0

1’

3.84

P

A R

TIC

IPA

T

ION

-

1.17

-

0.30

3.

12

0.62

P

RIM

E

22.9

8’

7.71

F

ED

FU

ND

_.

._

- 10

9.97

h

~ 28

.80

FO

RE

IGN

-

75.0

6h

- 10

.89

CO

NS

TR

UC

T-S

ING

FA

M

~

7.78

~

0.73

-

21.5

2 -

1.92

~

58.3

5h

~ 4.

49

10.8

6 1.

34

CO

NS

TR

UC

T-M

lJL

.TIF

A

M

4.57

0.

37

11.1

4 0.

78

-- 7

.89

- 0.

49

48.3

0h

3.50

C

ON

ST

RU

CT

-NO

NR

ES

-

9.71

-

0.96

~

8.89

~

0.87

-

3.48

~

0.29

45

.13h

5.

63

CO

NS

TR

UC

T-R

ES

EC

L’R

ED

7.

05

0.68

32

.66h

3.

03

22.8

0 1.

85

17.4

1‘

2.14

RI

0.33

” 0.

22”

0.24

” 0.

19”

Num

ber

of o

bser

vatio

ns

28,3

48

23,6

58

24,0

80

24,8

24

“Int

erce

pts

wer

e in

clud

ed

for

each

ba

nk

in t

he

sam

ple,

an

d th

e R

’s

refl

ect

the

prop

ortio

n of

va

rian

ce

expl

aine

d cr

fter

thes

e in

terc

epts

. hS

igni

tican

tly

diff

eren

t fr

om

zero

at

th

e 1%

lev

el.

two-

side

d.

‘Sig

nifi

cant

ly

diff

eren

t fr

om

zero

at

th

e 5%

lev

el,

two-

side

d.

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 con- cessionary 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 (COLLATERAL- TOTAL 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 fixed- and floating-rate samples (with the exception that collateral becomes signifi- cantly 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 fixed- and 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 gener- ally 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 cross-section data and results

The individual loan data in the previous section were useful for revealing ex ante evaluations 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 propor- tions 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 distin-

guish 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

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.

35

Sample Number of means observations

Dependent variables

C&I CHARGE-OFFS/

C&l LOANS

CL I PAST DUE 30-89/

C&l LOANS

C&I PAST DUE r 90/

C&I LOANS

C&I NONACCRUAL/

C&z I LOANS

C&I RENEGOTIATED/

C&I LOANS

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

Ratio of net charge-offs to loans for all C&I loans over a semi- annual 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 Re- port 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.

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 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 correspond- ing 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.

1.04% 2,861

2.48% 2,889

1.30% 2,889

4.88% 2.671

1.28% 2,671

_a

--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 perfor- mance 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 collat- eral 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 se- cured borrowers and their loans have substantially poorer than average

‘For example, a $2 million loan with 3-years duration will have 6 times the average portfolio representation as a $1 million loan with l-year duration.

“By the accounting rules governing the Call Report, only the construction and land develop- ment loans that are not secured by real estate are included under C&I loans.

“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 fixed- rate 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 hypothe- sis 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.

CO

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2.76

-

19.5

2 -

0.92

98

.44”

3.

10

109.

79

1.50

21

2.50

1.

50

~ 5

59.8

4 ~

0.6

9 -

136.

12

- 0.

12

~ 2

15.7

9 ~

0.6

6 -

84.9

7”

- 3.

27

0.04

2,

671

Tab

le 4

Poo

led

tim

e-se

ries

cro

ss-s

ecti

on r

egre

ssio

ns

of b

ank

per

form

ance

on

col

late

ral

and

con

trol

var

iabl

es (

depe

nde

nt

vari

able

s in

bas

is p

oin

ts).

Var

iabl

e

C&

I C

HA

RG

E-O

FF

S/

C&

I P

AST

D

UE

30

-89/

C

&I

PA

ST

DU

E

> 9

O/

C&

I N

ON

AC

CR

UA

L/

C&

I R

EN

EG

OT

IAT

ED

/ C

&I

LO

AN

S C

&i I

LO

AN

S C

&I

LO

AN

S C

&I

LO

AN

S C

&I

LO

AN

S

Coe

ffic

ien

t t-

stat

isti

c C

oeff

icie

nt

r-st

atis

tic

Coe

ffic

ien

t f-

stat

isti

c C

oeff

icie

nt

r-st

atis

tic

Coe

ffic

ien

t t-

stat

isti

c

INT

ER

CE

PT

67

7.01

a C

OL

LA

TE

RA

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18

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AL

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--I

93.6

2b

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TE

RA

L

-FIX

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3.02

C

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LA

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34

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CO

LL

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ER

AL

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- 10

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CO

LL

AT

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-6

24.5

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L-F

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65

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RA

L

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13.2

8 C

OL

LA

TE

RA

L-F

IXE

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0 13

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LL

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AL

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-II

41.7

1 C

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L-F

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.?

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0 C

OL

LA

T

-FIX

ED

T

OT

AL

28

5.75

”

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

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4.31

696.

52a

43.9

0 79

.02b

43

.34

12.9

5 -

32.3

9 70

.86

45.3

7 27

.08

23.8

7 71

.16b

18

.03

2.04

40

5.22

’

CO

LL

AT

ER

AL

-FL

OA

TIN

G-I

7.

89

0.21

-

18.5

6 C

OL

LA

TE

RA

L-F

LO

AT

ING

-?

10.8

2 0.

33

- 23

.36

CO

LL

A

TE

RA

L-F

LO

AT

ING

-3

20.1

7 0.

62

- 10

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1.66

78

5.81

” 1.

05

29.6

1 2.

14

- 2.

44

1.19

20

.41

0.35

-

9.95

-

0.89

-

5.86

1.

90

53.5

8 1.

25

17.0

7 0.

74

- 15

.13

0.67

43

.45

1.96

27

.20

0.52

13

.47

0.06

-

5.49

7.

24

165.

91”

-0.5

4 0.

70

- 0.

77

- 2.

84

- 0.

34

10.7

1

8.38

0.

69

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6 0.

55

- 0.

26

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6 1.

40

0.46

-0

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1.19

0.

73

0.38

-0

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2.81

0.02

-

0.09

0.

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1613

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4.

71

- 54

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- 0.

33

185.

85

1.30

-

23.6

7 -0

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257.

66

1.80

8.

42

0.06

19

4.97

1.

35

- 10

9.61

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259.

80

1.83

14

7.83

1.

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19.2

6 0.

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211.

48

1.56

90

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0.67

11

88.2

2’

5.40

- 29

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- 0.

24

-48.

15

- 0.

43

- 68

.69

- 0.

62

1672

.27a

5.

01

- 40

3.04

” -

2.61

37

.03

0.28

-

223.

87

- 1.

69

222.

64

1.65

78

.03

0.58

15

0.09

1.

11

- 13

8.18

-1

.05

102.

14

0.76

11

6.76

0.

90

- 15

.83

-0.1

2 95

.92

0.75

13

3.29

1.

06

154.

97

0.75

- 15

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- 0.

65

- 70

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- 0.

67

- 15

8.38

-

1.51

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-by- observed-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 nondiversifi- able 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 propor- tion of loans which would otherwise be made on a secured basis.

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