1

FDIC Banking Review 

Differentiating Among Critically 

Undercapitalized Banks and Thrifts* 

by Lynn Shibut, Tim Critchfield, and Sarah Bohn**

The Prompt Corrective Action (PCA) provisions

in Federal Deposit Insurance Corporation

Improvement Act of 1991 (FDICIA) require that

regulators set a threshold for critically undercapitalized institutions, and that regulators promptly

close institutions that breach the threshold unless

they quickly recapitalize or merge with a healthier

institution. Many economists expected these provisions to result in dramatically reduced loss rates,

or even zero loss rates, for bank failures. 

Bank regulators set the threshold for critically

undercapitalized institutions to 2 percent tangible

capital. There are a number of reasons why a

threshold above O percent is appropriate. Since

the value of many types of bank assets is opaque

and difficult to estimate, and since troubled banks

have an incentive to overstate asset values, it is

* Reprinted with minor edits from Prompt Corrective Action in Banking: 10 

Years Later, edited by George G. Kaufman, pp. 143–202, © 2002, with permission of Elsevier. 

not unusual for the capital levels of troubled banks

to be overstated.1 Thus a threshold slightly above

O percent may better approximate insolvency on a

market­value basis. In addition, a higher threshold may increase the likelihood that a private­sector solution can be found for a failing institution. 

Critics have complained that the 2 percent capital

threshold set by regulators is too low. For example, Benston and Kaufman (1997, p. 154) argued

that it appears to be "much too low" and should

be increased, citing as evidence the likelihood that

most banks with 2 percent tangible capital already

have negative market­value capital, the ability of

troubled banks to change risk exposure quickly by

using derivatives, and the loss rates of postFDICIA failures.2

** Lynn Shibut is the Section Chief for Consulting Services in the Division of 

Insurance and Research, FDIC. Tim Critchfield is a Senior Financial Analyst 

in the Division of Insurance and Research, FDIC. Sarah Bohn is in graduate 

school at the University of Maryland. The authors wish to thank Tyler Davis, 

Heather Gratton, Toni Holloman-Spinner, Terry Kissinger, Jennifer Merrill, and 

Katherine Samolyk for assistance, and Charles Collier, Robert Ferrer, Grovetta 

Gardineer, Alton Gilbert, Herb Held, James Marino, Rae-Ann Miller, Larry Mote, 

Dan Nuxoll, John O’Keefe, Bob Storch, Bob Walsh, and Jim Wigand for valuable comments. 

1 If troubled banks overstate asset values, their capital is artificially 

increased: this may allow them to avoid or delay adverse consequences. 

The U. S. General Accounting Office (1992) documented the problem. There 

is also ample evidence that examinations of troubled banks often result in 

increased reserve levels (which cause capital to fall). See for example Dahl 

et al. (1998) and Gunther and Moore (2000). 

2 Throughout the article, loss rates are defined as the FDIC loss divided by 

total assets of the failed bank. 

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FDIC Banking Review 

Setting the thresholds involves making trade­offs.

Peek and Rosengren (1996, p. 5O) summarized

them as follows:

It is easy to identify a problem bank at the time of

its failure. The challenge is to identify a problem

bank in time to prevent its failure or at least in

time to alter its behavior in order to limit the losses to the deposit insurance fund. Thus an appropriate slogan for early intervention might be "the

earlier the better." However, such an approach

must be tempered by giving appropriate weight to

the costs associated with supervisory intervention

in banks that are incorrectly identified as "troubled."

In this article, we studied institutions insured by

the Federal Deposit Insurance Corporation

(FDIC) that crossed the 2 percent tangible capital

threshold or failed between 1994 and 2OOO. We

separated these banks3 into four groups (low­cost

failures, high­cost failures, near­failures that survived, and near­failures that were purchased), and

we analyzed differences among the groups. If

there are consistent differences that separate failed

banks (and particularly high­cost failed banks)

from other seriously troubled banks, there may be

opportunities to improve the regulatory treatment

of troubled banks-either through a change in the

PCA threshold for a critically undercapitalized

bank or by other means.

This article begins by providing background information, including a discussion of related literature

and the tradeoffs associated with setting the

threshold for critically undercapitalized banks.

The article then discusses the data and methodology and reports the results of various comparisons

across groups. The final sections provide concluding remarks and make recommendations.

Background and Literature Review 

The PCA provisions in FDICIA were motivated

by a desire to reduce supervisory forbearance and

failure costs in the banking industry. Many peo3 Throughout this article, we use the term banks to mean all FDIC-insured 

depository institutions. 

ple, including members of Congress, believed that

regulators should have supervised banks and

thrifts differently in the 198Os. Appendix 1 provides a summary of these provisions.

Carnell (1997b) concisely described the overarching goal of PCA: "to resolve the problems of

insured depository institutions at the least­possible

long­term loss to the deposit insurance fund (i.e.,

to avoid or minimize loss to the fund)." The

means for achieving the goal center on incentives.

For banks, PCA was designed to reduce the

"moral hazard" inherent in federal deposit insurance by giving the owners and managers of troubled banks an incentive to avoid taking excessive

risks by encouraging them to maintain enough

capital and by limiting their discretion if capital is

impaired. For regulators, PCA was designed to

encourage aggressive action against troubled banks

by limiting their ability to practice forbearance

and by requiring audits after failures. PCA also

clarified the rules of the game for both bankers

and regulators.

Technically, the goal of PCA can be accomplished

by reducing either the loss rate of failed banks or

the failure rate of banks (or both). The limits triggered by the thresholds for an undercapitalized

bank focus largely on avoiding failure and thus

reducing the failure rate. In contrast, the closure

rules triggered by the threshold for a critically

undercapitalized bank focus more heavily on

reducing the loss rate by ensuring prompt closure

of nonviable banks. But the closure rules could

also reduce the failure rate by encouraging banks

to seek capital earlier than they would if closure

occurred when banks become insolvent on a bookvalue basis.4

4 Since the 2 percent threshold allows the bank only 90 days to improve its 

condition before closure, the bank’s viability at this point is almost entirely 

determined by events that occurred before it reached the threshold. If banks 

have not begun seeking capital well before reaching the 2 percent threshold, 

the 90-day time limit is tantamount to closure. However, regulators are 

allowed to delay closure for up to 270 days (inclusive of the first 90-day period) if the primary supervisor determines, and the FDIC concurs, that the delay 

would better achieve the purpose of FDICIA. The act also prescribes extremely narrow and explicit conditions for a delay beyond 270 days. 

2003, VOLUME 15, NO. 2 2 
FDIC Banking Review 

We found no empirical studies that concentrated

specifically on the 2 percent PCA threshold.

However, several studies have focused on early

intervention and the likelihood that the new

thresholds reduced regulatory forbearance. Peek

and Rosengren (1996) studied commercial banks

and savings banks in New England from 1988

through 1994, and they found that more than

two­thirds of banks downgraded to a CAMEL rating of 4 had a tangible capital ratio indicating they

were adequately capitalized under PCA. Peek and

Rosengren concluded that "examiners usually

identify problems before PCA guidelines are triggered."5 The U. S. General Accounting Office

(GAO, 1996) came to a similar conclusion in its

review of activities from 1992 through 1995. Peek

and Rosengren (1997) also studied the pattern of

formal and informal enforcement actions imposed

on commercial banks and savings banks in New

England from 1989 through 1992. They found

that "formal regulatory actions tend to occur well

before most banks become undercapitalized

according to PCA capital thresholds, and they

include restrictions on bank behavior that tend to

be more comprehensive than those in the PCA

provisions." Jones and King (1995) studied commercial banks operating from 1984 through 1989,

and they concluded that the risk­based capital

(RBC) thresholds for undercapitalized banks do

little to limit supervisory forbearance.6 Thus several authors suggest that the PCA capital thresholds do little to force earlier or stronger

intervention by regulators at the stage when the

probability of failure is most likely to be reduced

by such intervention.7

Two studies investigated changes to the RBC

threshold for undercapitalized institutions. Berger

et al. (1991) examined RBC thresholds as part of

5 Peek and Rosengren (1996, p. 49). They tested only for the 4 percent 

leverage-ratio threshold. There are two other capital thresholds that define 

undercapitalized banks, and some of the banks with leverage ratios above 4 

percent may have breached one of the other thresholds. 

6 Jones and King found that regulators have already downgraded the institutions to a CAMEL 4/5 rating before the institutions meet the RBC threshold 

for an undercapitalized institution. 

7 PCA may have had significant indirect effects on the behavior of both banks 

and regulators that were not addressed in these studies. For example, banks 

might protect themselves from the PCA restrictions by holding more capital 

than they would have held absent PCA. 

an analysis of the problems associated with market­value accounting proposals. Noting that the

GAO (199O) had found that some banks in poor

condition underreport their loan­loss reserves,

they explored several alternative RBC standards

that incorporated adjustments to the loan­loss

reserve based on nonperforming loan data. Using

Call Report data for all banks from 1982 through

1989, they developed statistical methods for estimating alternative loan­loss reserves. They used

the revised loan­loss reserve figures to adjust the

RBC ratios for all banks as of year­end 1989, and

compared the adjusted results with the actual

RBC ratios. All of their alternative measures

resulted in substantial increases in the number of

banks that would have been classified as undercapitalized.8 They concluded that the revision

would expand and probably improve the distribution of regulatory scrutiny. 

Jones and King (1995) developed an alternative

RBC threshold by using data on classified assets

and noncurrent loans to enhance the current

threshold. To measure the effectiveness of their

alternative, they estimated the prediction error of

the actual and revised RBC standard, assuming

that all troubled institutions should be classified as

undercapitalized under the RBC standard.9 Their

revised RBC standard resulted in a significant

reduction in the prediction error. 

These studies suggest that an investigation of the

tradeoffs associated with the 2 percent threshold

might bear fruit. If one can identify the tradeoffs

related to the threshold, consider the costs inherent in each, and consider the effects of changing

the threshold, one can make a well­informed judg8 Depending on the method used, from 214 to 314 banks would have been 

adequately capitalized under the RBC standards in place but would have been 

undercapitalized under the revised RBC standards. Very few banks that were 

undercapitalized under the RBC standard in place at that time would have 

become adequately capitalized under the revised method. The authors normalized the RBC levels across banks so that they captured only differences in 

the distribution of RBC across banks, instead of a combination of an 

increased standard and changes in the distribution across banks. 

9 They used three definitions for troubled institutions: (1) all institutions with 

a CAMEL rating of 4 or 5, (2) all institutions with tangible capital below 2 

percent, and (3) all institutions that met either of the first two standards. 

Their estimates of prediction error assumed that all such institutions should 

be classified as undercapitalized and that all other institutions should not. 

3 2003, VOLUME 15, NO. 2 
 

FDIC Banking Review 

ment about the threshold that provides the highest net benefit to society.1O In this article, we are

most interested in the potential costs and benefits

associated with a marginal change in the 2 percent

threshold for critically undercapitalized institutions.11 We examine the consequences of thresholds that identify failure candidates either before

or after the optimum time. 

We first review the costs associated with the delay

of closure. Theoretically, this includes the operating costs of running an insolvent institution, plus

any additional costs associated with risks taken by

the banks in hope of surviving. Gilbert (1992)

analyzed commercial banks and concluded that

both of these costs may not be very high. Based

on a review of commercial bank failures from 1985

through 199O, he found no statistically significant

differences in the loss rates of BIF­member banks

that were undercapitalized for different lengths of

time before closure. Thus, whether PCA encourages regulators to close undercapitalized banks

earlier (or later), Gilbert predicts that the loss

rates do not change significantly. However, the

U.S. savings­and­loan crisis and Japan's banking

crisis provide ample evidence that these costs can

be extremely high-particularly if regulators do

not carefully monitor undercapitalized banks and

limit their activities, or if the delay lasts a long

time.

One might argue that the costs of delay can be

calculated as the cost of failure (for both the

FDIC and other creditors of the receivership),

since regulators have the authority to close banks

when their market value is zero.12 However, a

large part of these costs is probably attributable to

the difficulties of measuring the market value of a

troubled bank, large shifts in market value, or

fraud, rather than the capital threshold used for

10 The literature on public finance supports the assumption that government 

intervention should strive to maximize the net benefit to society. Therefore, 

when setting PCA thresholds, banking regulators should not focus solely on 

minimizing losses to the insurance funds. 

11 More general discussions of the benefits of PCA can be found in Carnell 

(1997a) and Mishkin (1997). 

12 Regulators have the authority to close banks when they are operating in an 

unsafe or unsound manner, regardless of capital levels. Presumably any bank 

with no market value would meet this criterion. 

PCA.13 It is very difficult to separate losses attributable to measurement errors inherent in certain

assets or market shifts from losses attributable to a

delay in closure caused by a sub­optimal PCA

threshold-especially since regulators have the

authority to define capital (effectively changing

accounting standards) as well as choose the capital threshold.14 The cost associated with fraud

may be somewhat easier to ascertain, but one

should not automatically assume that there were

no delays in closure for bank failures that involved

fraud.

Next, we look at the consequences of a PCA

threshold that flags failure candidates too soon.

The associated costs differ depending on the outcome of the PCA action. Possible outcomes

include

•	 The bank is closed.

•	 The bank survives, either with or without a
 

capital infusion.
 

•	 The bank is quickly sold to another institution.

If the bank is closed, the costs are relatively high.

The owners suffer a loss of the freedom to control

their assets and the stigma of failure. They also

bear costs that would have been avoided if the

bank had stayed open.15 In some circumstances,

13 Several authors have noted the difficulties in measuring capital at banks. 

See, for example, GAO (1992). 

14 Regulators are not allowed to set standards that are less stringent than 

GAAP. Under Section 37 of the FDI Act, regulators have the authority to set 

standards that are more stringent than GAAP. Note, however, that taking 

such an action introduces new costs into the equation (including a new regulatory burden for banks and reduced comparability of bank financial reports 

and information from other related industries) and thus should not be undertaken lightly—particularly if alternative means of meeting the objective are 

available. 

15 Benston and Kaufman (1997, p. 146) argue that the closure of an institution that is solvent on a market-value basis “would not be a ‘taking’ by the 

government as any remaining capital would be returned to the shareholders.” 

From a legal standpoint, they are probably correct. However, there are real 

costs, including the overhead expenses associated with administering a 

receivership, prepaid expenses that are rendered worthless at failure, transaction costs associated with transferring information from the seller to a buyer, 

and any lost asset value associated with the stigma of failure. Owners could 

potentially be subject to a Directors and Officers claim as well. In addition, 

the FDIC must pay the cost of administering insurance and preparing the resolution. From an economic standpoint, these costs should be considered 

when an increase in the PCA threshold is being contemplated. 

2003, VOLUME 15, NO. 2	 4 
 

FDIC Banking Review 

the bank's customers or the local community

could suffer.16

If the bank survives intact, then the costs to the

owners may be very low.17 If the bank reaches the

2 percent threshold and the owners sell the bank

to another institution, then the costs are probably

somewhere between those of the other outcomes.18

The dynamic effects of the PCA threshold should

also be considered. For some banks, the PCA

threshold for critically undercapitalized institutions increases the bank's resolve to act decisively

or accelerates a search for new capital or potential

acquirers. If the PCA threshold triggers a more

effective response by the bank, and the improvement in the bank's response causes the bank to

avoid failure, then the PCA threshold would yield

a net benefit to the bank (and, most likely, to the

FDIC).19

Under the current PCA threshold, we see little

evidence that viable banks have been closed; thus

to date the costs of unnecessary closure have

probably been negligible or zero.2O It is more diffi16 Some of the bank’s creditors could suffer losses if contracts are abrogated 

or if the receivership does not pay creditors as much as they would have 

received had the bank stayed open. In addition, if the bank was not located 

in a competitive market, the bank’s customers (and others in the community) 

may not be able to obtain credit or make deposits at equivalent terms. 

These effects could, in turn, harm the local economy more broadly. This 

appears to be most likely for small, rural banks in situations where the FDIC closes the bank with no acquirer (a payout) or where the acquirer has less 

interest in the local community than the failed bank. See Gilbert and Kochin 

(1989) for an analysis of these effects. 

17 If crossing the 2 percent threshold did not cause any material change in 

strategy, the cost would probably be negligible. If there was a change in 

strategy that improved the allocation of capital, there could be an economic 

benefit; if the opposite occurred, there could be an economic loss. But these 

results relate to the PCA threshold to the extent that the threshold (rather 

than the weak condition of the bank) caused the change. In addition, for the 

most part, the owners would retain the freedom to control their assets. 

18 Like owners that suffer from closure, owners of a bank that was purchased 

would lose the freedom to control their assets, and they would have to pay 

the transaction costs associated with transferring information to a buyer. 

Because of the 90-day deadline, they might also be required to sell the franchise for a below-market price. 

19 Stronger action by the bank could also yield improvements to the bank 

owner’s earnings. 

20 There have been some receiverships with a surplus. However, a surplus 

can occur for reasons other than an avoidable closure. For example, it can 

occur because of positive developments in the markets after failure or 

because the FDIC was able to reverse or mitigate problems experienced at the 

bank (such as excessive employee compensation or self-dealing on the part of 

management). Shibut and Critchfield (2000) found no evidence of wrongful 

closure in their review of low-cost failures. 

cult to gauge the net cost for the other two outcomes (survival or a quick merger) because they

involve predicting the relationship between the

threshold level and outcomes of troubled banks,

estimating the associated economic effects, and

balancing the economic effects against the lost

freedom experienced by the bank owners.

The optimum threshold for critically undercapitalized banks may well vary across the business cycle.

Because banks that breach the PCA threshold

have such a short time to recapitalize, it appears

likely that the costs imposed by a threshold that

was set too high would be heavier during times

when the market for bank franchises is weak, and

lower during times when markets are strong.21

The cost of lengthy delays can be extremely high,

although the cost of brief delays (if accompanied

by close supervision) may be low. In some unusual

circumstances, the cost of identifying banks too

early could be substantial. For example, the application of PCA thresholds on money­center banks

during the less­developed­country crisis in the

early 198Os might have caused serious damage to

the economy.22 Thus the GAO (1996, pgs.

56-57) states that "we see the issue as one of

striking a proper balance between the need for sufficient regulatory discretion to respond to circumstances at a particular institution and the need for

certainty for the banking industry about what constitutes an unsafe and unsound condition and

what supervisory actions would be expected to

result from those conditions." Mishkin (1997) discusses the need for discretion in unexpected situations that involve systemic risk.23 In more normal

circumstances, the cost of identifying banks too

early would be much lower. 

21 The tradeoff may also be influenced by the nature of the problems experienced by the banking industry. For example, a PCA threshold that is optimum for banks undergoing a regional recession may not work as well for 

stresses associated with high interest rates or subprime lending. 

22 Federal Deposit Insurance Corporation (1997, chapter 5). While it appears 

that the sudden application of PCA rules as the crisis mounted would have 

had terrible consequences, it is difficult to gauge what the results might have 

been if PCA triggers and the current accounting rules had been in place long 

enough to alter the banks’ behavior well in advance of the crisis. 

23 He states that the systemic-risk exception in FDICIA allows regulators 

enough discretion to avoid excessive costs associated with the PCA thresholds. Thus concerns about extreme situations need not be considered unless 

they do not involve systemic risk. If, however, an unexpected event were to 

make the PCA threshold inappropriate for a large number of banks at the 

same time, the systemic-risk exception would become untenable. 

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FDIC Banking Review 

Few studies have attempted to quantify the net

cost or benefit of changing the capital thresholds

for closure. The FDIC (1997, chapter 12) made

an admittedly very rough estimate of the benefits

if PCA had been in effect from 198O through 1992

by assuming that a failing bank's operating losses

(excluding loan­loss provisions) would have been

avoided if closure had occurred according to the

PCA rules. The authors of the FDIC study found

that 343 failed banks would have been closed earlier, yielding a cost savings of about $825 million.

They identified 143 banks that breached the 2

percent PCA threshold but did not fail, but they

made no attempt to estimate the associated costs. 

Barakova and Carey (2OO1) shed some light on

these tradeoffs in their analysis of the characteristics of banks that recover from distress and the

pace of their recovery.24 Using data from FDICinsured commercial banks from 1984 through

1999, they identified 345 banks whose equity

ratios fell below 2 percent but did not fail. Only

51 percent of those banks recovered in one year,

71 percent recovered in two years, and 91 percent

in four years.25 They found that the typical

sources of bank recapitalization differed depending

on the recovery time frame. Equity infusions

played a substantial role regardless of the recovery

time frame, but for banks that recovered more

slowly, the role of equity infusions was smaller and

the role of income larger. They concluded that

regulators should insist on the rapid issuance of

sufficiently large amounts of capital if they want a

rapid recovery of a seriously undercapitalized

bank. This analysis indicates that if an increase in

the PCA threshold causes viable banks to be classified as critically undercapitalized, it may well

force some banks to merge (or possibly even fail)

that would otherwise survive intact.

Barakova and Carey also found that most distressed banks did not issue new equity until after

loan losses began to decline-both before and

after FDICIA, perhaps because the cost of equity

is prohibitive when significant loan losses are

24 The study concentrated on the pace of recovery and the implications for 

selecting an appropriate time horizon for Value-at-Risk (VaR) models. 

25 Recovery was defined as reaching a leverage ratio of 5 percent. 

being reported. This indicates that viable banks

that breach the threshold while their loan losses

have not clearly declined may be most likely to

suffer from the costs associated with a PCA

threshold above the optimum level. 

By analyzing groups of critically undercapitalized

banks since FDICIA, we provide insight into the

effects of the 2 percent threshold to date, and we

highlight areas where changes in the threshold

could provide a net benefit to society. Note, however, that the best regulatory change may be something other than a change in the PCA

threshold-particularly if the differences between

the outcome groups are unrelated to capital. 

Data and Methodology 

This section discusses the data and methods used

for analysis.

Sample Selection

For 1994 to 2OOO, we analyzed the bank failures

plus any bank that fell below the 2 percent capital

requirement established in FDICIA. We began in

1994 because the FDICIA provisions aimed at

reducing the FDIC's losses would have been fully

implemented by then. For failed institutions, we

segregated those with relatively low resolution

costs from other failures. For each institution that

fell below 2 percent tangible capital without failing, we looked to see what happened to the institution after getting into capital trouble. We found

that about one­half of this group was absorbed by

other organizations and one­half survived independently. 

There were 48 failures from 1994 through 2OOO.

Three notable failures were eliminated from our

analysis because of massive fraud or other extraordinary circumstances.26 We labeled 16 failed

26 The Meriden Trust and Safe Deposit Bank, Meriden, Connecticut, was eliminated because it had no insured deposits and was part of a cross-guaranty 

case. The First National Bank of Keystone, Keystone, West Virginia, and BestBank in Boulder, Colorado, were eliminated because the fraud involved in 

these failures was large enough to seriously distort their financial reports. 

2003, VOLUME 15, NO. 2 6 
 

FDIC Banking Review 

institutions with a resolution cost of less than 12

percent of assets as low­cost failures. This left 29

institutions with an estimated resolution cost of

over 12 percent of assets. We selected 12 percent

as the cutoff for low­cost failures because it was

well below the average loss rate experienced by

the FDIC and also provided a reasonable sample

size. We used two different reference periods to

analyze the data: for the 32 institutions that

became critically undercapitalized before failure,

we used the date when they breached the threshold; for the remaining 13 institutions, we used the

final Call Report date.27 For the remainder of the

article, the analysis date refers to the date an institution fell below the PCA capital limit (although

in fact for a few institutions it refers to the failure

date).28

We found 44 institutions that fell below 2 percent

tangible capital from 1994 to 2OOO and that did

not fail as of the fourth quarter of 2OO1. Five

institutions in this group were eliminated because

of special circumstances.29 The remaining 39

institutions consisted of 21 institutions that were

absorbed into another organization within one

year of breaching the threshold and 18 institutions

that survived intact for at least one year.3O We

refer to the total group of 39 as near­failures.

27 We use the term Call Report as shorthand for both the Call Report and the 

Thrift Financial Report. 

28 All but 7 of the 32 institutions that fell below the threshold failed within 

one year. Three institutions fell below the PCA threshold more than one year 

but fewer than three years before failure, and for them we decided to use 

the PCA date for our analysis. Four institutions fell below the PCA threshold 

more than three years before failure, but because they fell below the PCA 

threshold between 1990 and 1993 (before PCA was fully implemented), we 

decided to use their failure date. 

29 (1) New Meridian Trust was excluded because it was a bridge bank created by the FDIC to operate a failed institution until a buyer could be found. 

(2) Valley First Community Bank, Scottsdale, Arizona, reported a capital level 

below 2 percent on its very first regulatory report after establishing its operations. (3) Chicago-Tokyo Bank, Chicago, Illinois, was held by a foreign bank 

holding company and it reported virtually zero assets on its last report. (4) 

Suntrust Bankcard, National Association, Orlando, Florida, had one quarter during a downsizing that produced a low capital ratio because the denominator 

was based on average assets that did not reflect the end-of-period level of 

assets. (5) Continental Savings Bank, Seattle, Washington, filed a report with 

a missing value for capital in one quarter. 

30 Of the 21, 14 were absorbed by mergers that eliminated their charters and 

7 were purchased by a holding company. 

Pre-PCA Condition Data

We collected a large volume of data from Call

Reports and Thrift Financial Reports (TFR) on all

the institutions selected. We constructed variables from both reports that correspond to consistent measures of financial performance for the

two­year period before an institution failed or fell

below 2 percent tangible capital; for institutions

that survived after reaching the threshold, we also

collected data for the year after they fell below 2

percent tangible capital. We developed annualized performance ratios from a rolling set of the

previous four quarters. This calculation allowed

us to create aggregate data from any ending quarter that would contain annual ratios that covered

all four seasons, and this tended to smooth the

effects of sudden changes in accounting or performance.

Examination data were collected for each institution from any full examination over the three

years immediately before failure or the three years

before an institution fell below 2 percent tangible

capital. Classified loans were taken from the last

full examination before the institution fell below

the PCA capital limit. These loans were measured

against assets reported in the quarter after the

examination was completed or, if a failed institution did not file its last report, the previous quarter.31

To measure local bank distress, we created a problem bank index based on CAMELS 4­ and 5­rated

banks in each metropolitan statistical area (MSA)

or county. The index represents the percentage of

deposits in each bank's location (or locations) that

were held by problem banks.32

31 There were 6 institutions out of the 84 institutions studied that failed to 

file a financial report in the quarter after their last full examination before 

falling below the PCA capital limit. 

32 To create a problem bank index we summarized the branch-level deposits 

of all FDIC-insured institutions that were rated CAMELS 4 or 5 for every market which was located in a metropolitan statistical area (MSA) or county for 

any county that was not in an MSA. We divided this sum by the total 

deposits for the MSA or county and then multiplied these percentages by the 

percent of each institution’s deposits located in each market. Then we 

summed the totals for each market that an institution had a presence in for 

the mid-year before the institution fell below the PCA capital limit of 2 percent or before failure. 

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FDIC Banking Review 

We also used branch­level data to create a 

geographic distribution index, similar to the

Herfindahl­Hirschman Index (HHI). Instead of

measuring market concentration, our geographic

index measures an institution's exposure to each

market in which it operates (from O, complete

diversification, to 1, no diversification).33

To test for understated reserve levels, we followed

the technique adopted by Jones and King (1995).

Jones and King calculated an adjusted reserve

level based on a pooled time­series regression,

where charge­offs over a two­year period were

regressed against the amount of classified loans as

of the beginning of the two­year period. The

resulting equation is as follows:34

Two­year Charge­offs = O.OO6 + O.95*Loss 

+ O.57*Doubtful + O.15*Substandard 

+ O.O4*Special Mention
 

Jones and King estimated the appropriate reserves

by applying these parameter estimates to the classified assets held by each bank in the sample

(implicitly assuming that reserves should cover

about two years of charge­offs).35 We compared

the total from this calculation with the reserves

reported in the financial reports filed in the quarter closest to the examination date. 

Resolution and Receivership Data

For each failed institution, we collected data about

the resolution and the receivership. The resolution data came from FDIC Board cases and associ33 This was calculated as of the middle of each year before the institution 

falls below the PCA capital limit using data from the annual Summary of 

Deposits. We calculated the percentage of an institution’s deposit in each 

market that it operates and then squared these percentages. Then we 

summed the squared percentages for all markets in which an institution operates so that the results ranged from 0 to 1. This measure was based on the 

work of Katherine Samolyk. 

34 All parameter estimates were statistically significant at the 1 percent level. 

The R2 was .33. The sample included commercial banks during 1984–1989. 

35 Under GAAP, the allowance for loan-and-lease losses (ALLL) should cover 

probable estimated credit losses in the loan portfolio, not a specified number 

of years of expected charge-offs. 

ated working papers; the data included the number of bidders, the premiums for the top two bids,

and the FDIC's estimated loss at the time of failure. The receivership data came primarily from

the FDIC's general ledger, the FDIC Failed Bank

Cost Analysis, and pro forma financial statements

prepared at bank closing. For the most part, we

reported results over the life of the receivership;

for receiverships that had not terminated as of

year­end 2OOO, results through year­end 2OOO were

used.36 To improve comparability across banks,

we reported many items as a percentage of total

assets at failure. The FDIC general ledger was the

source for asset loss rates, assets sold to acquirers,

balance sheet composition (including adjustments

to the balance sheet made during the receivership), and receivership income and expense items.

The primary source for the FDIC loss rates used in

this study was the Failed Bank Cost Analysis,

which relies on FDIC general ledger data. Appendix 2 includes a discussion of the method we used

to calculate FDIC loss rates.

We also collected receivership data from the pro

forma statements prepared during the institution

closings. These provided the banks' balance

sheets as of the date of failure, adjustments made

to the balance sheet to switch from Generally

Accepted Accounting Principles (GAAP)

accounting to the cash­basis accounting used by

the FDIC's receiverships, and the initial balance

sheet recorded on the FDIC's general ledger. 

The accounting differences between ongoing institutions and receiverships are substantial; thus any

comparison of pre­failure and post­failure results

must be done carefully. Appendix 2 discusses the

major accounting differences, as well as certain

adjustments we made to improve the comparability of the data across institutions and across time.

36 Of the 45 receiverships, 32 (71 percent) had terminated by year-end 2000. 

As of year-end 2000, the remaining receiverships held $79 million in assets 

for sale, which was 6.8 percent of the assets held by these receiverships at 

failure. 

2003, VOLUME 15, NO. 2 8 
FDIC Banking Review 

Statistical Analysis Methods

With the wealth of data collected for the institutions chosen for this study, we required a statistical

test for finding significant differences in measures

of central tendency for any two of the institution

groupings. We were limited by the relatively low

number of institutions in each group, since the

study period was only 1994-2OOO. The groups

ranged in size from 16 to 45. In addition, many of

the financial ratios in this study tended not to be

distributed normally. Although a two­sample t­test

is robust against minor normality departures, this

study's data contain extreme outliers and are highly skewed. Thus, the reliability of a simple parametric test is suspect.37 In this study, a

nonparametric analysis appears more appropriate

since it does not involve assumptions of normality

and is not sensitive to the presence of outliers.

We used the Wilcoxon rank sum test to compare

the location of two given distributions with the

same general shape, which is essentially a difference of medians test. Given a financial or other

ratio and two groups of institutions, the Wilcoxon

statistic tests the null hypothesis that the two

groups have the same probability distribution versus the alternative hypothesis of a location shift

(that is, a difference in medians). The test provides a p­value for use in determining the conclusion of the test, given a level of significance. We

are particularly interested in financial ratios for

the compared groups that are statistically different

at the 1 percent, 5 percent, and 1O percent significance levels. 

In addition to the Wilcoxon test, we applied the

Kendall's tau test (test of independence) to the

failed bank data. As explained above, failures

were split into two groups-high­cost and lowcost-for the purpose of refining the differentiation between undercapitalized institutions.

Dividing the failures into the two groups involved

an educated but somewhat arbitrary selection of a

37 In fact, comparisons of simple averages for analysis were unsatisfactory 

because of outliers, and comparisons of weighted averages were unsatisfactory because they tended to represent only the largest banks in the sample. 

Thus, we relied most heavily on medians, distributions, and the Wilcoxon test 

for analysis. 

loss rate threshold. Although the group­by­group

analysis is valuable especially for comparing failures with near­failures and low­cost failures with

near­failures, we also chose to study the correlation of failure loss rate with other variables for the

entire set of institutions in the study. The

Kendall's tau test involves computing a correlation

statistic based on paired­sign statistics. Like the

Wilcoxon test, this non­parametric test is robust

to outliers and does not involve assumptions of

normality. The test provides a sample correlation

coefficient (ranging from -1 to 1) and a p­value

testing the null hypothesis of independence of two

variables versus the alternative hypothesis that the

two variables are correlated. 

Results 

We begin with a comparison of failures and nearfailures. This is followed by comparisons of lowcost and high­cost failures, low­cost failures and

near­failures, near­failures by outcome (survived

versus purchased), and surviving near­failures over

time (PCA date versus one year later). Most of

the comparisons were based on the PCA date,

defined as the quarter­end date when the institution breached the 2 percent threshold; for failed

institutions that never breached the threshold, the

final Call Report date was treated as the PCA date.

Failures versus Near-failures

This section discusses failures compared with nearfailures that breached the PCA threshold. Table 1

provides the comparison of failures and near­failures as of the quarter that the institutions

breached the PCA threshold; table 2 provides the

same comparison one year earlier. The 45 failures

in our study held $4.1 billion in assets as of the

PCA date, and the near­failures held $9.O billion

in assets at the PCA date. The average size for

the failures was just $92 million, while the average

size for the near­failures was more than twice as

large ($23O million). However, the difference in

the median size was much smaller and statistically

insignificant.

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Table 1	 As of the last examination

Comparison of Failures and Near-Failures
 

Failures Near-Failures Difference 

AS OF THE PCA DATE 

Number of Institutions 45 39 (6) 

Median Total Assets ($MM) 64 57 (7) 

Median Asset Growth (12.61) (8.52) 4.09* 

Institutions with CAMELS Rating of 4 or 5 (%) 93.30 84.60 (8.70) 

Median Balance Sheet Percentages 

Investment Securities 9.40 11.51 2.11 

1-4 Family Mortgages 11.08 16.38 5.30 

Commercial Real Estate 16.27 11.03 (5.24) 

Multifamily Real Estate 1.40 1.04 (0.36) 

Commercial & Industrial Loans 10.91 11.84 0.93 

Deposits 97.00 98.00 1.00 

Median Rolling Four-Quarter Performance Ratios 

Return on Assets (5.62) (5.76) (0.14) 

Return on Equity (124.93) (124.24) 0.69 

Yield on Earning Assets 8.40 8.77 0.37 

Cost of Funding Earning Assets 3.85 4.37 0.52 

Net Interest Margin 4.62 4.69 0.07 

Noninterest Income to Earning Assets 1.58 1.12 (0.46) 

Noninterest Expense to Earning Assets 8.19 6.66 (1.53)* 

Net Operating Income to Assets (5.60) (6.04) (0.44) 

Net Charge-offs to Loans 3.44 3.98 0.54 

Loss Provision to Average Assets 3.37 3.58 0.21 

Loss Provision to Net Charge-offs 120.18 122.95 2.77 

Efficiency Ratio 135.07 123.03 (12.04) 

Median Condition Ratios 

Loss Allowance to Loans 4.37 4.50 0.13 

Loss Allowance to Noncurrent (Coverage Ratio) 43.00 63.98 20.98** 

Noncurrent & OREO to Assets 9.93 6.69 (3.24)* 

OREO to Assets 2.47 0.80 (1.67)* 

Noncurrent RE Loans to RE Loans 7.97 3.54 (4.43)** 

Noncurrent Loans & Leases to Loans & Leases 9.54 7.24 (2.30) 

Noncurrent Loan Growth 31.20 29.44 (1.76) 

Equity Capital Ratio 1.39 0.97 (0.42) 

Core Capital (Leverage) Ratio 1.28 0.87 (0.41)** 

Equity Capital plus Reserves to Assets 4.22 4.03 (0.19) 

AS OF MID-YEAR BEFORE PCA DATE, OR MOST RECENT EXAM 

Median Economic Condition Measures 

Problem Bank Index 0.08 0.02 (0.06) 

Geographic Diversification Index 1.00 1.00 0.00 

Median Examiner Classification Ratios 

Reserves to Estimated Reserves 101.34 94.81 (6.52) 

Classified Assets to Assets 13.00 10.47 (2.52) 

before failure, 93 percent of the

failures and 85 percent of the

near­failures had a CAMELS

rating of 4 or 5. None of the

institutions had a CAMELS

rating of 1, but six institutions

were rated 2; only one of the

six failed.

The median ratios calculated

for condition and performance

of each group revealed a few

differences but for many items

there was no meaningful difference. At the quarter when the

failures fell below the PCA

threshold, their condition was

predictably worse than that of

the near­failures in most

respects except capital. The

failures had higher median

equity capital ratios than the

near­failures (1.39 compared

with O.97 percent), and statistically significant higher core

capital (leverage) ratios (1.28

versus O.87 percent). The 13

failures that had capital above

the PCA threshold appear to

have lifted the core capital

ratio for the entire group of

failed institutions, which would

explain the significant differences in capital ratios.38

For both groups, median capital declined precipitouslyover 4OO basis points-during

the year before they breached

the PCA threshold or failed.

Note: For failed institutions that did not file a Call Report or TFR showing that they were critically undercapitalized, 

the Final Call Report or TFR was used.	 38 Throughout the section on results, we use 

the terms significant and insignificant to mean * Significant at the 10% level. statistically significant and statistically insignifi­ ** Significant at the 5% level. cant. 

*** Significant at the 1% level. 

2003, VOLUME 15, NO. 2	 10 
 

 

 

 

 

 

 

 

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Table 2 in securities while the near­failComparison of Failures and Near-Failures 

Failures Near-Failures Difference 

ures held 12 percent. For the

failed institutions this ratio

held at 9 percent for the year

AS OF ONE YEAR BEFORE THE PCA DATE before they fell below the PCA

Number of Institutions 45 39 (6) threshold, but the near­failures

Median Total Assets ($MM) 71 59 (12) actually increased their median Median Asset Growth (4.21) 0.34 4.55 value for the percentage of Institutions with CAMELS Rating of 4 or 5 (%) 77.80 64.10 (13.70) assets held in securities from 1O

Median Balance Sheet Percentages percent in the year before Investment Securities 8.93 10.35 1.42 falling below the threshold. 1-4 Family Mortgages 12.20 15.28 3.08 The composition of the loan Commercial Real Estate 14.38 10.76 (3.62)
 

Multifamily Real Estate 1.60 1.00 (0.60) portfolios for the failed instituCommercial & Industrial Loans 10.07 8.85 (1.22) tions appeared slightly more
 Deposits 92.00 92.00 0.00 risky than for the near­failures,
 

Median Rolling Four-Quarter Performance Ratios but again none of these differReturn on Assets (1.20) (0.72) 0.48 ences were significant. The
 Return on Equity (18.01) (12.89)
 5.12 median value for the percent­ Yield on Earning Assets 8.76 8.46 (0.30) age of assets held in 1­ to 4­ Cost of Funding Earning Assets 3.94 4.04 0.10 family mortgages was 11 percent Net Interest Margin 4.93 4.68 (0.25)* 

Noninterest Income to Earning Assets 1.22 1.54 0.32 for the failed institutions while

Noninterest Expense to Earning Assets 6.10 6.21 0.11 the near­failures held 16 perNet Operating Income to Assets (1.27) (0.75) 0.52 cent of this typically less risky Net Charge-offs to Loans 1.40 1.48 0.08 loan. For commercial real

Loss Provision to Average Assets 1.46 1.21 (0.25) estate (excluding multifamily Loss Provision to Net Charge-offs 123.47 129.06 5.59 residential property), a general­ Efficiency Ratio 97.83 90.36 (7.47) ly riskier loan type that caused Median Condition Ratios many failures in the 198Os, the Loss Allowance to Loans 2.62 2.07 (0.55)
 

Loss Allowance to Noncurrent (Coverage Ratio) 44.74 43.47 (1.27) median value was 16 percent
 

Noncurrent & OREO to Assets 8.56 4.62 (3.94) for the failures while the nearOREO to Assets 2.66 1.08 (1.58) failures held just 11 percent.
 Noncurrent RE Loans to RE Loans 4.10 3.14 (0.96)
 For the failed institutions, the Noncurrent Loans & Leases to Loans & Leases 5.62 4.33 (1.29) 16 percent actually constituted Noncurrent Loan Growth 10.64 3.92 (6.72) an increase in the median Equity Capital Ratio 5.85 6.28 0.43 

Core Capital (Leverage) Ratio 5.71 5.94 0.23 value for commercial real

Equity Capital plus Reserves to Assets 8.14 7.77 (0.37) estate (from 14 percent) during

Note: For failed institutions that did not file a Call Report or TFR showing that they were critically undercapitalized, the year before falling below

the Call Report or TFR one year before the Final Report was used. the PCA threshold. This

* Significant at the 10% level. increase may relate to the ** Significant at the 5% level. 

*** Significant at the 1% level. higher median shrinkage in

total assets experienced by the

This provides evidence that a modest increase in the PCA threshold failures in the year preceding

may not result in a the PCA date (12.61 percent substantial change in the timing of the associated versus 8.52 percent), as they regulatory actions for institutions that breach the threshold. sold off more marketable assets

The portfolio composition measures showed median values that were to improve their capital ratios.

generally worse for the institutions that eventually failed, but the differences in medians were statistically insignificant. On the balance The patterns in troubled loans

sheet, the failed institutions held a median of just 9 percent of assets showed more pronounced dif11 2003, VOLUME 15, NO. 2 
FDIC Banking Review 

ferences between the two groups. The level of

nonperforming assets was significantly higher for

failed institutions than for near­failures. For failed

institutions, the median value for the noncurrent

loans plus other real estate owned (OREO) as a

percentage of assets was 9.93 percent at the PCA

date. The near­failures reported a median level of

6.69 percent of assets, which was 3.24 percent less

than the failed institutions reported. One year

earlier the failures registered a median of 8.56 percent while the near­failures were at 4.62 percent,

but the difference was not statistically significant.

The source of higher nonperforming assets was

real estate loans: at the PCA date, the median

ratio of noncurrent real estate loans to total real

estate loans was 7.97 percent for failures-significantly higher than the 3.54 percent median value

for near­failures. Moreover, the failures held significantly more OREO (2.47 percent of assets for

the failures versus O.8O percent for the near­failures, as of the PCA date). For commercial and

industrial (C�I) loans the differences were not as

dramatic nor were they significant, but the problems in these loans arose earlier for the failures.

The failed institutions increased to a median noncurrent rate of 8.73 percent for C�I loans when

they hit the PCA threshold from 5.46 percent one

year earlier. The near­failures experienced a

sharper change, from 1.7O percent one year before

hitting the PCA threshold to a median noncurrent

rate of 7.23 percent at the PCA date. However, in

neither period was the median growth in noncurrent loans significantly different between the

groups.

The differences in nonperforming assets across the

groups were not accompanied by significant differences in loan provisions and charge­offs. The

charge­off rates were very similar for the two

groups, with the near­failures recording slightly

higher rates than the failures in both years. In the

last four quarters the median net charge­off rate

on loans more than doubled from one year earlier

for both groups. Loan­loss provisions (as a percentage of average assets) showed a similar

increase over time. In both periods, the ratio of

loss provisions to average loans was also comparable across groups. The ratio of the loss allowance

to total loans was higher for the failures one year

before the PCA date (2.62 percent versus 2.O7

percent). As of the PCA date, the ratio had

increased substantially for both groups, but the

increase was larger for near­failures. Thus at the

PCA date, the ratio of loss allowance to total

loans was 4.37 percent for failures and 4.5O percent for near­failures. 

At the PCA date, the relationship between

reserves and noncurrent loans was significantly

weaker for the failed institutions than for the

near­failures. The coverage ratio, by measuring

the dollar amount of reserves set aside for each

dollar of noncurrent loans, gives a relative measure of the protection available for charge­offs

before earnings and capital must suffer. The median coverage ratio was slightly higher for the failures than the near­failures one year before the

PCA date (44.74 percent versus 43.47 percent).

While the near­failures improved their ratios in

the following year (to 63.98 percent), the coverage

ratio for the median failure deteriorated to 43.OO

percent as of the PCA date. Although the nearfailures' capital levels were slightly lower, their

reserve levels were much better than those of the

institutions that eventually failed. Figure 1 shows

the distribution of coverage ratios across the

groups as of the PCA date: more than one­half of

the high­cost failures had coverage ratios below 4O

percent, but less than 3O percent of the low­cost

failures and near­failures had coverage ratios

below 4O percent.

The failures had a higher level of classified assets

(13.OO percent versus 1O.47 percent), but the difference was smaller than that of the nonperforming assets and was insignificant. Surprisingly, the

Jones and King estimate of reserves was lower

than actual reserves for the failures but higher

than actual reserves for the near­failures; however,

the difference between the groups was insignificant. Two phenomena might explain this result.

First, when examiners find that a bank is insolvent, they stop identifying losses and instead shift

to preparing for failure. Therefore the classified

asset figures are probably incomplete (and thus

understated) for some, perhaps most, of the fail2003, VOLUME 15, NO. 2 12 
 

FDIC Banking Review 

Figure 1 the near­failures had a median ratio

of 7.77 percent. Thus, when noncurDistribution of Coverage Ratio as of PCA Date rent loan levels are not considered,

Institutions (%) 

40 

30 

20 

10 

0 

Coverage Ratio 

ures. Second, the formula relied on examination

data from the last full exam before an institution

fell below the PCA threshold. Because we had to

reach back in time to get the data for this

approach, the formula would not have captured

changes to the balance sheet during the intervening period. Since many of the institutions experienced increases in noncurrent loans during the

year before they breached the PCA threshold, and

the institutions may also have experienced deterioration in classification levels,39 we think that the

classified loan data were too distant from the date

these institutions fell below the PCA threshold. 

Comparing capital plus reserves as a percentage of

assets when these institutions fell below the PCA

limit, the differences between the failed institutions and the near­failures virtually disappear.

The median ratio of equity capital plus reserves to

assets was 4.22 percent for the failed institutions

while the near­failures were not significantly different at 4.O3 percent. Even one year before, the

failures had a median ratio of 8.14 percent while

39 Examiners use four classification levels to indicate the level of impairment. 

The distribution of classifications across these levels has a strong influence 

on the Jones and King estimate. 

the difference between the groups

was insignificant.

The performance up to the quarter

when all these institutions fell below

the PCA threshold was remarkably

similar. The return on assets (ROA)

for the four quarters leading up to the

violations of the PCA threshold was

poor for both groups. The failures

had a median ROA of -5.62 percent,

which was slightly lower than the

median loss for the near­failures, at

-5.76 percent. Compared with the

near­failures, the failures had somewhat lower earnings the year before

(-1.2O percent ROA for failures,

-O.72 percent ROA for near­failures).

The failures began reporting quarterly losses in

their last seven quarters before falling below the

PCA threshold. The near­failures showed median

quarterly losses for their last five quarters before

falling below the PCA threshold.4O

Some performance ratios showed significant differences. The failed institutions generated a median

ratio of noninterest expenses to earning assets of

8.19 percent in their last four quarters, up from

6.1O percent one year earlier. The near­failures

generated a noninterest expense ratio of just 6.66

percent, up from 6.21 percent one year earlier.

Although the two groups started at nearly the

same level of noninterest expenses, the failing

institutions generated more losses from noninterest expenses than the near­failures. This may

relate to the higher levels of troubled assets, since

asset workouts are resource intensive. 

0 0-20% 20-40% 40-60% 60-80% 80­

100% 

100­

120% 

120­

140% 

140­

160% 

160­

180% 

180%+ 

Low-Cost 

Failures 

Near 

Failures 

High-Cost 

Failures 

40 Our results are consistent with those of Barakova and Carey (2001). They 

briefly compared the components of changes in equity of failed banks with 

the components of banks that recovered. The authors found no marked differences in performance between these two groups, although failed banks raised 

somewhat less equity and experienced higher cumulative losses than the 

banks that recovered. 

13 2003, VOLUME 15, NO. 2 
 

 

 

 

FDIC Banking Review 

The only statistically significant difference in performance between the groups in the year before

these institutions fell below the PCA threshold

was net interest margin. The failing institutions

reported a net interest margin of 4.93 percent,

while the near­failures reported 4.68 percent, a

statistically significant 25 basis point difference.

The yield on assets was a statistically insignificant

3O basis points higher for the failures and the cost

of funding earning assets was a statistically

insignificant 1O basis points lower for the failures.

Although it is difficult to discern from the financial reports, the net­interest­margin advantage

enjoyed by the failures may have been from interest accruals on loans that became past due as

these institutions came closer to failing. 

In summary, the most important differences

between failures and near­failures related to

reserve levels and nonperforming assets. On balance sheet composition, capital, and performance,

these two groups were not very different.

Low-cost Failures versus 

High-cost Failures

A comparison of low­cost and high­cost failures

gives insight into whether the current capital

threshold provides the optimal intervention timing

for banks and regulators, and whether additional

items might assist regulators to reduce losses to

the insurance funds.

Comparisons of Pre-failure Financial 

Condition and Performance 

Table 3 compares low­cost and high­cost failures

as of the PCA date, and table 4 makes the same

comparison one year earlier. Table 5 provides

Kendall's tau correlations for the FDIC loss rate

for both periods.41 There were 16 low­cost failures, holding $1.4 billion in assets as of the PCA

date.42 There were 29 high­cost failures, holding

41 All correlations and tests of significance are based on the Kendall’s tau test 

and compare the FDIC loss rate with the item being discussed. 

42 Eight low-cost failures and five high-cost failures did not breach the PCA 

threshold before failure. For these institutions, we treated the final Call 

Report date as the PCA failure date. 

$2.7 billion in assets, as of the PCA date. There

were no significant differences related to asset size.

As expected, most of the median condition ratios

were worse for the high­cost failures than for the

low­cost­failures. The median capital ratio of the

low­cost failures was 2.O9 percent, down from 6.68

percent one year before the institution fell below

the PCA threshold. The median for the high­cost

failures was O.78 percent, down from 5.65 percent

one year earlier. The median capital held by lowcost failures was significantly higher than for the

high­cost failures when they fell below the PCA

threshold, but the difference one year earlier was

insignificant. Similarly, the median for the core

capital (leverage) ratio was 2.O3 percent for the

low­cost failures and O.98 percent for the highcost failures (significantly different). However, the

correlations between the capital measures and the

FDIC loss rate were insignificant. 

A related difference appeared in the level of

deposits funding assets. The low­cost failures

ended up with deposits that were 95 percent of

assets, while the high­cost failures ended up with

deposits as 97 percent of assets. This significant

difference may have been caused simply by the

lower level of capital backing the assets of the

high­cost failures. 

The low­cost failures had a higher proportion of

low­risk assets than the high­cost failures. The

low­cost failures had a median ratio of securities

to assets of 1O percent, while the high­cost failures

had 9 percent. The low­cost failures had a median 15 percent in securities one year earlier, while

the high­cost failures had even less-8 percent.

In both periods, there was a statistically significant

correlation between securities holdings and FDIC

losses (-O.27 as of the PCA date; -O.28 one year

earlier). The median for single­family residential

mortgages was 16 percent for the low­cost failures,

and 9 percent for the high­cost failures. The year

before falling below the PCA threshold both

groups held just slightly more: 17 percent and 1O

percent, respectively. In both periods the difference in medians and the correlations with the

FDIC loss rate were significant. The two groups

2003, VOLUME 15, NO. 2 14 
 

 

 

 

 

 

 

 

 

 

 

 

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Table 3 showed a slight insignificant

Comparison of Low-Cost and High-Cost Failures
 

Low-Cost High-Cost Difference 

AS OF THE PCA DATE 

Number of Institutions 16 29 13 

Median Total Assets ($MM) 66 64 (2) 

Median Asset Growth (18.04) (11.92) 6.12 

Institutions with CAMELS Rating of 4 or 5 (%) 93.80 93.10 (0.70) 

Median Balance Sheet Percentages 

Investment Securities 10.14 8.74 (1.40) 

1-4 Family Mortgages 15.61 9.42 (6.19)* 

Commercial Real Estate 15.93 17.57 1.64 

Multifamily Real Estate 1.55 1.01 (0.54) 

Commercial & Industrial Loans 10.51 11.00 0.49 

Deposits 95.13 97.30 2.17* 

Median Rolling Four-Quarter Performance Ratios 

Return on Assets (5.23) (5.92) (0.69)* 

Return on Equity (106.23) (134.99) (28.76)* 

Yield on Earning Assets 8.11 8.42 0.31 

Cost of Funding Earning Assets 3.69 4.16 0.47* 

Net Interest Margin 4.41 4.80 0.39 

Noninterest Income to Earning Assets 1.36 2.02 0.66 

Noninterest Expense to Earning Assets 7.87 9.23 1.36 

Net Operating Income to Assets (4.85) (5.73) (0.88)* 

Net Charge-offs to Loans 3.85 3.09 (0.76) 

Loss Provision to Average Assets 2.44 3.71 1.27 

Loss Provision to Net Charge-offs 129.41 119.57 (9.84) 

Efficiency Ratio 117.61 138.75 21.14 

Median Condition Ratios 

Loss Allowance to Loans 4.50 4.37 (0.13) 

Loss Allowance to Noncurrent (Coverage Ratio) 69.03 37.85 (31.18)*** 

Noncurrent & OREO to Assets 6.44 12.92 6.48*** 

OREO to Assets 0.46 4.17 3.71** 

Noncurrent RE Loans to RE Loans 4.35 9.20 4.85** 

Noncurrent Loans & Leases to Loans & Leases 5.67 11.37 5.70** 

Noncurrent Loan Growth 14.21 31.86 17.65 

Equity Capital Ratio 2.09 0.78 (1.31)* 

Core Capital (Leverage) Ratio 2.03 0.98 (1.05)** 

Equity Capital plus Reserves to Assets 5.79 3.95 (1.84) 

AS OF MID-YEAR BEFORE PCA DATE, OR MOST RECENT EXAM 

Median Economic Condition Measures 

Problem Bank Index 0.01 0.11 0.10* 

Geographic Diversification Index 1.00 1.00 0.00 

Median Examiner Classification Ratios 

Reserves to Estimated Reserves 114.13 97.02 (17.12) 

Classified Assets to Assets 12.20 13.77 1.57 

Note: For failed institutions that did not file a Call Report or TFR showing that they were critically undercapitalized, 

the Final Call Report or TFR was used. 

* Significant at the 10% level. 

** Significant at the 5% level. 

*** Significant at the 1% level. 

difference in commercial real

estate (excluding multifamily

residential properties) as a percentage of assets. The low­cost

failures had a median 16 percent of assets in commercial

real estate, while the high­cost

failures had 18 percent; both

medians had increased by 2

percent from the year before.

These mortgages are much

more difficult to dispose of

when an institution tries to

shrink as capital becomes

scarce. 

Nonperforming assets, including noncurrent loans and

OREO, were dramatically different for the two groups of

failures. The median percentage of nonperforming assets to

total assets for the low­cost

failures was 6.44 percent, up

from 4.32 percent one year earlier. The median percentage of

nonperforming assets for the

high­cost failures was 12.92

percent, up from 9.79 percent

one year earlier. The high­cost

failures started at a higher level

and remained about double the

median rate of the low­cost

failures. There was a O.33 correlation between nonperforming assets and the FDIC loss

rate as of the PCA date, and a

O.26 correlation one year earlier. The differences in medians

and the correlations were statistically significant in both

periods. 

A primary cause of these differences was the level of noncurrent commercial real estate,

which was significantly higher

15 2003, VOLUME 15, NO. 2 
 

 

 

 

 

 

 

 

 

 

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Table 4 whereas the low­cost failures

Comparison of Low-Cost and High-Cost Failures had only O.46 percent. Total

noncurrent loans exhibited a Low-Cost High-Cost Difference 

pattern similar to all nonperAS OF ONE YEAR BEFORE THE PCA DATE forming assets, except that in

Number of Institutions 16 29 13 the year before the institutions Median Total Assets ($MM) 72 71 (1) fell below the PCA threshold

Median Asset Growth 5.06 (5.94) (11.00) the difference was insignificant. Institutions with CAMELS Rating of 4 or 5 (%) 75.00 79.30 4.30 At the PCA date, the median Median Balance Sheet Percentages level of noncurrent loans as a Investment Securities 15.00 8.39 (6.61)* percentage of total loans was 1-4 Family Mortgages 16.95 10.20 (6.75)*
 

Commercial Real Estate 14.35 15.85 1.50 5.67 percent for the low­cost
 

Multifamily Real Estate 1.42 1.66 0.24 failures, and 11.37 percent for
 Commercial & Industrial Loans 7.43 12.93 5.50 the high­cost failures. The
 Deposits 90.66 92.23 1.57
 median net charge­off rate on

Median Rolling Four-Quarter Performance Ratios loans during the last four quar­ Return on Assets (0.09) (1.56) (1.47) ters of operation was 3.85 per­ Return on Equity (4.43) (23.27) (18.84) cent for the low­cost failures, Yield on Earning Assets 8.66 8.86 0.20* 

Cost of Funding Earning Assets 3.90 4.05 0.15 and 3.O9 percent for the highNet Interest Margin 4.70 5.32 0.62** cost failures-not a significant

Noninterest Income to Earning Assets 1.16 1.42 0.26 different. Despite similar net

Noninterest Expense to Earning Assets 5.57 6.96 1.39 charge­offs, the high­cost fail­ Net Operating Income to Assets (0.81) (1.73) (0.92) ures left behind higher levels of Net Charge-offs to Loans 0.56 1.81 1.25 noncurrent loans. Loss Provision to Average Assets 0.99 1.73 0.74 

Loss Provision to Net Charge-offs 149.17 103.65 (45.52) 

Efficiency Ratio 97.91 97.91 0.00 At failure, reserves and capital

Median Condition Ratios serve to cover losses in the

Loss Allowance to Loans 2.00 2.91 0.91 loan portfolio. In this respect,

Loss Allowance to Noncurrent (Coverage Ratio) 58.67 37.83 (20.84) the groups looked similar. The

Noncurrent & OREO to Assets 4.32 9.79 5.47*** median ratio of equity capital OREO to Assets 0.97 3.15 2.18** plus reserves to assets was 5.79 Noncurrent RE Loans to RE Loans 2.22 6.53 4.31* percent for the low­cost fail­ Noncurrent Loans & Leases to Loans & Leases 3.63 6.93 3.30 


Noncurrent Loan Growth 43.67 1.90 (41.77)* ures, and a slightly lower 3.95
 

Equity Capital Ratio 6.68 5.65 (1.03) percent for the high­cost failCore Capital (Leverage) Ratio 6.56 5.59 (0.97) ures (an insignificant differ­ Equity Capital plus Reserves to Assets 8.19 8.14 (0.05)
 ence). One year earlier these Note: For failed institutions that did not file a Call Report or TFR showing that they were critically undercapitalized, ratios were within 5 basis the Call Report or TFR one year before the Final Report was used. 

points, at 8.19 percent and * Significant at the 10% level. 

** Significant at the 5% level. 8.14 percent, respectively.

*** Significant at the 1% level. However, the high­cost failures

needed more reserves to cover

for the high­cost failures one year before they fell below the PCA their higher level of noncurrent

limit. The median low­cost failure had no noncurrent commercial loans. The coverage ratio,

real estate, while the high­cost failures had a median noncurrent rate reserves to noncurrent loans,

for commercial real estate of 3.93 percent. Commercial real estate was much lower for the highprobably influenced the OREO levels, which were significantly higher cost failures. The median covfor the high­cost failures in both periods. As of the PCA date, the erage ratio for the low­cost

high­cost failures had a median 4.17 percent of assets in OREO, failures was 69.O3 percent,

2003, VOLUME 15, NO. 2 16 
 

 

 

 

 

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Table 5 

Kendall Tau Correlation Coefficients 

One Year At PCA
 

Before Date
 

PRE-FAILURE ITEMS VERSUS TOTAL LOSS RATE
 

Number of Institutions 45 45 

Median Total Assets ($MM) (0.07) (0.06) 

Median Asset Growth (0.08) 0.01 

Median Balance Sheet Percentages 

Investment Securities (0.28)*** (0.27)*** 

1-4 Family Mortgages (0.24)** (0.26)** 

Commercial Real Estate (0.07) (0.09) 

Multifamily Real Estate 0.00 (0.07) 

Commercial & Industrial Loans (0.02) (0.02) 

Deposits 0.12 0.22** 

Median Rolling Four-Quarter Performance Ratios 

Return on Assets (0.10) (0.26)** 

Return on Equity (0.11) (0.18)* 

Yield on Earning Assets 0.24** 0.18* 

Cost of Funding Earning Assets 0.06 0.20* 

Net Interest Margin 0.23** 0.07 

Noninterest Income to Earning Assets 0.09 0.15 

Noninterest Expense to Earning Assets 0.14 0.21** 

Net Operating Income to Assets (0.04) (0.27)*** 

Net Charge-offs to Loans 0.23** 0.09 

Loss Provision to Average Assets 0.14 0.15 

Loss Provision to Net Charge-offs (0.07) 0.07 

Efficiency Ratio 0.00 0.12 

Median Condition Ratios 

Loss Allowance to Loans 0.10 0.01 

Loss Allowance to Noncurrent (Coverage Ratio) (0.19)* (0.12) 

Noncurrent & OREO to Assets 0.26** 0.33*** 

OREO to Assets 0.26** 0.28*** 

Noncurrent RE Loans to RE Loans 0.09 0.20* 

Noncurrent Loans & Leases to Loans & Leases 0.07 0.16 

Noncurrent Loan Growth (0.21)* 0.08 

Equity Capital Ratio 0.01 (0.15) 

Core Capital (Leverage) Ratio (0.01) (0.15) 

Equity Capital plus Reserves to Assets (0.01) (0.14) 

* Significant at the 10% level. 

** Significant at the 5% level. 

*** Significant at the 1% level. 

while the high­cost failures had just 37.85 percent-a statistically significant difference of 31.18 percent. One year

earlier, the low­cost failures had a less favorable median of

58.67 percent, while the high­cost failures recorded virtually the same level, 37.83 percent; the difference between

groups was insignificant. Surprisingly, the correlation

between the coverage ratio and the FDIC loss rate was

insignificant as of the PCA date. One year earlier, it was

-O.19 and significant at the 1O percent level.

During the year leading up to the PCA

date, the correlation between ROA and

the FDIC loss rate was -O.26 (negative as

expected) and significant; it was insignificant one year earlier. The low­cost failures

had a median loss of 5.23 percent of assets

in the year leading up to the PCA date,

while the high­cost failures had a loss of

5.92 percent of assets. The difference in

medians was relatively small (O.69) but significant at the 1O percent level. The net

interest margin and the yield on earning

assets favored the high­cost failures in

both periods, and some of the differences

were significant. One possible explanation

for this result is that the high­cost institutions might have accrued more interest

that was never subsequently collected, but

this is hard to determine from the data we

collected. The low­cost failures had a

lower cost of funds in both periods: the

difference in medians was 47 basis points

(3.69 percent versus 4.16 percent) and significant in the institutions' last year of

operations before falling below the PCA

limit. During that period, the median

ratio of noninterest income to earning

assets favored the high­cost failures by 66

basis points but was statistically insignificant. The median ratio of noninterest

expenses to earning assets favored the lowcost failures by 136 basis points. Although

the difference in medians was insignificant,

the correlation was O.21 and significant.

The low­cost failures operated in markets

with much lower levels of problem banks

than the high­cost failures. We measured

the level of deposits held by problem banks

from each market that the failing institutions operated in during the month of June

before they fell below the PCA limit. The

low­cost failures registered a median index

of just O.O1, while the high­cost failures

showed an index of O.11 for their markets.

This difference was significant and indicates that the markets in which they operated were troubled enough to stress other

17 2003, VOLUME 15, NO. 2 
FDIC Banking Review 

institutions as well as their Figure 2 

own resources.43 This relates
 

to an important characteristic
 

of the sample. While the
 

near­failures and low­cost failures were more or less evenly
 

distributed over the sample
 

period, most of the high­cost
 

failures occurred in 1994 and
 

1995. Figure 2demonstrates
 

this phenomenon. 

Therefore, some of the differences between the high­cost
 

failures and the other institutions were probably related to
 

the economy, industry conditions, and�or regime changes
 

rather than to the characteristics of the banks. As confirmed by the problem bank index, many of the
 

high­cost failures occurred when a substantive
 

number of nearby banks were experiencing difficulties and some markets (notably commercial real
 

estate, particularly in New England) were still suffering from excess supply. When our sample period began (January 1994), over 1O percent of BIF
 

member banks had CAMEL ratings of 3, 4, or 5
 

(indicating that the banks had significant problems); by year­end 1995, only 5 percent of BIF
 

members had CAMEL ratings of 3, 4, or 5. 

These results indicate that failures may well be
 

more expensive during periods of stress-regardless of PCA.44 Periods of stress are often characterized by large, and sometimes sudden, shifts in
 

market values. For example, fixed­rate mortgage
 

loan values plummeted in the late 197Os and early
 

43 The correlation was 0.16 and insignificant. This could relate to idiosyncratic differences between banks (particularly the amount of loss attributable to 

fraud or mismanagement) and the small sample size. 

44 McDill (2002) confirmed that a relationship exists. Using data from 1980 

through 2000, she regressed the FDIC loss rates of failed banks against various business-cycle items and bank-specific characteristics. She found that 

loss rates were higher for institutions located in states with falling personalincome levels—particularly if the reduction occurred after a “boom.” She also 

found that loss rates increased for institutions located in states that had concentrations of problem banks. As an example of the effects, she used the 

results to estimate the loss rate for a typical Texas failure in 1988 and 1999. 

The loss rate in 1988 was 6 percentage points higher than the loss rate in 

1999. 

0 

10 

20 

30 

40 

50 

60 

70 

12/93 12/94 12/95 12/96 12/97 12/98 12/99 12/00 12/01 

Failure Date 

Loss Rate (%) 

High-Cost 

Low-Cost 

FAILURES 

Loss Rates Over Time 

Low-Cost and High-Cost Failures 

198Os, farm prices fell sharply during the agricultural crisis in the mid­198Os, and commercial real

estate prices plunged in Texas and New England

during regional recessions. Some of the high­cost

failures may have become market­value insolvent

before the markets bottomed out.45 Because markets are often thin during periods of stress, asset

valuation becomes more difficult as well. Therefore, it may not be feasible or desirable to create a

regulatory regime that results in near­zero losses to

the insurance funds during periods of stress.46

We briefly looked at the relationship between coverage ratios and industry stress. Coverage ratios

were not correlated with the problem bank index.

As shown in figure 3, coverage ratios varied widely

over the sample (both for failed banks and nearfailures) throughout the sample period. 

45 Capital is basically a measure of current condition and largely ignores the 

likelihood of future deterioration. Mailath and Mester (1993) developed a theoretical model on optimum bank closure policy and found that regulators 

should consider the future prospects and probable actions of a failing bank in 

order to minimize losses to the insurance funds. A more forward-looking 

measure of capital might mitigate losses during periods of stress, but it would 

introduce more subjectivity into the equation as well. 

46 If regulators imposed either extremely tight restrictions on risk (that is, narrow banking) or very high capital requirements, it might be feasible to largely 

eliminate insurance fund losses during periods of stress. However, there 

would probably be substantial costs in the form of tighter credit availability 

and higher credit costs for a large number of borrowers. 

2003, VOLUME 15, NO. 2 18 
 

FDIC Banking Review 

Figure 3 

0 

40 

80 

120 

160 

200 

2/15/94 2/10/95 2/5/96 1/30/97 1/25/98 1/20/99 1/15/00 1/9/01 

Failure Date 

Coverage Ratio (%) 

Failure 

Near Failure 

Coverage Ratio Over Time* 

Low-Cost and High-Cost Failures 

* Eight outlying observations have been omitted from this graph. 

In addition, some of the costs experienced by the

high­cost failures might be attributable to changes

in the regulatory regime. Most banks that crossed

the 2 percent PCA threshold after 1995 would

have experienced their entire period of decline

after FDICIA had been passed and the enabling

regulations were in place. Those banks that

crossed the threshold in 1994 (and perhaps also in

1995) might have had a less rigorous incentive

structure in place during the early phases of their

decline.

We also investigated the level of classified loans in

each group based on the last full examination

before falling below the PCA limit, but none of

our measures showed a significant difference

between the groups. The median low­cost failure

had 12.2O percent of its loans classified by examiners during their last full examination, while the

median high­cost failure had 13.77 percent.

Based on classified loans, we estimated appropriate

levels of reserves and compared these with the

actual reserves reported. The median low­cost

failure reported reserves that were 114 percent of

the estimated level, while the median high­cost

failure reported reserves of 97 percent of the estimated level. This difference was not significant,

but this does support the higher level of reserves to noncurrent loans held by the low­cost

failures. 

As in the comparison between

failures and near­failures, the

coverage ratio and the level of

nonperforming assets were

important characteristics that

distinguished between low­cost

and high­cost failures. These

items appear to be the most

fruitful ones to consider for

any policy changes that might

reduce loss rates for failed

banks. There were also important differences related to

industry stress and the timing

of the failures. We found evidence that failure costs were

influenced by industry conditions. However,

changes in the regulatory treatment of seriously

troubled banks may not influence the level of

industry stress. Some differences in asset composition and performance were also statistically significant and may prove useful. 

Comparisons at Resolution

We examined the number of institutions that submitted bids at failure. The median number of bidders was four for both the low­cost and high­cost

failures, and the distribution around the median

was similar. Thus, it appears that the market

exposure was sufficient for both groups.47 The bid

results (shown in tables 6 and 7) were somewhat

different. As anticipated, low­cost failures generally had deposit franchises that were worth more

than those of high­cost failures. At 2.88 percent,

the median bid­to­deposit ratio for low­cost failures was 51 percent higher than the median for

47 The market for deposits was strong throughout the period. There could be 

a different result during periods of lower demand. 

19 2003, VOLUME 15, NO. 2 
 

 

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high­cost failures. Although Table 6 

the differences in medians were Comparison of Low-Cost and High-Cost Failures

insignificant, we found a rela­ At Resolution and In Receivership 

tively strong (-O.22) and significant correlation between this

ratio and the FDIC loss rate.

Even so, the differences

explain only a relatively small

portion of the overall cost differences.

Comparisons of Receilership

Performance

Table 6 provides comparisons

of receivership performance

and table 7 provides correlation statistics to loss rates. As

anticipated, there was a strong

relationship between asset loss

rates and the FDIC loss rate.

The asset type with the largest

losses was OREO: even the

low­cost failures suffered a 21

percent median loss rate on

sales of those assets. The asset

types with the largest differences between the two groups

were OREO (21.O3 percentage

points), C�I loans (13.11 percentage points), and other

assets (1O.14 percentage

points). The asset type with

the strongest correlation with

the FDIC loss rate was C�I

loans (O.44). The correlations

were significant for most asset

types. Thus it appears that

asset quality at the high­cost

institutions was worse across

the board. 

The median loss rate on total

assets for high­cost institutions

was more than 2 1�2 times the

loss rate for low­cost institutions. The difference exceeded

the differences for most asset

Low-Cost High-Cost Difference 

Number of Institutions 16 29 13 

Median Total Assets ($MM) 66 64 (2) 

Median Resolution Data 

Total Bid/Total Deposits 3.01 2.38 (0.63) 

Deposit Bid/Total Deposits 2.88 1.42 (1.46) 

Median Loss Rates over Life of the Receivership 

(as % of Total Assets at Failure) 

Investment Securities Loss a 0.00 0.00 0.00 

C&I Loan Loss 15.00 28.11 13.11*** 

Mortgage Loan Loss b 14.99 16.22 1.23 

Consumer Loan Loss 9.26 13.37 4.11 

Owned Real Estate (OREO) Loss 21.27 42.30 21.03** 

Investment in Subsidiaries Loss 3.03 0.00 (3.03) 

Other Asset Loss 5.97 16.11 10.14 

Total Asset Loss 7.99 21.96 13.97*** 

Median Receivership Reserve Ratios 

Reserves/Total Assets 2.84 3.04 0.20 

Reserves/Total Asset Losses 28.67 14.27 (14.40)** 

Median Performance over Life of the Receivership 

Percentage Point Change in Cost Estimate 

(Original to 12/2000) 2.88 8.03 5.15*** 

(as % of Total Assets at Failure) 

Receivership Income 0.51 4.58 4.07*** 

Receivership Expenses (Excludes Holding Costs) 2.97 11.24 8.27*** 

Holding Costs 0.58 3.34 2.76*** 

Assets Passed to Acquirer 62.99 24.84 (38.15)** 

(as % of Total Expenses) 

Legal Expenses 1.47 1.61 0.14 

OREO Expenses (Net of OREO Income) 0.72 3.03 2.31* 

Indirect Expenses 59.74 62.57 2.83 

Note: The total bid includes all items that reduce the FDIC’s cost; the deposit bid includes the amount that is directly 

tied to deposits. Receivership income and expenses exclude gains and losses from asset-sales adjustments made 

directly to equity. 

* Significant at the 10% level. 

** Significant at the 5% level. 

*** Significant at the 1% level. 

a For failures that occurred in 2000, gains/losses on investment securities that were sold to the acquirer are excluded 

from these figures. Also note that some acquirers may bid for investment securities at par and adjust their bid for 

deposits accordingly, particularly if the deposit bid is contingent on receiving the securities as well. 

b Mortgage loss rates (and possibly the difference between low-cost and high-cost failures) may be understated because 

of foreclosure activity. If the FDIC forecloses on a property, the receivership reclassifies the asset to an OREO asset 

prior to sale. Thus the losses from selling the underlying real estate are recorded under OREO instead of mortgages. 

We calculated the change in the percentage of the banks’ assets held in OREO over the course of the receivership, and 

we found that the median increase (in percentage points) was 2 percent for the both groups of banks. 

2003, VOLUME 15, NO. 2 20 
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Table 7 

Kendall Tau Correlation Coefficients 

Resolution and Receivership Items 

versus Total Loss Rate 

All Failures 

Number of Institutions 45 

Median Total Assets ($MM) (0.06) 

Median Resolution Data 

Total Bid/Total Deposits (0.15) 

Deposit Bid/Total Deposits (0.22)** 

Median Loss Rates over Life of the Receivership 

(as % of Total Assets at Failure) 

Investment Securities Loss (0.03) 

C&I Loan Loss 0.44*** 

Mortgage Loan Loss 0.19* 

Consumer Loan Loss 0.25** 

Owned Real Estate (OREO) Loss 0.28*** 

Investment in Subsidiaries Loss 0.09 

Other Asset Loss 0.09 

Total Asset Loss 0.48*** 

Median Receivership Reserve Ratios 

Reserves/Total Assets (0.03) 

Reserves/Total Asset Losses (0.27)** 

Median Performance over Life of the Receivership 

Percentage Point Change in Cost Estimate 

(Original to 12/2000) 0.44*** 

(as % of Total Assets at Failure) 

Receivership Income 0.27*** 

Receivership Expenses (Excludes Holding Costs) 0.48*** 

Holding Costs 0.24** 

Assets Passed to Acquirer 0.30*** 

(as % of Total Expenses) 

Legal Expenses 0.09 

OREO Expenses (Net of OREO Income) (0.18)* 

Indirect Expenses 0.21** 

* Significant at the 10% level. 

** Significant at the 5% level. 

*** Significant at the 1% level. 

types, indicating that the high­cost failures held

more of their portfolios in the types of assets that

experienced significant losses.48 This comports

with our comparisons of low­cost and high­cost

failures before failure (presented above).49

48 We investigated the amount of foreclosure activity during receivership but
 

found that the median change in OREO (as a percentage of total assets)
 

between closing weekend and the final receivership balance increased about
 

the same amount (2 percentage points) for both groups.
 

49 Appendix 3 provides more detail about the asset composition at failure and
 

in receivership.
 

The receivership data showed that reserve ratios

were about the same for the two groups (3 percent

of total assets). Although the reserve ratios were

much higher than industry averages (as a percentage of loans), reserves were much lower than the

asset losses experienced by the receiverships.

Reserves covered only 29 percent of the losses for

the low­cost failures and a mere 14 percent for the

high­cost failures.5O Just as the coverage ratio was

significantly higher for the low­cost failures, so

were the reserves relative to actual losses. Many

of these banks, and particularly the high­cost

banks, may have had inadequate reserves; however, we do not have enough information to determine this with certainty.51

Receivership income, receivership expenses, and

holding costs were all much higher for the highcost institutions, and both the correlations and

the differences in medians were highly significant.52 There was almost no receivership income,

and there were almost no holding costs, for the

low­cost institutions. For the high­cost institutions, median receivership income over the life of

the receivership was 4.58 percent of total assets at

failure, and median holding costs over the life of

the receivership were 3.34 percent of total assets

at failure.53 The difference in median receivership expenses to total assets at failure was even

larger: 2.97 percent for low­cost failures versus

11.24 percent for high­cost failures. At O.48, the

correlation between receivership expenses and the

FDIC loss rate was quite strong. Even with the

50 These loss rates were calculated as the difference between the gross book 

value recorded by the receivership and the sales proceeds. If the costs associated with selling the assets had been included, the difference between the 

two groups of institutions would have been larger. 

51 There are several reasons why reserves may be sufficient under GAAP for 

an operating institution but still be smaller than the asset losses experienced 

at liquidation. Some of the reasons are changes in market conditions, 

changes in interest rates, and differences in the value anticipated for an asset 

held in a long-term portfolio versus the value if the asset is sold relatively 

quickly. 

52 Receivership income and expenses were calculated on a cash basis, and 

they excluded gains and losses from asset sales, asset write-offs, holding 

costs, income from the bid premium and other equity adjustments. Holding 

costs were defined as the FDIC’s lost income from funding the deposits up 

front and waiting for recovery from the receivership. Appendix 2 provides 

more information on the composition of these items. 

53 Because the level of activity and the duration (in time) of receiverships 

vary substantially, all income and expense items are reported as the total 

amount over the life of the receivership divided by total assets at failure. 

21 2003, VOLUME 15, NO. 2 
FDIC Banking Review 

extra income as an offset, it is clear that the net

effect of these items made up a large portion of

the cost differential between these two groups of

institutions.54

These differences were closely Figure 4 

tied to the enormous divergence in the portion of assets

passed (that is, sold) to the

acquirer.55 The FDIC passed

62.99 percent of assets to

acquirers for the median lowcost institution, but only 24.84

percent for the median highcost institution (figure 4).56

For 75 percent of the highcost institutions, the FDIC

retained over one­half of the

assets in the receivership. On

the surface, the cause of these

immense differences appears to

be the FDIC's sales methods.

The true cause is not as simple.

One likely reason for the difference in assets sold

to the acquirer is the quality of the assets held by

the banks: assets of the high­cost banks were generally riskier and of lower quality. The markets for

the riskier assets are typically thinner, and it may

be harder to estimate the market price. The duediligence effort required for lower­quality assets is

more extensive as well. Thus there might have

been fewer interested buyers, and the odds of find54 One cannot merely subtract receivership income from receivership expenses 

and assume that the difference (as a percentage of total assets at failure) is 

the amount of loss in percentage points attributable to receivership operations. Many receiverships start with substantial amounts of positive equity 

(partly because reserves are reversed at failure). In addition, some losses and 

gains do not flow through the receivership income statement, and some 

receivership losses are borne by creditors other than the FDIC. 

55 An acquirer is defined as the institution (or institutions) that purchased the 

deposits. 

56 The simple average for the percentage of assets sold to the acquirer was 

30.01 percent for the high-cost institutions and 65.73 percent for the low-cost 

institutions. 

ing a buyer to purchase the deposits and the riskier assets simultaneously-and on a relatively tight

time line-would probably have been much 

smaller.

0 

10 

20 

30 

40 

50 

60 

70 

Loss Rate (%) 

0 10 20 30 40 50 60 70 80 90 100 

Pass Rate (%) 

High-Cost 

Low-Cost 

FAILURES 

Loss Rate versus Pass Rate 

Low-Cost and High-Cost Failures 

The marketplace differences discussed above

would almost certainly have affected the asset pass

rates and the loss rates for some types of assets.

As noted above, a relatively large proportion of

the high­cost failures occurred when the industry

was experiencing distress and the markets were

still absorbing large amounts of troubled assets.

Our analysis suggests that real estate loans were a

large problem for the high­cost failures. Vacancy

rates for office space dropped precipitously during

the early years of our sample period, and the volume of sales transactions for office space increased

dramatically in 1998.57 Therefore, some of the

57 National vacancy rates for office space were 16.8 percent at the beginning 

of our sample period. They dropped to 13.8 percent at year-end 1995, and 

9.9 percent at year-end 1997. The changes in sales volume were larger. 

According to Torto-Wheaton Research, there were only 18 sales made of Class 

A office buildings nationwide in 1996, and 35 in 1997—but 238 were made 

in 1998. The results were similar for other types of office buildings. 

2003, VOLUME 15, NO. 2 22 
FDIC Banking Review 

variance in receivership performance across the

groups almost certainly was related to differences

in market conditions. The marketing process

works best when the economy and the industry

are performing well.58

Policy changes made at the FDIC may also have

contributed to this result. The FDIC combined its

Division of Resolutions and its Division of Depositor and Asset Services in 1996. The merger facilitated a more cohesive sales approach that starts

before failure and continues past resolution with

few disruptions from changes in staff or strategy at

resolution. In 1997, the FDIC adopted a new

asset sales procedure called "joint asset marketing." It emphasizes marketing homogeneous pools

of assets to a large number of potential buyers

quickly (preferably at resolution). The new procedures, combined with the strong economy, have

considerably shortened the pace of asset sales from

FDIC receiverships since the mid­199Os.

In summary, the results of the comparison of resolution and receivership results is consistent with

the results from the pre­failure period. For the

most part, these results merely demonstrate the

way losses are realized when banks fail. However,

the differences in asset pass rates and receivership

performance may provide some evidence that

FDIC losses are influenced by market conditions.

To the extent that this is the case, it may be difficult or impossible to develop changes in regulatory

treatment that yield near­zero losses to the insurance funds during times of industry stress. 

Low-cost Failures versus Near-failures

A comparison between low­cost failures and nearfailures allows us to search, at the margin, for differences that distinguish failing banks from banks

that can survive serious problems. This compari58 The results concerning changes in the FDIC loss estimates over time were 

also consistent with the concept that losses may increase when markets are 

weak. For high-cost failures, the FDIC loss estimates increased substantially 

between the original estimate and the latest estimate; for low-cost failures, 

they increased only a little bit. This supports the theory that markets deteriorated during the life of the receivership, or it might indicate that asset valuation at closure was more difficult for the high-cost institutions that carried 

riskier assets. 

son investigates the robustness of the earlier comparison between all failures and near­failures. Any

marginal distinctions may provide useful insights

when considering marginal changes in the PCA

threshold, or they might be helpful to examiners

of similar banks. Our comparisons, shown in

tables 8 and 9, reveal only a few significant differences between the low­cost failures and the nearfailures. Some of these differences, such as capital

levels, may relate to the fact that whereas some

failures never fell below the PCA limit, all nearfailures fell below the limit. 

Median capital levels of the low­cost failures were

significantly higher than those of the near­failures.

The median equity capital ratio for the low­cost

failures was 2.O9 percent at our analysis date,

while the near­failures reported O.97 percent when

they fell below the PCA limit. The core capital

(leverage) ratio also was significantly higher for

the low­cost failures, at 2.O3 percent compared

with O.87 percent for the near­failures. And

deposits of the two groups were significantly different: deposits as a percentage of assets were a

median 95 percent for low­cost failures but 98 percent for the near­failures. This difference was

probably caused by the difference in capital. 

For the performance of the two groups, the overall

ROA was not significantly different, but the operating ROA was. The difference in overall ROA

and operating ROA by our measures stemmed

from the gains on the sales of securities and

extraordinary items. The low­cost failures reported a loss on assets of 5.23 percent, but this loss

was less for operating earnings-4.85 percent.

The near­failures reported a median loss of 5.76

percent of assets, but a median operating loss of

6.O4 percent of assets. Although we did not calculate a ratio for the level of gains on the sales of

securities or extraordinary items, the result on

operating earnings implies that the low­cost failures reported non­operating gains that reduced

their loss, while the near­failures reported nonoperating losses that increased their loss. The

low­cost failures also reported significantly lower

median costs of funding earning assets, at 3.69

percent, while the near­failures reported 4.37 

percent.

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Table 8 The levels of nonperforming

Comparison of Low-Cost Failures and Near-Failures
 

Low-Cost Near-Failures Difference 

AS OF THE PCA DATE 

Number of Institutions 16 39 23 

Median Total Assets ($MM) 66 57 (9) 

Median Asset Growth (18.04) (8.52) 9.52 

Institutions with CAMELS Rating of 4 or 5 (%) 93.80 84.60 (9.20) 

Median Balance Sheet Percentages 

Investment Securities 10.14 11.51 1.37 

1-4 Family Mortgages 15.61 16.38 0.77 

Commercial Real Estate 15.93 11.03 (4.90) 

Multifamily Real Estate 1.55 1.04 (0.51) 

Commercial & Industrial Loans 10.51 11.84 1.33 

Deposits 95.13 98.00 2.87** 

Median Rolling Four-Quarter Performance Ratios 

Return on Assets (5.23) (5.76) (0.53) 

Return on Equity (106.23) (124.24) (18.01) 

Yield on Earning Assets 8.11 8.77 0.66 

Cost of Funding Earning Assets 3.69 4.37 0.68** 

Net Interest Margin 4.41 4.69 0.28 

Noninterest Income to Earning Assets 1.36 1.12 (0.24) 

Noninterest Expense to Earning Assets 7.87 6.66 (1.21) 

Net Operating Income to Assets (4.85) (6.04) (1.19)* 

Net Charge-offs to Loans 3.85 3.98 0.13 

Loss Provision to Average Assets 2.44 3.58 1.14 

Loss Provision to Net Charge-offs 129.41 122.95 (6.46) 

Efficiency Ratio 117.61 123.03 5.42 

Median Condition Ratios 

Loss Allowance to Loans 4.50 4.50 0.00 

Loss Allowance to Noncurrent (Coverage Ratio) 69.03 63.98 (5.05) 

Noncurrent & OREO to Assets 6.44 6.69 0.25 

OREO to Assets 0.46 0.80 0.34 

Noncurrent RE Loans to RE Loans 4.35 3.54 (0.81) 

Noncurrent Loans & Leases to Loans & Leases 5.67 7.24 1.57 

Noncurrent Loan Growth 14.21 29.44 15.23 

Equity Capital Ratio 2.09 0.97 (1.12)** 

Core Capital (Leverage) Ratio 2.03 0.87 (1.16)*** 

Equity Capital plus Reserves to Assets 5.79 4.03 (1.76) 

AS OF MID-YEAR BEFORE PCA DATE, OR MOST RECENT EXAM 

Median Economic Condition Measures 

Problem Bank Index 0.01 0.02 0.01 a 

Geographic Diversification Index 1.00 1.00 0.00 a 

Median Examiner Classification Ratios 

Reserves to Estimated Reserves 114.13 94.81 (19.32) a 

Classified Assets to Assets 12.20 10.47 (1.73) a 

* Significant at the 10% level. 

** Significant at the 5% level. 

*** Significant at the 1% level. 

a Untested. 

assets reported by these groups

did not differ very much, but

the low­cost failures reported

significantly lower net chargeoffs on real estate loans. Their

net charge­offs were just O.O5

percent of real estate loans,

while the near­failures had net

charge­offs that were 1.26 percent of real estate loans over

the last four quarters before

the institutions fell below the

PCA limit. Both at the PCA

date and one year earlier, the

median coverage ratio was

higher for the low­cost failures

than for the near­failures. The

differences in medians were

relatively large (69.O3 versus

63.98 at the PCA date; 58.67

versus 43.47 one year earlier),

but not statistically significant. 

Because the results for lowcost failures were remarkably

similar-and in many ways

superior-to the results for

near­failures, one might conclude that the failed institutions could have survived,

given the chance. However,

only two of these failures

resulted in no costs to the

FDIC; our review of these two

extraordinary cases indicates

that failure was appropriate.59

The remaining low­cost failures had loss rates that ranged

from 3 percent to 12 percent

of assets. On the other hand,

without the sale to another

organization or an infusion of

capital, some of the near­failures could easily have joined

59 See Shibut and Critchfield (2000) for details. 

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Table 9 Near-failures Purchased

Comparison of Low-Cost Failures and Near-Failures versus Near-failures That

Survived Low-Cost Near-Failures Difference a 

AS OF ONE YEAR BEFORE THE PCA DATE 

Number of Institutions 16 39 23 

Median Total Assets ($MM) 72 59 (13) 

Median Asset Growth 5.06 0.34 (4.72) 

Institutions with CAMELS Rating of 4 or 5 (%) 75.00 64.10 (10.90) 

Median Balance Sheet Percentages 

Investment Securities 15.00 10.35 (4.65) 

1-4 Family Mortgages 16.95 15.28 (1.67) 

Commercial Real Estate 14.35 10.76 (3.59) 

Multifamily Real Estate 1.42 1.00 (0.42) 

Commercial & Industrial Loans 7.43 8.85 1.42 

Deposits 90.66 92.00 1.34 

Median Rolling Four-Quarter Performance Ratios 

Return on Assets (0.09) (0.72) (0.63) 

Return on Equity (4.43) (12.89) (8.46) 

Yield on Earning Assets 8.66 8.46 (0.20) 

Cost of Funding Earning Assets 3.90 4.04 0.14 

Net Interest Margin 4.70 4.68 (0.02) 

Noninterest Income to Earning Assets 1.16 1.54 0.38 

Noninterest Expense to Earning Assets 5.57 6.21 0.64 

Net Operating Income to Assets (0.81) (0.75) 0.06 

Net Charge-offs to Loans 0.56 1.48 0.92 

Loss Provision to Average Assets 0.99 1.21 0.22 

Loss Provision to Net Charge-offs 149.17 129.06 (20.11) 

Efficiency Ratio 97.91 90.36 (7.55) 

Median Condition Ratios 

Loss Allowance to Loans 2.00 2.07 0.07 

Loss Allowance to Noncurrent (Coverage Ratio) 58.67 43.47 (15.20) 

Noncurrent & OREO to Assets 4.32 4.62 0.30 

OREO to Assets 0.97 1.08 0.11 

Noncurrent RE Loans to RE Loans 2.22 3.14 0.92 

Noncurrent Loans & Leases to Loans & Leases 3.63 4.33 0.70 

Noncurrent Loan Growth 43.67 3.92 (39.75) 

Equity Capital Ratio 6.68 6.28 (0.40) 

Core Capital (Leverage) Ratio 6.56 5.94 (0.62) 

Equity Capital plus Reserves to Assets 8.19 7.77 (0.42) 

a None of these differences were found to be statistically significant at the 10% level. 

our list of failures. All in all, this comparison highlights the difficulty

of predicting failure. Given the lack of quantitative differences

between these two groups, it is hard to identify changes in the PCA

threshold that would consistently improve the cost tradeoffs.

We split the near­failures into

groups based on the way they

survived. Twenty­one institutions were absorbed into other

organizations within one year

after they fell below the PCA

limit, and eighteen near­failures survived their problems

and remained independent for

at least one year. We expected

that the institutions that survived might have been in

slightly better condition than

the institutions absorbed by

other organizations, but very

few differences were significant

(see tables 1O and 11). 

Both the median and the average size of the survivors was

much greater than the institutions purchased, but there were

no statistically significant differences in asset size. As of the

PCA date, the 18 survivors had

a median asset size of $91 million; the 21 institutions purchased had a median asset size

of $38 million. 

The survivors had significantly

higher noninterest income

(which is common for larger

institutions) in the year before

they fell below the PCA limit.

They reported a median 2.19

percent of earning assets in

noninterest income, while the

institutions that were purchased reported O.83 percent.

This difference dwindled as the

survivors approached the PCA

threshold, and it was less significant by the time they fell

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Table 10 

Comparison of Near-Failures by Outcome 

Survivors Purchased Difference 

AS OF THE PCA DATE 

Number of Institutions 18 21 3 

Median Total Assets ($MM) 91 38 (53) 

Median Asset Growth (7.24) (12.57) (5.33) 

Institutions with CAMELS Rating of 4 or 5 (%) 77.80 90.50 12.70 

Median Balance Sheet Percentages 

Investment Securities 12.46 8.83 (3.63) 

1-4 Family Mortgages 18.01 16.38 (1.63) 

Commercial Real Estate 6.42 12.39 5.97 

Multifamily Real Estate 1.42 0.53 (0.89) 

Commercial & Industrial Loans 6.44 12.60 6.16 

Deposits 97.44 97.89 0.45 

Median Rolling Four-Quarter Performance Ratios 

Return on Assets (4.20) (6.04) (1.84) 

Return on Equity (116.31) (125.72) (9.41) 

Yield on Earning Assets 8.55 9.12 0.57 

Cost of Funding Earning Assets 4.15 4.46 0.31 

Net Interest Margin 4.75 4.69 (0.06) 

Noninterest Income to Earning Assets 1.49 0.83 (0.66)* 

Noninterest Expense to Earning Assets 8.11 6.14 (1.97) 

Net Operating Income to Assets (4.33) (6.07) (1.74) 

Net Charge-offs to Loans 3.17 4.56 1.39 

Loss Provision to Average Assets 1.96 4.08 2.12** 

Loss Provision to Net Charge-offs 118.54 124.65 6.11 

Efficiency Ratio 133.11 115.98 (17.13) 

Median Condition Ratios 

Loss Allowance to Loans 2.73 5.05 2.32 

Loss Allowance to Noncurrent (Coverage Ratio) 61.40 74.66 13.26 

Noncurrent & OREO to Assets 7.89 6.02 (1.87) 

OREO to Assets 1.10 0.57 (0.53) 

Noncurrent RE Loans to RE Loans 2.98 3.54 0.56 

Noncurrent Loans & Leases to Loans & Leases 8.53 6.36 (2.17) 

Noncurrent Loan Growth 22.71 29.44 6.73 

Equity Capital Ratio 1.00 0.88 (0.12) 

Core Capital (Leverage) Ratio 0.77 1.26 0.49 

Equity Capital plus Reserves to Assets 3.80 4.11 0.31 

AS OF MID-YEAR BEFORE PCA DATE, OR MOST RECENT EXAM 

Median Economic Condition Measures 

Problem Bank Index 0.05 0.01 (0.03) 

Geographic Diversification Index 1.00 1.00 0.00 

Median Examiner Classification Ratios 

Reserves to Estimated Reserves 104.05 91.97 (12.08) 

Classified Assets to Assets 5.93 12.97 7.04 

* Significant at the 10% level. 

** Significant at the 5% level. 

*** Significant at the 1% level. 

below the PCA limit. At that

time they reported 1.49 percent, while the purchased

institutions reported O.83 percent.

The near­failures that were

purchased held a higher median percentage of commercial

real estate and C�I loans, but

this difference was not significant. Capital plus reserves

were higher for the purchased

institutions, but again, the difference was not significant.

The level of nonperforming

assets was lower for the purchased institutions, but not

significantly. The performance

of the purchased institutions

seemed worse because of higher losses, higher net chargeoffs and higher provisions for

loan losses, but none of these

differences was significant.

Buyers may have been attracted to the higher levels of capital and reserves and lower

nonperforming assets of the

aquired institutions, but the

lower performance might make

a buyer take pause. 

Tracking Near-failures That

Survived One Year Later

For the 18 near­failures that

survived, we compared their

results during the year they

crossed the PCA threshold to

the following year; the results

are in table 12. This group

showed many significant differences across the two periods,

mainly related to capital and

performance. 

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Table 11 that both failed banks and

Comparison of Near-Failures by Outcome 

Survivors Purchased Difference 

near­failures had capital infusions, but the infusions were

larger for the near­failures. We

AS OF ONE YEAR BEFORE THE PCA DATE found that some of the failed

Number of Institutions 18 21 3 

Median Total Assets ($MM) 82 44 (38) 

Median Asset Growth (1.08) 0.34 1.42 

Institutions with CAMELS Rating of 4 or 5 (%) 66.70 61.90 (4.80) 

Median Balance Sheet Percentages 

Investment Securities 8.51 10.55 2.04 

1-4 Family Mortgages 16.65 14.99 (1.66) 

Commercial Real Estate 7.41 13.95 6.54 

Multifamily Real Estate 1.91 0.69 (1.22) 

Commercial & Industrial Loans 4.41 10.41 6.00 

Deposits 90.64 92.09 1.45 

Median Rolling Four-Quarter Performance Ratios 

Return on Assets (0.75) (0.72) 0.03 

Return on Equity (12.91) (12.37) 0.54 

Yield on Earning Assets 8.21 8.70 0.49 

Cost of Funding Earning Assets 3.65 4.12 0.47 

Net Interest Margin 4.59 4.68 0.09 

Noninterest Income to Earning Assets 2.19 0.83 (1.36)** 

Noninterest Expense to Earning Assets 7.30 4.52 (2.78)* 

Net Operating Income to Assets (0.78) (0.75) 0.03 

Net Charge-offs to Loans 1.74 1.46 (0.28) 

Loss Provision to Average Assets 1.14 1.25 0.11 

Loss Provision to Net Charge-offs 101.73 129.64 27.91 

Efficiency Ratio 94.12 86.45 (7.67) 

Median Condition Ratios 

Loss Allowance to Loans 1.82 2.09 0.27 

Loss Allowance to Noncurrent (Coverage Ratio) 51.35 35.08 (16.27) 

Noncurrent & OREO to Assets 5.96 4.50 (1.46) 

OREO to Assets 1.80 0.71 (1.09) 

Noncurrent RE loans to RE Loans 7.02 2.25 (4.77) 

Noncurrent Loans & Leases to Loans & Leases 5.24 3.61 (1.63) 

Noncurrent Loan Growth (2.63) 20.08 22.71 

Equity Captial Ratio 5.71 6.47 0.76 

Core Capital (Leverage) Ratio 5.32 6.02 0.70 

Equity Capital plus Reserves to Assets 7.28 8.03 0.75 

* Significant at the 10% level. 

** Significant at the 5% level.
 

*** Significant at the 1% level.
 

All measures of capital were significantly higher, largely because of

capital infusions. Equity capital increased from a median of 1.OO percent as of the PCA date to 5.28 percent one year later. These results

provide some evidence of the importance of capital infusions that are

large enough to ensure survival. Barakova and Carey (2OO1) found

institutions in our sample also

had capital infusions, but the

amounts were insufficient.

Regulators might be able to

use this evidence to encourage

troubled banks to seek enough

capital to materially improve

their survival chances.

The capital position of the

eighteen survivors improved

much faster than nonperforming assets. Nonperforming

assets as a percentage of total

assets improved from a median

level of 7.89 percent to 4.8O

percent, but this was insignificant. Behind these numbers

was a significant improvement

in noncurrent C�I loans,

down from 7.62 percent to

3.56 percent. 

Over this one­year time span

performance improved significantly, but these 18 institutions still reported a median

loss on assets of O.73 percent,

down from a loss of 4.2O percent one year earlier. The

lower provision expense was a

driving force in reducing losses. Provision expenses fell significantly from a median of

1.96 percent of average assets

to O.6O percent. Net chargeoffs declined, but not significantly. Provisions declined

significantly from a median of

119 percent of net charge­offs

to 54 percent one year later. 

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Table 12 Concluding Remarks 

Comparison of Survivors 

Contrary to the expectations At PCA One Year 

Date Later Difference of many economists, PCA has

Number of Institutions 18 18 0 

Median Total Assets ($MM) 91 104 13 

Median Asset Growth (7.24) (4.74) 2.50 

Median Balance Sheet Percentages 

Investment Securities 12.46 10.26 (2.20) 

1-4 Family Mortgages 18.01 20.39 2.38 

Commercial Real Estate 6.42 7.59 1.17 

Multifamily Real Estate 1.42 1.25 (0.17) 

Commercial & Industrial Loans 6.44 10.83 4.39 

Deposits 97.44 92.23 (5.21)*** 

Median Rolling Four-Quarter Performance Ratios 

Return on Assets (4.20) (0.73) 3.47*** 

Return on Equity (116.31) (19.16) 97.15*** 

Yield on Earning Assets 8.55 8.48 (0.07) 

Cost of Funding Earning Assets 4.15 4.11 (0.04) 

Net Interest Margin 4.75 4.54 (0.21) 

Noninterest Income to Earning Assets 1.49 1.01 (0.48) 

Noninterest Expense to Earning Assets 8.11 6.81 (1.30) 

Net Operating Income to Assets (4.33) (0.77) 3.56*** 

Net Charge-offs to Loans 3.17 1.37 (1.80) 

Loss Provision to Average Assets 1.96 0.60 (1.36)*** 

Loss Provision to Net Charge-offs 118.54 54.30 (64.24)** 

Efficiency Ratio 133.11 98.59 (34.52) 

Median Condition Ratios 

Loss Allowance to Loans 2.73 2.23 (0.50) 

Loss Allowance to Noncurrent (Coverage Ratio) 61.40 51.27 (10.13) 

Noncurrent & OREO to Assets 7.89 4.80 (3.09) 

OREO to Assets 1.10 1.06 (0.04) 

Noncurrent RE Loans to RE Loans 2.98 3.08 0.10 

Noncurrent Loans & Leases to Loans & Leases 8.53 5.73 (2.80) 

Noncurrent Loan Growth 22.71 (29.97) (52.68)* 

Equity Capital Ratio 1.00 5.28 4.28*** 

Core Capital (Leverage) Ratio 0.77 5.28 4.51*** 

Equity Capital plus Reserves to Assets 3.80 7.58 3.78*** 

* Significant at the 10% level. 

** Significant at the 5% level. 

*** Significant at the 1% level. 

not resulted in the FDIC's

experiencing little or no loss

when a depository institution

fails. From 1994 to 2OOO, most

failures imposed significant

costs (as a percentage of

assets) on the insurance funds.

However, 55 percent of the

FDIC­insured institutions that

fell below the PCA threshold

for critically undercapitalized

institutions avoided failure,

and almost 3O percent of the

failed institutions never

breached the PCA threshold. 

We explored the tradeoffs

associated with the PCA

threshold for critically undercapitalized institutions. When

market­value solvent institutions breach the threshold,

both financial costs and nonfinancial costs (loss of freedom)

are imposed on many of the

bank owners and, to a lesser

extent, supervisors. These

costs are probably offset by

savings associated with prompt

closure (for failed institutions)

and with higher survival rates

(for troubled institutions, if the

threshold successfully aids in

recapitalizing some institutions

that would otherwise fail).

Ideally, the PCA threshold

would be set at the level that

balances these tradeoffs to

yield the highest net benefit to

society. Because the tradeoffs

are difficult to measure, we do

not know the optimum level.

However, the decidedly mixed

outcomes of the institutions

2003, VOLUME 15, NO. 2 28 
 

FDIC Banking Review 

that have breached the threshold to date provide

us with some assurance that the current threshold

is not too wide off the mark. If almost all of the

institutions that breached the threshold had survived or if almost all of them had failed, then it

would be more likely that an adjustment to the

threshold level would yield substantial benefits. 

We investigated the differences between the critically undercapitalized institutions that did and did

not fail, in hopes of finding information that could

be used to improve the regulatory treatment of

seriously troubled banks. The most meaningful

differences across outcomes are related to nonperforming assets, coverage ratios (calculated as

reserves divided by noncurrent loans), and the

local economy. The failed institutions had median

nonperforming ratios that were significantly higher

than those of the near­failures, particularly for real

estate loans. The high­cost failures had nonperforming levels that were roughly double those of

the low­cost failures. The nonperforming levels of

the low­cost failures and of the near­failures were

roughly the same.

Of all the measures we tested, the coverage ratio

appears to be the most useful indicator that serious losses may await the FDIC. At the PCA date,

the median coverage ratio was 43 percent for the

failures and 64 percent for the near­failures. However, the median coverage ratio was higher for the

low­cost failures than for the near­failures (69 percent versus 64 percent). The median coverage

ratio for high­cost failures was only 38 percent. 

The high­cost failures were much more likely to

be located in areas where a relatively large number

of banks were experiencing problems. Unlike the

low­cost failures and near­failures, they were also

more likely to have occurred in 1994 and 1995-a

period at the end of the banking crisis, when some

markets for troubled assets (particularly commercial real estate markets) were sluggish. Thus some

of the differences in outcome are probably related

to the marketplace rather than to individual bank

characteristics. During periods of stress, asset values sometimes experience steep declines, and

slow­moving markets increase the difficulty of

measuring asset values. Because of these phenomena, the FDIC may experience higher loss rates

during periods of stress than during good times,

even with PCA.

The differences in other items were smaller. The

failed banks had somewhat riskier asset portfolios

than the near­failures; likewise, the high­cost failures had riskier asset portfolios than the low­cost

failures. The performance measures were similar

across groups. There were few differences

between the low­cost failures and near­failures,

and-surprisingly-the differences tended to favor

the low­cost failures. The near­failures that were

purchased were also similar to those that survived,

except that the surviving institutions were larger.

There are several reasons why these results may

not be robust in the future. First, our sample period did not include a full business cycle, and we

have found evidence, albeit limited, that the

results vary across the business cycle. Second, a

disproportionate number of the high­cost failures

occurred in 1994-1995; thus, regime changes

(which we were unable to isolate) may have influenced the results. Third, we did not test for

intangible items such as the quality of bank management, which could be important. Finally, 

historical results do not always provide a good

indicator of future performance.

Recommendations 

The PCA regulations emphasize capital and not

reserves. Because we found that loan­loss reserves

differentiate relatively strong and weak institutions that have already fallen below the PCA

threshold, we think the level of reserves should be

studied more closely. Instead of trying to find a

better threshold capital level for critically undercapitalized institutions, regulators may want to

refine the rules governing reserves or limit the discretion of seriously troubled banks to set their own

29 2003, VOLUME 15, NO. 2 
FDIC Banking Review 

reserve levels.6O If troubled banks consistently

adjust their reserves so they are always adequate

to absorb the estimated credit losses associated

with the banks' loan portfolios, then capital would

become a better measure of condition. 

We recommend that regulators attempt to develop

a formula for minimum reserve levels that could

potentially be used to improve the supervision of

seriously troubled institutions.61 Our results give

us hope that such a formula would be feasible.62

Once such a formula were developed, regulators

could require that seriously troubled banks set

reserve levels by using the higher of their normal

reserving procedures or the formulaic approach60 Regulators often use cease-and-desist orders to force troubled banks to use 

appropriate methods for setting reserves. Because we did not collect data on 

formal and informal actions, we do not know whether there was a relationship between such orders and coverage ratios. In addition, the heterogeneity 

of banks, coupled with the amount of subjective judgment inherent in reserve 

levels, may make the task of limiting bank discretion through industry-wide 

rules a daunting one. 

61 This idea is not original with us. See also U. S. Department of the Treasury (1991), Berger et al. (1991), and Jones and King (1995). 

62 Based on our research, the formula might use a combination of classified 

assets, asset types, changes in and levels of noncurrent loans and OREO, and 

data about local-market conditions. Given the results of the Jones and King 

(1995) estimate, the appropriate formula for banks approaching insolvency 

may differ from the optimum formula for less-troubled banks. Thus it would 

be important to match the regulatory use of the formula to the methods and 

data used in its development. 

at least for calculating regulatory capital.63 Alternatively, seriously troubled banks could be allowed

to record reserve levels that fell below the formula

only if approved by an examiner or the FDIC.64

Because many troubled institutions are slow to

adjust reserve levels for deteriorating conditions,

this approach could potentially reduce insurance

fund costs by hastening the closure of non­viable

banks. This approach might also improve the

tradeoffs associated with the 2 percent PCA

threshold, since it appears that the high­cost failures would be more seriously impacted by such a

change than the low­cost failures or the near­failures. Alternatively, regulators could adopt other,

less prescriptive ways to use this information in

the supervisory process. 

63 A formulaic approach may not meet GAAP, in which case the regulatory 

burden associated with a change may increase. The regulatory burden could 

be mitigated if the formula were used solely for setting regulatory capital 

standards. 

64 If such a policy were adopted, it might be beneficial for a separate team 

of FDIC staff (including examiners) to be involved in such decisions. This 

might reduce the potential for inefficiencies stemming from the principal-agent 

problem, which is eloquently described by Mishkin (1997). 

2003, VOLUME 15, NO. 2 30 
FDIC Banking Review 

APPENDIX 1 

Summary of the PCA Provisions in FDICIA 

The PCA provisions in FDICIA require that banking regulators take prespecified actions whenever bank

capital levels fall below established levels. Table A1­1 summarizes the requirements for each capital

level, except for critically undercapitalized institutions (defined as institutions with a leverage ratio below

2 percent). 

Table A1-1 

Capital-Ratio Thresholds under PCA 

Capital Ratio 

Capital Category Total Risk-based Tier 1 Risk-based Leverage 

Well-capitalized a 10% or more, and 6% or more, and 5% or more 

Adequately capitalized 8% or more, and 4% or more, and 4% or more 

Undercapitalized b Less than 8%, or Less than 4%, or Less than 4% 

Significantly undercapitalized Less than 6%, or Less than 3%, or Less than 3% 

Source: GAO (1997). 

a An institution is not considered to be well-capitalized if it is subject to a formal regulatory action 

that requires the institution to meet and maintain a specific capital level. 

b The leverage ratio can be as low as 3% if the institution has a CAMELS rating of 1. 

Table A1­2 provides a summary of the required actions and limits set by FDICIA. The limits are additive. For example, the restrictions for a significantly undercapitalized bank include those for an undercapitalized institution as well.

Table A1-2 

Mandatory Actions under the PCA Provisions of FDICIA 

Well-capitalized and adequately capitalized 

• None 

Undercapitalized 

• May not pay dividends or management fees. 

• Subject to increased monitoring. 

• Must implement acceptable capital plan. 

• Asset growth restricted. 

• Approval needed for acquisitions, branching, and new business lines. 

• May not issue brokered deposits. 

• Access to discount window restricted. 

Significantly undercapitalized 

• Subject to provisions applicable to undercapitalized institutions. 

• Compensation of senior officers restricted. 

• Unless action would not further the purposes of PCA, supervisor shall 

— Require bank to raise additional capital or be merged. 

— Enforce Section 23A of Federal Reserve Act as if exemptions in act 

do not apply. 

— Restrict deposit interest rates to those prevailing in region. 

Source: Primarily Jones and King (1995). 

Critically undercapitalized 

• After 60 days, may not make payments on subordinated debt without 

approval. 

• Must be placed in receivership 

— Within 90 days, unless action would not achieve the purposes 

of PCA. 

— Within 270 days, unless specific statutory requirements are met. 

• Access to discount window restricted more than for undercapitalized. 

• May not do the following without FDIC approval 

— Undertake material transactions, except in usual course of business. 

— Extend credit for any highly leveraged transaction. 

— Make any material change in accounting methods. 

— Undertake covered transactions, as defined in Section 23A of 

Federal Reserve Act. 

— Pay excessive compensation or bonuses. 

— Pay interest on liabilities above prevailing market rates. 

31 2003, VOLUME 15, NO. 2 
FDIC Banking Review 

APPENDIX 2
 

Details on Selected Calculations
 

This appendix discusses selected calculations and

accounting policies that influence the data sources

used for this article. Some of the accounting policies inhibit the comparability of failed­bank data

across time. The more material items are discussed here.

Calculation of FDIC Loss 

At resolution, the FDIC generally bases its loss

estimate on an Asset Valuation Review (AVR).

The AVR estimate of loss is calculated as the difference between the FDIC's anticipated outlay

and the net present value of the funds recovered

from the receivership.65 As the receivership progresses, the loss calculation (as published by the

FDIC in the Failed Bank Cost Analysis (FBCA))

changes somewhat: it is essentially calculated as

the FDIC's resolution outlays minus the funds

recovered from the receivership and the estimated

funds to be recovered from the receivership in the

future.66 Both cost figures exclude pre­closing

expenses associated with preparing the bank for

resolution and determining which deposit

accounts are insured.67 These items tend to be a

relatively small component of FDIC losses. 

65 The steps of the calculations are (1) estimate the net present value of the 

assets; (2) estimate the volume of creditors in each creditor class; (3) estimate payments to each creditor class, given the discounted asset value estimates; and (4) estimate the FDIC loss as the difference between the FDIC’s 

claim and its anticipated recoveries. 

66 For terminated receiverships, all cash flows are undiscounted. For ongoing 

receiverships, actual recoveries to date are undiscounted, and expected future 

recoveries are discounted to the date of the estimate. For example, assume 

that a failure occurred in 1995. The FDIC loss as of year-end 2000 would be 

calculated as the 1995 outlay minus all dividends paid through year-end 2000 

minus estimated future dividends (which would be based on the anticipated 

future recoveries from the receivership, discounted to year-end 2000). In 

addition, the interest due to the FDIC is calculated and placed on the 

receivership books in cases in which the FDIC expects some or all of the 

interest to be paid in the near future. This interest is excluded from the cost 

reported in the Failed Bank Cost Analysis (FBCA) even in cases in which it is 

reported and/or paid by the receivership. 

67 Differences in the cost of insurance determination are considered when the 

FDIC determines the least-cost bid. The cost of preparing for resolution is a 

sunk cost that would not influence the selection of a winning bidder and 

thus is excluded from the least-cost test. 

The FDIC is required to fund the insured deposits

at the time of failure; however, the receivership

pays dividends to the FDIC and other creditors as

assets are sold (after meeting expenses). Thus, the

FDIC has working capital requirements during the

interim period between the failure date and the

dates when dividends are paid by the receivership. 

Because funding costs are a real cost to the FDIC

(in the form of lost interest income to the insurance fund) but are largely excluded from the

FBCA, we estimated the funding cost for each

bank where the FBCA figure was the most up­todate published figure.68 To make the estimate, we

collected the FDIC claim and dividend payments

made through year­end 2OOO. For open receiverships, we assumed that the remaining asset value

(based on discounted cash flow, net of expenses)

would be paid to the FDIC on December 31,

2OOO. Then, we calculated the interest that the

receivership owed to the FDIC on the portion of

its claim that either had been paid or that we

assumed would have been paid on December 31,

2OOO. We used the FDIC's average yield on its

investments as the interest rate. We also treated

two items included in the FBCA figure as holding

costs: interest earned on the receivership's cash

balances (which reduced holding costs) and interest paid to other creditors by the receivership

(which increased holding costs). This allows for a

more accurate comparison of the economic costs

across receiverships, and of the initial cost estimate and the latest available cost estimate.69

Across the full sample of banks, the median difference between the FBCA cost and the cost used in

this article was 3.75 percent of total assets as of

the quarter­end date before failure. 

68 Our estimates do not follow FDIC regulations and practices for the payment of interest to receivership creditors. 

69 This adjustment still does not result in a fully consistent comparison of 

economic costs. The initial cost estimate uses risk-adjusted discount rates 

based on the asset composition of the bank, whereas the adjustment to the 

2000 FBCA cost estimate effectively uses the FDIC’s cost of funds for discounting. 

2003, VOLUME 15, NO. 2 32 
FDIC Banking Review 

Differences between GAAP and the FDIC’s 

Receivership Accounting Policies 

Whereas open banks normally prepare financial

statements and Call Reports using accounting

principles that are predicated on their being ongoing concerns, receiverships use cash­basis accounting. The primary difference between bank and

FDIC receivership accounting policies relates to

the treatment of accrued items, reserves for troubled assets, and intangible assets. Receiverships

do not typically record accrued items or intangible

assets, except in cases where certain intangible

assets are recorded on the books at one dollar for

control purposes.7O Receiverships record loans at

gross book value, thereby reversing partial chargeoffs or reserves recorded by the failed bank.71

These differences sometimes result in large

changes in the equity ratio of a bank when the

basis of accounting shifts at failure, at times resulting in a receivership's initially showing more equity than the failed bank. As assets are sold,

receivership equity inevitably declines, reflecting

the asset recovery received. 

At closing, the FDIC calculates the institution's

closing balance sheet and then makes adjustments

to conform to the receivership accounting policies.

Although we collected both initial balances, we

relied primarily on the institution's closing balance

sheet for analysis because it is more comparable to

the Call Report. Thus, all references to the bank's

balance sheet at closing exclude the adjustments

made to conform to receivership accounting policies unless an exception is cited.

The data on asset composition as of the failure

date and during the receivership should be interpreted with care, partly because of differences in

the accounting basis (discussed above) and partly

70 For example, assume that a bank sells a portfolio of mortgages but retains 

the servicing rights. The servicing rights may have real value, but they are 

typically recorded on the books of the receivership at one dollar. 

71 Gross book value is typically defined as the historical cost or unpaid loan 

balance minus any charge-offs that were recorded by the bank. If there are 

partial charge-offs recorded by the failed bank that are identifiable at failure, 

they will normally be reversed; however, this is frequently not the case. 

Thus, the treatment of charge-offs is not always consistent across receiverships. 

because of different asset categories.72 It appears

likely that many-perhaps even all-of the failed

banks used asset categories for their general

ledgers that did not align closely with the asset

categories used on the Call Report.73 Differences

in asset category may be quite small for securities

and other real estate owned (OREO) but appear

much larger for loans. Other differences may

occur because of limits in data availability and

time. Therefore, the results should be interpreted

with these caveats in mind. 

The financial statements of a receivership differ

substantially from those of an ongoing bank or

thrift in other ways as well. For example, the liabilities are grouped according to claims that have

been proven (or remain unproven), and the

income statement does not include interest

expenses for most classes of creditors.74

Asset Losses, Charge-offs, and Reserves 

Asset losses are a primary factor that determines

the FDIC loss. There are substantial disparities in

the information collected about asset losses of a

failed bank over its life cycle. This section discusses certain adjustments made to the receivership loss figures to improve the comparability

across the bank's life cycle and summarizes differences in policies and practices between the asset

losses estimated at resolution and the asset losses

recorded by the receivership. 

72 By asset categories, we mean types of assets grouped by loan purpose 

(such as single-family mortgages or C&I loans). 

73 Banks frequently categorize assets on their general ledgers on the basis of 

the subsystems used in servicing the assets. For example, all fixed-rate amortizing instruments may be boarded on one subsystem (regardless of loan purpose), and the general ledger may carry one set of accounts that is used for 

all such instruments. To prepare the Call Reports, a bank would typically use 

information from both the general ledger and the subsystems. At failure, the 

FDIC attempts to align the general ledger categories with those used internally by the FDIC receiverships (stratified by loan purpose, as in the Call Report), 

but time constraints and the differences in asset categories limit the FDIC’s 

ability to succeed in this effort. 

74 For many creditors, receiverships pay post-insolvency interest only after the 

principal is paid in full. The FDIC’s current policy is to calculate and record 

the interest cost due to creditors when 95 percent of the principal balance 

has been repaid. Such costs are treated as an adjustment to equity rather 

than an interest expense, because the bulk of such claims are typically 

accrued but not paid and receiverships use cash-accounting principles. 

33 2003, VOLUME 15, NO. 2 
FDIC Banking Review 

In preparation for resolution, the FDIC prepares

an Asset Valuation Review (AVR) that estimates

asset losses for the entire portfolio. These losses

are based on total assets-gross of reserves-as

per the Call Report.75 To prepare the estimates,

analysts review the available documentation, project all cash flows (including associated income

and expenses), and discount the cash flows using a

market­based discount rate. These estimates are

made for various pools of assets, which are typically packaged to facilitate the marketing process.

Because these packages do not always align with

the asset categories recorded on the Call Report,

we have largely omitted such comparisons. 

Receiverships record asset losses as the difference

between the gross asset balance (after reversing

reserves and, to some extent, charge­offs) and the

sales price. The receivership asset­loss figures are

not discounted, and they exclude sales expenses

and net income (loss) received prior to the sale.76

Because both the definition of asset loss and the

asset categories differ between the AVR and the

receivership, we made no attempt to analyze

changes in loss estimates between resolution and

year­end 2OOO by asset category.

Receiverships record judgments (awards made in a

court) and certain deficiency balances (chargedoff assets) at gross book value, whereas they are

typically not recorded at all on the Call Reports.

For this analysis, we exclude judgments and deficiencies (both balances and losses) recorded by

the receiverships to improve the comparability of

losses over the life of the failed bank.

Some of the receiverships had unsold assets as of

year­end 2OOO. To facilitate comparisons across

the full sample of failed institutions, we estimated

future losses by asset type and incorporated these

75 Charge-offs are not reversed.
 

76 Although accounting policy excludes all asset-sales expenses from these figures, occasionally certain asset-sales expenses incurred at the time of sale are
 

included.
 

estimates into the asset loss rates for the receiverships. To prepare the estimates, we relied upon

supporting documents for the FDIC's year­end

financial statements for 2OOO . 

In addition to differences in practices and policies,

the asset­loss data are difficult to interpret because

of activity that occurs between the last Call

Report and the failure date. Because no financial

statements are filed during this period, we have no

information about charge­offs, loss provisions,

asset sales, or realized losses. If a bank sells a substantial amount of assets during the period, we

have no record of the transaction. We can merely

make inferences based on balance sheet changes

between these dates. Comparisons also become

difficult when interest rates or the health of the

economy change during the course of the resolution and the receivership.

In summary, comparisons of financial data over

time and across the stages of a failed bank (prefailure, failure, receivership) are difficult to interpret because of differences in accounting policy

and data collection, changes in the economy, and

missing data for a brief period. For a typical failed

bank, the initial equity balance of the receivership

is markedly higher than the closing equity found

on the Call Report. Because many receiverships

begin with positive equity balances, losses recorded on the income statement of the receiverships

usually exceed the FDIC's loss on its receivership

claim.77 Asset losses recorded by the receivership

may be either higher or lower than the reserve

levels recorded by the bank and the original AVR

asset­loss estimates because of different calculation

methods-even in cases where original expectations are met exactly. Therefore, one must be

careful when interpreting comparisons of results

over the life of a failed bank.

77 This could also occur because of losses incurred by other creditors. However, most at-risk creditors flee banks before failure, so the FDIC and the 

bank’s stockholders typically bear almost all the losses at failure. 

2003, VOLUME 15, NO. 2 34 
 

FDIC Banking Review 

APPENDIX 3
 

Comparison of Asset Composition: Call Report vs. Receivership
 

We collected high­level balance sheet data as

recorded both by the failed bank's general ledger

on the date of failure, and by the initial balance

sheet of the receivership (that is, at failure), after

adjustments were made over the life of the

receivership.78 We also compared these to the

final Call Report data filed by the institution. The

comparison is found in table A3­1. All figures are

shown as a percentage of total assets.

The data on asset composition at receivership are

difficult to interpret, partly because of accounting

differences and partly because of different asset

categories. Appendix 2 discusses these obstacles

to straightforward comparison. The results should

be interpreted with these caveats in mind.

Both the low­cost and high­cost failures shrank

between the final Call Report date and failure.

The median shrinkage was 7.1O percent for lowcost failures and 9.34 percent for high­cost failures. Between failure and receivership, both

groups experienced a small increase in assets

(attributable largely to the reversal of reserves). 

From the final Call Report date to failure, the

median level of securities increased slightly for

low­cost institutions and decreased slightly for

high­cost institutions. A review of the results for

individual institutions indicates that a few of the

low­cost institutions apparently sold a material

amount of loans during the intervening period. 

The mortgage results were puzzling. From the

final Call Report date to failure, the median percentage of mortgages to total assets increased:

from 23.OO percent to 27.53 percent for low­cost

failures, and from 17.58 percent to 22.O9 percent

for high­cost failures. The increase continued in

78 Some of the adjustments made during the receivership relate to post-closing 

activities rather than the institution’s asset balance at closing. We dropped 

all adjustments that we could identify as occurring after failure. 

the receivership. The change in median levels

between the failure date and the receivership is

negligible for the low­cost failures but large (22.O9

percent at failure; 3O.25 percent in receivership)

for the high­cost failures. Reviewing the results by

institution, one infers that the largest factor is differences in asset category definitions.79 Some portion of the increase between the Call Report date

and failure is probably attributable to reductions in

other types of assets (thereby increasing the proportion of mortgages). 

Like the mortgage results, the C�I loan results

were characterized by substantial swings that frequently appear to be changes in asset type definitions at failure. The median percentages dropped

for the low­cost failures. For the high­cost failures, they increased between the Call Report date

and failure but decreased during the receivership.

The OREO results appear to be untainted by differences in asset category definitions. Between

the final Call Report and failure, there was a substantial change in the median ratio for high­cost

failures (3.64 percent on the final Call Report,

5.69 percent at failure) but little change for the

low­cost failures (O.69 percent on the final Call

Report, O.91 percent at failure). The median

increase in OREO during the receivership, calculated in percentage points of total assets at failure,

was similar (1.96 percent for low­cost failures,

2.19 percent for high­cost failures). These figures

include foreclosure activity. 

There were few changes in reserves between the

final Call Report date and failure. Reserves are

reversed in receivership.

79 Unfortunately, we were not always able to determine which report provides 

the most accurate picture of the institutions. For some of these institutions, 

there was a change in receivership that appears to bring the institution’s balance sheet into closer conformity with the Call Report. This type of change 

probably indicates that the asset types recorded on the institution’s general 

ledger did not align well with the Call Report. 

35 2003, VOLUME 15, NO. 2 
FDIC Banking Review 

There were substantive changes in the median

levels of other assets (including cash and fed

funds). From the final Call Report date to failure,

the median level of other assets increased slightly

for low­cost institutions (16.21 percent to 18.92

percent) and decreased slightly for high­cost institutions (19.54 percent to 14.24 percent). Most

institutions reduced their balances of other assets

between the Call Report and failure, although a

Table A3-1 

few institutions showed substantive increases

because of apparent asset sales. We were unable

to ascertain the underlying reasons for the persistent reductions shortly before failure. There were

also large and persistent reductions in the

receivership: the most likely cause was write­offs

of intangible assets, accrued interest, and prepaid

expenses. 

Comparison of Median Balance Sheet Composition before and at Failure 

(as a percentage of total assets) 

Low-Cost Failures (%) High-Cost Failures (%) All Failures (%) 

Bank Bank Bank 

Call General Receiver- Call General Receiver- Call General ReceiverReport Ledger ship Report Ledger ship Report Ledger ship 

Securities 10.82 12.91 18.01 9.73 7.55 12.04 10.06 9.10 12.56 

Mortgages 23.00 27.53 28.98 17.58 22.09 30.25 18.94 27.27 28.98 

C&I Loans 31.74 26.31 23.90 33.95 44.47 36.36 33.50 32.62 33.08 

Other Loans 3.07 3.58 3.54 4.00 5.18 5.31 3.92 5.01 4.97 

OREO 0.69 0.91 3.80 3.64 5.69 5.33 2.47 4.15 5.14 

Reserves –2.76 –2.58 0.0 –2.68 –3.09 0.0 –2.72 –3.04 0.0 

Other Assets 16.21 18.92 7.04 19.54 14.24 6.35 19.33 14.35 6.51 

2003, VOLUME 15, NO. 2 36 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

FDIC Banking Review 

BIBLIOGRAPHY 

Aggarwal, Raj, and Kevin Jacques. 2OOO. The Impact of FDICIA and Prompt Corrective

Action on Bank Capital and Risk: Estimates Using a Simultaneous Equations

Model. Journal of Banking and Finance 25:1139-6O.

Barakova, Irina, and Mark Carey. 2OO1. How Quickly do Troubled U.S. Banks

Recapitalize? With Implications for Portfolio VaR Credit Loss Horizons. Working

paper presented at the 2OO1 Financial Management Association meetings.

Benston, George J., and George G. Kaufman. 1997. FDICIA after Five Years. Journal of

Economic Perspectiles 11, no. 3:139-58.

Barth, Mary E., Wayne R. Landsman, and James M. Wahlen. 1995. Fair Value

Accounting: Effects on Banks' Earning Volatility, Regulatory Capital, and Value of

Contractual Cash Flows. Journal of Banking and Finance 19:577-6O5.

Berger, Alan N., K. K. King, and J. M. O'Brien. 1991. The Limitations of Market Value

Accounting and a More Realistic Alternative. Journal of Banking and Finance

15:753-83.

Carnell, Richard Scott. 1997a. FDICIA After Five Years: What has Worked and What

has Not? in FDICIA: Bank Reform File Years Later and File Years Ahead, edited by

George G. Kaufman, 11-16, JAI Press. 

---. 1997b. A Partial Antidote to Perverse Incentives: The FDIC Improvement Act

of 1991 in FDICIA: Bank Reform File Years Later and File Years Ahead, edited by

George G. Kaufman, 199-233, JAI Press. 

Dahl, Drew, John P. O'Keefe, and Gerald A. Hanweck. 1998. The Influence of Examiners

and Auditors on Loan­Loss Recognition. FDIC Banking Reliew 11, no. 4:1O-25.

Eisenbeis, Robert A., and Larry D. Wall. 2OO2. Reforming Deposit Insurance and FDICIA.

Federal Reserve Bank of Atlanta Economic Reliew 87, no. 1:1-16.

Federal Deposit Insurance Corporation (FDIC). 1997. History of the Eighties-Lessons for

the Future: An Examination of the Banking Crises of the 1980s and Early 1990s. 2

vols. FDIC.

Gilbert, R. Alton. 1992. The Effects of Legislating Prompt Corrective Action on the Bank

Insurance Fund. Federal Reserve Bank of St. Louis Reliew 42, no. 4:3-22.

Gilbert, R. Alton, and Levis A. Kochin. 1989. Local Economic Effects of Bank Failures,

Journal of Financial Serlices Research 3:333-45.

Gunther, Jeffrey W., and Robert R. Moore. 2OOO. Financial Statements and Reality: Do

Troubled Banks Tell All? Federal Reserve Bank of Dallas Economic and Financial

Reliew (Third Quarter), 3O-35.

Jones, David S., and Kathleen Kuester King. 1995. The Implementation of Prompt

Corrective Action: An Assessment. Journal of Banking and Finance 19:491-51O.

Kaufman, George G. 1997. FDICIA After Five Years: What has Worked and What has

Not?, in FDICIA: Bank Reform File Years Later and File Years Ahead, edited by

George G. Kaufman, 35-43, JAI Press. 

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Mailath, George J., and Loretta J. Mester. 1993. When Do Regulators Close Banks? When

Should They? Working Paper no. 93-1O. Federal Reserve Bank of Philadelphia. 

McDill, Kathleen. 2OO2. Federal Deposit Insurance Corporation memo on the loss rates of

bank failures over the business cycle (January 28). 

Mishkin, Frederick S. 1997. Evaluating FDICIA in FDICIA: Bank Reform File Years Later

and File Years Ahead, edited by George G. Kaufman, 17-32, JAI Press. 

Peek, Joe, and Eric S. Rosengren. 1996. The Use of Capital Ratios to Trigger Intervention

in Problem Banks: Too Little, Too Late. Federal Reserve Bank of Boston New

England Economic Reliew (September�October), 49-58. 

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Southern Economic Journal (July), 268-8O.

Shibut, Lynn, and Timothy Critchfield. 2OOO. An Analysis of Low Cost Failures.

Unpublished manuscript (December 11). FDIC.

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and Reforms Needed to Strengthen the Fund. GAO�AFMD­9O­1OO. 

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for Safer, More Competitile Banks. Publication L, no. 1O1-73.

2003, VOLUME 15, NO. 2	 38 
 

FDIC Banking Review 

Do Local Economic Data Improve Off-Site 

Bank-Monitoring Models? 

by Daniel A. Nuxoll, John O'Keefe, and Katherine Samolyk*

Researchers at U.S. bank regulatory agencies

have developed several types of statistical models

to monitor potential problems at individual banks

off-site (that is, without having to visit bank premises). These off-site monitoring models tend to be

"unconditional" forecasting models that use available data on a bank's current and past condition

to predict its future condition; they do not require

the user to "condition" the forecast on assumptions about the future values of any of the variables in the model. Generally the models attempt

to predict one of two phenomena: either that a

bank will fail or that its condition has deteriorated

enough that it will receive a downgrade in its

supervisory rating (composite safety-and-soundness rating) during the next on-site examination.

Although most models use fairly standard measures of banking conditions, variables describing

conditions in the broader economy in which banks

operate have not been important features of the

* Daniel A. Nuxoll is Senior Economist, John O'Keefe is Chief of the Financial 

Risk Measurement Section, and Katherine Samolyk is Senior Financial 

Economist in the Division of Insurance and Research, Federal Deposit 

Insurance Corporation. The views expressed here are those of the authors 

and not necessarily those of the Federal Deposit Insurance Corporation or its 

staff. 

models.1 And whereas historical episodes of

regional recessions and banking-sector difficulties

have been studied, the contribution of economic

data in forecasting future bank distress has

received relatively little attention in empirical

banking research.2

Improving off-site monitoring capabilities would

enable regulatory agencies to allocate supervisory

resources more efficiently and intervene more

promptly and would reduce the costs associated

with bank failures. For these reasons we investigate the extent to which state-level economic data

could be used to improve the performance of standard types of statistical models that forecast a

bank's condition off-site. Specifically, we focus on

the linkages between economic conditions and

problems of bank performance between the mid1 For discussions of off-site monitoring models, see: Cole, Cornyn, and Gunther 

(1995); Gilbert, Meyer, and Vaughan (1999); and Reidhill and O'Keefe (1997). 

2 Samolyk (1994a) finds linkages between state banking conditions and state 

personal-income growth during the 1980s and early 1990s that are consistent 

with the existence of a regional credit channel. Neely and Wheelock (1997) 

conclude that the dispersions in state-level bank earnings can be attributed 

largely to disparities in state economic conditions; similarly, Samolyk (1994b) 

finds that state economic conditions explain significant amounts of observed 

differences in bank asset quality and bank profitability during the1980s and 

the early 1990s. 

39 2003, VOLUME 15, NO. 2 
FDIC Banking Review 

198 s and the early 199 s-a period characterized

by significant regional disparities in both bankingsector and broader economic conditions. The

national economic expansion that followed the

recession of the early 198 s was an uneven one:

agricultural and oil-producing states alike experienced local economic problems and serious banking-sector difficulties. In addition, the national

recession of the early 199 s was largely concentrated along the coasts and was linked to bank

failures in New England and California. Since the

early 199 s the U.S. banking industry has consolidated into larger, more geographically diverse

institutions, so one might argue that the industry

is now less vulnerable to local economic conditions of the type experienced in the 198 s and

early 199 s. Nonetheless, for thousands of small

U.S. banks, linkages between local economic conditions and bank performance are likely to remain

significant. 

Our empirical strategy is to take variables

measuring economic conditions in the state where

a bank is located and add them to statistical

models that attempt to identify institutions likely

to experience financial difficulties. We study the

contribution of state-level economic variables in

three types of forecasting models-specifically,

those that forecast bank failures, those that

forecast changes in the quality of bank assets, and

those that forecast risky bank growth (as indicated

by supervisory rating downgrades). The sole

criterion for success is whether these variables

improve the accuracy of forecasts.

By way of preview, the addition of state-level economic variables generally does not improve upon

the forecasts generated by models using only data

on a bank's condition. Indeed, the models forecasting bank failures and changes in the quality of

bank assets perform about the same or worse when

state-level economic variables are included. The

models predicting risky bank growth, however,

show a more consistent, albeit modest, improvement. These findings do not imply that economic

conditions are unimportant for a bank's performance. Rather, as we discuss in the conclusion, it is

possible that factors not considered in our models

contribute to this finding of no, or little, predictive

improvement. 

The next section discusses the conceptual link

between state-level economic data and bank performance. The subsequent three sections present

the results of incorporating state-level economic

data into models forecasting the three aspects of

bank performance that we focus on (failures,

changes in asset quality, and risky growth). The

final section presents our conclusions and discusses the implications of our findings for future

research on bank off-site monitoring. 

Conceptual Link between Local Economic 

Conditions and Bank Performance 

Because the purpose of our study is to investigate

whether local economic variables can improve the

ability of statistical models to forecast which banks

will experience difficulties, we judge the success of

each model in terms of the accuracy of its forecasts relative to the forecasts of an otherwise

equivalent model that does not include the economic variables. Before we turn to the models we

develop, however, it will be helpful to discuss the

conceptual link between local economic conditions and bank performance. 

Some theories posit that the main comparative

advantage of banks relative to other financial

firms lies in banks' information about and

expertise in lending locally. This advantage is

viewed as particularly important for smaller, morelocalized banking institutions. In making its

lending decisions, bank management must address

the risk that local economic conditions will affect

the profitability of local borrowers and the

subsequent performance of loans granted to those

borrowers. Bank lending tends to move

procyclically as borrowers seek to fund profitable

business opportunities in economic expansions

and to retrench during economic downturns.

Once loans are issued, a bank's profitability and

credit quality will depend to some extent on the

economic fortunes of its borrowers. Indeed, when

economic conditions change dramatically, we

2003, VOLUME 15, NO. 2 40 
 

 

FDIC Banking Review 

expect to find a correlation between

these conditions and the likelihood

that a bank will fail.) Thus, when

local economic conditions vary

substantially, we expect to find some

relationship between these variations

and the performance of local banks.

And because profitability and asset

quality are key factors affecting bank

supervisory ratings, we also expect to

see a link between local economic

conditions and the on-site examination ratings received by institutionsall other things being equal. 

But all other things may not be equal.

The relationship between local economic conditions and a bank's performance also is affected by the

management of the bank. Differences in credit cultures, lending

strategies, underwriting standards,

and asset-and-liability management

will lead to differences in the exposure of institutions to local economic

developments. We expect that "better-managed" banks will be able to

weather local economic downturns

better than poorly managed banks.

Because management is so important

to a bank's success, it receives particular attention during on-site safetyand-soundness examinations. The

summary, or composite, safety-andsoundness rating (CAMELS rating)

reflects not only the bank's current

profitability, asset quality, and capital

adequacy but also the soundness of

3 But since bank failure is an extreme event, its correlation with standard measures of local economic conditions 

(such as income growth or unemployment rates) may be 

more complex than the correlation of continuous performance measures, such as bank asset-quality ratios. In addition, external capital injections or friendly mergers can 

prevent bank failures from occurring. 

the bank's current management.4 The linkages among the

local economic conditions a bank faces, its management policies, its profitability and asset quality, its on-site composite

safety-and-soundness examination rating, and its survival are

depicted in figure 1. 

Despite the multiplicity of factors at play, banks operating in

poorly performing economies are nonetheless more likely to

perform worse than banks in healthier environments. This

suggests that local economic data have the potential to

improve the performance of the statistical models used for

identifying banks that are likely to experience problems.

Whether these data do improve the models' performance is

ultimately an empirical question. But the fairly dramatic

regional differences in U.S. economic conditions and bank performance during the 198 s and early 199 s present a good

opportunity to study this question (especially given the regulatory structure of the industry at the time, and in particular the

interstate banking restrictions that to a large degree delineated

banking activities along state lines). 

Figure 1 

Using Economic Data to Monitor Bank Risk 

Local
 

Economic
 

Conditions
 

Bank
 

Risk-Management
 

Practices and
 

Profit Strategies
 

Profitability 

and 

Asset Quality 

Bank Survival 

or 

Failure 

CAMELS 

Rating 

Data Considerations 

A number of considerations influenced our decision to investigate the usefulness of state-level economic data in off-site

monitoring models. First, we wanted to use economic variables that were consistently reported for all regions during the

study period. Second, we wanted to use variables that would

have been available in a timely fashion for inclusion in off-site

monitoring models. Third, we wanted to use economic data

4 CAMEL stands for Capital, Asset quality, Management, Earnings, and Liquidity. In 1997, the ratings became CAMELS with the addition of a market Sensitivity rating. However, because most of 

our data are from the period before 1997, we refer to CAMEL ratings. 

41 2003, VOLUME 15, NO. 2 
FDIC Banking Review 

measured for the type of geographic

region that reasonably could be

expected to reflect the conditions

faced by many banks. Various data

series are available for counties (or

parishes), states, or Census-level divisions, but given our selection criteria,

state-level data seemed the best

choice.5 A fair number of data series

are available for all states within a

reasonable time frame.6 In addition,

interstate banking restrictions and

state banking laws delineated banking

markets along state lines. Therefore,

for the U.S. banking industry of the

198 s and early 199 s, state-level

economic data seemed to be reasonable measures of the local economic

conditions affecting banks. 

Predicting Bank Failures 

The first part of our study examines

the contribution that state-level economic variables make when added to

standard models predicting bank failures.7 Patterns in the state-level data

during the 198 s and early 199 s suggest that regional economic conditions were related to the incidence of

bank failure. More specifically, states

experiencing economic booms followed by busts tended to have high

failure rates. Figure 2 shows this by

comparing state personal-income

growth rates and bank-failure rates for

Texas and for Massachusetts.

5 State-level economic variables can contribute to off-site 

monitoring models without being perfect measures of the 

relevant economic conditions because they bear on all 

banks. What is necessary is only that the economic variables provide reasonable approximations of the relevant 

"local" conditions for most banks in the sample. 

6 In contrast, although employment and (annual) income 

data are produced at the county level, the latter are not 

available until 18 months after the end of the year. 

7 For more detail, see Nuxoll (2003). 

Although there were also regions where weak economic performance was not followed by high bank-failure rates, these

tended to be regions where the economic weakness had not

been preceded by an economic boom. 

Here we look at whether measures of state-level economic

conditions would have helped supervisors identify the institutions that ultimately failed during the late 198 s and early

199 s. Taking what have become fairly standard logistic

regression models, we use bank financial data at the beginning

Figure 2 

Relationship between State-Level Economic 

Conditions and Bank Failures 

A. Texas 

Boom to 1982—Bust after 1986 Percentage 

15 

10 

5 

0 

-5 

-10 

Bank Failure 

Personal-Income Growth* 

1980 1982 1984 1986 1988 1990 1992 1994 

B. Massachusetts 

Boom to 1986—Bust after 1991 Percentage 

8 

-4 

1980 1982 1984 1986 1988 1990 1992 1994 

Personal-Income Growth* 

Bank Failure 

* Personal-income growth is defined here as the difference between the state personal-income 

growth rate and that for the United States. 

6 

4 

2 

0 

-2 

2003, VOLUME 15, NO. 2 42 
 

FDIC Banking Review 

of a period to predict the likelihood that an institution will fail sometime during a subsequent twoyear interval. In these models, the precise

relationships used to assign bank-failure probabilities are based on the historical relationships

observed for failures during the prior two-year

interval. That is, first we estimate statistical relationships about the conditions preceding failures

during the previous two years, and then we use

these relationships to forecast specific failures during the subsequent two years. 

Because these models generate a failure probability

for each bank, one must choose a critical (or cutoff) probability in order to classify banks as survivors or failures. For example, a critical

probability of 5 percent indicates that all banks

having estimated failure probabilities greater than

5 percent are classified as "predicted failures."

Obviously, choosing a lower, more-stringent 

critical probability will yield a greater number of

predicted bank failures than will a higher, lessstringent one. Furthermore, the accuracy of failure-model predictions is measured in terms of two

types of forecast errors that the model can make:

one, bank failures that are not predicted (missed

failures); and two, surviving banks are erroneously

identified as failures (missed survivors). Thus, in

choosing a critical failure probability, a model user

faces a trade-off in terms of the types of prediction

errors that will be obtained from the model. By

choosing a lower critical probability, a user can

generally reduce the percentage of missed failures

but will increase the percentage of missed survivors. A more accurate failure-prediction model

is one that gives the user a better trade-off in

terms of these forecast errors. In other words,

given the percentage of missed failures yielded by

the user's cutoff, a more accurate model will yield

fewer missed survivors (and a less-accurate model

will yield more). 

Here we report forecast results for two periods. For

the first period, we use the relationship between

bank and state-level economic conditions as of

year-end 1986 and actual failures in the years

1987 and 1988 to predict failures occurring in

1989 and 199 . For the second period, we use the

relationship between bank and state-level economic conditions as of year-end 1988 and actual

failures in the years 1989 and 199 to predict failures occurring in 1991 and 1992. 

Table 1 lists the variables in the bank failureprediction models. As indicated in the top panel,

the basic "banking" model uses fairly standard

bank financial data and supervisory (CAMEL) ratings to predict failure/survival during the subsequent two years. The statistical relationships

yielded by the models for the subperiods studied

here are generally consistent with those reported

by other researchers. All else being equal, banks

with less capital, more asset-quality problems, and

lower supervisory ratings for management and liquidity are assigned higher projected failure probabilities. We next examine the contribution to the

basic banking model made by various proxies

measuring state-level economic conditions (see

the bottom panel of table 1). Model results are

displayed in figure ). 

The solid line in figure )A illustrates the prediction-error trade-off yielded by the banking model

using actual failures in 1987 and 1988 to predict

Table 1 

Variables Included in Bank Failure-Prediction 

Models 

Standard Bank Financial Data 

• Asset-quality measures 

• CAMEL ratings 

• Capital/asset ratios 

Other Bank Data 

• Five years of loan growth, asset growth 

• Growth associated with mergers 

• Mean and standard deviation of operating income 

• Average salary 

• Loan-to-asset ratio 

• Other 

Proxies for Economic Conditions 

(during previous five years) 

• State personal-income growth 

• State employment growth 

• State unemployment rate 

•	 Growth in total loans issued by insured banks headquartered 

in the state 

•	 Growth in total assets held by insured banks headquartered 

in the state 

43	 2003, VOLUME 15, NO. 2 
FDIC Banking Review 

failure in 1989 and 199 . Here the

prediction-error trade-off is not as

good as that depicted in figure )B.

There is a greater trade-off between

minimizing missed survivors and minimizing missed failures. The broken

line summarizes the predictive accuracy of the model when measures of

Figure 3 

Results of Bank Failure-Prediction Models with and 

without State-Level Economic Condition Variables 

A. Forecast Errors 

1988 Data/1989–90 Failures Percentage
 

50
 

Bank Data Only 

Bank and Economic Data 

0 1 2 5 7 10 20 30 40 50 

state-level personal-income growth 40 

are added to the pure banking model:

Type I Error Type I Error

the addition of the economic data
 

(Missed Failures) 30 

materially reduces the accuracy of the
 

bank-failure predictions for 1989 and
 20 

199 . 

10 

The solid line in figure )B illustrates

the prediction-error trade-off yielded 0 

by the banking model using actual

failures in 199 and 1991 to predict

failure in 1992 and 199). The model

predicts fairly well, in the sense that

one could have chosen a lower

critical probability (fewer missed

survivors) without dramatically

increasing the proportion of missed

failures. The broken line summarizes

the predictive accuracy of the model

when measures of state-level

personal-income growth are added to

the pure banking model: the

economic data do not materially

improve our ability at year-end 1991 10 

to predict bank failures. 

0 

Type II Error 

(Missed Survivors in percentage) 

Note: Forecasts use model estimates generated from 1986 data and actual 1987–88 failures. 

B. Forecast Errors 

1991 Data/1992–93 Failures Percentage 

50 

40 

Bank Data Only 

Bank and Economic Data 

0 1 2 5 7 10 20 30 40 50 

(Missed Failures) 30 

20 

Although evidence about the contribution of state-level economic data in

off-site monitoring models is sparse,

our findings are consistent with what

has been reported. The most relevant work in this area was conducted

by researchers at the Federal Reserve

System when they were developing

their near-term-prediction Financial

Institution Monitoring System

(FIMS) in the early 199 s. These

researchers' systemwide effort yielded

two models that have been modified

and improved over time. The developers of the FIMS model found that

Type II Error 

(Missed Survivors in percentage) 

Note: Forecasts use model estimates generated from 1989 data and actual 1990–91 failures. 

including state-level data on unemployment rates, personal

income, and housing permits did not significantly improve

upon predictions based solely on bank-examination and bankfinancial data.8

8 Cole, Cornyn, and Gunther (1995) report on the development of the Federal Reserve System's 

failure-prediction and CAMEL-prediction models. Various prototypes included state-level data on 

unemployment rates, personal-income growth, and housing permits; however, the explanatory 

power of the state-level economic variables "is attenuated by the inclusion of bank-specific variables in the model" (p. 8). Other researchers have estimated bank failure-prediction models that 

include economic proxies, but they do not assess the contribution of the economic variables in 

their models. 

2003, VOLUME 15, NO. 2 44 
 

 

 

1

FDIC Banking Review 

Predicting Changes in the Credit 

Quality of Bank Assets 

Since the goal of off-site monitoring

models is to identify emerging banking problems, accurate forecasts of

bank nonperforming-asset ratios are

useful, insomuch as declining asset

quality generally is a precursor of

more serious banking problems.

Thus, the second part of our study

investigates whether state-level economic variables would improve the

performance of reduced-form models

that predict changes in bank profitability and asset quality. Here we

report results for models that predict

changes in nonperforming-asset

ratios.9

As with the incidence of bank failure,

one can find examples of states where

poor economic conditions have been

correlated with higher-than-average

bank asset-quality problems. Figure

4A illustrates a situation in which the

nonperforming-asset ratio of banks in

a state is inversely related to the

state's economic health. However,

one also can find examples of states

where bank asset-quality problems are

not clearly related to state-level economic conditions. As figure 4B

shows, the nonperforming-asset ratio

of California banks was high even

when the state's economy was

healthy.1 

9 The nonperforming-asset ratio equals the sum of 

total loans and leases more than 90 days past due 

plus nonaccruing loans and leases plus other real 

estate owned as a share of total assets. 

10 Because the nonperforming-asset ratios of very large 

banks reflect the national and international scale of 

their activities, banks with more than $20 billion 

(1994) in assets were excluded from the calculations 

illustrated in figure 4. 

The nature of bank asset-quality ratios makes them an attractive candidate to study. First, as discussed above, the economic conditions affecting a bank's borrowers should be directly

related to the credit quality of the bank's loan portfolio. Second, unlike bank failure (which is a discrete event occurring

only when a bank's condition worsens beyond some threshold

level), the quality of bank assets is measured in the same continuous fashion as economic variables; hence, it may exhibit a

more systematic correlation with economic variables. 

Figure 4 

1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 

-3 

-2 

-1 

0 

1 

2 

Percentage 

Personal-Income 

Growth 

Nonperforming 

Asset Ratio Unemployment Rate 

A. Wisconsin 

B. California 

* State-level variables are measured relative to comparable variables calculated at the 

national level. Thus, personal-income growth equals the difference between state 

personal-income growth and that for the United States. The unemployment rate equals 

the difference between the state unemployment rate and the U.S. unemployment rate; 

and the nonperforming-asset ratio equals the difference between the nonperformingasset ratio of banks headquartered in the state and the nonperforming-asset ratio of 

all U.S. banks. 

Relationship between Bank Asset Quality and State-Level Economic Conditions,* 1986–95 

1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 

Percentage 

Personal-Income 

Growth 

Nonperforming 

Asset Ratio 

Unemployment 

Rate 

-3 

-2 

-1 

0 

1 

2 

3 

45 2003, VOLUME 15, NO. 2 
FDIC Banking Review 

One difference, however, between this part of

the study and the first part is that bank supervisory staff do not currently use "standard"

models that forecast a bank's profitability or

asset quality. Thus, we begin by using bank

financial data from prior periods to construct

reduced-form linear models that predict the

change in a bank's nonperforming-asset ratios

one year forward. We then include a variety

of state-level economic data to see whether

they improve upon the forecasts yielded by the

bank financial data. 

We evaluate the forecasts of asset-quality

changes by using a standard summary measure

of a linear model's prediction error. The root

mean-squared error (RMSE) measures the

square root of the average value of a model's

squared forecast errors. Forecast errors are

squared before averaging so that negative

errors and positive errors count equally, and

larger errors are given more weight. 

In the models we use here, the RMSE summarizes those differences in asset-quality changes

across banks that are not explained by the

model. To put the size of the RMSE in perspective, we compare each model's RMSE

with the RMSE we obtain when we use only

the historical mean change in nonperformingasset ratios (no banking or economic data) to

predict future changes.11

Because U.S. banks vary greatly in size, we

want to account for the possibility that the

11 Thus, for each bank size class and each sample period, we estimate 

the following models: 

(1) Nonperfi = αj + �βjk Bankki + εi (bank model) 

(2) Nonperfi = αj + �βjk Bankki + �Γjl Econli + εi , (banking & economic model) 

(3) Nonperfi = αj + εi	 (naïve model) 

where 	 j = jth bank size class (1-6). 

i = ith observation in size class j. 

k = kth right-hand-side banking variable. 

l = lth right-hand-side economic variable. 

In sample, the RMSE of the naïve model regressions will be very close 

to the standard deviation of the dependant variable for each sample of 

banks. Out of sample, the RMSE of the naïve model forecasts can differ from the standard deviation of realized asset-quality changes 

because the forecasts are based on the average changes in nonperforming-asset ratios evident historically, and these average changes can 

differ from the realized mean. 

link between state-level economic variables and nonperforming-asset ratios could vary with a bank's size.

First, very large banks (those with assets of more than

�2 billion in 1994 dollars) are excluded from all

analyses because they operate in markets that are

much larger than the state in which they are headquartered. We divide the remaining institutions into

five classes based on asset size in 1994 dollars, and we

estimate separate models for each size class. This

allows the measured link between state-level data and

the quality of bank assets to differ for each class of

banks. Table 2 identifies the bank size classifications. 

Table 2 

Number of Banks in the Analysis Samples 

Bank Asset-Size Class Sample period 

(1994 dollars) 1986–89 1991–94 1990 1995 

Very small: less than $25 million 8,382 5,514 1,752 873 

Small: $25 million to $100 million 19,572 15,843 4,247 3,074 

Medium: $100 million to $300 million 7,826 7,669 1,926 1,605 

Medium-large: $300 million to $1 billion 2,386 2,564 675 553 

Large: $1 billion to $20 billion 1,425 1,471 391 342 

Here we report results for models that measure the

link between lagged bank conditions and annual

changes in bank nonperforming-asset ratios during

two periods: 1986 through 1989 and 1991 through

1994.12 We assess each of these models in terms of

the accuracy of its out-of-sample predictions of assetquality changes in the year following each model's

estimation period-that is, in 199 and 1995. In

modeling changes in asset quality, we include lagged

values of the bank's financial variables that are most

likely to be related to the quality of bank assets.1)

These measures are identified in the top two panels of

table ). We then include a set of economic variables

(identified in the bottom two panels of table )) in

what we refer to as "banking and economic models."14

12 Observations for all four years in a given sample period are pooled in what is 

called a cross-sectional time-series analysis. The four-quarter change in a bank's 

asset-quality ratio is measured as the percentage change in the ratio of nonperforming assets to total assets. Nonperforming assets include loans 90 days past due 

and still accruing, nonaccruing loans and leases, and other real estate owned. 

13 Because we are linking bank data over time, we adjust data where necessary to 

reflect bank mergers so as to get a consistent historical series for each bank. 

14 To control for variations in the national economy during a given sample period, the 

set of economic variables also includes one lag of U.S. personal-income growth and 

one lag of the percentage-point change in the GDP deflator (as a proxy for inflation). 

2003, VOLUME 15, NO. 2	 46 
FDIC Banking Review 

Figure 5 illustrates the amount of variation in

nonperforming-assets-ratio changes that is not

predicted by the models linking past conditions to asset-quality changes during the previous four years. All the results we report here

include the same sets of banking and economic variables. Hence differences in results

across specifications can be attributed to the

inclusion of the economic variables, differences in bank size, and differences in the sample period under scrutiny. 

As indicated in figure 5A, the reduced-form

models using Call Report variables predict

only modest change in bank asset quality during 199 , and the economic variables do not

materially improve upon these forecasts. 

Figure 5B shows that historical relationships

observed during the early 199 s do not help

Table 3 

Variables Used to Predict 

One-Year-Forward Asset-Quality Changes 

Bank Balance Sheet Variables 

(current and previous four quarters) 

• Ratio of equity to assets 

• Ratio of total loans to assets 

• Ratio of nonperforming loans to assets 

• Ratio of other real estate owned to assets 

• Ratio of 30-90 days past-due loans to assets 

Bank Income and Growth Variables 

(previous four quarters) 

• Annual asset-growth rate 

• Return on average assets 

• Net charge-offs 

State-Level Economic Variables 

(current and previous four quarters) 

• State-level percentage of 1-4 family mortgages 

90 days past due 

• State unemployment rate 

• Log of state personal income per worker 

• State-level personal-income growth 

• Log of state failed-business liabilities per worker 

Macroeconomic Variables 

(rate of change during previous four quarters) 

• U.S. personal-income growth 

• Change in the GDP deflator 

predict changes in bank nonperforming-asset ratios

during 1995. For this period, the inclusion of statelevel economic variables would have made our prediction errors larger. 

In summary, this part of our study indicates that future

changes in bank asset quality are hard to predict even

with data on recent trends in bank asset-quality measures. And state-level economic data do not generally

improve upon these predictions. These results suggest

that, at least for the periods we study, a reasonable

predictor of a bank's nonperforming-asset ratios one

year forward is the bank's current nonperforming-asset

ratios. 

Figure 5 

0 

0.5 

1.0 

1.5 

2.0 

2.5 

RMSE 

Very Small Small Medium Medium-Large Large 

Size Class 

0 

0.3 

0.6 

0.9 

1.2 

RMSE 

Standard 

Deviation Bank 

Model 

Bank and 

Economic 

Model 

Standard 

Deviation Bank 

Model 

Bank and 

Economic 

Model 

Very Small Small Medium Medium-Large Large 

Size Class 

A. Out-of-Sample Forecast Errors for 1990 

Based on 1986–89 Model 

B. Out-of-Sample Forecast Errors for 1995 

Based on 1991–94 Model 

Predicting Changes in Bank Nonperforming-Asset Ratios* 

* Out-of-sample forecast errors yielded by the Bank Model and the Bank 

and Economic Model are compared to the actual standard deviation of 

nonperforming-asset ratio changes during the forecast year. 

47 2003, VOLUME 15, NO. 2 
 

FDIC Banking Review 

Predicting Risky Bank Growth 

The manner in which a bank grows

has important implications for its

overall safety and soundness. Imprudent or ill-timed growth can lead to

risky loan concentrations, funding

problems, or other difficulties for

bank management.15 Bank regulators are aware of these possibilities

and have included appropriate safeguards in the supervisory process.

Most relevant to this article is the

FDIC's growth-monitoring system

(GMS), which seeks to identify risky

bank growth ex ante.16 We propose

that economic conditions in a bank's

market might provide a useful context for assessing the potential risks of

bank growth and might therefore

contribute to bank off-site monitoring

models. To see whether our proposal

is correct, we next test whether data

on state economic conditions added

meaningful information to GMS.17

Before we describe those tests, it is

useful to look at the past correlation

between bank safety and soundness

(that is, risky bank growth) and state

economic conditions. The U.S. banking experience of the 198 s and early

199 s suggests that deteriorating economic conditions were associated

with declines in the condition of

15 A thorough analysis of the causes of the U.S. banking 

crises of the 1980s and early 1990s found that a 

"boom/bust" cycle in banking markets was a common 

feature; the analysis also examined the implications of 

these cycles for bank growth. See Federal Deposit Insurance Corporation (1997). 

16 Bank supervisors also can place restrictions on bank 

growth. Regulatory capital requirements are perhaps the 

most general restriction and limit the degree to which a 

bank can engage in leveraged growth. Moreover, bank 

management may be required to obtain supervisory 

approval before engaging in some types of new activities. 

17 For an extensive description of the FDIC's GMS during 

the late 1980s and early 1990s, see 

Reidhill and O'Keefe (1997). 

Figure 6 

1984 1986 1988 1990 1992 1994 1996 

-8 

-4 

0 

4 

8 

12 

16 

Mean Percentage Change 

in Unemployment Rates 

-0.4 

-0.2 

0 

0.2 

0.4 

0.6 

0.8 

Mean Change in 

Composite CAMEL Rating 

-10 -0.6 

Change in State 

Unemployment Rates 

Change in 

CAMEL Rating 

1984 1986 1988 1990 1992 1994 1996 

-6 

0 

6 

12 

18 

-0.2 

0 

0.2 

0.4 

0.6 

Change in State 

Unemployment Rates 

Change in 

CAMEL Rating 

Mean Percentage Change 

in Unemployment Rates 

Mean Change in 

Composite CAMEL Rating 

Changes in Unemployment Rates and 

CAMEL Ratings for Banks Examined in a Quarter 

A. Southwest Banks 

B. Northeast Banks 

banks. Figure 6A illustrates that sharp increases in state

unemployment rates in the southwestern United States during

the mid-198 s coincided with deteriorating banking conditions, as identified through composite CAMEL ratings of

banks.18 (In the figure, positive changes in the average composite CAMEL rating for the region's banks indicate a widespread decline in banks' safety and soundness because the

rating is an ordinal index that increases in value the poorer a

bank's assessed safety and soundness.) As indicated in figure

6B, the correlation between adverse changes in state unem18 The mean percentage change in state unemployment rates for examined banks is weighted by 

the number of banks examined within a state each quarter. This was done to ensure that the 

economic conditions shown in figure 6 reflect those faced by the banks whose CAMEL rating 

changes also are shown in figure 6. 

2003, VOLUME 15, NO. 2 48 
FDIC Banking Review 

ployment rates and declines in CAMEL ratings Table 4 

was particularly pronounced in the northeastern Banking and Economic Variables Included in United States.19 Bank Growth Models 

Although informative, these simple comparisons Portfolio Changes (current quarter) Peers for Ranking 

do not tell us whether data on state economic

conditions add to off-site growth-monitoring models. To answer this question, we develop and compare two off-site growth-monitoring models

designed to rank banks in terms of the relative

riskiness of their growth (that is, we designed two

risky-growth indexes). The first model ("bank

model") serves as our basis of comparison and uses

information on a bank's portfolio composition,

changes in portfolio composition, and supervisory

assessments of bank condition to construct a riskygrowth index. The bank model excludes measures

of state economic activity, however. The second

model ("bank and economic model") includes all

the information the bank model contains plus

measures of state-level economic activity. The

measures of economic activity we test are quarterly changes in both state unemployment rates and

state personal-income growth. Because our conclusions are the same for both of these economic

activity measures, for brevity we present only the

results of tests that use changes in state unemployment rates. 

The premise behind the bank model is that all

other things being equal, the risks to a bank's

future safety and soundness increase when growth

(1) proceeds too quickly, (2) increases the concentration in risky activities, or ()) increases the

reliance on volatile sources of funding. In addition, it is presumed that the poorer a bank's initial

condition, the greater the future risks from

growth. As shown in table 4 the bank model uses

11 variables to capture the factors that can lead to

risky bank growth. More specifically, the bank

model uses 5 measures of portfolio change: the

annualized rates of growth in total assets, gross

loans and leases, the ratio of loans plus securities

19 The Pearson's correlation coefficient (and p-values in parentheses) between 

the mean percentage change in unemployment rates and changes in CAMEL 

ratings for the period 1984 through 1997 is 0.24 (0.0734) for the Southwest 

and 0.73 (0.0001) for the Northeast. 

• Asset growth All banks 

• Gross loan growth All banks 

• Growth of loans and securities 

as a percentage of assets All banks 

• Growth of volatile liabilities 

as a percentage of assets All banks 

• Growth of equity 

as a percentage of assets All banks 

Portfolio Ratios (current quarter) 

• Loans and securities 

as a percentage of assets Region & size peers 

• Volatile liabilities 

as a percentage of assets Region & size peers 

• Equity as a percentage of assets Region & size peers 

• Portfolio concentration 

(a summary measure) All banks 

Supervisory Variables 

• Initial supervisory rating (composite CAMEL rating) 

• Number of days since last bank examination 

Economic Variables 

• Change in state unemployment rate: 


current and previous four quarters 


• Alternatively, state personal-income growth: 


current and previous four quarters
 

with maturities of five years or more to assets, the

ratio of volatile liabilities2 to assets and the ratio

of equity capital to assets. In addition, the bank

model uses 4 portfolio ratios: the ratios of loans

plus securities with maturities of five years or more

to assets, volatile liabilities to assets, equity capital

to assets, and a summary measure of portfolio concentration. The summary measure of loan concentration is used to capture potentially risky

shifts in business activity and is based on the

Herfindahl-Hirschman Index (HHI). To calculate

the concentration measure we first compute the

shares of total loans held in 15 well-defined cate20 Volatile liabilities are defined here as the sum of time deposits over 

$100,000, foreign deposits, federal funds and securities sold under repurchase 

agreements, demand notes issued to the U.S. Treasury, and other borrowed 

money. 

49 2003, VOLUME 15, NO. 2 
FDIC Banking Review 

gories of loans and leases. Next we square and

sum the loan shares.21 Rather than using the raw

values of these measures of portfolio change and

portfolio ratios, we use a bank's percentile ranking

for each measure, based on either a peer group or

a national ranking, as appropriate.22 Finally, the

bank model includes 2 supervisory measures: a

bank's composite CAMEL rating as of the quarterend, and the number of days since the bank's last

on-site safety-and-soundness examination as of

the quarter-end. 

The final step in computing the bank-model

growth index is to combine the 11 variables into a

summary growth index. We do this by weighting

each variable in terms of its importance in

explaining downgrades in composite CAMEL ratings during the prior period and then summing the

weighted variables.2) The reason we choose this

approach is that a growth index is most useful to

bank supervisors if it can be used to anticipate

changes in bank safety and soundness (which is

measured by composite CAMEL ratings). 

In the banking and economic model, we study the

contribution of economic data in growth monitoring by including state-level economic variables as

additional explanatory variables. This article presents the results of tests based on the quarterly percentage change in state unemployment rates. To

construct the bank and economic model growth

index, we use the same approach as with the bank

model but add percentage changes in state unemployment rates for the current quarter and four

prior quarters. 

21 We use the same approach to constructing the loan concentration index 

that Reidhill and O'Keefe (1997) used. Specifically, certain risky loan concentrations are weighted more heavily in the HHI. 

22 National rankings are used for all measures of portfolio change as well as 

for the summary measure of portfolio concentration. All remaining ratios are 

ranked with the use of peer groups. To form peer groups, we stratified 

banks into eight broad U.S. geographic regions and two asset-size classes 

("large" or "small" depending on whether the asset size is greater or less 

than $1 billion). 

23 Specifically, we used the year-end percentile rankings of the 9 financial 

measures and the raw values of the 2 supervisory measures in the bank 

model as explanatory variables in a logistic regression model to explain the 

incidence of composite CAMEL downgrades during the subsequent three-year 

period. The weights obtained from a given three-year estimation period are 

applied out-of-sample as weights to the 11 variables, and the weighted sum is 

used as the growth index. 

As we stated at the outset, useful risky-growth

indexes should anticipate declines in bank safety

and soundness. Hence, to assess each index's usefulness, we rank banks on the basis of their growth

indexes and group the ranked banks into "risk"

quintiles. Next we measure the proportion of

banks receiving CAMEL downgrades (during the

subsequent three years) in each of the quintiles.24

For example, we construct bank-model growth

indexes as of year-end 1988 and then compare the

distribution of CAMEL downgrades between 1989

and 1991 across risk quintiles. 

Here we report results for banks that were examined during five three-year periods. For each period, we compute risky-growth indexes (with and

without the state economic data) on the basis of

the methodology described above. We then compare the downgrade experiences of the risk quintiles generated by the bank model with those of

the risk quintiles generated by the bank and economic model. We measure the contribution of the

state economic variables by comparing the proportion of downgrades in each risk quintile across the

two models. The model that performs "better"

will be the one with a higher proportion of downgrades in its highest-risk quintile and a lower proportion of downgrades in its lowest-risk quintile. 

Figure 7A shows the percentage of CAMEL downgrades (during the indicated three-year period)

occurring in the highest-risk quintile as classified

by each model. Except for the 199 to 1992 period (which coincided with a national recession),

the proportion of downgrades occurring in the

highest-risk quintile identified by the bank and

economic model is somewhat larger than the proportion in the same quintile for the bank model.

Figure 7B shows the percentage of downgrades

received by banks in the lowest-risk quintile.

Here the proportion of future downgrades occurring in the lowest-risk quintile identified by the

bank and economic model is generally lower than

24 Reidhill and O'Keefe (1997) indicate that there may be a three- to five-year 

lag between periods of excessive growth and subsequent declines in bank 

safety and soundness. 

2003, VOLUME 15, NO. 2 50 
16 

14 

12 

10 

FDIC Banking Review 

Figure 7 

Percentage of CAMEL Downgrades Occurring in the Highest- and Lowest-Growth Quintiles 

A. Highest Growth Quintile 

Percent Downgraded 

34 

32 

30 

28 

26 

24 

22 

20 

Bank and 

Economic Model 

Bank Model 

1989-91 1990-92 1991-93 1992-94 1993-95 1994-96 

Forecast Period 

B. Lowest Growth Quintile 

Percent Downgraded 

18 

Bank and 

Economic Model 

Bank Model 

1989-91 1990-92 1991-93 1992-94 1993-95 1994-96 

Forecast Period 

8 

the proportion in the same quintile for the bank model. These

results suggest that state-level economic data might be useful

in identifying imprudent bank growth. Although the improvement in the performance of the growth-monitoring model in

anticipating future downgrades is somewhat modest, it is fairly

consistent over time and is in line with evidence about historical patterns of local economic conditions, portfolio growth, and

subsequent bank performance.25 

25 A study by Avery and Gordy (1998) examines the extent to which recent loan growth (that is, 

growth during the previous two years) has been associated with a bank's current profitability and 

asset-quality ratios. The models in their study include a broad range of economic variables constructed from economic data at the county, state, and national levels. Although their study does 

not attempt to predict emerging banking problems, it does indicate that loan growth should be 

measured relative to economic fundamentals. 

Conclusion 

This study investigates the usefulness

of state-level economic data in statistical off-site monitoring models. Our

results indicate that state-level economic data do not contribute to the

models that forecast bank failures

and changes in the quality of bank

assets. The results for the model predicting risky bank growth are more

encouraging, indicating that the

inclusion of state-level economic data

slightly improves the predictive power

of this model. 

Although these results run counter to

our initial expectations, we can offer

possible reasons for the findings;

some of the reasons might be

addressed by future research. It

makes sense to expect that broad

measures of economic conditions,

such as state unemployment rates

and personal-income growth, have

varying relevance to individual banks.

This variation would be partly due to

wide variation not only in the services and products offered by banks but

also in the composition of state

economies. We are limited in investigating this possibility because banks

do not publicly report business activity (for example, loans) by the geographic markets and industry sectors

served. Given this limitation, it is

difficult to determine which economic variables are likely to be most relevant to a bank's current condition

and future performance. Our hope

was that broad measures of economic

conditions would have had relevance

for most banks and therefore for offsite monitoring models. 

We also anticipate that bank management plays a very significant role in

determining how economic condi51 2003, VOLUME 15, NO. 2 
FDIC Banking Review 

tions affect a bank's performance. Prior research

by the FDIC and others has suggested that bankspecific attributes such as the quality of management, loan underwriting, and risk-management

practices should have an important influence on a

bank's performance and its susceptibility to

adverse economic conditions. Although these

characteristics are hard to quantify, bank supervisors do collect data in some of these areas. For

example, all federal bank regulators conduct periodic surveys of bank underwriting practices. The

FDIC is pursuing research on the contribution

that the data in its semiannual underwriting survey might make to off-site monitoring models. 

2003, VOLUME 15, NO. 2 52 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

FDIC Banking Review 

BIBLIOGRAPHY 

Avery, Robert B., and Michael B. Gordy. 1998. Loan Growth, Economic Activity, and

Bank Performance. Working Paper. Board of Governors of the Federal Reserve

System.

Cole, Rebel A., Barbara G. Cornyn, and Jeffery W. Gunther. 1995. FIMS: A New Monitoring System for Banking Institutions. Federal Reserve Bulletin (January), 1-15.

Federal Deposit Insurance Corporation (FDIC). 1997. History of the Eighties, Lessons for the

Future: An Examination of the Banking Crises of the 1980s and Early 1990s. 2 vols.

FDIC.

Gilbert, R. Alton, and Sangkyun Park. 1994. Value of Early Warning Models in Bank

Supervision. Unpublished mimeo. Federal Reserve Bank of St. Louis..

Gilbert, R. Alton, Andrew P. Meyer, and Mark D. Vaughan. 1999. The Role of Supervisory

Screens and Econometric Models in Off-Site Surveillance. Federal Reserve Bank

of St. Louis Review (November/December), )1-56.

Hooks, Linda M. 1995. Bank Asset Risk: Evidence from Early Warning Models. Contemporary Economic Policy 1), no. 4:)6-5 .

Neely, Michelle Clark, and David Wheelock. 1997. Why does Bank Performance Vary

across States? Federal Reserve Bank of St. Louis Review (March/April), 27-4 .

Nuxoll, Daniel A. 2 ). The Contribution of Economic Data to Bank Failure Models,

Working Paper. FDIC. 

Reidhill, Jack, and John O'Keefe. 1997. Off-Site Surveillance Systems. In History of the

Eighties, Lessons for the Future: An Examination of the Banking Crises of the 1980s and

Early 1990s. FDIC.

Samolyk, Katherine A. 1994a. Bank Performance and Regional Economic Growth: Evidence of a Regional Credit Channel. Journal of Monetary Economics )4, 259-278.

---. 1994b. U.S. Banking Sector Trends: Assessing Disparities in Industry Performance. Federal Reserve Bank of Cleveland Economic Review, Quarter 2, 2-17.

Swamy, P.A.V.B., James R. Barth, Ray Y. Chou, and John S. Jahera, Jr. 1995. Determinants

of U.S. Commercial Bank Performance: Regulatory and Econometric Issues.

Finance and Economics Discussion Paper, no. 95-29. Board of Governors of the

Federal Reserve System. 

53 2003, VOLUME 15, NO. 2 
FDIC Banking Review 

Recent Developments Affecting 

Depository Institutions 

by Lynne Montgomery*

REGULATORY AGENCY ACTIONS 

Interagency Actions 

Final Patriot Act Regulations on Customer

Identification

On April 30, 2003, the Federal Reserve Board

(FRB), the Office of the Comptroller of the

Currency (OCC), the Office of Thrift Supervision

(OTS), the Federal Deposit Insurance

Corporation (FDIC), the National Credit Union

Administration (NCUA), the Commodity Futures

Trading Commission, the Securities and Exchange

Commission, and the Department of the Treasury

and its Financial Crimes Enforcement Network

issued final regulations requiring certain financial

institutions to establish procedures to verify the

identity of new account holders. The regulations

implement section 326 of the USA Patriot Act,

which mandates that rules be issued requiring

financial institutions to implement reasonable procedures to (1) verify the identity of a person opening an account, (2) maintain records of the

*Lynne Montgomery is a senior financial analyst in the FDIC’s Division of
 

Insurance and Research.
 

Reference sources: American Banker (AB), BNA’s Banking Report (BBR), and
 

Federal Register (FR).
 

information used to verify the person's identity,

and (3) determine whether the person appears on

any list of known or suspected terrorists or terrorist organizations. The rules apply to banks and

trust companies, savings associations, credit

unions, securities brokers and dealers, mutual

funds, futures commission merchants, and introducing brokers. Institutions subject to the final

rules will be required to establish a program for

obtaining identifying information from customers

who open new accounts. Financial institutions

are also required, among other things, to set forth

procedures for verifying the identity of customers

within a reasonable period of time. Financial

institutions must be in full compliance with the

new regulations by October 1, 2003.

PR-FRB, 4/30/03.

Guidance on Managing Credit-Card Accounts

On January 8, 2003, the FRB, the OCC, the OTS,

and the FDIC issued guidance on account management and loss­allowance practices for creditcard lending and called for conservative

management of credit­line assignments. The guidance outlines the supervisory agencies' expecta2003, VOLUME 15, NO. 2 54 
FDIC Banking Review 

tions for prudent risk­management, income­recognition, and loss­allowance practices. The guidance, which applies to all banks and thrift

institutions, requires that lenders justify their credit­management decisions with careful analysis of

borrower repayment history, risk scores, and other

relevant criteria. The guidance is intended to

help financial institutions conduct credit card

lending activities in a safe and sound manner

while meeting the needs of their customers. The

agencies developed the guidance in response to

recent examinations that disclosed a number of

inappropriate account­management, risk­management, and loss­allowance practices. 

BBR, 1/13/03, p. 40.

Advisory on Mortgage Banking Activities

The FRB, the OCC, the OTS, and the FDIC on

February 24, 2003, issued an advisory letter providing guidance on mortgage banking activities.

The guidance, which applies to all banks and

thrift institutions, was developed in response to

recent examinations and market developments.

The guidance details the agencies' expectations

regarding risk­management activities, including

valuation and modeling processes, hedging activities, management information systems, and internal audits. The guidance also states that the

agencies may require additional capital from institutions that fail to incorporate into their risk­management programs the sound practices set forth in

the advisory letter. PR-14-2003, FDIC, 2/25/03.

Federal Deposit Insurance Corporation 

Final Rule on Limited Liability Companies

On January 31, 2003, the FDIC adopted a final

rule making banks that are organized as limited

liability companies (LLCs) eligible for federal

deposit insurance. An LLC offers an optional

business organization model that has both the limited liability benefits of a corporation and the

"pass­through" taxation benefits of a partnership.

Under the new rule, the regulators will retain their

bank supervisory powers over banks operating as

LLCs, including the power to take prompt corrective action and issue enforcement orders to LLC

banks that become critically undercapitalized. 

BBR, 2/3/03, pp. 195-96.

Bank Failure

On February 7, 2003, the California Commissioner

of Financial Institutions closed Southern Pacific

Bank, Torrance, California, and named the FDIC

as receiver. Beal Bank, S.S.B., Plano, Texas, paid

the FDIC a premium of $500,000 to assume

approximately $834.0 million of Southern Pacific's

insured deposits and to purchase approximately

$201.5 million of the failed bank's assets. The

FDIC estimates that the cost of the failure to the

Bank Insurance Fund will be $134.5 million. This

was the first failure of an FDIC­insured institution

in 2003 and the first bank failure in California

since 2000. PR-11-2003, FDIC, 2/7/03.

Federal Reserve Board 

Identity Theft Booklet

On January 16, 2003, the Federal Reserve Bank of

Boston released a new booklet designed to help

consumers protect themselves against identity

theft. The Identity Theft booklet describes some

common sense precautions consumers should take

to protect personal information, shows consumers

how to monitor for signs of identity theft, and

offers a guide for consumers whose identities have

been stolen. The booklet also has contact information for the national credit bureaus, federal

agencies, and nonprofit organizations that advise

consumers and businesses. The booklet is available online at the Federal Reserve Bank of

Boston's Web site: http://www.bos.&rb.org/consumer/identity/index.htm. PR-FRB, 1/16/03.

Updated Check-Processing Operations

The Federal Reserve Banks on February 6, 2003,

announced changes to their back­office checkprocessing operations intended to improve operating efficiency and reduce check­cashing costs to

the government. Reflecting the ongoing shift in

55 2003, VOLUME 15, NO. 2 
 

FDIC Banking Review 

consumer and business preferences from checks to

electronic payments, the Reserve Banks intend to

reduce their check service operating costs through

a combination of streamlining their check management structure, reducing staff, and consolidating their check­processing locations. Check

payments continue to be the most popular form of

noncash retail payment; however, their share of all

noncash retail payments has declined from 85 percent in 1979 to 60 percent today. The changes,

which are projected to be completed by the end of

2004, are expected to reduce operating costs for

check services by approximately $60 million in

2005 and $300 million over the subsequent five

years. PR-FRB, 2/6/03; BBR, 2/10/03, p. 242.

Amendments to Regulation B-Equal Credit

Opportunity Act

On February 19, 2003, the FRB approved a final

rule amending Regulation B, which implements

the Equal Credit Opportunity Act (ECOA). The

ECOA prohibits discrimination on the basis of a

credit applicant's national origin, marital status,

religion, color, sex, race, age, receipt of public

assistance benefits, or the exercise of rights under

the Consumer Credit Protection Act. The FRB in

1976 adopted a general prohibition against nonmortgage lenders' inquiring about applicant characteristics. The final rule creates an exception

that allows nonmortgage lenders to collect data

about borrowers' personal characteristics as long

as the lenders keep the data confidential and use

the information to assess their own compliance

with the ECOA. PR-FRB, 2/19/03.

Online Resource Center

The FRB announced on March 28, 2003, the

launch of an online resource for researchers, educators, program directors, and others interested in

advancing financial education programs. The

resource-the Financial Education Research

Center-was developed by the Federal Reserve

Bank of Chicago to encourage research and disseminate information through its repository of

studies related to financial education and its 

listing of major financial education programs

throughout the country. The Web site for the

Research Center is www.chicago&ed.org/cedric/

index.c&m. PR-FRB, 3/28/03. 

Office of the Comptroller of the Currency 

Simplified Application Process

The OCC announced on March 21, 2003, a new

national bank service that simplifies the corporate

application process. National banks can use the

new "E­Corp" system to electronically complete

and submit branch and relocation applications to

the OCC. E­Corp is available on National

Banknet, the OCC's secure extranet Web site

available exclusively to national banks. E­Corp is

one component of the agency's continuing effort

to eliminate unnecessary regulatory burden, simplify administrative processes, enhance communications, reduce paperwork, and take full

advantage of e­government mandates. 

NR 2003-24, OCC, 3/21/03.

Office of Thrift Supervision 

Appointment of Gilleran as FFIEC Chairman

OTS Director James E. Gilleran was named

Chairman of the Federal Financial Institutions

Examination Council (FFIEC) for a two­year term

beginning April 1, 2003. Mr. Gilleran succeeds

Donald E. Powell, Chairman of the FDIC. Mr.

Gilleran was sworn in as director of the OTS on

December 7, 2001. Before joining the OTS, he

served as chairman and chief executive officer of

the Bank of San Francisco from 1994 to 2000 and

as superintendent of the California State Banking

Department from 1989 to 1994. He also served as

chairman of the Conference of State Bank

Supervisors (CSBS) from 1993 to 1994 and as a

member of the CSBS's Bankers Advisory Council

until 2000. From 1991 to 1992, Mr. Gilleran was

chairman of the State Liaison Committee of the

FFIEC. OTS 03-14, 4/1/03.

2003, VOLUME 15, NO. 2 56 
 

FDIC Banking Review 

Guidance on Third-Party Arrangements

On March 19, 2003, the OTS issued a bulletin

offering guidance to federal thrift institutions on

how to monitor the operational and financial performance of third­party firms that provide key

business services. The guidance, published in

Thrift Bulletin 82, cautions institutions to exercise

appropriate due diligence before entering into

third­party arrangements and to maintain effective

oversight and controls for the duration of the

arrangement. OTS examiners will review internal

controls and management of third­party arrangements when conducting safety­and­soundness

examinations. Thrift institutions contract with

third­party firms who provide security services, tax

services, legal advice, and an array of other services. This guidance complements existing OTS

guidance on third­party arrangements in two other

prominent areas: information technology and

internal audits. 

OTS 03-10, 3/10/03; BBR, 3/24/03, p. 502.

National Credit Union Administration 

Appointment of Johnson as Vice Chair

On January 15, 2003, the NCUA Board of

Directors named board member JoAnn Johnson as

the board's vice chair. The three­member board

has been without a vice chair since 1997. Before

joining the NCUA board in March 2002, Ms.

Johnson was a member of the Iowa Senate, having

been elected to that body in 1994. She chaired

the Senate's Ways and Means Committee from

1996 to 2000 and the Commerce Committee from

2000 until resigning her seat to join the NCUA

board. NR03-0115, NCUA, 1/15/03.

Broader Access to SBA Loan Program

A February 14, 2003, legal ruling by the Small

Business Administration (SBA) allows all credit

unions to seek SBA approval to participate in the

SBA's guaranteed business loan program under

Section 7(a) of the Small Business Act (the SBA

guarantees up to 85 percent of Section 7(a)

loans). When the guaranteed business loan program was first established, the SBA allowed all

credit unions to participate. However, ten years

ago the SBA reinterpreted its regulations to mean

that only credit unions whose members had a geographic common bond were eligible because only

those credit unions were considered "open to the

public," as required by SBA rules. Other credit

unions-such as those bound together on the

basis of common occupational relationships-were

not considered open to the public and were therefore ineligible. Under the new ruling, all credit

unions are once again eligible to seek approval for

participation in the program. BBR, 2/24/03, p. 350.

Updated Chartering and Membership Rules

On March 27, 2003, the NCUA adopted a regulation that revises federal credit union chartering

and field­of­membership rules by expanding the

choices for groups that wish to establish federally

chartered credit unions. The regulation allows a

proposed field of membership to include a trade,

industry, or profession. Another major feature of

the regulation is a provision for single­sponsor

credit unions that allows a field of membership to

be diversified beyond a single employer. The regulation also provides that multiple­group occupational credit unions with fewer than 3,000

members no longer need an economic analysis to

determine if each group could sustain a separate

credit union. In addition, the regulation assumes

that any metropolitan statistical area with a population of up to 1 million can serve as the credit

union's local community. NR03-0327, NCUA, 3/27/03.

Foreign Branching

The NCUA adopted a final rule that establishes

the requirements for federally insured credit

unions to branch outside the United States. The

rule requires credit unions to receive approval

from the host country and the NCUA. The

NCUA recognizes that a host country will have

some regulatory authority over a foreign branch

office; however, the NCUA retains the right to

57 2003, VOLUME 15, NO. 2 
 

FDIC Banking Review 

examine a foreign branch and take any necessary credit unions wishing to set up a foreign branch

enforcement actions. The final rule, which submit a business plan to the NCUA. 

becomes effective July 1, 2003, also requires that NCUA, 12 CFR Part 741.

STATE LEGISLATION AND REGULATION
 

Georgia

On January 22, 2003, the OTS announced that

federal law preempts provisions of the Georgia Fair

Lending Act (GFLA) from applying to federal savings associations and their operating subsidiaries.

The GFLA imposes various restrictions on loans

based on the annual percentage rate and amount

of points and fees charged. The preemption is

based on the Home Owners' Loan Act and OTS

regulations that comprehensively and exclusively

regulate lending by federal savings associations.

The OTS also determined that with respect to

terms of credit, loan­related fees, disclosures, and

the origination or refinancing of a loan, the GFLA

conflicts with OTS regulations governing lending

operations. OTS 03-02, 1/22/03.

On March 7, 2003, Georgia Governor Sonny

Perdue signed legislation (SB 53) that eases burdens on lenders and others under the Georgia Fair

Lending Act. The GFLA is one of the nation's

most controversial and criticized anti­predatorylending statutes. In February 2003, Standard &

Poor's and Moody's Investors Service refused to

rate Georgia mortgage­backed securities because

of worries that loan assignees and other parties

could be liable under the GFLA; after SB 53 was

signed, however, both credit agencies agreed to

rate the mortgage­backed securities. Under SB 53

assignee liability applies only to high­cost loans,

which are defined as loans on which the interest

charged is 8 percentage points above the interest

rate on comparable U.S. Treasury bills. SB 53 also

changed the reasonable tangible net benefit testwhich required lenders to determine whether a

refinanced loan presents a tangible net benefit to

the borrower-so that it applies only to high­cost

loans. In addition, SB 53 removed a provision in

the GFLA that included mortgage insurance premiums and Veteran Administration funding fees in

the cap on points and fees. The new legislation

also removed a state fee from the point­and­fee

cap. BBR, 3/10/03, p. 421.

New York

The OTS announced on January 30, 2003, that

federal law preempts provisions of the New York

predatory lending law from applying to federal savings associations and their operating subsidiaries.

The New York law restricts loans based on the

annual percentage rate and amount of points and

fees charged. The preemption is based on the

Home Owners' Loan Act and OTS regulations

that comprehensively and exclusively regulate

lending by federal savings associations. The OTS

also determined that, with respect to terms of

credit, loan­related fees, disclosures, advertising,

and the origination, refinancing, or servicing of a

loan, the New York statute conflicts with OTS

regulations governing lending operations. 

OTS 03-04, 1/30/03.

New York Attorney General Eliot Spitzer

announced on February 11, 2003, that ten banks

had signed agreements to block customers from

using their credit cards for online gambling. It is

illegal in New York to promote or facilitate unauthorized betting or gambling, online or off. The

agreements apply to lending activities arising in

New York or affecting New York residents, but the

attorney general expects that the banks will block

all gambling transactions across their entire systems. The ten banks involved are: Cayuga Bank,

Chemung Canal Trust Company, First Consumers

National Bank, First Premier Bank, Merrick Bank,

Peoples Bank, Trustco Bank, USAA Federal

Savings Bank, US Bank NA, and Wells Fargo

2003, VOLUME 15, NO. 2 58 
FDIC Banking Review 

Financial Bank. Eight months earlier Citibank

signed a similar agreement. BBR, 2/17/03, p. 300.

On March 28, 2003, New York State Banking

Superintendent Elizabeth McCaul resigned, citing

personal reasons for her departure. Ms. McCaul

had been the state's longest­serving superintendent, spending six years in the position. On April

2, 2003, New York Governor George Pataki

named Barbara �ent the state's acting banking

superintendent. Ms. �ent joined the New York

Banking Department in 1988 and has been the

department's deputy for consumer affairs for the

past four years. AB, 4/2/03.

Tennessee

On April 17, 2003, Tennessee Governor Phil

Bredesen signed legislation (P.A. 03­32) that

makes it easier for branches of Tennessee banks to

be acquired. Previous law required branches of

Tennessee banks to have been opened and

engaged in the banking business for at least five

years before being acquired. The new legislation

reduces the length of business from five years to

three years. BBR, 4/28/03, p. 687.

RECENT ARTICLES AND STUDIES
 

The percentage of community banks selling mortgages into the secondary market has jumped from

less than one­half in 2000 to approximately 72

percent in 2002, according to findings of the

America's Community Bankers 10th annual Real

Estate Lending Survey. The upward trend can be

explained by an overall increase in mortgage originations, as well as a favorable environment for

sales into the secondary market. The dollar volume of mortgage sales into the secondary market

has increased even more dramatically, rising from

17 percent of total mortgage originations in 2000

to 41 percent in 2001 and to 45 percent in 2002.

Nearly 33 percent of the survey respondents said

they anticipate selling more loans into the secondary market in 2003, and 43 percent expected to

sell about the same level of loans in 2003 as in

2002. The findings are based on survey responses

from 320 community banks. BBR, 2/10/03, pp. 257-58.

BANK AND THRIFT PERFORMANCE
 

Fourth-�uarter ���� Results for Commercial

Banks and Savings Institutions

FDIC­insured commercial banks and savings institutions earned $25.6 billion during the fourth

quarter of 2002, an increase of $3.5 billion from

earnings in the fourth quarter of 2001. �ey factors in the higher earnings were gains on sales of

securities and other assets, an increase in service

charges, a decrease in expenses for credit losses,

and strong growth in interest­earning assets. The

average return on assets (ROA) was 1.23 percent

in the fourth quarter, up from 1.12 percent one

year earlier. The number of commercial banks

and savings institutions on the FDIC's "Problem

List" declined from 142 in the third quarter of

2002 to 136 in the fourth quarter, and assets of

"problem" banks fell from $42 billion to $39 billion. Eleven FDIC­insured institutions failed during 2002, and two of those failures occurred in the

fourth quarter. 

FDIC �uarterly Ban�ing Profile, Fourth �uarter 2002.

59 2003, VOLUME 15, NO. 2 
 

FDIC Banking Review 

INTERNATIONAL DEVELOPMENTS
 

Argentina

Under an agreement with the International

Monetary Fund signed on January 24, 2003, the

Argentine government pledged to pay banks $5.3

billion in compensation for major losses they sustained following the country's financial crisis in

late 2001 and the devaluation of the peso in

January 2002. The government also agreed to a

major restructuring of Argentina's three large public banks-Banco de la Nacion Argentina, Banco

de la Provincia de Buenos Aires, and Banco de le

Ciudad de Buenos Aires. BBR, 2/3/03, p. 230.

On March 28, 2003, Argentine President Eduardo

Duhalde signed a decree to lift all the restrictions

on bank withdrawals that had been in place since

the financial crisis of 2001. Under the decree,

savers will receive approximately two­thirds of

their term deposits in cash and the remainder in a

ten­year government bond. BBR, 3/31/03, p. 552.

Basel Committee

On February 25, 2003, the Basel Committee on

Banking Supervision issued guidelines for managing and supervising operational risk. The guidelines discuss what banks will be expected to do to

protect themselves from operational risk and also

provide a framework of ten core principles for

effective management of such risk. Institutions

are expected to establish and maintain systems for

the "identification, assessment, monitoring, and

mitigation�control" of operational risk. The guidelines also recommend public disclosure of these

systems "to allow market participants to assess

their approach to operational risk management."

For regulators, the guidelines recommend assessing

existing risk controls at supervised banks and

establishing systems that allow timely communication of changes in an institution's risk position.

AB, 2/26/03.

Canada

A final package of regulations under Canada's

Bank Act eases restrictions on the ability of foreign and domestic banks to undertake information

technology (IT) activities. The regulations follow

the implementation of the Financial Consumer

Agency of Canada Act, which is a new financial

services framework that permits banks to engage

in IT activities in a financial context. The regulations ease existing restrictions by reducing the

"primarily financial" standard to a new "materially

related" standard. The regulations also provide

financial institutions with added flexibility to

invest in entities that do not necessarily meet the

materially related standard, provided that the size

of the investments falls below a certain threshold.

BBR, 3/3/03, p. 399.

The Office of the Superintendent of Financial

Institutions issued a new guideline outlining the

policies and procedures that banks operating in

Canada are expected to have in place to deter and

detect money laundering and terrorist financing

activities. The updated Guideline B­8 on

Deterring and Detecting Money Laundering incorporates legislative changes made since the release

of the original guideline in September 1996. The

updated guideline omits much of the material

related to transaction identification and reporting

and shifts the focus to identifying and mitigating

risks related to money laundering. 

BBR, 4/14/03, pp. 627-28.

China

China's banking industry regulator, the People's

Bank of China, issued new rules to combat money

laundering. The new rules require financial institutions and their employees to participate in the

fight against money laundering and cooperate with

law enforcement officials in anti­money­laundering efforts. Financial institutions that fail to comply with the new rules will be subject to

prosecution. The new rules, which became effective March 1, 2003, are part of a broader campaign to stop illegal capital flows and to bring the

industry regulations into compliance with international standards. BBR, 1/20/03, p. 120.

2003, VOLUME 15, NO. 2 60