Banking Review 

2003 VOLUME 15, NO. 4 

Options for Pricing Federal Deposit Insurance (page 1) 

by Eric P. Bloecher, Gary A. Seale, and Robert D. Vilim 

The Federal Deposit Insurance Corporation has been exploring several options for reforming 

the risk-based deposit insurance system by more effectively differentiating risk among insured 

institutions. Each option involves trade-offs among a number of desirable attributes, since no 

one option possesses all of the attributes to the highest degree. This article describes the 

options under consideration and examines the trade-offs among them. 

Evaluating the Vulnerability of Banks and 

Thrifts to a Real Estate Crisis (page 19) 

by Charles Collier, Sean Forbush, and Daniel A. Nuxoll 

As part of its extensive off-site monitoring efforts, the Federal Deposit Insurance Corporation 

has evaluated banks’ and thrifts’ vulnerability to a real estate crisis similar to the crisis that 

occurred in New England in the early 1990s. This article discusses the resulting Real Estate 

Stress Test (REST) and current trends in REST ratings. That model indicates that a very 

large number of banks and thrifts in the West and the Southeast are heavily concentrated in 

commercial real estate. 

The views expressed are those of the authors and do not necessarily reflect official positions of the Federal Deposit Insurance 

Corporation. Articles may be reprinted or abstracted if the FDIC Banking Review and author(s) are credited. Please provide the 

FDIC’s Division of Insurance and Research with a copy of any publications containing reprinted material. 

Chairman Donald E. Powell 

Director, Division of Insurance Arthur J. Murton 

and Research
 

Deputy Director Fred Carns
 

Executive Editor George Hanc
 

Managing Editors Jack Reidhill
 

Diane Ellis 

Editorial Committee Christine E. Blair 

Valentine V. Craig 

Rose M. Kushmeider 

Publication Manager Geri Bonebrake 
Options for Pricing Federal Deposit Insurance 

Options for Pricing Federal Deposit 

Insurance 

Eric P. Bloecher, Gary A. Seale, and Robert D. Vilim* 

Editors Note: 

The following article discusses some 

deposit insurance pricing options that are 

under consideration by the FDIC. The 

specific pricing examples are presented 

only to illustrate the general types of 

options being considered and should not 

be regarded as a comprehensive set. This 

article is intended to highlight the practical trade-offs posed by the choice among 

different types of pricing systems. 

In April 2001, the FDIC released a document 

entitled “Keeping the Promise: Recommendations 

for Deposit Insurance Reform” (the recommendations paper), which laid out the Corporation’s 

recommendations for merging the insurance 

funds, eliminating the designated reserve ratio as 

the trigger for charging premiums, considering 

rebates if the merged fund grows too rapidly, and 

indexing insurance coverage. The paper also recommended charging regular, risk-based insurance 

premiums to all banks,1 and it included some 

examples of how the FDIC might enhance the 

current nine-cell premium matrix (see table 1 in 

the next section) to better price for risk. 

Since the FDIC released the recommendations 

paper, our work has focused on further exploring 

the options for pricing deposit insurance. Generally, we have been reviewing three primary 

methodologies: expanded use of supervisory ratings, use of statistical models, and a combination 

of the two. Choosing a system for deposit insurance pricing involves trade-offs among a number 

of desirable attributes. We summarize the options 

being explored and discuss the trade-offs without 

offering a judgment as to which attributes of 

deposit insurance pricing are most desirable from 

a policy standpoint. 

* All the authors are with the Division of Insurance and Research at the 

Federal Deposit Insurance Corporation. Eric P. Bloecher and Robert D. Vilim 

are senior financial analysts, and Gary A. Seale is a senior financial 

economist. The authors would like to thank John O’Keefe and Richard 

Jones for helpful comments, and Christine Brickman for research assistance. 

1 Throughout this article, the term banks refers to all FDIC-insured 

institutions. 

FDIC BANKING REVIEW 1 2003, VOLUME 15, NO. 4 
Options for Pricing Federal Deposit Insurance 

After providing some historical background on 

FDIC premiums, we lay out the desirable attributes, or general requirements, of deposit insurance 

pricing; present the pricing options we have been 

considering, along with the relative merits of each 

option; and then describe pricing for two categories of banks that could be priced separately: 

new banks and large banks. The final section 

concludes with a brief summary of the trade-offs 

that need to be evaluated before a new deposit 

insurance pricing system is selected. 

Historical Background 

For most of the FDIC’s history, deposit insurance 

coverage was funded by a premium system under 

which all insured institutions were charged an 

identical flat rate for deposit insurance. The rate 

was set by the Banking Act of 1935 as 1/12 of 1 

percent of total domestic deposits.2 Thus, deposit 

insurance premiums did not vary with the level of 

risk that an institution posed to the insurance 

fund. 

After passage of the Banking Act, the banking 

industry stabilized quickly, and bank failures 

remained low through the 1940s. The rapid 

increase in lending after the war was not accompanied by the high loan losses that many had 

anticipated; instead, the FDIC was faced with the 

possibility that the insurance fund could grow 

unchecked. To address this issue, Congress passed 

the Federal Deposit Insurance Act of 1950, which 

provided for assessment credits to be distributed 

to banks in years when the FDIC’s assessment 

income exceeded its losses and expenses. The 

credits were distributed on a pro rata basis, with 

the FDIC retaining up to 40 percent of the Corporation’s net assessment income and banks 

receiving up to 60 percent. The system of credits 

was a way to control the growth of the insurance 

fund by allowing premium income to be reduced 

2 This rate was calculated to be the annual assessment rate that would 

have been required to cover actual losses on deposits in banks that failed 

between 1865 and 1934, excluding “crisis” years when losses were 

unusually high. 

in periods with low failure rates, while the FDIC 

retained the ability to make full use of premiums 

during periods of higher failure rates. 

Although many observers recognized from the 

beginning that the original pricing system had 

weaknesses, the full implications of flat-rate insurance assessments did not attract significant attention until the bank and savings and loan 

insurance funds experienced record losses in the 

late 1980s. Two main problems were identified. 

First, a flat-rate system provided an inducement 

for a bank or thrift to undertake higher-risk business strategies to maximize profits. These strategies could be pursued without the banks incurring 

additional insurance expense; failure costs generated by increased risk taking were instead passed 

on to the insurer (and perhaps the taxpayer). 

Second, in a flat-rate premium system, sound and 

well-managed institutions were subsidizing highrisk, poorly managed institutions: low-risk banks 

paid more for insurance than they should, whereas risky banks paid less. The subsidy funded by 

low-risk banks represented an economic burden 

that caused them to operate at a competitive disadvantage. These two problems pointed to the 

conclusion that a more equitable and economically supportable deposit insurance pricing system 

would require high-risk institutions to pay more 

than low-risk institutions. 

The Federal Deposit Insurance Corporation 

Improvement Act of 1991 (FDICIA) required 

that a risk-based premium system be implemented 

by January 1, 1994. The FDIC implemented a 

risk-based system on January 1, 1993, a year early. 

Separate but identical assessment rate schedules 

were adopted for the Bank Insurance Fund (BIF) 

and the Savings Association Insurance Fund 

(SAIF). Institutions were assigned to one of nine 

risk categories by the use of capital ratios and 

other relevant information, mainly supervisory 

ratings. Originally, assessment rates ranged from 

23 cents per $100 of assessable deposits for the 

lowest-risk institutions to 31 cents per $100 of 

assessable deposits for the highest-risk institutions. When the funds were recapitalized, premiums were lowered. The Deposit Insurance Funds 

2003, VOLUME 15, NO. 4 2 FDIC BANKING REVIEW 
Options for Pricing Federal Deposit Insurance 

Act of 1996 prohibits the FDIC from charging 

premiums to institutions that are well capitalized 

and highly rated by supervisors as long as the 

insurance fund is above 1.25 percent of insured 

deposits. Table 1 presents the current nine-cell 

matrix for the combined fund—that is, a hypothetical fund in which the BIF and SAIF are 

merged—and gives the number and percentage of 

banks in each cell as of year-end 2002. As the 

table indicates, over 90 percent of institutions are 

in the 1A category. Currently, these institutions 

are not assessed for deposit insurance. 

Table 1 

Matrix Distribution, Risk-Related 

Premium System 

(Bank Insurance Fund and Savings Association 

Insurance Fund Combined, Year-End 2002) 

Supervisory Subgroupb 

A B C 

Capital Subgroupa (CAMELS 1 or 2) (CAMELS 3) (CAMELS 4 or 5) 

1—Well Capitalized 8,583 523 115 

91.7% 5.6% 1.2% 

2—Adequately Capitalized 113 17 14 

1.2% 0.2% 0.1% 

3—Undercapitalized 1 0 6 

0.0% 0.0% 0.1% 

Note: The figures in the cells refer to the number and percentage of all FDICinsured institutions. 

a Assignments to capital subgroups are made in accordance with section 327.4(a) 

(1) of the FDIC’s Rules and Regulations. “Well capitalized” means a total riskbased capital ratio that equals or exceeds 10 percent, a Tier-1 risk-based capital 

ratio that equals or exceeds 6 percent, and a Tier-1 leverage capital ratio that 

equals or exceeds 5 percent. “Adequately capitalized” means not well capitalized 

and a total risk-based capital ratio that equals or exceeds 8 percent, a Tier-1 riskbased capital ratio that equals or exceeds 4 percent, and a Tier-1 leverage capital 

ratio that equals or exceeds 4 percent. “Undercapitalized” means neither well 

capitalized nor adequately capitalized. 

b Assignments to supervisory subgroups are made in accordance with section 327.4 

(a) (2) of the FDIC’s Rules and Regulations. Subgroup A consists of financially 

sound institutions that have only a few minor weaknesses; this subgroup generally 

corresponds to the primary federal regulator’s composite CAMELS rating of 1 or 2. 

Subgroup B consists of institutions with demonstrable weaknesses that, if not 

corrected, could lead to a significant deterioration of the institution and an 

increased risk of loss to the relevant insurance fund; this subgroup generally 

corresponds to the primary federal regulator’s composite CAMELS rating of 3. 

Subgroup C consists of institutions that pose a substantial probability of loss to 

the relevant insurance fund unless effective corrective action is taken. This 

subgroup generally corresponds to the primary federal regulator’s composite 

CAMELS rating of 4 or 5. 

Key Attributes of a Deposit Insurance Pricing 

Structure 

Ideally, any pricing system adopted by the FDIC 

would possess some combination of five attributes: 

accuracy, simplicity, flexibility, appropriate incentives, and fairness. 

Accuracy 

Perhaps the most important consideration for any 

proposed pricing system is that the criteria used to 

rank or categorize banks accurately reflect the relative risk that institutions pose to the insurance 

fund. Accuracy is generally measured against the 

insurable event, which in this case is bank 

failure.3 Banks that are in higher-premium categories should have a more frequent occurrence of 

failure than banks in lower-premium categories. 

Additionally, for any pricing methodology that 

relies extensively on data provided by banks or 

other outside parties, the integrity of the data 

must be adequate. Reported data must be timely, 

accurate, and verifiable. They must be available 

to regulators early enough in the assessment cycle 

to allow for premiums to be calculated. 

Simplicity 

The methodology selected should be available to 

the public, insured banks, and other outside parties, and members of all three groups should find 

it comprehensible. Moreover, bankers should be 

able to compute their risk categories or ratings 

without undue difficulty—preferably, early in the 

assessment cycle. For some pricing systems, the 

FDIC may need to provide software or some other 

form of technical assistance to help bankers perform the calculations. 

3 For certain groups of banks or within certain time periods, data on 

failures are often insufficient to allow meaningful statistical comparisons. 

As a result, to compare the pricing methodologies considered here, we also 

use historical data on the frequency of examination rating downgrades. 

FDIC BANKING REVIEW 3 2003, VOLUME 15, NO. 4 
Options for Pricing Federal Deposit Insurance 

Flexibility 

The factors that are most predictive of bank failure can change over time. Moreover, we expect 

that the FDIC’s ability to measure risk exposure 

will improve over time. Consequently, it is 

important that any pricing system allow for periodic changes in the risk-assessment criteria. 

Allowing for periodic changes will enable the 

FDIC to continually evaluate which factors are 

relevant at any particular time. Updates should 

be infrequent enough to allow banks a measure of 

stability for planning purposes, yet frequent 

enough to ensure that all the criteria remain relevant. 

Appropriate Incentives 

The measures of bank risk included in the pricing 

structure should provide incentives for bank management to act responsibly. While some measures 

of risk may perform well in statistical tests, their 

inclusion in a pricing system may not be appropriate because of the perverse incentives they create 

for sound bank management. For example, a 

measure that penalized banks for increasing levels 

of charge-offs might create an incentive for managers to avoid charging off loans simply to reduce 

their insurance premiums. 

Fairness 

Closely associated with the idea that the classification is to be correlated with risk is the idea that 

banks with similar characteristics should be treated in a like manner. Institutions with similar risk 

structures should pay approximately equal premiums. 

Pricing Options for Well-Capitalized and 

Highly-Rated Institutions 

A primary objective of deposit insurance pricing 

reform is to better differentiate among the bestrated institutions on the basis of risk, thereby 

reducing subsidies paid by low-risk institutions to 

riskier ones and moderating incentives for 

increased risk taking.4 Pricing that incorporates 

greater sensitivity to risk would achieve the goal 

of making the deposit insurance system more 

equitable and economically efficient.5 

The options currently being considered for banks 

(other than large banking organizations) include 

expanded use of supervisory ratings, use of statistical models (both in a continuous and discrete format), use of a combination of statistical models 

and supervisory ratings, and a scorecard that uses 

expert judgment in conjunction with a statistical 

model. 

Expanded Use of Supervisory Ratings 

A simple method of providing further risk differentiation within the best insurance category is to 

make expanded use of the CAMELS ratings.6 

This expansion could involve either the use of 

composite ratings alone or the use of composite 

and component ratings combined. If composite 

ratings alone are used, composite 1-rated institutions would pay a lower premium than composite 

2-rated institutions. Table 2 presents examples of 

how the FDIC might use both the composite rating alone and the composite rating combined 

with the component ratings to subdivide the 1A 

4 Because of the statutory prohibition noted above, currently subsidies are 

paid only when the insurance fund is less than 1.25 percent of insured 

deposits. For purposes of this article, we concentrate on banks within the 

best insurance category (the 1A category). Institutions that are not well 

capitalized and highly rated are generally subject to a higher level of 

supervisory review and, in some cases, may be operating under specific 

enforcement actions. 

5 This article is concerned primarily with differentiating banks according to 

the risk they pose to the insurance funds, not with determining the 

absolute amounts that individual banks should pay for insurance (that is, 

not with determining the “break-even” or “actuarially fair” amounts). 

Actuarial pricing is the goal of most private insurers and as a general 

approach has much to recommend it, but adopting a strict actuarial 

framework would be impractical for the FDIC, mainly because if the FDIC 

were to charge the highest-risk institutions such a premium, the premium 

would be high enough to threaten these banks with failure. 

6 The CAMELS rating is assigned by a bank’s primary regulator. The 

acronym stands for Capital, Assets, Management, Earnings, Liquidity, and 

Sensitivity to market risk. A rating from 1 (the best) to 5 (the worst) is 

assigned for each of these component elements, and an overall composite 

rating based on the component ratings is then assigned to the bank. 

2003, VOLUME 15, NO. 4 4 FDIC BANKING REVIEW 
 

Options for Pricing Federal Deposit Insurance 

insurance category. In option 1 of the table 

(composite ratings only), banks rated a composite 

1 are placed in the lower-premium category 

(1A1) and banks rated composite 2 are placed in 

the higher-premium category (1A2). As of yearend 2002, 40.4 percent of institutions would have 

fallen in the 1A1 category and 59.6 percent of 

institutions would have fallen in the 1A2 category. In option 2 (composite and component ratings combined) the institutions rated composite 1 

are placed in the 1A1 category as in option 1. 

However, composite 2-rated banks would be 

divided into two groups based on their component ratings: most would categorized into the 1A2 

category, while those banks having weaker component ratings would be placed into the 1A3 category. Banks in the 1A3 category would pay the 

highest premium rates among the 1A banks. 

Table 2 

Options for Pricing Well-Capitalized and 

Highly-Rated Institutions Using 

CAMELS Ratings (Year-End 2002) 

Subcategories 

1A1 1A2 1A3 

Option 1: Using Composite Ratings 

Composite 1 Rated 3,501 

40.4% 

Composite 2 Rated 5,169 

59.6% 

Option 2: Using Composite and Component Ratings 

Composite 1 Rated 3,501 

40.4% 

Composite 2 Rated 

and Sum of Components <= 12 

and No More Than One 

Component Rated 3 or Worse 4,271 

49.3% 

Composite 2 Rated 

and Sum of Components > 12 

or Two or More Components 

Rated 3 or Worse 898 

10.4% 

Note: The table shows two options for subdividing the 1A insurance category 

using supervisory ratings. The 1A1 subgroup represents the least risk and the 1A3 

subgroup represents the greatest risk. The figures in the cells refer to the number 

and percentage of 1A institutions. 

Statistical Models 

Pricing methods that rely on statistical models 

have been developed to provide options that 

incorporate objective financial data reported by 

banks. The two statistical models under consideration use reported financial data to rank banks in 

the 1A category. One is a failure-prediction 

model, and the other is a CAMELS downgradeprediction model. 

Failure-Prediction Model 

The failure-prediction model is a statistical model 

that relates historical Call Report ratios to bank 

failures to determine an estimated failure probability for each bank.7 This failure probability can 

be used to rank banks for pricing. Table 3 illustrates an example of a failure-prediction calculation for a hypothetical bank. Column A shows 

the coefficients produced by the model. These 

coefficients are the same for all banks and represent the relative weight placed on each ratio for 

determining a probability of failure. The hypothetical bank’s financial ratios, which can be 

obtained from the Call Report, are in column B. 

These ratios are multiplied by the corresponding 

coefficients in column A to obtain the values in 

column C. The sum of these values produces a 

raw score, which is then transformed to obtain 

the estimated failure probability.8 For the hypothetical bank, this probability is 0.39 percent. 

The estimated probability of failure for each bank 

ranges between 0 and 100 percent. This value 

represents the likelihood of a bank’s failing over a 

five-year period. Under a continuous pricing format, in which each institution receives an individual score, banks could be ranked according to 

their estimated failure probabilities and assessed 

according to their ranking. 

7 The model is a logistic model of the general form ρ{1|X,β} = ez/(1 + 

ez) where Z = α + Σβi

xi, the number 1 represents bank failure within a 

specified period, and xi represents the i th financial-ratio variable. 

8 The transformation follows the formula in the above footnote. 

FDIC BANKING REVIEW 5 2003, VOLUME 15, NO. 4 
 

Options for Pricing Federal Deposit Insurance 

Table 4 shows how failure probabilities from the statistical failure model would have been distributed 

among all well-capitalized and highly-rated FDICinsured banks for selected years between 1985 and 

2002. Most banks would have had a low estimated 

probability of failure, especially after 1995. Thus, it is 

reasonable to assume that during periods of relative 

stability, most banks would pay an amount close to 

the average premium for that category. 

Table 3 

Failure-Prediction Model, Hypothetical Bank 

Coefficient Financial 

(Weight) Ratio Scorea 

Scoring Factor (A) (B) (C) 

Intercept –3.91 N/A –3.91 

Nonaccrual Loans / Total Assets 35.47 .002 0.07 

Loans Past Due 90+ Days / 

Total Assets 37.10 .010 0.37 

ORE / Total Assets 30.46 .015 0.45 

Loans Past Due 30–89 days / 

Total Assets 30.45 .005 0.15 

Pretax Net Operating Income / 

Average Assets –15.17 .030 –0.45 

Noncore Funding / Total Assets 5.20 .120 0.62 

Equity & Reserves / Total Assets –21.69 .130 –2.82 

Total Score –5.52 

Note: This table demonstrates how the results of the failure-prediction model can be 

used to create an individual expected-failure probability for each institution. 

a The raw score is the product of columns A and B. Via the formula in note 7, the total 

score produces the expected probability of failure (Pr(default)) through the transformation. 

e-5.52 

Pr(default) = = 0.39% -5.52)(1 + e 

Table 4 

Figures 1 and 2 show how the failure-prediction model would have performed historically 

in identifying both CAMELS downgrades 

(figure 1) and failures (figure 2). In figure 1, 

we used Call Report data at each year-end to 

rank banks according to their expected failure 

probabilities; we then divided the rank listing 

into three numerically equal groups and, for 

each group, calculated the percentage of 

banks that were actually downgraded from a 

CAMELS 1 or 2 to a CAMELS 3, 4, or 5 over 

the subsequent five-year period. As the figure 

shows, the group with the highest expected 

failure rate consistently has the highest percentage of banks downgraded. Likewise, 

banks in the middle group of the three consistently have a higher percentage of downgrades 

than banks in the group with the lowest 

expected rate of failure. 

Figure 2 shows the percentage of banks in 

each of the three groups that actually failed 

over the subsequent five-year period. This figure, too, shows a consistently higher failure 

rate for the group of banks having the highest 

expected failure rate. The distinction is not 

as clear for the middle and lowest thirds, however, primarily because of the low overall 

number of failures in these groups, especially 

after 1992. (This problem of the low overall 

number of actual failures distorting the percentages after 1992 is common to all the pricing systems evaluated here.) 

Continuous Pricing Distribution of Well-Capitalized and Highly-Rated 

Institutions Based on Projected Failure Probabilities 

Projected Range of Failure Probability 

<= 0.5 0.5–1.0 1.0–1.5 1.5–2.0 2.0–2.5 2.5–3.0 3.0–3.5 3.5–4.0 4.0–4.5 > 4.5 

2002 42.3%a 28.1% 12.7% 5.8% 3.0% 2.0% 1.3% 0.9% 0.7% 3.2% 

2000 42.5 27.4 12.6 5.6 3.5 2.3 1.3 0.9 0.7 3.3 

1995 55.5 27.2 8.5 3.0 1.8 1.0 0.7 0.4 0.3 1.6 

1990 36.5 29.7 12.8 6.6 3.9 2.2 1.7 1.1 0.9 4.7 

1985 28.6 27.4 14.1 7.4 5.2 3.0 2.2 1.7 1.4 8.9 

Note: Data are as of year-end. 

a The percentages are those of 1A institutions in each of the failure-probability ranges. These percentages are based on the 

model shown in table 3. 

2003, VOLUME 15, NO. 4 6 FDIC BANKING REVIEW 
Options for Pricing Federal Deposit Insurance 

CAMELS Downgrade-Prediction Model 

The Statistical CAMELS Off-site Rating (SCOR) 

model is similar to the failure-prediction model 

but was designed specifically to estimate the likelihood that a bank will receive a CAMELS downgrade over the next year. The FDIC currently 

uses this model for the off-site risk monitoring of 

banks. The model produces an expected 

CAMELS rating for the bank, which is expressed 

as a number between 1.00 (the best) and 5.00 

(the worst). The SCOR rating could be used to 

rank banks by risk for pricing purposes. In historical tests of downgrades and failures, SCOR performs much like the failure-prediction model, 

producing results very similar to those shown in 

figures 1 and 2. 

Continuous versus Discrete 

Separate from the choice of whether supervisory 

ratings or statistical models should be used to 

rank institutions is the question of whether a discrete or continuous format should be used. The 

failure-prediction model and the SCOR model 

produce a continuous ranking. (After each bank 

receives an individual score based on the results 

Figure 1 

of the models, premium amounts are established 

on the basis of the relative ranking of each bank.) 

However, it is possible to create a discrete pricing 

structure by superimposing a fixed number of categories on the results of the models; for example, 

to create figures 1 and 2 we arbitrarily divided the 

banks into three groups with an equal number of 

institutions in each group. But the groups do not 

necessarily have to be of equal size. Rather, 

groups could be established that minimized the 

difference in expected failure probabilities 

between the best and worst banks in each group. 

Doing this is desirable, since grouping makes it 

inevitable that some banks will pay a somewhat 

higher premium than their expected failure probabilities will warrant, while others will pay a 

somewhat lower premium. 

Discrete formats may offer greater simplicity than 

continuous formats, but they also create the 

potential that small changes in a measured variable could produce large changes in the deposit 

insurance premium (“cliff effects”). The existing 

nine-cell matrix is an example of a discrete format with cliff effects; however, it is based on wellestablished and generally accepted thresholds. 

Figure 2 

Relationship between Probability of Failure 

and Actual CAMELS Downgrades within 

Five Years of Failure-Probability Calculation 

Percentage of Downgraded Banks 

in Failure-Probability Group 

60 

Relationship between Probability of Failure 

and Actual Failures within Five Years of 

Failure-Probability Calculation 

Percentage of Failed Banks 

in Failure-Probability Group 

8 

1A3 Highest Expected Rate of Failure 50 

1A3 Highest Expected Rate of Failure 

6 

40 

1A2 30 1A1 Lowest Expected 

Rate of Failure 

20 

4 1A1 Lowest Expected Rate of Failure 

1A2 

10 

0 

2 

0 

1985 1987 1989 1991 1993 1995 1997 

Year-End 

1985 1987 1989 1991 1993 1995 1997 

Year-End 

FDIC BANKING REVIEW 7 2003, VOLUME 15, NO. 4 
 

Options for Pricing Federal Deposit Insurance 

For example, there is widespread understanding of 

the difference between a CAMELS 2-rated institution and a CAMELS 3-rated institution, and 

there is even different supervisory treatment for 

the two. Additionally, the capital thresholds are 

the same as the thresholds established by regulation for purposes of Prompt Corrective Action. 

The problem of creating cliff effects can be mitigated by the use of a more graduated pricing 

structure. Any of the statistical methods could 

achieve this result. However, the greater the 

number of categories created, the more stringent 

the requirements that are placed on the specified 

system. If too many categories are created, the 

distinctions between them become less clear, and 

fairness becomes an issue. This is also the case 

with continuous pricing systems, when the number of categories is essentially equal to the number of banks. The methodologies described above 

could be used to create as many or as few pricing 

categories as required to achieve an acceptable 

trade-off, but it is important that expected failure 

rates be progressively higher for successively higher risk-pricing categories. 

Combination of Supervisory Ratings and 

Statistical Models 

Although using statistical models to price 

deposit insurance premiums might be appealing 

because of their reliance on objective financial 

data reported by banks, strictly applying a statistical model would inevitably result in some 

CAMELS 1-rated institutions paying more than 

some CAMELS 2-rated institutions. Since 

banks rated a composite 2 fail more frequently 

than banks rated a composite 1, it would seem 

logical to make the case that the 2-rated institutions in general should pay a higher premium for 

deposit insurance. 

An alternative that addresses this concern is to 

combine supervisory ratings with one of the statistical models. A combined approach would 

preserve the CAMELS rating by initially classifying banks according to whether they were 

ranked CAMELS 1 or 2 and would then use the 

statistical models to create subcategories. 

There are a number of potential possibilities for 

combining supervisory ratings and statistical models. Table 5 illustrates one way in which a pricing 

system might operate using CAMELS ratings and 

SCOR. In the table, we divide the 1A insurance 

category as of year-end 2002 into three subcategories. All CAMELS 1-rated banks are placed in 

the 1A1 category. CAMELS 2-rated banks with a 

SCOR value of less than 1.25 also are placed in 

the 1A1 category. The remaining CAMELS 2­

rated banks are classified as either 1A2 (SCOR 

rating anywhere from 1.25 to 1.75) or 1A3 

(SCOR rating higher than 1.75). The distributions shown in Table 5 would vary depending on 

the threshold values chosen for the SCOR ratings. 

Combined approaches tend to perform much like 

the statistical methodologies in identifying downgrades and failures. Banks that are in higher-premium groups are more likely to be downgraded or 

to fail than banks in lower-premium groups. 

Table 5 

Option for Pricing Well-Capitalized and 

Highly-Rated Institutions Using CAMELS and 

SCOR Ratings (Year-End 2002) 

Subcategories 

1A1 1A2 1A3 

All Composite 1 Rated 

and Composite 2 Rated with 

SCOR Rating < 1.25 3,618 

41.7% 

Composite 2 Rated with 

SCOR Rating >= 1.25 

and <= 1.75 2,767 

31.9% 

Composite 2 Rated with 

SCOR Rating > 1.75 2,285 

26.4% 

Note: SCOR values are calculated from December 31, 2002, Call Report data, and 

these values are combined with December 2002 exam ratings. The 1A1 subgroup 

represents the least risk and the 1A3 subgroup represents the greatest risk. The 

figures in the cells refer to the number and percentage of 1A institutions. 

2003, VOLUME 15, NO. 4 8 FDIC BANKING REVIEW 
Options for Pricing Federal Deposit Insurance 

Scorecard 

The scorecard uses an expert system to	 9 This equity measure, which includes loss reserves, performed better in statistical tests 

than equity alone. In addition, we believe that including loss reserves could create a develop gradations of risk for each variable disincentive to charge off loans purely to avoid higher nonperforming-asset scores. in the failure-prediction model, thus lessening the cliff effects. The original example of a scorecard appeared in the Table 6 

recommendations paper in April 2001. 

Since then, we have held numerous 

meetings with other regulators, industry 

groups, and academics to solicit ideas on 

the scorecard. The comments received 

from these groups led us to make adjustments to correct for criteria that unduly 

penalized a particular class of banks. 

Other changes were designed to improve 

the estimation techniques that had been 

used to create the original scorecard. The 

most significant change is that the failure-prediction model was reestimated for 

banks in the 1A insurance category only, 

rather than for the entire industry. 

Table 6 shows the most recent version of 

the scorecard. In this example, the 1A 

category of the current pricing matrix 

(table 1) is divided into three subcategories. The scoring framework allows 

banks to be classified as 1A1 (least risk), 

1A2, or 1A3 (most risk). The noncorefunding adjustment factor at the bottom 

of the table is included to address the 

unique funding strategies used by large 

banks and is discussed more fully below. 

This version of the scorecard places 

greater emphasis on asset-quality measures than the original scorecard. This 

version also includes more gradations of 

risk within each of the three subcategories. The modified scorecard does 

maintain the net income, noncore funding, and equity elements of the original 

scorecard, but the weight placed on these 

measures has been reduced, as would be 

more appropriate for CAMELS 1- and 2­

rated banks. Also, the equity measure 

has been changed to include loss 

reserves.9 

Scorecard 

(Weightings Based on Well-Capitalized and Highly-Rated 

Institutions Only) 

Range of Maximum 

Scores Score 

Scoring Factor 

Nonaccrual Loans / Total Assets < 0.5% 30 30 

= 0.5–1.0% 26 

= 1.0–1.5% 23 

= 1.5–2.0% 21 

= 2.0–2.5% 20 

> 2.5% 0 

Loans Past Due 90+ Days / Total Assets < 0.5% 25 25 

= 0.5–1.0% 22 

= 1.0–1.5% 20 

= 1.5–2.0% 18 

= 2.0–2.5% 13 

> 2.5% 0 

ORE / Total Assets < 0.5% 20 20 

= 0.5–1.0% 16 

= 1.0–1.5% 14 

= 1.5–2.0% 12 

= 2.0–2.5% 11 

> 2.5% 0 

Loans Past Due 30–89 Days / Total Assets < 0.5% 14 14 

= 0.5–1% 12 

= 1–1.5% 10 

= 1.5–2% 9 

= 2–2.5% 8 

> 2.5% 0 

Pretax Net Operating Income / Average Assets > 0.5% 7 7 

= 0–0.5% 4 

< 0% 0 

Noncore Funding / Total Assets <= 40% 3 3 

> 40% 0 

Equity & Reserves / Total Assets > 7.0% 1 1 

<= 7.0% 0 

Total 100 

Application of Scoring Framework 

If institution is 1A and total score is >= 97, classify as 1A1 

If institution is 1A and total score is < 97 and >= 87, classify as 1A2 

If institution is 1A and total score is < 87, classify as 1A3 

Adjustment Factor if Noncore Funding / Total Assets > 40% 

Market Adjustment for Standard and Poor’s AA– or Better 3 

Market Adjustment for Standard and Poor’s A– to A+ 1 

Market Adjustment for Standard and Poor’s BBB+ or Worse 0 

FDIC BANKING REVIEW	 9 2003, VOLUME 15, NO. 4 
 

Options for Pricing Federal Deposit Insurance 

Table 7 shows the distribution that would have 

resulted under this structure. It is clear that the 

distribution can shift significantly over different 

periods. Although 43 percent of the banks would 

have been classified in the best category at the 

end of 2002, only 24 percent would have been in 

this category in 1985. Thus, a certain amount of 

migration into and out of categories can be 

expected as the banking industry passes into and 

out of periods of stress. 

Table 7 

Distribution of Well-Capitalized and 

Highly-Rated Institutions Based 

on the Scorecard 

(1985–2002) 

Subcategories 

Year 1A1 1A2 1A3 

2002 43.5% 38.6% 17.9% 

2000 44.5 38.0 17.6 

1995 45.5 42.2 12.3 

1990 30.0 43.3 26.7 

1985 23.8 41.4 34.9 

Note: For each selected year, 1A institutions are scored on the 

basis of their reported financial ratios at year-end and are then 

placed into one of the three subcategories demarcated in the 

section of table 6 called “Application of Scoring Framework”. 

Relative Merits of Proposed Pricing Options 

To recap, the pricing options presented here for 

banks (other than large banking organizations) 

include expanded use of supervisory ratings, use of 

statistical models (both in a continuous and discrete format), use of a combination of statistical 

models and supervisory ratings, and a scorecard 

that uses expert judgment in conjunction with a 

statistical model. How do these options fare relative to the desirable attributes outlined earlier? 

Accuracy 

We can compare accuracy, or the ability to differentiate risk, through the use of power curves for 

each of the deposit insurance pricing options discussed. Figure 3 is a power curve that represents 

how well each of the options performs in identifying failures. The horizontal axis of the figure 

shows the percentage of total institutions scored 

by each method. The institutions are sorted left 

to right, from those having the worst score (most 

likely to fail) to those having the best score. The 

vertical axis shows the cumulative percentage of 

total failures identified. The point identified on 

the figure shows that the first 10 percent of the 

institutions ranked according to SCOR values 

contained 62 percent of the total failures. The 

closer the curve is to the upper-left corner of the 

graph, the more accurate the particular method is 

at identifying failures. The diagonal line essentially represents a system with no predictive 

power, where the number of failures identified is 

proportional to the percentile of observations. 

A failure identification score can be developed by 

measuring the area between an option’s respective 

curve and the diagonal line. Based upon this 

score, the CAMELS-downgrade model has the 

most predictive power (37.05), followed by the 

failure-prediction model (35.66), the scorecard 

(28.49), and finally the supervisory-based structure (16.74).10 

Simplicity 

The supervisory ratings approach has an advantage over the other options when considering 

simplicity because of the level of familiarity with 

and acceptance of the CAMELS rating system. 

The CAMELS rating system is well understood 

and accepted by the banking industry, and broad 

agreement exists as to what each of the five ratings means in terms of a bank’s condition. In 

contrast, the statistical models are more complicated than other methods. They also are less 

10 This is not to say that supervisory ratings are inaccurate. Rather, 

CAMELS ratings provide a relatively broad measure of risk. They are good 

at separating healthier institutions from those showing more pronounced 

financial weaknesses, but are not specifically designed to differentiate 

among better-rated institutions. The statistical models, on the other hand, 

were developed to fit the historical failure and CAMELS downgrade data. 

In a sense, they are designed to excel at tests of historical accuracy (ex 

post). It is not clear that statistical models would fare as well relative to 

CAMELS ratings going forward (ex ante), where the task is to identify 

emerging risk factors that may or may not be reflected in historical 

experience. 

2003, VOLUME 15, NO. 4 10 FDIC Banking Review 
 

 

Options for Pricing Federal Deposit Insurance 

transparent to insured institutions because the 

mechanics of the models are not observable. The 

scorecard represents an attempt to simplify the 

purely statistical approaches by combining an 

expert system with the failure-prediction model. 

Flexibility 

The supervisory ratings approach also holds an 

advantage over the other options in terms of flexibility. When examiners assign a CAMELS rating, they have access to and can analyze a wide 

range of data, including information about management, underwriting, and various intangible 

factors. Statistical models and scorecard can 

never completely reflect the current financial 

condition of a bank because they rely on Call 

Report information and because they cannot be 

tailored to reflect the unique aspects of individual 

banks. With a supervisory ratings approach, 

changes in virtually any factor predictive of bank 

failure, as well as improvements in supervisors’ 

ability to measure risk exposure, would automatically be incorporated into the deposit insurance 

pricing system. To achieve flexibility, the statistical models and scorecard would need to be updated with some frequency to ensure that they 

Figure 3 

Power Curves for Pricing Methodologies 

Cumulative Percentage of Failures CAMELS Downgrade
 

100
 

80 

60 

40 

20 

0 

Percentage of Institutions 

CAMELS 

Scorecard 

Failure-Prediction 

10 20 30 40 50 60 70 80 90 100 

continue to reflect the factors most closely associated with risk, thus making them more difficult to 

implement. 

Appropriate Incentives 

A supervisory approach also would best avoid perverse-incentive problems because examiners 

would verify on-site that operating results were 

achieved through safe and sound management 

practices. Supervisory and insurance ratings 

would therefore be closely aligned. Purely statistical approaches could create unintended incentive 

problems because they rely completely on Call 

Report data. The scorecard was designed to 

reduce the possibility of perverse incentives 

inherent in the statistical approach. The expert 

system incorporated into this approach would 

allow choices to be made regarding the factors 

used and threshold values to avoid these problems. 

Fairness 

Even though a goal of each of the pricing options 

is to treat banks equitably, almost always there 

will be cases in which the classification of a particular bank may be seen as unfair. The combined supervisory ratings and statistical models 

approach was developed, in part, to address a fairness issue. A purely statistical approach probably 

would result in some CAMELS 1-rated institutions paying higher premiums than some 

CAMELS 2-rated institutions. A combined 

approach could prevent this outcome. 

Another way to evaluate fairness is in terms of an 

option’s objectivity. The statistical models are 

the most objective since they rely completely on 

a bank’s reported data. The scorecard would be 

the next most objective, followed by the supervisory ratings approach, which would rely a great 

deal upon examiners’ judgment. 

FDIC BANKING REVIEW 11 2003, VOLUME 15, NO. 4 
 

Options for Pricing Federal Deposit Insurance 

Pricing Options for New Banks 

A separate pricing system is being considered for 

new institutions because of their special characteristics. Risks in new institutions often result 

from the fact that these institutions operate with 

unproven business plans in markets served by 

established competitors. The risks inherent in 

new institutions are not easily identified by the 

methods that can be useful for detecting risk in 

seasoned institutions. For instance, new institutions typically have high capital-to-asset ratios 

and low levels of problem assets compared with 

their seasoned-institution counterparts, yet new 

institutions have generally displayed a higher failure rate than seasoned institutions. As a result, 

pricing structures that rely on financial ratios 

would be less effective in identifying risk in new 

institutions. 

No consensus exists as to when a new bank takes 

on the characteristics of a seasoned institution. 

For purposes of deposit insurance pricing, we 

define new banks as those existing for five years 

or less. Figure 4 shows that at the end of this 

five-year period, failure rates of new institutions 

approach failure rates of seasoned institutions. 

Moreover, five years should allow new institutions 

enough time to confirm the viability of their business plans. Conversely, using a period longer 

than five years could discourage bank formation 

because of the relatively higher premiums to be 

paid by new institutions. 

Two options for setting the assessment rate for 

new institutions are currently being considered. 

They are based on the premise that, although new 

banks should pay a risk premium that reflects 

their historical failure experience, the premium 

should not be so high as to discourage new firms 

from entering the industry. The two options are 

(1) automatically charge new institutions the 

highest rate paid by well-capitalized, highly-rated 

banks, or (2) charge new institutions a separate 

rate. The difference between the two lies in the 

maximum rate that could be charged to these new 

institutions. 

In the second option—charging new institutions 

a separate rate—the assessment rate for these new 

institutions could be based on the historical risk 

profile of new institutions as a group, and the rate 

could be capped so that these new institutions 

would pay a rate lower than the rate paid by institutions that are less than well capitalized and not 

highly rated. As in option 1, new institutions 

that fall outside the best insurance assessment risk 

class would pay the same rate as other institutions 

in their particular class. 

Figure 4 

Years to Failure for Institutions Established 

between 1985 and 1994 

(Failure Data through 2002) 

Percentage of Failures 

2.5 

2.0 

1.5 

1.0 

0.5 

0.0 

Average Failure Rate of 

Seasoned Institutions, 

1985--2002a 

<=1 1.1--2.0 2.1--3.0 3.1--4.0 4.1--5.0 5.1--6.0 6.1--7.0 7.1--8.0 

Years after Establishment that Institution Failed
 

aSeasoned institutions are institutions that have been in operation for at least five years.
 

2003, VOLUME 15, NO. 4 12 FDIC BANKING REVIEW 
Options for Pricing Federal Deposit Insurance 

Pricing Options for Large Banks 

Large banks also have special characteristics, 

which may not be captured by the more traditional approaches to risk assessment. This unique status is explicitly acknowledged by FDICIA, which 

allows for separate pricing based on institution 

size. Although a pricing system that relies primarily on financial ratios derived from Call Report 

data may be suitable for small and medium-sized 

banking institutions, it may not be the best 

approach to identifying and equitably charging for 

risk in larger, more complex institutions. 

In the long term, Basel II holds some promise for 

pricing large bank risk because it incorporates 

default probabilities derived from the institutions’ 

own internal credit-risk models.11 Such a system 

will not be possible before the Basel II capital 

guidelines are implemented (the scheduled date is 

2007). In the meantime, information derived 

from the financial markets—alone or in combination with supervisory information—may provide a 

more accurate way to evaluate and price risk in 

large and complex organizations than an accounting-based system. 

Developing a pricing system specifically for large 

and complex banking organizations will first 

require establishing criteria to select the institutions that would be subject to such an alternative 

system. The simplest and most commonly used 

criterion for delineating the group of large and 

complex banking organizations is asset size. 

Another criterion could be market capitalization, 

or a measure of complexity such as market participation or foreign operations. 

Aside from the criterion used to define large 

banks, the ability to implement a pricing system 

that relies upon financial market data depends 

upon the availability of market data for these 

larger institutions. Equity data are generally 

available for most large banking companies. 

11 As the requirements of Basel II are formalized and as institutions opt to 

adhere to them, we would expect the FDIC to incorporate information about 

the bank’s internal credit rating systems, operational risk, and market risk 

into its pricing of deposit insurance. 

However, other market data, such as subordinated 

debt price quotes, are not available for several 

large banks. 

Pricing Framework Based on 

Supervisory Ratings 

A simple method for categorizing large, well-capitalized and highly-rated institutions according to 

risk is the method already proposed for small and 

medium-sized banks: creating two or more subgroups based on CAMELS ratings. Using supervisory ratings to set assessment rates for large 

institutions is appealing for several reasons. Large 

banking organizations are subject to frequent and 

thorough on-site review. Continuous supervision 

programs, which provide real-time and continuous evaluations of risk, have been established by 

the Office of the Comptroller of the Currency, 

the Federal Reserve Board, and the FDIC. Also, 

ratings assigned by regulators to large banks 

reflect information from a variety of sources, 

including the financial markets. 

Pricing Framework Based on Market 

Measures of Risk 

As the scale and complexity of the banking 

industry has increased, interest in using market 

information as a regulatory tool has grown. Regulators already use market signals extensively to 

monitor bank risk, and a variety of market indicators hold promise for pricing deposit insurance for 

large and complex institutions. These include 

price data such as stock price volatility and subordinated debt yield spreads; credit ratings assigned 

by companies such as Moody’s, Standard and 

Poor’s (S&P), and Fitch; and estimated default 

frequencies calculated using option pricing-type 

models such as the one developed by KMV Corporation.12 A combination of these measures— 

and others that might prove suitable—could 

provide a more robust and balanced pricing tool 

for large banks than one based entirely on either 

12 KMV’s model calculates a company’s probability of default from its stock 

price volatility, current capital structure, and value of its assets. 

FDIC BANKING REVIEW 13 2003, VOLUME 15, NO. 4 
 

Options for Pricing Federal Deposit Insurance 

supervisory ratings or financial ratios. A disadvantage of relying entirely on market measures is 

that the insurer would forgo the benefits of information gleaned by examiners with access to confidential information. 

A key issue is whether the data used for insurance 

pricing should originate at the bank or the parent 

holding-company level. In general, data related 

to the depository institution are of greater value 

to the insurer, since they reflect the consensus 

opinions of investors about the condition and 

performance of the entity having the most direct 

access to the federal safety net. Data related to 

the depository institution are all the more important in light of the increasing diversification of 

financial holding companies into business lines 

unrelated to banking; market information about a 

parent company may not accurately reflect the 

performance of an insured subsidiary. 

Unfortunately, market data are often unavailable 

at the insured-institution level. The equity and 

debt instruments that would provide information 

useful for deposit insurance pricing are more typically issued by the parent holding companies of 

banks. This practice may compel the use of holding-company data for pricing. 

Measuring the Predictive Ability of Market 

Factors 

Because so few large institutions have failed, 

insufficient data are available to establish statistical relationships between the probability of bank 

failure and market measures in the same way failure was correlated with Call Report data to develop the failure-prediction model. To establish the 

usefulness of market measures as predictive factors, therefore, we tested three market measures 

against supervisory downgrades from CAMELS 1 

or 2 to CAMELS 3 or lower over the period from 

1987 through 1999 for the largest 25 banks as of 

year-end 1999. 

Figure 5 shows the degree to which stock price 

volatility has predicted downgrades. We calculated a coefficient of variation for stock price (as a 

measure of volatility) and grouped the institutions 

by high, medium, or low volatility. The bars in 

the figure show the percentage of banks in each 

category that were downgraded to a composite 3 

rating or worse within two years of our calculation. The results show a relationship between 

stock volatility and supervisory downgrades, indicating that stock price volatility may be an effective way to differentiate institutions for pricing 

purposes. 

Figure 6 shows how well S&P credit ratings perform in predicting CAMELS composite downgrades. These aggregate results show a certain 

degree of differentiation between higher and 

lower investment-grade ratings, and a significant 

differentiation between investment- and noninvestment-grade ratings. 

Figure 5 

High Volatility Medium Volatility Low Volatility 

0 

1 

2 

3 

4 

5 

6 

7 

Percentage of Institutions Downgraded 

to CAMELS 3 or Worse within 2 Years 

of Volatility Calculation 

Aggregate CAMELS Downgrades in Relation to 

Stock Price Volatility for Top 25 Institutions 

(Coefficient of Variation, 1987–1999) 

2003, VOLUME 15, NO. 4 14 FDIC BANKING REVIEW 
 

 

Options for Pricing Federal Deposit Insurance 

Figure 7 shows how KMV-estimated default frequencies perform in predicting downgrades during 

subsequent two-year periods. Again, a correlation 

is evident between the market measure and higher probabilities of downgrades. 

Methodology for Assigning Scores 

A large-bank pricing system using market measures could be constructed in a number of ways. 

One would be to use an individual measure—for 

example, credit ratings—on its own. Currently, 

S&P credit ratings for the parent companies of 

the 50 largest insured institutions range from 

AA– to BBB–. Given this fairly wide distribution, assessment rates could be assigned either to 

each credit rating category individually or to larger groups made up of more than one rating category. Alternatively, a combination of market 

measures could be weighted and summed to produce a single score per institution. 

Pricing Framework Based on Combination 

of Supervisory and Market Measures 

Another way to create subcategories in the best 

insurance group would be to use supervisory ratings in combination with a select set of market 

Figure 6 

measures of bank risk. Such a system could take 

the form of either an integrated system in which 

supervisory ratings and market measures were 

combined and equally represented or a system in 

which market measures would serve as trip wires 

to adjust insurance classifications based mainly on 

supervisory ratings. An integrated system would 

require a method of weighting the various factors—composite ratings and market measures—to 

produce a single score. 

A pricing system with trip wires might incorporate any of the market measures mentioned above 

(or others, such as price-to-book ratios) to adjust 

institution scores after the institutions had initially been categorized by supervisory ratings. For 

example, banks might be placed into separate 

CAMELS 1 and CAMELS 2 categories, and 

CAMELS 2-rated institutions that had relatively 

poor credit ratings might then be relegated to a 

third category. In table 8, the composite 2-rated 

group is subdivided into two categories: those 

with S&P credit ratings of A– or better (the 1A2 

group) and those with ratings worse than A– (the 

1A3 group). Similarly, high subordinated debt 

yield spreads, high stock price volatility, or low 

price-to-book ratios might serve as the secondary 

means of differentiation. 

Figure 7 

Aggregate CAMELS Downgrades in Relation to 

Credit Rating for Top 25 Institutions 

(Coefficient of Variation, 1987–1999) 

Percentage of Institutions Downgraded 

to CAMELS 3 or Worse within 2 Years 

60 

50 

40 

30 

20 

10 

0 

A-- or Better BBB+ to BBB-­ BB+ or Lower 

Aggregate CAMELS Downgrades in Relation to 

KMV for Estimated Default Frequency (EDF) 

Percentage of Institutions Downgraded 

to CAMELS 3 or Worse within 2 Years 

15 

12 

9 

6 

3 

0 

EDF <.33 .33 <= EDF < .67 EDF >= .67 

FDIC BANKING REVIEW 15 2003, VOLUME 15, NO. 4 
Options for Pricing Federal Deposit Insurance 

Pricing with a Scorecard 

The disadvantages of using a financial ratio-based 

pricing system for large banks are discussed above. 

However, it may be possible to modify the scorecard approach in ways that would eliminate unintended adverse effects on large banks. Larger 

institutions tend to be penalized by the noncore 

funding component of the scorecard because they 

often operate with higher levels of wholesale 

funding and lower levels of capital than the 

smaller institutions that compose the bulk of 

observations used to calibrate the scorecard risk 

weights. To compensate, selected market measures could be incorporated into the scorecard 

either to replace certain of its elements as measures of risk or to offset elements that unduly 

penalize large banks. One approach we explored 

is the use of credit ratings. 

Table 8 

For example, the lowest section of table 6 (see 

page 9) shows the part of a revised scorecard that 

includes an adjustment for institutions’ S&P credit ratings. The rating adjustment relates to a 

bank’s noncore funding score: banks that have 

ratings of A– or better receive an upward scoring 

adjustment to reflect their enhanced ability to 

obtain capital in the debt markets. Table 9 shows 

the effect of the rating adjustment on the distribution of large-bank rankings based on the modified scorecard. The rating adjustment results in 

an increase in the percentage of large banks 

placed in the risk category 1A1, though this percentage remains below the percentage of small 

banks in the 1A1 category. The percentage gap 

between large and small 1A1 institutions may 

reflect the relatively stronger asset-quality measurements for smaller institutions. 

Table 9 

Options for Pricing Large Institutions 

Using CAMELS Ratings with Credit Ratings 

(Year-End 2002) 

Subcategories 

1A1 1A2 1A3 

Composite 1 Rated 15 

32.6% 

Composite 2 Rated 

and Credit Rating A– or better 24 

52.2% 

Composite 2 Rated 

and Credit Rating Worse than A– 7 

15.2% 

Note: The figures in the cells refer to the number and percentage of the 46 

largest institutions for which S&P ratings are available. 

Distribution of Well-Capitalized and 

Highly-Rated Institutions by Size Based on 

the Scorecard (Year-End 2002) 

Subcategories 

1A1 1A2 1A3 

Small Banks 43.5% 38.5% 18.0% 

Large Banksa with Noncore 

Funding Adjustment 36.7 57.1 6.1 

Large Banksa without 

Noncore Funding Adjustment 32.7 59.2 8.2 

Note: The scorecard-derived scores (see table 6) produce the distribution shown 

here. In the scorecard, the adjustment for noncore funding rewards institutions 

rated by S&P as AA– or better with a 3-point upward adjustment, and institutions 

rated A– to A+ with a 1-point upward adjustment. 

a Large banks are the top 50 banks by asset size. 

2003, VOLUME 15, NO. 4 16 FDIC BANKING REVIEW 
Options for Pricing Federal Deposit Insurance 

Conclusion 

All the options discussed in this article involve 

trade-offs among the desirable attributes of a 

deposit insurance pricing system. As applied to 

historical data, the statistical approaches tend to 

provide greater risk differentiation than the supervisory ratings approach but also tend to be more 

complex, more difficult to implement, and more 

likely to create unintended perverse incentives. 

The scorecard has an advantage over pure statistical models in terms of simplicity, flexibility, and 

incentives, but it is less accurate. 

The combined statistical and supervisory 

approach was presented as an option that can 

ensure that CAMELS 1-rated institutions never 

pay more than CAMELS 2-rated institutions. 

Also, the combined approach supplements the 

informational content of the CAMELS ratings 

with the more recent information reported in 

Call Reports. However, the combined approach 

does not eliminate all the disadvantages of either 

of the two pure approaches. For example, if a 

combined methodology breaks the well-capitalized and highly-rated group of institutions into 

three or four subcategories, there is still the 

potential for cliff effects—small changes in a 

measured variable that produce large changes in 

the deposit insurance premium. In addition, a 

combined system is more complex than a system 

based on CAMELS ratings alone. 

Nonetheless, combining the statistical and supervisory approaches can mitigate several of the concerns relating to either approach in isolation. 

The combined approaches and perhaps the scorecard approach provide the opportunity to make 

practical trade-offs and achieve the right balance 

among desirable attributes and policy objectives. 

Separate deposit insurance pricing options were 

presented for new banks to address their special 

characteristics. Additional options also were presented for large banks that incorporate market 

data, which may better identify risk in larger, 

more complex institutions. Ultimately, the selection of one or another approach will reflect a particular weighting of the desirable attributes and a 

judgment regarding the approach that achieves 

the best balance. 

FDIC BANKING REVIEW 17 2003, VOLUME 15, NO. 4 
Vulnerability of Banks and Thrifts to a Real Estate Crisis 

Evaluating the Vulnerability of Banks and 

Thrifts to a Real Estate Crisis 

Charles Collier, Sean Forbush, and Daniel A. Nuxoll* 

As part of its extensive off-site monitoring efforts, 

the Federal Deposit Insurance Corporation (FDIC) 

has evaluated banks’ and thrifts’ vulnerability to 

the stress of a real estate crisis similar to the crisis 

that occurred in New England in the early 1990s.1 

Asking what would happen to banks and thrifts 

today if the real estate market were to experience a 

downturn similar to the one in New England a 

decade ago, we developed the history of the collapse of the New England real estate market into a 

stress test—the Real Estate Stress Test (REST)— 

that produces ratings comparable to the CAMELS 

ratings.2 The REST ratings indicate the severity of 

the exposure to real estate and therefore identify 

institutions that appear vulnerable to real estate 

* All the authors are on the staff of the Federal Deposit Insurance Corporation 

(FDIC). Charles Collier and Sean Forbush are with the Division of Supervision 

and Consumer Protection (DSC), Collier as chief of the Information 

Management Section and Forbush as a senior financial analyst. Daniel Nuxoll 

is with the Division of Insurance and Research (DIR) as a senior economist. 

This article reports the results of a close collaboration among numerous 

people in both the DSC and the DIR. In addition, the staff of the FDIC’s San 

Francisco Regional Office encouraged the project and provided the authors 

with helpful comments. 

The opinions expressed here are those of the authors and do not necessarily 

reflect the views of the FDIC. 

1 See Collier et al. (2003) for a more general discussion of the objectives and 

problems. The ratings direct the attention of 

examiners to particular institutions and indicate 

that the FDIC should be especially concerned 

about the management of real estate lending at 

these institutions. Poor practices there could 

expose the FDIC to substantial losses. 

In addition, REST is able to identify particular 

areas of the country where a high fraction of the 

banks and thrifts are vulnerable—areas where the 

real estate markets might be of concern to bank 

examiners. Although these markets may be 

healthy at the moment, the extent of bank lending 

in them means that the FDIC must pay particular 

attention to conditions there. 

The results of our research with REST indicate 

that the institutions most vulnerable to real estate 

crises today are headquartered in the West and a 

2 CAMELS ratings are based on examiners’ assessments of Capital, Asset 

quality, Management, Earnings, Liquidity, and market Sensitivity. The ratings 

range from 1 to 5, with 1 being the best. Banks and thrifts with a rating of 

1 or 2 are considered sound, whereas supervisors have definite concerns 

about institutions with a rating of 3. Institutions with a rating of 4 or 5 are 

considered problem banks. The Sensitivity rating was added only in 1997, so 

strictly speaking, ratings before that year are CAMEL ratings. This article 

methods of the FDIC’s off-site models. uses “CAMELS” throughout, despite the anachronism. 

FDIC BANKING REVIEW 19 2003, VOLUME 15, NO. 4 
 

Vulnerability of Banks and Thrifts to a Real Estate Crisis 

handful of southern cities.3 The real estate markets in these locations are currently healthy, but 

because banks—and by extension the FDIC—have 

substantial exposure to these markets, bank supervisors need to be especially alert to any indication 

of problems there. 

We also find that the most critical risk factor is 

construction lending, a finding that confirms the 

conventional wisdom that construction lending is 

particularly risky. Many accounts of the savings 

and loan crisis of the late 1980s and early 1990s 

discuss commercial and residential construction 

projects that went awry.4 

Because the stress test was developed on data from 

New England, it may well reflect the distinctive 

characteristics of events in that region. However, 

when REST was backtested on data from Southern 

California in the late 1980s and early 1990s, it was 

successful in identifying institutions that later had 

problems. More importantly, REST was also successful in identifying troubled banks in parts of the 

country where real estate downturns were moderate. These successes suggest that even if a repetition of the severe problems of New England or 

Southern California is very unlikely, REST can 

still help identify banks that might suffer difficulties during less severe real estate downturns. 

The REST model should not be interpreted as a 

condemnation of construction lending. The 

model does, however, emphasize that risk control 

is especially important for these loans. The success 

of a construction loan depends on the future, not 

the present, of the real estate market, so 

construction lending is intrinsically more risky 

3 Clearly, our project is most directly related to the FDIC’s function as an 

insurer, not a supervisor. Consequently, this article discusses all banks and 

thrifts, whether or not they are supervised by the FDIC. 

It must also be observed that banks are identified by their headquarters. 

Consequently, for purposes of this stress test, the Bank of America is located 

in Charlotte, N.C., although the vast majority of its business is outside the 

Charlotte metropolitan statistical area and outside the state of North Carolina. 

However, the number of megabanks is relatively small, and few of the banks 

in our project have many operations that are outside a small area. 

4 A number of popular accounts—for example, see Mayer (1990), chapter 5— 

report that Edwin Gray, the chairman of the Federal Home Loan Bank Board 

from 1983 to 1987, became aware of the depth of the S&L crisis while 

watching a videotape of abandoned projects in the Dallas area. 

than forms of lending that are secured by liens on 

real property. 

The obvious question is why one should focus on 

New England. There are three reasons. First, 

problems among the banks in New England can be 

traced directly to the real estate market.5 Second, 

the number of banks in the region was large 

enough that statistical models can be estimated 

relatively easily. Third, the New England experience is hardly unique. As the FDIC (1997) documents, commercial real estate was a factor in 

several distinct sets of banking problems during the 

1980s and early 1990s.6 In addition, commercial 

real estate has been a factor in bank crises in a 

number of other countries.7 Thus, events in New 

England constitute a relatively clear case of a problem that is endemic to banking. 

Importantly, REST uses Call Report data, so it 

cannot evaluate pricing, terms, or underwriting— 

factors critical to controlling the risk of real estate 

lending. Moreover, REST does not estimate the 

condition of the real estate market in any region, 

state, or metropolitan statistical area (MSA); it 

identifies markets where banks are exposed to 

potential real estate problems, not markets where 

such problems actually exist. What the REST 

model can do is identify the banks that are at most 

risk in the event real estate problems should occur. 

In so doing, it sharpens the focus of questions 

about risk control and real estate markets and 

therefore makes an important contribution to the 

FDIC’s off-site monitoring. 

This article explains how the model was built with 

the use of New England data and was tested with 

the use of data from other historical real estate 

crises. The REST results for December 2002 are 

presented and analyzed, and recent trends—both 

nationally and for selected states—are discussed. 

5 See FDIC (1997), chapter 10, for a discussion of this issue. In contrast, the
 

Texas banking crisis during the late 1980s and early 1990s was caused only
 

partly by commercial real estate.
 

6 Ibid., especially chapter 3.
 

7 See Herring and Wachter (1999). 

2003, VOLUME 15, NO. 4 20 FDIC BANKING REVIEW 
 

Vulnerability of Banks and Thrifts to a Real Estate Crisis 

Method of Examining New England 

The central question for the team that built the 

REST model was whether any model could detect 

those healthy banks that would be in most danger 

during periods when real estate became a problem. 

To answer this question, we examined the New 

England real estate crisis of the early 1990s. In 

1987, the economy and the banking industry in 

New England could have been described as 

vibrant, but by 1990 the problems were obvious.8 

The first stage in developing the REST model 

involved comparing the banks in New England in 

1987 with the banks there in 1990. All the banks 

were healthy in 1987, but by 1990 a substantial 

fraction of them were troubled. Our analysis used 

statistical procedures and data from 1987 to find 

the traits common to the institutions that later 

had severe difficulties. This approach seeks to 

answer the question whether as early as 1987 one 

could have identified the riskiest banks in New 

England. 

Because the purpose of our project was to evaluate 

banks’ ability to withstand a crisis such as the one 

in New England in 1991–1993, banks that had a 

special function or were somehow atypical were 

eliminated from the analysis. Banks considered 

atypical were those that had equity-to-asset ratios 

greater than 30 percent or loan-to-asset ratios less 

than 25 percent. A total of 13 special-purpose or 

atypical (or new) banks were eliminated from the 

December 1987 sample.9 

In addition, consolidation just before the crisis had 

to be taken into account. In December 1987, 289 

New England banks filed Call Reports, but in 

8 We could have used data from years other than 1987 and 1990 to develop 

the REST model, but for a terminal date, 1990 is the obvious choice. The 

problems in New England were not that apparent until 1990, yet in 1991 a 

significant number of banks failed. We are especially interested in banks 

that are so troubled they eventually fail; thus, a later terminal date would 

ignore some important information. The start date of 1987 corresponds 

closely to the peak in the New England economy, but 1986 or 1988 could 

equally well have been used. Experiments indicate that the REST results 

would have been similar for any of those three years. 

9 Also excluded was a Connecticut bank that at the end of 1988 apparently 

sold its regular banking operations and continued as a special-purpose 

institution. 

December 1990 the number had shrunk to 255. 

Much of the consolidation appears to have been 

achieved by mergers of different banks owned by 

the same holding company. Regardless of the reason for the consolidation, the performance of the 

bank resulting from a merger was undoubtedly 

affected by the characteristics of the banks 

absorbed in the merger. Consequently, this project 

used data adjusted for mergers.10 

Finally, because growth rates between 1985 and 

1987 were included in the model, only banks that 

had been in existence for five years (1985–1990) 

were part of the sample. 

The sample contained a total of 203 banks.11 

In the first stage—comparing the conditions and 

balance sheets of banks at the end of 1987 with 

the same banks’ conditions and balance sheets at 

the end of 1990—the model considers 12 variables 

as measures of health at the end of 1990 (previous 

work has shown that these variables are closely 

related to CAMELS ratings, and the FDIC has 

developed a Statistical CAMELS Off-site Rating 

[SCOR] model using them).12 The 1987 data 

include the same 12 variables as well as 12 

10 To adjust the data, we combined the data for separate institutions that 

later merged. For example, if two banks merged in January 1988, the 1987 

data for the resulting bank would be the combined balance sheets and 

income statements for the two banks as of December 1987. 

11 Our discussion of New England does not refer to thrifts because the savings 

banks were excluded from the sample. During this period, savings banks 

filed a slightly different Call Report from the one filed by commercial banks, 

so some data provided by commercial banks are missing for savings banks.. 

More importantly, during this period many mutual savings banks converted to 

stockholder-owned savings banks, and after conversion, these institutions 

behaved quite differently. See FDIC (1997). The development of the stress 

test assumes that the institutions in the sample had a generally stable 

strategy, and clearly many of the savings banks in New England did not. 

Our discussion of Southern California does not include thrifts because before 

1991, data on thrifts in that region are limited. 

12 See Collier et al. (2003). A model could be developed that would forecast 

CAMELS ratings directly. However, the deterioration among banks in New 

England was extremely sudden, and CAMELS ratings change only after an 

examination (or, occasionally, after an off-site review). CAMELS ratings at the 

end of 1990 probably do not reflect the extent of the problems in New 

England because examiners were overwhelmed and had not changed the 

ratings at some troubled institutions. We developed a model to forecast 

CAMELS ratings directly, and although it identified the same types of 

institutions as the REST model, in backtests it was found to be slightly less 

accurate than the REST model. 

2003, VOLUME 15, NO. 4 21 FDIC BANKING REVIEW 
 

Vulnerability of Banks and Thrifts to a Real Estate Crisis 

variables that measure (as a fraction of assets) the types 

of loans made by the bank, a variable that measures the 

bank’s growth rate between 1985 and 1987, and a 

variable that measures the bank’s size in 1987. 

The basic results appear in tables 1 and 2. The reason 

the two tables differ is that only 3 of the 12 SCOR variables (equity, provisions for loan losses, and net income) 

can be less than zero, while another of the variables 

Table 1 

The REST Model: Coefficients Estimated with OLS 

Income 

before 

Equity Provisions Taxes Loans 

Intercept 6.111 –3.830 4.933 22.523 

Log assets –0.315 0.283 –0.245 

Growth –0.020 0.008 –0.009 0.046 

Lagged Variables 

Equity 0.535 

Loan-loss reserves 1.346 

Loans past due 30–89 days 0.253 –0.354 –0.960 

Loans past due 90+ days –0.804 

Nonaccrual loans –0.755 1.029 

Other real estate 

Charge-offs 1.084 

Provisions for loan loss 

Income before taxes 0.815 0.378 

Noncore liabilities –0.032 

Liquid assets 0.032 

Loans and long-term securities 0.689 

Loan Types 

Agriculture loans 

C&I loans –0.040 0.054 –0.075 

Credit card loans 0.059 0.072 

Other consumer loans 

Loans to depositories 

Municipal loans 

Agricultural real estate loans 0.858 

Construction loans –0.229 0.217 –0.366 

Multifamily-housing loans 

Nonresidential real estate loans 

1–4 family mortgages 

Leases –0.214 0.376 

R2 0.5212 0.3702 0.4474 0.5834 

F-Statistic 0.676 0.868 0.557 0.611 

Degrees of freedom 16,176 18,176 16,176 22,176 

Note: The data are for 203 New England banks. The independent variables are from 

December 1987 and the dependent variables are from December 1990. Charge-offs, 

provisions, income, and growth are all based on merger-adjusted data. 

(loans and long-term securities) can be zero 

in principle but in fact was substantially 

greater than zero for the whole sample. 

These 4 variables were handled by the usual 

regression technique—ordinary least squares 

(OLS). Table 1 reports the results for these 

4 variables. 

The other 8 SCOR variables cannot, in 

principle, be less than zero. These variables—loan-loss reserves, loans past due 

30–89 days, past due 90+ days, nonaccruals, 

other real estate, charge-offs, volatile liabilities, and liquid assets—were fit with a Tobit 

model.13 Table 2 reports the results for these 

variables. For a number of the 8 variables, 

the results do not differ appreciably from 

OLS because, as reported in table 2, there 

were very few zero values.14 

As mentioned above, the independent variables for the REST model include all the 

1987 values for SCOR variables, 12 categories of loans as a fraction of assets, asset 

growth, and bank size. 

The SCOR variables represent the condition 

of the bank in 1987. In fact, the condition 

of the bank results partly from the characteristics of the bank, so these 12 variables are 

proxies for the characteristics of the bank. 

For example, one cannot directly observe 

the quality of a bank’s underwriting, but presumably tighter underwriting results in fewer 

past-due loans—so the data on loans past 

due 30–89 days can be seen as a proxy for 

underwriting standards. 

The loan-type variables are important 

because the New England crisis was a real 

estate crisis. Our project included data on 

13 Other real estate consists mostly of real estate that banks own 

because of foreclosures. Charge-offs are gross, not net, so they 

cannot be less than zero. 

14 In fact, all banks had some loans past due 30–89 days, but the 

OLS estimates differ from Tobit because of a handful of values that 

are close to zero. Tobit considers the possibility that these values 

are greater than zero by chance. 

2003, VOLUME 15, NO. 4 22 FDIC BANKING REVIEW 
Vulnerability of Banks and Thrifts to a Real Estate Crisis 

several types of real estate loans (1–4 family residential, multifamily housing, agricultural, construction and development, and other nonresidential) 

as well as other loans (unsecured commercial, to 

municipalities, to depository institutions, credit 

card, other consumer, agricultural production). 

Table 2 

Presumably banks that held large amounts of real 

estate loans would be the ones most severely 

affected by the crisis. 

Asset growth between 1985 and 1987 was included 

because rapidly growing banks are considered 

The REST Model: Coefficients Estimated with Tobit 

Past Past Other 

Dues Dues Real Noncore Liquid 

Reserves 30–89 90+ Nonaccrual Estate Charge-offs Liabilities Assets 

Intercept –2.385 5.756 2.595 –5.627 –0.706 –3.026 –8.015 24.764 

Log assets 0.224 –0.278 –0.113 0.444 0.181 0.803 0.963 

Growth 0.003 0.006 0.007 –0.042 –0.052 

Lagged Variables 

Equity 0.130 0.045 0.597 

Loan-loss reserves 0.554 –0.582 

Loans past due 30–89 days 0.646 0.373 0.440 0.256 –0.930 

Loans past due 90+ days 0.455 

Nonaccrual loans 0.790 0.416 –2.621 

Other real estate –0.869 –1.407 

Charge-offs –2.002 0.712 –11.673 

Provisions for loan loss 11.304 

Income before taxes 

Noncore liabilities 0.028 0.026 0.696 

Liquid assets –0.036 0.428 

Loans and long-term securities –0.247 

Loan Types 

Agricultural loans 

C&I loans 0.031 0.044 0.019 

Credit card loans 0.105 

Other consumer loans 0.025 0.044 

Loans to depositories 0.189 –0.819 

Municipal loans –0.150 –0.121 –0.240 

Agricultural real estate loans –0.076 0.820 

Construction loans 0.113 0.077 0.071 0.250 0.155 0.088 –0.197 

Multifamily-housing loans 0.144 0.132 

Nonresidential. real estate loans 0.069 0.025 –0.125 

1–4 family mortgages –0.012 

Leases 

Pseudo-R2 0.3689 0.4597 0.1306 0.3588 0.3219 0.4380 0.7679 0.5862 

Chi-Squared Statistic 12.45 7.67 9.43 16.76 17.05 12.44 9.00 17.51 

Degrees of Freedom 20 15 18 20 19 17 15 20 

Zero values 0 0 21 19 14 1 3 0 

Note: The data are for 203 New England banks. The independent variables are from December 1987 and the dependent variables are from 

December 1990. Charge-offs, provisions, income, and growth are all based on merger-adjusted data. 

2003, VOLUME 15, NO. 4 23 FDIC BANKING REVIEW 
 

Vulnerability of Banks and Thrifts to a Real Estate Crisis 

especially risky. Total assets were included in the 

model because it is usually thought that larger 

banks can more easily diversify risk away.15 

All estimations were done with a stepwise procedure.16 This method starts with all 26 variables 

(12 SCOR variables, 12 loan-type variables, asset 

growth, and size) and eliminates those that are not 

statistically significant. The stepwise method was 

necessary because some variables have coefficients 

that are very large but statistically insignificant. 

Although inclusion of these variables improves the 

in-sample fit of the model, it does so only very 

slightly. If the coefficients are large, however, 

inclusion of these variables in out-of-sample forecasting would almost certainly have an effect on 

the forecasts despite the complete absence of statistical evidence that these variables matter at all. 

Their elimination made very little difference to 

the fit of the model. 

New England Results 

As noted above, all the results were estimated with 

a stepwise procedure, and this procedure did not 

result in a significantly worse fit than if all the 

variables had been used. In general, the two sets 

of estimates are completely consistent with each 

other. In fact, most of the coefficients estimated 

with a stepwise procedure are very similar to those 

estimated when all the variables are used.17 

Although alternative methods produced similar 

estimates, one should be cautious about interpreting these results. For example, one cannot conclude that the ratio of loans and long-term 

15 The number actually used is the logarithm of total assets. 

16 The statistical software SAS supports a stepwise method for OLS but not 

for Tobit. The variables with the Tobit specification were also estimated with 

stepwise OLS and with a full Tobit model (one that includes all 26 variables). 

The variables that were insignificant in both the stepwise OLS and the full 

Tobit specification were dropped. The Tobit was reestimated, and the more 

insignificant variables were dropped. In the final estimation, all variables 

were significant at least at the 15 percent level. 

17 It should be noted that because these equations were estimated with a 

stepwise procedure, the coefficients and t-statistics cannot be interpreted in 

the textbook manner. However, the estimated coefficients and t-statistics are 

very similar when all the variables are included. 

securities to assets did not affect asset quality, 

although that variable has a zero coefficient in all 

the asset-quality equations (loans past due 30–89 

days, loans past due 90+ days, nonaccrual loans, 

other real estate, charge-offs, and provisions for 

loan loss). The effect might be small or inconsistent. Statistical tests reveal correlation, not causation.18 When the correlation is strong and 

consistent with theory, however, there is good reason to take statistical results seriously. With that 

in mind, one should note several features of the 

results. 

First, this approach captures much of the variation 

between banks. Near the bottom of both table 1 

and table 2 there is a line reporting that R2 is 

between 0.30 and 0.60 for most of the results.19 

This means that 1987 data can account for about 

30–60 percent of the differences between banks in 

1990. The major exception to this result is that 

the variable “loans past due 90+ days” has an R2 of 

only 0.1306.20 

Second, most variables are mean-reverting. That 

is, the banks that were exceptional in 1987 tended 

to resemble the average (mean) bank more closely 

by 1990. The coefficients on the lagged variables 

show this effect. For example, consider the effect 

of lagged equity on equity. From table 1, the estimated coefficient is 0.535. This means that an 

extra 1 percent equity would lead to an extra 0.535 

percent equity in 1990. Importantly, the coefficient is between 0 and 1, indicating that banks 

with unusually high levels of equity in 1987 still 

had unusually high levels of equity in 1990, but 

18 The stepwise procedure complicates the usual warning about reasoning 

from correlation to causality. The coefficient on a correlated variable might 

well incorporate the effect of an omitted variable. 

19 The numbers reported for the Tobit are pseudo-R2s. They are calculated in 

a manner analogous to the manner in which OLS R2s are calculated, except 

that with the Tobit numbers the calculation allows for the fact that the 

variables can never be less than zero. 

20 The test statistics for the hypothesis that the omitted variables have a zero 

coefficient are also included. By way of comparison, the 5 percent 

significance level for a Chi-squared statistic with 15 degrees of freedom is 

25.00, while the comparable F-statistic with 20 and 200 degrees of freedom 

is 1.62. However, because the model was fitted with a stepwise procedure, 

the statistics in the tables are not useful for classical hypothesis testing. 

They merely indicate that excluding the variables has very little effect on the 

fit of the model. 

2003, VOLUME 15, NO. 4 24 FDIC BANKING REVIEW 
Vulnerability of Banks and Thrifts to a Real Estate Crisis 

other things being equal, differences in equity levels shrank during those three years. An inspection 

of tables 1 and 2 shows that the only variables 

without a strong mean-reverting component are 

provisions, reserves, nonaccrual loans, and other 

real estate. 

This observation suggests that most of the SCOR 

variables reflect something fundamental about the 

operations of a bank. Banks with higher than 

average levels of loans past due 30–89 days tend to 

have higher than average levels even three years 

later. Conceivably, high levels of past-due loans 

may reflect a less cautious underwriting philosophy. 

This interpretation of the SCOR variables is supported by the data. For example, high levels of 

loans past due 30–89 days might be considered a 

sign that the bank is more willing to take risks. In 

fact, high levels of loans past due 30–89 days in 

1987 are associated with lower net income and 

more nonaccrual loans, more other real estate, 

more charge-offs, and more provisions in 1990. 

The third feature of our New England REST 

results is that most of the loan-type variables have 

the expected coefficients. High levels of commercial real estate loans in 1987 were associated with 

poor performance in 1990. It should be noted that 

construction and development loans in particular 

were problems for New England banks. Although 

other types of commercial real estate (nonresidential real estate and multifamily housing) were associated with problems, construction loans were the 

major problem: they were significant in almost 

every regression, and they generally had a larger 

effect than other types of commercial real estate 

loans. High levels of commercial and industrial 

(C&I) loans and other consumer loans also seem 

to have been a risk factor. Credit card loans were 

not a special problem, and loans to municipalities 

helped shield banks from the downturn. 

Fourth, high asset growth between 1985 and 1987 

also resulted in poor performance by 1990. The 

signs on log assets are consistent with the theory 

that larger institutions were more diversified and 

more aggressive in facing their problems in 1990. 

Large institutions had fewer past-due loans; on the 

other hand, they had more nonaccrual loans, 

reserves, charge-offs, and provisions. They also 

had lower net income, but that result seems to be 

driven completely by the higher provisions. 

There are some other interesting features of the 

results. Banks with high net income in 1987 tended to have higher equity in 1990. Banks with high 

levels of reserves in 1987 performed better in 1990. 

This last finding is consistent with the interpretation that more-conservative banks tend to recognize losses more quickly and reserve against them. 

This interpretation, in turn, is consistent with the 

observation that charge-offs in 1987 are negatively 

correlated with loans past due 30–89 days in 1990. 

Banks that relied on noncore liabilities also tended 

to have more difficulties in 1990 (lower income, 

more past dues 30–89 days, and more other real 

estate). This result is consistent with the notion 

that banks that use noncore liabilities may be more 

aggressive and take more risks. 

And there are some anomalies. High levels of 

other real estate in 1987 are correlated with low 

levels of loans past due 90+ days and nonaccrual 

loans in 1990. This might reflect differences in 

workout policies. 

Out-of-Sample Testing 

Although these results are intrinsically interesting 

as an analysis of past events in New England, the 

goal of our project was to develop a forecasting 

tool that could identify banks most likely to have 

difficulties during future real estate downturns. To 

test whether REST had forecasting power, we 

applied it to other real estate crises. Because these 

tests involved banks that were not in the sample 

used to build the model, they are called “out-ofsample” tests. 

Southern California experienced a real estate crisis 

at about the same time as New England. To test 

the validity of the New England results, we 

forecasted 1991 SCOR ratios on the basis of 1988 

data for Southern California banks. The banks 

2003, VOLUME 15, NO. 4 25 FDIC BANKING REVIEW 
Vulnerability of Banks and Thrifts to a Real Estate Crisis 

included all California banks overseen by the 

FDIC’s Los Angeles East, Los Angeles West, and 

Orange County field offices. Again, all 

institutions with loan-to-asset ratios less than 25 

percent or equity-to-asset ratios greater than 30 

percent were excluded. The sample contained 242 

banks, 173 of which had a composite CAMELS 

rating of 1 or 2 as of year-end 1988. 

The banks in California differed from those in 

New England in a number of ways. First, California banks in 1988 were generally in worse shape 

than New England banks in 1987. None of the 

New England banks in the sample had a CAMELS 

rating worse than 3 at year-end 1987, but 20 (8.3 

percent) of the Southern California banks were 

rated 4 at year-end 1988. Second, the shock in the 

New England economy and therefore to the 

region’s banks was both shorter and more severe. 

Of the 203 New England banks, 33—16.3 percent—failed, a percentage slightly higher than the 

percentage in California (33 of 240, or 13.8 percent). Moreover, in New England the bulk of the 

failures (29) were concentrated in a two-year period (1991 and 1992), whereas in Southern California the failures were spread out over three years 

(1992–1994). Third, structural differences 

between the two regions’ banking industries were 

significant: California had permitted statewide 

branching for decades, whereas the banking industry in New England was more segmented. 

The stress test did not do particularly well at forecasting individual ratios. For example, the model 

was not able to identify those banks that experienced large increases in nonaccrual loans. This is 

not too surprising because management’s decisions 

about handling problems determine how the problems affect the bank’s balance sheet and income 

statement, and if bank management delays dealing 

with real estate problems, the bank will tend to 

have higher other real estate owned or nonaccrual 

loans. If management deals with the problems 

aggressively, those same problems may affect the 

bank’s provisions, charge-offs, income, and capital. 

Even a perfect model cannot forecast how management will deal with problems. 

However, bank supervisors do not evaluate banks 

in terms of individual ratios but in terms of the 

overall condition of the bank. Consequently, the 

major issue is whether the stress test can forecast 

bank condition. The SCOR model can be used to 

translate the 12 SCOR ratios into a forecasted 

CAMELS rating.21 These ratings are the REST 

ratings. 

Table 3 compares 1988 REST ratings with 

CAMELS ratings and with failures between 1992 

and 1995. All the banks used for compiling table 

3 were 1 or 2 rated as of December 1988, and all 

banks survived until at least December 1991. If 

the bank failed between 1992 and 1995, the bank 

is identified as a failure. Otherwise, the bank’s 

reported CAMELS rating is the worst rating it 

received between 1992 and 1995.22 

Several considerations underlie this approach. 

First, the ultimate concern of supervision is troubled banks; hence, one should concentrate on the 

worst ratings. Second, banks that are rated 3 or 

worse have already been identified as potential 

problems, and the critical question is which banks 

currently regarded as sound are likely to develop 

problems.23 Third, as noted above, events in 

Southern California evolved over a number of 

years. Problems at a bank that were obvious at the 

end of 1993 might not have been evident at the 

end of 1991. Using the worst rating during the crisis years 1992–1995 avoids the issue of timing. 

This method considers the banks that encountered 

difficulties, regardless of when the problems actually occurred. 

21 Our project focuses on the information that could have been known at the 

time. Consequently, the REST ratings are computed with the same 

coefficients that could have been used to produce the December 1988 SCOR 

ratings. There is one complication: the coefficients were estimated using 

revised Call Report data and a complete set of examination ratings. Neither 

would have been available if someone had estimated the SCOR model in 

1989. 

22 Three banks are excluded because although they survived until December 

1991, they merged before they were examined. The mergers were not 

assisted; that is, the banks did not fail. 

23 The results are not materially different if one includes banks that were 

rated 3, 4, or 5 as of 1988. 

2003, VOLUME 15, NO. 4 26 FDIC BANKING REVIEW 
 

 

Vulnerability of Banks and Thrifts to a Real Estate Crisis 

Forecasting models have two types of error: they fail to identify the banks that are downgraded (Type I error), and they 

identify banks that are not downgraded (Type II error).24 

This article analyzes the number of banks that the model 

correctly identified, so it refers to Type I accuracy and Type 

II accuracy. The emphasis is on problem banks (banks with 

a CAMELS rating of 4 or 5) and failures. Failures cost the 

FDIC money to resolve the bank, and problem banks are in 

danger of failing and take considerably more supervisory 

resources. 

Panel A of table 3 shows the raw numbers, while panel B 

reports Type I accuracy and panel C reports Type II accuracy. Ideally all banks that failed would have REST ratings 

worse than 4.5 (100 percent Type I accuracy), and the 

“failed” line in panel B would have a 100 percent in the last 

24 For a more extended explanation of Type I and Type II errors, see Collier et al. (2003). 

Table 3 

Performance of Stress Test in Southern California 

Worst REST Rating 

Exam Rating 1.0–2.5 2.5–3.5 3.5–4.5 4.5–5.0 Total 

A. Number of Banks 

1 or 2 12 12 18 4 46 

3 12 17 19 5 53 

4 or 5 4 14 17 21 56 

Failed 1 2 8 4 15 

Total 29 45 62 34 170 

B. Percentage by Worst Rating 

1 or 2 26.09 26.09 39.13 8.70 

3 22.64 32.08 35.85 9.43 

4 or 5 7.14 25.00 30.36 37.50 

Failed 6.67 13.33 53.33 26.67 

Total 17.06 26.47 36.47 20.00 

C. Percentage by REST Rating 

1 or 2 41.38 26.67 29.03 11.76 27.06 

3 41.38 37.78 30.65 14.71 31.18 

4 or 5 13.79 31.11 27.42 61.76 32.94 

Failed 3.45 4.44 12.90 11.76 8.82 

Note: All banks were California banks supervised from the FDIC’s Los Angeles East, Los 

Angeles West, and Orange County field offices. All banks had a 1 or 2 CAMELS composite 

rating as of December 1988. REST ratings are based on December 1988 Call Reports. The 

worst rating was the worst CAMELS composite rating assigned between 1992 and 1995, and all 

failures occurred between 1992 and 1995. 

column. In addition, ideally all the banks 

with REST ratings of 4.5 or worse would 

eventually have a CAMELS rating of 5 

or would fail (100 percent Type II accuracy). In that case, the column for REST 

ratings greater than 4.5 in panel C would 

have numbers that sum to 100 percent in 

the lines for CAMELS ratings of 4 or 5 or 

for failures. 

Table 3 shows that the model is not perfect but that it does correctly identify a 

large percentage of problem banks and 

failures. Consider Type I accuracy first. 

Panel A indicates that 15 banks failed; 4 

of the 15 had REST ratings worse than 

4.5, while 8 of the 15 had REST ratings 

between 3.5 and 4.5. Panel B shows this 

is 27 percent and 53 percent of the failures, respectively. Thus, if banks with 

REST ratings of 3.5 or worse are targeted, 

the Type I accuracy for failures is 80 percent. A similar analysis shows that for 

problem banks, REST has a Type I accuracy of 68 percent. 

The analysis of Type II accuracy also 

shows that REST is quite accurate. 

Panel C indicates that among the banks 

with REST ratings of 4.5–5, 62 percent 

became problem banks and 12 percent 

failed; put differently, 74 percent either 

failed or were in danger of failing. For 

REST ratings of 3.5–4.5, 28 percent were 

problem banks, and 13 percent failed. In 

other words, just over 40 percent had 

severe difficulties. 

Several points about this backtesting 

should be emphasized. All the banks 

were rated 1 or 2 at the time of the 

December 1988 Call Report, and the 

examination ratings were given three to 

seven years after the Call Report. In 

short, REST did a reasonably good job of 

identifying which sound banks were most 

likely to encounter difficulties three to 

seven years later. 

2003, VOLUME 15, NO. 4 27 FDIC BANKING REVIEW 
 

 

 

Vulnerability of Banks and Thrifts to a Real Estate Crisis 

However, the example of Southern California was chosen 

precisely because real estate problems were severe there. 

This is a critical piece of information. The stress test identifies banks that could become problems if there were a real 

estate crisis. REST does not identify real estate markets 

that are susceptible to crisis. The backtest was successful 

because the Southern California market did in fact have a 

crisis; REST did not identify that market as one vulnerable 

to crisis. In the jargon of forecasting, the stress test provides 

conditional, not unconditional, forecasts.25 

25 Earlier in the same period Texas had a major crisis, which we did not use for two 

reasons. First, large bank-holding companies present a number of difficulties because of the 

connections between banks in the holding company. Second, the real estate problems in 

Texas began after many banks in the state had already gotten into trouble because of loans 

to the oil and gas industry. However, tests on the 1986 data from Texas show results 

similar to those presented in the text for Southern California. As of December 1986, only 

34 banks had a composite CAMELS rating of 1 or 2 and a REST rating of 5. Of those 34, 

13 (38 percent) failed and 13 (38 percent) became problem banks. Only 1 maintained a 1 

or 2 rating until 1993. In contrast, 338 banks had a REST rating of 2, and only 12 (3 

percent) failed, while 43 (13 percent) became problem banks. 

Table 4 

Performance of Stress Test in Atlanta 

Worst REST Rating 

Exam Rating 1.0–2.5 2.5–3.5 3.5–4.5 4.5–5.0 Total 

A. Number of Banks 

1 or 2 2 7 7 4 20 

3 3 3 4 4 14 

4 or 5 0 2 6 4 12 

Failed 0 0 1 0 1 

Total 5 12 18 12 47 

B. Percentage by Worst Rating 

1 or 2 10.00 35.00 35.00 20.00 

3 21.43 21.43 28.57 28.57 

4 or 5 0.00 16.67 50.00 33.33 

Failed 0.00 0.00 100.00 0.00 

Total 10.64 25.53 38.30 25.53 

C. Percentage by REST Rating 

1 or 2 40.00 58.33 38.89 33.33 42.55 

3 60.00 25.00 22.22 33.33 29.79 

4 or 5 0.00 16.67 33.33 33.33 25.53 

Failed 0.00 0.00 5.56 0.00 2.13 

Note: All banks were headquartered in the Atlanta MSA. All banks had a 1 or 2 CAMELS 

composite rating as of December 1987. REST ratings are based on December 1987 Call Reports. 

The worst rating was the worst CAMELS composite rating assigned between 1991 and 1994, 

and the failure occurred between 1991 and 1994. 

Both New England and Southern California suffered from extremely bad real 

estate problems. REST has also been 

backtested on episodes of less severe real 

estate problems. For example, table 4 

reports the results (based on December 

1987 data and examination ratings from 

the period 1991–1994) for banks headquartered in the Atlanta MSA. 

The problems in Atlanta were clearly less 

severe than those in New England or 

Southern California. Only one bank 

failed, and no banks received a CAMELS 

5 rating. Nonetheless, institutions identified by the stress test were more likely to 

have severe difficulties. Only 2 of the 17 

institutions (12 percent) with REST ratings better than 3.5 received a CAMELS 

4 rating, but 11 of the 30 institutions (37 

percent) with REST ratings worse than 

3.5 later became problem banks or failed. 

Again, all these banks were CAMELS 

rated 1 or 2 at year-end 1987.26 

Forecasts Based on December 2002 

Data 

The stress test has been run at the FDIC 

since 1999, and the ratings are distributed 

every quarter to FDIC examiners and 

analysts as well as to the other banking 

regulatory agencies. Tables 5, 6, and 7 

summarize a recent set of ratings—those 

based on the December 31, 2002, Call 

Report data. In contrast to the backtests, 

these tables report on all institutions 

regardless of CAMELS rating.27 However, institutions with equity-to-asset ratios 

exceeding 30 percent and loan-to-asset 

ratios less than 25 percent are omitted. 

26 A handful of other backtests have been done and have 

produced similar results.
 

27 There is a second difference as well: thrifts are included in
 

the December 2002 data.
 

2003, VOLUME 15, NO. 4 28 FDIC BANKING REVIEW 
Vulnerability of Banks and Thrifts to a Real Estate Crisis 

Table 5 reports the results by FDIC region, table 6 by 

state (omitting U.S. territories), and table 7 by selected 

MSAs. 

Table 5 shows that the banks in the San Francisco and 

Atlanta regions are unusually vulnerable to real estate 

problems. Of the 699 institutions in the San Francisco 

region, 162 (23.2 percent) had ratings of 3.5–4.5, and 

250 (35.8 percent) fell into the worst category, with ratings of 4.5–5.0. This last number is the most significant, since these are the institutions that the model 

identifies as especially vulnerable to real estate problems. In the Atlanta region, 244 (21.2 percent) had 

ratings between 3.5 and 4.5, and 332 (28.8 percent) had 

ratings worse than 4.5. In the rest of the nation, only 

12.7 percent were rated between 3.5 and 4.5, and only 

9.5 percent were rated worse than 4.5. In short, the 

Table 5 

model indicates that institutions in the West 

and Southeast are approximately three times 

more likely to be vulnerable to a real estate 

crisis than institutions in other parts of the 

country. 

Table 5 also indicates some regions of secondary concern, notably the Dallas and Memphis 

regions. 

In table 6 (the results reported by state) the 

states are ranked by the percentage of institutions with stress-test ratings worse than 4.5.28 

This table clearly indicates that the vulnerable institutions are concentrated geographically, with 6 of the top 10 states being in the 

San Francisco region. In addition, there are 

only 11 states in which 30 percent or more of 

the institutions are extremely vulnerable, and 

only 4 more in which the percentage is 

REST Ratings by FDIC Region 

(Based on December 2002 Call Report) 

FDIC Region 1.0–1.5 1.5–2.5 2.5–3.5 3.5–4.5 4.5–5.0 Total 

A. Number 

Boston 2 174 128 34 18 

New York 14 364 231 84 58 

Atlanta 24 261 291 244 332 

Memphis 10 217 220 118 79 

Chicago 59 927 528 284 182 

Kansas City 136 1,238 397 202 138 

Dallas 37 486 252 165 189 

San Francisco 17 129 141 162 250 

Total 299 3,796 2,188 1,293 1,246 

Excluding 

Atlanta & 

San Francisco 258 3,406 1,756 887 664 

B. Percentage 

Boston 0.6 48.9 36.0 9.6 5.1 

New York 1.9 48.5 30.8 11.2 7.7 

Atlanta 2.1 22.7 25.3 21.2 28.8 

Memphis 1.6 33.7 34.2 18.3 12.3 

Chicago 3.0 46.8 26.7 14.3 9.2 

Kansas City 6.4 58.6 18.8 9.6 6.5 

Dallas 3.3 43.0 22.3 14.6 16.7 

San Francisco 2.4 18.5 20.2 23.2 35.8 

Total 3.4 43.0 24.8 14.7 14.1 

Excluding 

Atlanta & 

San Francisco 3.7 48.9 25.2 12.7 9.5 

356 

751 

1,152 

644 

1,980 

2,111 

1,129 

699 

8,822 

6,971 

between 20 percent and 30 percent. 

Table 7 presents the data by MSA, though it 

includes only MSAs where at least 10 banks 

or thrifts are headquartered.29 Again, the 

MSAs are ranked by the percentage of institutions with stress-test ratings worse than 4.5. 

Only the top 20 MSAs are reported in the 

table, and the table confirms that these 

MSAs are unusual. On average, REST 

assigns almost 60 percent of the banks and 

thrifts in these MSAs a rating of 4.5 or worse, 

whereas for all other MSAs the comparable 

number is approximately 20 percent. Clearly, 

the FDIC should be especially concerned 

about the health of real estate markets in 

these MSAs.30 

28 The totals in table 5 include banks and thrifts in U.S. territories. 

29 Unfortunately, some cities with very high percentages of poor 

REST ratings (for example, Provo, Utah, and Fort Collins, Colo.) are 

excluded from the table because too few institutions are 

headquartered in them. 

30 Some preliminary work also shows that new banks have unusually 

poor REST ratings. As a group, banks that are less than three years 

old have REST ratings comparable to those in the MSAs listed in 

table 7. 

2003, VOLUME 15, NO. 4 29 FDIC BANKING REVIEW 
 

Vulnerability of Banks and Thrifts to a Real Estate Crisis 

Table 6 Table 7 

REST Ratings by State 

(Based on December 2002 Call Report) 

Number of Institutions 

State 1.0–1.5 1.5–2.5 2.5–3.5 3.5–4.5 4.5–5.0 Total 

Percentage 

4.5–5.0 

AZ 1 1 5 6 23 

NV 6 3 2 3 16 

WA 2 14 11 18 49 

UT 3 10 8 3 25 

NC 0 16 15 22 48 

OR 0 3 9 7 15 

GA 6 64 64 56 119 

CA 1 32 62 90 103 

CO 4 46 21 25 52 

FL 8 38 59 79 94 

ID 0 2 5 6 6 

AK 0 0 4 1 2 

MI 1 43 49 34 41 

SC 2 20 29 26 20 

DC 0 2 0 2 1 

TN 3 56 56 48 40 

NM 1 24 11 9 11 

DE 2 7 8 5 5 

VA 3 24 57 31 25 

AL 4 53 48 24 26 

MD 6 49 35 15 19 

TX 19 277 157 105 98 

MO 11 169 89 60 43 

LA 1 71 54 19 18 

OK 13 139 63 26 28 

WI 9 113 101 54 31 

NH 0 14 13 0 3 

MT 2 35 20 17 8 

NJ 2 63 33 18 12 

IL 29 414 149 99 69 

KS 30 219 54 32 33 

AR 3 62 66 29 15 

IN 6 86 69 28 16 

MN 21 256 111 52 37 

KY 5 110 71 38 16 

WY 2 23 10 9 3 

MA 1 103 62 23 12 

NY 0 92 66 17 11 

MS 3 28 44 22 6 

CT 0 23 24 6 3 

NE 32 171 39 18 11 

SD 8 60 13 8 3 

OH 9 161 89 31 9 

IA 14 299 75 27 11 

PA 4 151 86 24 4 

HI 0 2 5 1 0 

ME 0 18 16 4 0 

ND 20 64 16 5 0 

RI 1 4 6 0 0 

VT 0 12 7 1 0 

Total 299 3,796 2,188 1,293 1,246 

36 

30 

94 

49 

101 

34 

309 

288 

148 

278 

19 

7 

168 

97 

5 

203 

56 

27 

140 

155 

124 

656 

372 

163 

269 

308 

30 

82 

128 

760 

368 

175 

205 

477 

240 

47 

201 

186 

103 

56 

271 

92 

299 

426 

269 

8 

38 

105 

11 

20 

8,822 

63.9 

53.3 

52.1 

51.0 

47.5 

44.1 

38.5 

35.8 

35.1 

33.8 

31.6 

28.6 

24.4 

20.6 

20.0 

19.7 

19.6 

18.5 

17.9 

16.8 

15.3 

14.9 

11.6 

11.0 

10.4 

10.1 

10.0 

9.8 

9.4 

9.1 

9.0 

8.6 

7.8 

7.8 

6.7 

6.4 

6.0 

5.9 

5.8 

5.4 

4.1 

3.3 

3.0 

2.6 

1.5 

0.0 

0.0 

0.0 

0.0 

0.0 

14.1 

REST Ratings by MSA 

(Based on December 2002 Call Report) 

Number of Institutions Percentage 

MSA State Total 3.5–4.5 4.5–5.0 4.5–5.0 

Atlanta GA 76 5 64 84.2 

Raleigh NC 11 1 8 72.7 

Seattle WA 36 6 26 72.2 

Grand Rapids MI 20 5 14 70.0 

Portland OR-WA 13 2 9 69.2 

Naples FL 10 4 6 60.0 

Sacramento CA 10 1 6 60.0 

Phoenix AZ 27 4 16 59.3 

Nashville TN 21 6 12 57.1 

Las Vegas NV-AZ 24 4 13 54.2 

Birmingham AL 19 4 10 52.6 

Norfolk VA-NC 14 4 7 50.0 

San Jose CA 10 3 5 50.0 

Riverside CA 19 5 9 47.4 

Dallas TX 73 12 34 46.6 

Orlando FL 24 4 11 45.8 

Stockton CA 11 4 5 45.5 

Memphis TN-AR-MS 25 6 11 44.0 

Salt Lake City UT 32 1 14 43.8 

Denver CO 33 5 14 42.4 

Top 20 MSAs 508 86 294 57.9 

All MSAs 4,243 873 1,009 23.8 

All but the top 20 MSAs 3,735 787 715 19.1 

Note: This table includes only MSAs in which at least 10 banks or thrifts are 

headquartered. Data are reported for only the top 20 such MSAs. 

2003, VOLUME 15, NO. 4 30 FDIC BANKING REVIEW 
Vulnerability of Banks and Thrifts to a Real Estate Crisis 

Analysis of the December 2002 Forecasts 

The results of the stress test can be analyzed much 

as the SCOR model is. With the SCOR model, 

one can attribute the reasons for a forecasted 

CAMELS downgrade to specific variables by comparing the bank’s ratios with the median ratios of 

all banks currently rated 2. The same technique 

can be used with REST.31 

For purposes of examining the REST ratings, we 

defined the benchmark as the median ratios of all 

institutions currently rated 1 or 2. This standard 

of comparison cannot be identified with any 

existing institution; it is a composite—the 

“average” institution with a 1 or 2 rating.32 This 

benchmark is used to calculate “weights” that trace 

the reason for poor ratings back to specific ratios. 

The weights are in terms of percentages so they 

necessarily sum to 100 percent. The percentages 

31 See appendix 2 in Collier et al. (2003) for an explanation of the method 

for deriving SCOR weights. The method used by REST is slightly more 

complicated because some variables (for example, nonaccruing loans) can 

never be less than zero. 

32 SCOR uses the median ratios of the banks that received a rating of 2 

within the previous year. 

Table 8 

can be negative if the ratio is better than the 

standard. Importantly, the weights are not used in 

the estimation; they are merely a method of 

comparing an institution that has received a poor 

rating with an average institution. 

Tables 8 and 9 illustrate how the weighting 

procedure can be used to analyze a result. Each 

table is for a hypothetical institution. The 

institution described in table 8 has a stress-test 

rating of 4.86 but a CAMELS rating of 2 and a 

SCOR rating of 1.51. However, almost 12 percent 

of the institution’s assets are construction loans, 

and those loans make up about 81 percent of the 

difference between this institution and the typical 

1- or 2-rated bank. Other factors contributing to 

the poor REST rating are nonresidential real estate 

(18.52 percent of the portfolio, with a weight of 

6.38 percent), multifamily housing loans (weight 

5.47 percent), and C&I loans (weight 4.71 

percent). This institution does have some strong 

points, though they are not important enough to 

change the stress-test rating. It holds 0.89 percent 

of its assets in its loan-loss reserves. These reserves 

have a weight of –0.64 percent, indicating that 

although they are a positive factor, they are 

Sample Stress-Test Rating, Hypothetical Bank A 

Cert Charter 

Found State 

Stress 4.86 Region 

Field Office 

CAMELS 2 SCOR 1.51 

Current Data Weight 

Assets 100,000 1.87 

Growth 25.42 1.66 

SCOR Ratios Portfolio 

Equity 10.20 0.94 Construction 

Loss Reserves 0.89 –0.64 Nonresidential Real Estate 

Loans Past Due 30–89 Days 0.62 0.23 Multifamily 

Loans Past Due 90+ Days 0.04 –0.06 1–4 Family 

Nonaccrual Loans 0.26 –0.33 C&I 

Other Real Estate 0.00 0.00 Credit Card 

Charge-offs 0.08 0.22 Other Consumer 

Provisions for Loss 0.18 0.11 Agricultural Operating 

Pretax Income 1.63 –0.43 Agricultural Real Estate 

Noncore Liabilities 14.41 –0.15 Depository 

Liquid Assets 36.19 –0.91 Municipality 

Loans and Long-Term Securities 65.98 0.07 Leases 

Ratio 

11.97 

18.52 

3.34 

5.96 

12.56 

1.07 

5.08 

0.15 

0.07 

0.00 

0.00 

0.00 

Weight 

81.02 

6.38 

5.47 

0.63 

4.71 

–0.64 

–0.26 

0.00 

0.11 

0.00 

0.00 

0.00 

2003, VOLUME 15, NO. 4 31 FDIC BANKING REVIEW 
Vulnerability of Banks and Thrifts to a Real Estate Crisis 

negligible in comparison with the size of the 

construction loan portfolio. 

Table 9 shows an institution that has a stress-test 

rating of 4.88. In contrast to the rating of the bank 

in table 8, this rating is not driven by construction 

loans. In fact, the bank illustrated in table 9 has no 

construction loans, and the stress test evaluates this 

as a positive factor (weight –10.27 percent). However, the institution is concentrated in multifamily 

housing (weight 74.07 percent). Secondary factors 

include a concentration in nonresidential real estate 

(weight 21.33 percent) and a reliance on noncore 

liabilities (weight 16.74 percent). This institution is 

relatively large (assets of $500 million), and on balance its size is a slight negative factor (weight 7.60 

percent). 

Table 10 presents an overview of the weights for 

banks that are currently rated CAMELS 1 or 2 but 

have REST ratings of 4.5 or worse. The variables 

are ordered by the median weight. Construction 

loans, with a median weight of almost 75 percent, 

are clearly the most important factor in the model. 

Of the 800 institutions with ratings of 4.5 or 

worse, 777 have weights for construction loans 

that exceed 5 percent. In 16 cases (as for the 

Table 9 

hypothetical bank in table 9), construction loans 

are a significant positive factor and have weights 

that exceed –5 percent. The median bank that is 

identified as extremely vulnerable holds 13.05 percent of its assets as construction loans, compared 

with 0.50 percent of the banks that receive REST 

ratings of between 1.50 and 2.50. 

In some cases nonresidential real estate loans, C&I 

loans, and multifamily housing loans are also significant risk factors. In addition, large weights are 

regularly assigned to low levels of liquid assets, 

high levels of noncore liabilities, and high levels of 

loans past due 30–89 days. Moreover, banks with 

poor ratings tend to be larger and to have grown 

more rapidly. Most variables seldom, if ever, have 

significant positive or negative weights. Mortgages 

on 1–4 family homes generally have a positive 

weight, but it is never significant. 

Table 11 shows that although construction loans 

have the most weight, they are not the only factor 

driving the ratings. All institutions holding construction loans exceeding 20 percent of their total 

assets are identified as extremely vulnerable, but 12 

institutions that have no construction loans 

received REST ratings of 4.5 or worse. 

Sample Stress-Test Rating, Hypothetical Bank B 

Cert Charter 

Found State 

Stress 4.88 Region 

Field Office 

CAMELS 2 SCOR 1.51 

Current Data Weight 

Assets 500,000 7.60 

Growth 40.37 3.69 

SCOR Ratios Portfolio 

Equity 7.61 –1.65 Construction 

Loss Reserves 0.94 –0.90 Nonresidential Real Estate 

Loans past due 30–89 Days 0.10 –6.55 Multifamily 

Loans past due 90+ Days 0.00 –0.16 1–4 Family 

Nonaccrual Loans 0.05 0.35 C&I 

Other Real Estate 0.00 0.00 Credit Card 

Charge-offs 0.02 0.91 Other Consumer 

Provisions for Loss 0.24 0.23 Agricultural. Operating 

Pretax Income 2.24 –2.61 Agricultural Real Estate 

Noncore Liabilities 37.65 16.74 Depository 

Liquid Assets 12.27 6.35 Municipality 

Loans and Long-Term Securities 87.33 –0.16 Leases 

Ratio 

0.00 

43.70 

43.32 

0.94 

0.00 

0.00 

0.00 

0.00 

0.00 

0.00 

0.00 

0.00 

Weight 

–10.27 

21.33 

74.07 

0.89 

–8.66 

0.04 

–1.35 

0.00 

0.11 

0.00 

0.00 

0.00 

2003, VOLUME 15, NO. 4 32 FDIC BANKING REVIEW 
Vulnerability of Banks and Thrifts to a Real Estate Crisis 

Table 10 

Reasons for Ratings of 4.5–5.0 

Median 

Weight 

Negative Factors 

(Weights of 5% or More) 

Number Percent 

Positive Factors 

(Weights of –5% or Less) 

Number Percent 

Medians 

4.5–5.0 1.5–2.5 

Number 

Percentage with CAMELS 1 Rating 

SCOR Rating 

REST Rating 

Construction Loans 73.46 

Nonresidential Real Estate Loans 4.37 

C&I Loans 4.34 

Noncore Liabilities 3.75 

Growth 2.88 

Assets 2.03 

Liquid Assets 1.71 

Multifamily Housing Loans 0.78 

Loans and Long-Term Securities 0.20 

Provisions for Loan Loss 0.20 

1–4 Family Mortgages 0.13 

Agricultural Real Estate Loans 0.12 

Charge-offs 0.08 

Loans Past Due 30–89 Days 0.02 

Other Real Estate 0.00 

Agricultural Loans 0.00 

Loans to Depositories 0.00 

Loans to Municipalities 0.00 

Leases 0.00 

Credit Card Loans –0.02 

Loans Past Due 90+ Days –0.04 

Pretax Income –0.12 

Nonaccrual Loans –0.13 

Equity –0.29 

Other Consumer Loans –0.32 

Loan-Loss Reserves –0.56 

777 97.1 

357 44.6 

378 47.3 

339 42.4 

248 31.0 

210 26.3 

55 6.9 

104 13.0 

0 0.0 

4 0.5 

0 0.0 

0 0.0 

6 0.8 

259 32.4 

0 0.0 

0 0.0 

0 0.0 

0 0.0 

0 0.0 

0 0.0 

6 0.8 

34 4.3 

0 0.0 

20 2.5 

2 0.3 

0 0.0 

16 2.0 

6 0.8 

38 4.8 

22 2.8 

4 0.5 

28 3.5 

3 0.4 

0 0.0 

0 0.0 

0 0.0 

0 0.0 

0 0.0 

21 2.6 

182 22.8 

45 5.6 

0 0.0 

0 0.0 

12 1.5 

35 4.4 

4 0.5 

0 0.0 

42 5.3 

42 5.3 

0 0.0 

0 0.0 

26 3.3 

800 3,565 

23.5 53.0 

1.77 1.56 

4.94 2.06 

13.05 0.50 

17.00 5.17 

12.60 6.11 

23.72 13.20 

32.08 7.15 

162,870 62,404 

21.39 36.82 

0.93 0.07 

79.30 68.57 

0.25 0.08 

14.20 17.98 

0.06 2.99 

0.08 0.07 

0.70 0.63 

0.00 0.00 

0.00 3.28 

0.00 0.00 

0.00 0.00 

0.00 0.00 

0.09 0.00 

0.01 0.04 

1.46 1.47 

0.19 0.11 

8.56 10.28 

3.51 6.24 

0.88 0.73 

Table 11 

REST Ratings and Construction Loans 

Construction Loans REST Rating as a Percentage 

of Assets 1.0–1.5 1.5–2.5 2.5–3.5 3.5–5.5 4.5–5.0 Total 

0 203 897 123 35 

0–5 134 2,657 2,084 548 

5–10 2 11 239 745 

10–15 2 90 

15–20 3 

20–25 

25–30 

30–35 

35–40 

40–45 

45–50 

60–65 

65–70 

Total 339 3,565 2,448 1,421 

12 

31 

159 

306 

164 

74 

21 

20 

6 

3 

2 

1 

1 

800 

1,270 

5,454 

1,156 

398 

167 

74 

21 

20 

6 

3 

2 

1 

1 

8,573 

2003, VOLUME 15, NO. 4 33 FDIC BANKING REVIEW 
 

 

Vulnerability of Banks and Thrifts to a Real Estate Crisis 

Table 11 also shows the reason that using the ratio 

of construction loans to total assets by itself is 

inadequate. A bank could have 7 percent of its 

assets in construction loans and receive almost any 

REST rating. If the bank has no other risk factors, 

it will receive a rating of 1–1.5, but if other risk 

factors are present, it may receive a rating of 5. 

Before assigning ratings, the stress test considers 

several aspects of a bank’s operations, allowing for 

both mitigating and exacerbating factors. A single 

ratio is only one number and is meaningful only 

after it has been put in a broader context.33 

Trends in Stress-Test Ratings 

Figures 1 and 2 show the history of stress-test ratings since December 1986 for the United States as 

well as some individual states. Both figures show 

the percentage of institutions receiving ratings of 

3.5 or worse as a percentage of all institutions with 

REST ratings.34 Figure 1 shows ratings in the 

33 Gilbert, Meyer, and Vaughan (1999) make this point forcefully. 

34 REST uses the SCOR model to assign ratings that are comparable to 

CAMELS ratings. Using the data on the characteristics of banks assigned 

CAMELS 5 ratings after actual examinations, SCOR estimates coefficients that 

describe the characteristics of a 5-rated bank. In 1998, there were few banks 

with CAMELS 5 ratings, so for that year the SCOR characterization of a 5­

rated bank relies on very little data and is consequently imprecise. This 

imprecision affects REST ratings worse than 4 because a rating midway 

between 4 and 5 draws on the characterizations of both 4-rated and 5-rated 

banks. The imprecision in SCOR (and REST) resulted in better ratings for 

Figure 1 

REST Ratings Worse Than 3.5, 1986–2002 

(United States, California, and Massachusetts) 

Percentage of All Banks 

80 

60 

40 

20 

0 

United 

States 

California 

Massachusetts 

1986 1988 1990 1992 1994 1996 1998 2000 2002 

United States and in two states that have already 

been discussed—Massachusetts and California. 

Figure 2 shows ratings in Arizona, Georgia, and 

Illinois. Both figures also show a definite trend in 

stress-test ratings: since 1993, the ratings for the 

United States and for all five states have become 

worse. 

In figure 1 the effects of the real estate crises in 

Massachusetts and California are clear. Large percentages of the financial institutions in both states 

were vulnerable in the late 1980s, and the percentages of vulnerable institutions then declined dramatically. Figure 1 also shows that institutions in 

the two states have followed quite different paths 

in the last decade. Whereas the REST ratings for 

California banks and thrifts have again become 

substantially worse than those for the United 

States as a whole, ratings for Massachusetts banks 

have generally become better. 

banks with very poor financials. If one takes a set of very poor financial 

ratios and assigns a rating based on pre-1997 coefficients or coefficients 

estimated on data from 1999 or later, the ratings would all be similar. 

However, the 1998 coefficients produce better ratings for the weakest 

financial ratios (that is, those ratios that would have been assigned a rating 

worse than 4 by coefficients from other periods). The data for the worst 

ratings are misleading in 1998 because the coefficients for 1998 are 

imprecise, and the ratings based on those coefficients do not reflect the 

innate weakness of the banks in the worst condition. 

Figure 2 

REST Ratings Worse Than 3.5, 1986–2002 

(Arizona, Georgia, and Illinois) 

Percentage of All Banks 

80 

60 

40 

20 

0 

1986 1988 1990 1992 1994 1996 1998 2000 2002 

Arizona 

Georgia 

Illinois 

2003, VOLUME 15, NO. 4 34 FDIC BANKING REVIEW 
Vulnerability of Banks and Thrifts to a Real Estate Crisis 

Figure 2 shows that Arizona banks and thrifts have 

followed a pattern similar to California’s, with very 

poor ratings in the mid-1980s, a very rapid 

improvement, and a subsequent deterioration. 

Ratings in Georgia, in contrast, have gradually 

deteriorated up to the present. Georgia today has 

a very high percentage of banks and thrifts with 

poor ratings. Ratings in Illinois have followed the 

national pattern quite closely, with some increase 

before the recession of the early 1990s, a decline 

during the recession, and a gradual but definite 

increase in the percentage of poor ratings after 

1993. However, ratings in Illinois have generally 

been a little better than ratings in the rest of the 

country. Both figures illustrate quite clearly that 

although national trends may be significant, each 

state has a story of its own. 

Conclusion 

This article has explained the development of a 

real estate stress test and the test’s most significant 

results. The stress test highlights institutions 

whose lending practices deserve scrutiny; it 

therefore spotlights markets that should be 

inspected for evidence of incipient real estate 

problems. REST indicates that a large fraction of 

banks and thrifts in the West and the Southeast 

may be vulnerable to problems in the real estate 

market, mostly because of large concentrations in 

construction and development lending. REST 

does not, however, show that any real estate 

market is either overbuilt or on the verge of a 

crisis. There are, after all, a multitude of ways for 

institutions to manage and mitigate the risk of 

construction lending. 

This article raises the questions of whether 

institutions that have exposures to the real estate 

market have adequately protected themselves and 

whether the real estate markets in the West and 

Southeast are inherently healthy. The history of 

banking suggests that these questions are vitally 

important to the FDIC. 

2003, VOLUME 15, NO. 4 35 FDIC BANKING REVIEW 
 

 

 

 

 

Vulnerability of Banks and Thrifts to a Real Estate Crisis 

REFERENCES
 

Collier, Charles, Sean Forbush, Daniel A. Nuxoll, and John O’Keefe. 2003. The SCOR 

System of Off-Site Monitoring: Its Objectives, Functioning, and Performance. 

FDIC Banking Review 15, no. 3:17–32. 

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

the Future. Vol. 1, An Examination of the Banking Crises of the 1980s and Early 

1990s. FDIC. 

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): 31–56. 

Herring, Richard J., and Susan M. Wachter. 1999. Real Estate Booms and Banking 

Busts—An International Perspective. Occasional Paper No. 58. Group of Thirty. 

Mayer, Martin. 1990. The Greatest-Ever Bank Robbery. Charles Scribner’s Sons. 

FDIC BANKING REVIEW 36 2003, VOLUME 15, NO. 4