Bank Loan-Underwriting Practices: Can Examiners’ Risk 

Assessments Contribute to Early-Warning Systems? 

Working Paper 2003-06 

John O’Keefe, Virginia Olin, and Christopher A. Richardson* 

November 2003 

* John O’Keefe is Chief of the Financial Risk Measurement Section, Division of Insurance and 

Research, FDIC. Virginia Olin is a Senior Financial Economist, Division of Insurance and 

Research, FDIC. Christopher A. Richardson is an Economist with the Center for Responsible 

Lending, Washington, DC and was at the U.S. Department of Justice, Civil Rights Division 

when this paper was written. The authors thank Andrew Davenport and Michael Jenkins of the 

FDIC and the participants in the 2003 annual meetings of the Western Economic Association, for 

helpful comments and suggestions. The views expressed here are those of the authors and not 

necessarily those of the Federal Deposit Insurance Corporation or the Department of Justice. 
 

ABSTRACT 

Loan underwriting practices are the primary determinant of bank credit risk and bank 

credit availability. For this reason, U.S. bank supervisors conduct periodic surveys to assess 

bank underwriting practices and their riskiness. In early 1995 the Federal Deposit Insurance 

Corporation (FDIC) introduced a comprehensive examination questionnaire, or survey, of bank 

underwriting practices at FDIC-supervised banks; FDIC bank examiners complete the survey at 

the end of each FDIC-supervised bank examination. The survey covers lending practices both in 

general and in specific loan categories. This study investigates (1) the relationships between 

examiners’ assessments of the riskiness of bankers' lending practices and subsequent changes in 

bank condition, and (2) the question of whether these relationships can enhance supervisors’ 

early-warning systems. We find that higher (lower) risk in underwriting practices is associated 

with subsequent increases (decreases) in nonperforming assets generally. We also find that 

assessments of underwriting risk contribute to off-site surveillance models that predict safetyand-soundness examination ratings. However, this contribution is largely subsumed by that of 

concurrent safety-and-soundness examinations ratings. Thus, underwriting survey data are best 

used as diagnostic measures of the sources of financial distress. 

1 
 

1. Supervisory Tools for Monitoring Bank Loan-Underwriting Practices 

To maintain public confidence in insured depository institutions and to protect the deposit 

insurance funds (the Bank Insurance Fund and the Savings Association Insurance Fund), federal 

regulatory agencies must promote the safety and soundness of commercial banks and savings 

associations. To a great extent, the risk to an institution's soundness and to the insurance funds 

is determined by the quality of the institution's loan portfolio. Loans typically make up the 

largest portion of the institution's asset structure, and they ordinarily present the greatest credit 

risk and therefore the greatest potential loss exposure to banks. 

The most common supervisory tools used by the regulatory agencies in promoting safety 

and soundness are on-site examinations and off-site surveillance systems. Each serves a unique 

purpose. For the on-site examination, the objective is to evaluate effectively the safety and 

soundness of the FDIC-insured depository institution (hereinafter “bank”) and to analyze all 

aspects of the bank’s operations, including loan portfolios and policies on making sound loans. 

For off-site surveillance systems, the objective is to provide bank supervisors with an early 

warning of potential problems in banks’ condition. Some off-site systems model financial data 

that banks file with regulators (the modeling is to determine whether additional supervisory 

attention is warranted before the next regularly scheduled on-site examination; examination 

frequency is discussed below in the section on sample and data). Other off-site systems use 

different indicators to predict changes in banks’ condition. 

In early 1995 the FDIC introduced a third tool, a hybrid of on-site examinations and off-site 

surveillance systems: a questionnaire, or survey, that supplements on-site examinations. Because 

loan underwriting practices are the primary determinant of a bank’s credit risk and credit 

availability, the survey’s focus was on current underwriting practices. The survey is intended to 

2 
provide an early warning of potential credit-quality problems. When examiners look at credit 

administration during the examination, they identify weaknesses that may exist, and they note that if 

these practices are not improved, the bank’s condition will probably worsen because of deterioration 

in the quality of loans. On the basis of this review, they complete the survey. Thus, the 

underwriting survey is a way to anticipate future problems by relying on examiners’ risk 

assessments of current underwriting practices. In this study we assess the survey’s contribution to 

early-warning systems by examining the predictive content of survey data, using two models: one 

forecasts changes in banks’ supervisory ratings, and the other forecasts banks’ nonperforming 

assets. 

In the rest of this section, we briefly explain on-site examinations, off-site surveillance 

systems, and the FDIC supplemental questionnaire. In the next section we discuss three areas of 

related empirical research: underwriting cycles in property and casualty insurance markets, bank 

supervisors’ off-site surveillance systems, and the predictive content of bank supervisors’ 

surveys of underwriting practices. Then we describe our sample and data. After that we present 

the methodology used to examine the potential contribution of the FDIC underwriting survey to 

early-warning models, and the results of our empirical tests. Finally, we summarize and 

conclude. 

1.1 On-Site Examinations 

There are four fundamental reasons for on-site examinations. First, they help maintain 

public confidence in the integrity of the banking system and individual banks. The existence of 

unhealthy or deteriorating conditions, which may threaten this integrity, should be disclosed 

through the examiners’ evaluations. Second, periodic on-site examinations provide the best 

3 
 

means of determining banks’ adherence to laws and regulations. Third, the examination process 

can help prevent problem situations from remaining uncorrected and deteriorating to the point at 

which costly financial assistance by the FDIC becomes unavoidable. Finally, examinations 

supply supervisors with an understanding of the nature, relative seriousness, and ultimate cause 

of a bank’s problems and thus provide a sound factual foundation on which to base corrective 

measures, recommendations, and instructions. 

The appraisal of lending and collection policies and of the bank’s adherence to those 

policies, as well as the evaluation of individual loans, is only part of the on-site examination. To 

be sure, examiners are instructed to review loan policies and portfolios, but they are also 

capturing a kind of information different from that captured in off-site surveillance systems. 

Specifically, they review lending policies to ensure that the policies are clearly defined and 

explicit enough to provide the directors and senior officers with effective supervision. They 

check to see that loan policies are up-to-date and have been approved by the board of directors. 

And they check to see that the actions taken by officers and employees adhere to established 

policies. The examiners’ manual contains an extensive list of broad areas of consideration and 

concern that lending policies should address.1 

At the end of each on-site examination, the bank is assigned a safety-and-soundness 

rating. The basis for this rating is the Uniform Financial Institutions Rating System (UFIRS) 

designed to evaluate banks’ condition on a uniform basis and to identify banks requiring special 

attention or concern. Each examined bank is assigned a composite rating that uses six essential 

components of the bank's financial condition and operations. These components address the 

1

See FDIC (2002). 

4 
adequacy of capital (C), the quality of assets (A), the capability of management (M), the quality 

and level of earnings (E), the adequacy of liquidity (L), and the sensitivity to market risk (S). 

Hence, the composite rating is called a CAMELS rating. Composite and component ratings are 

assigned on a 1 to 5 numerical scale. A "1" indicates the highest rating (strongest performance 

and risk management practices, and the least degree of supervisory concern), while a "5" 

indicates the lowest rating (weakest performance, inadequate risk management practices, and 

therefore the highest degree of supervisory concern). 

In sum, on-site examinations are the best way for supervisors to track the condition of 

banks; however, since examiners cannot be continuously on-site, regulators also use off-site 

surveillance to help span the gap between regularly scheduled on-site examinations. 

1.2 Off-Site Surveillance 

Off-site surveillance provides supervisors with an early warning of potential problems in 

the bank’s condition. Some off-site systems use statistical techniques that analyze previous 

financial data that banks file with regulators (Call Reports) to predict future CAMELS composite 

and component ratings. Other systems are not statistically based, but they still have predictive 

qualities. Both kinds of systems provide information that helps regulators determine whether 

additional supervisory attention is warranted before the next regularly scheduled on-site 

examination. Use of off-site surveillance, however, is not a substitute for periodic on-site 

examinations. Instead, it is a valuable complement to the examination process. Off-site 

surveillance has several advantages. For instance, it is far less intrusive and uses fewer 

personnel than on-site examinations. And since off-site surveillance can identify banks that 

5 
 

show signs of financial distress, it also helps regulators allocate on-site examination resources 

efficiently. 

1.3 The FDIC Supplemental Questionnaire 

The supplemental questionnaire, or survey, introduced in 1995 asks examiners to respond 

to questions only about underwriting practices. Underwriting practices can generally be 

characterized by the criteria used to qualify borrowers, loan pricing, repayment terms, sources of 

repayment, and collateral requirements. Underwriting practices also encompass the management 

and administration of the loan portfolio, including its growth, concentrations in specific markets, 

out-of-area lending, and adherence to written underwriting policies.2 

FDIC bank examiners complete the survey at the end of each FDIC-supervised bank 

examination. They assess material changes in underwriting practices and evaluate them in 

relation to supervisory standards, rating the risk associated with a bank’s underwriting practices 

in absolute terms: low, medium, or high.3

 FDIC examiners also classify the frequency of 

2

 Part 364 of the FDIC Rules and Regulations covers “Credit Underwriting” in a general sense, 

and Part 365 provides a list of specific factors for underwriting standards. Part 365 also covers 

loan administration. 

3 Low: the level of risk imposed on the institution does not warrant notice by bank supervisors 

even when factors that might offset the risk are ignored. Medium: the level of risk should be 

brought to the attention of bank supervisors. There may or may not be factors that offset the risk 

incurred by the institution; however, the level of risk raises concerns when considered apart from 

these offsetting factors. High: the level of risk is high and therefore should be brought to the 

6 
 

 

specific risky underwriting practices for overall lending as “never or infrequently,” “frequently 

enough to warrant notice,” or “commonly or as standard procedure.”4 Specifically, the FDIC 

survey asks examiners about the risk in current underwriting practices, loan portfolios, purchased 

loan participations, loan growth and/or significant changes in lending activities, and loan 

administration. 

In addition, examiners rate the frequency of the following risky practices in overall 

lending: lending in amounts that result in concentrations to one industry or borrower, out-of-area 

lending, failing to adjust loan pricing for risk, failing to require principal reductions before 

renewing loans terms, and deviating from written lending policies. Finally, examiners classify 

the frequency of specific risky underwriting practices in seven major loan categories: business, 

residential and commercial (nonresidential) real estate construction, commercial real estate, 

home equity, agriculture, consumer, and credit cards.5 

immediate attention of bank supervisors. There may or may not be factors that offset the risk 

incurred by the institution; however, the level of risk is high when viewed in isolation. 

4 Never or infrequently: the institution does not engage in the practice, or does so only to an 

extent that does not warrant notice by bank supervisors. Frequently enough to warrant notice: 

the institution engages in the practice often enough for it to be brought to the attention of bank 

supervisors. There may or may not be factors that offset the risks the practice imposes on the 

institution. Commonly or as standard procedure: the practice is either common or standard at the 

institution and therefore should be brought to the attention of bank supervisors. There may or 

may not be factors that offset the risks the practice imposes on the institution. 

5

 The survey also asks examiners to identify potentially risky loan categories in which the bank is 

actively lending, such as unguaranteed portions of small business administration loans, subprime 

7 
 

The designers of the first survey were staff from the FDIC’s former Division of Research 

and Statistics and Division of Supervision (now the Division of Insurance and Research and the 

Division of Supervision and Consumer Affairs, respectively). Before selecting the questions, the 

designers reviewed the underwriting practices of banks that had failed during the banking crisis 

of the 1980s.6

 Once questions were chosen, test surveys were sent to FDIC examiners and 

Division of Supervision regional directors for comment and feedback. Although some of the 

survey questions have changed over time to account for emerging underwriting concerns and to 

incorporate feedback from examiners, the majority of questions have remained intact. 

Other U.S. bank regulators also conduct underwriting surveys; however, the FDIC’s 

survey is unique in the extent to which it quantifies the level of risk and the frequency of specific 

risky underwriting practices. The Federal Reserve Board conducts a Senior Loan Officer 

Opinion Survey on Bank Lending Practices, but its primary focus is credit availability. The 

Comptroller of the Currency’s annual Survey of Credit Underwriting Practices is closer in spirit 

to the FDIC’s survey in that it surveys bank examiners and has questions on credit risk, but it 

does not request information about the frequency of specific risky practices. 

2. Empirical Studies 

Regulatory assessments of underwriting standards become especially important as banks 

respond to incentives to change their standards from time to time. In our view, one incentive that 

loans, dealer paper loans, low-documentation business loans, high loan-to-value home equity 

loans, or any other category not listed. In addition, the survey asks examiners to comment on 

which, if any, of these loan categories pose more-than-normal risk to the bank. 

6

 For a complete history of this crisis, see FDIC (1997). 

8 
 

 

 

may induce changes in underwriting standards over time is capacity constraints—periodic 

constraints on the amount of risk banks can safely assume and, consequently, on the amount of 

credit banks can extend. Capacity constraints in the banking industry can arise from various 

sources: binding risk-based capital constraints, limited opportunities for deposit growth in 

particular markets, increases in large time-deposit funding costs, or macroeconomic weakness. 

In addition, examiner responses to the FDIC survey on underwriting practices indicate that the 

level of market competitiveness influences underwriting standards. Examiners have typically 

commented that the main reason banks loosen underwriting practices is competitive forces.7 

Here we offer a possible connection between periodic capacity constraints, the level of market 

competitiveness, and underwriting cycles in banking by drawing on research in property-casualty 

insurance markets. 

Gron (1994) applies the theory of capacity constraints to explain underwriting cycles in 

property-casualty insurance markets. She describes four stages in a property-casualty insurance 

underwriting cycle. The first stage is characterized by low profitability (and prices) and 

relatively high insurance capacity. In the second stage, profitability and prices rise abruptly in 

response to an unanticipated crisis, while the availability of insurance falls as a result of capacity 

constraints. In the third—post-crisis—stage, profitability and prices remain high while industry 

capacity gradually increases to more normal levels as new firms enter the market and existing 

firms expand. Finally, in the fourth stage, competitive pressures cause profitability and prices to 

fall while capacity remains relatively high. In our adaptation of the theory of capacity 

constraints to banking, we anticipate that banks loosen underwriting standards at the fourth stage 

7

 FDIC, Report on Underwriting Practices, various issues. 

9 
 

rather than reduce the price of the loan (which directly affects profits). Gron’s finding of a 

negative relationship between capacity and underwriting margins (a measure of profitability) 

supports the capacity constraint theory in insurance markets over arbitrage theories that suggest 

no systematic relationship between capacity and underwriting margins. Although we do not 

empirically test the capacity constraint theory in this paper, we propose that banks’ responses to 

capacity constraints can create an underwriting cycle in which banks tighten or loosen their 

underwriting standards in response to competitive pressures. 

Haskel and Martin (1994) provide additional empirical support for the theory of capacity 

constraints as applied to industrial firms. Haskel and Martin find a positive relationship between 

profits and direct measures of capacity constraints for industrial firms in the United Kingdom. 

Previous empirical research to develop off-site surveillance models has accounted for 

underwriting standards indirectly through their impact on financial variables. These models 

estimate the probability of individual banks’ failure or their downgrade in CAMELS rating. 

With the rapid increase in the number of bank and thrift failures in the late 1980s and early 

1990s, bank regulators took particular interest in off-site models. Several researchers have built 

models based on the experience of those years. Gilbert, Meyer, and Vaughan (2002) empirically 

compare the predictive ability of the Federal Reserve Board’s SEER failure prediction model 

with a model they develop to predict downgrades in bank condition from CAMELS ratings 1 or 

2 to ratings 3, 4, or 5. They find that over one- and two-year test windows, their specialized 

downgrade model outperformed the SEER failure model by only a small margin. They conclude 

that a model specified to predict downgrades adds little predictive value to existing off-site 

failure prediction models. One limitation on applying these results more generally is that their 

study estimates the models on the basis of 1989 data and considers out-of-sample performance 

10 
 

for a period—1991 to 1998—that was relatively stable. The stability of their test period makes it 

difficult to discern how their model would perform in more volatile economic states. 

Kolari et al. (2002) develop failure prediction models for national banks using both 

parametric logit and nonparametric trait recognition methods. They find that although both 

perform well with regard to in-sample classification of failing and nonfailing banks, trait 

recognition outperforms logit in predicting failures out of sample. Our study, in contrast, uses 

data on FDIC-supervised institutions, thus providing us with more data for both in-sample 

estimation and out-of-sample prediction. 

Estrella, Park, and Peristiani (2000) use three types of capital ratios—risk-weighted, 

leverage, and gross revenue—to predict bank failure.8

 They find that the most complex of the 

ratios—the risk-weighted ratio—is the strongest predictor of bank failure over long time 

horizons. However, the risk-weighted ratio is sometimes outperformed by the leverage ratio and 

the gross revenue ratio over horizons of less than two years. Their analysis illustrates the 

centrality of the capital ratio as a determinant of bank failure: the use of the capital ratio alone in 

a logistic regression model results in pseudo R-squared values as high as 13.5 percent. 

Data from underwriting surveys also inform monetary and supervisory policy makers 

about current banking industry practices and conditions and therefore have the potential to 

8

 Estrella, Park, and Peristiani (2000) define the three capital ratios as follows: The risk-weighted 

capital ratio is the ratio of Tier 1 capital (which includes common stock, common stock surplus, 

retained earnings, and some perpetual preferred stock) to risk-weighted assets. The leverage 

ratio is Tier 1 capital divided by the quarterly average of total tangible assets. The gross revenue 

ratio is the ratio of Tier 1 capital divided by total interest and noninterest income before the 

deduction of any expenses. 

11 
 

contribute to econometric forecasting models. Lown, Morgan, and Rohatgi (2000) and Lown 

and Morgan (2003) use the Federal Reserve’s Senior Loan Officer Opinion Survey to analyze the 

importance of credit availability to the economy. They conclude that shocks to lending standards 

(credit tightening or loosening) have a significant impact on both the volume of commercial 

loans and real output. To date, however, there has been no published research on the potential 

contribution of underwriting survey data to statistical models that predict bank performance and 

financial distress. We propose that survey data can shed light not only on the potential future 

effect of credit availability on the economy (as shown by Lown, Morgan, and Rohatgi [2000] 

and Lown and Morgan [2003]) but also on future bank performance. Using the FDIC survey 

data for underwriting practices, we test this hypothesis by controlling directly for bank 

examiners’ assessments of underwriting standards in off-site surveillance systems. 

3. Sample and Data 

To investigate whether FDIC underwriting survey data can contribute to bank 

supervisors’ early-warning models, we used FDIC survey data from the period August 1, 1995, 

through December 31, 2002, a period that includes 6,937 survey observations.9 This period was 

relatively benign for U.S. banks (the few bank failures that occurred were often attributed to 

idiosyncratic factors),10 so it should provide a useful test of examiners’ ability to detect 

9

 Although the survey began in February 1995, not until August of that year were several key 

questions added to it. 

10 Between 1995 and 2002, 46 FDIC-insured banks failed. Fraud was considered a significant 

factor in 7 (15.2 percent) of the failures, whereas of the 472 FDIC-insured bank failures that 

occurred between 1990 and 1994, fraud was found to be a significant factor in 27 (5.7 percent). 

12 
 

idiosyncratic weaknesses in underwriting practices as opposed to broad-based problems of credit 

quality. During this period, the requirements set by the Federal Deposit Insurance Corporation 

Improvement Act (FDICIA) for examination frequency were in effect. Specifically, annual 

safety-and-soundness examinations were required for all FDIC-insured banks except those that 

had assets under $250 million and composite CAMELS ratings of 1 or 2; these banks had to be 

examined once every 18 months. The FDIC is the federal supervisor for state-chartered banks 

that are not members of the Federal Reserve System, and to avoid duplication of examination 

expenses and the associated burdens on banks, the FDIC generally alternates examinations with 

state supervisory authorities. As a result, FDIC survey data are typically available for every 

second examination of FDIC-supervised banks, or about one examination every two to three 

years. 

Table 1 presents the survey questions and responses for the 1995–2001 period. As 

discussed above, the survey has undergone revision over time, and we control for this in our 

empirical analysis. The most significant survey change was made in October 1998, when the 

possible responses to several questions on risk in general underwriting practices were changed 

from relative to absolute risk rankings. That is, the possible responses were changed from 

characterizing risk as above average, average, or below average11 to high, medium, or low based 

on supervisory standards. The change was made to improve the clarity and ease of interpretation 

of survey results. Relative risk rankings can be difficult to interpret, especially when overall 

industry risk is changing. To see this, one need only consider the likely differences in the 

11 As noted above, the survey was designed in consultation with senior examiners. They agreed 

that examiners had a general feel for what was considered “below average,” “average,” and 

“above average” risk. No formal definitions were written. 

13 
absolute risk levels between banks with “above-average” risk in current underwriting practices 

during a banking crisis and banks with "above-average" risk during a period of record 

profitability. More is said below about these changes and how we control for them in the 

analysis. 

Since the purpose of this study is to investigate the potential contribution of FDIC survey 

data to off-site surveillance models, we use FDIC off-site surveillance models as our basis for 

comparison. The primary off-site surveillance model used by the FDIC is the Statistical 

CAMELS Off-Site Rating (SCOR) model. SCOR uses the balance-sheet and income-statement 

information that banks are required to report to their primary federal supervisor each calendar 

quarter-end (Call Reports). The SCOR model attempts to predict CAMELS ratings by relating 

12 financial ratios to each bank’s future composite rating (similar models are developed for the 

component ratings). In addition to the 12 SCOR variables, we include controls for economic 

conditions in banks’ local markets by using current and lagged growth rates of state personal 

income. Previous research by the FDIC (Nuxoll, O'Keefe, and Samolyk [2003]) suggests that 

the boom and bust economic cycle that contributed to bank failures in the 1980s and 1990s is 

captured by state personal-income growth. 

4. Methodology 

We examine the predictive content of the FDIC survey data using two types of models. 

The first type forecasts banks’ examination ratings (CAMELS ratings) received during a given 

year on the basis of prior year-end values of variables that we hypothesize determine bank safety 

and soundness. The second type models nonperforming assets using similar explanatory 

variables. We selected these two models because the dependent variables (CAMELS ratings and 

14 
 

nonperforming assets) are the broadest and, from a supervisory perspective, the most significant 

indicators of bank safety and soundness. The two dependent variables are also measured in very 

different ways; that is, discrete CAMELS ratings are reported by bank supervisors, and 

continuous nonperforming assets are reported by bank management. As a result, the two models 

provide fairly robust tests of the potential contribution of survey responses to off-site 

surveillance models. 

For the first type of model, we estimate four alternative specifications of the CAMELSrating forecast model to show the relative contributions of groups of explanatory variables. 

Specification 1, which serves as our basis of comparison, uses as explanatory variables (a) past 

financial variables contained in the FDIC’s SCOR model, and (b) past measures of state 

economic conditions, as measured by state personal-income growth lagged one to five years. 

Specification 2 differs from specification 1 by including as additional explanatory variables (c) 

past FDIC underwriting survey responses. Specification 3 expands the set of explanatory 

variables used in specification 2 to include (d) past CAMELS ratings. Equation 1 presents the 

broadest specification (specification 3) of the model in general form: 

CAMELS Rating = f ( financials , economic conditions , underwriting , CAMELSrating ) (1) t t −1 t −1 t−1 t −1 

Specification 4 of the model is used to test the potential overlap between the information 

contained in the past FDIC surveys and past CAMELS ratings. This specification excludes past 

FDIC surveys as explanatory variables but includes all the other items in the 3rd, or full, 

specification. 

The SCOR model uses the same set of financial variables to predict the CAMELS 

composite and six component ratings (Capital, Asset quality, Management, Earnings, Liquidity, 

15 
and Sensitivity to market risk). A review of the FDIC survey questions as presented in table 1, 

however, indicates that they should have a narrower scope of influence on examination ratings 

than the SCOR financial variables. We anticipate that underwriting practices are most closely 

related to examiners’ assessments of the quality of bank management, as measured by the 

management component rating, although the timing of the relationship would seem to be 

concurrent, since the survey focuses on “current” lending practices. It is reasonable, however, to 

expect that current underwriting practices affect future loan quality and future overall safety and 

soundness. For these reasons, we estimate equation 1 using three dependent variables: 

composite CAMELS ratings, the asset component ratings, and the management component 

ratings. We estimate all specifications of the prediction model in equation 1 using ordered 

logistic regression. All financial measures are expressed as percentages of contemporaneously 

measured bank assets. In specifications where lagged CAMELS ratings are used as explanatory 

variables, the ratings are from examinations occurring in the year from which year-end financial 

data are used. Similarly, the CAMELS ratings used as dependent variables are from 

examinations occurring within one year of the date of the financial data used as explanatory 

variables. 

CAMELS composite and component ratings are ordinal indices ranging from 1 (best 

rating) to 5 (worst rating). Because banking conditions were very favorable during the 1995– 

2002 period for which we have FDIC survey data, there have been very few poorly rated banks. 

Between 1995 and 2002, of the approximately 6,900 examinations that make up our sample, the 

distribution of composite CAMELS ratings was as follows: 40.7 percent rated 1, 50.9 percent 

rated 2, 7.6 percent rated 3, 0.7 percent rated 4, and 0.1 percent rated 5. The small size of some 

CAMELS rating groups made it impossible to estimate ordinal logistic regressions using all five 

16 
 

rating levels, as would normally be done. We therefore grouped CAMELS ratings of 3, 4, and 5 

into one category and maintained the CAMELS 1 and 2 rating categories. We did this for both 

the composite and the component ratings. Since the focus of bank supervisors’ off-site 

surveillance is potential deterioration in CAMELS 1- and 2-rated banks, we believe the use of a 

three-level CAMELS rating does not compromise our analysis.12 

As shown in table 1, FDIC survey responses for general underwriting practices are threelevel ordinal responses, with level 1 representing the lowest perceived risk (below-average risk, 

low absolute risk, or low frequency of risky practices) and level 3 representing the highest 

perceived risk (above-average risk, high absolute risk, or frequent risky practices). To allow for 

differences in outcomes (future condition or CAMELS rating) across perceived risk levels, each 

survey question was measured using two dummy variables: a dummy set equal to one for lowrisk responses, and zero otherwise; and a dummy set equal to one for high-risk responses, and 

zero otherwise. The middle or average responses were the omitted dummy. 

For our second type of model (nonperforming assets), equation 2 presents the basic 

model in general form: 

Nonperforming assets = f ( financials , economic conditions , underwriting , CAMELSrating ) (2) t t−1 t−1 t −1 t−1 

Our approach to estimation of equation 2 is parallel to the approach we used for equation 1. 

Specifically, we use the same four specifications of the model (explanatory variables) as before. 

12 Once a bank is CAMELS-rated 3 or worse, supervisors increase surveillance and rely more on 

on-site examinations than on off-site surveillance. See Collier, Forbush, Nuxoll, and O’Keefe 

(2003) for additional discussion of this focus. 

17 
 

The major difference in estimating equation 2 is that we model only total nonperforming assets, 

expressed as a percentage of bank assets, as the dependent variable. Nonperforming assets are 

defined as the sum of all loans and leases past due 30–89 days, loans and leases past due 90 days 

or more, nonaccrual loans and leases and all other real estate owned (included repossessed real 

estate). Equation 2 is estimated using ordinary least squares. 

5. Results 

The FDIC survey could contribute to off-site surveillance in at least two ways. The first 

is by contributing to models that forecast supervisory ratings and other quantifiable bank risk 

measures, such as nonperforming assets. These contributions are perhaps the easiest to quantify. 

Here we document them and show that they are statistically and economically meaningful. The 

second way the FDIC survey could contribute to off-site surveillance is by helping bank 

supervisors understand the actual, current sources of bank risk. We also document this latter 

contribution and show that individual survey questions have persistent and logical relationships 

with future bank risk. 

5.1 Prediction of CAMELS Ratings: In-Sample Results 

Tables 2–4 show the results of logistic regressions of the four specifications of equation 1 

discussed above. The regressions are designed to predict the likelihood of receiving a poorer 

future CAMELS rating. To account for the changes in possible responses to questions in 

October 1998, we estimate the models using survey data from two periods: January 1996 – 

September 1998 and October 1998 – December 2001 (hereinafter "1996–1998" and "1998– 

2001," respectively). We combine all responses within each of the two periods and estimate the 

18 
 

resulting panels of data using ordered logistic regression.13 We are interested in panel 

estimations for both periods for several reasons. First, any test of the relationships between 

survey results and bank CAMELS ratings is also a test of the ability of the survey to accurately 

measure the risks in underwriting practices. The change from relative to absolute risk rankings 

in the survey was intended to improve the survey’s measurement of underwriting risk, and we 

wish to see if this intended improvement is supported by the data. Second and more importantly, 

we wish to see if the relationships between underwriting practices and banks’ CAMELS ratings 

are robust over time. 

Generally speaking, as we explain in the pages that follow, all results are in close 

agreement with our expectations. Discussed here are the results for composite CAMELS ratings 

and then for the asset quality and management component ratings. Because of the large number 

of explanatory variables included in the models as well as the number of model specifications 

tested, this section summarizes results and addresses only statistically significant relationships 

(statistical significance levels between 1 and 10 percent). 

Tables 2a and 2b show that for both the 1996–1998 and 1998–2001 periods, the 

likelihood of receiving a poorer composite CAMELS rating is negatively related to equity 

capitalization and income before taxes and extraordinary items. The likelihood of receiving a 

poorer composite CAMELS rating is also negatively related to liquid assets and gross loan 

charge-offs in the 1998–2001 period, but not consistently so for all model specifications. For 

both the 1996–1998 and 1998–2001 periods, the likelihood of receiving a poorer composite 

CAMELS rating is positively related to the allowance for loan losses, loans past due 30–89 days, 

13 Because we control for changes in economic conditions using state personal-income growth, 

we do not include dummy variables for time or geographic region in the models. 

19 
 

loans past due 90 days or more, nonaccrual loans and leases, other real estate owned, volatile 

liabilities, and provisions for loan losses. State personal-income growth rates show a lessconsistent relationship with future composite CAMELS ratings, but one- and two-year lagged 

growth rates are positively related to the likelihood of receiving poorer composite CAMELS 

ratings in the 1998–2001 period. 

Similarly, the relationships between FDIC survey results and the likelihood of receiving a 

poorer composite CAMELS rating are in strong agreement with our expectations. Tables 2a and 

2b show that the likelihood of receiving a poorer composite CAMELS rating is positively 

associated with the dummy for high perceived risk in underwriting practices (above-average risk, 

high absolute risk, or frequent risky practices) and negatively related to the dummy for low 

perceived risk in underwriting practices (below-average risk, low absolute risk or low frequency 

of risky practices). Further, for 1996–1998 and 1998–2001 we find that FDIC survey data add 

significant explanatory power to the SCOR model. Comparisons of model specifications 1 and 2 

in tables 2a and 2b show that for both periods, the FDIC survey data increase the overall 

explanatory power of the model. This can be seen when one compares the Akaike Information 

Criteria with Somers’ D statistics.14 A Chi-Square test of the joint significance of the survey 

data in specification 2 shows that for both periods, the survey explanatory variables are jointly 

significant at the 95 percent confidence level. One interesting result is that the changes in the 

survey responses from relative to absolute risk levels (as seen when one compares results for the 

two periods) do not alter the significance of the survey data in the forecasting models. In 

addition, comparisons of specifications 2 and 3 show that the inclusion of dummy variables for 

14 Larger values for the Somers’ D statistic indicate better model fit, while smaller values for the 

Akaike Information Criteria indicate better model fit. 

20 
 

 

initial composite CAMELS ratings in the models reduces the number of statistically significant 

survey explanatory variables. It is reasonable to expect that since survey data characterize one 

portion of the information used by examiners to determine CAMELS ratings, initial CAMELS 

ratings will subsume some of the informational content of survey data.15 While specifications 3 

and 4 are extremely close in overall explanatory power, a Chi-Square test of the joint 

significance of the survey data in specification 3 shows that for both periods, the survey 

explanatory variables are jointly significant at the 95 percent confidence level. 

Tables 3a and 3b show the results for models that attempt to predict asset component 

ratings. The relationships between the SCOR explanatory variables and future asset component 

ratings are similar to those seen with future composite CAMELS ratings. Equity capitalization 

and income before taxes and extraordinary items are negatively related to the likelihood of 

receiving a poorer asset component rating for both periods, and for the 1998–2001 period the 

negative relationship is true for liquid assets and loans plus securities with maturities of five 

years or more. For both the 1996–1998 and 1998–2001 periods the likelihood of receiving a 

poorer asset component rating is positively related to allowance for loans and lease losses, loans 

past due 30–89 days, loans past due 90 days or more, nonaccrual loans and leases, and other real 

estate owned. Similarly, the relationships between the survey data and asset component ratings 

are very similar to those between the survey data and CAMELS composite ratings. Finally, 

changes in the explanatory power of the model across all four specifications of the asset 

15 The initial composite CAMELS rating and survey data are taken from the same bank 

examination, so the overlap in information is not surprising. 

21 
 

component forecast model are very similar to those seen with the CAMELS composite rating 

prediction. For brevity, therefore, we will not repeat that discussion. 

The results of estimation of the management component rating model are presented in 

tables 4a and 4b. The relationships that the SCOR and survey explanatory variables have with 

future management component ratings are very similar to those seen with future composite 

CAMELS and asset component rating forecast models. In addition, the marginal changes in 

model explanatory power across the four specifications are the same as those seen with the future 

composite CAMELS and asset component ratings forecast models.16 One result we should 

highlight is that the general explanatory power of the forecast models is less when the attempt is 

made to forecast management component ratings than it is in the attempt to forecast composite 

CAMELS and asset component ratings. 

At this point, one may question the value of the underwriting survey responses for offsite surveillance, given that the explanatory power of survey responses is largely subsumed by 

CAMELS ratings that are contemporaneous with survey responses. The FDIC does not, 

however, include CAMELS ratings as explanatory variables in the SCOR off-site surveillance 

model. CAMELS forecast models that include lagged CAMELS ratings as explanatory variables 

16 Robustness tests were performed on model specifications 2 and 3 for all CAMELS rating 

models. Specifically, we included only those survey questions that were consistently defined for 

both the 1996–1998 and 1998–2001 periods. The consistently defined survey questions 

measured risk in terms of the frequency of risky practices (see table 1). The model specifications 

were identical to those shown in tables 2–4 in all other respects. Our results regarding the 

marginal contribution of the survey responses are essentially the same as those seen in tables 2– 

4. Finally, pooled regressions that combined data for both periods also yielded similar results. 

22 
 

 

tend to produce forecasts that are clustered around those lagged CAMELS values, since 

CAMELS ratings change infrequently. We expect, however, that the underlying riskiness of 

banks is more evenly distributed than the clustered results would suggest. That is, not all 

CAMELS 1-rated banks are of equal risk; the same can be said for other CAMELS ratings. In 

addition, if a bank’s financial condition has changed since the last examination, off-site models 

that omit lagged CAMELS ratings will place greater weight on this changed condition.17 For 

these and other reasons, from our perspective the most important tests of the contribution of 

underwriting survey data to off-site surveillance models are those based on models without 

lagged CAMELS ratings as explanatory variables, that is, model specifications 1 and 2.18 

5.2 Prediction of CAMELS Ratings: Out-of-Sample Results 

The true measure of the contribution of survey data to off-site surveillance models is their 

contribution to out-of-sample forecasts. This section presents the results of out-of-sample 

forecasts of CAMELS ratings, using essentially the same approach as for the in-sample forecasts. 

To allow for several forecast periods, we use an annual forecast model. That is, forecast models 

are designed to show how explanatory variables measured during one calendar year predict 

CAMELS ratings received over the next calendar year. For example, we estimate the ordered 

logistic regression for model specification 2 using survey data for 1996, year-end 1996 Call 

17 Since bank examinations are required only every 12 to 18 months, there can be a considerable 

difference between the dates of lagged CAMELS ratings and the financial data that are used as 

explanatory variables in off-site surveillance models. 

18 The reasons lagged CAMELS ratings are excluded from the FDIC’s SCOR model are 

discussed at length in Collier, Forbush, Nuxoll, and O’Keefe (2003), appendix 1. 

23 
 

Report values of the SCOR variables, and lagged state personal-income growth rates. The 

dependent variable in this estimation is the composite CAMELS rating received during 1997. 

We next apply these estimated relationships (estimated explanatory variables’ coefficients) to 

survey data and SCOR variables for 1997 to predict the composite CAMELS ratings that banks 

would receive in 1998. The model specifications are otherwise identical to those discussed in the 

section on in-sample results, with one exception. Since data on one survey question (the 

frequency with which the bank fails to require a material principal reduction before renewing 

loan terms) did not become available until October 1996, we drop this variable from the forecast 

model. Dropping it allows us to extend the sample period back to include all of 1996 and adds 

another forecast year to our tests. For the sake of brevity, we do not present the estimated model 

coefficients for the annual logistic regressions. Rather, we proceed directly to measures of 

predictive accuracy. 

Figure 1 shows the overall predictive accuracy of the CAMELS rating forecasts for all 

model specifications. In measuring overall accuracy, we round the predicted CAMELS rating to 

the nearest integer value and use that value as the forecast.19 The results generally parallel those 

seen with the in-sample results discussed above. The inclusion of survey data (specification 2) 

19 For example, a predicted CAMELS rating of 2.55 is rounded up to 3, while a predicted 

CAMELS rating of 2.45 is rounded down to 2. Since actual CAMELS ratings are integer values 

ranging from 1 to 5, some method of translating predicted CAMELS ratings into integers is 

needed in order to assess the accuracy of predictions. While rounding is the simplest way to 

accomplish this, other approaches could have been used. For example, one might round up all 

values that are more than 0.25 from the nearest integer, so that predicted CAMELS of 1.26 and 

2.39 are translated into ratings of 2 and 3, respectively. 

24 
enhances the overall accuracy of models that predict CAMELS composite, asset component, and 

management component ratings, relative to specification 1. In addition, specifications 3 and 4 

have very similar predictive accuracy for all categories of CAMELS ratings tested. 

As shown in figures 2–4, similar results are also generally found when assessed by initial 

CAMELS rating groups. These latter comparisons, however, require us to consider Type 1 and 2 

error rates. For example, a greater proportion of CAMELS 1-rated banks might be forecast 

correctly at the expense of forecasting fewer CAMELS 2- or 3-rated banks correctly. Figures 2– 

4 do not show the Type 1 and Type 2 error rates, but only the percentage of forecasts that are 

correct. Figures 2–4 show that in cases when model specification 2 had greater accuracy than 

specifications 3 or 4 in predicting any one CAMELS-rating group, it was less accurate in 

forecasting some other CAMELS rating group. 

As an alternative test of the predictive accuracy of the models, we compute the Pearson’s 

partial correlation coefficient between actual future CAMELS ratings and the predicted ratings 

(without rounding the predicted ratings to integer values). We combine forecasts across both the 

1996–1998 and 1998–2001 periods, since previous results indicate similar predictive accuracy 

for both periods. As shown in table 5, the correlation coefficient increases when survey data are 

added to the model (compare specifications 1 and 2) for all CAMELS rating forecasts. In 

addition, the correlations between actual and predicted CAMELS are greatest for specifications 3 

and 4. 

5.3 Nonperforming-Asset Models 

Equation 2 provides an additional test of the potential contribution of FDIC survey data 

to off-site surveillance models. If lapses in underwriting practices occur, we expect the bank to 

25 
be more exposed to borrowers who are unable to meet interest and principal payments. Tables 6 

and 7 present the results of ordinary least squares regressions of the relationships between 

nonperforming assets and the same types of explanatory variables used for CAMELS prediction. 

Nonperforming assets are hypothesized to be determined by prior year-end nonperforming 

assets, prior year-end values of all SCOR model financial variables, state personal-income 

growth rates lagged one to five years, initial examination ratings, and survey data. We also 

include as an explanatory variable the interval (in days) between the date of the survey data and 

the date of the Call Report from which the dependent variable (nonperforming assets) was 

measured. Since it may take some time for lending practices to result in problem loans, a control 

for this interval is needed. In all other respects, the four specifications we use to estimate the 

nonperforming asset prediction model (equation 2) are the same as those for CAMELS 

prediction. 

Tables 6 and 7 show that nonperforming assets are positively related to lagged 

nonperforming assets and equity capitalization for both the 1996–1998 and 1999–2001 periods 

but show no consistent relationship with other financial variables across both periods. However, 

nonperforming assets increase with high-risk underwriting practices and decrease with low-risk 

underwriting practices for both the 1996–1998 and 1999–2001 periods. Finally, tests of the joint 

significance of the survey explanatory variables in specification 1 show that the survey data are 

jointly significant, and similar tests of specification 3 yield the same result. We did not perform 

out-of-sample tests of the nonperforming-asset prediction models because of the limited number 

of forecasts that might be produced. As constructed, our models would have allowed for only 

two out-of-sample estimations, and we do not feel this provides enough information to assess the 

out-of-sample predictive accuracy of the models. 

26 
6. Summary and Conclusions 

We conclude that the examiners’ risk assessments of underwriting practices do contribute 

to early-warning systems in at least two ways. First, these assessments, as captured by the FDIC 

underwriting survey, contribute to models that forecast CAMELS ratings. Survey responses can 

be used to predict not only composite CAMELS ratings but also asset component ratings and, to 

a lesser extent, management component ratings. Moreover, our results indicate that inclusion of 

survey responses in the FDIC’s SCOR off-site surveillance model would improve SCOR’s 

predictive accuracy. In addition, we find that survey responses contribute to models that attempt 

to forecast nonperforming assets. Specifically, we find that higher (lower) risk in underwriting 

practices is associated with subsequent increases (decreases) in nonperforming assets generally. 

Second, we argue that survey responses add to the usefulness of off-site surveillance models by 

pointing to additional potential sources of bank risk. Bank supervisors need to know not only 

which banks have increased in riskiness but also what the likely causes of the increase in risk are. 

It is important for bank supervisors to know the possible reasons for increased bank risk so that 

they know where to direct their efforts. Our results show that survey responses give clear and 

consistent signals about the contribution of underwriting risk to overall risk and therefore 

provide useful direction to bank supervisors. 

27 
REFERENCES 

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

Off-Site Monitoring. FDIC Banking Review 15, no. 3:17–32. 

Estrella, Arturo, Sangkyun Park, and Stavros Peristiani. 2000. Capital Ratios as Predictors of 

Bank Failure. Federal Reserve Bank of New York Economic Policy Review 6, no. 2:33– 

52. 

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. 

———. February 2002. Section 3. DOS Manual of Examination Policies. FDIC. 

Gilbert, R. A, Andrew P. Meyer, and Mark D. Vaughan. 2002. Could a CAMELS Downgrade 

Model Improve Off-Site Surveillance? Federal Reserve Bank of St. Louis Economic 

Review 84, no. 1:47–64. 

Gron, Anne. 1994. Capacity Constraints and Cycles in Property-Casualty Insurance Markets. 

RAND Journal of Economics 25, no. 1:110–23. 

28 
 

Haskel, Johnathan, and Christopher Martin. 1994. Capacity and Competition: Empirical 

Evidence on U.K. Panel Data. Journal of Industrial Economics 42, no. 1:23–44. 

Kolari, James, Dennis Glennon, Hwan Shin, and Michele Caputo. 2002. Predicting Large U.S. 

Commercial Bank Failures. Journal of Economics and Business 54:361–87. 

Lown, Cara S., and Donald P. Morgan. Forthcoming. The Credit Cycle and the Business Cycle: 

New Findings Using the Survey of Senior Loan Officers. In Financial Market Behavior 

and Appropriate Regulation over the Business Cycle, Proceedings of the 38th Annual 

Conference on Bank Structure and Competition. Federal Reserve Bank of New Chicago. 

Lown, Cara S., Donald P. Morgan, and Sonali Rohatgi. 2000. Listening to Loan Officers: The 

Impact of Commercial Credit Standards on Lending and Output. Federal Reserve Bank 

of New York Economic Policy Review 6, no. 2:1–15. 

Nuxoll, Daniel, John O’Keefe, and Katherine Samolyk. 2003. Do Local Economic Data 

Improve Bank Off-Site Monitoring Models? FDIC Banking Review 15, no. 2:39–53. 

29 
Figure 1. Overall Accuracy of CAMELS Rating Predictions 

Composite CAMELS Rating Prediction 

0.0 

20.0 

40.0 

60.0 

80.0 

100.0 

Year 

Percent Correct 

Specification 1 62.1 68.8 66.9 69.2 

Specification 2 67.8 73.6 72.4 71.3 

Specification 3 84.7 84.6 84.6 82.8 

Specification 4 85.2 84.5 84.5 82.2 

1997 1998 2000 2001 

Asset Quality Rating Prediction 

0.0 

20.0 

40.0 

60.0 

80.0 

100.0 

Year 

Percent Correct 

Specification 1 31.3 61.3 66.1 62.9 

Specification 2 42.8 67.2 71.2 66.5 

Specification 3 49.2 70.8 77.9 76.6 

Specification 4 51.1 71.8 77.9 76.7 

1997 1998 2000 2001 

Management Rating Prediction 

0.0 

20.0 

40.0 

60.0 

80.0 

100.0 

Year 

Percent Correct 

Specification 1 46.3 62.1 61.3 63.4 

Specification 2 53.8 67.3 66.0 68.9 

Specification 3 73.7 78.6 79.4 80.1 

Specification 4 75.7 79.1 79.4 80.0 

1997 1998 2000 2001 

30 
 

Figure 2. Accuracy of CAMELS Composite Rating Predictions by Rating Group 

Predicted Group 1: Composite CAMELS Rating Prediction 

0.0 

20.0 

40.0 

60.0 

80.0 

100.0 

Year 

Percent Correct 

Specification 1 79.0 73.6 67.5 72.3 

Specification 2 75.4 75.4 72.1 76.7 

Specification 3 88.0 87.6 88.9 86.3 

Specification 4 88.1 87.2 88.9 85.9 

1997 1998 2000 2001 

Predicted Group 2: Composite CAMELS Rating Prediction 

0.0 

20.0 

40.0 

60.0 

80.0 

100.0 

Year 

Percent Correct 

Specification 1 57.3 65.8 67.3 67.6 

Specification 2 64.7 72.5 73.5 69.2 

Specification 3 83.2 84.7 84.9 81.5 

Specification 4 83.5 84.7 84.8 80.2 

1997 1998 2000 2001 

Predicted Group 3: Composite CAMELS Rating Prediction 

0.0 

20.0 

40.0 

60.0 

80.0 

100.0 

Year 

Percent Correct 

Specification 1 42.3 64.7 55.0 70.5 

Specification 2 35.9 61.9 62.3 68.3 

Specification 3 59.5 53.5 64.1 76.0 

Specification 4 69.2 55.8 63.8 79.0 

1997 1998 2000 2001 

31 
Figure 3. Accuracy of CAMELS Asset Quality Rating Predictions by Rating Group 

Predicted Group 1: Asset Quality Rating Prediction 

0.0 

20.0 

40.0 

60.0 

80.0 

100.0 

Year 

Percent Correct 

Specification 1 77.1 78.4 74.1 64.7 

Specification 2 84.9 83.8 76.6 70.0 

Specification 3 89.9 86.9 83.7 83.3 

Specification 4 90.0 86.7 83.6 83.7 

1997 1998 2000 2001 

Predicted Group 2: Asset Quality Rating Prediction 

0.0 

20.0 

40.0 

60.0 

80.0 

Year 

Percent Correct 

Specification 1 30.2 43.8 54.8 52.8 

Specification 2 33.9 49.9 62.5 55.4 

Specification 3 36.6 54.6 70.8 65.9 

Specification 4 38.0 55.8 71.1 65.5 

1997 1998 2000 2001 

Predicted Group 3: Asset Quality Rating Prediction 

0.0 

20.0 

40.0 

60.0 

80.0 

100.0 

Year 

Percent Correct 

Specification 1 21.7 57.1 61.3 78.3 

Specification 2 25.6 51.5 66.3 82.9 

Specification 3 28.3 48.3 68.5 84.7 

Specification 4 30.1 50.9 66.4 88.0 

1997 1998 2000 2001 

32 
Figure 4. Accuracy of CAMELS Management Rating Predictions by Rating Group 

Prediction Group 1: Management Rating Prediction 

0.0 

20.0 

40.0 

60.0 

80.0 

100.0 

Year 

Percent Correct 

Specification 1 68.8 69.0 58.7 60.4 

Specification 2 76.3 69.2 63.1 68.0 

Specification 3 85.9 87.1 86.7 83.9 

Specification 4 85.6 87.1 86.7 83.8 

1997 1998 2000 2001 

Prediction Group 2: Management Rating Prediction 

0.0 

20.0 

40.0 

60.0 

80.0 

100.0 

Year 

Percent Correct 

Specification 1 48.9 58.3 62.5 64.0 

Specification 2 53.3 65.2 67.8 68.4 

Specification 3 72.3 74.4 77.6 79.4 

Specification 4 75.0 74.7 77.7 78.6 

1997 1998 2000 2001 

Prediction Group 3: Management Rating Prediction 

0.0 

20.0 

40.0 

60.0 

80.0 

100.0 

Year 

Percent Correct 

Specification 1 26.8 85.7 66.7 74.0 

Specification 2 34.5 82.4 71.9 73.6 

Specification 3 46.2 63.6 66.2 72.8 

Specification 4 48.3 67.3 66.0 76.3 

1997 1998 2000 2001 

33 
 

 

 

 

 

 

Table 1. 

Survey Responses for General Underwriting Practices 

1/96 - 9/98 10/98 - 12/01 

Question Responses (1996-1998 / 1998-2001) Number Percent Number Percent 

How would you characterize the Below Average/Low 745 28.9 2,691 61.7 

potential risk associated with the Average/Medium 1,565 60.8 1,475 33.8 

institution's current UW practices? Above Average/High 264 10.3 196 4.5 

How would you characterize the Below Average/Low 925 35.9 2,722 62.4 

potential credit risk of the institution's Average/Medium 1,362 52.9 1,433 32.9 

overall loan portfolio? Above Average/High 288 11.2 207 4.7 

To what extent has recent lending 

been made in amounts that resulted Never or Infrequently 2,055 79.8 3,382 77.5 

in--or contributed to--concentrations Frequently Enough to Warrant Notice 339 13.2 660 15.1 

of credit to one borrower or industry? Commonly or as Standard Procedure 181 7.0 320 7.3 

How would you characterize the Below Average/Low 828 32.2 2,644 60.6 

risk associated with loan administration? Average/Medium 1,389 53.9 1,467 33.6 

Above Average/High 358 13.9 251 5.8 

To what degree does the institution Never or Infrequently 1,828 71.0 3,809 87.3 

fail to adjust its loan pricing on Frequently Enough to Warrant Notice 611 23.7 453 10.4 

different-quality loans to reflect Commonly or as Standard Procedure 136 5.3 100 2.3 

differences in risk? 

To what extent does the institution Never or Infrequently 1,589 61.7 3,308 75.8 

fail to require a material principal Frequently Enough to Warrant Notice 842 32.7 913 20.9 

before renewing term loans? Commonly or as Standard Procedure 144 5.6 141 3.2 

To what extent do the institution's Never or Infrequently 1,863 72.3 3,297 75.6 

written lending policies differ from Frequently Enough to Warrant Notice 603 23.4 909 20.8 

actual practices? Commonly or as Standard Procedure 109 4.2 156 3.6 

34 
 

 

 

 

 

 

 

 

 

 

Table 2a 

Composite CAMELS Rating for Next 12 Months Prediction 

Logistic Regressions 

FDIC Survey Data 1996-1998 

Specification 1 Specification 2 Specification 3 Specification 4 

Coefficient Coefficient Coefficient Coefficient 

Explanatory Variable Estimate (standard error) Estimate (standard error) Estimate (standard error) Estimate (standard error) 

Intercept 3 -3.4875 *** (0.6322) -1.8018 *** (0.6887) 0.3411 (0.8672) -0.1626 (0.8314) 

Intercept 2 0.7643 (0.6200) 2.8482 *** (0.6826) 6.0667 *** (0.8842) 5.4731 *** (0.8449) 

Equity -0.0845 *** (0.0137) -0.0932 *** (0.0146) -0.0132 (0.0167) -0.0082 (0.0164) 

Allowance for loan losses 0.2870 ** (0.1144) 0.2822 ** (0.1203) -0.1051 (0.1424) -0.1059 (0.1400) 

Loans past due 30-89 days 0.4019 *** (0.0556) 0.2672 *** (0.0581) 0.3131 *** (0.0715) 0.3581 *** (0.0695) 

Loans past due 90 days or more 0.3989 *** (0.1182) 0.2531 ** (0.1188) 0.2872 ** (0.1391) 0.3377 ** (0.1423) 

Nonaccrual loans and leases 0.8790 *** (0.0849) 0.6806 *** (0.0890) 0.4132 *** (0.1005) 0.4380 *** (0.0980) 

Other real estate owned 1.1677 *** (0.1597) 1.0409 *** (0.1633) 0.4772 *** (0.1609) 0.4619 *** (0.1514) 

Volatile liabilities 0.0127 *** (0.0046) 0.0160 *** (0.0048) 0.0048 (0.0060) 0.0027 (0.0059) 

Liquid assets -0.0024 (0.0054) -0.0084 (0.0057) -0.0035 (0.0072) -0.0014 (0.0072) 

Loans and securities with 

maturities of 5 years or more 0.0090 (0.0058) 0.0029 (0.0061) -0.0017 (0.0077) 0.0005 (0.0077) 

Gross charge-offs on loans & leases 0.1665 (0.1851) 0.0030 (0.1837) -0.2528 (0.1939) -0.2225 (0.1919) 

Provisions for loan & lease losses 0.2955 * (0.1698) 0.3304 * (0.1803) 0.3814 ** (0.1744) 0.3229 * (0.1734) 

Income before taxes and

 extraordinary items -1.4850 *** (0.0803) -1.4271 *** (0.0841) -0.6367 *** (0.0893) -0.6314 *** (0.0879) 

State personal-income growth, lag 1 0.0074 (0.0346) 0.0103 (0.0363) 0.0041 (0.0457) 0.0058 (0.0453) 

State personal-income growth, lag 2 -0.0050 (0.0367) -0.0129 (0.0384) -0.0342 (0.0485) -0.0296 (0.0483) 

State personal-income growth, lag 3 0.0527 (0.0389) 0.0238 (0.0409) 0.0625 (0.0520) 0.0694 (0.0513) 

State personal-income growth, lag 4 -0.0194 (0.0291) -0.0259 (0.0305) -0.0525 (0.0382) -0.0508 (0.0380) 

State personal-income growth, lag 5 -0.0569 ** (0.0281) -0.0547 * (0.0295) -0.0788 ** (0.0373) -0.0737 ** (0.0371) 

Composite CAMELS dummy 1 -6.6069 *** (0.3037) -6.8582 *** (0.2850) 

Composite CAMELS dummy 2 -3.0459 *** (0.2618) -3.1515 *** (0.2480) 

Concentrations of credit dummy 1 -0.1187 (0.1371) 0.0256 (0.1729) 

Concentrations of credit dummy 3 -0.0140 (0.2128) -0.0880 (0.2640) 

Fail-to-adjust pricing dummy 1 -0.3832 *** (0.1131) -0.3032 ** (0.1438) 

Fail-to-adjust pricing dummy 3 -0.0915 (0.2239) -0.2768 (0.2745) 

Principal reduction dummy 1 -0.2410 ** (0.1053) -0.1171 (0.1345) 

Principal reduction dummy 3 0.3994 * (0.2314) 0.2375 (0.2741) 

Written policies versus practices dummy 1 -0.4237 *** (0.1205) 0.0059 (0.1543) 

Written policies versus practices dummy 3 0.4564 * (0.2682) 0.1354 (0.3182) 

Current underwriting practices dummy 1 -0.1206 (0.1517) 0.0712 (0.1997) 

Current underwriting practices dummy 3 0.4282 ** (0.2174) 0.6263 ** (0.2555) 

Portfolio credit risk dummy 1 -0.3879 *** (0.1388) -0.2721 (0.1824) 

Portfolio credit risk dummy 3 0.1406 (0.1960) -0.2552 (0.2327) 

Loan administration dummy 1 -0.4847 *** (0.1294) -0.1457 (0.1703) 

Loan administration dummy 3 0.2528 (0.1849) -0.1127 (0.2274) 

Akaike Information Criteria 3490.31 3246.88 2264.28 2,261.74 

Somers' D Statistic 65.9% 71.2% 83.2% 82.6% 

-2 x log likelihood, L(specification) 3,452.31 3,180.88 2,194.28 2,219.74 

Likelihhood-ratio test statistic for

 significance of survey variables: L(1) - L(2) L(4) - L(3) 

Chi-square, degress of freedom,

 5 percent critical value 271.43, 7, 14.07 25.46, 7, 14.07 

* indicates significance at 10% level, ** indicates signifcance at 5% level and *** indicates significance at 1% level 

35 
 

 

 

 

Table 2b 

Composite CAMELS Rating for Next 12 Months Prediction 

Logistic Regressions 

FDIC Survey Data 1998-2001 

Specification 1 Specification 2 Specification 3 Specification 4 

Coefficient Coefficient Coefficient Coefficient 

Explanatory Variable Estimate (standard error) Estimate (standard error) Estimate (standard error) Estimate (standard error) 

Intercept 3 -2.8577 *** (0.4645) -0.9439 * (0.5111) 0.5114 (0.6457) 0.0835 (0.6093) 

Intercept 2 0.9511 ** (0.4598) 3.3963 *** (0.5138) 5.8893 *** (0.6560) 5.3746 *** (0.6172) 

Equity -0.1039 *** (0.0094) -0.1002 *** (0.0096) -0.0588 *** (0.0114) -0.0561 *** (0.0113) 

Allowance for loan losses 0.2726 *** (0.0896) 0.1362 (0.0918) -0.0856 (0.1098) -0.0777 (0.1084) 

Loans past due 30-89 days 0.3849 *** (0.0404) 0.2975 *** (0.0425) 0.2936 *** (0.0506) 0.3128 *** (0.0497) 

Loans past due 90 days or more 0.5573 *** (0.0906) 0.4152 *** (0.0941) 0.4367 *** (0.1062) 0.4699 *** (0.1050) 

Nonaccrual loans and leases 0.7969 *** (0.0632) 0.5514 *** (0.0673) 0.3093 *** (0.0770) 0.3452 *** (0.0752) 

Other real estate owned 0.9775 *** (0.1229) 0.7682 *** (0.1299) 0.4682 *** (0.1442) 0.4815 *** (0.1455) 

Volatile liabilities 0.0117 *** (0.0032) 0.0154 *** (0.0033) 0.014 *** (0.0040) 0.0124 *** (0.0040) 

Liquid assets -0.0071 * (0.0039) -0.0108 *** (0.0041) -0.0036 (0.0052) -0.0020 (0.0051) 

Loans and securities with

 maturities of 5 years or more 0.0005 (0.0044) -0.0038 (0.0046) -0.0063 (0.0058) -0.0046 (0.0057) 

Gross charge-offs on loans & leases -0.2150 (0.1463) -0.3008 ** (0.1374) -0.3312 ** (0.1441) -0.3308 ** (0.1416) 

Provisions for loan & lease losses 0.5744 *** (0.1327) 0.4678 *** (0.1386) 0.4233 *** (0.1469) 0.4434 *** (0.1486) 

Income before taxes and 

 extraordinary items -1.2041 *** (0.0473) -1.1661 *** (0.0494) -0.5350 *** (0.0541) -0.5081 *** (0.0534) 

State personal-income growth, lag 1 0.0590 *** (0.0217) 0.0649 *** (0.0228) 0.0402 (0.0286) 0.0353 (0.0284) 

State personal-income growth, lag 2 0.0844 *** (0.0213) 0.0960 *** (0.0223) 0.1003 *** (0.0282) 0.0931 *** (0.0279) 

State personal-income growth, lag 3 -0.0184 (0.0270) -0.0285 (0.0283) -0.0293 (0.0355) -0.0274 (0.0352) 

State personal-income growth, lag 4 -0.0232 (0.0248) -0.0394 (0.0260) -0.0544 * (0.0326) -0.0509 (0.0323) 

State personal-income growth, lag 5 0.0573 ** (0.0250) 0.0559 ** (0.0262) 0.0616 * (0.0326) 0.0591 * (0.0323) 

Composite CAMELS dummy 1 -6.4065 *** (0.1996) -6.7808 *** (0.1820) 

Composite CAMELS dummy 2 -2.7633 *** (0.1633) -2.9736 *** (0.1485) 

Concentrations of credit dummy 1 -0.2569 *** (0.0972) -0.0375 (0.1206) 

Concentrations of credit dummy 3 -0.2200 (0.1536) -0.1257 (0.1878) 

Fail-to-adjust pricing dummy 1 -0.1899 (0.1208) -0.1831 (0.1453) 

Fail-to-adjust pricing dummy 3 0.5867 ** (0.2608) 0.5157 * (0.3012) 

Principal reduction dummy 1 -0.2126 ** (0.0972) -0.0914 (0.1185) 

Principal reduction dummy 3 -0.0571 (0.2275) -0.1358 (0.2583) 

Written policies versus practices dummy 1 -0.2257 ** (0.0993) 0.1558 (0.1213) 

Written policies versus practices dummy 3 0.4069 * (0.2202) 0.2335 (0.2528) 

Current underwriting practices dummy 1 -0.3246 *** (0.1126) -0.2106 (0.1398) 

Current underwriting practices dummy 3 0.4666 * (0.2572) 0.0033 (0.2824) 

Portfolio credit risk dummy 1 -0.4559 *** (0.1035) -0.0741 (0.1291) 

Portfolio credit risk dummy 3 0.3801 (0.2319) -0.2249 (0.2550) 

Loan administration dummy 1 -0.7749 *** (0.1002) -0.3636 *** (0.1249) 

Loan administration dummy 3 0.3401 (0.2198) 0.1814 (0.2491) 

Akaike Information Criteria 6,374.13 5,814.50 4,055.66 4,076.28 

Somers' D Statistic 65.7% 72.2% 84.7% 84.3% 

-2 x log likelihood, L(specification) 6,336.13 5,748.50 3,985.66 4,034.28 

Likelihhood-ratio test statistic for

 significance of survey variables: L(1) - L(2) L(4) - L(3)

 Chi-square, degress of freedom,

 5 percent critical value 587.76, 7, 14.07 48.62, 7, 14.07 

* indicates significant at 10% level, ** indicates signifcant at 5% level and *** indicates significant at 1% level 

36 
 

 

Table 3a

 Asset Quality Rating for Next 12 Months Prediction 

Logistic Regressions 

FDIC Survey Data 1996-1998 

Specification 1 Specification 2 Specification 3 Specification 4 

Coefficient Coefficient Coefficient Coefficient 

Explanatory Variable Estimate (standard error) Estimate (standard error) Estimate (standard error) Estimate (standard error) 

Intercept 3 -5.0433 *** (0.6415) -3.5824 *** (0.7029) -1.3509 * (0.7819) -1.6506 ** (0.7448) 

Intercept 2 -1.9807 *** (0.6296) -0.2155 (0.6933) 2.6114 *** (0.7863) 2.2749 *** (0.7491) 

Equity -0.0530 *** (0.0134) -0.0538 *** (0.0140) -0.0265 * (0.0141) -0.0240 * (0.0138) 

Allowance for loan losses 0.4428 *** (0.1113) 0.4001 *** (0.1151) 0.1066 (0.1235) 0.0783 (0.1213) 

Loans past due 30-89 days 0.4561 *** (0.0536) 0.3414 *** (0.0561) 0.3180 *** (0.0608) 0.3430 *** (0.0598) 

Loans past due 90 days or more 0.5949 *** (0.1206) 0.4589 *** (0.1182) 0.4213 *** (0.1250) 0.4567 *** (0.1262) 

Nonaccrual loans and leases 1.1681 *** (0.0880) 0.9364 *** (0.0888) 0.5105 *** (0.0940) 0.5196 *** (0.0936) 

Other real estate owned 1.1672 *** (0.1613) 0.9804 *** (0.1693) 0.4466 *** (0.1634) 0.4466 *** (0.1563) 

Volatile liabilities 0.0010 (0.0046) 0.0042 (0.0048) 0.0031 (0.0052) 0.0025 (0.0051) 

Liquid assets 0.0017 (0.0055) -0.0037 (0.0059) -0.0055 (0.0064) -0.0048 (0.0063) 

Loans and securities with

 maturities of 5 years or more 0.0096 (0.0060) 0.0043 (0.0064) 0.0042 (0.0069) 0.0059 (0.0068) 

Gross charge-offs on loans & leases 0.4576 ** (0.1843) 0.3358 * (0.1719) 0.0010 (0.1628) -0.0051 (0.0010) 

Provisions for loan & lease losses -0.0209 (0.2007) -0.1239 (0.1641) 0.0317 (0.1611) 0.0317 (0.1638) 

Income before taxes and

 extraordinary items -0.5027 *** (0.0719) -0.4193 *** (0.0712) -0.1533 ** (0.0743) -0.1541 ** (0.0739) 

State personal-income growth, lag 1 0.0403 (0.0349) 0.0452 (0.0364) 0.0659 * (0.0396) 0.0692 * (0.0394) 

State personal-income growth, lag 2 -0.0052 (0.0373) -0.0191 (0.0389) -0.0011 (0.0426) 0.0063 (0.0423) 

State personal-income growth, lag 3 0.1004 ** (0.0400) 0.0700 * (0.0418) 0.1001 ** (0.0461) 0.1098 ** (0.0458) 

State personal-income growth, lag 4 0.0109 (0.0296) -0.0032 (0.0309) -0.0072 (0.0335) -0.0050 (0.0334) 

State personal-income growth, lag 5 -0.0612 ** (0.0286) -0.0570 * (0.0300) -0.0738 ** (0.0328) -0.0761 ** (0.0326) 

Asset component dummy 1 -4.4103 *** (0.2709) -4.8278 *** (0.2407) 

Asset component dummy 2 -1.9780 *** (0.2336) -2.1607 *** (0.2129) 

Concentrations of credit dummy 1 -0.1930 (0.1338) -0.1672 (0.1467) 

Concentrations of credit dummy 3 0.9020 (0.2052) -0.0230 (0.2239) 

Fail-to-adjust pricing dummy 1 -0.2734 ** (0.1122) -0.1256 (0.1245) 

Fail-to-adjust pricing dummy 3 -0.2843 (0.2095) -0.2022 (0.2311) 

Principal reduction dummy 1 -0.3490 *** (0.1044) -0.1127 (0.1169) 

Principal reduction dummy 3 0.3506 (0.2137) 0.1761 (0.2309) 

Written policies versus practices dummy 1 -0.2147 * (0.1171) -0.0625 (0.1309) 

Written policies versus practices dummy 3 0.0659 (0.2442) -0.2283 (0.2626) 

Current underwriting practices dummy 1 -0.1734 (0.1689) -0.1433 (0.1861) 

Current underwriting practices dummy 3 0.4647 ** (0.1970) 0.2944 (0.2132) 

Portfolio credit risk dummy 1 -0.7150 *** (0.1496) -0.2278 (0.1674) 

Portfolio credit risk dummy 3 0.2805 (0.1799) -0.1278 (0.1980) 

Loan administration dummy 1 -0.1503 (0.1389) -0.0053 (0.1538) 

Loan administration dummy 3 0.5469 *** (0.1711) 0.2209 (0.1893) 

Akaike Information Criteria 3,557.07 3,291.75 2,821.08 2,817.89 

Somers' D Statistic 61.8% 67.7% 77.0% 76.6% 

-2 x log likelihood, L(specification) 3,519.07 3,225.75 2,751.08 2,775.89 

Likelihhood-ratio test statistic for

 significance of survey variables: L(1) - L(2) L(4) - L(3)

 Chi-square, degress of freedom,

 5 percent critical value 293.32, 7, 14.07 24.81, 7, 14.07 

* indicates significance at 10% level, ** indicates signifcance at 5% level and *** indicates significance at 1% level 

37 
 

 

 

 

 

 

 

Table 3b.

 Asset Quality Rating for Next 12 Months Prediction 

Logistic Regressions 

FDIC Survey Data 1998-2001 

Specification 1 Specification 2 Specification 3 Specification 4 

Coefficient Coefficient Coefficient Coefficient 

Explanatory Variable Estimate (standard error) Estimate (standard error) Estimate (standard error) Estimate (standard error) 

Intercept 3 -3.8036 *** (0.4828) -1.8211 *** (0.5321) -0.1754 (0.5850) -0.4275 (0.5579) 

Intercept 2 -1.0818 ** (0.4774) 1.3132 ** (0.5309) 3.5402 *** (0.5902) 3.2439 *** (0.5621) 

Equity -0.0691 *** (0.0097) -0.0553 *** (0.0099) -0.0336 *** (0.0105) -0.0349 *** (0.0105) 

Allowance for loan losses 0.5808 *** (0.0949) 0.3902 *** (0.0914) 0.2182 ** (0.0950) 0.2363 ** (0.0944) 

Loans past due 30-89 days 0.4357 *** (0.0392) 0.3729 *** (0.0409) 0.3411 *** (0.0433) 0.3489 *** (0.0428) 

Loans past due 90 days or more 0.6863 *** (0.0929) 0.5744 *** (0.0943) 0.4142 *** (0.0967) 0.416 *** (0.0961) 

Nonaccrual loans and leases 1.1558 *** (0.0703) 0.9341 *** (0.0719) 0.5490 *** (0.0743) 0.5576 *** (0.0735) 

Other real estate owned 1.0509 *** (0.1256) 0.7823 *** (0.1299) 0.3725 *** (0.1306) 0.3902 *** (0.1293) 

Volatile liabilities -0.0022 (0.0032) -0.0005 (0.0034) 0.0056 (0.0036) 0.0056 (0.0036) 

Liquid assets -0.0076 ** (0.0041) -0.0117 *** (0.0043) -0.0105 ** (0.0047) -0.0090 * (0.0046) 

Loans and securities with

 maturities of 5 years or more -0.0003 (0.0045) -0.0036 (0.0048) -0.0038 (0.0052) -0.0026 (0.0052) 

Gross charge-offs on loans & leases 0.0231 (0.1467) -0.1038 (0.1303) -0.3803 *** (0.1284) -0.4239 *** (0.1274) 

Provisions for loan & lease losses 0.4020 *** (0.1437) 0.1321 (0.1317) 0.3084 ** (0.1374) 0.3878 *** (0.1406) 

Income before taxes and 

 extraordinary items -0.4705 *** (0.0427) -0.4099 *** (0.0425) -0.2574 *** (0.0437) -0.2532 *** 0.0437 

State personal-income growth, lag 1 0.0239 (0.0219) 0.0236 (0.0230) -0.0061 (0.0253) -0.0132 (0.0252) 

State personal-income growth, lag 2 0.0509 ** (0.0214) 0.0509 ** (0.0226) 0.0265 (0.0247) 0.0243 (0.0245) 

State personal-income growth, lag 3 0.0263 (0.0272) 0.0207 (0.0286) 0.00659 (0.0313) 0.0099 (0.0311) 

State personal-income growth, lag 4 0.0377 (0.0250) 0.0379 (0.0263) 0.0414 (0.0287) 0.0433 (0.0285) 

State personal-income growth, lag 5 0.0425 ** (0.0253) 0.0402 (0.0266) 0.0217 (0.0290) 0.0211 (0.0287) 

Asset component dummy 1 -4.2971 *** (0.1851) -4.9213 *** (0.1603) 

Asset component dummy 2 -1.8219 *** (0.1569) -2.2215 *** (0.1399) 

Concentrations of credit dummy 1 -0.3837 *** (0.0948) -0.2294 ** (0.1035) 

Concentrations of credit dummy 3 -0.2432 (0.1498) -0.2084 (0.1626) 

Fail-to-adjust pricing dummy 1 -0.1620 (0.1158) -0.1309 (0.1249) 

Fail-to-adjust pricing dummy 3 0.4016 (0.2509) 0.3297 (0.2617) 

Principal reduction dummy 1 -0.3284 *** (0.0925) -0.1446 (0.1017) 

Principal reduction dummy 3 0.2088 (0.2235) 0.1252 (0.2331) 

Written policies versus practices dummy 1 -0.2130 ** (0.0941) 0.0306 (0.1039) 

Written policies versus practices dummy 3 0.1708 (0.2131) -0.0085 (0.2232) 

Current underwriting practices dummy 1 -0.2284 ** (0.1075) -0.1423 (0.1198) 

Current underwriting practices dummy 3 0.3570 (0.2480) 0.1441 (0.2543) 

Portfolio credit risk dummy 1 -0.7685 *** (0.0982) -0.1618 (0.1115) 

Portfolio credit risk dummy 3 0.9107 *** (0.2298) 0.3766 (0.2394) 

Loan administration dummy 1 -0.5139 *** (0.0954) -0.1487 (0.1071) 

Loan administration dummy 3 0.2865 (0.2059) 0.1072 (0.2155) 

Akaike Information Criteria 6,554.66 5,906.48 5,011.04 5,035.56 

Somers' D Statistic 61.1% 69.3% 78.3% 77.3% 

-2 x log likelihood, L(specification) 6,516.66 5,840.48 4,941.04 4,993.56 

Likelihhood-ratio test statistic for

 significance of survey variables: L(1) - L(2) L(4) - L(3)

 Chi-square, degress of freedom,

 5 percent critical value 676.18, 7, 14.07 52.52, 7, 14.07 

* indicates significant at 10% level, ** indicates signifcant at 5% level and *** indicates significant at 1% level 

38 
 

 

Table 4a. 

Management Rating for Next 12 Months Prediction 

Logistic Regressions 

FDIC Survey Data 1996-1998 

Specification 1 Specification 2 Specification 3 Specification 4 

Coefficient Coefficient Coefficient Coefficient 

Explanatory Variable Estimate (standard error) Estimate (standard error) Estimate (standard error) Estimate (standard error) 

Intercept 3 -2.3644 *** (0.5764) -0.6106 (0.6265) 0.6608 (0.7412) 0.3989 (0.7084) 

Intercept 2 0.9232 (0.5718) 3.0220 *** (0.6273) 5.2142 *** (0.7530) 4.9206 *** (0.7194) 

Equity -0.0105 (0.0113) -0.0128 (0.0117) 0.0055 (0.0121) 0.0068 (0.0119) 

Allowance for loan losses 0.0395 (0.1041) 0.0421 (0.1087) -0.0948 (0.1232) -0.1117 (0.1210) 

Loans past due 30-89 days 0.4284 *** (0.0521) 0.3022 *** (0.0539) 0.3125 *** (0.0613) 0.3325 *** (0.0606) 

Loans past due 90 days or more 0.4731 *** (0.1125) 0.3223 *** (0.1103) 0.3077 ** (0.1263) 0.3457 *** (0.1274) 

Nonaccrual loans and leases 0.6573 *** (0.0765) 0.4546 *** (0.0790) 0.2609 *** (0.0859) 0.2806 *** (0.0839) 

Other real estate owned 0.7736 *** (0.1393) 0.6424 *** (0.1404) 0.3602 *** (0.1344) 0.3609 *** (0.1304) 

Volatile liabilities 0.0092 ** (0.0042) 0.0127 *** (0.0044) 0.0011 (0.0050) -0.0007 (0.0050) 

Liquid assets -0.0022 (0.0050) -0.0089 * (0.0053) -0.0863 (0.0062) -0.0064 (0.0061) 

Loans and securities with

 maturities of 5 years or more -0.0055 (0.0054) -0.0128 ** (0.0056) -0.0101 (0.0066) -0.0080 (0.0066) 

Gross charge-offs on loans & leases 0.1855 (0.1719) 0.0592 (0.1680) -0.1549 (0.1752) -0.1435 (0.1748) 

Provisions for loan & lease losses 0.2536 (0.1597) 0.2728 * (0.1522) 0.2922 * (0.1607) 0.2776 * (0.1606) 

Income before taxes and 

 extraordinary items -0.9734 *** (0.0681) -0.0893 *** (0.0685) -0.5014 *** (0.0718) -0.5044 *** (0.0706) 

State personal-income growth, lag 1 0.0197 (0.0322) 0.0222 (0.0334) -0.0166 (0.0398) -0.0161 (0.0395) 

State personal-income growth, lag 2 -0.0038 (0.0343) -0.0092 (0.0356) -0.0075 (0.0422) -0.0070 (0.0421) 

State personal-income growth, lag 3 0.0608 * (0.0363) 0.0392 (0.0380) 0.0659 (0.0450) 0.0682 (0.0446) 

State personal-income growth, lag 4 -0.0109 (0.0272) -0.0174 (0.0282) -0.0262 (0.0330) -0.0249 (0.0329) 

State personal-income growth, lag 5 -0.0628 ** (0.0262) -0.0640 ** (0.0272) -0.0474 (0.0320) -0.0431 (0.0318) 

Managemenent component dummy 1 -5.4436 *** (0.2262) -5.6733 *** (0.2047) 

Managemenent component dummy 2 -2.3823 *** (0.1867) -2.5035 *** (0.1704) 

Concentrations of credit dummy 1 -0.1293 (0.1270) 0.0947 (0.1469) 

Concentrations of credit dummy 3 -0.0981 (0.1953) 0.0416 (0.2255) 

Fail-to-adjust pricing dummy 1 -0.2739 *** (0.1048) -0.1789 (0.1233) 

Fail-to-adjust pricing dummy 3 0.0252 (0.2050) -0.0048 (0.2329) 

Principal reduction dummy 1 -0.2824 *** (0.0977) -0.0723 (0.1158) 

Principal reduction dummy 3 0.3843 * (0.2109) 0.2479 (0.2326) 

Written policies versus practices dummy 1 -0.5265 *** (0.1126) -0.0191 (0.1320) 

Written policies versus practices dummy 3 0.1761 (0.2417) -0.1514 (0.2608) 

Current underwriting practices dummy 1 -0.1733 (0.1404) -0.0167 (0.1726) 

Current underwriting practices dummy 3 0.0054 (0.1971) -0.1639 (0.2192) 

Portfolio credit risk dummy 1 -0.2932 ** (0.1291) -0.1369 (0.1568) 

Portfolio credit risk dummy 3 0.0485 (0.1796) -0.1006 (0.2008) 

Loan administration dummy 1 -0.4674 *** (0.1194) -0.1074 (0.1476) 

Loan administration dummy 3 0.6598 *** (0.1723) 0.3053 (0.1930) 

Akaike Information Criteria 4,172.93 3,890.73 2,956.62 2,943.49 

Somers' D Statistic 54.0% 61.4% 78.6% 78.7% 

-2 x log likelihood, L(specification) 4,134.93 3,824.73 2,886.62 2,901.49 

Likelihhood-ratio test statistic for

 significance of survey variables: L(1) - L(2) L(4) - L(3)

 Chi-square, degress of freedom,

 5 percent critical value 310.20, 7, 14.07 14.87, 7, 14.07 

* indicates significance at 10% level, ** indicates signifcance at 5% level and *** indicates significance at 1% level 

39 
 

 

 

 

 

Table 4b

 Management Rating for Next 12 Months Prediction 

Logistic Regressions 

FDIC Survey Data 1998-2001 

Specification 1 Specification 2 Specification 3 Specification 4 

Coefficient Coefficient Coefficient Coefficient 

Explanatory Variable Estimate (standard error) Estimate (standard error) Estimate (standard error) Estimate (standard error) 

Intercept 3 -3.2464 *** (0.4409) -1.4971 *** (0.4796) -0.1521 (0.5744) -0.3912 (0.5454) 

Intercept 2 -0.0023 (0.4362) 2.2018 *** (0.4799) 4.5996 *** (0.5809) 4.2996 *** (0.5507) 

Equity -0.0470 *** (0.0085) -0.0421 *** (0.0087) -0.0303 *** (0.0102) -0.0303 *** (0.0100) 

Allowance for loan losses 0.1169 (0.0856) 0.0022 (0.0872) -0.0820 (0.1026) -0.0464 (0.1005) 

Loans past due 30-89 days 0.4165 *** (0.0390) 0.3274 *** (0.0411) 0.2871 *** (0.0468) 0.2963 *** (0.0461) 

Loans past due 90 days or more 0.6349 *** (0.0893) 0.4871 *** (0.0924) 0.5262 *** (0.1032) 0.5695 *** (0.1022) 

Nonaccrual loans and leases 0.7168 *** (0.0607) 0.5006 *** (0.0640) 0.3452 *** (0.0723) 0.3774 *** (0.0700) 

Other real estate owned 0.7367 *** (0.1160) 0.5391 *** (0.1218) 0.2836 ** (0.1292) 0.3064 ** (0.1279) 

Volatile liabilities 0.0065 ** (0.0030) 0.0094 *** (0.0031) 0.0070 * (0.0036) 0.0059 (0.0036) 

Liquid assets 0.0038 (0.0037) 0.0020 (0.0039) -0.0001 (0.0046) -0.0001 (0.0045) 

Loans and securities with

 maturities of 5 years or more 0.0013 (0.0041) -0.0019 (0.0043) -0.0027 (0.0051) -0.0021 (0.0050) 

Gross charge-offs on loans & leases -0.1387 (0.1387) -0.2191 * (0.1257) -0.3369 ** (0.1446) -0.3088 ** (0.1399) 

Provisions for loan & lease losses 0.4143 *** (0.1260) 0.3260 ** (0.1296) 0.3896 *** (0.1394) 0.3672 *** (0.1382) 

Income before taxes and 

 extraordinary items -0.9127 *** (0.0424) -0.8680 *** (0.0441) -0.4569 *** (0.0478) -0.4427 *** (0.0473) 

State personal-income growth, lag 1 0.0776 *** (0.0208) 0.0884 *** (0.0216) 0.0491 * (0.0258) 0.0416 (0.0256) 

State personal-income growth, lag 2 0.0923 *** (0.0204) 0.1054 *** (0.0212) 0.0917 *** (0.0254) 0.0843 *** (0.0252) 

State personal-income growth, lag 3 -0.0117 (0.0257) -0.0236 (0.0268) -0.0258 (0.0320) -0.0178 (0.0317) 

State personal-income growth, lag 4 -0.0101 (0.0236) -0.0228 (0.0245) -0.0145 (0.0292) -0.0097 (0.0290) 

State personal-income growth, lag 5 0.0290 (0.0238) 0.0230 (0.0248) 0.0460 (0.0296) 0.0492 * (0.0293) 

Managemenent component dummy 1 -5.8093 *** (0.1688) -6.1199 *** (0.1536) 

Managemenent component dummy 2 -2.6227 *** (0.1343) -2.8071 *** (0.1225) 

Concentrations of credit dummy 1 -0.3316 *** (0.0925) -0.1929 * (0.1100) 

Concentrations of credit dummy 3 -0.0734 (0.1462) 0.0121 (0.1709) 

Fail-to-adjust pricing dummy 1 -0.0299 (0.1151) 0.0613 (0.1322) 

Fail-to-adjust pricing dummy 3 0.8753 *** (0.2539) 0.8527 *** (0.2794) 

Principal reduction dummy 1 -0.3406 *** (0.0924) -0.1581 (0.1071) 

Principal reduction dummy 3 -0.1008 (0.2186) -0.1759 (0.2381) 

Written policies versus practices dummy 1 -0.3648 *** (0.0944) 0.1044 (0.1102) 

Written policies versus practices dummy 3 0.2319 (0.2120) 0.1525 (0.2305) 

Current underwriting practices dummy 1 -0.1143 (0.1079) 0.0718 (0.1267) 

Current underwriting practices dummy 3 0.4949 ** (0.2475) -0.1799 (0.2636) 

Portfolio credit risk dummy 1 -0.3131 *** (0.0990) -0.1405 (0.1167) 

Portfolio credit risk dummy 3 0.0307 (0.2242) -0.1769 (0.2407) 

Loan administration dummy 1 -0.8780 *** (0.0965) -0.3749 *** (0.1132) 

Loan administration dummy 3 0.6612 *** (0.2091) 0.4272 * (0.2305) 

Akaike Information Criteria 7,156.96 6,551.42 4,824.15 4,846.22 

Somers' D Statistic 56.2% 64.7% 81.3% 80.8% 

-2 x log likelihood, L(specification) 7,118.96 6,485.42 4,754.15 4,804.22 

Likelihhood-ratio test statistic for

 significance of survey variables: L(1) - L(2) L(4) - L(3)

 Chi-square, degress of freedom,

 5 percent critical value 633.54, 7, 14.07 50.07, 7, 14.07 

* indicates significance at 10% level, ** indicates signifcance at 5% level and *** indicatece significant at 1% level 

40 
 

 

 

 

 

 

 

 

 

 

 

 

 

Table 5. 

Pearson's Partial Correlation Coefficients between Actual and Predicted CAMELS Ratings 

(1996 - 2002) 

Actual Composite CAMELS Rating 

Composite CAMELS Prediction Correlation (p-value)

 Specification 1 0.582 < 0.0001

 Specification 2 0.648 < 0.0001

 Specification 3 0.793 < 0.0001

 Specification 4 0.793 < 0.0001 

Actual Asset Quality Rating 

Asset Quality Rating Prediction Correlation (p-value)

 Specification 1 0.420 < 0.0001

 Specification 2 0.534 < 0.0001

 Specification 3 0.656 < 0.0001

 Specification 4 0.663 < 0.0001 

Actual Management Rating 

Management Rating Prediction Correlation (p-value)

 Specification 1 0.412 < 0.0001

 Specification 2 0.543 < 0.0001

 Specification 3 0.736 < 0.0001

 Specification 4 0.739 < 0.0001 

There were 5,075 observations for each category of CAMELS rating prediction. 

41 
 

 

 

 

 

 

 

 

 

Table 6. 

Total Nonperforming Assets Prediction (one year ahead) 

Ordinary Least Squares Regression 

FDIC Survey Data 1996-1998 

Explanatory Variable 

Specification 1 

Coefficient 

Estimate (standard error) 

Specification 2 

Coefficient 

Estimate (standard error) 

Specification 3 

Coefficient 

Estimate (standard error) 

Specification 4 

Coefficient 

Estimate (standard error) 

Intercept 0.1655 (0.3024) 0.5447 * (0.3201) 0.8840 *** (0.3373) 0.7424 ** (0.3181) 

Nonperforming assets (t-1) 0.6842 *** (0.0145) 0.65516 *** (0.0151) 0.63065 *** (0.0160) 0.6341 *** (0.0160) 

Equity (t-1) 0.0091 ** (0.0043) 0.0100 ** (0.0043) 0.0110 ** (0.0043) 0.0109 ** (0.0043) 

Allowance for loan losses (t-1) 0.0000 ** (0.0000) 0.0000 0.0000 0.0000 (0.0000) 0.0000 0.0000 

Volatile liabilities (t-1) -0.0003 (0.0024) -0.0004 (0.0023) -0.0008 (0.0023) -0.0006 (0.0023) 

Liquid assets (t-1) -0.0027 (0.0027) -0.0035 (0.0027) -0.0033 (0.0027) -0.0026 (0.0027) 

Loans and securities with 

maturities of 5 years or more (t-1) 0.0037 (0.0029) 0.0025 (0.0029) 0.0026 (0.0029) 0.0035 (0.0029) 

Gross charge-offs on loans & leases (t-1) -0.1791 ** (0.0904) -0.2326 *** (0.0903) -0.2541 *** (0.0904) -0.2264 ** (0.0901) 

Provisions for loan & lease losses (t-1) 0.3507 *** (0.0931) 0.3591 *** (0.0929) 0.3629 *** (0.0933) 0.3503 *** (0.0932) 

Income before taxes and 

extraordinary items (t-1) 0.0402 * (0.0214) 0.0471 ** (0.0214) 0.0589 ** (0.0215) 0.0607 *** (0.0214) 

State personal-income growth, lag 1 -0.0316 ** (0.0154) -0.0287 * (0.0153) -0.0286 * (0.0153) -0.0301 ** (0.0153) 

State personal-income growth, lag 2 0.0019 (0.0173) 0.0014 (0.0173) 0.0002 (0.0172) 0.0013 (0.0172) 

State personal-income growth, lag 3 0.0101 (0.0213) 0.0067 (0.0212) 0.00727 (0.0211) 0.0076 (0.0211) 

State personal-income growth, lag 4 0.0056 (0.0158) 0.0032 (0.0158) 0.0056 (0.0157) 0.0073 (0.0157) 

State personal-income growth, lag 5 0.0266 * (0.0143) 0.0268 * (0.0143) 0.0290 ** (0.0143) 0.0304 ** (0.0142) 

Asset component dummy 1 

Asset component dummy 2 

-0.4775 

-0.2608 

*** 

** 

(0.1328) 

(0.1225) 

-0.6382 

-0.3422 

*** 

*** 

(0.1170) 

(0.1130) 

Survey lag (calldate - survey date) 0.0000 (0.0002) 0.0000 (0.0002) 

Concentrations of credit dummy 1 

Concentrations of credit dummy 3 

-0.1140 

0.0007 

* (0.0630) 

(0.0969) 

-0.1016 

-0.0058 

(0.0628) 

(0.0966) 

Fail-to-adjust pricing dummy 1 

Fail-to-adjust pricing dummy 3 

-0.0294 

-0.1197 

(0.0509) 

(0.1054) 

-0.0235 

-0.1110 

(0.0508) 

(0.1052) 

Principal reduction dummy 1 

Principal reduction dummy 3 

-0.0748 

0.3781 *** 

(0.0475) 

(0.1060) 

-0.0542 

0.3689 *** 

(0.0476) 

(0.1058) 

Written policies versus practices dummy 1 

Written policies versus practices dummy 3 

-0.0139 

0.1829 

(0.0555) 

(0.1243) 

-0.0035 

0.1345 

(0.0554) 

(0.1245) 

Current underwriting practices dummy 1 

Current underwriting practices dummy 3 

0.0248 

-0.0764 

(0.0688) 

(0.1039) 

0.0187 

-0.0836 

(0.0685) 

(0.1035) 

Portfolio credit risk dummy 1 

Portfolio credit risk dummy 3 

-0.1431 

0.1604 

** 

* 

(0.0640) 

(0.0962) 

-0.0919 

0.0895 

(0.0650) 

(0.0983) 

Loan administration dummy 1 

Loan administration dummy 3 

-0.0838 

-0.0851 

(0.0581) 

(0.0885) 

-0.0726 

-0.1405 

(0.0579) 

(0.0891) 

Adjusted R Squared 56.0% 56.9% 57.3% 56.9% 

F Statistic 

5 percent critical value 

214.29 

1.67 

108.07 

1.46 

102.61 

1.46 

194.80 

1.67 

Sepecifications compared for 

F-statistic test that all 

survey terms are not significant, 

5 percent critical value 

Spec(1) and (2) 

4.47, 1.67 

Spec(3) and (4) 

2.40, 1.67 

* indicates significant at 10% level, ** indicates signifcant at 5% level and *** indicates significant at 1% level 

42 
 

 

 

 

 

 

 

 

Table 7. 

Total Nonperforming Assets Prediction (one year ahead) 

Ordinary Least Squares Regression 

FDIC Survey Data 1998-2001 

Specification 1 Specification 2 Specification 3 Specification 4 

Coefficient Coefficient Coefficient Coefficient 

Explanatory Variable Estimate (standard error) Estimate (standard error) Estimate (standard error) Estimate (standard error) 

Intercept 0.6817 *** (0.2174) 0.8790 *** (0.2340) 1.0790 *** (0.2415) 1.1988 *** (0.2266) 

Nonperforming assets (t-1) 0.7318 *** (0.0118) 0.6979 *** (0.0124) 0.6748 *** (0.0130) 0.68178 *** (0.0129) 

Equity (t-1) 0.0057 * (0.0034) 0.0061 * (0.0034) 0.0080 ** (0.0034) 0.0086 ** (0.0034) 

Allowance for loan losses (t-1) 0.0000 ** 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 * 0.0000 

Volatile liabilities (t-1) 0.0067 *** (0.0017) 0.0068 *** (0.0017) 0.0072 *** (0.0017) 0.0068 *** (0.0017) 

Liquid assets (t-1) -0.0085 *** (0.0019) -0.0087 *** (0.0019) -0.0085 *** (0.0019) -0.0080 *** (0.0019) 

Loans and securities with 

 maturities of 5 years or more (t-1) 0.0009 (0.0021) 0.0004 (0.0020) 0.0002 (0.0020) 0.0005 (0.0020) 

Gross charge-offs on loans & leases (t-1) 0.1432 ** (0.0582) 0.1466 ** (0.0579) 0.1407 ** (0.0577) 0.1342 ** (0.0576) 

Provisions for loan & lease losses (t-1) -0.2156 *** (0.0590) -0.2419 *** (0.0588) -0.2422 *** (0.0585) -0.2329 *** (0.0585) 

Income before taxes and 

extraordinary items (t-1) -0.0086 (0.0166) -0.0071 (0.0166) 0.0063 (0.0167) 0.0157 (0.0166) 

State personal-income growth, lag 1 -0.0748 *** (0.0109) -0.0689 *** (0.0108) -0.0698 *** (0.0108) -0.0766 *** (0.0107) 

State personal-income growth, lag 2 -0.0181 * (0.0106) -0.0230 ** (0.0105) -0.0248 ** (0.0105) -0.0212 ** (0.0105) 

State personal-income growth, lag 3 0.0249 * (0.0130) 0.01952 (0.0129) 0.0178 (0.0129) 0.02341 * (0.0128) 

State personal-income growth, lag 4 0.0197 * (0.0119) 0.0215 * (0.0118) 0.0210 * (0.0118) 0.0207 * (0.0118) 

State personal-income growth, lag 5 0.0374 ** (0.0133) 0.0000 0.0338 ** (0.0132) 0.0327 ** (0.0132) 0.0350 *** (0.0132) 

Asset component dummy 1 -0.4322 *** (0.0916) -0.5727 *** (0.0767) 

Asset component dummy 2 -0.2150 *** (0.0828) -0.3020 *** (0.0744) 

Survey lag (calldate - survey date) 0.0005 *** (0.0002) 0.0006 *** (0.0002) 

Concentrations of credit dummy 1 -0.0340 (0.0476) -0.0158 (0.0475) 

Concentrations of credit dummy 3 -0.0026 (0.0741) 0.0043 (0.0738) 

Fail-to-adjust pricing dummy 1 0.0990 (0.0602) 0.1056 * (0.0600) 

Fail-to-adjust pricing dummy 3 -0.0611 (0.1278) -0.0513 (0.1273) 

Principal reduction dummy 1 -0.1373 *** (0.0448) -0.1118 ** (0.0448) 

Principal reduction dummy 3 -0.0158 (0.1153) -0.0347 (0.1151) 

Written policies versus practices dummy 1 -0.1094 ** (0.0477) -0.0856 * (0.0477) 

Written policies versus practices dummy 3 -0.0179 (0.1155) -0.0251 (0.1151) 

Current underwriting practices dummy 1 0.0515 (0.0544) 0.0549 (0.0543) 

Current underwriting practices dummy 3 0.1319 (0.1547) 0.1048 (0.1542) 

Portfolio credit risk dummy 1 -0.0402 (0.0494) 0.0407 (0.0512) 

Portfolio credit risk dummy 3 0.2625 * (0.1407) 0.1888 (0.1437) 

Loan administration dummy 1 -0.1969 *** (0.0471) -0.1633 *** (0.0474) 

Loan administration dummy 3 0.0301 (0.1142) 0.0054 (0.1143) 

Adjusted R Squared 56.4% 57.3% 57.6% 57.3% 

F Statistic for test that all variables are significant, 346.31 174.06 165.25 314.71 

5 percent critical value 1.67 1.46 1.46 1.67 

Sepecifications compared for Spec(1) and (2) Spec(3) and (4) 

F-statistic test that all 6.34, 1.67 3.06, 1.67 

survey terms are not significant,

 5 percent critical value 

* indicates significant at 10% level, ** indicates signifcant at 5% level and *** indicates significant at 1% level 

43