FDIC Banking Review Recently Chartered Banks’ Vulnerability to Real Estate Crisis by Chiwon Yom *
Even while the U.S. banking industry continues to
consolidate and the number of banks continues to shrink, de novo banking
activity remains vigorous. De novo banks
play important roles in preserving competition in the market, providing credit
to small businesses (DeYoung, Goldberg, and White ), and promoting an
entrepreneurial spirit (Brislin and Santomero ).1 At the same time, however, these fledgling
institutions are financially fragile and more susceptible to failure. Although they are sound in their early years,
with large capital cushions and low levels of nonperforming loans, their
financial condition typically deteriorates as capital reserves and the quality
of their loans move toward industry levels but earnings remain low. Furthermore—and this may not be widely
known—new banks are vulnerable to real estate crises because they concentrate
heavily in real estate loans. The extent
of new banks’ exposure to the real estate market is reflected in their poor
ratings on the Real Estate Stress Test (REST).
This model measures the severity of a bank’s exposure to real estate
lending, projecting what would happen to a bank if the real estate market
experienced a downturn similar to the New England real estate crisis in the
The FDIC closely monitors recently chartered banks and
thrifts. For purposes of offsite
monitoring, the FDIC defines young banks as commercial banks and thrifts that
are eight years old or younger based on studies showing that new banks need
more than three years to fully mature
(DeYoung , DeYoung and Hasan ).
Newly chartered banks tend to be small, and roughly 80 percent of all
young banks are located in metropolitan statistical areas (MSAs). This study examines these young banks.
Specifically, it examines the vulnerability
of these young banks to real estate problems: how their financial condition
evolves over time, the degree of risk they bear because of their real estate
lending, how they compare with established banks in this respect, and what explains
the heightened vulnerability of young banks to real estate crises.
For our benchmark group we choose small established banks,
defined for this study as institutions that are more than eight years old, have
assets of less than $300 million, and are located in MSAs. In addition, our benchmark group excludes
special-purpose institutions, such as credit card banks and banks with
extensive trust operations.
This study is preceded by a review of the literature and
followed by a summary and conclusion.
The Purpose of This Study in Relation to the Literature
Recent studies have furthered our understanding of newly
established banks by examining the determinants of bank start-ups and
identifying the factors that determine the performance of de novo banks. De novo banking activity is more likely
during periods of favorable economic conditions (Dunham ) and in areas
that have undergone merger activity (Dunham , Berger, Bonime, Goldberg,
and White , Seelig and Critchfield ). Moreover, new banks tend to locate in urban
areas (DeYoung ) and in markets where economic growth is high (Moore and
Among researchers who identify the factors that determine
the performance of de novo banks, DeYoung (2003) finds that the relationship
between external conditions (for example, intense competitive rivalry or slow
economic growth) and higher failure rates is more systematic for the de novo
banks than for established banks.
Hunter, Verbrugge, and Whidbee (1996) find that adverse economic
conditions have contributed to the failure of recently chartered thrifts.
Endogenous factors have also been found to play a
significant role in the performance and survival of newly chartered banks. Hunter, Verbrugge, and Whidbee (1996) find
that credit risk, low capital stocks, and cost inefficiencies have contributed
to the failure of de novo banks. Hunter
and Srinivasan (1990) find that differences in operating costs, credit policy,
and leverage account for most of the performance variations among the sample
banks relative to the established target group during the early years of
operation. Arshadi and Lawrence (1987)
find that operating costs, deposit growth, composition of loan portfolios, and
deposit pricing are important in determining the performance of newly chartered
banks; they conclude that the performance of new banks is a function of
Other studies relate the performance of de novo banks to the
banks’ business strategies and risk management.
Brislin and Santomero (1991) find that de novo banks in the third
Federal Reserve district (Pennsylvania, New Jersey, and Delaware) tend to
concentrate in single types of loans—for example, real estate loans—and they
caution that because of the lack of diversification, such strategies increase
portfolio risks. Gunther (1990)
attributes the large number of failures of new Texas banks in the 1980s to the
banks’ aggressive strategies, such as concentrating in commercial and
industrial (C&I) loans, maintaining low liquidity, and relying heavily on
purchased funds. Hunter and Srinivasan
(1990) find that real estate lending has consistent and significant effects on
the performance of new banks in the later years of operation.
The present study adds to the literature by exploring the role
of real estate lending in relation to the performance and lending strategies of
banks established between 1995 and 2003.
In the latter half of the 1990s, after severe problems in the banking
industry during the 1980s and early 1990s, de novo banking activity picked
up. Table 1 reports the number of banks
and savings institutions chartered in the United States between 1995 and 2003
that were not affiliates of a holding company.3 The table disaggregates de novo institutions
by state and type of charter (national bank charter, state bank charter, and
savings institution charter). During
this period, the five states with the highest number of new start-ups were
Florida, Georgia, Illinois, California, and Texas at 121, 96, 81, 85, and 64,
respectively. State charters, at 877,
constituted the largest share of new institutions (69.7 percent); there were
257 national charters (20.4 percent) and 125 new savings institutions (9.9
For a number of reasons, this new batch of de novo
institutions may differ in performance and viability from the de novo banks in
the 1980s. First, economic conditions
are more favorable now than they were in the 1980s, when many banking
institutions operated under severe regional recessions. Second, regulation and supervision are more
stringent now. The Federal Deposit
Insurance Corporation Improvement Act of 1991 (FDICIA) requires all
institutions, including those with national charters, to apply formally to the
FDIC for federal deposit insurance.
Before FDICIA, the FDIC granted deposit insurance to national banks as a
matter of law once the Office of the Comptroller of the Currency had approved a
bank’s charter. In contrast, the
chartering of state banks depended heavily on whether the FDIC approved the
bank’s application for insurance: without the FDIC’s approval of the
application, a state was unlikely to grant a bank charter.
Third, once chartered, a new bank is now supervised more
closely by its regulatory agency. The
FDIC conducts a limited-scope examination at each newly chartered state
nonmember bank within the first six months of operation, followed by a
full-scope examination within the first twelve months. Subsequently, each state nonmember bank is
examined each year until the end of the third year, although the FDIC may
alternate with the state supervisors in conducting the examination.4 Similarly, the Federal Reserve Banks examine
newly chartered state member banks at a higher frequency compared to
established banks, conducting full-scope examinations for safety and soundness
at newly chartered state member banks at 6-month intervals (whereas established
banks are examined every 12 to 18 months) and continuing to schedule exams at
this frequency until the bank receives a strong composite CAMELS ratings (a rating
of 1 or 2) in two consecutive exams (DeYoung ).5
Fourth, new banks are required to maintain a higher capital
ratio than their established counterparts.
Normally the FDIC requires all proposed depository institutions to start
with enough capital to provide “a Tier 1 capital to assets leverage ratio (as
defined in the appropriate capital regulation of the institution’s primary
federal regulator) of not less than 8.0% throughout the first three years of
operation.”6 These temporary
capital requirements are meant to ensure that new banks have enough capital
cushion to absorb the negative earnings and rapid asset growth of the first few
Finally, bank supervisors typically place restrictions on
dividend payouts by new banks, limit the amount of debt that new bank holding
companies can issue, and require new banks to maintain minimum levels of
loan-loss reserves (DeYoung ).
The Life Cycle of the Performance of Young Banks
We begin our examination of young banks’ exposure to the
real estate market by describing the evolution of the performance of young
banks. To document this evolution, we
group young banks chartered between 1995 and 2003 into classes according to the
year they are chartered. For example,
new banks chartered in 1997 and 1998 are grouped into Class 1997 and Class
1998. Grouping young banks this way is
motivated by recent studies that have found that newly chartered banks follow a
distinct life-cycle pattern (DeYoung [1999, 2000]).
Figures 1 through 5 graph the median values of financial
ratios for each of our classes of young banks, starting when the banks are four
quarters old (the flow variables are four-quarter sums). For each ratio, the financial performance of
all the classes of young banks is compared with the median 2—the median
financial ratio of all institutions with a CAMELS composite rating of 2—serving
as an industry benchmark.
The figures show that in the early years of young banks, the
banks’ financial ratios follow similar time paths regardless of the year of chartering. Figure 1 shows that the median bank of each
class earned negative profits in the first few years. But although the median banks start to earn
profits after about two years, they continue to underperform established banks
(the median 2). In the early years,
however, young banks’ negative or low earnings are offset by a large initial
capital and low nonperforming assets: as figures 2 and 3 show, in the first few
years young banks have very high capital and very few nonperforming loans. For instance, in their fourth quarter since
establishment, the median equity-to-assets ratios for Class 1995, Class 1998,
and Class 2000 are 17.40 percent, 18.46 percent, and 18.77 percent,
respectively. This is substantially
higher than the median 2 equity-to-assets ratio of 8.96 percent. Similarly, the median
nonaccruing-loans-to-total-assets ratio is zero for all classes of young banks
in their fourth quarter, compared with 0.24 percent for the median 2. A number of years after having been
chartered, however, young banks experience financial deterioration, as their
capital cushions are depleted by low earnings and fast growth. Figure 4 shows that the median asset growth (annualized) of young banks is very high in the first few years.
But as the rapid rise in nonaccrual loans indicates, assets
begin to show signs of trouble. Notably,
young banks’ performance begins to deteriorate for the most part after the
third year (12th quarter), the age when supervisors stop paying close attention
to these institutions. The poor
performance continues for a number of years until these banks reach full
maturity and perform much like established banks. Young banks’ reliance on noncore funds, too,
remains high up to the eighth year.
Figure 5 shows that throughout the sample period, young banks have a
higher median ratio of noncore-funds-to-total-assets than the median 2.
These findings are consistent with those of studies that
examined the performance trend of new banks chartered in the 1980s. Using the sample of new banks chartered
between 1980 and 1985, DeYoung and Hasan (1998), and DeYoung (2000), conclude
that it takes many years for de novo banks to reach full maturity and perform
as well as established banks. In fact,
DeYoung and Hasan (1998) find that it takes nine years for new banks to become
as efficient, in terms of profitability, as established banks.
The Real Estate Exposure of Young Banks Compared with That of Established Banks
We have seen that young banks are financially fragile. This is well known. What is less well known is that they
concentrate heavily in risky assets—more heavily than established banks
do. Table 2 reports the median REST
ratings of young and established banks across time and the number of banks in each
of the two groups, and figure 6 represents the two “median” columns
graphically. As figure 6 shows, the
median REST ratings of young banks are consistently worse than those of
established banks. A formal test using
Kendall’s rank correlation confirms that the REST ratings of young banks are
worse (with statistical significance) than those of established banks. Like Pearson’s correlation coefficient,
Kendall’s rank correlation takes values between –1 (perfect negative
correlation) and +1 (perfect positive correlation). Moreover, figure 6 shows that the REST
ratings of both young and established banks steadily worsened in the latter
half of the sample period—yet the gap between the median ratings widened. It can be inferred, therefore, that the REST ratings
of young banks deteriorated more rapidly than those of established banks.
Figures 7 and 8, whose solid lines trace the percentage of
young and established banks with poor REST ratings over time, show the
percentages of both young and established banks with REST ratings of 4 or 5
rose between 1993 and 2004, but at the same time, the percentage of young banks
with poor ratings became higher. It rose
from 21 percent in 1993 to 77 percent in 2004, whereas the percentage for
established banks with poor ratings rose from 8 percent in 1993 to 40 percent
in 2004. These figures show the extent
to which young banks are more vulnerable to the stress of a real estate crisis
than their established counterparts are.
(Parenthetically, figures 7 and 8 also trace the percentage
of young and established banks with poor CAMELS composite ratings. The broken lines in these figures show the
percentage of banks with a CAMELS rating of 4 or 5. During the period 1993–2004, when the percentage
of institutions with poor REST was rising, the percentage of institutions with
poor exam ratings was falling. The
contrast between the trend in the health of the banking industry and the trend
in the industry’s risk exposures to real estate lending is consistent with the
high cyclicality of the real estate market.
During periods of favorable economic conditions, the real estate market
expands, and meeting the increasing demand for real estate loans leads banks to
The reason the REST ratings of young banks are worse than
the ratings of established banks is that the kinds of real estate lending done
by young banks are riskier than the kinds done by established banks. Table 3 shows that as of December 2004, young
banks tended to have more commercial and industrial (C&I), construction and
development (C&D), and nonresidential real estate loans—the three types
generally considered risky.
Specifically, the new institutions’ median ratio of C&I loans to
total assets was roughly twice that of the established peer: young banks’ 11.85
percent versus established banks’ 6.59 percent.
Similarly, nonresidential real estate lending made up a higher
percentage of total assets for new institutions than for established
banks. And most importantly, a typical
new bank had 8.58 percent of total assets in construction loans—more than twice
the percentage for the established peer.
Previous studies have found construction and development lending to be
the primary risk factor of real estate crises because the success of
construction loans is highly dependent on the future of the real estate market
(Collier, Forbush, and Nuxoll ) and because commercial real estate
projects are highly leveraged and more sensitive to changes in market
conditions (Freund, Curry, Hirsch, and Kelley ).
In contrast, the relatively safer real estate 1–4 family
loans make up a smaller share of assets for young banks than for established
As table 3 also shows, the comparison between young and
established banks holds for rate of growth and reliance on noncore funds. Young banks grow more rapidly than
established banks: a typical young bank grows at the rate of 22.32 percent
annually—roughly four times the median growth rate of established banks. And to fuel such rapid growth, young banks
rely more heavily on noncore funds, which are expensive sources of funds and
the first to be demanded in times of stress.
Noncore liabilities make up 24.72 percent of total assets for a typical
young institution, compared with 17.33 percent for established banks.
In sum, the statistics presented in table 3 suggest that the
poor REST ratings of new banks are likely to be attributable to higher
concentrations in construction, C&I, and nonresidential real estate loans;
to rapid growth; and to heavy reliance on noncore funds.
Possible Explanations for Young Institutions’ More Risky Lending
How does one explain young institutions’ heavier engagement
in riskier real estate lending activities?
One might attribute it to the geographic location of these
institutions. Young banks tend to start
up in areas of rapid economic and population growth (Moore and Skelton )
and therefore young banks may simply be meeting the local market’s growing
demand for real estate loans.
Alternatively, perhaps young banks simply engage in more aggressive risk
To evaluate these two possible explanations, we first
determined whether young banks in fact are concentrated in rapidly growing
states; we then compared the average REST rating of each state with the
percentage of new banks in the state. On
the one hand, if states with large percentages of new banks have high average
REST ratings, that finding will support the first explanation. For if the geographic location of young
institutions is important in explaining the institutions’ poor REST ratings,
other established institutions in the same states will also have poor ratings,
and the REST rating of a typical bank in these states will be high. On the other hand, if states with large
percentages of new banks do not show high average REST ratings, the second
explanation—more aggressive risk management—is the more likely. For if young institutions’ poor REST ratings
are unrelated to their geographic locations, typical banks in the same states
will not necessarily have a poor REST rating.
And if aggressive risk management is the answer, what factors might
Table 4 reports, by state, the number of young institutions,
the total number of institutions, the ratio of young institutions to total
institutions, and the median REST rating for the state, based on December 2004
data. In the aggregate, young banks make
up 13.3 percent (1,197 out of 8,975) of all banks. But rather than being evenly distributed,
young banks are concentrated in a few states.
For instance, in Nevada and Arizona young banks make up more than 50 percent
of all banks, but Alaska, Hawaii, and Vermont have no young banks.
Table 4 also shows that states with large percentages of
young banks tend to have poor median REST ratings. Arizona, where young banks constitute 64
percent of all banks, has the worst median REST rating—4.98. Seven of the ten states with the largest
percentages of young banks have ratings worse than 4.
A formal statistical test—again, Kendall’s rank
correlation—confirms the positive correlation noted above between the ratio of
new to all banks in a state and the median REST rating for the state. There is strong evidence that states with
larger percentages of young banks tend to have worse median REST ratings. The rank correlation between these two
variables is 0.47 and is highly significant.
This result is consistent with the first explanation for
young banks’ relatively heavier engagement in real estate lending—that the
geographic location of these banks is an important contributor to their poor
REST ratings. As earlier studies have
showed, young banks are concentrated in urban and rapidly growing markets
(Moore and Skelton , DeYoung ), and it is plausible that in such
markets there are growing amounts of deposits and increasing demands for loans,
including commercial and real estate loans.
By supplying the loan demands of the local market, both young and
established banks lend more heavily to the real estate sector. Consequently, banks in rapidly growing states
have poor REST ratings.
Risk Management: Young Banks vs. Established Banks
Although geographic location—a heavy concentration in rapidly
growing markets—can be considered an explanation for young banks’ poor REST
ratings, it may not necessarily offer a full explanation. To explore whether young and established
institutions engage in similar lending activities in high-growth states, we
compared the loan portfolio composition of young banks in three high-growth
states with that of established banks in the same states. As noted above, young banks are predominantly
small and urban, so the established institutions with which we compared them
are small and located in metropolitan statistical areas.
Three states with relatively large percentages of young
banks are Florida, Georgia, and New Jersey.
In Florida, the percentage is 40.6; in Georgia, 28.5; and in New Jersey,
28.4. Moreover, in these states the
number of institutions, too, is relatively large, so statistical tests can be
performed. Florida and Georgia have
median REST ratings worse than 4, but a typical bank in New Jersey has a REST
rating of 2.9.
To test whether, in these three states, young banks’ loan
ratios are ranked worse than the ratios of established banks, we used Kendall’s
rank correlation statistic. (Rank
correlation is estimated for each bank’s loan ratio and a dummy variable, valued
1 if a young bank and 0 if an established bank.) The results of the rank correlation test are
reported in table 5.
In our three states, young banks generally use riskier
lending strategies. They tend to devote
greater shares of their assets to loans, and they concentrate in riskier loans,
such as C&I and construction loans.
Moreover, they grow more rapidly than established banks.
In New Jersey, young banks had a statistically significantly
higher concentration of riskier loans (C&I loans, C&D loans, and
nonresidential real estate loans). And
asset- and loan-growth rates were significantly higher for young banks. In contrast, young banks had lower
concentrations of safer loans (for example, loans to municipalities and 1-4
family real estate loans), which help shield banks from downturns (Collier,
Forbush, and Nuxoll ).
In Florida the picture was similar. Young banks had a greater percentage of their
assets in a riskier loan type (C&D loans).
They grew more rapidly (higher asset growth) and relied more on noncore
liabilities. And they had fewer loans to
depository institutions and municipalities.
In Georgia, young banks concentrated more heavily than
established banks in C&I and multi-family loans and grew more rapidly, but
their ratio of construction loans did not differ significantly from the ratio
for established banks. Nevertheless,
young banks in Georgia had a higher percentage of assets devoted to
construction loans than did the young banks of New Jersey and Florida. In Georgia young banks devoted more than 20
percent of their assets to construction loans, whereas the comparable
percentages in Florida and New Jersey were 10.43 percent and 5.18 percent,
Explanations for Aggressive Risk Management
These findings suggest that geographic location alone does not
fully explain young banks’ concentration in riskier loans and greater
vulnerability to real estate crises.
Even within rapidly growing states, young banks pursue more aggressive
lending strategies than established banks.
We now explore other factors that may explain young banks’ pursuit of
One such factor may be young banks’ desire for rapid growth. Arshadi and Lawrence observe that growth in
the first few years is vitally important for new banks’ survival and sound
performance (Arshadi and Lawrence ).
With low business volume, new banks are likely to spend proportionately
more on salaries and overhead expenses,7 and to become profitable,
they need to grow and use their facilities and staff more efficiently. This need may drive young banks to grow
rapidly using noncore liabilities and relaxed underwriting standards.
Another reason that young banks may be attracted to the
riskier assets is that these assets tend to generate immediate income. For instance, commercial real estate loans
have large up-front fees. A third
possible reason is that specialized business strategies require expertise in
fewer areas and may help young institutions find their market niche (Brislin
and Santomero ).
Fourth, young banks’ concentration in risky activities may
result from the growth constraints they encounter. Unlike their established counterparts, young
banks lack established customer relationships and market recognition. As a result, their growth is constrained by
limitations on deposits and on good investment opportunities. Young banks may be left to lend to the pool
of borrowers with poor credit and to finance highly risky ventures. Economists refer to this phenomenon as
Whatever the rationale for the aggressive lending strategies
undertaken by young banks, they are particularly vulnerable to downturns in the
real estate market, as the experience of the new Texas banks in the 1980s
demonstrates.8 New Texas
banks in the early 1980s were heavily concentrated in growing markets;
according to Gunther, new banks made up 54 percent of the banks in the five
largest and most rapidly growing markets in Texas (Austin, Dallas, Fort
Worth–Arlington, Houston, and San Antonio).
Gunther’s analysis suggests that new banks pursued riskier strategies,
such as concentrating on riskier loans and relying more heavily on wholesale
After oil prices plummeted in 1986, Texas entered a
recession and experienced a real estate crisis.
Although many banks suffered, it was evident that the recession had an
especially great effect on de novo banks.
In the subsequent four years 39 percent of de novo banks failed, but
only 21 percent of established banks.
Finding that new banks with capital levels similar to the levels of
established banks and risk did not fail at significantly higher rates than
mature banks, Gunther concludes that new banks’ relatively higher risk postures
led to the high incidence of failure.
The experience of the new Texas banks offers a scenario of
what could happen to the current vintage of young banks if the markets now
experiencing rapid growth—and where there are many young banks—were to
experience busts. Young banks in these
states would be likely to experience greater failures and losses. Of course, one must be cautious when
extrapolating from a banking experience in the 1980s to a banking experience
today, for even if economic conditions were to become comparable to those in
the 1980s, the regulatory environment, as noted above, differs greatly from
what it was in the 1980s.
Summary and Conclusion
It is well known that new banks are financially fragile and
more susceptible to failure than established banks. What is less well known is that new banks
have a substantial exposure to the real estate market. The extent of this exposure is reflected in
the poor REST ratings of new banks. For
example, in December 2004 the median REST rating of young banks was 4.54,
whereas the median for established banks was 3.03. This difference is statistically significant.
Part of the explanation for young banks’ vulnerability to a
real estate crisis is geographic location.
Young banks tend to locate in rapidly growing markets, where economic
activity is greater and the demand for riskier real estate loans (such as
C&D loans and C&I loans) is also greater.
But if geographic location fully explained young banks’
vulnerability to a real estate crisis, the established banks in the same market
would use strategies roughly similar to those used by the young banks. Our research shows that they do not. A closer examination of young banks in three
rapidly growing states—Florida, Georgia, and New Jersey—suggests that the young
banks in those states use riskier lending strategies than their established
The disproportionate use by young banks of the risky
strategies may have a number of explanations.
Young banks may undertake aggressive business strategies in order to
grow rapidly, bolster low earnings, and become profitable. More importantly, their heavy concentration
in risky loans may reflect the severe problem of adverse selection that they
encounter: lacking a well-established customer base, new banks may find that a
disproportionately large share of the loan applications they receive are from
borrowers with risky ventures who have been turned down by other banks. In other words, the financial vulnerabilities
of young banks may in fact be augmented by these institutions’ asset composition.
Past experiences hint at the extent to which adverse
episodes in the real estate market could affect these fledgling
institutions. Thus, regulators are
further motivated to closely monitor not only the banks’ performance but also
their risk management.
Arshadi, Nasser, and Edward C.
Lawrence. 1987. An Empirical Investigation of New Bank
Performance. Journal of Banking and
Finance 11, no. 1:33–48.
Berger, Allen, Seth D. Bonime,
Lawrence G. Goldberg, and Lawrence J. White.
The Dynamics of Market Entry: The Effects of Mergers and Acquisitions on
Entry in the Banking Industry. Journal
of Business (forthcoming).
Brislin, Patricia, and Anthony M.
Santomero. 1991. De Novo Banking in the Third District. Federal Reserve Bank of Philadelphia Business
Review (January/February): 3–10.
Collier, Charles, Sean Forbush, and
Daniel Nuxoll. 2003. Evaluating the Vulnerability of Banks and
Thrifts to a Real Estate Crises. FDIC
Banking Review 15, no. 4:19–36.
Critchfield, Tim, Tyler Davis, Lee
Davison, Heather Gratton, George Hanc, and Katherine Samolyk. 2004.
Community Banks: Their Recent Past, Current Performance, and Future
Prospects. FDIC Banking Review
16, no. 3:1–56.
DeYoung, Robert. 1999.
Birth, Growth, and Life or Death of Newly Chartered Banks. Federal Reserve Bank of Chicago Economic
Perspectives 23, no. 3:18–35.
2000. For How Long Are Newly
Chartered Banks Financially Fragile? FRB
Chicago Working Paper 2000-09. Federal
Reserve Bank of Chicago.
2003. De Novo Bank Exit. Journal of Money, Credit, and Banking
35, no. 5:711–28.
DeYoung, Robert, Lawrence G. Goldberg,
and Lawrence J. White. 1999. Youth, Adolescence, and Maturity of Banks:
Credit Availability to Small Business in an Era of Banking Consolidation. Journal of Banking and Finance 23, no.
DeYoung, Robert, and Iftekhar
Hasan. 1998. The Performance of De Novo Commercial Banks:
A Profit Efficiency Approach. Journal
of Banking and Finance 22, no. 5:565–87.
Dunham, Constance R. 1989.
New Banks in New England. New
England Economic Review, 30–41.
Federal Deposit Insurance Corporation
(FDIC). 1997. History of the Eighties—Lessons for the
Future. Vol. 1. FDIC.
Gunther, Jeffery W. 1990.
Financial Strategies and Performance of Newly Established Texas
Banks. Federal Reserve Bank of Dallas Financial
Industry Studies (December): 9–14.
Hunter, William C., and Aruna
Srinivasan. 1990. Determinants of De Novo Bank
Performance. Federal Reserve Bank of
Atlanta Economic Review 75, no. 2:14–25.
Hunter, William C., James A.
Verbrugge, and David A. Whidbee.
1996. Risk Taking and Failure in
De Novo Savings and Loans in the 1980s. Journal
of Financial ServicesResearch, 10:235–71.
Keeton, William, Jim Harvey, and Paul
Willis. 2003. The Role of Community Banks in the U.S.
Economy. Federal Reserve Bank of Kansas
City Economic Review 88, no. 2:15–43.
McDill, Kathleen. 2004.
Resolution Costs and the Business Cycle.
Working Paper 2004-01. FDIC.
Moore, Robert R., and Edward C.
Skelton. 1998. New Banks: Why Enter When Others Exit? Federal Reserve Bank of Dallas Financial
Industry Issues (Q1), 1-7.
Nuxoll, Daniel A., John O’Keefe, and
Katherine Samolyk. 2003. Do Local Economic Data Improve Off-Site
Bank-Monitoring Models? FDIC Banking
Review 15, no. 2:39–53.
Seelig, Stephen, and Timothy
Critchfield. 2003. Merger Activity as a Determinant of De Novo
Entry into Urban Banking Markets.
Working Paper 2003-01. FDIC.
* The author is a senior financial economist in the Division of Insurance and Research at the Federal Deposit Insurance Corporation. The author would like to thank Daniel A. Nuxoll, George Hanc, Kenneth Jones, Valentine V. Craig and Christine E. Blair for valuable comments and suggestions.
1 In this article, the terms banks and institutions refer to all insured institutions-commercial banks, savings banks and thrifts.
2 See Collier, Forbush, and Nuxoll (2003). The stress test was developed on the basis of the New England real estate crisis in the 1990s, and information from a bank's balance sheet and income statement are used to rate the institution. The REST ratings are directly comparable to CAMELS ratings; a REST rating of 1 indicates least vulnerable to a real estate crisis, and a rating of 5 indicates most vulnerable. The REST model is part of the FDIC's offsite monitoring system and is used to help identify and monitor the institutions that are most vulnerable to a real estate crisis.
3 I thank Tim Critchfield at the FDIC's Division of Insurance and Research for providing these data.
4 I thank Don Hamm at the FDIC's Division of Supervision and Consumer Protection for referring me to the Manual of Examination Policies, Section 1.1 Basic Examination Concepts and Guidelines.
5 CAMELS is an acronym for Capital, Asset quality, Management, Earnings, Liquidity, and market Sensitivity.
6 The FDIC Statement of Policy on Applications for Deposit Insurance.
7 For instance, Brislin and Santomero (1991) note that overhead expenses account for 92 percent of total expenses in the first quarter of operation at a typical de novo bank in the third Federal Reserve district (Pennsylvania, New Jersey, and Delaware).
8 Gunther (1990) tracks the failure rates during the period 1986 to 1989 of banks that had been established between 1980 and 1985. Accordingly, new banks in his study were ten years old and younger.