Working Paper Series

The Entry, Performance, and Risk

Profile of De Novo Banks

Yan Lee

Federal Deposit Insurance Corporation

Chiwon Yom

Federal Deposit Insurance Corporation

Current Version: April 2016

FDIC CFR WP 2016-03

fdic.gov/cfr

NOTE: Staff working papers are preliminary materials circulated to stimulate discussion and critical

comment. The analysis, conclusions, and opinions set forth here are those of the author(s) alone and do

not necessarily reflect the views of the Federal Deposit Insurance Corporation. References in publications

to this paper (other than acknowledgment) should be cleared with the author(s) to protect the tentative

character of these papers.
The Entry, Performance,

and Risk Profile of De Novo Banks*

Yan Lee

Federal Deposit Insurance Corporation

Washington, DC 20429

Chiwon Yom

Federal Deposit Insurance Corporation

Washington, DC 20429

April 7, 2016

Abstract

From 2000 to 2008, 1,042 new community banks were chartered. Despite the largest 

financial crisis in decades, the vast majority of these new banks either survived or were

financially healthy when merged with another bank. In this paper we investigate whether 

the patterns of community bank de novo formation and failure for this cohort that formed 

just prior to the recent financial crisis were the same as for new institutions from earlier 

periods. Similar to previous periods, we find that many of the new banks chartered were 

in markets that experienced bank mergers or acquisitions, and were large and growing. 

Consistent with a “life-cycle theory” of de novos, compared with small established banks, 

recent de novos were financially fragile and failed at higher rates during the crisis. 

Discrete-time hazards models confirm that failed de novos from the recent period 

invested heavily in construction and development lending prior to the crisis, similar to the 

concentrated loan portfolios exhibited by earlier cohorts. 

*The views expressed here are solely of the authors and do not necessarily reflect the 

views of the Federal Deposit Insurance Corporation. 
1

In the period before the recent crisis, 1,042 de novo community bank institutions were 

chartered, of which 133 eventually failed. In this paper, we investigate whether the 

reasons for recent de novo entry and failure conform to what has been established in the 

literature, and whether the recent crisis period failures were similar to or different from 

those of past crises. In particular, we document recent trends in community bank de novo 

chartering activity from 2000 to 2008, the years immediately preceding the recent 

financial crisis. Using vintage analysis, we present characteristics of these de novo 

banks, including growth, earnings, capital positions, and loan performance, to determine 

recent de novo attributes at each year of their life cycle. Then we compare differences in 

financial characteristics among these de novos based on whether they eventually failed, 

were acquired, or survived. 

Previous studies show that de novo entry occurs when economic conditions are favorable, 

and where merger and consolidation activity reduced the number of banks. For example, 

Berger et al. (2004) examine entry activity in more than 2,700 local banking markets in 

the United States for the 1980 to 1998 period, and find that the probability of de novo 

entry is higher in markets that had experienced mergers or acquisitions in the previous 

three years, especially those involving large banks. Examining de novos that started in 

1997, Moore and Skelton (1998) find that de novos are more likely to start in states that 

experience rapid population growth, decreased unemployment, with a small number of 

small banks, and the lowest percentage of bank loans in small-business lending, and for 

urban markets, where there was less banking competition. For the 1977 to 1988 period, 

Amel and Liang (1997) find that entry was more likely to occur in large, growing, and 

urban markets. Similarly, Dunham (1989) finds that de novo entry in New England in 

the early 1980s was more frequent in states where bank profits were high, and where 

bank mergers were reducing the number of banks. 

DeYoung (1999, 2000, 2003) has developed and written extensively on a “life-cycle”

theory of de novo evolution. Studying the 303 new commercial banks chartered in 1985 

(DeYoung, 1999), he finds that new banks are unprofitable, but unlikely to fail given 

their large initial capital cushions. As these banks rapidly grow their assets via lending, 

their equity cushions decline to similar levels as established banks. While de novos have 

high capital cushions relative to benchmark banks for their first three years of life, their 

return on assets (ROA) do not approach established banks until about their 11th year.

1

 

Therefore, they tend not to fail during their early years of negative profit, but during their 

subsequent years of positive profit. DeYoung further confirmed this pattern when 

investigating the long-run (14-year) financial fundamentals and failure patterns for the 

1,664 commercial banks chartered between 1980 and 1985 (DeYoung, 2000; DeYoung, 

2003). However, this intertemporal pattern varies with economic conditions at the time 

of start-up, such that de novos’ fragility also depends on how their life cycle is positioned 

relative to the business cycle. De novos chartered just prior to the bank failure period of 

the late 1980s experienced extreme versions of this life-cycle pattern, whereas those 

chartered earlier had similar failure patterns as those of established banks.

 1 For de novos chartered between 1980 and 1994, DeYoung and Hasan (1998) find that it takes nine years 

on average for these new banks to reach the profit efficiency of established banks.
2

In addition to weak financial fundamentals and negative external economic conditions, 

lack of portfolio diversification and reliance on non-core funds were found to contribute 

to de novo fragility and failure.

2

 Looking at banks chartered between 1995 and 2003, 

Yom (2005) finds that these young banks had greater concentrations than established 

banks in riskier assets such as commercial and industrial (C&I), construction and 

development (C&D), and commercial real estate loans. Further, these banks relied more 

heavily on non-core funds, which are also more likely to be demanded in times of 

economic stress. Inadequate capital, economic stress, poor management, operating 

inefficiencies, as well as use of brokered deposits and rapid asset growth were found to 

contribute to failures of savings and loans de novos operating in the 1980 to 1986 period 

(Hunter, Verbrugge, and Whidbee, 1996). Gunther (1990) attributes the large number of 

failures of new Texas banks in the late 1980s to these banks’ aggressive strategies, such 

as concentrating in C&I lending, maintaining low liquidity, and relying heavily on 

purchased funds. 

As in the literature for earlier periods, we find that many community banks chartered in 

the most recent period were in markets that experienced bank mergers or acquisitions, 

and were large and growing.

3

 However, contrary to earlier new charters, these de novos 

were formed in less, rather than more, concentrated markets. Consistent with a life-cycle 

theory of de novos, compared with small established banks, these de novos were 

financially fragile and took many years to reach maturity, relying more on non-core

funding, and failed at higher rates during the recent financial crisis. Discrete-time hazard 

models confirm that the de novo banks that failed tended to be financially unhealthy and, 

similar to the concentrated loan portfolios exhibited by earlier cohorts, failed de novos 

from the most recent period invested heavily in C&D lending. We conclude, therefore, 

that de novo entry prior to and failure during the most recent crisis were similar in many 

regards to patterns found in the literature studying de novo banks chartered in 1970s to 

early 2000s.

4

From a deposit insurance fund perspective, de novo failures were a relatively minor 

contributor to the cost of the crisis. To the extent that the formation of new banks is 

important to the vitality of the community banking sector and to the local economies 

which they serve, the cost of de novo failures should be weighed against the economic 

benefits of allowing new banks to be formed. The new banks that failed were relatively 

small, averaging $216 million in assets.5 The cost of resolving these new bank failures 

 2 Core deposits are those funds collected by banks from regular customers in their geographic lending 

market, for example, through savings accounts. These are stable sources of funds that are acquired at 

relatively predictable costs. Non-core funds, such as federal funds purchased, Federal Home Loan Bank 

advances, and large time deposits, can be volatile sources of funds that can leave the bank on the basis of 

small rate differentials. 

3 De novo banks examined in earlier studies include those chartered from the 1970s to the 1990s. 4 At the same time, how new banks are chartered have changed compared with earlier periods. Before 

FDICIA, national banks and state member banks were granted Federal Deposit Insurance upon being 

chartered by the OCC and the Federal Reserve Banks while state nonmember banks needed the FDIC 

approval of insurance before a state granted a new charter. 

5 Bank balance sheet figures, including assets and total loans and leases, are obtained from regulatory 

Reports of Condition and Income, also known as “CALL Reports,” which are mandated under FFIEC 

reporting requirements.
3

was estimated to be $8.9 billion.6 As such, these banks represented 27 percent of all 

failures from 2008 to 2015, but only 14.3 percent of the total costs of failure. In contrast, 

a single, large institution, IndyMac, had estimated losses exceeding $12 billion. In 

addition, the remaining de novos have contributed to both maintaining a competitive 

banking system and to lending to their community. Without the new bank formation that 

took place from 2000 through 2008, there would have been 634 fewer FDIC-insured 

institutions as of September 2015, which would have represented a reduction of roughly 

10 percent from the actual population of 6,270 institutions at that time. At the end of the 

third quarter 2015, these 634 institutions held $214 billion in total loans and leases.7

 

These findings suggest that de novo banks’ weak performance and high failure rate in 

early years are balanced against the important roles they play, such as providing local 

small-business loans and increasing competition by curbing the exercise of market power

via entry. To promote de novo banks to become healthy, established institutions, it is 

important to monitor and supervise effectively their risk-taking behavior and enforce

good risk management practices. While it is beyond the scope of this paper to 

comprehensively evaluate the benefits of de novos against the costs of their failure, we 

instead provide context for the most recent crisis period in order to gain understanding in 

how to support new banks such that they can contribute to the vitality of the community 

banking sector and the local economies in which they serve.

The paper is organized as follows: Section I presents recent trends in de novo entry and 

models the determinants of entry. Using vintage analysis, Section II explores several 

aspects of de novo performance before and during the crisis. Section III uses a discretetime hazard model to explore which financial characteristics affect recent de novos’ 

probability of failure. Section IV concludes.

1. De Novo Entry

1.A Recent Descriptive Trends in De Novo Entry 

Our study examines the set of traditional de novo institutions that are also community 

banks. Not all newly chartered institutions are traditional de novo banks. Traditional de 

novo banks refer to newly formed institutions that have to build their deposit and loan 

customers from scratch. For instance, existing nonbank financial institutions, such as 

credit unions, can change charters to become newly chartered commercial banks, but are 

not de novo institutions for our purposes. This section describes the criteria used to 

determine when a “new” institution is a “traditional community bank de novo.” It then 

documents the volume and geographic location of these new institutions and their areas 

of lending specialization. 

 6 Costs of resolution are obtained from the FDIC’s Failure Transactions Database. 7 In comparison, SBA reported $118.8 billion total loans on their balance sheet for fiscal year 2015. 

https://www.sba.gov/sites/default/files/aboutsbaarticle/Agency_Financial_Report_FY_2015.pdf.
4

Between 2000 and 2008, 1,341 new institutions were chartered in the United States. Of 

these new banks, 225 were part of a multibank holding company at inception, which we

excluded from the traditional de novo population. Of the remaining institutions, 34 

began with a large level of assets (greater than $100 million). Bank-by-bank 

investigation of these 34 entities indicated that 19 were not traditional de novos. Of the 

89 de novo institutions that were not considered a community bank according to the 

FDIC’s research definition8

, we retain 34 that eventually appeared on the community 

bank list, three years after charter.9

 In total, we identified 1,042 institutions that we 

consider community bank de novos.

Figure 1 shows total community bank de novos, by year of charter, for these 1,042

institutions. The left axis shows the number of new charters each year, while the right 

axis shows the share of community bank de novos to total community banks. There are 

two peaks in community bank de novo chartering activity in our period of study. The 

first occurred in 2000, with 159 new charters. The second occurred in 2006, with 151 

new charters. Figure 1 shows that de novos are typically a small share of existing 

community banks, never exceeding 2 percent of all community banks in a given year. 

Figure 2 shows total de novo charters by year and primary regulator. The majority of 

community bank de novos are state-chartered and regulated by the Federal Deposit 

Insurance Corporation (FDIC). In total, 76.5 percent of the de novos in the period were 

chartered as state nonmember banks. The majority (83.6 percent) of these community 

bank de novos were headquartered in Metropolitan Statistical Areas (MSAs).10 Figure 3 

shows the share of de novos chartered in our study period, by the nine census regions. 

The largest share, 32.6 percent (339 banks), was chartered in the South Atlantic, which 

consists of Delaware, the District of Columbia, Florida, Georgia, Maryland, North 

Carolina, South Carolina, Virginia, and West Virginia. The Pacific region, consisting of 

Alaska, California, Hawaii, Oregon, and Washington, had the next largest share, at 15.6 

percent (162 banks). New England had the smallest share, with 2 percent (21 banks)

community bank de novo charters. 

Figures 4 and 5 explore chartering activity by state. Figure 4 shows the location of total 

charters over the period, by state. The states with the largest volume of de novos were 

California, Florida, Georgia, and Texas, with 123, 118, 112, and 72 charters, respectively. 

Figure 5 shows the share of de novos to the total number of community banks at the 

 8 The FDIC defines community banks as those banking organizations with assets less than $1 billion in 

2010. For banking organizations above that size threshold, other considerations are employed, such as a 

loan-to-assets ratio greater than 33 percent, core deposits-to-assets ratio greater than 50 percent, and the 

number of offices, location, and geographic dispersion of the bank’s offices. For the exact definition, see 

“FDIC Community Banking Study,” FDIC, December 2012, 

https://www.fdic.gov/regulations/resources/cbi/report/cbi-full.pdf. 9

 At inception, a de novo may not yet have the characteristics of a community bank, such as a high loan-toassets ratio. Of the 34 retained, 32 appeared on the community bank list one year after chartering, another 

appeared two years after chartering, and one more three years after chartering. The remaining 55 

institutions (89 less 34) did not appear on the community bank list even up to five years after chartering.

10 In comparison, 61 percent of the banks are state nonmember banks and 52 percent are headquartered in 

MSAs as of December 2008. 
5

beginning of the period. Arizona had the largest share of de novo-to-existing institutions, 

at 95.5 percent, followed by Nevada (66.7 percent), and California (47.1 percent). 

In summary, from 2000 to 2008, there were 1,042 newly chartered community bank de 

novos, with the majority FDIC-regulated and formed in MSAs. The South Atlantic region 

had the largest overall share of de novo chartering activity, while the individual states of 

California, Florida, and Georgia received the most new banks.

1.B Regression Analysis of Recent De Novo Entry 

In this section, we model the determinants of de novo entry in local markets from 2000 to 

2008 using regression analysis. Similar to earlier studies—Keeton (2000), Seelig and 

Critchfield (2003), Berger et al. (2004)—we model de novo entry into local areas by 

controlling for merger and acquisition (M&A) activity, local market conditions, and the 

financial conditions of existing banks in the local market.

In this analysis, data on bank mergers and acquisitions were acquired from the FDIC’s 

proprietary merger history database, while variables on bank deposits were derived from 

the FDIC’s Summary of Deposits (SOD) data, and bank financial ratios and portfolio 

shares were obtained from the Report of Condition and Income, also known as the 

regulatory CALL Report.11 Statistics for population and income growth were obtained 

from the Bureau of Economic Analysis, and for house price index growth from the 

Federal Housing Finance Agency.

The probability of a new bank entry in a county is

EEEEEii = α + β1MMMMMM aaa AAAAAAAAAA Ai, −1, −2, −3 + Xβ2 + ε (1)

The dependent variable, ENTRYit, measures whether a new bank’s headquarters was 

formed in county i in year t. We further assume that the error term ε has a standard 

logistic distribution. 

The explanatory variables are measured as the average of the previous three years (t-3, t2, and t-1) or as of the end of year t-1. Two variables are used to measure M&A

activities in counties: Merger Deposits and Acquisition Deposits. Merger Deposits is 

defined as a share of county deposits held by banks involved in mergers (where the 

charters of the banks involved in the merger are consolidated), averaged over the 

previous three years. Acquisition Deposits is defined as the county share of deposits held 

by banks involved in acquisitions (where the acquired bank retains its own separate 

charter, but its bank holding company ownership changes), averaged over the previous 

three years. 

 11 The SOD is collected by the FDIC from banks as of June 30 of each year, and contains branch level 

geographic and deposit information. The CALL Report is filed quarterly by all banking institutions and 

contains basic financial information related to banks’ balance sheets and income statements. 
6

Mergers and acquisitions can encourage de novo entry when they reduce small-business

lending and other banking services locally. This can occur when small banks focused on 

providing banking services in the local market merge with or are acquired by large banks 

or distant banks. Small banks tend to specialize in small-business lending and other 

services that emphasize personal customer contact. Keeton (2000) finds that relationship 

between entry and mergers is strongest when small banks were taken over by large banks 

or local banks by distant banks. His findings support that a positive relationship between 

mergers and entry by de novo banks can be attributed to reductions in small-business

lending or other services to customers with preferences for personal contact. Therefore, if

mergers and acquisitions result in these services being diminished because large banks

are less likely to focus on these types of services, then there is potential for de novo banks 

to meet these needs. 

Mergers and acquisitions can also lead to de novo entry when displaced senior managers 

of merged or acquired banks start a new bank. Or, instances where individuals repeatedly 

start new banks with the goal of selling them can result in a positive correlation between 

mergers and acquisitions and de novo entry. 

The impacts of mergers and acquisitions on de novo entry can differ because mergers 

tend to be more disruptive to a bank than an acquisition (Berger, Bonime, Goldberg, and 

White, 2004). For instance, mergers can replace the senior managers and board of 

directors of one of the banks, changing policies and procedures, and integrating financial 

and accounting systems. Acquisitions, in contrast, typically involve fewer organizational 

changes. As a result, we expect the estimated coefficients on Merger Deposits and 

Acquisition Deposits to be positive, and to potentially differ.

Other variables, X, in equation (1) include local market conditions, such as share of 

deposits owned by large banks, market concentration, market size, recent market growth 

rate, and financial ratios of existing local banks. 

Summary statistics on counties with and without de novo entry are reported in Table 1. 

The variables in Panel A describe a county’s bank market environment and structure. 

Shares of county deposits held by banks involved in mergers and acquisitions are both 

higher in counties with de novo entry. For instance, 4.2 percent of county deposits are 

held by banks involved in mergers in counties with de novo entry, compared with 2.6

percent in counties without de novo entry. Counties with de novo entry have 

substantially higher deposits held by large banks, higher population, and higher deposits,

compared with counties without de novo entry. Moreover, counties with de novo entry 

have higher population growth, bank deposit growth, state income growth, and House 

Price Index (HPI) growth compared with those without de novo entry. 

The variables in Panel B report the financial ratios of existing banks in a county. These

financial ratios are constructed as the deposit-weighted average value of financial ratios 

for the existing banks, with more than 50 percent of their deposits in the county. For 

instance, ROA refers to a deposit-weighted average ROA ratio of all existing banks in the 

county with over 50 percent of their deposits in the county.
7

Panel B shows that in counties with de novo entry, banks with the majority of their 

deposits in the county have lower average equity and non-performing loans to assets

ratios compared with banks in counties without de novo entry. Moreover, banks in 

counties with de novo entry tend to have higher concentration in both C&D and CRE 

loans. This finding is consistent with Yom (2005), who found that de novo banks were 

located in markets where an average bank was more susceptible to a real estate crisis 

resulting from higher concentration in real estate loans. For instance, the depositweighted average C&D loans-to-assets ratio is 9 percent in counties with de novo entry,

compared with 4.3 percent in counties without de novo entry. In contrast, there is no 

statistical difference in average single-family residential loans-to-assets ratio across 

counties with and without de novo entry. 

Table 2 reports the results of the de novo entry regression analysis. Column (1) reports 

the results supporting the hypothesis that bank mergers and acquisitions increase the 

probability of de novo entry into the market where mergers and acquisitions occur. The 

estimated coefficients on Merger Deposits and Acquisition Deposits are positive and 

statistically significant. Mergers and acquisitions increasing the probability of 

subsequent entry is consistent with the earlier studies’ findings (Keeton, 2000; Seelig and 

Critchfield, 2003; Berger, Bonime, Goldberg, and White, 2004). 

To evaluate the economic significance of Merger Deposits and Acquisition Deposits

variables on entry, we estimate the change in predicted probability of entry when there 

are no bank mergers or acquisitions in the county. To approximate the de novo entry 

probability of an average county, we compute the predicted entry probability using mean 

values of the explanatory variables. For our sample period, an “average” county has an 

annual entry probability of 1.64 percent. Calculating the probability of entry when the 

Merger Deposits value is its mean value and comparing that to when the value is zero 

yields a decrease in entry probability of 0.25 of a percentage point. Thus, when an 

average county has no mergers, the entry probability is lowered by 15.2 percent. A 

similar exercise using the Acquisition Deposits variable shows that the effect of bank 

acquisitions on de novo entry is smaller than that of mergers. Calculating the effect of a 

change in Acquisition Deposits from its mean value to zero lowers entry probability by 

0.14 of a percentage point, or by 8.5 percent. 

The model includes a variable Large Bank Deposit Share, which measures the share of 

deposits owned by large banks ($1 billion or more in assets) in the county. To the extent 

that large banking organizations focus less on small-business lending and other services

that emphasize personal contact, counties with a higher share of large bank deposits 

should attract de novo entry. However, Column (1) shows that the estimated coefficient 

on Large Bank Deposits is statistically insignificant. 

Market concentration, measured by the Herfindahl-Hirschman Index (HHI) on deposits, 

is negative and statistically significant, indicating that entry probability is higher for 
8

counties with a less concentrated deposit market.12 This finding is consistent with the 

presence of barriers to entry. 

We include two market size variables: log of county population and log of county

deposits. The estimated coefficients on these variables are positive and statistically 

significant, indicating that de novo banks are attracted to large markets. Conceivably, 

there is greater demand for services of de novo banks in large markets. 

To measure recent market growth, we include the population growth rate, deposit growth 

rate, state income growth rate, and housing price index growth rate. The estimated 

coefficients on population growth rate and state income growth rates are positive and 

statistically significant. This finding is consistent with the theory that de novo entrants 

are attracted to markets that are growing and expanding, with increasing demand for 

banking services. Although positive, the estimated coefficients on HPI growth rate and 

county deposit growth rate are statistically insignificant. 

The regression also includes census region dummy variables. The New England region is 

excluded as the base case. The estimated coefficients on the census region indicators are 

positive and statistically significant, indicating that the probability of de novo entry in 

other census regions differs from that in the New England region.13

Column (2) of Table 2 expands the model specification by including existing banks’ 

financial ratios, which are deposit-weighted average values of incumbent banks with over 

50 percent of their deposits in the county. We include measures of financial health of 

these local market banks, given that more efficient or financially sound incumbents 

should make stronger competitors, which is likely to deter entry. On the other hand,

strong financial performance of incumbent banks can signal favorable economic 

conditions, which would encourage entry. 

The coefficient on Nonperforming Loans is negative and statistically significant, which 

is consistent with favorable economic conditions in the county and good credit quality

customers, which encourages entry. An alternative explanation is that incumbent banks 

in counties with higher de novo entry concentrate in C&D loans and that interest reserves 

on these loans keep their nonperforming loans ratios low. Frequently, banks fund an 

interest reserve upon origination of a C&D loan which is used to pay interest on the loan 

until the construction and development project is completed. This feature can mask 

credit problems on C&D loans and result in low nonperforming loans ratios. 

The estimated coefficients on the ROA and Equity are statistically insignificant. 

Counties where existing banks have high concentration in C&D loans have higher de 

novo entry. This suggests that investors of new banks are attracted to markets with 

investment opportunities in C&D loans. At the same time, established banks’ high share

in C&D loans may be a proxy for growing markets. Established banks’ concentration in 

 12 The greater the county’s HHI, the more concentrated are its deposits. 13 The estimated coefficients on the census regional dummies are not reported.
9

other types of loans, commercial real estate (CRE), single-family residential, and smallbusiness loans do not affect entry probability with statistical significance. 

In summary, regression analysis on de novo entry in local markets shows that new bank 

start-ups are more likely to occur in counties that have had bank mergers or acquisitions. 

This finding is consistent with the proposition that new banks enter markets to fill in the 

gap of reduced banking services resulting from mergers or acquisitions. New banks are

also more likely to occur in large, growing, and less concentrated markets. Moreover, 

they are more likely to occur in counties where existing banks have low nonperforming 

loans and high concentrations in C&D loans. 

2. Vintage Analysis of Recent De Novo Bank Performances

In this section, we examine how the recent cohort of de novo banks perform once 

chartered, especially during and before the crisis. Here, all data presented come from 

regulatory CALL Reports. 

We group de novo banks by the year in which they are chartered, or class, because 

existing research suggests that newly chartered banks follow a distinct life-cycle 

pattern.

14 Following literature conventions, Figures 6 through 10 show the median values 

of financial ratios for each class of de novo banks. These figures also show the median 

ratios of a benchmark group of banks, which are comparable in size and located in 

metropolitan areas.15 We choose as a benchmark small established community banks 

older than 15 years, with asset sizes less than the asset size of de novo banks at the 95th

percentile at each quarter, and headquartered in a metropolitan statistical area. As shown 

in Section 1, de novo banks are predominantly urban, so the established institutions with 

which we compared them are located in metropolitan statistical areas.

Figure 6 shows that de novo banks grow rapidly in their first few years. For instance, 

during their first year of operation, the median one-year asset growth for the de novo 

bank classes ranged between 169 percent and 246 percent. In comparison, the median 

growth rate for established banks ranged from 3.8 percent to 6.6 percent during the same

period. Growth in the first few years is important for de novo banks’ survival and sound 

performance.16 With low business volume, these banks are likely to spend 

disproportionally more on salaries and overhead expenses. To become profitable and 

viable, these new institutions need to grow and use their facilities and staff efficiently. 

De novo banks lack established customer relationships and market recognition. Many of 

the recent de novo banks had growth aspirations that exceeded the availability of core 

deposits. To grow, the de novo banks studied here relied heavily on non-core sources of 

 14 See DeYoung (1999, 2000). 15 The comparison of de novos with small established banks as benchmark is the common practice in the de 

novo literature. For example, Dunham (1989) compares de novos with commercial banks in the same state 

with less than $200 million in assets, Hunter and Srinivasan (1990) and Moore and Skelton (1998) with 

banks with less than $100 million in assets, and DeYoung (1999, 2000) with banks with less than $500 

million in assets. 16 Arshadi and Lawrence (1987).
10

funds. Figure 7 shows that although initially de novo banks have lower non-core fundsto-assets ratios, the ratio quickly rises in the early stages of a de novo’s life cycle and 

remains higher than that of established banks.17 

Figure 8 shows that de novo banks lose money in early years. Even after de novo banks 

earn positive income, they continue to underperform relative to small established banks, 

often for many years. For the vintages of de novo banks studied here, their median 

earnings ratios lag those of small established banks. Especially during the recent crisis, de 

novo banks earned lower income than established banks. While the earnings of some 

early cohorts of de novo banks caught up in 2006 and 2007, they deteriorated during the 

crisis, often more severely than for small established banks. And, the de novo banks of 

later vintages, specifically those that were chartered at the beginning of the recession or 

were very young when the recession began, suffered lower earnings than de novo banks 

of earlier classes. This is consistent with DeYoung (2000), who finds that the 

performance of de novo banks also depends on the position of their formative years 

relative to the stage of the business cycle. 

Figure 9 shows that de novo banks have very high initial capital-to-assets ratios, with 

median ratios for de novo bank classes ranging from 48 percent to 77 percent. As these 

banks grow, their high start-up capital ratios converge quickly to those of established 

banks. The decline in capital ratios is driven by high growth rates and low earnings in 

early years. 

De novo loan performance measures also exhibit a life-cycle pattern, although the pattern 

is not as strong as in some other performance measures. Figure 10 shows median nonperforming loans-to-assets ratios, by de novo vintage. While the recession figures 

prominently in the loan performance of all classes, de novo banks typically have low 

non-performing loan ratios in early years, because a large share of their loan portfolio is 

unseasoned. After a few years in operation, de novo banks’ problem loans begin to 

increase as their loans season. Of course, strong business cycles can swamp this 

seasoning effect. Starting in late 2007 and early 2008, de novos experienced a sharp 

increase in non-performing loans regardless of the charter year. During the crisis, the 

median non-performing loans ratios for de novo bank classes were worse than those for 

established banks except for the youngest classes, Classes 2007 and 2008, which had a 

higher share of unseasoned loans. 

In summary, the performance of the most recent cohort of de novo banks is similar to that 

found for earlier cohorts--de novo banks are financially fragile and take many years to 

reach full maturity (DeYoung and Hasan, 1998, and DeYoung, 2000). The previous

figures generally show that the financial ratios of de novo banks display a distinctive lifecycle pattern, following similar time paths regardless of the year of chartering. They 

appear sound in early years, with large capital cushions and low levels of non-performing

loans. However, financial performance measures deteriorate as they grow their loan 

 17 Non-core funds are specifically defined as a sum of time deposits over $100,000, foreign office deposits, 

fed funds purchased and securities sold under agreement to repurchase, and other borrowed money. 
11

portfolios and their loans season, with earnings typically remaining below that of 

established peers. 

3. De Novo Bank Failures, Mergers, and Survivors

3.A De Novo Outcomes

De novo banks studied here were chartered under an economic environment 

characterized by rapid expansion, followed by a severe recession. In this section, we 

investigate the outcomes of these fledgling institutions. Data on institution failures and 

supervisory CAMELS ratings were obtained from proprietary FDIC datasets.18 

Out of the 1,042 de novo banks chartered between 2000 and 2008, 133 (12.8 percent) 

failed while 205 (19.7 percent) exited without failing, specifically, via merger or 

liquidation.

19 In comparison, 4.9 percent of small established banks exited via failure

while 25.3 percent exited without failing. Thus de novo banks’ failure exit rate is more 

than twice the rate of small established banks, while their non-failure exit rate is lower 

than that of small established banks. De novo banks’ higher failure rate is consistent with 

previous studies, which found that they are financially fragile and more susceptible to 

failure, especially when business cycle conditions deteriorate.20 Among 205 non-failure 

de novo exits, 195 were mergers and 10 were liquidations. Figure 11 shows the number 

of de novo exits via failure or acquisition each year. De novo failures are concentrated in 

the crisis period, starting in 2008, with sharp rises from 2009 to 2011. De novo 

acquisitions are dispersed across 2002 to 2014, with a sharp rise in 2012 and 2013. 

Are there differences in financial health across de novo banks with different exit 

outcomes? We compare their financial health by investigating de novo banks’ last 

CAMELS rating before exit. CAMELS ratings are based on assessments of banks’ 

capital, asset quality, management, earnings, liquidity and market sensitivity, assigned by 

bank regulators. The ratings are integer-valued and range from 1 (best) to 5 (worst). 

Table 3 reports the last composite CAMELS rating of failed, acquired, and liquidated de 

novo banks. While all 133 failure exits were CAMELS 5 rated, more than half (60.5

percent) of acquisitions were 1 or 2 rated at their last examination. Among liquidations, 

four banks were 2 rated, while the remaining six were rated 3, 4, or 5, suggesting that de 

novo banks tend to be poorly performing when they decide to liquidate. 

Table 4 lists five states with the highest number of de novo bank failures. Georgia had 

the highest number of failures with 42 banks, followed by Florida with 23. Illinois, 

California, and Arizona are the remaining states with 11, 8, and 6 failures, respectively. 

 18 CAMELS ratings stand for: Capital adequacy (C), Asset quality (A), Management quality (M), Earnings 

(E), Liquidity (L), and Sensitivity to Market Risk (S).

19 De novo bank outcomes are as of September 2014. 20 For example, DeYoung (2003) finds that the relationship between external conditions (such as intense 

competitive rivalry or slow economic growth) and higher failure rates is more systematic for de novo banks 

than for established banks. 
12

Figure 12 shows de novo failures by census region as a percent of total de novo failures

in the United States. More than half (56 percent) of all failed de novo banks are 

headquartered in the South Atlantic region. In comparison, other regions have 

substantially lower shares of de novo failures. For instance, the regions with the second 

largest shares of failed de novos are Mountain and East North Central, with 11 percent

each. The disproportionately large share of failed de novos in the South Atlantic region 

is explained in part by the large share of de novos that were chartered in that region. 

Figure 13 shows de novo failures by census region, as a percent of total de novos in that 

region. When measured as a percentage of the total number of de novo banks in the 

region, the de novo failure rate in the South Atlantic region is 22 percent (compared with

56 percent when the base is all de novos). The South Atlantic region was not only the 

most active charting region, it also had the highest rate of new charters failing. The three 

regions with the highest rate of de novos failing remain South Atlantic (22 percent), 

Mountain (20 percent), and East North Central (16 percent). 

Figure 14 shows that 34 percent of the de novo banks that exited via mergers were 

headquartered in the South Atlantic region. Other regions had substantially lower shares 

of de novo mergers. The second and third regions with the largest share of merged de 

novos were the Pacific (17 percent) and West South Central regions (15 percent). Figure 

15 shows the number of de novo bank exits via merger in each census region as a percent 

of the total number of de novo banks in that region. The New England and West South 

Central regions had the highest percentage, where 33 percent of all de novo banks 

chartered were merged, followed by the mid-Atlantic (23 percent) and Pacific (21

percent) regions. Figures 13 and 15 show that 5 percent of de novo banks exited via 

failure and 33 percent exited via mergers in the New England region. In contrast, de 

novo banks headquartered in the South Atlantic, East North Central and Mountain 

regions exited via failures at a higher rate than via mergers. 

3.B Comparison of Failed, Merged, and Survived De Novo Banks

Next, we group de novo banks into three groups—survived, merged out, and failed—and 

compare their median financial ratios starting five years prior to exit outcome. For 

survived banks, the exit period is assumed to be the end of their 29th quarter.

21 Here, as 

before, bank financial ratios and portfolio shares come from regulatory CALL Reports.

Figure 16 shows that roughly three years prior to exit, the median equity to assets ratio 

for failed banks started to decline and deviate from those of the other two groups. In their 

last quarter prior to failure, the median equity ratio was close to zero percent. For the 

earnings to assets ratio, Figure 17 shows that failed de novos had the highest median ratio 

five years prior to exit. Roughly three years prior to exit, however, their median earnings 

ratio began to decline, reaching as low as –6 percent before failure. Figure 18 shows that 

a rise in the median non-performing loans-to-assets ratio at failed de novos’ started three 

years prior to exit. 

 21 The average age of failed and merged de novos was 29 and 28 quarters, respectively. Hence, we 

assigned an exit age for survived de novos as 29 quarters. 
13

These figures show that financial deterioration of failed de novos began roughly three 

years prior to exit. They also show that median financial ratios of survived and merged 

de novos remained comparable throughout the five-year period. Figures 19 and 20 show 

that failed de novos relied more heavily on non-core funds and brokered deposits. Last, 

Figures 21 to 23 show that failed de novos also invested more heavily in C&D loans 

compared with survived and merged de novos, and that they had lower concentrations in 

C&I and CRE loans. 

3.C Determinants of De Novo Outcomes

What factors determine the different exit outcomes of de novo banks? In this section, we 

employ Shumway’s (2001) discrete-time hazard model to investigate whether the 

financial characteristics of de novo banks affect their probability of failure.

22 

Summary statistics for the variables used in the hazard model are reported in Table 5, 

which include measures of de novo banks’ financial characteristics, such as equity, 

income before taxes, non-core funds, non-performing loans-to-assets ratios, and one-year 

asset growth rate. Also included are measures of the banks’ loan portfolio composition, 

such as C&D, C&I, 1-to-4 family residential, CRE, and small-business loans-to-assets 

ratios.23

Table 5 shows that compared with survived de novo banks, those that failed had lower 

equity, earnings, and asset growth rate on average. At the same time, failed de novo 

banks relied more heavily on non-core funds and had higher non-performing loans to 

assets ratio. In terms of loan concentration, failed de novo banks had higher C&D loans 

and lower small-business loans-to-assets ratios. 

Table 6 reports the estimation results of the hazard models for de novo bank failures. 

Column (1) shows that de novo banks with higher equity and income before taxes-toassets ratios were less likely to fail. In contrast, those with higher ratios of non-core

funds and non-performing loans to assets were more likely to fail. These results are 

similar to the findings on established bank failure models.24 Column (2) reports the 

results of the model when the loan concentration ratios of de novo banks are added to the 

model. The estimated coefficients on the financial ratios remain similar. The exception 

is the estimated coefficient on the non-core funds ratio, which is no longer statistically 

significant. Among the loan concentration ratios, only the C&D-to-assets ratio is positive 

and statistically significant. 

 22 Since the likelihood function of a multi-period logit model is equivalent to that of a discrete-time hazard 

model, Shumway shows that the discrete-time hazard model can be estimated using standard logistic 

regression estimation method on pooled time series of bank data. The test statistics produced by a logit 

program need to be adjusted to account for the lack of independence between bank-year observations. 23 Small-business loans are proxied by C&I loans with original loan amount less than $1 million. 24 See Cole and Wu (2009) and Wheelock and Wilson (2000).
14

These results suggest that de novo banks in poor financial health, with lower equity, 

lower earnings, and higher non-performing loans were more likely to fail. In addition, de 

novo banks that failed tended to invest more heavily in C&D loans. 

4. Conclusion 

Of the 1,042 de novo community bank institutions chartered before the recent crisis, 133 

eventually failed. We investigate trends in recent de novo entry and reasons for failure, 

and compare our results to the patterns and findings of earlier studies, which examine de 

novo banks chartered between the 1970s and the early 2000s. For de novos chartered 

between 2000 and 2008, we find—as in earlier periods—that they were more likely to 

form in markets that experienced bank mergers or acquisitions, and that were large and 

growing. However, contrary to earlier new charters, de novos of the recent period were 

formed in less, rather than more, concentrated markets. 

Consistent with the life-cycle theory of de novos, these de novos were financially fragile, 

relied more on non-core funding, and failed at higher rates than small established banks 

during the recent financial crisis. Discrete-time hazard models confirm that the de novo 

banks that failed had invested heavily in C&D lending, similar to the concentrated loan 

portfolios exhibited by earlier cohorts (Yom, 2005). 

We conclude that the patterns of distress for the de novos formed prior to the most recent 

crisis were similar in many ways to those found for de novos of earlier periods. Excessive 

risk-taking, namely, concentration in riskier types of loans such as C&D and heavy 

reliance on non-core funds, resulted in these fledgling institutions being vulnerable to 

periods of economic stress. These findings highlight the importance of sound risk 

management practices, for example, establishing limits on loan concentration and 

reliance on non-core sources of funds, at de novo banks. Moreover, the findings provide 

some guidance to regulators on the indicators to be monitored in evaluation of de novo 

health and risk management. 
15

REFERENCES

1. Adams, Robert M., and Dean F. Amel. 2007. “The Effects of Past Entry, Market 

Consolidation, and Expansion by Incumbents on the Probability of Entry.” FRB 

Finance and Economics Discussion Series 2007–51. Federal Reserve Board. 

2. Amel, Dean F., and J. Nellie Liang (1997). “Determinants of Entry and Profits in 

Local Banking Markets.” Review of Industrial Organization, vol. 12, no. 1, pp. 

59–78.

3. Arshadi, Nasser, and Edward C. Lawrence. 1987. “An Empirical Investigation of 

New Bank Performance.” Journal of Banking and Finance, 11, no. 1:33–48.

4. Berger, Allen N., Seth D. Bonime, Lawrence G. Goldberg, Lawrence J. White. 

2004. “The Dynamics of Market Entry: The Effects of Mergers and Acquisitions on 

Entry in the Banking Industry.” Journal of Business 77 (October 2004), 797–834.

5. Cole, Rebel A., and Qiongbing Wu. 2009. “Predicting Bank Failures Using a 

Simple Dynamic Hazard Model.” De Paul University, Working Paper. 

6. DeYoung, Robert. 2003. “De Novo Bank Exit.” Journal of Money, Credit, and 

Banking, 35, no. 5:711–28.

7. ———. 2000. “For How Long Are Newly Chartered Banks Financially Fragile?” 

FRB Chicago Working Paper 2000–09. Federal Reserve Bank of Chicago.

8. ———. 1999. “Birth, Growth, and Life or Death of Newly Chartered Banks.”

Federal Reserve Bank of Chicago Economic Perspectives 23, no. 3:18–35.

9. 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.

10. Dunham, Constance R. 1989. “New Banks in New England.” New England 

Economic Review, 30–41.

11. Goldberg, Lawrence G., and Lawrence J. White. 1997. “De Novo Banks and 

Lending to Small Businesses: An Empirical Analysis.” NYU Working Paper no. 

FIN-98-039.

12. Gunther, Jeffery W. 1990. “Financial Strategies and Performance of Newly 

Established Texas Banks.” Federal Reserve Bank of Dallas Financial Industry 

Studies (December): 9–14.

13. 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 Services Research, 10:235–71.
16

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.

15. Keeton, William R. 2000. “Are Mergers Responsible for the Surge in New Bank 

Charters?” Federal Reserve Bank of Kansas City Economic Review, First Quarter 

2000. 

16. 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.

17. Seelig, Steven A., and Tim Critchfield. 2003. “Merger Activity as a Determinant 

of De Novo Entry into Urban Banking Markets.” FDIC Working Paper 2003–01. 

18. Shumway, Tyler. 2001. “Forecasting Bankruptcy More Accurately: A Simple 

Hazard Model.” Journal of Business, 74, no. 1:101–124.

19. Wheelock, David C., and Paul W. Wilson. 2000. “Why Do Banks Disappear: 

The Determinants of U.S. Bank Failures and Acquisitions.” Review of Economics 

and Statistics, 82(1):127–138.

20. Yom, Chiwon. 2005. “Recently Chartered Banks’ Vulnerability to Real Estate 

Crisis.” FDIC Banking Review, 17, no.2:1–15.
17

Table 1. Descriptive Statistics on De Novo Entry

Entry1 No Entry

Mean STD Mean STD

A. Bank Market Environment and Structure

Merger Deposits2 4.2*** 4.0 2.6 3.6

Acquisition Deposits3 2.7*** 2.7 1.9 3.2

Large Bank Deposit Share 68.4*** 23.9 36.8 31.7

Herfindahl-Hirschman Index 0.2*** 0.1 0.3 0.2

Population, County 709,246. *** 1,235,501* 73,262* 176,120*

Bank Deposits, County 16,104,698.**** 37,047,347* 1,172,712* 5,577,288*

Population Growth 1.5*** 1.6 0.5 1.7

Deposit Growth 7.8*** 13.6 4.5 14.2

State Income Growth 3.1*** 2.1 2.5 2.1

House Price Index Growth 1.3*** 2.6 0.8 2.2

B. Bank Financial Ratios

ROA, County 1.1*** 0.8 1.1 0.8

Equity, County 9.7*** 2.8 10.5 3.2

Non-performing Loans, County 1.1*** 0.9 1.5 1.2

C&D, County 9.0*** 8.1 4.3 5.6

CRE, County 16.1*** 8.4 11.5 7.6

Single Family Residential, 

County

20.5*** 12.7 19.9 12.5

Small-business Loans, County 6.8*** 4.1 7.8 4.9

1

Entry refers to a de novo bank branch formation in a county. The sample includes de novo bank 

branch formations from 2000 to 2008.

2

Deposits at merged-out banks.

3

Deposits at acquired banks.

*** Mean value at counties with de novo entry are significantly different from that in counties 

without de novo entry at the 1% level.
18

Table 2. De Novo Entry Logistic Regression Model1

Variable Entry

Intercept -18.44*** -18.67***

Merger Deposits2 0.07*** 0.07***

Acquisition Deposits3 0.05*** 0.05***

Large Bank Deposit Share -0.002*** -0.005***

Herfindahl-Hirschman Index -1.17*** -0.93***

Log of Population, County 0.51*** 0.56***

Log of Deposits, County 0.55*** 0.53***

Population Growth 0.15*** 0.13***

Deposit Growth 0.001** 0.001**

State Income Growth 0.05*** 0.05***

House Price Index Growth 0.01*** 0.02***

ROA, County 0.07***

Equity, County -0.01***

Nonperforming Loans, County -0.18***

C&D, County 0.03***

CRE, County -0.003**

Single Family Residential, County 0.01***

Small-business Loans, County -0.01***

Likelihood Ratio 2495*** 2301***

Regional Dummies Yes Yes

Number of Observations 1

Entry refers to a de novo bank branch formation in a county. The sample includes de novo 

bank branch formations from 2000 to 2008.

2

Deposits at merged-out banks. 3

Deposits at acquired banks.

*** indicates statistical significance at 1%, ** indicates statistical significance at 5%, and * 

indicates statistical significance at 10%.
19

Table 3. Last CAMELS Rating of De Novo Banks, Before Exit*

Last Rating Failures Acquisitions Liquidations

1 14 (7.2%)

2 104 (53.3%) 4 (40.0%)

3 41 (21.0%) 1 (10.0%)

4 22 (11.3%) 3 (30.0%)

5 133 (100%) 12 (6.2%) 2 (20.0%)

No rating 2 (1.0%)

Total 133 195 10

 *De Novo outcomes and CAMELS ratings are as of September 2014.

Table 4. Five States With Highest Number of De Novo Bank Failures*

STATE FAILURES (PERCENT) MERGERS (PERCENT) SURVIVED (PERCENT)

GA 42 (38%) 13 (12%) 56 (50%)

FL 23 (19%) 25 (21%) 71 (60%)

IL 11 (27%) 4 (10%) 26 (63%)

CA 8 (7%) 26 (21%) 89 (72%)

AZ 6 (29%) 2 (9%) 13 (62%)

*De Novo outcomes and CAMELS ratings are as of September 2014.
20

Table 5. Descriptive Statistics for Variables Used in Failure Hazards Models

Variable1

Failed Survived

Mean 

(%)

STD 

(%)

Mean 

(%)

STD 

(%)

Equity 7.2*** 5.3 11.6 4.6

Income Before Taxes -2.9*** 2.9 -0.1 1.7

Non-core Funds 32.9*** 11.4 26.0 11.1

Non-performing Loans 6.5*** 4.8 1.3 2.1

One-year Asset Growth 14.6*** 37.7 30.5 45.5

C&D Loans 19.5*** 11.9 9.5 8.8

1-to-4 Family Residential Loans 14.7*** 10.3 16.1 11.3

CRE Loans 24.7*** 10.7 24.7 11.8

Small-business Loans 7.2*** 6.1 8.7 6.3 1

One-year asset growth rate is annual change in total assets in percentage. The remaining 

variables are ratios scaled by total assets in percentage.

*** The failed bank means are significantly different from the survived bank means at the 1% 

level. 

Table 6. Failure Hazards Models

Variable Failure

Intercept -3.41*** -4.38***

Equity -0.18*** -0.19***

Income Before Taxes -0.21*** -0.25***

Non-core Funds 0.03*** 0.03***

Nonperforming Loans 0.23*** 0.19***

One-year Asset Growth 0.01*** 0.003**

C&D Loans 0.09***

1-to-4 Family Residential Loans 0.01***

CRE Loans 0.003**

Small-business Loans -0.02***

Likelihood Ratio 330*** 409***

Number of Observations 1,012 1,012

*** indicates statistical significance at 1%, ** indicates statistical significance at 5%, and 

* indicates statistical significance at 10%.
21

159

102

73

94

103

138

151

147

75

1.8

1.2

0.9

1.1

1.3

1.7

1.9 1.9

1.0

0.0

0.5

1.0

1.5

2.0

2.5

0

20

40

60

80

100

120

140

160

180

2000 2001 2002 2003 2004 2005 2006 2007 2008

Percent of Community Banks

Number of New Institutions

Year

Figure 1. Total and Share of Community Bank De Novos, 

by Charter Year

Total De Novos Share De Novo (Pct)

0

20

40

60

80

100

120

140

160

180

2000 2001 2002 2003 2004 2005 2006 2007 2008

Number of New Institutions

Year

Figure 2. De Novos, by Regulator and Charter Year

Total De Novos FDIC FED OCC OTS
22

Figure 3. 2000-2008 De Novos by Census Region

New England

(CT, ME, MA, NH, RI, VT), 21

West N. Central

(IA, KS, MN, MO, NE, ND, SD), 75

East N. Central

(IL, IN, MI, OH, WI), 90

East S. Central

(AL, KY, MS, TN), 90

West S. Central

(AR, LA, OK, TX), 91

Pacific

(AK, CA, HI, OR, WA), 162

Mountain

(AZ, CO, ID, MT, NV, NM, UT, WY), 

76

South Atlantic

(DE, DC, FL, GA, MD, NC, SC, VA, 

WV), 339

Mid-Atlantic

 (NJ, NY, PA), 97
23

Figure 4. Total De Novos, 2000–2008

Figure 5. Total De Novos (2000–2008) to Community Banks in 2000
24

Figure 6. Median One-Year Asset Growth Rates, by De Novo Charter Year

Figure 7. Median Non-core Funds-to-Assets Ratios, 

by De Novo Charter Year

-50

0

50

100

150

200

250

300

Mar-00Mar-01Mar-02Mar-03Mar-04Mar-05Mar-06Mar-07Mar-08Mar-09Mar-10Mar-11Mar-12Mar-13Mar-14

%

Asset growth_2000

Asset growth_2001

Asset growth_2002

Asset growth_2003

Asset growth_2004

Asset growth_2005

Asset growth_2006

Asset growth_2007

Asset growth_2008

Small Established Banks

0

5

10

15

20

25

30

35

Mar-00Mar-01Mar-02Mar-03Mar-04Mar-05Mar-06Mar-07Mar-08Mar-09Mar-10Mar-11Mar-12Mar-13Mar-14

% of total assets

Noncore funds_2000

Noncore funds_2001

Noncore funds_2002

Noncore funds_2003

Noncore funds_2004

Noncore funds_2005

Noncore funds_2006

Noncore funds_2007

Noncore funds_2008

Small Established Banks
25

Figure 8. Median Income Before Taxes-to-Assets Ratio,

by De Novo Charter Year

Figure 9. Median Equity-to-Assets Ratios, by De Novo Charter Year

-5

-4

-3

-2

-1

0

1

2

Mar-00Mar-01Mar-02Mar-03Mar-04Mar-05Mar-06Mar-07Mar-08Mar-09Mar-10Mar-11Mar-12Mar-13Mar-14

% of total assets

Earnings_2000

Earnings_2001

Earnings_2002

Earnings_2003

Earnings_2004

Earnings_2005

Earnings_2006

Earnings_2007

Earnings_2008

Small Established Banks

0

10

20

30

40

50

60

70

80

90

Mar-00Mar-01Mar-02Mar-03Mar-04Mar-05Mar-06Mar-07Mar-08Mar-09Mar-10Mar-11Mar-12Mar-13Mar-14

% of total assets

Equity_2000

Equity_2001

Equity_2002

Equity_2003

Equity_2004

Equity_2005

Equity_2006

Equity_2007

Equity_2008

Small Established Banks
26

Figure 10. Median Non-performing Loans-to-assets Ratios,

by De Novo Charter Year

Figure 11. Number of De Novo Bank Failures and Acquisitions, by Year

0

0.5

1

1.5

2

2.5

3

Mar-00Mar-01Mar-02Mar-03Mar-04Mar-05Mar-06Mar-07Mar-08Mar-09Mar-10Mar-11Mar-12Mar-13Mar-14

% of total assets

Nonperforming Loans &

Leases_2000

Nonperforming Loans &

Leases_2001

Nonperforming Loans &

Leases_2002

Nonperforming Loans &

Leases_2003

Nonperforming Loans &

Leases_2004

Nonperforming Loans &

Leases_2005

Nonperforming Loans &

Leases_2006

Nonperforming Loans &

Leases_2007

Nonperforming Loans &

Leases_2008

Small Established Banks

0

5

10

15

20

25

30

35

40

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Number of Failures/Mergers

Failures

Mergers
27

Figure 12. De Novo Failures in Each Region, as Percent of Total De Novo Failures

Figure 13. De Novo Failures as Percent of Total De Novos, by Region

New England (CT, 

ME, MA, NH, RI, 

VT)

1%

Mid-Atlantic (NJ, 

NY, PA)

6%

East North 

Central (IL, IN, 

MI, OH, WI)

11%

West North Central 

(IA, KS, MN, MO, 

NE, ND, SD)

4%

South Atlantic (DE, 

DC, FL, GA, MD, 

NC, SC, VA, WV)

56%

East South Central 

(AL, KY, MS, TN)

1%

West South Central 

(AR, LA, OK, TX)

2%

Mountain (AZ, CO, 

ID, MT, NV, NM, 

UT, WY)

11%

Pacific (AK, 

CA, HI, OR, 

WA)

8%

5

8

16

8

22

2 3

20

6

0

5

10

15

20

25
28

Figure 14. De Novo Acquisitions in Each Region, as Percent of Total De 

Novo Acquisitions

Figure 15. De Novo Acquisitions, as Percent of Total De Novos, By Region

New England 

(CT, ME, MA, NH, 

RI, VT), 4

MidAtlantic 

(NJ, NY, 

PA), 11

East North 

Central (IL, 

IN, MI, OH, 

WI), 5

West North 

Central (IA, KS, 

MN, MO, NE, ND, 

SD), 4

South Atlantic 

(DE, DC, FL, GA, 

MD, NC, SC, VA, 

WV), 34

East South 

Central (AL, KY, 

MS, TN), 5

West South 

Central (AR, LA, 

OK, TX), 15

Mountain (AZ, 

CO, ID, MT, NV, 

NM, UT, WY), 4

Pacific (AK, CA, 

HI, OR, WA), 17

33

23

11 11

20

11

33

9

21

0

5

10

15

20

25

30

35
29

Figure 16. Median Equity Ratio for Failed, Merged, and Survived De Novos

Figure 17. Median Earnings Ratio for Failed, Merged, and Survived De 

Novos

0

2

4

6

8

10

12

14

20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1

% of total assets

Age in quarters prior to exit

Failed

Merged

Survived

-7

-6

-5

-4

-3

-2

-1

0

1

2

20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1

% of total assets

Age in quarters prior to exit

Failed

Merged

Survived
30

Figure 18. Median Non-performing Loans Ratio for Failed, Merged, and 

Survived De Novos

Figure 19. Median Non-core Funds for Failed, Merged, and Survived De 

Novos

0

2

4

6

8

10

12

14

20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1

% of total assets

Age in quarters prior to exit

Failed

Merged

Survived

0

5

10

15

20

25

30

35

40

20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1

% of total assets

Age in quarters prior to exit

Failed

Merged

Survived
31

Figure 20. Median Brokered Deposits for Failed, Merged, and Survived De Novos

Figure 21. Median C&D Loans for Failed, Merged, and Survived De Novos

0

2

4

6

8

10

12

20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1

% of total assets

Age in quarters prior to exit

Failed

Merged

Survived

0

5

10

15

20

25

30

20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1

% of total assets

Age in quarters prior to exit

Failed

Merged

Survived
32

Figure 22. Median C&I (<$1 million) for Failed, Merged, and Survived De 

Novos

Figure 23. Median CRE Loans for Failed, Merged, and Survived De Novos

0

1

2

3

4

5

6

7

8

9

10

20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1

% of total assets

Age in quarters prior to exit

Failed

Merged

Survived

0

5

10

15

20

25

30

20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1

% of total assets

Age in quarters prior to exit

Failed

Merged

Survived