credit union business modelsfmaconferences.org/sandiego/papers...534 copeland hall 222 copeland hall...

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CREDIT UNION BUSINESS MODELS

David L. Stowe1

John D. Stowe2

Contacts: 1 David L. Stowe 2 John D. Stowe

Ohio University Ohio University

534 Copeland Hall 222 Copeland Hall

Athens, OH 45701 Athens, OH 45701

740-593-2445 740-593-9439

[email protected] [email protected]

mailto:[email protected]

mailto:[email protected]

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Table of Contents Abstract ...........................................................................................................................................3

Introduction ....................................................................................................................................4

Literature review and background .............................................................................................5

General overview......................................................................................................................5

Credit union classifications ......................................................................................................7

Data and methods ..........................................................................................................................9

Sample and data ........................................................................................................................9

Cluster analysis methods ........................................................................................................10

Empirical results .........................................................................................................................11

Overall cluster analysis results ...............................................................................................11

Business models for the six clusters .......................................................................................14

Conclusion ...................................................................................................................................19

References ....................................................................................................................................22

Tables ............................................................................................................................................25

Endnotes........................................................................................................................................34

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Abstract Credit union decisions on how funds are raised and invested and what services to

provide are guided by their business models and should be reflected by credit union financial

statements. We use cluster analysis to group credit unions using common size financial

statement variables such that the financial statements are similar within credit union groups and

distinct across groups. This allows the assignment of credit unions to groups by knowing their

essential elements but without predefining the groups. In this paper, we present six credit union

strategic groups differentiated from each other by their asset-liability management choices and

the services they provide members. Identifying the various credit union groups provides a

clearer picture of their business models and the economic roles that different credit unions play.

Keywords credit unions, depository institutions, asset-liability management, business

models, strategic groups

JEL Classification G21

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1 Introduction

Like other financial intermediaries, credit union business models govern how they invest their

assets and what claims they issue to raise funds. In addition, credit unions choose a variety of

other services to provide their members. Credit union boards and managers make these

decisions based on local and national economic conditions that affect financial markets, their

membership, government regulations, and their market competition. Because these decisions

arereflected in credit union financial statements, we use a cluster analysis of common size

statement variables to reveal systematic patterns in these investing, financing, and operational

decisions. A clear picture of the strategic policy decisions that guide managerial policy-making

is valuable to regulators, credit union boards and managers, and credit union members.

Credit unions (CUs) have been categorized in many ways, such as by their membership

orientation, size, geographic locations (NCUA region, state, farm versus non-farm, etc.),

occupational membership base, state versus national charter, or stage in life cycle. Our approach

is agnostic to these traditional, more arbitrary, methods of classification. We use their financial

statements and employ cluster analysis of common size variables to form groups of credit unions

that are similar within groups and distinct across groups.

We use 41 common size balance sheet and income statement variables for 1,528 large

U.S. credit unions at December 31, 2015 to form these groups. In the paper, we present the

analysis for a set of six groups. Just like other intermediaries like banks or mutual funds, CUs

show several different business models. The asset allocation between investment in investment

securities versus loans, and the allocations within the loan portfolio (e.g., to auto loans, real

estate loans, and other loans) differ markedly across the six clusters. Credit unions also differed

greatly in the revenues and expenses generated by providing services to their members. Cluster

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analysis is successful in forming groups of credit unions with alternative business models by

analyzing the structures of their common size financial statements.

2 Literature review and background

2.1 General overview

A business model is a general, integrative framework encompassing a firm’s strategies and

activities. Intuitively, a business model is “how a firm does business,” or “how a firm makes its

money.” Researchers, of course, are much more specific. For businesses in general, they report

a fairly large number of critical components of a business model, although for specific firms or

industries they often boil it down to a small number that make the industry or firm distinctive.1

For financial intermediaries, their different combinations of their assets and their funding

sources, their asset-liability management, is of prime importance. And then they may offer

specialized services for their clients, either for their depositors or their borrowers. Heterogeneity

across banking business models has a decided effect on bank risk and return, financial health,

and system stability (Vander Vennet and Mergaerts (2016), Ayadi et al (2016), and Hryckiewicz

and Kozlowski (2017)).2

Mutual funds are the most transparent financial intermediary, with easily recognized

business models. For the thousands of such funds, their investment policies and their financial

performance are readily available, as well as their fee structures, investment philosophies, and

marketing strategies. Legally, their shareholders are the funds’ owners and they usually use

limited leverage. In contrast, commercial banks have liquidity and capital requirements and

most of their assets are non-publicly-traded and thus not priced in a market. Asset-liability

management is a core activity of bank management.

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While financial economists and the public have a strong interest in all financial

intermediaries, general knowledge of credit unions is undoubtedly much lower than, say, for

commercial banks and mutual funds. This paper has a narrow focus—deriving credit union

groups (with differing business models) based on the overall structure of their financial

statements.3

Credit unions are less studied than other financial institutions for various reasons. Credit

unions are smaller, individually and in the aggregate, than these institutions. At December 2015,

the 6,147 federally insured credit unions in the U.S. had $1.22 trillion of assets. In contrast, the

5,338 FDIC insured banks had $14.89 trillion of assets, including 576 banks with assets

exceeding one billion dollars. The mean size (total assets) of commercial banks was fourteen

times the mean size of credit unions. There were four banks (JPMorgan Chase, Bank of

America, Citigroup, and Wells Fargo) that each had substantially more assets than the entire

credit union industry. The mutual fund industry is also quite large relative to credit unions.

9,521 large U.S. funds had total assets of $15.65 trillion. They owned 25% of all U.S. corporate

equity, and they held 54% of all defined contribution fund assets and 48% of all IRA assets.

Unlike credit unions, shares of mutual funds and many banks are publicly traded. There

are 15 commercial banks (with a total market value of shares of $660.5 billion) that are members

of the S&P 500 Index and 1,132 banks that are listed on U.S. exchanges or the pink sheets.

Mutual funds and publicly traded commercial banks are followed by security analysts as well as

by professional investors and the investing public. As mutual organizations, credit unions, of

course, attract no scrutiny by equity investors or financial economists relying on market pricing

information.

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Notwithstanding the larger scale of banks and mutual funds, the total assets of credit

unions are large, many people do their “banking” with credit unions, and much of their consumer

finance activity (car loans, personal loans, retirement planning, mortgages, and even small

business lending) is conducted through this type of financial institution.

2.2 Credit union classifications

While we infer the dominant business models for credit unions from their financial

statement structures, credit unions have been classified in several ways depending on the

purposes of the researcher, manager, regulator, or customer.

Based on whose interests are best served, borrowers or savers, Taylor (1971) introduced a

three-way classification system. Credit unions are saver-dominated credit unions (if the interests

of savers dominate), borrower-dominated credit unions (if the interest of borrowers dominate),

and neutral credit unions (if neither of the two groups dominate). This system depends on the

member orientation of the credit union—which group is receiving more than its share of

benefits.4

Ferguson and McKillop (2000) classify credit unions globally as nascent (formative),

transitional, and mature, based on where they are in an organizational life cycle. The classes

depend on, among other things, asset size, size of membership, regulatory frameworks, common

bond restrictions, product diversification, technology, and degree of professionalism of

management.

A recent paper by Malikov, Restrepo-Tobon, and Kumbhakar (2014) demonstrates an

issue with treating credit unions as homogeneous. They note that some empirical researchers

had concluded that returns to scale had been largely exhausted in the industry. But, segregating

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credit unions into three groups and reassessing their performance reversed this conclusion. The

three groups were credit unions that focused on 1) consumer loans and investments, 2) consumer

loans, real estate loans, and investments, and 3) consumer, real estate, and business loans and

investments. Within groups, the continued presence of returns to scale put a positive light on

mergers and growth of credit union size.

There are other typologies that can suit the particular interests of members, regulators,

and researchers. One of these is farm versus non-farm credit unions. Credit union size also an

important characteristic. Another interesting class of credit unions is the occupational credit

unions, who may face special opportunities and risks based on their occupational orientation.

Examples of these could be trade-union-specific credit unions, employer-specific credit unions,

university credit unions, and the like. Another is commercial credit unions, who are large

enough and operating in a competitive, deregulated environment such that they compete more

directly with other financial institutions in their markets. These credit unions have lost some of

the characteristics of the common bond that has been an important part of credit union history.

Credit unions may also be distinguished by operating in one of the five NCUA regions and by

their state and local market. In the past, there were also different regulations for Federal versus

state chartered credit unions.

Our purpose in this paper is not to specify groups of credit unions and then explore how

this is manifested in their financial and operating strategies, but to do the opposite. We take the

financial statements of a large group of credit unions, and cluster the credit unions into groups

that have similar financial statement structures and similar business models.

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3 Data and methods

3.1 Sample and data

Table 1 goes here

A summary of the call report data for the 6,147 U.S. credit unions is in Table 1, showing their

assets and liabilities for the end of Q4 2015 in Panel A and their income and expenses for the

year ending December 31, 2015 in Panel B. The $1.22 trillion of total assets included $96 billion

of cash and equivalents, $276 billion of investments, $796 billion of loans, and $51 billion of

other assets. The credit unions also had $1,029 billion of deposits, $57 billion of other liabilities,

and $133 billion of net worth. They had $56 billion of gross income and total expenses of $47

billion and net income of $9 billion. Table 1 provides the NCUA codes for the data in the table,

and the variables we use in this study follow the logic of the NCUA summary financial

statements presented in the table.

We are using common-size variables from credit union balance sheets and income

statements to form groups of credit unions that are similar within groups and distinct across

groups. We use sixteen asset variables, indicated by A1 through A16 on the right-hand side of

Table 1, as well as nine liability variables (L1 through L9), seven revenue variables (R1 through

R7), and nine expense variables (E1 through E9). The asset and liability variables are

standardized by dividing by total assets, and the revenue and expense variables are divided by

gross revenue. With no duplication and no omissions, each of the four sets of common size

variables sums precisely to 1.

Our empirical analysis focuses on the 1,528 of the largest U.S. credit unions with assets

of at least $100 million on December 31, 2015. Within this sample, 251 credit unions had total

assets exceeding $1 billion. Even with the minimum size requirement, there were a handful of

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credit unions with such unique financial structures that they distort the statistics for the rest of the

sample. After eliminating 25 of these, summary data for the 41 common size variables for the

final set of 1,528 credit unions are given in Table 2. The sample credit unions ranged in size

from total assets of $100.02 million to $73.279 billion, with an average size of $719.6 million

and collective total assets of $1.0995 trillion. The 1,528 sample credit unions include 24.9% of

all U.S. credit unions and held 90.2% of all credit union assets.

Table 2 goes here

The variables we use in this study are common size statement variables created from a

percentage breakdown of each credit union’s balance sheet and income statement. Table 2

provides descriptive statistics for the 41 common size statement variables. The dispersions of

the variables, seen in their standard deviations and the ranges across their percentile values, show

the diversity of credit union financial structures and provide the basis for clustering the credit

unions into more homogeneous groups.

3.2 Cluster analysis methods

We use an iterative partitioning, non-hierarchical cluster technique to form the credit

union clusters. The K-means procedure iteratively assigns the credit unions to clusters that

minimize the sum of the squared distances of the credit union variables from their assigned

cluster means. The specific program we used was SAS FASTCLUS, which is an efficient

algorithm for clustering large data sets. The analyst can choose the number of clusters to form,

and the algorithm assigns each credit union to one and only one cluster, attempting to minimize

the total squared Euclidian distance (squared deviations from cluster means) of the credit unions

from their assigned clusters.

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The results of the cluster analysis are evaluated by comparing the cluster profiles based

on the variables used to form the clusters. We further assess the clusters with additional

variables (such as size, location, and financial ratios) that were not used to form the clusters.

Credit unions managers make decisions about how funds are raised and invested and what

services to provide. These decisions should be reflected in credit union financial statements and

in the cluster analysis groupings based on this data.

4 Empirical results

4.1 Overall cluster analysis results

Table 3 goes here

The number of clusters to form is an empirical and judgmental issue. The summary statistics for

numbers of clusters ranging from 2 through 20 shown in Table 3 (pseudo-F statistic, approximate

expected over-all R-squared, and cubic clustering criterion) can help guide the number of

clusters used. Prior knowledge and empirical considerations also are guides since there is no

theoretically correct number of clusters. The meaningfulness of the solution is often the best

guide. Thus, the economic interpretation of the statistics is a crucial part of our project, which,

along with a desire for parsimony, led us to use six clusters. The R-squares in Table 3 are the

proportions of the total variance explained by group membership (the between-group variance

divided by the total variance). As Table 3 shows, the R-square increases with the number of

clusters formed, and the R-square would continue to increase and approach unity if the number

of clusters approached the sample size. As the number of clusters increases, the cubic clustering

criterion increases and the clusters are more homogeneous. With the large number of variables

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in this study, there is no dramatic “break” in the cluster statistics—the statistics continue to

improve as the number of clusters increases.

Table 4 goes here

Table 4, Panel A, shows the number of credit union members in each of the six clusters,

ranging from a high of 430 for Cluster 2 to a low of 48 for Cluster 5. Unlike other multivariate

techniques, such as factor analysis, the ordering and naming of the clusters is arbitrary. In Panel

A, Clusters 2 and 4 have the lowest dispersion of the of the common size variables about their

centroids (cluster means), and Cluster 1 has the greatest dispersion within cluster. Clusters 2

and 4 are closer to each other than any other pair of clusters.

Panel B of Table 4 shows the distances between centroids for all pairs of the clusters.

The closest pairs of clusters are Clusters 2 and 4 (0.2527), Clusters 2 and 3 (0.2810), Clusters 4

and 6 (0.3004), and Clusters 5 and 6 (0.3004). Cluster 1 has the distinction of being the most

distant cluster from all five of the other clusters (2, 3, 4, 5, and 6) and is most distant from

Cluster 3 (0.5845). The bottom row of Panel B shows the distance of the six cluster centroids

from the mean centroid of the entire 1,528 credit union sample. In the bottom row, Cluster 1 is

the farthest away from the overall mean, and Cluster 2 is the closest.

Table 5 goes here

Table 5 shows the total standard deviations for the 41 common size variables and the

within cluster standard deviations. The total standard deviation for each variable can be

decomposed into the within group variation (variance about the assigned group means) and the

residual variance. The R-square is the proportion of the total variance that is explained by

cluster membership. It is also seen as the between group variance (total variance minus within

group variance) divided by the total variance. The over-all R-square for the 41 common size

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variables is 29.00%, and the R-squares for the individual variables are sometimes much higher

than the average and others very low.

For the asset variables, the highest R-squares for A9 Real estate first mortgages

(50.96%), A13 Used auto loans (48.08%), A15 Other loans (36.89%), A5 Investments 1-3 years

(36.44%), A6 Investments 3-5 years (30.49%), and A12 New auto loans (27.65%). At the other

extreme, six asset categories had R-squares below 5.00%, including A2 Cash on deposit at FI’s,

A3 Cash equivalents, A8 Investments gt 10 years, A10 Other real estate loans, and A14 Non-

federally insured student loans. On the liability side of the balance sheet, the highest R-squares

are for L2 Regular shares (46.65%) and L3 Money market accounts (24.23%), and the lowest are

for L5 IRA/Keough accounts (3.37%) and L7 All other shares (1.24%).

The R-squares for the income statement variables tend to be lower than for the balance

sheet variables. Two revenue accounts had R-squares above 30% (R1 Interest on loans and R3

Investment income), one was 17.22% (R5 Fee income), and the other revenue variables had R-

squares below 10%. For the expense variables, one was above 20% (E1 Share dividends is

20.37%), four were below 10% (E3 Loan servicing expenses, E4 Other non-interest expenses, E5

Interest on borrowed money, and E7 Interest on deposits. The other four expense variables had

R-squares between 10% and 20%.

An equally weighted average of the R-squares for the 41 variables is 15.30%, while the

variance-weighted R-square is 29.00%.5 This occurs because the larger financial statement

categories and those with higher variances tended to have higher R-squares. Although the

income statement variables have lower R-squares than the balance sheet variables, they are still

informative and we chose to present our cluster analysis with 41 balance sheet and income

statement variables rather than the balance sheet variables alone.

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4.2 Business models for the six clusters

Tables 6-8 present information about the business models for the six CU groups, based on what

the financial statement breakdowns reveal about their financial and operating orientations. For

convenience, we will give a name and very brief description for each of the clusters before going

on with more details.

• Cluster 1 – Saver-oriented CUs. These CUs have very high investments and low loan

balances and have very low non-interest expenses. They focus on raising funds from

depositors, investing in securities (and not member loans), and have low fee income and

expenses associated with member services. They are clearly saver- or depositor-oriented

credit unions and not borrower-oriented credit unions.

• Clusters 2 and 3 – Traditional (1) and Traditional (2) CUs. These CUs have investments

and loans closer to the overall mix of all credit unions, although they do have a slant

towards investments. They have high fee income and non-interest expenses, showing an

emphasis on member services. Although Cluster 3 has more real estate loans and more

regular shares than Cluster 2 while Cluster 2 has more investments, they both have an

overall balance of decisions that meets the stereotypes for a traditional credit union.

• Clusters 4, 5, and 6 – Auto Lenders, Other Lenders, and Real Estate Lenders,

respectively. These CUs have very high loan balances relative to investment balances.

Although they have a strong borrower-orientation, they still have fairly diversified

financial structures with a strong overweight in a type of loan class (auto loans, other

loans, and real estate loans, respectively). Their other fees and expenses are affected by

their chosen lending markets.

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Table 6 goes here

Having previewed the nature of each of the six credit union clusters, we discuss each

cluster in more detail, referencing Tables 6, 7, and 8. Table 6 provides the means of the common

size variables for each of the six credit union clusters as well as the means for the complete 1,528

CU sample for comparison. Table 7 provides additional properties of each cluster, such as the

sizes of the CUs, some combined financial statement categories (such as total loans, which is the

sum of loan variables A9 through A15), four basic financial ratios, federal versus state charters,

and the NCUA region memberships. Table 8 lists the largest five CUs in each cluster and their

respective total assets. Of course, financial statement variables are inter-related. For example, if

a credit union invests more heavily in a particular loan or investment category, the other balance

sheet and income statement accounts adjust in a systematic manner.

Cluster 1 Saver-oriented CUs: Relative to the other credit unions, the Cluster 1 credit

unions have a saver orientation rather than a borrower or a neutral orientation. The Cluster 1

credit unions have the largest commitments to investments, with A4 through A8 totaling 59.1%

of total assets, which is more than double that of the other credit unions as a whole. In contrast,

Clusters 2 and 3 have roughly 30% in investments, and the remaining clusters have substantially

under 20%. Cluster 1 has the lowest commitment to loans, with total loans (A9-A15) averaging

only 30.5% of assets. In stark contrast, Clusters 2 and 3 committed 53% and 57% to loans, and

clusters 4, 5, and 6 averaged 75%, 77%, and 71%. This cluster has the lowest A16 other assets

(“bricks and mortar”), lowest R1 Interest on loans, highest R3 Investment income, highest E6

Share dividends, and the lowest levels of E1 Labor expense, E2 Office expenses, E3 Loan

servicing costs, E4 Other non-interest expenses, and E8 provision for loan/lease losses. For the

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calendar year, Cluster 1 had the highest E9 Net income (22.87%). As a financial intermediary,

this cluster’s business model favors investments over loans to members and has the lowest

revenues and expenses for member services. This cluster’s business model is reflected

consistently in all parts of the balance sheets and income statements.

Cluster 2 Traditional (1) CUs: As shown in Panel B of Table 4, the 430 credit unions in

this cluster are closer to the overall financial statement structure of the entire sample than any

other cluster. Their investments, loans, and financing are fairly representative of the total CU

sample, although this cluster has tilts towards investments and away from the loan categories

compared to the overall sample of CUs. Their average investments are the second highest

among the six clusters and their loans are the second lowest. Based on their revenue and expense

categories, this group of credit unions shows a commitment to member services. Their A16

Other assets and their R5 Fee income are high, as are their E1 Labor expense and E2 Office

expense. Unfortunately, Cluster 2 also has the lowest E9 Net income. This cluster may be

providing the most typical loan, services, and savings products, which, at least for calendar year

2015, had the lowest profitability.

Cluster 3 Traditional (2) CUs: The financial statement profile of the Cluster 3 CUs

resembles that of the Traditional (1) Cluster 2 profile, with slightly less in investments and more

in mortgages. Cluster 3 has the highest funding from L2 Regular shares (55.61%) and very low

funding from L3 Money market accounts (4.09%). E5 Interest on borrowed money and E8

Provision for loan/lease losses are low. Compared to Cluster 2, the Cluster 3 credit unions have

lower investment allocations and rely much less heavily on L3 Money market accounts. In

general, almost all of the 41 common size accounts for the Cluster 3 (and Cluster 2) credit unions

tend to be closer to the overall averages than the Cluster 1 credit unions. The CU’s in the

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Traditional (1) and Traditional (2) clusters were smaller, on average, than the other four clusters,

were less profitable (with the lowest return on assets), and also maintained the highest levels of

liquidity. They had the highest non-interest expense of the six clusters.

Cluster 4 Auto Lenders: These credit unions have the largest commitment to auto loans,

with A12 New auto loans and A13 Used auto loans totaling 39.54% of assets, which is roughly

double the commitment of the other clusters. Their auto loans, many of which are indirect loans

through auto dealerships, generate the highest E3 Loan servicing expenses and E8 Provision for

loan/lease losses. Cluster 4 has low investments (totaling only 11.2% of assets), and R3

Investment income, consequently, is low. This cluster is over-represented in NCUA Region 4

(Austin, TX), and under-represented in NCUA Region 1 (Albany, NY). The financial statement

structure of this cluster, except for the high auto loans and low investments (and some related

revenues and expenses), is very similar to that of the overall sample.

Cluster 5 Other Lenders: This group is also very low on investments (totaling 10.9%)

and has the second-highest commitment to real estate loans (A9 and A10 sum to 31.79%).

However, Cluster 5 is distinguished because it has the largest commitment to A15 Other loans

(26.50%), which is unsecured loans and various business loans. Other loans are three to eight

times as large as any other cluster. This group has low R3 investment income, high R1 Interest

on loans, and high E8 Provision for loan losses. Cluster 5 is closest to the other lending clusters

(Clusters 4 and 6), but it is the second most distant of the six clusters from the overall CU

averages.

Cluster 6 Real Estate Lenders: The 403 credit unions in Cluster 6 tend to specialize in

real estate lending. The average size of the CUs in this cluster is greater than any other cluster,

and the cumulative total assets of the Cluster 6 CUs is more than twice as large as any other

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cluster. A9 Real estate first mortgages are 38.62% of assets, which is almost double the

commitment of the credit unions in the other five clusters. Combining A9 with A10 Other real

estate loans, the Cluster 6 credit unions invest 46.34% of assets in real estate loans. Its non-

interest operating costs (E1-E4) are below average and its interest paid (E5-E7) is slightly above

average.

Table 7 goes here

Table 7 shows additional properties of the credit union clusters. The total assets of the

Cluster 6 CUs are $488 billion, which is twice as large as the next biggest cluster (Cluster 4,

Auto lenders) and constitutes 44.4% of the assets of all the CUs in our sample. The average size

of these CUs was also the largest, with a mean of $1.211B and median of $0.431B. The smallest

credit unions, on average, were the traditional credit unions in Cluster 2 and Cluster 3. The most

profitable CUs were in Cluster 6 (based on return on assets) and in Cluster 1 (based on net profit

over revenue), and the least profitable CUs were in Cluster 2, although we would expect

profitability to vary over time. Except for the Cluster 1 CUs that had a median capital-to-assets

ratio of 13.43%, the medians for this ratio were in the 10.1% to 10.7% range for the other five

clusters. Cluster 1 had the lowest liquidity ratio (5.38%), with the others in the 6.4% to 8.4%

range. The median Loan-to-assets ratio was 29.2% for Cluster 1, roughly 55% for Clusters 2 and

3, and roughly 75% for Clusters 4, 5, and 6.

There is variation of the types of CUs across the five NCUA regions. For example, in

Region 1 (northern states, headquartered in Albany, NY), slightly over 40% of the CUs are the

real estate CUs, which is more than double their representation in any other NCUA region.

Region 1 has the lowest share of the traditional Cluster 2 CUs. In Region 4, covering the

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southwest and Midwest, headquartered in Austin, TX), the Auto loan Cluster 4 CUs were heavily

concentrated. The Cluster 5 Other lenders were also concentrated in Region 4.

Table 8 goes here

Table 8 identifies the largest five CUs in each cluster along with their total assets. Unlike

large commercial banks and mutual funds, these large CUs are not household names.6 Four of

the five CUs with total assets of more than $10 billion are in Cluster 6. Investing in real estate

allows them to be of a larger scale than those that invest in other asset classes. A common bond

of members and the fact that many are local or regional institutions can limit credit union size.

Credit unions, like other businesses, operate in a very complex environment. They must

exist within national and local financial markets, and they face competition from other credit

unions as well as banks, securities firms, insurance companies, and other intermediaries. Many

credit unions have a membership common bond that can enhance or detract from their

performance. And, of course, the regulatory environment and the quality of their management

and staffs play crucial roles. The cluster analysis of common size statement data allows us to

form clusters or groups of credit unions with relatively homogeneous financial structures within

each cluster. The makeup of the asset, liability, revenue, and expense variables reflect the

decisions made by credit union managers and speaks to their business models.

5 Conclusion

Business models for credit unions revolve around the allocation of assets into various investment

securities and loans, the makeup of their liabilities, and the management of the interactions

between them. In addition to asset-liability management, credit unions also provide a variety of

services to their members. Relative to other intermediaries, this may be a distinguishing

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characteristic. For example, they may provide banking services for low-income customers,

investment advice to customers with low net worth, loans that are small (and perhaps to less

creditworthy customers), and other personal financial services. Credit unions frequently are

serving a narrower geographic and demographic constituency than other financial intermediaries.

Their financial statements should reflect their business models.

For the 1,528 credit unions in our study, we present the results of forming six clusters

from their common size statements. The investment/loan mix is a central focus of the CU

business models, with Cluster 1 very high on investments and low on loans, with Clusters 4, 5,

and 6 at the other extreme (high loans and low investments). Clusters 4, 5, and 6 were

specialized lenders in auto loans, other loans, and real estate loans, respectively. Clusters 2 and 3

were more balanced, but with a modest investment slant. Fee income and non-interest expenses

are high for credit unions (such as for Cluster 2) that provide a high level of services to members.

Cluster 1 is the lowest on this dimension. Loan-servicing costs and provision for loan losses are

related to the types of lending. Any decisions affecting one type of account (like an asset

account) had logical impacts on other accounts in the balance sheets and income statements. The

financial statements to a large degree revealed distinct CU business models, each with differing

financial and operating orientations.

Other financial intermediaries are rated and classified in various ways. Mutual funds, for

example, are classed by their expense structures and investment portfolio styles. A large-cap,

low-expense fund would perform differently than a high expense, mid-to small-cap growth fund.

While credit unions are not as narrowly specialized as mutual funds, their overall business

models are distinct.

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The business model or strategic orientation of a credit union is useful information. In

assessing the performance of management, a credit union board should compare its performance

with similar credit unions. For example, the performance of credit unions like the traditional

credit unions in Cluster 2 is not comparable with performance for the real estate lenders in

Cluster 6 or the saver-oriented credit unions in Cluster 1. Members may wish to belong to a

credit union that provides the loans, services, or savings products they need. Of course, credit

union managers may need to adapt their business models as local and national economic

conditions change. Because credit unions have distinct business models, national and local

economic events will affect them differently. These distinct business models should be expected

in a dynamic and competitive industry.

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25

TABLE 1 Breakdown of U.S. credit union balance sheet and income statements, 2015 Q4

Panel A: Balance sheets

NCUA Code Amount $ billions Proportion of total assets

Total assets Acct_010 1,219.23 1.0000

Total cash & equivalents (lt 3 months) Acct_730 96.18 0.0789 Cash on hand Acct_730A 9.98 0.0082 A1

Cash on deposit in FI’s Acct_730B 80.79 0.0663 A2

Cash equivalents Acct_730C 5.41 0.0044 A3

Total Investments (gt 3 months) Acct_799I 276.00 0.2264 Investments lt 1 year Acct_799A1 68.62 0.0563 A4

Investments 1-3 years Acct_799B 102.85 0.0844 A5

Investments 3-5 years Acct_799C1 72.02 0.0591 A6

Investments 5-10 years Acct_799C2 27.94 0.0229 A7

Investments gt 10 years Acct_799D 4.56 0.0037 A8

Total loans Acct_025B 796.47 0.6533 Real estate loans *** 402.31 0.3300 Real estate first mortgage Acct_703 326.91 0.2681 A9

Other real estate loans Acct_386 75.40 0.0618 A10

Credit cards Acct_396 49.30 0.0404 A11

Auto loans *** 264.54 0.2170 New autos Acct_385 100.93 0.0828 A12

Used autos Acct_370 163.61 0.1342 A13

Non-fed ins. student loans Acct_698A 3.53 0.0029 A14

Other loans *** 76.80 0.0630 A15

Other assets *** 50.58 0.0415 A16

Total liabilities and net worth Acct_014 1,219.23 1.0000

Total deposits Acct_018 1,029.09 0.8440 Share drafts Acct_902 152.49 0.1251 L1

Regular shares Acct_657 356.89 0.2927 L2

Other deposits *** 519.71 0.4263 Money market accounts Acct_911 234.58 0.1924 L3

Share certificate accounts Acct_908C 192.27 0.1577 L4

IRA/Keogh accounts Acct_906C 77.37 0.0635 L5

Non-member deposits Acct_880 6.76 0.0055 L6

All other shares Acct_630 8.73 0.0072 L7

Other liabilities *** 56.92 0.0467 L8

Net worth Acct_997 133.22 0.1093 L9

26

TABLE 1 (Continued)

Panel B: Income and expense statements

NCUA Code Amount $ billions Proportion of gross income

Gross income *** 55.68 1.0000

Total interest income Acct_115 39.67 0.7124 Interest on loans Acct_110 35.29 0.6338 R1

Interest refunds Acct_119 (0.07) -0.0012 R2

Investment income Acct_120 4.44 0.0798 R3

Trading income Acct_124 (0.00) -0.0001 R4

Total non-interest income Acct_117 16.01 0.2876 Fee income Acct_131 7.63 0.1370 R5

Other operating income Acct_659 8.02 0.1441 R6

Other (including gains/losses) *** 0.37 0.0066 R7

Total expenses *** 42.82 0.7690

Total non-interest expense Acct_671 36.73 0.6596 Labor expense Acct_210 18.63 0.3345 E1

Office expenses *** 9.39 0.1686 E2

Loan servicing expenses Acct_280 2.61 0.0469 E3

Other non-interest expenses Acct_260 6.86 0.1232 E4

Total interest expense Acct_350 6.09 0.1095 Interest on borrowed money Acct_340 0.80 0.0143 E5

Share dividends Acct_380 4.73 0.0849 E6

Interest on deposits Acct_381 0.57 0.0103 E7

Provision for loan/lease losses Acct_300 4.02 0.0722 E8

Net income Acct_661A 8.84 0.1587 E9

The format of this table and the variables used follow the NCUA’s current version of “Summary of

Federally Insured Credit Union Call Report Data.” The NCUA codes for each variable are given in the

second column. (*** indicates a variable that is a combination of two or more call report accounts). The

codes for the 41 variables we use (e.g., A1 through A16) are in the right-hand-side column.

27

TABLE 2 Descriptive statistics for common size statement variables for large credit unions

Common Size Variables Mean Standard

Deviation

Percentile Values

10th 25th 50th 75th 90th

A1 Cash on hand 0.0101 0.0076 0.0032 0.0057 0.0090 0.0128 0.0181

A2 Cash on deposit at FI’s 0.0678 0.0479 0.0205 0.0364 0.0589 0.0873 0.1236

A3 Cash equivalents 0.0048 0.0212 0.0000 0.0000 0.0000 0.0000 0.0062

A4 Investments lt 1 year 0.0616 0.0610 0.0056 0.0193 0.0444 0.0854 0.1365

A5 Investments 1-3 years 0.0890 0.0791 0.0105 0.0278 0.0672 0.1283 0.1991



A8 Investments gt 10 years 0.0045 0.0157 0.0000 0.0000 0.0000 0.0012 0.0124

A9 Real estate first mort 0.2366 0.1295 0.0815 0.1413 0.2191 0.3114 0.4124

A10 Other real estate loans 0.0654 0.0563 0.0110 0.0248 0.0517 0.0900 0.1364

A11 Credit cards 0.0305 0.0235 0.0000 0.0128 0.0275 0.0445 0.0623

A12 New auto loans 0.0751 0.0653 0.0158 0.0294 0.0534 0.1029 0.1642

A13 Used auto loans 0.1538 0.0960 0.0419 0.0811 0.1390 0.2097 0.2801

A14 Non-fed ins. student loans 0.0034 0.0120 0.0000 0.0000 0.0000 0.0005 0.0092

A15 Other loans 0.0717 0.0608 0.0209 0.0335 0.0563 0.0932 0.1349

A16 Other assets 0.0485 0.0229 0.0219 0.0324 0.0458 0.0616 0.0783

L1 Share drafts 0.1504 0.0620 0.0674 0.1134 0.1485 0.1851 0.2270

L2 Regular shares 0.3170 0.1242 0.1761 0.2324 0.3027 0.3866 0.4758

L3 Money market accounts 0.1681 0.1060 0.0055 0.0940 0.1658 0.2385 0.3018

L4 Share certificate accounts 0.1551 0.0748 0.0665 0.1044 0.1484 0.1978 0.2496

L5 IRA/Keough accounts 0.0619 0.0295 0.0304 0.0437 0.0589 0.0761 0.0970

L6 Non-member accounts 0.0060 0.0184 0.0000 0.0000 0.0000 0.0000 0.0187

L7 All other shares 0.0083 0.0298 0.0000 0.0000 0.0002 0.0031 0.0179

L8 Other liabilities 0.0237 0.0371 0.0002 0.0043 0.0094 0.0269 0.0686

L9 Net worth 0.1096 0.0270 0.0832 0.0919 0.1042 0.1213 0.1420

R1 Interest on loans 0.6231 0.1012 0.4986 0.5654 0.6267 0.6851 0.7500

R2 Interest refunds -0.0014 0.0156 0.0000 0.0000 0.0000 0.0000 0.0000

R3 Investment income 0.0915 0.0873 0.0169 0.0353 0.0662 0.1190 0.1952

R4 Trading income -0.0001 0.0012 0.0000 0.0000 0.0000 0.0000 0.0000

R5 Fee income 0.1597 0.0814 0.0654 0.1027 0.1502 0.2051 0.2647

R6 Other operating income 0.1211 0.0709 0.0201 0.0702 0.1253 0.1667 0.2068

R7 Other (including gains/losses) 0.0060 0.0249 -0.0070 -0.0011 0.0009 0.0076 0.0242

E1 Labor expense 0.3672 0.0658 0.2870 0.3271 0.3695 0.4089 0.4486

E2 Office expenses 0.1888 0.0634 0.1117 0.1435 0.1829 0.2297 0.2766

E3 Loan servicing expenses 0.0477 0.0297 0.0153 0.0263 0.0424 0.0634 0.0864

E4 Other non-interest expenses 0.1217 0.0567 0.0575 0.0774 0.1104 0.1590 0.1995

E5 Interest on borrowed money 0.0052 0.0137 0.0000 0.0000 0.0000 0.0029 0.0155

E6 Share dividends 0.0736 0.0600 0.0123 0.0365 0.0647 0.0944 0.1333

E7 Interest on deposits 0.0104 0.0274 0.0000 0.0000 0.0000 0.0000 0.0454

E8 Provision for loan/lease losses 0.0496 0.0469 0.0011 0.0197 0.0429 0.0729 0.1058

E9 Net income 0.1358 0.0854 0.0367 0.0777 0.1271 0.1844 0.2442

This table provides the means, standard deviations, and percentile values for the 41 common size

statement variables for the 1,528 large credit unions.

28

TABLE 3 R-Squares for alternative numbers of clusters

Number of

Clusters R-Square

Pseudo F

Statistic

Approximate

Expected Over-

all R-Square

Cubic

Clustering

Criterion

2 0.1212 210.42 0.0826 23.38

3 0.1947 184.38 0.1414 26.01

4 0.2433 163.30 0.1747 33.47

5 0.2585 132.76 0.2000 28.83

6 0.2900 124.31 0.2193 35.99

7 0.3124 115.18 0.2335 41.93

8 0.3329 108.34 0.2456 48.25

10 0.3630 96.12 0.2646 58.45

20 0.4409 62.58 0.3211 69.32

The R-squares are the proportion of the total variance of the variables that is explained by cluster

membership for alternative numbers of clusters. The common size balance sheet variables include 16

asset variables and 9 liability/net worth variables. The income statement variables include 7 revenue

variables and 9 expense variables.

29

TABLE 4 Cluster summary statistics

Panel A: Frequency distribution of the 1,528 large credit unions across six clusters

Cluster Frequency RMS Std

Deviation

Maximum

Distance from

Seed to

Observation

Nearest

Cluster

Distance

between

Cluster

Centroids

1 67 0.0729 0.8488 2 0.4400

2 430 0.0492 0.6502 4 0.2527

3 192 0.0542 0.6895 2 0.2810

4 388 0.0478 0.7745 2 0.2527

5 48 0.0597 0.8985 6 0.3004

6 403 0.0514 0.7065 4 0.3004

The cluster analysis is performed with the full set of 41 common size statement variables. A squared

Euclidian distance is the sum of the squared differences between two objects across the variables. The

Euclidian distance, which is the “Distance” reported in the table, is the square root of the squared

Euclidian distance.

Panel B: Distances between all pairs of cluster centroids

Cluster 1 2 3 4 5 6

1 0

2 0.4400 0

3 0.4474 0.2810 0

4 0.5845 0.2527 0.3527 0

5 0.4063 03878 0.4371 0.3067 0

6 0.5547 0.3189 0.3736 0.3004 0.3004 0

ALL 0.4645 0.1471 0.2467 0.1755 0.2956 0.2021

The body of the table shows the distances between all pairs of cluster centroids. The bottom row is the

distance of each cluster centroid from the overall mean of the 1,528 credit unions. The distances reported

in the table are Euclidian distances.

30

Table 5 Cluster variable descriptive statistics

Variable Total STD Within STD R-Square RSQ/(1-RSQ)

A1 Cash on hand 0.0077 0.0074 0.0752 0.0813

A2 Cash on deposit at FI’s 0.0479 0.0473 0.0271 0.0279

A3 Cash equivalents 0.0212 0.0212 0.0056 0.0056

A4 Investments lt 1 year 0.0610 0.0567 0.1403 0.1632

A5 Investments 1-3 years 0.0791 0.0632 0.3644 0.5733



A8 Investments gt 10 years 0.0157 0.0154 0.0312 0.0322

A9 Real estate first mort 0.1295 0.0908 0.5096 1.0390

A10 Other real estate loans 0.0563 0.0553 0.0373 0.0388

A11 Credit cards 0.0235 0.0230 0.0418 0.0437

A12 New auto loans 0.0653 0.0556 0.2765 0.3821

A13 Used auto loans 0.0960 0.0693 0.4808 0.9259

A14 Non-fed ins. student loans 0.0120 0.0120 0.0094 0.0095

A15 Other loans 0.0608 0.0484 0.3689 0.5846

A16 Other assets 0.0229 0.0219 0.0872 0.0956

L1 Share drafts 0.0620 0.0583 0.1178 0.1335

L2 Regular shares 0.1242 0.0908 0.4665 0.8745

L3 Money market accounts 0.1060 0.0924 0.2423 0.3199

L4 Share certificate accounts 0.0748 0.0691 0.1497 0.1760

L5 IRA/Keough accounts 0.0295 0.0290 0.0337 0.0349

L6 Non-member accounts 0.0184 0.0177 0.0822 0.0896

L7 All other shares 0.0298 0.0297 0.0124 0.0125

L8 Other liabilities 0.0371 0.0354 0.0918 0.1011

L9 Net worth 0.0270 0.0261 0.0689 0.0740

R1 Interest on loans 0.1013 0.0780 0.4080 0.6891

R2 Interest refunds 0.0156 0.0156 0.0039 0.0039

R3 Investment income 0.0873 0.0568 0.5772 1.3652

R4 Trading income 0.0012 0.0012 0.0090 0.0091

R5 Fee income 0.0814 0.0742 0.1722 0.2080

R6 Other operating income 0.0710 0.0683 0.0761 0.0824

R7 Other (including gains/losses) 0.0249 0.0248 0.0096 0.0097

E1 Labor expense 0.0658 0.0617 0.1233 0.1406

E2 Office expenses 0.0634 0.0592 0.1309 0.1507

E3 Loan servicing expenses 0.0297 0.0293 0.0331 0.0342

E4 Other non-interest expenses 0.0567 0.0564 0.0131 0.0133

E5 Interest on borrowed money 0.0137 0.0133 0.0625 0.0667

E6 Share dividends 0.0600 0.0536 0.2037 0.2559

E7 Interest on deposits 0.0274 0.0274 0.0044 0.0045

E8 Provision for loan/lease losses 0.0470 0.0424 0.1860 0.2284

E9 Net income 0.0854 0.0808 0.1075 0.1205

OVER-ALL 0.0612 0.0517 0.2900 0.4084

For each common size variable, Table 5 shows the total dispersions and dispersions about their assigned

cluster means and the R-squares (proportions of variances explained by cluster membership).

31

TABLE 6 Cluster variable means

Cluster 1 2 3 4 5 6 ALL

Number of members

67

Saver

Oriented

430

Traditional

(1)

192

Traditional

(2)

388

Auto

Lenders

48

Other

Lenders

403

Real

Estate

1528

All

A1 Cash on hand 0.0046 0.0121 0.0103 0.0115 0.0066 0.0081 0.0101

A2 Cash on deposit at FI’s 0.0634 0.0761 0.0793 0.0622 0.0674 0.0597 0.0678

A3 Cash equivalents 0.0086 0.0060 0.0066 0.0031 0.0025 0.0041 0.0048

A4 Investments lt 1 year 0.0930 0.0877 0.0779 0.0330 0.0378 0.0510 0.0616




A8 Investments gt 10 years 0.0170 0.0045 0.0048 0.0028 0.0041 0.0039 0.0045

A9 Real estate first mort 0.1398 0.1669 0.2336 0.1787 0.2196 0.3862 0.2366

A10 Other real estate loans 0.0372 0.0613 0.0602 0.0611 0.0983 0.0772 0.0654

A11 Credit cards 0.0145 0.0334 0.0247 0.0346 0.0267 0.0292 0.0305

A12 New auto loans 0.0277 0.0642 0.0619 0.1321 0.0436 0.0498 0.0751

A13 Used auto loans 0.0522 0.1380 0.1163 0.2633 0.1089 0.1053 0.1538

A14 Non-fed ins. student loans 0.0015 0.0029 0.0026 0.0029 0.0069 0.0049 0.0034

A15 Other loans 0.0325 0.0641 0.0713 0.0815 0.2650 0.0541 0.0717

A16 Other assets 0.0274 0.0497 0.0449 0.0572 0.0455 0.0443 0.0485

L1 Share drafts 0.0802 0.1725 0.1282 0.1582 0.1503 0.1415 0.1504

L2 Regular shares 0.3986 0.3292 0.5126 0.2803 0.2193 0.2440 0.3170

L3 Money market accounts 0.1258 0.1973 0.0409 0.1657 0.1873 0.2047 0.1681

L4 Share certificate accounts 0.1456 0.1201 0.1247 0.1802 0.1999 0.1789 0.1551

L5 IRA/Keough accounts 0.0824 0.0565 0.0596 0.0633 0.0600 0.0642 0.0619

L6 Non-member accounts 0.0012 0.0007 0.0020 0.0123 0.0209 0.0063 0.0060

L7 All other shares 0.0108 0.0047 0.0097 0.0074 0.0218 0.0102 0.0083

L8 Other liabilities 0.0155 0.0125 0.0105 0.0276 0.0282 0.0390 0.0237

L9 Net worth 0.1400 0.1064 0.1117 0.1050 0.1123 0.1111 0.1096

R1 Interest on loans 0.4732 0.5486 0.6368 0.6505 0.7638 0.6780 0.6231

R2 Interest refunds -0.0031 -0.0006 -0.0026 -0.0006 -0.0003 -0.0024 -0.0014

R3 Investment income 0.3641 0.1130 0.1126 0.0371 0.0357 0.0724 0.0915

R4 Trading income -0.0006 -0.0001 0.0000 0.0000 -0.0001 0.0000 -0.0001

R5 Fee income 0.0817 0.1952 0.1524 0.1804 0.1241 0.1226 0.1597

R6 Other operating income 0.0680 0.1393 0.0963 0.1259 0.0705 0.1236 0.1211

R7 Other (including gains/losses) 0.0167 0.0046 0.0046 0.0067 0.0064 0.0058 0.0060

E1 Labor expense 0.2846 0.3923 0.3783 0.3586 0.3579 0.3583 0.3672

E2 Office expenses 0.1287 0.2190 0.1977 0.1813 0.1742 0.1713 0.1888

E3 Loan servicing expenses 0.0333 0.0482 0.0403 0.0548 0.0452 0.0465 0.0477

E4 Other non-interest expenses 0.1115 0.1263 0.1311 0.1217 0.1011 0.1165 0.1217

E5 Interest on borrowed money 0.0058 0.0020 0.0020 0.0046 0.0062 0.0105 0.0052

E6 Share dividends 0.1800 0.0488 0.0775 0.0663 0.0828 0.0865 0.0736

E7 Interest on deposits 0.0110 0.0089 0.0079 0.0112 0.0170 0.0115 0.0104

E8 Provision for loan/lease losses 0.0163 0.0416 0.0373 0.0811 0.0774 0.0361 0.0496

E9 Net income 0.2287 0.1131 0.1279 0.1204 0.1382 0.1628 0.1358

This table provides the means for the common size variable for each of the six credit union clusters. For

comparison, the overall means are repeated in the right-most column.

32

TABLE 7 Properties of credit unions by cluster

Cluster 1 2 3 4 5 6 ALL

Number of members

67

Saver

Oriented

430

Traditional

(1)

192

Traditional

(2)

388

Auto

Lenders

48

Other

Lenders

403

Real

Estate

1528

All

Total assets of cluster ($B) 59.51 191.00 108.20 231.78 21.14 487.92 1099.55

Mean size ($B) 0.888 0.444 0.564 0.597 0.440 1.211 0.720

Median size ($B) 0.303 0.225 0.194 0.310 0.242 0.431 0.277

Combined categories

Total investments (A4-A8) 0.5907 0.3252 0.2885 0.1118 0.1091 0.1773 0.2322

Total loans (A9-A15) 0.3053 0.5309 0.5705 0.7543 0.7689 0.7066 0.6365

Fees/Other op inc (R5-R6) 0.1497 0.3345 0.2487 0.3063 0.1946 0.2462 0.2808

Labor/Office exp (E1-E2) 0.4133 0.6113 0.5760 0.5399 0.5321 0.5296 0.5560

Total non-int exp (E1-E4) 0.5581 0.7857 0.7474 0.7164 0.6784 0.6927 0.7254

Total interest exp (E5-E7) 0.1969 0.0596 0.0874 0.0821 0.1060 0.1085 0.0892

Financial ratios (Median)

Return on assets 0.0061 0.0048 0.0050 0.0064 0.0061 0.0070 0.0059

Capital-to-assets ratio 0.1343 0.1016 0.1044 0.1015 0.1017 0.1069 0.1042

Liquidity 0.0538 0.0842 0.0843 0.0703 0.0725 0.0639 0.0720

Loan-to-assets 0.2918 0.5426 0.5646 0.7581 0.7797 0.7123 0.6529

Charter (Fed = 1, State = 0) 58.21% 53.72% 55.21% 47.42% 54.17% 48.88% 51.24%

NCUA region membership

Region 1 Albany, NY 20.90% 14.65% 18.23% 10.82% 20.83% 40.20% 21.34%

Region 2 Alexandria, VA 22.39% 16.51% 20.83% 13.66% 10.42% 14.39% 15.84%

Region 3 Atlanta, GA 14.93% 23.02% 19.27% 21.91% 8.33% 11.41% 18.39%

Region 4 Austin, TX 19.40% 22.56% 20.83% 37.37% 35.42% 14.89% 24.35%

Region 5 Tempe, AZ 22.39% 23.26% 20.31% 16.24% 25.00% 18.11% 19.76%

Region 8 0.00% 0.00% 0.52% 0.00% 0.00% 0.99% 0.33%

Table 7 provides additional financial characteristics for the credit unions in each cluster. This includes

the total assets of all of the credit unions in each cluster as well as the mean and median size of the

members in each cluster. For each cluster, the table combines some of the common size variables for

interpretation, provides four popular financial ratios, the percent of Federal charters, and a breakdown of

NCUA region membership.

33

TABLE 8 Largest credit union members in each cluster

Cluster Number Rank in Cluster Credit Union Name Total Assets

1 1 Star One 7,857,993,558

n = 67 2 ESL 5,666,403,125

Saver-oriented 3 Wings Financial 4,227,852,463

4 DFCU Financial 4,020,891,185

5 State Farm 3,943,461,167

2 1 The Golden 1 9,676,953,370

n = 430 2 Redstone 4,285,558,786

Traditional (1) 3 Desert Schools 3,837,933,267

4 State Employees 3,042,780,974

5 Wescom Central 3,007,815,087

3 1 Boeing Employees 14,471,060,884

n = 192 2 Alliant 8,673,603,672

Investments (2) 3 American Airlines 5,961,680,116

4 United Nations 4,357,291,509

5 Hudson Valley 4,249,485,540

4 1 Security Service 9,237,936,319

n = 388 2 America First 7,181,010,913

Auto Lenders 3 Alaska USA 6,149,860,075

4 Mountain America 5,077,886,756

5 Pennsylvania State Employees 4,487,443,051

5 1 San Antonio 2,801,772,675

n = 48 2 Connexus 1,319,357,085

Other Lenders 3 Advantis 1,227,427,941

4 Hanscom 1,117,610,110

5 USAlliance 1,092,874,058

6 1 Navy Federal Credit Union 73,279,078,865

n = 403 2 State Employees' 31,820,568,729

Real Estate Lenders 3 Pentagon 19,460,442,369

4 SchoolsFirst 11,745,352,319

5 First Technology 8,649,723,431

This table lists the five largest credit unions in each cluster and their respective total assets (at December

31, 2015).

34

Endnotes

1 There are a large number of business model components that are applied to an industry or to a

specific firm. For a general overview of this literature, see Zott, Amit, and Massa (2011) and

Foss and Saebi (2017).

2 Hryckiewicza and Kozlowskib (2017) established how four distinct banking business models

affected global bank profitability and risk during the recent financial crisis. Ayadi (2106) and

Avadi et al. (2016) studied the differential performance of five business models for 2500

European banks during the crisis. These papers also argue that a diversity of business models

can reduce the severity of a banking crisis and that the analysis of banking business models can

allow regulators, supervisors, depositors, market participants, and shareholders to better

understand bank risks.

3 For excellent overviews on the background, economics, and empirical studies on the industry,

please see recent papers by McKillop and Wilson (2010, 2015) and the extensive resources

available from the National Credit Union Association (www.ncua.gov). There are outstanding

research papers on credit unions, delving into their history, organizational objectives, taxation

and regulation, cost structures and returns to scale, demutualization, and general outlook. Basic

governance and organizational topics are addressed by Smith, Cargill, and Meyer (1981),

Goddard, McKillop, and Wilson (2008 and 2009), and Ralston, Wright, and Garden (2001).

Frame, Karels, and McClatchey (2003) and Tatom (2005) analyze the taxation of credit unions.

Papers on CU performance, CU service provision, and CU risks are Bauer (2008), Bauer, Miles,

and Nishikawa (2009), Frame, Karels, and McClatchey (2002), Karels and McClatchey (1999),

Fried, Lovell, and Yaisawarng (1999), Walter (2006), and Wheelock and Wilson (2011).

http://www.ncua.gov/

35

4 Walker and Chandler (1977) show how regulations result in a pro-borrower bias for credit

unions. Davis (2001) details other issues (intergenerational conflicts, capital adequacy, and

incentives to convert to joint stock form) inherent in the governance structures of credit unions.

5 The individual R-squares for the balance sheet variables tend to be greater than the R-squares

for the income statement variables. Compared to the complete set of common size variables

presented here, the overall R-square using balance sheet variables alone is higher, and the overall

R-square using income statement variables alone is lower. Because the total variance is greater,

we use the larger 41-variable set which allows a richer analysis than using a smaller variable set.

6 Many credit unions have changed their names and no longer have a company or industry

affiliation.

credit union business modelsfmaconferences.org/sandiego/papers...534 copeland hall 222 copeland hall...

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