Predicting and Managing a Credit Union’s Expense Ratio

February 16, 2005

This is the executive summary to a 67-page research monograph that we recently completed for the Filene Research Institute that explores how to manage a credit union’s operating expense ratio. We describe research that has created a new tool to help credit unions manage their expenses, as measured by the expense-to-asset ratio.

The operating expense ratio (operating expenses divided by average total assets) is a popular expense management tool involving both a numerator and a denominator. Sometimes, however, managers and regulators underestimate the importance of the denominator in formulating business strategy. They may concentrate on slashing operating expenses to achieve a lower expense ratio without fully considering alternative approaches. A 4% expense ratio for a credit union with $4 in expenses and $100 in assets can be reduced to 3% by (1) lowering expenses to $3, (2) raising assets to $133, or (3) altering expenses and assets simultaneously. Each of these three general alternatives can be pursued through a variety of product and service strategies.

Our research involved examining a wide range of potential predictors of expense ratios including total assets, deposits and loans per member, loan-to-asset ratios, delinquency rates, real estate loans, auto loans, average wages, products and services offered, sponsor subsidies, branches, and characteristics of the field of membership.

Leading Expense-Ratio Drivers

A wide variation exists in expense-to-asset ratios. Significant influences on expense ratios include:

1. Deposits per member. This is the most powerful expense ratio predictor identified in this research. As deposits (shares) per member increase, expense ratios decrease substantially.

2. Credit union size. Increases in asset size are generally associated with somewhat lower expense ratios, even controlling for other influences. However, many individual small credit unions have very low expense ratios.

3. Ratio of loans to assets. As the loan-to-asset ratio rises, the expense ratio rises.

4. Average loan size. Larger average loans tend to reduce the expense to asset ratio.

5. Real estate loans. An increase in the percentage of the loan portfolio devoted to real estate lending reduces the expense ratio.

Our research confirms what some credit union managers have concluded intuitively – attracting additional assets alone is not nearly as effective in reducing expense ratios as building deposits and loans per member. The dollar value of loans, however, is not listed above because of its high correlation with both deposits per member and loans to assets. Needing to avoid highly correlated variables in the predictive equation, we did not include loans per member directly.

Why are assets per member such a strong expense ratio predictor? Members with very low deposit balances require many of the same services and incur many of the same expenses as members with very large deposit and loan balances. Successful credit union expense managers develop strategies to attract a larger share of wallet from each member, offering incentives and new products through effective marketing programs. They also consider actions designed to limit the number of very low balance accounts on their books. A recent Filene report discusses the phenomenon of “free ridership,” observing that in a traditional cooperative, members receive benefits in proportion to the amount they contribute to the enterprise. But as the organization becomes more heterogeneous, we find “free riders,” individuals who derive benefits without contributing to the organization.

Benchmarking

In addition to showing the best expense ratio predictors, this report introduces a tool for comparing a credit union’s expense performance against a benchmark for similar-sized credit unions using similar strategies. Using six actual credit unions as examples, we apply our predictor to create a benchmark and to simulate changes for each organization. Benchmarks for these six credit unions provide examples in which the predictor benchmark is both a tighter and a looser standard than a simple peer group average. We provide summaries of results and their implications.

The appendix shows how to use the Expense Predictor Equation, and gives specifics on how a credit union can use our expense ratio predictor using their data. It shows how to calibrate the predictor for a particular credit union, and how to use it for benchmarking and simulations to help manage expense to asset ratios more effectively.

Many credit unions today use peer group averages to benchmark their expense ratio. If the peer group is based on asset size, this method omits many ways in which the credit union may differ from its peer group and the many ways in which credit unions that make up the peer group differ among themselves. Credit unions circumvent some of these problems by choosing their peer groups based on criteria other than size. However, the ideal peer group has as much as possible in common with the credit union using it to benchmark expense ratios. In practice this ideal is difficult to achieve because credit unions vary in many ways.

The benchmarking tool developed here deals with the peer group problem in a way that mimics an ideal situation in which all credit unions within the peer group exhibit key characteristics identical to the credit union performing the benchmark.

To develop this tool we start with an asset-size grouping and estimate statistically how much a number of key characteristics affect the expense ratio on average for credit unions in that group. These characteristics include such variables as assets, shares per member, the mix of types of loans in the portfolio, loan-to-asset ratio, field of membership characteristics, number of branches, and local cost of living measures. We use a well-established statistical procedure to estimate the size of each influence, on average, within the group. We then use these estimates to generate an expense ratio predictor.

The predictor can then do two important things:

• A credit union can enter the values for its own particular characteristics, and the predictor will provide the expense ratio that credit unions in its group would have on average, if they had many of the same characteristics as Credit Union X. The predictor produces a benchmark expense ratio that controls not just for asset size, but for other key variables that can influence expense to asset ratios.

• The tool simulates the effect on the expense ratio of changes in key variables. If a credit union has a target expense ratio, it can simulate changes in assets, shares per member, or other characteristics to measure their effect on the expense ratio of each variable while controlling for other variables.

Joanne M. Doyle, James Madison University, and William A. Kelly, Jr., University of Wisconsin-Madison, wrote Predicting and Managing a Credit Union’s Expense Ratio for Filene Research Institute. For more information, click here.