Analyzing credit union performance against segmented peer groups can improve benchmarking. While broad industry averages can help in tracking large-scale trends, at times the comparison can be misleading for evaluating individual credit union performance. Is share growth of 5% a good performance? If so, how good is it? Benchmarking performance with a peer group can give more meaning to a credit union’s performance.
There are some standard methods that credit unions have used to help select a peer group that is meaningful. Some samples include:
- Multiple Credit Union Comparables -- Some credit unions define small peer groups with just three or four other credit unions that share the same market or have similar fields of membership. Another method is to compare the credit union to multiple large peer groups in order to isolate where the opportunities for improvement are. For example, an $800 million credit union with 12 branches was concerned about operating expenses. The credit union created a peer group of all credit unions over $500 million in assets and a peer group of all credit unions between $500 million and $1 billion with at least 10 branches to compare office occupancy and office operations as a percent of assets. This type of analysis allows credit unions to see the opportunities to improve performance.
Below is a graph on growth of membership at the four largest credit unions by asset size in Arizona.
- Asset based -- This is the most common method for selecting a peer group. Callahan & Associates uses 10 standard asset based peer groups. Performance variations by size can be significant. The average credit union over $1 billion in assets increased shares 8.4% for the 12 months ending December 31, 2007. The average credit union with assets between $50 million and $100 million increased shares 3.9%.
- Geographic based -- Credit union trends by state can vary widely. For example, in Illinois the average credit union increased shares 3.6% for the 12 months ending December 31, 2007. California credit unions collectively increased shares 4.2% for the same period. As the challenges from the real estate crisis become clearer, we may see more variations in the data by geography.
- Efficiency based -- Another method for developing peers can be based on efficiency. A credit union with a high average share balance will most likely have a different operating model than a credit union with much a much lower average share balance. For example, the 2,021 credit unions over $50 million in assets have an average share balance of $7,924. This group of credit unions has an operating expense to average assets ratio of 3.31%. The 210 credit unions with an average share balance over $12,250 have an operating expense to average assets ratio of 1.91%. Peer groups based on operating efficiency may provide a better understanding of the strength of a performance.
- Membership based -- The type of member served by the credit union will impact performance. Does the credit union predominantly serve lower ranking military? Is the field of membership mostly teachers or government workers? Does the credit union serve a certain demographic? Even credit unions that have moved from a core sponsor to a more community based field of membership could have their performance impacted by the type of membership.
- Credit Union Supplier Analysis -- During a search for a new supplier for a service at a credit union, peer analysis can be valuable. For example, some credit unions have developed peer groups by core processor to find trends in operating expenses and other efficiency measures.
Analyzing data both by peer group and for the industry at large can help credit unions to understand where opportunities for growth or improvement lie.