As one point of contact for questions regarding Callahan & Associates' financial analysis software, I have firsthand knowledge of how many people in the industry select a "peer group" of credit unions for benchmarking. Almost all of you are doing it wrong.
Ok, now that I have your attention, "wrong" might be a little too strong, but you could be doing it a whole lot better. Hear me out.
The problem is that many people select credit unions based solely on asset size. (Anyone visit NCUA.gov recently?) However, using this single factor can be misleading and lead to a false understanding of your performance. Each credit union's performance is driven by an innumerable group of factors; however, there are four filters that you can use to more accurately identify a "peer group" that will lead to better decisions.
- Asset Size
While it may appear as though I just contradicted myself by listing asset size as the first criterion for benchmarking, that is not the case. Asset size is a valuable data point to use in conjunction with other factors. After all, there is something to be said for economies of scale (although many technologically savvy credit unions are turning this cliché on its head) and shear buying power (read: being able to negotiate better pricing with vendors because you deliver more business). Defining a peer group based on asset size alone is the problem that many credit unions get themselves into.
While we at Callahan & Associates often discuss important national trends in our articles, perhaps nothing is more important to a credit union's performance than the conditions of their local market. For example, as of June 2009 the national figure for ROA is 0.27 percent; however, if we remove the "Sand States" of California, Nevada, Arizona, and Florida, the industry average more than doubles to 0.55 percent. One significant factor (other than the Corporate elephant) is local real estate prices. Another is unemployment—both largely local phenomena.
While national trends provide context that is absolutely imperative for an interdependent industry like credit unions, it is necessary to analyze your local market when setting performance targets. Otherwise, you could just be spinning your wheels trying to achieve the unattainable. Or worse yet, underperforming.
- Field of Membership & Average Member Relationships
The demographic you serve can have a substantial impact on your performance. Consider: teachers have more stable, albeit often lower-paid, employment than pharma- or airline-based credit unions. Credit unions that have community charters may in some cases serve a more diverse membership; in others, they may serve wealthy zip codes.
So, how can you figure this out using standard 5300 data? TOM (type of membership) codes are a good place to start but pair this with more specific data like Average Share Balance. Credit unions with a high average share typically have a different operating model than a credit union with a much lower average share balance.
• As of 2Q 2009, there were 2,165 credit unions with over $50 million in assets. These credit unions reported an average share balance of $8,758 and an operating expense ratio of 1.56 percent.
• Of those 2,165 credit unions 202 had an average share balance over $14,000. These credit unions reported a significantly lower operating expense ratio of 0.88 percent.
- Business Model
Defining a peer group based on your business model is hugely valuable but equally complicated. If you have already built your peer group using our first three criteria, and now you attempt to further narrow your peer group based on your delivery strategy, loan portfolio, or deposit offerings, you may find yourself with only a handful of comparisons.
So, now you have two options: first, you can broaden the parameters of the criterion you have used to this point, which would leave you with a peer group that is loosely defined by asset size, geography, or membership and would more heavily be based off of an element of your business model. Or you could use several peer groups to benchmark your performance in different areas of your performance while still heavily incorporating the previous criteria mentioned.
In most cases I would suggest the latter. Let me elaborate. If you define your differentiating factor as "ease of access" and have invested heavily in delivery channels, use factors like number of branches, ATMs, CUSO-relationships (shared branching) and even number of electronic services offered to find other credit unions like yourself. However, you probably won't find many in your geography. So use the business model peer group to analyze your performance in some areas, while also using a geographic peer group to understand local influences that may help you shine a light on differences in performance in the first group
Sound complicated? It doesn't have to be. Investing time (and a little money) up front when selecting a peer group can save you time (and a lot of money) down the road. To learn more about peer analysis, sign up for the complimentary webinar "Unleash the Power of Data for Better Decision-Making:Strategic Benchmarking."