How to Solve Community Bank Problems When Organic Growth is Not the Answer

Part One: Picking the Right Target

Last month’s Bank Insights revealed unsettling balance sheet trends that indicate trouble on the horizon for community banks. This article is part of a series that will detail why M&A is the best solution—as long as banks use proper M&A analytics in their deliberations. 

By Kamal Mustafa, Invictus Chairman

Continued low loan interest rates combined with rock-bottom cost of funds have created major challenges for community banks going forward. Those banks that continue to pursue aggressive organic growth in an effort to increase earnings will sink further into the upcoming morass of declining Net Interest Margins and profits. Earnings compression will aggravate issues such as rising loan-to-deposit ratios, loan concentrations and increasing regulatory capital requirements.

Unfortunately, organic growth aggravates all these problems. And that means the only practical solution lies in properly targeted, identified, analyzed, priced and executed acquisitions. But that is easier said than done. Most community banks rely on traditional M&A analytics based on historical data that fail to take into consideration the very economic and regulatory factors causing distress. As a result, these legacy analytics and their underlying logic fail in identifying and pricing the best targets a bank should pursue.

This series will focus on the proper methodology and analytics that are required to identify, price and consummate transactions that will help community banks mitigate the negative pressures created by current economic conditions and monetary policy. This article will review pre-due diligence target identification and pro forma analytics.

Pro forma statements generated using traditional methodologies often provide misleading results. We will highlight the difference in results between traditional reports and those that use vintage analytics, taking into account when a loan was originated.

The methodology of generating a target’s pro forma is generally the same as it was in the 1970s. Yet so much has changed since then in banking. Using these outdated techniques completely ignores the recession of 2008, the post-recession economic gyrations that affected probabilities-of-default and loss-given-default, and most importantly, the continuing, unprecedented monetary policy that has artificially lowered interest rates.

The following chart of outstanding quarterly loans is a simplistic yet realistic depiction of the prevailing methodology in use by analysts, investment bankers and accountants in the community banking market. As the chart shows, in an effort to focus on “expected performance,” there is an undue emphasis on more current loan yields and trends. As a result, the target bank’s performance in “older” years is completely ignored. This is unfortunate as the resulting performance metrics are not only inaccurate but also grossly misleading.

For practical purposes, the chart above seems logical. It shows projected growth trends consistent with recent history and an estimated gross yield based on the most current financial data. This gross yield is then extrapolated for the balance of the projected period. In reality, this approach serves only to mislead acquiring management’s focus and direction. The flaws become evident when “vintage analytics” are utilized in analyzing potential targets.

In the following chart, Invictus uses its proprietary methodology to segment out the vintage layers that constitute the successive quarterly loan balances as the portfolios age. This allows an appropriate focus on the new/refinanced loans underwritten each quarter and, equally importantly, their progression since they were originated.

As can be seen from the chart above, the latest outstanding loan balance will cascade down over time based on the inherent maturities/repayments built into the loan portfolios. The slope of this “net loan outstanding” level is different for different banks and has a critical impact on that bank’s future performance, profitability and loan level sustainability.

Now examine the next chart. The “vintage analytics” start to incorporate gross yield rates into each of the vintage segments based on prevailing interest rates at the point of origination. This process accurately estimates the gross yield distribution across each quarterly total loan portfolio, in segment and in total. The amortization built into the segments allows an extrapolation of these portfolios into the future, with each pro forma quarter reflecting its unique yield distribution and resulting net total gross yield.

Interest rates on loans are determined by competition in the market at the time of origination.  Invictus estimates the prevailing rates in each historical period to quantify the yield contribution of each loan in the portfolio. The bands are colored to indicate periods when rates were relatively high (green) or low (red).

The previous chart creates the foundation for a pro forma system that is not only far more realistic and accurate, but as will be evident from the following charts, produces radically different results than the prevalent legacy analytics.

Traditional Versus Vintage Analytics

The yield, risk, and amortization schedule of loans differs by loan type.  Each portfolio should be assessed individually and then aggregated together to forecast total loans.

As is evident from the following two charts, implied growth rates in the traditional analysis ignore the amortizations built into the target’s portfolio. Given the wide variances between different target banks, this portfolio runoff is critical when evaluating what is truly being purchased in an M&A transaction. (Question to ask your investment banker: Is this data being captured?)

Look closely at the next vintage analysis chart. It highlights and quantifies the reality built into every single bank’s portfolio. Loans have been color-coded in 20 percentile bands that reflect their gross yield based on prevailing interest rates at point of origination. An examination of the vintage analysis chart during the pro forma period shows that higher-rate loans originated during the 2009-2013 periods are running off rapidly. These higher, gross yielding loans continue to be replaced with “red” loans done at prevailing/expected low rates. The refinanced loans coupled with organic growth at these low rates continue to further drive down gross yields.

The bottom line: A very different pattern in pro forma gross yields emerges when comparing the traditional analytics and those that use vintage.

Legacy analytics have no basis or logic for adjusting pro forma gross yields, so they tend to project a constant based on the target’s current assets. This produces misleading results. The vintage analytics consider this critical factor, allowing the acquirer to truly evaluate the potential value of the assets under consideration.

Our advice: Know what you are buying.

These differences in simple pro forma projections barely scratch the surface of the value in using vintage M&A analytics over the legacy analytics that have permeated the market. Subsequent articles will examine in more detail how vintage analytics, using only public data, can allow acquirers a far greater and more accurate assessment of their targets. They can also help quantify their value proposition, solving or mitigating the acquirer’s limitations and constraints.   <