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Loan Betas: A Key ALM Assumption for NIM as Rates Rise

Darryl Mataya
June 30, 2022
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What Are the Loan Betas in Your Plan for Rising Interest Rates?

Loan betas, or how rates for new and repriced loans will act as interest rates rise, are critical ALM inputs for NIM forecasts. 

You might also like this webinar, "Banking in a Rising Rate Environment: Myth Busters Panel."

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Yield vs. Funding Costs

Forecasting NIM improvements as rates rise

With the expected ongoing increases in market rates, many institutions hope to improve net interest margin via an increase in yield on earning assets outstripping any increases in funding costs. However, in prior rising-rate environments, this expected gain was often not as big as it was modeled in an asset/liability management (ALM) model.

 

Why is that? The reason is that the true loan betas, or the actual increase in yields earned, do not fully match the increases in market rates. When I ask about the projected betas used in modeling, I'm often told they are assumed to be 1, meaning any new assets booked or repriced will adjust at 100% of recent market rate increases.

Responses to Rate Hikes

What is a loan beta

When we define a loan beta, we refer to the change in rates charged on new loans or repriced loans relative to market-rate changes, not the change in overall loan yield. You will sometimes see a loan beta analysis that only calculates the overall change in loan yields relative to market rate changes. However, this dilutes and reduces the actual beta by including the yield on loans that cannot be repriced. An institution with a high mix of fixed-rate lending will report lower betas, when in fact the true loan beta can be much higher.

Why loan betas often don’t match rising rates

Looking at some actual experience in the 2016-2019 rising-rate environment reveals that a beta of 1 is probably not the best assumption; the actual rate response to rising rates is often less than 100%. This is due to many factors:

  • Many "variable" loans have floors that keep them from lifting off until rates rise enough, although a good model should incorporate that factor.
  • Competitive loan pricing pressure can keep rate increases down as commercial loans reset. A market rate like prime or SOFR may have gone up, but you may have decided to reduce spreads to remain competitive.
  • Consumer loans can have lower betas in competitive markets when institutions hesitate to raise rates in order to maintain origination volume.
  • Sometimes beta values are driven more by a shift in product mix or credit tier. For example, an institution may have reduced the number of loans it made to lower credit grades over a study period, and therefore, the overall average rates declined relative to market rates.
  • Not all market indices gain the same – the yield curve shifts.

Learn more about best practices for loan pricing during a rising-rate environment

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Rate Increases & Mortgage Betas

Loan betas in the last rising-rate environment

In the last rising-rate environment, average 30-year mortgage rates had a beta reaction of 84% compared to the 10-year Treasury, which it correlates very highly with. The mortgage beta was lower when compared to shorter-term Treasuries but then higher when compared to the fed funds rate. However, the lag (reaction period) was about a year when compared to fed funds but zero relative to the 10-year Treasury.

 

This illustrates another challenge in forecasting actual loan yields: using a realistic rate and yield curve projection. Using traditional immediate and permanent rate shocks to calculate future income potentially distorts loan betas because it relies on any possible market rate index to move by the same amount. In practice, this never happens, so modeling potential changes to yield-curve shape becomes an important forecasting tool.

Considerations & Approaches

Calculating historical loan betas

To calculate historical loan betas for your institution, you need:

  • access to data on historical loan pricing and originations
  • access to historical market rates, and
  • some basic statistical methods to calculate the relative changes in your rates compared to a market rate

However, a simple approach may miscalculate a true beta if it does not also attempt to correlate your rate changes with a closely matching market rate. Some loans may be priced off the prime rate and others off matching longer-term Treasury or borrowing rates. A simple approach is also not going to consider or calculate the lag, or the delay in the time it takes for your pricing to respond to market changes. For these reasons, institutions often use third parties or special software tools to calculate betas.

We have done some of these calculations using data and pricing over several institutions from the 2016-2019 rising rate environment. The following table summarizes beta calculations from consumer loan categories, which are primarily fixed-rate loans.

loan betas for consumer loans

Variable-Rate Loans After Rate Hikes

Commercial loan betas

There are no commercial loan categories in this analysis. Calculating beta values for variable rate commercial loans requires a more complex data set in order to extract both the actual rate changes and the spreads and indexes that those changes were based on.

It is unlikely that you will have enough information to accurately calculate your historical betas for commercial loans. So, when projecting beta value assumptions for commercial loan categories, it is helpful to understand your institution’s overall loan pricing philosophy and methodology.

If you price strictly off market indices and rarely do exception pricing to match competitive offers, then it is more likely those loans will have betas closer to 1. If you make a lot of exceptions or allow a significant amount of room in negotiating rates, your beta values are likely to be lower as rates rise.

So have you reviewed or evaluated the forecast loan betas for your institution? If not, it is a good idea to consider that assuming a simple 100% reaction may not accurately reflect what is more likely to take place.

Stay competitive as the market changes.

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About the Author

Darryl Mataya

Senior Advisor
Darryl Mataya is a Senior Advisor at Abrigo, where he manages the deposit and loan pricing services and consults regularly with institutions on funding strategies, pricing analysis, and loan strategies. He has been part of the banking industry since the 1980s, first as a designer and developer of software solutions. Before joining Abrigo,

Full Bio

About Abrigo

Abrigo enables U.S. financial institutions to support their communities through technology that fights financial crime, grows loans and deposits, and optimizes risk. Abrigo's platform centralizes the institution's data, creates a digital user experience, ensures compliance, and delivers efficiency for scale and profitable growth.

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