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Observations from ALM Model Validations: Extremely Profitable New Business ROA in Static Balance Sheet Simulations

In this installment of our series on observations from model validations, we’ll focus in on the results from traditional income simulations, specifically static balance sheet simulations. We often see results that show low risk despite the credit union having a material amount of fixed-rate, long-term assets.

Take the example below which shows the NII results from a static balance sheet simulation. In year 1, the NII volatility is -15.62% in a +300 bp rate environment, which would be considered lower risk, and year 2 is even better at -8.45%. Keep in mind most policies have NII volatility limits of 20-30%, so this particular credit union looks pretty good. But why?

Static balance sheet showing NII volatility
While there could be a number of reasons, what we’ve found is that static balance sheet simulations assume the new business will always be extremely profitable if rates increase. The example below shows a credit union that has a base case ROA of 0.78% that jumps 153 bps to 2.31% in a +300 rate environment.

Static balance sheet simulation with new business ROA over 2{f36f94659acab79cca6adb0c2cb87abd9a89960d2b05b787f21b160005154717}

As we discussed in a previous blog, Observations from ALM Model Validations: Cost of Funds Back Testing, static balance sheet simulations assume that the deposit mix will not change as rates change, even though history suggests otherwise. It also generally assumes that a credit union could never have the loan-to-asset ratio drop, and often assumes the institution will be able to raise its loan rates 100% of the rate change.

Clearly, relying on a new business ROA north of 2% is not reasonable. These unrealistic assumptions about new business understate the risk of an institution.

Often when presented with this evidence, the response is that there is no way that such a high ROA is being assumed because the results show a decline in ROA for the first year (see example below). The reason is that often places only look out one year, maybe two. So the new business impacts the results but is smaller than the existing business.

To prove this out, look out at year 5 in your static simulation. You may not fully see the ROA over 2%, since most institutions are having the strain from their existing commitments holding the ROA back, but it is likely you will see an ROA that is above a level the credit union has ever experienced. If you want to see an even less defendable answer, look out at year 5 for a +500, and you will most likely see an ROA that far exceeds the earnings experienced the last time rates were at 5% (2006-2007).

Static balance sheet hiding critical new business assumption

Seeing results from this perspective, it is hard to call a static balance sheet a risk simulation.

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Strategic Planning for Payment Options

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We all know there is tremendous uncertainty with respect to the payments battle. There are numerous players in this market. Just a few are shown below.

Payment options

It is not enough for credit unions to just add multiple payment options for members. If credit unions want to be able to generate revenue in this environment they must think through, early on, a critical component of the payments strategy—figuring out how to get the credit union’s card to be used, and hopefully be THE CARD that is at the top of members’ wallets (including in-app purchases).

While it may be obvious that credit unions need to market their payment options and get employees to enthusiastically tout the benefits to members, quite often when we ask “what are your initiatives to get members to set your card and forget it?” the response is, “that’s a great question.”

Think about the opportunities you’ve had to get your members to use your card exclusively—in other words, it is not likely that your members think about which card to use, since there are so many options available to them. For example:

  • The Apple Store
  • Amazon Prime
  • Amazon Dash Buttons
  • Starbucks
  • Netflix
  • Uber

The list goes on and on.

Any roll out of new payment options is incomplete without a strategy to convince members they should regularly use your credit union’s card with the new payment option.

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Observations from ALM Model Validations: Optimistic New Volume Rate Assumptions

When running static or dynamic balance sheet income simulations, assumptions regarding the interest rates received on new business are needed. On the surface, this seems to be an easier assumption to make relative to some of the other assumptions needed in asset/liability management modeling (ALM modeling). However, in model validations we have performed, we have seen several issues with this assumption that result in far reaching consequences on the modeling.

For example, most credit unions tout that their rates are better than bank rates. Assuming new production at lower rates can make sense from an earnings and risk to earnings perspective.

The challenge is that many will assume that their net economic value (NEV) discount (market) rates are the same as their new production rates. To represent the fair value of assets (NEV), it is important to represent the yield that the market would demand to purchase the loans from the credit union. Assuming a low discount rate creates optimistic market values. Therefore, it is necessary to increase the discount rate to make the NEV more reasonable.

A Modeling Challenge
While, in this example, increasing the discount rate helps to produce more reasonable NEV results, it creates more optimistic earnings projections because quite often institutions use the same assumption for both new production and NEV discount rates.

One Solution
If your credit union is running a model that uses offering rates as discount rates, and any adjustments to the offering rate affects both the income simulation and the NEV, consider doing two different model runs—one for the income simulation with new volume rates representative of recent production and one for NEV with the adjusted discount rates.

Rates Change
One other consideration is the new volume rate in shocked rate environments. The most common assumption seen is that loan rates will go up 100% of the rate change and the rate increase will not hurt new volume production. Considering that the industry has been experiencing significant loan growth, resulting in an increased loan-to-asset level; is it reasonable to assume that current levels will be maintained (or assumed growth continued) despite taking all loan rates up 300 bps?

Competitive forces may not allow pricing to move this fast. Consider adjusting the base case to incorporate a slower pricing change. If not making this assumption in the base, minimally run a what-if scenario to understand the sensitivity of the results to the assumption.

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A Tough Question in a Short Message

Why is it critical to position for quality deposit acquisition, NOW?

Because:

  • The competitive environment is complex and changing at lightning speed.
  • Technology companies are redefining banking for just about everyone—not just Millennials.
  • Many credit unions have more than 65% of their deposits with members who are 60 years old or older.

Hopefully the traditional life cycle will play out. But hope is not a strategy. Consider the marketing, sales, and operational impact of the number of members needed from the younger generation to compensate for the loss of one large depositor. Some credit unions say it can be as high as 50 to 1. Do you know this ratio for your credit union?

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Observations from ALM Model Validations: NEV – Loans Devalue in Rate Shocks – or Do They?

When considering valuation as a measure of interest rate risk, and value volatility as an indicator of changes in interest rate risk, many institutions perform net economic value (NEV) analysis. When working with credit unions, or performing model validations, a concern many have is ensuring the models have the “right” assumptions. What is the “right” discount rate? Should credit risk spreads be incorporated? What effective discount rate or what yield curve should be used to discount cash flows – which method is “more right”?

All of the above may be questions to consider but they are distractions from simple analyses credit union management teams can perform when determining if answers are reasonable. For example, take an auto loan portfolio in which the valuation methodology derives a value of $210M in the base rate environment. This same portfolio devalues to $200M in a +300 bp shock. Said differently, the value volatility in a +300 bp shock is -5.00%. From a quick reasonableness test, this is within a 4-6% devaluation range in a +300 bp shock – very reasonable for an auto loan portfolio.

However, does it change the reasonableness answer if the current book value of the auto loans is $199M? While the devaluation of the loan portfolio is certainly reasonable, the resulting answer implies that the loan portfolio could be sold at a 0.50% gain if rates increased 300 bps instantly. That answer is certainly less reasonable. It is important to remember, in Chapter 13 of NCUA’s Examiner’s Guide, NEV is defined as the fair value of assets less the fair value of liabilities. Would it be reasonable to assume a fair value gain on an auto loan portfolio if rates increased 300 bps?

When measuring NEV volatility, the starting value still matters. High starting values can be driven by low starting discount rates. It is good to evaluate both the effective discount rate and the difference between value and book in the current environment. Some models are unable to calculate an effective discount rate. We have found that sometimes in this situation the effective discount rate does not match what the user intended. If you are in the situation of the model not being able to show the current discount rate, extra attention should be given to the value versus book and how the value compares to book in different environments. Optimistically high starting and shocked values can hide risk and volatility; this connects with the cautions brought out in our blog regarding high starting NEV ratios posted on September 25, 2015.