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Does Your NEV Analysis Really Capture Fair Value Of Assets?

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If your credit union runs a Net Economic Value (NEV) analysis as part of the A/LM process, how do you determine the credit union’s loan discount rate assumptions?

Sometimes we hear credit unions say that their current loan offering rates are used as the discount rates in their NEV models.  We do not believe this is the best method.

While current offering rates reflect what a specific credit union is willing to accept in terms of yield on new volumes, they do not necessarily reflect what an investor looking to purchase an existing portfolio may demand.  For example, if the credit quality of a credit union’s auto loan portfolio has materially degraded since the loans were made—a situation many credit unions are experiencing—then an investor is going to require a return that exceeds today’s low rates to compensate for the risk.

Additionally, there may be times when a credit union is intentionally pricing a portfolio above or below market to affect growth in that portfolio.  Many credit union executives would say that their loan rates are better than banks—in other words, they are “below market rates.”

As far as the credit risk component is concerned, lately we have heard comments suggesting that credit risk should not be factored into an NEV analysis.  This is simply not true.

In Letter to Credit Unions 99-CU-12, the NCUA says “NEV equals the fair value of assets minus the fair value of liabilities.”  What is the definition of fair value?  According to 12CFR NCUA, Section 704.2, fair value is defined as “the amount at which an instrument could be exchanged in a current, arms-length transaction between willing parties.”  The definition further states, “Valuation techniques should incorporate assumptions that market participants would use in their estimates of values, future revenues, and future expenses, including assumptions about interest rates, default, prepayment, and volatility.”

If you have gone through the process of having a third party value your portfolio, this can be helpful.  Note, in many cases such as mortgages, the price will only be for a portion of the loans, not for 100% of the portfolio.  Often, a material portion of the portfolio will not receive a price since there is not an efficient market for mortgages made several years ago that now have high LTVs.

If you do not have market values then for each portfolio, as you set assumptions, consider if you would have to take a loss selling all of the parts (the good and the bad).  Not only should your unique credit experience play a role in this answer, but so should the geographic region in which the loans were made.  A credit union selling loans from any of the sand states will typically take larger losses.

Once you establish an assumed price, you can use that price to calculate the assumed discount rate.  Once the base simulation is done, run alternate sets of assumptions to calculate the sensitivity to your results.

Establishing Concentration Limits

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Establishing concentration limits that enable you to make sustainable, sound business decisions while trying to satisfy new regulatory pressure is very tricky.

The supervisory letter on concentration risk states that examples of concentrations within an asset class include…

“Residential Real Estate Loans—collateral type, lien position, geographic area, non-traditional terms (such as interest-only, payment option, or balloon payment), fixed or variable interest rates, low or reduced underwriting documentation, and loan-to-value (LTV).”

If you are contemplating multifactor concentration limits as described above, consider the following example and how this approach could impact your strategy and business decisions.

Let’s assume:

8 real estate types, with
4 different LTV ranges for
20 ZIP codes (geographic areas) and
6 credit score ranges, would result in

3,840 total risk limits for the Residential Real Estate Loans

Keep in mind the above example is just for Residential Real Estate.  Imagine applying the same multifactor approach to other asset categories.  The number of limits can become daunting and unmanageable.

We recommend listing every limit on a single piece of paper to help decision makers understand the magnitude of their potential policy commitments.

Slicing and dicing portfolios absolutely is a key component of portfolio analysis and risk management.  However, we are concerned that the establishment of these limits in policy is being rushed in anticipation of the next exam or, during the exam process, examiners are pressuring credit unions to establish concentration limits quickly.

Rushing to establish concentration limits without appropriate analysis, including potential impact to strategy and business model, could result in unintended consequences with serious implications.  Not to mention the red flag noted in the supervisory letter regarding changing concentration limits if a credit union is outside of policy.

We highly recommend following a deliberate process to establish limits.  Test drive your limits under various economic scenarios to understand, in advance, how they will impact strategy and business decisions.  This includes the changes that may be necessary to the credit union’s business model in order to manage within the new limits.

This blog addresses only a sliver of the issues regarding concentration limits.  There certainly will be more to follow, such as the correlation between the speed with which concentration increases and poor financial performance.