Valuing Contingent Consideration: Challenges and Solutions
Experts discuss pros, cons of different approaches to fair value measurements of earnouts.
When the FASB statement on business combinations was revised (modifying Statement no. 141 into Statement no. 141(R), now codified as FASB Accounting Standards Codification (ASC) Topic 805, Business Combinations), one change required that all items of consideration transferred by the acquirer be measured and recognized at fair value at the acquisition date, including consideration that is transferred only if some future specified event occurs. Sometimes this is referred to as an “earnout.” This type of contingent consideration often results when an acquirer and acquiree cannot completely agree on an acquisition price.
Because there is inherent uncertainty in any item of a contingent nature, the fair value measurement can often be complex, resulting in diversity in practice. The JofA gathered an expert panel to address the fair value measurement of contingent consideration (see sidebar, “Valuation Discussion Panelists,” at bottom of page). This article contains excerpts from that conversation.
Mark L. Zyla: What is the controversy about contingent consideration? The valuation community asked FASB to weigh in at a November 2010 Valuation Resource Group meeting. Can you tell us more about what the issue was, and whether the FASB eventually provided any guidance?
Daniel Peckham: There has been a lot of diversity in practice and debate in this area, with the lead accounting firms working together informally for months trying to develop best practices. The discussions among the firms came to a point where two leading views emerged. Those views are based on different interpretations of the accounting guidance.
View A, the liability view, is the view that the transaction premise is based on a transfer of a contingent consideration liability to a counterparty such as a bank or an insurance company, rather than based on the amount that the selling shareholders would be willing to receive in exchange for the earnout. So the conceptual basis for the measurement is to estimate the consideration required to entice a counterparty to step into an obligation to hold the liability to maturity and pay the earnout.
From an accounting perspective, those that were proponents of View A cited the language in paragraph 15 of ASC [Topic] 820 [Fair Value Measurements and Disclosure] that indicates a fair value measurement assumes that the liability is transferred to a market participant at the measurement date, and that the liability to the counterparty continues without settlement so it is not sold, but transferred.
View B, the asset view, is essentially based on the measurement of the corresponding asset. Proponents of View B noted that the guidance in (FASB Accounting Standards Update no.) 2009-05 [Fair Value Measurements and Disclosures (Topic 820)—Measuring Liabilities at Fair Value] indicates a liability can be measured based on the trading price of corresponding assets, and carried forward this guidance to conclude that Level 3 measurements of liabilities could also be based on the value of the asset.
View B notes that, in the absence of a real market, a hypothetical market is assumed, and furthermore that the market is efficient. Therefore, any arbitrage opportunity would be captured, forcing the fair value of the contingent consideration as a liability to approximate the fair value as an asset.
The VRG members generally supported an approach similar to View B and recommended that the FASB amend ASC [Topic] 820 to clarify that it is acceptable at all levels of the hierarchy, including Level 3 inputs, to use an asset valuation method when measuring fair value of a contingent consideration liability. In a subsequent fair value measurement meeting with IASB and the FASB, the recommendation that a liability can be measured based on the exit price of the asset holder was adopted, putting to rest most of the differences in views on the conceptual basis for the measurement.
Zyla: What are some of the outstanding issues that are involved in the valuation of contingent consideration? Why are they so hard to value?
David Dufendach: To illustrate, let’s first talk about something that’s not as hard to value.
In a typical business combination, there’s a known purchase price, there’s a forecast; and an IRR (internal rate of return) can be calculated from these facts. The forecast is typically a risky forecast. The actual cash flows may turn out to be higher or lower. If in fact the cash flows do turn out to be 10% higher, for example, we might expect the value of the equity, the value of the enterprise, to be higher by some similar amount. The relationship between cash flows and value is linear.
An earnout, however, is often characterized by its nonlinearity. It is also a function of business performance, but in a nonlinear way. If we assume, for example, that an earnout is structured so that there’s an extra $5 million payment only if revenues exceed a certain threshold, or for some other reason, then, this contingent payment is nonlinear. If the revenues equal or exceed the target, the payment will be $5 million. If the revenues fall short by 1%, 10% or any other amount, the payment will be zero. It’s clearly not a linear relationship.
If you have that kind of nonlinear payoff, you can’t value that earnout with a single scenario. You have to think about multiple scenarios that address the full range of possible outcomes.
Zyla: Risk is an important component in valuing contingent consideration. What kinds of risks need to be considered?
Amanda A. Miller: There are three kinds of risks that need to be considered when determining the discount rate for an earnout. The first one is the risk associated with the underlying metric. In many cases, the earnout will be based on revenues or EBITDA or another metric that is related to the value of the business. In those cases, that underlying metric is typically viewed as being subject to the firm level of risk, so the IRR or the weighted average cost of capital (WACC).
The second kind of risk is associated with the shape of the payoff. For example, the earnout might have a high likelihood of getting an extra $5 million fixed payment. That’s clearly going to be a lot less risky than the WACC. On the other hand, some other earnouts might have very low probability, very high risk, or have an option-like payout that pays a percentage of revenues or earnings above some high threshold. Those kinds of payouts are going to be much riskier than the WACC.
The last risk is the counterparty risk, because usually these payments aren’t in escrow waiting to be doled out. Generally, the acquirer is responsible for making the payments so the risk is associated with the acquirer’s cost of debt.
Zyla: Suppose an acquirer agrees to pay $5 million next year if the target’s revenue in that year exceeds a certain threshold. How would you value that?
Travis Chamberlain: The earnout payment is fixed and contingent on revenue exceeding a fixed threshold. There are two potential outcomes: either an earnout of $5 million or no earnout payment at all. The payment is contingent on whether the revenue threshold is met.
As David mentioned, due to the nonlinear payoff structure, and random nature of the underlying earnout metric, it is necessary to consider multiple scenarios and the expected future distribution of possible outcomes for revenue. As such, an option-pricing model or a scenario-based model is needed to value this type of earnout.
Since it can be difficult to estimate discount rates for nonlinear payoffs, some practitioners advocate the use of an option-pricing model instead of a scenario-based model or to estimate the discount rates for scenario-based models. Under an option-pricing model, the risk of the underlying metric in our example revenue is incorporated through a volatility estimate. Then option-pricing theory is used to estimate the value of the earnout as an option, whereby the future payoffs are estimated in a risk-neutral probability framework and discounted back at the risk-free rate to the valuation date.
In financial terms, the earnout formula in our example is analogous to a binary or digital option. As such, a modified version of the Black-Scholes option-pricing model could be used to value the earnout. While there are some embedded assumptions in the option-pricing framework that inspire debate and some practical challenges in estimating volatility for metrics like revenue, option-pricing models remain a viable and appealing methodology for valuing contingent consideration.
Zyla: One of the inputs to the option-pricing model is volatility. How do you measure the volatility as it relates to the underlying asset? What would you use as proxy for volatility for contingent consideration?
Miller: I’ve seen it done a couple ways. One way is that, since typically we’re dealing with a metric that’s related to the value of the total enterprise, like revenues or EBITDA, you can actually look at asset volatilities for comparable companies, comparable to the target.
Another way is to take comparable companies and directly observe the volatilities of the revenues or the volatilities of EBITDA. That can be challenging because there isn’t as much data. We tend to view the asset volatility as the more reliable measure.
Zyla: We’ve talked about, for a single cash payment contingent consideration, some of the things that you have to consider as the probability of getting the payment. Then you choose a discount rate that captures the risk associated with that payment.
But really earnouts are more complex than that. Some of them typically specify that the sellers get a payment based on the amount by which some metric exceeds a threshold. For example, it might be 50% of EBITDA above some level. How do people value those types of contingent consideration structures?
Chamberlain: Let’s assume that the earnout payment is a function of the amount by which EBITDA exceeds a certain amount. Under this scenario, the payoff is a nonlinear function of EBITDA with an asymmetrical distribution. So a method employing only a success or failure scenario will be inadequate—you’ll need multiple scenarios or an option-pricing method.
Under the previously mentioned scenario- based model, the expected payment would be calculated based upon the target’s real-world scenarios, the resulting payment under each scenario and the probability associated with each scenario. Although the payments would be probability-weighted, they would not be risk-free. The expected probability-weighted payment would be discounted to present value using the risk-adjusted discount rate.
Under an option-pricing method, the earnout formula would, in this example, be analogous to a regular call option instead of an all-or-nothing digital option. As such, the fair value of the earnout could be estimated using a Black-Scholes-type option-pricing model.
The methodology can become complicated if there is some form of path dependency in the earnout formula. For example, if an earnout provision specifies contingent payments over multiple years with catchup provisions, whereby the payments in later years are dependent on what happened in the intervening years, then a simulation model will likely be necessary.
Zyla: Does it matter what the underlying metric is? Does it matter, for example, whether it’s revenue or EBITDA or something else?
Dufendach: Yes, it does matter. Earnouts require valuation methods to be tailored to the unique factors that affect the underlying metric used to trigger the payment. A critical step in the valuation process is matching the expected distribution of this metric to an appropriate model.
In the case of EBITDA or other earnings measures, the expected distribution of future results will be affected by the volatility of top-line revenues, the correlation of these revenues with variable costs and the level of fixed costs, as well as other factors. The resulting distribution might be approximately normal, or bimodal, or some other pattern specific to the circumstances. The same entity’s expected revenue distribution, on the other hand, will be unaffected by most of these factors and may exhibit a very different pattern. An option- or scenario-based model that appropriately captures the distribution and associated probabilities of expected EBITDA for purposes of valuing contingent consideration may therefore be significantly different from the model employed for a revenue-based target.
Zyla: How can companies realistically deal with these issues? What are you seeing in practice?
Miller: The two things that companies, or their valuation specialists, need to think about are: (1) What’s the full range of outcomes for the earnout and the probabilities of those outcomes, and (2) how do you discount taking into account the risks associated with the resulting earnout payment? Option-pricing theory is one way to do that.
There are a number of specialists who are gaining experience at doing this, and many accounting and consulting firms have specialist groups that deal with these issues. It’s always a good idea to get your audit firm on board in advance and have the right people reviewing these complex structures.
To comment on this article or to suggest an idea for another article, contact Kim Nilsen, executive editor, at knilsen@aicpa.org or 919-402-4048.
Valuation Discussion Panelists
Travis Chamberlain, CPA/ABV/CFF, ASA, is a valuation specialist with Clifton Gunderson LLP and is a member of the AICPA Forensic and Valuation Services Executive Committee. He is also a member of the AICPA Equity Securities Task Force that is rewriting the guide Valuation of Privately-Held- Company Equity Securities Issued as Compensation. He specializes in the valuation of businesses, intangible assets, contingent consideration and complex securities.
David Dufendach, CPA/ABV, ASA, is a partner with Grant Thornton LLP. He is the U.S. director of quality control with respect to valuations for financial reporting purposes. He specializes in the valuation of businesses and business segments, intangible assets, intellectual property, financial instruments, derivatives, and related matters for purposes of financial statement reporting, corporate planning and other purposes. He is a member of the AICPA Task Force that is rewriting the practice aid Assets Acquired in a Business Combination to Be Used in Research and Development Activities.
Daniel Peckham, ASA, CFA, is a Deloitte Financial Advisory Services LLP principal specializing in the valuation of businesses, intangible assets and stock options. He has performed valuation engagements for mergers and acquisitions, purchase price allocation, financing, tax planning and litigation support. In addition to valuation, he has performed a variety of financial consulting projects that include buy-side and sell-side transaction advice, lease-vs.-buy analyses, and analyses of business plans for startup companies. He is a frequent presenter on fair-value-related financial reporting requirements.
Amanda A. Miller, Ph.D., is an executive director in the Complex Securities Valuation Practice at Ernst & Young LLP. Her specific areas of focus include valuation of privately held company securities, private equity and venture capital investments, illiquid credit instruments, and contingent considerations. She is a member of the AICPA Equity Securities Task Force that is rewriting the guide Valuation of Privately-Held-Company Equity Securities Issued as Compensation and has given presentations on fair value for private-company securities, financial instruments and contingent considerations at recent AICPA and American Society of Appraisers (ASA) conferences.
Mark L. Zyla, CPA/ABV, CFA, ASA, is managing director of Acuitas Inc., a valuation and litigation consulting firm based in Atlanta. He specializes in the valuation of businesses, intangible assets and various equity instruments. He is the chairman of the AICPA’s Fair Value Measurement Conference Committee and also serves on the AICPA’s Impairment Task Force, which is working on the guide Testing Goodwill for Impairment. He is also the author of Fair Value Measurements: Practical Guidance and Implementation, published by John Wiley & Sons.
Valuation Resource Group
The Valuation Resource Group (VRG) is a support group for FASB that provides the staff with information about implementation issues regarding fair value measurements. The VRG comprises industry representatives, including financial statement preparers, auditors, users and valuation experts.
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