## Industry Trends

## Largest Transactions Closed

- Target
- Buyer
- Value($mm)

Whether you’re looking to buy or sell a business, navigating the process for contingent considerations—otherwise known as “earnouts”—can be complicated. In M&A transactions, buyers and sellers often use earnouts to bridge the valuation gap or to share risk when future events are uncertain. From identifying and quantifying sources of applicable risk to selecting the appropriate valuation inputs, however, the earnout process can present a significant challenge for accounting teams, financial advisors, and business owners alike. So let’s take a more in-depth look at some of the challenges and complexities often required in the valuation process.

An earnout is a commercial agreement between a buyer and a seller in which a portion of the purchase price is postponed and later paid to the seller if the acquired firm meets certain financial or non-financial benchmarks. In the past, under Statement of Financial Accounting Standards (SFAS) 141, the value of an earnout was not recognized until it was paid. But under SFAS 141R, implemented in 2008—and now under Accounting Standards Codification (ASC) Topic 805, *Business* *Combinations*—acquiring entities must record the fair value of an earnout and other contingent payments at the date of acquisition, as part of the total purchase price. In addition, ASC 805 requires a revaluation of most earnouts at each subsequent reporting period until final settlement of the obligation.

Each earnout is characterized by terms and conditions unique to the specific transaction and is contingent upon meeting certain financial or non-financial metrics. An earnout can take the form of an all-or-nothing payout or a proportional share of a maximum sum, for example. These mechanisms are especially effective when the buyer and the seller produce different valuations, typically thanks to opposing perspectives on future expectations or on the probability of a specific future event.

When attempting to align their plans with various dynamic factors, buyers and sellers must carefully analyze all possibilities, which can make agreeing on a purchase price difficult. While the ability to accommodate mismatched views on value is the most common reason to choose an earnout, other benefits can make earnouts attractive to both parties as well.

When an earnout is paid over time, buyers defer paying part of the purchase price, which means they can use any earnings generated from the acquired business to help pay the earnout. Buyers also benefit from sellers’ tendency to work hard to ensure a smooth transition. Through the deferred payout arrangement, sellers maintain “skin in the game.”

Yet not all the benefits go to the buyer. When sellers realize the full purchase price of their business at a later date (even from a buyer who initially doubted that value), that deferred payment stream can enable them to defer or even reduce taxes.

Determined through negotiations between buyers and sellers, the structure of an earnout may be simple, based on a fixed percentage of an underlying metric such as revenue or EBITDA (known as a “linear structure”). Or the structure can be rather complex, where the payoff is a nonlinear function of the underlying metric, incorporating thresholds, tiers, caps, or catch-up payments. The type of structure—linear or nonlinear—establishes both the risk and the discount rate used in ascertaining the fair value of the earnout. The structure also drives the choice of valuation model or method to use when estimating the fair value of the earnout.

A simple linear earnout absent any complicating conditions can be estimated based on the payoff associated with the expected probability-weighted outcomes of the underlying metric. Most earnouts are not linear, however, and for those with a nonlinear structure, the potential payoff is not symmetrical. Accordingly, a nonlinear structure should consider the probability distribution of possible outcomes of the underlying metric and the related payoff for each possible outcome.

Common earnout structures are shown below, with each diagram indicating the relationship between the payoff structure (vertical axis) and the performance of the underlying metric (horizontal axis):

In a constant earnout structure, for instance, the probability of achieving the underlying metric is 100%, and the payoff is fixed—the equivalent of a deferred payment (this type of structure is commonly referred to as “debt-like”). Compare this with a binary structure, where the payoff is fixed once the underlying metric is achieved. Both of these are considered nonlinear earnouts and often come with significant risks, which increase further with the introduction of multiple contingencies, such as a combined threshold and cap, or a payoff calculated as a percentage of an excess above a certain threshold.

An earnout with a linear structure, on the other hand, represents a fixed payment and incorporates a direct correlation between the payoff and the underlying metric. The payoff in a linear earnout often is a fixed percentage of an underlying metric (such as revenue or EBITDA), and the risk associated with both the underlying metric and the payoff is equivalent.

To use an earnout in a transaction is one thing; to value it is quite another. Best practices for determining the fair value of an earnout can be found in the “Practice Aid,” an influential valuation guide published by the Appraisal Foundation. Valuation specialists (and those who use their services) should start with a thorough understanding of these guidelines.

The Practice Aid acknowledges that no single method for valuing an earnout is superior in all respects and circumstances, and ultimately recognizes the trade-offs involved in selecting one method over another. It does conclude, however, that certain methods are most appropriate for certain types of earnouts. In particular, the Practice Aid recommends two commonly used valuation methodologies:

- The scenario-based method (SBM)
- The option pricing method (OPM)

There are two key differences between the SBM and the OPM. The first difference comes from each method’s ability to account for both the risk associated with the underlying metric and, where applicable, the risk associated with the earnout’s payoff structure. The second critical difference comes from the complexity associated with the process for each method, including the required inputs and the process of capturing the subsequent output.

The scenario-based method uses various forecasts (“scenarios”) for the underlying metric and the payoff associated with each scenario. Each scenario is assigned a probability based on its rate of occurrence, and the associated payoff is then probability-weighted, producing the expected earnout payment. Then, to calculate the expected present value of the earnout, the payment is discounted at the appropriate rate. The challenge in this valuation method lies in reasonably estimating the range of outcomes or scenarios for the underlying metric.

Using the SBM is appropriate when the risk of the underlying metric is diversifiable (as with a non-financial metric), such as the achievement of a certain milestone or event. The SBM is also suitable when the payoff structure is linear, such as a fixed percentage of revenue or EBITDA, and contains no thresholds, caps, or catch-up payments, for example.

The option pricing method, like the SBM, builds on an infinite number of scenarios given the underlying mathematical characteristics. Because of the underlying complexities of the math within the OPM framework, however, this method can be more complex and less transparent, and thus is less commonly understood. Yet the OPM has its advantages: it is useful in estimating the probability of achieving the underlying metrics based on a quantitative framework, and it eliminates qualitative guesswork.

The OPM is commonly used in valuing nonlinear earnouts when the underlying metric is financial in nature or when the risk of the underlying metric is non-diversifiable. This two-step method discounts the underlying metric and the expected payoff separately by applying the discount rate to the underlying metric, resulting in a risk-neutral framework for that metric, and then estimating the payoff within that same risk-neutral framework.

Within the OPM framework, earnouts can be structured as independent or dependent. An independent earnout structure does not make each payoff or underlying metric dependent on achievement of the prior payoff or underlying metric, and it permits the use of separate option pricing methods for each payoff. A dependent earnout, in contrast, contains multiple interdependent, non-diversifiable metrics (such as an earnout with catch-up provisions or multiyear caps) and requires a technique that can accommodate those complexities, such as a Monte Carlo simulation.

A Monte Carlo simulation is a probabilistic numerical method used to estimate the outcome of a given, uncertain (stochastic) process. In other words, it simulates future events or metrics (such as revenue or EBITDA) that cannot be modeled implicitly. Using key assumptions, such as growth rates and volatility, you can estimate a number of outcomes for a single forecast. After repeating the process hundreds or thousands of times and calculating the average result, you end up with a value that often affords a significantly higher degree of confidence compared with the single forecasted value.

In the context of an earnout, you would use a Monte Carlo simulation to account for multiple time horizons or multiple interdependent, underlying metrics by incorporating an assumed distribution for each metric and period. With multiple metrics, you’ll require an estimate of the correlation between the metrics—based on either the historical correlation or an assessment of that relationship (provided by management)—in order to ascertain the joint distribution.

After the metrics’ forecast has been risk-adjusted within the OPM’s risk-neutral framework, you’ll calculate the payoff for each trial according to the earnout’s structure. Then you’ll discount the payoff from the expected payment date(s) to the transaction close date at the risk-free rate, and make any adjustment for counterparty credit risk (if applicable). You’ll then calculate the value of the earnout as the mean present value of the payoffs across all the simulation’s trials.

Once you have selected the appropriate methodology to value the earnout, an applicable discount rate must be established so you can estimate the present value of the earnout as of the measurement date. The discount rate incorporates elements of default and recovery, and is applied to a certain metric or cash flow according to the methodology employed.

If the underlying metric is non-financial or the payoff structure is linear, then using the SBM is appropriate (as mentioned earlier). In such cases, to calculate the earnout’s present value, you would apply probability weighting to the payoff and discount the payoff at the appropriate rate. The discount rate accounts for the time value of money (this is the “risk-free rate”) and a premium for counterparty credit risk, when appropriate.

If the underlying metric is financial and comprises non-diversifiable risk, then the discount rate components increase and should take into account the time value of money; a premium for counterparty credit risk, when appropriate; and a risk premium that is appropriate for the metric itself (the “metric risk premium”). To accurately incorporate the additional risk premium, you’ll need to consider additional points such as the expected volatility in the metric’s growth rate, the time remaining to settlement, and the value of the metric relative to the structural features. Estimating an appropriate discount rate for these additional complexities is difficult, and therefore the SBM is not recommended when an earnout is based on an underlying metric that has non-diversifiable risk or a nonlinear payoff structure.

When the underlying metric is financial and comprises non-diversifiable risk, or when the payoff is nonlinear—both options that are mentioned earlier in this article—the discount rate components increase, and you would consider a metric risk premium in determining the appropriate discount rate. Equivalent to the discount rate that risk-adjusts the metric projections, this premium represents the required return that a market participant might expect. It might be similar to the acquired company’s weighted average cost of capital (WACC) or the transaction’s internal rate of return (IRR), although in many situations, the financial metric or payoff is not directly related to the company’s value.

Two methods are commonly used in estimating the metric risk premium:

- The top-down method
- The bottom-up method

When determining the metric risk premium using the top-down method, you would first consider the beta used to estimate the WACC or implied transaction IRR for the acquired business. As outlined earlier, differences in risk between the business’s long-term cash flows and the underlying metric are likely. Therefore, you would need to adjust the beta for items such as the short-term nature of the underlying metric and the differences between the acquired company’s capital structure and the degree of financial leverage compared with the underlying metric. In addition, you might need to adjust the starting beta for a size premium, a country risk premium, or even a company-specific risk premium.

Conversely, the bottom-up method estimates the underlying metric’s beta by assessing its volatility relative to the volatility of a proxy for the broader market. You would then make adjustments to account for differences in the underlying metric’s measurement period, size, company-specific risk, and other relevant factors.

Regardless of the chosen approach, it is important to understand which factors are relevant and applicable to the underlying metric, particularly when considering which additional risk premiums (size premiums, company-specific risk premiums, country risk premiums, etc.) have been incorporated into the acquired business’s WACC or transaction IRR.

Counterparty credit risk represents a contingent obligation of the obligor (the buyer) to make future payments—something that should be considered when accounting for the potential default risk of the buyer. This risk should also account for the seniority of the earnout claim in the buyer’s capital structure as well as for the expected timing of the payment. A decision about the fair value of the earnout is based on the buyer’s own specific credit risk, not the credit risk of a market participant, because under ASC 820, it is presumed that the earnout would be transferred to a market participant of equal credit standing.

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Whether you are evaluating an earnout as part of the initial recognition and fair value measurement requirements under ASC 805 or contemplating the sale of a business to private equity groups, public companies, or ESOPs, it’s important to fully understand the challenges associated with the valuation of an earnout. Consequently, you should consider partnering with a trusted and experienced advisor to help you navigate the valuation process.

**Valuation**

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