Valuation of earn-outs

published on 22 November 2023 | reading time approx. 3 minutes

Earn-outs are popular clauses in company purchase agreements. They provide for an additional amount to be paid by the buyer to the seller if the company achieves previously defined targets, usually of a financial nature (e.g. sales or EBIT/EBITDA), after the transaction date. They often require a valuation for accounting purposes (e.g. IFRS 3, HGB). Besides, value deliberations may also take place during purchase price negotiations or in the context of investment controlling. The following article gives a short overview of the valuation of earn-outs.

Earn-outs are used in particular in situations with increased uncertainty, for financing the purchase price by way of deferral of payment, for mitigation of the principal-agent conflict or information asymmetry, or for bridging differing value expectations of the buyer and the seller. Although earn-outs are subject to individual contractual arrangements, mark-ups between 20 and 40 per cent can often be observed in practice. The amount of payment is not necessarily fixed but can be derived on a variable basis depending on the respective KPI. They may be accompanied by safeguards for the buyer in the form of a cap (maximum payment) and for the seller in the form of a floor (minimum payment). The combined agreement on both a cap and a floor is called a collar. As an alternative to cash, earn-out payments can be made in shares or other instruments, for example, convertible bonds.

Structuring

Earn-outs can be legally structured in various ways. The options most often used in practice are the structuring as a variable element of the purchase price in the purchase agreement or as put or call options (often mutual). The disclosure of an earn-out in the balance sheet largely depends on the structure of the earn-out and whether reporting takes place according to HGB or IFRS. Since this article primarily deals with the valuation of earn-outs, the accounting obligation will not be addressed in detail. In this respect, we refer you to the article written by our colleague Tobias Neukirchner: Earn-Out Komponenten bei Kaufpreisallokationen | Rödl & Partner (roedl.de)

Apart from accounting purposes, value deliberations may also be of relevance in purchase price negotiations or for investment controlling.

Valuation procedure

Valuation of earn-outs during purchase price negotiations may be of advantage, on the one hand, for information purposes, so as to determine the expected payment amount or to assess any risks of higher payments. Moreover, valuation is necessary as part of the purchase price allocation, since earn-outs are regarded as an element of the purchase price and, thus, of the consideration to be paid.

In practice, earn-outs are usually valued using simulation models, in particular in the case of non-linear payment profiles (for example in caps and floors). The model that is most often applied in this case is the so-called Monte Carlo simulation. It is a stochastics theory or probabilistic model that relies on repeated random sampling. It aims at obtaining numerical results based on sampling for problems that cannot be solved or can be solved only with great effort by an analytical approach. The simulation heavily relies on the law of large numbers.

The future development of the earn-out target value is simulated based on an estimated range of values for independent variables, e.g. sales growth or EBIT/EBITDA margin. For this purpose, an appropriate probability distribution is first defined for each independent variable. They usually comprise normal distribution or lognormal distribution but in some cases also more “exotic” ones such as beta distribution.

Then, a large number of random samples are taken. Computer-based Monte Carlo simulations use random numbers for simulating random events. In each simulation run, the value of each independent variable is determined according to distribution and random numbers. Then, based on the independent variables, the dependent variable is calculated, in our case the earn-out.

For a Monte Carlo simulation to deliver a meaningful result, usually at least 5,000 to 10,000 runs have to be performed.

The result of the simulation is an (approximate) probability distribution of the earn-out, which can then be analysed. In particular, it is possible to derive distribution-related values (expected value, standard deviation) and to determine the probability of certain ranges for the earn-out. Most of these analyses serve information purposes, for example, assessing the probability for the buyer that the earn-out will exceed a certain critical value. For accounting purposes, in particular the expected value is important, because it represents the fair value of an earn-out under IFRS 13. This value must therefore (depending on the structuring) be disclosed in the balance sheet as contingent liability and/or provision.

Less complex earn-outs may also be valued directly using option pricing models like for example the Black Scholes model. The Black Scholes model provides an analytical solution for determining the option price. In respect of the valuation of debtor warrants the same principles apply as to the valuation of earn-outs. A debtor warrant is a promise made by a debtor in the case of a creditor's waiver of a claim saying that the debtor will repay all or part of the debts initially forgiven if his/her financial situation improves. In terms of valuation, they thus work like a “negative earn-out”.

Conclusion

Earn-outs allow risk management based on payment of the purchase price linked to a performance indicator. Especially given the current macro-economic uncertainty and increased inflation, earn-outs are an important instrument in purchase price negotiations. Since earn-outs provide room for manoeuvre when it comes to structuring, they should be valued individually in practice, which sometimes can be quite a complex process. Depending on the accounting regime, it may also be necessary to update valuations on subsequent valuation dates. This requires thorough verification and adjusting past assumptions.