Valuing Intellectual Property of an Early Stage Company

Congress shall have the power to promote the progress of science and useful arts, by securing for limited times to authors and inventors the exclusive right to their respective writings and discoveries

 US Constitution, Article 1, Section 8, Clause 8

The Issue

Intellectual property (IP) was recognized as having value by the founding fathers of the country.  As intangibles and intellectual property become the true source of where corporate value lies, correctly valuing IP takes on an increased level of importance.  IP refers to creations of the mind and the related rights to use these in commerce. While it certainly includes patents, trademarks, licenses and permits, it also includes internally developed knowhow related to manufacturing a product and/or developing a service.  Professor Baruch Lev of the Stern School at NYU has indicated that a significant percentage of the value of public firms is attributable to various types of IP, the bulk of which does not appear on their balance sheets. While the contribution of IP to overall firm profitability varies, what we do know is that it provides corporate owners with a decided competitive advantage which allows them to earn rates of return that exceed their respective costs of capital.  The upshot is that firms that have IP that is long-lived will also have market values of equity that far exceed their corresponding book values.

In cases where the firm is a startup that has IP but little or no revenue, valuing the IP is both complex and difficult. Because of these facts, the value of startup firm IP is often based on its reproduction cost. However, in those cases where there is a demonstrated market for the firm’s product that incorporates the IP, basing the IP value on its reproduction cost will almost certainly undervalue it. The reason for this outcome is that the return to the owners of the IP will likely be very high. Just think what the revenue might be if the owning firm licensed the IP to another firm and collected a 2% royalty rate on licensee sales that incorporated the IP. If the market is likely to be large and the firm owning the IP has the capacity to take advantage of the business opportunity, then the IP will have a value that far exceeds its reproduction costs.

But opportunity aside, startup risk for most early stage firms is very great, and while IP can mitigate this risk profile to some degree, the inability of a startup to exploit its IP will more than likely reduce its value to both the owners and potential acquirers.  One has no idea when sales will occur and how large they will be for firms that own IP but have limited or no sales history.  In these instances, any value attributed the IP will be uncertain and highly suspect.  Since IP values are in part determined by what a hypothetical licensee might pay in the form of a royalty rate, any selected rate—like expected sales—is subject to a great deal of uncertainty particularly where the IP is unproven.

But what if we knew what the range of sales would likely be in any future year and we also knew what the range of royalty rates might be? Knowing this, we could produce thousands of IP values based on randomly combining sales and royalty rates and arranging these in such a way that we form a distribution which shows the percentage of the total associated with each value.  These percentages are probabilities associated with each of the values produced.  The value of the IP is the sum of the product of each value multiplied by its associated probability. The beauty of this approach is that it does not require revenue projections which are notoriously optimistic and generally wrong both in terms of timing and size.  Below we report the results of employing this method to valuing IP of a startup firm with small first year sales.

 Case Study

Axiom’s client capitalized the costs of acquired technology, knowhow, trade secrets and identifiable costs incurred to develop, file, and defend the company’s patents and new patents or provisional patent applications. This knowledge base allowed the firm to produce proprietary chemical products that eliminated harmful microbes in a variety of customer settings.

Axiom undertook a comprehensive FASB 142 Step 1 analysis of the assumptions made by management which is the basis of its baseline forecast. This included a review of source-based market analysis undertaken by management and its consultants. In addition, we tested to see whether the number of customers implied by their projections is reasonable in light of recent developments at the company. Management has also provided details on its sales pipeline as substantive backup to its projections.

Based on this analysis we concluded that the IP may be impaired and that a determination of the fair value of the IP is required.  Since the firm had relatively little revenue and projections were generally unreliable, we used a Monte Carlo approach which required the following information:

  1. Current annual revenue
  2. Revenue growth range for each projected year over the eleven year economic life of the IP
  3. Range of royalty rates
  4. Cost of capital

The only input that is problematic in this analysis is item two above.  Axiom developed a unique data set which shows revenue growth rates for startup firms by industry for each year after each firm’s formation.  This data set was the basis for revenue growth ranges used in the Monte Carlo.  The essential characteristic of startup firm performance is that revenue growth could be 300% in one year and zero or even negative in the next.  For startups and early stage firms, revenue growth is essentially random and the Monte Carlo framework can easily accommodate this randomness.

The range of royalty rates within an industry can be determined in a reasonably straightforward way since there is a great deal of data on royalty rates. In our case the range varied between 1% and 4% and the rate was randomly selected.  The analysis consisted of a Monte Carlo simulation that generated 1,000 random revenue growth rates over the 11 year asset life and 1,000 random royalty rates. These growth rates were applied to a starting revenue number which was then multiplied by a randomly generated royalty rate to calculate the royalty fee income. We then summed the present value of the after-tax royalty fee income and added back the implied tax amortization benefit to arrive at the fair value of IP for each of the 1,000 growth and royalty paths. The histogram of IP values is shown below:

Histogram of IP

 We used the histogram as the basis for determining the distribution that most accurately approximates the IP generating function.  Using this distribution, the probability weighted IP value was calculated. This value, $4,145,100, is the fair value of the IP.  The beauty of the Monte Carlo is that one can determine not only the expected value of the IP but the probability that the IP is within a certain range.  For example, the probability that the value of the IP is $12 million (1.2E+7) is about 1%, whereas the probability that the value of the IP is $3.2 million is 26%.

Valuing a Startup as an Exercise in Quantum Physics

It seems to be clear, therefore, that Born’s statistical interpretation of quantum theory is the only possible one. The wave function does not in any way describe a state which could be that of a single system; it relates rather to many systems, to an ‘ensemble of systems’ in the sense of statistical mechanics. (Albert Einstein, on Quantum Theory, 1936)

The value of a startup and the character of a wave function in quantum theory are similar in that neither is best described by one value but reflect an ensemble of values for which there is a central tendency. Valuing a startup business is not only difficult and complicated but there are many factors that more often than not lead to significant miscalculation.  What we know is that there is no valuation model that is designed to address the spectrum of risks, and of course opportunities, that characterize startups as a group.

Axiom works with startups all the time, and the valuation framework most often used is  discounted cash flow. The accompanying assumptions typically include a  rapid growth phase  to reflect the “market opportunity” and a very high cost of capital, to reflect the myriad of undefined risks expected to be encountered, which is then used to discount the expected super growth in free cash flow.  The values that result from using this approach may pass the proverbial “smell test” but given the undisclosed trajectories that a startup can take, it is virtually impossible for early stage investors to use the business plan valuation as  anything more than a hope and a prayer. On the other hand, early stage investors that are considering investing in the startup generally have a good idea of whether the business model makes sense and also whether the commercial market is large enough to support some level of activity that the startup represents.  With this in mind,  investors proceed by first giving the business plan valuation a significant haircut and then determine what percentage of the startup they need to own to produce the desired rate of return. The end result is that the entrepreneur gets some financial capital but at a very stiff price.

In order to properly value a startup  that will yield higher business values and financial terms from investors that are likely to be less oppressive, entrepreneurs need to recognize two fundamental factors. First, they need to transparently incorporate, but not limited to, the factors noted below.

  1. There are a multitude of risks, including not getting to market on time,  the product/service does not function as anticipated, operating expenses are greater than expected, and efficiencies of scale do not develop as anticipated (meaning that expense levels are far higher than anticipated and the investment required to sustain commercial viability is greater than expected).
  2. Sales may be greater or less than expected and customer segments may not be as receptive as originally thought, while others may be more receptive but in order to take advantage of these developments costly modifications of the product or service are required.

Second, and most importantly, entrepreneurs need to understand that the factors noted above, along with others, will likely combine in unexpected ways that will lead to sales and expense trajectories that the entrepreneur has not anticipated but will nevertheless be expected to cope with. Each of these outcomes is associated with a different business value which means that a startup is really not characterized by one value- the business plan value- but rather a distribution of values which reflect the market place the business will develop in.

The question then becomes how do these factors combine? Is there a logic that may provide guidance?  For startups, more so then for established firms, there are many more known unknowns and this along with the plethora of unknown unknowns makes placing a final and precise value on a startup almost an exercise in futility.  However, all hope is not lost once we are willing accept the view that we do not know what trajectory the startup will take and certainly we generally know almost nothing about when the startup will become commercially viable.  If we consider the various factors that govern a startup and assume they will randomly combine over a future time period, then we can study what the valuation implications of these combinations are.  If we randomly combined the factors described in 1 and 2 above, for example, what would this tell us?

First and foremost it would generate a distribution of firm values and the associated probabilities of achieving these values.  The strength of this analysis is not in simply understanding the valuation  implications if specific things go awry but rather what happens if some things go right and others  do not since inevitably this will be the case.  In the end, we do not really know how future values of various valuation drivers  might combine in ways that may be detrimental to the startup or enhance its prospects. What we do know is that there are series of events that could occur and we want to know how these in combination will impact the prospects of the startup and its value.

The Exhibit below is constructed based on 27,000 valuations which are produced by randomly combining the multitude of paths that numerous variables that drive valuation can take. The startup is an Axiom client selling its unique products and related services to an energy-related business client base.

Probability Density Function

The founder was in the midst of his first capital raise and was receiving feedback that the value he placed on the firm was too high.  Given this feedback and accompanying term sheets that were hard to fully come to grips with, he turned to Axiom for help to better understand what the value of the firm really is and how to use what he learned to negotiate terms that are more consistent with the firm’s central tendency value.

There are two vertical lines indicating the value assigned by the entrepreneur in the business plan and the value implied by a VC investor group’s term sheet.   Based on Axiom’s analysis, the central tendency of the firm’s value distribution is in the neighborhood of $12 million.

So what is the value of this startup?   The answer is the value is likely to be between $10.5 and $13.6 million since this range covers a significant portion of the value distribution.   The eventual transaction value is based on a multitude of factors but two that are critical are:  level of investor competition and liquidity in the market for startups.

From October’s Axiom on Value