The income or net present value (NPV) approach is based on the cash flow that results from introducing the covered technology to the marketplace. The intellectual property value as calculated by this method is the present value of the expected future income stream. The theory behind this approach is that the licensee will be willing to pay some portion of their economic gain from using the intellectual property. It is based on the fact that successful technology commercialization or licensing means, for the owner or licensee, increased profits because of the use of the intellectual property protected technology.
The income approach to valuation involves making educated guesses or more precise measures if possible, as to the amount of income that the new technology will generate over time. It involves taking all the costs of production, sales, and overhead into account and subtracting those from the sales or revenues produced each month, quarter, or year. Specifically there are three parameters associated with this method. They are: 1. Amount of the income stream, 2. Duration of the income stream, and 3. Risk associated with the realization of the income. This involves preparing a spreadsheet involving all the cash flows inflows and outflows, for the term of the agreement and then calculating the current or net present value of the technology. Note that this NPV calculation requires selection of a discount rate which is the cost of capital adjusted for risk, examples of which are shown in the “Risk Premiums” figures. Obviously, this method is only as good as the precision of the data that is put into it.
NET PRESENT VALUE METHODS The net present value or income approach is based upon the economic principle of anticipation. The investor expects a certain income stream to be earned from the ownership of the intangible asset. This future income stream is converted to present net worth after analysis of all the risk factors that impact the generation of this future income.
An example utilizing this methodology to value biotechnology companies and their intellectual property follows. It includes in the calculation the probability of different discounted cash flows being realized. When doing so it’s important to work backwards.
Step one is to start with the commercialization stage. To determine the value from market launch the end of the product life, estimates of the following are made: 1. Forecasted cash flow from market launch until the end of product life. 2. The discount rate to apply to the cash flows. 3. The net present value at the market launch date is then determined by applying the discount rate to the forecasted cash flows.
Forecasting cash flows from market launch to product expiry (product life) requires a quantification of: expected revenue; cost of sales; operating costs; and income taxes.
Step Two is to create the value back through the R&D stage. Expected future cash flows during the development stage until market launch are made up substantially of the costs of preclinical R&D and conducting clinical trials related to the specific indication. Therefore the valuation must estimate the: 1. Costs, 2. Timing, and 3. Risks; for each stage of the preclinical and clinical trials. A detailed buildup of the budgeted trial costs is developed from a bottom-up estimate. For such R&D processes the estimate of value can be improved by applying a decision tree to the model. This is particularly true for biotechnology and other nascent technical areas. Each stage of the R&D or clinical steps is a journey toward obtaining market approval from a regulatory authority or consumer base. By assessing the probabilities of achieving success at each stage of the development process, the specific assumptions can be quantified directly in the valuation model. The advantage of using this methodology is that it improves the quality of the valuation. The disadvantage is that the calculation is no better than the cost and probability estimates Incorporated into the calculation. For incremental and some next-generation work these estimates can be pretty solid. For advanced next-generation and breakthrough work this methodology can be subject to large errors.
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