Despite the early enthusiasm, real option valuation (ROV) has hitherto failed to “revolutionize” the way companies value their uncertain real-world assets. The reluctance to embrace ROV is largely due to its complexity and limited applicability to real- world problems. In this paper, we will introduce, discuss, and demonstrate the potential of one of the most promising ROV techniques: Least- Squares Monte Carlo Simulation (LSM). LSM is a significant step in overcoming the limitations in valuation techniques. It is particularly powerful when investment decisions are a function of multiple uncertain factors and when compounded options which may be exercised at any time exist. The paper will also include a real-world application, illustrating the power and flexibility of the LSM method. Developing and using a fit-for-purpose ROV model for a realistic investment opportunity is no simple task. However, the result of an ROV is not only an estimated value that includes the value of flexibility, but a complete strategy map showing the optimal choices as uncertainties are resolved. This, together with the creative thinking required to come up with valuable options and identifying the key uncertainties, creates insight and clarity for the decision-makers.
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