Imagine a company contemplating whether it should invest in a new technology with uncertain market potential. A traditional approach, such as a Cost Benefit Analysis (CBA), might suggest rejecting the project if the expected cash flow doesn’t exceed the upfront costs associated with investing. However, such an analysis overlooks some of the strategic options the company might have. What if the company could delay the investment until they better understand the potential future outcomes? What if they could expand the project if there proves to be a high demand for their product—or abandon the project with minimal losses if it fails to deliver?

For a more flexible approach, this company might want to try a real options analysis (ROA). This method acknowledges that investments often involve a sequence of choices over time, not just a single "go/no-go" choice. As it suggests in its name, a real options analysis allows decision-makers to consider real options, which are the opportunities, but not the obligations, to make future decisions based on how uncertainties unfold. Such options could include deferring, expanding, scaling down, or even outright abandoning a project.¹ 

To conduct an ROA, companies may start by first identifying the real options within their project and then identifying the relevant variables that may impact this option. After estimating the volatility of each asset, a company can then calculate the value of each option using decision-making software or mathematical formulas, incorporating estimates for uncertainties like changing market prices. By assigning a monetary value to these options, ROA provides a more complete picture of an investment's true economic potential.^1,2

Real Option: The opportunity, but not the obligation, that a company has to take an action, such as deferring, expanding, or abandoning a project. These “real” options relate to tangible assets like machinery, property, or materials, as opposed to financial options, which are based on separate measurements.³

Abandonment Option: A type of real option that allows a decision-maker to abandon a project if future conditions are unfavorable. This option is particularly valuable for projects with high potential losses.²

Volatility: A measure of the uncertainty or variability of an investment's returns. In ROA, higher volatility generally increases an option’s value because it means that there’s a higher potential for large price swings in the company’s favor. When there’s more uncertainty, there’s also more likelihood of a high return on investment.³ 

Net Present Value (NPV): The difference between the present value of an investment’s future cash flows and the initial cost. In ROA, NPV acts as a baseline to help measure the added value of flexibility in decision-making.

Binomial Tree Model: A common method in ROA that uses a tree-like diagram to model the possible future values of an investment over time. Each node in the tree represents a possible future state, and branches represent possible options.

Black-Scholes Model: This model provides a mathematical equation using variables like time and interest rate to estimate the value of different potential options. It considers several key variables that impact an option's value, including the current asset price, strike price, time to expiration, interest rates, and volatility. The model's insights and formulas have been foundational to the development of modern options trading and risk management practices and laid the foundation for real options analysis.¹

Financial Options Pricing Theory: A branch of financial economics that provides a theoretical framework for determining the fair value of option contracts. This theory is built on the Black-Scholes model, which helps estimate values for different options. Together, the Black-Scholes model and financial options pricing theory have enabled the development of sophisticated options trading strategies and risk management techniques in financial markets.¹

Real option analysis has its roots in the Black-Scholes model, developed by Fischer Black and Myron Scholes in the 1970s. This model is foundational to financial options pricing theory, providing a mathematical formula to define the hypothetical price of different financial options. This model was groundbreaking in the field of financial analysis, as it helped leaders to compare different options more objectively. 

Several years later, economist Stewart Myers used the Black-Scholes model to develop the concept of “real options,” which are tangible assets with physical value like land, property, equipment, and natural resources. This focus on tangible investments introduced the idea of viewing corporate assets as future growth opportunities. Myers argued that the total value of a firm should include not just its present-day resources but also its potential for future growth, which is dependent on its current assets and the choices those assets provide.¹

By the 1980s, economists such as Myers applied the theory to investments in real assets, giving rise to the method we now recognize as real options analysis. With the support of academics like Lenos Trigeorgis, who helped popularize the concept, ROA expanded beyond the corporate world and found applications in diverse fields, including natural resource management, manufacturing, supply chain management, real estate, research and development, and more.¹ Since ROA excels in guiding decision-makers in particularly unpredictable contexts, the framework has thrived in the environmental and power sectors, which both involve high degrees of volatility and uncertainty.

Fischer Black

An American economist best known as one of the authors of the Black–Scholes equation, which provided a mathematical framework for pricing options and laid the groundwork for the development of ROA.⁴

Myron Scholes

A Canadian–American financial economist and co-originator of the Black–Scholes options pricing model, Scholes is a professor of finance at the Stanford Graduate School of Business and a Nobel Laureate in Economic Sciences.⁴

Stewart Myers 

Myer, often credited with coining the term “real options” in 1977, was one of the first to apply the theory to non-financial assets. He is a professor of economics at the MIT Sloan School of Management, known for his work on capital structure and valuation, as well as the theory and practice of corporate finance.⁵

Lenos Trigeorgis

A leading academic and professor in the field of ROA, Trigeorgis has made substantial contributions to the theory and practice of real options analysis and pricing, including the textbook Real Options in Capital Investment: Models, Strategies, and Applications.⁶

ROA has the potential to reshape how investment decisions are made, especially for projects with a high degree of uncertainty. Its impacts stretch beyond mere financial calculations and can give us a strategic decision-making process applicable to many realms, particularly the environmental sector. 

Simplifying Complexity

Making crucial decisions within a large organization is already complex enough. Trying to imagine every possible outcome from selecting a potential option is not only overwhelming but also impossible. However, we can still benefit significantly by incorporating information about any of the real options that are foreseeable. Leaders looking to gather as much data as they can in order to make informed decisions may be drawn to ROA for this very reason. 

Unfortunately, the real options analysis process can be complicated and daunting for many people. Researchers are actively exploring ways to simplify ROA, making it more accessible to a broader range of decision-makers. Efforts include developing user-friendly software tools and creating standardized frameworks for specific applications. Simplifying the framework into a step-by-step process or using a computer system to help visualize the impacts of each option feels reminiscent of a choose-your-own-adventure book, mapping the various options available to stakeholders.⁷

Many organization leaders may also feel overwhelmed when confronted with so many complicated calculations, as many traditional quantitative analyses include things like revenue, stock prices, or material costs. Fortunately, a successful real options analysis can involve looking at these quantitative measures alongside qualitative aspects of ROA, like employee morale, management style, or a company’s potential for technological advancement.

Conducting a real options analysis also demands a clear articulation of assumptions about uncertainties, decision points, and potential future actions. Thus, stakeholders must be transparent with one another, as clear communication facilitates more robust decision-making processes. This streamlined communication may help simplify some of the internal complexities of large-scale decisions.²

Environmental Impact Assessment

As environmental impact has become a larger area of concern for many organizations, ROA has helped decision-makers weigh the value of potential environmental initiatives. Although most scientists are certain of our society’s need to adjust to the changing climate, exactly how we should adapt to climate change is still a topic of debate. Fortunately, ROA has given leaders a framework for understanding the long-term implications of uncertain future demands, technological advancements, and environmental regulations.⁷

For example, ROA can help assess the value and the economic feasibility of renewable energy projects, accounting for uncertainties in factors like electricity prices, technology costs, the availability of oil and gas, and changing legal regulations. The framework also involves laying out all possible options and acknowledging uncertainty. The environment’s future is incredibly uncertain, but the ROA framework encourages flexible adaptation strategies in the face of potential volatility. As unforeseen environmental impacts emerge, and when new information becomes available, leaders can adjust their plans of action, re-evaluating their decisions to invest or opt out of future projects.

As another example, a city may consider building a new railway system to encourage commuting and reduce the pollution caused by car emissions. However, a city that’s poorly designed for a rail system may see little additional value generation if citizens are unlikely to use the new transport option. ROA could provide a framework for weighing the costs and benefits of railway construction while incorporating potential future impacts. Perhaps hybrid or self-driving cars will become the norm, reducing the need for a railway. Alternatively, there’s the possibility of the invention of an ultra-sustainable and affordable train technology, making the switch to a rail system a better choice. 

The Power Industry

Another example of where we’ve seen ROA at play is in the power industry. ROA has often been employed to analyze decisions related to the expansion of power plants. These decisions usually involve two stages. The first is the construction stage, which involves decisions about whether to build new power plants, taking into account uncertainties like electricity demand, fuel prices, and construction costs. The second stage is the operating stage, which involves decisions about how to operate existing power plants, including choices like switching between different generating units based on electricity prices or deciding between decommissioning and equipment replacement.^7,8 In both stages, ROA can help power plant managers make sense of the many variables at play when deciding whether or not to expand. 

Despite its potential benefits, ROA is not without its limitations. Specifically, a real options analysis can struggle with particularly complex or uncertain situations and may invite subjectivity and bias during probability calculation. 

Complexity

Although one of its main strengths is its ability to tackle complex decisions, implementing ROA can be intricate, often requiring specialized expertise in areas like financial modeling, probability theory, and decision analysis. Most project managers or decision-makers lack this kind of expertise, making the complicated equations and calculations involved in ROA a potential hindrance. While more data is often a good thing, a real options analysis with many variables can involve countless value estimations that are difficult to make amidst constantly changing market conditions and regulatory practices. This complexity can hinder wider adoption and make it challenging for decision-makers unfamiliar with its technicalities to interpret results effectively.⁷

Limited Applicability

Critics argue that ROA's reliance on quantifying uncertainties and assigning probabilities might not be appropriate for situations characterized by deep uncertainty, where the range of possible outcomes is poorly understood, or where complex social, political, and environmental factors interact in unpredictable ways.

To illustrate, consider another city planning project, this time evaluating real estate development in an area vulnerable to climate change, such as a beachfront property. Using ROA, planners might assess the value of delaying development until more data on sea level rise is available or the flexibility to scale back the project if environmental regulations tighten. However, deep uncertainty complicates this analysis. The climate impact on the area is difficult to quantify due to unpredictable interactions between environmental, social, and political factors, such as shifts in local policy, unexpected environmental events, and changes in public sentiment. ROA’s reliance on assigning probabilities to outcomes, such as the likelihood of regulatory changes or the precise impact of flooding, might be insufficient to capture these highly complex and interdependent factors.¹

In situations like these, the range of potential outcomes is so broad and uncertain that attempting to quantify each scenario could lead to unreliable price evaluations. The unpredictable nature of climate-related risks, especially when combined with social and political factors, challenges the assumption that future conditions can be accurately forecasted. Critics argue that in such cases, decision-makers might be better served by approaches that focus on flexibility under uncertainty, like scenario planning or adaptive management, which allow company strategies to evolve without requiring specific probabilities for every possible outcome.

Subjectivity

Since so much of a real options analysis relies on assumptions about future uncertainties and predictions about the likelihood of different scenarios, there are built-in limitations. Our decision criteria will never be perfect, and neither will our thought processes. Any assumptions we make in the ROA process can be inherently subjective, potentially guided by personal heuristics, which may lead to biased outcomes.

This time, let’s imagine an energy company evaluating investment options in various renewable energy sources, like wind and solar. If they were relying on ROA, the company must estimate the likelihood of future regulatory changes, energy prices, and technological advancements, which are inherently uncertain. Unfortunately, concepts like the optimism bias could subtly influence the assumptions made in ROA. Maybe decision-makers are overly hopeful about the speed of technological advancements, in which case they may overvalue future solar technologies, leading to a heavier investment in solar power despite the uncertain regulatory environment or fluctuating energy prices.

Moreover, confirmation bias might cause decision-makers to favor information that supports their preferred scenario (like increasing demand for renewable energy) while downplaying contradictory data )like a potential oversupply in the market). Similarly, anchoring could lead analysts to rely too heavily on initial probability estimates, even as new information arises that should adjust these expectations. These cognitive biases can result in assumptions and probability estimates that skew the ROA analysis, leading to potentially biased investment choices that might not align with real-world outcomes, especially in a complex market with unpredictable variables and not-always-objective factors at play.⁹

Naju Agricultural Dam Solar Power Project

South Korea recently aimed to install land and water-based solar power systems in unused areas of the Naju Agricultural Dam. The study used an ROA to evaluate the project’s potential profitability, which was not initially apparent using traditional Net Present Value (NPV) calculations. The project’s NPV analysis, which didn’t include all real options, suggested the project would be unprofitable, yielding a negative result of -$6.67 million.¹¹ However, after incorporating an abandonment option using ROA, the project's profit potential was calculated at $38.17 million—which is obviously a significant increase. This difference in outcomes highlights how ROA can reveal hidden value by accounting for flexibility that traditional methods, like NPV, don’t reveal. 

With the Korean dam project, several factors contributed to the unpredictable nature of the project's profitability. The first uncertainty was fluctuating electricity selling prices; these prices are subject to change due to factors like government policies, the balance of electricity supply and demand, and the specific type and capacity of the solar power system. Variable weather conditions also pose a risk; there is significant unpredictability regarding how much solar power would be generated. Lastly, costs related to construction, operation, and maintenance can vary, particularly for water-based solar power systems, where installation challenges can arise depending on water depth and other environmental factors.

Investors used the binomial tree model to calculate the value of the abandonment option for the dam project. This model visually represents the project's possible outcomes at different stages, allowing stakeholders to assess the value of continuing or abandoning the project at each decision point. By incorporating the abandonment option (in this case, halting the dam construction) and applying the binomial tree model, the investors could map out alternative pathways based on different potential electricity supply and demand levels. This use of the ROA framework helped guide the investors in navigating the many uncertainties, and thanks to the abandonment option, investors now have the right to halt the project during the construction phase if market conditions become unfavorable. Although the project has yet to be passed, the ROA provided a more accurate assessment of the dam’s true worth: it has given investors a significantly improved proposed profit potential compared to the initial negative NPV calculation, which may ultimately affect whether or not the construction proceeds. 

Using ROA to Enhance Flood Risk Management

The Ijssel River in the Netherlands, faced with the effects of climate change, aging dikes, and soil subsidence, is in desperate need of improved flood protection. The traditional approach to flood risk management primarily relies on dike reinforcement, but in this case, that might not be sufficient or even the most cost-effective in the long term.¹⁰

Dutch researchers looking for a solution introduced the “room for the river” approach, which offers a more adaptable and resilient alternative to dike reinforcement by creating more space for the river to flood naturally, reducing the pressure on dikes. The researchers used ROA to evaluate two flood risk management strategies: the dike strategy by itself, which focuses solely on dike reinforcement, or the room for the river approach (which would include the dike reinforcement, among other things). They modeled the uncertainty in over 500 possible extreme-river condition scenarios, considering various factors like climate change predictions and potential updates to flood protection standards.

Ultimately, the real options analysis approach revealed that incorporating the room for the river measures could be more cost-effective, particularly if there were to be any extreme changes to the water levels in the future. When researchers used a more traditional analysis method to determine the costs and benefits of each option, it seemed that only the ROA was able to capture the severity of the risks associated with extreme environmental changes. Thus, while recognizing the inherent uncertainty in future climate conditions, real options analysis may continue to provide a helpful framework for making decisions under such uncertain and complex conditions. 

TDL Perspectives: Addressing The Climate Crisis 

As ROA has often been used in real-world environmental policy decisions, read a perspective piece from TDL senior consultants on environmental policy and how we can address the climate crisis.

Mental models for business decisions with Roger Martin 

When a decision environment isn’t conducive to conducting a ROA, stakeholders may fall back on mental models. Give this podcast episode a listen to learn more about mental models for business decisions. 

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2. Hayes, A. (2021, April 23). Real option: Definition, valuation methods, example. Investopedia. Reviewed by M. James. Fact-checked by K. Ávila Munichiello. https://www.investopedia.com/terms/r/realoption.asp 
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4. Goldman Sachs. (n.d.). 1973: Black-Scholes changes the world of finance. https://www.goldmansachs.com/our-firm/history/moments/1973-black-scholes
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7. Kwakkel, J. H. (2020). Is real options analysis fit for purpose in supporting climate adaptation planning and decision-making? Wiley Interdisciplinary Reviews: Climate Change, 11(3), e638. https://doi.org/10.1002/wcc.638 
8. Dittrich, R., Wreford, A., & Moran, D. (2016). A Survey of Decision-Making Approaches for Climate Change Adaptation: Are Robust Methods the Way Forward? Ecological Economics, 122, 79-89. https://doi.org/10.1016/j.ecolecon.2015.12.006 
9. Nur, G. N., MacKenzie, C. A., & Min, K. J. (2023). A Real Options Analysis Model for Generation Expansion Planning Under Uncertain Demand. Decision Analytics Journal, 8, 100263. https://doi.org/10.1016/j.dajour.2023.100263 
10. Kind, J. M., Baayen, J. H., & Wouter Botzen, W. J. (2018). Benefits and Limitations of Real Options Analysis for the Practice of River Flood Risk Management. Water Resources Research, 54(4), 3018-3036. https://doi.org/10.1002/2017WR022402 
11. Na, S., Kim, K., Jang, W., & Lee, C. (2022). Real Options Analysis for Land and Water Solar Deployment in Idle Areas of Agricultural Dam: A Case Study of South Korea. Sustainability, 14(4), 2297. https://doi.org/10.3390/su14042297