Investments in Pinus elliottii Engelm. Plantations: Real Options Analysis in Discrete Time

Background: The commonly used methods for the financial evaluation of plantation forest investment projects do not incorporate uncertainties and ignore the value related to flexibility. The real options analysis makes it possible to capture these values in investment projects, increasing their value and return. Despite this, studies involving real options in forest investment projects are scarce, specifically those related to Pinus spp. Therefore, this study aimed to: (a) analyze whether the real options analysis adds value to investment projects of Pinus elliottii Engelm. plantations; and (b) make the real options analysis more accessible to forest managers and potentially increase its use in the investment projects of Pinus spp. plantations. Methods: We evaluated two investment projects in P. elliottii plantations in southern Brazil, which differed in the way of obtaining the land for planting: with lease or purchase of land on a planning horizon of 21 years. In the real options analysis, we used deferral, expansion, and abandonment. Results: Individually, the deferral, expansion, and abandonment options add value to investment projects in Pinus elliottii plantations. The option to expand the forested area is one that adds the most value to the investment project with land lease. In the investment project with land purchase, it is abandonment. Conclusions: Investment projects in Pinus elliotti plantations that contemplate the land purchase analyzed through the real options analysis present higher financial returns than those that consider land lease, inverting the result provided by the traditional analysis.

Real Options for Risk Analysis in Estimating the Capitalization Rate

A suitable cap-rate is generally determined through an analogical process in order to estimate the value of any real estate through the capitalization of the incomes. The analogy relates to the risk and duration of similar investments. There are numerous methods to rationalize the valuation of the cap-rate. Appraisals have a certain degree of uncertainty in all these methods. This paper proposes a methodology which removes any uncertainty when evaluating the cap-rate. This is achieved through the combination of the formal logic of the Ellwood’s model and the Real Options Analysis.

Real options analysis of revenue risk sharing in post-disaster housing reconstruction

Purpose Post-disaster housing reconstruction (PDHR) demands a considerable percentage of global property investment, yet the post-disaster environment presents intricate challenges to reconstruction financing for governments and at the same time, revenue uncertainty for private investors. The purpose of this study is to develop a methodology for tackling land shortage and the financial challenges of PDHR in the aftermath of a disaster. Design/methodology/approach This study developed a methodology based on a combined minimum revenue guarantee and maximum revenue cap model using a well-established real options analysis (ROA) for revenue risk sharing in PDHR projects and land readjustment (LR) for finance. The applicability of the purported model is demonstrated through an illustrative example. Findings The results show that flexibility in the options could increase the PDHR contractor’s risk profile by increasing the expected value of the contractor investment and reducing the probability of investment loss. On the other side, a cap on the contractor revenue stream would allow the government to benefit from any excess in revenue and would counterbalance the value of the option. Practical implications The framework proposed in this study could serve as a practical risk-revenue sharing in PDHR projects. Governments and policymakers could use the findings to enable the successful delivery of PDHR projects and consequently bring the quality of life of affected people to pre-disaster conditions. Originality/value This study can be considered as a first attempt toward the use of the Australian barrier style options structure, and the trinomial lattice valuation model in PDHR projects, which incorporates LR, public-private partnerships, governmental guarantees and PDHR concepts in one ROA-based framework.

Optimized Expansion Strategy for a Hydrogen Pipe Network in the Port of Rotterdam with Compound Real Options Analysis

The port of Rotterdam is the largest seaport in Europe. To maintain its position, the harbor will have to anticipate global transitions such as transferring to sustainable energy. Hydrogen is seen as a promising energy carrier; however, future demand is uncertain. The current research investigates decision making under uncertainty and values flexibility. Compound real options analysis is applied to optimize the time-variant expansion strategies for a hydrogen pipe network. The trade-off between early investments and missed revenues when not investing in time determines the optimized expansion strategy. Moreover, the real options approach also provides the levelized unit price for hydrogen distribution, to cover the life cycle costs of the optimal expansion strategy. Finally, this real options approach offers flexibility to a decision maker as it allows for enhancing future decisions. The academic contribution of this research is a distinct perspective on a compound real options approach where the optimal strategic path is the key result of interest. This in contrast to other real options applications in the literature which focus on option value, exchange with limiting the options or do not visualize a strategic path. Moreover, this research demonstrates how stepwise expansion and decision making under uncertainty facilitate transitions such as the transition toward clean energy.

THE ROLE OF MACHINE LEARNING FOR FLEXIBILITY AND REAL OPTIONS ANALYSIS IN ENGINEERING SYSTEMS DESIGN

Flexibility analysis helps improve the expected value of engineering systems under uncertainty (economic and/or social). Designing for flexibility, however, can be challenging as a large number of design variables, parameters, uncertainty drivers, decision making possibilities and metrics must be considered. Many available techniques either rely on assumptions that are not suitable for an engineering setting, or may be limited due to computational intractability. This paper makes the case for an increased integration of Machine Learning into flexibility and real options analysis in engineering systems design to complement existing design methods. Several synergies are found and discussed critically between the fields in order to explore better solutions that may exist by analyzing the data, which may not be intuitive to domain experts. Reinforcement Learning is particularly promising as a result of the theoretical common grounds with latest methodological developments e.g. decision-rule based real options analysis. Relevance to the field of computational creativity is examined, and potential avenues for further research are identified. The proposed concepts are illustrated through the design of an example infrastructure system.