A Real Options Approach to the Valuation of a Forestry Investment

Insley, Margaret

doi:10.1006/jeem.2001.1209

Cited by 168 publications

(144 citation statements)

References 14 publications

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“…The price uncertainty is likely to increase when using e.g., GBM [35][36][37] instead of GMR [32][33][34] as process for future price increments. The increasing price variation increases also the degree of yield value prediction uncertainty.…”

Section: Forest Property Dmentioning

confidence: 99%

“…A more advanced, and also complicated, approach is to try to predict future timber price development based on realized past price development, by which long-term trends can be depicted and factors causing price peaks identified. Such predictions can be carried out e.g., by using geometric mean-reverting (GMR, [32][33][34]) or geometric Brownian motion (GBM, [35][36][37]) price processes.…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty in Forest Net Present Value Estimations

Holopainen

Mäkinen

Rasinmäki³

et al. 2010

Forests

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Uncertainty related to inventory data, growth models and timber price fluctuation was investigated in the assessment of forest property net present value (NPV). The degree of uncertainty associated with inventory data was obtained from previous area-based airborne laser scanning (ALS) inventory studies. The study was performed, applying the Monte Carlo simulation, using stand-level growth and yield projection models and three alternative rates of interest (3, 4 and 5%). Timber price fluctuation was portrayed with geometric mean-reverting (GMR) price models. The analysis was conducted for four alternative forest properties having varying compartment structures: (A) a property having an even development class distribution, (B) sapling stands, (C) young thinning stands, and (D) mature stands. Simulations resulted in predicted yield value (predicted NPV) distributions at both stand and property levels. Our results showed that ALS inventory errors were the most prominent source of uncertainty, leading to a 5.1-7.5% relative deviation of property-level NPV when an interest rate of 3% was applied. Interestingly, ALS inventory led to significant biases at the property level, ranging from 8.9% to 14.1% (3% interest rate). ALS inventory-based bias was the most significant in mature stand properties. Errors related to the growth predictions led to a relative standard deviation in NPV, varying from 1.5% to 4.1%. Growth model-related uncertainty was most significant OPEN ACCESSForests 2010, 1 178 in sapling stand properties. Timber price fluctuation caused the relative standard deviations ranged from 3.4% to 6.4% (3% interest rate). The combined relative variation caused by inventory errors, growth model errors and timber price fluctuation varied, depending on the property type and applied rates of interest, from 6.4% to 12.6%. By applying the methodology described here, one may take into account the effects of various uncertainty factors in the prediction of forest yield value and to supply the output results with levels of confidence.

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Section: Forest Property Dmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Uncertainty in Forest Net Present Value Estimations

Holopainen

Mäkinen

Rasinmäki³

et al. 2010

Forests

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“…Yin [29] discusses harvest timing, land acquisition, and entry decisions combining forest level analysis and options valuation approach. Other authors have studied the impact of assuming that stochastic prices follow a mean reverting process rather than geometric brownian motion, see e.g., [16] and [13].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Real options, however, show the investment to be highly valuable. Insley & Rollins [16] model the harvesting problem assuming infinite rotations as a linear complementarity problem that was solved numerically. The accuracy of the solution, obviously, depends on the grid used.…”

Section: Literature Reviewmentioning

confidence: 99%

A Stochastic Dynamic Programming Model for Valuing a Eucalyptus Investment

Cunha

Fontes

2008

Springer Optimization and Its Applications

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This work proposes an exercise-dependent real options model for the valuation and optimal harvest timing of a forestry investment in eucalyptus. Investment in eucalyptus is complex, as trees allow for two cuts without re-plantation, and have a specific time and growth window in which they are suitable for industrial processing into paper pulp. Thus, path dependency in the cutting options is observed, since the moment of exercise of the first option determines the time interval in which the second option may be exercised. Therefore, the value of the second option depends on the history of the state variables rather than on its final value. In addition, the options to abandon the project, or convert land to another use, are also considered. The option value is estimated by solving a stochastic dynamic programming model. Results are reported for a case study in the Portuguese eucalyptus forest which show that price uncertainty postpones the optimal cutting decisions. Moreover, optimal harvesting policies deviate from present practice of forest managers and allow for considerable gains.

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“…Among others, Pindyck (2000) applied the real option theory to environmental policy adoption; Conrad (2000) studied land use decisions; Insley (2002) treated forest logging as the exercise of a real option; Kassar and Lasserre (2004) analyzed biodiversity preservation decisions; Saphores (2003) studied the exploitation of an endangered species; Saphores and Shogren (2005) showed how to optimize the use of pesticides.…”

Section: Introductionmentioning

confidence: 99%