Abstract
As a long-term investment, timberland investments offer financial benefits including portfolio diversification, attractive risk/return profile, an inflation hedge, and the potential of cash flow. Based on interviews with experts regarding ranges of input parameters used in single-hectare financial models and Monte Carlo simulation method, we examine what are the main factors that influence internal rates of returns (IRRs) in several global timber plantation investment opportunities: loblolly pine on the U.S. Atlantic coastal plain; Douglas-fir plantations in the western U.S.; loblolly pine and eucalyptus plantations in Brazil; radiata pine and eucalyptus plantations in Chile; and pine and oak stands in Poland. The results show that excluding the price of land, biological growth and timber prices were the most influential variables that impacted the IRRs across global timberland investments. In addition, some country-specific factors, such as planting costs (Chile) and management costs (Poland and the U.S.), were identified as crucial when considering timberland investments in these countries. Investments in South America’s pine plantations are characterized by the same level of returns as eucalyptus opportunities, but with lower risk. The same was found for Douglas-fir investments in the Pacific Northwest compared to loblolly pine in the U.S. South. If Poland were an investable alternative, which is not the case so far, any investments in oak and pine stands are not recommended yet, given that for the same level of risk, better returns may be achieved in Douglas-fir plantations in the U.S. PNW. The Monte Carlo method utilized provides easily interpretable representation of the robustness of timberland investment estimates in selected regions and should become standard practice in forest-business decision making. However, more accurate probability density functions need to be determined in further research, using, for instance, historical data and kernel density estimation, rather than “lack of information” (triangular) distributions.
Published Version
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