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Profitability and risk sources in global timberland investments

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  • Chudy, R.P.
  • Chudy, K.A.
  • Kanieski da Silva, B.
  • Cubbage, F.W.
  • Rubilar, R.
  • Lord, R.

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.

Suggested Citation

  • Chudy, R.P. & Chudy, K.A. & Kanieski da Silva, B. & Cubbage, F.W. & Rubilar, R. & Lord, R., 2020. "Profitability and risk sources in global timberland investments," Forest Policy and Economics, Elsevier, vol. 111(C).
    • Handle: RePEc:eee:forpol:v:111:y:2020:i:c:s1389934119302588
    DOI: 10.1016/j.forpol.2019.102037
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    References listed on IDEAS

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    1. Wan, Yang & Clutter, Michael L. & Mei, Bin & Siry, Jacek P., 2015. "Assessing the role of U.S. timberland assets in a mixed portfolio under the mean-conditional value at risk framework," Forest Policy and Economics, Elsevier, vol. 50(C), pages 118-126.
    2. Bin Mei & David N. Wear & Jesse D. Henderson, 2019. "Timberland Investment under Both Financial and Biophysical Risk," Land Economics, University of Wisconsin Press, vol. 95(2), pages 279-291.
    3. Changyou Sun & Daowei Zhang, 2001. "Assessing the Financial Performance of Forestry-Related Investment Vehicles: Capital Asset Pricing Model vs. Arbitrage Pricing Theory," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(3), pages 617-628.
    4. Hildebrandt, Patrick & Knoke, Thomas, 2011. "Investment decisions under uncertainty--A methodological review on forest science studies," Forest Policy and Economics, Elsevier, vol. 13(1), pages 1-15, January.
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