This post includes topics addressed in the (virtual) Applied Forest Finance course on May 16^th, 2023.

Investors and managers in the forest industry often make decisions with imperfect and incomplete information. Therefore, we benefit by having an approach for dealing with uncertainty and minimizing errors when analyzing timberland and forestry investments.

Common Errors when Analyzing Forestry Investments

To start, it helps to understand where errors occur when evaluating forestry investments. In practice, most errors relate to the data used (inputs) or the math in spreadsheets. These errors can pollute decisions because inputs affect estimated cash flows, which then impact valuations when discounted to the present in traditional discounted cash flow (DCF) work.

Generally, we observe three categories of errors in Excel models and spreadsheets.

1. Analytic errors. Across the spreadsheet models we review, approximately one-third have an error in a formula (that we find). Often, these link with “relative” and “absolute” referencing, where a formula pulls in data from the wrong cell. Professor Ray Panko at the University of Hawaii, a spreadsheet expert, noted in a Wall Street Journal interview, that “you’re going to have undetected errors in about 1% of all spreadsheet formulas.”
2. Application errors. For example, analysts can err by mixing real and nominal discount rates and cash flows, or inappropriately comparing before and after-tax results. Also, using the incorrect metric can be problematic, such as misapplying cash-on-cash return or internal rate of return (IRR).
3. Errors of omission. Unfortunately, we find analysis often omits key facts, including relevant costs and potential revenues. Confirm that the assumed costs and revenues are current and reflect the best available, accessible information. In short, know what’s knowable.

Prioritize Clean and Orderly Data and Analysis

When screening and evaluating analysis, I start by confirming that what we have in hand is clean and accurate. Building a history of error-free, detail-oriented work builds trust and puts you in a better position to influence decisions and grow. For strategy and market projections, I prefer clean data and analysis over rushed, subjective intel. Ideally, we have both, but if given the choice, choose clean, with an “as of” date, over speculation on today’s unconfirmed costs or prices or market intel.

I have written about the importance of “clean over current” elsewhere with respect to strategy and projections, and it’s how we report things at Forisk, since, like many analysts, we use government data and other sources that often lag actuals by months or quarters. If a report has multiple errors, then I doubt everything it contains. Errors inject doubt. If it’s clean but a little behind, we can still support investment decisions and assess performance.

Suggestions for Minimizing Errors

Our team observes several practices to minimize errors. For example, we label tabs and worksheets, and date all files. Then, when we revisit a model in the future, we can retrace our steps. In addition, if we correct an error or make an improvement, we know which version is the most current.

We also set aside time to check each other’s work before publishing or delivering results. Imposing and embracing milestones for deliverables or reviewing work throughout a project or research effort institutionalizes a level of quality control. In the end, some level of paranoia and self-awareness is required for quality analysis of forestry, or any other, investments.