Can public data benchmark a private timber business?

Partly, and the split is clear. On prices, public data is a workable benchmark: stumpage, delivered log and lumber prices, and producer price indices, compared at the right level and on the same basis. On returns and operations, it gives context rather than a score. The main timberland return index covers institutional properties. Listed companies report at company level. And mill-level efficiency data appears in research surveys, published as averages that hide how far the average mill sits from the best.

What the usual answer says

Answers to this question mostly point to market reports, return indices or paid benchmarking products, with caveats about size, product mix and region. The caveats are right. What they leave out is that prices, returns and operating efficiency are different kinds of benchmark, and public data supports each to a very different degree.

Prices: public data works, with care

Public price series measure the market a business trades in. Set your own stumpage paid against a regional average for the same product and period, and the comparison means something, provided the level, units and basis match. Check how many sales sit behind the average. One state forest agency's annual summary for 2025, dated 24 February 2026, rests its statewide pine sawlog price on 248 reported sales, and its mixed hardwood sawlog price in one region on three. A price built on three sales tells you where the market might be, and little more.

Returns: an index built for other owners

For timberland, the usual public benchmark is NCREIF's Timberland Index. Its first-quarter 2024 release describes it as "443 investment-grade timber properties with a market value of approximately $27.2 billion". Its data "enhances the ability of institutional investors to evaluate the risk and performance of timberland investments". A family or industrial owner is outside that sample by construction. Brooks Mendell of Forisk, a timber research firm, set out its limits in March 2026. In his words, "NCREIF does a better job of summarizing returns through economic cycles from professionally managed, industrial timberlands than it does comparing manager skills, cost effectiveness, or local timber markets."

Operations: filings and surveys

Listed companies do publish operating figures. One listed engineered wood and plywood producer's 2025 annual report gives capacity and production by product. Its plywood and veneer lines produced 1,842 million square feet against a capacity of 2,725 million, about 68%. That is a real public operating rate. But it is one company, across many plants and products, and its capacity is its own "estimated annual capacity at the end of each year based on machinery capabilities". It shows the direction of an industry, not where your mill stands.

Mill-level comparison needs mill-level data. A 2021 study in Forest Science by Sun, Marcille and Daniels applied efficiency analysis to "a unique dataset for 146 mills over four survey years" in one major timber-producing state, with data from 2003 to 2017. Scale efficiency was "generally high", but "overall technical efficiency is only 85%". In 2017, "the most efficient mills as a group used 14% fewer employees, processed 18% more sawlogs, and produced 32% more lumber than an average mill". Productivity grew "only 0.14% per year", mostly from changes in technology, and the catch-up of individual mills toward the best was negative.

The gap between average and best is large, so beating the average says little. The data is now nine years old, and the public sees the result of the analysis rather than the peer data.

Is the best mill a fair comparison?

A fair objection is that the most efficient mills differ in scale, log supply and equipment, so their level is out of reach for most. The study partly answers it. With scale efficiency generally high, size explains little of the gap, and the authors conclude that improvement "relies on better management of operations and resources by individual mills". Technology still drove most of the growth, though, and capital is not equally available to every mill. The honest reading is that the frontier shows what is possible for a similar mill with similar kit, which is exactly what a public average can't tell you.

What to do instead

Benchmark prices against public series, with the sales count noted. Use the return index and listed-company filings for direction, not as a score. Benchmark operations mainly against your own history: recovery, output per hour and cost per unit by month and line, with definitions held constant. Where a few businesses agree to share data under common definitions, often through an association or an adviser, a small peer group shows the spread that averages hide. Treat any published average as a floor.

Where AI helps

AI helps most with the internal layer. It can hold definitions constant across years of records, rebuild historical measures when systems change, and explain month-on-month movement. It can also read public price reports and filings into a comparable form, with their counts, bases and footnotes attached.

When it doesn't apply

Businesses that already belong to a data-sharing group with detailed peer benchmarks have better data than any public source for operations. For a business that only needs to know whether it paid a fair market price, the public price series may be all it needs.

Quarri for finance and strategy teams is built for the people who close the month, explain the margin and answer the board.

Quarri is an AI-native data platform for the timber supply chain. It connects buying, production, sales and inventory for forest management, sawmill, wood products and pulp, paper and packaging operators.