How reliable is public stumpage price data?

It depends on two things, and the reports show evidence of only one. How many sales sit behind a figure, how it was averaged and how tons were converted to board feet decide how noisy it is, and most reports print all three. Who reported the sales, or which land they came from, decides whether it is biased, and a large count doesn't fix that. A pine sawtimber price built from a few hundred sales is a steady guide to what reporters saw. A hardwood price in the same table can rest on three.

What the usual answer says

Most descriptions call public stumpage data a useful regional guide that is less reliable than actual sale prices, because it relies on voluntary reports, varies by region and product, and lags the market. That is fair. The reports themselves say more precisely how far a given figure can be trusted.

What one annual summary shows

One state forest agency's annual stumpage summary for 2025, dated 24 February 2026, prints the working behind each price. Pine sawlogs rest on 248 reported sales across the state, and pine pulpwood on 236. Mixed hardwood sawlogs rest on 30 statewide, and in one of its two regions on three. Its footnote suppresses any figure with "less than three sales", so the three-sale hardwood price sits at the edge of what the agency will publish. Federal sales are excluded.

The summary prints three averages for every product: unweighted, volume-weighted, and a simple average of the two. For pine sawlogs in one region, the unweighted price was 33.04 per ton and the volume-weighted price 30.68, a gap of about 8% from the choice of average alone. In our reading, the weighted figure suits valuing volume and the unweighted one describes a typical sale.

Units add another layer. Where a sale reports no conversion factor, the agency applies defaults: "pine sawtimber--1 MBF = 8 tons", hardwood sawtimber 9 tons, and board feet on the Doyle log scale. A buyer converting on a different factor or log rule will see a different price from the same data.

Who reports

Counts measure the sample, not whether it represents the market. The same agency's FAQ explains that its figures come from a regular voluntary survey of a few dozen reporters, such as consultants, loggers, dealers and occasionally landowners. It says the report "should not be used to judge the fair market value of a specific timber sale, which might vary considerably due to many factors". If the reporters' sales differ systematically from the sale a reader is pricing, a large count gives a precise figure for the wrong market.

Why public data is thin

A state revenue department's stumpage value report, used to value private forest land for tax, explains the underlying problem: "private landowners are not required to disclose the terms of timber sales", so it relies on public land sales. Across twelve fiscal years its four zones had 127, 71, 21 and 7 of them. With "few sales in the central zone and almost none in the Eastern zone", the department fits a regression model rather than averaging winning bids. The model's latest output for the thinnest zone was negative, "not economical" to harvest there in fiscal 2025, and the report left it out of the average. That is a model built on seven sales reaching its limit, not an observed price.

Checks before using a figure

Read the number of sales behind each price; in our rule of thumb, under ten in a year is an indication rather than a price. Know which average is used, and keep to one. Confirm the conversion factor and log rule, and convert the business's own figures on the same basis. Check who reports and what is excluded. Where counts are low, widen the region or period, or use the trend in a related product, and say so.

Where AI helps

Public stumpage reports come as PDFs and tables with counts and footnotes in small print. AI can extract each price with its sales count, average type, unit and conversion factor, and flag figures below a chosen count. Set beside a business's own sales on the same basis, that shows how much weight a published price can bear.

When it doesn't apply

A business with many recent sales of its own in a liquid market has better data than any public report. And no public average says what a specific stand will fetch.

Quarri for forest management is built around how a forest operation runs, from the cruise to the settled account.

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.