Natural Resources Institute Finland (Luke) - Statistical Database

The national statistics office of Finnish forestry, and one of the quietest deep shelves in the catalog. Natural Resources Institute Finland (Luke) - Luonnonvarakeskus in Finnish - compiles Finland's official forest statistics and publishes them through its Statistical Database in Finnish, Swedish and English, and Datadory decodes that whole shelf into one keyed dataset. Eleven subject areas sit behind the single record: roundwood removals and drain of the growing stock, commercial fellings, forest protection, forest accounts, silvicultural and forest improvement work, stumpage earnings, volumes and prices in energy-wood trade, volumes and prices in roundwood trade, a weekly roundwood-trade monitor compiled with the Finnish Forest Industries Federation, wood consumption, and a discontinued-statistics archive that keeps the retired series readable instead of orphaning them.

History is the headline number nobody else hands you. Roundwood trade series run back to 1949, removals by ownership category to 1985, and monthly trade tables layer on from 2020 - three generations of Nordic timber-market context in one keyed grid, which is depth most commercial timber series never survive long enough to accumulate. Get a sample of this dataset.

One flat row per measure x year x price region x type of sale x assortment, typed and ready for a model. Four verified observations exactly as they deliver:

INFO    : M3T        (measure: volume)
Year    : 2024
MPKH    : SSS        (price region: whole country)
KAUP    : KAUP_YHT   (type of sale: all forms)
PTL     : PTL_YHT    (assortment: all)
value   : 48727      unit : 1,000 m3

INFO    : M3T        (measure: volume)
Year    : 2024
MPKH    : SSS        (price region: whole country)
KAUP    : KAUP_YHT   (type of sale: all forms)
PTL     : TUK_YHT    (assortment: sawlogs, all species)
value   : 22462      unit : 1,000 m3

INFO    : M3T        (measure: volume)
Year    : 2025
MPKH    : SSS        (price region: whole country)
KAUP    : KAUP_YHT   (type of sale: all forms)
PTL     : PTL_YHT    (assortment: all)
value   : 39378      unit : 1,000 m3

INFO    : M3T        (measure: volume)
Year    : 2025
MPKH    : SSS        (price region: whole country)
KAUP    : KAUP_YHT   (type of sale: all forms)
PTL     : TUK_YHT    (assortment: sawlogs, all species)
value   : 17714      unit : 1,000 m3

The four rows sketch the whole shelf. Finland's roundwood trade reached 48,727 thousand cubic metres in 2024 across all assortments, with sawlogs taking 22,462; the running 2025 vintage already carries 39,378 and 17,714 on identical keys, so a year-over-year chart needs no remapping between vintages. Flip the measure selector and the same grid reports thousand euros instead of thousand cubic metres; switch the type-of-sale dimension and standing sales separate from delivery sales; walk down the assortment ladder and pine sawlogs part company with spruce.

Eight documented fields define every observation, and each is a coded dimension rather than a prose label - which is why a 1949 figure and a 2025 figure join on identical keys, and why a regional price spread computes without a mapping table of your own. Every definition below was verified against the delivered data structure during research, not inferred from documentation.

Derived reshapes - real-price deflators, standing-versus-delivery spreads, regional price indices, extracts aligned to companion forestry tables - depend on the cut you specify, so they fold under additional fields on request and are confirmed with your sample rather than guessed at here.

Three chips, and the first is the one analysts underestimate - Finland is small, but it is instrumented like nowhere else.

• Geography: the whole country plus the eight official roundwood price regions, with several subject areas additionally split by forest ownership category - private versus state and other public.
• Temporal: roundwood trade from 1949, removals by ownership category from 1985, monthly trade tables from 2020; annual and monthly reference periods sit side by side in the same dictionary.
• Granularity: annual and monthly observations keyed by price region, ownership category, type of sale, assortment and tree species, with a measure selector flipping volumes in thousand cubic metres to values in thousand euros.

Set against the wider catalog - average quality score 7.81 across all 1,744 datasets - this slice scores 9/10, carried by fully verified field definitions and histories measured in generations rather than fiscal quarters.

API, files, or your warehouse. Daily, weekly, or hourly.

Pick the channel and the cadence; the rows arrive identical either way - decoded once from the source grid into one fully keyed observation per row, typed against the field dictionary above, with classifier codes and labels held stable so a re-pull diffs cleanly against last quarter's extract.

• Timber-price and stumpage analysts track standing versus delivery sales across the eight price regions to read who is capturing value between forest owner and mill - the workflow pattern sits on our price monitoring page.
• Fiber-security and raw-material planners watch removals, drain and wood consumption series for the earliest signal of tightening supply; sizing work like this is sketched on the market sizing use-case page.
• Bioenergy and energy-wood analysts size the fuelwood trade separately from pulpwood and sawlogs, with demand-side patterns on demand forecasting.
• Quant researchers and macro strategists lift seventy-plus years of trade history as a supply-side factor behind Nordic timber exposures - the pattern is written up on quant backtesting.
• Academics and policy researchers cite official figures whose classifiers, ownership splits and discontinued-series handling are explicit rather than buried; the pattern lives on citation-grade research.

Market researchers and consultants get the fastest route to a defensible Nordic timber-market sizing - one official frame, price regions ready-made, no stitching of press releases. Investors and quant researchers get a seventy-year supply-and-price backdrop for timber and bioenergy positions, long enough to hold several complete housing cycles. Data scientists and ML engineers get a fully keyed, schema-stable panel whose classifier codes survive releases intact, deep enough to train on without augmentation. Developers building data products get typed rows that load straight into a warehouse and diff cleanly across vintages. Journalists, academics and students get citable official statistics whose caveats are documented dimensions rather than buried footnotes. Persona workflows: market researchers x forest products, investors & quants x forest products, data scientists x forest products, developers & builders x forest products, journalists & academics x forest products.

The rest of the forest-products shelf reads as zoom levels around this national close-up.

• Eurostat Roundwood and Wood Products Statistics (FOR_BASIC) - the European frame around the Finnish one: thirty-two countries, twelve basic products, euro-valued flows collected through the questionnaire Luke itself feeds.
• FAOSTAT Forestry - Forest Production and Trade - the global baseline: roughly 285 areas and 104 products back to 1961, into which these Finnish rows slot as the deepest national texture.
• Statistics Canada - Lumber Production, Shipments and Stocks - the Atlantic counterpart, monthly and provincial. The head-to-head continues on our Luke vs Statistics Canada lumber page.
• FAO Global Forest Resources Assessment (FRA) Data Platform - the resource-side companion: forest area and growing stock behind the removals counted here.
• Roundwood, explained, stumpage price, explained and roundwood removals and drain, explained - the ten-minute primers on the vocabulary this dictionary uses.

Three things worth knowing upfront.

First, the shelf is broader than trade. Stumpage earnings, silvicultural work, forest protection, wood consumption and forest accounts are adjacent subject areas, not rows you are missing - if your model needs the silviculture or protection side, say so in the sample request and we will scope those tables alongside.

Second, the grids are honest about their edges. Annual trade tables and monthly trade tables differ in how far back they run, and the discontinued-statistics archive holds the retired series separately rather than splicing them silently into current ones - treat the seam as a documented fact, not noise.

Third, the cube carries volumes and values only. There are no geolocation, stand-composition or certification attributes here, so sourcing-risk screening wants a complementary dataset layered on top rather than a deeper cut of this one.

Because the useful test is a join, not a glance. In the raw artifact the observations sit behind coded classifier variables - regions, sale forms, assortments and species named by short codes whose labels live in separate metadata - and decoding them correctly is a chore nobody should repeat per project. Datadory decodes once, types every column, and ships rows cut to the price regions, assortments, sale forms and years you nominate, with keys held stable across releases so a re-pull diffs cleanly against last quarter's extract.

Start small: get a sample of this dataset scoped to the slice you will actually use, or browse the forest products data hub, the best forest products datasets ranking, the Luke source profile, or the full catalog.