Soft Sawnwood Monthly Price - Yen per Metric Ton

Data as of March 2026

Range
Mar 2016 - Mar 2026: 12,585.070 (35.30%)
Chart

Description: Soft Sawnwood, average export price of Douglas Fir, U.S. Price, Yen per Metric Ton

Unit: Yen per Metric Ton



Source: International Monetary Fund

See also: Agricultural production statistics

See also: Top commodity suppliers

See also: Commodities glossary - Definitions of terms used in commodity trading

Overview

Soft sawnwood is dimension lumber cut from softwood species such as pine, spruce, fir, and spruce-pine-fir mixes. It is typically priced by volume, with market quotations commonly expressed in US dollars per cubic meter. In commodity references, a widely used benchmark is the average export price from the United States, which reflects tradeable grades rather than retail lumber sold to end users. Soft sawnwood is a standardized industrial material used in structural framing, roof trusses, flooring substructures, pallets, packaging, and general construction. It is distinct from hardwood lumber because it is generally lighter, easier to machine, and more closely tied to mass housing and repair-and-remodel activity. Pricing is influenced by grade, moisture content, dimensions, and whether the product is kiln-dried or green. Because lumber is bulky and costly to transport relative to value, regional supply chains and freight access play an important role in market formation.

Supply Drivers

Soft sawnwood supply is shaped by forest biology, harvesting cycles, and mill capacity. Major producing regions include Canada, the United States, Scandinavia, and parts of Russia and Central Europe, where conifer forests are extensive and suited to mechanized harvesting. Unlike annual crops, timber supply depends on long growth cycles, so output responds slowly to changes in planting, thinning, and final harvest decisions. Weather affects both forest growth and logging access: drought, storms, wildfire, insect outbreaks, and freeze-thaw conditions can disrupt standing timber availability and transport. Insect and disease pressure can also alter the quality mix of logs available to mills.

Processing capacity is another constraint. Sawmills require steady log flows, energy, labor, and capital-intensive equipment, so outages or maintenance can tighten supply even when timber is available. Transport bottlenecks matter because logs and lumber are heavy and low-value relative to freight cost; rail, truck, and port access shape export competitiveness. Regional housing cycles can also affect sawmill utilization, since mills often adjust output to match construction demand and inventory conditions. Because lumber can be stored only for limited periods without quality loss, producers manage production around seasonal logging conditions and downstream demand.

Demand Drivers

Demand for soft sawnwood is driven primarily by residential construction, renovation, and light industrial uses. Structural framing in houses and low-rise buildings is the largest end use in many markets, especially where wood-frame construction is common. Demand also comes from pallets, crates, packaging, and temporary works, which link lumber consumption to manufacturing, logistics, and trade flows. In colder climates, seasonal building patterns often create stronger demand during construction seasons, while repair-and-remodel activity provides a steadier baseline.

Substitution plays an important role. Soft sawnwood competes with steel, concrete, engineered wood products, and in some applications plastic or composite materials. Wood-frame construction is favored where labor practices, building codes, and material costs support it, while engineered products can replace some dimensional lumber in structural applications. Demand is also influenced by population growth, household formation, and income conditions because housing starts and home improvement spending respond to broader economic cycles. In packaging and pallets, demand is tied to goods movement and manufacturing output rather than housing alone. Building codes, fire standards, and efficiency requirements shape the mix of wood products used, but the basic role of soft sawnwood as a versatile structural material remains persistent.

Macro and Financial Drivers

Soft sawnwood prices are sensitive to general economic activity because construction and industrial packaging are cyclical. A stronger US dollar can affect export competitiveness by making US lumber more expensive in foreign-currency terms, while a weaker dollar can support export demand. Interest rates matter because housing and construction are credit-sensitive; higher borrowing costs typically reduce building activity and lumber consumption through the financing channel. Storage and financing costs also influence market structure: lumber is bulky, degrades if poorly stored, and ties up working capital, so inventories are costly to carry. This can produce periods of backwardation when nearby supply is tight or contango when inventories are ample. Lumber prices may also correlate with broader cyclical assets through their link to construction, manufacturing, and freight conditions, though the relationship is driven by physical demand rather than financial speculation alone.

MonthPriceChange
Mar 201635,648.85-
Apr 201635,506.68-0.40%
May 201636,240.292.07%
Jun 201634,884.04-3.74%
Jul 201633,362.23-4.36%
Aug 201632,631.94-2.19%
Sep 201634,071.794.41%
Oct 201634,329.330.76%
Nov 201635,230.862.63%
Dec 201637,472.636.36%
Jan 201736,584.59-2.37%
Feb 201735,321.30-3.45%
Mar 201733,252.27-5.86%
Apr 201734,817.014.71%
May 201735,645.082.38%
Jun 201736,585.642.64%
Jul 201736,957.291.02%
Aug 201735,293.56-4.50%
Sep 201734,613.17-1.93%
Oct 201736,730.126.12%
Nov 201737,804.652.93%
Dec 201736,968.37-2.21%
Jan 201838,186.173.29%
Feb 201836,387.30-4.71%
Mar 201835,680.82-1.94%
Apr 201835,612.17-0.19%
May 201838,209.037.29%
Jun 201839,201.992.60%
Jul 201839,355.240.39%
Aug 201837,482.16-4.76%
Sep 201838,325.992.25%
Oct 201837,969.09-0.93%
Nov 201838,616.151.70%
Dec 201841,667.697.90%
Jan 201938,988.31-6.43%
Feb 201937,802.94-3.04%
Mar 201938,472.831.77%
Apr 201937,405.14-2.78%
May 201940,186.557.44%
Jun 201938,220.84-4.89%
Jul 201938,594.220.98%
Aug 201939,140.291.41%
Sep 201939,063.34-0.20%
Oct 201937,680.98-3.54%
Nov 201939,707.395.38%
Dec 201937,507.75-5.54%
Jan 202039,938.116.48%
Feb 202037,132.56-7.02%
Mar 202038,843.234.61%
Apr 202038,637.86-0.53%
May 202038,053.17-1.51%
Jun 202037,703.41-0.92%
Jul 202035,708.41-5.29%
Aug 202035,807.040.28%
Sep 202035,034.30-2.16%
Oct 202036,920.435.38%
Nov 202037,564.291.74%
Dec 202034,270.84-8.77%
Jan 202136,708.427.11%
Feb 202135,152.80-4.24%
Mar 202139,906.9813.52%
Apr 202143,208.458.27%
May 202145,973.506.40%
Jun 202148,045.824.51%
Jul 202140,652.40-15.39%
Aug 202136,311.69-10.68%
Sep 202132,460.54-10.61%
Oct 202135,141.778.26%
Nov 202135,327.270.53%
Dec 202135,864.181.52%
Jan 202237,608.914.86%
Feb 202238,992.823.68%
Mar 202255,221.8941.62%
Apr 202255,810.281.07%
May 202255,592.96-0.39%
Jun 202255,867.740.49%
Jul 202254,809.57-1.89%
Aug 202258,177.176.14%
Sep 202258,045.35-0.23%
Oct 202257,075.44-1.67%
Nov 202255,244.20-3.21%
Dec 202249,676.98-10.08%
Jan 202340,074.40-19.33%
Feb 202344,005.339.81%
Mar 202341,531.12-5.62%
Apr 202340,336.20-2.88%
May 202346,692.6815.76%
Jun 202344,254.63-5.22%
Jul 202344,915.821.49%
Aug 202346,761.454.11%
Sep 202349,558.845.98%
Oct 202347,571.91-4.01%
Nov 202345,555.22-4.24%
Dec 202345,594.450.09%
Jan 202449,065.067.61%
Feb 202449,067.300.00%
Mar 202451,135.844.22%
Apr 202448,529.43-5.10%
May 202450,827.144.73%
Jun 202448,523.66-4.53%
Jul 202448,783.450.54%
Aug 202444,628.80-8.52%
Sep 202447,201.205.76%
Oct 202443,684.91-7.45%
Nov 202447,393.668.49%
Dec 202454,143.7114.24%
Jan 202549,123.32-9.27%
Feb 202551,387.264.61%
Mar 202549,596.21-3.49%
Apr 202546,032.07-7.19%
May 202547,098.702.32%
Jun 202548,653.453.30%
Jul 202549,563.381.87%
Aug 202546,480.45-6.22%
Sep 202544,441.94-4.39%
Oct 202541,908.47-5.70%
Nov 202544,934.227.22%
Dec 202545,625.891.54%
Jan 202647,951.255.10%
Feb 202647,182.36-1.60%
Mar 202648,233.922.23%

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