Soft Sawnwood Monthly Price - Russian Ruble per Metric Ton

Data as of March 2026

Range
May 2003 - Apr 2013: 1,650.555 (19.41%)
Chart

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

Unit: Russian Ruble 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
May 20038,504.90-
Jun 20038,845.974.01%
Jul 20039,016.881.93%
Aug 20038,833.14-2.04%
Sep 20038,957.651.41%
Oct 20038,613.67-3.84%
Nov 20038,441.96-1.99%
Dec 20037,935.56-6.00%
Jan 20047,937.810.03%
Feb 20048,652.349.00%
Mar 20048,767.691.33%
Apr 20048,602.09-1.89%
May 20049,977.8015.99%
Jun 20049,959.72-0.18%
Jul 200410,101.501.42%
Aug 200410,381.252.77%
Sep 200410,014.36-3.53%
Oct 200410,155.531.41%
Nov 20049,374.39-7.69%
Dec 20048,152.97-13.03%
Jan 20059,216.4013.04%
Feb 20058,846.97-4.01%
Mar 20059,939.6912.35%
Apr 20059,060.48-8.85%
May 20059,352.763.23%
Jun 20059,332.51-0.22%
Jul 20058,554.75-8.33%
Aug 20059,521.7911.30%
Sep 20059,396.08-1.32%
Oct 20059,472.180.81%
Nov 20059,585.891.20%
Dec 20059,199.41-4.03%
Jan 20068,717.07-5.24%
Feb 20069,149.684.96%
Mar 20068,851.64-3.26%
Apr 20068,518.92-3.76%
May 20068,861.124.02%
Jun 20069,440.666.54%
Jul 20069,634.172.05%
Aug 20069,869.152.44%
Sep 20069,501.39-3.73%
Oct 200610,009.265.35%
Nov 20069,309.51-6.99%
Dec 20069,646.993.63%
Jan 20079,420.07-2.35%
Feb 20079,730.273.29%
Mar 20078,969.99-7.81%
Apr 20079,126.421.74%
May 20079,353.542.49%
Jun 20078,800.30-5.91%
Jul 20077,892.92-10.31%
Aug 20076,377.59-19.20%
Sep 20077,984.8525.20%
Oct 20077,347.24-7.99%
Nov 20077,498.002.05%
Dec 20077,641.351.91%
Jan 20087,908.693.50%
Feb 20087,644.44-3.34%
Mar 20087,079.32-7.39%
Apr 20086,858.28-3.12%
May 20087,252.195.74%
Jun 20086,949.49-4.17%
Jul 20087,757.9811.63%
Aug 20087,620.77-1.77%
Sep 20087,899.823.66%
Oct 20088,582.828.65%
Nov 20089,116.396.22%
Dec 20088,887.80-2.51%
Jan 200911,429.2928.60%
Feb 200911,397.93-0.27%
Mar 200910,655.91-6.51%
Apr 200910,558.29-0.92%
May 200910,548.31-0.09%
Jun 200910,113.74-4.12%
Jul 20099,917.50-1.94%
Aug 20099,937.490.20%
Sep 20098,978.16-9.65%
Oct 20098,485.56-5.49%
Nov 20098,957.735.56%
Dec 20099,668.617.94%
Jan 20109,380.30-2.98%
Feb 20109,239.75-1.50%
Mar 20109,325.070.92%
Apr 20108,860.79-4.98%
May 20109,364.765.69%
Jun 20109,582.742.33%
Jul 20109,616.230.35%
Aug 20109,733.881.22%
Sep 20109,911.581.83%
Oct 20109,535.51-3.79%
Nov 20108,565.41-10.17%
Dec 20109,723.9613.53%
Jan 20119,199.97-5.39%
Feb 20118,564.53-6.91%
Mar 20118,071.04-5.76%
Apr 20117,694.44-4.67%
May 20117,993.753.89%
Jun 20118,186.982.42%
Jul 20118,582.514.83%
Aug 20119,163.166.77%
Sep 20119,384.122.41%
Oct 20119,774.014.15%
Nov 20119,713.85-0.62%
Dec 20119,347.49-3.77%
Jan 20129,724.364.03%
Feb 20129,449.52-2.83%
Mar 20128,853.25-6.31%
Apr 20129,452.706.77%
May 201210,213.878.05%
Jun 201210,477.982.59%
Jul 201210,186.20-2.78%
Aug 201210,339.731.51%
Sep 201210,164.35-1.70%
Oct 20129,795.21-3.63%
Nov 201210,262.384.77%
Dec 20129,201.48-10.34%
Jan 20139,132.24-0.75%
Feb 20139,201.950.76%
Mar 20139,607.474.41%
Apr 201310,155.465.70%

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