Soft Sawnwood Monthly Price - Bolivar Fuerte per Metric Ton

Data as of March 2026

Range
Feb 2008 - Aug 2018: 71,553,590.000 (10,695,820.00%)
Chart

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

Unit: Bolivar Fuerte 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
Feb 2008668.99-
Mar 2008639.65-4.39%
Apr 2008625.37-2.23%
May 2008655.504.82%
Jun 2008630.53-3.81%
Jul 2008712.5913.01%
Aug 2008675.68-5.18%
Sep 2008670.32-0.79%
Oct 2008696.393.89%
Nov 2008714.562.61%
Dec 2008676.30-5.35%
Jan 2009746.6210.40%
Feb 2009682.52-8.59%
Mar 2009660.71-3.20%
Apr 2009674.842.14%
May 2009708.194.94%
Jun 2009698.39-1.38%
Jul 2009674.93-3.36%
Aug 2009672.68-0.33%
Sep 2009625.69-6.99%
Oct 2009618.16-1.20%
Nov 2009664.467.49%
Dec 2009690.283.89%
Jan 2010780.6313.09%
Feb 2010779.84-0.10%
Mar 2010818.254.93%
Apr 2010781.24-4.52%
May 2010796.151.91%
Jun 2010796.590.06%
Jul 2010813.602.13%
Aug 2010830.412.07%
Sep 2010834.430.48%
Oct 2010815.16-2.31%
Nov 2010718.06-11.91%
Dec 2010817.4213.84%
Jan 20111,316.4761.05%
Feb 20111,254.62-4.70%
Mar 20111,217.52-2.96%
Apr 20111,176.04-3.41%
May 20111,227.644.39%
Jun 20111,254.962.23%
Jul 20111,318.535.07%
Aug 20111,366.873.67%
Sep 20111,307.64-4.33%
Oct 20111,341.222.57%
Nov 20111,351.990.80%
Dec 20111,272.51-5.88%
Jan 20121,337.025.07%
Feb 20121,359.191.66%
Mar 20121,294.43-4.77%
Apr 20121,374.556.19%
May 20121,423.233.54%
Jun 20121,366.40-3.99%
Jul 20121,343.32-1.69%
Aug 20121,387.373.28%
Sep 20121,387.800.03%
Oct 20121,351.22-2.64%
Nov 20121,400.593.65%
Dec 20121,283.19-8.38%
Jan 20131,295.450.96%
Feb 20131,646.1027.07%
Mar 20131,959.7319.05%
Apr 20132,036.583.92%
May 20132,249.4910.45%
Jun 20132,191.46-2.58%
Jul 20132,194.690.15%
Aug 20132,171.13-1.07%
Sep 20132,074.10-4.47%
Oct 20132,037.97-1.74%
Nov 20132,089.372.52%
Dec 20132,222.286.36%
Jan 20142,127.52-4.26%
Feb 20142,141.660.66%
Mar 20142,227.944.03%
Apr 20142,098.80-5.80%
May 20142,081.33-0.83%
Jun 20142,085.220.19%
Jul 20142,025.52-2.86%
Aug 20142,010.06-0.76%
Sep 20142,058.642.42%
Oct 20141,989.89-3.34%
Nov 20142,120.356.56%
Dec 20142,133.800.63%
Jan 20152,208.713.51%
Feb 20152,117.08-4.15%
Mar 20152,021.88-4.50%
Apr 20152,119.664.84%
May 20152,081.52-1.80%
Jun 20151,926.11-7.47%
Jul 20151,894.37-1.65%
Aug 20151,931.141.94%
Sep 20151,909.71-1.11%
Oct 20151,933.521.25%
Nov 20151,980.282.42%
Dec 20151,855.10-6.32%
Jan 20161,976.706.55%
Feb 20162,057.454.09%
Apr 20163,220.8356.54%
May 20163,314.592.91%
Jun 20163,298.53-0.48%
Jul 20163,200.78-2.96%
Aug 20163,214.240.42%
Sep 20163,333.843.72%
Oct 20163,298.43-1.06%
Nov 20163,265.32-1.00%
Dec 20163,225.12-1.23%
Jan 20173,180.23-1.39%
Feb 20173,115.99-2.02%
Mar 20172,934.94-5.81%
Apr 20173,155.497.51%
May 20173,167.360.38%
Jun 20173,290.553.89%
Jul 20173,279.98-0.32%
Aug 20173,202.97-2.35%
Sep 20173,118.29-2.64%
Oct 20173,243.574.02%
Nov 20173,337.342.89%
Dec 20173,264.82-2.17%
Jan 20183,438.585.32%
Feb 20186,530,952.00189,831.50%
Mar 201812,944,770.0098.21%
Apr 201819,045,050.0047.13%
May 201825,490,550.0033.84%
Jun 201829,570,050.0016.00%
Jul 201844,434,720.0050.27%
Aug 201871,554,260.0061.03%

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