Soft Sawnwood Monthly Price - Iceland Krona per Metric Ton

Data as of March 2026

Range
May 2006 - Jan 2019: 19,224.440 (81.79%)
Chart

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

Unit: Iceland Krona 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 200623,504.05-
Jun 200626,109.3011.08%
Jul 200626,619.661.95%
Aug 200625,967.14-2.45%
Sep 200624,919.21-4.04%
Oct 200625,518.532.41%
Nov 200624,162.45-5.31%
Dec 200625,455.855.35%
Jan 200724,907.35-2.15%
Feb 200724,902.70-0.02%
Mar 200723,012.16-7.59%
Apr 200723,086.580.32%
May 200722,864.86-0.96%
Jun 200721,321.29-6.75%
Jul 200718,708.53-12.25%
Aug 200716,186.76-13.48%
Sep 200720,168.1324.60%
Oct 200717,914.17-11.18%
Nov 200718,638.234.04%
Dec 200719,356.713.85%
Jan 200820,761.137.26%
Feb 200820,739.40-0.10%
Mar 200821,356.582.98%
Apr 200821,592.151.10%
May 200822,975.566.41%
Jun 200823,264.591.26%
Jul 200826,057.6312.01%
Aug 200825,722.44-1.29%
Sep 200828,512.4310.85%
Oct 200837,048.8629.94%
Nov 200845,087.4421.70%
Dec 200839,102.05-13.28%
Jan 200943,096.0810.21%
Feb 200936,225.92-15.94%
Mar 200935,330.61-2.47%
Apr 200939,848.0612.79%
May 200941,723.304.71%
Jun 200941,246.02-1.14%
Jul 200940,080.23-2.83%
Aug 200939,880.77-0.50%
Sep 200936,353.91-8.84%
Oct 200935,711.10-1.77%
Nov 200938,339.697.36%
Dec 200940,255.055.00%
Jan 201039,598.70-1.63%
Feb 201039,264.43-0.84%
Mar 201040,239.972.48%
Apr 201038,714.42-3.79%
May 201039,799.792.80%
Jun 201039,495.83-0.76%
Jul 201038,748.20-1.89%
Aug 201038,255.39-1.27%
Sep 201037,568.36-1.80%
Oct 201035,122.10-6.51%
Nov 201030,982.34-11.79%
Dec 201036,476.5317.73%
Jan 201135,847.03-1.73%
Feb 201134,083.18-4.92%
Mar 201132,705.97-4.04%
Apr 201130,976.86-5.29%
May 201132,759.875.76%
Jun 201133,663.022.76%
Jul 201135,709.436.08%
Aug 201136,470.192.13%
Sep 201135,605.36-2.37%
Oct 201136,249.061.81%
Nov 201136,869.421.71%
Dec 201135,879.13-2.69%
Jan 201238,528.297.38%
Feb 201239,089.681.46%
Mar 201238,119.75-2.48%
Apr 201240,617.186.55%
May 201242,094.293.64%
Jun 201240,624.59-3.49%
Jul 201239,415.64-2.98%
Aug 201238,872.81-1.38%
Sep 201239,752.712.26%
Oct 201239,035.86-1.80%
Nov 201241,595.456.56%
Dec 201237,775.29-9.18%
Jan 201338,858.182.87%
Feb 201338,944.160.22%
Mar 201339,072.730.33%
Apr 201338,514.32-1.43%
May 201343,315.4212.47%
Jun 201343,166.17-0.34%
Jul 201342,708.39-1.06%
Aug 201341,338.70-3.21%
Sep 201339,963.67-3.33%
Oct 201339,151.56-2.03%
Nov 201340,498.693.44%
Dec 201341,552.772.60%
Jan 201439,198.67-5.67%
Feb 201438,932.05-0.68%
Mar 201440,044.952.86%
Apr 201437,518.57-6.31%
May 201437,313.17-0.55%
Jun 201437,746.001.16%
Jul 201436,842.79-2.39%
Aug 201437,106.320.72%
Sep 201439,016.915.15%
Oct 201438,254.34-1.95%
Nov 201441,706.139.02%
Dec 201442,431.701.74%
Jan 201546,301.189.12%
Feb 201544,497.37-3.90%
Mar 201544,021.78-1.07%
Apr 201546,023.204.55%
May 201543,893.50-4.63%
Jun 201540,544.99-7.63%
Jul 201540,414.83-0.32%
Aug 201540,497.840.21%
Sep 201538,935.45-3.86%
Oct 201538,912.58-0.06%
Nov 201541,265.466.05%
Dec 201538,392.45-6.96%
Jan 201640,976.766.73%
Feb 201642,004.732.51%
Mar 201640,088.85-4.56%
Apr 201639,982.34-0.27%
May 201641,061.952.70%
Jun 201640,793.68-0.65%
Jul 201639,141.91-4.05%
Aug 201637,994.60-2.93%
Sep 201638,365.450.98%
Oct 201637,764.78-1.57%
Nov 201636,727.20-2.75%
Dec 201636,382.43-0.94%
Jan 201736,425.930.12%
Feb 201734,922.98-4.13%
Mar 201732,161.44-7.91%
Apr 201734,935.808.63%
May 201732,747.50-6.26%
Jun 201733,421.302.06%
Jul 201734,496.143.22%
Aug 201734,069.02-1.24%
Sep 201733,252.95-2.40%
Oct 201734,318.933.21%
Nov 201734,902.011.70%
Dec 201734,298.80-1.73%
Jan 201835,482.723.45%
Feb 201834,032.90-4.09%
Mar 201833,533.44-1.47%
Apr 201832,984.16-1.64%
May 201836,179.579.69%
Jun 201838,071.505.23%
Jul 201837,592.87-1.26%
Aug 201836,324.82-3.37%
Sep 201837,894.394.32%
Oct 201839,397.533.97%
Nov 201841,862.056.26%
Dec 201844,978.497.44%
Jan 201942,728.49-5.00%

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