Soft Sawnwood Monthly Price - Czech Koruna per Metric Ton

Data as of March 2026

Range
May 2016 - Mar 2026: -1,506.440 (-18.99%)
Chart

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

Unit: Czech Koruna 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 20167,934.25-
Jun 20167,957.550.29%
Jul 20167,844.01-1.43%
Aug 20167,764.44-1.01%
Sep 20168,051.243.69%
Oct 20168,101.580.63%
Nov 20168,175.310.91%
Dec 20168,296.781.49%
Jan 20178,116.43-2.17%
Feb 20177,924.33-2.37%
Mar 20177,440.07-6.11%
Apr 20177,914.936.38%
May 20177,632.23-3.57%
Jun 20177,722.411.18%
Jul 20177,430.61-3.78%
Aug 20177,099.45-4.46%
Sep 20176,836.63-3.70%
Oct 20177,123.004.19%
Nov 20177,292.592.38%
Dec 20177,092.04-2.75%
Jan 20187,204.821.59%
Feb 20186,916.87-4.00%
Mar 20186,938.020.31%
Apr 20186,844.19-1.35%
May 20187,557.8510.43%
Jun 20187,865.004.06%
Jul 20187,809.16-0.71%
Aug 20187,506.35-3.88%
Sep 20187,513.220.09%
Oct 20187,571.060.77%
Nov 20187,770.182.63%
Dec 20188,402.118.13%
Jan 20198,041.62-4.29%
Feb 20197,763.67-3.46%
Mar 20197,858.751.22%
Apr 20197,653.71-2.61%
May 20198,429.1010.13%
Jun 20198,017.92-4.88%
Jul 20198,123.291.31%
Aug 20198,541.135.14%
Sep 20198,541.040.00%
Oct 20198,109.09-5.06%
Nov 20198,426.583.92%
Dec 20197,888.87-6.38%
Jan 20208,302.825.25%
Feb 20207,758.29-6.56%
Mar 20208,704.2412.19%
Apr 20208,985.853.24%
May 20208,869.92-1.29%
Jun 20208,310.30-6.31%
Jul 20207,726.43-7.03%
Aug 20207,470.06-3.32%
Sep 20207,508.320.51%
Oct 20208,105.807.96%
Nov 20208,059.07-0.58%
Dec 20207,149.14-11.29%
Jan 20217,599.376.30%
Feb 20217,138.94-6.06%
Mar 20218,081.3513.20%
Apr 20218,569.086.04%
May 20218,872.593.54%
Jun 20219,220.943.93%
Jul 20218,006.45-13.17%
Aug 20217,153.16-10.66%
Sep 20216,350.37-11.22%
Oct 20216,824.477.47%
Nov 20216,880.630.82%
Dec 20217,077.902.87%
Jan 20227,084.920.10%
Feb 20227,294.762.96%
Mar 202210,583.6445.09%
Apr 20229,989.54-5.61%
May 202210,102.551.13%
Jun 20229,760.51-3.39%
Jul 20229,690.13-0.72%
Aug 202210,436.357.70%
Sep 202210,036.04-3.84%
Oct 20229,700.21-3.35%
Nov 20229,268.13-4.45%
Dec 20228,423.78-9.11%
Jan 20236,840.78-18.79%
Feb 20237,343.587.35%
Mar 20236,871.80-6.42%
Apr 20236,464.72-5.92%
May 20237,391.9314.34%
Jun 20236,853.30-7.29%
Jul 20236,877.140.35%
Aug 20237,139.693.82%
Sep 20237,672.797.47%
Oct 20237,405.62-3.48%
Nov 20236,909.41-6.70%
Dec 20237,085.372.55%
Jan 20247,584.137.04%
Feb 20247,670.451.14%
Mar 20247,949.753.64%
Apr 20247,452.89-6.25%
May 20247,470.000.23%
Jun 20247,079.54-5.23%
Jul 20247,220.601.99%
Aug 20246,978.92-3.35%
Sep 20247,446.966.71%
Oct 20246,765.97-9.14%
Nov 20247,327.968.31%
Dec 20248,498.5015.97%
Jan 20257,629.84-10.22%
Feb 20258,141.666.71%
Mar 20257,692.52-5.52%
Apr 20257,121.90-7.42%
May 20257,191.680.98%
Jun 20257,252.890.85%
Jul 20257,121.26-1.81%
Aug 20256,634.98-6.83%
Sep 20256,231.27-6.08%
Oct 20255,786.67-7.13%
Nov 20256,082.305.11%
Dec 20256,067.64-0.24%
Jan 20266,319.124.14%
Feb 20266,237.90-1.29%
Mar 20266,427.813.04%

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