Soft Sawnwood Monthly Price - Sri Lanka Rupee per Metric Ton

Data as of March 2026

Range
Jun 2006 - Jan 2019: 29,035.010 (80.18%)
Chart

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

Unit: Sri Lanka Rupee 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
Jun 200636,211.48-
Jul 200637,218.682.78%
Aug 200638,277.782.85%
Sep 200636,408.50-4.88%
Oct 200639,327.898.02%
Nov 200637,732.47-4.06%
Dec 200639,579.804.90%
Jan 200738,542.80-2.62%
Feb 200740,181.764.25%
Mar 200737,561.40-6.52%
Apr 200738,699.043.03%
May 200740,156.023.76%
Jun 200737,681.86-6.16%
Jul 200734,506.08-8.43%
Aug 200727,899.98-19.14%
Sep 200735,861.5428.54%
Oct 200733,372.26-6.94%
Nov 200733,884.941.54%
Dec 200733,933.150.14%
Jan 200834,943.722.98%
Feb 200833,650.11-3.70%
Mar 200832,132.33-4.51%
Apr 200831,436.88-2.16%
May 200832,946.554.80%
Jun 200831,698.22-3.79%
Jul 200835,767.6312.84%
Aug 200833,948.32-5.09%
Sep 200833,714.39-0.69%
Oct 200835,093.534.09%
Nov 200836,652.884.44%
Dec 200835,119.83-4.18%
Jan 200939,610.7412.79%
Feb 200936,254.97-8.47%
Mar 200935,200.54-2.91%
Apr 200936,932.794.92%
May 200938,609.714.54%
Jun 200937,418.28-3.09%
Jul 200936,161.85-3.36%
Aug 200936,027.33-0.37%
Sep 200933,492.32-7.04%
Oct 200933,091.04-1.20%
Nov 200935,478.467.21%
Dec 200936,804.303.74%
Jan 201035,976.27-2.25%
Feb 201035,078.98-2.49%
Mar 201036,024.582.70%
Apr 201034,580.15-4.01%
May 201034,914.280.97%
Jun 201034,895.86-0.05%
Jul 201035,470.491.65%
Aug 201036,005.171.51%
Sep 201036,185.090.50%
Oct 201035,139.45-2.89%
Nov 201030,904.93-12.05%
Dec 201035,019.2313.31%
Jan 201134,054.54-2.75%
Feb 201132,457.64-4.69%
Mar 201131,325.36-3.49%
Apr 201130,239.23-3.47%
May 201131,429.603.94%
Jun 201132,064.802.02%
Jul 201133,661.054.98%
Aug 201134,989.633.95%
Sep 201133,578.13-4.03%
Oct 201134,457.282.62%
Nov 201134,999.751.57%
Dec 201133,790.63-3.45%
Jan 201235,503.465.07%
Feb 201237,148.614.63%
Mar 201237,877.991.96%
Apr 201241,229.638.85%
May 201242,850.233.93%
Jun 201242,062.72-1.84%
Jul 201241,598.22-1.10%
Aug 201242,718.322.69%
Sep 201242,630.23-0.21%
Oct 201240,648.30-4.65%
Nov 201242,561.344.71%
Dec 201238,451.71-9.66%
Jan 201338,309.68-0.37%
Feb 201338,626.170.83%
Mar 201339,535.932.36%
Apr 201340,843.793.31%
May 201345,212.8110.70%
Jun 201345,288.230.17%
Jul 201345,768.541.06%
Aug 201345,545.34-0.49%
Sep 201343,722.43-4.00%
Oct 201342,515.59-2.76%
Nov 201343,579.042.50%
Dec 201346,266.896.17%
Jan 201444,258.07-4.34%
Feb 201444,579.110.73%
Mar 201446,304.983.87%
Apr 201443,625.20-5.79%
May 201443,205.00-0.96%
Jun 201443,231.690.06%
Jul 201441,978.61-2.90%
Aug 201441,642.09-0.80%
Sep 201442,672.962.48%
Oct 201441,357.96-3.08%
Nov 201444,176.736.82%
Dec 201444,488.640.71%
Jan 201546,250.193.96%
Feb 201544,713.68-3.32%
Mar 201542,758.91-4.37%
Apr 201544,826.914.84%
May 201544,214.14-1.37%
Jun 201541,038.89-7.18%
Jul 201540,300.73-1.80%
Aug 201541,136.542.07%
Sep 201542,192.452.57%
Oct 201543,357.232.76%
Nov 201544,719.513.14%
Dec 201542,344.54-5.31%
Jan 201645,276.286.92%
Feb 201647,122.844.08%
Mar 201645,388.50-3.68%
Apr 201646,463.912.37%
May 201648,396.374.16%
Jun 201648,042.90-0.73%
Jul 201646,661.00-2.88%
Aug 201646,917.000.55%
Sep 201648,729.393.86%
Oct 201648,566.68-0.33%
Nov 201648,366.72-0.41%
Dec 201648,136.53-0.48%
Jan 201747,852.61-0.59%
Feb 201747,112.23-1.55%
Mar 201744,555.55-5.43%
Apr 201748,004.837.74%
May 201748,367.980.76%
Jun 201750,415.804.23%
Jul 201750,533.290.23%
Aug 201749,190.26-2.66%
Sep 201747,799.54-2.83%
Oct 201749,925.744.45%
Nov 201751,407.642.97%
Dec 201750,129.97-2.49%
Jan 201853,018.485.76%
Feb 201852,218.37-1.51%
Mar 201852,412.290.37%
Apr 201851,738.62-1.29%
May 201854,993.846.29%
Jun 201856,677.023.06%
Jul 201856,297.11-0.67%
Aug 201854,119.43-3.87%
Sep 201856,344.834.11%
Oct 201857,662.992.34%
Nov 201860,202.144.40%
Dec 201866,604.7010.64%
Jan 201965,246.49-2.04%

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