Soft Sawnwood Monthly Price - Pakistan Rupee per Metric Ton

Data as of March 2026

Range
Apr 2006 - Jan 2019: 31,142.310 (167.95%)
Chart

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

Unit: Pakistan 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
Apr 200618,543.10-
May 200619,680.946.14%
Jun 200621,055.886.99%
Jul 200621,579.682.49%
Aug 200622,250.403.11%
Sep 200621,487.21-3.43%
Oct 200622,574.465.06%
Nov 200621,246.61-5.88%
Dec 200622,348.935.19%
Jan 200721,623.66-3.25%
Feb 200722,468.723.91%
Mar 200720,856.80-7.17%
Apr 200721,481.953.00%
May 200721,975.282.30%
Jun 200720,593.89-6.29%
Jul 200718,673.04-9.33%
Aug 200715,054.27-19.38%
Sep 200719,182.5627.42%
Oct 200717,916.15-6.60%
Nov 200718,698.114.36%
Dec 200719,035.961.81%
Jan 200819,765.713.83%
Feb 200819,097.19-3.38%
Mar 200818,294.02-4.21%
Apr 200818,585.641.59%
May 200820,724.5011.51%
Jun 200819,806.33-4.43%
Jul 200823,541.3918.86%
Aug 200823,489.60-0.22%
Sep 200824,173.022.91%
Oct 200826,087.687.92%
Nov 200826,656.022.18%
Dec 200824,940.16-6.44%
Jan 200927,593.0710.64%
Feb 200925,329.97-8.20%
Mar 200924,769.94-2.21%
Apr 200925,343.192.31%
May 200926,638.465.11%
Jun 200926,414.88-0.84%
Jul 200925,887.34-2.00%
Aug 200926,003.110.45%
Sep 200924,199.59-6.94%
Oct 200924,011.77-0.78%
Nov 200925,889.597.82%
Dec 200927,079.064.59%
Jan 201026,629.42-1.66%
Feb 201026,026.63-2.26%
Mar 201026,645.132.38%
Apr 201025,500.92-4.29%
May 201025,902.911.58%
Jun 201026,220.501.23%
Jul 201026,852.522.41%
Aug 201027,433.002.16%
Sep 201027,624.680.70%
Oct 201027,034.33-2.14%
Nov 201023,700.61-12.33%
Dec 201027,035.2014.07%
Jan 201126,320.46-2.64%
Feb 201124,975.04-5.11%
Mar 201124,240.92-2.94%
Apr 201123,218.69-4.22%
May 201124,391.605.05%
Jun 201125,117.432.98%
Jul 201126,465.385.37%
Aug 201127,625.434.38%
Sep 201126,681.53-3.42%
Oct 201127,186.041.89%
Nov 201127,406.170.81%
Dec 201126,525.61-3.21%
Jan 201228,146.126.11%
Feb 201228,754.792.16%
Mar 201227,404.20-4.70%
Apr 201229,072.886.09%
May 201230,278.644.15%
Jun 201230,032.38-0.81%
Jul 201229,584.28-1.49%
Aug 201230,577.733.36%
Sep 201230,623.040.15%
Oct 201230,060.93-1.84%
Nov 201231,371.084.36%
Dec 201229,103.48-7.23%
Jan 201329,465.291.24%
Feb 201329,899.121.47%
Mar 201330,607.212.37%
Apr 201331,886.664.18%
May 201335,244.7610.53%
Jun 201334,966.84-0.79%
Jul 201335,169.640.58%
Aug 201335,619.021.28%
Sep 201334,810.53-2.27%
Oct 201334,486.38-0.93%
Nov 201335,758.633.69%
Dec 201337,876.575.92%
Jan 201435,718.00-5.70%
Feb 201435,844.270.35%
Mar 201435,408.49-1.22%
Apr 201432,623.66-7.86%
May 201432,699.080.23%
Jun 201432,711.400.04%
Jul 201431,840.44-2.66%
Aug 201432,099.920.81%
Sep 201433,591.374.65%
Oct 201432,586.70-2.99%
Nov 201434,396.115.55%
Dec 201434,278.98-0.34%
Jan 201535,445.043.40%
Feb 201534,203.97-3.50%
Mar 201532,776.11-4.17%
Apr 201534,329.144.74%
May 201533,744.71-1.70%
Jun 201531,212.78-7.50%
Jul 201530,681.22-1.70%
Aug 201531,483.992.62%
Sep 201531,715.200.73%
Oct 201532,186.741.49%
Nov 201533,246.223.29%
Dec 201530,934.40-6.95%
Jan 201633,007.346.70%
Feb 201634,285.723.87%
Mar 201633,022.47-3.68%
Apr 201633,826.072.43%
May 201634,813.422.92%
Jun 201634,616.18-0.57%
Jul 201633,645.34-2.80%
Aug 201633,749.560.31%
Sep 201634,979.103.64%
Oct 201634,631.41-0.99%
Nov 201634,309.63-0.93%
Dec 201633,896.33-1.20%
Jan 201733,429.21-1.38%
Feb 201732,748.16-2.04%
Mar 201730,851.46-5.79%
Apr 201733,169.827.51%
May 201733,293.490.37%
Jun 201734,597.773.92%
Jul 201734,729.730.38%
Aug 201733,842.83-2.55%
Sep 201732,952.82-2.63%
Oct 201734,281.874.03%
Nov 201735,279.582.91%
Dec 201735,695.781.18%
Jan 201838,108.986.76%
Feb 201837,284.25-2.16%
Mar 201837,747.641.24%
Apr 201838,289.191.43%
May 201840,268.705.17%
Jun 201842,549.545.66%
Jul 201844,166.803.80%
Aug 201841,876.76-5.18%
Sep 201842,537.741.58%
Oct 201844,167.783.83%
Nov 201845,604.343.25%
Dec 201851,341.4812.58%
Jan 201949,685.42-3.23%

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