Soft Sawnwood Monthly Price - Forint per Metric Ton

Data as of March 2026

Range
Apr 2011 - Jan 2019: 50,287.020 (99.91%)
Chart

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

Unit: Forint 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 201150,333.55-
May 201153,174.555.64%
Jun 201154,219.111.96%
Jul 201157,679.306.38%
Aug 201160,452.394.81%
Sep 201163,166.854.49%
Oct 201167,717.797.20%
Nov 201171,846.366.10%
Dec 201168,495.45-4.66%
Jan 201274,013.838.06%
Feb 201269,603.16-5.96%
Mar 201266,680.86-4.20%
Apr 201272,005.177.98%
May 201275,900.885.41%
Jun 201274,666.22-1.63%
Jul 201273,034.92-2.18%
Aug 201272,703.77-0.45%
Sep 201271,409.70-1.78%
Oct 201268,489.90-4.09%
Nov 201272,125.295.31%
Dec 201265,075.39-9.77%
Jan 201366,750.502.57%
Feb 201366,693.04-0.09%
Mar 201372,954.549.39%
Apr 201374,386.391.96%
May 201380,689.028.47%
Jun 201379,457.49-1.53%
Jul 201378,659.33-1.00%
Aug 201377,699.32-1.22%
Sep 201374,164.44-4.55%
Oct 201370,137.13-5.43%
Nov 201373,379.264.62%
Dec 201377,704.895.89%
Jan 201475,105.12-3.35%
Feb 201477,421.623.08%
Mar 201479,952.423.27%
Apr 201474,313.25-7.05%
May 201473,389.56-1.24%
Jun 201474,683.841.76%
Jul 201473,717.52-1.29%
Aug 201475,367.022.24%
Sep 201479,531.055.53%
Oct 201476,884.36-3.33%
Nov 201483,043.168.01%
Dec 201485,430.442.87%
Jan 201595,803.7012.14%
Feb 201591,084.77-4.93%
Mar 201590,137.36-1.04%
Apr 201593,781.884.04%
May 201590,870.16-3.10%
Jun 201585,319.85-6.11%
Jul 201585,331.020.01%
Aug 201585,979.140.76%
Sep 201584,630.63-1.57%
Oct 201585,257.360.74%
Nov 201591,531.917.36%
Dec 201585,311.11-6.80%
Jan 201691,143.186.84%
Feb 201691,567.560.47%
Mar 201688,430.40-3.43%
Apr 201688,710.950.32%
May 201692,323.954.07%
Jun 201692,300.66-0.03%
Jul 201691,194.09-1.20%
Aug 201689,211.28-2.17%
Sep 201692,007.923.13%
Oct 201692,070.880.07%
Nov 201693,429.831.48%
Dec 201695,629.162.35%
Jan 201792,736.48-3.02%
Feb 201790,554.27-2.35%
Mar 201785,244.91-5.86%
Apr 201791,876.567.78%
May 201789,143.83-2.97%
Jun 201790,619.521.66%
Jul 201787,624.87-3.30%
Aug 201782,709.52-5.61%
Sep 201780,918.48-2.17%
Oct 201785,638.665.83%
Nov 201788,936.103.85%
Dec 201786,639.59-2.58%
Jan 201887,520.271.02%
Feb 201885,071.06-2.80%
Mar 201885,259.950.22%
Apr 201884,076.83-1.39%
May 201893,171.8210.82%
Jun 201898,466.055.68%
Jul 201898,156.81-0.31%
Aug 201894,363.01-3.87%
Sep 201895,306.541.00%
Oct 201894,940.90-0.38%
Nov 201896,710.911.86%
Dec 2018104,980.008.55%
Jan 2019100,620.60-4.15%

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