Soft Sawnwood Monthly Price - New Zealand Dollar per Metric Ton

Data as of March 2026

Range
Apr 2016 - Mar 2026: 50.622 (10.80%)
Chart

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

Unit: New Zealand Dollar 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 2016468.54-
May 2016488.084.17%
Jun 2016470.48-3.61%
Jul 2016450.75-4.19%
Aug 2016445.79-1.10%
Sep 2016457.042.52%
Oct 2016461.951.07%
Nov 2016456.78-1.12%
Dec 2016458.810.44%
Jan 2017449.23-2.09%
Feb 2017432.39-3.75%
Mar 2017419.50-2.98%
Apr 2017453.578.12%
May 2017458.080.99%
Jun 2017456.90-0.26%
Jul 2017447.70-2.01%
Aug 2017438.89-1.97%
Sep 2017431.18-1.76%
Oct 2017459.896.66%
Nov 2017485.675.60%
Dec 2017470.81-3.06%
Jan 2018475.661.03%
Feb 2018461.52-2.97%
Mar 2018463.620.46%
Apr 2018456.41-1.56%
May 2018500.979.76%
Jun 2018513.422.49%
Jul 2018520.301.34%
Aug 2018506.00-2.75%
Sep 2018519.172.60%
Oct 2018515.25-0.75%
Nov 2018503.88-2.21%
Dec 2018541.537.47%
Jan 2019528.11-2.48%
Feb 2019501.44-5.05%
Mar 2019506.410.99%
Apr 2019497.74-1.71%
May 2019557.3711.98%
Jun 2019536.20-3.80%
Jul 2019533.25-0.55%
Aug 2019572.277.32%
Sep 2019572.720.08%
Oct 2019550.45-3.89%
Nov 2019570.623.66%
Dec 2019522.68-8.40%
Jan 2020553.185.84%
Feb 2020528.16-4.52%
Mar 2020599.4413.50%
Apr 2020597.32-0.35%
May 2020582.99-2.40%
Jun 2020543.84-6.72%
Jul 2020507.83-6.62%
Aug 2020511.990.82%
Sep 2020497.16-2.90%
Oct 2020529.176.44%
Nov 2020526.09-0.58%
Dec 2020466.58-11.31%
Jan 2021491.805.40%
Feb 2021460.48-6.37%
Mar 2021514.2311.67%
Apr 2021555.708.06%
May 2021583.855.07%
Jun 2021613.545.09%
Jul 2021528.53-13.86%
Aug 2021474.73-10.18%
Sep 2021417.95-11.96%
Oct 2021441.025.52%
Nov 2021440.08-0.21%
Dec 2021465.845.85%
Jan 2022485.854.30%
Feb 2022507.654.49%
Mar 2022679.3833.83%
Apr 2022652.17-4.00%
May 2022674.473.42%
Jun 2022656.27-2.70%
Jul 2022646.35-1.51%
Aug 2022686.816.26%
Sep 2022681.84-0.72%
Oct 2022683.250.21%
Nov 2022641.98-6.04%
Dec 2022576.93-10.13%
Jan 2023480.28-16.75%
Feb 2023526.789.68%
Mar 2023500.41-5.01%
Apr 2023486.96-2.69%
May 2023546.9012.31%
Jun 2023511.51-6.47%
Jul 2023511.940.08%
Aug 2023538.385.17%
Sep 2023566.215.17%
Oct 2023538.80-4.84%
Nov 2023508.80-5.57%
Dec 2023508.900.02%
Jan 2024542.336.57%
Feb 2024535.87-1.19%
Mar 2024561.344.75%
Apr 2024530.45-5.50%
May 2024537.711.37%
Jun 2024500.62-6.90%
Jul 2024513.352.54%
Aug 2024502.12-2.19%
Sep 2024530.025.56%
Oct 2024479.04-9.62%
Nov 2024520.348.62%
Dec 2024613.3117.87%
Jan 2025557.43-9.11%
Feb 2025595.746.87%
Mar 2025580.90-2.49%
Apr 2025549.51-5.40%
May 2025548.08-0.26%
Jun 2025558.661.93%
Jul 2025562.730.73%
Aug 2025533.38-5.22%
Sep 2025509.94-4.39%
Oct 2025480.44-5.79%
Nov 2025513.556.89%
Dec 2025506.68-1.34%
Jan 2026527.184.05%
Feb 2026505.64-4.08%
Mar 2026519.172.67%

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