Prices through the supply chain: Softwood lumber

Release date: September 2, 2022

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Context

In 2021, the cost of constructing a single-detached home increased by 22.5%.^Note This record annual increase in construction costs was driven by higher prices for softwood lumber. Between June 2020 and May 2021, the producer price^Note of softwood lumber more than tripled, amid various COVID-related developments in the economy. Demand for softwood lumber increased as declining interest rates and excess household savings^Note underpinned greater spending on new home construction and pandemic home-improvement projects, key downstream markets for the commodity. Domestic supply of softwood lumber declined initially amid mill closures and curtailments following the COVID-19 outbreak. Even as production picked up in the summer of 2021, longer-term supply constraints in British Columbia due to beetle infestations and wildfires limited increases in Canadian softwood lumber production.

To better understand the unprecedented volatility in lumber prices and the ensuing price transmission, this analysis evaluates the price movements across the domestic supply chain for lumber since the summer of 2020 using producer price indexes.

Highlights

The increase in producer prices for lumber was not passed along proportionately to consumers. This partly reflects the fact that wholesalers and retailers reduced their markups, presumably to remain competitive.
Even though wholesalers and retailers reduced their percentage markup on lumber, they still earned a higher gross profit on each unit of lumber sold.
Roughly two thirds (65%) of the record increase in construction costs for single-detached homes between the first quarter of 2020 and the first quarter of 2022 can be attributed to rising prices for wood, plastics, and composites, specifically, softwood lumber prices.

Analysis

Overview of the supply chain for softwood lumber

Figure 1 illustrates a simplified supply chain for lumber, excluding international trade. First, logs are harvested from the forests. Next, these logs are sorted for their intended end uses. Logs that are deemed suitable for dimensional lumber are transported to sawmills for processing. Producers sell their lumber to wholesalers, who distribute it to various downstream users. This includes contractors, who purchase the lumber for use, notably in residential construction, where it is used in walls, floors, and ceilings. It also includes retailers who purchase lumber for resale, at a markup, to household consumers. While lumber has other uses, this analysis focuses on its journey from log to end-use, in single-home construction and home improvement.

Forest products supply chain (excluding international trade) — Figure 1 Description

This figure illustrates the supply chain for lumber, from forest to end use in home construction and improvement. It excludes international trade.

**Note:** This figure was altered from the original figure to highlight a specific path, represented by the red arrows

**Source:** WWF Living Forests Report, 2012.
https://files.worldwildlife.org/wwfcmsprod/files/Publication/file/5ub3o4pav8_living_forests_report_ch4_forest_products
.pdf?_ga=2.97272157.354968229.1646840601-364187257.1646840600

Measuring prices across the supply chain

Figure 2 outlines the specific data used to monitor price movements along the supply chain for lumber. To begin with, the Raw Materials Price Index (RMPI) measures price changes for logs and bolts that are purchased for further processing by manufacturers operating in Canada. Moving downstream, the Industrial Product Price Index (IPPI) tracks changes in producer prices, in this case, the prices that producers receive for the sale of softwood lumber.^Note The Wholesale Services Price Index (WSPI) quantifies price movements for lumber distributed to, and sold by, wholesalers. There is no retail selling price index for lumber per se. However, the Retail Services Price Index (RSPI)^Note measures movements in the price that consumers pay for lumber at home centres. Finally, the Building Construction Price Index (BCPI) measures price movements for wood products used in home construction as well as aggregate price movements in construction costs for single-detached homes. The BCPI measures quarterly price changes, whereas each of the four other indexes measures monthly price changes.^Note

Figure 2: From logs to single-home construction―Measuring prices along the supply chain for lumber — Figure 2 Description:

This figure illustrates the stages along the domestic supply chain for lumber. Each stage is associated with a Producer Prices Program that measures prices at the stage. In turn, an individual price series is identified under each Program that most closely tracks the path of lumber along the supply chain.

NAPCS-2017

NAICS-2017

Contributors to unprecedented inflation

The first significant price change in the supply chain for lumber since the start of the pandemic was captured by the IPPI. In July 2020, producer prices for softwood lumber increased 22% from the previous month. At that point, demand for lumber had begun trending upwards, mostly driven by new residential construction and spending on home improvement. Housing starts across Canada rallied in May and June, following a decline in April. As a result, housing starts in the second quarter of 2020 totalled roughly 74,600―their highest levels since the second quarter of 1987, and 35% higher than their annual average over the second quarters of the previous five years. Likewise, housing starts in the U.S., a key driver of demand for Canadian lumber, increased after an initial decline in April. In July 2020, the U.S. recorded 138,700 housing starts,^Note up 21% year-over-year.

Amid the ongoing uncertainty surrounding the impacts of the COVID-19 pandemic, some sawmills shut down, while others curtailed production temporarily, resulting in a dip in domestic softwood lumber production in the second quarter of 2020. Domestic lumber production increased over the following three quarters, averaging 8% higher in the first quarter of 2021 compared to the first quarter of 2020. However, demand for lumber outgrew this increase in supply.

Domestic residential construction investment, including spending on new construction and renovations, surpassed pre-COVID levels by August 2020. Thereafter, it increased steadily and peaked in April 2021, to a level 34% higher than its average level over the decade spanning from 2010 to 2019, adjusted for inflation. At the same time, U.S. housing starts increased in the third quarter of 2020 and rose even further thereafter putting significant upward pressure on lumber prices.

Other factors may have contributed to inflation across the supply chain for lumber. Indeed, during COVID-19, businesses were exposed to various forms of supply chain disruptions, such as truck driver shortages, interruptions to trade, and reduced workforce productivity. To the extent that these disruptions reduced the supply of production inputs, the relative scarcity for these goods would have created upward pressure on their prices. The ensuing increase to production costs, or a portion thereof, would have then been passed on to customers.

Against this backdrop, producer prices for softwood lumber rose steadily, increasing in eight of the ten months beginning in August 2020. Double-digit price growth was recorded in six of these eight months. Prices peaked in May 2021, 209% above their June 2020 levels. This volatility in lumber prices was unprecedented, as illustrated in Chart 1.

Chart 1

Data table for Chart 1

Data table for chart 1
Table summary
This table displays the results of Data table for chart 1 Year-over-year percentage change in producer prices for lumber (appearing as column headers).
1957
	Year-over-year percentage change in producer prices for lumber
January	-8.7
February	-8.7
March	-8.6
April	-8.6
May	-10.2
June	-8.6
July	-7.1
August	-7.3
September	-5.0
October	-5.8
November	-4.2
December	-3.4
1958
January	-1.7
February	-2.6
March	-4.3
April	-3.4
May	-1.7
June	-3.4
July	-2.6
August	1.7
September	1.7
October	0.9
November	0.9
December	0.9
1959
January	3.5
February	5.4
March	8.0
April	8.8
May	9.7
June	9.7
July	8.8
August	6.0
September	6.0
October	7.0
November	6.1
December	7.9
1960
January	6.0
February	5.1
March	0.8
April	-0.8
May	-2.4
June	-4.8
July	-5.6
August	-8.1
September	-11.3
October	-10.6
November	-9.1
December	-10.6
1961
January	-11.3
February	-11.3
March	-8.2
April	-7.4
May	-6.6
June	-4.2
July	-2.6
August	-0.9
September	1.8
October	0.0
November	2.7
December	3.6
1962
January	3.6
February	4.5
March	4.5
April	3.5
May	5.3
June	7.1
July	6.1
August	5.3
September	6.3
October	7.3
November	4.4
December	3.5
1963
January	6.1
February	7.0
March	4.3
April	5.1
May	4.2
June	4.1
July	5.8
August	6.7
September	4.2
October	4.2
November	5.1
December	5.9
1964
January	4.1
February	5.7
March	7.4
April	6.5
May	6.5
June	4.0
July	1.6
August	2.4
September	2.4
October	2.4
November	1.6
December	0.8
1965
January	4.0
February	1.5
March	0.0
April	-0.8
May	-0.8
June	0.0
July	-1.5
August	1.5
September	3.9
October	3.2
November	3.2
December	4.8
1966
January	1.5
February	2.3
March	4.6
April	8.5
May	8.4
June	6.9
July	7.0
August	3.0
September	0.8
October	1.5
November	1.5
December	0.8
1967
January	1.5
February	3.7
March	2.2
April	0.0
May	-0.7
June	1.4
July	2.9
August	4.4
September	7.5
October	7.6
November	9.1
December	11.3
1968
January	11.9
February	12.9
March	17.1
April	17.7
May	18.4
June	19.0
July	19.9
August	21.8
September	23.8
October	24.6
November	26.4
December	27.0
1969
January	27.8
February	29.7
March	32.3
April	28.9
May	21.6
June	7.1
July	-1.8
August	-7.5
September	-10.7
October	-14.7
November	-15.4
December	-17.6
1970
January	-21.8
February	-26.3
March	-31.8
April	-31.3
May	-26.6
June	-17.7
July	-10.8
August	-6.9
September	-4.4
October	0.0
November	-5.2
December	-5.8
1971
January	-1.3
February	6.0
March	14.2
April	15.0
May	13.4
June	18.8
July	26.4
August	28.2
September	23.2
October	19.2
November	24.0
December	24.7
1972
January	30.9
February	26.9
March	21.3
April	24.3
May	24.9
June	20.3
July	18.7
August	20.4
September	27.4
October	40.6
November	43.1
December	42.3
1973
January	38.5
February	37.4
March	43.9
April	44.8
May	40.8
June	39.4
July	28.4
August	28.3
September	26.6
October	15.4
November	10.0
December	6.6
1974
January	-1.1
February	-4.7
March	-1.4
April	-3.9
May	-5.4
June	-9.1
July	-8.4
August	-18.3
September	-23.0
October	-26.7
November	-23.5
December	-14.1
1975
January	-18.0
February	-15.0
March	-21.3
April	-15.1
May	-5.3
June	-2.6
July	2.7
August	9.1
September	13.9
October	18.2
November	17.0
December	12.7
1976
January	27.4
February	23.9
March	24.5
April	14.5
May	5.3
June	6.5
July	6.7
August	14.1
September	15.6
October	18.2
November	17.6
December	16.9
1977
January	11.8
February	13.6
March	14.4
April	14.8
May	15.0
June	17.1
July	23.1
August	25.0
September	25.7
October	22.4
November	21.0
December	19.2
1978
January	26.6
February	27.0
March	26.1
April	26.7
May	28.6
June	24.4
July	18.5
August	16.5
September	16.2
October	25.7
November	28.7
December	24.5
1979
January	22.3
February	22.5
March	22.6
April	19.9
May	20.8
June	22.1
July	23.7
August	24.9
September	25.5
October	15.4
November	6.4
December	0.6
1980
January	-0.8
February	-4.8
March	-10.7
April	-21.4
May	-22.4
June	-17.1
July	-15.3
August	-21.4
September	-25.7
October	-21.8
November	-14.7
December	-10.7
1981
January	-15.9
February	-14.6
March	-11.8
April	4.9
May	6.7
June	1.9
July	0.0
August	-4.2
September	-9.2
October	-12.5
November	-15.6
December	-9.9
1982
January	-9.7
February	-10.0
March	-6.5
April	-6.9
May	-8.0
June	-4.3
July	-7.8
August	-6.3
September	1.3
October	4.4
November	9.2
December	10.1
1983
January	22.3
February	22.7
March	22.6
April	23.9
May	37.5
June	41.7
July	38.2
August	25.7
September	16.3
October	17.9
November	10.5
December	10.2
1984
January	3.4
February	10.6
March	10.5
April	4.2
May	-13.2
June	-24.7
July	-25.1
August	-11.2
September	-6.5
October	-7.8
November	-4.2
December	-7.5
1985
January	-5.4
February	-11.4
March	-12.9
April	-10.6
May	2.4
June	11.4
July	14.4
August	8.6
September	11.6
October	9.7
November	5.8
December	7.6
1986
January	5.3
February	5.1
March	16.2
April	22.6
May	11.8
June	2.1
July	2.3
August	11.6
September	18.2
October	16.4
November	16.0
December	8.2
1987
January	4.6
February	8.8
March	-3.7
April	-9.3
May	-6.7
June	2.2
July	3.9
August	-0.6
September	-6.0
October	-5.3
November	-1.6
December	2.0
1988
January	6.9
February	2.4
March	3.5
April	3.9
May	2.3
June	2.2
July	1.0
August	-4.8
September	-5.5
October	-4.4
November	-4.8
December	-4.0
1989
January	-4.3
February	-2.9
March	-2.4
April	-2.6
May	0.0
June	-0.6
July	0.8
August	3.9
September	4.7
October	5.7
November	2.9
December	0.8
1990
January	-1.0
February	1.2
March	1.8
April	1.4
May	1.6
June	-1.2
July	-3.5
August	-4.5
September	-4.5
October	-7.2
November	-6.1
December	-5.4
1991
January	-7.2
February	-10.2
March	-8.5
April	-4.8
May	-1.0
June	8.8
July	5.2
August	-1.4
September	-1.2
October	0.2
November	4.3
December	6.1
1992
January	12.7
February	24.3
March	24.1
April	15.9
May	11.8
June	3.3
July	4.8
August	14.7
September	22.1
October	21.8
November	25.8
December	31.5
1993
January	48.1
February	59.9
March	68.9
April	56.8
May	28.6
June	18.4
July	17.5
August	31.8
September	32.1
October	37.7
November	47.2
December	54.8
1994
January	32.5
February	10.9
March	0.7
April	1.3
May	24.9
June	41.6
July	40.6
August	29.1
September	16.2
October	10.1
November	-0.2
December	-12.0
1995
January	-12.1
February	-10.6
March	-12.9
April	-11.7
May	-18.9
June	-29.4
July	-17.2
August	-21.1
September	-11.4
October	-14.5
November	-17.7
December	-16.7
1996
January	-15.9
February	-12.7
March	-7.9
April	3.0
May	26.2
June	40.6
July	24.8
August	37.5
September	32.7
October	33.4
November	44.7
December	36.4
1997
January	34.6
February	29.8
March	26.2
April	29.3
May	8.5
June	3.3
July	3.9
August	-4.9
September	-11.0
October	-11.3
November	-18.4
December	-13.9
1998
January	-14.3
February	-12.8
March	-15.2
April	-16.5
May	-17.7
June	-17.2
July	-14.9
August	-10.0
September	-11.1
October	-6.2
November	-4.4
December	-1.1
1999
January	1.8
February	0.7
March	9.8
April	6.5
May	16.0
June	28.3
July	36.4
August	16.4
September	16.4
October	6.3
November	9.2
December	8.1
2000
January	5.2
February	3.3
March	-1.6
April	-5.8
May	-15.3
June	-23.8
July	-34.3
August	-30.1
September	-26.8
October	-19.0
November	-19.0
December	-22.5
2001
January	-26.1
February	-23.5
March	-20.0
April	-10.6
May	13.8
June	13.0
July	12.1
August	25.6
September	20.2
October	5.1
November	4.5
December	7.2
2002
January	16.8
February	20.6
March	25.1
April	14.0
May	-8.0
June	-13.9
July	-6.7
August	-15.5
September	-14.5
October	-0.9
November	-5.1
December	-4.2
2003
January	-8.3
February	-11.0
March	-22.7
April	-23.2
May	-24.5
June	-14.7
July	-11.5
August	-5.4
September	8.1
October	-3.8
November	-5.3
December	-4.7
2004
January	1.1
February	6.0
March	15.4
April	25.3
May	44.9
June	37.9
July	29.9
August	32.8
September	16.2
October	11.2
November	4.1
December	9.7
2005
January	6.0
February	3.2
March	1.4
April	-7.1
May	-19.1
June	-16.2
July	-17.7
August	-25.9
September	-22.3
October	-10.7
November	-1.3
December	-5.7
2006
January	-3.9
February	-10.7
March	-11.3
April	-9.9
May	-7.0
June	-10.5
July	-7.2
August	-6.3
September	-6.9
October	-8.1
November	-9.5
December	-9.0
2007
January	-7.1
February	-8.0
March	-8.6
April	-7.9
May	-9.4
June	-4.3
July	-6.6
August	-3.2
September	-7.8
October	-9.3
November	-8.9
December	-9.4
2008
January	-15.8
February	-15.6
March	-14.4
April	-12.4
May	-8.7
June	-8.4
July	-6.3
August	-1.4
September	1.6
October	8.4
November	9.3
December	6.4
2009
January	4.3
February	8.8
March	8.8
April	5.6
May	0.2
June	0.3
July	1.3
August	-3.7
September	-4.5
October	-8.0
November	-6.9
December	-4.3
2010
January	0.2
February	7.8
March	6.3
April	11.9
May	20.1
June	4.7
July	0.3
August	1.2
September	3.5
October	6.8
November	7.8
December	6.5
2011
January	7.6
February	-3.9
March	-3.1
April	-9.3
May	-16.0
June	-4.7
July	-0.8
August	-1.1
September	0.8
October	-1.1
November	-4.4
December	-3.8
2012
January	-4.1
February	-2.0
March	1.9
April	6.2
May	18.6
June	18.9
July	15.4
August	16.5
September	9.7
October	10.2
November	16.8
December	20.1
2013
January	27.0
February	29.8
March	30.4
April	29.6
May	13.5
June	6.1
July	7.5
August	9.4
September	15.9
October	19.3
November	18.3
December	11.9
2014
January	7.2
February	5.8
March	1.5
April	-0.9
May	7.7
June	10.4
July	11.5
August	11.2
September	9.4
October	7.0
November	2.7
December	5.2
2015
January	5.4
February	5.2
March	1.9
April	-1.2
May	-5.1
June	1.8
July	5.8
August	3.7
September	-0.6
October	-2.6
November	0.6
December	1.1
2016
January	0.2
February	-3.4
March	0.5
April	6.8
May	12.9
June	8.6
July	4.8
August	4.5
September	7.2
October	8.2
November	4.3
December	2.6
2017
January	1.4
February	10.1
March	13.0
April	17.2
May	13.4
June	9.4
July	9.7
August	12.6
September	14.1
October	22.8
November	28.0
December	25.1
2018
January	22.0
February	22.8
March	23.0
April	16.6
May	28.0
June	34.4
July	26.8
August	8.2
September	4.3
October	-15.8
November	-19.7
December	-16.6
2019
January	-14.7
February	-14.7
March	-17.5
April	-20.3
May	-28.9
June	-29.8
July	-24.7
August	-16.3
September	-11.6
October	2.6
November	7.0
December	5.4
2020
January	5.4
February	7.2
March	8.6
April	2.8
May	8.1
June	12.8
July	34.4
August	69.4
September	98.9
October	82.2
November	51.0
December	81.3
2021
January	104.8
February	102.7
March	115.8
April	165.9
May	226.0
June	170.0
July	47.9
August	-4.5
September	-20.7
October	-7.6
November	13.5
December	20.0
2022
January	21.5
February	22.1
March	24.7
April	-4.8
May	-24.9
Source: Statistics Canada. Table 18-10-0266-01 Industrial product price index, by product, monthly.

Most of the increase in lumber prices between June 2020 and May 2021, dissipated over the following three months. As some consumers became increasingly reluctant to pay higher prices for lumber, domestic renovation spending moderated. This decrease in demand was accompanied by higher North American lumber supply.^Note In Canada, easing supply pressures for lumber were evidenced by rising inventory levels at sawmills. Overall, producer prices for lumber declined by 53% between May and August 2021, at which point prices were 44% above their levels in June 2020.

More recently, in December 2021, producer prices for lumber were trending upward again, underpinned by robust residential construction in the U.S. and supply constraints resulting from severe flooding in Vancouver. As of May 2022, producer prices were 132% above their levels in June 2020.

Price movements across the supply chain

Prices across the supply chain for lumber are interconnected. For example, the producer price for softwood lumber is influenced by upstream developments such as higher prices of logs. Rising input costs can cause lumber producers to increase their output price to remain profitable, an example of cost-push inflation. At the same time, the producer price for softwood lumber is also influenced by downstream developments. A growing number of building permits, signalling an expansion in future construction, will eventually translate into higher demand for building materials including lumber. As these building intentions materialize into supply agreements, lumber producers may increase output prices to maximize their potential earnings, an example of demand-pull inflation. Other factors such as innovations in home construction can also influence producers prices. Ultimately, prices at every stage of the supply-chain will be influenced by a unique combination of factors outlined above. However, this section focuses on describing price movements across the supply chain instead of quantifying their determinants.

Chart 2 illustrates the price changes in lumber across the supply chain since June 2020. It is interesting to note that price increases did not transmit evenly across the supply chain. For example, wholesale selling prices for lumber peaked at 132% above June 2020 levels, while retail selling prices for dimensional lumber surpassed June 2020 levels by 86%. This incomplete transfer reflects the fact that wholesalers and retailers reduced their markups.^Note In other words, wholesalers did not pass on the entire cost increase from producers to retailers, nor did retailers pass on the entire cost increase from wholesalers on to consumers.

In addition, price increases did not always appear simultaneously. For example, it took five months for prices of logs and bolts to increase 5% above June 2020 levels. The remainder of this section discusses movements across the supply chain in greater detail.

Chart 2

Data table for Chart 2

Data table for chart 2
Table summary
This table displays the results of Data table for chart 2 Logs and bolts (RMPI), Producer price for softwood lumber (IPPI), Wholesale selling price for softwood lumber (WSPI), Selling price for dimensional lumber at home centres (RSPI) and Construction costs for single-detached house (BCPI), calculated using Cumulative percentage change in prices units of measure (appearing as column headers).
	Cumulative percentage change in prices
	Logs and bolts (RMPI)	Producer price for softwood lumber (IPPI)	Wholesale selling price for softwood lumber (WSPI)	Selling price for dimensional lumber at home centres (RSPI)	Construction costs for single-detached house^{Data table for chart 2 Note 1} (BCPI)
2020
June	0.0	0.0	0.0	0.0	0.0
July	-1.5	22.5	0.0	14.8	1.0
August	-1.2	51.1	13.3	22.9	1.9
September	0.3	81.1	33.2	31.9	2.9
October	1.9	66.2	33.7	32.2	4.2
November	4.1	41.3	21.5	27.5	5.5
December	9.6	69.5	31.6	28.0	6.8
2021
January	12.1	92.7	49.3	37.9	9.0
February	13.6	105.6	60.7	42.6	11.1
March	16.7	121.9	66.8	45.5	13.3
April	22.0	147.4	98.3	65.7	16.8
May	28.6	208.6	132.2	85.7	20.4
June	39.4	170.0	112.4	84.5	24.0
July	44.4	81.2	73.1	60.1	25.9
August	44.2	44.2	59.5	44.7	27.8
September	40.1	43.7	47.9	29.5	29.7
October	42.8	53.6	44.3	38.9	31.3
November	47.8	60.4	37.5	39.2	32.9
December	55.0	103.4	47.2	51.4	34.5
2022
January	60.7	134.1	80.6	77.5	37.3
February	67.3	151.0	96.1	83.5	40.2
March	76.1	176.8	104.9	89.9	43.0
Note 1 Quarterly series converted to monthly series through linear interpolation. Return to note 1 referrer Source: Statistics Canada. Table 18-10-0268-01 Raw materials price index, monthly; Statistics Canada. Table 18-10-0266-01 Industrial product price index, by product, monthly; Internal calculation of indexes by NAPCS from Statistics Canada’s Wholesale Price Report (survey number 5106); Internal calculation of indexes by NAICS from Statistics Canada’s Retail Price Report (survey number 5135); Statistics Canada. Table 18-10-0135-01 Building construction price indexes, by type of building.

Raw materials

While prices for logs and bolts eventually rose, the transfer from producer prices for lumber was imperfect. Between June 2020 and March 2022, price movements for logs and bolts appeared to lag movements in producer prices. At the same time, the price increase for logs and bolts was more moderate compared to softwood lumber, with prices for the raw material peaking 44% above June 2020 levels in July 2021. Since then, log prices have continued to increase, reaching 76% above June 2020 levels by March 2022.

Manufacturing

As highlighted earlier, the unprecedented increase in lumber prices originated in manufacturing. Producer prices for lumber grew substantially amidst an imbalance between supply and demand for lumber that was spurred by various pandemic-related developments. Between June 2020 and May 2021, producer prices for softwood lumber increased by an average of 12% per month.

Wholesale

Between June 2020 and March 2022, wholesale selling price movements for lumber largely coincided with movements in producer prices for lumber. What is more, producer and wholesale prices peaked simultaneously, in May 2021, before moderating in unison thereafter.

Chart 3 illustrates how the prices paid by wholesalers to producers of lumber grew faster than the prices they charged retailers for it. In other words, wholesalers were not passing along the entire increase in lumber prices from their suppliers on to their customers, opting to reduce markups somewhat to stay competitive. Nonetheless, the wholesale margin price, which represents the dollar value spread between the average selling price and the average purchase price, increased. This reflects the fact that at higher prices, wholesalers pocketed a larger gross difference between the price they paid for lumber and the price at which they sold it for. A lower wholesale markup, or margin measured as a percent of the selling price, did not prevent wholesalers from earning three times as much profit per unit of lumber sold in May 2021, compared to June 2020. Chart 4 depicts a hypothetical example showing how margins can increase even as markups decline.

Chart 3

Data table for Chart 3

Data table for chart 3
Table summary
This table displays the results of Data table for chart 3 Purchase, Selling and Margin, calculated using Wholesale price index for softwood lumber units of measure (appearing as column headers).
	Wholesale price index for softwood lumber
	Purchase	Selling	Margin
2020
June	100.0	100.0	100.0
July	102.4	100.0	101.9
August	115.4	113.3	126.9
September	137.5	133.2	133.6
October	134.2	133.7	154.1
November	116.4	121.5	181.2
December	137.2	131.6	199.0
2021
January	152.9	149.3	155.6
February	170.5	160.7	189.9
March	184.6	166.8	151.3
April	209.3	198.3	206.8
May	250.4	232.2	257.8
June	227.8	212.4	273.6
July	177.4	173.1	310.4
August	159.0	159.5	261.0
September	145.5	147.9	257.3
October	165.0	144.3	252.8
November	153.8	137.5	242.2
December	156.1	147.2	250.3
2022
January	212.4	180.6	428.1
February	205.3	196.1	355.1
March	235.8	204.9	383.8
Source: Internal calculation of indexes under the North American Product Classification System from Statistics Canada’s Wholesale Price Report (survey number 5106).

Chart 4

Data table for Chart 4

Data table for chart 4
Table summary
This table displays the results of Data table for chart 4 Purchase price, Margin, Selling price and Markup, calculated using dollars and percent units of measure (appearing as column headers).
	dollars			percent
	Purchase price	Margin	Selling price^{Data table for chart 4 Note 1}	Markup
Period 1	20	5	25	20^{Data table for chart 4 Note 2}
Period 2	40	8	48	17^{Data table for chart 4 Note 2}
Note 1 Selling price = Purchase price + Margin Return to note 1 referrer Note 2 Markup percentage = Margin / Selling price Return to note 2 referrer

Retail

At home centres, one of two important end-markets for lumber that are examined in this analysis, movements in selling prices for lumber resembled movements in wholesale selling prices over the period between June 2020 and March 2022.^Note Moreover, retail prices for lumber also peaked in May 2021, before moderating thereafter.

However, in retail trade, margin increases were lower than in wholesale trade. Like wholesalers, the prices that retailers paid their suppliers for lumber grew faster than the prices they charged consumers for it. As a result, the percentage markup on each piece of lumber sold shrank. In fact, markups on dimensional lumber at home centres declined between November 2020 and March 2021, reaching their lowest levels over the history of available data, which began in 2012. Despite lowering markups, retailers still earned higher profits on each unit of lumber sold, due to higher prices. Between July 2020 and May 2021, retailers earned an average of 28% more profit per unit of lumber, compared to June 2020. (See Chart 5.)

It is interesting to note that the margin increases for retailers were noticeably lower than those for wholesalers. One reason retailers are often more limited in their ability to transfer upstream price increases to their customers is the competitive nature of their markets, in which failing to match competitor prices can result in the loss of market share.

Chart 5

Data table for Chart 5

Data table for chart 5
Table summary
This table displays the results of Data table for chart 5 Purchase, Selling and Margin, calculated using Retail price index for dimensional lumber units of measure (appearing as column headers).
	Retail price index for dimensional lumber
	Purchase	Selling	Margin
2020
June	100.0	100.0	100.0
July	117.1	114.8	109.6
August	125.0	122.9	118.1
September	134.4	131.9	126.1
October	132.1	132.2	132.4
November	127.8	127.5	126.9
December	131.6	128.0	119.6
2021
January	144.4	137.9	122.5
February	151.3	142.6	122.1
March	157.0	145.5	118.6
April	175.9	165.7	141.9
May	191.0	185.7	173.3
June	187.2	184.5	178.3
July	152.2	160.1	178.8
August	139.3	144.7	157.5
September	127.8	129.5	133.5
October	140.3	138.9	135.8
November	140.8	139.2	135.4
December	146.0	151.4	163.9
2022
January	179.9	177.5	172.0
February	186.8	183.5	175.7
March	190.5	189.9	188.4
Source: Internal calculation of indexes under the North American Industry Classification System from Statistics Canada’s Retail Price Report (survey number 5135).

Home construction

The pattern for inflation in single-detached home construction does not resemble the pattern for upstream inflation in lumber prices. This may partly reflect a transmission lag due to fixed purchase agreements by contractors. Furthermore, prices to construct homes encompass more than only lumber. Unlike the first four elements of the supply chain that all represent one individual product, home construction is much more complex, involving many materials as well as labour. As such, changes to the costs to construct single-detached homes reflect more than simply changes to lumber prices.

Chart 6 shows price movements between the first quarter of 2020 and the first quarter of 2022 for the six largest cost divisions in single-detached home construction. Together these six divisions account for 77% of the cost to construct a single-family home.

Price trends for these divisions can be separated into two orders of magnitude. In the first, which includes five divisions, prices increased from a low of 9% over the reference period, for general requirements (e.g., building permit costs and general contractors fee), to a high of 27% for concrete. In the second, there is only one division―wood, plastics, and composites―where prices increased by an unparalleled 142% over the reference period. Price increases in this division were specifically driven by wood products, with contractors reporting 100% price increases for most wood products and increases as high as 186% for wood truss framing.

Chart 6

Data table for Chart 6

Data table for chart 6
Table summary
This table displays the results of Data table for chart 6 Wood, plastics and composites, General Requirements, Thermal and moisture protection, Concrete, Openings and Finishes, calculated using Price index of key cost divisions in single-detached home construction units of measure (appearing as column headers).
	Price index of key cost divisions in single-detached home construction
	Wood, plastics and composites	General Requirements	Thermal and moisture protection	Concrete	Openings	Finishes
2020
Q1	100.0	100.0	100.0	100.0	100.0	100.0
Q2	100.8	100.2	100.5	100.8	100.3	100.5
Q3	111.3	100.3	101.3	102.2	101.1	101.2
Q4	127.0	101.4	101.8	103.3	101.4	101.7
2021
Q1	152.9	101.7	104.8	105.6	104.2	103.5
Q2	191.6	102.7	110.8	111.1	110.0	108.7
Q3	208.3	103.9	114.7	115.5	113.6	112.5
Q4	218.2	106.1	119.1	119.9	118.1	117.6
2022
Q1	242.4	108.7	126.0	127.0	124.3	123.3
Source: Internal calculation of indexes from Statistics Canada’s Building Construction Price Index (survey number 2317).

Based on product level data collected through the Building Construction Price Index, wood, plastics, and composites accounted for 65% of the increase in the cost to construct a single-detached home between the first quarter of 2020 and the first quarter of 2022. Put differently, the share of costs in single-detached home construction^Note attributed to wood, plastics, and composites rose from an average of 21.6% in 2019 to 31.3% by the first quarter of 2022. (See Chart 7.)

Chart 7

Data table for Chart 7

Data table for chart 7
Table summary
This table displays the results of Data table for chart 7 Wood, plastics, and composites percent share of single-detached home construction costs (appearing as column headers).
2017
	Wood, plastics, and composites percent share of single-detached home construction costs
Q1	20.1
Q2	20.1
Q3	20.4
Q4	21.2
2018
Q1	21.2
Q2	21.4
Q3	21.7
Q4	21.8
2019
Q1	21.8
Q2	21.6
Q3	21.6
Q4	21.5
2020
Q1	21.4
Q2	21.4
Q3	21.5
Q4	23.1
2021
Q1	23.1
Q2	26.2
Q3	30.0
Q4	31.2
2022
Q1	31.3
Source: Internal calculation of indexes from Statistics Canada’s Building Construction Price Index (survey number 2317).

Conclusion

There are various takeaways from this analysis. Firstly, strong increases in demand for softwood lumber coupled with domestic supply constraints contributed to record lumber prices, which peaked in May 2021. Secondly, the transmission of prices throughout the supply chain for lumber was imperfect. Notably, wholesalers and retailers reduced their markups, likely to remain competitive. Consequently, the increase in producer prices was not passed along proportionately to consumers. Finally, higher prices for softwood lumber drove the unprecedented increase in construction costs for single-detached homes in 2021. In fact, two thirds of the increase in the cost to produce a single-detached home since the start of 2020 can be attributed to only one division—wood, plastics, and composites. Detailed product data within this division points towards wood products as the dominant driver behind this inflation.

Notes

Note 1

This cost increase was recorded across a composite of 11 Census Metropolitan Areas (CMAs), including St. John's, Moncton, Halifax, Montréal, Ottawa-Gatineau (Ontario part), Toronto, Winnipeg, Saskatoon, Calgary, Edmonton and Vancouver. In 2021, these 11 CMAs accounted for a combined 42.1% of single home building permit value.

Return to note 1 referrer

Note 2

The producer price represents the price received by producers for the sale of softwood lumber.

Return to note 2 referrer

Note 3

Net household saving rose to $211.9 billion in 2020, due to lower household spending on discretionary services such as travel and dining out, and government transfers to manage the economic impacts of the COVID-19 pandemic. This compares to an average annual net household saving of $25.0 billion between 2015 and 2019. (Source: Statistics Canada. Table 36-10-0612-01 Adjusted household disposable income, Canada, provinces and territories).

Return to note 3 referrer

Note 4

While export prices are not considered in this analysis, they were found to closely mirror producer prices for lumber. This reflects the influence of the larger U.S. market on prices in Canada.

Return to note 4 referrer

Note 5

Each of the five series chosen to analyze price movements across the supply chain for lumber represent products. However, the retail price index is the only index where the product is not strictly defined through the North American Product Classification System (NAPCS). Instead, the index represents movements in average prices for a detailed category of product sold across home centres, as defined by the North American industry Classification System (NAICS).

Return to note 5 referrer

Note 6

The aggregate index for the BCPI represents a composite of indexes for 11 Census Metropolitan Areas (CMAs).

Return to note 6 referrer

Note 7

United States Census Bureau, New Residential Construction-Historical Data. https://www.census.gov/construction/nrc/historical_data/index.html

Return to note 7 referrer

Note 8

Lance Lambert, “Lumber’s epic boom and bust, explained in 8 charts,” Fortune, September 15, 2021.

Return to note 8 referrer

Note 9

In this report, margin is defined as the gross difference between average selling price over average purchase price. In turn, the markup is defined as the ratio of the margin to the selling price. Margins and markups do not necessarily move in tandem. For example, a wholesaler may purchase a product from a supplier for $20 today and resell this product to a retailer for $25. The margin would be $5, while the markup would be 20%. Tomorrow that same wholesaler may purchase the identical product from the same supplier for $40 and resell it to a retailer for $48. The margin would have increased to $8, however, the markup would have declined to 17%. To maintain their initial markup of 20%, the wholesaler would have had to resell their product for $50.

Return to note 9 referrer

Note 10

Based on results from a vector autoregression model (VAR) focusing on the period between January 2019 and December 2021, a one percentage point increase in wholesale prices for softwood lumber would increase retail prices for dimensional lumber by 0.46 percentage points in the following month. By 24 months, the cumulative increase in retail prices as a result of the one percentage point increase in wholesale lumber prices would be 0.7 percentage points.

Return to note 10 referrer

Note 11

Share of costs in single-detached home construction measured across a composite of 11 Census Metropolitan Areas (CMAs), including St. John's, Moncton, Halifax, Montréal, Ottawa-Gatineau (Ontario part), Toronto, Winnipeg, Saskatoon, Calgary, Edmonton and Vancouver.

Return to note 11 referrer

Date modified:: 2022-09-02

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Prices through the supply chain: Softwood lumber

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