Review of Alternative 

Measures of Softwood 

Sawtimber Prices in the 

United States

Henry Spelter

United States

Department of

Agriculture

Forest Service

Forest

Products

Laboratory

Research

Paper

FPL–RP–629
Abstract

This study compares prices from various timber market reports and an estimate of timber value derived from 

product-selling prices and manufacturing costs. In the 

South, two primary sources of timber price information are 

Forest2Market (F2M) and Timber Mart-South (TMS). Comparisons showed that F2M prices are generally higher than 

TMS prices for both stumpage and delivered timber. Residual value (RV) estimates tended to vary from these at any 

given time. Over 5 years, however, the negative and positive 

deviations largely offset each other, resulting in roughly the 

same average price levels, at least compared with TMS. The 

RV estimates also tended to lead the direction of reported 

prices and were useful as leading indicators of reported market price directions. 

Comparison of various price reports from public and private 

agencies in the West showed that Forest Service prices were 

substantially lower than those recorded by other agencies 

and RV calculations. The discounts appear to reflect lower 

quality offerings and more restrictive harvest regulations 

that increase harvest costs. 

This report proposes a method of pricing timber based on 

RV calculations as one means to reduce the variability in 

lumber sawmilling profits.

Keywords: softwood timber prices, residual value, stumpage, delivered logs, lumber manufacturing costs.

June 2005

Spelter, Henry. 2005. Review of alternative measures of softwood sawtimber prices in the United States. Research Paper FPL-RP-629. Madison, WI: 

U.S. Department of Agriculture, Forest Service, Forest Products Laboratory. 16 p.

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Contents

Background .......................................................................... 1

Sources of Stumpage and Delivered Log Prices .................. 1

 U.S. South ........................................................................ 1

 U.S. West .......................................................................... 2

Sawtimber Residual Value ................................................... 2

Timber Price Comparisons ................................................... 3

 U.S. South ........................................................................ 3

 U.S. West .......................................................................... 6

Discussion ............................................................................ 8

Literature Cited .................................................................... 9

Appendix A―Model Forms Used by Timber MartSouth to Gather Timber Market Information ......................10

Appendix B―Residual Delivered Log Value .....................11

Appendix C―Model Sawmill Costs and Residual 

Values for Five States ..........................................................14
Background

Shares of thousands of companies trade daily in the stock 

market. Because simultaneously tracking the movements of 

all stocks is impossible, representative indicators that give 

a timely sense of the market’s general direction are useful. 

Numerous such indexes mirror the movements of the stocks 

they were designed to characterize. 

The market for timber is similarly diverse. The value of 

individual timber stands is influenced by many variables 

such as tree size, tree quality, species mix, sales volume, 

quantity per acre, logging conditions, nearness to roads, 

distance to mills, and intended end product. As with stocks, 

both buyers and sellers need general indicators of timber 

prices by which to measure the value of specific sales. A 

difficulty in developing such an indicator is that, unlike with 

stocks, there is no central exchange where all transactions 

are tallied and reported. Timber markets are therefore less 

transparent, imposing fact-gathering costs on market participants. Those needing a sense of direction in timber markets 

have several options.

One option is to track movements in transactions data periodically compiled and published by public agencies that 

manage large tracts of timberland. Most published data 

represent recent weighted average contract prices from an 

agency’s timber sales program. Sometimes prices paid for 

harvested timber are also reported. Harvested timber data 

represent sales prices contracted at earlier times, adjusted 

for market changes in the meantime where escalation 

clauses in the contract allow for modifications. Final harvest 

prices can vary from current contract prices and some view 

them as better indicators because of opportunities for arbitrage in the stumpage market (Adams and Haynes 1989).

Another option is to obtain data from market reporting 

services that gather stumpage prices (analogous to contract 

prices for timber sales above) and delivered log prices 

(similar to, but not the same as, final timber harvest prices). 

Market reporting services incorporate data on timber transactions from consulting foresters, timber brokers, and sawmill operators and reflect both posted list prices and actual 

contract prices from summary reports of recent timber sales.

A third option is to obtain reports compiled and averaged 

by state forestry agencies and marketing extension offices, 

which are gathered from sources similar to the market 

reporting services previously mentioned.

Various reports suffer from assorted handicaps. They 

employ different sampling methods and obtain data from 

different subpopulations, which introduce variability and 

complicate comparability among them. As some appear with 

a lag of several quarters or months, timeliness is often an 

issue. Different agencies also operate with dissimilar fiscal 

years, and some data are based on timber appraisals while 

other data are based on actual sale receipts. Some reports do 

not segregate different products and oftentimes combine different units of measure. Consequently, each report provides 

its own version of market reality. In this report, therefore, I 

review various regional reports and compare them to examine the degree to which their signals about timber price levels and directions are consistent. 

A parallel objective is to develop a complementary indicator of timber value that provides an alternative point of 

reference based on more transparent downstream product 

markets. By subtracting the cost of processing from final 

product prices, estimates of timber’s residual worth can be 

derived. Such constructed residual value (RV) estimates 

offer an additional timely source of information on potential 

timber value. 

Sources of Stumpage and 

Delivered Log Prices

U.S. South

In the U.S. South, about 90% of the timber comes from private timberlands. A widely used reference for prices since 

1976 is the publication Timber Mart-South (TMS). The 

TMS collects data using standard forms provided to a broad 

cross section of reporters in the timber industry, but it also 

accepts and uses any available information that reasonably 

reflects timber transactions (see Appendix A for a copy of 

forms). The reporting pool includes companies and individuals actively engaged in the day-to-day operation of selling and buying timber on the stump or delivering it to yards 

and mills. From the reports returned each quarter, the data 

are sorted and tabulated to arrive at a grouping of ranges 

for low and high prices, and from these a simple average 

is obtained for each state, parts of states, and product. Four 

Review of Alternative Measures of Softwood 

Sawtimber Prices in the United States

Henry Spelter, Economist

Forest Products Laboratory, Madison, Wisconsin
Research Paper FPL–RP–629

2

product grades are reported: veneer, sawtimber, chip-n-saw, 

and pulpwood.

More recently, timber price data have become available 

from a web-based firm, Forest2Market (F2M). The F2M 

obtains ongoing timber sales data from a cross section of 

industry participants on prices and 17 other attributes associated with each sale, the most important of which is average 

sale diameter. Each report is scrutinized by a forester and 

analyzed to ensure data quality. If the report passes scrutiny, then the data are assigned to one of 39 micro markets, 

reflecting well-defined homogeneous timber baskets that 

often cross state boundaries. The files are updated continually so clients have access to information more akin to real 

time by product category and region, down to the county 

level if desired. The F2M also produces printed reports in 

which volume-weighted regional price averages are 

published. 

U.S. West

In contrast to the U.S. South, a large part of timber supply in 

the U.S. West originated from Federal forests, of which the 

National Forests managed by the U.S. Department of Agriculture Forest Service (Forest Service) provided the bulk. 

Because of the once large volume of timber involved, price 

data on Forest Service sales and harvests were historically 

relied upon to measure timber pricing trends. Around the 

early 1990s, however, both the quantity and quality of Federal timber sales began to decline. Federal timber sales thus 

became less representative of the timber used by most mills, 

with resulting prices that departed from the mainstream. 

To fill the void, a number of alternative sources emerged. 

Among these was the report Log Lines (LL) published 

since 1989 by Arbor Pacific Forestry Services, Inc. (Mount 

Vernon, Washington). This company collects market price 

information monthly from over 100 producers and purchasers of logs in six western Oregon and Washington regions. 

Prices reflect both offered and actual prices for delivered 

loads of unsorted logs and are based on long log scale using 

the Scribner decimal C system. Each listing represents an 

unweighted average of prices reported by a minimum of 

three sources. The reporting is specific to various grades and 

species of timber. 

A similar service has been provided since 1977 by the 

Oregon Department of Forestry, which publishes prices on 

domestically processed logs delivered to mills (Pond Value) 

within four coastal Oregon regions. Mills report the data.

In the interior West, two widely referenced sources are from 

the University of Montana Bureau of Business and Economic Research (BBER) and Northwest Management, Inc. 

(NMI), a private forest management company. The BBER 

report provides a summary of recent prices in Montana 

reported by primary wood processors. The prices are averages of individual quotes. The NMI obtains prices from 

many of the same sources, but it sorts the data into high and 

low categories and determines an average for each rather 

than a universal average. Coverage extends to Idaho and 

eastern Washington.

Sawtimber Residual Value

An alternative measure of timber worth is the residual 

value obtained by subtracting manufacturing costs from 

end-product prices. This approach was traditionally used by 

the Forest Service to set minimum bids on timber sales. To 

generate these benchmarks, the Forest Service periodically 

sampled mills to obtain current manufacturing costs. These, 

along with margins for profit, risk, log harvesting, and 

transportation costs, were then subtracted from indexes of 

mill realizations (net selling prices) to arrive at an implicit 

value of standing sawtimber. This method was abandoned in 

the 1980s, as the cost and administrative burden of the data 

gathering were deemed excessive. In its stead, transactions 

evidence, a method based on statistical analysis of many 

recent sales, was adopted. Residual value estimation, however, remains a useful metric for those, such as occasional 

timber sellers, who do not have access to a large body of 

current timber sales data.

A lower-cost way to derive an RV estimate is to model 

the costs of representative mills. Sawmills number in the 

thousands and exhibit a wide spectrum of costs. However, 

the bulk of the timber is processed by a relatively few highvolume commodity-oriented operations. In Washington, for 

example, 85% of the sawtimber is processed by mills with 

8-hour shift capacity of 120,000 board feet or more (Larsen 

2002). Modeling a composite mill from this group is an 

alternative to survey-intensive data gathering. For this study, 

I have derived inferred dollar values for delivered logs and 

timber on the stump (stumpage) for three southern and two 

western representative states. Background on the derivations is provided in Appendix B. Current updated values 

are posted every quarter on the Forest Products Laboratory 

website (www.fpl.fs.fed.us). 

Figure 1— Georgia sawtimber stumpage price trends 

(2000–2004), by source (RV, residual value; TMS, Timber 

Mart-South; F2M, Forest2Market).
Review of Alternative Measures of Softwood Sawtimber Prices in the United States

3

Timber Price Comparisons

The comparisons here generally cover the period from the 

first quarter of 2000 to the fourth quarter of 2004. Data from 

F2M, however, were available only since the first quarter of 

2002. 

U.S. South

Stumpage 

Figure 1 illustrates relative quarterly price trends in Georgia 

for timber on the stump, as indicated by the two southern 

market reports and our RV calculations. Between TMS and 

F2M, differences in a given quarter could be significant, but 

they largely evened out over the entire 12 quarters in 

Georgia. The variation was as much as +11% to –8%, but 

the average F2M price was just 2% higher than TMS. 

The tendency for F2M prices to be higher than TMS was 

evident in most states over the 3 years in which both sets of 

data were available. Table 1 indicates disparities of +36% 

to –7%, with an average of +12% for the 10 southern states 

combined, a statistically significant difference.

As shown in Table 1, data are also available for subregions 

of states, and these illustrate how variability increases as 

the focus of reporting narrows. In northern Georgia, for 

example, F2M averaged 13% higher than TMS, whereas in 

coastal southern Georgia it was 7% less. 

Quarter-to-quarter correlations in prices between the two 

sources were rather weak. In many regions correlation coefficients were insignificant or even negative. That means 

movement in one direction by one source was often not 

matched or was even contradicted by the other.

In comparisons with residual value estimates, in any given 

quarter the differences between reported stumpage prices 

and calculated RV could be quite high. Georgia’s 22% 

greater to 28% less than TMS was typical. Especially 

noticeable is the tendency of the discrepancies to be cyclical, with long periods of lower values alternating with 

extended periods of higher values. However, over the entire 

19 quarters (Quarter 2, 2000–Quarter 4, 2004), the differences largely canceled. Overall aggregate differences in 

three states (Table 2) were rather small, indicating that timber prices, as reported by market observers, tend to balance 

out over longer time spans with value estimates derived 

from product selling prices through RV calculations. 

Comment on Stumpage

An important metric for appraisal of softwood timber is 

prices of other sales. Generally, benchmarks should be current and local. But timeliness and local relevancy involve 

tradeoffs. As the focus narrows to a given area and time, the 

amount of available data necessarily drops. Conversely, for 

larger areas or longer time spans, the data and their reliability increase, but their relevancy as a guide to contemporary 

Table 1—Differences and correlation coefficients in average stumpage prices between prices reported by different 

sources

Q 1, 2002–Q 4, 2004 Q 1, 2000–Q 3, 2004

State and region 

of state

Difference (%)

F2Ma – TMSb

Correlation coefficient (%) 

F2M – TMS 

Difference (%)

Statec – TMS 

Correlation coefficient (%)

State – TMS 

Virginia-west 36 62

N. Carolina-west 32 49

Louisiana-north 31 45 3 49

Arkansas-north 27 –26

Louisiana-south 22 –17 17 16

Texas-north 22 61 –2 78

Florida-west 21 29

Arkansas-south 17 45

Georgia-north 14 38

S. Carolina-east 9 77

Texas-south 9 26 9 28

Virginia-east 9 24

N. Carolina-east 9 65

S. Carolina-west 8 12

Florida-east 4 29

Alabama-north 3 –38

Mississippi-south –0 46 –1 63

Mississippi-north –1 42 –1 79

Alabama-south –2 1

Georgia-south –7 17

Southd 12 59

aForest2Market timber price information.

bTimber Mart-South timber price information. cState data available from Louisiana, Texas, and Mississippi.

dThe South as a whole. This is the average for all states and regions listed above.
Research Paper FPL–RP–629

4

local conditions diminishes. For most reports, quarterly 

reporting was found to best balance concerns over timeliness. Area coverage is usually broken down into micro markets or other substate agglomerations, as procurement radii 

tend to be less than 100 miles for most mills (Spelter 2003). 

Estimates of sawtimber prices from the two main southern 

price reports were often quite different. One possible reason 

is that F2M prices are tied strongly to diameter, and their 

reported prices generally rise and fall as diameters increase 

or decrease. In relying on expert opinion for high and low 

estimates, TMS is apt to get results in which reporters average variations related to diameter and other factors and give 

figures for “typical” sales, omitting some of the variation 

inherent in F2M data. Weighting sale prices by volume is 

another potential factor because large sales, which would 

normally command a premium because of harvesting economies, carry more weight in F2M than in TMS. 

Other methodological variations include volume conversions. The F2M prices are converted to weight on the basis 

of sale-specific conversion factors supplied by the data 

sources. In recent years, TMS has also been requesting price 

data to be reported by weight but uses generic constants to 

convert from various scales to a common platform where 

weight is not supplied. Conversion factors vary considerably, as can be seen from Texas state data (Texas Forest 

Service 1982–2004) where volumes are provided in both 

local scale and by weight (Fig. 2). If the 15,000 pounds per 

thousand board feet (Scribner) factor used in TMS 

conversions is too high, then resulting prices per weight will 

be too low compared with actual conversions. 

In comparing F2M to TMS, one further noticeable trend 

is the narrowing of differences between them. Overall, the 

arithmetic average of F2M prices in the 10 states was 12% 

higher than TMS, but the largest discrepancies were in the 

first year that F2M was in full operation (Fig. 3). Since then, 

the differences have narrowed to smaller though fairly constant levels. 

Larger discrepancies exist at the substate level of detail, as 

the samples upon which the averages are based decrease. 

In the case of F2M, its 12 quarters of data were based upon 

15,515 entries, which average to 1,293 per quarter. When 

further broken down into the 20 subregions, the average 

falls to 65. As the data are further divided among reports for 

pulpwood, chip-n-saw, sawtimber, stumpage, and delivered 

prices, additional data dilution inevitably increases the influence of outliers. This is no less the case for TMS, and both 

TMS and F2M exhibit occasional erratic changes in prices 

as their focus narrows.

Correlations between the two sources were low, but this is 

unsurprising given the short period over which both data 

sets were available. Over longer intervals, the correspondence would likely improve because of greater exposure 

to common cycles and trends. This notion is reinforced by 

six comparisons with state data from Louisiana, Mississippi, and Texas, which provide additional benchmarks. 

State-published Louisiana prices (Louisiana Department of 

Agriculture 1980–2005) were originally reported in dollars 

per thousand board feet (Doyle scale). These prices align 

well with TMS data when converted here to weight using 

a conversion factor of 18,300 pounds per thousand board 

feet. Prices provided by Mississippi (Mississippi State University 1990–2005) align equally well with both reports. 

Prices reported by the state of Texas (Texas Forest Service 

1982–2004), on the other hand, show better correspondence 

with the higher F2M averages. 

In relation to reported prices, RV estimates in three selected 

states show much greater variability. The RV estimates 

derive from product sales prices. The link between product 

and raw material markets is not exact over short intervals, 

and this is reflected in the tendency for RV values to show 

greater swings in extremes from highs to lows. Whereas 

discrepancies are large, they also tend to be cyclical, and 

as such, often lead trends in reported prices (Figs. 1 and 3). 

When the RVs are lower, reported prices tend to eventually 

follow; when the opposite is true, reported market prices 

tend to rise. In that sense, RV estimates have utility as leading indicators of market direction. 

Which market reports provide the better coverage? As 

there is no absolute standard of prices against which the 

reports can be compared, this question is impossible to 

answer. Timberland owners and timber users with their 

own transaction records can determine how these alternatives correspond to their experiences and best serve their 

needs. The RV estimates developed here do provide another 

Table 2—Differences and correlation coefficients between estimated residual value (RV) and reported 

stumpage prices, dollars per ton

Q 2, 2002–Q 4, 2004

Market region

Difference (%) 

RV – TMS1

95% confidence interval

 Lower bound Higher bound

Correlation coefficient (%) 

RV – TMS 

Alabama –4 –11 +2 37

Georgia –4 –11 +3 71

Texas 6 –3 +14 50

1Timber Mart-South timber price information.
5

Review of Alternative Measures of Softwood Sawtimber Prices in the United States

point of reference and show closer correspondence overall 

with TMS data, but F2M data were not available over the 

same period; thus the comparison is not determinative. The 

F2M does bring a new dimension to timber price reporting 

through continuous updating and posting of its information. 

The level of detail available in sale attributes also allows for 

compartmentalizing prices to better fit the particular circumstances of a sale in a way that general indexes do not.

Delivered

Figure 4 illustrates the relative quarterly price trends for 

delivered timber as indicated by TMS and F2M data and 

RV calculations. For Georgia, F2M prices were consistently 

above TMS, with the average 9% higher than TMS. By 

2004, however, the two series had nearly converged.

By contrast, following the pattern in stumpage, Georgia RV 

and TMS prices averaged at about the same over the entire 

5-year period, with TMS just 2% higher. Likewise, the differences tended to be cyclical, with long periods of lower 

RV values followed by extended periods of higher values. 

The variation in any given quarter is as much as +14% to 

–20%, but the overall correlation was high at 83% (Table 3). 

For the other two states where RV and TMS comparisons 

were made, the results were similar except the correlations 

were not as high.

The relationship over the shorter available period between 

RV and F2M values in Georgia was not as strong, showing a 

correlation coefficient of only 43%. The F2M averaged 13% 

higher than RV. 

For the 10 southern states combined, F2M averaged 15% 

higher than TMS and 11% higher than RV. In every comparison, average F2M prices were higher. 

The quarter-to-quarter correspondences between TMS and 

the other two sources, as measured by correlation coefficients, were weak. Only Texas showed a strong correspondence between TMS and F2M. Several of the others had 

correlation coefficients close to zero or negative (Table 3). 

Comment on Delivered

As was the case with stumpage prices, F2M delivered prices 

tended to be higher than other estimates. The F2M prices 

showed a looser connection to product prices as reflected in 

RV prices. The TMS values exhibited less variability and 

a moderate tendency to follow RV levels. Over time, F2M 

prices have tended to get closer to TMS, but gaps remain in 

most regions.

Over the course of a business cycle, the often wide discrepancies between reported timber prices and derived RV 

values (for both stumpage and delivered logs) largely cancel. The RV approach to valuation assigns value to timber 

on the basis of final product prices and costs and assumes 

constant downstream profit rates. In actual timber markets, 

these forces are opposed by supply influences, such as timber management costs and timber-owner expectations. This 

reduces the responsiveness of timber markets to developments in product prices and results in the well known cyclicality in wood-product profit margins. 

Figure 2—Average weight per thousand board feet (mbf) 

(Doyle) for Texas sawtimber.

Figure 3—Average sawtimber stumpage price trends 

(2000–2004) southwide by source (RV, residual value; 

TMS, Timber Mart-South; F2M, Forest2Market).

Figure 4—Georgia delivered sawtimber price trends 

(2000–2004) by source (RV, residual value; TMS, Timber 

Mart-South; F2M, Forest2Market).
Research Paper FPL–RP–629

6

The limited ability to pass through changes in input costs to 

product prices, or its inverse, the limited ability to impose 

changes in product prices on inputs, is encapsulated in a 

sector’s elasticity of price transmission—the average percentage change in one market (lumber) relative to a percentage change in its associated market (timber). That has been 

estimated for southern yellow pine lumber by researchers 

at different times with values ranging from 0.36 to 0.64 

(Haynes 1977). Employing the TMS data for the three 

southern states to estimate this statistic results in values that 

fall into the lower end of that range (Table 4). This means 

that the prices reported to and processed by TMS tend to lag 

product prices both on the way up and on the way down. 

The stickiness in pricing manifests itself in and is a major 

contributor to the corresponding swings in downstream 

product profitability.

U.S. West

Stumpage

Figure 5 shows quarterly Forest Service and State of Oregon 

sawtimber price trends on the stump in coastal Oregon compared with RV estimates. 

Forest Service reports do not differentiate timber sales by 

grade beyond generic categories such as sawtimber, pulpwood, and firewood. Total volume sales and offerings per 

sale were small in this period. Between 2000 and 2003, the 

Forest Service in the Pacific Northwest, Region 6, made 

98,511 sales in which it sold 1.3 billion (×109) board feet 

of timber for an average per sale amount of 12.8 thousand 

board feet. By contrast, between 1987 and 1990, 236,281 

sales were made in which 17.1 billion (×109) board feet 

of timber were sold for an average per sale figure of 72.5 

thousand board feet. These recent sales tended to emphasize 

salvage and thinnings (material that would normally fall 

into lower grades), and prices were not very representative 

of underlying market conditions as reflected by RV calculations. On average, residual value estimates were 79% higher 

than Forest Service sales. There was also no significant correlation between the two series. 

By contrast, the match between State of Oregon timber 

prices and RV figures was much better, and over the common period of availability, the two series differed by only 

3%. The correlation coefficient was 39%, relatively high 

considering that prices were flat and trendless during the 

period.

Table 3—Differences and correlation coefficients in average delivered timber prices between prices reported by 

different sources, dollars per ton

Q 1, 2002–Q 4, 2004 Q 2, 2000–Q 4, 2004

State and region of state

Difference (%)

F2Ma – TMSb

Correlation coefficient (%) 

F2M – TMS 

Difference (%)

RVc – TMS

Correlation coefficient (%) 

RV – TMS 

Virginia-east 38 10

Virginia-west 33 28

N. Carolina-west 31 –46

Florida-west 30 –5

Louisiana-south 24 8

S. Carolina-east 17 –54

N. Carolina-east 16 44

Louisiana-north 16 –22

S. Carolina-west 15 24

Georgia-north 12 –2 –2 83

Mississippi-south 11 –64

Arkansas-north 11 –0

Texas-north 10 50 –5 18

Arkansas-south 10 43

Florida-east 9 62

Texas-south 8 84 –5 18

Alabama-south 7 2 –2 79

Georgia-south 7 44 –2 83

Mississippi-north 5 –14

Alabama-north 4 62 –2 79

Southd 15 10 3 20

aForest2Market timber price information.

bTimber Mart-South timber price information. cResidual value estimates. Data for RVs are for entire states only and repeat within each part of a state.

dThe South as a whole. This is the average for all states and regions listed above.

Table 4—Estimates of the elasticity of price 

transmission for southern yellow pine 

Source

Elasticity of price 

transmission

Haynes 0.64

Holley cited in Haynes 0.60

Adams cited in Haynes 0.36

Present study

Georgia 0.36

Alabama 0.35

Texas 0.30
7

Review of Alternative Measures of Softwood Sawtimber Prices in the United States

Higher volume of sales in Idaho and Montana (Forest Service Region 1) makes Forest Service data there more likely 

to be representative. This is indicated by the higher correlation (72%) with residual value estimates (Fig. 6). But 

a striking feature of the two data sets is the consistent gap 

between them. Unlike in the South, where the differences 

between residual values and private market prices largely 

evened out over the 5-year period, the discrepancies here 

persisted. The RV prices were, on the average, 80% higher 

than prices obtained for Forest Service timber. 

Comment on Stumpage

Lower quality timber and more environmentally constrained 

logging are two likely reasons for generally lower Forest 

Service stumpage values. Especially in Region 1, much of 

the timber sold after 2000 was fire-killed salvage. Because 

Forest Service timber sales involve long administrative 

processes before timber can be harvested, much of the wood 

became infected with blue stain and experienced weather 

check. Further, in many cases mandated helicopter logging 

and other restrictive harvest measures increased costs and 

therefore decreased the amounts buyers could pay. 

The effect of these conditions can be further gauged by 

comparing Forest Service prices with those obtained by the 

state of Montana (Table 5). After salvage timber began to 

dominate sales in 2001, the quicker, more direct harvesting 

procedures permitted under Montana regulations meant the 

quality of the timber was less debased and extraction costs 

were lower. These factors resulted in Montana stumpage 

prices diverging from those of the Forest Service, from a 

discount in 2000 to rising premiums that reached over 100% 

by 2003. During the 4-year period covered by Montana’s 

fiscal years 2001–2004, the Forest Service sold 587 million 

board feet of sawtimber in Region 1 for $71 million. During 

the same time, Montana stumpage prices for essentially the 

same type of timber reaped about a 60% premium. Had Forest Service sales procedures been more like those of Montana and similar prices been achieved, revenues would have 

been $40 million higher. The environmental benefits captured by the Forest Service sales procedures can be weighed 

against the foregone revenues implied by comparison with 

the State of Montana results.

Delivered

Figure 7 illustrates the price of #2 Douglas-fir sawlogs 

obtained by averaging four regional quotes from Log Lines 

and three Oregon Department of Forestry (ODF) regions 

(Oregon Department of Forestry 2001–2004). Residual 

value estimates are also shown, though these are not entirely 

comparable because they cover a mix of logs. This mix of 

logs includes lower grades, such as #3, and less valuable 

species, such as whitewood (hemlock), and therefore has 

lower aggregate value. 

Figure 5—Coastal Oregon stumpage by source (RV, 

residual value; FS, Forest Service; ODF, Oregon Department of Forestry).

Figure 6—Montana stumpage by source (RV, residual 

value; FS, Forest Service).

Table 5—Comparative stumpage prices for Forest 

Service (FS) Region 1 and State of Montana timber 

FS Region 1 

($/1,000 bf)

State of Montana 

($/1,000 bf)

Difference 

(%)

FY 2000a 227 209 –8

FY 2001 150 195 30

FY 2002 116 184 59

FY 2003 77 157 105

FY 2004 154 220 43

aMontana’s fiscal year runs from July 1 through June 30.

Figure 7— Oregon #2 Douglas-fir delivered log prices by 

source (RV, residual value; LL, Log Lines; ODF, Oregon 

Department of Forestry).
Research Paper FPL–RP–629

8

The ODF and LL prices are generally close, and over 

the entire period their averages are essentially the same 

(Table 6). The RV estimates, aside from the bias noted 

above, exhibit a similar relation to published market prices 

as observed in the southern states—a tendency to cycle 

about the market prices.

Figure 8 shows three similar prices for Montana. The NMI 

price was the average of their published high and low 

quotes. The two market report prices again show close 

agreement and no significant bias among them. The residual 

value estimates averaged 6% below the other two. The correlation coefficients were also high: 87% with BBER prices 

and 80% with NMI quotes.

Comment on Delivered

The close correspondence of data between NMI and BBER 

is likely explained by large overlap in information sources. 

In Montana especially, the remaining operating mills are 

few and limit the size of the sample pool, which means both 

sources essentially mine the same data.

The elasticity of price transmission for western markets has 

been estimated from 0.35 to 0.45 (Haynes 1977), indicating 

a 35–45 % change in timber prices for a 100% change in 

product prices. The data in this study for our two western 

states broadly agree with these results (Table 7).

Discussion

Unlike stocks or bonds, timber sales are not interchangeable. Each has its own characteristics, making it problematic 

to derive a representative index of timber prices. With communications through fax machines and the internet facilitating the transfer of complex information, it is more feasible 

to assimilate and analyze larger blocks of data. Some of the 

uncertainty in timber price reports could be eased by reporting, alongside average prices, other sale attributes such 

as tree diameter, stand-quality, and species mix. Defining 

attributes that make average timber prices “high” or “low” 

would be one way to illuminate the meaning of such price 

characterizations. Information on many sale attributes is 

already available on web-based data of organizations such 

as F2M, which thus affords greater insight into timber 

pricing evaluation. 

Table 6—Differences and correlation coefficients in average stumpage prices between prices reported by different 

sources, dollars per thousand board feet (Scribner) 

Q 1, 2002–Q 3, 2004

Grade and species by region

Difference (%) 

LLa – ODFb 

Correlation coefficient (%) 

LL – ODF 

Difference (%)

BBERc – NMId 

Correlation coefficient (%) 

BBER – NMI 

Northwest Oregon

#2 DFe 3 95

#3 DF –1 85

#2 WWf 3 91

#3 WW 11 96

Southwest Oregon

#2 DF 2 78

#3 DF 7 63

South central Oregon

#2 DF –2 90

#3 DF 2 78

Oregon

#2 DF 1 92

#3 DF 3 91

#2 WW 3 93

Montana

DF Sawlogs 0 72

aLog Lines.

bOregon Department of Forestry.

cUniversity of Montana Bureau of Business and Economic Research.

dNorthwest Management, Inc.

eDouglas-fir..

f Whitewood/hemlock. 

Figure 8—Montana #2 Douglas-fir delivered log prices 

by source (RV, residual value; BBER, Bureau of 

Business and Economic Research; NMI, Northwest

Management, Inc.).
9

Review of Alternative Measures of Softwood Sawtimber Prices in the United States

Nevertheless, all price reports are based on samples with all 

the attendant variability associated with the technique. The 

difficulty in homing in on a sawtimber price is shown by the 

disparate estimates from different organizations illustrated 

in this summary. Occasional statistically large differences 

and resultant conflicting signals indicate that issues concerning sampling, weighting of data, and converting different 

log scales to common units remain. 

As a complement to timber price reports, another reference point is residual value determination based on prices 

and manufacturing costs in downstream lumber markets. 

This metric too is ultimately based on sampling, but from a 

more transparent market dealing in more fungible products. 

Indexes of timber valuation that are derived from manufacturing costs and revenues of representative sawmills can add 

insight of values that are realistic in the context of downstream markets. 

An objection to such a constructed measure is that it represents only the timber processor’s perspective. Valuation 

from the timber grower’s perspective on the basis of timber 

growing costs is equally valid, and the contrast of those two 

views determine bargaining and the ultimate market clearing price. Over the long run, however, I argue that residual 

values must embody both seller and buyer interests. Timber prices need to be high enough to keep land in timber 

production. Those costs in turn must ultimately be passed 

through product prices or capacity erosion will cause supply scarcity, driving prices up. This contention is supported 

by the near equivalence of RV estimates with timber prices 

over the span of the 5 years examined. As illustrated, RV 

estimates are often precursors of trends in timber markets 

and as such can play a role as an adjunct to timber price 

reports in the calculus of market price determination.

Based on this logic, a further possible use of RV estimates 

could be as a vehicle for adjusting prices in long-term 

timber contracts. The appeal of this to processors is that it 

would buffer them better against declines in product selling prices. Conversely, it would assure sellers of higher 

compensation when downstream prices improve. However, 

timber sellers would likely demur at the prospect of the 

wholesale transfer to them of market volatility and risk that 

this would entail. Extreme periods of depressed prices, even 

if eventually made up, would reduce the attraction of timber 

growing. In such applications, therefore, some floor and 

ceiling price levels would likely need to be set relative to 

reported market prices so as to temper the extreme swings 

that can result from ties to volatile lumber prices.

Literature Cited

Adams, Darius; Haynes, Richard. 1989. A model of National Forest timber supply. Forest Science. 35(2): 401–424.

Cahill, J. 1984. Log scale conversion factors. In: Snellgrove, T., 

and others, eds. User’s guide for cubic measurement. Published 

cooperatively by USDA Forest Service and University of Washington, College of Forest Resources, Seattle, WA. 

Grist, Charles; Karmous, Moustafa. 1988. Energy use and conservation in Oregon’s lumber and wood products industry. Salem, OR: 

Oregon Department of Energy.

Haynes, Richard. 1977. A derived demand approach to estimating 

the linkage between stumpage and lumber markets. Forest Products 

Journal 23(2): 281–287.

IWFR. 1994–2005. International Woodfiber Report. San Francisco: 

Paperloop.com.

Keegan, Charles E.; Fiedler, Carl; Wichman, Daniel. 1995. Costs 

associated with harvest activities for major harvest systems in 

Montana. Forest Products Journal 45(7/8): 78–82.

Larsen, David M. 2002. Washington mill survey–1998. Series 

Report # 15. Olympia, WA: Washington State Department of Natural Resources. 

Louisiana State. 1980–2005. Quarterly reports of forest products. 

Baton Rouge: Louisiana Department of Agriculture. 

Mississippi State University. 1990–2005. Mississippi timber price 

report. Mississippi State, MS: Mississippi State University. 

Oregon Department of Forestry. 2001–2004. Oregon Annual Timber Harvest Reports. http://oregon.gov/ODF/STATE_FORESTS/

FRP/annual_reports.shtml

Paperloop, Inc. 1996–2005. International Woodfiber Report. San 

Francisco: Paperloop, Inc.

Spelter, Henry; Alderman, Matthew. 2003. Average Log Hauling 

Distance in Michigan. Unpublished manuscript on file at the U.S. 

Forest Products Laboratory. Madison, WI.

Spelter, Henry; Alderman, Matthew. 2003. Profile 2003: Softwood 

sawmills in the United States and Canada. Research Paper FPL–

RP–608. Madison, WI: U.S. Department of Agriculture, Forest 

Service, Forest Products Laboratory. 

Steele, P. H.; Wagner, F. G.; Taylor, F. W. 1988. Relative softwood 

sawmill conversion efficiency by region of the United States. Forest Products Journal 38(2):33–37. 

Texas Forest Service. 1982–2004. Texas timber price trends. College Station, TX: Texas A&M University.

U.S. Bureau of Labor Statistics 1958–2003. Washington, DC: 

http://www.bls.gov/

U.S. Department of Energy. 2005. Washington, DC: http://www.

eia.doe.gov/cneaf/electricity/epm/table5_6_a.html

United States Census Bureau. Annual Survey of Manufacturing. 

1958–2003. Washington, DC: U.S. Census Bureau.

United States Census Bureau. 2004. 2002 Census of Manufacturing. Sawmills. Washington, DC: U.S. Census Bureau. 

Table 7—Estimates of the elasticity of price 

transmission for various western regions

Market region

Elasticity of price 

transmission Source

Western 0.45 Haynes

West Coast 0.38 Haynes

Rocky Mountain 0.35 Adams cited in Haynes

West Coast 0.36 Vaux cited in Haynes

Oregon 0.27 Present study

Montana 0.42 Present study
Research Paper FPL–RP–629

10

 Reporting Form

State Date:

Area Quarter:

County email: tmart@arches.uga.edu

Reported By:

Stumpage

Product

Market

(G = Good, 

F = Fair, 

P = Poor )

Stumpage

LOW

Stumpage

HIGH

Scale

S = Scribner, 

D = Doyle

I = International, 

T = Tons, C= Cords

Pine Sawtimber

Pine Ply Logs

Oak Saw Timber

Mix Hdwd Saw

Pine Pole 

Timber

Pine Chip-n-Saw

Pine Pulpwood

Hwd Pulpwood

Delivered

Product

Market

(G = Good, 

F = Fair, 

P = Poor )

Stumpage

LOW

Stumpage

HIGH

Scale

S = Scribner, 

D = Doyle

I = International, 

T = Tons, C= Cords

Pine Sawtimber

Pine Ply Logs

Oak Saw Timber

Mix Hdwd Saw

Pine Pole 

Timber

Pine Chip-n-Saw

Pine Pulpwood

Hwd Pulpwood

Comments:

Appendix A―Model Forms Used by Timber Mart-South to Gather Timber 

Market Information
11

Review of Alternative Measures of Softwood Sawtimber Prices in the United States

Appendix B―Residual Delivered 

Log Value

For the past 5 years, I have been deriving quarterly residual 

log value estimates for five U.S. regions as an aid to understanding log markets. My focus is on sawtimber, the main 

use for which is lumber, so the data derived here are from 

sawmilling alone. Although sawtimber can be used for other 

products (chiefly plywood), over the past decades the economics of plywood have been superceded by engineered 

wood (OSB), leaving lumber as the prime user of sawtimber. Therefore, to avoid unduly complicating the analysis, I 

focus solely on lumber. I selected five states on the basis of 

availability of reported selling prices specifically tied to a 

locality: Georgia with reported Southern Pine lumber prices 

for the southeast, Alabama with lumber prices for the southcentral, Texas with prices in the southwest, Oregon with 

prices for coastal Pacific Northwest, and Montana for prices 

reported for the inland West.

This estimation process consisted of two steps. First, I 

estimated net selling values of lumber and its byproducts. 

Second, I estimated processing costs for a representative 

sawmill. Subtracting the processing cost from the net selling 

value yielded the residual value of delivered logs. Tables 

B1 and B2 show the assumed sawmill configurations and 

sequence of calculations for 2002 in the regions chosen. 

Mill type. Four main types of mills operate in the United 

States: stud (2-in-thick lumber in lengths of 7–10 feet), 

dimension (2-in. lumber in varying lengths), timber 

(5-in. and thicker lumber), and board (less then 2-in. thick). 

Dimension mills process the bulk of the timber followed 

by stud mills. These two mills types are broadly similar 

in terms of product except for the differences in lengths. I 

therefore chose dimension sawmills as most representative 

for log valuation purposes in four of the regions. In the fifth 

I modeled a mill with an output blend of 80% dimension 

and 20% timber.

Production volume. I selected an annual production volume 

with a value near the middle of the range among permanent, 

high-volume mills for each region. For example, in 2002 a 

coastal Oregon mill with 168 million board feet of output 

represented the mid-point of that region’s cumulative production capacity. In this example, one half of the 5.6 billion board feet of capacity was accounted for by mills with 

annual capacity of less than 168 million board feet (Spelter 

and Alderman 2003). 

Mill sales (realization). Softwood lumber market prices 

are reported weekly by three independent firms: Crows, 

Madison’s Lumber Reporter, and Random Lengths. Each 

employs a staff of reporters who survey mills, brokers, 

wholesalers, distributors, and treaters weekly. The reported 

prices that emerge from these inquiries reflect the judgment 

of the staff about the prices of transactions at the producer 

level (they are not statistical, volume-weighted sales averages). Over time, the prices reported by the three tend to be 

similar, and for simplicity I used the data from Crows. 

Lumber prices tend to be strongly correlated, so to represent 

mill sales of primary products, I selected only one or two 

high-volume lumber grades (usually Standard and Better, 2 

by 4). I reduced these prices by 6% to account for discounts 

and lower grades in a typical product mix that would pull 

the average realization down. 

I estimated revenue from byproducts from calculations of 

the amount of chips and shavings produced, depending on 

the Lumber Recovery Factor (LRF), discussed below. Chip 

prices were obtained from a wood fiber price reporting 

source (IWFR 1994–2005).

Staffing and wages. Each regional mill was assumed to contain the full range of milling processes including log sorting, 

sawing, grading, drying, trimming, edging, and planing. 

Staffing levels typical for these functions, based on two 

shifts, were specified. Supervisory and maintenance staffs 

appropriate to mill size were also included. 

Reference wages were obtained from the 2002 Census of 

Manufacturers with non-Census years derived by adjusting 

the reference wage by indexes from the Bureau of Labor 

Statistics. Cost estimates are fully loaded and included vacation pay, unemployment insurance, retirement, Social Security payments, and health insurance costs.

Table B1—Model sawmill configurations for five regions, 2002

 Region and mill type

W. Oregon

dimension

Montana

dimension

Georgia

dimension

Texas

dimension

Alabama

dimension and timber 

Production (mmbfa) 168 106 105 120 90

Line workers 163 142 139 150 122

Cost/worker ($/wk) 958 882 752 749 762

Log diameter (in.) 10 10 10 10 10.5

Electricity (kwh/bfb) 0.12 0.17 0.17 0.17 0.17

Fuel cost ($/mbfc) 2.2 2.2 2.2 2.2 2.2

Depreciation of assets 

(millions of dollars)

18 13 15 16 11

aMillion board feet

bKilowatt hours per board foot cDollars per thousand board feet
Research Paper FPL–RP–629

12

Energy. Average electricity use by sawmill was estimated 

from Census of Manufacturers and energy usage studies 

(Grist and Karmous 1988). For mills drying all their lumber, 

the level was set at 170 kilowatt hours per thousand board 

feet. For Oregon, where up to half of output is sold green, 

a lower level of kilowatt hours per thousand board feet 

was set. Electricity costs to industrial users, by state, were 

obtained from the U.S. Department of Energy (2005).

Fuel costs. Purchased fuel costs per thousand board feet of 

output were calculated from Annual Survey of Manufacturers (U.S. Census Bureau 1958–2003).

Supplies. The cost per unit of materials, components, containers, and parts were calculated from the 2002 Census of 

Manufacturers. The Producer Price Index was employed to 

extend the 2002 estimate to other years. 

Administrative costs. This includes general administrative 

costs for items such as insurance, property taxes, communications, and office personnel. Costs were estimated on the 

basis of assumptions for staffing and expense items.

Depreciation. An overall industry-wide estimate of the gross 

value of depreciable assets is contained in the 2002 Census 

of Manufacturers. I adjusted this for specific mill types to 

take into account plant scale. I calculated the annual depreciation charge on the basis of a 10-year write-off period. I 

included the opportunity cost of the capital tied up in the 

facility (the interest paid or the interest revenue foregone) 

based on contemporary rates for bonds rated Baa.

Total revenues, less the total costs, gives the residual value 

of the timber, stated in dollars per thousand board feet, lumber tally basis. To convert these to log scale basis, two additional metrics are needed. 

Lumber recovery factor (LRF). The recovery rate of lumber 

per cubic foot of log for most mills lies in the 7–8 (nominal) 

board feet per cubic foot range (Steele and others 1988). 

Recoveries depend on the inherent capabilities and the 

maintained condition of the equipment, the size of the logs 

(the smaller the log, the lower the LRF) and the number of 

sawlines (large timbers requiring fewer cuts lose less volume to sawdust than thin boards). Over the years, as scanning and optimization capabilities advanced, LRFs tended 

to benefit from the increased ability to find optimal cuts 

based on log shape. Also important were similar technologies applied to the edging of boards, as the ability to fully 

exploit permissible wane allowances could significantly 

boost recoveries. 

The average log diameter was assumed to be 10 in. for 

dimension mills, whereas 10.5 in. was chosen for the partial 

timber mill. The LRFs for the larger western mills were 

assumed to be nearer the high end of the range, whereas 

southern mills were placed in the middle. Because of its 

product mix and larger log size, the combination dimension–timber mill was given a slightly higher LRF. 

Board-to-cubic conversion factor. The board-foot to cubicfoot conversion factor is mainly a function of log diameter 

and the log scale (log length, taper, and defects also play 

roles). Here the Scribner log scale was used as the frame of 

reference, but on the basis of the scaling protocols employed 

in particular regions. 

For a given log diameter, conversion factors in the coastal 

Pacific Northwest are lowest because the conservative measurement rounding conventions and the longer log lengths 

underestimate the board feet contained within a log. In the 

other regions, where shorter logs are the norm, the volume 

omission is relatively lower, and thus the projected board 

Table B2—Model sawmill costs for five regions, 2002a

W. Oregon Montana Georgia Texas Alabama

Lumber value 296 285 298 285 313

+ Chip value 29 31 28 31 28

+ Shavings 11 10 7 7 7

 = Revenues 335 326 333 323 348

Profit and risk 17 16 17 16 17

+ Line labor 46 59 50 47 52

+ Electricity 5 9 9 9 7

+ Fuels 2 2 2 2 2

+ Supplies 9 9 10 9 9

+ Administrative 9 10 9 8 9

+ Depreciation 15 17 20 18 17

= Manufacturing costs 104 123 117 110 113

Residual 231 204 216 213 235

× LRFb 7.9 bf/ft3 7.7 bf/ft3 7.5 bf/ft3 7.5 bf/ft3 7.5 bf/ft3

= $/ft3 1.83 1.57 1.62 1.60 1.81

÷ Conversion factor 3.85 bf/ft3 4.79 bf/ft3 4.79 bf/ft3 4.79 bf/ft3 4.90bf/ft3

 = Residual log value 477 327 339 335 369

aRevenue, value, and cost factors are shown in dollars per thousand board feet. 

bLumber recovery factor.
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Review of Alternative Measures of Softwood Sawtimber Prices in the United States

footage is higher. The relevant conversion factors in each 

region were derived from the assumed log sizes using 

published equations relating the conversion factor to average log diameter of a population of logs (Cahill 1984). 

Multiplying the residual board foot, lumber tally price 

value by the LRF and dividing the result by 1,000 yields 

the equivalent price per cubic foot. Dividing this by the 

board-to-cubic conversion factor and multiplying by 1,000 

generates the residual value in terms of the local board-foot 

log scale. Appendix C contains the annual estimates for the 

regions over the period 2000–2004. For these price calculations over time, improvements in mill output efficiency of 

about 2% per year were factored in. 

Residual stumpage value. The next step in obtaining stumpage values is to deduct additional costs for converting 

standing trees to delivered logs. These include tree felling, 

delimbing, bucking, skidding, loading, and hauling. The 

variability in these costs, however, depends on a multitude 

of logging and tree conditions. These can result in highly 

automated or labor-intensive logging operations and variability that is substantially greater than mill manufacturing 

costs. Stump-to-truck costs can vary from an average of 

$87 per thousand board feet for a mechanical ground-based 

system to $164 per thousand board feet for an uphill skyline cable system. Helicopter logging boosts them further 

to $233 per thousand board feet (Keegan and others 1995). 

Data on average industry conditions relating to these variables are lacking; thus, modeling these costs along the lines 

above to obtain a representative industry average would be 

largely conjectural regarding average conditions. 

A simpler approach is to compare available reported data on 

stumpage and delivered wood costs. Although not accurate 

in the short term because logs delivered often reflect stumpage prices paid in the past, a long-term analysis with several 

wood market cycles would tend to average out and minimize this lag effect.

Where such data were not available, as in the West, historical Forest Service estimates were brought forward in time 

by using an index of logging costs provided by the Bureau 

of Labor Statistics.
Table C2—Model sawmill costs for Idaho and Montana 2000–2004a

2000 2001 2002 2003 2004

Lumber value 313 290 285 284 405

+ Chip value 40 38 31 31 30

+ Shavings 10 10 10 10 10

 = Revenues 363 339 326 325 446

Profit and risk 18 17 16 16 22

+ Labor 59 59 59 60 62

+ Electricity 4 10 9 7 7

+ Fuels 2 2 2 3 4

+ Supplies 9 9 9 10 10

+ Administrative 10 10 10 10 10

+ Depreciation 20 18 17 17 16

= Manufacturing costs 122 126 123 122 131

Residual 242 213 204 203 315

× LRFb 7.6 bf/ft3 7.6 bf/ft3 7.7 bf/ft3 7.7 bf/ft3 7.7 bf/ft3

= $/ft3 1.84 1.62 1.57 1.56 2.42

÷ Conversion factor 4.79 bf/cft 4.79 bf/cft 4.79 bf/cft 4.79 bf/cft 4.79 bf/cft

 = Residual log value 384 339 327 326 506

aRevenue, value, and cost factors are shown in dollars per thousand board feet.

bLumber recovery factor.

The tables below (C1–C5) show the annual estimates for the regions over the period 2000–2004. For these price calculations 

over time, improvements in mill output efficiency of about 2% per year were factored.

Table C1—Model sawmill costs for Oregon 2000–2004a

2000 2001 2002 2003 2004

Lumber value 306 300 296 307 418

+ Chip value 35 34 29 29 29

+ Shavings 11 11 11 11 11

 = Revenues 352 344 335 347 458

Profit and risk 18 17 17 17 23

+ Labor 45 46 46 47 48

+ Electricity 4 5 5 7 7

+ Fuels 2 2 2 3 4

+ Supplies 9 9 9 9 9

+ Administrative 9 9 9 9 9

+ Depreciation 18 16 15 14 14

 = Manufacturing costs 104 104 104 106 115

Residual 248 240 231 240 343

× LRFb 7.9 bf/ft3 7.9 bf/ft3 7.9 bf/ft3 7.9 bf/ft3 7.9 bf/ft3

= $/ft3 1.97 1.90 1.83 1.91 2.72

÷ Conversion factor 3.85 bf/ft3 3.85 bf/ft3 3.85 bf/ft3 3.85 bf/ft3 3.85 bf/ft3

 = Residual log value 512 494 477 496 707

aRevenue, value, and cost factors are shown in dollars per thousand board feet.

bLumber recovery factor.

Appendix C―Model Sawmill Costs and Residual Values for Five States

Research Paper FPL–RP–629

14
15

Review of Alternative Measures of Softwood Sawtimber Prices in the United States

Table C3—Model sawmill costs for Georgia 2000–2004a

2000 2001 2002 2003 2004

Lumber value 327 317 298 322 375

+ Chip value 31 29 28 31 30

+ Shavings 7 7 7 7 7

 = Revenues 366 354 333 360 413

Profit and risk 18 18 17 18 21

+ Labor 47 48 50 50 51

+ Electricity 7 7 9 12 11

+ Fuels 2 2 2 3 4

+ Supplies 10 10 10 11 11

+ Administrative 9 9 9 9 9

+ Depreciation 23 21 20 20 19

= Manufacturing costs 116 116 117 123 125

Residual 250 238 216 238 288

× LRFb 7.5 bf/ft3 7.5 bf/ft3 7.5 bf/ft3 7.5 bf/ft3 7.5 bf/ft3

= $/ft3 1.88 1.79 1.62 1.79 2.17

÷ Conversion factor 4.79 bf/ft3 4.79 bf/ft3 4.79 bf/ft3 4.79 bf/ft3 4.79 bf/ft3

 = Residual log value 392 374 339 373 452

aRevenue, value, and cost factors are shown in dollars per thousand board feet.

bLumber recovery factor.

Table C4—Model sawmill costs for Texas 2000–2004a

2000 2001 2002 2003 2004

Lumber value 318 309 285 310 366

+ Chip value 34 33 31 32 32

+ Shavings 7 7 7 7 7

 = Revenues 360 350 323 349 405

Profit and risk 18 17 16 17 20

+ Labor 44 45 47 48 49

+ Electricity 9 9 9 11 10

+ Fuels 2 2 2 3 4

+ Supplies 9 9 9 10 10

+ Administrative 8 8 8 8 8

+ Depreciation 21 20 18 18 18

 = Manufacturing costs 110 110 110 115 119

Residual 249 239 213 234 286

× LRFb 7.5 bf/ft3 7.5 bf/ft3 7.3 bf/ft3 7.5 bf/ft3 7.5 bf/ft3

= $/ft3 1.87 1.80 1.60 1.76 2.15

÷ Conversion factor 4.79 bf/ft3 4.79 bf/ft3 4.79 bf/ft3 4.79 bf/ft3 4.79 bf/ft3

 = Residual log value 391 376 335 368 450

aRevenue, value, and cost factors are shown in dollars per thousand board feet.

bLumber recovery factor.
Table C5—Model sawmill costs for Alabama 2000–2004a

2000 2001 2002 2003 2004

Lumber value 355 332 313 325 385

+ Chip value 29 28 28 29 31

+ Shavings 7 7 7 7 7

 = Revenues 392 367 348 361 424

Profit and risk 20 18 17 18 21 

+ Labor 50 50 52 52 54

+ Electricity 7 7 7 9 9

+ Fuels 2 2 2 3 4

+ Supplies 8 8 9 8 8

+ Administrative 10 9 9 10 10

+ Depreciation 20 18 17 17 16

 = Manufacturing costs 117 114 113 116 122

Residual 275 254 235 245 302

× LRFb 7.5 bf/ft3 7.5 bf/ft3 7.5 bf/ft3 7.5 bf/ft3 7.5 bf/ft3

= $/ft3 2.12 1.95 1.81 1.88 2.32

÷ Conversion factor 4.9 bf/ft3 4.9 bf/ft3 4.9 bf/ft3 4.9 bf/ft3 4.9 bf/ft3

 = Residual log value 442 408 377 393 485

aRevenue, value, and cost factors are shown in dollars per thousand board feet.

bLumber recovery factor.

Research Paper FPL–RP–629

16