A Process to Establish and Use 

Base Period Prices for National 

Forest System Transaction 

Evidence Timber Appraisal

Richard W. Haynes

Kenneth E. Skog

Richard Aubuchon

General Technical Report

FPL–GTR–242

United States Department of Agriculture

Forest 

Service

Forest Products

Laboratory

December

2016
Abstract

The USDA Forest Service is required to appraise timber 

prior to it being offered for sale. Currently the Forest Service uses a transaction evidence based approach, but concerns have been raised about availability—both in number 

and applicability—of timber sales used as the basis of this 

approach. In addition to the problem of few sales, in certain 

situations a notable fraction of the sales may be made at the 

minimum rate. This transaction evidence system and the 

application of a rollback factor in setting a minimum bid 

rate can result in appraised prices declining over time in 

areas with low competition, even though primary product 

prices are increasing. This study evaluates several changes 

that may be needed to address these concerns. The changes 

should be able to provide a base period price (BPP) that 

does not rely on local transactions. The estimated BPP could 

rapidly respond to change in timber and product market 

conditions and after local cost adjustments would provide 

an estimated fair market value for sales with mixes of sawtimber and nonsawtimber products. We propose use of price 

mark-up rules to estimate BPPs for various timber product 

classes. These price mark-up rules could be estimated for 

various geographic zones and would predict quarterly average stumpage prices for recent sales based on primary product price indexes. The mark-up equation estimates of average base period stumpage price are compared to actual high 

bid values for 810 recent Forest Service timber sales. The 

December 2016

Haynes, Richard W.; Skog, Kenneth E.; Aubuchon, Richard. 2016. A process to establish and use base period prices for National Forest System 

transaction evidence timber appraisal. General Technical Report FPLGTR-242. Madison, WI: U.S. Department of Agriculture, Forest Service, 

Forest Products Laboratory. 43 p.

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comparisons highlight a number of issues that need to be 

addressed and require decisions by forest management decision makers as part of implementation. These issues include 

selection of geographic zones where mark-up equations 

may be needed to estimate BPP, appropriate size of timber 

appraisal zones when mark-up equations are used, assignment of sawtimber and nonsawtimber volumes to appraisal 

groups, structure and extent of uniformity of cost adjustments, treatment of high- and low-value hardwoods, adjustments for bid premiums, and selection of rollback factors.

Keywords: timber appraisal, mark-up equations, base period 

price, bid rate, advertised rate
Contents

Preface............................................................................................................................ii

Introduction....................................................................................................................1

The Problem...................................................................................................................2

A Revised Method to Calculate Base Period Prices for a Timber Sale .........................4

Overview of Timber Appraisal Calculations...............................................................4

Model for Estimating Base Period Price.....................................................................4

Appraisal Zones.............................................................................................................5

Product Codes and Appraisal Groups .........................................................................6

Data Sets ........................................................................................................................7

Forest Service Timber Sold Data ................................................................................7

Primary Product Price Data.........................................................................................8

Timber Sales Data for Individual Sales ......................................................................9

Matching Appraisal Zones and Product Prices...........................................................10

Results............................................................................................................................10

Estimates of Price Mark-Up Equations.......................................................................10

Model Tests by Forest Service Region........................................................................12

Escalation of Timber Sales Prices after the Sale.........................................................22

Discussion......................................................................................................................24

Adjustment Issues.......................................................................................................24

Does the Forest Service Get a Fair Price?...................................................................25

Implementation Guidance ...........................................................................................25

Acknowledgments..........................................................................................................26

Literature Cited ..............................................................................................................26

Glossary .........................................................................................................................27

Appendix 1—Assignment of National Forests to Appraisal Zones...............................30

Appendix 2—Product Codes and Product Names.........................................................34

Appendix 3—National Forest Timber Sale Volume and Price by Appraisal Zone, 

FY2009 Q1 to FY2014 Q3 ............................................................................................35

Appendix 4—Appraisal Information Collected from Individual Sales.........................41

Appendix 5—Methods to Improve Predictions of Base Period Price and Estimate 

the Probability Distribution of High Bid Price ..............................................................42

Decreasing the Error in Prediction of Base Period Price ............................................42

Estimating the Probability Distribution of High Bid Price for a Given Sale..............42

Estimation of Base Period Price by a Mark-Up Equation Given Possible 

Patterns in Error Terms ...............................................................................................42
ii

Preface

This report is part of an effort to examine and potentially address various issues involved 

in appraising USDA Forest Service timber sales. This study provides information that will 

help in further discussions among timber purchasers and Forest Service timber appraisers 

to address selected issues. The intent of this report is to review a number of contemporary 

issues and suggest ideas that would address those issues and provide an appraisal process 

that is transparent, consistent across Regions, and more specific to the mix of products being 

sold by the Forest Service.

This report was prepared by a small group of individuals that have been involved in the 

appraisal process. This group selected a number of issues to discuss and address. The Forest 

Service and timber purchasers will need to further consider the extent to which individual 

issues will be addressed in any revision to appraisal methods. Extensive review comments 

from a variety of individuals and organizations have greatly influenced the development of 

the final report.
A Process to Establish and Use Base 

Period Prices for National Forest System 

Transaction Evidence Timber Appraisal

Richard W. Haynes, Natural Resource Economist

Kenneth E. Skog, Research Forester Emeritus

USDA Forest Service, Forest Products Laboratory, Madison, Wisconsin

Richard Aubuchon, Forester

USDA Forest Service, Sale Administration & Appraisals, Fort Collins, Colorado

Introduction

The USDA Forest Service is required to appraise timber 

prior to it being offered for sale. The objectives of timber 

appraisal (see section 2420.2 of the Forest Service Manual) 

are to

1. estimate fair market value for National Forest timber 

offered for sale,

2. set an advertised rate that encourages sufficient competition for National Forest System (NFS) timber that 

results in values that are fair to both the Government and 

purchaser,

3. bring advertised values close to bid values,

4. ensure that appraisals are sensitive to changes in the 

market for forest products, and

5. redetermine stumpage values to ensure fair market value 

received for timber under contracts of more than seven 

years duration.

Although appraisal methods have changed over time, transaction evidence methods are currently used in Regions 1–9 

and the residual value method is used in Region 10. In the 

past several years, there has been growing concern about 

availability—both in number and applicability—of timber 

sales used as the basis of transaction evidence methods in 

certain Regions. In addition to the problem of few sales, 

in certain situations a notable fraction of the sales may be 

made at the minimum rate. With this transaction evidence 

system and the application of a rollback factor in setting an 

advertised rate, Base Period Prices (BPPs) can decline over 

time even though primary product prices are increasing. 

This study is intended to address this concern by suggesting use of price mark-up equations to estimate BPPs, where 

Forest Service managers decide they are needed, as a substitute for existing transaction evidence prices. The proposed 

price mark-up equations should have the following features:

• Be available nationwide

• Work with current or revised system of local cost 

adjustments

• Rely on data readily available in the public sector

• Be easy to understand and operate, using variables that 

have a “real world” basis

• Rapidly adjust BPPs in response to change in timber and 

product market conditions

• Predict BPP that can be used in combination with cost and 

sale adjustments to set a fair market value for sales with 

mixes of sawtimber and nonsawtimber products

• Evaluate predicted average BPPs for appraisal zones by 

comparing them to bid rates for recent historic sales (such 

as prior 2–3 years)

• Can eventually be implemented using the Timber Information Manager (TIM) program

A successful method is one that meets these objectives and 

could be effectively implemented by a diminishing timber 

sales staff. The intent would be to have a tool that could periodically estimate equations that would be used by staff in 

each appraisal zone to estimate BPPs. Use of systematically 

developed BPPs could increase societal confidence that the 

agency is estimating “fair market values” and may also increase financial returns to the treasury (and payments to the 

States) by reducing the number of situations where timber 

is sold at appraised prices that do not reflect current market 

prices.

We define BPP per unit of timber volume for a given appraisal group and appraisal zone as the estimated value 

from the high bid price for a sale in the current calendar 

quarter where the sale has volume-weighted average characteristics. These average characteristics include average 

costs (including stump to landing, haul) and average market 

demand conditions. Average market demand conditions are 

determined by an estimated primary product price and the 

average conversion efficiency of mills in the zone. These 

average market conditions will determine the average price 

mills can pay for stumpage. The BPP implicitly includes 

any average bid premium that the winning bidders have paid 

above the recent average appraised prices.

This definition (label as A) differs to a degree from the 

definition used in various Forest Service Regions (label as 

B). In definition B, BPP is generally defined as representing 
General Technical Report FPL–GTR–242

2

the appraisal group’s volume-weighted average winning bid 

price of the competitively sold timber sales in the base period, with the exceptions of Regions 1, 8, and 9 and Regions 5 

and 6 biomass. For Region 1, it is the average of the winning bid price plus road maintenance costs, environmental 

costs, temporary development costs and specified road costs 

and is called the “actual past average value.” For Regions 8 

and 9, it includes the application of a rollback factor. For 

Region 5 biomass, it is an estimated value. For Region 6 

biomass, it is the delivered chip price converted per hundred 

cubic feet (CCF). In general the BPP reflects average sale 

characteristics of the base period sales.

In definition A, BPP is the estimated average high bid price 

for a sale in the current quarter where the sales characteristics are average for a recent period. In definition B, BBP is 

the average high bid price for a sale of average characteristics over a historic period as close to the present as possible 

and extending back a number of quarters.

The Transaction Evidence Appraisal (TEA) system has been 

used by the Forest Service (except in Region 10) for the past 

25 years. It was intended to reduce appraisal costs (by reducing the need for cost collection) and to reduce the difference (bid premium) between appraised and high bid prices. 

To function as intended, TEA depends on a number of transactions (sales) in competitive markets (that is, with multiple 

bidders). The downturn in the Forest Service timber sales 

program starting in the mid-1990s has led to fewer sales in 

the time periods that comprise the various transactions data 

bases and, in many areas, fewer bidders. The great recession of 2008–2009 further reduced the basis for transaction 

evidence, raising questions about whether timber sale appraisals reflect fair market value, especially in declining or 

rapidly changing markets. (The Forest Service is required by 

36 CFR 223.60 to sell timber at “fair market value.” From 

an economics perspective, this value is like a reserve price 

that represents the agency’s view of a sufficient price, taking 

into account local cost adjustments and the average mix of 

bidders.) Specific problems are the drop in competition, inadequate number of transactions, outdated appraisal zones, 

lack of transparency in regional cost adjustments, increased 

use of stewardship contracting, and the tendency to lag market price changes. The primary objective of the study is to 

provide a method to estimate BPP (that is, stumpage prices, 

or net returns to the landowner from selling timber, generally expressed as dollars per CCF in Forest Service sales) that 

may be used when transaction evidence data are limited and 

to provide a preliminary evaluation of their use. These average base period stumpage prices are estimated for a given 

timber appraisal group (such as softwood sawtimber) as a 

function of primary product (such as softwood lumber) price 

in a given appraisal zone. Depending on decisions of Forest 

Service managers, this method might be used to estimate 

BPPs in a limited set of Regions with minimal alterations 

to the current appraisal system or for all BPPs with more 

extensive system alterations. The implementation section 

discusses options for managers. This study suggests that 

BPPs be estimated for six appraisal groups—for each of two 

species groups (softwoods and hardwoods) there are three 

product groups (sawtimber, nonsawtimber, and fuelwood). 

These groups are flexible and could be divided into low- and 

high-value subgroups. The BPPs are estimated for the current quarter for a given appraisal group in a given appraisal 

zone. For an individual sale, a BPP for an appraisal group 

is further adjusted by local cost adjustments and potentially 

by a rollback factor to provide an indicated advertised rate 

(before base rate adjustment).

The Problem

Figure 1 shows variations in primary wood product prices 

that drive what wood product mills can pay for Forest Service stumpage. These lumber and wood pulp prices drive 

prices that can be paid for logs at mills and the stumpage 

price that can be paid for offered timber. (We implicitly assume that given longer Forest Service timber sale contracts, 

bidders base their bids on expectations for future product 

values, which may or may not be reflective of current product values.) A problem for the timber appraisal system is 

how to account for this known influence of primary product 

price on appraised price. Our proposed solution to this problem is to select and use primary product prices (such as lumber prices) familiar to bidders to estimate current stumpage 

prices (discussed in a later section).

For lumber prices, the market swings are notable. These are 

commodity markets that reflect key movements in overall 

economic conditions. Also notable are differences between 

softwood species (the lower group of lines) and hardwood 

(HWD) species, reflecting differences in end uses and markets of these species groups.

Figure 1b shows one of the price series for wood pulp. 

Although showing less volatility than for lumber, it still reflects general market cycles. Lumber prices were more volatile than pulp prices because of greater volatility in housing 

construction markets over this period than in paper end-use 

markets. Both charts illustrate the dilemma facing agencies 

when market conditions are relatively uncertain and additional known variables (such as sale volumes, species, product groups, logging system requirements, haul distances, 

slash and road maintenance adjustments, and other local sale 

adjustments) influence sale value.

Although strong economic theory indicates that primary 

product prices will influence what mills pay for stumpage, 

our ability to detect the degree of influence using data series 

will depend on variation in primary product prices, variation in other factors that influence stumpage prices, and the 

length of the data series. While Figure 1 shows notable variation in primary product prices over 13 years, we use data 

from a shorter time, since 2009, to estimate the relationships 

between primary product prices and stumpage prices.
A Process to Establish and Use Base Period Prices for National Forest System Transaction Evidence Timber Appraisal

3

The intent of an appraisal system is to provide an advertised value as the starting point for a bidding process. This 

starting point needs to represent a “fair market value” in 

the sense that such a value is one that both willing buyers 

and sellers would agree on. For most sales of public timber 

in the United States, the final market price is set by public 

auction, where bidders bid based on their perceptions of the 

value of timber being offered and future market conditions. 

(In the United States, public agencies selling timber use 

various ways to develop appraised prices, but most include 

some form of product prices.) Most agencies do not attempt 

to estimate a final price, because it is not possible to do so 

without knowing who the purchaser will be or what economic conditions will be at time of harvest, which is often 

two or more years after a sale. (USDA Forest Service timber 

sale contacts can range in length from less than 1 year to 

10 years; most sales are in the 2–4 year range.) Often the 

goal of the appraisal process is to attract multiple bidders 

and have a high likelihood of successfully selling the sale at 

initial offering. The issue in developing an appraisal process 

$/MBF, lumber scale

 100

0

 200

 300

 400

 500

 600

 700

 800

 900

Hardwood 

lumber

Coast, softwood 

lumber

Inland, softwood 

lumber

Southern Pine 

lumber

West, spruce, 

pine, fir lumber

East, spruce, 

pine, fir lumber

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

(a)

2006 2007 2008 2009 2010 2011 2012 2013 2014

$/metric ton

0

200

400

600

800

1,000

(b) 1,200

Figure 1—U.S. prices for (a) lumber, by region, and (b) wood pulp. (Lumber prices from Random Lengths (2014), 

Luppold (2015), and WWPA (2011); June 2006 world wood pulp price from World Bank (2015), U.S. wood pulp 

price index from USBLS (2015a).)
General Technical Report FPL–GTR–242

4

is how to include expectations of lumber market conditions. 

TEA systems were intended to provide a minimum advertised value that reflects current market conditions; but as 

Figure 1a illustrates, market conditions can change rapidly 

and TEA systems typically use data from a number of quarters in the past. Given how forest product markets, including 

stumpage, can be characterized as experiencing relentless 

change (Haynes 2008), can we develop an appraisal method 

that ties changes in stumpage prices to product prices, and 

how would that method work?

A Revised Method to Calculate Base 

Period Prices for a Timber Sale

In this section we discuss three key parts of the appraisal 

method and possible revisions to those parts. We first give 

an overview of appraisal calculations then review the model 

that will be used to estimate the BPP. Next we review spatial 

aggregations for which we develop relationships between 

product prices and Forest Service sold prices. We call the 

spatial aggregations “appraisal zones,” but unlike the former 

use of appraisal zones for appraising timber, which tended to 

be small (often subparts of a Forest Service Region), these 

are larger, follow broad timber market areas, and combine 

several National Forests and, in some cases, parts of more 

than one Forest Service Region. In most cases, these larger 

appraisal zones follow the various regional definitions used 

in some of the spatial timber market models from various 

Resources Planning Act (RPA) timber assessments (see 

Adams and Haynes 1980). Then discuss the timber product 

(appraisal group) framework for this study.

Overview of Timber Appraisal Calculations

An initial objective for the appraisal system is to calculate a 

predicted bid rate, PBRs

igz. This value is for a particular sale 

(i), for volumes in each of several timber appraisal groups 

(g), in a given appraisal zone (z). For simplicity, we drop the 

g and z notation for appraisal group and appraisal zone.

The PBR for a sale, i, is calculated based on (1) an expected 

average current price for stumpage in the appraisal zone and 

(2) an adjustment to that expected average price (and average sale conditions) to account for the difference between 

average costs to get stumpage to a mill and specific sale i

costs to get stumpage to a mill:

(1)

where BPs is the estimated current average stumpage price 

for stumpage in appraisal group g for the stumpage market in appraisal zone z. In the TEA system, this is called 

∑the BPP. ( ) = = + − n

j i Cji Cj 1

s s 

BP is the estimated current average cost for cost 

center j in a given appraisal zone. Cost centers include 

felling/bucking/yarding, hauling, and road maintenance; 

include routine adjustment for quality or risk or for any unusual adjustment; and include any deposits required by the 

PBRi

s

 = BPs + 

j =1

n

∑ Cj − Cji ( )

purchaser. They include any cost to a purchaser that would 

cause them to adjust their bid. Cji is the estimated cost for 

cost center j or adjustment for quality or risk or unusual circumstance specifically for sale i.

In some ways, Equation (1) follows the process a bidder 

uses to develop site-specific prices (called site prices; for a 

discussion, see Haynes (2008)) that recognize local differences in operations and costs compared to regional averages. The adjustments Cj − Cji are not increased or decreased 

by a multiplier, on the assumption that these differences 

accurately indicate the adjustments to average costs that bidders effectively would use when calculating a bid price. We 

assume that bidders do not modify these estimated costs, by 

increasing or decreasing them, when including them in a bid 

calculation.

The appraisal system begins with a BPP and volume for 

each appraisal group (an aggregation of species, such as 

lodgepole pine and grand fir) in a sale. Several adjustments 

are applied to each BPP, including market adjustments and 

local cost adjustments from recent average costs (such as 

haul cost). Adjustments may also be made for quality, risk, 

and a catch-all—“unusual adjustment.” The result is the 

PBR for each appraisal group, which is adjusted by a competition or “rollback” factor and any property value added 

to the sale. The result is the indicated advertised rate for 

the appraisal group. This value is compared to a base rate 

for each appraisal group in the sale. The base rate is calculated by selecting, for each appraisal group, the higher 

of a minimum rate or a minimum deposit to the National 

Forest Fund ($0.25/CCF), plus the cost of reforestation on 

regeneration acres and any timber property value over the 

amount used to offset deficit values (FSH2409.18,45.42). If 

this base rate is higher than indicated advertised rate (a deficit appraisal group), it is used as the value for the appraisal 

group. The indicated advertised rates of other appraisal 

groups are adjusted downward in proportion to their positive (nondeficit) value to recover the upward price adjustment in the deficit appraisal group(s). The current minimum 

rates are $1/CCF for a low-value appraisal group, $3/CCF 

for a medium-value group, and $5/CCF for a high-value 

group (FSM2431.31b). The result after any substituting and 

adjusting due to base rates is the advertised rate for each 

appraisal group, which is intended to be a fair market value. 

There are some differences among Forest Service Regions; 

for example, in Regions 8 and 9, a rollback factor is used in 

calculating the BPP, whereas other Regions apply the factor 

to the PBR.

Model for Estimating Base Period Price

We propose a method to estimate BPPt

s

, which is average 

stumpage price in the current quarter t as a function of price 

in the current quarter of the product that is made from the 

timber, Pt

p

:

BPP (2) t

s

 = c1 + c2Pt

p
A Process to Establish and Use Base Period Prices for National Forest System Transaction Evidence Timber Appraisal

5

This approach for estimating a raw material price is common in agricultural markets (Wohlgenant 2001). The price 

change for the factor of production (raw material such as 

stumpage) is estimated as a function of price change in 

product markets. Discussion of the link between factor and 

product markets is a common aspect of production economics. In the case of timber, stumpage markets are considered 

to be derived from product markets where stumpage is one 

factor in the production of lumber or wood pulp (other factors are typically labor and capital). Economic theory and 

empirical evidence indicate that as product prices increase, 

the ability of mills to pay higher prices for stumpage increases as well. Conversely, as product prices decrease, the 

ability of mills to pay for stumpage decreases. Finally, we 

are proposing not that the price mark-up rule could predict 

the high bid for a specific sale but rather that it estimates 

the high bid if a sale has average costs and average bid premium. Models for predicting the specific high bid would 

be more complex and would include sale characteristics, 

market condition expectations, number of bidders, and other 

factors. Our goal is to estimate the BPP, the starting point 

for timber sale appraisal, which would then be modified by 

local cost adjustments and potentially a rollback factor and 

then compared to the legal minimums to determine the advertised rate.

We estimate current quarterly average stumpage prices (by 

product class) as a function of current quarterly primary 

product price (for lumber or wood pulp). The data are quarterly, and past studies suggest that lumber and stumpage 

prices adjust to changes in either market within a month 

(Haynes 2008). We use nominal price data (inflation is not 

removed) for both stumpage and primary products. This 

implies that any adjustment in product price within a quarter 

will be reflected in an adjustment to stumpage price within 

the same quarter. For our method, we assume that we can 

use the latest reported product price to estimate current average stumpage price.

In Equation (2), BPPs is the price in the stumpage market 

and Pp is the price in, say, the lumber market. Coefficients 

c1 and c2 are estimated from historic price data for the two 

markets. In agricultural literature, this is called a marketing 

margin or sometimes a price mark-up rule and used to relate 

farm level prices to wholesale product prices. (George and 

King (1971) summarize this use and discuss how marketing 

margins can be used to develop derived demand functions.) 

In forestry, this relation has been used to understand how 

changes in the product market are transmitted to the stumpage market (Haynes 1977, Spelter 2005).

Depending on estimates for c1 and c2, there are three possible types of relations between the factor (stumpage) and 

the product (such as lumber, wood pulp, biomass): The 

contribution of factor cost to product cost is fixed if c1 is 

significant and c2 is zero, is proportional to the product price 

if c1 is zero, or is mixed if both are significant.

A caution to be aware of in interpreting the coefficients is 

that measurement scales are different between volumes in 

product markets and volumes in stumpage markets. For 

example, lumber prices are dollars per thousand board feet 

lumber tally, whereas Forest Service stumpage prices are 

in dollars per hundred cubic feet ($/CCF). This means that 

the estimated coefficients will include implicit conversions 

between measurement scales and cannot be used to estimate 

price elasticity of stumpage prices with respect to product 

prices. In past studies, price mark-up rules have been used 

to estimate elasticity of price transmission, which can be 

used to estimate elasticity in the stumpage market from elasticity in the product market. This is useful in determining 

how price changes in one market are transmitted to the other 

market (Haynes 1977).

For the case where only c2 is used, the actual calculation of 

aggregate BPP for all appraisal groups in a sale is a volumeweighted average price across the product groups calculated 

as 

(3)

where w1, w2, and w3 are proportions of softwood sawtimber, softwood nonsawtimber, and softwood fuelwood, 

respectively; Ps

p is price of softwood lumber; and C1

2 and 

C2

2 are softwood sawtimber and nonsawtimber coefficients, 

respectively; Pwp is wood pulp price; F is firewood permit 

fee for the appraisal zone (a proxy for fuelwood prices because of a lack of reported prices); y1, y2, and y3 are proportions of hardwood sawtimber, hardwood nonsawtimber, and 

hardwood fuelwood, respectively; Ph

p is price of hardwood 

lumber; and D1

2 and D22 are hardwood sawtimber and nonsawtimber coefficients, respectively. (Prices of softwood 

lumber, wood pulp, and hardwood lumber are from the 

primary product data set shown in Table 3; hardwood sawtimber and nonsawtimber coefficients are estimated for a 

single-coefficient equation in Table 6.)

In actual application, Equation (3) will be modified for species and product groupings in the various appraisal zones.

Appraisal Zones

Forest Service timber appraisals have long depended on 

using appraisal zones to represent similar timber market, 

timber production, and milling cost conditions. Traditionally 

these have been relatively small geographic areas reflecting 

timbersheds or working circles for clusters of forest product 

manufacturing facilities. The number of these zones varies 

by Forest Service Region, but they are geographically specific. The past three decades have seen steady consolidation 

of the forest products industry, where there are now fewer 

but larger facilities drawing timber from broader areas. During this same period, the Forest Service shifted its timber 

appraisal processes away from residual value to transaction 

w C P w C P w F y D P y D P y3F 2 wp 2 2

p

h

1

3 1 2

2 wp 2 2 p

s 1 BPP = 1 2 + + + + +

w C P w C P w F y D P y D P y3F 2 wp 2 2

p

h

1

3 1 2

2 wp 2 2 p

s 1 BPP = 1 2 + + + + +
General Technical Report FPL–GTR–242

6

evidence (TEA) processes, reducing the need for local 

manufacturing cost collection used for the residual value 

method. (Region 10 is the one exception, where the residual 

value approach is stilled used.) With the TEA method, the 

role of appraisal zones, while still being based on log flows 

to a milling center, changed to being areas used to make 

cost adjustments to the average transaction evidence price 

to reflect sale-specific conditions. At the same time, western 

products markets for Forest Service timber have become 

less diverse as panel markets have changed and softwood 

lumber and associated residue (including biofuels) markets 

have increased (see the discussion in Haynes (2008) for an 

explanation). Coinciding with the diminishing diversity of 

products markets is the increase in bidders for Forest Service timber that operate in multiple Forests and/or Forest 

Service Regions. Some of these bidders question the differences between Forest Service Regions in appraisal and sale 

practices. Another component of this evolution has been 

changes in lumber market price reporting. There are now 

broad composite indexes for regional species (and grade) 

groupings that are reported monthly. These broad price 

composite indexes are a key component in the method we 

propose to estimate BPP. The relationship between stumpage and lumber prices is used to estimate average stumpage 

price for each product appraisal group used in each zone. 

These lumber prices are broad regional aggregates including 

a range of species available in the region.

After considering the availability of stumpage price data for 

National Forest locations and appropriate matching to markets and lumber price index data, we propose nine appraisal 

zones for the country, not counting Alaska (see Appendix 1 

for assignment of National Forests to each appraisal zone):

1. Coast—Western Oregon, Washington and northern 

California (15 National Forests)

2. Inland, North—eastern Oregon, Washington, all of 

Region 1 plus Boise, Payette, and Salmon–Challis 

National Forests in Idaho, Montana (30 National Forests)

3. Inland, Central—Idaho/Nevada border National 

Forests, Nevada, Utah, Colorado, Wyoming, South 

Dakota (20 National Forests), most of Regions 2 and 4 

(minus the Idaho Forests of Region 4 north of the Snake 

River) (31 National Forests)

4. Inland, South—All of Region 3, southern California 

(16 National Forests)

5. Sierra—middle forests in California (9 National Forests)

6. Southeast—National Forests in Region 8 east of Texas 

(26 National Forests)

7. Mid-South—Texas, Arkansas, Missouri (8 National 

Forests)

8. Lake States—Minnesota, Wisconsin, Michigan 

(8 National Forests)

9. Northeast—Region 9 without the Lake states 

(8 National Forests)

Why these nine appraisal zones? They each correspond to 

a broad lumber market. The coast appraisal zone is roughly 

the same as the Western Wood Products Association’s 

(WWPA’s) coast region and the area covered by the Northwest Forest Plan (NWFP), and contains two traditional 

lumber markets (Douglas-fir and redwood markets). The 

three inland appraisal zones (North, Central, and South) correspond roughly to the WWPA’s Inland region. The North 

Inland appraisal zone is roughly the old inland empire 

timber market that has pine and Douglas-fir markets. The 

Central Inland appraisal zone is an area where production is 

scattered but mostly in the eastern part focusing on mixed 

softwood species. The South Inland appraisal zone includes 

National Forests where there is little product production and 

the Forest Service has had difficulty in sustaining a sales 

program; there are some local markets for pine lumber. The 

Sierra appraisal zone includes National Forests in eastern 

California where there are active markets for lumber production in the Sierra Mountains; this zone has a mix of species 

including pine and true firs. The Southeast appraisal zone 

includes both the product-diverse southern pine markets and 

Appalachian hardwood markets in several of the states associated with the furniture industry. The Mid-South appraisal 

zone (mid-south west of Mississippi) contains traditional 

pine sawtimber and hardwood markets of the Ozarks. The 

Lake States appraisal zone includes the three lake states with 

a mix of hardwood and softwood markets. The Northeast 

appraisal zone includes both the northeast States and Ohio 

valleys with primarily Appalachian hardwood markets.

Product Codes and Appraisal Groups

The Forest Service has an extensive list of timber product 

codes (Appendix 2). Thirteen product codes (shown in bold 

in App. 2) are included in the Forest Service Timber Cut and 

Sold reports (one of the basic data sets used in this study). 

A combination of a product code and one or more species 

codes are used to define an appraisal group for which cost 

adjustments are made for individual timber sales. For example, sale number 11054 (FY2014) in Region 9 has a total 

volume 1,894 CCF divided among four species and two 

product codes (saw timber and pulpwood), for a potential 

total of eight appraisal groups. In the case of sale number 

11054, volumes are identified for each of four species codes 

and two product codes; cost adjustments are applied separately for each. There are no limits to the number of possible 

combinations of species and product codes for each sale. 

Each combination is treated as a possible appraisal group 

requiring cost adjustments to an average base period appraised price for volume in each group. Cost adjustments are 

made for logging costs (felling, bucking; skidding, loading) 

and for hauling, road maintenance, contracting costs (such as 

lopping and scattering vegetation along roads or away from 

leave trees), temporary development costs, unusual sale-related costs, specified road costs, and for other risks to arrive 

at an advertised rate. (Temporary development costs vary 

among regions. Most would include temporary bridges and 
A Process to Establish and Use Base Period Prices for National Forest System Transaction Evidence Timber Appraisal

7

landings and their decommissioning. Some regions include 

seed and fertilizer costs in this category, whereas others 

list it separately, called in these cases “seed and fertilizer” 

or “erosion control.”) In our approach to form appraisal 

groups, we have grouped species into two broad groups 

(hardwoods and softwoods) and grouped timber products 

into three product groups (sawtimber, nonsawtimber, and 

fuelwood) resulting in a maximum of six possible appraisal 

groups (Table 1). These groupings represent actively functioning stumpage markets within the appraisal zones. The 

most widespread markets for National Forest timber are for 

softwood sawtimber; in the east, there are also markets for 

hardwood sawtimber. In the east and especially the South, 

there are also functioning markets for nonsawtimber that 

include both traditional pulpwood and chip or fiber input for 

other engineered products. Although characterized by less 

formal markets, there are various markets and permit systems for fuelwood or biomass used for energy applications.

Data Sets

Three data sets are constructed for this study. The first two 

(Forest Service data on timber sold volume and price, and 

data on primary product prices) give averages by quarter. 

The third data set contains 810 Forest Service timber sale 

records. (These sales represent the variety of timber sales offered over the past several years, including regular, thinning 

and salvage sales. Each sale reflects unique circumstances 

that may lead to out of the ordinary bidding as illustrated in 

various figures later.) For a selection of sales, these records 

include sale dates, sale volume, species and product groups, 

cost data, and appraised and high bid prices. The third 

data set includes all sales for several fiscal years for some 

regions and all sales in fiscal year 2014 for other regions. 

The first data set includes timber volume sold and volumeweighted average price by quarter and covers the period 

fiscal year 2009 to the third quarter of fiscal year 2014. The 

second data set, which includes quarterly price for lumber 

and wood pulp, covers the fourth quarter of 2008 to the second quarter of 2014 (a total of 23 quarters). This period was 

chosen because it includes the most recent 20 quarters available at the start of this project (which was assumed to be the 

minimum sample size for estimation and to represent recent 

supply and demand trends that set prices). Generally this is 

a period where timber markets reflected both the full effect 

of the great recession of the 2000s and the beginning of the 

economic recovery.

Forest Service Timber Sold Data

All the Forest Service sold data for fiscal years 2009 to 

2014.3 were entered in two Excel spreadsheets. These data 

cover the calendar year period of 2008.4 to 2014.2. Each 

row contains the sold volume and bid prices for up to 13 

products included in the sold data for that period. Aggregate 

volumes of sawtimber, nonsawtimber, and fuelwood sold 

by quarter are shown in Figure 2. Of the total 2.55 billion 

cubic feet of timber sold over this period, 86.4% was softwoods and 13.6% hardwoods. For the three product groups, 

57.8% was sawtimber, 24.3% was nonsawtimber, and 17.9% 

was fuelwood. These proportions varied among regions, 

Table 1—Assignment of FS products into product 

groups used in the revised appraisal method

Product 

group

FS products, 

names

FS products, 

numbers

Sawtimber Sawtimber, Poles, Pilings, Ties 01, 03, 04, 09

Nonsawtimber Pulpwood, Posts, Nonsawtimber 02, 06, 08

Fuelwood Fuelwood, Miscellaneous 

Convertible, Cull Logs, Small 

Roundwood, Green biomass 

Convertible, Dry biomass 

Convertible

07, 14, 18, 19,

20, 21

Figure 2—Volumes sold by the Forest Service Timber Sales Program, FY2009–2014 (USDA Forest Service 2014).

Sawtimber

Nonsawtimber

Fuelwood

Total

2009 2010 2011 2012 2013 2014

CCF

 0

 500,000

 1,000,000

 1,500,000

 2,000,000

 2,500,000

 3,000,000

 3,500,000
General Technical Report FPL–GTR–242

8

with higher proportions of sawtimber sold in the western 

National Forests. (Proportions among the three product 

groups were 66.5%, 8.9%, and 24.5% for western appraisal 

zones and 41.7%, 52.9%, and 5.4% for the eastern appraisal 

zones.) Spikes in the sales program in Figure 2 reflect a consistently higher number of sales, and corresponding volume, 

sold in the fourth quarter of each fiscal year (third quarter of 

each calendar year).

The total value of all timber sold during this period was 

$759.7 million. Table 2 shows total timber volume and value for each of the new appraisal zones. It shows that there 

are differences between the volume and value proportions 

by appraisal zone. An important difference is that all eastern 

zones have higher value proportions than volume proportions, reflecting higher competition, more active markets for 

nonsawtimber material, and higher value species than western zones (except for the Coast zone).

Primary Product Price Data

Primary product prices are shown in Table 3 and Figure 1. 

These are presented in their nominal dollar form and reflect 

both long-term market trends and short-term market adjustments, such as the drop in prices in 2008–2009 accompanying the great recession.

Softwood and hardwood lumber prices. The product prices 

are market price proxies that bidders for Forest Service 

sawtimber are reacting too as they bid for Forest Service 

sawtimber. To serve this purpose for our appraisal method 

and for bidders, they need to be widely available (reported) 

and representative of demand for timber provided by Forest 

Service sales.

In this section, we describe the sources, data compilation 

issues, and various propositions about the price series. The 

lumber price series represent the product markets for sawtimber sold by the Forest Service. A secondary purpose is 

to discuss the relation between the lumber price indexes 

collected by the Bureau of Labor Statistics and stumpage 

prices, to provide background to discussions of how they 

can be used to adjust stumpage prices to actually be paid 

after timber is sold. Some of the price series and indexes are 

periodically published in two Forest Service publications 

(Howard and Westby 2013, Zhou 2013). The sources we 

use for softwood and hardwood lumber prices are Random 

Lengths, the Hardwood Market Report, and the Western 

Wood Products Association (WWPA). See the series and 

their sources in Table 3.

Various lumber price indexes are published by the Bureau of 

Labor Statistics (BLS) and in Howard and Westby (2013). 

BLS lumber price indexes have been simplified in recent 

years, but they still report a national price for all lumber, 

softwood lumber, and hardwood lumber. These are the 

lumber price index (WPU081 1982=100), the softwood 

lumber index (WPU0811), and the hardwood lumber index 

(WPU0812). These changes have made them useful for illustrating broad trends but less useful for developing base 

prices that need to reflect regional differences in species 

mixes.

Some of the most widely used softwood lumber price series 

in the West are the WWPA Coast and Inland lumber price 

series. Summaries of the WWPA price data are published by 

Zhou (2013). Random Lengths (“the weekly report on North 

American forest products markets,” which uses the rule that 

monthly prices are averages of weekly prices starting with 

the week that has the first Thursday of the month) collects 

and publishes nearly 300 lumber species and grade prices, 

but most useful for our estimation methods are 6 of their 

11 lumber composite prices: Coast Dry Random and Stud, 

Inland, Southern Pine, Western SPF, Eastern SPF, and Green 

Douglas Fir (Random Lengths 2014). These lumber composite prices are collected and published weekly by Random 

Lengths, intended to enable readers to quickly grasp price 

comparisons among different species, end uses, and regions. 

The weekly prices are also compiled into monthly averages 

and published as part of their monthly market overviews 

and annual yearbooks. For our purposes we compute a price 

series equivalent to the WWPA coast price series using 

two Random Lengths composite series. This price series is 

formed as the weighted average of the Coast Dry Random 

and Stud and green Douglas Fir composite prices using the 

proportions of kiln dried and green lumber for the coast and 

inland regions published by WWPA in their annual yearbooks (for example, WWPA (2011)). The various softwood 

lumber price series we use are shown in Figure 1a.

Hardwood lumber prices. We use an unpublished composite 

series developed by Bill Luppold (USFS, Northern Research 

Station). He developed a weighted price for Number 1 Common grade of hardwood lumber that uses 1C lumber prices 

by species weighted by the volume that each particular species is in the forest inventory for the Appalachian and northern area. (The species and weights are Ash, 0.04; Basswood, 

0.01; Beech, 0.01; Birch, 0.01; Cherry, 0.04; Hickory, 0.03; 

Hard maple, 0.09; Soft maple, 0.06; Red oak, 0.36; White 

Table 2—Total volume and value of Forest Service 

timber sales, FY2009–2014

Volume 

(×106 CCF)

Value

($)

Volume

(%)

Value

(%)

Coast 3.64 137.74 14.24 18.13

Inland North 5.86 148.88 22.95 19.60

Inland Central 3.26 32.09 12.77 4.22

Inland South 1.39 9.44 5.46 1.24

Sierra 2.43 44.00 9.53 5.79

Southeast 3.40 130.10 13.29 17.12

Mid-South 2.30 90.57 9.00 11.92

Lake States 2.67 110.47 10.44 14.54

Northeast .60 56.44 2.32 7.43

Total 25.55 759.73 100.00 100.00
A Process to Establish and Use Base Period Prices for National Forest System Transaction Evidence Timber Appraisal

9

oak, 0.18; Yellow poplar, 0.16; and Walnut, 0.01.) Common 

grade is often referred to as Cabinet grade because of its 

adaptability to the standard sizes of kitchen cabinet doors 

and is also used in the manufacture of furniture parts. Lumber prices by species are published in the Hardwood Market 

Report (various years). (The Hardwood Market report is 

published weekly and provides benchmark pricing and market commentary on North American lumber and hardwood 

products industry.) To represent quarterly prices, he uses 

prices reported for the first of the month for January, April, 

July, and October. Although hardwood lumber prices appear 

in Figure 1a to have risen sharply, the deflated recent prices 

are below levels reached in the mid-2000s. The one exception is black walnut, which enjoys strong export markets.

Wood pulp prices. Nonsawtimber can include small logs 

that can be sold to make several types of products that can 

vary by appraisal zone. Traditionally, nonsawtimber has 

been sold as pulpwood to make pulp and composite panel 

products (Table 1). More recently, some small logs could 

be used by small log sawmills to make lumber. These 

markets are predominantly in the eastern United States 

and are emerging in the West, where sellers are considered 

to arbitrage small logs between pulp, panel, and lumber 

markets. A stumpage owner will sell stumpage in the market 

with the highest return. Because the pulpwood markets will 

have to match prices for other small log uses, we use pulp 

prices to predict the nonsawtimber stumpage markets in the 

Eastern United States.

There is no single market pulp price for the United 

States, but there is a BLS national wood pulp price index 

((WPU09110501) 2006 (June) = 100) (USBLS 2015a) that 

we use to construct a price series for wood pulp. A wood 

pulp price specific to the United States can be constructed 

by taking the June 2006 world wood pulp price ($697.3/mt 

or $632.6/t) and multiplying it by the U.S. wood pulp index 

in 2006 and thereafter. (The world wood pulp price is the 

price ($/mt) for Swedish wood pulp, especially softwood 

sulphate bleached, air-dry weight pulp at North Sea Ports 

(World Bank 2015).) The resulting price is shown in Figure 1b and Table 3.

Timber Sales Data for Individual Sales

The third data set is a collection of individual sales data sets 

from each Forest Service Region except for Region 10. We 

use these data sets to compare BPP estimates to final sales 

prices and show local sales costs.

Table 3—Primary wood product price data

Year Qtr

Inland West 

softwood lumber

($/MBF)

Southern Pine 

lumber

($/MBF)

West spruce, pine, 

and fir lumber

($/MBF)

East spruce, pine 

and fir lumber

($/MBF)

West Coast 

softwood lumber

($/MBF)

Hardwood 

lumber

($/MBF)

Wood pulp

($/t)

2008 4 263.00 262.33 185.00 263.33 191.03 583.10 813.78

2009 1 251.00 235.33 158.33 235.33 171.64 559.60 750.56

2 256.67 236.00 178.67 245.67 181.88 489.90 701.51

3 295.67 246.00 211.67 273.33 213.69 483.10 704.07

4 298.33 246.67 220.00 277.67 212.81 515.90 753.12

2010 1 352.33 314.67 278.67 319.33 259.50 536.10 804.49

2 373.67 350.33 275.33 340.67 282.21 598.90 897.93

3 316.67 254.33 235.67 297.33 234.38 621.65 956.50

4 343.67 257.33 268.67 309.33 264.95 616.90 943.49

2011 1 363.00 287.33 289.67 331.00 292.45 578.80 946.51

2 347.33 254.67 246.67 302.67 273.77 556.70 959.99

3 348.33 252.67 255.33 311.00 278.60 558.10 959.53

4 338.67 249.33 239.33 302.33 260.68 558.70 906.76

2012 1 358.33 279.67 265.33 326.00 277.33 545.65 902.35

2 388.67 321.00 302.33 365.00 301.26 550.55 901.42

3 389.67 310.00 311.33 378.00 318.67 550.85 906.99

4 395.00 338.67 331.33 387.00 324.27 551.85 888.17

2013 1 455.33 422.67 382.33 439.00 393.50 585.75 868.64

2 455.33 395.33 336.67 416.67 372.87 657.75 872.59

3 434.33 363.00 323.33 378.00 351.98 676.70 877.24

4 475.33 359.33 359.00 406.00 394.21 691.80 881.19

2014 1 485.67 395.67 361.00 416.00 409.32 772.50 878.40

2 481.33 399.00 338.33 409.00 362.60 843.25 902.81

Data sources: All softwood lumber series, Random Lengths (2014); Hardwood lumber, Hardwood Lumber Market Report as compiled by Luppold (2015); 

Wood pulp, USBLS (2015a) and World Bank (2015) for the June 2006 dollar value per metric ton. Data from Random Lengths and the Hardwood Market 

Report are used with permission of the publishers.
General Technical Report FPL–GTR–242

10

These data were compiled in Excel spreadsheets from appraisal information on FS-2400-17 forms kept for each timber sale. The data include sales of timber from final harvest, 

intermediate treatments, and salvage treatments but do not 

include timber removed as part of stewardship contracts 

where timber goods are traded for ecosystem restoration 

treatments. All the sales were sold using sealed or oral auction procedures. These data sets include sale identifiers, 

administrative variables, general sale information, and cost 

center and other adjustments. For details on data they usually contain, see Appendix 4. The data sets vary among Regions in the variables they include and the number of sales. 

There are at least 50 sales for each Region, and several Regions sent data for the FY2010–2014 period or for all sales 

in a fiscal year (most frequently FY2014) (Table 4).

We added several summary variables to each sale. Depending on the variables in the regional data sets, these include 

(1) fractions of volume that are in certain categories 

(such as, percentage sawtimber, percentage softwoods) 

(2) volume-weighted average costs (logging, haul costs, 

slash disposal, temporary roads, sale contact costs, specified 

road costs), (3) advertised bid price (after local cost adjustments), (4) high bid price, and (5) bid premium (difference 

between the advertised rate and high bid price). The actual 

data sets contain more detail for each product and species 

combination. For example, the Region 9 data set has 14 possible appraisal groups that are subject to cost adjustments 

for each sale.

Matching Appraisal Zones and Product Prices

Table 5 indicates the primary product prices that are used in 

each appraisal zone. Assignment of primary product prices 

was guided by the need to estimate stumpage prices for species groups that are sold in each zone. Differences between 

eastern and western regions in the primary product price 

series used for hardwood species and nonsawtimber products will be explained in the next section.

Results

First, we present estimated price mark-up rules for each appraisal group in each appraisal zone. This includes discussion of caveats for using the equations. Second, using data 

on individual sales, we compare our estimates of stumpage 

price from the mark-up equations to high bid prices for 

individual sales. In addition to the obvious goal of learning if our estimates are above or below actual high bid, this 

comparison provides a basis for subsequent discussions 

about local cost adjustments and possible requirements for 

rollback factors that would need to be applied to initial appraised price.

Estimates of Price Mark-Up Equations

Price mark-up rules, in the form of linear equations, were 

estimated for the four groups, hardwoods and softwoods, 

sawtimber and nonsawtimber, for all appraisal zones. Regressions were made first using two coefficients (c1, c2) in 

the form shown in Equation (2), second with a constant term 

only (c1), and third with a coefficient for primary product 

price only (c2). We evaluated the predictive ability of the 

equations by estimating root mean square error (rmse) comparing predicted quarterly average stumpage price to actual 

quarterly average stumpage price and r2 values for the equations. Equations with a single coefficient for primary product price did not have strong predictive power for stumpage 

price (low r2), but the predictions were always better than 

using the previous quarter’s stumpage price as a prediction 

of the current quarter’s price. Use of a recent period’s local stumpage price to estimate current BPP is the current 

Table 4—Time periods and number of sales for 

individual timber sales data by Region and zone

Region and new zone Time period

Number

of sales

Region 1 (Inland North) FY2010.3–FY2015.1 64

Region 2 (Inland Central) FY2010–FY2014 171

Region 3 (Inland South FY2010–FY2014 62

Region 4 (Inland Central) FY2010–FY2014 142

Region 5 (Coast) FY2007.4–FY2014 10

Region 5 (Sierra) FY2007.4–FY2014 40

Region 5 (Inland South) 0

Region 6 (Coast) FY2014 41

Region 6 (Inland North) FY2014 19

Region 8 (Mid-South) FY2014 24

Region 8 (Southeast) FY2014 76

Region 9 (Mid-South) FY2014 26

Region 9 (Lake States) FY2014 103

Region 9 (Northeast) FY2014 32

Table 5—Match up among appraisal zones stumpage 

prices and primary product price series

Softwood Hardwood

Appraisal

zone

SawtimberNonsawtimberSawtimberNonsawtimber

Coast Coast Coast N/A N/A

Inland North Inland Inland N/A N/A

Inland Central West SPFa West SPF N/A N/A

Inland South West SPF West SPF N/A N/A

Sierra Inland Inland N/A N/A

Southeast Southern 

Pine

Wood pulp Hlumb Wood pulp

Mid-South Southern 

Pine

Wood pulp Hlum Wood pulp

Lake States East SPF Wood pulp Hlum Wood pulp

Northeast East SPF Wood pulp Hlum Wood pulp

a

Spruce–pine–fir lumber.

b

Hardwood lumber.
A Process to Establish and Use Base Period Prices for National Forest System Transaction Evidence Timber Appraisal

11

transaction evidence appraisal method. The regression with 

a constant term gave lower rmse than the equation with a 

single coefficient on primary product price for 18 of 26 appraisal groups. The two-coefficient regressions gave somewhat lower rmse and higher r2

 values, but coefficients were 

often not statistically significant. We use single-coefficient 

equations to estimate stumpage price (Eq. (4)) because, with 

competition among mills for stumpage, the stumpage price 

would consistently increase with primary product price 

(coefficients are shown in Table 6):

BPPs

t

   = c2Pp

t (4)

We made preliminary tests to determine if the data could 

be transformed to improve the predictive equation (for example, by testing for stationarity of prices series and adjustments to attain stationary series for prediction equations). 

Initial tests did not suggest predictions would be altered notably. Appendix 5 discusses possible ways to improve BPP 

predictions.

Certain modifications were made in forming the equations. 

In the western appraisal zones, hardwood volume was a 

small part (1.1% of the total sales program, and 84.3% of 

hardwood volume was sold as fuelwood) of the total Forest 

Service timber sales program. Consequently the mark-up 

equations for sawtimber and nonsawtimber stumpage price 

were estimated using the price for total (both softwood and 

hardwood) volumes sold. Also, in the West, few timber sales 

are sold solely containing nonsawtimber. These relatively 

small volumes of nonsawtimber leads to greater price volatility in the nonsawtimber markets, as shown in Figure 3 for 

two example western regions.

Because the volume and value of western sales are predominantly for sawtimber, western bidders likely develop their 

bid price for sawtimber volumes while considering the value 

they can obtain from the associated nonsawtimber volumes. 

(This same type of behavior has been observed in the South, 

where the issue is hardwood volumes in softwood stands. 

The hardwoods are referred to as the “come along volumes,” 

and it may be that some hardwood value is assigned by bidders to the softwood bid price and would be one explanation 

for lower hardwood pulpwood prices relative to softwood.) 

In cases where nonsawtimber is a biddable product, bidders may bid higher on nonsawtimber to keep sawtimber 

bids lower. The higher the potential bid for sawtimber (as 

influenced by lumber price), the higher the possible bid for 

nonsawtimber. For the few sales with significant nonsawtimber volumes, bidders still adjust bid price in accordance 

with current lumber prices if they think they can sell some 

nonsawtimber logs for sawtimber prices; the price for the 

portion of nonsawtimber not suitable for sawtimber uses 

would be influenced by chip prices. Another explanation for 

high overbids is that Forest Service cruising procedures are 

incorrectly calling logs nonsaw when they actually contain 

a sawlog. The purchaser reveals this information via their 

bid prices. There is also the case where the nonsaw product 

class consists of very different product types that have very 

different prices. For example, the nonsaw product class in 

Region 1 consists mostly of pulp and post and pole materials. Pulp logs typically sell for $25 to $30 per ton in specific 

areas in Region 1. Post and pole material ranges in value 

from $50 to $65 per ton, which at the upper end of the range 

rivals sawtimber prices. This highlights the issue of creating 

product classes ignoring the values of the material and “not 

appraising” the nonsaw material and advertising the material 

at $1/CCF. 

Western chip prices are mostly proprietary except for chip 

export prices (see table 49 in Zhou (2013)), which are 

shown in Figure 3a and are generally for mill residues. 

(Chip export prices, which are arbitraged against other 

products for stumpage, can be used as a proxy for the driver 

of domestic nonsawtimber stumpage prices (Haynes 1999, 

Busby 2006).) Export prices are not immediately available 

to use in predictions. Both parts of Figure 3 indicate that 

western nonsawtimber prices are more volatile than chip 

export prices. It seems likely that this volatility is due to the 

influence of volatile lumber prices in the bidding process. 

The volatility of nonsawtimber prices may result from the 

bidding process where their price is established in joint 

bids with sawtimber. It seems likely that both chip prices 

Table 6—Estimated coefficients for stumpage price 

equationsa

Sawtimber RSQ

Nonsawtimber RSQ

East equations—softwood

Lake States 0.22360 0.417 0.048832 –0.119

Northeast 0.12757 0.960 0.048832 –0.026

Southeast 0.21052 0.775 0.026354 –0.327

Mid-South 0.14644 0.911 0.014885 –0.253

East equations—hardwood

Lake States 0.11845 0.102 0.03136 0.155

Northeast 0.41791 0.146 0.00419 –0.095

Southeast 0.08085 0.260 0.00919 –0.085

Mid-South 0.11690 0.260 0.00961 0.004

West equations

Coast 0.14916 0.298 0.02514 –0.043

Inland North 0.10203 0.910 0.03515 0.484

Inland Central 0.04000 0.726 0.04967 0.395

Inland South 0.02448 0.843 0.00513 0.556

Sierra 0.08599 0.626 0.03888 0.258

a

Coefficients are for the equation y = ax, where y is stumpage price 

in $/CCF and x is product price for lumber in $/MBF or pulp in 

$/ton. The R2 (RSQ) values are computed as [1 – (residual sum of 

squares)/(centered sum of squares)]. The value is negative in cases 

where a constant value has a lower error of estimate for stumpage 

values than the equation estimate.
General Technical Report FPL–GTR–242

12

and sawtimber/ lumber prices influence the movement of 

nonsawtimber prices. As a first approximation to track the 

volatility of nonsawtimber prices, we use lumber prices 

to estimate nonsawtimber prices. If chip price data can be 

found that are available in the current period to make predictions, then they may be used to improve predictions of 

nonsawtimber price.

In the four eastern appraisal zones, there are clear differences between sawtimber and nonsawtimber markets. These 

differences are illustrated in Figure 4 from the southeast 

appraisal zone. Prices in both sawtimber and nonsawtimber 

markets vary in broadly similar ways, unlike those shown in 

Figure 3.

Estimated coefficients are shown in Table 6. For the eastern 

appraisal zones, equations for softwoods and hardwoods 

(four equations for each appraisal zone) follow a general 

format:

1. Sawtimber stumpage price is a function of lumber price.

2. Nonsawtimber stumpage price is a function of wood pulp 

price.

For the west, equations for total volumes only (two equations for each appraisal zone) follow a general format:

1. Sawtimber stumpage price is a function of lumber prices.

2. Nonsawtimber stumpage price is a function of lumber 

prices.

Model Tests by Forest Service Region

The new method for calculating BPPs can be evaluated 

using data on individual sales by Region. Our equation 

estimates an initial stumpage price, which is the estimated 

Figure 3—(a) Softwood sawtimber, nonsawtimber, and chip export prices for the Coast Appraisal 

Zone (sawtimber and nonsawtimber, USDA Forest Service (2014); Chip export price, Zhou (2013)) and 

(b) softwood sawtimber and nonsawtimber prices for the Inland Central Appraisal Zone (USFS 2014).

0

10

20

40

50

60

70

80

90

Sawtimber

Nonsawtimber

Export chip prices

$/CCF

2010 2011 2012 2013 2014

30

2009

0

10

20

30

40

50

60

70

80

Sawtimber

Nonsawtimber

$/CCF

2009 2010 2011 2012 2013 2014

(a)

(b)
A Process to Establish and Use Base Period Prices for National Forest System Transaction Evidence Timber Appraisal

13

price for an average cost sale, before individual sale cost 

corrections and application of rollback factors, and with an 

average bid premium. In the simplest form, we can compare 

a new estimate of the BPP using prices from our mark-up 

equations to the high bid price for the individual sales. 

Results of interest include

1. the number of sales where BPP is higher than the high 

bid price (if the new BPP was used without cost corrections, these sales would likely have resulted in no bid 

sales) and

2. the number of sales where an adjusted BPP is higher than 

the high bid price where the BPP for each is adjusted for 

some local costs.

These results will indicate (1) the need for cost adjustments 

in the development of the initial appraised price, (2) modifications that may be needed in how nonsawtimber volumes 

are valued, and (3) how haul cost differences impact local 

sale prices in spatially diverse regions (such as Region 3).

Common influences over time on timber sales used include 

shifts in Forest Service management and shifts in market 

conditions. Since the mid-1990s, the Forest Service has 

shifted management objectives from harvesting certain areas 

for volumes of timber to objectives to treat forest land to attain certain ecosystem outcomes. The consequences include 

more volume from thinning treatments, limited harvest of 

large trees (over 22 in. (56 cm) in the west), lower volumes 

per acre harvested, shifts to logging systems that result in 

higher costs, and increasing harvest volumes of difficult-toutilize timber (such as more dead or small-diameter timber). 

In addition to various cost impacts, if Forest Service utilization definitions lag industry utilization practices, we could 

find that appraised prices are less than high bid prices as 

bidders achieve greater product recovery from material that 

the agency might have considered unmerchantable. (This is 

not a new phenomenon; it was observed in the late 1970s in 

the disposition of yarded unmerchantable material (YUM) 

in Region 6.) As has been noted in discussion of Figure 1a, 

sales data sets generally cover the end of the period associated with the “great recession” and most of the figures for 

the test results, if they include multiple years, demonstrate 

the downward price trends in 2008–2010 and recovery afterwards. The sale data sets do not include stewardship sales 

or in many cases direct sales. (The Region 4 dataset is an 

exception and has some direct sales.)

Forest Service Region 1. Region 1 is part of the proposed 

Inland North Appraisal Zone. Results comparing BPP to 

high bid for individual sales in Region 1 are shown for 

sales ordered chronologically in Figure 5a. There were 

64 sales from the period FY2010 to FY2015. The BPP 

does not include cost corrections for individual sales, and 

its values for nonsawtimber are higher than their assumed 

price in the appraisals (which values all nonsawtimber at 

$1/CCF). Figure 5b shows BPP values for two different 

values assigned to nonsawtimber. The line BPP-R1 indicates 

the appraised price using Region 1 default appraisal values, 

and the BPP line indicates the BPP with the new (higher) 

appraised price for nonsawtimber. There are no cost 

adjustments for prices in Figure 5. BPP is computed using 

Equation (3) modified to include just softwood species. 

In these examples we assigned a value of $6.25/CCF for 

fuelwood, hardwood, and softwood (based on converting a 

$5/cord fuelwood collection permit fee from cords to CCF 

by the ratio 1.25 CCF/cord). 

Individual sales are shown on the x axis, with the oldest 

sale (FY2010.3) on the left and the newest (FY2015.1) on 

the right. In this simplest approach to sale appraisal, BPP 

Figure 4—Forest Service sold prices for softwood sawtimber, nonsawtimber, and fuelwood 

for the Southeast Appraisal Zone (USDA Forest Service 2014).

0

20

40

50

60

70

80

90

100

Sawtimber

Nonsawtimber

Fuelwood

$/CCF

2010 2011 2012 2013 2014

10

30

2009
General Technical Report FPL–GTR–242

14

is higher than high bid price for 34% of all sales. Figure 5b 

shows BPP with and without use of the default R1 price of 

$1/CCF for nonsawtimber. Using the R1 price for nonsawtimber, we overestimate high bid price for 26% of sales.

These results suggest that further thought needs to be given 

to how to appraise sales in a time of changing sawtimber/

nonsawtimber definitions. Figure 5b reveals that groups 

of sales (for example, sales 20–23, the four sales from the 

Flathead National Forest offered in FY2013 that are plotted as circles) have sufficient nonsawtimber volumes (48% 

in these sales) to reduce appraised prices and consequently 

lower the high bid to levels less than might have been 

expected.

Figure 5—Region 1 (a) BPPs and actual high bid price for sales from 2010 to 2015 and (b) BPPs using two nonsawtimber 

value assumptions. (BPP from mark-up equations and Table 6 applied to appraisal group volumes from USDA Forest 

Service Region 1 (2014); high bid from USDA Forest Service Region 1 (2014); BPP-R1 is the same as BPP except R1 

minimum bid prices are used for nonsawtimber volumes.)

Base period price

High bid

2013 2014 2015

$/CCF

0

20

40

60

80

100

120

140

160

180

2010

2012

Base period price

Base period price, 

Region 1

2013 2014 2015

$/CCF

0

10

20

30

40

50

60

20102012

(a)

(b)
A Process to Establish and Use Base Period Prices for National Forest System Transaction Evidence Timber Appraisal

15

Forest Service Region 3. Region 3 is part of the proposed 

Inland South appraisal zone. Results comparing BPP to high 

bid prices for Region 3 are shown in Figure 6. This Region 

provided a data set that had 62 sales covering fiscal years 

2010–2014. The chart is plotted chronologically, with earliest sales on the left and latest sales on the right. There are 

three prices plotted in Figure 6: BPP, high bid prices, and 

BPP adjusted for differences in haul costs. BPP was less 

than high bid price on 18% of these sales. In an attempt to 

improve its performance, we adjusted the BPP by the difference from the average of haul costs, after which the adjusted 

price was less than the high bid price for 68% of the sales. 

This is “better” performance in that the adjusted price overestimates the high bid 32% of the time. However, the fact 

that the adjustment reduced estimated prices on average indicates that the adjustments were for a set of sales with costs 

that were, on average, greater than the longer-term average.

Our comparison of estimated BPPs (and adjusted BPPs) 

to high bid prices is imperfect to the degree that the time 

period of sales used to estimate the BPP equations (20+ 

months), the time period for the sample of recent sales, and 

the time period used to compute average costs used to make 

actual appraisals can differ. The degree to which estimated 

BPP over- or underpredicts will be influenced by these differences. If the time periods and average costs were all the 

same, we would expect estimated BPP to underestimate (or 

overestimate) the sample set of sales 50% of the time.

Regions 2 and 4. These Regions are considered together 

because they share the same appraisal zone, Inland Central. 

This appraisal zone is unique in that nonsawtimber products 

are valued higher than sawtimber products. This may reflect 

diverse trends, such as the lack of traditional sawtimber 

markets, active small log markets that range from corral 

poles and posts, house logs to small logs for “chip and saw” 

type of manufacturing, and a large volume of dead and 

dying timber.

Both are large data sets (Region 2 has 171 sales, Region 4 

has 142) and cover 2010–2014. Comparisons of high bid 

and BPP for Regions 2 and 4 are shown in Figures 7 and 8. 

Like other Regions, both Regions 2 and 4 exhibit price 

spikes associated with highly competitive bidding for select 

sales. Prices are generally higher in Region 4, reflecting the 

larger proportion of nonsawtimber on sales in that Region.

In Region 2, BPP exceeded high bid 57% of the time, and in 

Region 4 it exceeded high bid 31% of the time. If we reduce 

BPP by the amount of average bid premium in each Region, 

we reduce the percentage of cases where adjusted BPP exceeds high bid to 49% and 19%, respectively. There are no 

cost adjustments or rollback factors applied to BPP in either 

Figure 7 or 8.

Both of these Regions are in the proposed Inland Central appraisal zone, so the price mark-up rule estimation uses sold 

information for both Regions. In the potential implementation, local cost adjustments will be applied across the Regions comprising this appraisal zone. There are some differences in the cost data for these two Regions, and these may 

account for some of the differences between the Regions 

Figure 6—Region 3 BPP, actual high bid price, and adjusted BPP for sales from 2010 to 2014. (BPP from mark-up 

equations and Table 6 applied to appraisal group volumes from USDA Forest Service Region 3 (2014); high bid from 

USDA Forest Service Region 3 (2014); adjusted haul costs from BPP minus the difference between average haul cost 

and haul cost for each sale.)

Base period price

High bid

Base period price, 

adjusted for haul 

costs

2010 2011 2012 2013 2014

$/CCF

–100

–20

–40

–60

–80

0

20

80

60

40

100

120
General Technical Report FPL–GTR–242

16

shown in Figures 7 and 8. As shown in Table 7, there is a 

difference in sale yarding costs between the Regions that 

make combined cost comparisons difficult. These costs are 

typically the largest of the cost centers and may be due to 

use of more expensive yarding systems in Region 2 than in 

Region 4. Hauling, road, and slash costs seem more uniform 

between the Regions.

Some other differences would especially affect BPP estimates. Table 8 shows sawtimber, nonsawtimber, and fuelwood proportions for Regions 2 and 4. Because BPP is 

computed as a volume-weighted average, the differences 

between the regional proportions will lead to different estimates of BPP.

Figure 7—Region 2 BPP and actual high bid price for sales from 2010 to 2014. (BPP from mark-up equations 

and Table 6 applied to appraisal group volumes from USDA Forest Service Region 2 (2014); high bid from USDA 

Forest Service Region 2 (2014).)

Figure 8—Region 4 BPP and actual high bid price for sales from 2010 to 2014. (BPP from mark-up equations 

and Table 6 applied to appraisal group volumes from USDA Forest Service Region 4 (2014); high bid from USDA 

Forest Service Region 4 (2014).)

Base period price

High bid

2010 2011 2012 2013 2014

$/CCF

0

20

10

30

50

40

60

Base period price

High bid

2010 2011 2012 2013 2014

$/CCF

0

20

30

10

40

50

60

70

80

90
A Process to Establish and Use Base Period Prices for National Forest System Transaction Evidence Timber Appraisal

17

Both Regions have experienced beetle outbreaks, and perhaps appraisers in Region 4 assigned more of the beetle 

killed timber to the nonsawtimber and fuelwood product 

groups. As noted elsewhere, BPP overvalues fuelwood relative to current Forest Service practices. Because the data 

used to estimate the price mark-up equations relies on high 

bids, it does include results of sales where bidders have 

higher utilization expectations for nonsawtimber than used 

in the original appraisal. This may occur in those cases 

where nonsawtimber includes green small (4–9 in. (10–

23 cm) small-end diameter) sawtimber. In Region 4, however, nonsawtimber prices could be low because much of this 

material might be dead material, not used as sawtimber and 

perhaps appraised as fuelwood. Another factor contributing 

to lower prices in Region 4 is the relatively high proportion 

of direct sales (a third of the sales sold without competition) 

in the data set.

Forest Service Region 5. Region 5 is divided into three 

of the proposed appraisal zones (Coast, Sierra, and Inland 

South). Sales data were provided for 50 sales for National 

Forests in the Coast and Sierra zones. The BPP and the high 

bid price for sawtimber are shown in chronological order 

(oldest to newest) in Figure 9.

Figure 9a shows five sales with an estimated negative adjusted BPP that would be appraised at base rates. The figures show that for each zone the majority of BPP estimates 

are above the high bid price (50% for the Coast appraisal 

zone). Adjusting estimated BPP for each sale by the sale’s 

difference in costs from the average makes the most difference (reduces BPP on average) for sales in the Coast appraisal zone. To attain advertised rates that exceed high bid 

prices in fewer cases will require adjusting BPP with cost 

adjustments and rollback factors. Rollback factors would 

ideally provide an advertised rate that has a low likelihood 

of no bid and has a low likelihood of high overbid. Bidders 

or agencies often have expectations about adequate levels of 

bid premium expressed in percentage terms. If the starting 

point is lowered by various cost adjustments, the expectations for the high bid will be lower. For individual sales, 

there could be a correction to BPPs (up or down) depending 

on how individual sales costs differ from the average.

Region 5 costs illustrate another point: The sum of various 

costs is significant. Average costs for the Coast and Sierra 

zone sales are shown in Table 9.

Not counting log and haul costs, the average of these costs 

is $57.97/CCF. Most of the remaining costs are various 

“deposits” and have become part of the way costs are 

assigned in timber management programs.

Forest Service Region 6, west of the Cascade Mountains. 

Results for the west side of Region 6 are shown in Figure 10 

for 41 fiscal year 2014 coast appraisal zone timber sales 

(the data set also included 19 sales from eastside national 

forests). As in other Regions, if the BPP was not adjusted, a 

majority (51%) of sales would not have sold. Three different 

prices are shown in Figure 10.

The BPP is computed using Equation (3) with appropriate 

coefficients and lumber prices. The adjusted appraised price 

is the BPP adjusted by the degree to which four costs in 

each sale differ from average cost in the recent base period 

(yarding, hauling, temporary roads, and specified roads). 

The high bid price is from each sale.

The fact that cost adjustments bring BPP closer to high bid 

prices suggests that cost adjustments are important. However, there are still differences between adjusted BPP and 

high bid for some groups of sales, which suggests further 

attention is needed on how to make adjustments for certain 

types of sales. For example, judging from sale names, nearly 

50% of the westside sales are thinning sales, which typically 

have higher logging costs and often lower value material. 

As suggested by the Capital Forest Study (Curtis and others 

2004) switching logging methods from two stage logging to 

Table 7—Average local sales costs by cost center for 

Regions 2 and 4

Sale 

yard

Sale 

haul

Sale 

road

Sale 

slash

Spec 

road

High 

bid

Appraised 

price

Region 2 81.47 45.24 4.48 2.95 2.32 13.68 11.15

Region 4 31.91 50.42 3.28 3.64 12.37 18.66 12.32

Average 58.98 47.59 3.93 3.26 6.88 15.94 11.68

Table 8—Average timber product mix for 

Regions 2 and 4

Sawtimber

(%)

Nonsawtimber

(%)

Fuelwood

(%)

Region 2 89.47 10.52 0.0007

Region 4 25.17 48.92 25.90

Average 60.30 27.94 11.75

Table 9—Average individual sales costs for 

Region 5 Coast and Sierra zones

Cost center Cost ($/CCF)

Sale PAL Fire Protect 8.62

Sale stump to truck 94.87

Sale Haul Cost 50.35

Sale Specific Cost 4.84

Sale Slash Cost 6.90

Sale Erosion Control 5.25

Sale Specified Road Cost 6.31

Sale Road Maintenance 12.66

Sale BD Deposit 3.64

Sale Surface Replacement Deposit 4.27

Sale Road Maintenance Deposit 2.67

Sale Engineering Deposit 0.66

Sale Temp Rd 2.15
General Technical Report FPL–GTR–242

18

thinning would increase logging costs by nearly $15/CCF 

(expressed in 2014 dollars) which should reduce bid prices 

(stumpage prices) by a corresponding amount.

Given that BPP reflects an average, not correcting for higher 

logging costs associated with some sales could lead to appraised prices higher than bidders would be willing to pay 

for that sale.

Figure 9—Region 5 (a) Coast zone and (b) Sierra zone BPP, BPP adjusted, and high bid price for sales. 

(BPP from mark-up equations and Table 6 applied to appraisal group volumes from USDA Forest Service 

Region 5 (2014); high bid from USDA Forest Service Region 5 (2014).)

Table 10—Proportion of volume sold in fiscal year 2014 

by appraisal group in the Southeast and Mid-South 

appraisal zones

Appraisal zone

Softwood (%) Hardwood (%)

Sawtimber

Nonsawtimber SawtimberNonsawtimber

Southeast 41 40 9 11

Mid-South 68 28 2 3

Base period price

High bid

Base period price, 

adjusted for 

average costs

Base period price

High bid

Base period price, 

adjusted for 

average costs

2007 2008 2013 2014

$/CCF

–60

–20

–40

0

20

40

60

80

100

120

2008 2014

$/CCF

–60

–20

–20

20

0

60

40

80

100

120

140

160

180

200

2007 2013 2009 2012

(a)

(b)
A Process to Establish and Use Base Period Prices for National Forest System Transaction Evidence Timber Appraisal

19

Forest Service Region 8. Region 8 is divided into two 

proposed appraisal zones, the Mid-South and the Southeast. 

Region 8 provided 100 FY2014 sales, and the results for 

the two appraisal zones are shown in Figure 11. There is a 

difference between the sales in the two appraisal zones in 

terms of species and product groupings (Table 10). Region 8 

did not assign any timber to the fuelwood product group.

In spite of the Mid-South having a greater proportion of 

softwood sawtimber sales, average BPP values are 13% 

higher in the southeast because of the high proportion of relatively higher value hardwood sawtimber in the southeast.

Both figures illustrate the tendency of BPP values to exceed 

both high bid and advertised prices in a majority of cases. 

Only 37% of the BPP values fell below high bid prices in 

these two examples. Two points should be considered: The 

first is minor, but this data set included sales awarded in 

FY2014 and some sales may have been appraised in 2013 

using lower product prices. Second, this points out the need 

to consider various cost adjustments. For example, when 

average costs and bid premium are subtracted from BPP values, they are less than high bid 99% of the time.

Forest Service Region 9. Results for the three appraisal 

zones in Region 9 are shown in Figure 12 (a–c) for 161 

FY2014 timber sales. Region 9 is divided among three 

appraisal zones: Mid-South (Mark Twain NF), Lake States 

(6NF), and Northeast (7NF). There were 26 sales in the 

Mid-South appraisal zone, 103 sales in the Lake States 

appraisal zone, and 32 sales in the Northeast appraisal zone. 

The BPPs for eastern regions are computed as a volumeweighted average using the general relation shown in 

Equation (3).

The BPP values vary among the three appraisal zones largely because of differences in the mix of species and products 

among the zones. These differences are shown in Table 11 

and largely account for range in BPP values that average 

$61.95, $42.61, and $145.11/CCF for the Mid-South, Lake 

States, and Northeast national forests, respectively. In these 

first estimates, BPP values exceed actual high bids 38% of 

Figure 10—Region 6 West side BPP, adjusted BPP, and high bid price for sales. (BPP from mark-up equations and 

Table 6 applied to appraisal group volumes from USDA Forest Service Region 6 (2014); adjusted BPP is BPP adjusted 

by each sales difference in costs from the average costs, USDA Forest Service Region 6 (2014); high bid from USDA 

Forest Service Region 6 (2014).)

Table 11—Volume proportions of Region 9 sales by product group and appraisal zone

Hardwood (%)

Total 

(%)

Softwood (%) Sawtimber

Sawtimber Nonsawtimber Fuelwood Low High Nonsawtimber Fuelwood

Mid-South 10 14 0 49 17 0 9 100

Lake States 12 20 0 4 4 60 0 100

Northeast 2 4 0 1 48 45 0 100

Region 9 10 16 0 25 47 2 100

2013 2014

$/CCF

–60

–40

–20

0

20

40

60

80

100

120

140

160

180

200

Base period price

High bid

Base period price, 

adjusted for 

average costs
General Technical Report FPL–GTR–242

20

board roads, mats). Some species, such as oak, are used both 

for appearance and industrial products, complicating this 

differentiation. This is important because the proportion of 

lumber values passed on to stumpage prices is the highest 

for hardwood lumber. The lumber price being used is based 

on higher value uses and will raise the appraised price for 

hardwood sawtimber including lower value species such as 

Aspen (Populus tremuloides).

This raises the question of how to adjust for these lowvalue species. In 2014, the hardwood lumber price averaged 

Figure 11—Region 8 (a) Southeast appraisal zone and (b) Mid-South appraisal zone BPP, advertised price, and 

actual high bid price for FY2014 sales. (BPP from mark-up equations and Table 6 applied to appraisal group 

volumes from USDA Forest Service Region 8 (2014); appraised price and high bid from USDA Forest Service 

Region 8 (2014).)

1 2 3 4

$/CCF

0

20

40

60

80

100

120

Quarter

1 2 3 4

$/CCF

0

20

40

60

80

100

120

Quarter

(b)

(a)

Base period price

High bid

Advertised price

Base period price

High bid

Advertised price

the time. One caution to note is that BPP values for all three 

zones share what is probably an overestimate of the value of 

fuelwood at $6.25/CCF. A more important factor illustrated 

here is how “low-value” hardwoods are treated. In terms 

of applications, hardwood species are differentiated into 

primary and secondary species, with primary species being 

those used for some type of appearance application, such as 

cabinet and furniture parts. Here we refer to these species 

as high-value species. Secondary species (or low-value species) are those used for industrial products (pallets, ties, and 
A Process to Establish and Use Base Period Prices for National Forest System Transaction Evidence Timber Appraisal

21

$836.31/MBF and the average price for aspen lumber was 

$348.33/MBF, a ratio of 0.417. Using this proportion and 

the information from Table 11, we modify Equation (3) and 

recompute BPP:

(5)

where y1a and y1b are the low and high sawtimber proportions from Table 11 and the parameter L is the ratio of lowto high-value hardwood species appropriate to the region/

appraisal zone. 

Making this adjustment for the Region 9 sales in the MidSouth and Lake States appraisal zones is shown in Figure 

12d. Average BPP values are reduced 36% for sales in the 

Mid-South and 4% for sales in the Lake States appraisal 

zones. This adjustment increases the number of sales where 

BPP values are less than the high bid, but they still exceed 

high bid 35% of the time. In some ways, Table 11 explains 

the reasons for the larger impacts in the Mid-South sales. 

In the Mid-South, more sawtimber is coded as low-value 

hardwoods (species code 4; hardwood other), whereas in the 

Lake States, aspen is frequently assigned to the nonsawtimber product group.

Finally, the Region 9 cost adjustment process differs from 

the west. In Region 9, felling/bucking and skidding/loading adjustments are not compared against a recent average. 

Instead, regional guidance recommends that adjustments 

be made when these costs fall outside a normal range. Two 

other costs vary notably among sales—haul costs and road 

maintenance costs. For the sales in FY2014, these averaged 

$23.54/CCF and $5.91/CCF, respectively.

The lack of cost adjustments and the somewhat higher BPP 

values compared to high bid values in Figures 12 (a–c) suggest that a rollback factor might be an appropriate way to 

adjust BPP and to increase the likelihood that sales will be 

sold.

Adjustments for bid premium. The BPP reflects both average costs incurred by bidders and average bid premium 

(difference between the advertised rate and high bid price). 

The excess amount bid on Forest Service sales can be large 

depending on the extent of competition, sale attributes, and 

economic conditions at the time of sale (Table 12). Some 

agencies, such as Oregon Department of Forestry, have bid 

premium targets as a measure of their appraisal system’s 

performance. They attempt to keep appraised prices within a 

set ratio with final bid prices.

Adjusting BPP for the average bid premium increases the 

number of times it is less than the high bid price. For example, in both Regions 3 and 6, a downward adjustment of 

BPP for average bid premium about doubled the number of 

sales where the BPP is less than the high bid price.

w C P w C P w F y aD P y bD LP y D P y3F 2 wp 2 2

p

h

1

1 2

p

h

1

3 1 2

2 wp 2 2 p

s 1 BPP = 1 2 + + + + + +

C P w F y aD P y bD LP y D P y3F 2 wp 2 2

p

h

1

1 2

p

h

1

3 1 2

2 wp 2 2 

+ + + + +

One inference of these significant bid premiums is the need 

to adjust for them either directly or as part of any rollback 

feature of any revised appraisal process.

Lessons from these examples. First, without any adjustments, BPPs exceeded high bids for 72% of the sales nationally. Making corrections for local cost variation can improve 

performance, but a need for rollback factors is still likely to 

adjust the BPP prior to or after these adjustments. Second, 

there are examples of sales with higher than average nonsawtimber volumes where use of BPP would be higher than 

use of Minimum Rates. This was demonstrated in multiple 

regions and suggests more consideration if base rates are 

set too low Our estimates of BPP might be improved (lower 

rmse) if western chip prices are used as predicting variables. 

Third, cost adjustments are necessary and reflect how bidders differentiate among sales characteristics and the heterogeneity of the timber sales. The example from Region 3 using haul cost differences to adjust BPP illustrates this point. 

Sales with lower haul costs (sales that are closer to mills) 

often had higher bid prices.

The example from Region 9, where we adjusted for lowand high-value hardwood species, demonstrates both the 

ability to do this in a meaningful way and the difficulty of 

using the somewhat arbitrary species code assignments. If 

this is to be pursued, greater consistency will be needed in 

the use of various catch-all species codes, such as Softwood, 

Hardwood other (species codes 1 and 4), and combined 

Softwoods and Hardwoods (species codes 7 and 8). There 

will also need to be a general understanding of the definitions of high- and low-value hardwood sawtimber.

Although not tested as part of this study, use of higher 

sale-specific local costs that reduce the advertised price 

(the starting point in bidding) may reduce high bids overall as bidders tend to cluster about perceived levels of bid 

premium for competitive sales. (These local costs require 

justification so that there is not the perception that they are 

being used to manipulate an appraisal in order to achieve 

Table 12—Average bid premium and ratio of high bid to 

appraised price by Region for individual sales data

Region Appraisal zone

Average bid 

premium 

($/CCF)

Ratio of average 

high bid to 

appraised price

1 North Inland 26.44 1.88

2 Central Inland 2.53 1.23

3 South Inland 3.99 2.30

4 Central Inland 6.34 1.51

5 Sierra 11.68 1.58

6 Coast 30.62 1.94

8 Southeast, Mid-South 9.63 1.28

9 Central Inland, Lake 

States, Northeast

31.88 1.70
General Technical Report FPL–GTR–242

22

a predetermined price that may not reflect a fair market 

value.) For example, in Region 6, average bid premium is 

94% of the advertised price but bidders may collectively 

consider sales that have bid premiums in excess of 50% 

to be “highly” competitive, regardless of the starting point 

(minimum acceptable bid price); once sales price reaches 

50% over the minimum acceptable bid, bidding may slow 

except in special cases.

Escalation of Timber Sales Prices 

after the Sale

After a timber sale is sold, many months may go by before 

the timber is harvested. The value of the timber at harvest 

for each appraisal group (and the payment due for the timber) can be calculated by applying the percentage change 

in producer price indexes (from the U.S. Bureau of Labor 

Statistics) that drives the price for each stumpage appraisal 

group. The percentage increase or decrease in value of 

Figure 12—Region 9 (a) Mid-South, (b) Lake States, and (c) Northeast BPP, sale advertised price, and high bid 

price for sales. (d) Mid-South and Lake States BPP and BPP adjusted for low- and high-value hardwood sawtimber 

(BPP2). (BPP from mark-up equations and Table 6 applied to appraisal group volumes from USDA Forest Service 

Region 9 (2014); appraised price and high bid from USDA Forest Service Region 9 (2014).)

Base period price

High bid

Advertised price

1 2 3 4

$/CCF

0

20

40

60

80

100

120

140

160

180

Quarter

Base period price

High bid

Advertised price

1 2 3 4

$/CCF

0

20

40

60

80

100

120

140

160

180

Quarter

(b)

(a)
A Process to Establish and Use Base Period Prices for National Forest System Transaction Evidence Timber Appraisal

23

timber in each appraisal group would be determined by 

the percentage increase or decrease in the primary product 

producer price index between the month of the sale and the 

month of the harvest.

The basis for using changes in producer price indexes is the 

same as the basis for using changes in product prices to predict BPP. Economic theory and empirical evidence indicate 

that as product prices increase, the ability of mills to pay 

higher prices for stumpage increases as well. Conversely, 

as product prices decrease, the ability of mills to pay for 

stumpage decreases.

Three producer price indexes (not seasonally adjusted) 

could be used to increase price for sawtimber and nonsawtimber. To adjust softwood and hardwood sawtimber prices 

in each appraisal zone, these are the national level softwood 

and hardwood lumber producer price indexes, WPU0811 

and WPU0812, respectively (USBLS 2015a). For nonsawtimber in the West, the softwood lumber price index would 

Figure 12 (con.)—Region 9 (a) Mid-South, (b) Lake States, and (c) Northeast BPP, sale advertised price, and high bid 

price for sales. (d) Mid-South and Lake States BPP and BPP adjusted for low- and high-value hardwood sawtimber 

(BPP2). (BPP from mark-up equations and Table 6 applied to appraisal group volumes from USDA Forest Service 

Region 9 (2014); appraised price and high bid from USDA Forest Service Region 9 (2014).)

1 2 3 4

$/CCF

0

100

50

150

250

350

450

200

300

400

500

Quarter

$/CCF

0

10

20

30

40

50

60

70

80

90

Individual 2014 sales

Mid-South

Base period price

 Base period price 

 (adjusted)

Lake States

Base period price

Base period price 

(adjusted)

(c)

(d)

Base period price

High bid

Advertised price
General Technical Report FPL–GTR–242

24

be used. For softwood and hardwood nonsawtimber in the 

East, the wood pulp producer price index, WPU0911, could 

be used.

The Bureau of Labor Statistics provides guidance on how 

to include language in contracts that would escalate prices 

from a base month to a final sales month (USBLS 2015b). 

The producer price indexes for a particular month are typically published on the BLS web site by the 10th to 18th 

of the following month. All producer prices indexes that 

are not seasonally adjusted are routinely subject to revision once, four months after original publication (USBLS 

2015b). The BLS recommends that escalation adjustments 

always use the latest version of the PPI data available at the 

time escalation calculations are made.

Discussion

Here we discuss several inferences for implementation. 

These include two adjustment issues in timber appraisal that 

are frequently made to reduce the likelihood of no bid sales; 

some thoughts on the question “Does the Forest Service receive a fair price for timber it sells?”; guidance for the Forest Service price adjustment mechanism after a sale is made; 

and finally some thoughts about how to implement the approach described here.

Adjustment Issues

Local cost adjustments. The examples illustrate the 

importance of cost center adjustments to BPPs and the 

variety of approaches to these adjustments. Many of the 

examples illustrate that adjusting for sale-specific local costs 

are a necessary and important step to reduce the likelihood 

of no bid sales. Adjustments for logging and hauling costs 

are the largest; as illustrated in Region 6, adjustments for 

different logging systems may be important, especially 

considering the increased costs associated with thinning 

sales. Adjusting for haul costs is important especially in 

areas with limited processing facilities. The example from 

Region 3 is such a case. It also provides an empirical 

example of how transportation costs can impact market 

prices in a way that is consistent with our understanding of 

spatial economics.

Implications for rollback factors. In the original discussion 

of adoption of TEA systems, rollback factors were seen 

as a way to reduce the likelihood of no bid sales. No bid 

sales were and still are seen as being unnecessarily time 

consuming.

Below is an equation we suggest as a framework for discussing the components that determine a final high bid price 

for an individual sale in the context of many sales in an 

appraisal zone over a historic time period. We use this equation to suggest how to estimate a rollback factor for a current sale (after the historic period) given historic sales data.

We discuss a simplified situation where sales have an Indicated Advertised Rate (the rate before comparison to Base 

Rates) that is the same as the Advertised Rate. We represent 

these as each having timber in a single appraisal group. The 

discussion would be similar if sales had timber in multiple 

appraisal groups and Advertised Rate differed from Indicated Advertised Rate.

Over a number of quarters, t = 1 to n, in a given appraisal 

zone, a number of timber sales will provide sale prices for 

timber in various appraisal groups. We may view those sales 

prices as being composed of several components:

(6)

where HBPt,i is the high bid price per unit volume for sale 

i that occurs in quarter t; c2Pt

p

 is the BPP per unit volume 

calculated by multiplying the primary product price (such 

as softwood lumber) by coefficient c2, which was computed by a mark-up equation over the periods t = 1 to n; 

(C − Ci) is the volume-weighted average costs for all sales 

over quarters t = 1 to n minus the total cost for sale i; and 

(BPMi − BPM) is bid premium for sale i minus the volumeweighted average bid premium per unit volume for all sales 

over the quarters t = 1 to n.

Equation (6) can be used in two ways. First, it can be used 

to retrospectively explain the components that make up high 

bid price for a set of sales over a historic period; and second, 

it can be used prospectively for a sale in a quarter soon after 

the historic period (such as for four quarters after) to discuss 

how to estimate a roll back factor based on historic data for 

(BPMi − BPM).

Retrospectively, Equation (6) gives components that explain 

high bid price for sales over a historic period. These include 

the first two terms that would be used to compute advertised 

rate—before any rollback—for a given new sale i during 

the historic period. The first term is a computed BPP, c2Pt

p

. 

For this retrospective evaluation (and prospective use of 

the equation is discussed later), we assume that the c2 coefficient is estimated using sales data over the historic period. 

This BPP already includes an adjustment for average costs 

and an adjustment for average bid premium. For an individual sale i in the historic period, we need the second and third 

terms to adjust for sale i specific costs and bid premium, 

(C − Ci) + (BPMi − BPM).

The bid premium adjustment will only be known retrospectively for a group of sales. Note that the bid premium values 

used are the difference between an advertised rate (without 

any rollback), given by the first two terms, and the high bid. 

The bid premium we compute is not exactly the one a bidder would have computed because they could have different 

estimates of a BPP and cost adjustments. The bid premium 

adjustment term (BPMi − BPM) includes any errors we 

made in the first two terms in estimating a bidder’s estimate 

of market value and cost adjustments.

HBPt,i = c2Pt

p

 + (C − Ci) + (BPMi − BPM)
A Process to Establish and Use Base Period Prices for National Forest System Transaction Evidence Timber Appraisal

25

Based on the sales that occurred over quarters t = 1 to n, our 

bid premium correction factor will have a probability distribution that will range from negative to positive values.

Now consider that we would like to estimate an Advertised 

Rate, including a rollback factor, for a sale j that occurs in 

a quarter m somewhat later than the historic period (such as 

up to four quarters later).

We would use the first two terms of Equation (6) to estimate 

an Advertised Rate (before any rollback) for sale j. We assume this estimate would include a historic average amount 

of bid premium. If we assume that the probability distribution in the new current quarter for (BPMi − BPM) is the 

same as for the historic period and that the distribution is 

symmetric around the average bid premium, then there is a 

50% chance that the bid premium for sale j could be higher 

or lower than the average.

The historic distribution for the bid premium correction factors can be used directly to help select a rollback factor. For 

any given rollback factor, we can estimate the probability 

that the final bid will exceed the Advertised Rate minus a 

given rollback factor.

For example, with no rollback factor and based on our assumptions, we would estimate a 50% chance that the final 

bid will be higher than the Indicated Bid Price estimate. If 

we take a rollback factor that is equal to one standard deviation of the bid premium correction factor (and the bid premium correction factor is normally distributed), then there 

will be an 83% chance that the final bid will be higher.

The distribution of bid premium correction factors can be 

estimated using historic sales data and used to determine the 

probability that a given rollback adjustment will result in a 

high bid that exceeds the Advertised Rate.

Does the Forest Service Get a Fair Price?

Given that predicted BPPs (without individual sales cost 

adjustments) exceeded high bids by roughly 70% of the 

378 timber sales in our regional examples, it is reasonable 

to ask if the initial starting points (BPPs) were a fair price. 

Given the assumed values for nonsawtimber and fuelwood, 

these are reasonable starting points. The BPP values indicate 

the values actually paid on average for timber sales. They 

include the effect of some sales with large bid premiums. 

That there are such large bid premiums on individual sales 

that are competitive is reflective of the diverse bidders with 

different costs structures and product utilization abilities or 

factors that TEA may not be sensitive to, such as restrictions 

on when a purchaser can operate on a specific sale.

The final high bids reflected in the BPP values will be the 

truest measure of average fair market stumpage prices available. They represent what an actual purchaser is willing to 

pay given their expectations for product recovery and their 

cost management. At the same time, the BPP is the point of 

central tendency in a non-normal distribution of a very heterogeneous product called a timber sale.

There are opportunities to reduce bid premiums, especially 

in the appraisal of nonsawtimber volumes. Figure 4 illustrates the way that the small sawtimber (generally less than 

11 in. (28 cm)) market is differentiating itself and offering 

higher market values than the base rates commonly used. 

In the South, chip and saw technologies are widespread 

and create utilization opportunities that maximize returns 

for logs down to 4 in. (10 cm) small-end diameter. Similar 

technologies are evolving in the west where there are higher 

value market opportunities (including export markets) for 

clean mill residues. These opportunities in timber appraisals 

would not necessarily increase bid prices because bidders 

are already making assumptions that exceed those embedded in the current appraisal process. These types of assumptions are factors that can lead to higher than expected bid 

premiums on some sales.

Part of the motivation for adopting the TEA appraisal 

process in the mid-1980s was as an attempt to reduce bid 

premiums, which at the time were perceived to be too large. 

Recent experience shows that this goal is not currently being 

achieved with the present TEA appraisals. Although some of 

the blame may be attributed to a backward-looking system 

to track market volatility, other causes of high bid premiums include local supply issues, underestimating utilization 

abilities of potential high bidders (for material tagged as 

nonsawtimber or fuelwood), and excessive use of local cost 

reductions that reduce appraised prices relative to market 

prices.

Implementation Guidance

Forest Service managers could decide to use price mark-up 

equations to estimate BPPs for the new appraisal groups 

while maintaining the existing appraisal zones and the existing cost centers used to make local adjustments. Alternatively, they could use the new BPPs for some or all of the 

appraisal zones and/or a set of cost centers that are more 

standardized across appraisal zones. One strength of the 

method to estimate BPP is its simple, transparent approach 

that is also conceptually robust in that it is based on wellestablished theory on the relation between product and 

stumpage markets. Alternative appraisal zones suggested 

match broad lumber market indexes, such as the Coast region or the Inland north region. That is, bidders for National 

Forest timber in these areas are competing in similar markets. High bid prices for individual sales should primarily be 

due to differences in unique sale characteristics (species and 

grade composition, sale length, and size) and costs (such as 

logging costs, haul distances).

The mark-up equations needed to estimate BPPs would be 

updated periodically (such as quarterly or biannually) by 

Forest Service staff using new data on average quarterly 

prices by appraisal zone and prices or price indices for primary products.
General Technical Report FPL–GTR–242

26

The mark-up equations for each appraisal group in an appraisal zone will be estimated using quarterly average 

stumpage sales values for a certain number of most recent 

quarters. For our equations we used 23 quarters. The estimated average price (BPP) for a sale in the current quarter 

will implicitly include the average local costs for the sales 

that occurred over those recent quarters. They will also include an average bid premium over those quarters.

The local cost factors used to adjust the BPP (Cji values in 

Eq. (1)) must be the deviations from the weighted average 

local costs over the same set of quarters where sales prices 

are used to estimate mark-up equations. Forest Service mangers will need to decide how many quarters of data are to be 

used to estimate both the mark-up equations and the average 

local costs. Because average local costs would be estimated 

by weighting by volumes sold, the mark-up equations would 

need to be estimated using volume-weighted sales data. To 

maintain the integrity of these assumptions, we assume that 

the BPP would be implemented with National/Regional 

guidance at the National Forest level. Each National Forest 

would use the BPP from the appraisal zone it is assigned to. 

It would also use the cost adjustments from average costs 

that are appropriate for their appraisal zone. If adjustments 

are to be made for low- and high-value species, there will 

need to be a more consistent use of species codes across 

National Forests.

If the new appraisal zones are used, there will need to be an 

effort to reorganize information on sales by the new zones 

that are now grouped by areas within Regions. For the new 

appraisal zone arrangement, most Forest Service Regions 

are parts of several appraisal zones, so this restructuring 

would involve collaboration across Regions but should lead 

to greater transparency for bidders who operate across Forest Service Regions.

Acknowledgments

We acknowledge the assistance of many people: Beth 

Boddiger and Sara Qian of the Forest Service helped develop summaries of the Cut & Sold reports; Austin Rempel 

(ECONorthwest) converted and summarized the Cut & 

Sold reports as EXCEL worksheets; Jeff Prestemon (SRS) 

reviewed the initial Study Plan and provided some southern 

lumber price series for comparisons; Xiaoping Zhou (PNW) 

provided various data series for western lumber species; 

James Howard (FPL) provided background material on the 

price series published at FPL; Bill Luppold (NRS) provided 

a hardwood lumber price set he developed and maintains 

as well as insights about northern hardwood lumber markets; Steve Verrill (FPL) provided statistical assistance in 

preparing Table 6 and guidance on how to interpret results; 

and Mike Niccolucci, Matt Etzenhouser, Paul Fink, Tim 

Howard, Mike Daugherty, Larry Mahler, Ricky Rodriguez, 

and Charlotte Bofinger (all of the Forest Service) helped 

assemble the regional sales data sets.

Literature Cited

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timber assessment market model: structure, projections, and 

policy simulations. Forest Science Monograph No. 22. 62 p.

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Forest Service, Pacific Northwest Research Station. 14 p.

Curtis, R.O.; Marshall, D.D.; DeBell, D.S., eds. 2004. Silvicultural options for young-growth Douglas-fir forests: the 

Capitol Forest study—establishment and first results. Gen. 

Tech. Rep. PNW-GTR-598. Portland, OR: U.S. Department 

of Agriculture, Forest Service, Pacific Northwest Research 

Station. 110 p.

George, P.S.; King, G.A. 1971. Consumer demand for food 

commodities in the United States with projections for 1980. 

Giannini Foundation Monograph # 26. Berkeley, CA: California Agricultural Experiment Station. 161 p. 

Hardwood Market Report. [Various years.] Memphis, TN: 

Hardwood Market Report.

Haynes, R.W. 1977. A derived demand approach to estimating the linkage between stumpage and lumber markets. Forest Science. 23(2):281–288.

Haynes, R.W. 1999. Export chip prices as a proxy for nonsawtimber prices in the Pacific Northwest. Res. Note. PNWRN-537. Portland, OR: U.S. Department of Agriculture, 

Forest Service, Pacific Northwest Research Station. 25 p.

Haynes, R.W. 2008. Emergent lessons from a century of 

experience with Pacific Northwest timber markets. Gen. 

Tech. Rep. PNW-GTR-747. Portland, OR: U.S. Department 

of Agriculture, Forest Service, Pacific Northwest Research 

Station. 45 p.

Howard, J.L.; Westby, R.M. 2013. U.S. timber production, 

trade, consumption and price statistics 1965-2011. Research 

Paper FPL-RP-676. Madison, WI: U.S. Department of Agriculture, Forest Service, Forest Products Laboratory. 91 p.

Luppold, W. 2015. Personal communication. Economist, 

USDA Forest Service, Northern Research Station, Princeton, WV.

Random Lengths. 2014. 2013 yearbook. Eugene, OR: 

Random Length Publications. 319 p.

Spelter, H. 2005. Review of alternative measures of softwood sawtimber prices in the United States. Res. Pap. FPLRP-629. Madison, WI: U.S. Department of Agriculture, Forest Service, Forest Products Laboratory. 16 p.

USBLS. 2015a. Databases, tables & calculators by subject. 

Washington, DC: U.S. Bureau of Labor Statistics. http://

www.bls.gov/data/. Accessed April 1, 2015.
A Process to Establish and Use Base Period Prices for National Forest System Transaction Evidence Timber Appraisal

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USBLS. 2015b. Escalation guide for contracting prices. 

Washington, DC: U.S. Bureau of Labor Statistics. http://

www.bls.gov/ppi/ppiescalation.htm. Accessed April 1, 2015.

USDA Forest Service. 2014. Timber sale accounting system 

of the US Forest Service.

USDA Forest Service, Region 1. 2014. Region 1 Timber 

sales database.

USDA Forest Service, Region 2. 2014. Region 2 Timber 

sales database.

USDA Forest Service, Region 3. 2014. Region 3 Timber 

sales database.

USDA Forest Service, Region 4. 2014. Region 4 Timber 

sales database.

USDA Forest Service, Region 5. 2014. Region 5 Timber 

sales database.

USDA Forest Service, Region 6. 2014. Region 6 Timber 

sales database.

USDA Forest Service, Region 8. 2014. Region 8 Timber 

sales database.

USDA Forest Service, Region 9. 2014. Region 9 Timber 

sales database.

WWPA. 2011. Statistical yearbook of the western lumber 

industry. Portland OR: Western Wood Products Association. 

26 p.

Wohlgenant, M.K. 2001. Marketing margins: empirical 

analysis. In: Gardner, B.; Rausser, G., eds. Handbook of 

agricultural economics, volume 1. Amsterdam: Elsevier Science B.V. 934–970. Chapter 16.

World Bank. 2015. World Bank data, global economic monitor commodities, woodpulp price, $/mt, current Statistisches Bundesamt, Germany; Allman Manadsstatistik, Sweden; 

Washington, DC: The World Bank Group. http://databank.

worldbank.org/data/views/variableselection/selectvariables.

aspx?source=Global-Economic-Monitor-(GEM)-Commodities. Accessed April 23, 2015.

Zhou, X. 2013. Production, prices, employment and trade in 

northwest forest industries, all quarters 2012. Resour. Bull. 

PNW-RB-265. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 

163 p.

Glossary

Advertised Rates—The minimum acceptable bid rates for 

timber (specific condition A/AT4) in the sale advertisement 

and bid form. They are the indicated advertised rates, as adjusted, so that all advertised rates are at least as high as base 

rates.

Appraisal Zone—A geographic area encompassing a Region, a National Forest, a group of Ranger Districts, or combinations thereof. The zone boundary is usually determined 

by similarities in haul distance, species composition, timber 

quality, market area competition, or logging methods.

Appraised Value—The estimated fair market value of 

standing timber that will be converted to commercial products, such as lumber or pulpwood.

Base Period—The time period used for identifying competitive sales for determining the Base Period Price.

Base Period Price—With the exceptions of Regions 1, 8, 

and 9 and Regions 5 and 6 biomass, the value representing 

the appraisal group’s volume-weighted average winning 

bid price of the competitively sold timber sales in the base 

period. For Region 1, it is the average of the winning bid 

price plus road maintenance costs, environmental costs, 

temporary development costs, and specified road costs and 

is called the “Actual Past Average Value.” For Regions 8 

and 9, it includes the application of a rollback factor. For 

Region 5 biomass, it is an estimated value. For Region 6 

biomass, it is the delivered log price converted to CCF. It 

reflects the average sale characteristics of the base period 

sales and is the starting point of a transaction evidence 

appraisal.

Base Rates—The lowest rates of payment for timber that 

are authorized by the contract (specific condition A/AT4). 

Base rates are not subject to change by rate redetermination, 

except for reduction under contract provisions B/BT3.31 

for environmental modification, B/BT3.32 for catastrophic 

damage, and B/BT3.33 for market change during suspension. Base rates are the higher of either (a) the applicable 

minimum rates (FSM 2431.31b) or (b) the Knutson–Vandenberg deposits for essential reforestation, plus timber 

property value, plus $0.25 per hundred cubic feet (CCF).

Bid Premium Rates—The amounts by which the purchaser 

bids in excess of the advertised rates (specific condition A/

AT4). Bid premium rates remain constant during the term of 

the contract, except for reduction under contract provisions 

B/BT3.31 for environmental modification, B/BT3.32 for 

catastrophic damage, and B/BT3.33 for market change during suspension.

Bid Rates—The contract rates bid by the timber sale purchaser, exclusive of required deposits (specific condition 

A/AT4). These rates may be subject to escalation (FSM 

2431.34 and contract provision B/BT3.2).
General Technical Report FPL–GTR–242

28

Comparative Rates—Determination of stumpage value by 

comparison with stumpage rates received by comparative 

bids on similar sales (sec. 44.5).

Competition Adjustment—See Rollback Factor.

Cost Adjustment—Adjustments to the Base Period Price 

to reflect operating cost differences between the base period 

sales and the sale being appraised. These adjustments are for 

differences in logging cost, specified road construction cost, 

haul cost, and other costs that significantly affect value differences between timber sales.

Deficit Appraisal—An appraisal where the average indicated advertised rate is less than the average base rate.

Fair Market Value—The value at which property (timber) 

would change hands between a willing and knowledgeable 

seller and a willing and knowledgeable buyer, neither under compulsion to sell or buy and both having reasonable 

knowledge of the relevant facts. In addition to type, quality, 

and quantity of timber, fair market value reflects the time of 

sale, the highest and most profitable use, the location, a reasonable time to find a purchaser, and an open and competitive market. It can be viewed as an estimate of market value 

that reflects the price an operator of average efficiency who 

is able to pay that price and retain sufficient profit to maintain long-run operations is willing to pay.

Fuelwood—Wood used by conversion to some form of 

energy.

Indicated Advertised Rates—The estimated value of the 

timber after accounting for all differences between the sale 

and the sales in the base period including timber property 

value, any market adjustments, and the rollback factor. If 

necessary, this value is adjusted to increase it to base rates.

Market Adjustment—An adjustment to the appraisal data 

so that it represents current market conditions.

Minimum Rates—The lowest rate at which the Forest 

Service may sell timber (FSM 2431.31b), except under contract provisions B/BT3.31 for environmental modification, 

B/BT3.32 for catastrophic damage, B/BT3.33 for market 

change during suspension, an administrative use sale or permit under 36 CFR 223.2, stewardship contracts sold under 

36 CFR part 223.1, incidental amounts of material not meeting utilization standards of the timber sale contract, or to 

provide for the removal of insect-infested, diseased, dead, or 

distressed timber (36 CFR 223.61).

Nonsawtimber—Logs of insufficient size or quality to be 

suitable for conversion to lumber.

PAL—Project Activity Level, used in Region 5 where it is 

an industrial operation’s fire precaution system. Its costs are 

assessed on a per CCF basis.

Predicted Bid Rate—With the exceptions of Regions 8 and 

9, the value after cost adjustment to the Base Period Price 

for differences in average sale characteristics, timber quality, risk, and market conditions. With the exception of Region 1, the predicted bid rate is adjusted downwards by the 

rollback factor to arrive at the indicated advertised rate. For 

Region 1, a Rapid Market Adjustment is considered following the rollback, prior to arriving at the indicated advertised 

rate. The indicated advertised rate will also include any 

Timber Property Value in the sale.

Public Works Road Construction Cost—The total cost of 

constructing all permanent roads specified in the timber sale 

contract, estimated as if construction is to be accomplished 

by an independent contractor. It includes the additional costs 

of a public works contract.

Pulp—In the context of this paper, the wood chips from 

which pulp is produced, usually derived from mill edgings 

and small timber.

Purchaser Credit—Credit earned by purchaser’s construction of specified roads. Purchaser credit was discontinued 

in contracts advertised after March 31, 1999, but may be a 

factor in redetermining rates or damage calculations for contracts advertised prior to then.

Quality Adjustment—An appraisal adjustment to reflect 

differences in timber quality between the base period sales 

and the sale being appraised.

Rate Redetermination—An adjustment of bid rates to reflect current appraised values. The rate redetermination may 

be necessary because of contract extension, contract modification, catastrophic damage, or other reasons specified in 

the timber sale contract.

Risk Adjustment—An appraisal adjustment to reflect 

unusual conditions not reflected in market, quality, or cost 

adjustments and that is tied to uncertainties surrounding the 

sale.

Road Construction Cost—The total cost of constructing all 

permanent roads specified in the timber sale contract, estimated as if construction is to be accomplished by the timber 

purchaser. The road construction cost is the cost amount 

used in timber appraisal calculations.

Rollback Factor—A downward adjustment applied to the 

volume-weighted average winning bid price of the competitively sold timber sales in the base period for Regions 8 and 

9; and to the predicted bid rate for all other Regions. For 

Regions 2, 3, and 4, the downward adjustment is determined 

by applying the factor to the Adjusted Base Period Price. 

For Regions 5 and 6, the adjustment is determined by applying the factor to the predicted bid rate itself. The rollback 

factor may also be referred to as the competition adjustment, 

competition amount, or competition factor. In the appraisal 

process, the rollback factor is intended to (a) set advertised 

rates at a level to capture sufficient competition for the establishment of an actual fair market value, (b) compensate 
A Process to Establish and Use Base Period Prices for National Forest System Transaction Evidence Timber Appraisal

29

for possible changes in the market between the base period 

and the bid date, (c) reflect differences in sale conditions 

that may not have been reflected in the appraisal, (d) allow 

for the risk that timber volumes, sale characteristics, or values may vary from those used in the appraisal, and (e) allow 

for the risk that markets or other economic conditions may 

change during the period of the contract.

Sawtimber—Logs of sufficient size and quality as to be 

suitable for conversion to lumber.

Specified Roads—Roads planned for future recurrent uses 

for which the timber sale contract specifies location, standards, and construction specifications.

Standard Rates—The lowest rates at which the Forest Service may sell timber without a supporting appraisal calculation (FSM 2431.31a).

Stumpage Value—The value of uncut timber.

Timber Property Value—The cost of manufacture from 

the standing tree to the state of manufacture where the sale 

is made, which is usually in decks. This would include the 

constructed value of felling and bucking, skidding, decking, slash treatment, erosion prevention, and a proportionate 

share of overhead and depreciation. In trespass cases where 

the timber is found at a mill, add transportation value and 

an estimated value for loading and unloading. This cost is 

added to the stumpage value of the standing tree (sec. 47.3).

Transaction Evidence Appraisal (TEA)—An appraisal 

method designed to estimate fair market value of timber 

based on bid rates of past timber sale transactions. The base 

data is compiled from sales sold competitively over a period 

of time known as the base period.

Unusual Adjustment—An adjustment to the appraisal data 

to reflect known unusual conditions, not reflected in other 

market, quality, risk, or cost adjustments.
General Technical Report FPL–GTR–242

30

Appendix 1—Assignment of National Forests to Appraisal Zones

Region

Appraisal 

zone

Forest 

number Forest name

5 1 5 Klamath NF

5 1 8 Mendocino NF

5 1 10 Six Rivers NF

5 1 14 Shasta–Trinity NF: Shasta NF, Trinity NF

6 1 3 Gifford Pinchot NF

6 1 6 Mount Hood NF

6 1 5 Mt. Baker–Snoqualmie NF: Mt. Baker NF, Snoqualmie NF

6 1 9 Olympic NF

6 1 10 Rogue River–Siskiyou NF: Rogue River NF, Siskiyou NF

6 1 12 Siuslaw NF

6 1 15 Umpqua NF

6 1 18 Willamette NF

1 2 2 Beaverhead–Deerlodge NF: Beaverhead NF, Deerlodge NF

1 2 3 Bitterroot NF

1 2 4 Idaho Panhandle NFs: Couer d’Alene NF, St. Joe NF, Kaniksu NF

1 2 5 Clearwater NF

1 2 8 Custer NF

1 2 10 Flathead NF

1 2 11 Gallatin NF

1 2 12 Helena-Lewis and Clark NF: Helena NF, Lewis and Clark NF

1 2 14 Kootenai NF

1 2 16 Lolo NF

1 2 17 Nez Perce NF

4 2 2 Boise NF

4 2 12 Payette NF

4 2 13 Salmon–Challis NF: Salmon NF, Challis NF

6 2 1 Deschutes NF

6 2 2 Fremont–Winema NF: Fremont NF, Winema NF

6 2 4 Malheur NF

6 2 7 Ochoco NF

6 2 14 Umatilla NF

6 2 16 Wallowa–Whitman NF: Willowa NF, Whitman NF

6 2 17 Okanogan–Wenatchee NF: Okanogan NF, Wenatchee NF

6 2 21 Colville NF

2 3 2 Bighorn NF

2 3 3 Black Hills NF

2 3 4 Grand Mesa, Uncompahgre, and Gunnison NFs: Grand Mesa NF, Uncompahgre NF, Gunnison NF

2 3 6 Medicine Bow–Routt NFs and Thunder Basin National Grassland: Medicine Bow NF, Routt NF, 

Thunder Basin National Grassland

2 3 7 Nebraska NF and Grasslands: Samuel R. McKelvie NF, Buffalo Gap, Fort Pierre, and Oglala 

National Grasslands, Nebraska NF

2 3 9 Rio Grande NF 
A Process to Establish and Use Base Period Prices for National Forest System Transaction Evidence Timber Appraisal

31

Region

Appraisal 

zone

Forest 

number Forest name

2 3 10 Arapaho & Roosevelt NFs, Pawnee National Grassland: Arapaho NF, Roosevelt NF, Pawnee 

National Grassland

2 3 12 Pike and San Isabel NFs, Cimarron and Comanche National Grasslands: Pike NF, San Isabel NF, 

Cimarron National Grassland, Comanche National Grassland

2 3 13 San Juan NF

2 3 14 Shoshone NF

2 3 15 White River NF

4 3 1 Ashley NF

4 3 3 Bridger–Teton NF: Bridger NF, Teton NF

4 3 7 Dixie NF

4 3 8 Fishlake NF

4 3 10 Manti–LaSal NF 

4 3 14 Sawtooth NF

4 3 15 Caribou–Targhee NF: Caribou NF, Targhee NF

4 3 17 Humboldt–Toiyabe NF: Humboldt NF, Toiyabe NF

4 3 19 Uinta–Wasatch–Cache NF: Uinta NF, Wasatch NF, Cache NF

3 4 1 Apache–Sitgreaves NFs: Apache NF, Sitgreaves NF

3 4 2 Carson NF

3 4 3 Cibola NF

3 4 4 Coconino NF

3 4 5 Coronado NF

3 4 6 Gila NF

3 4 7 Kaibab NF

3 4 8 Lincoln NF

3 4 9 Prescott NF

3 4 10 Santa Fe NF

3 4 12 Tonto NF

5 4 1 Angeles NF

5 4 2 Cleveland NF

5 4 7 Los Padres NF

5 4 12 San Bernardino NF

5 5 3 Eldorado NF

5 5 4 Inyo NF

5 5 6 Lassen NF

5 5 9 Modoc NF

5 5 11 Plumas NF

5 5 13 Sequoia NF

5 5 15 Sierra NF

5 5 16 Stanislaus NF

5 5 17 Tahoe NF

5 5 19 Lake Tahoe Basin Management Unit

8 6 1 National Forests in Alabama: William B. Bankhead NF, Talladega NF, Tuskegee NF, Conecuh NF

8 6 2 Daniel Boone NF

8 6 3 Chattahoochee–Oconee NFs: Chattahoochee NF, Oconee NF
General Technical Report FPL–GTR–242

32

Region

Appraisal 

zone

Forest 

number Forest name

8 6 4 Cherokee NF

8 6 5 National Forests in Florida: Apalachicola NF, Osceola NF, Ocala NF

8 6 6 Kisatchie NF

8 6 7 National Forests in Mississippi: Bienville NF, Delta NF, De Soto NF, Holly Springs NF, 

Homochitto NF, Tombigbee NF

8 6 8 George Washington and Jefferson NFs: George Washington NF, Jefferson NF 

8 6 11 National Forests in North Carolina: Nantahala NF, Pisgah NF, Uwharrie NF, Croatan NF 

8 6 12 Francis Marion and Sumter NFs: Francis Marion NF, Sumter NF 

8 6 60 Land Between the Lakes National Recreation Area

8 7 9 Ouachita NF

8 7 10 Ozark–St. Francis NFs: Ozark NF, St. Francis NF

8 7 13 National Forests and Grasslands in Texas: Angelina NF, Davy Crockett NF, Sabine NF, Sam 

Houston NF, Caddo–Lyndon B. Johnson National Grasslands

9 7 5 Mark Twain NF

9 8 3 Chippewa NF

9 8 4 Huron–Manistee NFs: Huron NF, Manistee NF

9 8 7 Ottawa NF

9 8 9 Superior NF

9 8 10 Hiawatha NF

9 8 13 Chequamegon–Nicolet NF: Chequamegon NF, Nicolet NF

9 9 8 Shawnee NF

9 9 12 Hoosier NF

9 9 14 Wayne NF

9 9 19 Allegheny NF

9 9 21 Monongahela NF

9 9 20 Green Mountain & Finger Lakes NFs: Green Mountain NF, Finger Lakes NF

9 9 22 White Mountain NF
A Process to Establish and Use Base Period Prices for National Forest System Transaction Evidence Timber Appraisal

33

REGION 1

Northern

REGION 8

Southern

REGION 9

Eastern

REGION 6

Pacific

Northwest

LEGEND

USFS Administrative Regions

USFS Forests 0 50 100 200 300 400 500

Miles

0 80 160 320 480 640 800

Kilometers

REGION 4

Intermountain

REGION 3

Southwestern

REGION 2

Rocky Mountain REGION 5

Pacific Southwest

Forest Service Administrative Regions and proposed appraisal zones.

1 2

3

4

5

6

7

8

9

0 50 100 200 300 400 500

Miles

0 80 160 320 480 640 800

Kilometers

LEGEND

Proposed Appraisal Areas

USFS Forests
General Technical Report FPL–GTR–242

34

Appendix 2—Product Codes and Product Names

01 SAWTIMBER

02 PULPWOOD

03 POLES

04 PILINGS

05 MINE PROPS

06 POSTS

07 FUELWOOD

08 NON-SAW

09 TIES

10 COOP BOLTS

11 ACID/DIST.

12 FLOAT LOGS

13 TRAP FLOAT

14 MISC-CONV.

15 XMAS TREES

16 NAV STORES

17 NON CONV.

18 CULL LOGS

19 SM RND WD

20 GRN BIO CV

21 DRY BIO CV

26 SPWOODPR

27 BEE TREES

28 TRANSPLANT

29 LIMB/BOUGH

30 FOLIAGE

31 NEEDLES

32 BARK

33 CONES-GRN

34 CONES-DRY

35 SEED

36 NUTS/SEED

37 FRTS/BERYS

38 TREE SAP

39 TREE RESIN

40 ROOTS

41 BULBS

42 MUSHROOMS

43 FUNGI

44 MOSSES

45 HERBS

46 FERNS

47 WILDFLOWRS

48 GRASS

49 AQTC PLNTS

50 VINES

51 MSLTO/SPMS

52 CACTI

53 GRNBIO-NCV

54 DRYBIO-NCV

55 OTHER PLNT

60 WORMS

61 INSECTS

64 ANML ARTF

65 OTHR ANM
A Process to Establish and Use Base Period Prices for National Forest System Transaction Evidence Timber Appraisal

35

Appendix 3—National Forest Timber Sale Volume and Price by 

Appraisal Zone, FY2009 Q1 to FY2014 Q3 Sawtimber Nonsawtimber Fuelwood Fiscal year & quarter Softwood volume (CCF) Softwood value ($/CCF) Hardwood volume (CCF) Hardwood value ($/CCF) Softwood volume (CCF) Softwood value ($/CCF) Hardwood volume (CCF) Hardwood value ($/CCF) Softwood volume (CCF) Softwood value ($/CCF) Hardwood volume (CCF) Hardwood value ($/CCF) Coast 2009.1 41292 17.10 0 0 20 17.33 0 0 10866 6.80 3194 5.00 2009.2 40511 25.14 0 0 474 1.53 0 0 7866 6.33 2 10.00 2009.3 77464 10.90 0 0 364 14.24 0 0 17236 4.33 8 28.96 2009.4 417301 24.74 0 0 17742 1.13 1 20 8354 4.66 0 0.00 2010.1 90339 15.87 0 0 18 17.80 0 0 27056 4.90 628 10.04 2010.2 11013 24.14 0 0 17 27.35 0 0 17794 4.52 1036 10.00 2010.3 92376 25.27 0 0 1759 0.59 88 0 16767 5.64 729 10.00 2010.4 301563 33.75 0 0 12469 0.08 0 0 28441 5.36 1 19.80 2011.1 82818 62.87 0 0 10402 21.14 0 0 12057 7.58 2389 5.00 2011.2 55985 35.33 0 0 93 2.78 0 0 23635 3.77 5104 6.28 2011.3 64152 54.09 0 0 3026 0.15 0 0 12660 7.87 3294 5.00 2011.4 386108 55.88 0 0 2267 0.21 0 0 50201 2.84 5030 5.23 2012.1 32894 83.80 0 0 52 3.63 0 0 14012 18.91 3367 5.00 2012.2 65466 59.90 0 0 78 3.31 0 0 11095 8.02 4440 5.00 2012.3 39533 40.94 0 0 3069 0.38 0 0 11209 9.38 3583 5.00 2012.4 466652 54.40 0 0 9198 0.95 0 0 64593 3.16 3681 5.00 2013.1 83051 49.58 0 0 36 4.61 0 0 9545 9.64 3851 5.71

2013.2 92434 56.15 0 0 1396 5.13 0 0 12081 6.20 5778 6.322013.3 145296 63.00 0 0 231 1.47 0 0 13347 6.54 4364 5.952013.4 298408 40.19 2 10.00 7927 1.09 0 0 23587 4.05 4764 5.842014.1 21353 26.15 0 0 13 18.20 0 0 12296 7.77 2775 5.002014.2 86640 72.78 1 26.89 24 19.00 0 0 11403 8.49 6863 6.222014.3 80205 53.96 0 0 17 13.77 0 0 14003 6.35 3 10.00Inland North2009.1 132056 18.90 0 0 23302 2.88 0 0 53554 5.92 0 02009.2 50193 11.35 0 0 14768 7.72 0 0 11164 1.03 0 02009.3 52242 18.09 0 0 25276 9.60 0 0 29374 5.19 0 02009.4 336695 22.51 0 0 142276 10.46 0 0 70130 3.00 0 02010.1 107455 24.85 0 0 41534 40.81 0 0 59242 7.71 0 02010.2 52368 30.93 0 0 33963 14.46 23 6 33952 1.06 0 02010.3 94854 11.87 0 0 62214 7.68 0 0 94741 4.79 0 02010.4 386815 30.53 4 8.33 128533 12.43 1 40.00 173375 6.80 0 02011.1 68656 35.65 0 0 9733 17.63 0 0 55763 6.36 0 02011.2 9902 41.11 0 0 8656 8.81 0 0 20536 4.59 0 02011.3 102312 25.63 0 0 29377 1.95 0 0 99111 10.33 0 0
General Technical Report FPL–GTR–242

36 Sawtimber Nonsawtimber Fuelwood

Fiscalyear &quarter

Softwood

volume

(CCF)

Softwood

value

($/CCF)

Hardwood

volume

(CCF)

Hardwood

value

($/CCF)

Softwood

volume

(CCF)

Softwood

value

($/CCF)

Hardwood

volume

(CCF)

Hardwood

value

($/CCF)

Softwood

volume

(CCF)

Softwood 

value

($/CCF)

Hardwood 

volume

(CCF)

Hardwood

value

($/CCF)

Inland North—con.2011.4 361141 40.37 0 0 105311 20.36 0 0 177851 19.69 0 02012.1 42987 29.89 0 0 17210 3.51 0 0 60484 6.17 0 02012.2 53699 21.52 0 0 20641 32.36 0 0 35372 15.33 0 02012.3 66386 50.96 4 8 14984 1.74 0 0 97366 6.69 0 02012.4 444313 39.22 0 0 80587 4.97 0 0 141047 9.79 6593 0.272013.1 46954 53.02 0 0 4012 15.57 0 0 73762 7.24 0 02013.2 52275 34.81 0 0 5577 1.57 0 0 12130 7.17 4 02013.3 72623 57.68 0 0 18970 1.43 0 0 91640 5.04 0 02013.4 496741 56.81 0 0 40695 9.92 0 0 151636 9.11 0 02014.1 64700 65.50 0 0 26920 18.59 0 0 43132 6.15 0 02014.2 79350 40.15 0 0 27053 0.98 0 0 9079 4.44 0 02014.3 79956 75.89 0 0 31482 57.42 0 0 96096 4.49 0 0Inland Central2009.1 33205 34.79 17 11.76 2064 4.10 672 1.04 25275 8.30 17019 23.092009.2 67283 15.73 2 11.25 19 33.13 0 0 3317 8.35 7 15.712009.3 32493 11.68 88 11.35 4321 71.74 10 20.99 13093 7.50 102 12.892009.4 310583 5.29 1346 2.06 4068 7.59 67 3.17 67272 7.18 245 6.882010.1 78686 6.97 527 1.65 520 11.99 351 1.11 23155 10.00 98 5.912010.2 100475 13.11 0 0 137 4.55 0 0 5907 7.10 5 15.282010.3 71238 13.50 17 12.70 807 22.95 8 20.00 30439 5.48 4673 5.122010.4 157510 8.97 1978 1.35 2504 9.63 21 3.76 98974 6.39 3802 4.192011.1 95249 12.05 12 11.25 812 5.79 73 2.18 23298 9.40 34 10.662011.2 103327 8.78 0 0 306 1.97 517 1.00 9567 7.56 0 02011.3 77026 5.56 971 8.26 82 24.93 6 20.00 20849 10.90 4133 10.272011.4 133113 7.84 823 2.34 1437 8.57 8 21.58 85006 7.79 445 3.172012.1 67240 5.24 76 2.60 1440 3.47 3 28.51 23041 8.79 13915 4.022012.2 36513 4.46 0 0 42 24.53 0 0 6462 7.03 11560 3.952012.3 57006 3.60 31 11.81 3238 2.99 5 24.00 31113 7.78 4908 1.552012.4 270107 11.33 60 12.61 7814 12.25 95 1.63 92633 6.75 3675 11.272013.1 138409 10.60 16 12.37 1226 7.94 4 20.00 25299 8.86 52 11.422013.2 76975 13.57 0 0 724 2.02 0 0.00 10453 8.52 12 17.942013.3 68064 13.59 36 10.51 999 45.35 6 20.00 32180 6.11 64 10.202013.4 167671 15.48 1605 1.51 7345 6.68 1 20.00 99495 5.82 10824 1.222014.1 71216 11.34 13 12.57 38 43.21 1 40.00 18040 8.54 506 1.242014.2 54175 14.34 0 0 885 8.85 0 0 24618 15.01 7 8.932014.3 64948 19.99 40 12.13 527 6.94 29 4.95 23146 8.54 9725 5.72
A Process to Establish and Use Base Period Prices for National Forest System Transaction Evidence Timber Appraisal

37

Sawtimber Nonsawtimber Fuelwood

Fiscalyear &quarter

Softwood

volume

(CCF)

Softwood

value

($/CCF)

Hardwood

volume

(CCF)

Hardwood

value

($/CCF)

Softwood

volume

(CCF)

Softwood

value

($/CCF)

Hardwood

volume

(CCF)

Hardwood

value

($/CCF)

Softwood

volume

(CCF)

Softwood 

value

($/CCF)

Hardwood 

volume

(CCF)

Hardwood

value

($/CCF)

Inland South2009.1 2927 9.50 17 7.62 8165 0.15 0 0 30829 7.44 183 31.542009.2 901 5.48 3 11.53 546 3.30 0 0 5612 10.91 34 24.422009.3 6082 4.11 24 7.98 884 2.54 0 0 20481 6.54 22 12.382009.4 35704 6.98 38 7.04 41170 0.10 3 1.00 33876 6.11 66 14.362010.1 7109 9.86 17 9.82 796 3.05 0 0 30007 7.60 132 30.622010.2 11340 10.90 0 0 14962 0.06 0 0 6389 8.62 71 31.302010.3 29083 8.89 19 8.53 19798 0.14 0 0 22958 6.08 36 33.612010.4 43743 5.74 29 4.85 12119 0.54 0 0 48437 7.29 260 30.032011.1 6836 6.62 10 8.28 2219 1.24 0 0 36889 8.40 239 27.572011.2 164 6.79 7 8.66 268 1.65 0 0 8315 7.88 133 27.212011.3 5369 7.32 498 1.22 648 3.27 0 0 19139 6.26 423 13.832011.4 75433 4.79 125 2.19 35652 0.60 180 1.00 42806 7.30 758 5.632012.1 34658 6.06 2 16.54 11583 1.03 0 0 30640 7.94 211 26.922012.2 12267 12.48 0 0 24681 0.01 0 0 7926 9.02 118 26.682012.3 30894 3.94 3 19.77 5971 0.55 0 0 19566 6.08 68 33.932012.4 13576 7.38 101 4.34 2874 1.43 0 0 36134 7.69 160 23.652013.1 29649 4.28 22 30.10 5545 1.06 5 1.00 26810 8.42 193 27.892013.2 23416 7.33 0 0 2758 1.01 0 0 6938 9.27 75 30.502013.3 25351 6.06 15 23.42 9457 3.09 0 0 16519 8.66 83 14.762013.4 167993 11.53 9 19.30 47944 4.34 0 0 32223 7.62 132 22.832014.1 7597 3.86 54 3.63 1059 1.31 50 1.00 23041 8.73 154 28.422014.2 14472 4.31 0 0 3343 1.00 0 0 6147 9.36 80 29.132014.3 20475 9.81 19 32.05 2523 1.58 5 36.99 17347 6.16 36 22.10Sierra2009.1 7393 17.22 0 0 1959 9.44 8 8.34 14285 9.22 535 10.182009.2 5014 5.07 0 0 12 2.12 0 0 494 10.99 0 02009.3 18976 5.61 0 0 13659 0.40 10 8.33 15344 11.00 9 20.002009.4 244235 12.85 18 5.56 96709 7.50 5 8.34 21158 5.94 51 20.002010.1 33222 10.02 16 5.56 7407 2.09 4 8.33 16212 8.75 666 11.822010.2 20672 19.42 0 0 6800 2.14 0 0 8674 5.12 319 14.542010.3 46690 18.61 0 0 23211 2.78 0 0 20114 10.31 685 11.742010.4 224310 18.77 0 0 107346 10.05 150 1.00 31130 7.70 1353 11.552011.1 44954 101.38 18 5.56 10759 32.37 0 0 12921 9.56 911 13.782011.2 14249 11.38 0 0 8869 2.05 0 0 5074 14.74 423 17.582011.3 2399 5.77 18 5.56 678 3.26 0 0 23981 8.98 1165 13.542011.4 177397 24.74 0 0 29292 4.81 7 8.34 80817 9.70 1163 13.492012.1 14007 10.34 0 0 3968 8.62 14 8.33 25535 6.05 874 14.41
General Technical Report FPL–GTR–242

38Sawtimber Nonsawtimber Fuelwood

Fiscalyear &quarter

Softwood

volume

(CCF)

Softwood

value

($/CCF)

Hardwood

volume

(CCF)

Hardwood

value

($/CCF)

Softwood

volume

(CCF)

Softwood

value

($/CCF)

Hardwood

volume

(CCF)

Hardwood

value

($/CCF)

Softwood

volume

(CCF)

Softwood 

value

($/CCF)

Hardwood 

volume

(CCF)

Hardwood

value

($/CCF)

Sierra—con.2012.2 3610 37.65 0 0 462 18.85 0 0 6977 8.86 559 15.032012.3 41624 27.24 0 0 5912 19.39 11 8.33 24433 8.18 707 10.002012.4 235822 22.90 0 0 6789 0.07 0 0 84128 7.57 682 10.752013.1 6479 10.56 14 5.56 4 26.09 2 8.34 20837 9.06 728 12.942013.2 17654 19.16 4 5.56 0 0.00 0 0 9691 5.88 67 10.002013.3 31250 46.16 11 5.56 10 59.96 8 8.34 25140 8.81 802 10.152013.4 254146 24.11 18 5.56 479 0.64 0 0 28243 6.15 534 10.002014.1 18611 24.75 0 0 33 92.99 5 8.34 11499 10.90 464 12.052014.2 26453 38.54 7 5.56 17 4.41 1 1.00 17182 16.30 272 17.392014.3 81040 30.27 14 5.56 13 30.25 5 8.34 21987 7.14 521 10.22Mid-South2009.1 18216 93.08 5766 78.26 10825 19.66 3194 4.84 0 0.00 5114 2.992009.2 75449 37.01 7879 80.11 11421 20.86 244 7.07 356 0.14 2093 2.232009.3 27634 41.82 13817 73.47 8082 16.84 119 1.72 103 0.68 1054 1.882009.4 64814 43.04 12130 42.31 36700 17.45 107 2.82 342 0.58 1478 4.042010.1 30269 54.17 10803 63.40 8565 14.15 931 8.00 0 0.00 5044 3.682010.2 29999 65.03 17064 52.59 12977 9.06 2914 28.02 3 13.33 3089 3.242010.3 56279 83.49 35916 60.83 33226 16.34 2291 9.19 1 20.00 617 2.462010.4 84273 56.07 55666 50.30 56243 18.62 8287 4.86 1861 0.00 2099 3.302011.1 35549 63.24 13604 48.05 17369 16.99 4064 11.16 12 2.50 4777 3.842011.2 3101 46.95 9354 67.00 2606 7.11 2371 12.79 50 1.00 1960 3.602011.3 83550 54.20 39257 45.79 39805 13.56 5069 11.97 0 0.00 548 2.532011.4 112149 30.38 34502 60.33 40343 5.71 2204 2.84 1020 21.70 2314 3.022012.1 23136 23.41 7604 45.62 6542 14.96 4827 5.93 794 3.59 3789 3.842012.2 11324 46.69 16876 53.29 15207 8.65 1321 4.83 242 4.91 1749 3.352012.3 37612 27.08 31213 67.45 31019 7.95 6653 11.50 1316 1.46 2266 17.492012.4 111885 36.49 43787 60.28 60602 14.07 5000 11.85 645 2.62 1797 12.092013.1 14614 40.25 6749 92.64 6745 13.72 703 1.02 642 4.08 3863 6.592013.2 44223 60.51 14495 95.76 31111 7.73 3449 12.33 762 11.38 4063 8.592013.3 47235 44.52 16660 100.35 47753 19.92 3701 11.30 81 20.37 2888 8.962013.4 65749 35.76 27363 54.35 58858 12.35 7641 11.45 1320 11.87 4202 13.902014.1 10582 46.76 8475 112.61 7944 10.37 1572 1.52 428 5.00 5102 16.412014.2 71177 64.43 21063 107.89 32136 12.19 7756 12.77 436 9.15 8326 8.692014.3 11199 19.82 22005 84.66 22905 5.07 444 1.88 75 6.60 5491 15.73
A Process to Establish and Use Base Period Prices for National Forest System Transaction Evidence Timber Appraisal

39

Sawtimber Nonsawtimber Fuelwood

Fiscalyear &quarter

Softwood

volume

(CCF)

Softwood

value

($/CCF)

Hardwood

volume

(CCF)

Hardwood

value

($/CCF)

Softwood

volume

(CCF)

Softwood

value

($/CCF)

Hardwood

volume

(CCF)

Hardwood

value

($/CCF)

Softwood

volume

(CCF)

Softwood 

value

($/CCF)

Hardwood 

volume

(CCF)

Hardwood

value

($/CCF)

Southeast2009.1 19146 89.73 4883 21.24 14350 19.79 3855 5.94 675 4.76 961 7.092009.2 23286 69.42 8086 47.86 36789 18.90 7735 12.25 694 3.92 1407 5.932009.3 32475 74.78 9947 46.60 74875 22.02 12451 7.02 1646 51.35 183 6.202009.4 59119 63.15 30900 40.07 116652 23.00 18066 4.80 619 3.55 461 5.702010.1 29115 62.95 10779 31.89 35117 27.64 9100 6.69 433 4.71 5796 5.582010.2 38907 74.33 5651 38.35 75362 25.56 11637 9.04 421 16.11 5945 5.162010.3 14150 75.24 1988 82.15 37241 32.53 1201 11.03 56 5.71 1147 5.362010.4 101002 75.49 32420 58.63 134481 20.42 18445 11.88 39950 10.71 3732 5.672011.1 23496 49.15 6539 34.49 41811 26.05 6123 6.82 6297 5.99 6666 5.652011.2 45747 67.20 10061 38.06 39033 21.00 8222 4.55 369 6.50 4318 5.512011.3 47909 63.76 12636 54.82 61267 22.92 9370 6.33 5746 13.87 2892 7.842011.4 110977 61.74 33556 52.40 160746 21.95 20041 5.90 28695 8.84 3738 5.222012.1 31039 64.74 5198 55.21 11330 20.55 4775 5.09 14371 7.03 2230 6.742012.2 25753 71.33 2162 93.03 29549 29.71 2735 8.97 1599 1.79 4348 6.382012.3 49015 60.11 9500 44.37 47686 22.99 10692 6.04 3666 13.87 316 5.512012.4 166157 62.48 60800 54.11 218664 22.80 39577 6.73 3849 15.51 3038 5.122013.1 11592 57.31 5067 76.37 22844 21.90 5484 7.81 634 5.24 5286 5.532013.2 29785 73.91 5987 47.73 33798 21.21 4146 13.39 4877 6.17 1666 6.232013.3 43500 60.81 4909 37.35 82108 26.98 4550 8.97 204 5.20 953 5.502013.4 136658 64.99 29932 48.63 139533 21.77 32281 8.92 1157 1.67 2824 5.192014.1 4061 55.66 2472 28.30 1050 18.85 1513 10.79 581 5.53 4926 5.522014.2 43323 65.81 6943 61.33 40368 23.06 14275 10.35 625 5.86 4285 5.552014.3 68761 73.35 7556 40.11 40472 19.22 5972 9.02 186 7.60 1295 5.31Lake States2009.1 11631 77.09 3122 50.27 16303 50.23 9611 21.67 162 1.85 3317 5.362009.2 6296 53.72 1320 92.15 9637 51.96 31618 25.90 -516 -0.31 1118 5.032009.3 21871 45.34 2856 58.68 24511 40.03 15829 25.90 604 0.44 10355 5.242009.4 22368 54.68 9284 72.80 34000 40.21 98354 26.68 473 0.66 4614 4.892010.1 12824 60.02 5088 50.37 12503 28.17 30970 24.48 92 1.80 4498 5.122010.2 22201 75.10 4311 52.52 23693 50.66 48056 23.98 693 0.17 2111 6.032010.3 14114 58.31 2585 60.45 21847 30.00 48384 27.66 913 1.62 10555 4.942010.4 44448 67.15 9692 55.73 51892 43.10 92058 30.52 2697 1.38 5494 5.342011.1 20303 93.02 7932 41.73 23251 26.40 35926 26.56 2631 1.33 6023 5.942011.2 6769 83.69 2811 30.78 11509 36.92 24527 25.64 1021 1.22 1621 6.172011.3 21300 97.43 8165 59.17 23331 53.86 61908 33.42 299 5.00 10345 5.222011.4 32316 60.78 11419 119.64 50043 27.67 122740 32.09 1171 2.44 5213 5.322012.1 12372 61.78 2788 66.15 16414 59.60 15676 23.24 159 5.00 3230 4.962012.2 14151 79.10 4352 72.74 26344 48.87 58410 33.73 326 5.00 2430 5.82
General Technical Report FPL–GTR–242

40 Sawtimber Nonsawtimber Fuelwood

Fiscalyear &quarter

Softwood

volume

(CCF)

Softwood

value

($/CCF)

Hardwood

volume

(CCF)

Hardwood

value

($/CCF)

Softwood

volume

(CCF)

Softwood

value

($/CCF)

Hardwood

volume

(CCF)

Hardwood

value

($/CCF)

Softwood

volume

(CCF)

Softwood 

value

($/CCF)

Hardwood 

volume

(CCF)

Hardwood

value

($/CCF)

Lake States—con.2012.3 19728 80.15 8100 75.65 28820 47.77 75405 28.42 202 5.00 7371 5.562012.4 46638 86.16 10949 77.34 61253 45.48 120456 29.18 1211 3.89 4600 5.372013.1 9861 74.50 3135 92.14 30652 27.43 49279 24.85 354 4.12 2674 5.382013.2 13821 74.71 8383 49.57 29982 62.42 39385 24.06 665 2.56 1765 6.032013.3 28482 65.57 4448 70.31 27433 25.59 43775 28.05 2698 1.05 7735 5.212013.4 47973 96.94 12134 71.01 31537 41.60 114718 27.50 700 2.25 5080 5.362014.1 8000 91.10 2532 129.90 11703 63.23 21061 28.18 642 1.76 1830 5.342014.2 9500 101.80 8536 51.61 20418 29.97 56020 22.68 375 3.89 1983 5.892014.3 26643 103.96 11622 125.31 24188 52.58 91089 33.82 434 4.55 9001 5.29Northeast2009.1 10 87.85 0 0 -767 -0.15 3408 2 0 0.00 269 11.672009.2 3472 22.66 4970 128 3240 3.61 2782 2 0 0.00 248 8.062009.3 1997 40.34 1412 30 520 1.87 4650 12 0 0.00 209 10.342009.4 7322 57.66 19580 167 3902 7.18 20952 7 0 0.00 22 5.452010.1 1019 69.87 3490 303 269 4.10 3658 3 1848 6.25 799 6.032010.2 631 31.29 5208 323 793 27.02 3622 2 453 6.25 1249 6.902010.3 221 22.50 7005 300 51 2.51 6865 2 714 6.25 3818 8.032010.4 7603 32.28 34652 224 2625 3.65 28085 8 497 6.25 582 7.462011.1 2090 159.90 4568 265 427 4.82 6464 4 1508 6.25 581 6.732011.2 310 14.44 6387 235 187 1.91 6270 2 402 6.25 1114 6.722011.3 232 5.19 8496 266 704 0.97 10181 2 650 6.25 716 9.082011.4 6414 46.38 32956 164 2786 4.48 25416 5 620 6.04 581 8.612012.1 1590 25.91 2156 362 513 1.29 2487 3 1472 6.24 606 6.612012.2 895 55.71 6662 202 550 4.92 8529 2 534 6.25 1473 6.342012.3 2621 38.63 7129 286 4559 5.18 5844 3 535 6.25 460 8.862012.4 13248 39.87 28301 183 3568 4.51 27998 7 423 6.25 1761 3.812013.1 35 37.84 4507 402 411 1.13 4605 3 1514 6.25 551 7.352013.2 1014 92.98 1497 266 24 23.86 1443 3 386 6.13 1330 6.852013.3 5336 32.18 11826 258 1380 2.04 7645 2 718 6.25 1706 4.402013.4 11885 35.36 16523 251 4466 3.80 22953 4 485 6.25 642 7.552014.1 2059 26.87 8601 104 1932 3.44 2127 2 1236 6.25 513 7.022014.2 860 50.28 6399 360 784 2.83 6545 3 513 6.25 1324 6.712014.3 308 2.39 7682 399 374 1.00 9339 2 759 6.23 670 8.72
A Process to Establish and Use Base Period Prices for National Forest System Transaction Evidence Timber Appraisal

41

Administrative information

Region

Forest

FY 

Quarter

Sale Number

Green/Salvage

SBA

Deficit

Timber Property

Bid Method

General sale information

Product/Unit of Measure

Species Name/Group

Species Code

Volume

Quadratic Mean DBH

Base Period Price

Cost centers and other adjustments

Market Adjustment

Rapid Market Adjustment

GBCv-Nonsaw Adjustment 

Falling and Bucking

Skidding and Loading

Logging Adjustment

Base Skid-Yard Cost

Sale Skid-Yard Cost

Skid-Yard Cost Adjustment

Base Stump to Truck

Sale Stump to Truck

Stump to Truck Adjustment

Base PAL Fire Protection

Sale PAL Fire Protection

PAL Fire Protection Adjustment

Base Haul Cost

Sale Haul Cost

Appendix 4—Appraisal Information Collected from Individual Sales

Haul Cost Adjustment

Base Road Maintenance Cost

Sale Road Maintenance Cost

Road Maintenance Cost Adjustment

Surface Replacement Deposit

Road Maintenance Deposit

Base Contract Cost

Sale Contract Cost

Contract Cost Adjustment

Base Slash Cost

Sale Slash Cost

Slash Cost Adjustment

Base BD Deposit

Sale BD Deposit

BD Deposit Cost Adjustment

Base Development and Other Cost

Sale Development and Other Cost

Development and Other Cost Adjustment

Base Temporary Road Cost

Sale Temporary Road Cost

Temporary Road Cost Adjustment

Base Erosion Control Cost

Sale Erosion Control Cost

Erosion Control Cost Adjustment

Base Other Costs

Sale Other Costs

Other Costs Adjustment

Quality Adjustment

Unusual Adjustment

Risk Adjustment

Sale Specified Road Cost

Engineering Deposit

Timber Property Value

Market Area Adjustment (R1 only)

Minimum bid price (advertised price)

High bid price (or “no bid”)
General Technical Report FPL–GTR–242

42

Appendix 5—Methods to Improve Predictions of Base Period Price and 

Estimate the Probability Distribution of High Bid Price

Decreasing the Error in Prediction of 

Base Period Price

Base Period Price (BPP) may be estimated in the current 

quarter for a given appraisal group in a given zone using a 

mark-up regression equation linking the current quarter’s 

primary product price to stumpage price:

BPPt

s

 = c2Pt

p

 + ut (A1)

In Equation (A1), BPP is the estimated high bid price for 

sales with average costs and average bid premium. Equation 

(A1) is fit on quarterly average stumpage price and primary 

product price data over a historic period and includes an 

error term ut, which is the difference between the predicted 

average high bid price for quarter t (c2Pt

p ) and the actual 

average high bid for quarter t. The difference ut is the 

amount by which actual quarterly average sales costs and 

actual average high bid premium differ from the grand average over the entire period of the data used to fit the regression equation.

For a given mark-up equation, we define an improvement in 

the prediction of BPPt

s

 as a reduction in the standard deviation of the error term ut

 for the predicted value.

We would be able to decrease this error by using two or 

more smaller zones rather than one larger appraisal zone. 

The standard error of predictions would be less for at least 

one of the smaller zones but not necessarily all the smaller 

zones.

Estimating the Probability Distribution of 

High Bid Price for a Given Sale

If we make two key assumptions, we may use a more 

detailed equation that shows the components of High Bid 

Price for sales over a historic period and may be used to 

predict the probability distribution of high bid price for a 

sale in one to a few quarters after the historic period:

(A2)

(A3)

We assume that (1) the average sales costs for the follow-on 

quarters are the same as for the historic period and (2) the 

distribution of bid premium correction factors as defined 

in Equation (A3) are the same as for the historic period. 

Equation (A2) gives the components that explain the high 

bid price for each individual sale over the historic period. 

As for Equation (A1), the term c2Pt

p is an estimate of the 

high bid price for a sale with average costs and average bid 

premium over the historic period of data for individual sales. 

HBPt,i = c2Pt

p + (C − Ci)+ (BPMi − BPM)

(BPMi − BPM) = HBPt,i − [c2Pt

p + (C − Ci)]

Note that there is no error term because we assume that we 

compute (BPMi − BPM) using data over the historic period 

using Equation (A3).

Technically the bid premium correction factor as defined 

here includes any error in using c2Pt

p to predict the market 

stumpage value when there are average costs and average 

bid premium in quarter t and any error in using (C − Ci) to 

estimate cost corrections that the high bidder used.

If we assume the distribution of (BPMi − BPM) terms 

over the historic period remains the same for a few followon quarters, we may use that distribution to select a bid 

premium correction factor for a sale in a follow-on quarter—using Equation (A2)—where the selected bid premium 

correction factor gives an estimate of HBPt,i, which has a 

certain probability of being greater than the actual high bid 

price. This would be the probability of a no bid sale.

Estimation of Base Period Price by a 

Mark-Up Equation Given Possible 

Patterns in Error Terms

Recall that the mark-up equation given by Equation (A1) 

predicts stumpage price for a given appraisal group. It is fit 

using quarterly data on stumpage prices and primary product 

prices. The error term ut in equation A1 may be expressed as

(A4)

where Ct is the average costs for sales in quarter t of the historic period, C is the average of costs for all sales over the 

historic period, and the bid premium correction factor for all 

sales in quarter t of the historic period is defined as

There may be patterns in the error terms ut that can lead to 

incorrect estimates of the parameter c2. For example, it is 

possible for the error terms to be serially correlated, or to 

have differing variance over time. It is also possible that 

certain data points are outliers and have undue influence in 

comparison to other data points.

The error term in Equation (A4) is composed of two variables—the cost difference and the bid premium difference. 

The cost difference term is determined by the choices forest 

managers make in selection of timber sales and the cost features of those sales. Any pattern in those differences would 

be due to patterns in those decisions. The bid premium 

difference term is influenced by many factors, including 

the mix of timber products for sale, the location relative to 

mills, number of bidders, and strength of markets for products. Also it is possible that the two terms could be correlated for some sets of time periods, and this would influence 

ut = (C − Ct) + (BPMt − BPM)

(BPMt − BPM) = BPt

 − [c2Pt

p + (C − Ct)]
A Process to Establish and Use Base Period Prices for National Forest System Transaction Evidence Timber Appraisal

43

the pattern of errors. It is not clear that even if there were 

patterns in the errors over some periods that these patterns 

would persist. Also, cost pattern conditions and bid pattern 

conditions in one appraisal zone would not necessarily happen in another zone.

A basic strategy in estimating mark-up equations would be 

to correct for serial correlation with autoregressive equations when indicated, transform variables when needed to 

adjust for heteroscedasticity, and trim outlier data points to 

reduce their influence. These methods were used for a sample of mark-up equations. Some equations exhibited serial 

correlation and corrections modified c2 slightly and could be 

used in place of uncorrected estimates. Visual inspection of 

residuals could indicate if transformation of variables may 

help reduce heteroscedasticity. Visual inspection of several 

mark-up equations did not find systematic trends in variance in residuals. Inspection of residuals identified a number 

of outliers, and estimates without them modified c2 values 

slightly.

Although the c2 estimates can be improved by these and 

other measures, additional predictor variables would likely 

be needed to reduce standard deviation of errors for estimating Base Period Price notably.

A strategy that is very likely to reduce error in predicting Base Period Price is to use smaller rather than larger 

appraisal zones to the extent that there are a sufficient number of sales to fit equations for the smaller zones.