risk and return profile of forestry investment fund in latvia

Thesis

Risk and Return Profile of Forestry

Investment Fund in Latvia

Author: Ģirts Tihomirovs, Class 16a

Supervisor: Anete Pajuste, Ph.D.

Riga 2010

Ģirts Tihomirovs Risk and Return Profile of Forestry Investment Fund in Latvia Page ii

Guarantee

Name of the author in full:

Ģirts Tihomirovs

Title of the Thesis as approved by the RBS Council:

Risk and Return Profile of Forestry Investment Fund in Latvia

I confirm that my Master of Business Administration (MBA) Thesis has been prepared by

myself. The data, definitions, citations that are taken from other sources are indicated in

my work. This work, nor any part of it, in one form or another has never been handed in to

some other commission and has never been published.

Signed

____________________

Ģirts Tihomirovs

Date

____________________

Ģirts Tihomirovs Risk and Return Profile of Forestry Investment Fund in Latvia Page iii

Executive Summary

The purpose of the thesis is to analyze forestry in Latvia as an investment

opportunity, evaluating the forestry industry from investor’s perspective and identifying

factors affecting potential return from forestry investments, researching historic

performance and determining the expected return of forestry investments and associated

risk, and suggesting appropriate implementation of investment strategy to capture profit

opportunities in the forestry industry. Forestry in the thesis is referred to as managing

forests to generate income from sales of timber and eventual sales of forests. The main

problem of the thesis as defined by the author is determining expected return and risk of

forestry investment fund in Latvia.

Firstly, forestry industry analysis is carried out, to understand how it operates and

to identify the factors affecting potential return and risk of forestry investments. The

industry analysis includes determining industry life cycle and business cycle reaction,

analysis of external factors using PESTEL model, and analysis of supply and demand. In

order to perform the industry research, interviews with industry experts are carried out,

industry statistics are analyzed, publications about forestry industry and other relevant

public information is reviewed.

It is found that forestry in Latvia is a highly regulated industry, and return potential

is significantly limited by legislative restrictions to cut trees under a certain age and

diameter. Also, forest land transformation is a complex and costly process, which means

that forest land faces little competition from other uses besides forestry. Another factor

negatively affecting forestry returns is environmental protection that does not adequately

compensate forest owners for the restrictions imposed. Factors positively affecting forestry

returns include beneficial tax regime for forest land, social trend of increasing popularity of

ecological products, and technological improvements that increase production efficiency.

Demand and supply analysis reveals that Latvia’s State Forests which manages

about 50% of all forests in Latvia has a major role in the supply side of the industry, since

its output decisions are used by private forest owners to determine their strategy. On

demand side, a major challenge faced by Latvian foresters is decreasing demand for

pulpwood from Scandinavia, which can potentially be substituted by regional demand for

energywood. Decreased demand for lumber and veneer due to global economic slowdown

is a short-term problem and is already showing signs of recovery.

Secondly, calculations of historical and expected return and risk of forestry

investments in Latvia are performed, applying methodology used in previous researches in

this field in USA, Sweden and other countries. The results are also analyzed from financial

theory perspective to draw conclusions on forestry’s performance as an investment in

comparison to other investment opportunities.

Analysis of historical return and risk of forestry in Latvia reveals that average

nominal return on forestry in 2001-2009 was 14.75% per annum, with variance of 0.1364.

A detailed analysis revealed that a very substantial part of both returns and variability

stems from timber price developments. Comparison of the historical return with findings of

other researches reveals that historic returns on forestry in Latvia are comparable to those

of other countries, which are in the range of 7.3% to 16.1% per annum with average value

of 11.8%. All of the other researches reveal variances lower than the one calculated for

Latvian forestry returns, with average variance in other researches of 0.038. This can be

attributed to longer periods covered in other researches, thus evening out short-term

Ģirts Tihomirovs Risk and Return Profile of Forestry Investment Fund in Latvia Page iv

fluctuations, and the fact the time period covered in the thesis includes significant

economical imbalances in both local and global economy.

Although industry classification suggests forestry as being a defensive industry, no

empirical evidence is found to support such argument. As forestry returns of a period are

found to compensate for inflation in the next period, it can be concluded that forestry

provides a hedge against inflation. Comparison with other asset classes reveals that

forestry has demonstrated rather strong risk-adjusted returns with Sharpe ratio of 0.29, and

forestry offers substantial diversification benefits, since its returns are weakly correlated

with other asset classes reviewed.

General conclusion of the thesis is that the expected real return of forestry industry

in Latvia is 2.6% per annum; therefore, an investment fund that does not have a specific

investment strategy should have expected yearly real returns of 2.6%. Analysis of more

sophisticated investment strategies according different species, forest and timber types

reveal that the expected return maximizing strategy is to invest in specific types of forests

with specific dominant species. If the forestry investment fund pursues this investment

strategy, the expected real rate of return of the fund is in the range of 5.3% to 8.3%.

Although the expected risk of the forestry investment fund as measured by variance

of forestry returns cannot be precisely calculated, based on the historical data and expected

convergence with average variability levels of forestry on a global level, it can be

speculated that the near term variance should be in the range of 0.038 to 0.136.

The recommended investment strategy of selecting investments according forest

types and dominant species is best implemented through focused acquisition target search

and careful evaluation. Since the thesis provides information on the expected return of

various combinations of forest types and dominant species, each forest’s expected return

can theoretically be evaluated, thus allowing more informed decision making.

Finally, the suggested implementation strategy to mitigate possible risks and

maximize return includes building a team of investment management and forestry experts

to ensure professional forest management and compliance with effective regulations,

avoiding purchasing forests that could potentially be included in protected nature

territories, and purchasing even-aged forests with approaching felling age or diameter.

Also, the investment fund should use the opportunity to buy underpriced forests and

machinery from distressed forest owners, as well as focus on roundwood production and

cooperate with energywood consumers to limit its exposure to decreasing demand for

pulpwood in the region.

Ģirts Tihomirovs Risk and Return Profile of Forestry Investment Fund in Latvia Page v

Anotācija latviešu valodā

Maģistra darba „Latvijas kokrūpniecības investīciju fonda riska un atdeves profils”

mērķis ir analizēt kokrūpniecību Latvijā kā investīciju iespēju, izvērtējot nozares pievilcību

no investoru redzespunkta un identificējot meža investīciju potenciālo atdevi iespaidojošos

faktorus, noteikt vēsturisko un sagaidāmo atdevi un risku kokrūpniecības investīcijām, kā

arī ieteikt investīciju stratēģijas ieviešanu peļņas iespēju izmantošanai. Kā galvenā

maģistra darba problēma ir izvirzīta kokrūpniecības investīciju fonda sagaidāmās atdeves

un riska noteikšana.

Izvirzīto mērķu sasniegšanai tiek izmantotas vairākas izpētes metodes un

informācijas avoti. Pirmkārt, nozares analīzei tiek pielietots PESTEL modelis,

piedāvājuma un pieprasījuma analīze, kā arī tiek noteikts nozares attīstības cikls un biznesa

cikla reakcija, balstoties uz intervijām ar nozares ekspertiem, industrijas statistikas datiem,

nozares publikācijām un citu nozīmīgu publisku informāciju. Atdeves un riska noteikšanai

tiek izmantota ārvalstu pētījumos pielietota metodoloģija, izmantojot Latvijas

kokrūpniecības datus par koksnes cenām, koku augšanu un mirstību, zemes cenām, mežu

platībām un citiem raksturojošiem datiem, kas iegūti no nozares pētījumiem un dažādu

asociāciju statistikas datiem, kā arī Centrālā Statistikas Biroja datiem.

Darbā secināts, ka vidējā nominālā atdeve kokrūpniecībā Latvijā periodā 2001-

2009 bija 14.75% gadā, ar dispersiju 0.1364. Sagaidāmā reālā atdeve nefokusētam

kokrūpniecības investīciju fondam ir 2.6%; savukārt, ja kokrūpniecības investīciju fonds

izvēlas mežus pēc to tipiem un valdošās sugas, fonda sagaidāmā reālā atdeve ir 5.3%–

8.3%. Lai arī sagaidāmo risku jeb dispersiju nevar aprēķināt, tas varētu būt 0.038–0.136.

Maģistra darbs ir rakstīts angļu valodā, tā kopējais lappušu skaits ir 79 lappuses, un

tajā ir 28 tabulas, 7 formulas, 8 attēli, kā arī 6 pielikumi.

Ģirts Tihomirovs Risk and Return Profile of Forestry Investment Fund in Latvia Page vi

Table of Contents

1. INTRODUCTION ............................................................................................................ 1

1.1. Purpose of the Thesis ....................................................................................... 1

1.2. Problem Statement ........................................................................................... 1

1.3. Limitations of the Thesis .................................................................................. 1

1.4. Research Methods ............................................................................................ 3

1.5. Structure of the Thesis ...................................................................................... 4

2. GLOSSARY OF TERMS AND ABBREVIATIONS ................................................................. 5

3. THEORETICAL FRAMEWORK ......................................................................................... 7

3.1. Industry analysis ............................................................................................... 7

3.2. Forestry Industry Return and Risk Analysis .................................................. 10

3.2.1. Review of Previous Researches in the Field ............................................ 10

3.2.2. Methodology Used in the Thesis .............................................................. 13

4. EMPIRICAL FINDINGS ................................................................................................... 15

4.1. Forestry Industry Analysis ............................................................................. 15

4.1.1. General Information on Forestry in Latvia ............................................. 15

4.1.2. Industry Classification ............................................................................. 17

4.1.3. External Factor Analysis ......................................................................... 18

4.1.4. Supply and Demand Analysis .................................................................. 26

4.2. Return and Risk of the Forestry Investment Fund ......................................... 29

4.2.1. Data Description ...................................................................................... 29

4.2.2. Analysis of Historical Return and Risk .................................................... 31

4.2.3. Determining Expected Return and Risk of the Fund ............................... 41

5. RECOMMENDATIONS AND IMPLEMENTATION ............................................................. 50

5.1. Recommended Investment Strategy ............................................................... 50

5.2. Suggestions for Implementation ..................................................................... 54

6. CONCLUSIONS............................................................................................................... 57

7. SUGGESTIONS FOR FURTHER RESEARCH .................................................................... 60

8. APPENDIX I. LIST OF INTERVIEWS ............................................................................... 61

9. APPENDIX II. FOREST LAND AREA AND PRICES ........................................................... 62

10. APPENDIX III. FOREST TYPES AND MOST APPROPRIATE SPECIES ....................... 63

11. APPENDIX IV. DATA ON FOREST LAND AND TIMBER ............................................ 64

12. APPENDIX V. EXPECTED RETURN ACCORDING SPECIES ....................................... 68

13. APPENDIX VI. EXPECTED RETURN ACCORDING SPECIES AND FOREST TYPE ...... 72

14. BIBLIOGRAPHY ........................................................................................................ 76

Ģirts Tihomirovs Risk and Return Profile of Forestry Investment Fund in Latvia Page vii

List of Tables

Table 1: Main felling area stand age ................................................................................... 24

Table 2: Main felling area average basal diameter .............................................................. 25

Table 3: Return on forestry, 2001-2009 .............................................................................. 31

Table 4: Returns of different asset classes 2001-2009, % ................................................... 35

Table 5: Summary of comparison with different asset classes ............................................ 36

Table 6: Correlation between returns, 2001-2009 ............................................................... 38

Table 7: Results of other researches .................................................................................... 39

Table 8: Expected return of generic investment strategy .................................................... 42

Table 9: Sensitivity of expected returns on generic strategy ............................................... 42

Table 10: Expected return of different timber types............................................................ 43

Table 11: Sensitivity of expected returns, different timber types........................................ 43

Table 12: Expected return of different species .................................................................... 44

Table 13: Sensitivity of expected returns, different species ................................................ 45

Table 14: Expected return of different forest types ............................................................. 45

Table 15: Expected return, different timber and forest types .............................................. 46

Table 16: Sensitivity of expected returns, different timber and forest types ....................... 47

Table 17: Expected returns for each species, return maximizing forest types .................... 47

Table 18: Expected returns for each forest type, return maximizing species ...................... 48

Table 19: Summary of investment strategies ...................................................................... 50

Table 20: Different species and forest type investment summary ....................................... 51

Table 21: Different timber and forest type investment summary ........................................ 52

Table 22: Different forest type investment summary .......................................................... 52

Table 23: Strategy to mitigate investment fund risks and maximize return ........................ 55

Table 24: Forest land area and prices according regions, 2009 .......................................... 62

Table 25: Forest types and most appropriate species .......................................................... 63

Table 26: Data on forest land and timber ............................................................................ 64

Table 27: Expected return calculations according species .................................................. 68

Table 28: Expected return calculations according species and forest type ......................... 72

Ģirts Tihomirovs Risk and Return Profile of Forestry Investment Fund in Latvia Page viii

List of Equations

Equation 1: Faustmann land value formula ......................................................................... 10

Equation 2: Mills and Hoover return formula ..................................................................... 10

Equation 3: Redmond and Cubbage return formula ............................................................ 11

Equation 4: Lundgren return formula .................................................................................. 12

Equation 5: Historic return on timberland investment formula ........................................... 13

Equation 6: Stumpage price estimation formula ................................................................. 14

Equation 7: Expected return ................................................................................................ 14

List of Figures

Figure 1: Stages in the industry life cycle ............................................................................. 7

Figure 2: Forest cover in Latvia (%), forest ownership structure ........................................ 15

Figure 3: Age structure of major species (thousand ha) ...................................................... 15

Figure 4: Forest sector organizations and governance ........................................................ 16

Figure 5: Protected nature territories in Latvia .................................................................... 21

Figure 6: Return and GDP growth ....................................................................................... 32

Figure 7: Return and inflation ............................................................................................. 33

Figure 8: Return and lagged inflation .................................................................................. 34

Ģirts Tihomirovs Risk and Return Profile of Forestry Investment Fund in Latvia Page 1 of 79

1. Introduction

1.1. Purpose of the Thesis

Forestry has historically been one of the major exporting industries of Latvia.

About 50% of territory of Latvia is covered with forests, and about 47% of Latvian forests

are privately owned. Forests are used as investments both on private individual level and

on investment fund level.

The purpose of the thesis is to analyze forestry in Latvia as an investment

opportunity, evaluating the forestry industry from investor’s perspective and identifying

factors affecting potential return from forestry investments, researching historic

performance and determining the expected return of forestry investments and associated

risk, and suggesting appropriate implementation of investment strategy to capture profit

opportunities in the forestry industry.

1.2. Problem Statement

Investment management and consulting company “Capitalia” is considering to

create a forestry investment fund that would acquire, manage, and eventually sell forests.

In order to attract investors to the fund, the company has to provide justified information

about return and risk profile of such investments. Therefore, the managerial problem is

determining expected return and risk of forestry investment fund in Latvia.

1.3. Limitations of the Thesis

The scope of the thesis is to analyze forestry investments in Latvia, by forestry

referring to managing forests (land and trees) to generate income from sales of timber and

eventual sales of forests. This means that wood-processing, sales of seedlings, income


from hunting and grazing, or other activities related to forests and timber are not

considered in the analysis. It must also be noted that analyzing non-monetary benefits,

such as scenic value of forests, is out of the scope of this paper.

Return on forestry investments is expected to stem from income from sales of

timber; therefore, no windfall income such as discovery of oil resources in the timberland

is considered in the thesis. Risk is referred to as the variation in expected cash flows (i.e.

return); operation risks such as risk of fire damage or risk of biological diseases are

incorporated in the expected return, therefore, not explicitly analyzed.

Some limitations of the thesis also stem from assumptions behind the model to

estimate expected return. The most significant limitations are as follows:

It is assumed that forest management fully complies with the effective

legislation. Although the existence of illegal activities in forest management,

including but not limited to bribery, tax avoidance and unapproved tree cutting,

is possible, the unavailability of related information prevents the inclusion of

such factors in the analysis.

It is assumed that timber volume that can be sold at prevailing prices is

unlimited. Although such assumption holds for relatively small volumes, it

imposes limitations to application of findings on large volumes of timber.

It is assumed that the effective legislation will stay unchanged. The limitation

of this assumption is that possible legislation changes are not modeled and thus

can potentially make the findings less valid if there are significant changes in

legislation.


Another important limitation is the relatively short period analyzed due to

unavailability of data for longer periods.

These limitations and potential effects should be considered when using the

findings to make investment decisions; nevertheless, the analysis is considered valid to

provide insights about forestry investments in Latvia.

1.4. Research Methods

The thesis employs a number of research methods to solve the problem stated.

Firstly, forestry industry analysis is carried out, to understand how it operates and to

identify the factors affecting potential return and risk of forestry investments. The industry

analysis includes determining industry life cycle and business cycle reaction, analysis of

external factors using PESTEL model, and analysis of supply and demand. In order to

perform the industry research, interviews with industry experts are carried out, industry

statistics are analyzed, publications about forestry industry and other relevant public

information is reviewed.

Next, calculations of historical and expected return and risk are performed. The

methodology for calculations is based on previous researches in the field in USA, Sweden

and other countries, applying the models to forestry industry in Latvia. It must be noted

that due to some peculiarities of forestry industry in Latvia as revealed by industry

analysis, the models are adjusted to reflect those specific factors.

Financial theory of investment portfolios is used to analyze investment fund’s

theoretical performance in context of an investment portfolio, focusing on its

diversification potential.


1.5. Structure of the Thesis

The thesis consists of several major parts. First, introduction sets out the general

information about the research, including the aim and scope of the research, relevance of

the issue, and structure of the paper. Next, in the theoretical framework section, relevant

literature is reviewed to come up with the methodology to be used in the research.

Presentation and analysis of empirical findings is the core part of the research, where

forestry industry in Latvia is analyzed to identify factors affecting investment return and

risk, historical return and risk of forestry in Latvia are examined, and expected return and

risk of a forestry investment fund are determined. The thesis is concluded by

recommendations on implementation of investment strategy to capture potential return

opportunities.


2. Glossary of Terms and Abbreviations

basal area – Sum of cross section areas of trees, measured in square meters per hectare, at

1.3 meters above ground.

clear cutting – Harvest of all trees in a site.

diameter breast height – Diameter of trees at 1.3 meters.

even-aged – Refers to forests in which trees have been established at about the same time

and are thus roughly the same age.

forest – Refers to land and trees combined.

gross domestic product (GDP) – The market value of all goods and services produced by

residents of a nation in a year.

hardwood – Non-coniferous or deciduous trees, e.g. birch, alder, aspen, oak.

main felling area diameter – minimum average diameter of diameter breast height to

allow felling before reaching the harvesting age.

optimal rotation age – The tree harvest age at which net present value of a forest is

maximized.

partial cutting – Any harvest removing part of the timber without clear-cutting.

pre-commercial thinning – A thinning where stumpage prices are negative, so that cut

trees are left in the woods (logging and hauling costs exceed the delivered log price).

present value – The value on a given date of a future payment, discounted to reflect the

time value of money and other factors such as investment risk.

reforestation – Establishing trees on formerly forested areas.

rotation – Age, in years, at which mature timber is harvested.


roundwood – Harvested wood in round or log form.

softwood – Coniferous trees, e.g. pine, spruce.

stand – A group of standing live trees.

stumpage – Standing trees in the forest.

stumpage value – The estimated or actual amount that buyers would pay for standing

timber for immediate harvesting.

stumpage price – Stumpage value expressed as a sum per unit volume.

thinning – The practice of removing some trees in a crowded stand so that the remaining

ones can grow faster.

timber – Refers to trees alone, excluding land, for purposes of producing forest products.

timberland – Forest area available for commercial timber production.

Note: Terms and abbreviations compiled from Klemperer (1996) and Field (2001).


3. Theoretical Framework

In this section, relevant literature and researches in the field are reviewed to come

up with the methodology for the thesis. Theoretical models are evaluated and compared to

determine the most appropriate framework for solving the stated problem.

3.1. Industry analysis

There are numerous approaches used to analyze industries, differing in their

purposes, perspective, depth and other dimensions. The purpose of industry analysis in the

thesis is to identify the factors affecting return in the industry; therefore, the analysis is

carried out from investment perspective.

A useful tool for starting an industry analysis is to determine the stage in the

industry life cycle model, as industry maturity largely determines the competitive

environment and return in the industry (Hill and Jones, 2008). The model shows the

connection between total demand, passage of time, and the maturity stage of an industry.

Although the number of stages slightly varies, the most common set of industry stages is as

follows: pioneer, growth, mature and decline (CFA Institute, 2009).

Figure 1: Stages in the industry life cycle

D

eman

d

Pioneer Growth Mature Decline

Time

Source: CFA Institute, 2009. Illustration by the author.


According CFA Institute (2009), in pioneer phase, the industry is just beginning to

develop, product acceptance is uncertain, and there is a high risk and many failures. In

growth stage, product acceptance is established; both sales and earnings expand rapidly,

significant returns are yielded. As new competitors enter the market and it becomes

saturated, growth slows down and profitability declines to “average” levels, and industry

enters mature stage. Mature industries are usually stable and sales growth follows

economic growth. The last stage is the decline phase, when the demand for the industry’s

products decreases, profit margins diminish, and many companies exit the industry.

An industry can be characterized by its business cycle reaction, based on how it

correlates with economic cycles. CFA Institute (2009) classifies industries into growth,

defensive and cyclical industries. Growth industries exhibit above-normal expansion in

both sales and profits, independent of the business cycle. Typically, these are new

technology and pharmacy companies. Defensive industries show a stable performance

throughout business cycles, and they usually fall into the mature category. Typically, these

are industries which face inelastic demand, such as food, shelter, utilities or government

contractors. Cyclical industries are those which follow business cycle, benefiting from

economic growth and suffering from economic downturn.

External factor analysis is useful to analyze outside influences affecting industry,

including sales, earnings and returns on investment. The most commonly used framework

for external factor analysis is PEST model, which analyzes political, economic, social and

technological factors affecting an industry. This model has been extended to include also

environmental and legal factors, forming a PESTEL model. This model is often used for

business and strategic planning; nevertheless, the model itself or its conceptual framework

is used in financial researches as well, as it offers a structured approach to analyze an

industry. Therefore, PESTEL is used in the thesis to analyze the influence of external


factors on the industry, and determine the necessary adjustments to models used in

expected return calculations.

Supply and demand analysis of the industry provides understanding of the

industry dynamics and key drivers of return in the industry. This analysis involves

identifying and studying customers and client groups, determining drivers of demand for

industry’s products, researching the competitive environment in the industry, assessing

bargaining power of suppliers and customers.


3.2. Forestry Industry Return and Risk Analysis

3.2.1. Review of Previous Researches in the Field

Researches of forestry investments in other countries reveal several approaches to

value timberland investments and risk and return of such investments. Methodology,

results and implications of these researches are reviewed to decide on the methodology to

be used in the thesis.

Faustmann (1849) was one of the first famous researchers of forestry investments.

He developed the most commonly used formula for land value (MV):

Equation 1: Faustmann land value formula

MV = [NG1 × (1 + r)T-tg1

+ NG2 × (1 + r)T-tg2

+ NS – SK × (1 + r)T-tsk

] ×

[(1 + r)T

– 1]-1

– FK

where NG1 = net from the first thinning (net of cutting costs), NG2 = net from the second thinning, NS is net

income from final sales of timber, SK is silviculture (reforestation) costs, FK = discounted annual general

administration costs, r = calculated interest, T = circulation period, tg1 = the time of the first thinning, tg2 =

time of the second thinning, tsk = time at which silvicultural measures are carried out (Lonnstedt and

Svensson, 2000).

Mills and Hoover (1982) develop a framework to analyze risk and return of

forestry investments, basing their model on portfolio theory by Markowitz (1952). They

analyze timberland investments as a part of a diversified portfolio (with investments in

stocks, bonds, and other agricultural investments) by using the following return formula:

Equation 2: Mills and Hoover return formula

Rt = ∑[Pt+1,s ((1 – Mts)Vts + Its(1 – Gts)(1 – Mts)) – PtsVts + (Lt+1 – Lt) – Ct] × [PtsVts + Lt]-1

where Rt = annual rate of return per acre for the forest investment; P = estimated stumpage price for species s

at the beginning period (t) and beginning of period (t+1); M = mortality of species s in period t to t+1; G =

growth loss of species s in period t to t+1; I = expected growth of species s in period t to t+1; V = volume of

growing stock at beginning of period t; L = land value at beginning (t) and beginning of period (t+1); C =

expenses in period t to t+1.


Redmond and Cubbage (1988) use Capital Asset Pricing Model (CAPM) with

Jensen (1969) approach to estimate alpha (risk adjusted return) and beta (sensitivity to

market return or systematic risk) coefficients on forest investments. Formula:

Equation 3: Redmond and Cubbage return formula

Ri – Rf = α + β × (Rm - Rf) + ε

where Ri = realized return on the investment, Rm = realized return on market portfolio, Rf = return on risk-

free asset, ε = error term

It must be noted that they used yearly average stumpage price plus growth changes

as a measure of realized investment returns, arguing that change in land prices is

incorporated in the price change variable, and returns are driven only by timber price

changes and timber growth.

Washburn and Binkley (1990) argue that CAPM application by Redmond and

Cubbage (1988) is flawed, as average stumpage prices are used together with period-end

market returns, causing errors due to different timing. They suggest using averages of

market returns and risk-free rates to improve robustness.

Lonnstedt and Svensson (2000) use similar approach as Mills and Hoover (1982)

to analyze return and risk of timberland investments in Sweden – they apply portfolio

theory by Markowitz (1952). However, Mills and Hoover (1982) used existing data of land

values, while Lonnstedt and Svensson (2000) use Faustmann (1849) formula to calculate

market value of land.

Sun and Zhang (2001) compare results of CAPM and Arbitrage Pricing Theory

(APT) and conclude that both yield similar results, while APT returns more robust results.

However, use of APT in this research is counter-intuitive, as applied APT factors are

synthetically assigned (rotated factor loading method), not factors existing in real life.


Lundgren (2005) uses CAPM like Redmond and Cubbage (1988), but the return is

defined differently:

Equation 4: Lundgren return formula

RT,t = [ ∆(LVt + TVt) + OCFt ] × [ LVt-1 + TVt-1 ]-1

where LV = the value of bare land, TV = the value of standing timber (biomass stock), OCF = PH - C =

operating cash-flow, P = price of timber, H = timber harvest, C = cost of harvesting, investment, and

maintenance costs.

Other researchers, such as Gjolberg and Guttormsen (2001), Kaayire and Nanang

(2002) use real option methodology to value forestry investments, arguing that it is the

most appropriate method, as forest owners have many implicit options, such as cut-down

option, land abandonment option, land use conversion option, and option to delay

investments. Though such method can be applied for investment valuation, it is not

suitable for analyzing and comparing risk and return profile from the perspective intended

in the thesis, therefore, this method is not further considered.

Researchers generally agree that timberland investments have strong historical risk-

adjusted returns, they provide a hedge against inflation, and the negative or limited

correlation with other asset classes makes them attractive for diversification purposes.


3.2.2. Methodology Used in the Thesis

The thesis applies modified methodology used by Mills and Hoover (1982),

calculating historic returns of timberland investments in Latvia. The return calculations

used in this thesis omit land-value changes from the original Mills and Hoover (1982)

model. Bare and Waggener (1980) argue that changes in stumpage prices cause these land-

value changes, and inclusion of land-value changes would therefore double count the

change in stumpage price. As Mills and Hoover (1982) themselves admit, this holds under

the assumption that the land is used exclusively for forestry. Since, as later stated in the

thesis, forest land transformation for other uses in Latvia is very limited and difficult to

make, such assumption is believed to be reasonable in this analysis; therefore, land-value

changes are not taken into account, when calculating return. Also, growth loss is omitted

from the formula, since growth loss is already included in available data of mortality. The

formula used in the thesis for calculating forestry investment return is as follows:

Equation 5: Historic return on timberland investment formula

Rt = ∑ [ Pt+1,s ((1 – Mts)Vts + Its(1 – Mts)) – PtsVts – Ct ] × [PtsVts + Lt]-1

where Rt = annual rate of return per hectare for the forest investment; P = estimated stumpage price for

species s at the beginning period (t) and beginning of period (t+1); M = mortality of species s in period t to

t+1; I = expected growth of species s in period t to t+1; V = volume of growing stock at beginning of period

t; L = land value at beginning (t); C = expenses in period t to t+1.

The risk measure used in the thesis is the variance (squared standard deviation) of

annual rates of return. As argued by Mills and Hoover (1982), it provides an excellent

proxy for investment risk, if the return distribution is symmetric. Also, it is commonly used

in financial economics to measure risk of other investments; therefore, using variance as a

risk measure serves for comparison purposes as well.


Since the model uses stumpage prices (price of standing timber), but price data is

available for roundwood (logs), stumpage prices are estimated by a conversion surplus

approach, as also used by Mills and Hoover (1982), using the following formula:

Equation 6: Stumpage price estimation formula

Pst = -at + ∑ Pst Ys

Where Pst = estimated stumpage price for species s in period t; a = harvesting and milling costs and profits to

convert stumpage to logs in period t; Pi= price of the logs of the sth

species in period t; Ys = the proportional

yield of the sth species.

All other model input data, including timber volume, land prices, growth and

mortality of species in Latvia, are obtained as is from various researches and statistical

databases.

The methodology above is used on historical data to evaluate overall historical

return and risk of forestry investments in Latvia.

Then, expected return for the forestry investment is determined, using the return

formula above with the expected values of variables included in the formula: timber prices,

mortality, volume, growth, management expenses, and land value. The modified return

formula for determining expected return:

Equation 7: Expected return

E[R]t = ∑ [ E[Pt+1,s] × ((1 – E[Mts]) × E[Vts] + E[Its] × (1 – E[Mts])) – E[Pts] × E[Vts] –

E[Ct] ] × [ E[Pts] × E[Vts] + E[Lt] ]-1

where Rt = expected rate of return per hectare for the forest investment; P = expected stumpage price for

species s at the beginning period (t) and beginning of period (t+1); M = expected mortality of species s in

period t to t+1; I = expected growth of species s in period t to t+1; V = expected volume of growing stock at

beginning of period t; L = expected land value at beginning (t); C = expected expenses in period t to t+1.

Expected returns for different investment strategies are determined to draw

conclusions about the most appropriate investment strategy and suggest its

implementation.


4. Empirical Findings

4.1. Forestry Industry Analysis

4.1.1. General Information on Forestry in Latvia

Forest is one of the most important natural resources of Latvia, as about 50% of

territory of Latvia is covered with forests. State owns half of forests, 47% of forests are

privately owned, and municipalities own 3% of Latvian forests. The thesis is concerned

with privately owned forests, as these are potential investment targets of the investment

fund.

Figure 2: Forest cover in Latvia (%), forest ownership structure

Source: Forest Industry in Latvia, 2008

According to data of Ministry of Agriculture, forests in Latvia are dominated by

pine (28.8%), birch (28.2%), spruce/fir (17.4%), other species are below 10% of total. The

following graph shows the age structure of major species in Latvia:

Figure 3: Age structure of major species (thousand ha)



Since forest management and related industries are among the most important

sectors of Latvia, there have been established many associations and other institutions to

help coordinating and developing forest industry in Latvia.

Figure 4: Forest sector organizations and governance


The thesis uses reports and researches of these organizations (e.g. Forest Industry

Institute “Silava”, Latvian Forest Industries Federation) as a source of information and data

about forestry in Latvia.


4.1.2. Industry Classification

Latvian forest industry, according the industry life cycle model, is in a long-term

mature development stage. It has been present for centuries, the product acceptance has

been established long ago, the industry is saturated with suppliers, and industry turnover is

experiencing steady growth. Since the use of timber is not expected to diminish in the

foreseeable future, it is highly unlikely that the mature stage will turn into a decline stage.

According to the model, forest industry, like other industries in a mature

development stage, should provide rather moderate return on investment, considering

relatively low risk.

If classified according business cycle reaction, forest industry falls into defensive

industry category. Forest industry is definitely not a growth industry, as also indicated by

the industry life cycle classification. Demand in the industry is rather stable throughout

business cycles – although it is affected by general state of economy, it does not highly

correlate with economic cycles.

Based on industry classifications, it is expected that both return and risk in the

forest industry in Latvia is in the low to moderate range due to the mature development

stage, but since the industry is defensive, it is expected to provide a diversification

characteristics for portfolio as a hedge against fluctuations in the market and inflation. This

is also concluded in several foreign researches, as discussed in the theoretical framework

section of the thesis.


4.1.3. External Factor Analysis

4.1.3.1. Political Factors

The main political factors (legal factors are viewed separately) influencing forest

industry are related to forest ownership, taxation and regulation in the industry.

As stated above, about 50% of forests in Latvia are state owned; they are managed

by State’s Forest Services. Due to such large state ownership, the government might be

biased in terms of decisions regarding forestry industry. Another aspect of such large

ownership by state is that it can possibly crowd out private forests owners – as industry

experts comment, since a political decision for the state to buy up more forest land was

made, forest land prices sharply increased and private owners had to bid higher prices to

purchase forest land.

Regarding taxation, owning forest land is an attractive investment, since no land tax

is paid on new or renewed forests (Regulations of Cabinet of Ministers no.76).

Nevertheless, personal income tax is applied on income from forests owned by private

parties.

Forestry is a highly regulated industry with lots of government intervention. Forest

management, including thinning, harvesting, reforestation, is strictly prescribed. More on

regulations in the Legal factors section. It must be noted that plantations (agricultural land

artificially afforested for special purposes) do not fall under the forest regulations;

however, they also do not enjoy the land tax benefits, and in Latvia are considered an

inferior alternative to forests by industry experts due to inappropriate soil chemical

structure and higher probability of illnesses. Therefore, plantations are not in the scope of

this paper.


4.1.3.2. Economic Factors

Since timber is a regional commodity (although timber is sometimes delivered to

other continents, typically, it is transported within a region), timber producers face a

regional demand and competition. Consequently, the industry is affected by economic

developments in the region, for instance, decrease of construction levels in Scandinavia

decrease demand for lumber of Latvian timber producers, as Scandinavia is the main

trading partner for Latvian foresters.

Another economic factor affecting the industry is the availability of financing.

Since forest management is financial capital intensive industry (forest land purchase,

machinery), high interest rates and/or tight crediting is bad for the industry.

4.1.3.3. Social Factors

The main social factor affecting forestry industry is the increasing awareness about

sustainable development and ecological products.

Awareness about sustainable development is mostly concerned with the

preservation of environment, which leads to an important environmental factor –

reforestation (see Environmental factors section).

Increasing popularity of ecological products has a positive impact on demand of

timber, since products made of wood are among the most natural products one can get.

4.1.3.4. Technological Factors

Technological developments have lead to increasing cost-efficiency in tree felling,

since more advanced machinery allows decreasing the labor component in forest

management cost. As an example, one can compare lumberjack (a person who manually

fells trees) with an axe and a harvester tractor that cuts trees in seconds.


A technological factor negatively influencing forestry is the development of new

materials, which substitute wood as a material (e.g. PVC window frames substituting

wooden window frames). However, technological advancements also find new uses for

timber products (e.g. composite materials made of wood and plastic) that significantly

improve material characteristics. Therefore, the effect of technological advancements

cannot be unequivocally said to be negative.

4.1.3.5. Environmental Factors

Regarding the environment, an important factor for the industry is reforestation.

According the legislation, reforestation must be carried out within 3 years since harvesting.

In addition, at least 80% of forests owned must be reforested before getting approval for

new cutting sites.

Another important environmental factor for forestry is the natural environment

itself – soil fertility, weather conditions, spread of vermin, and other nature related aspects.

Although all of them influence returns in forest management, the thesis uses soil fertility

(i.e. forest type) as a major differentiating factor in analyses. Such approach is used also in

other researches reviewed, since soil fertility or site quality is considered the most

influential of the mentioned environmental factors on timber growth.

An important environmental aspect affecting forestry industry is the existence of

protected nature territories, where economic activity is significantly limited or fully

prohibited. Latvia has joined the EU initiative for protection of species and habitats called

Natura 2000, which is a network of protected territories and microreserves. According to

the Ministry of the Environment, currently 11.3% of land territory of Latvia is within

Natura 2000, from which about 50% is forest land, both publicly and privately owned.


Representatives of the Ministry of the Environment have expressed determination to

extend Natura 2000 to additional 0.25% in the nearest future.

Figure 5: Protected nature territories in Latvia

Source: Nature Conservation Agency

Since land owners have virtually no information on what the expansion territories

will be and they have limited ability to discuss an inclusion of a land area in protected

territories, environment protection poses a negative effect on forestry industry. As stated

by many private forest owners in the forestry industry conference Future of forest industry

in Latvia – opportunities and solutions (2010), they have not received any compensation

for forests included in Natura 2000 (and thereby restricted from economic activities) for

several years.

Considering all this information, it can be said that environmental protection causes

decreased returns for forest owners.


4.1.3.6. Legal Factors

Forest land registration and transformation

According to the Civil Law article nr.994 (available on Likumi.lv, 2010), land

ownership rights are based on the records of the Land Register, and since forests can be

fully managed (including tree felling and buy/sell transactions) only by land owners, it is

clear that forests must be registered with the Land Register to achieve the best benefit from

them. In Latvia, 36% of forest land has not been registered with the Land Register. All the

forest land that has not yet been registered belongs to the state through Latvia’s State

Forests, until it is registered with the alleged owner. It must be noted that the largest part of

this unregistered land is actually owned by the state with no alleged private owners, and

Latvia’s State Forests has also begun registering such land with the Land Register.

There are two basis for registering initial ownership by private owners: renewal of

ownership rights (based on previous ownership, including through inheritance) or through

privatization of land that has been received in long term use. First registration in either case

requires land boundary map, request for registration approved by a notary and payment of

registration fee. Both processes are relatively lengthy and can take up to 3-4 months, and

can cost up to LVL 1’000-1’500 per hectare. Considering the cost of registration and prices

for forest land, it is often cheaper to buy registered land than to register own land.

Renewal of ownership rights requires evidential documents of previous ownership,

besides the documents mentioned above. Based on these evidences, the decision is made

whether or not to allow registering the ownership rights on a forest.

Privatization on the basis of long term use is possible only until 31.08.2010 for

forest lands that had been prior assigned in long term use, for a fee determined by cadastral

value and forest inventory data. Privatization process is generally controlled by local


municipality. After 31.08.2010, privatization of state owned land (including forests) will

only be possible through auctioning.

Forest land transformation is regulated by Regulations of Cabinet of Ministers

no.806, and it states that land transformation can only be initiated by the legal (i.e.

registered with Land Register) owner. Land transformation requires the following

documents and approvals: topographical plan and construction board approval, local

municipality approval of new planning, forest inventory and forest district authority

approval, and boundary map registration with State Land Service. It must be noted that

there are several restrictions on land transformation, and often transformation is denied by

one of the mentioned institutions. The process is very lengthy and with all approvals can

take up to a year, with associated costs up to LVL 1’000-1’500 per hectare, which often

makes the land transformation unfeasible.

Although land transformation is possible, due to the complexity, time and resource

consumption of the process, it can be assumed to be feasible only in exceptional cases.

This implies that forest land faces little competition from other uses besides forestry, which

is one of the assumptions behind the chosen return estimation model.

Forest management

Forest management in Latvia is regulated by a special law – Law on Forest

(available on Likumi.lv, 2010). The Law allows felling trees in:

Main felling area – for harvesting timber, after reaching the minimum age or

the minimum diameter in the felling area.

Improvement cutting (thinning) – for improving growing conditions and

healthiness of forests.


Sanitary felling area – illness, animal, vermin or any other way damaged tree

felling.

Reconstructive felling area – unproductive forest felling, according laws and

regulations.

Other felling areas – for creating and maintaining forest infrastructure, forest

land transformation, scenic landscaping, removal of dangerous trees, and

nature preservation purposes, according laws and regulations.

For the purpose of the thesis, the relevant points are regarding felling in main

felling areas and improvement cutting (thinning). While thinning can provide marginal

income from forests, usually this income is rather insignificant, compared to income from

harvesting in main felling areas.

In order to start harvesting trees, a special license must be obtained from forest

district, which approves such license on the basis of forest inventory performed by

independent valuator and harvesting plan compliance with effective legislation. Forest

guards are controlling forest management to ensure that all legislation requirements are

fulfilled.

Harvesting in main felling areas is subject to minimum average age of dominant

species, as regulated by the Law on Forest:

Table 1: Main felling area stand age

Dominant species Ia and I II-III IV and lower

Oak 101 121 121

Pine, larch 101 101 121

Spruce, ash, lime-tree 81 81 81

Birch 71 71 51

Alder 71 71 71

Aspen 41 41 41

Source: Law on Forest (available on Likumi.lv, 2010)


However, this regulation of minimum age can be ignored, if the average diameter

in a stand is above the minimum average diameter as defined by the Regulations of

Cabinet of Ministers no.892:

Table 2: Main felling area average basal diameter

Dominant species

Site quality

Ia I II III

Average diameter (centimeters)

Pine 40 36 32 28

Spruce 32 30 30 28

Birch 32 28 26 23

Source: Regulations of Cabinet of Ministers no.892 (available on Likumi.lv, 2010)

These restrictions of main felling area harvesting have a direct impact on potential

profitability – as argued by industry experts (interviews with Sandijs Lūkins, Raitis

Zverblis), the optimal rotation age (to maximize profits) is far less than the minimum

felling age allowed; therefore, the realization of profitability potential is limited. It must be

noted that restriction in other countries, such as Sweden and Finland, allow felling younger

trees, offering better returns for forest owners.

Clear cutting, which is the most efficient for harvesting purposes, is allowed for up

to 5 hectares (in some areas of good site qualities, up to 10 hectares is allowed, if at least

20 pines per hectare are left for seedling purposes).

Thinning is allowed, if basal area (sum of cross section areas of trees) exceeds the

minimum threshold set by Regulations of Cabinet of Ministers no.892. Additionally, a

felling permit must be obtained, prior to thinning. Since regulations regarding thinning are

relatively in line with the need of thinning, this requirement is not considered as restricting.


4.1.4. Supply and Demand Analysis

Supply in this industry stems from forest owners who harvest timber from forests in

their possession. Two distinct types of suppliers are public and private forest owners, and

the main differences are the underlying drivers for supplying materials to the market.

Public forests are being managed by Latvia’s State Forests according to a

preapproved five year plan of harvesting amounts in cubic meters. Although the actual

harvest might deviate from the initial plan, this plan is the target for public forests.

Private owners, on the other hand, usually plan for only one year ahead, since tree

felling licenses are usually obtained for the year ahead, and the licenses are a right, not an

obligation to fell the trees. It must be noted that private forest owners often plan their

harvesting in relation to Latvia’s State Forests harvesting plan, since it is the single largest

player in the industry and provides about a half of total timber to the market, thereby, its

decisions move the prices significantly. Hence, private owners are better off using a

follower strategy not to sell their timber on an oversupplied market, and pushing their

capacity in an undersupplied market.

A problem currently faced by the supply side of the industry is that Latvia’s State

Forests have not yet made a harvesting plan for period 2010-2014; therefore, private forest

owners currently have no guidance of what their harvesting strategy should be. The plan is

expected to be approved only in summer 2010.

Demand in the industry is composed from several components, since there are

many uses of timber depending on dimensions and quality of trees: (1) saw logs are

demanded by sawmills for producing lumber, (2) plywood or veneer logs are used

primarily by veneer producers, (3) packing timber is used for producing solid wood


packaging such as pallets and boxes , (4) pulpwood is used for production of pulp and

paper, sometimes also used in Oriented Strand Board (OSB), (5) energywood is timber and

residuals used for producing energy, often interchangeable with pulpwood (6) poles are

special category of saw logs, which are used for producing electricity and other poles.

In order to analyze demand dynamics in the industry, it should be noted that

demand for timber largely stems from the demand for products in later stages in the value

chain – for instance, sawmills purchase round timber to produce lumber, which is later

used in construction of buildings and wooden product manufacturing. Also, the fact that

timber is a regional commodity should be taken into account.

In the short run, there has been a decrease in demand for lumber and veneer due to

global economic slowdown and resulting decrease in construction and manufacturing

outputs; however, this trend is expected to reverse in the medium term, since the global

economy is already showing signs of recovery. Other timber product categories have

remained relatively stable, with the exception of pulpwood and energywood. One of the

major buyers for pulpwood historically has been pulp and paper industry in Scandinavia;

however, there is a trend of moving pulp and paper production to more southern regions

(Future of forest industry in Latvia, 2010), causing demand for pulpwood in the region to

decrease. As a result, timber producers in Latvia must switch to supplying pulpwood as

energywood; however, in the short run, there might be difficulties to do so, since the

energy sector is yet to develop to absorb such amounts.

Overall, it can be concluded that on the supply side, private forest owners in Latvia

face large competition from public sector due to large volumes offered by Latvia’s State

Forests. Therefore, private owners have to adjust their strategy based on the strategy of the

largest player; however, they experience more flexibility with regards to postponing

harvesting.


Analyzing demand dynamics and forces in the market, it can be concluded that

timber producers (i.e. suppliers) have lower bargaining power than buyers. Although the

industries are interdependent, timber is a commodity not a scarce resource, therefore,

buyers can easily switch between suppliers, and this is exactly what is happening in pulp

industry.


4.2. Return and Risk of the Forestry Investment Fund

4.2.1. Data Description

The thesis uses the following data in analyzing return and risk of forestry

investments:

Forest area according dominant species and forest type (Lazdiņš, 2008).

Timber volume according dominant species and forest type (Lazdiņš, 2008).

Historical forest area and timber volume data (Central Statistical Bureau, 2001-

2009).

Growth according dominant species and forest type (Lazdiņš, 2008).

Mortality according dominant species and forest type (Lazdiņš, 2008).

Timber prices for different timber types (Latvian Wood databases, 2001-2009).

Forest land prices according different regions (Forest valuation report, 2010).

Forest land area according different regions (Central Statistical Bureau, 2009).

Forest management costs (Forest valuation report, 2010).

The analysis will use findings of Lazdiņš et al (2008) within Latvian State Forest

Research Institute “Silava”, who has researched the average annual growth rates and

mortality of timber in Latvia. According to the research data, average annual growth rate is

8.17 m3 per ha, and annual mortality (due to illnesses, weather etc.) calculated from the

data presented in the research is approximately 2.01%. For detailed data, refer to Appendix

IV. Since the general composition of forests and the climate has not changed significantly


over the last decade and no historical data is available, such growth and mortality rates are

applied for whole period analyzed.

Historical forest area and timber volume data are used to calculate average timber

volume per hectare.

Historical stumpage prices are calculated, based on historical roundwood prices as

retrieved from Latvian Wood databases, subtracting harvesting milling costs per m3,

which, according to the Forest valuation report (2010), are approximately LVL 7 per m3.

Average forest land price per ha was calculated by weighting forest land value

according different regions of Latvia as from Forest valuation report (2010), applying

weights of forest areas of different regions of Latvia, as from Central Statistical Bureau

(2009). Forest management costs, including administrative costs and infrastructure

maintenance costs, are LVL 6 per ha per annum, based on the Forest valuation report

(2010).


4.2.2. Analysis of Historical Return and Risk

4.2.2.1. Historical Return and Risk Calculations

Based on the Mills and Hoover (1982) framework, the following annual returns on

forestry were calculated for forestry investments in Latvia on aggregate (i.e. average) level.

It must be noted that throughout historical return analysis, nominal rates of return are used.

Table 3: Return on forestry, 2001-2009

Period Return,

%

Stumpage

price,

LVL/m3

Mortality,

% of vol.

Volume,

m3/ha

Growth,

m3/ha

Expenses,

LVL/ha

Land

value,

LVL/ha

2001 1.5% 10.09 2.01% 187.5 8.17 6.00 468.47

2002 3.2% 10.29 2.01% 199.5 8.17 6.00 468.47

2003 11.6% 11.55 2.01% 197.7 8.17 6.00 468.47

2004 46.0% 17.62 2.01% 194.6 8.17 6.00 468.47

2005 18.7% 20.96 2.01% 192.9 8.17 6.00 468.47

2006 -5.8% 19.21 2.01% 200.2 8.17 6.00 468.47

2007 96.2% 39.19 2.01% 199.0 8.17 6.00 468.47

2008 -8.6% 34.96 2.01% 204.0 8.17 6.00 468.47

2009 -29.1% 23.70 2.01% - 8.17 6.00 468.47

Source: Author’s calculations; sources of input data covered in section “Data description”.

Returns on forestry investments were found to have relatively high rates of return

in most periods; however, the return rates were fluctuating to a great extent. In fact, if

return components are analyzed, it can be seen that most variability comes from the

fluctuations in stumpage prices of timber. It must be noted that additional volatility would

have been added if historic forest land prices were used instead of constant; however, as

argued in the theoretical section of the thesis, since the forest land in Latvia is best used for

forestry and there is limited alternative uses, doing so would double-count the effects of

timber price fluctuations.

Mean annual return of this period was 0.1475 or 14.75%, with standard

deviation of 0.3693 and variance of 0.1364. This return seems high on an absolute level,


considering the general knowledge of returns on various asset classes; however, it must be

taken into account that the time-series of 2001-2009 used in calculations include years of

rapid economic growth (often referred to as “economic bubble”) in Latvia, when returns on

other types of investments were unusually (and overly) high as well. As the matter of fact,

timber price, which has a direct effect on returns on forestry, increased more than 100% in

year 2007 alone. Timber price mean annual increase was 15.24% over the period, with

variance of 0.1660, thus it can be seen that forestry performed with little less return but

with much less volatility (i.e. risk as measured by variance).

4.2.2.2. Analysis in the Context of Macroeconomic Developments

To analyze forestry returns in the context of macroeconomic developments, historic

forestry returns are compared with the developments of GDP and inflation. Although

timber is generally an input to production, one might suggest that producer price index

developments should be examined; however, since forestry is analyzed from investment

perspective and investments are typically compared against consumer price index

developments (i.e. general inflation), the latter approach is used.

Figure 6: Return and GDP growth

-40

-20

0

20

40

60

80

100

-20

-10

0

10

20

30

40

50

2001 2002 2003 2004 2005 2006 2007 2008 2009

Real GDP growth Latvia, % Real GDP growth EU, % Forestry return, %

Ret

urn

GD

P g

row

th

Source: Author’s calculations, GDP data from International Monetary Fund


Since timber is a regional commodity, both GDP growth of Latvia and EU were

analyzed. Comparing forestry historical returns with GDP developments does not imply

any significant relationship between GDP and forestry returns. Correlation coefficient,

which shows the co-movement or linear association between two variables (CFA Institute,

2009), is 0.55 for Latvia and 0.58 for EU, which shows a rather weak correlation between

GDP and forestry returns. Weak correlation with GDP combined with low volatility of

forestry returns would approve forestry being a defensive industry; however, since the

volatility cannot be regarded as low, such statement cannot be approved.

Figure 7: Return and inflation

-40

-20

0

20

40

60

80

100

-8

-4

0

4

8

12

16

20

2001 2002 2003 2004 2005 2006 2007 2008 2009

Inflation Latvia, % Inflation EU, % Forestry return, %

Infl

atio

n

Ret

urn

Source: Author’s calculations, inflation data from International Monetary Fund

When forestry returns are compared to inflation data, it can be seen that they are

not exhibiting a straight-forward co-movement; this is also proved by a low correlation

coefficient of 0.27 with Latvia’s inflation and 0.11 with inflation in EU. This suggests that

forestry investments are not a good hedge against inflation, since a good hedge would be

one that moves together with inflation, yielding high returns in times of inflationary

environment, and rather low returns in times of low inflation.


However, if inflation in Latvia and forestry returns are analyzed in more detail, a

certain pattern can be recognized – inflation lags forestry return by one period. The

following graph shows historic forestry returns and inflation data lagged by one period:

Figure 8: Return and lagged inflation

-40

-20

0

20

40

60

80

100

-8

-4

0

4

8

12

16

20

2001 2002 2003 2004 2005 2006 2007 2008 2009

Inflation Latvia, lag(1), % Forestry return, %

Infl

atio

n

Ret

urn

Source: Author’s calculations, historic inflation data and forecast for 2010 (shown as 2009 with a lag of one

period) from International Monetary Fund

The correlation coefficient between forestry returns and lagged inflation is 0.81,

meaning a strong co-movement of these variables. This can also be noticed visually from

the graph above – the two lines are moving in tandem, with the exception of year 2006.

Existence of such correlation does not mean a perfect hedge against inflation; however, it

can be argued that owners of forestry investments are rewarded for future inflation, i.e.

forestry returns of a period compensate for inflation in the next period. Therefore, in a

sense, forestry provides an indirect hedge against inflation.

4.2.2.3. Comparison with Other Asset Classes

In order to draw conclusions about forestry attractiveness as an investment and to

identify its characteristics as a portfolio diversifying asset, the analysis will be extended to

include other types of assets. For comparison purposes, historic performance of stock

markets and government bonds are taken. Since investing in forestry in Latvia means a


regional exposure, as timber is a regional commodity, alternative investment opportunities

in both Latvia and Europe are considered.

Latvian stock exchange index OMXR is used as a proxy for investments in Latvian

stock market; similarly, Baltic Benchmark index OMXBBGI is used to represent

performance of investments in listed Baltic companies. FTSE Eurotop 100 index is used to

represent investments in European stock market – it is a capitalization-weighted index of

100 most highly capitalized companies in Europe (conceptually similar to widely used

S&P 500 in USA). To compare forestry to government bonds, EU 10 year bond benchmark

is used, which is a synthetic bond, composed of Euro area country government bonds.

Table 4: Returns of different asset classes 2001-2009, %

Period Forestry

returns

Latvian

stock

exchange

index

OMXR

Baltic stock

exchange

benchmark

index

OMXBBGI

European

stock

exchange

index FTSE

Eurotop 100

EU 10 year

bond

benchmark

2001 1.5% 45.2% 4.0% -16.2% 5.0%

2002 2.5% -14.1% 36.6% -24.5% 4.9%

2003 11.5% 48.4% 45.0% 13.6% 4.2%

2004 45.9% 43.5% 57.6% 7.5% 4.1%

2005 18.6% 63.5% 47.5% 16.7% 3.4%

2006 -5.8% -3.1% 22.4% 10.7% 3.9%

2007 96.5% -9.2% -8.6% 3.8% 4.3%

2008 -9.0% -54.4% -66.7% -31.3% 4.4%

2009 -29.2% 2.8% 37.8% 22.1% 4.0%

Source: Author’s calculations, historic returns for stock indices from Nasdaq OMX Baltic, Reuters,

Bloomberg, historic yields for bonds from European Central Bank

As can be seen from the table above, EU 10 year bond benchmark has been

generating relatively stable yields, while all other asset classes have been rather highly

fluctuating, yielding higher returns in certain years, and lower or even negative in others.

The following table summarizes mean performance and variability of the

aforementioned asset types:


Table 5: Summary of comparison with different asset classes

Forestry

returns

Latvian

stock

exchange

index

OMXR

Baltic stock

exchange

benchmark

index

OMXBBGI

European

stock

exchange

index FTSE

Eurotop 100

EU 10 year

bond

benchmark

Mean returns 14.7% 13.6% 19.5% 0.3% 4.3%

Variance 0.136 0.149 0.150 0.037 0.000

Standard deviation 0.369 0.386 0.387 0.193 0.005

Sharpe ratio 0.290 0.249 0.401 -0.194 n/a

Source: Author’s calculations, historic returns for stock indices from Nasdaq OMX Baltic, Reuters,

Bloomberg, historic bond yields from European Central Bank

Mean returns represent average annual arithmetic returns for period 2001-2009,

variance and standard deviation represent the variability of these returns. As can be seen

from the table, highest annual return in the period was achieved by Baltic stock exchanges

with 19.5%, followed by forestry in Latvia with 14.7%, and Latvian stock exchange with

13.6%. European stock exchanges, as shown by the proxy, performed surprisingly bad,

with only 0.3%, which is below EU government bonds. This can be explained by the

aftermath of dot-com bubble burst in the beginning of the decade, which hardly hit

European markets, but had limited downside in the Baltic States.

Regarding risk or variability measures, EU government bonds, as expected, show

the lowest values – zero variance (with three decimal places) and a standard deviation of

0.005. EU stock market has the second lowest variance, 0.037. Forestry variance of 0.136

is exceeded both by Latvian and Baltic stock exchanges, 0.149 and 0.15 respectively. In

the light of mean returns, forestry is a better investment than Latvian stock market in terms

of mean-variance framework, since it provides better return and lower variance (i.e. risk)

than Latvian stock market. Comparison of forestry with other asset classes is not as straight

forward, since others have either both higher returns and risk (Baltic stock exchange), or

both lower returns and risk (EU stock market and EU bonds). However, it can be inferred


that EU bonds have outperformed EU stock market in the period, providing both better

returns and lower variability.

Sharpe ratio, which is a common measure for risk-adjusted returns of an

investment, shows the average return in excess of the risk-free rate divided by the standard

deviation of return (CFA Institute, 2009). As the risk-free rate, EU 10 year bond

benchmark is used. According to Damodaran (2009), the most applicable measure for risk-

free rate is the yield of 10 year default free government bond that matches the market and

the currency of the underlying asset. Since Latvia is an EU member, it can be argued that

all of the mentioned assets are within one market, and, although Latvia still has its own

currency and there is a currency risk, the fact that shares on Baltic stock exchanges are

traded also in euro and cash flows for forestry investments generally are in euro (due to

exports to EU markets) imply that use of euro-denominated bonds is sensible. Therefore,

benchmark of euro area 10 year government bonds with value 4.03% is used as the risk-

free rate.

Sharpe ratio analysis shows that on risk-adjusted return level, forestry in Latvia

(Sharpe ratio 0.29) is superior to Latvian stock market (0.249), but inferior to Baltic stock

market (0.401). Since European stock market, as measured by FTSE Eurotop 100,

generated lower annual returns than risk-free rate, its Sharpe ratio is negative, meaning that

investors are not compensated for taking additional risk, and thus are worse off than

owning a risk-free asset or any other of the mentioned assets. For EU bonds, Sharpe ratio is

not calculated, since they themselves are considered risk-free assets.

To analyze diversification potential of forestry investments, the basic assumption

behind portfolio construction under Modern Portfolio Theory – including an asset that is

not perfectly correlated with the portfolio will increase the risk-adjusted return of total

portfolio (CFA Institute, 2009). Correlation coefficient can take values in the range of -1


(perfectly negatively correlated) to +1 (perfectly positively correlated), and for

diversification purposes, the lower the correlation between two assets, the greater

diversification benefits of such asset.

The following table summarizes correlation between different asset types for period

2001-2009:

Table 6: Correlation between returns, 2001-2009

Forestry

returns OMXR

OMX

BBGI

FTSE

Eurotop

100

EU 10

year

bond

Forestry returns

0.13 0.01 0.11 -0.02

Latvian stock exchange index

OMXR 0.13 0.73 0.56 -0.27

Baltic stock exchange

benchmark index OMXBBGI 0.01 0.73 0.66 -0.30

European stock exchange

index FTSE Eurotop 100 0.11 0.56 0.66 -0.74

EU 10 year bond benchmark -0.02 -0.27 -0.30 -0.74

Source: Author’s calculations

As can be seen from the table, forestry returns are weakly correlated with other

asset classes, since the range of correlation coefficients is from -0.02 and 0.13. This means

that, all other things held constant, if an investor’s portfolio consists of other assets, he/she

will increase the risk-adjusted returns of portfolio by adding forestry to the portfolio.

Another observation made is that EU 10 year bond benchmark is negatively

correlated with all other asset types, while all of the stock exchanges are rather strongly

correlated (this partly is because they are not exclusive, e.g. Latvian stock market is also

included in Baltic benchmark index). Therefore, without a more detailed analysis, it can be

speculated that mean-variance optimal portfolio would include forestry, EU bonds and at

least one of the stock markets.


4.2.2.4. Comparison with Other Researches in the Field

The following table summarizes findings with other researches in the field. Since

there are no similar researches on Latvian forestry, findings of other researches

internationally were taken for comparison purposes.

Table 7: Results of other researches

Country Period

Number

of years

Mean

returns Variance

Forestry returns, thesis Latvia 2001-2009 9 14.7% 0.136

Mills and Hoover (1982) USA 1969-1978 10 10.1% 0.034

Mills and Hoover (1982) USA 1959-1978 20 7.3% 0.018

Redmond and Cubbage

(1988) USA 1951-1985 35 10.8% 0.038

Lonnstedt and Svensson

(2000) Sweden 1968-1994 27 14.9% 0.091

Lundgren (2005) Sweden 1965-1999 35 16.1% 0.006

Note: for other researches where mean returns and variance were calculated for different areas/species,

averages were calculated and presented in this table.

Source: Author’s calculations, respective researches

When comparing the findings of this thesis to other researches in the field, it can be

concluded that mean annual returns of forestry in Latvia of 14.7% are comparable to those


of 11.8%.

However, all of the other researches reveal variances lower than the one calculated

for Latvian forestry returns (0.136), with the closest value being 0.091 in Sweden. The

average variability of return in other researches was 0.038, which is much lower than the

one calculated about Latvia in the thesis. This can mainly be attributed to the time period

covered by the researches, which has twofold consequences. Firstly, longer time periods in

other researches even out short-term fluctuations, therefore, lowering the variability of


returns. Secondly, the time period covered in the thesis (2001-2009) includes significant

economical imbalances in both local and global economy, with speculative commodity

bubble in 2008 and following global recession among other things. Therefore, it can be

argued that although such high volatility is justified for this period, a longer time horizon

would probably show much lower variability in returns, thus converging with the levels as

presented in other researches.


4.2.3. Determining Expected Return and Risk of the Fund

In this section, expected return and risk of a forestry investment fund in Latvia will

be estimated, based on the findings of previous sections about historical performance of

forestry investments and factors affecting potential returns. First, the expected long-term

performance of a fund pursuing a generic investment strategy will be determined, followed

by estimating long-term returns for different forest investment strategies. It must be noted

that expected returns determined are real rates of return.

4.2.3.1. Expected Return of Generic Investment Strategy

A forestry fund pursuing generic investment strategy in this thesis is regarded as a

fund which does not follow a certain investment strategy based on forest types, tree species

or other characteristics of forests. Returns of such investment fund should be equal to

“average” return of forestry, since such fund would not differentiate its investments and

therefore should possess “average” forests. Effectively, this means that determining

expected return of such investment fund means determining the expected returns of

forestry in Latvia on an aggregate level.

Considering the forestry return formula used in this thesis, expected return of

forestry investments are determined by values of and changes in timber prices, mortality,

volume, growth, management expenses, and land value.

According to Ernst&Young (2009) report, median real long-term timber stumpage

price change used in standing forest valuation is 1.00% per annum. Since timber is a

regional commodity, in the long run, prices within a region should converge; therefore,

1.00% is used in the thesis as expected real price growth for Latvian timber.


Expected values for mortality, volume, growth, management expenses and land

value in this analysis are expected to remain on current level, since due to stable climate

the mortality and growth rates are not expected to change significantly, changes in land

prices are ignored in this model, there is no reasonable basis for timber volume per ha and

management expenses to change.

Table 8: Expected return of generic investment strategy

Expected

return

Price

growth

Current

stumpage

price,

LVL/m3

Mortality,

% of

volume

Volume,

m3/ha

Growth,

m3/ha

Expenses,

LVL/ha

Land

value,

LVL/ha

2.6% 1% 23.62 2.01% 204.01 8.17 6.00 468.47


Based on such assumptions, the expected annual return for generic investment

strategy is 2.6% per annum. However, it must be noted that the expected return is highly

dependent on the expected stumpage price growth; therefore, a sensitivity analysis is

performed.

Table 9: Sensitivity of expected returns on generic strategy

Price growth Expected return

0% 1.6%

1% 2.6%

2% 3.5%

3% 4.4%

4% 5.4%

5% 6.3%

10% 10.9%


As can be seen from the sensitivity analysis, there is a non-linear relationship

between expected price growth and expected returns on investment fund, pursuing generic

investment strategy. With 0% expected price growth, the expected return is 1.6%, for 10%

price growth, the expected return is 10.9%, which means only a 0.9% excess return over

price growth rate.


4.2.3.2. Expected Return of Different Timber Types Investment Strategies

A more sophisticated investment strategy for the fund is to be focusing on either

softwood or hardwood forest types; therefore, expected returns for such investment funds

are calculated, similarly as in the previous section, but using more detailed data for

softwood or hardwood forests (refer to Appendix IV for more detailed data). Since there is

no statistical information available for land value differences between softwood and

hardwood forests, average value is used.

Table 10: Expected return of different timber types

Expected

return

Price

growth

Current

stumpage

price,

LVL/m3

Mortality,

% of

volume

Volume,

m3/ha

Growth,

m3/ha

Expenses,

LVL/ha

Land

value,

LVL/ha

Softwood

2.2% 1% 30.40 2.14% 229.90 8.29 6.00 468.47

Hardwood

2.9% 1% 17.16 1.86% 180.92 8.07 6.00 468.47


As can be seen from the table above, investing in softwood forests has expected

returns of 2.2%, while investing in hardwood forests has expected returns of 2.9% per

annum. Based on this information, all other things held constant, the investment fund

generates greater return by investing in hardwood forests.

Table 11: Sensitivity of expected returns, different timber types

Price growth Expected return,

Softwood

Expected return,

Hardwood

0% 1.2% 2.0%

1% 2.2% 2.9%

2% 3.1% 3.8%

3% 4.1% 4.7%

4% 5.0% 5.6%

5% 6.0% 6.5%

10% 10.7% 10.9%



As can be concluded from the sensitivity analysis, the greatest expected return

differences between softwood and hardwood investment strategies are at low price

changes, while with higher price growth this difference shrinks.

4.2.3.3. Expected Return of Different Species Investment Strategies

Similarly to the previous investment strategy, the investment fund could focus on

certain tree species; therefore, expected long-term return for different species investment

strategies are calculated, using species specific data (refer to Appendix IV for more

detailed data and Appendix V for detailed calculations).

Table 12: Expected return of different species

Expected

return

Price

growth

Current

stumpage

price,

LVL/m3

Mortality,

% of

volume

Volume,

m3/ha

Growth,

m3/ha

Expenses,

LVL/ha

Land

value,

LVL/ha

Pine

1.8% 1% 33.00 2.19% 248.82 7.97 6.00 468.47

Spruce

3.0% 1% 27.80 2.03% 198.68 8.81 6.00 468.47

Birch

2.4% 1% 24.40 2.32% 175.06 7.44 6.00 468.47

Black alder

4.1% 1% 16.60 0.50% 212.77 9.19 6.00 468.47

White alder

4.1% 1% 15.20 1.63% 134.36 8.14 6.00 468.47

Aspen

2.6% 1% 11.80 1.81% 234.40 9.70 6.00 468.47

Oak

3.6% 1% 17.80 0.35% 216.47 7.60 6.00 468.47


From the table above, it can be concluded that investing in forests with alder as the

dominant species would yield average expected return 4.1% per annum. Oak forests have

expected yield of 3.6%, followed by spruce forests with 3.0% per annum.


Table 13: Sensitivity of expected returns, different species

Price

growth

Expected return

Pine Spruce Birch Black alder White alder Aspen Oak

0% 0.8% 2.0% 1.5% 3.2% 3.3% 1.7% 2.7%

1% 1.8% 3.0% 2.4% 4.1% 4.1% 2.6% 3.6%

2% 2.7% 3.9% 3.4% 5.0% 5.0% 3.5% 4.5%

3% 3.7% 4.9% 4.3% 6.0% 5.8% 4.4% 5.4%

4% 4.6% 5.8% 5.2% 6.9% 6.7% 5.2% 6.3%

5% 5.6% 6.7% 6.1% 7.8% 7.5% 6.1% 7.3%

10% 10.4% 11.5% 10.7% 12.4% 11.8% 10.5% 11.9%


From the sensitivity analysis it can be seen that white alder generates the largest

expected return at 0% price growth, is in par with black alder at 1% and 2% price growth,

and black alder yields higher expected returns than other species.

4.2.3.4. Expected Return of Different Forest Types Investment Strategies

Calculating expected return for different forest types (refer to Appendix III for

forest type explication, Appendix IV for more detailed data and Appendix V for detailed

calculations), the highest expected return is generated by forest type Av, 6% per annum,

followed by Lk and Kp (both 4.6%), then Vrs and Grs (both 4.4%).

Table 14: Expected return of different forest types

Forest type Expected return Forest type Expected return

Sl 3.0% Nd 3.0%

Mr 3.2% Db 3.7%

Ln 3.0% Lk 4.6%

Dm 0.5% Av 6.0%

Vr 2.2% Am 3.7%

Gr 4.1% As 2.7%

Mrs 3.6% Ap 4.4%

Dms 3.2% Kv 3.6%

Vrs 4.4% Km 3.7%

Grs 4.4% Ks 2.7%

Pv 3.1% Kp 4.6%



Based on the sensitivity analysis (refer to Appendix V), this forest type generates

the highest expected returns for lower levels of expected price growth. At 10% expected

price growth, Kp forest type generates the highest expected returns.

4.2.3.5. Expected Return of Different Timber and Forest Types Investment

Strategies

Expected returns of combination of different timber type (softwood versus

hardwood) and forest type investment strategies are presented in the table below:

Table 15: Expected return, different timber and forest types

Forest type Softwood Hardwood Forest type Softwood Hardwood

Sl 3.1% 0.3% Nd 3.1% 2.8%

Mr 3.3% 4.9% Db 5.1% 3.5%

Ln 3.0% 3.9% Lk 0.7% 4.6%

Dm -0.2% 2.3% Av 6.7% -

Vr 2.3% 2.0% Am 3.7% 4.5%

Gr 4.1% 3.9% As 2.6% 2.8%

Mrs 3.7% 2.9% Ap 5.1% 4.1%

Dms 3.0% 3.6% Kv 4.0% 0.4%

Vrs 4.6% 4.1% Km 3.7% 4.1%

Grs 3.6% 4.3% Ks 2.6% 2.9%

Pv 3.3% 1.9% Kp 4.7% 4.4%


As can be seen from the table, for softwood, forest type Av generates the highest

expected return of 6.7%, while for hardwood, forest type Mr generates the highest

expected return, 4.9% per annum.

Sensitivity analysis (refer to Appendix V for more details) reveals that for softwood

Av forest type generates the highest expected returns for lower levels of expected price

growth, while Ap forest type leads, when price growth is higher.


Table 16: Sensitivity of expected returns, different timber and forest types

Price

growth

Highest expected return

Softwood Hardwood

Forest type Expected return Forest type Expected return

0% Av 6.0% Mr 4.1%

1% Av 6.7% Mr 4.9%

2% Av 7.4% Mr 5.6%

3% Av 8.2% Lk 6.5%

4% Av 8.9% Lk 7.4%

5% Av 9.6% Lk 8.3%

10% Ap 13.9% Lk 12.9% Source: Author’s calculations

For hardwood, Mr forest type generates highest expected returns, if price growth is

in the range of 0%-2%, and Lk forest type, if price growth is 3% or more.

Surprisingly, it must be noted that when differentiated according forest type,

softwood generates higher expected returns on all price levels than hardwood, which was

the leader if only timber type, not forest type was the investment differentiator.

4.2.3.6. Expected Return of Different Species and Forest Types Investment Strategies

Finally, the most sophisticated investment strategy for the forestry investment fund

would be to differentiate both on species and forest type level. Expected return for each

species and forest type combination, based on different expected price levels are calculated

(refer to Appendix V for details). The following table summarizes highest expected returns

for each species and return maximizing forest types:

Table 17: Expected returns for each species, return maximizing forest types

Species Forest Expected return

Pine Av 7.0%

Spruce Mrs 5.5%

Birch Mr 5.5%

Black alder Vr 5.0%

White alder Kp 5.9%

Aspen Db 8.3%

Oak Kp 5.3% Source: Author’s calculations


Based on this table, it can be concluded that highest expected return is generated by

aspen within forest type Db, with expected return 8.3%. Next, pine within forest type Av

generates expected return of 7.0%, and white alder within forest type Kp generates

expected return 5.9%.

Analyzing data from the forest type perspective, the following table summarizes

highest expected returns and return maximizing species for each forest types:

Table 18: Expected returns for each forest type, return maximizing species

Forest type Species Expected

return Forest type Species

Expected

return

Sl Pine 3.2% Nd Black alder 4.0%

Mr Birch 5.5% Db Aspen 8.3%

Ln Spruce 5.0% Lk White alder 5.7%

Dm Black alder 4.8% Av Pine 7.0%

Vr Black alder 5.0% Am Birch 5.0%

Gr Black alder 4.6% As White alder 5.3%

Mrs Spruce 5.5% Ap White alder 5.3%

Dms White alder 4.9% Kv Pine 4.1%

Vrs Spruce 4.7% Km Spruce 5.1%

Grs Aspen 4.9% Ks White alder 5.1%

Pv Pine 3.4% Kp White alder 5.9%


Sensitivity analysis (Appendix V) reveals that the return maximizing combinations

of species and forest type hold in all analyzed range of expected price changes, except that

at high price change levels, black alder maximizes return in Gr forests, and spruce

maximizes return in Ap forests; Dms forests maximize return with black alder, Grs

maximize return with black alder and Ap forests maximize return with spruce. However, it

must be noted that the differences of expected returns between these species/forest types

and those presented in tables above are not significant, usually below 1% in expected

return at 10% price growth.


4.2.3.7. Expected Risk

Risk of investments, as argued in the methodology section of the thesis, is best

measured by the variance (squared standard deviation) of annual rates of return. Expected

risk, therefore is the variance of expected annual rates of return. As argued in previous

sections, most variability in forestry returns stems from changes in timber prices, since

other return drivers, such as timber growth, are relatively stable over time. Therefore,

determining expected risk of the investment fund requires estimating future price volatility;

however, forecasting price volatility is even more complex than forecasting future prices.

Approach often used by researchers in estimating future volatility is to analyze

historical volatility to infer information about expected volatility. As found in the thesis,

historical variance of returns in forestry in Latvia for period 2001-2009 was 0.136, while in

other researches compared, the average variance was 0.038. As argued before, such high

variance is Latvia can be attributed to relatively short time period analyzed and economical

imbalances in both local and global economy during the period covered. Therefore,

expected future variance of forestry in Latvia is likely to be smaller than historical,

provided that there are no adverse global economic events. Since timber is a regional

commodity, country specific issues have limited impact on forestry, therefore, it is

expected that in the future, volatility of forestry in Latvia would converge with the lower

levels of regional and global forestry. Hence, it can be speculated that the near term

variance of forestry returns in Latvia should be in the range of 0.038 to 0.136, with the

average value 0.087.


5. Recommendations and Implementation

5.1. Recommended Investment Strategy

The following table summarizes the investment strategy alternatives for the forestry

investment fund, the highest expected return within each alternative, the combination of

species and forest type that provides such return, and forest land area that suits the

specifications. It must be noted that all expected returns presented are gross expected

returns for the fund, i.e. management fees are not accounted for.

Table 19: Summary of investment strategies

Investment strategy Highest

expected return Species

Forest

type

Area,

th.ha

Generic 2.57% All All 3’141

Different timber types 2.92%

Hardwood (birch, black

alder, white alder, aspen,

oak)

All 1’660

Different species 4.13% Black alder, white alder All 468

Different forest types 6.00% All Av 3.5

Different timber and

forest types 6.74% Softwood (pine, spruce) Av 3.5

Different species and

forest types 8.25% Aspen Db 0.8


These alternatives are arranged in a growing order of investment strategy

sophistication. Generic strategy, which is not to differentiate investments and thus invest in

“average” forests, is the easiest to implement, but it generates the lowest expected returns

of 2.57% per annum. Different timber types (hardwood or softwood) investment strategy

expectedly will generate 2.92% per annum, while investing in certain species has the

expected returns of 4.13%. Investing according forest types (ignoring timber types and

species) will yield 6% expected return, while considering the best timber type results in

6.74% expected returns.


The highest expected return is for the most narrow investment strategy – to invest

in a certain forest type and dominant species, which has the expected return of 8.25% per

annum, if Db type forest with aspens are chosen.

Although it seems that the latter investment strategy is the one to recommend

because it has the highest expected returns, it must be evaluated against the opportunities

to implement such investment strategy on a reasonably large scale. Since the investment

fund has the planned fund amount of LVL 10 to 20 million, and the available forest land

for the investment strategy should be adequately large to execute the selected investment

strategy (e.g. available area at least 10 times larger than planned land for purchasing), it

can be concluded that at least 40 thousand ha land area within a chosen investment strategy

should be available. Therefore, the investment strategy should also be evaluated by the

land available to execute it, prior making recommendation.

As can be seen from the table above, the return maximizing species and forest

combinations within last three investment strategies are not viable, since such land

available is below the set threshold of 40 thousand ha. Therefore, a further analysis has to

be made. The following tables summarize the top 10 opportunities within each of the three

investment strategies in question.

Table 20: Different species and forest type investment summary

Expected

return Forest type Species Area, th.ha

Cumulative

area, th.ha

8.3% Db Aspen 0.75 0.75

7.0% Av Pine 3.53 4.28

5.9% Kp White alder 8.26 12.54

5.7% Lk White alder 1.15 13.69

5.5% Mrs Spruce 4.7 18.39

5.5% Mr Birch 4.9 23.29

5.3% Ap White alder 31.92 55.21

5.3% As White alder 33.54 88.75

5.3% Kp Oak 2.01 90.76

5.2% Ap Spruce 23.73 114.49 Source: Author’s calculations


As can be seen from the table above, cumulative forest land area for the strategy

exceeds 40 thousand ha threshold with white alder and Ap forest type combination with

5.3% expected return, meaning, the expected return of this investment strategy is in the

range of 5.3% - 8.3%.

The following table shows that the necessary area threshold for different timber and

forest type investment strategy is exceeded only with hardwood and forest type Mr, which

means that the expected return on this investment strategy is in the range of 4.9% - 6.7%.

Table 21: Different timber and forest type investment summary

Expected

return Forest type Timber type Area, th.ha

Cumulative

area, th.ha

6.7% Av Softwood 3.53 3.53

5.1% Ap Softwood 25.46 28.99

5.1% Db Softwood 6.36 35.35

4.9% Mr Hardwood 4.9 40.25

4.7% Kp Softwood 11.16 51.41

4.6% Lk Hardwood 5.5 56.91

4.6% Vrs Softwood 18.62 75.53

4.5% Am Hardwood 6.4 81.93

4.4% Kp Hardwood 71.12 153.05

4.3% Grs Hardwood 20.32 173.37 Source: Author’s calculations

If the strategy to invest in different forest types is analyzed, the threshold is

exceeded with Kp forest type; hence, the expected return for this strategy is 4.6% - 6.0%.

Table 22: Different forest type investment summary

Expected return Forest type Area, th.ha Cumulative area, th.ha

6.0% Av 3.53 3.53

4.6% Kp 82.28 85.81

4.6% Lk 6.08 91.89

4.4% Ap 151.27 243.16

4.4% Vrs 96.58 339.74

4.4% Grs 22.89 362.63

4.1% Gr 73.55 436.18

3.7% Db 93.06 529.24

3.7% Am 52.39 581.63

3.7% Km 68.51 650.14 Source: Author’s calculations


Based on the analysis, the recommendation for the forestry investment fund is to

pursue investment strategy that selects investments according forest types and dominant

species. If the forestry investment fund pursues this investment strategy, the expected real

return of the forestry investment fund is in the range of 5.3% - 8.3%.


5.2. Suggestions for Implementation

The recommended investment strategy – to select investments according forest

types and dominant species – is not only the expected return maximizing strategy, but also

the most complex strategy to implement, since it anticipates very narrow investment

targets.

The major challenge for implementing this investment strategy is to purchase the

specific forest types with the dominant species, given that forests are usually sold in bulk

areas. There are two solutions for this challenge:

Identify regions where the forests in question are most widespread, and search

acquisition targets within these regions.

Make bulk purchases of forests, identify forests that are in line with the

investment strategy and keep them, reselling the rest.

The second solution has two significant drawbacks: (1) it requires more resources

than actually needed to implement the investment strategy, since it anticipates purchasing

“excessive” forests, and (2) potential liquidity problems might arise, since it might not be

easy to resell them straight away. Therefore, the first alternative of focused acquisition

target search and careful evaluation is more reasonable to be used.

Other challenges of implementing the investments strategy stem from the various

external factors, covered in the previous sections of the thesis. The following table

summarizes different factors affecting forestry investment returns, and author’s

suggestions of implementation strategy to mitigate the risks and maximize the returns of

the forestry investment fund.


Table 23: Strategy to mitigate investment fund risks and maximize return

Factor affecting returns Strategy to mitigate risks and maximize return

High level of government

regulation in the industry

Gather a team of both investments management and

forestry industry experts to ensure professional forest

management and compliance with effective regulations.

Potential expansion of

protected nature territories

Prior to purchasing a forest, investigate the flora and the

fauna in the forest for rare and/or protected species,

avoiding purchasing such forests.

Considering that new protected nature territories are

usually introduced by expanding the existing (i.e. not

creating separated areas), forests that border existing

protected nature territories should be avoided.

Restrictions on tree felling

according age/diameter

Purchase forests with approaching felling age/diameter

to be able to fell the trees as soon as possible.

Purchasing even-aged forest stands increases the

efficiency of forest management due to economies of

scale of tree felling, thereby increasing potential returns.

Low demand for pulp wood

from Scandinavia

Since pulp wood is also used as energywood, a risk

mitigating strategy is to cooperate with local

energywood consumers to sell such timber.

Also, the fund should focus on producing roundwood,

since it is a more valuable product with no demand

problems in the foreseeable future.


Costly to register forests for

the first time

Consider only registered forest land for purchases,

avoiding potential arbitrage deals with unregistered

forest owners.

Costly to transform forest

land for other uses

If such opportunities exist, purchase forest land with

approved transformation plans, since the option to

transform the forest land for other uses adds value to the

forest.

New technologies in forest

management

Purchase or lease the newest machinery and employ

most modern forest management techniques to increase

the efficiency and reduce costs, thereby maximizing the

potential return.

Low recent historic returns Since due to price decreases forest owners faced

negative returns on their forestry investments, the fund

might be able to purchase forests and machinery from

distressed forest owners for relatively low prices.


6. Conclusions

Several relevant conclusions regarding forestry in Latvia as an investment

opportunity can be drawn from the empirical research findings of the thesis.

Firstly, it is found that forestry in Latvia is a highly regulated industry, and return

potential is significantly limited by legislative restrictions to cut trees under a certain age

and diameter. Also, forest land transformation is a complex and costly process, which

means that forest land faces little competition from other uses besides forestry. Another

factor negatively affecting forestry returns is environmental protection that does not

adequately compensate forest owners for the restrictions imposed. Factors positively

affecting forestry returns include beneficial tax regime for forest land, social trend of

increasing popularity of ecological products, and technological improvements that increase

production efficiency.

Next, demand and supply analysis reveals that Latvia’s State Forests which

manages about 50% of all forests in Latvia has a major role in the supply side of the

industry, since its output decisions are used by private forest owners to determine their

strategy. On demand side, a major challenge faced by Latvian foresters is decreasing

demand for pulpwood from Scandinavia, which can potentially be substituted by regional

demand for energywood. Decreased demand for lumber and veneer due to global economic

slowdown is a short-term problem and is already showing signs of recovery.

Analysis of historical return and risk of forestry in Latvia reveals that average

nominal return on forestry in 2001-2009 was 14.75% per annum, with variance of 0.1364.

A detailed analysis revealed that a very substantial part of both returns and variability

stems from timber price developments. Comparison of the historical return with findings of

other researches reveals that historic returns on forestry in Latvia are comparable to those



of 11.8%. All of the other researches reveal variances lower than the one calculated for

Latvian forestry returns, with average variance in other researches of 0.038. This can be

attributed to longer periods covered in other researches, thus evening out short-term

fluctuations, and the fact the time period covered in the thesis includes significant

economical imbalances in both local and global economy.

Although industry classification suggests forestry as being a defensive industry, no

empirical evidence is found to support such argument. As forestry returns of a period are

found to compensate for inflation in the next period, it can be concluded that forestry

provides a hedge against inflation. Comparison with other asset classes reveals that

forestry has demonstrated rather strong risk-adjusted returns with Sharpe ratio of 0.29, and

forestry offers substantial diversification benefits, since its returns are weakly correlated

with other asset classes reviewed.

General conclusion of the thesis is that the expected real rate of return of forestry

industry in Latvia is 2.6% per annum; therefore, an investment fund that does not have a

specific investment strategy should have expected yearly real returns of 2.6%. Analysis of

more sophisticated investment strategies according different species, forest and timber

types reveal that the expected return maximizing strategy is to invest in specific types of

forests with specific dominant species. If the forestry investment fund pursues this

investment strategy, the expected real return of the fund is in the range of 5.3% to 8.3%.

Although the expected risk of the forestry investment fund as measured by variance

of forestry returns cannot be precisely calculated, based on the historical data and expected

convergence with average variability levels of forestry on a global level, it can be

speculated that the near term variance should be in the range of 0.038 to 0.136.


The recommended investment strategy of selecting investments according forest

types and dominant species is best implemented through focused acquisition target search

and careful evaluation. Since the thesis provides information on the expected return of

various combinations of forest types and dominant species, each forest’s expected return

can theoretically be evaluated, thus allowing more informed decision making.

Finally, the suggested implementation strategy to mitigate possible risks and

maximize return includes building a team of investment management and forestry experts

to ensure professional forest management and compliance with effective regulations,

avoiding purchasing forests that could potentially be included in protected nature

territories, and purchasing even-aged forests with approaching felling age or diameter.

Also, the investment fund should use the opportunity to buy underpriced forests and

machinery from distressed forest owners, as well as focus on roundwood production and

cooperate with energywood consumers to limit its exposure to decreasing demand for

pulpwood in the region.


7. Suggestions for Further Research

During the thesis writing process, the author identified areas, which might be

interesting and relevant to research, but due to limited resources and data availability, was

not able to perform himself.

Firstly, analyzing historical returns for a longer time period would yield more

robust results, since short-term fluctuations even out. Unavailability of longer period data

at the time of this research limits the applicability of the findings to long-term analysis,

and, since forestry is generally considered as long time horizon investment, such

perspective would be very useful.

Next, the author has chosen to use a methodology that ignores changes in forest

land value when calculating forestry returns, since many researchers believe that inclusion

of land value changes would double-count the effects of timber price fluctuations as land

value is a function of timber prices; however, it would be interesting to analyze the effects

of including changes of market prices of land in the calculations. However, it must be

noted that a prerequisite for such analysis would be usage of rather long time horizon,

since due to real estate bubble land prices were significantly distorted from their “true”

values.

Finally, comparison of forestry investments on a Baltic level would potentially

reveal interesting results, since all three Baltic countries have differences in legislations,

land utilization, and other factors affecting forestry investment returns.


8. Appendix I. List of Interviews

Sandijs Lūkins. Managing director, Foran Real Estate. Interview date: 19.02.2010.

Foran Real Estate is a forest management company owned by Norway pension company

Storebrand, and it manages over 46’000 ha of forests in Latvia. Sandijs Lūkins has been

with the company for 11 years and he has consulted over 100 companies internationally on

implementing Forest Stewardship Council (FSC) certification.

Raitis Zverbulis. Private forest owner. Interview date: 12.02.2010.

Rautis Zverbulis has 12 year experience in forest management and he has been privately

managing over 1’200 ha of forests in Latvia.

Mārtiņš Martinsons. Former legal consultant, Latvia’s State Forests. Interview date:

25.02.2010

Juris Filipovičs. Former administrative director, State Land Service of the Republic of

Latvia. Interview date: 05.03.2010

Kārlis Šķērstens. Head of legal department, State Land Service of the Republic of Latvia.

Interview date: 05.03.2010


9. Appendix II. Forest land area and prices

Table 24: Forest land area and prices according regions, 2009

Region Forest land area,

th.ha

Land price,

LVL/ha

Aizkraukles region 157.30 522.84

Alūksnes region 136.20 493.87

Balvu region 114.70 389.06

Bauskas region 74.00 333.06

Cēsu region 162.50 493.87

Daugavpils region 93.40 373.93

Dobeles region 47.40 429.09

Gulbenes region 114.40 493.87

Jelgavas region 53.40 429.09

Jēkabpils region 151.00 429.09

Krāslavas region 92.50 373.93

Kuldīgas region 142.50 481.06

Liepājas region 180.60 481.06

Limbažu region 139.00 493.87

Ludzas region 105.20 389.06

Madonas region 183.30 429.09

Ogres region 102.90 522.84

Preiļu region 69.60 389.06

Rēzeknes region 86.40 389.06

Rīgas region 198.80 522.84

Saldus region 102.40 481.06

Talsu region 165.80 521.48

Tukuma region 111.70 521.48

Valkas region 149.90 493.87

Valmieras region 125.20 493.87

Ventspils region 161.00 521.48

Weighted average 468.47

Source: Author’s compilation of data from Central Statistical Bureau, Forest valuation report (2010)


10. Appendix III. Forest Types and Most Appropriate Species

Table 25: Forest types and most appropriate species

Abbreviation Forest type (in Latvian) Pine Spruce Birch Black Alder White Alder Aspen Oak

Sl Sils x

Mr Mētrājs x

Ln Lāns x

Dm Damaksnis x x x

x

Vr Vēris

x x x x x x

Gr Gārša

x x x x x x

Mrs Slapjais mētrājs x

Dms Slapjais damaksnis x x x

x x

Vrs Slapjais vēris

x x x x x x

Grs Slapjā gārša

x x x x x x

Pv Purvājs x

x

Nd Niedrājs x x x

Db Dumbrājs

x x x

x

Lk Liekņa

x x x

x

Av Viršu ārenis x

Am Mētru ārenis x

As Šaurlapju ārenis x x x x

Ap Platlapju ārenis

x x x

x x

Kv Viršu kūdrenis x

Km Mētru kūdrenis x

Ks Šaurlapju kūdrenis x x x x

Kp Platlapju kūdrenis

x x x

x x

Source: Author’s compilation, information from Regulations of Cabinet of Ministers no.398


11. Appendix IV. Data on Forest Land and Timber

Table 26: Data on forest land and timber

Species\Forest Sl Mr Ln Dm Vr Gr Mrs Dms Vrs Grs Pv Nd Db Lk Av Am As Ap Kv Km Ks Kp Total

Pine

Area, th.ha 21.2 103.5 108.7 208.8 12.6 2.3 39.2 40.0 1.7 0.3 77.5 29.7 0.6 0.0 3.5 37.5 89.5 1.7 18.4 50.2 74.8 0.6 922.1

Growth, mil.m3 0.1 0.7 0.9 2.0 0.1 0.0 0.3 0.3 0.0 0.0 0.2 0.2 0.0 0.0 0.0 0.4 0.9 0.0 0.1 0.4 0.7 0.0 7.4

Volume, mil.m3 2.7 21.6 29.5 67.2 3.0 0.8 7.5 11.0 0.4 0.1 6.7 4.9 0.0 0.0 0.1 10.5 28.8 0.6 1.5 10.7 21.8 0.1 229.4

Mortality,% of vol. 0.3 0.8 1.0 4.9 0.3 0.0 0.4 1.1 0.1 0.0 0.4 0.5 0.0 0.0 0.0 0.5 1.9 0.1 0.0 0.6 1.7 0.0 14.6

Mortality, mil.m3 0.0 0.2 0.3 3.3 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.5 0.0 0.0 0.1 0.4 0.0 5.0

Growth, m3 per ha 3.8 7.1 8.6 9.8 7.9 8.8 6.6 8.2 5.8 0.0 3.1 5.1 0.0 0.0 2.8 9.6 10.4 11.6 3.3 7.4 9.4 0.0 8.0

Volume, m3 per ha 129 208 271 322 234 344 191 275 220 444 87 165 17 0 28 281 322 347 81 213 291 190 248.8

Spruce

Area, th.ha 0.0 1.0 3.7 101.2 145.1 6.9 4.7 31.8 16.9 2.3 0.6 8.3 5.8 0.6 0.0 8.5 122.0 23.7 0.3 7.6 57.3 10.6 558.8

Growth, mil.m3 0.0 0.0 0.0 0.9 1.4 0.1 0.0 0.2 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.1 0.3 0.0 0.0 0.5 0.1 4.9

Volume, mil.m3 0.0 0.0 0.4 23.5 28.4 1.8 0.3 5.6 3.1 0.6 0.1 1.2 0.8 0.1 0.0 1.0 24.2 4.8 0.0 0.8 12.4 2.2 111.0

Mortality,% of vol. 0.0 0.0 0.1 2.2 3.4 0.2 0.0 0.6 0.3 0.1 0.0 0.2 0.2 0.1 0.0 0.2 2.0 0.7 0.0 0.1 1.6 0.3 12.0

Mortality, mil.m3 0.0 0.0 0.0 0.5 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5 0.0 0.0 0.0 0.2 0.0 2.3

Growth, m3 per ha 0.0 0.0 5.5 9.1 9.8 10.2 4.3 6.3 8.3 8.7 0.0 3.6 6.9 0.0 0.0 4.7 9.0 11.0 0.0 5.3 8.7 9.5 8.8

Volume, m3 per ha 0 29 110 232 196 255 70 175 185 239 82 150 138 103 0 111 198 202 0 103 217 206 198.7

Birch

Area, th.ha 0.5 4.9 3.9 132.6 217.4 10.1 7.1 39.8 37.2 4.9 7.0 28.6 45.7 1.6 0.0 6.2 138.6 44.7 1.1 9.5 118.8 42.1 902.2

Growth, mil.m3 0.0 0.0 0.0 0.9 1.8 0.1 0.0 0.2 0.3 0.0 0.0 0.1 0.3 0.0 0.0 0.0 1.1 0.4 0.0 0.1 0.9 0.4 6.7

Volume, mil.m3 0.0 0.3 0.8 24.8 40.5 2.1 0.5 5.8 5.7 1.1 0.4 2.7 6.5 0.3 0.0 0.6 27.2 9.6 0.1 1.1 19.7 8.4 157.9

Mortality,% of vol. 0.0 0.0 0.2 2.4 4.0 0.2 0.2 0.8 0.7 0.2 0.2 0.6 0.6 0.0 0.0 0.0 2.6 1.0 0.0 0.1 2.2 0.8 16.7

Mortality, mil.m3 0.0 0.0 0.0 0.6 1.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.7 0.1 0.0 0.0 0.4 0.1 3.7

Growth, m3 per ha 0.0 4.1 7.7 7.0 8.4 6.9 2.8 6.0 6.7 8.2 1.4 3.2 5.9 6.1 0.0 4.9 8.1 9.6 0.0 5.3 7.5 9.3 7.4

Volume, m3 per ha 64 63 194 187 186 205 65 146 153 224 53 95 143 152 0 91 196 215 81 115 166 199 175.1

(table continued in the next page)


(Table continued)


Black alder

Area, th.ha 0.0 0.0 0.0 1.2 23.7 1.5 0.3 2.9 21.8 5.3 0.0 3.5 34.5 2.7 0.0 0.0 17.0 13.5 0.0 0.0 13.4 17.6 158.9

Growth, mil.m3 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.2 0.1 0.0 0.0 0.3 0.0 0.0 0.0 0.1 0.1 0.0 0.0 0.1 0.2 1.5

Volume, mil.m3 0.0 0.0 0.0 0.2 3.4 0.5 0.0 0.4 4.5 1.6 0.0 0.5 7.9 0.7 0.0 0.0 3.0 3.5 0.0 0.0 2.7 4.9 33.8

Mortality,% of vol. 0.0 0.0 0.0 0.0 0.4 0.1 0.0 0.0 0.4 0.1 0.0 0.1 1.0 0.1 0.0 0.0 0.3 0.3 0.0 0.0 0.4 0.5 3.7

Mortality, mil.m3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2

Growth, m3 per ha 0.0 0.0 0.0 8.1 8.0 13.2 0.0 6.9 9.2 13.3 0.0 5.8 8.7 11.1 0.0 0.0 8.2 10.4 0.0 0.0 7.5 12.5 9.2

Volume, m3 per ha 0 0 0 171 143 318 0 144 209 297 0 139 230 266 0 0 175 260 0 0 200 279 212.8

White alder

Area, th.ha 0.0 0.0 0.0 17.9 159.4 26.7 0.0 4.2 10.6 5.1 0.0 0.0 5.8 1.2 0.0 0.0 33.5 31.9 0.0 0.0 5.0 8.3 309.6

Growth, mil.m3 0.0 0.0 0.0 0.1 1.4 0.2 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.4 0.0 0.0 0.0 0.1 2.5

Volume, mil.m3 0.0 0.0 0.0 1.3 21.6 4.2 0.0 0.3 1.1 0.6 0.0 0.0 0.4 0.2 0.0 0.0 4.4 5.7 0.0 0.0 0.7 1.2 41.6

Mortality,% of vol. 0.0 0.0 0.0 0.3 2.6 1.0 0.0 0.1 0.1 0.1 0.0 0.0 0.3 0.0 0.0 0.0 0.5 0.7 0.0 0.0 0.1 0.2 6.1

Mortality, mil.m3 0.0 0.0 0.0 0.0 0.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.7

Growth, m3 per ha 0.0 0.0 0.0 3.9 8.5 7.9 0.0 4.8 5.7 5.9 0.0 0.0 3.5 8.7 0.0 0.0 8.3 11.0 0.0 0.0 8.0 9.7 8.1

Volume, m3 per ha 0 0 0 70 135 157 0 74 107 116 0 0 61 139 0 0 132 177 0 0 147 148 134.4

Aspen

Area, th.ha 0.0 0.0 0.7 32.8 108.7 11.1 1.1 5.3 8.4 1.7 0.0 0.6 0.8 0.0 0.0 0.2 36.4 25.5 0.0 1.2 6.8 1.2 242.4

Growth, mil.m3 0.0 0.0 0.0 0.3 1.1 0.1 0.0 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4 0.3 0.0 0.0 0.1 0.0 2.4

Volume, mil.m3 0.0 0.0 0.0 6.1 26.3 3.2 0.0 1.5 1.6 0.4 0.0 0.0 0.1 0.0 0.0 0.0 8.7 6.6 0.0 0.3 2.0 0.0 56.8

Mortality,% of vol. 0.0 0.0 0.0 0.9 3.1 0.4 0.0 0.1 0.3 0.1 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.7 0.0 0.1 0.2 0.0 6.9

Mortality, mil.m3 0.0 0.0 0.0 0.1 0.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 1.0

Growth, m3 per ha 0.0 0.0 0.0 7.9 10.2 12.6 0.0 9.5 8.4 12.0 0.0 0.0 13.3 0.0 0.0 0.0 9.9 9.8 0.0 8.1 10.4 0.0 9.7

Volume, m3 per ha 0 0 0 185 242 284 36 284 191 246 0 0 133 0 0 0 239 259 0 250 294 0 234.4



(Table continued)


Oak

Area, th.ha 0.0 0.0 0.0 5.4 10.1 15.0 0.0 0.0 0.0 3.5 0.0 0.0 0.0 0.0 0.0 0.0 1.1 10.2 0.0 0.0 0.0 2.0 47.4

Growth, mil.m3 0.0 0.0 0.0 0.0 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.4

Volume, mil.m3 0.0 0.0 0.0 0.9 2.2 3.4 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 2.4 0.0 0.0 0.0 0.4 10.3

Mortality,% of vol. 0.0 0.0 0.0 0.2 0.2 0.6 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.1 0.4

Mortality, mil.m3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Growth, m3 per ha 0.0 0.0 0.0 5.5 8.9 7.3 0.0 0.0 0.0 8.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7.8 0.0 0.0 0.0 10.0 7.6

Volume, m3 per ha 0 0 0 157 214 225 0 0 0 287 0 0 0 0 0 0 81 234 0 0 0 189 216.5

Softwood

Area, th.ha 21.2 104.5 112.4 309.9 157.7 9.1 43.9 71.8 18.6 2.6 78.1 38.0 6.4 0.6 3.5 46.0 211.5 25.5 18.7 57.8 132.1 11.2 1 480.9

Growth, mil.m3 0.1 0.7 1.0 3.0 1.5 0.1 0.3 0.5 0.2 0.0 0.2 0.2 0.0 0.0 0.0 0.4 2.0 0.3 0.1 0.4 1.2 0.1 12.3

Volume, mil.m3 2.7 21.6 29.9 90.7 31.3 2.5 7.8 16.5 3.5 0.7 6.8 6.1 0.8 0.1 0.1 11.5 53.0 5.4 1.5 11.5 34.2 2.3 340.5

Mortality,% of vol. 0.3 0.7 1.0 4.2 3.1 0.1 0.4 0.9 0.2 0.1 0.4 0.4 0.2 0.1 0.0 0.5 1.9 0.6 0.0 0.5 1.7 0.2 2.1

Mortality, mil.m3 0.0 0.2 0.3 3.8 1.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 1.0 0.0 0.0 0.1 0.6 0.0 7.3

Growth, m3 per ha 3.8 7.0 8.5 9.6 9.6 9.9 6.4 7.4 8.1 7.8 3.1 4.7 6.3 0.0 2.8 8.7 9.6 11.0 3.2 7.1 9.1 9.0 8.3

Volume, m3 per ha 129 207 266 293 199 277 178 230 189 261 87 162 127 103 28 250 251 212 80 199 259 205 229.9

Hardwood

Area, th.ha 0.5 4.9 4.7 190.0 519.3 64.4 8.5 52.2 78.0 20.3 7.0 32.6 86.7 5.5 0.0 6.4 226.6 125.8 1.1 10.7 144.0 71.1 1 660.3

Growth, mil.m3 0.0 0.0 0.0 1.3 4.6 0.6 0.0 0.3 0.6 0.2 0.0 0.1 0.6 0.1 0.0 0.0 1.9 1.3 0.0 0.1 1.1 0.7 13.4

Volume, mil.m3 0.0 0.3 0.8 33.2 94.0 13.3 0.5 8.0 13.0 4.6 0.4 3.2 14.9 1.1 0.0 0.6 43.4 27.7 0.1 1.4 25.1 14.9 300.4

Mortality,% of vol. 0.0 0.0 0.2 2.0 3.2 0.6 0.2 0.6 0.5 0.1 0.2 0.5 0.8 0.1 0.0 0.0 1.9 0.7 0.0 0.1 1.8 0.6 1.9

Mortality, mil.m3 0.0 0.0 0.0 0.6 3.0 0.1 0.0 0.0 0.1 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.8 0.2 0.0 0.0 0.4 0.1 5.6

Growth, m3 per ha 0.0 4.1 6.5 6.8 8.8 8.5 2.4 6.3 7.4 9.4 1.4 3.4 6.9 9.1 0.0 4.7 8.4 9.9 0.0 5.6 7.6 10.0 8.1

Volume, m3 per ha 64 63 163 175 181 206 59 154 166 228 53 98 172 205 0 88 191 220 81 131 174 209 180.9



(Table continued)


TOTAL

Area, th.ha 22 109 117 500 677 74 52 124 97 23 85 71 93 6 4 52 438 151 20 69 276 82 3 141

Growth, mil.m3 0.1 0.8 1.0 4.3 6.1 0.6 0.3 0.9 0.7 0.2 0.3 0.3 0.6 0.1 0.0 0.4 3.9 1.5 0.1 0.5 2.3 0.8 25.7

Volume, mil.m3 3 22 31 124 125 16 8 25 16 5 7 9 16 1 0 12 96 33 2 13 59 17 641

Mortality,% of vol. 0.3 0.7 0.9 3.6 3.2 0.5 0.4 0.8 0.4 0.1 0.4 0.4 0.8 0.1 0.0 0.5 1.9 0.7 0.0 0.5 1.7 0.6 2.0

Mortality, mil.m3 0.0 0.2 0.3 4.5 4.0 0.1 0.0 0.2 0.1 0.0 0.0 0.0 0.1 0.0 0.0 0.1 1.9 0.2 0.0 0.1 1.0 0.1 12.9

Growth, m3 per ha 3.7 6.9 8.5 8.5 9.0 8.7 5.7 6.9 7.6 9.2 2.9 4.1 6.9 8.2 2.8 8.2 9.0 10.1 3.0 6.9 8.3 9.8 8.2

Volume, m3 per ha 128 200 262 248 185 215 159 198 171 232 84 132 169 196 28 230 220 219 80 188 215 209 204

Source: Author’s compilation of data from Central Statistical Bureau, Lazdiņš (2008)


12. Appendix V. Expected Return According Species

Table 27: Expected return calculations according species

Expected return Price growth Stumpage price, LVL/m3 Mortality, % of volume p.a. Volume, m3/ha Growth, m3/ha p.a. Expenses, LVL/ha p.a. Land value, LVL/ha

Pine

0.8% 0% 33.00 2.19% 248.82 7.97 6.00 468.47

1.8% 1% 33.00 2.19% 248.82 7.97 6.00 468.47

2.7% 2% 33.00 2.19% 248.82 7.97 6.00 468.47

3.7% 3% 33.00 2.19% 248.82 7.97 6.00 468.47

4.6% 4% 33.00 2.19% 248.82 7.97 6.00 468.47

5.6% 5% 33.00 2.19% 248.82 7.97 6.00 468.47

10.4% 10% 33.00 2.19% 248.82 7.97 6.00 468.47

Spruce

2.0% 0% 27.80 2.03% 198.68 8.81 6.00 468.47

3.0% 1% 27.80 2.03% 198.68 8.81 6.00 468.47

3.9% 2% 27.80 2.03% 198.68 8.81 6.00 468.47

4.9% 3% 27.80 2.03% 198.68 8.81 6.00 468.47

5.8% 4% 27.80 2.03% 198.68 8.81 6.00 468.47

6.7% 5% 27.80 2.03% 198.68 8.81 6.00 468.47

11.5% 10% 27.80 2.03% 198.68 8.81 6.00 468.47

Birch

1.5% 0% 24.40 2.32% 175.06 7.44 6.00 468.47

2.4% 1% 24.40 2.32% 175.06 7.44 6.00 468.47

3.4% 2% 24.40 2.32% 175.06 7.44 6.00 468.47

4.3% 3% 24.40 2.32% 175.06 7.44 6.00 468.47

5.2% 4% 24.40 2.32% 175.06 7.44 6.00 468.47

6.1% 5% 24.40 2.32% 175.06 7.44 6.00 468.47

10.7% 10% 24.40 2.32% 175.06 7.44 6.00 468.47



(Table continued)


Black alder

3.2% 0% 16.60 0.50% 212.77 9.19 6.00 468.47

4.1% 1% 16.60 0.50% 212.77 9.19 6.00 468.47

5.0% 2% 16.60 0.50% 212.77 9.19 6.00 468.47

6.0% 3% 16.60 0.50% 212.77 9.19 6.00 468.47

6.9% 4% 16.60 0.50% 212.77 9.19 6.00 468.47

7.8% 5% 16.60 0.50% 212.77 9.19 6.00 468.47

12.4% 10% 16.60 0.50% 212.77 9.19 6.00 468.47

White alder

3.3% 0% 15.20 1.63% 134.36 8.14 6.00 468.47

4.1% 1% 15.20 1.63% 134.36 8.14 6.00 468.47

5.0% 2% 15.20 1.63% 134.36 8.14 6.00 468.47

5.8% 3% 15.20 1.63% 134.36 8.14 6.00 468.47

6.7% 4% 15.20 1.63% 134.36 8.14 6.00 468.47

7.5% 5% 15.20 1.63% 134.36 8.14 6.00 468.47

11.8% 10% 15.20 1.63% 134.36 8.14 6.00 468.47

Aspen

1.7% 0% 11.80 1.81% 234.40 9.70 6.00 468.47

2.6% 1% 11.80 1.81% 234.40 9.70 6.00 468.47

3.5% 2% 11.80 1.81% 234.40 9.70 6.00 468.47

4.4% 3% 11.80 1.81% 234.40 9.70 6.00 468.47

5.2% 4% 11.80 1.81% 234.40 9.70 6.00 468.47

6.1% 5% 11.80 1.81% 234.40 9.70 6.00 468.47

10.5% 10% 11.80 1.81% 234.40 9.70 6.00 468.47



(Table continued)


Oak

2.7% 0% 17.80 0.35% 216.47 7.60 6.00 468.47

3.6% 1% 17.80 0.35% 216.47 7.60 6.00 468.47

4.5% 2% 17.80 0.35% 216.47 7.60 6.00 468.47

5.4% 3% 17.80 0.35% 216.47 7.60 6.00 468.47

6.3% 4% 17.80 0.35% 216.47 7.60 6.00 468.47

7.3% 5% 17.80 0.35% 216.47 7.60 6.00 468.47

11.9% 10% 17.80 0.35% 216.47 7.60 6.00 468.47

Softwood

1.2% 0% 30.40 2.14% 229.90 8.29 6.00 468.47

2.2% 1% 30.40 2.14% 229.90 8.29 6.00 468.47

3.1% 2% 30.40 2.14% 229.90 8.29 6.00 468.47

4.1% 3% 30.40 2.14% 229.90 8.29 6.00 468.47

5.0% 4% 30.40 2.14% 229.90 8.29 6.00 468.47

6.0% 5% 30.40 2.14% 229.90 8.29 6.00 468.47

10.7% 10% 30.40 2.14% 229.90 8.29 6.00 468.47

Hardwood

2.0% 0% 17.16 1.86% 180.92 8.07 6.00 468.47

2.9% 1% 17.16 1.86% 180.92 8.07 6.00 468.47

3.8% 2% 17.16 1.86% 180.92 8.07 6.00 468.47

4.7% 3% 17.16 1.86% 180.92 8.07 6.00 468.47

5.6% 4% 17.16 1.86% 180.92 8.07 6.00 468.47

6.5% 5% 17.16 1.86% 180.92 8.07 6.00 468.47

10.9% 10% 17.16 1.86% 180.92 8.07 6.00 468.47



(Table continued)


TOTAL

1.6% 0% 23.62 2.01% 204.01 8.17 6.00 468.47

2.6% 1% 23.62 2.01% 204.01 8.17 6.00 468.47

3.5% 2% 23.62 2.01% 204.01 8.17 6.00 468.47

4.4% 3% 23.62 2.01% 204.01 8.17 6.00 468.47

5.4% 4% 23.62 2.01% 204.01 8.17 6.00 468.47

6.3% 5% 23.62 2.01% 204.01 8.17 6.00 468.47

10.9% 10% 23.62 2.01% 204.01 8.17 6.00 468.47



13. Appendix VI. Expected Return According Species and Forest Type

Table 28: Expected return calculations according species and forest type


Pine

Price +0% 2.2% 2.4% 2.0% -2.0% 2.9% 2.4% 2.7% 1.8% 2.3% 0.0% 2.5% 2.3% -0.6% - 6.2% 2.7% 1.2% 3.1% 3.2% 2.6% 1.3% -0.1% 0.8%

Price +1% 3.2% 3.3% 3.0% -1.1% 3.8% 3.4% 3.7% 2.7% 3.3% 0.9% 3.4% 3.2% 0.0% - 7.0% 3.7% 2.1% 4.1% 4.1% 3.6% 2.3% 0.8% 1.8%

Price +2% 4.1% 4.3% 3.9% -0.1% 4.8% 4.3% 4.6% 3.7% 4.2% 1.9% 4.3% 4.1% 0.5% - 7.7% 4.6% 3.1% 5.1% 5.0% 4.6% 3.2% 1.8% 2.7%

Price +3% 5.0% 5.2% 4.9% 0.8% 5.8% 5.3% 5.6% 4.7% 5.2% 2.9% 5.2% 5.1% 1.1% - 8.4% 5.6% 4.1% 6.1% 5.9% 5.5% 4.2% 2.7% 3.7%

Price +4% 5.9% 6.2% 5.9% 1.7% 6.7% 6.3% 6.5% 5.6% 6.2% 3.8% 6.1% 6.0% 1.6% - 9.2% 6.6% 5.0% 7.1% 6.7% 6.5% 5.2% 3.6% 4.6%

Price +5% 6.8% 7.2% 6.9% 2.7% 7.7% 7.3% 7.5% 6.6% 7.1% 4.8% 7.0% 7.0% 2.2% - 9.9% 7.6% 6.0% 8.1% 7.6% 7.4% 6.1% 4.5% 5.6%

Price +10% 11.5% 12.0% 11.7% 7.4% 12.6% 12.2% 12.3% 11.5% 11.9% 9.7% 11.4% 11.7% 4.9% - 13.6% 12.5% 10.9% 13.0% 12.0% 12.3% 11.0% 9.2% 10.4%

Spruce

Price +0% - -0.5% 4.1% 1.5% 1.2% 3.5% 4.6% 2.6% 3.8% 3.2% -0.2% 1.9% 4.2% -0.2% - 3.4% 2.1% 4.3% -1.3% 4.2% 2.1% 3.9% 2.0%

Price +1% - 0.2% 5.0% 2.4% 2.1% 4.5% 5.5% 3.6% 4.7% 4.2% 0.6% 2.8% 5.1% 0.6% - 4.3% 3.1% 5.2% -1.3% 5.1% 3.0% 4.9% 3.0%

Price +2% - 0.8% 5.9% 3.4% 3.1% 5.4% 6.3% 4.5% 5.7% 5.1% 1.4% 3.7% 6.0% 1.5% - 5.2% 4.0% 6.2% -1.3% 6.0% 4.0% 5.8% 3.9%

Price +3% - 1.4% 6.8% 4.3% 4.0% 6.4% 7.2% 5.4% 6.6% 6.1% 2.3% 4.6% 7.0% 2.4% - 6.1% 5.0% 7.2% -1.3% 6.9% 4.9% 6.8% 4.9%

Price +4% - 2.1% 7.7% 5.3% 4.9% 7.4% 8.0% 6.4% 7.6% 7.1% 3.1% 5.5% 7.9% 3.2% - 7.0% 5.9% 8.1% -1.3% 7.8% 5.9% 7.8% 5.8%

Price +5% - 2.7% 8.6% 6.2% 5.9% 8.4% 8.9% 7.3% 8.5% 8.0% 3.9% 6.5% 8.8% 4.1% - 7.9% 6.9% 9.1% -1.3% 8.7% 6.8% 8.7% 6.7%

Price +10% - 5.9% 13.2% 10.9% 10.5% 13.2% 13.2% 12.0% 13.3% 12.9% 8.1% 11.1% 13.5% 8.4% - 12.4% 11.6% 13.9% -1.3% 13.2% 11.6% 13.5% 11.5%

Birch

Price +0% -0.3% 4.6% 3.3% 1.0% 0.2% 2.8% 2.9% 2.8% 3.2% 3.1% 1.5% 2.1% 2.9% 3.4% - 4.2% 1.2% 3.0% -0.2% 3.7% 1.9% 3.4% 1.5%

Price +1% 0.5% 5.5% 4.3% 2.0% 1.1% 3.7% 3.7% 3.7% 4.1% 4.1% 2.2% 2.9% 3.9% 4.3% - 5.0% 2.1% 4.0% 0.6% 4.6% 2.8% 4.3% 2.4%

Price +2% 1.2% 6.3% 5.2% 2.9% 2.0% 4.7% 4.5% 4.6% 5.0% 5.0% 3.0% 3.8% 4.8% 5.2% - 5.9% 3.0% 4.9% 1.4% 5.5% 3.7% 5.3% 3.4%

Price +3% 2.0% 7.1% 6.1% 3.8% 2.9% 5.6% 5.3% 5.5% 5.9% 6.0% 3.7% 4.6% 5.7% 6.2% - 6.8% 3.9% 5.9% 2.2% 6.4% 4.6% 6.2% 4.3%

Price +4% 2.8% 7.9% 7.1% 4.7% 3.8% 6.6% 6.1% 6.4% 6.8% 7.0% 4.5% 5.5% 6.6% 7.1% - 7.6% 4.9% 6.8% 3.0% 7.3% 5.5% 7.2% 5.2%

Price +5% 3.5% 8.7% 8.0% 5.6% 4.7% 7.5% 6.9% 7.3% 7.8% 7.9% 5.2% 6.3% 7.5% 8.0% - 8.5% 5.8% 7.8% 3.8% 8.2% 6.4% 8.1% 6.1%

Price +10% 7.4% 12.8% 12.8% 10.2% 9.3% 12.2% 10.9% 11.9% 12.4% 12.7% 9.0% 10.6% 12.1% 12.6% - 12.9% 10.4% 12.5% 7.8% 12.7% 11.0% 12.8% 10.7%



(Table continued)


Black alder

Price +0% - - - 3.9% 4.1% 3.7% -1.3% 3.7% 3.4% 3.9% - 3.2% 2.3% 3.5% - - 3.6% 3.2% - - 2.8% 3.5% 3.2%

Price +1% - - - 4.8% 5.0% 4.6% -1.3% 4.6% 4.3% 4.8% - 4.0% 3.2% 4.5% - - 4.5% 4.1% - - 3.7% 4.4% 4.1%

Price +2% - - - 5.7% 5.8% 5.6% -1.3% 5.5% 5.2% 5.8% - 4.9% 4.1% 5.4% - - 5.4% 5.1% - - 4.6% 5.4% 5.0%

Price +3% - - - 6.6% 6.7% 6.5% -1.3% 6.4% 6.1% 6.7% - 5.8% 5.0% 6.4% - - 6.3% 6.0% - - 5.5% 6.3% 6.0%

Price +4% - - - 7.5% 7.6% 7.5% -1.3% 7.2% 7.0% 7.7% - 6.6% 6.0% 7.3% - - 7.2% 6.9% - - 6.4% 7.3% 6.9%

Price +5% - - - 8.4% 8.5% 8.5% -1.3% 8.1% 7.9% 8.6% - 7.5% 6.9% 8.3% - - 8.1% 7.9% - - 7.3% 8.2% 7.8%

Price +10% - - - 12.9% 12.9% 13.2% -1.3% 12.5% 12.5% 13.4% - 11.8% 11.4% 13.0% - - 12.6% 12.6% - - 11.8% 12.9% 12.4%

White alder

Price +0% - - - 3.3% 2.6% 3.2% - 4.1% 3.7% 3.7% - -1.3% 3.1% 4.9% - - 4.4% 4.4% - - 4.2% 5.0% 3.3%

Price +1% - - - 4.0% 3.4% 4.0% - 4.9% 4.5% 4.5% - -1.3% 3.8% 5.7% - - 5.3% 5.3% - - 5.1% 5.9% 4.1%

Price +2% - - - 4.7% 4.3% 4.9% - 5.6% 5.3% 5.3% - -1.3% 4.5% 6.6% - - 6.1% 6.2% - - 5.9% 6.8% 5.0%

Price +3% - - - 5.5% 5.1% 5.8% - 6.4% 6.2% 6.1% - -1.3% 5.2% 7.5% - - 7.0% 7.1% - - 6.8% 7.6% 5.8%

Price +4% - - - 6.2% 6.0% 6.6% - 7.1% 7.0% 7.0% - -1.3% 5.9% 8.4% - - 7.9% 8.0% - - 7.7% 8.5% 6.7%

Price +5% - - - 6.9% 6.8% 7.5% - 7.9% 7.8% 7.8% - -1.3% 6.6% 9.2% - - 8.7% 8.9% - - 8.6% 9.4% 7.5%

Price +10% - - - 10.6% 11.0% 11.8% - 11.6% 11.9% 11.9% - -1.3% 10.1% 13.6% - - 13.0% 13.4% - - 12.9% 13.8% 11.8%

Aspen

Price +0% - - -1.3% 2.5% 0.6% 3.4% -0.7% 2.6% 3.2% 3.9% - -1.3% 7.4% - - -1.3% 2.5% 2.5% - 2.5% 2.8% -1.3% 1.7%

Price +1% - - -1.3% 3.4% 1.5% 4.3% -0.2% 3.6% 4.0% 4.9% - -1.3% 8.3% - - -1.3% 3.4% 3.4% - 3.4% 3.7% -1.3% 2.6%

Price +2% - - -1.3% 4.2% 2.4% 5.2% 0.3% 4.5% 4.9% 5.8% - -1.3% 9.1% - - -1.3% 4.3% 4.3% - 4.3% 4.6% -1.3% 3.5%

Price +3% - - -1.3% 5.1% 3.2% 6.1% 0.7% 5.4% 5.8% 6.7% - -1.3% 9.9% - - -1.3% 5.2% 5.2% - 5.2% 5.5% -1.3% 4.4%

Price +4% - - -1.3% 5.9% 4.1% 7.0% 1.2% 6.3% 6.6% 7.6% - -1.3% 10.8% - - -1.3% 6.1% 6.1% - 6.1% 6.4% -1.3% 5.2%

Price +5% - - -1.3% 6.8% 5.0% 7.9% 1.7% 7.2% 7.5% 8.5% - -1.3% 11.6% - - -1.3% 6.9% 7.0% - 7.0% 7.3% -1.3% 6.1%

Price +10% - - -1.3% 11.1% 9.3% 12.5% 4.1% 11.7% 11.8% 13.0% - -1.3% 15.9% - - -1.3% 11.4% 11.4% - 11.4% 11.9% -1.3% 10.5%



(Table continued)


Oak

Price +0% - - - 2.7% 3.4% 2.2% - - - 2.4% - - - - - - -0.3% 2.7% - - - 4.4% 2.7%

Price +1% - - - 3.5% 4.3% 3.1% - - - 3.4% - - - - - - 0.4% 3.6% - - - 5.3% 3.6%

Price +2% - - - 4.4% 5.2% 4.0% - - - 4.3% - - - - - - 1.2% 4.5% - - - 6.2% 4.5%

Price +3% - - - 5.3% 6.1% 4.9% - - - 5.3% - - - - - - 1.9% 5.4% - - - 7.1% 5.4%

Price +4% - - - 6.2% 7.1% 5.9% - - - 6.2% - - - - - - 2.7% 6.4% - - - 8.1% 6.3%

Price +5% - - - 7.1% 8.0% 6.8% - - - 7.1% - - - - - - 3.4% 7.3% - - - 9.0% 7.3%

Price +10% - - - 11.5% 12.6% 11.4% - - - 11.9% - - - - - - 7.2% 11.9% - - - 13.6% 11.9%

Softwood

Price +0% 2.2% 3.1% 3.0% 2.8% 4.2% 2.7% 3.2% 2.9% 3.9% 2.5% 2.8% 2.6% 4.3% -0.2% 6.0% 3.2% 3.5% 4.5% 3.2% 3.2% 3.2% 3.9% 1.2%

Price +1% 3.1% 3.3% 3.0% -0.2% 2.3% 4.1% 3.7% 3.0% 4.6% 3.6% 3.3% 3.1% 5.1% 0.7% 6.7% 3.7% 2.6% 5.1% 4.0% 3.7% 2.6% 4.7% 2.2%

Price +2% 4.0% 5.0% 4.9% 4.8% 6.2% 4.6% 5.1% 4.9% 5.8% 4.5% 4.6% 4.4% 6.1% 1.6% 7.5% 5.2% 5.5% 6.5% 4.9% 5.1% 5.2% 5.8% 3.1%

Price +3% 4.9% 5.9% 5.9% 5.8% 7.1% 5.6% 6.0% 5.8% 6.7% 5.4% 5.5% 5.4% 7.1% 2.4% 8.2% 6.1% 6.4% 7.5% 5.8% 6.1% 6.2% 6.8% 4.1%

Price +4% 5.9% 6.9% 6.9% 6.8% 8.1% 6.6% 7.0% 6.8% 7.7% 6.4% 6.3% 6.3% 8.0% 3.3% 8.9% 7.1% 7.4% 8.4% 6.7% 7.1% 7.1% 7.7% 5.0%

Price +5% 6.8% 7.9% 7.8% 7.7% 9.1% 7.5% 8.0% 7.8% 8.7% 7.4% 7.2% 7.3% 8.9% 4.2% 9.6% 8.1% 8.4% 9.4% 7.5% 8.0% 8.1% 8.7% 6.0%

Price +10% 11.4% 12.7% 12.7% 12.6% 13.9% 12.4% 12.7% 12.6% 13.5% 12.2% 11.6% 12.0% 13.6% 8.5% 13.2% 13.0% 13.3% 14.3% 11.9% 12.8% 13.0% 13.6% 10.7%

Hardwood

Price +0% -0.4% 4.1% 3.0% 1.5% 1.1% 3.0% 2.2% 2.8% 3.2% 3.4% 1.2% 2.0% 2.6% 3.7% - 3.7% 1.9% 3.2% -0.3% 3.3% 2.0% 3.5% 2.0%

Price +1% 0.3% 4.9% 3.9% 2.3% 2.0% 3.9% 2.9% 3.6% 4.1% 4.3% 1.9% 2.8% 3.5% 4.6% - 4.5% 2.8% 4.1% 0.4% 4.1% 2.9% 4.4% 2.9%

Price +2% 1.0% 5.6% 4.8% 3.2% 2.9% 4.8% 3.6% 4.5% 5.0% 5.2% 2.5% 3.6% 4.4% 5.5% - 5.4% 3.7% 5.0% 1.2% 5.0% 3.8% 5.3% 3.8%

Price +3% 1.7% 6.3% 5.7% 4.1% 3.8% 5.7% 4.3% 5.4% 5.9% 6.2% 3.2% 4.4% 5.2% 6.5% - 6.2% 4.6% 6.0% 1.9% 5.9% 4.7% 6.3% 4.7%

Price +4% 2.4% 7.1% 6.6% 5.0% 4.6% 6.6% 5.0% 6.3% 6.8% 7.1% 3.9% 5.2% 6.1% 7.4% - 7.0% 5.5% 6.9% 2.7% 6.7% 5.6% 7.2% 5.6%

Price +5% 3.1% 7.8% 7.5% 5.9% 5.5% 7.5% 5.7% 7.1% 7.7% 8.0% 4.6% 6.1% 7.0% 8.3% - 7.8% 6.4% 7.8% 3.4% 7.6% 6.4% 8.1% 6.5%

Price +10% 6.6% 11.5% 11.9% 10.3% 9.9% 12.1% 9.3% 11.5% 12.2% 12.7% 7.9% 10.1% 11.5% 12.9% - 11.8% 10.9% 12.4% 7.2% 11.9% 10.9% 12.7% 10.9%



(Table continued)


TOTAL

Price +0% 2.1% 2.3% 2.0% -0.4% 1.2% 3.1% 2.7% 2.3% 3.4% 3.4% 2.3% 2.1% 2.8% 3.6% 5.3% 2.7% 1.8% 3.5% 2.8% 2.7% 1.8% 3.6% 1.6%

Price +1% 3.0% 3.2% 3.0% 0.5% 2.2% 4.1% 3.6% 3.2% 4.4% 4.4% 3.1% 3.0% 3.7% 4.6% 6.0% 3.7% 2.7% 4.4% 3.6% 3.7% 2.7% 4.6% 2.6%

Price +2% 3.8% 4.2% 3.9% 1.5% 3.1% 5.0% 4.5% 4.2% 5.3% 5.3% 3.9% 3.9% 4.6% 5.5% 6.6% 4.6% 3.7% 5.4% 4.4% 4.6% 3.7% 5.5% 3.5%

Price +3% 4.7% 5.1% 4.9% 2.4% 4.0% 6.0% 5.4% 5.1% 6.2% 6.3% 4.8% 4.8% 5.6% 6.5% 7.3% 5.6% 4.6% 6.3% 5.2% 5.5% 4.6% 6.5% 4.4%

Price +4% 5.6% 6.0% 5.8% 3.3% 4.9% 6.9% 6.3% 6.0% 7.2% 7.2% 5.6% 5.7% 6.5% 7.4% 7.9% 6.5% 5.5% 7.3% 6.1% 6.5% 5.5% 7.4% 5.4%

Price +5% 6.5% 7.0% 6.8% 4.2% 5.8% 7.9% 7.3% 7.0% 8.1% 8.2% 6.4% 6.6% 7.4% 8.4% 8.6% 7.5% 6.5% 8.2% 6.9% 7.4% 6.5% 8.4% 6.3%

Price +10% 10.9% 11.6% 11.5% 8.9% 10.4% 12.6% 11.9% 11.6% 12.8% 13.0% 10.6% 11.1% 12.0% 13.1% 11.8% 12.2% 11.2% 13.0% 11.1% 12.1% 11.1% 13.2% 10.9%



14. Bibliography

Bare, B. B., Waggener, T.R. (1980). Forest Land Values and Return on Investment.

Forest Science, vol.26, 91-96.

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