Download Direct Investing In Farmland and Real Assets

Direct Investing In Farmland and Real Assets: Opportunities
and Risks for Capital Market Investors
George Martin, Senior Advisor, Wood Creek Capital
October, 2011
Overview
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Introduction: Investing in Real Assets
Institutional Interest in US and Brazilian Farmland.
Alternate Means for Investors to Access Agricultural Returns
Factor Based Modeling of US Farmland
Investing in Biofuel Feedstock Production: Brazilian Sugar Cane
Factor Based Modeling of Brazilian Farmland
Conclusions
Note that this presentation draws heavily upon: H. Geman and G. Martin, “Understanding Farmland Investment as Part of
Diversified Portfolio” May, 2011, an independent research project sponsored by Bunge Global Agribusiness Financial Services
Group.
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What are “Real Assets”?
Investors are increasingly seeking real asset investments that have more than one of the
properties listed below. They pursue returns that:
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Are positively correlated with US or European price inflation;
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Preserve value during periods of financial market contagion or substantial changes in
the economic environment due to changes in the business cycle;
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Benefit directly from the increasing scarcity of production inputs, particularly in core
economic sectors such as energy, manufacturing, and agriculture;
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Are essential components to economic infrastructure, including the built environment of
commercial and residential real estate; transportation, including roads, rail, shipping
and air; and major projects, such as telecommunications and pipelines; and,
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Offer long-term risk and return properties suitable for investors seeking to fund longterm liabilities.
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Defining Real Assets
Assets that are typically mentioned in conjunction with these characteristics can be divided
into “traditional” and “new” real assets. Institutional investors, particularly endowments,
have pursued investments in traditional real assets for many decades. These real assets
include:
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Equities
Inflation-Linked Bonds or Derivatives
Commodities and Commodity-Linked Derivatives
Direct or Indirect Real Estate
Gold
Timber
As part of the quest for real exposure, newer real asset classes have come within the
purview of investors. These “new” real asset classes include:
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Infrastructure
Farmland, and
Intellectual Property
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Inflation and Real Asset Classes: Summary
Source: Wood Creek
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Increased Institutional Interest in Farmland Assets
Recent years have seen increased interest by institutional investors in the returns available from direct
ownership of real assets. Of particular interest to investors has been direct ownership of farmland assets,
with global private investment in farmland by financial investors estimated to be USD 10-25B (HighQuest,
2010).
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The rationale for such investment has typically centered around three themes:
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Farmland as an inflation hedge: as a real asset that is linked to food and energy production,
farmland is expected to be a hedge against inflation.
Farmland as a diversifying source of return: as a private market investment subject to its own
physical and economic dynamics, and an asset that is for the most part privately held, and
often indirectly stabilized by government subsidy, farmland’s returns are not, in the short run,
directly linked to financial markets.
Farmland as asset positioning for a food and energy scarcity theme: economic and
demographic growth are theorized to create demand for agricultural products that outstrips
current productive capacity, leading to the development of new farmland and price
appreciation in existing farmland assets.
However, the respective merits of each one of these investment themes are largely untested.
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U.S. Farmland as an Institutional Benchmark
In order to understand the key features of farmland in general, we first undertake an analysis of some of the
key characteristics of US farmland.
This focus is driven by three considerations:
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US farmland is a relatively stable, mature asset and its history is free from wholesale
disruptions in market structure, organizational form and political economy,
The amount of available quantitative data on farmland and agriculture (both time series as
well as depth of information) for the US is greater than for any other country, thus facilitating
analysis of the long run investment properties of the asset; and
The organizational form of farmland in the US—largely privately held, market-based but
subject to meaningful government regulation and activity—largely mirrors (in mature form) the
state of existing international markets for farmland (for example, Brazil, Australia, and to a
lesser extent Eastern Europe).
This background is essential for demonstrating that farmland, which, while subject to evolutionary forces in
agricultural technology and organizational form, has had relatively stable properties, including its
relationship to macroeconomic factors that are of concern to institutional investors.
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A Capital Markets Perspective on Brazilian Farmland
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However, this background analysis in US farmland, while useful, is insufficient to
understand the dynamics of farmland in the frontier farmland markets which are also of
interest to institutional investors.
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We focus our discussion on one particular market for farmland: Brazil. As a major producer
of sugar cane, Brazilian farmland markets exist at the intersection of agricultural and
energy markets.
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We evaluate the economics of sugar cane production in Brazil and identify key risks that
institutional investors must consider when investing in production assets.
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Getting Exposure to Ag: Direct, Commodity Futures or Agribusiness Equity?
Capital market investors considering non-operating investments in agricultural assets have
three primary approaches to get access to those assets:
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Purchase of agricultural futures or related derivative instruments.
Ownership of listed equities in agricultural firms, and
Ownership and leasing out of farmland,
Each one of these approaches to investing has distinct advantages and disadvantages, and
provides access to different points in the agricultural value chain.
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Access to Agricultural Exposure via short-dated commodity futures
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Capital market investors have historically accessed agricultural futures through index
based products, such as the S&P GSCI.
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The S&P GSCI index is a so-called “first generation” commodity index that narrowly
focuses on giving investors liquid exposure to near term price appreciation or depreciation
in commodities, as well as potential benefits associated with the “roll” from front to nextout futures contracts, and an implicit momentum-based strategy associated with an index
weighting scheme based on accumulated value.
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Since its inception in 1990 until the end of 2009, the S&P GSCI Agricultural Sub-Index has
returned an average of -1.3% p.a.
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While well-known, we do not believe that the GSCI is an efficient commodity index,
particularly for accessing agricultural returns, and therefore not fully representative of the
agriculturally-related returns available via futures markets. We believe that there are other,
more efficient indexes available, and should disclose that that the author has relationships
with other commodity index providers.
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Owning Agribusiness Equities
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Investors may also access commodity oriented returns via investment in agricultural equities.
Companies with listed equities are active at all points in the value chain. While various index providers
have recently created ex-post indexes of agriculturally-related firms, these indexes:
• are of a relatively short time horizon (typically 2000 onward) and
• suffer from significant survivorship bias or insufficient attention to the changing nature of the
non-agriculturally-related industrial activity conducted by firms.
The best long term equity index focused on agricultural equities is that created by Ken French (“KF”) from
CRSP and Compustat data.
• The index is value weighted, rebalanced annually, and requires that a firm have a
contemporaneous agricultural sector classification (SIC codes = 0100 - 1000) at each rebalance
point, and not just a current classification.
Average annualized returns to the KF index since 1950 have been 11.9%, with a volatility of 24.4%. This
compares to an annualized return of 12.5% for the S&P500 and a volatility of 17.8% over the same time
period, with a correlation of .55.
This compares to pure, non-rental returns, to holding US farmland of 6.1%, with a volatility of 6.6%. We
estimate that rental returns to land have a long run average level of approximately 6% over this time
period, though more recently are close to 4%1.
• These farmland returns have a correlation with the S&P of -.26, suggesting greater portfolio
diversification benefits to an investor already holding equities.
1. Source: Understanding Farmland Investment as Part of a Diversified Portfolio (May 2011, Prof. Helyette Geman and George Martin)
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Owning Farmland or Owning Farms
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Kastens (2001) estimates the total return to owning farmland over the period 1951-1999 at
11.5%, against an average return of the KF agricultural index over this time period of 10.7%.
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Though we have some methodological reservations, Kastens calculates the returns to
operating farms based on a sample of 2000 Kansas farms for the period 1973-1999.
Operating returns over this period average 6.8% p.a., and he estimates land returns to be
8.9% over the corresponding period.
• Rolling 5-year returns for farms vs. farmland indicate that the returns to farming
have been less than land holding for all rolling periods except for those
terminating in the late 1980’s.
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This compares to an average of 13.3% p.a. for agricultural equities as proxied by the KF
index for this time period.
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Benefits of Direct Ownership of Farm Land
While equities allow investors to access returns available in the value chain associated with
agricultural production, including the sale of inputs like fertilizer, machinery and (transgenic)
seeds, as well as agricultural distribution for food, fuel and feed, ownership of land in the US
context has a number of distinct advantages.
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The Capitalization of Government Subsidies in Land Values and Rental Rates
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US Farmland is the key point of focus for government-sponsored agricultural support,
insurance and stabilization payments.
These payments are capitalized into the value of land, tending to raise values, and
providing a floor to possible price depreciation.
Estimates vary widely: Goodwin et al. (2003) quote studies which estimate that between
7% and 69% of farmland values is attributed to capitalized government payments, with
land in the Northern Great Plains most dependent on government payments.
Studies that look at the cross sectional variation of land prices within particular regions,
such as Kirwan et al. (2010), and Gardner (2003), find that land prices are not sensitive to
government payments. Gardner hypothesizes that this may be because subsidies, while
commodity-specific in implementation, do not have commodity-specific impact on land
values since land use is highly flexible, particularly over the long time horizons associated
with capitalization.
Studies additionally suggest that land rental rates are largely set – except in the loosest of
markets -- so that owners of the land can capture such subsidies.
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What Are the Determinants of Crop Yield?
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Crop Production is largely a process of transforming solar energy into chemical potential energy.
Land is the platform upon and through which that process occurs.
Yield (per unit of land per unit of time) can be decomposed as:
Y=Q x I x E x H
• Y is Yield.
• Q is total solar radiation over the area per period.
• I is the fraction of solar radiation captured by the crop canopy.
• E is photosynthetic efficiency of the crop (total plant dry matter per unit of solar radiation).
• H is harvest index (fraction of total dry matter that is harvestable).
Q is largely a function of geography and weather.
Over the short run, I is the most variable, as it depends on the extent to which crops have been able to
deploy canopy; total dry matter production (absent severe stressed of drought, etc.) is largely a linear
function of captured solar radiation.
Increases in H have largely accounted for increases in yields to key grains over the 20th century.
E varies little, but is the subject of much research, as for a given species, I and H have been changed for
many key crops and are difficult to change further. Crops such as sugarcane have high efficiencies
relative to other crops.
Source: Hay& Porter, Physiology of Crop Yield (2006)
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Capture in Land Values of technological improvements
Yield enhancing technologies, such as genetic modification of seeds, while raising the
costs of specific inputs, allow owners of land to capture the bulk of the economic
benefit (particularly in developing countries).
Carpenter (2010) summarizes 49 peer reviewed surveys on the distribution of benefits
of genetically modified seeds.
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Understanding Sources of Return in US Farmland Values: History of Farmland Real
Estate Values
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It clearly appears that a strong trend
emerged around 1940, corresponding
to a consistent annual growth rate
since 1940 of 5.85%.
Note that this does not include
ancillary cash flows, such as lease
payments
There have only been two deviations
from this trend: a strong rising trend
starting in 1973, reverting to a lower
value in 1986, and a weaker higher
trend beginning with the recent
commodities boom starting in the early
2000s.
Based on this evidence, current US
agricultural land prices appear to be
back on a strong upward trending line
of consistent growth of almost 6% p.a.
Source: USDA
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Factor Modeling of US Farmland Returns
Source: USDA
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Factor Modeling Results
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The model is statistically significant, with an adjusted-R2 of 0.73. Interestingly, we find that the most
significant variables are:
US CPI, which shows that the returns to US farmland have been a significant hedge against inflation
risk;
Yield to Worst, which is an indicator of the level of interest rates, with a negative coefficient. This
suggests, as is intuitive, that higher interest rates are associated with lower farmland returns. This is
likely linked to both the business cycle, as higher interest rates are associated with contraction in
monetary policy, and to the fact that higher interest rates are likely to put downward pressure on land
prices as higher discount rates bite into the present expected value of future agricultural proceeds from
the land.
Industrial Production, which is positive, suggests that land prices are pro-cyclical. This is particularly
interesting and somewhat counterintuitive as it implies that farmland should be considered as part of a
“growth” story like equities, rather than a “store of value” like gold.
DXY, which suggests a stronger dollar is associated with increases in land price. This may be a proxy for
monetary policy, or may actually reflect the impact of increased external demand for US farm products
on both land values and the price of the dollar.
We note in particular that, historically, changes in the spot price of commodities, corn, wheat and oil,
are not statistically significant determinants.
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Model Fit
We evaluated the robustness of this model in two ways; by comparing the “goodness of fit” with historical
data, and through a test of stability of the regression coefficients. We compared the results predicted by
the model (“Fitted”) to the “Actual” farmland returns. We see, from inspecting the graph, that model fit has
been relatively strong over the entire period, and is not driven by outliers, etc.
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Principal Component Analysis of State-Level Returns
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While the above analysis was conducted on an overall average of US Farmland returns, on
a practical level such an investment is not available to an investor.
Investors seeking to include farmland in a portfolio must make a selection of specific
properties, and cannot invest in such an average. This typically means a basket of
investments in farmland across a number of geographies.
In seeking to understand the heterogeneity across geographies, we more closely analyzed
farmland returns across US states.
We conducted a principal components analysis for state level returns for the period 19732009.
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The results suggest that there is a common factor (“first principal component”) across US
farmland which explains 56% of the cross-sectional variation in farmland returns.
Looking at the correlations between the first principal component and state-level returns we
see that Kansas and Missouri returns are highly correlated (.90 and .91, respectively) with this
common “first principal component”. This suggests that the risk and returns associated with
Kansas and Missouri are representative of US farmland returns, and vice versa.
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Principal Component Analysis of State-Level Returns
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Cluster Analysis of State-Level Returns
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Increasing Importance of Biofuels as Marginal Source of Demand
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Historically, Energy and Agricultural commodity prices were largely uncorrelated with each other
With elevated prices of fossil fuels, biofuel production has become commercially viable.
Linkages between the pricing of energy product and biofuel feedstock have increased (beyond the
increase in commodity price correlation that has come from the “financialization” of liquid commodity
markets
To the extent that elevated energy commodity prices will persist, these linkages will persist and grow
stronger as the economic infrastructure necessary to exploit pricing differences becomes more
established and more efficient.
• In Brazil, full substitutability of Gasoline and Ethanol at the pump has driven up correlation
between gasoline and sugar
• Brazil is generally the world’s low *cost* producer of sugar, but is still smaller than global crude
market. Crude prices have increasing causality for sugar.
What does the data show?
• Prior to 2008, relatively clear separation in futures trading across commodity complexes
• After 2009, Corn, Wheat, Soybeans and Soy Oil are now part of the energy complex (NG now
trades indepently of the energy complex)
• Sugar is not yet integrated into the energy complex
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Commodity Price Causality: 2000-2007 (weekly)
Note that CL Causes other Energy complex variables and
metals, but NOT Ag
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Commodity Price Causality: 2010-5/2011 (weekly)
Note that CL, HO, RB join Ags BO,SY, CN and WH in a causal group; other groups are industrial metals,
meats, softs/precious metals whose grouping may reflect currency effects; note as well that NG has been
decoupled; and sugar is not linked directly to the energy/ag complex
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Why Sugar Cane?
Source: Wood Creek
Ethanol can be derived from Sugar Cane far more efficiently (per unit of land) than
from corn or other feedstock.
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Brazil is the Dominant Source of Sugarcane and Ethanol
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Regions of Brazil
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Economic Geography of Brazilian Sugarcane Production
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While Sugarcane
was first grown in
the north, it is
primarily a crop
for the South
East.
Going forward
most new
cropping is in
Center West.
Yields vary
significantly by
region.
This, and other USDA sourced figures, are from C. Valdes,
Brazil’s Ethanol Industry: Looking Forward,” USDA,, June 2011
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Brazilian Sugar and Ethanol Markets are highly integrated, but because of subsidies
and international competition, there are opportunities for millers
Sugar and Ethanol are highly integrated because of predominance of flex fuel
cars in Brazil. Has its origins in government policy (Proalcool).
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Ethanol Production Process
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Sugarcane is converted into
sugar, ethanol, and bagasse
(which is used for plant
operation and electricity cogeneration.
Multiprocessing Mills can
alter sugar/ethanol output
between 40%-60%.
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Ethanol Production Costs
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Increasing costs of
inputs:
• Cane
• Labor
• Other costs
increases
greater in %
but not $
value
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Increasing Productivity of Brazilian Farmers and Millers
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Brazilian Exports of Ethanol
Aside from
Tariffs, key
determinant
is USD/BRL
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US Corn Ethanol vs. Brazilian Sugarcane Ethanol
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According to Itau, excluding import
tariffs and corn-ethanol subsidies,
the price of Brazilian ethanol in the
U.S. would drop to $3.23 per gallon
(from $3.77) and the price of cornbased ethanol would rise to $3.18
per gallon (from $2.73).
Even excluding distortions, the
price of Brazilian ethanol would
still be higher than the price of
American ethanol, and exporting it
would not be viable.
Until 2008, even with current
distortions, the price of Brazilian
ethanol was at the same level as
the price of U.S. ethanol.
USDBRL is the major determinant
of import viability
Chart excerpted from Itau, “Macro Vision”, July, 5, 2011
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Factor Analysis of Brazilian Farmland
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Similar to the United States, Brazil has widely varying land resources for agricultural
production.
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Brazil also has an infrastructure of varying quality, which greatly affects the cost of
transporting agricultural goods to market (typically the port).
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This raises an important question for the investor seeking to allocate to Brazilian
farmland: how similar are the returns to farmland investing across the various regions of
Brazil?
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Although data on farmland prices is limited, we have accessed a reasonable timeframe of
quality data for analysis.
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We used semi-annual farmland valuation surveys conducted in 13 Brazilian states from
2001 to 2009 available from Brazilian research company AgraFNP. The limited number of
data points prevented us from performing a robust regression analysis of macro factors.
Note that data is price appreciation-only.
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Brazilian Land Prices
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Principal Components Analysis of Regional Brazilian Farmland
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Our principal components analysis suggests
that there is a common factor that explains
75% of the cross-sectional variation in
returns to Brazilian farmland values.
This is a relatively large “beta” effect, and
may suggest that there may be limited
benefit to investment strategies that are
focused only on regional selection.
A closer inspection of the results suggests
that there are distinct dynamics that affect
the value of land in and near the state of São
Paulo:
• The correlation of Minas Gerais and
Sao Paulo the lowest at .61 and .70
with the common factor.
• Minas Gerais and Sao Paulo have
the highest correlation with the
second largest common factor,
which explains 12% of the cross
sectional variation in returns.
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Considerations for Investing in Brazilian Sugar
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Non-BRL investors in Brazilian agricultural
assets must consider substantial currency risk
– long horizon IV’s > 20%.
Hedging BRL currency risk is generally
infeasible as local currency interest rates >
12%.
Long term bank finance of farm land purchase
not available.
Farmland transactions are usually paid in
installments over 3 years, which is more an
issue for exit with local purchaser.
Government approvals of outright foreign
purchase of land has slowed substantially.
Non-land, agricultural assets may be viable
substitute for land purchase, but at increased
operating risk.
USDBRL Implied Volatility as of Sep 29, 2011
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Themes and Conclusions
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Real assets provide institutional investors with significant portfolio benefits
Global demographic and economic trends support increased demand for agricultural
products and correspondingly productive agricultural assets.
Direct ownership of farmland has been an efficient means for the capital market investor to
access returns to agriculture.
Factor modeling of US Farmland shows that at national and state-levels, farmland has been
positively correlated with US inflation, and negatively correlated with interest rates.
Factor modeling of US Farmland shows that core returns to farmland have been available
with state level investments.
Increasing integration between agricultural and energy markets has created opportunities in
biofuel feedstock production, in particular sugarcane based ethanol.
Changes in Brazilian farmland prices in different states have been highly correlated over the
past decade.
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Disclaimers
IMPORTANT DISCLOSURES
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42
Contact Information
Wood Creek Capital Management, LLC
Connecticut Financial Center
157 Church Street, 20th floor
New Haven, CT 06510
Phone: 203.401.3220
Fax: 203.286.1972
[email protected]
www.woodcreekcap.com
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