Direct Investing In Farmland and Real Assets: Opportunities and Risks for Capital Market Investors. George Martin, Senior Advisor, Wood Creek Capital

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1 Direct Investing In Farmland and Real Assets: Opportunities and Risks for Capital Market Investors George Martin, Senior Advisor, Wood Creek Capital October, 2011

2 Overview 1. Introduction: Investing in Real Assets 2. Institutional Interest in US and Brazilian Farmland. 3. Alternate Means for Investors to Access Agricultural Returns 4. Factor Based Modeling of US Farmland 5. Investing in Biofuel Feedstock Production: Brazilian Sugar Cane 6. Factor Based Modeling of Brazilian Farmland 7. 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. 2

3 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: Are positively correlated with US or European price inflation; Preserve value during periods of financial market contagion or substantial changes in the economic environment due to changes in the business cycle; Benefit directly from the increasing scarcity of production inputs, particularly in core economic sectors such as energy, manufacturing, and agriculture; 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, Offer long-term risk and return properties suitable for investors seeking to fund longterm liabilities. 3

4 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: 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: Infrastructure Farmland, and Intellectual Property 4

5 Inflation and Real Asset Classes: Summary Source: Wood Creek 5

6 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). The rationale for such investment has typically centered around three themes: 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. 6

7 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: 1. US farmland is a relatively stable, mature asset and its history is free from wholesale disruptions in market structure, organizational form and political economy, 2. 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 3. 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. 7

8 A Capital Markets Perspective on Brazilian Farmland 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. 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. We evaluate the economics of sugar cane production in Brazil and identify key risks that institutional investors must consider when investing in production assets. 8

9 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: 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. 9

10 Access to Agricultural Exposure via short-dated commodity futures Capital market investors have historically accessed agricultural futures through index based products, such as the S&P GSCI. 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. 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. 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. 10

11 Owning Agribusiness Equities 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 = ) 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) 11

12 Owning Farmland or Owning Farms Kastens (2001) estimates the total return to owning farmland over the period at 11.5%, against an average return of the KF agricultural index over this time period of 10.7%. Though we have some methodological reservations, Kastens calculates the returns to operating farms based on a sample of 2000 Kansas farms for the period 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. This compares to an average of 13.3% p.a. for agricultural equities as proxied by the KF index for this time period. 12

13 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. 13

14 The Capitalization of Government Subsidies in Land Values and Rental Rates 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. 14

15 What Are the Determinants of Crop Yield? 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 20 th 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) 15

16 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. 16

17 Understanding Sources of Return in US Farmland Values: History of Farmland Real Estate Values 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 17

18 Factor Modeling of US Farmland Returns Source: USDA 18

19 Factor Modeling Results The model is statistically significant, with an adjusted-r2 of 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. 19

20 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. 20

21 Principal Component Analysis of State-Level Returns 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 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. 21

22 Principal Component Analysis of State-Level Returns 22

23 Cluster Analysis of State-Level Returns 23

24 Increasing Importance of Biofuels as Marginal Source of Demand 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 24

25 Commodity Price Causality: (weekly) Note that CL Causes other Energy complex variables and metals, but NOT Ag 25

26 Commodity Price Causality: /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 26

27 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. 27

28 Brazil is the Dominant Source of Sugarcane and Ethanol 28

29 Regions of Brazil 29

30 Economic Geography of Brazilian Sugarcane Production 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

31 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). 31

32 Ethanol Production Process 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%. 32

33 Ethanol Production Costs Increasing costs of inputs: Cane Labor Other costs increases greater in % but not $ value 33

34 Increasing Productivity of Brazilian Farmers and Millers 34

35 Brazilian Exports of Ethanol Aside from Tariffs, key determinant is USD/BRL 35

36 US Corn Ethanol vs. Brazilian Sugarcane Ethanol 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,

37 Factor Analysis of Brazilian Farmland Similar to the United States, Brazil has widely varying land resources for agricultural production. Brazil also has an infrastructure of varying quality, which greatly affects the cost of transporting agricultural goods to market (typically the port). 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? Although data on farmland prices is limited, we have accessed a reasonable timeframe of quality data for analysis. 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. 37

38 Brazilian Land Prices 38

39 Principal Components Analysis of Regional Brazilian Farmland 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. 39

40 Considerations for Investing in Brazilian Sugar 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,

41 Themes and Conclusions 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. 41

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