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The drivers of farmland values: An analysis of global row crop farmland pricing
Farmland has been proven to deliver strong returns over time with low volatility and little correlation with other asset classes and the broader global economic cycle. Since 2001, U.S. farmland prices have risen by 6.8% p.a.,1 while farmland in Poland has risen by 11.8% p.a.2 and in Brazil by 12.9% p.a.3 (in local currency terms). These return characteristics are proving attractive to many investors by providing diversification to their portfolios.
This paper analyses the key drivers of global farmland values and how these drivers may have changed over time. In doing so, it explores several key themes, including:
- Row crop farmland values as a function of farm income and cost of capital
- The change in components that drive farmland values (particularly time and region)
- Farmland productivity as a growing positive factor driving farmland values in all regions, coupled with a decrease in the influence of interest rates
Row crop farmland: reviewing an implied pricing model
The implied value of farmland can be modelled using the cash flows an owner expects to receive from farming the land and the owner’s cost of capital.4 Cash flows from farming are also useful in determining the cash flows due to a farmland owner from a tenant, as owners will take a share of the return to land in the form of rent. Furthermore, in the absence of other factors such as change of land use or tax benefits, the ability to generate farm income is highly relevant to any resale value, given the most likely buyers will be farmers or other investors. Farmland cash flows are determined by calculating revenues (crop yield multiplied by crop sales price) less expenses (cost of production,5 both direct and indirect). In some countries, revenues also include government subsidies and other support provided to the farmer. Each landowner or farmer will have their own cost of capital dependent on their financial situation, in this analysis the owner’s cost of capital is represented by either industry farm lending rates or the long-term risk free rate in the country of operation. These provide a proxy for broad changes in the cost of financing in each country.
For row crop farmland there is a clear link between crop yield, crop price, cost of production, interest rates and land values. An implied price can be formulated as a result of income from crop production and the interest rates, as follows:
F = (C * Y + S – P) / I
is freehold land price
is the farm gate price of crops sold by the farmer, weighted according to the typical crop rotation on the farmland
is the crop yield, weighted according to the typical crop rotation
is any subsidy due to a crop grower
is the cost of crop production per unit of area, weighted by typical crop rotation
is the farm lending rates or long-term risk-free rate (longest available local government bond yield)
The relationship between actual and implied prices in many of the key agricultural commodity-exporting regions around the world are shown in the figures below. In order to mirror farmer decision-making processes, this analysis uses five-year moving-average implied pricing which more closely aligns with farmer decision making rather than conditions resulting from a single year. The analysis focused on the U.S, Brazil and Poland, which are agricultural exporting countries. In the U.S. and Brazil, the major producing states of Illinois and Mato Grosso were selected. For Poland, country level averages were used due to a lack of granularity for some data (most notably production cost history). All farmland values and commodity prices are in local currency terms.
The U.S. Midwest is the country’s major corn and soybean production region. Farmers in the Midwest are amongst the most efficient globally in producing corn and soybeans (roughly in a 53:47 rotation), largely due to the productive soils, perfect climatic conditions for corn and soybeans but also due to the ongoing adoption of new technology. Figure 1 shows the comparison between actual farmland values in Illinois (one of the Midwest’s two most significant agricultural-producing states and the state in the Midwest attracting most institutional investor interest) compared to the modelled implied pricing since 2005. The correlation between implied and actual farmland values is 93.3%, indicating a very strong association between the modelled implied pricing and actual prices over time.
Given the strong mathematical link between the components of farm income, interest rates and farmland values, the components can be converted into a regression analysis to analyze the relative magnitude of different farmland price drivers and how they have changed over time. In the regression analysis, changes in yields and cost of production were combined to indicate how farm productivity has driven farmland values. Productivity improvements have been led by seed technology developments, more efficient application of inputs, and improvements in equipment, to name a few.
The regression analysis in Illinois has a R-squared of 96.3%, which indicates very strong explanatory power of the variables in determining farmland values. On average, changes in commodity prices has explained 14% of the movement in Illinois farmland values since 2005. There was a slight increase in commodity price contribution from 2009 to 2016, when prices of corn and soybeans experienced strong price movements (both upwards and downwards) but that price volatility has since stabilized. One of the dominant market themes over the analysis period has been the declining interest rate environment. As a result, interest rates have had a large and positive influence on farmland values. However, as interest rates have declined to relatively low levels (figure 3), the impact on farmland values has also declined to 50% in 2018 from 73% in 2005.
Productivity is the component which has grown in its influence on farmland values during this period from 18% in 2005 to 35% in 2018, indicating that improvements in yields and more efficient practices are one of the key drivers of farmland values. Given the current introduction of technology focused on big data collection and analysis, and its implementation into agricultural equipment and decision making programs, productivity is likely to continue to be a key contributor to rising farmland values going forward.
Selecting an individual state for the analysis in Brazil was important given the geographic spread of states driving differences in inland transportation costs and certain states being able to grow two crops per season due to the soil type and rainfall. Mato Grosso is Brazil’s largest agricultural commodity-producing state, with main railroad hubs located over 2,000 km from deepwater export terminals. The cost of transportation is reflected in the farm-gate price that farmers receive, and in this analysis is included in the commodity price component. Mato Grosso is able to grow two crops per season, leading to 150% utilization of the farmland (a typical season’s rotation is 100% soybean followed by 20% corn and 30% cotton on better quality land). The five-year average implied price for farmland in Mato Grosso has a correlation of 97.0% with actual prices, again showing very strong association between the model and actual prices.
The regression analysis has an R-squared of 97.9%, but interestingly there are differences in the magnitude of the various components’ contribution to movements in farmland values in Brazil relative to the U.S. Firstly, commodity prices had a higher influence at an average of 30% over the period. This higher contribution can be partly explained by currency. Commodity prices received by Brazilian farmers are referenced to U.S. dollars (USD). Given the considerable volatility in the exchange rate between the USD and the Brazilian Real over the period, the devaluation of the Real has resulted in an increasing contribution of commodity pricing to Brazilian farmland values. Secondly, a reduction in freight costs driven by infrastructure improvements has resulted in higher farm-gate prices and has also inflated the contribution of this variable.
Interest rates have had a much lower influence in Brazil at an average of 5%. While there has been considerable volatility in Brazilian interest rates over this time, farmers in Brazil benefit from subsidized government loans which have experienced less frequent changes hence reducing the overall contribution relative to the U.S.
Productivity on average has explained 64% of changes in farmland values in Mato Grosso. This has been driven by the introduction of new seed varieties, improved agronomic practices, adoption of larger equipment, and the development of large farming operations that have significant economies of scale.
The analysis in Poland was focused at a national level given limited data availability at the provincial level for historical costs of production. Despite using less granular data, the correlation of the five-year average implied pricing relative to actual prices remained high at 98.0%. The implied price did, however, overestimate actual prices which we believe is the result of the farm debt market in Poland being in its infancy. Farmers often prefer to buy land with cash rather than with credit (regardless of whether credit is available). This resulted in the average purchasing power being lower than interest rates would imply and therefore actual farm prices have not tracked the implied pricing model. At the same time, new regulations introduced in 2016 imposed a pre-emption right for the Polish government on agricultural land transactions as well as eligibility criteria. The uncertainty of this regulation reduced overall transactions and restricted the universe of potential buyers.
In Poland, the regression analysis delivered an R-squared of 98.8% and highlights that productivity accounted for an average of 77% of movement in farmland values. This is significantly higher than the U.S. and Brazil. With its accession into the European Union in 2004, foreign purchases of Polish farmland accelerated and brought improved farming practices and productivity to the country. Given farmers in Poland have been predominately cash buyers and foreign investor funding costs are likely to have been in their country of origin, interest rate movements in Poland have had a significantly lower contribution to farmland values compared to the U.S.
Commodity price contribution to Polish farmland values is the lowest of all countries analyzed at an average of 9.0%. Poland typically has more diversified farming operations, and it is difficult to define a typical rotation. In this analysis, nearby MATIF wheat prices were used for the commodity price variable; wheat accounts for the majority of crop mix in the country at 50%. In addition to farms producing a broad mix of crops, some of these crops are fed to animals rather than sold at market prices and other crops maybe under contract with downstream processors. As a result of Poland’s EU accession, farmers started to receive subsidy payments which have gradually increased to their current levels of between zł700 and zł800 (around €200) per hectare. This analysis assumed the subsidy payments increased farm revenues, however the growth in subsidy payments has not been linked to commodity price movements. These three factors translate into a lower explanatory power of commodity prices on Polish farmland values.
Overall, the analysis demonstrates that the key drivers of row crop farmland values globally are a function of farm income and cost of capital. There are nuances to investing in different regions, which highlights the importance of having investment professionals that are located in and have experience of each region to be able to invest with confidence.
The magnitude of each variable’s influence on farmland values has changed over time. To forecast future farmland values, investors need to form a view on the direction of commodity prices, cost of capital and productivity improvements. Productivity has been one of the key variables that has influenced global row crop farmland values over time. Developing countries such as Poland and Brazil have benefited from the introduction of farming practices that have rapidly improved productivity. Even in the U.S., which is a more mature producing region, productivity continues to play an important role in lifting farmland values. Given the plethora of new technology being introduced to farming operations, it is rational to expect this trend to continue.
1 U.S. Farmland: USDA Illinois farmland in USD.
2 Poland farmland: Polish National Statistics Office (GUS) in EUR.
3 Brazil farmland: Informa FNP, Mato Grasso region, in BRL.
4 Based on asset pricing models first developed by JB Williams in The Theory of Investment Value (1938)
5 Cost of production includes seeds, fertilizer, pesticides, fuel, labor, machinery, overheads and repairs and maintenance.
The views and opinions expressed are for informational and educational purposes only as of the date of production/writing and may change without notice at any time based on factors such as market conditions or legal and regulatory developments. All information has been obtained from sources believed to be reliable, but its accuracy is not guaranteed. This material may contain “forward-looking” information that is not purely historical in nature. Such information may include, among other things, projections, forecasts, estimates of market returns, and proposed or expected portfolio composition. Any changes to assumptions made in preparing this material could have a material impact on the information presented herein. Past performance is no guarantee of future results. Investing involves risk; principal loss is possible. All information has been obtained from sources believed to be reliable, but its accuracy is not guaranteed. There is no representation or warranty as to the current accuracy, reliability or completeness of, nor liability for, decisions based on such information and it should not be relied on as such.
A word on risk
As an asset class, agricultural investments are less developed, more illiquid, and less transparent compared to traditional asset classes. Agricultural investments will be subject to risks generally associated with the ownership of real estate-related assets, including changes in economic conditions, environmental risks, the cost of and ability to obtain insurance, and risks related to leasing of properties.
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