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Agriculture analytics: Solutions reflect farmland’s true value

Each percentage point shift in farmland value moves the national ledger to the tune of $26 billion. Photo Courtesy of 123rf.com | © Péter Gudella

Each percentage point shift in farmland value moves the national ledger to the tune of $26 billion.
Photo Courtesy of 123rf.com | © Péter Gudella

Joseph ByrumBy Joseph Byrum

Agricultural land has long been considered an exceptionally stable source of value, but that is changing. The price of farms has recently declined, but this devaluation may be the result of investors acting on insufficient data. Old-fashioned cropland valuation techniques are perhaps painting an inaccurate picture of the nation’s food production capacity, turning a “sure thing” investment into something less reliable than it once was. Data analytics can help correct the imbalance.

Save for a brief dip at the end of the Great Recession [1] in 2009, farm prices in the Midwest have steadily climbed year-over-year since the 1980s. This is not a particularly surprising result, as a rapidly growing global population – the world will have to feed two billion more people by 2050 [2] – guarantees a steady and strong increase in demand for agricultural products. Consequently, many institutional investors have seen farmland as the sort of reliable asset that can withstand all but the most severe economic downturns [3].

Falling Farm Values

So why have farm values begun sliding now [4] in a time of relative prosperity? Nationwide, the average crop land real estate value is $4,090 per acre [5], the lowest this figure has been since 2013. Surveys sent to farmers indicate that cash rental rates in Illinois, Indiana, Iowa and Wisconsin were down 7 percent in 2017 compared to the previous year [6]. Even prices for the highest quality farmland remained stagnant, while less attractive parcels have dropped in value.

Why should you care about how much someone pays for a few acres of land in Iowa? It matters because agriculture contributes nearly a trillion dollars to the U.S. gross domestic product [7]. Add up the value of all farmland (including the buildings that sit on them), and you are talking about $2.6 trillion in wealth [8]. So each percentage point shift in farmland value moves the national ledger to the tune of $26 billion. No wonder the Federal Reserve banks pay such close attention to farm values.

Economists at the Chicago Fed point to collapsing commodity prices as the prime culprit in the recent decline: “This downturn has hit the Midwest hard, as seen in lower farmland values and cash rental rates for cropland” [9]. Compared to two years ago, the inflation-adjusted price for wheat has dropped 23 percent and corn is down 8 percent. Soybean has nearly recovered and is down just 2 percent over this period.

For farmers operating on the edge of profitability, a commodity price downturn is devastating. While it can mean the difference between success and failure for an individual grower’s operation, the land will outlast the commodity price trend. This suggests the measure of value for a long-term asset like land should not just be the short-term fluctuation in commodity price, but the land’s inherent productive capacity. But this should be determined with greater precision than can be had from a glance at the latest commodity futures price chart.

Data Analytics = More Precise Valuations

The use of data analytics for more precise valuations could recapture billions in lost value. Land is, of course, the foundation of food production. Aside perhaps from fish, just about everything we eat depends on cropland, whether that means the field that grows corn for the supermarket or the soybean field that supplies the feed for cattle.

The United States is a net food exporter [10], and agriculture is the only major U.S. industry that has consistently enjoyed a trade surplus [11]. That is largely due to the Midwest serving as the world’s primary supplier of corn and soybean, a status that is not likely to change any time soon. We have the ideal climate, experienced farmers and the best technology. That links the demand for the output of American farms to global demand for food.

Despite fluctuations related to weather and stockpile levels, that global demand can only head in one direction: up. According to the United Nations, the world population will increase by 82 million within the next year [12]. That is equivalent of adding an entire country the size of Germany to global demand. Now extend the time horizon to 16 years from now, and global producers will have to increase output to cover the needs of the population of India – over 1.2 billion people – more than the entire world population at the middle of the 19th century.

Of course, farmers going about their business are not making plans based on what they think might happen in the year 2033. They tend to move from harvest to harvest, taking into account factors such as market conditions, expected weather, cash on hand and how much a given field can produce in adjusting their choice of crop for planting. This is how they make the most of the supply and demand situation, and it is how they have done things for decades.

But modern operations research techniques have opened new opportunities for increasing the productive output of agriculture [13]. The same insights that increase the yield per acre of land can also provide the insights needed to improve land valuation.

Rather than just raising and lowering values based on market conditions in isolation, the worth of a particular parcel should be based on its expected productive output, under the present market conditions. Data analytics can play a big role in enhancing not just the estimate of productive output, but also increasing that productive output itself.

For instance, if the cost of corn stays the same over five years, or maybe even dips a bit, the value of the land would probably drop. But if yield were to increase enough to offset the price drop, the land’s true value ought to be positive, not negative. Thus, the increased value one can expect from the land ought to be factored into the price.

Data analytics have only recently begun to play a part on the farm thanks to the development of new technologies. Remote sensing and satellite technologies offer unprecedented insight into the layout of fields and their chemical and biological condition, all in real time. Combined with models of weather patterns, water, nutrient and other resource availability, it is possible to generate an expected range of output from a given field. That knowledge can be combined with forecasted commodity values, as well as property taxes and other regulatory costs, to achieve a far more precise expression of a particular field’s monetary value.

Farm Management Software

Already, some startup companies are integrating data-backed farm management software with systems that track farm values. The company Granular has raised $18.7 million [14] to develop the AcreValue software package that tracks 40 million farm parcels in a database containing three years’ worth of land transaction records, plus public sources of crop rotation, soil and environmental data.

The software uses two different models to create its valuation estimates. The first considers the individual characteristics of a piece of land. “This controls why a parcel of land on one side of the road might have a very different value than a parcel of land on the other side,” the company explains [15]. The second model uses all of the available financial data affecting the overall market, including interest rates and commodity prices. Granular says the combination of the two models is what provides the most powerful estimate of land value.

These pioneers of data-backed valuation have also pointed toward many of the relevant factors that are not – at least for now – part of the analysis. There is the potential for commercial development, mineral rights, special leasing arrangements, soil erosion, the quality of tillage and drainage, and the history of past land use that would have contaminated the location.

Those are some of the big-picture data that would refine the overall market analysis, but more can and should be done to boost the precision of the first type of individual analysis. Unfortunately, that is much more easily said than done.

Farmers would need to spend the up-front cash to install the latest remote sensing equipment. While many have done so, not all have been convinced of the value of data. Farming is an industry steeped in tradition, where it can be hard to break the old analog habits – why change what works? Of course, show farmers the value that can be derived from data analytics systems, and the farmer’s practical nature turns the tide in your favor. Demonstrating a return on the up-front investment in technology systems is the key to convincing farmers of the value of operations research.

If, in addition to showing that the productivity increases from data analytics will drive greater profits, we could also show that greater precision in data collection will result in more stable and higher land values as the long-term adoption rates of data gathering technologies will rise.

Ultimately, I believe this is where the agricultural industry is headed. Operations research is the future of our industry, and that is why the data analytics community will be needed more than ever to help guide the way.

Joseph Byrum, Ph.D., MBA, PMP, is senior R&D and strategic marketing executive in Life Sciences – Global Product Development, Innovation and Delivery at Syngenta. He writes about agricultural innovation. Connect on Twitter @ByrumJoseph. He is a member of INFORMS.

References

  1. https://www.federalreservehistory.org/essays/great_recession_of_200709
  2. http://www.un.org/en/development/desa/news/population/2015-report.html
  3. http://www.economist.com/news/finance-and-economics/21637379-hardy-investors-are-seeking-way-grow-their-money-barbarians-farm-gate
  4. http://www.kiplinger.com/tool/business/T019-S000-kiplinger-s-economic-outlooks/
    https://www.conference-board.org/data/usforecast.cfm
  5. Page 4: http://usda.mannlib.cornell.edu/usda/current/AgriLandVa/AgriLandVa-08-05-2016.pdf
  6. https://www.chicagofed.org/publications/agletter/2015-2019/may-2017
  7. https://www.ers.usda.gov/data-products/ag-and-food-statistics-charting-the-essentials/ag-and-food-sectors-and-the-economy.aspx
  8. Page 17: http://usda.mannlib.cornell.edu/usda/current/AgriLandVa/AgriLandVa-08-05-2016.pdf
  9. https://www.chicagofed.org/events/2016/ag-conference
  10. https://www.ers.usda.gov/data-products/foreign-agricultural-trade-of-the-united-states-fatus/us-agricultural-trade-data-update/
  11. https://www.usitc.gov/research_and_analysis/trade_shifts_2014/us_trade_by_industry_sector.htm
  12. https://esa.un.org/unpd/wpp/Download/Standard/Population/
  13. https://www.informs.org/ORMS-Today/Public-Articles/June-Volume-42-Number-3/Edelman-Award-Syngenta-earns-2015-INFORMS-Edelman-Award
  14. https://techcrunch.com/2015/07/22/granular-rakes-in-18-7-million-to-manage-big-farms/
  15. https://www.acrevalue.com/faq/#valuationestimates

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