Cropland prices across the United States have appreciated at record levels in recent years. After a U.S. recession, in which a bubble in real estate prices was a factor, some have questioned if cropland prices are in a bubble. Schnitkey, Sherrick, and Kuethe (2014) show that the increase in cropland returns and the decline in interest rates have justified the rise in cropland prices. They showed how historically the capitalized value (the sum of future cash rent payments divided by the 10-year Treasury note rate) closely follows the actual sale price as seen in their Figure 1. However, it is notable that in recent years the capitalized value has greatly outpaced actual sale prices. This article explores two causes of this phenomenon. First and most intuitively, if high crop prices are used to calculate capitalized value, the high prices must be perceived as permanent for buyers and sellers of land to actually transact near the capitalized values implied by high prices. Second, the expected utility model of uncertainty predicts that risk-averse buyers of land will not be willing to pay the full, capitalized value of land if they perceive that the high prices will prevail only in the presence of greater volatility as well.
Commodity Prices
The sale of commodities generates an income stream from an investment in cropland. When the expected commodity prices rise, the capitalized value of cropland also rises. Figure 1 and 2 show the price of corn and soybeans, respectively, since 2004 using data from Farmdoc. Corn and soybeans occupy the most acres of cropland of all commodities grown in Illinois (“Illinois Agriculture”).
Tables 1 and 2 show the estimated revenue and capitalized value for different price and yield combinations. In figure 1, different prices are multiplied by possible yields to estimate Net operating income (NOI) per acre. Figure 2 displays the capitalized values for the different price and yield combinations.
The lower price and yield combinations result in negative cash flows per acre, the cost of production is more than revenue generated; this results in the negative theoretical capitalized values shown in the table. Because negative cropland prices are not feasible in the long run (or even the medium run) as predicted by current levels of commodity price, yield, interest rate, and operating cost combinations, it is clear that either commodity prices must rise or operating costs will fall (because yield growth is slow and interest rates are already near zero).
For soybeans, a low price and yield combination still provides a positive profit. At high price it provides a much lower return compared to corn. It appears corn provides an opportunity for greater return than soybeans. It also has a larger variability in its net operating income per acre.
Impact of Variability in Commodity Prices
Although the expected commodity price drives the capitalized value of land, the variability in commodity prices also matters to actual land values. Trujillo-Barrera, Mallory, and Garcia (2012) show that the annual volatility in corn prices have recently experienced sustained periods of historically high prices.
For a risk-averse individual, a riskless outcome is preferred over risky one. For example, a risk-averse farmer may prefer corn at a guaranteed price of $6/bushel over corn with an expected price of $7/bushel, but more variability.
Figure 3 illustrates how risk affects the value of cropland.
The y-axis represents the utility of cropland and the x-axis represents the net operating income to that cropland. The curved orange line shows the diminishing marginal utility to the farmer of increasing net operating income.
When there is variability in the expected net operating income, the expected utility of cropland can be shown by determining possible outcomes and weighing the probability of each outcome to find the expected income. For example, if you expect the net operating income per acre to be either $150 or $450 both with a 50 percent probability of happening, the expected net operating income is $300.
Drawing a horizontal line from the midpoint of the black line to the orange line at Utility* identifies the sacrifice in return the risk-averse individual would be willing to accept to eliminate risk in net operating income (Identified as $255 in figure 5). Now imagine net operating income with even greater variability (represented by the blue line) but identical expected net operating income of $300, the return the risk-averse individual’s expected utility is reduced to Utility** and what he would be willing to accept with certainty decreases to $200.
The key takeaway from figure 3 is that increased variability in net operating income reduces a risk-averse individual’s expected utility from owning the land, which reduces his willingness to pay for cropland. Given that most farmers use some kind of forward-pricing mechanism, such as forward contracts or futures contracts, there is evidence that farmers are risk averse.
Conclusion
In conclusion, we explored two reasons for the recent divergence between cropland capitalized values and actual sales prices. We identified that the recent high commodity prices likely were not perceived as permanent. In addition, commodity price volatility has remained at historically high levels. The increased volatility in net operating income this implies weighs heavy on the price of cropland as well.
Works Cited
“10-Year Treasury Constant Maturity Rate.” Economic Research. Federal Reserve Bank of St. Louis, n.d. Web. 4 Dec. 2014. <http://research.stlouisfed.org/fred2/series/DGS10>.
“Marketing & Outlook.” Farmdoc. University of Illinois at Urbana- Champaign, n.d. Web. 1 Nov. 2014. <http://www.farmdoc.illinois.edu/manage/uspricehistory/us_price_history.html>.
Schnitkey, G., B. Sherrick and T. Kuethe. “Farmland Price Outlook in 2014 and Beyond.” farmdoc daily (4):155, Department of Agricultural and Consumer Economics, University of Illinois at Urbana-Champaign, August 19, 2014
Trujillo-Barrera, Andrés, Mindy Mallory, and Philip Garcia. “Volatility Spillovers in U.S. Crude Oil, Ethanol, and Corn Futures Markets.” Journal of Agricultural and Resource Economics 37.2 (2012): 247-62. Web. 27 Feb. 2015. <http://ageconsearch.umn.edu/bitstream/134275/2/pp247-262,Trujillo-Barrera.pdf>.
United States of America. USDA. “Illinois Crop Production.” N.p., 10 Nov. 2014. Web. 8 Dec. 2014. <http://www.nass.usda.gov/Statistics_by_State/Illinois/Publications/Current_News_Release/20141110-IL_Crop_Production.pdf>.
United States. USDA. Illinois Department of Agriculture. “Illinois Agriculture.” N.p., 1 Apr. 2011. Web. 18 Feb. 2015. <http://www.nass.usda.gov/Statistics_by_State/Illinois/Publications/Farmfacts/farmfact.pdf>.
Zwilling, Bradley L. “Costs to Produce Corn and Soybeans in Illinois—2013.” Farm Business Management (2014): n. pag. Web. 4 Oct. 2014. <http://www.farmdoc.illinois.edu/manage/corn_soybeans_costs.PDF>.