How Does Farm Income Affect Farmland Values?
The value of farmland across the central United States is influenced by a number of factors, including stability, a dynamic market, the willingness of buyers to pay a premium for the location, and crop direct income. By understanding these factors, farmers can make informed decisions about where to invest when buying land.
To determine the relationship between farmland values and farm income across the central United States, the FBN® Data Science team assessed a wide range of FBN and USDA data points. For the purposes of this article, we describe the difference between the actual farmland value and the value that would be expected based on crop direct income as farmland premium.
Key Takeaways: What Does the Relationship Between Farm Income and Farmland Values Mean for Farmers?
Crop direct income is a major determinant of farmland values, but not the only one. Areas with a high premium have other factors that drive up land values, such as stability, a dynamic market, and buyers willing to pay a premium for the location.
Farmers who are only interested in the productive potential of land should look for areas with lower premium, as this will mean higher profitability. However, farmers who are interested in farmland value appreciation should consider how the factors that drive premium are playing out in a given area and how they may evolve in the future. If forces are pushing for premium to increase, there may be an opportunity to buy land with a long-term view of taking advantage of the appreciation.
Differences in premium create opportunities. If direct income was the only return to farmland, there would be a flow of capital from areas with high premium (expensive) to areas with low premium (cheap). This could be possible, especially if there were a high farmland appreciation in some areas due to recent high income. In that context, farmers could start looking at low-premium areas as an opportunity for investment.
Crop Income Varies Between Regions
An important piece of this analysis is the direct crop income. As farmers know — and as the map illustrates, based on differences in soil, weather and other varying crop factors across that region — not all farms have the same direct income. The map shown here illustrates the average Direct Crop Income for corn and soybeans between 2016-2022.
The areas with the highest direct income are Idaho, Illinois, Indiana, Iowa, southern Minnesota, and the irrigated areas of Nebraska and Kansas. It’s important to note that, based on the trend line in figure 1, areas with higher direct income would be expected to have higher farmland values.
Higher Income Farms Have More Valuable Farmland
As you may expect, farmland values increase with higher crop direct income. However, while some areas have a high farmland value for the returns they generate, others have a low farmland value compared to the returns.
In the image shown here, note that the price per acre data points are FBN county cropland value estimates based on USDA data. The green line in each graph shows the trend using a quadratic-plateau model. Direct Crop Income is the average income for the four years prior to the sale (corn and soybeans).
Iowa, Illinois, Indiana, Ohio Have Favorable Premiums
The premium map here, which illustrates the average premium in the Midwest, shows that the I-states and eastern Ohio have a favorable premium, which tends to decrease when moving in any direction away from those central areas. When shifting to the west and north, it becomes negative.
In the map, dots show cities with a population above 200,000 people. The premium is the average for 2013-2022. It is calculated using FBN county cropland value estimates based on USDA data, and direct crop income for the four years before the sale.
(Remember that the premium is calculated considering the actual prices received, using local bids and adjusting for transportation. Consequently, those negative premiums should not be related to higher transportation costs to the final destinations.)
Premiums Have Changed Substantially Over Time
This image above illustrates the premium trend over time, keeping in mind that the premium is the average for the reference period. It is calculated using FBN county cropland value estimates based on USDA data, and direct crop income for the four years prior to the sale.
The premium over time maps above show how the premium has changed in the last 10 years. The main change is increased premiums in south Minnesota and West Ohio in recent years, which could be due to weather and income variability or to farmland market trends that start to value different regions differently over time.
Farmers who are considering a land purchase should carefully consider all relevant factors, including the crop direct income, farmland demand and farmland supply, before making a decision. By understanding the factors that influence farmland premium, farmers can make informed decisions that will help them achieve their financial goals.
Direct Income Calculations
Direct income = Gross farm revenue - Direct Costs
Gross farm revenue = Yield x Farm-gate Grain Price + government Payments + Crop Insurance Payments
Direct Cost = Seed + Fertilizer + Chemical + Machinery (fuel, repair, depreciation, labor) + Overhead + Drying Cost + Crop Insurance
The observed farmland value is obtained from USDA cropland values between 2013-2022. For each of those years, we calculated the direct income for the four years prior to the sale before averaging those years into a single value and running a quadratic-plateau regression model (figure 1) between the direct income and the farmland value. Finally, we calculated the premium using equation 1.
Direct Income Calculations
The direct income estimates used in this analysis leverage several data inputs. To calculate historical annual gross farm revenue, we used USDA county yields combined with FBN's internal datasets that cover key aspects of farm economics, including localized grain price, crop input costs and government farm payments.
We used each county's centroid to calculate farm-gate prices by county. Next, we took the average bid prices in the surrounding region near harvest (October and November) and adjusted for hauling cost as a function of distance to the county's centroid using historical hauling cost information (Grain Transportation Report). We then combined these data layers – modeled yields and commodity prices – to estimate historical gross farm revenue. We also added each crop's average government and crop insurance payment in the county.
Then we subtracted the direct costs, such as seed, fertilizer, machinery (fuel, repair, depreciation, labor), drying, overhead and crop insurance. The direct costs are localized, using amounts and rates conforming to standard practices in each region, and using historical prices for each of the components.
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