30+ Variables an Ag Lender Should Understand
Lending across nearly all loan categories continues to contract as banks tighten up their lending standards and brace for a potential recession. For mortgage lending specifically, lenders are losing money on loans for the first time in years as the cost to originate rises.
Meanwhile, deposits at community banks dropped $2 billion in a single week in May, according to the Federal Reserve, and most of the nation’s banks expect customers to fall behind on payments as delinquencies rise.
An important note regarding the $2 billion: the money was reallocated to higher yielding investments such as money market accounts, some of which were simply transfers from savings to money accounts at the same bank. Regardless, less money is available for active deposits in and loans out at smaller community banks, which rely on this system for loans. Again, many of these community banks can access capital from larger banks, but with a few basis points of cost, so the cost of capital is rising for community banks when they can’t access the ready capital from deposits. Again, this points to another reason to sharpen the pencil for new loans for risk analysis.
Ag Lending Offers Promise for Banks
Financial institutions are under significant pressure to remain competitive, and ag lending offers promise to banks that are struggling.
According to American Bankers Association’s latest Farm Bank Performance Report, ag lending increased by 8.1 percent in 2022 to $103.1 billion, despite ongoing economic and environmental challenges. The report also shows that banks held more than $43.8 billion in small farm loans at the end of last year, including $9.3 billion in micro farm loans.
Farmland continues to be a strong asset as land values grow, and farmers and landowners continue to seek capital to manage rising operating costs. This trend will likely continue – and banks should take note.
Assessing Risk Requires Significant Data
However, ag lending is much more complex than other markets and requires an enormous amount of data. Banks must go beyond FICO scores, employment verification and financial statements, and consider farm production information and other data, none which is either standardized or structured for ease of use.
Banks are then forced to manually collect information, which is extremely time-consuming and error prone due to the non-standardized formatting of the accounting data. This process also does not account for the assessment of management risk, which is directly related to the farm operation or asset value of the land, which is another task critical to loan underwriting which is often represented.
Here’s a look at the five main data categories banks must assess on farm loans:
1. Crop
Crops are the primary income generators for farms and can be complicated to evaluate without the right data. It’s not as simple as knowing the type of crop, like corn or soybean, and the earning potential or the previous season’s revenue.
Banks must also have access to other impactful crop metrics such as yield confidence intervals and volatility indexes to best plan and forecast their strategies.
Crop-related data variables include:
● Ag risk score
● Crop type
● Earning potential index
● Grassland biomass
● Gross revenue per acre
● Yield confidence interval
● Yield history
● Yield prediction (corn, soy, wheat)
● Yield volatility index (spatial)
● Yield volatility index (temporal)
2. Hazards
A farm’s hazard risk is its potential for being impacted by natural occurrences such as weather events. Assessing this type of risk is also challenging and goes beyond simply looking at floodplain proximity. Banks must know the risk of droughts or hail, as examples. Eutrophication risk, which impacts water quality, is also critical.
Hazard-related data variables include:
● Drought risk
● Eutrophication risk
● Flooding frequency/risk
● Floodplain proximity
● Hail risk
3. Land
Land values are rising. In fact, the U.S. Department of Agriculture reported a 14.3% increase in cropland value last year compared to the previous year. But this isn’t the only data point banks must consider when evaluating loans.
Land class and Land Use Code (LUC), which includes (1) irrigated cropland, (2) dry cropland, (3) improved pasture land, (4) native pastureland, (5) orchard, (6) wasteland, (7) timber production and (8) wildlife management, must be assessed.
Banks must also have data on if and how the land is irrigated and how that could impact production efficiency and yield. Consider that only 15% of farms have some form of irrigation yet account for over half of all U.S. crop sales.
Land data variables include:
● Irrigation
● Land Class
● Land Value
● Land Use Code (LUC)
● Vegetation diversity
4. Soil
A farm’s soil serves as the base for all farming activities, and its health and chemical makeup has a direct impact on production and profitability. In assessing the risk, banks must know things like the soil carbon accrual, which is based on the relationship between inputs (from vegetation) and outputs (from decomposition or disturbance). With the rise of ESG lending, soil carbon emissions are also important. To traditionally acquire this information, soil samples must be sent to an accredited laboratory for testing. Modern approaches, however, are available through technological innovations.
Soil-based data variables include:
● Soil carbon accrual
● Soil carbon baseline
● Soil carbon capacity
● Soil carbon emissions
● Soil carbon scenario forecast
5. Management
Understanding an operation’s land management practices also helps assess the risk. Information like planting and harvest dates are important, but banks also need information on whether the operation rotates crops, which helps return nutrients to the soil, interrupts pest and disease cycles, and improves overall yields.
Whether the operation has cover crops – plants grown after main crops have been harvested to ensure the success of future crops – is also important. This process also helps with soil erosion and can improve the overall health of soil, and therefore increase the operation’s profitability.
Management data variables include:
● Cover crop
● Crop residue
● Crop rotation
● Harvest date
● Mechanical emissions data
● Minimum viable water use
● Planting date
● Tillage
Banks Must Invest in Data Systems
If any of these data variables sound foreign, you’re not alone. After all, bankers are not farmers. But it doesn’t mean bankers can ignore these factors in assessing risk, especially if they expect to responsibly and successfully enter into agricultural investments and lending.
This also doesn’t mean it’s impossible for banks to pursue ag loans. To quickly capitalize on this market, banks must invest in data services that scale across their portfolio and utilize the latest tools in machine learning and artificial intelligence (AI), which can accurately identify and replicate field-level patterns and generate a field-level risk analysis unique and specific to an agriculture portfolio.
As banks explore new markets, true value will be found in adopting solutions that replace the untimely and resource heavy process of manually researching and compiling critical farm information by aggregating the data and presenting it in an actionable way.
But by using modern data collection tools, banks can gain a complete picture of a farm’s risk level and ensure their lending decisions are sound without worrying about what to collect and how to collect it. They can capitalize on a booming industry and focus on doing what they do best – banking.
About Author:
Jim O’Brien, CEO of Agrograph