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The Data Puzzle: How We Determine Land Value

By Marie Coffin

Our proprietary valuation process incorporates several pieces of data — some are publicly available, some are proprietary. Taken together, CIBO uses them to create a disruptive valuation process that will create an objective pricing framework in the agricultural industry.

Estimated County Mean of Price Per Acre

Estimated Means

Aggregate Survey Data

Aggregated or summarized land values are available from the USDA and are the results of regular censuses and surveys of agricultural land. There is no information disclosed about individual holdings, but the county-wide and state-wide averages give a good indication of the baseline prices at a fairly local level. This data can be used as a starting point to estimate the value of a specific property.

In some states, land sales are registered with the state department of revenue, and summaries of the transactions are published. Such transaction data can provide useful validation of census values, but they are not widely available across the country so employing them predictively is challenging. Similarly, auction and lease records are also available at local levels, but not widely or uniformly across states.

Productivity

In residential real estate, the conventional wisdom is that the three most important drivers of price are “location, location, location.” In valuing agricultural land, we might say “productivity, productivity, productivity.” The productivity of agricultural land is meant to reflect how much the land can produce. For row crops, we think of this in terms of the bushels per acre (or kilograms per hectare) that can be harvested from the land.

Just as value depends on productivity, so too productivity depends on many factors: soil quality, climate, topography, and drainage, length of the growing season, etc. Of course, the actual yield in a given year is different from the overall productivity: yield is affected by the above factors, but also sensitive to weather (as opposed to climate), the choice of the crop (a given year might turn out to be better for one crop than another), and the skill and management decisions of the grower. A “productivity index” is an attempt to capture the inherent quality of the land (sometimes called its “productivity potential”) independent of year-to-year fluctuations and grower-to-grower decisions.

ISPFMRA – Price Per Acre vs. PI

Illinois Productivity Index PI vs appraised price per acre 1

Figure 1:  Illinois Productivity Index (PI) vs appraised price per acre.

The development of productivity indices is a science in itself: some are more reflective of soil quality, while others attempt to also include the effects of climate in the local area. Most do not take into account growers’ management practices, even when those practices are wide-spread; for this reason, such indices are only somewhat predictive of actual crop yields. Nevertheless, as we see above, productivity is a good predictor of the sales price.

Yield Stability

Growers are accustomed to assessing their own fields in terms of the stability of yields over time. Even though the yield on a field will vary year by year depending on weather and management, some parts of the field will consistently perform well (high stability), while other parts will consistently perform poorly (low stability).  Typically, fields will also have unstable regions that yield well under certain conditions but poorly in other conditions. For example, a part of the field with higher elevation and sandy soil may do poorly in a dry year, but conversely, have a high yield in wet years; meanwhile, other parts of the field are draining poorly.

Yield Stability The field is mostly unstable red and has a low stability score 7 on a scale of 0 to 100 1

Figures 2a: Illustrates the field is mostly unstable (red), and has a low stability score (7 on a scale of 0 to 100).

Yield Stability the field on the right is mostly high and medium stable dark and light green and has a stability score of 73

Figures 2b: Illustrates the field on the right is mostly high and medium stable (dark and light green), and has a stability score of 73.

Yield stability tends to be correlated with selling price: fields that have a smaller proportion of low and unstable regions tend to command higher prices, probably because such fields require less intensive management.  At CIBO, we calculate a stability (or reliability) score for each field, and this rating is factored into the field valuation.

Blog 2 4

Figure 3: Illustrates the trend in stability scores, where more stable parcels generally command higher prices.

Urban Pressure

Urban pressure is the phenomenon in which farmland close to urban areas will typically sell for higher prices than comparable farmland that is farther from an urban area. This is because farmland close to urban areas can potentially be repurposed for residential development. In the near future, the CIBO valuation process will use urban pressure as a secondary factory in determining Valuation.

Improvements — Installed Irrigation, Tiling, Buildings, and Structures

Land improvements can be viewed as investments that increase the value of farmland. In dryland regions, for example, installing a pivot irrigation system can increase yields and thereby increase the value of the land itself. Other improvements of this type include installed and well-maintained tiling, maintained access roads, and buildings (Not only dwellings, but also modern barns and machine shops increase the aggregate value of farmland parcels).

Detecting such improvements on a broad scale is a challenging problem. Without conducting a first- hand inspection, it is difficult to know how much value a house adds to a farmland parcel. At a distance, a  modern new machine shop may be hard to distinguish from an old decaying barn. CIBO’s Computer Vision system is tackling these problems, and we anticipate that as we make advances in this area, our land valuations will become more nuanced over time.

Conclusion

Many factors come into play in CIBO’s valuation process — some are intuitive, while others require more complex interpretation. In the end, we arrive at a value that can be objectively applied to land across the United States, changing the game and providing standard pricing factors for all land transactions.

About Marie Coffin

Marie Coffin is the VP, Science and Modeling at CIBO, a science-driven software startup. She has focused on being a biostatistician at agriculture companies. Prior to CIBO, she worked for Monsanto, Icoria, Paradigm Genetics, and was an assistant professor at Clemson University. She holds a BS in Mathematics from South Dakota State University and a Ph.D. in Statistics from Iowa State University.

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