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Solving Farmland Valuation

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Why land valuation is so hard, and how a new, proprietary approach finally makes it easy for anyone to set accurate and consistent land prices.


The U.S. land transactions market is a big one: hundreds of millions of acres representing an estimated worth of $400 billion.

Many types of purchasers buy this farmland—from people who make a living on the land, developing a deep connection to the soil along the way, to investors who purchase new land to expand their existing portfolio. Despite their innate differences, every type of land buyer—and seller—faces the same dilemma:

How can I accurately determine the worth of any given parcel?

Economists say the “value” of a good or service is what the buyer is willing to pay and the seller is willing to accept at the same point in time. It’s a clear-cut definition that applies to other markets—but unfortunately, it doesn’t apply to land transactions.

That’s because a wider variety of criteria must be considered in order to accurately determine land valuation. At the same time, the perspective through which different types of land buyers value land varies widely according to their intention for the land.

Due to these contingencies, until recently, the market lacked valid and objective land data upon which buyers and sellers could base purchase decisions—a situation that resulted in different people pricing the same parcel of land in wildly different ways.

But now there’s a new way to confidently and accurately determine land value, with objectivity and validity. By uniquely blending publicly available data with proprietary, science-driven insights, a new valuation process gives stakeholders who aren’t personally familiar with a piece of land comprehensive data about it—and a new, common language for understanding it.

With it, anyone evaluating U.S. farmland can enjoy an unprecedented scale and depth of information about each parcel.

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The Trouble with Standard Land Valuation

Several key elements historically made determining land value a complicated endeavor.

  • Different types of land buyers
  • Varied, incomplete land valuation methods
  • Complex information about land located across many different, disconnected sources
  • Different geographies and climates
  • Other unpredictable factors

Different Land Buyers

Buyers prioritize different aspects about land when they attempt to determine its value. That’s because different types of buyers have different intentions for the land.

The Flipper

A purchaser who buys land to resell in the short-term, the flipper hunts for land they believe they can buy at a bargain price and sell for a significant profit. As a result, flippers generally are interested in the land’s current price and short-term price trends, not its long-term outlook.

What they prioritize >> Current/short-term price trends.

The Long-Term Investor

The long-term investor believes land value will appreciate over an extended period of time because land is a limited resource. As a result, the long-term investor seeks out—and often is willing to pay a premium price for—quality land because they expect the price for that land will continue to increase over time despite any short-term fluctuations.

What they prioritize >> Land quality.

The Lessor/Landlord

Often a variant of the long-term investor, the lessor/ landlord buys land for the purpose of generating short-term income through leases and typically intends to own the land for a significant period of time. As a result—and since lease rates in any geographic area tend to be quite restricted—the lessor/landlord primarily is interested in local lease rates and their own expected return on investment (ROI) when it comes to land valuation.

What they prioritize >> Local lease rates, ROI.

The Grower-Owner

As a purchaser who buys land to farm it directly, the grower-owner wants to invest in good land that returns a profit year over year while appreciating over time. While the grower-owner cares about price, this investor also cares about factors that influence the land’s production capacity—things like land quality, historical farming practices, location, present/future operating costs, climate forecasts, and the present/ future selling price of crops.

What they prioritize >> Price, production cost.

The Hobby-Owner

As someone who buys land for its recreational value— perhaps to raise a small herd of livestock, start a truck farm, or simply live in the country—the hobby-owner does not expect to generate a large income from the land. As a result, the hobby-owner cares most about buying affordable land in a convenient location, and is not as concerned about the land’s quality or long-term price trends.

What they prioritize >> Location, price.

Differing Land Valuation Methods

Disagreement often arises regarding pricing for the same parcel of land due to the wide variety of different land valuation approaches commonly used—none of which alone can accurately, consistently determine land values.

Hedonic Regression Valuation

Widely used in real estate to estimate the selling price of an individual property based upon certain predictive or explanatory regressors, Hedonic models use some form of regression—multiple linear regression, ridge regression, generalized linear regression, etc.—to arrive at an overall price that represents the contributory value of each characteristic.

The Math Behind It

The selling price for a single-family dwelling, for example, might depend upon the number of bedrooms, number of bathrooms, lot size, school district, and distance to the nearest grocery store.

Why It Doesn’t Work for Land Valuation

Finding the agricultural value of land involves complex variables based upon soil quality, weather, productivity history, and other considerations which aren’t included in typical residential real estate estimates.

Tax Assessor Valuation

Tax assessors use the County Assessor’s Valuation method to assess farmland values, which is based upon three key pieces of information: the average value of farmland in the region (e.g., the county), the average productivity rating for the region, and the weighted productivity rating of the individual parcel. It can be used with any productivity rating—such as Iowa’s CSR2 or Minnesota’s CPI—to arrive at results independent of the scale of the productivity rating.

The Math Behind It

1. Calculate the average value of a productivity point:

Value per productivity point = Avg. farmland value in county / Avg. CSR in county

2. Apply this value to the weighted productivity rating of the parcel:

Value = Value per productivity point x Weighted productivity for parcel

Why It Doesn’t Work for Land Valuation

Within a county, the parcel value determined by this method is purely a function of agronomic value—it doesn’t consider all of the other variables that drive land value. This method of valuation also relies upon the calculation of an average, countywide farmland value—derived from data collected only from farmers who participate in an annual survey—and is sensitive to macroeconomic factors.

Income Capitalization Valuation

The income capitalization valuation method assesses farmland’s investment value based upon the expected returns—i.e., the present value of a future cash flow. Since this method views farmland as an investment, the rate of return can be estimated as the long-term rate of return on another investment of comparable risk (e.g., a stock or bond).

The Math Behind It

To estimate the present value of farmland, estimate its estimated yearly cash flow and the rate of return.

1. The classic present value equation:


2. When n -> ∞ (i.e., cash flow runs to perpetuity):

Screen Shot 2020 04 14 at 10.58.30 AM

Why It Doesn’t Work for Land Valuation

Despite the appearance of mathematical validity, estimating a farm’s cash flow using income capitalization is a complex, error-prone undertaking that delivers unreliable results.

Comparable Sales Valuation

The housing market widely relies upon comparable sales (comparables) to level-set asking prices—either using comparables along with several other inputs to arrive at the final asking price or arriving at a valuation simply by taking a weighted average of comparable selling prices.

The Math Behind It

1. Locate similar houses that have sold recently (e.g., those in the same or a nearby neighborhood that are of the same size, same age, same condition, etc.).

2. Use the average selling prices of those homes as a benchmark for valuation.

Why It Doesn’t Work for Land Valuation

To account for macro- and micro-geographic trends, comparables usually are applied on a very local level. Yet less than one percent of farm parcels are sold in any given year, leaving estimators with a tiny sample size that shrinks even more once they restrict their analysis to truly comparable farm parcels.

In addition, the factors that contribute to a “comparable” in residential and commercial real estate are well-defined, generally agreed-upon, and easily measured. Yet there exists no such general agreement with regards to agricultural land, where some parcels are more easily measured than others and a larger number of factors have the potential to make farms comparable.

Nearest-Neighbor Interpolation Valuation

Related to the comparables approach, nearest-neighbor interpolation operates by taking a weighted average of the n-closest sales records as an estimate of the sale price of a parcel.

Why It Doesn’t Work for Land Valuation

Geographic trends show this approach works only if the n-closest sales records are clustered closely around the parcel in question. Yet, because sales data is so sparse, there often will be few records geographically close enough to be relevant, and the resulting small sample size makes the interpolation behave erratically.

Macro-Economic Valuation

Most approaches to estimating farmland value have taken place at the macroeconomic scale. These macro-economic valuations strive to provide broad insights into farmland value’s “boom/bust” cycles by looking at spatial-temporal trends in farm prices versus inflation factors.

Why It Doesn’t Work for Land Valuation

The calculations are too broad and can’t estimate different values for different farms located in the same county.

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Complex Information Located Across Many Different, Disconnected Sources

Compounding the valuation and buyer problems outlined above is the land data itself: its volume, complexity, and disconnected nature historically have prevented the multivariate analysis required to arrive at accurate, consistent land prices.

Aggregate Survey Data

Aggregated (summarized) land values from regular censuses and surveys of agricultural land are available from the USDA and can be used as a starting point to estimate the value of a specific property. While these aggregations don’t disclose information about individual holdings, the countywide/statewide averages can be good indicators of baseline land prices at a fairly local level. Yet, as aggregates, they alone cannot account for—and accurately value—a parcel based upon the specific features of the land.

Aggregate Transaction Data

Land sales in some states are registered with the state’s Department of Revenue, which records and publishes transaction summaries. While transaction data like this can be useful validation of census values, it’s challenging to employ the data in a predictive manner since it’s not widely available across the country.

Similarly, auction and lease transaction records also are available in some states, but not widely or uniformly across all states.

Productivity Data

When considering a land purchase, some buyers want to know how much the land can produce—they want to understand its “productivity.”

Productivity depends on many factors, like soil quality, climate, topography, drainage, and length of the growing season. But the actual “yield” in any given year is different from the land’s overall productivity. Like productivity, yield is affected by the above factors; it’s also, however, sensitive to local weather, crop choice, and the grower’s skill and management decisions.

In an attempt to capture the inherent quality of the land independent of year-to-year fluctuations and grower-to-grower decisions a “productivity index” (PI) can be produced.

Blog 2 1

CIBO Lab’s yield simulator for a parcel of land where corn is to be planted for “Warm and Dry” conditions.

While PIs help with land pricing efforts, the methods used to calculate them can vary widely, and they’ve proven only somewhat able to accurately predict actual crop yields. That’s because some PIs consider soil quality more heavily in their calculations while others attempt to include in their results the effects of climate in the local area. In addition, most calculations don’t take into account growers’ management practices, even when those practices are widespread.

Yield Stability Data

Even though a field’s yield varies year-by-year depending upon weather and management, parts of the field may consistently perform better or worse than other parts. The parts that consistently perform well are referred to as having “high stability,” while parts that consistently perform poorly have “low stability.”

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

CIBO’s proprietary Stability Insights show this field is mostly unstable (shown in red), resulting in a low stability score of 7 (on a scale of 0 to 100).

Fields also may possess unstable regions that yield well under certain conditions but poorly in other conditions (e.g., a part with higher elevation and sandy soil may perform poorly in a dry year but have a high yield in wet years, while other parts of the field drain poorly).

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

CIBO’s proprietary Stability Insights show this field possesses mostly high (shown in dark green) to medium stability (shown in light green), resulting in a stability score of 73 (on a scale of 0 to 100).

Yield stability tends to correlate with selling prices for land: fields with fewer low-stability and unstable regions tend to command higher prices.

Urban Pressure Data

Urban pressure is the phenomenon in which farmland located close to urban areas typically sells for higher prices than comparable farmland located farther from urban areas—usually because farmland close to urban areas has the potential to be repurposed for residential development.

Land Improvement Information

Land improvements are investments that increase the value of farmland—things like new irrigation, tiling, access roads, buildings, and other structures implemented on the land, like modern barns and machine shops. This is because installing a pivot irrigation system, for example, can increase crop yields and thereby increase the value of the land itself.

It’s hard to detect such improvements on a broad scale because it’s difficult to know how much value a house adds to a farmland parcel without conducting a first-hand inspection (e.g., it might be hard to distinguish from a distance the differences between a modern, new machine shop and an old decaying barn).

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Different Geographies and Climates

Land’s geographical location and climate also greatly impact its price, making it difficult to generalize land prices across the nation.

The below graph visualizes the tumultuous nature of land prices over time. From 1995 to present day, some states experienced substantial changes to land prices per acre due to natural events like droughts or floods, while other states remained largely immune to these kinds of fluctuations.

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Year-over-year trends for agricultural land’s price-per-acre by state indicate New Jersey land prices vary greatly from land prices in Montana.  Source: USDA survey of agricultural land prices over time, by state.

CASE STUDY: Urban vs. Rural Land Price Fluctuations in Illinois

To illustrate how local trends vary within a state, let’s look at Illinois, which boasts a mixture of highly urbanized areas in and near Chicago, as well as rural areas composed almost entirely of farmland.

As shown in these statewide trends, overall Illinois land prices:

  • rose sharply from 2004 to 2008;
  • dropped slightly from 2008 to 2010, reflecting the housing real estate bubble;
  • rose sharply from 2011 to 2014, driven in part by strong commodity futures; and
  • dropped slightly from 2014 to 2018.

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State-wide agricultural land trends in Illinois. Source: Quick Stats USDA Survey Data.

Yet, when we compare a metropolitan county in Illinois to a rural county, a very different picture emerges. The land price differences between these two types of counties are stark, with Illinois counties tending to show one of two trends: the “urban” trend of Cook County or the “rural” trend of Edgar County. For instance:

  • Land prices for urban Cook County— where Chicago is located—peaked in 2007, decreased somewhat in 2012 (likely because of the housing bubble), then steeply rose again in 2017.
  • By contrast, land prices in Edgar County, a rural county located in the central-east agricultural portion of Illinois, have risen gradually but steadily over the past 15 years, remaining largely unaffected by the housing bubble.


Land price trends for two distinctly different counties in Illinois. Source: Quick Stats USDA Survey Data.

Other Unpredictable Factors

A variety of other common factors also can influence how individual investors value land.

Relatives, for example, might unwittingly over-price land owned by their family for generations because they’re unable to remain objective due to family considerations and sentimental (or historical) attachment.

In another scenario, purchasers might assess a parcel from several different viewpoints because they plan to do several different things with the land—perhaps they plan to grow crops on one portion, enjoy the recreational possibilities of a second portion, and lease the rest of the land. For this purchaser, the land’s “value per acre” is some aggregate value taking into account the different valuations representing different subsets of the overall parcel.

Introducing a New Way to Confidently, Fairly Price U.S. Land

It’s clear only an entirely new approach will empower any type of buyer to accurately and objectively determine a land parcel’s true value.

That’s why we at CIBO developed an online platform that delivers unmatched intelligence about land. Our breakthrough machine learning, modeling, and simulation capabilities combine the most accurate aspects of each existing valuation method with our own proprietary insights, and take into account more variables from public, private, and proprietary data sources than any other method.

The result?

An unmatched depth and scale of farmland information now easily available to anyone who needs it.

Why CIBO’s Approach Works Better than Any Other Land Valuation Method

  1. CIBO’s proprietary valuation and land scoring processes blend more than one petabyte of proprietary data with tough-to-reach public information.
  2. One-of-a-kind CIBO Insights generate simple scores anyone can use—on their own or paired with other publicly available land data—to efficiently evaluate and compare parcels of land, understand past and in-season management practices and yields, and predict a parcel’s future productivity and value.
  3. A self-correcting valuation model automatically detects anomalies, then either displays corrected valuations and/or updates the CIBO model with additional, conditional factors.

Find out for yourself—view CIBO land reports and estimated land/ lease values, run easy yield simulations, and quickly search land data anytime, FREE!

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