United States

Department of

Agriculture

Economic

Research

Service

Economic

Information

Bulletin

Number 92

February 2012

Trends in U.S. Farmland 

Values and Ownership

Cynthia Nickerson, Mitchell Morehart, 

Todd Kuethe, Jayson Beckman, 

Jennifer Ifft, and Ryan Williams

Average farm real estate values, 1980-2010 

$ per acre

1980 85 90 95 2000 05 10

0

500

1,000

1,500

2,000

2,500

Nominal

Inflation adjusted (2005=100)
The U.S. Department of Agriculture (USDA) prohibits discrimination in all its programs 

and activities on the basis of race, color, national origin, age, disability, and, where 

applicable, sex, marital status, familial status, parental status, religion, sexual 

orientation, genetic information, political beliefs, reprisal, or because all or a part of an 

individual's income is derived from any public assistance program. (Not all prohibited 

bases apply to all programs.) Persons with disabilities who require alternative means 

for communication of program information (Braille, large print, audiotape, etc.) should 

contact USDA's TARGET Center at (202) 720-2600 (voice and TDD). 

To file a complaint of discrimination write to USDA, Director, Office of Civil Rights, 1400 

Independence Avenue, S.W., Washington, D.C. 20250-9410 or call (800) 795-3272 

(voice) or (202) 720-6382 (TDD). USDA is an equal opportunity provider and employer.

Recommended citation format for this publication: 

Nickerson, Cynthia, Mitchell Morehart, Todd Kuethe, Jayson Beckman, 

Jennifer Ifft, and Ryan Williams. Trends in U.S. Farmland Values and 

Ownership. EIB-92. U.S. Dept. of Agriculture, Econ. Res. Serv. February 2012.

Photos: USDA, Natural Resources Conservation Service.

www.ers.usda.gov

Visit Our Website To Learn More!

www.ers.usda.gov
United States

Department

of Agriculture

www.ers.usda.gov

A Report from the Economic Research Service

Abstract

Because farm real estate represents much of the value of U.S. farm sector assets, large 

swings in farmland values can affect the fi nancial well-being of agricultural producers. 

This report examines both macroeconomic (interest rates, prices of alternative investments) and parcel-specifi c (soil quality, government payments, proximity to urban areas) 

factors that affect farmland values. In the last few years, U.S. farmland values have been 

supported by strong farm earnings, which have helped the farm sector in many regions 

to withstand the residential housing downturn. Historically low interest rates are likely a 

signifi cant contributor to farming’s current ability to support higher land values. About 

40 percent of U.S. farmland has been rented over the last 25 years. Non-operators (landowners who do not themselves farm) owned 29 percent of land in farms in 2007, though 

that proportion has declined since 1992. 

Keywords: Farmland values, farmland prices, farmland ownership, tenure

Acknowledgments

We appreciate the thoughtful reviews from Joy Harwood of USDA’s Farm Service 

Agency; Kevin Barnes of USDA’s National Agricultural Statistics Service; Michael Duffy 

of Iowa State University; Ashok Mishra of Louisiana State University; Mary Ahearn, 

Patrick Sullivan, Utpal Vasavada, and Marca Weinberg of USDA’s Economic Research 

Service (ERS); and Andrew Morton (formerly in ERS). We also are grateful for the 

excellent assistance from Dale Simms (editorial input), Wynnice Napper (graphic design), 

and David Marquardt (geographic information systems).

Trends in U.S. Farmland Values 

and Ownership

Economic 

Information 

Bulletin 

Number 92

February 2012

Cynthia Nickerson, cnickerson@ers.usda.gov, 

Mitchell Morehart, Todd Kuethe, Jayson Beckman, 

Jennifer Ifft, and Ryan Williams
ii

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

Contents

Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Farmland Values: Trends Over Time, by Region, and Land Use . . . . . . 3

 Farmland Values Vary by Region and by Land Use Type . . . . . . . . . . . 3

Performance of Farmland As An Investment . . . . . . . . . . . . . . . . . . . . . . 5

 Rent-to-Value Ratios Declined Over the Last 50 Years . . . . . . . . . . . . . . 5

 Affordability of Farm Real Estate Has Varied Over Time . . . . . . . . . . . 7

 Farmland Values Were Supported by Farm Earnings in 2009-10 . . . . . 10

Parcel-Specifi c Factors Affecting Farmland Values. . . . . . . . . . . . . . . . 14

 Farm-Related Factors Affecting Farmland Values . . . . . . . . . . . . . . . . . 14

 Nonfarm-Related Factors Affecting Farmland Values . . . . . . . . . . . . . . 21

Farmland Ownership Trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

 Many Non-Operating Landlords Live in Towns and Urban Areas. . . . . 32

 Relatively Little U.S. Farmland Is Foreign Owned. . . . . . . . . . . . . . . . . 32

Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Appendix A: Data Inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

Appendix B. Alternative Measures of Urban Infl uence . . . . . . . . . . . . . 47

Errata

This report, originally released in February 2012, was reissued in 

March 2012 with the following corrections:

• Starting on the bottom of page 18, a paragraph about the effects of 

government programs on land values was revised to clarify program 

attributes.

• Starting on the bottom of page 32, a paragraph was amended to 

make it clear that the Farm Service Agency compiles data on foreign 

ownership of farm and forest land, and to delete the fi nal sentence on 

State regulation of foreign ownership.
iii

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

Summary

Farm real estate (land and structures) is the major asset on the farm sector 

balance sheet, accounting for 84 percent of the total value of U.S. farm assets 

in 2009. Because of this, changes in agricultural land values are a critical 

barometer of farm sector performance and the fi nancial well-being of agricultural producers. These changes also have implications for a wide range 

of policy issues, including agricultural competitiveness, industry structure, 

commodity programs, conservation payments, farmland protection, and local 

property taxes. In addition to being the largest single investment in a typical 

farmer's portfolio, farm real estate is the principal source of collateral for farm 

loans, enabling farm operators to purchase additional farmland and equipment 

or to fi nance current operating expenses and meet household needs.

What Is the Issue?

Interest in farmland values has grown in recent years due to dramatic 

increases in these values—more than 10 percent in 2005 and 2006—and the 

boom-and-bust cycle evident in the residential land market. Questions abound 

about the extent to which farmland values are affected by macroeconomic 

factors such as alternative investments, interest rates, and debt servicing capability. In addition to these farm fi nance considerations, interest also centers 

around the extent to which parcel-specifi c factors—such as soil productivity, 

proximity to delivery points like grain elevators and highways, and the land’s 

development potential—affect farmland values. Changes in farmland markets 

have also raised questions about tenure patterns, and the extent to which the 

benefi ts of farmland ownership are accruing to owners actively engaged in 

farming or to non-operating farmland owners. 

What Did the Study Find?

Many factors that can affect farmland values and ownership, both macroeconomic and parcel-specifi c, change simultaneously and they interact in 

complex ways. Though multivariate analysis is needed to provide defi nitive 

answers, examining individual factors one at a time through trend and correlation analyses provides a fi rst step in understanding potential relationships. 

Trends in farm incomes, cash rents, and interest rates suggest that 

farmland values were supported by farm earnings in 2009 and 2010, 

but there have been periods of imbalances in the recent past. Since 2009, 

though farmland values have been high, the discounted returns from renting 

farmland have been higher. Also, in the last 2 years, average income from 

farming has been more than suffi cient to service the debt on farm real estate 

purchases at current mortgage rates. A “speculative bubble” forming in farmland markets cannot be ruled out, but at a national level, farmland values 

have been supported of late by fundamental farm factors such as farm earnings. However, over 2005-08 and during 1978-85, farming income was insuffi cient to service debt on farm real estate purchases. Historically low interest 

rates are likely a signifi cant contributor to farming’s current ability to support 

higher land values. Increases in interest rates would likely put downward 

pressure on farmland values. 
iv

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

Strong farm earnings appear to have helped farmland markets withstand the signifi cant downturn in the residential housing market 

in recent years, though some regions may have experienced modest 

impacts. In addition to its value in a farming use, farmland near urban 

areas derives value from its potential to be developed for residential housing 

and other nonfarm purposes. Signifi cant volatility in urban real estate 

markets over the last decade has raised questions about the extent to which 

competing land markets are affecting farmland values. A comparison of 

rural housing and farmland values during the 2001-04 “boom” years of the 

housing market reveals that farmland values grew faster than rural housing 

values in many States. During the housing market downturn (2007-09) that 

affected all but the Plains and Delta regions, farmland values generally 

declined more moderately than rural housing values. In 19 States (notably 

California, Oregon, Washington, and Nevada), farmland values continued 

to increase even though rural housing values declined. Strong gains in agricultural returns and declining interest rates helped dampen the effects of 

the housing “bust” on farm real estate values.

Many of the factors that help explain the variation in farmland values 

across the country are parcel-specifi c. Farmland with higher soil quality 

requires fewer production inputs and management time, and land that 

produces more can enhance expected farming returns and thus farmland 

values relative to land with poorer soils. In the most rural areas where urban 

pressures are largely nonexistent, our analysis shows that cropland with better 

soils is correlated with higher land values. However, a positive correlation 

between soils and land values is diffi cult to detect near urban areas because 

so much of the land’s value derives from potential development uses and not 

farm factors.

The correlation between government payments and cropland values 

varies regionally and by payment type. Government agricultural 

program payments increase income from agricultural production, and 

when they do so in a consistent way, the expectation of future payments 

may be capitalized into the value of farmland. Although farmland values 

generally increase with insurance premium subsidies, land values are 

inconsistent for land with different levels of direct and countercyclical 

program (DCP) payments. Also, the ratio of DCP and insurance premium 

payments to land values varies regionally, a consequence of what crops 

are—and have historically been—grown and whether they are eligible for 

program payments. 

Non-operating landowners play a signifi cant role in U.S. agriculture.

Ownership status affects whether the benefi ts and risks of owning farmland 

accrue to active farmers or non-operating landowners. Three of the top four 

regions in terms of land in agriculture (Northern and Southern Plains and the 

Corn Belt) have non-operating owners owning more than 30 percent of the 

land. Non-operators owned 29 percent of all land in farms in 2007, and they 

owned 77 percent of farmland that is rented. Non-operators tend to be older, 

less likely to live on the farm, and less likely to participate in conservation 

programs. Despite recent increases in foreign ownership of forest land, as of 

February 2009, only 1.7 percent of privately owned land in farms or forest, or 

22.8 million acres, was owned by foreigners.
v

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

How Was the Study Conducted?

This report uses trend and correlation analyses to identify general relationships between farmland values and both macroeconomic and parcel-specifi c 

factors. The analyses use cropland and pastureland value estimates developed 

by USDA’s National Agricultural Statistics Service (NASS) and data from 

NASS’ June Area Survey, in conjunction with data on a variety of factors—

such as proximity to urban areas, soil quality, and irrigation status—that are 

likely to affect farmland values. Our analyses on farmland ownership trends 

largely use data from NASS’ 2007 Census of Agriculture, NASS’ 1999 and 

1988 Agricultural Economics and Land Ownership Surveys, and ERS-NASS 

Agricultural and Resource Management Surveys. Because trends in farmland 

markets are determined by complex interactions among many potential infl uences, these trend analyses can help inform the design of detailed statistical 

analyses that would more defi nitively identify those factors that are signifi -

cantly affecting trends in farmland values and ownership.
1

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

Introduction

U.S. farmland values and the factors infl uencing changes in those values have 

long been of considerable interest, and the subject of a great deal of economic 

research. Farm real estate (land and structures) is the major asset on the farm 

sector balance sheet, and accounted for 84 percent of total U.S. farm assets 

in 2009. Farm real estate is the principal source of collateral for farm loans, 

enabling farm operators to fi nance the purchase of additional farmland and 

equipment or to meet current operating expenses. Many farm households rely 

on landholdings as a retirement fund, so farm real estate is an investment 

strategy as well. Because of this, changes in agricultural land values are a 

critical barometer of farm sector performance and the fi nancial well-being of 

agricultural producers. Changes in land values also have implications for a wide 

range of policy issues, including agricultural competitiveness, industry structure, commodity programs, conservation payments, and farmland protection. 

About 922 million acres, or 40 percent of the Nation’s land base, was in 

farms in 2007 (latest year for which data are available). Most farmland is in 

the Midwest, but it exists in all States so fl uctuations in farmland markets can 

be both a regional and national concern (fi g. 1).

Figure 1

Acres of land in farms, 2007

Source: USDA, National Agricultural Statistics Service.

Northern

Plains

Corn Belt

Lake States

Southern

Plains

Pacific

Mountain

Delta States

Appalachia

Northeast

Southeast

United States:

40.2 percent

Percent of county land in farms

70 - 90

90 or more

50 - 70

10 - 30

30 - 50

Less than 10
2

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

Because farmland is such a signifi cant asset in the farm sector, farmers, 

lenders, researchers and policymakers have long been interested in the 

trends in, and determinants of, farmland values. This interest has grown in 

recent years due to dramatic increases in farmland values; rapid changes in 

commodity prices arising, in part, from renewable energy policy; and volatility in other land markets. For example, residential housing prices escalated 

in 2005-06 and then dropped precipitously, coincident with a signifi cant 

increase in commodity prices. These changes have raised questions about the 

extent to which farmland markets respond to changes in fundamental farmrelated factors, such as commodity prices and farm income, and the extent to 

which they are subject to risks and changes in other land markets. 

Sharply rising farmland values also evoke reminders of the trends that 

preceded the farm crisis of the 1980s. Farmland values rose throughout 

much of the post-World War II period; from 1969 to 1978, farmland prices 

increased 73 percent as agricultural producers responded to high returns and 

Federal policies that increased incentives for investing in agriculture (Barnett, 

2010). In 1980, farmland prices began to decline in response to Federal 

monetary policy that raised interest rates to combat infl ation. The rapid 

escalation in interest rates and energy prices precipitated a farm fi nancial 

crisis that led to farm bankruptcies and bank failures throughout the 1980s 

(Duncan, 2008). 

Farmland values are determined by factors that affect the land market as 

a whole, as well as factors that affect the value of individual parcels relative to each other. Examining factors at both of these scales is important in 

understanding how and why farmland values are changing. Macroeconomic 

factors behind farmland values—such as expected returns from agricultural uses, alternative investment opportunities, interest rates, and use of 

debt fi nancing—and other sector-level factors help inform whether farmland continues to be attractive as an investment and whether farmland 

values appear to be supported by fundamental farm factors. A micro-level 

perspective reveals how farmland values vary spatially due to variation 

in parcel-level characteristics like soil productivity, the land’s proximity 

to markets, eligibility for farm program payments, and opportunities for 

nonfarm income. In this report, we examine both macro and micro aspects 

of farmland markets. 
3

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

Farmland Values: Trends Over Time, 

by Region, and Land Use

Since the farm crisis of the mid-1980s, farm real estate values (including 

land and buildings) have been rising in both nominal and real (i.e., infl ationadjusted) terms (fi g. 2). Between 1994 and 2004, real values increased 

between 2 and 4 percent annually. In 2005 and 2006, values jumped 16 

percent and 11 percent, before slowing to 6-7 percent in 2007 and 2008. 

Farmland Values Vary by Region 

and by Land Use Type 

Average land values for the Nation mask wide regional variation. Cropland 

values (the land value excluding buildings) exceeded $5,000 per acre in 

2007 in the Northeast and Pacifi c regions, where high-value crops—fruits, 

vegetables, and orchards—are grown and development pressures are high 

(fi g. 3). Cropland values in 2007 were only about $1,000 per acre in the 

Northern Plains, an area dominated by wheat and other row crop farms. 

Figure 2

Average farm real estate values, 1980-2010 

$ per acre

Source: USDA, National Agricultural Statistics Service.

1980 85 90 95 2000 05 10

0

500

1,000

1,500

2,000

2,500

Nominal

Inflation adjusted (2005=100)

Figure 3

Average farmland value by land use and region, as of 2007 (in 2005 $) 

$ per acre

Source: USDA, ERS analysis of National Agricultural Statistics Service June Area Survey data.

Pastureland

Cropland

0

1,000

2,000

3,000

4,000

5,000

6,000

Northeast

Corn Belt

Lake States

Northern Plains

Appalachian

Southeast

Delta States

Lower 48 States

Pacific

Mountain

Southern Plains
4

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

Also, though national average cropland values changed little between 

1999 and 2004, cropland values in some regions were more variable. For 

example, in the Pacifi c region, cropland values increased between 2 and 4 

percent over 1999-2003, but decreased 16 percent between 2003 and 2004.

Average farmland values also obscure differences by type of land use. 

Historically, crop and pasture land prices did not diverge signifi cantly 

because pasture was essential for feeding farm animals, which were an 

important part of the farming enterprise (Raup, 2003). More recently, when 

measured, nationally cropland values have exceeded pasture values due 

primarily to higher returns associated with cropland. Cropland enjoys a 

substantial premium over pastureland values in most regions (fi g. 3), especially in the Pacifi c States where vineyards command higher land prices 

(Reynolds and Johnston, 2003). 

However, cropland value premiums have declined over the past 10 years in 

every region except the Northeast. Though premiums exceeded 20 percent 

at some point within the last 10 years, by 2008, cropland values in the 

Appalachian and Southern Plains regions exceeded average pastureland 

values by only 3-4 percent. In the Southern Plains, nonfarm factors such as 

recreational income from hunting leases are contributing to increasing farmland values (Guiling et al., 2008; Henderson and Moore, 2006; Pope, 1985). 

(Nonfarm income streams that accrue to pastureland would contribute to 

declining cropland premiums.) In the Southeast and Delta regions, average 

pasture values rose more quickly in the last 10 years and have exceeded 

average cropland values since 2005 (table 1). In Georgia, Flanders et al. 

(2004) observe that more pastureland than cropland is near urban areas, 

drawing stronger demand for housing or recreational development.

Table 1

Percentage of premium cropland over pastureland values (in 2005 $)

Region 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Percent

Northeast 31 35 37 37 37 52 56 62 56 61

Lake States 133 128 116 113 104 97 80 69 65 67

Corn Belt 136 122 114 105 104 102 98 90 85 93

Northern Plains 196 189 183 186 184 176 167 153 148 148

Appalachian 14 16 20 19 18 215223

Southeast 15 16 10 4 2 14 -14 -12 -12 -13

Delta States 4 3 7 4 0 -2 -10 -11 -18 -17

Southern Plains 28 30 28 32 37 44 21 13 7 4

Mountain 315 310 318 319 318 254 231 191 178 171

Pacifi c 360 396 404 399 390 218 262 247 215 193

Lower 48 States 180 184 183 181 178 178 178 140 146 153

Source: USDA, ERS analysis of National Agricultural Statistics Service Agricultural Land Values and Rents Final Estimates.
5

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

Performance of Farmland As An Investment

Recent sharp increases in farmland values raise questions about whether 

farmland prices have reached levels that cannot be sustainably supported by 

expected agricultural returns. Examining how farmland values change relative to farmland rents, whether farm income is suffi cient to service real estate 

debt, and how farmland compares to alternative investments indicate how 

farmland has been performing as an investment. These comparisons also 

reveal that the recent high farmland prices are not occurring under the same 

conditions that contributed to the farm crisis of the 1980s, at least for the 

farm sector as a whole.

At the most basic level, capital asset pricing theory posits that farmland 

values are derived from the expected stream of returns that can be earned 

into the future by having the land in an agricultural use.1 Farm incomes and 

land values were closely linked in the fi rst half of the 20th century, but have 

become less so since (Shalit and Schmidt, 1982), with little correlation at the 

national level in recent decades (fi g. 4).2,3

Rent-to-Value Ratios Declined Over 

the Last 50 Years 

The farmland rent-to-value (RTV) ratio, calculated as the cash rent per acre 

divided by the land value per acre, is a proxy for how quickly an asset will 

pay for itself.4 The roughly 45-year trend reveals a decreasing RTV ratio 

(fi g. 5); if agricultural rents were the sole source of returns from farmland, 

the farmland would have paid for itself in about 14 years in 1951, but would 

take more than 33 years in 2007. However, in many places, nonagricultural 

returns can be earned from the land, such as returns from developing the land 

(Livanis et al., 2006). Decreasing RTV ratios are consistent with the growing 

importance of nonagricultural factors in determining land values. Indeed, 

Flanders et al. (2004) fi nd that in Georgia, nonagricultural factors have a 

stronger infl uence on land values than cash rents.

1Numerous studies have examined 

the applicability of the present value 

model to farmland prices (e.g., Falk, 

1991; Clark et al., 1993) and considered 

other factors that can affect farmland 

values, such as infl ation and risk relative to nonfarm investments (Chavas 

and Thomas, 1999; Irwin et al., 1988) 

and local (parcel-specifi c) factors 

discussed later in this report, such as 

amenities and the value of land in a 

developed use (e.g., Libby and Irwin, 

2003; Livanis et al., 2006).

2Net farm income in fi gure 4 is that 

for the farm sector as a whole. It is 

computed by determining the gross 

value of goods and services generated 

by farm assets and subtracting the 

expenses incurred during the year. All 

cash and noncash items are included 

in gross farm income and in expenses, 

regardless of whether they were earned 

or incurred by farm operators, contractors, or farmland owners.

3The farm sector includes about 

800,000 farms where the operator 

identifi es farming as the primary occupation and 1.2 million farms where the 

operator is not engaged in farming as 

a primary occupation. As a result, the 

latter group does not generate as much 

income from farming. For this group 

there is a substantial gap between 

farm income and the value of land 

and buildings on their operations. In 

contrast, the remaining 800,000 farm 

businesses that do generate substantial 

income from farming have more consistency between land/building values 

and farm earnings.

4The relationship between land rents 

and values was a popular research 

topic during the run-up and later 

collapse of land prices during the 

1970s and 1980s. A central question 

of this research was whether a simple 

capitalization formula (value=[annual 

rent / capitalization rate]) could predict 

land prices in the current year based on 

current rental rates, or whether a more 

sophisticated model is needed (e.g., 

Melichar 1979, Barry 1980, Alston 

1986, Burt 1986). Rents were found 

to help explain land price variation 

in some parts of the country, while in 

other areas the increasing infl uence of 

non-agricultural factors required more 

sophisticated modeling approaches 

(e.g., Featherstone and Baker 1987).

Figure 4

Land values and farm sector net income, 1980-2009 (in 2005 $) 

Net farm income

($ per acre)

Source: USDA, ERS Farm Income Accounts data, available at 

http://www.ers.usda.gov/data/FarmIncome/Finfidmuxls.htm

1980 82 84 86 88 90 92 94 96 98 2000 02 04 06 08

0

20

40

60

80

100

120

0

500

1,000

1,500

2,000

2,500

Net farm income per acre

(left axis)

Land value per acre (right axis)

Land value

($ per acre)
6

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

Survey data collected by USDA’s National Agricultural Statistics Service 

(see box, “June Area Survey Data”) show that since 1999, almost every 

USDA region has seen a decrease in RTV ratios (fi g. 6). The Lake States and 

Southern Plains experienced the largest percentage decreases in RTV ratios, 

at 48.3 percent and 51.2 percent. The Delta and Mountain regions had the 

lowest percentage decreases in RTV ratios between 1999 and 2008, at 4.2 

percent and 8.8 percent. The Delta, Mountain, and Pacifi c regions experienced the most volatility in RTV ratios.5 The regions with the highest RTV 

ratios are the Northern Plains, Delta, Mountain, and Pacifi c regions. 

Declining rent-to-value ratios in many regions may be due to the infl uence of 

nonfarm factors on land values, as RTV ratios are generally lower near urban 

5More farmers in these three regions 

use irrigation, and some of the volatility could be due to our averaging of 

irrigated and non-irrigated rents and 

land values. 

Figure 5

Cropland rent-to-value, 1967-2011

Percent

1967 72 77 82 87 92 97 2002 07

0

1

2

3

4

5

6

7

8

Sources: For 1997-2011, USDA-NASS Quickstats (http://quickstats.nass.usda.gov/). For 

earlier data, see USDA-ERS cash rent and Agricultural Land Values Survey data.

Figure 6

Cropland rent-to-value by region, 1999-20081 

Percent

1Ratio of per-acre cash rent for cropland to per-acre value of cropland in the June Area Survey, 

including both irrigated and non-irrigated cropland.

Source: USDA, ERS analysis of National Agricultural Statistics Service June Area Survey data.

1999 2000 01 02 03 04 05 06 07 08

0

1

2

3

4

5

6

7

8

Northern Plains

Pacific

Southern Plains

Northeast

Delta

Corn Belt

Southeast

Mountain

Lake States

Appalachia
7

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

areas (fi g. 7). In regions such as the Corn Belt, that are heavily dominated 

by agriculture,6 other factors may be playing a role in declining RTV ratios, 

such as increased recreational (hunting) values and long-term rental arrangements with rental prices that adjust slowly to rapidly appreciating farmland 

values (fi g. 8). Indeed, studies have found that farmland prices are more volatile than rents (Falk, 1991; Featherstone and Baker, 1987). Rising incomes 

in the nonfarm rural economy in recent years may also have contributed to 

increases in farmland values (e.g., Henderson and Novack, 2005), with a 

more limited impact on rents. 

Affordability of Farm Real Estate 

Has Varied Over Time 

As farmland values surge and rent-to-value ratios decline, questions arise 

about the affordability of farmland. Figure 9 shows U.S. total farm real 

estate values from 1970, and the maximum farm real estate value that 

current farming income can “cash fl ow” (service debt on) in each year.7

Although affordability will fl uctuate above and below real estate values due 

to price and yield variation, during two periods in particular—1978-85 and 

2005-08—income from farming alone was insuffi cient to service the debt 

on farm real estate purchases. The low levels of affordability indicate that 

nonfarm factors likely had a large role in determining farmland values during 

these periods.8 In other periods, such as before 1974 and between 1986 and 

2004, farming incomes were more aligned with the value of land. 

When farmland values are high and affordability is low, some farmers might sell 

land to capture the capital gains benefi ts arising from appreciated land values. 

6Although some Corn Belt States 

like Ohio have extensive areas of lowdensity urban infl uence, ERS estimates 

that nearly three-quarters of farmland 

in the Corn Belt is not subject to urban 

infl uence.

7The amount of cash available to 

service debt on the purchase of all land 

and buildings is calculated as revenues 

from all crops and livestock produced, 

government payments, insurance payments, and cooperative dividends less 

operating and ownership expenses (i.e., 

adjusted net returns). To determine 

the maximum value of real estate that 

can be purchased from the adjusted net 

returns, we assume a loan term of 30 

years and use average annual mortgage 

rates for this type of loan. This is then 

used to calculate the maximum real 

estate value that could be mortgaged 

based on agricultural production only. 

Trends over time in farm real estate 

affordability can be examined using 

data from the farm sector accounts 

(http://www.ers.usda.gov/Briefi ng/

FarmIncome/). 

8Nonfarm factors include purchases 

for recreation uses or for residential or 

other types of development.

USDA’s NASS survey each June asks farmers to report their estimates of per acre 

cropland, pastureland and total real estate value (including building value), as well 

as cropland rents. The JAS is based on a probability area frame with an annual 

sample of about 10,800 segments of approximately 1 square mile. Land values and 

cash rents are collected from all farmers having operations within the sampled 

segments (NASS, 2009a). The Sample tool in the Spatial Analyst extension of 

ArcGIS 9.3 was used to extract the values of other data in GRID format to the 

JAS segment points, so that associations can be made between the JAS data and 

characteristics of other data, listed in Appendix A: Data Inputs. A key objective of 

the JAS spatial data development process is to capture the time-series lineage of 

the survey at unique spatial locations to generate a single set of points in a spatial 

dataset that represent survey locations over time. 

JAS segments are surveyed longitudinally over several consecutive years, so by 

design, each point represents at least one survey year. We developed the spatial 

dataset for the years 1999 through 2008. MS Access was used to develop a table 

of unique sample points, defi ned by latitude and longitude fi elds. Each of these 

31,242 unique spatial locations was assigned an identifi er. A separate lookup table 

is used to join the year, State, and segment identifi er to the unique location identifi er, 

creating a one-to-many type join. Any extractions of the gridded data to the JAS 

segment points yielded a table including the spatial location identifi er tabbed with 

any values intersected in the sample process. The lookup table provides the full 

linkage between the extracted grid data and the JAS tabular data. 

June Area Survey (JAS) Data 
8

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

Figure 7

County average rent-to-value ratios1, 1999-2008

Source: USDA, ERS analysis of National Agricultural Statistics Service June Area Survey data.

Rent-to-value, in percent

Cities with population

>50,000

0 - 1.5

1.6 - 3.0

3.1 - 4.0

4.1 - 6.0

6.1 -12.0

12.1 - 50.0

1Ratio of per acre cash rent for cropland to per acre value of cropland in the 

June Area Survey, including both irrigated and non-irrigated cropland.

Note: The nonshaded areas either did not have any farms included in the JAS or had insufficient observations due to low levels 

of agricultural production.

Figure 8

Annual changes in per-acre average cropland value and rent 

in the Corn Belt, 2000-08 

Percent change

Source: USDA, ERS analysis of National Agricultural Statistics Service June Area Survey data.

2000 02 04 06 08

-5

0

5

10

15

20

25

Average cropland rent

Average cropland value
9

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

Yet, other farmland owners will not necessarily react by immediately selling 

their land. Farmland markets have historically been very thin, with some estimates indicating about 0.5 percent of U.S. farmland is sold annually.9 Farmland 

owners may hold onto land if the family has held it for a long time or because 

they derive other nonfi nancial (lifestyle) benefi ts from owning the land. Indeed, 

studies of farmland market activity reveal that sales of farmland are often forced 

due to death or retirement rather than a result of affordability levels (Raup, 2003). 

Nonetheless, for farm operators who owned the land they operated in 2007, about 

53 percent of land was acquired at arm’s length from an unrelated seller, about 25 

percent of acres owned were inherited, and 19 percent were purchased from relatives.10 For purchases from relatives at least, evidence suggests the buyers negotiate terms that are more favorable, which can help in periods of high farmland 

prices or low levels of affordability. Robison et al. (2002) fi nd that farmland was 

sold to relatives at a discount when compared to land sold to nonrelatives. Debtservicing diffi culties and high real-estate transaction costs have led to increased 

demand for leasing rather than owning land (Sherrick and Barry, 2003).

The current environment of low interest rates has two positive effects on 

agricultural real estate values (Schnitkey, 2010). First, for those who have to 

purchase land with debt capital, it lowers the total cost of purchasing land. The 

effect of the recent declines in interest rates is evident in fi gure 9 by the sharply 

upward trend in the amount of land value that can be cash-fl owed with falling 

farm mortgage rates. Indeed, farm mortgage interest rates have been at record 

lows in very recent years, and farmland purchasers are increasingly using debt 

fi nancing. Agricultural and Resource Management Survey (ARMS) data reveal 

that land purchases by farm operators in 2008 and 2009 were fi nanced predominantly by credit (74 and 65 percent, respectively) and, in 2009, over half of 

farm real estate transactions involved debt fi nanced for 75 percent or more 

of the purchase price. Second, interest rates represent returns on competing 

fi xed investments, and when they are low, farmland looks more attractive as 

an investment alternative. Coupled with increases in crop prices and improved 

agricultural credit conditions, some regions experienced double-digit increases 

in farmland values in 2010 (Oppendahl, 2010; Briggeman and Akers, 2010). 

Low interest rates also aid those who are buying farmland for other uses of the 

land (see box, “Trends in Farmland Purchases”).

9Estimated using USDA-NASS data 

from the 1999 Agricultural and Land 

Economics Survey (Sherrick and Barry 

2003).

10These estimates are based on USDANASS/ERS’ Agricultural Resource 

Management Survey of farm operators, 

and account for about 71 percent of land 

in farms. Estimates are not available on 

how land was acquired by non-operating 

landlords, who owned 29 percent of 

farmland in 2007. Non-operating landlords own farmland and rent it to farmers 

rather than operate it themselves. 

Figure 9

Current value of farm real estate versus maximum affordable value 

$ per acre

Sources: USDA, ERS Farm Balance Sheet data, 

http://www.ers.usda.gov/Briefing/FarmIncome/Data/Constant-dollar-table.xls.

1970 75 80 85 90 95 2000 05 10

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

Actual farm real estate value

Farm real estate value that current 

income can cash flow
10

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

Farmland Values Were Supported 

by Farm Earnings in 2009-10

From an investment perspective, capitalized values are used to quantify 

impacts of both cash rent and interest rate changes on land values. A capitalized value represents the estimated discounted value of all future cash fl ows 

to farmland. The capitalized value is calculated by dividing cash rent (a 

measure of cash fl ow generated from agricultural production) by a discount 

factor, typically an appropriate interest rate. When farm real estate values 

divided by the capitalized value (price-to-value ratio) exceed 1, farmland 

values are not supported by the stream of cash fl ows the farmland could earn. 

During 2004-08, farmland values were not justifi ed based on farm earnings 

alone (fi g. 10).11 Subsequently, market signals indicate that farmland was 

somewhat undervalued at current interest rates, so it is not surprising that 

farmland values increased in 2009-10. 

11This is consistent with the low affordability fi ndings noted in fi gure 9.

The sharp increases in farmland values in recent years have sparked an interest in 

the characteristics and intent of farmland buyers. In the 10th District of the Federal 

Reserve1, farmland appears to have been purchased by nonfarmers much more for 

recreation than for development or residential purposes. Purchases of farmland for 

development in this region have been decreasing since 2002, while purchase of 

farmland for recreation weakened in 2008. Henderson and Akers (2009) report that 

this decline in demand has led to a stabilization of land prices after several years of 

increasing prices. Based on the Iowa 2009 Farmland Value Survey, about 23 percent 

of land sales were to investors, a decrease from previous years. Likewise, more 

farmers were reported to buy land (Duffy, 2009). 

Trends in Farmland Purchases

Reason for farmland purchases by nonfarmers (10th Federal Reserve 

District)1

Percent of respondents2

0 

10 

20 

30 

40 

50 

60 

70 

1Includes Colorado, Kansas, Nebraska, Oklahoma, Wyoming and portions of 

western Missouri and northern New Mexico.

2Respondents were asked the most common reasons for farmland purchases by individuals

other than farmers. Respondents could choose more than one response, and therefore 

percentages will not sum to 100.

Source: Henderson and Akers (2009).

2002 2005 2008

Recreation Residential/Development

1Includes Colorado, Kansas, Nebraska, Oklahoma, Wyoming, and portions of western 

Missouri and northern New Mexico.
11

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

The separate effects of changes in the interest rate are also evident in fi gure 

10. In 2000, current rates were equal to the long-term average Treasury note 

rate of 6 percent. The dotted green line shows changes in the cropland priceto-value ratio when the interest rate is held constant at this 6-percent average. 

Since 2000, this price-to-value ratio has declined below what it would have 

been had interest rates remained at the long-term average. In 2010, the price-tovalue ratio was about 0.9 based on current rates. If interest rates were to jump 

to the long-term average, the price-to-value ratio would sharply increase to over 

1.5, signaling that land values would not be supported by the stream of rents 

the land could earn at historical interest rates. Farmland markets could subsequently react with sharp reductions in farmland values. Although in the 1980s 

interest rates did increase abruptly (and farmland values declined dramatically), 

such abrupt changes are not common. Gradual changes in interest rates have 

a more muted effect on farmland markets because expectations change and 

market participants have more time to adjust. 

Some predict that stable farm incomes and interest rates will keep farmland values stable, although increasing volatility in the agricultural markets 

could lead to reductions in farmland values (Henderson and Akers, 2009; 

Schnitkey, 2010). In addition to limited changes in interest rates, recent 

trends in several other macroeconomic factors differ from patterns during 

the farm crisis of the 1980s. In the early 1980s, increasing crop supplies, a 

strong dollar, and weakened export demand contributed to rapid declines in 

farmland values. At present, the demand for crops for food and as an energy 

source is keeping commodity stocks tight and export demand for corn and 

other commodities strong. Also, some predict that the dollar is likely to 

remain weak for some time (Henderson, 2010). However, some caution that 

the size of the U.S. trade defi cit increases the risk of investing in farmland. 

If other countries become less willing to cover the U.S. defi cit, interest rates 

could climb and debt-fi nanced real estate values could fall (Raup, 2003). 

Figure 10

Cropland price-to-value ratios, 1998-2011 

Ratio

2011 forecast.

1Ratios are calculated as the average cropland value per acre divided by the capitalized 

values. Capitalized value = per-acre cropland cash rent / interest rate on 10-year note.

The dotted line represents the same ratio, with capitalized value calculated using a constant 

6% interest rate.

Sources: USDA, ERS analysis of National Agricultural Statistics Service farmland value 

and rent data.

1998 2000 02 04 06 08 10

0

0.5

1.0

1.5

2.0

2.5

Ratio at constant 6% interest rate1

Ratio at prevailing interest rates1

The orange and green dotted lines represent the average cropland price divided by the 

capitalized value under different assumptions about interest rates. When this ratio is 

equal to or less than 1, cropland prices are supported by fundamental farm factors.
12

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

Farmland has historically been a stable investment and has become 

more attractive relative to other investments in recent years

Farmland serves both as a productive asset and capital investment. Like any 

capital investment, an appreciation in farmland price carries important (unrealized) capital gains. To examine the returns to investing in farmland relative 

to other investments, we estimate returns to farmland versus the S&P 500 

and gold. Returns are measured as the percentage change in value from year 

to year. The percentage change is a simple measure that refl ects the value of 

holding an asset for one additional period, which should refl ect the expected 

income generated from the asset. 

The expected return of an asset is a function of the investment horizon. 

Assets that are held for many decades can yield returns that are quite 

different than assets held for shorter time periods. To account for this, we 

present the return of a $1,000 investment in (1) farmland, (2) a stock fund 

tied to the S&P 500, and (3) gold over three time horizons: a return in 2008 

to purchases in 1970, 1980, and 1990.

Our comparisons are based on real returns using the GDP price defl ator (with 

a base year of 2007), so that the annual returns represent an appreciation in 

asset value without the infl uence of infl ation. In addition, the initial investment of $1,000 is expressed in real terms in 2007 dollars. For example, in 

real terms, a $1,000 initial investment in farmland is equivalent to $4,630 in 

1970; $2,183 in 1980; and $1,376 in 1990.

Given an initial investment in farm real estate made in 1970, both gold 

and the S&P 500 composite index outperform farmland returns (fi g. 11a). 

Although gold exhibits the highest degree of variability, the average annual 

rate of return is 7 percent, followed by the S&P at 3.2 percent and farmland 

at 2.3 percent.

Over a shorter investment horizon—when farm real estate is purchased in 

1980 rather than 1970—the S&P 500 is the favorable investment, with an 

Figure 11a

Returns to farm real estate versus stocks and gold, 1970-2008 

$1,000

Sources: Farmland returns are derived from the USDA, National Agricultural Statistics Service 

June Area Survey estimates of average value per acre of all farms and buildings in the 

continental United States.The S&P 500 and gold prices are based on end-of-year settlement 

prices.

1970 75 80 85 90 95 2000 05

0

5

10

15

20

25

30

35

40

45

Farmland

S&P 500

Gold
13

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

average annual appreciation rate of 4.9 percent (fi g. 11b). Farmland returns 

appreciate 1 percent per year while gold exhibits a net loss of less than 1 

percent per year, on average. The low returns to farmland are not surprising 

given the farm crisis of the early 1980s, during which farmland values were 

greatly diminished. Between 1984 and 1985, farmland values declined by a 

record 14 percent.

Over an even shorter investment horizon, the same investment in 1990 nets 

similar returns for farmland (4.4 percent) and the S&P 500 (4.6 percent 

per year) (fi g. 11c). Although gold declined in value throughout the 1990s, 

returns rebounded in 2002, yielding an average annual return of 2.5 percent. 

Farmland values exhibited a relatively stable increase compared to the other 

investment alternatives, across all three investment horizons.

Figure 11c

Returns to farm real estate versus stocks and bonds, 1990-2008 

$1,000

Sources: Farmland returns are derived from the USDA, National Agricultural Statistics Service 

June Area Survey estimates of average value per acre of all farms and buildings in the 

continental United States. The S&P 500 and gold prices are based on end-of-year settlement 

prices.

Farmland

S&P 500

Gold

1990 92 94 96 98 2000 02 04 06 08

0

1

2

3

4

5

Figure 11b

Returns to farm real estate versus stocks and gold, 1980-2008 

$1,000

Sources: Farmland returns are derived from the USDA, National Agricultural Statistics Service 

June Area Survey estimates of average value per acre of all farms and buildings in the 

continental United States. The S&P 500 and gold prices are based on end-of-year settlement 

prices.

Farmland

S&P 500

Gold

1980 82 84 86 88 1990 92 94 96 98 2000 02 04 06 08

0

5

10

15
14

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

Parcel-Specifi c Factors Affecting 

Farmland Values

The previous section demonstrates how macroeconomic factors can affect 

farmland values. Yet, factors that are unique to each farm parcel also 

affect the parcel’s ability to generate returns, and these differences give 

rise to spatial variation in farmland values. Indeed, an indirect approach to 

measuring the impacts of net returns or rents on farmland value—one that is 

often employed in the economic literature—is to examine the relative importance of parcel-specifi c characteristics in explaining variation in farmland 

values across the country.12

Farmland is capable of generating several types of returns, depending on its 

use. In some areas, farmland value may be derived solely from its ability to 

support agricultural production, so parcel-specifi c factors such as soil quality, 

access to market terminals, and government payments are important determinants of land values. However, farmland close to urban areas may also 

generate nonfarm returns from the option to develop land for residential, 

commercial, or industrial uses. Parcel-specifi c factors related to development potential include distance from population concentrations and access to 

roads. Another nonfarm factor affecting farmland values is the amenity value 

of land. Amenity values arise from the scenic views, varied topography, mild 

temperatures, and other factors that attract people to rural areas and make 

certain locations particularly desirable. Surveys of real estate brokers (Duffy, 

2009) reveal—and economic studies confi rm—that both farm-related and 

nonfarm-related factors are likely to be important in explaining variation in 

farmland prices. Regardless of the type of returns land generates, the relative 

importance of such spatially explicit factors is likely to vary across location 

and time. 

The June Area Survey conducted by USDA’s National Agricultural Statistics 

Service (NASS) provides a unique opportunity to examine relationships 

between farmland values and some of the most commonly considered, 

spatially explicit determinants of farmland values (see box, “June Area 

Survey Data” on p. 7). National analysis using spatially explicit data is a 

complex task: it requires data sources that use consistent defi nitions for 

the entire country and signifi cant computational resources. Our analyses 

using these data demonstrate the importance of using spatially explicit data 

to understand local infl uences on farmland values. We construct estimates 

of land values as the weighted average of the values reported for the tracts 

within each segment (the sampling unit). Each segment contains a geocode, 

allowing segment-level values to be linked with a variety of spatially explicit 

data that may affect farmland values. 

Farm-Related Factors Affecting Farmland Values

More Productive Soils Are Correlated with Higher 

Cropland Values 

Farmland with better soil quality can improve yields and reduce production 

inputs and management time, which can enhance farming returns and thus 

farmland values. However, the contribution of better quality soils to farm12The hedonic technique—a statistical modeling approach economists 

often use when modeling the determinants of farmland values—assumes 

a parcel can be identifi ed by a unique 

set of attributes and that the value of a 

land parcel is an aggregation of the values of its individual attributes. It is an 

indirect approach that allows researchers to substitute proxies for actual measures of net returns which are typically 

not available (Freeman, 1993).
15

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

land values can vary depending on the proximity of the land to urban areas 

(Palmquist and Danielson, 1989). Some studies have found that demand for 

land in urban uses is a signifi cant component of land values in areas near 

large towns and cities (Chicoine 1981; Shi et al., 1997; Livanis et al., 2006; 

Hardie et al., 2001). In such cases, the effect of urban infl uence can be so 

strong that it masks the relationships between farmland values and characteristics that contribute to the agricultural earnings potential of the land.

In demonstrating the soil quality/land value relationship, we control for the 

effects of urban-related demand by limiting the JAS data to observations that 

were at least 25 miles from the borders of cities of at least 50,000 people. 

Our measure of soil quality is based on a “land capability class” measure 

that represents the soils with the fewest limitations for agricultural uses (see 

Appendix A). For this subset of more “rural” data points, cropland values 

were signifi cantly and positively correlated with better soil quality in some 

regions.13 These fi ndings are consistent with empirical studies that found 

a positive (and statistically signifi cant) relationship between soil quality 

measures and farmland values (Huang et al., 2006; Xu et al., 1993; Palmquist 

and Danielson, 1989; Chavas and Shumway, 1982). 

The effect of soil quality on cropland strengthened as we limited the data to 

even more rural parcels (those at least 40 miles from population concentrations of at least 50,000). However, patterns vary substantially across regions 

(fi g. 12). In 2008, there was a positive correlation between our soil quality 

measure and cropland values in the Corn Belt, Lake States, and North Dakota 

(in the Northern Plains region), but a negative or insignifi cant correlation 

in the Appalachian region. Also, we found that relationships between soil 

quality and land values varied by irrigation status. In the JAS data, respondents in eastern States were not asked to provide separate estimates of cropland values for irrigated and non-irrigated land, whereas respondents in the 

western States—where irrigation is more prevalent—were asked to do so. In 

most regions where cropland value data were available by irrigation status, 

irrigated land values exceeded average non-irrigated land values, as expected 

(fi g. 12). Also, the correlation between soil quality and irrigated land values 

was stronger (the slope of the irrigated land value line is steeper) than 

between soil quality and non-irrigated land values. 

Our analysis of the correlation between soil productivity and farmland values 

demonstrates the importance of examining parcel-specifi c factor/land value 

relationships at a very disaggregated level. For example, when analyzing the 

relationship at a national level, a positive correlation between better soils and 

cropland values was not evident over 2005-08.14 The analysis also demonstrates that controlling for parcel-specifi c factors other than urban infl uence is 

particularly important in some regions. A study by Palmquist and Danielson 

(1989) in North Carolina that did so found strong and signifi cant negative 

relationships between farmland values and erosion potential or wetness of the 

land (two characteristics that determine the Land Capability Class), contrary 

to the fi nding above for the Appalachian region. The more intuitive fi ndings 

in that study—that better soils increase farmland values— may also be attributable to the more detailed measures of erosivity and wetness that the authors 

were able to construct for their limited study area. 

13Results were similar when the population size of cities was set at 25,000.

14We examined the correlations using different years to test the sensitivity 

of the fi ndings, but the patterns did not 

vary across years.
16

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

Figure 12

Average per-acre cropland values and share of county soils in land capability class 1 or 2 by region, 2008

$1,000 per acre

Northeast

Avg. value

% soil in land capability class 1 or 2

$1,000 per acre 

<20 20-39 40-59 60-79 >80

0

2

4

6

8

10

<20 20-39 40-59 60-79 >80

0

2

4

6

8

10

% soil in land capability class 1 or 2

Appalachia

Avg. value

$1,000 per acre

<20 20-39 40-59 60-79 >80

0

2

4

6

8

10

% soil in land capability class 1 or 2

Corn Belt

$1,000 per acre

<20 20-39 40-59 60-79 >80

0

2

4

6

8

10

Avg. value

Avg. value - nonirrigated

Avg. value - irrigated

Northern Plains

% soil in land capability class 1 or 2

$1,000 per acre

% soil in land capability class 1 or 2

Mountain

$1,000 per acre

<20 20-39 40-59 60-79 >80

0

2

4

6

8

10 Southeast

% soil in land capability class 1 or 2

<20 20-39 40-59 60-79 >80

0

2

4

6

8

10

$1,000 per acre

% soil in land capability class 1 or 2

$1,000 per acre

% soil in land capability class 1 or 2

Lake States

Delta

$1,000 per acre

% soil in land capability class 1 or 2

Southern Plains

$1,000 per acre

% soil in land capability class 1 or 2

Pacific

Avg. value

Avg. value

NE, SD, KS

N. Dakota

Avg. value

Avg. value - nonirrigated

Avg. value - irrigated IL, IN, IA, OH

Missouri

<20 20-39 40-59 60-79 >80

0

10

20

30

40

Avg. value - nonirrigated

Avg. value - irrigated

<20 20-39 40-59 60-79 >80 0

2

4

6

8

10

Avg. value - nonirrigated

Avg. value - irrigated

<20 20-39 40-59 60-79 >80

0

2

4

6

8

10

Avg. value - nonirrigated

Avg. value - irrigated

<20 20-39 40-59 60-79 >80

0

2

4

6

8

10

Avg. value - nonirrigated

Avg. value - irrigated

Source: USDA, ERS analysis of National Agricultural Statistics Service, June Area Survey data.
17

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

Land Value Effects of Proximity to Market Channels 

Vary by Location

Because transportation costs increase with distance, the proximity of land to 

important points of delivery for crops can affect net returns and land values. 

Again, using spatially explicit data is important when examining these 

relationships, at least when points of delivery are unevenly distributed. For 

example, grain elevators are heavily concentrated in several Corn Belt States 

and along the Mississippi River. For farms in these regions, there is little variation in the distances between cropland parcels and a grain elevator, making 

it diffi cult to ascertain a relationship (fi g. 13). In the Northern Plains where 

there is more variation in farm distances to elevators, the expected relationship between proximity and cropland values is more evident. The cropland 

value-proximity relationship is even more pronounced when the data are 

limited to a single State (North Dakota) (fi g. 14).

Proximity to ethanol plants also illustrates location effects. In recent years, 

Federal energy policies supporting the production of biofuels through renewable fuel standards and tax credits have increased the demand for corn, 

which has elevated corn and other commodity prices. In addition to spurring 

general crop price increases, ethanol production reduces local transportation costs to terminal markets and provides a competing source of demand 

for grain. Research has found evidence of higher corn prices up to 68 miles 

from ethanol plants (McNew and Griffi th, 2005). Other research—based on 

a survey of agricultural lenders in the seven States covered by the Federal 

Figure 13

Spatial distrbution of grain elevators, 2002

Source: Baking Business magazine, 2002.
18

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

Reserve Bank of Kansas City—found that cropland adjacent to an ethanol 

plant can be valued more than $150 per acre higher than cropland more than 

50 miles away, with proximity to multiple ethanol plants producing a higher 

premium than proximity to a single plant (Henderson and Gloy, 2009).

Correlation Between Government Payments and Cropland 

Values Varies Regionally and by Payment Type

Government agricultural program payments may increase income from 

agricultural production. When they do so in a consistent way, the value of 

farmland is likely to increase due to expectations of a future income stream 

from government payments or reduced volatility in farm revenues (Gardner, 

2003).15 Although expectations of future government payments are diffi cult 

to observe, numerous studies have considered the degree to which payments 

under commodity programs are refl ected in land values (see Moss and 

Schmitz, 2003, and references therein). Farmers have received commodity 

program payments for many decades, though the programs are subject to 

change and most must be reauthorized as part of the Farm Bill every 5 years. 

Over 2004-07, commodity payments averaged less than $25 per acre, but in 

some regions exceeded $100 per acre (USDA-ERS, 2011a). 

The type of government payment or program can affect how it is refl ected 

in farmland values. Payments that are certain might be accounted for 

differently than payments that are realized only under low prices, yields, 

or revenue. Production fl exibility contract payments in effect for 1996-

2002— direct payments to producers with “base acreage” (i.e., historical 

rights to program benefi ts)—have been shown to increase cropland values 

(Goodwin et al., 2003; Roe et al., 2003). In contrast, programs that make 

payments based on low prices or yields, and thus support incomes, may not 

have as close a relationship with cropland values. Gardner (2003) indicates 

that a relationship may not be detectable because payments are non-existent 

when market prices are higher than pre-established target prices—i.e., 

at the time when land values increase the most. However, Goodwin et 

al. (2011) found that income support payments such as loan defi ciency 

payments—which pay when market prices fall below the local loan rate—

can add signifi cantly to the value of land, due to reduced variability of farm 

earnings and more certain cash fl ow. Studies on the impact of government 

15Other non-land inputs would also 

likely capture some of the benefi ts of 

government payments when farmers 

respond by increasing production.

Figure 14

Cropland value per acre by distance to nearest elevator, Northern Plains and North Dakota, 2008

$1,000 per acre

<5

5-10

10-15

15-20

20-25

25-30

30-35

35-40

40-45

45-50

>50

0

0.5

1.0

1.5

2.0

2.5

3.0

0

Northern Plains North Dakota

$1,000 per acre

0.5

1.0

1.5

2.0

2.5

<5

5-10

10-15

15-20

20-25

25-30

30-35

35-40

40-45

45-50

>50

Sources: USDA, ERS analysis of National Agricultural Statistics Service June Area Survey data and grain elevator location data.

Miles to nearest elevator Miles to nearest elevator
19

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

programs on cropland rents have commonly found that direct payments 

invariant to market conditions are more likely to be refl ected in rental rates 

than those that vary based on market prices (Latruffe and LeMouel, 2009). 

Two of the largest commodity programs enacted under the 2002 and 2008 

Farm Acts (in terms of dollars spent) provide fi xed direct payments and 

countercyclical payments. These programs make payments to farmers based 

on historical production (base acreage) of specifi c commodities such as 

corn, oilseeds, wheat, rice, and cotton, and payments are independent of 

farmers’ cropping decisions in the current year.16 Direct payments are fi xed 

during the 5-year periods of the Farm Acts, while countercyclical payments 

are price-dependent and less predictable—payment is triggered by market 

prices falling below a pre-defi ned price, with the payment not known until 

after harvest. The impacts of different government payments on cropland 

values are diffi cult to predict based only on theoretical models, so empirical 

studies provide important insight. However, empirical evidence of the size of 

the impact has not been conclusive. For example, estimates of the impact of 

direct payments on land values range from negligible in some studies to 10-50 

percent, depending on the region (Goodwin et al., 2003; Barnard et al., 1997).

Farms can also receive subsidized crop insurance premiums. Several Federal 

crop insurance plans—both for yield and revenue—are available to farmers. 

Almost all major commodities are covered by Federal crop insurance, 

which is administered by USDA’s Risk Management Agency and delivered 

to farmers through private agents. Farmers pay 50 to 60 percent of the total 

crop insurance premium, and the areas for which crop insurance is available 

and the number of crops insured have been increasing over the past decade. 

Currently, over 270 million acres are insured, with subsidies of over $5 

billion per year (USDA-ERS 2011a; 2011b). 

Figure 15 shows average cropland values for land with different levels of 

direct and counter-cyclical program (DCP) payments17 and crop insurance premium subsidies. Counties in the bottom 20th percentile of cropland 

acres are excluded to focus the analysis on more farm-dependent counties 

and to improve comparability of different payment levels. In 2007, crop 

insurance premium subsidies totaled $3.7 billion and DCP payments $6.2 

billion. Although cropland values generally increase with insurance premium 

subsidies, the pattern is less consistent for DCP payments. Crop insurance premium subsidies may have a more consistent impact in recent years, 

because farmers have been receiving about $2 in indemnity payments for 

every $1 of premiums paid by farmers (Goodwin et al., 2011). This suggests 

that crop insurance premium subsidies and DCP payments might have 

different impacts on land values, but controlling for other factors affecting 

land values will be important. 

There are considerable regional differences in the ratio of payments to cropland values (fi g. 16).18 Much of this regional diversity is likely based on 

differences in what crops are, and have historically been, grown. Cropland 

in the Delta and Southeast regions appears to have the largest level of DCP 

payments relative to cropland values. Cotton and rice production are high in 

these regions, especially the Delta (USDA-ERS, 2011c); DCP payments for 

these crops are the highest per acre, and the cotton base has most consistently 

received countercyclical payments. Some regions (the Northern and Southern 

16Payments are made to landowners 

if they operate the land. If the land is 

rented, the payments accrue to cash 

renters. Under a share-rent agreement, 

payments are split between the landowner and share renters. Landowners 

who rent out the land and don’t receive 

the payment directly may capture the 

benefi ts of the income stream through 

bidding up the lease payments.

17Administrative data reported for 

tax purposes (1099 tax information) 

were used to calculate direct payments. 

These data did not disaggregate direct 

and countercyclical payments, but in 

2007 the total expenditure through the 

DCP for direct payments was approximately $5.1 billion versus $1.1 billion 

in countercyclical payments. DCP 

payments from the 1099 tax information, and RMA indemnity payments 

by county, were converted to 1-mile 

resolution grids and combined with the 

JAS sample points. See Appendix A.

18Counties with ratios of both direct 

payments and premium subsidies to 

land values below 0.01, as well as 

counties with no JAS observations, 

appear as white on the map. In addition 

to excluding counties that are in the 

bottom 20 percent of cropland acres, 

counties without at least nine tractlevel observations from 2006 to 2008 

are excluded; these also appear as 

white. This ensures at least three farms 

were in the county sample from 2006 

to 2008, as farms remain in the JAS for 

5 years.
20

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

Figure 15

Cropland values by amount of selected government payments, 2007 

Cropland value per acre ($)

DCP = Direct and countercyclical program payments. 

Source: USDA, ERS analysis of National Agricultural Statistics Service June Area Survey data, 

Risk Management Agency and Farm Service Agency data, and Census of Agriculture data.

$1-5 $5-10 $10-15 $15-20 >$20

0

1,000

2,000

3,000

4,000

5,000

DCP Payments

Crop Insurance Premium Subsidy

Program payment per acre

Figure 16

Ratio of selected government payments to cropland value1, 2006-08

Source: USDA, ERS analysis of National Agricultural Statistics Service June Area Survey data, Risk Management Agency and Farm Service 

Agency data, and Census of Agriculture data.

Premium subsidies and direct payments ratio > 0.02

Premium subsidies ratio > 0.02

Direct payments ratio > 0.02

Direct payments or premium subsidies ratio > 0.01

1Ratio of per acre cash direct payments and per acre crop 

insurance premium subsidies to per acre value of cropland 

in National Agricultural Statistics Service June Area Survey, 

including both irrigated and non-irrigated cropland.
21

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

Plains) have high levels of both premium subsidies and DCP payments relative to cropland values. The Corn Belt and Lake States have the lowest level 

of subsidies and DCP payments relative to land values. Areas without DCP 

payments but higher premium subsidies tend to be areas where fruits, vegetables, and other crops (sugar beets, other specialty crops) not part of the DCP 

historical program base are grown.

The need for additional empirical study of the impacts of government 

payments on farmland values—and how the impacts vary by payment type 

and by region—is particularly important given the current trend of high 

commodity prices, large government defi cits, and debate over the future of 

agricultural payment programs. It is especially challenging to predict potential impacts on farmland values should programs be abruptly eliminated. 

Many studies have determined that agricultural payments raise farmland 

values. However, these studies use hedonic or other statistical regression 

approaches that capture only marginal changes (i.e., the change in per-acre 

values for a $1 change in payment) and do not take into account changes in 

commodity market prices, the emergence of replacement programs, and other 

signifi cant consequences of eliminating agricultural programs. Nonetheless, 

our analyses suggest that abrupt changes in agricultural payment programs 

are likely to affect farmland values, with varying regional effects.

Nonfarm-Related Factors Affecting 

Farmland Values

Empirical studies of farmland values have found that in some areas of the 

country, returns from agricultural production are not the most signifi cant 

contributor to farmland prices. Indeed, studies have estimated that well over 

half the variation in farmland prices is due to nonfarm factors in some States 

(Peterson, 1986; Plantinga et al., 2002). 

Farm Real Estate Values Are Positively Correlated 

with Proximity to Urban Areas

The demand for land that can be developed for urban use is the most signifi -

cant nonfarm factor affecting farmland values in areas that are more urbanized or are experiencing faster population growth (Livanis et al., 2006; 

Hardie et al., 2001). According to the classical model of urban form, land is 

allocated based on the needs of various economic agents who bid according 

to their desire to be close to markets or urban centers. The value of land, or 

location rent, declines as one moves away from urban centers. 

While proximity to population centers and increased access to markets and 

customers could infl uence farmland values by increasing expected agricultural returns, development demand for residential, commercial, and industrial uses tends to increase farmland values even more. The U.S. population 

continues to grow and disperse, and all States are experiencing urban-related 

infl uences on farmland values to some degree. To acquire land for nonagricultural purposes, developers typically must bid higher than the agricultural 

production value of the land. As a result, even a limited number of conversions of farmland to urban uses can lead to generally higher farmland values 

in areas infl uenced by urban demand for land.
22

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

A typical approach to identify the amount of land subject to urban infl uence is 

to examine the location of land relative to the size and number of population 

concentrations. The Population-Interaction Zones for Agriculture (PIZA) measure 

developed by ERS is based on the concept of a gravity model (Shi et al.,1997) 

that accounts for both size of populations within a 50-mile radius, as well as 

distance between a land parcel and the populations (see Appendices A and B). 

Using the PIZA index, more than three-quarters of farm real estate (farm land 

and buildings) were estimated to be rural in 2008 (fi g. 17). These are lands in 

tracts without any part of a town of 2,500 or more residents, and with the primary 

commuting pattern to sites within the tract. About one-fourth of farm real estate 

was estimated to be subject to some degree of urban infl uence. 

Farm real estate values tend to decline the farther the land is situated from 

urban areas or population centers. Figure 18 shows the mean value of farm 

real estate, including all buildings and structures, at increasing distance from 

the borders of population centers. As one moves farther from a population 

center, the average farm real estate value per acre declines regardless of the 

size of the population center (50,000, 25,000, 10,000, or 5,000 residents). The 

gradient steeply declines at a distance less than 10 miles and then declines 

more gradually with increasing distance. 

Further, the mean values are higher for larger cities. For example, the average 

value for agricultural parcels within 5 miles of a city of at least 50,000 residents is approximately $16,801 per acre, whereas the average value the same 

distance from a town of at least 5,000 residents is $10,705.

A major limitation of this defi nition of urban infl uence is that it provides little 

control for the relative size of population centers. For example, the defi nition 

of a city with at least 50,000 residents includes population centers such as 

Scranton, PA, with a population of just over 70,000 residents, and New York 

City, with a population of over 8 million residents. To mitigate this shortcoming and examine the robustness of the land value gradient, we evaluated 

two alternative measures of urban infl uence (see Appendix B). The additional 

analyses revealed very similar results.

Figure 17

Agricultural real estate subject to urban influence, 2008 

Note: Low, medium, and high interaction categories reflect the extent of interaction 

with population concentrations. 

Source: USDA, ERS analysis of NASS June Area Survey data and PIZA data 

(see Appendix A). Agricultural real estate includes land and buildings.

Rural

Low interaction

Medium interaction

High interaction

77%

7%

8%

8%
23

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

Farm Real Estate Values Fall as Access to Road

Transportation Declines

Economic theory suggests that access to transportation may play an important role in determining land values. Transportation infrastructure provides 

farmers with access to markets (urban centers, elevators, or other delivery 

points) and reduces input costs related to transportation. Figure 19 shows the 

average farm real estate value per acre relative to distance to major highways 

(interstates). Transportation effects may vary depending on urban infl uence, and the fi gure therefore divides the sample by PIZA classifi cation (see 

Appendix B).

The average value per acre exhibits a similar pattern across all PIZA classifi -

cations, declining as distance to interstate highways grows. The price decline 

is more pronounced at higher levels of population interaction. 

Farmland Values May Not Be Strongly Linked 

with Rural Housing Markets

Farmland values are highly correlated with distance to urban areas. As a 

result, farmland values near urban areas are tied to the value of competing 

land use activities. Yet, because farmland provides a source of revenue 

through the production of agricultural goods and services, the pattern of 

aggregate farmland values and the values in developed uses (mainly residential uses) may differ by location according to agricultural production characteristics. One national analysis (Plantinga et al., 2002) using 1997 data found 

that the infl uence of development potential was highest in places with urban 

areas and little agricultural land. On average, the development value contributed about 10 percent to the total value of agricultural land, ranging from 

more than 30 percent in many Northeastern States and Florida (the contribution was more than 80 percent in New Jersey) to less than 5 percent in most 

of the Northern and Southern Plains States. 

Figure 18

Farm real estate values by distance to population centers, 2008 

$ per acre

Sources: USDA, ERS analysis of National Agricultural Statistics Service June Area Survey data 

and Census of Population data. Agricultural real estate includes land and buildings.

< 5 10 15 20 25 30 35 40 45 50 50 +

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000 50,000 residents

25,000 residents

10,000 residents

5,000 residents

Miles
24

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

The contribution of development value to farmland values raises questions 

about the extent to which farmland markets are susceptible to changes in 

residential markets. Over the last decade, the residential real estate market 

has undergone dramatic changes, with a housing boom that peaked in 2005 

followed by a contraction. The contribution of residential investment to 

GDP dropped more than 20 percent from 2005 to 2007, with the contraction greatest in States where growth in residential home building was strong 

during the boom period (Siniavskaia, 2008). During 2001-04, California, 

Arizona, Oregon, Florida, Maryland, Connecticut, and Rhode Island all 

experienced increases of 20 percent or more in the Rural Housing Price 

Index (RHPI); in 2007-09, the RHPI in these States declined by at least 10 

percent (fi g. 20).19 In the Northern and Southern Plains and Delta regions, the 

housing downturn dampened growth in the RHPI rather than reversing the 

gains over the 2001-04 period. 

A comparison of farm real estate values between the boom-and-bust periods 

reveals that farm real estate values increased in all States during 2001-04, and 

the gains were often as great or greater than gains in the rural housing market 

(fi g. 20). From 2007 to 2009, farm real estate values declined in many States, 

but the reversals were generally more moderate than in the rural housing sector. 

In 19 States, notably California, Oregon, Washington, and Nevada, farm real 

estate values actually increased in 2007-09 while the RHPI declined. These 

trends suggest that strong gains in farm earnings and declining interest rates 

during 2007-09 helped the farmland market withstand the signifi cant downturn 

in housing markets. 

The relationship between farmland and rural housing markets is more evident 

when examining trends for particular States. The selected States (California, 

Iowa, and Massachusetts) represent three distinct areas in terms of agricultural production and residential housing. Average farm real estate value per 

acre and were obtained from the National Agricultural Statistics Service 

(USDA/NASS 2009a). Rural housing price index values were obtained from 

the Federal Housing Finance Agency (see footnote 19).

19The Housing Price Index (HPI) 

is published quarterly by the Federal 

Housing Finance Agency (FHFA, previously the Offi ce of Federal Housing 

Enterprise Oversight) (Calhoun, 1996). 

HPI is a broad measure of singlefamily house prices. The measure is a 

weighted, repeat-sales index drawing 

from repeated sales or refi nancing of 

the same property whose mortgages 

have been purchased or securitized 

by Fannie Mae or Freddie Mac. The 

HPI includes house price fi gures at a 

number of spatial intervals, including 

Census division, State, and metropolitan statistical area (MSA). The 

nonmetropolitan HPI measure (which 

we call RHPI) has been published at 

the State level by FHFA quarterly since 

1995. The measure includes all repeat 

sales mortgages in nonmetro areas as 

defi ned by the Offi ce of Management 

and Budget (OMB).

Figure 19

Farm real estate values by distance to nearest interstate highway 

by PIZA code, 2008 

$1,000 per acre

See appendix B for PIZA definitions.

Source: USDA, ERS analysis of National Agricultural Statistics Service June Area Survey data, 

PIZA data and interstate highway data (see Appendix A).

<5 10 15 20 25 30 35 40 45 50 50+

0

2

4

6

8

10

Rural

Low interaction

Medium interaction

Miles
25

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

California's real estate sector in 1996-2009 was characterized by rapid price 

appreciation followed by a downturn during the housing price bubble (fi g. 

21). The period of rapid price appreciation can also be observed in farm 

real estate values. From 1996 to -2007, both price series exhibited a similar 

average annual appreciation rate: 6 percent for rural housing and 8 percent 

for farm real estate. Farm real estate values continue to increase, albeit more 

slowly after the housing bubble burst in the late 2000s. 

The price trends are more tempered in Iowa, where rural housing prices 

exhibited a moderate increase throughout 1996-2007, with an average annual 

increase of 4 percent (fi g. 22). At the same time, farm real estate prices 

increased 7 percent per year, with gains particularly strong in 2004-07. 

Between 2007 and 2009, both price series leveled off.

As in California, rural housing prices in Massachusetts exhibited a boomand-bust pattern in 1996-2009 (fi g. 23). However, changes in farm real estate 

prices were less pronounced, increasing through 2006 and declining through 

2009. Although the downturn is similar in magnitude for both housing and 

farm real estate values, the price appreciation was much greater for residential real estate. Rural housing prices appreciated 9 percent per year in 1996-

2009, versus 4 percent for farm real estate.

Figure 20

Changes in farm real estate values and rural housing values by State, 2001-04 and 2007-09 

Sources: USDA, National Agricultural Statistics Service (2009a) and housing price index data (see footnote 19).

Farm real estate value change Rural housing price index change

Less than -20% 

-20% to -10%

-9.99% to 0%

0.01% to 10%

10.01% to 20%

20.01% to 30%

More than 30%

2007 - 2009

2001 - 2004
26

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

Figure 21

Farm real estate values and rural housing price index (RHPI) 

in California, 1996-2009 

Farmland value per acre ($)

Source: USDA, ERS analysis of National Agricultural Statistics Service 2009a and housing 

price index data (see footnote 19).

1996 98 2000 02 04 06 08

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

0

50

100

150

200

250

300

350

400

Farmland (left axis)

Rural housing (right axis)

RHPI

Figure 22

Farm real estate values and rural housing price index (RHPI) in Iowa, 

1996-2009 

Farmland value per acre ($)

Source: USDA, ERS analysis of National Agricultural Statistics Service 2009a and housing 

price index data (see footnote 19).

Farmland (left axis)

Rural housing (right axis)

RHPI

1996 98 2000 02 04 06 08

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

0

50

100

150

200

250

300

350

400

Figure 23

Farm real estate values and rural housing price index (RHPI) 

in Massachusetts, 1996-2009 

Farmland value per acre ($)

Source: USDA, ERS analysis of National Agricultural Statistics Service 2009a and housing 

price index data (see footnote 19).

Farmland (left axis)

Rural housing (right axis)

RHPI

1996 98 2000 02 04 06 08

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

0

50

100

150

200

250

300

350

400
27

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

Natural Amenities Are Positively Related 

to Farm Real Estate Values

A number of studies suggest that like other land values, farmland values are 

infl uenced by the amount and nature of natural amenities, such as scenic 

views and varied terrain that attract people to an area (Pope, 1985). ERS’ 

analysis of population changes suggests that high-amenity counties next to 

metropolitan areas are the most desirable places to live (USDA-ERS, 2009), 

so high-amenity areas could increase the development value of the land. 

The Natural Amenities Scale published by the Economic Research Service 

offers a quantitative measure of landscape amenities at the county level for 

the contiguous United States (see Appendix A). The scale is constructed 

based on the notion that positive natural amenities include mild climate, 

varied topography, and proximity to surface water, including ponds, lakes, 

and shoreline. The scale is constructed using information on land surface 

topography, winter temperatures and sunlight, summer temperatures and 

humidity, and water cover. The Natural Amenities Scale is categorized by 

deviations from mean amenity levels, ranging from 1 (low amenity) to 7 

(high amenity). The scale has been used in previous research, including 

a study of the effects of natural amenities on rural population change 

(McGranahan, 1999).

High levels of natural amenities have a large positive effect on farm real 

estate values, with a mean value per acre of $58,732 at the highest level of the 

index value (fi g. 24). At the lowest levels of natural amenities, mean values 

are $3,351 per acre. The impact of natural amenities falls greatly from the 

highest classifi cation (7) but remains relatively constant at lower levels (1-5). 

While natural amenities arising from topography and climate conditions are 

correlated with land values, other amenities arise from the farmland itself and 

tend not to be refl ected in the land’s value. These “rural” amenities include 

the habitat provision, groundwater recharge, and open-space benefi ts provided 

by having land in an agricultural use as opposed to a more intensive use 

(Hellerstein et al., 2002; Irwin et al., 2003). Though the landowner cannot 

Figure 24

Average farm real estate values and natural amenities, 2008 

$1,000 per acre

Source: USDA, ERS analysis of National Agricultural Statistics Service June Area Survey data 

and ERS Natural Amenity Scale data.

Amenity index (1=low level of amenities, 7=high)

1 23 4 6 5 7

0

10

20

30

40

50

60
28

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

typically capture the value associated with rural amenities through selling 

land on the market, evidence suggests that people do value these public goods 

and they are a primary reason for public support for farmland protection policies (e.g., Kline and Wichelns, 1996). Demand for protecting rural amenities 

is highest in suburban and urban fringe areas where the loss of agricultural 

lands to urban uses is highest (Nickerson and Hellerstein, 2003; Kline and 

Wichelns, 1994).

While the rural amenity value of land is not refl ected in farmland values, 

some policies to protect rural amenities can affect farmland values. State 

and local governments in every State have enacted measures to help retain 

farmland in an agricultural use and to prevent the loss of rural amenities (Hellerstein et al., 2002). Preferential assessment programs, in which 

landowners get property tax relief for keeping the land in agriculture, 

generally have little effect on farmland values—likely because many such 

programs contain penalties wherein a portion of the property tax savings 

must be paid upon conversion to a developed use (e.g., Parks and Quimio, 

1996; Wunderlich, 1997). Regulatory policies such as zoning often specify 

minimum lot sizes, and many zoning laws restrict the ability of landowners 

to convert farmland to urban uses. However, studies show that such policies 

reduce land values for the restricted farm parcels when development pressure 

is present (Gottlieb and Adelaja, 2009; Beaton, 1991). 

In some cases, impacts of farmland protection vary by location. Wisconsin’s 

exclusive agricultural zoning policy, which helps contain urban development, 

was found to decrease per-acre farmland prices for smaller agriculturally 

zoned parcels located closer to cities but increase prices for larger parcels 

far from cities (Henneberry and Barrows, 1990). Purchase of development 

rights programs, in which landowners voluntarily sell the rights to develop 

the land in exchange for a payment, result in permanent (or very long term, 

25-30 year) restrictions on nonfarm development. In theory, once the development rights have been sold, the market value of the preserved land will refl ect 

only its value in a farming use, which in urbanizing areas can be signifi -

cantly lower than its residential market value. However, several studies have 

found little evidence that such development restrictions signifi cantly lowered 

preserved farmland prices (Nickerson and Lynch, 2001; Lynch et al., 2007; 

Anderson and Weinhold, 2008). The authors speculated that landowners who 

farm as a recreational pursuit are outbidding “traditional” farmers for the 

preserved land.20

20Easements are also used to protect 

land from conversion to cropping uses. 

For example, Shultz and Taff (2004) 

found that easements to protect wetlands signifi cantly lowered land values 

in North Dakota.
29

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

Farmland Ownership Trends

The ownership of agricultural land can have far-reaching implications on the 

food and fi ber system. The most recent estimate of the number of farmland 

owners was in 1999, when USDA-NASS estimated that there were more than 

2.2 million agricultural landlords.21 Ownership arrangements can affect farmland values through their impacts on the decisionmaking behavior of farm 

operators, including production decisions, adoption of technologies, and conservation practices that can enhance the productivity of the land. For example, 

operators who are full tenants and own none of the land they operate are more 

likely to specialize in crops (versus livestock) than operators who own the land 

they operate (Hoppe et al., 2007). While there has been longstanding interest in 

the characteristics of farmland owners and the extent to which they are actively 

engaged in farming, this interest has increased in recent years with the increase 

in farmland prices and uncertainty over whether farmers or nonfarmers are 

reaping the benefi ts of farm program payments.22

The amount of land in farms that is rented by farm operators has fl uctuated over time, ranging between 34 percent and 43 percent of farmland over 

1964-2007 (fi g. 25). The rising share of rented land from 1982 (38 percent) 

to 1992 (43 percent) was partly due to the farm crisis in the early 1980s that 

forced some farms out of business and the owners to rent their land. However, 

since 1997 the amount of acreage rented has remained below 40 percent. The 

distribution of rented farmland varies across regions, with the highest proportions in Illinois, Iowa, Kansas, and the Mississippi Delta. Except for increases 

in a few areas (the Mississippi Delta region, southern Illinois, eastern North 

Dakota, and the Central Valley in California), the geographic concentration 

of counties with at least half of farmland rented or leased in 2007 was similar 

to 1950 (fi g. 26). With such a large amount of farmland rented, landlords 

clearly play a signifi cant role in U.S. agriculture, particularly in some regions. 

Farmland owners can be grouped into two categories: owner-operators (those 

owning all the land they operate; this includes farmers who operate some land 

and rent out additional land to other farmers) and non-operating land owners 

(NOLs). The proportion of land in farms owned by NOLs declined from 37 

percent in 1992 to 29 percent in 2007 (USDA-NASS, 2009). The amount of 

acres held by NOLs differs across regions (fi g. 27). In 2007, in three of the top 

21Also, in 1999, over 80 percent of 

landlords leased to only one tenant 

(USDA-NASS, 1999).

22One study estimated that of the 

$40 billion in total land value attributable to farm program payments in the 

Heartland, $25 billion is related to land 

owned by non-operators (Barnard et 

al., 2001).

Figure 25

Share of farmland acres rented or leased, 1964-2007 

Percent of acres

Source: Census of Agriculture, multiple years.

1964 69 74 78 82 87 92 97 2002 07

0

10

20

30

40

50
30

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

Figure 26

Percent of land in farms rented or leased, 2007

Source: USDA, National Agricultural Statistics Service.

United States:

38.0 percent

40 - 49

50 or more

30 - 39

20 - 24

25 - 29

Less than 20

Percent

Figure 27

Farmland owned by operators and non-operator landlords 

by region, 2007 

Million acres

Source: Census of Agriculture (2007).

Northeast

Lake States

Corn Belt

Northern Plains

Appalachia

Southeast

Delta

Southern Plains

Mountain

Pacific

0

50

100

150

200

250

Operators

Non-operators % owned by non-operators

24%

30%

22%

34%

19% 35% 28%

33%

38%

28%
31

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

four regions in terms of land in agriculture—the Corn Belt, Northern Plains, 

and Southern Plains—NOLs owned more than 30 percent of the land. NOLs 

also owned 30 percent or more of farmland in the Delta and Pacifi c regions. 

Many reasons contribute to this pattern, including laws in some States that limit 

farmland ownership by nonfarmers or by foreign owners. 

The characteristics of farm operators have been well documented (Hoppe 

et al., 2007), but much less is known about non-operator landlords. Data 

from a survey of private farmland owners in 1999 (the most recent available) 

revealed that most land owned by NOLs was concentrated in the hands of 

relatively few people who owned large amounts of land. In 1999, 4 percent 

of NOLs owned at least 1,000 acres of farmland, and collectively they 

controlled 44 percent of all acreage owned by NOLs (fi g. 28). However, about 

68 percent of NOLs owned less than 180 acres each; these landlords owned 

18 percent of the land owned by NOLs. 

Using a combination of the two most recent surveys of private farmland owners 

(1988 and 1999) and 2007 Census of Agriculture data indicates whether characteristics of farmland owners are changing over time. Age of farmland owners 

is of particular interest because land turnover is higher among older owners. A 

greater proportion of non-operators (than operators) are older than 70: 47 and 

42 percent of NOLs were older than 70 in 1988 and 1999, respectively, while 

only about 20 percent of owner-operators were older than 70 over the past three 

decades. Far fewer NOLs live on the farm, compared with owner-operators 

(29 versus 82 percent in 1999), though another 29 percent of NOLs live within 

5 minutes of the farm they own (Barnard et al., 2001). It also appears NOLs 

were less likely to be enrolled in the Conservation Reserve Program (CRP) or 

the Wetlands Reserve Program (WRP), at least in the 1980s and 1990s.23 The 

proportion of owner-operators participating in the CRP or WRP increased in 

1999-2007 from 10 percent to 16 percent; national data are not available to 

determine whether enrollment patterns among non-operators changed after 

1999. This is consistent with research that found that tenants were less likely 

than owner-operators to adopt conservation practices that provide benefi ts over 

the longer term (Soule et al., 2000). In 1999, non-operators also tended to own 

farm real estate that was about $100 per acre less valuable, on average, than 

land owned by owner-operators.

23USDA’s Conservation Reserve 

Program (CRP) and Wetlands Reserve 

Program (WRP) are two conservation 

programs that retire cropland from 

production. While non-operators might 

be more reluctant to participate due to 

contract lengths (at least 10 years in 

CRP, and 30 years in WRP), the benefi ts of enrolling land include reduced 

farm labor requirements and payments 

to the landowner. However, it is not 

always clear whether landlords are 

classifi ed as NOL or operators when 

they have land, particularly whole 

farms, enrolled in these long-term land 

retirement programs.

Figure 28

Number of non-operating owners and the acres they own, 1999 

1,000 acres

Source: USDA, National Agricultural Statistics Service Agriculture Economics and Land 

Ownership Survey (AELOS), 1999.

0

20

40

60

80

100

120

140

160

180

0

100

200

300

400

500

600

700

Less than

49 acres

More than

1,000 acres

500 to 999

acres

180 to 499

acres

50 to 179

acres

Number of acres (left axis)

Number of non-operating owners (right axis)

Non-operating

owners (1,000)
32

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

Many Non-Operating Landlords Live in Towns 

and Urban Areas

The Agricultural Economics and Land Ownership Survey (AELOS) indicates that in 1999 (the last update), 42 percent of all farm landlords lived in 

a city, town, or urban area.24 Some farm program payments are paid directly 

to landlords (see footnote 16), and when a large percentage of farmland 

owners may live in urban areas, it raises questions about whether—in addition to supporting farm incomes—farm program payments benefi t the rural 

economy as intended (Drabenstott, 2005). However, ERS analysis of 2004 

data on government payments made in Federal commodity and conservation 

programs revealed that over 90 percent of payments in 2004 were mailed to 

rural addresses outside of census-defi ned urban areas and clusters.

Relatively Little U.S. Farmland Is Foreign Owned

High levels of foreign demand for farmland could put upward pressure on 

farmland prices. Despite recent increases in foreign ownership of forest 

land, annual data compiled by USDA’s Farm Service Agency reveals that, 

as of February 2009, only 1.7 percent of privately owned land in farms or 

forest, or 22.8 million acres, was owned by foreigners (fi g. 29). The majority 

of foreign-owned land is owned by people or fi rms from two countries: 

Canada (34 percent) and the Netherlands (17 percent). Most foreign ownership is concentrated in Maine: 15.7 percent of Maine’s privately owned farm 

24The AELOS does not distinguish 

towns and cities based on population 

size, so it is possible some landlords 

report living in towns that would 

be classifi ed as “rural” according to 

Census Bureau defi nitions of urban 

areas and clusters.

Table 2

Characteristics of the average farmer and non-operating landowner

1988 1999 2007

Characteristic Non-Operator Owner-Operator Non-Operator Owner-Operator Owner-Operator

Percent

70 years or older 47.7 21.7 42.1 17.6 19.1

Live on farm 36.3 78.6 29.0 82.2 76.8

Enrolled in the Conservation or 

Wetlands Reserve Program 8.1 11.1 8.0 10.4 15.7

Sources: USDA, National Agriculture Statistics Service Agricultural Economics and Land Ownership Survey (1988 and 1999) and Census of 

Agriculture (2007).

Figure 29

Trends in foreign holdings of U.S. farmland, 1999-2009 

Million acres

Source: Blevins et al., 2010.

2000 01 02 03 04 05 06 07 08 09

0

2

4

6

8

10

12

14

16 Cropland

Pasture

Forest

Other agricultural land

 Non agricultural land
33

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

and forest land, or 2.82 million acres, is foreign owned. Of this amount, 

2.77 million acres are forest land. Other States with relatively high levels 

of foreign ownership of farm and forest land include Hawaii (8.8 percent), 

Washington (7.6 percent), Nevada (5.2 percent), and Alabama (5.1 percent). 

Foreign interests own 2.57 million acres in Texas. 
34

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

Conclusions

Farmland represents a signifi cant investment and a large portion of the wealth 

of many U.S. farmers. With farmland real estate values growing largely 

steadily since the farm fi nancial crisis in the mid-1980s, interest in the driving 

forces behind farmland values has grown, especially amid the residential 

housing downturn. Since 2005, increases in crop prices and cash rents, coupled 

with low interest rates and improved agricultural credit conditions, are giving 

rise to double-digit increases in farmland values in some regions. 

Historical relationships between cropland and pastureland values have 

changed in some regions. Cropland values have typically exceeded pasture 

values and have been at least twice as large as pasture values in the Midwest 

and West in the last decade. But the premium has shrunk recently, and in two 

regions—the Southeast and Delta—average cropland values have remained 

below average pasture values since 2004-05. Studies suggest that nonfarm 

factors could be contributing to the relative changes in crop and pasture 

values. Pastureland can provide a stream of recreational income from hunting 

leases, and in the Southeast, pastureland tends to be closer to urban areas and 

development demand. 

How has farmland been performing as an investment? Trends in farm 

incomes, cash rents and interest rates suggest farmland values were supported 

by farm earnings in 2009 and 2010. This suggests that in very recent years 

(when measured at the national level), farmland values are supported by 

fundamental farm factors. Yet, several macroeconomic measures indicate 

that over a longer horizon, farmland values are becoming less correlated with 

farm-related factors once thought to support those values. Declining rent-tovalue ratios indicate cash rents are increasingly smaller relative to farmland 

values, and the ratio is smallest for cropland close to urban areas. Also, the 

affordability of farmland has varied over time. While in 2009-2010 average 

income from farming has been more than suffi cient to service farm real 

estate debt, during 2005-08 and during 1978-1985, this was not the case. A 

lack of correlation with net farm incomes, declining rent-to-value ratios, and 

low levels of affordability all suggest that nonagricultural factors are increasingly important in determining farmland values. 

Farm program payments are under increased scrutiny amid a historic debate 

over the size of the Federal defi cit. Our analyses suggest that changes to 

some of the largest farm programs would likely have impacts that vary across 

regions. Direct and countercyclical (DCP) payments are a larger percentage 

of cropland values in the Delta and Southeast regions than elsewhere. While 

the extent to which farm program payments increase farmland values is 

uncertain, studies indicate at least some capitalization of farm program 

payments. Large reductions in DCP payments might have the greatest potential to reduce land values in the Delta and Southeast. The Corn Belt and Lake 

States regions generally have the lowest level of subsidies (through both the 

DCP program and crop insurance) relative to land values. In the Appalachia 

and Northeast regions, some areas have high levels of insurance premium 

subsidies relative to land values, but some areas also have low levels of DCP 

payments relative to land values. 
35

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

How does farmland compare to alternative investments? While farmland has 

historically been a stable investment, it has been outperformed by the S&P 

500 (and gold, when investments have been held for nearly 40 years). Yet in 

very recent years, farmland has become more attractive as an investment, as 

refl ected in surveys that indicate active investor interest in farmland, at least 

in some regions: nearly a quarter of farmland purchases in Iowa were made 

by investors in 2009. However, uncertainty about the renewal of major farm 

commodity programs and future interest rates could reduce the attractiveness 

of farmland as an investment relative to other alternatives.

While macroeconomic conditions affect farmland values, farmland values 

are also driven by parcel-specifi c factors that collectively determine the 

land’s potential returns from being in an agricultural use. The importance of 

using spatially disaggregated data, though complex and time consuming, is 

particularly evident when examining correlations between common proxies 

for net returns to agricultural production. Our analyses reveal that the positive relationships between better soils and cropland values became evident 

only when we controlled for proximity to large population concentrations (we 

retained land value observations only in the most rural areas). Parcel-specifi c 

effects also vary substantially across regions, with opposite effects in some 

cases (e.g., soil quality was negatively correlated with cropland values in the 

Appalachian region but positively correlated elsewhere). Cropland values 

generally increase with proximity to points of delivery (grain elevators, highways) in rural areas.

Signifi cant volatility in residential real estate markets over the last decade 

has raised questions about the extent to which competing land markets 

affect farmland values. A comparison of rural housing and farmland 

value trends in California, Iowa, and Massachusetts—three States that 

are distinct in terms of location, agricultural production and residential 

housing—reveals that the boom-and-bust cycle in the housing market in 

the last decade had no, or relatively modest, negative impacts on farmland markets. Of the three States, only farmland values in Massachusetts 

declined between 2006 and 2009, coincident with rapid losses in residential 

housing values. These comparisons suggest that farmland markets may be 

fairly independent of residential housing markets in some places but not in 

others. However, the moderating impacts of historically low interest rates 

and high crop prices almost certainly mitigated the downturn in value that 

farmland markets could have otherwise experienced.

Coupled with questions about the likely stability of farmland values are 

questions about whether active farmers benefi ted from the recent increases 

in those values. With about 40 percent of farmland being rented, and 

with more than 30 percent being rented from non-operating landowners 

in several regions (largely the Midwest and Pacifi c), it is clear that nonoperating landowners play a signifi cant role in U.S. agriculture. Not only 

do non-operators reap some of the benefi ts (and bear some of the risks) 

of changes in farmland values, land owned by non-operators is managed 

somewhat differently—for example, such lands may be less likely to be 

enrolled in conservation programs.

The trend analyses in this report provide an important foundation in understanding the factors driving farmland values and ownership. Nonetheless, 
36

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

the correlations of values and tenure status with macroeconomic and parcelspecifi c factors do not necessarily identify causal relationships. Many of the 

factors affecting land values and ownership are changing simultaneously, 

and it is diffi cult to know from trend analysis alone which or to what extent 

particular factors are contributing to changes. Additional research that statistically analyzes these complex interactions can help identify which of the myriad 

factors have the most signifi cant impacts on farmland values and ownership.
37

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

References

Alston, J. 1986. “An Analysis of Growth of U.S. Farmland Prices: 1963-

1982.” American Journal of Agricultural Economics 68:1-9.

Anderson, Kathryn, and Diana Weinhold. 2008. “Valuing Future 

Development Rights: The Costs of Conservation Easements,” Ecological 

Economics, Dec., v. 68 (1-2):437-46.

Barnard, C., R. Nehring, J. Ryan, R. Collender, and B. Quinby. 2001. 

“Higher Cropland Value from Farm Program Payments: Who Gains?” 

Agricultural Outlook, Nov., J. McLaughlin (ed.), U.S. Department of 

Agriculture, Economic Research Service.

Barnard, C., G. Whittaker, D. Westenbarger, and M. Ahearn. 1997. “Evidence 

of capitalization of direct government payments into U.S. cropland 

values,” American Journal of Agricultural Economics 79.

Barnett, Barry J. 2010. “The U.S. Farm Financial Crisis of the 1980s,” 

Agricultural History 74(2):366-380.

Barry, Peter J. 1980. “Capital Asset Pricing and Farm Real Estate,” American 

Journal of Agricultural Economics 62:547-53.

Bastian, Chris T., Donald M. McLeod, Matthew J. Germino, William 

A. Reiners, and Benedict J. Blasko. 2002. “Environmental Amenities 

and Agricultural Land Values: A Hedonic Model Using Geographic 

Information Systems Data,” Ecological Economics Nov., 40: 337-349.

Beaton, W.P. 1991. “The Impact of Regional Land-Use Controls on Property 

Values: The Case of the New Jersey Pinelands,” Land Economics

67(May):172-94.

Beattie, G., and R. Ransom. 1979. ‘‘Use Value Assessment: Its Causes, Its 

Characteristics, Its Effects,’’ Lincoln Institute of Land Policy, Cambridge, MA.

Blevins, Patricia, Lesa Johnson, and Dolores Smith. 2010. “Foreign Holdings of 

U.S. Agricultural Land through February 28, 2009.” USDA Farm Service 

Agency, http://www.fsa.usda.gov/Internet/FSA_File/afi da_2010.pdf

Briggeman, Brian, and Maria Akers. 2010. “Farmland Values Climb and 

Credit Conditions Improve.” Survey of 10th District Agricultural Credit 

Conditions. Federal Reserve Bank of Kansas City, 3rd quarter. http://

www.kansascityfed.org/publicat/research/indicatorsdata/agcredit/

AGCR3Q10.pdf

Burt, O.R. 1986. “Econometric Modeling of the Capitalization Formula 

for Farmland Prices,” American Journal of Agricultural Economics

68(1):10-26.

Calhoun, C.A. 1996. “OFHEO House Price Indexes: HPI Technical 

Description,” Offi ce of Federal Housing Enterprise Oversight. http://www.

fhfa.gov/webfi les/896/hpi_tech.pdf
38

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

Chambers, R. 1995. “The incidence of agricultural policies,” Journal of 

Public Economics 57, 317-335.

Chicoine, D.L., and J.T. Scott. 1983. “Agricultural use-valuation using farm 

level data,” Property Tax Journal, 2, 1-12. 

Chavas, Jean-Paul, and C. Richard Shumway. 1982. “A Pooled Time-Series 

Cross-Section Analysis of Land Prices,” Western Journal of Agricultural 

Economics 7(1):31-41.

Chavas, J.P., and A. Thomas. 1999. “A Dynamic Analysis of Land Prices,” 

American Journal of Agricultural Economics 81(4):772-884.

Chicoine, David L. 1981. “Farmland Values at the Urban Fringe: An Analysis 

of Sale Prices,” Land Economics 57 (Aug.):352-62.

Clark, J.S., M. Fulton, and J.T.Scott, Jr. 1993. “The Inconsistency of Land 

Values, Land Rents, and Capitalization Formulas,” American Journal of 

Agricultural Economics 75(1):147-55.

Conklin, H.E., and W. G. Lesher. 1977. “Farm value assessment as a means 

for reducing premature and excessive agricultural disinvestment in urban 

fringes,” American Journal of Agricultural Economics 59:755-759.

Duncan, Marvin. 2008. “Financing Agriculture in the 1980s,” Economic 

Review, Federal Reserve Bank of Kansas City, March.

Drabenstott, M. 2005. “Do Farm Payments Promote Rural Economic 

Growth?” Center for the Study of Rural America, Federal Reserve Bank 

of Kansas City.

Dobbins, C., and K. Cook. 2007. Indiana Farmland Values & Cash Rents 

Jump Upward, Purdue Agricultural Economics Report 1-8, Aug.

Duffy, M. 2009. Farmland Value Survey, Ag Decision Maker File C2-70. 

Iowa State University Extension, Dec. 

Falk, B. 1991. “Formally Testing the Present Value Model of Farmland 

Prices,” American Journal of Agricultural Economics 73(1):1-10. 

Featherstone, A.M., and T.G. Baker. 1987. “An Examination of Farm Sector Real 

Asset Dynamics,” American Journal of Agricultural Economics 69:532-46.

Flanders, A., F. White, and C. Escalante. 2004. “Equilibrium of Land Values 

from Agricultural and General Economic Factors for Cropland and 

Pasture Capitalization in Georgia,” Journal of Agribusiness 22: 49-60.

Freeman, M. 1993. The Measurement of Environmental and Resource 

Values: Theory and Methods. Washington, DC: Resources for the Future. 

Gardner, B. 2003. “U.S. Commodity Policies and Farmland Values,” in 

Government Policy and Farmland Markets: The Maintenance of Farmer 

Wealth, Charles B. Moss and Andrew Schmitz (eds.), Iowa State Press, IA.
39

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

Goodwin, B., A. Mishra, and F. Ortalo-Magné. 2011. “The Buck Stops 

Where? The Distribution of Agricultural Subsidies.” National Bureau of 

Economic Research Working Paper 16693, http://www.nber.org/papers/

w16693

Goodwin, B.K., A.K. Mishra and F.N. Ortalo-Magné. 2003. “Explaining 

Regional Differences in the Capitalization of Policy Benefi ts into 

Agricultural Land Values,” in Government Policy and Farmland Markets: 

The Maintenance of Farmer Wealth, Charles B. Moss and Andrew 

Schmitz (eds.), Iowa State Press, IA.

Gottlieb, P.D., A. Adelaja. 2009. “The Impact of Down-Zoning on Land 

Values: A Theoretical Approach,” Agricultural Finance Review 

69(2):206-27.

Guiling, P., B.W. Brorsen, and D. Doye. 2007. “How Much Infl uence Does 

Recreation Have on Agricultural Land Values?” Selected paper prepared 

for presentation at the Southern Agricultural Economics Association 

Annual Meeting, Mobile, AL, Feb. 4-7.

Hardie, Ian, T. Narayan and B. Gardner. 2001. “The Joint Infl uence of 

Agricultural and Nonfarm Factors on Real Estate Values: An Application 

to the Mid-Atlantic Region.” Amer. J. Agr. Econ. 83(1) (February 2001): 

120–132.

Hellerstein, D., C. Nickerson, J. Cooper, P. Feather, D. Gadsby, D. Mullarkey, 

A. Tegene, and C. Barnard. 2002. Farmland Protection: The Role 

of Public Preferences for Rural Amenities. USDA-ERS Agricultural 

Economic Report AER815, November. http://www.ers.usda.gov/

publications/aer815/

Henderson, Jason. 2010. “Will the Rural Economy Rebound in 2010?” 

Federal Reserve Bank of Kansas City Economic Review, 95(1):95-119.

Henderson, Jason, and Sean Moore. 2006. “The Capitalization of Wildlife 

Recreation Income into Farmland Values,” Journal of Agricultural and 

Applied Economics 38(3):597-610 (Dec.).

Henderson, Jason, and Brent A. Gloy. 2009. “The Impact of Ethanol Plants on 

Cropland Values in the Great Plains,” Agricultural Finance Review, v. 69, 

issue 1, pp. 36-48.

Henderson, J., and M. Akers. 2009. “Will High Farmland Values Hold?” The 

Main Street Economist, 4:6, Federal Reserve Bank of Kansas City.

Henderson, J., and N. Novack. 2005. “A Resurgent Rural Economy Spurs 

Farmland Values,” Federal Reserve Bank of Kansas City Economic 

Review, 1st quarter, v. 90 (1):59-82.

Henneberry, D.M., and R.L. Barrows. 1990. “Capitalization of Exclusive 

Agricultural Zoning into Farmland Prices,” Land Economics 66 (Aug.): 

249-58.
40

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

Hoppe, R., P. Korb, E.J. O’Donoghue, and D.E. Banker. 2007. Structure and 

Finances of U.S. Farms, 2007 Edition. EIB- 24, U.S. Department of 

Agriculture, Economic Research Service. June.

Hoppe, R. 2006. “Land Ownership and Farm Structure,” in Agricultural 

Resources and Environmental Indicators, Noel Gollehon and Keith Weibe 

(eds), EIB-16, U.S. Department of Agriculture, Economic Research 

Service.

Huang, Haixiao, Gay Y. Miller, Bruce J. Sherrick, and Miguel I. Gomez. 

2006. “Factors Infl uencing Illinois Farmland Values,” American Journal 

of Agricultural Economics, May, v. 88, issue 2, pp. 458-70.

Irwin, E., C. Nickerson, and L. Libby. 2003. “What Are Farmland Amenities 

Worth?” Choices, 3rd quarter.

Irwin, S., L. Forster, and B. Sherrick. 1988. “Return to Farm Real Estate 

Revisited.” American Journal of Agricultural Economics 70:580-87. 

Kline, J., and D. Wichelns, 1996. “Public preferences regarding the goals of 

farmland preservation programs,” Land Economics, 72: 538-549. 

Kline, J., and D. Wichelns. 1994. “Using referendum data to characterize 

public support for purchasing development rights to farmland,” Land 

Economics, 70(2): 223-33.

Latruffe, L., and C. Le Mouel. 2009. “Capitalization of Government Support 

In Agricultural Land Prices: What Do We Know?” Journal of Economic 

Surveys 23: 659-691.

Libby, L.W., and E.G. Irwin. 2003. “Rural Amenities and Farmland Values,” 

in Government Policy and Farmland Markets: The Maintenance of 

Farmer Wealth, Charles B. Moss and Andrew Schmitz (eds.), Iowa State 

Press, IA.

Livanis, Grigorios, Charles B. Moss, Vincent E. Breneman, and Richard F. 

Nehring. 2006. “Urban Sprawl and Farmland Prices,” American Journal 

of Agricultural Economics, Nov. 88(4): 915-29.

Luttrell, C.B. 1979. “The ‘Danger’ From Foreign Ownership of U.S. 

Farmland,” Federal Reserve Bank of St. Louis Review, Jan.:2-9.

Lynch, L., W. Gray, and J. Geoghegan. 2007. “Are farmland preservation 

program easement restrictions capitalized into farmland prices? What 

can a propensity score matching analysis tell us?” Review of Agricultural 

Economics 29 (3): 502-509.

McGranahan, D. 1999. Natural Amenities Drive Rural Population Change. 

AER-781, U.S. Department of Agriculture, Economic Research Service, 

http://www.ers.usda.gov/publications/aer781/aer781.pdf
41

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

McNew, K., and D. Griffi th. 2005. “Measuring the Impact of Ethanol Plants 

on Local Grain Prices,” Review of Agricultural Economics, Summer, v. 

27, issue 2, pp. 164-80.

Melichar, E. 1984. “A Financial Perspective on Agriculture.” Federal Reserve 

Bulletin 70:1-13.

Monke, J. 2006. “Farm Commodity Programs: Direct Payments, CounterCyclical Payments, and Marketing Loans.” CRS Report for Congress, 

Order Code: RL33271.

Moss, Charles B., and Andrew Schmitz (eds.). 2003. Government Policy 

and Farmland Markets: The Maintenance of Farmer Wealth. Iowa State 

Press, IA.

Moss C.B., and A.L. Katchova. 2005. “Farmland Valuation and Asset 

Performance,” Agricultural Finance Review 65(2):119–30. 

Nickerson, C.J., and D. Hellerstein. 2003. “Protecting rural amenities through 

farmland preservation programs,” Agricultural and Resource Economics 

Review, 32(1): 129-144.

Nickerson, C.J., and L. Lynch. 2001. “The Effect of Farmland Preservation 

Programs on Farmland Prices,” American Journal of Agricultural 

Economics, 83(2)(May):341-351.

Oppendahl, D.B. 2010. “Farmland Values and Credit Conditions,” The 

Agricultural Newsletter from the Federal Reserve Bank of Chicago, 

n. 1950, Nov. http://www.chicagofed.org/digital_assets/publications/

agletter/2010_2014/november_2010.pdf

Palmquist, R., and L.E. Danielson. 1989. “A Hedonic Study of the Effects of 

Erosion Control and Drainage on Farmland Values,” American Journal of 

Agricultural Economics 71(1) (Feb):55-62.

Parks, P.J., and W.R.H. Quimio. 1996. “Preserving Agricultural Land with 

Farmland Assessment: New Jersey as a Case Study,” Agricultural and 

Resource Economics Review April, 25(1): 22-27.

Peterson, W. 1986. “Land Quality and Prices,” American Journal of 

Agricultural Economics, Nov. 68(4): 812-819.

Plantinga, A., Ruben N. Lubowski, and Robert N. Stavins. 2002. “The effects 

of potential land development on agricultural land prices,” Journal of 

Urban Economics 52:561-581.

Pope, C.A. III. 1985. “Agricultural Productive and Consumptive Use 

Components of Rural Land Values in Texas,”American Journal of 

Agricultural Economics, Feb. 67(1):81-86.

Raup, Philip M. 2003. “Disaggregating Farmland Markets,” in Government 

Policy and Farmland Markets: The Maintenance of Farmer Wealth, 

Charles B. Moss and Andrew Schmitz (eds.), Iowa State Press, IA.
42

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

Reynolds, John E., and Warren Johnston. 2003. “Micro-Markets for 

Farmland: The Case of Florida and California,” in Government Policy 

and Farmland Markets: The Maintenance of Farmer Wealth, Charles B. 

Moss and Andrew Schmitz (eds.), Iowa State Press, IA.

Robison, Lindon J., Robert J. Myers and Marcelo E. Siles. 2002. “Social 

Capital and the Terms of Trade for Farmland,” Applied Economic 

Perspectives and Policy, 24(1):44-58.

Roe, T., A. Somwaru, and X. Diao. 2003. “Do Direct Payments Have 

Intertemporal Effects on U.S. Agriculture?” in Government Policy and 

Farmland Markets: The Maintenance of Farmer Wealth, Charles B. Moss 

and Andrew Schmitz (eds.), Iowa State Press, IA.

Schnitkey, Gary. 2010. “Farmland Price Outlook: Are Farmland Prices Too 

High Relative to Returns And Interest Rates?” Farm Economics Facts 

& Opinions FEFO 10-17, University of Illinois at Urbana-Champaign, 

Oct. 18. http://www.farmdoc.illinois.edu/manage/newsletters/fefo10_17/

fefo10_17.html

Schmitz, A., and R. Just 2003. “The Economics and Politics of Farmland 

Values.” Pgs 53-79 in C.B. Moss and A. Schmitz, eds. Government Policy 

and Farmland Markets: The Maintenance of Farmer Wealth. Iowa State 

Press, Ames, Iowa.

Shalit, H., and A. Schmidt. 1982. “Farmland Accumulation and Prices.” 

American Journal of Agricultural Economics 81 (Nov):710-19.

Sherrick, B.J. and P.J. Barry. 2003. “Farmland Markets: Historical 

Perspectives and Contemporary Issues,” in Government Policy and 

Farmland Markets: The Maintenance of Farmer Wealth, Charles B. Moss 

and Andrew Schmitz (eds.), Iowa State Press, IA.

Shi, Yue Jin, Timothy T. Phipps, and Dale Colyer. 1997. “Agricultural Land 

Values under Urbanizing Infl uences,” Land Economics, Feb. 73(1): 

90-100.

Shultz, Steven D., and Steven J. Taff. 2004. “Implicit Prices of Wetland 

Easements in Areas of Production Agriculture,” Land Economics, Nov., 

80(4): 501-12.

Siniavskaia, Natalia. 2008. “The Effect of the Home Building Contraction 

on State Economies.” Special study published by Housing Economics: 

The Economics Publication for America’s Housing Industry. http://www.

nahb.org/generic.aspx?sectionID=734&genericContentID=99676&chann

elID=311

 Soule, M.J., A. Tegene, and K.D. Wiebe. 2000. “Land Tenure and the 

Adoption of Conservation Practices,” American Journal of Agricultural 

Economics 82:993-1005.
43

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

U.S. Department of Agriculture, Economic Research Service, 2011a. Farm 

Risk Management: Government Programs and Risk. http://www.ers.usda.

gov/Briefi ng/RiskManagement/GovernmentProgramsandRisk.htm; 

U.S. Department of Agriculture, Economic Research Service, 2011b. Farm 

and Commodity Policy: Program Provisions: Crop Yield and Revenue 

Insurance. http://www.ers.usda.gov/briefi ng/farmpolicy/insure.htm

U.S. Department of Agriculture, Economic Research Service, 2011c. Farm 

and Commodity Policy: Government Payments and the Farm Sector 

Briefi ng Room (last updated September 9, 2011). http://www.ers.usda.gov/

Briefi ng/FarmPolicy/gov-pay.htm

U.S. Department of Agriculture, Economic Research Service, 2009. Rural 

Amenities: Natural Amenities Scale Briefi ng Room (last updated October 

22, 2009). http://www.ers.usda.gov/Briefi ng/RuralAmenities/naturalamenities.htm 

U.S. Department of Agriculture, National Agricultural Statistics Service. 

2009a. Agricultural Land Values and Cash Rents Annual Summary. 

http://usda.mannlib.cornell.edu/MannUsda/viewDocumentInfo.

do?documentID=1446.

U.S. Department of Agriculture, National Agricultural Statistics Service. 

2009b. The Censuses of Agriculture, 2007, 2002, 1997, and 1992. http://

www.agcensus.usda.gov/index.asp

U.S. Department of Agriculture, National Agricultural Statistics Service. 

1999. Agriculture Economics and Land Ownership Survey. http://www.

agcensus.usda.gov/Publications/1997/Agricultural_Economics_and_

Land_Ownership/index.asp

USDOT-Bureau of Transportation Statistics, 2004. National Transportation 

Atlas Database. Research and Innovative Technology Administration.

Wunderlich, G. 1997. “Land Taxes in Agriculture: Preferential Rate and 

Assessment Effects,” American Journal of Economics and Sociology, 

April, 56(2):215-28.

Xu, F., R.C. Mittelhammer, and P.W. Barkley. 1993. “Measuring the 

Contributions of Site Characteristics to the Value of Agricultural Land,” 

Land Economics, Nov., 69(4):356-69.

Zulauf, C. 2007. “Fixed Payments and the Major Farm Program Crops: An 

Overview.” AEDE-RP-0077-07, The Ohio State University, Department of 

Agricultural, Environmental, and Development Economics. 
44

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

Appendix A: Data Inputs

Euclidean Distance Metrics:

Distances to three types of features were calculated using a Euclidean 

distance (straight-line distance) tool in ArcGIS. The output cell size for these 

Euclidean distance grids was 1 mile.

Interstates

The source of the interstate data is the US National Transportation 

Atlas Interstate Highways, through ESRI, from 2002. A one-mile resolution Euclidean distance grid was calculated from interstates out to a 

maximum distance of 2,000 kilometers.

Grain elevators

The best available set of terminal and subterminal grain elevators are 

derived from Baking Business Magazine/Souza Publishing (2002). A 

1-mile resolution Euclidean distance grid was calculated from grain 

elevators out to a maximum distance of 2,000 kilometers. This set 

excludes country grain elevators.

Water ports

The port data contain physical information on commercial facilities 

at the principal U.S. Coastal, Great Lakes, and Inland Ports (USDOT 

Bureau of Transportation Statistics 2004). The data consist of listings 

of a port area's waterfront facilities, including information on berthing, 

cranes, transit sheds, grain elevators, marine repair plants, fl eeting 

areas, and docking/storage facilities. Collection of data is performed 

on a rotational basis to ensure onsite accuracy at each facility. A 1-mile 

resolution Euclidean distance grid was calculated from water ports out 

to a maximum distance of 2,000 kilometers.

Counties

Counties come from the Environmental Systems Research Institute counties 

from the year 2000. The county polygons were converted to a 30-meter resolution grid.

1099 tax information

Totals of FSA program payments by county were summarized and extracted 

from the FSA database for 1999-2008. These were joined to counties, and 

converted to a 1-mile resolution grid for each year.

Indemnity payment data

Totals of RMA indemnity payments by county were summarized for 1999- 

2008 and converted to 1-mile resolution grids.
45

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

Ethanol processing capacity surfaces

Using the ethanol capacity attribute of ethanol plants, ERS generated a grid 

using the ArcView’s kernel density function tool. The output cell size is 4 

kilometers, and the capacity is distributed over a 125-kilometer distance from 

each plant. This study considers the ethanol capacity grids from 2002 through 

2008. The kernel density approach is a way to demonstrate the expected 

decay of infl uence from ethanol plants on land value as a function of distance. 

Land capability class (LCC)

This is one of the attributes available in the Natural Resources Inventory 

(NRI). Its purpose is to classify soils into one of eight classes describing 

suitability for agricultural production. The county-level measure used in this 

study is the percent of the county cropland area composed of LCC classes 

1 and 2, the two best suitability classes. Inherent soil qualities—such as 

wetness, erosivity, stoniness, shallowness, and drought properties—are 

considered in the class assignment; however, anthropogenic soil enhancements are not considered.

For the offi cial NRCS defi nition of LCC, visit http://www.nrcs.usda.gov/technical/NRI/1997/summary_report/glossary.html.

For LCC class and subclass descriptions, visit http://www.nrcs.usda.gov/technical/NRI/maps/meta/m6175.html.

Crop yields

Average corn yields were derived from the NASS Crop County Estimates 

database. Yields were averaged from 2001 to 2006 at the county level to get 

an overall yield expectation.

PIZA

The ERS Population Interaction Indexes to Classify Agricultural Land 

(PIZA) is a measure of urban infl uence on agricultural lands available in a 

5-kilometer resolution grid. See Appendix B and http://www.ers.usda.gov/

Data/PopulationInteractionZones/accessdata.htm

Population Interaction Index (PII) 

The Population Interaction Index (PII) is designed to provide a cardinal 

measure of the potential interaction between nearby urban-related population and agricultural production activities in each grid cell. This is a 5- kilometer resolution grid. See Appendix B and http://www.ers.usda.gov/Data/

PopulationInteractionZones/accessdata.htm

Natural Amenities Scale

The ERS natural amenities scale is a measure of the physical characteristics 

of a county area that enhance the location as a place to live. The scale was 

constructed by combining six measures of climate, topography, and water 

area that refl ect environmental qualities most people prefer. These measures 
46

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

are warm winter, winter sun, temperate summer, low summer humidity, 

topographic variation, and water area. The data are available for counties in 

the lower 48 States. The fi le contains the original measures and standardized scores for each county, as well as the amenities scale. This county-level 

measure was converted to a 1-mile resolution grid. See http://www.ers.usda.

gov/Data/NaturalAmenities/
47

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

Appendix B. Alternative Measures 

of Urban Infl uence

Population Interaction Index (PII) 

The fi rst alternative measure of infl uence is the population interaction index 

(fi g. B1). The population interaction index ranks each location or area on a 

continuous scale in terms of the potential interaction between nearby urbanrelated population and agricultural production activities. The index is created 

for a series of 5-kilometer grid cells laid out across the contiguous 48 States. 

The index for a single location takes the form:

 1

N j

i

j ij

P

PII = D = å

where Pj is the population of cell j, and Dij is the distance between cell i and 

cell j. The measure is aggregated across N grid cells within a 50-mile radius 

of cell i. The index increases as population increases and/or as distance to the 

population decreases.

PII therefore provides a measure of urban infl uence that accounts for differences in population size of urban centers, as well as the potential for multiple 

nearby urban centers. Figure B1 demonstrates that the average value per acre 

for farmland declines as urban infl uence decreases. The decline is greatest 

at the highest levels of PII or the greatest amount of urban/rural interaction. 

At the greatest level of population interaction, the average value per acre is 

$9,515; at the lowest level, the value is $2,252.

Population-Interaction Zones for Agriculture (PIZA)

The population-interaction zones for agriculture (PIZA) identify agricultural 

land use in which urban-related residential, commercial, or industrial activities affect the economic and social environment of agriculture. Building on 

the PII grid cell data, PIZA classify grid cells into either rural or population 

interaction zones based on threshold values. The rural zone includes populaFigure B-1

Farmland value per acre by PII 

$1,000 per acre

100+ 75 50 25 < 25

0

2

4

6

8

10

12

PII

Sources: USDA, ERS analysis of National Agricultural Statistics Service June Area Survey

data and PIZA data.
48

Trends in U.S. Farmland Values and Ownership / EIB-92 

Economic Research Service/USDA

tion that supports active commercial farming, including related input and 

output industries. The threshold values vary for each of the 20 USDA Land 

Resource Regions to account for differences in farmland productivity. Grid 

cells initially classifi ed into the population-interaction zone were further classifi ed into one of three categories representing increasingly higher levels of 

population interaction.

PIZA classifi es each grid cell into one of four categories:

• 1 = rural

• 2 = low population interaction

• 3 = medium population interaction

• 4 = high population interaction.

The fi gure below depicts the average value per acre for all agricultural lands 

by PIZA classifi cation. Similar to the previous measures of urban infl uence, 

the value per acre is highest in areas with the greatest degree of population 

interaction and declines as population interaction decreases. For areas with 

high population interaction, the mean farmland value per acre is $16,041, 

versus $3,408 in rural areas.

Figure B-2

Farmland value per acre by PIZA 

$1,000 per acre

Sources: USDA, ERS analysis of National Agricultural Statistics Service June Area Survey

data and PIZA data.

PIZA

4 3 2 1

0

5

10

15

20