Introduction

From 1990 to 2000 the Boise, Idaho, metropolitan area consisting of Ada and Canyon

counties (Figure 1), was the seventh fastest growing metropolitan area in the nation and

the fastest growing such city in the Pacific Northwest. During the 1990s, the Boise

metro area population increased by 46.1 percent or 136,494 residents (from 295,851 to

432,345) (U.S. Census Bureau, 2001). The growth continues. The 2003 estimated

population of the Boise metro area was 476,659 (U.S. Census Bureau, 2005).

The rapid and substantial development that accompanies urban growth and the effects of

such development on farmland values are of particular concern to many residents,

appraisers, and policy makers in the Boise area. The Ada and Canyon counties have

highly diversified crop production on approximately 227,000 acres of the most

productive irrigated cropland in Idaho. Only two counties in Idaho have higher cropland

receipts per acre than Ada County and Canyon County.

2007 JOURNAL OF THE A|S|F|M|R|A

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Abstract

In areas where urban development is threatening quality farmland, zoning policies that encourage development on low

productivity land have a significant influence on market forces, resulting in upward pressure on the prices of low productivity

farmland. This helps identify potential permanent changes of use to a different highest and best use. Identifying the highest

and best use of land is fundamental in estimating a vacant land value and identifying comparable sales of vacant land.

Beasley is a former Graduate Research Assistant; Nelson is a Professor; Stroschein is an instructor of a farmland appraisal

course; and Hamilton is a Professor Emeritus, all in the Department of Agricultural Economics and Rural Sociology at the

University of Idaho. Stroschein is also a MAI Certified General Appraiser in Moscow, Idaho. On December 8, 2006, University

of Idaho Agricultural Economics and Rural Sociology lost a great friend and Colleague Dr. James R. Nelson. This article is

published posthumously and in memory of his dedicated work in rural land use change and valuation modeling.

These are Boise County, which has only about 1,900 acres of cropland, most of which is in nursery production; and Clark

County, where almost all of the 31,000 acres of cropland are in high value potato production (U.S. Department of Agriculture,

2004).

Knowledge of the effects of growth and development on farmland values around Boise, Idaho can also be insightful for

persons interested in land values and land use in other developing areas. Land owners, appraisers, and decision makers

in many areas of urban growth are concerned about such issues as land values, land development, land use planning, open

space preservation, and economic base lost as the growth of the metro area results in conversion of high quality agricultural

land to residential and commercial development. To effectively address such concerns, they need information about what

farmlands are most in demand for development, and why. Since development pressure on farmland is an economic force that

exerts upward pressure on land prices, a reasonable method for evaluating such pressure is to examine farmland prices

using analytical methods that decompose that value into quantifiable components related to both agricultural value and

development value. Once the value of the farmland is established, it can be compared to a value generated from alternative

uses. These are mechanisms real estate appraisers can utilize to analyze the market forces in concluding and empirically

supporting highest and best use conclusions.

Research Objectives

The overall objective of the research reported in this paper was to identify and evaluate factors that affect farmland values in

Ada and Canyon counties in Idaho with the intent that such information will help appraisers identify potential permanent

changes in highest and best use of land in developing areas, and will help policy makers better understand how to develop

and direct land use policy. Also, study results will hopefully provide other interested individuals with better knowledge and

understanding about the dynamics of land values. Specific objectives of this research were to identify land attributes that

affect farmland values and to interpret information about these attributes. This will provide information to interested

stakeholders about how land values are affected by relative levels of development pressure and other factors.

Land Value Models

Highest and best use analysis as conducted by appraisers involves four implicit criteria: 1) Physically possible; 2) Legally

permissible; 3) Financially feasible; and 4) Maximally productive. After considering the applications of what is

physically possible and legally permissible (or the reasonable probability of potential zoning changes), financially feasible

and maximally productive have traditionally been tested with economic models such as the discounted cash flow model. In

theory, the “proper” value of land is the present value of cash flow of the parcel’s future income stream (Elad, et al., 1994).

However, application of the discounted cash flow model to explain the present value of land which is likely to have future

changes can be quite problematic.

Another option for identifying those uses which will result in the highest land value, (short of completing an in depth

fundamental market analysis) is based on the assumption that the value of a differentiated good (such as land) can be

identified by a set of attributes. The value of the good is assumed to be the aggregation of the values of its individual

attributes. For farmland, individual attributes include agricultural productivity whereas development land value would

include proximity to employment centers, schools, and community services, etc. Modeling farmland value in this way

is consistent with the concept of decomposition of value into quantifiable components, including development pressure.

Models of this sort lend themselves well to estimation using regression techniques. They have been used by numerous

researchers including Bastian, et al. (2002); Torell and Bailey (2000); McLeod, et al. (1999); Vasquez, et al. (2002); Elad et

al. (1994); and others to explain values of different types of land assets with different types of economically valued attributes.

Data

The authors used Farm Credit Services data on 151 farm sales of irrigated cropland in Ada and Canyon counties that

transacted from 1994 through 2002. These data included, for each parcel, sale price (total dollars per parcel), acres, year of

sale, and township, range, and section. Sale price data were modified to form the adjusted sale price variable, calculated as

the sale price minus the value of any improvements. The value of the improvements was estimated by the Farm Credit

appraiser who analyzed the sale and is considered to be credible on that merit. Similarly, acres data were adjusted

downward by road and waste acreage to form the variable adjusted acres. Adjusted sale price, as defined above, was

designated as the dependent variable in this study. With the help of geographical information system specialists at

both the University of Idaho and the Idaho state office of the U.S. Department of Agriculture (2005), Natural Resources

Conservation Service (NRCS), more data were gathered. These included distances of parcels from cities greater than 10,000

population, presence of water bodies on or adjoining tracts, average slopes and elevations of sections containing data

parcels, and estimated productivity capabilities of soils present in tracts. Soil capability classes developed by NRCS

indicate the presence of soil limitations. The capability classes range from I-VIII, with one defined as land with slight

limitations and eight as land unsuitable for farming (Table 1). Since locations of parcels were known only by Government

Survey township, range and section identification, the percent of each soil capability class in the transacted parcel was

estimated as equal to the percent of each soil capability class in the section (defined by NRCS).

Whether the expected impacts of the independent variables on land values are positive or negative are specified in Table 2.

The expected impacts of most of the independent variables on the dependent variable (land value) were readily explainable

by economic theory, as follows:

• Parcels located further from cities were expected to be

worth less for development than parcels that are nearer to

cities.

• Lower numbered soil capability classes are more

productive for agriculture, thus were expected to be more

valuable than higher numbered classes.

• Since land prices tend to increase over time, and since the

base year for analysis was 1997, it was expected that

parcels sold before 1997 would be for less dollars than the

base year and parcels sold after 1997 would be for more.

Departures from the 1997 base year were modeled with

dummy variables.

However, for three of the independent variables considered

(elevation, slope, and rivers-lakes) economic theory does not

clearly suggest whether expected impacts on farmland values

should be positive or negative:

• Elevation tends to shorten growing seasons (negative

impact on agricultural value), but makes for desirable

views (positive impact on development value).

• Slope tends to make a parcel more difficult and costly to

farm (negative impact on agricultural value), but it is

usually considered to be esthetically interesting (positive

impact on development value, but still requires costly

excavation).

• Rivers and lakes on or adjoining a parcel add aesthetic

value (positive impact on development value), but “break

up” land, making it more difficult to farm (negative impact

on agricultural value).

Analysis and Results

An ordinary least squares (OLS) regression model was utilized to determine the influence of the independent variables on the

dependent variable. Model results indicated that the elevation, slope, and rivers-lakes variables were not significant. So,

since their theoretical rationales for inclusion were ambiguous (as mentioned above), they were dropped from the model.

Also, the year of sale dummy variables, except for the year 2001, were dropped from the model because they were not

significant. Their lack of significance suggests that land value inflation was minimal in the data analyzed.

Technical statistical assumptions were checked, and it was determined that the model should be converted to a weighted

least squares (WLS) model.1 This change does not affect any of the results mentioned above. Results of the WLS model are

shown in Table 3. The regression variables explain 62 percent of the price variation of farmland parcels in Ada and Canyon

counties. The independent variables in the model all have the expected signs. The only development pressure variable that is

significant is the distance to a city with greater than 10,000 population. The coefficient for variable I-1 (the number of acres of

irrigated soil capability class I in a parcel) is the highest of the soil capability class coefficients. This is consistent with

theory, since capability class 1 indicates highest productivity. Coefficients for variables I-2 and I-3 (acres of irrigated soil

capability classes II and III, respectively, in a parcel) are not inconsistent with theory, since they are very nearly equal and

less than the