Ecological Economics 40 (2002) 337–349
Environmental amenities and agricultural land values: a
hedonic model using geographic information systems data
Chris T. Bastian, Donald M. McLeod *, Matthew J. Germino,
William A. Reiners, Benedict J. Blasko
Department of Agricultural and Applied Economics,Uni6ersity of Wyoming,P.O.Box
Received 15 March 2001; received in revised form 12 October 2001; accepted 13 November 2001
Remote agricultural lands, which include wildlife habitat, angling opportunities and scenic vistas, command higher
prices per hectare in Wyoming than those whose landscape is dominated by agricultural production. Geographic
information systems (GIS) data are used to measure recreational and scenic amenities associated with rural land. A
hedonic price model is speciﬁed with GIS measures. It is used to estimate the impact of amenity and agricultural
production land characteristics on price per acre for a sample of Wyoming agricultural parcels. Results indicate that
the speciﬁcation performed well across several functional forms. The sampled land prices are explained by the level
of both environmental amenities as well as production attributes. Statistically signiﬁcant amenity variables included
scenic view, elk habitat, sport ﬁshery productivity and distance to town. This analysis permits a better estimation of
environmental amenity values from hedonic techniques. Improved estimation of amenity values is vital for policies
aimed at open space preservation, using agricultural conservation easements and land use conﬂict resolution. © 2002
Elsevier Science B.V. All rights reserved.
Environmental amenities; Geographic information systems data; Hedonic; Rural; Agriculture; Land values
Agricultural land values can be estimated by
summing the discounted productive rents (for a
useful summary see Robison et al., 1985). This
approach may reﬂect soil quality, capital improve-
ments, water supply and location to markets.
Agricultural land provides land for current and
future development, recreation, access to public
lands, wildlife habitat, and open space. Land,
* Corresponding author. Tel.: +1-307-766-3116; fax: +1-
email@example.com (D.M. McLeod).
Senior Authorship is shared by the ﬁrst two authors.
Authors are agricultural marketing specialist and assistant
professor from the Department of Agricultural and Applied
Economics, University of Wyoming, Laramie, WY; PhD at
Land Resources Department, Montana State University,
Bozeman, MT; professor and former research assistant from
the Department of Botany, from the University of Wyoming,
0921-8009/02/$ - see front matter © 2002 Elsevier Science B.V. All rights reserved.
C.T.Bastian et al.
40 (2002) 337–349
following Xu et al. (1993), can be viewed as an
input to production; space for amenities (provi-
sion of public goods via place); ﬁxed and taxable
(provision of public goods via net revenue); and
as an asset (capital good). Sale price should be the
outcome of the total market value of a parcel,
given existing and efﬁcient markets.
The demand for productive capacity by agricul-
turalists drives in part the demand for agricultural
land. Rural land prices may also reﬂect house-
holds’demand for homes with rural amenities.
Rural amenities in the Rocky Mountain region
include abundant public lands, recreational op-
portunities, wildlife, and open spaces.
Agricultural land is being converted into non-
agricultural uses across the US (Vesterby et al.,
1994) and the Rocky Mountain region. Rocky
Mountain counties containing or bordering na-
tional forest wilderness areas experienced popula-
tion gains from 1970 to 1985 (Rudzitis and
Johansen, 1989). Population in western states
grew by 16.7% from 1990 through 2000 (Center of
the American West, n.d.). Population in Utah,
Idaho and Colorado grew by 29.6, 28.5, and
30.6%, respectively, in that period (Taylor, n.d.).
Population in western Wyoming counties grew by
10–63% during the same period with Teton
county experiencing the highest growth at 63.3%
(Taylor, n.d.). The population in the rural (unin-
corporated) areas accounted for 55.2% of the
population growth in Wyoming (Taylor, n.d.).
Growth affects agricultural land in terms of
aggregate producer output and income. It can
have an impact on production practices via nui-
sance regulations or land use laws. Growth also
may affect the viability of input suppliers.
Public goods associated with agricultural land
(wildlife, scenery, open space), and the economic
as well as ﬁscal base of rural counties, may be
affected by growth as well. Rural residential de-
velopment is a leading cause of rural land frag-
mentation. Fragmentation of forests, rangeland
and watersheds impacts wildlife habitat, water
quality, recreation opportunities, and viewsheds.
These potential impacts of growth, and the
diversity of beneﬁts associated with agricultural
lands, suggest that agricultural land may be de-
manded in various input markets by competing
market segments. It is important for landowners,
land demanders and land policy analysts to recog-
nize what factors drive land prices. These factors
may increase the number of rent generating activ-
ities on a given agricultural parcel, thereby im-
proving the viability of a given operation. Such
knowledge may provide insight into land charac-
teristics that prompt the conversion of agricul-
tural land to other uses (identiﬁcation of open
space and wildlife habitat at risk of being devel-
oped). Insights given in this paper may lead to
differential property taxation approaches and im-
proved valuation and appraisal processes as they
relate to land use policies. Geographic informa-
tion systems (GIS) permit a quantitative means of
afﬁxing land characteristics to their location. This
paper demonstrates how GIS data can be used to
assess marketable attributes of agricultural lands
Speciﬁcally, our research objectives are as
1. Estimate a hedonic model with land price as a
function of productive and amenity attributes.
2. Incorporate parcel-speciﬁc GIS derived mea-
sures of amenities in the model.
2. Theory of hedonic price valuation
The hedonic technique is based on the premise
that goods traded in the market are made up of
different bundles of attributes or characteristics.
Hedonic price models (HPM), including GIS de-
lineated variables, permit inferring the impact of
land attributes on land values.
Beneﬁts of a change in valued land attributes
may be measured from the underlying demand for
the characteristic, or characteristics, of interest
HPM are based on a differentiated product (Z
that can be represented by a vector of product
(land) attributes (see Rosen, 1974; Bartik, 1987;
Palmquist, 1991). The Z
vector for this analysis is
based on two sets of characteristics, agricultural
production attributes, z
, and amenities at-
. These characteristics are thought to
appeal to two rural land market segments: agri-
cultural producers and demanders of rural resi-
dences. The observed price for Z
in the market is
C.T.Bastian et al.
40 (2002) 337–349
deﬁned as a hedonic function of its characteristics
The marginal impact on-parcel price, P(Z
can be estimated from this function (1).
Equation 1 is a reduced form model indicating
the attributes of a parcel relevant to both buyers
and sellers of agricultural land. The demand for/
supply of agricultural land can be considered as a
factor demand/supply model associated with a
production function including agricultural out-
puts, with non-agricultural rent generating oppor-
tunities and with a demand function for
residential sites. Economic theory does not
provide much direction concerning the functional
form of the HPM. Several functional forms are
examined in this study. Model evaluation is based
on overall goodness-of-ﬁt and, more importantly,
hypothesis testing for attributes affecting parcel
3. Study area
Wyoming can be considered as a large, rural
and heterogeneous land market. It consists of
irrigated basins and forested range in the west as
well as desert and high plains in the central and
eastern part of the state. The state is divided into
two regions for this analysis. The regions roughly
follow the Bureau of Land Management (BLM)
Ecoregions as reported in USDA/USDI (1993).
Region 1 (BLM Ecoregion 7 and 8, south part)
includes counties in the western part of the state.
These counties are directly south and east of
Yellowstone and Teton National Parks. The
Snake, Bighorn, and Green River basins, known
for their blue ribbon trout ﬁsheries, are located in
these counties. Region 2 (BLM Ecoregion 8, cen-
tral part and Ecoregions 4 and 5) covers the
central and eastern part of Wyoming. Federal
lands are found in both regions. US Forest Ser-
vice lands dominate in Region 1 with BLM as the
major federal land agency elsewhere.
Wyoming agriculture primarily consists of live-
stock and forage production followed by grain,
dry bean and sugar beet production. Region 2 has
the most crop acres. It has the highest value crop
and livestock operations compared to the remain-
der of the state. It is the dominant agricultural
region of the two as measured by agricultural
receipts (Wyoming Agricultural Statistics Service,
Population change on unincorporated lands has
not occurred uniformly across the state. Counties
with more public lands tended to grow the most.
The western portion of the state grew faster than
the remainder from 1990 through 2000 (US De-
partment of Commerce, Bureau of Census, 1996;
Taylor, n.d.). Generally, in-migrants to western
wilderness counties tend to be better educated,
have professional occupations, have higher in-
comes, are younger and have lived previously in
more populated places than area residents (Rudzi-
tis and Johansen, 1989). These immigrants seek
amenities in terms of improved climate, recre-
ation, scenery, and environmental quality (Rudzi-
tis and Johansen, 1989; for a Wyoming case see
McLeod et al., 1998). They are in a favorable
position to out compete agriculturalists for rural
land. This is demonstrated by ranchettes, small
acreage residential fragments of former ranches,
found all over the western US and particularly in
the Rocky Mountain region (Long, 1996; Reib-
4. Selective review of land value models
The literature examined reveals various compo-
nents of hedonic models regarding land values.
Agricultural land values are related to such at-
tributes as productivity, distance to markets, and
improvements (Xu et al., 1993, 1994; Torell and
Doll, 1991). Urban and rural amenity attributes
are analyzed in several articles. Few to date have
incorporated the spatial speciﬁcity afforded by
GIS measurement (see Kennedy et al., 1996; Ge-
oghegan et al., 1997).
Garrod and Willis (1992) examined neighbor-
hood or environmental characteristics of coun-
tryside parcels in the UK using a hedonic price
model. Measured attributes were compared to
perceived attributes. The view (of woodlands, for
example), as well as the presence of water, was
C.T.Bastian et al.
40 (2002) 337–349
important. McLeod (1982) used a bid-price ap-
proach to determine marginal willingness-to-pay
for urban residential properties in Perth, Aus-
tralia. River view, in addition to water and park
access, was important.
Spahr and Sunderman (1995) used Wyoming
ranchland sales data to model the contribution of
scenic and recreational quality to agricultural land
price. Low, medium and high quality amenity
levels, based on the judgment of area appraisers,
were represented by indicator variables in their
statistical model. These variables were statistically
signiﬁcant with high scenic quality contributing to
higher sale price. Spahr and Sunderman (1998)
examined agricultural land prices in the west us-
ing a hedonic approach. They found that uniform
taxation of agricultural lands based on productive
capacity encouraged speculation. Taxes paid on
non-scenic parcels subsidized scenic parcels, in
that overall market values diverged based on the
presence of valuable non-agricultural attributes.
The scenic value variables were indicator variables
multiplied by the deeded acres across little, good,
or great scenic levels, which were determined by
area appraisers. Scenery was signiﬁcant in ex-
plaining land values. GIS was not employed in
either study to specify or quantify the scenic land
attributes of individual parcels.
Bockstael (1996) estimated a hedonic model in
order to predict probabilities associated with con-
verting undeveloped land to developed lands. Im-
portant variables included lot size, public services,
zoning, proximity to population centers and vari-
ables associated with the percent of agricultural
use, forest lands and open space in the Patuxent
watershed. Bockstael’s (1996) model contributes
to understanding land use behavior and parcel
A hedonic rural land study using GIS was
provided by Kennedy et al. (1996). The analysis
identiﬁed rural land markets in Louisiana based
on economic, topographic and spatial variables.
GIS was used for deﬁning distance to market as
well as soil type variables.
Geoghegan et al. (1997) developed GIS data for
two landscape indices and incorporated them in a
hedonic model for Washington, DC, suburban
properties. Their measure of fragmentation is
deﬁned as perimeter to size ratio. The landcover
measure is an index of land use type that is a
surrogate for ﬂora and fauna habitat. They
provide an insightful array of landscape indices.
The approach provided in this paper expands
the above-mentioned research. GIS is a valuable
data source for HPM. Parcel-speciﬁc spatially
deﬁned attributes are quantiﬁed and modeled as
determinants of land values. The location and
amount of a particular amenity affords greater
validity in statistical estimation and potentially
more accurate estimation of attribute values
derived from hedonic models.
Data for this analysis came from appraisal data
sheets for transacted land sales during 1989
through 1995 used by Bastian et al. (1994) and
Bastian and Hewlett (1997) to assess Wyoming’s
agricultural land market. Approximately 1200
sales were screened for complete legal descriptions
and data that allowed accurate measurement of
GIS and agricultural variables hypothesized as
important. A random sample was then drawn
from these screened sales for each region, with
approximately equal observations from each,
given project budget constraints.
Past research indicates agricultural land values
are a function of both agricultural and amenity
variables given various demands for rural lands.
The variables used in estimating the HPM are
summarized in Table 1. The dependent variable of
the model (CDACRE) is the nominal price per
acre. CDACRE in nominal terms is negatively
correlated with the GNP implicit price deﬂator.
Cattle prices rose through 1993 and then fell
thereafter. Bastian and Hewlett (1997) found that
average agricultural land prices in Wyoming rose
and fell in a similar fashion. This was particularly
true for larger parcels. Thus, CDACRE was not
deﬂated based on concerns this would introduce
spurious measurement error into the dependent
variable. The use of nominal sale price follows
hedonic estimation in Spahr and Sunderman
(1995, 1998) and Xu et al. (1993, 1994). TREND
is included to account for price movements over
C.T.Bastian et al.
40 (2002) 337–349
Variable identiﬁcation, description and hypothesized sign
HypothesizedVariable Variable description/deﬁnition
CDACRE (dependent) Total ranch price in dollars divided by deeded acres (average per acre price by parcel).
Productivity rating of all pasture and meadow lands on-parcel, measured in AUMs per acre.PositivePASTMEDW
Productivity rating of all irrigated lands on-parcel (both sprinkler and gravity irrigated),PositiveIRIGAUAC
measured in AUMs per acre.
TOTAUMS Total carrying capacity of property, including deeded acres and assured leases, measured inNegative
TOTAMSQ Negative TOTAUMS squared.
Percentage of total AUMs of carrying capacity coming from railroad leases.PositiveRRUAPER
Indeterminate Total AUMs coming from BLM or state range which are an assured lease with the sale ofPUBAUPER
the property divided by total AUMs and multiplied by 100. Note: This percentage can be
more than 100.
Simpson’s diversity index (0–1) multiplied by 100. This number can then range between 0PositiveSIMPINDX
STRMAC Meters of stream on the property divided by deeded acres.N/A
Fish productivity average index on the property. The index comes from a Wyoming GameN/AFISHPROD
and Fish coverage.
FISHVALU STRMAC multiplied by FISHPROD, providing a measure of ﬁshing density per acre.Positive
Total dollar of improvements divided by deeded acres.PositiveIMPDOLAC
PositiveELKACPER Acres of spring–summer–fall and winter yearlong elk habitat divided by deeded acres.
Distance from edge of property to nearest incorporated town of 2000 inhabitants by road.NegativeTOWND
PositiveTREND Trend variable for years in sample of 1989–1995.
N/AREGN 0 or 1 indicator variable for two regions in state, 1 being high amenity Western region of
state, 0 being rest of state.
REGSIMP Interaction variable, SIMPINDX*REGN.Positive
REGFISH Interaction variable, FISHVALU*REGN.Positive
Interaction variable, ELKACPER*REGN.PositiveREGELKPR
The measures of agricultural productivity (see
Table 1) used are meant to address scaling issues
associated with parcel size (see Parsons, 1990).
Those agricultural production attributes hypothe-
sized to affect price are PASTMEDW (pasture and
sub-irrigated meadowlands); IRIGAUAC (irri-
gated croplands); TOTAUMS (operation size);
IMPDOLAC (on-parcel improvements); and leased
land (RRUAPER and PUBAUMS). The former is
railroad land interspersed with private, state, and
federal lands. The average value of railroad grazing
leases in Wyoming tends to be greater than those
found on public lands from 1990–1995 (Bastian et
al., 1994; Bastian and Hewlett, 1997).
Important amenity components include con-
sumptive and non-consumptive values of terrestrial
wildlife, availability of water-related recreation and
water quality indicated by trout habitat, accessibility
and scenic amenities (see Appendix A for GIS
protocols developed to quantify variables in these
categories). Accessibility to towns is important in
that it provides cultural and shopping opportunities
to rural residents. The town population threshold
of 2000 is chosen due to size thresholds that are
related to the presence of various retail trade and
service opportunities in Wyoming (Taylor and Held,
1998). Shonkwiler and Reynolds (1986) support the
inclusionofsuch a variable asaproxy for unspeciﬁed,
unknown non-agricultural amenities and services.
View characteristics examined in this analysis
include total view, relief, diversity and the amount
of edge between land cover classes (Germino et
al., 2001). These view variables include those that
were related to view cognition and preference
(Gobster and Chenoweth, 1989; Kaplan et al.,
1989; Steinitz, 1990; Baldwin et al., 1996; Ham-
mitt et al., 1994; Bishop, 1996) and that could be
calculated with the available data. Land cover
diversity, as measured by Simpson’s Index (Bar-
C.T.Bastian et al.
40 (2002) 337–349
Means, minimums and maximums for dollars per acre for agricultural land and agricultural production and GIS amenity variables
in Wyoming (N=138)
Variables Mean SD Minimum Maximum
CDACRE 2602.230430.655 442.630 28.538
1.271 1.465PASTMEDW 0.000 6.000
12480.000TOTAUMS 1445.347 96.0001990.008
33.753RRAUPER 0.317 0.0002.991
PUBAUPER 6.388 15.776 83.0340.000
45.430 3.07711.966 61.857SIMPINDX
REGSIMP 22.830 61.85726.292 0.000
5.1151.604 0.000REGFISH 43.839
13.352 31.504ELKACPER 0.000 100.000
REGELKPR 10.047 28.224 0.000 100.000
7.000TREND 4.920 1.529 1.000
bour et al., 1980), is used as a proxy to indicate
the view composition. The view composition,
rather than types of species, is used in the calcula-
tion for this analysis (see Geoghegan et al., 1997,
for a nice description of alternative landscape
composition indices). The index is calculated as
where Dis the diversity index ranging from 0 to 1
(0 being no diversity and 1 being maximum diver-
sity), lis land coverage type, and p
is the propor-
tion of view area occupied by each land type,
which can be seen from the centroid of the parcel.
Speciﬁcally, the amenity variables are elk (an
important big game species in Wyoming) habitat
for each land sale (ELKACPER);
urban goods and services
access (TOWND); and view composition
(SIMPINDX). Interaction terms are constructed
for view as well as for trout and elk habitat
(REGSIMP, REGFISH and REGELKPR).
These are the product of an indicator variable for
region 1 multiplied by the respective amenity
Descriptive statistics of these variables are pro-
vided in Table 2. All of the variables described are
thought to enhance the (per acre) sale price and
yield a positive coefﬁcient, except TOTAUMS
(parcel size). Per acre sale price is thought to
decrease with size and thus lead to a negative
Estimated expenditures for all elk hunters in Wyoming
grew by 22.6% from 1990 through 1995, with 1995 hunter
expenditures exceeding $29 million (Wyoming Game and Fish
Department, 1996). Total expenditures on elk hunting exceed
expenditures for hunting any other species in the state (Wyo-
ming Game and Fish Department, 1996).
Spring–Summer–Fall and Winter–Year-long habitat des-
ignations were chosen as they were thought to maximize
hunting and/or viewing opportunities (Lutz, 1998; Wyoming
Game and Fish Department, 1998).
Estimated expenditures on sport ﬁshing in Wyoming grew
by 11.7% from 1990 through 1995 with 1995 angler expendi-
tures being over $225 million (Wyoming Game and Fish
Department, 1996). Additional evidence on the importance of
trout ﬁshing in the Rocky Mountain region includes willing-
ness-to-pay estimates by Dalton et al. (1998) as well as
Dufﬁeld and Allen (1988), indicating the value of regional
C.T.Bastian et al.
40 (2002) 337–349
Coefﬁcient estimates and likelihood ratio tests for model speciﬁcations (N=138)
Linear coefﬁcientVariable Quadratic coefﬁcientSemi-log coefﬁcient
−358.176 (0.004)3.534 (0.000)Intercept −360.952 (0.004)*
PASTMEDW 64.907 (0.009) 0.246 (0.000) 64.665 (0.010)
IRIGAUAC 40.018 (0.000) 0.090 (0.000) 40.761 (0.001)
TOTAUMS −0.048 (0.236)−0.038 (0.000) −0.7×10
0.014 (0.159) 13.241 (0.000)13.012 (0.000)RRAUPER
−1.438 (0.342)−0.005 (0.263)PUBAUPER −1.549 (0.260)
5.527 (0.000) 0.021 (0.000) 5.600 (0.000)SIMPINDX
4.263 (0.043) 4.334 (0.050)REGSIMP 0.005 (0.237)
4.418 (0.321)0.025 (0.071)FISHVALU 4.057 (0.327)
REGFISH 28.456 (0.010) 27.981 (0.014)0.014 (0.374)
46.936 (0.010) 0.071 (0.067)TREND 46.722 (0.010)
0.707 (0.033)0.002 (0.000)IMPDOLAC 0.707 (0.035)
0.751 (0.102)ELKACPER 0.005 (0.001) 0.722 (0.116)
REGELKPR −2.721 (0.016)−0.005 (0.079)−2.780 (0.013)
1.847 (0.070)TOWND 0.005 (0.024) 1.852 (0.068)
0.559 0.573 0.556
154.224** (0.001)Breusch–Pagan X
22.733** (0.100) 154.397** (0.001)
127.916 (0.000)127.831 (0.000) 1816.557 (0.000)Likelihood ratio
The Breusch–Pagan chi-square test indicates the presence of heteroscedasticity prior to any adjustment. The values below the
chi-square indicate the associated tvalues.
*Values in parentheses below coefﬁcient estimates are Pvalues on the tstatistics. **Results adjusted using White’s consistent
covariance estimator (1980). OLS results are given, but with revised, robust covariance matrix (Greene, 1998). NOTE: The largest
condition index does not exceed 21 for any variable regardless of functional form.
6. Estimation diagnostics issues
Several functional forms are estimated (Table
The general model is thought to be well
speciﬁed, as evidenced by both the robustness of
the arguments and the highly signiﬁcant good-
ness-of-ﬁt measures across the functional forms.
A simple linear model and a semi-log model
(CDACRE logged) are equally preferred based on
the goodness-of-ﬁt measures, on the signiﬁcance
of the land attributes in explaining per acre parcel
price, and on the ease of interpretation. The
quadratic speciﬁcation is not deemed adequate
since the chief modiﬁcations to the base model,
size (TOTAUM) and size squared (TOTAMSQ),
A Box–Cox transformation of the model was
considered (Box and Cox, 1964), but not reported
due to the fact that most of the explanatory
variables in the model can take on zero values and
cannot be transformed (Greene, 1993).
over, logarithmic transformations of the indepen-
dent variables were not pursued due to the
potential for zero values.
Diagnostics indicate no signiﬁcant multi-
collinearity problems with the two preferred mod-
els. A condition index score was estimated for the
variables in the OLS estimated models as pre-
Cassel and Mendelsohn (1985) indicate the potential for
misleading and non-interpretable estimates when many of the
independent variables can take on zero values. Cropper et al.
(1988) ﬁnd that simple linear models perform well compared
to other functional forms, based on Monte Carlo simulations.
Milon et al. (1984) used a Box–Cox transformation to test
different functional forms of hedonic models explaining land
values in Florida and found that the linear and semi-log were
among the best speciﬁcations out of seven estimated functional
forms across three different locations.
Nonlinear forms of the explanatory variables ELKACPER
and TOWND are tried but do not improve the explanatory
power of the models as indicated by reduced goodness-of-ﬁt
compared to the reported models. Those results are not re-
ported here in the interest of brevity.
C.T.Bastian et al.
40 (2002) 337–349
scribed by Belsley et al. (1980).
and Gujarati (1995) indicate serious multi-
collinearity problems likely exist with condition
index scores over 30. All condition index scores
were less than 21. Moreover, variance inﬂation
factors were estimated for the models reported.
Only one value was greater than 10 (10.87), which
is the rule of thumb threshold recommended by
Gujarati (1995). A correlation analysis of the
independent variables indicated only one pair of
variables had a higher coefﬁcient than 0.55 (0.88).
All of these diagnostics point toward multi-
collinearity not being a signiﬁcant problem in
The model is comprised of cross sectional data
wherein the dependent variable varies greatly rela-
tive to the independent arguments. The chosen
units of the independent variables are intended to
reduce the potential effects of heteroscedasticity
thought, a priori, to be present in this data set.
Greene (1993) indicates that heteroscedasticity
arises primarily in the analysis of cross sectional
data. No a priori form of the heteroscedasticity is
hypothesized based on examination of the data.
The Breusch–Pagan statistic from White’s test
shows the presence of heteroscedasticity (1980).
White’s consistent estimator of the covariance
matrix is employed to provide consistent estimates
of the covariance matrix of the estimated i’s
The covariance estimator pro-
vides consistent outcomes in testing linear hy-
potheses of the estimated coefﬁcients. This is
critical since the two preferred models are linear
in nature and hypothesis tests regarding the sig-
niﬁcance of the parameter estimates is the primary
concern of this research.
HPM analyses are inherently spatial. Spatial
correlation is an efﬁciency issue in estimation that
may lead to incorrect test of hypotheses results. It
occurs due to omitted unobserved land character-
istics that are spatially correlated both to the
dependent variables and to each other (see Bock-
stael and Bell, 1998). Statistical tests and remedies
can be obtained (see Anselin, 1992, for a descrip-
). See also Anselin (1988, 1990),
Anselin and Hudak (1992) as well as Getis and
Ord (1992) for a more thorough discussion of
these diagnostic and estimation techniques.
Lagrange Multiplier tests for spatial error and
spatial lag dependence are performed for the sim-
ple linear model. A Lagrange Multiplier test is
used to test the null-hypothesis of no spatial
correlation. Neither spatial dependence nor spa-
tial error correlation is present when using a 400
mile distance band for the spatial weight matrix
=0.5294; prob.=0.4511; LM
prob.=0.4668). This distance describes a large
but heterogenous Wyoming agricultural land
The data exhibit heteroscedasticity and may
have spatial correlation problems when consider-
ing distance bands of less than 400-miles. How-
ever, the robustness of traditional diagnostics for
spatial correlation in the presence of heteroscedas-
ticity is questionable (Anselin and Rey, 1991). A
review of the literature indicates a lack of a joint
remedy for these conditions when the nature of
the heteroscedasticity is not known. When maxi-
mum likelihood estimation procedures were used
, Manual, Anselin (1995)) to ad-
dress potential spatial correlation, diagnostics in-
dicated signiﬁcant heteroscedasticity with
Breusch–Pagan statistics in excess of 100. It was
deemed that the heteroscedasticity was a more
serious problem than potential spatial
The condition number is estimated as the square root of
division of the maximum characteristic root by the minimum
characteristic root (Belsley et al., 1980). See Greene (1993) for
additional discussion on the estimation of this diagnostic.
The covariance matrix for bis |
and White’s consistent estimator is (X%X)
.‘‘LIMDEP produces this estimator as part of the
REGRESS procedure…’’ (Greene, 1998, pp. 291).
Transformations such as logarithms for the independent
variables were not used given the large incidence of variables
which could take on zero values.
Future research needs to resolve problems common to
most HPM estimation using spatially related data, such as
multicollinearity, heteroscedasticity and or spatial correlation
issues. The techniques currently available to deal with het-
eroscedasticity and spatial correlation jointly are limited to
techniques assuming a priori knowledge of the nature of the
non-constant variance of the errors.
C.T.Bastian et al.
40 (2002) 337–349
7. Empirical results
The following results are reported for the sim-
ple linear and semi-log estimations, respectively.
The signs on the signiﬁcant GIS constructed
amenity variables meet a priori expectations
across the two preferred models, save
REGELKPR (Table 3). REGELKPR has a nega-
tive coefﬁcient indicating decrease in western Wy-
oming ranch or farmland value due to the
presence of elk habitat. Possible rents extracted
from fee hunting for elk in western Wyoming are
diminished due both to elk occupying public land
during the hunting season and the large amount
of public land there (81% of region 1). Elk are a
source of property damage both to fences and hay
stacks; they may be viewed as a nuisance (Van
Tassell et al., 2000). Elk habitat (ELKACPER)
state-wide is positively related to sale price. Elk
state-wide offers rent seeking opportunities for
rural landowners due both to scarcity and tres-
pass. The central and eastern parts of the state
have less elk and public land (proportionately
more privately controlled land).
The level of view and trout habitat variables
help explain parcel price. Variables signifying
trout habitat for the simple linear estimation are
signiﬁcant in the western part of Wyoming (REG-
FISH) at h=0.01 level but not state-wide (FISH-
VALU). The latter outcomes indicate the regional
prominence of the afore-mentioned trout streams
in comparison to the balance of the state. The
most interesting result of the amenity variables is
the signiﬁcant and positive coefﬁcients of scenic
amenities state-wide (SIMPINDX) at h=0.01
level and in region 1 (REGSIMP) at h=0.000
level for the simple linear model. The results
indicate that view diversity, rather than unifor-
mity, is more highly valued. A diverse view com-
position is indicative of nearby diversity of
landscapes, landforms and associated wildlife
habitat. The view diversity could be valued by the
current owner as well as potentially bearing future
gains should the land be developed residentially.
The regional variables for view (REGSIMP) and
ﬁsh habitat (REGFISH) drop out of the semi-log
estimation. It is thought that the compressed scale
of the dependent variable is not sensitive to re-
gional variation as in the simple linear model.
State-wide ﬁsh habitat is signiﬁcant in the semi-
log but not so in the simple linear form. These
outcomes coincide with both Spahr and Sunder-
man’s (1995) as well as Spahr and Sunderman’s
Distance to social/urban amenities (TOWND)
is signiﬁcant in both models. It indicates that the
more distant and rural the agricultural property,
the higher the per acre price. This supports the
potential demand by agricultural interests due to
less urban-originated nuisance claims arising from
agricultural practices. The possible demand by
amenity seekers who enjoy untrammeled trout
streams, elk habitat, and scenic views also is
The signs on the signiﬁcant agricultural produc-
tion variables meet a priori expectations. The
agricultural production variables associated with
grazing (PASTMEDW) and irrigated crop pro-
duction (IRIGAUAC) are signiﬁcant for both
models. Capital improvements (IMPDOLAC)
also are signiﬁcant for both models in explaining
sale price. RRAUPER is positive and signiﬁcant
in the simple linear model, indicating the impor-
tance of secure (private) grazing leases. RRAU-
PER is not signiﬁcant in the semi-log model,
possibly due to the insensitivity of this functional
form to the few railroad leases. Ranch or farm
size (TOTAUMS) has a negative coefﬁcient and is
signiﬁcant in both estimations. The sign indicates
the diminishing marginal value (measured on a
per acre basis) associated with increasing size.
These ﬁndings are consistent with Torell and Doll
(1991) and Xu et al. (1994).
While the variable associated with public forage
(PUBAUPER) is not signiﬁcant, it is interesting
to note that the sign is negative. This result is
compatible with previous research. Torell and
Fowler (1986) found that proposals for increasing
grazing fees on federal lands and actual increased
grazing fees on New Mexico State trust lands lead
to a substantial percentage decline in ranch values
for ranches highly dependent upon public land
forage. Bastian and Hewlett (1997) concluded that
as the public-originated percentage of total forage
for a ranch increased beyond 24% the price per
animal unit declined for ranchlands sold during
1993 through 1995.
C.T.Bastian et al.
40 (2002) 337–349
Non-agricultural interests (amenity seekers)
would not be expected to associate value with
federal grazing leases. Subleasing of federal allot-
ments is generally prohibited and opportunities to
secure resale or rents by non-grazing interests are
TREND is positive and signiﬁcant. Wyoming
ranch and particularly farmland prices rose dur-
ing the study time period (Bastian et al., 1994;
Bastian and Hewlett, 1997).
The demand for amenities such as outdoor
recreation, scenery and open space is expected to
grow as population migration to less urban areas
continues. These pressures will increase the com-
petition for agricultural lands. Results of this
study indicate that remote agricultural lands,
which include wildlife habitat, angling opportuni-
ties and scenic vistas, command higher prices per
acre than those which primarily possess agricul-
tural production capacity. Amenity rich lands
may be at risk for conversion from agricultural
and open space function to residential use.
The contribution of this study is to utilize esti-
mated variables derived from GIS measures, the
values of which are uniquely speciﬁc to individual
land parcels. The GIS variables provide a means
to quantify amenity attributes and the opportu-
nity to include them in a hedonic price model.
The results point to an improved hedonic price
model speciﬁcation for agricultural lands, particu-
larly for the Rocky Mountain and Great Basin
regions. The GIS data development provides
more explicit variables and model speciﬁcations
than qualitative representations such as ordinal
ranking of land attribute levels or indicator vari-
ables signaling the presence of amenities. Estima-
tion of hedonic models using such techniques
stand to provide more accurate value estimates of
environmental amenities. This is important base-
line information for policies intended to preserve
environmental amenities, improve valuation of
agricultural conservation easements, and reduce
land use conﬂict resolutions.
The authors acknowledge the support from
USDA-NRI Rural Development (Grant c97-
35401-4347) for research funding, Farm Credit
Services of Nebraska and Wyoming as well as the
Wyoming Farm Loan Board for sales data, and
the Departments of Agricultural and Applied
Economics as well as Botany, both at the Univer-
sity of Wyoming, for general support. The au-
thors acknowledge the helpful comments from
Allen Torrell, Nancy Bockstael, Dale Menkhaus,
Ellen Axtemann and participants in the W-133
meetings held February 28–March 1, 2000.
Distance to Town
All parcels were ﬁrst digitized. The centroid
label of each parcel was calculated using the Arc
centroid label command. The centroid labels were
then copied into a point coverage that was used to
calculate distance from the centroid to the nearest
town with a population]2000.
The shortest path (along existing roads) to the
closest town center with a population]2000 was
calculated in the following steps. Three coverages
were required which include the parcel point cov-
erage; a road coverage derived from the TIGER
state-wide road coverage that has interstate high-
ways, state highways, connecting or county roads
and neighborhood roads (Spatial Data and Visu-
alization Center, 1997); and a coverage, derived
from the state-wide CITY coverage (Spatial Data
and Visualization Center, 1996a), for towns with
1. Distance from the centroid of the largest
polygon in a parcel to the closest node in a
road coverage was ﬁrst calculated using the
Arc command NEAR.
2. The city with a population ]2000 nearest (as
the crow ﬂies) to each parcel centroid label
was located and the distance from the city
label to the node nearest the city in the road
coverage was calculated using the Arc com-
C.T.Bastian et al.
40 (2002) 337–349
3. The Arcplot command, PATH, was used to
calculate the shortest path (along roads in the
road coverage) from the node closest to each
parcel centroid to the node closest to the
nearest city with a population]2000. PATH
was unable to generate a path for a few of the
parcels because the closest node (in the road
coverage) to the parcel centroid was on a dead
end road. The NEXT closest node to the
centroid in this case was manually selected
using the distance ruler in ArcEdit. Once this
node was determined it was used with the
4. Distances generated in steps 1 through 3 were
added together to get the total distance from
the road node closest to each parcel centroid
to the road node closest to the label of the
On-parcel Elk Habitat
Total area of elk habitat on each sale parcel
was calculated using the Arc IDENTITY com-
mand, which calculates the geometric intersection
of two coverages. Each sale coverage was inter-
sected with a coverage containing both Winter–
Year-long (WYL) and Spring–Summer –Fall
(SSF) elk habitat. Descriptions of the two habitat
types used are listed below (taken from the Wyo-
ming Game and Fish Department, 1998 coverage
A population or portion of a population of
animals makes general use of the documented
suitable habitat within this range on a year-round
basis. But during the winter months (between 12/1
and 4/30), there is a signiﬁcant inﬂux of addi-
tional animals into the area from other seasonal
Spring–Summer –Fall Habitat
A population or portion of a population of
animals use the documented habitats within this
range from the end of the previous winter to the
onset of persistent winter conditions (variable pe-
riod, but commonly from May through
Area of 6iewshed
View area was calculated for each parcel cen-
troid label using the Arc VISIBILITY command.
VISIBILITY requires an in–lattice (DEM), and
in–cover (point coverage containing the observer
location), an out–cover (this is the polygon cover-
age that is created by VISIBILITY and contains
all regions that can be seen from the observation
point speciﬁed in the in–cover).
The DEMs used in this analysis were adjusted
for vegetation height. A land cover coverage was
obtained for WY and surrounding states and the
land cover types were combined into a simpliﬁed
classiﬁcation containing only 10 types. The
polygon coverages were converted to 30 M grids,
and the cell values for the grid were then reclassed
into land cover height (an average height was
assigned to each land cover type). These land
cover heights were then added to the DEMs for
each state to create a DEM that was corrected for
The resulting visibility coverage shows all visi-
bility polygons that can be seen from the centroid
of the parcel at an observer height of 2 m.
Characteristics of Viewshed
The viewshed of each centroid label was draped
with the combined land cover coverage and the
area of each land cover type, as well as the
amount of ‘edge’(length of arcs between each
land cover type) was calculated using the SUR-
FACEDRAPE command in ARCPLOT, in order
to characterize each parcel view (Spatial Data and
Visualization Center, 1996c).
SURFACEDRAPE requires and x,y,anz
coordinate for each observer point (in this case,
the xand yvalues are the locations of the cen-
troid label for each parcel, and the zvalue is
elevation (+2 M) derived from a surface lattice
created by the DEM (adjusted for vegetation
height) of the centroid label.)
On-parcel Trout Streams
To calculate on-parcel trout productivity, each
parcel coverage was intersected (using the Arc
command INTERSECT) with a rivers coverage.
The rivers coverage is a subset containing streams
of 3rd order or higher of the Wyoming Gap
Analysis 1:100 000-scale hydrography coverage
(HYDRO) for Wyoming (Spatial Data and Visu-
C.T.Bastian et al.
40 (2002) 337–349
alization Center, 1996b). No digital coverage that
contains stream names or trout productivity for
Wyoming is currently available. The WY Game
and Fish Trout Stream Classiﬁcation Map (1991)
was used to assign stream names and class to each
stream arc in each intersected coverage. WY
Game and Fish provided a database containing
trout productivity, stream name, and township
and range information, and this was used to
manually assign productivity to each stream arc.
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