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Environmental Amenities and Agricultural Land Values: A Hedonic Model Using Geographic Information Systems Data


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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.
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Ecological Economics 40 (2002) 337349
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
1. Introduction
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-
E-mail address
: (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,
Laramie, WY.
0921-8009/02/$ - see front matter © 2002 Elsevier Science B.V. All rights reserved.
PII: S0921-8009(01)00278-6
C.T.Bastian et al.
Ecological Economics
40 (2002) 337349
following Xu et al. (1993), can be viewed as an
input to production; space for amenities (provi-
sion of public goods via place); xed 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 efcient markets.
The demand for productive capacity by agricul-
turalists drives in part the demand for agricultural
land. Rural land prices may also reect house-
holdsdemand 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
1063% 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 scal 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 benets 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 (identication 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
afxing land characteristics to their location. This
paper demonstrates how GIS data can be used to
assess marketable attributes of agricultural lands
in Wyoming.
Specically, our research objectives are as
1. Estimate a hedonic model with land price as a
function of productive and amenity attributes.
2. Incorporate parcel-specic 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.
Benets 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-
tributes, z
. 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.
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40 (2002) 337349
dened as a hedonic function of its characteristics
represented by
). (1)
The marginal impact on-parcel price, P(Z
any 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-t 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 sheries, 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-
same, 1999).
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 specicity 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.
Ecological Economics
40 (2002) 337349
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
signicant 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 signicant 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. Bockstaels (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
identied rural land markets in Louisiana based
on economic, topographic and spatial variables.
GIS was used for dening 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
dened as perimeter to size ratio. The landcover
measure is an index of land use type that is a
surrogate for ora 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-specic spatially
dened attributes are quantied 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.
5. Data
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 Wyomings
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 deator.
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
deated 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.
Ecological Economics
40 (2002) 337349
Table 1
Variable identication, description and hypothesized sign
HypothesizedVariable Variable description/denition
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.
Simpsons diversity index (01) multiplied by 100. This number can then range between 0PositiveSIMPINDX
and 100.
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 shing density per acre.Positive
Total dollar of improvements divided by deeded acres.PositiveIMPDOLAC
PositiveELKACPER Acres of springsummerfall 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 19891995.
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 19901995 (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 unspecied,
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 Simpsons Index (Bar-
C.T.Bastian et al.
Ecological Economics
40 (2002) 337349
Table 2
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
0.000 12.5003.4931.600IRIGAUAC
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
43.839FISHVALU 0.0005.5782.254
5.1151.604 0.000REGFISH 43.839
845.0000.000110.141IMPDOLAC 36.315
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
TOWND 110.2370.00027.54634.480
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.
Specically, the amenity variables are elk (an
important big game species in Wyoming) habitat
for each land sale (ELKACPER);
sh habitat
density (FISHVALU);
urban goods and services
access (TOWND); and view composition
(SIMPINDX). Interaction terms are constructed
for view as well as for trout and elk habitat
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 coefcient, except TOTAUMS
(parcel size). Per acre sale price is thought to
decrease with size and thus lead to a negative
estimated coefcient.
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).
SpringSummerFall and WinterYear-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 shing 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 shing in the Rocky Mountain region includes willing-
ness-to-pay estimates by Dalton et al. (1998) as well as
Dufeld and Allen (1988), indicating the value of regional
trout sheries.
C.T.Bastian et al.
Ecological Economics
40 (2002) 337349
Table 3
Coefcient estimates and likelihood ratio tests for model specications (N=138)
Linear coefcientVariable Quadratic coefcientSemi-log coefcient
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
TOTAMSQ 0.1×10
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)
0.617 0.604
0.559 0.573 0.556
154.224** (0.001)BreuschPagan X
22.733** (0.100) 154.397** (0.001)
127.916 (0.000)127.831 (0.000) 1816.557 (0.000)Likelihood ratio
The BreuschPagan 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 coefcient estimates are Pvalues on the tstatistics. **Results adjusted using Whites 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
specied, as evidenced by both the robustness of
the arguments and the highly signicant good-
ness-of-t measures across the functional forms.
A simple linear model and a semi-log model
(CDACRE logged) are equally preferred based on
the goodness-of-t measures, on the signicance
of the land attributes in explaining per acre parcel
price, and on the ease of interpretation. The
quadratic specication is not deemed adequate
since the chief modications to the base model,
size (TOTAUM) and size squared (TOTAMSQ),
are insignicant.
A BoxCox 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 signicant 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) nd that simple linear models perform well compared
to other functional forms, based on Monte Carlo simulations.
Milon et al. (1984) used a BoxCox 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 specications 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-t
compared to the reported models. Those results are not re-
ported here in the interest of brevity.
C.T.Bastian et al.
Ecological Economics
40 (2002) 337349
scribed by Belsley et al. (1980).
Belsley (1991)
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 ination
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 coefcient than 0.55 (0.88).
All of these diagnostics point toward multi-
collinearity not being a signicant problem in
these models.
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 BreuschPagan statistic from Whites test
shows the presence of heteroscedasticity (1980).
Whites consistent estimator of the covariance
matrix is employed to provide consistent estimates
of the covariance matrix of the estimated is
(Greene, 1998).
The covariance estimator pro-
vides consistent outcomes in testing linear hy-
potheses of the estimated coefcients. This is
critical since the two preferred models are linear
in nature and hypothesis tests regarding the sig-
nicance of the parameter estimates is the primary
concern of this research.
HPM analyses are inherently spatial. Spatial
correlation is an efciency 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-
tion of
). 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 signicant heteroscedasticity with
BreuschPagan 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 Whites 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.
Ecological Economics
40 (2002) 337349
7. Empirical results
The following results are reported for the sim-
ple linear and semi-log estimations, respectively.
The signs on the signicant GIS constructed
amenity variables meet a priori expectations
across the two preferred models, save
REGELKPR (Table 3). REGELKPR has a nega-
tive coefcient 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
signicant 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 signicant and positive coefcients 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
sh 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 sh habitat is signicant in the semi-
log but not so in the simple linear form. These
outcomes coincide with both Spahr and Sunder-
mans (1995) as well as Spahr and Sundermans
(1998) results.
Distance to social/urban amenities (TOWND)
is signicant 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 signicant agricultural produc-
tion variables meet a priori expectations. The
agricultural production variables associated with
grazing (PASTMEDW) and irrigated crop pro-
duction (IRIGAUAC) are signicant for both
models. Capital improvements (IMPDOLAC)
also are signicant for both models in explaining
sale price. RRAUPER is positive and signicant
in the simple linear model, indicating the impor-
tance of secure (private) grazing leases. RRAU-
PER is not signicant 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 coefcient and is
signicant in both estimations. The sign indicates
the diminishing marginal value (measured on a
per acre basis) associated with increasing size.
These ndings are consistent with Torell and Doll
(1991) and Xu et al. (1994).
While the variable associated with public forage
(PUBAUPER) is not signicant, 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.
Ecological Economics
40 (2002) 337349
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 signicant. Wyoming
ranch and particularly farmland prices rose dur-
ing the study time period (Bastian et al., 1994;
Bastian and Hewlett, 1997).
8. Conclusions
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 specic 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 specication for agricultural lands, particu-
larly for the Rocky Mountain and Great Basin
regions. The GIS data development provides
more explicit variables and model specications
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 conict 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 28March 1, 2000.
Appendix A
Distance to Town
All parcels were rst 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 rst calculated using the
Arc command NEAR.
2. The city with a population ]2000 nearest (as
the crow ies) 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-
mand, NEAR.
C.T.Bastian et al.
Ecological Economics
40 (2002) 337349
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
PATH command.
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
nearest city.
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 SpringSummer 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
WinterYear-long Habitat
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 signicant inux of addi-
tional animals into the area from other seasonal
SpringSummer 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
Viewshed Analysis
Area of 6iewshed
View area was calculated for each parcel cen-
troid label using the Arc VISIBILITY command.
VISIBILITY requires an inlattice (DEM), and
incover (point coverage containing the observer
location), an outcover (this is the polygon cover-
age that is created by VISIBILITY and contains
all regions that can be seen from the observation
point specied in the incover).
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 simplied
classication 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
vegetation height.
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.
Ecological Economics
40 (2002) 337349
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 Classication 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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In recent years, in the world, the anthropogenic impact on the soil, entailing various changes in its properties, is becoming more severe. Especially it is necessary to note the lands located on the boundaries of the protection zones of linear objects. This article studies the agrochemistry of soils that are under zones and linear protection objects and the effect of electric waves on wheat productivity. Overhead power lines have a negative impact on the environment, in particular on its ecological, social, and economic systems due to the high intensity of the electromagnetic field around the lead wires. High-voltage power lines do not significantly affect the content of humus and nutrients.
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The paper defines a synthetic, quantitative factor as a measure of spatial location of a parcel. To determine the factor the multivariate comparative analysis (MVA) was used. The location factor was determined on an original formula based on weighting of parcel distance to the selected places or objects. So determined factor enabled to the objective describe on of the location of the parcel and replaced the analysed location characteristics with one-variable. In this paper there was also proposed the use elaborated of synthetic measure as a similarity measure in the grouping of the parcels. To do this, spatial statistics methods was applied, based on the phenomenon of spatial autocorrelation. On the basis of designated global and the local Moran statistics, clusters of similar properties in terms of location have been identified. These so localized spatial clusters can have multiple application. They could be used for a variety of purposes realized in the municipality, where the distance to specific places is very important. The research was conducted for the vacant agricultural real estates. The researched area was the rural municipality called Krotoszyce, located in SouthWestern Poland.
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Изследването на поземлените отношения е част от процеса на изучаване на характера и състоянието на икономическите отношения при промените в стопанската среда, в която функционира селското стопанство. Характерът на поземлените отношения се определя от пряката им симбиоза с икономическите отношения в отрасъла. Процесите в поземлените отношения и темата за земята се разглеждат още при зараждането на икономическата теория. Това се дължи на факта, че земята е основният производствен фактор в системата земя - труд - капитал.
Hedonic price models are commonly used to estimate implicit prices for lake water quality across small geographic regions that might be assumed to be a part of a common real estate market. Yet recent studies expand the geographic scale of the hedonic model potentially obscuring important differences in implicit prices across markets. We estimate implicit prices for lake water quality across multiple states in the northeast and upper Midwest in the United States of America at three different geographic scales: substate, state, and multistate. We find implicit price estimates are heterogeneous at both the substate and state-levels, which is not accounted for in state-level or multistate hedonic models. Our results show that estimates across a broad geographic scale can be driven by a single subregion within the defined area. Overall, the study demonstrates that using a single hedonic model over a large geographic area may obscure important heterogeneity in implicit prices used to estimate potential benefits for water-quality improvements.
Forests offer a range of amenity values besides the production value of timber. We know from existing studies, that the trees' height and species affect the public's recreational values, but it is less clear how much the joy of owning a property with forest matters, and whether the joy depends on characteristics like tree height and species. In this paper, we estimate landowners' private amenity value of forests. We do this by estimating four hedonic land value models for different sizes of property. In the models, forests are described by the size of forest area with a canopy height above three meters, the size of forest area with a canopy height below three meters, mean canopy height, and the share of broadleaf forest. The value of an additional hectare of forest is predicted using the hedonic model and compared with the forest's expectation value based on accounting statistics for forest income, including hunting rents, discounted using an average real market interest rate of 1.7%. We find a difference of 6500–9900 EUR/ha between the estimated land value models and the production values for properties larger than 10 ha. We interpret this difference as the forest owner's willingness to pay for forest amenities in addition to the timber value. The results offer insight into the value of owning forests for the large majority of private forest owners who own small patches of land. They can be valuable in the design of land-use policies aiming to increase forest area and ecosystem service provision on private land that benefits both the public and the private owner.
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The Czech Republic is more or less unique in its dual system (official and market price) of agricultural land prices. In the case of the market price, there are several studies and approaches that assess the impact of individual variables on the market price of land. If we focus on the official price of land, its value in the Czech Republic is influenced by the evaluated soil ecological unit (ESEU) price. The ESEU price expresses the production potential of the land on the basis of soil quality indicators, which include the climatic region, the main soil unit, slope and exposure and, last but not least, the depth of the soil profile and skeletonisation. Climate change also means that the current values of the definition (e.g., for a climatic region, this refers to the average temperature or average precipitation) do not correspond to reality. No studies have looked in detail at the impact of soil quality indicators or climate change on the price of ESEUs. New and more accurate measurements of soil characteristics are increasing the number of ESEU codes, and prices have not yet been set for some codes. For this reason, we proposed the use of a hedonic method to determine shadow prices, which reflect the intensity and direction of the effect of each input variable on the price of an ESEU. A heteroscedastic corrected linear regression model was used to determine the coefficients, which presents in detail the effect of all included parameters on the final price of an ESEU in the results section. From the results, it was obvious that the shadow price coefficients themselves corresponded to basic generally accepted assumptions regarding the direction of effect. In the conditions of the Czech Republic, a significant influence on the price of an ESEU was mainly the slope with exposure and the depth of the soil profile with skeletonisation. These factors affected the productive capacity of the soil, which, in turn, translated into lower profitability of agricultural entities. The high explanatory ability of the hedonic model, with a high parametric significance for most of the used variables, was an important factor determining the robustness of the model as a repricing tool. The model set according to the prices of the applicable price decree can be used to set the prices of new or not-yet-valued ESEU codes under the conditions in the Czech Republic, or shadow prices can be used during climatic changes in the event of the transfer of the given soil unit to another climate region according to the general specification. It is therefore an important tool for the needs of the public administration.
Conference Paper
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This paper explores some of the ways in which a cognitive appreciation of landscape may be matched to the GIS context of DEM analysis. Physiographical characteristics of landscape cognition can be modelled using the technology associated with viewshed analysis. Relief, depth of view, horizon characteristics and shape could all be measured using GIS functionality. Uncertainty in surface feature classification may be identified by examining scale dependencies in the landsape and storing the results as feature membership functions. It is suggested that cognitive criteria such as drama, mystery and coherence may have measurable surrogates by using the modelled view as a basis for their definition.
Based on a large number of Monte Carlo simulation experiments on a regular lattice, we compare the properties of Moran's I and Lagrange multiplier tests for spatial dependence, that is, for both spatial error autocorrelation and for a spatially lagged dependent variable. We consider both bias and power of the tests for six sample sizes, ranging from twenty‐five to 225 observations, for different structures of the spatial weights matrix, for several underlying error distributions, for misspecified weights matrices, and for the situation where boundary effects are present. The results provide an indication of the sample sizes for which the asymptotic properties of the tests can be considered to hold. They also illustrate the power of the Lagrange multiplier tests to distinguish between substantive spatial dependence (spatial lag) and spatial dependence as a nuisance (error autocorrelation).
In the analysis of data it is often assumed that observations y1, y2, …, yn are independently normally distributed with constant variance and with expectations specified by a model linear in a set of parameters θ. In this paper we make the less restrictive assumption that such a normal, homoscedastic, linear model is appropriate after some suitable transformation has been applied to the y's. Inferences about the transformation and about the parameters of the linear model are made by computing the likelihood function and the relevant posterior distribution. The contributions of normality, homoscedasticity and additivity to the transformation are separated. The relation of the present methods to earlier procedures for finding transformations is discussed. The methods are illustrated with examples.
Introduced in this paper is a family of statistics, G, that can be used as a measure of spatial association in a number of circumstances. The basic statistic is derived, its properties are identified, and its advantages explained. Several of the G statistics make it possible to evaluate the spatial association of a variable within a specified distance of a single point. A comparison is made between a general G statistic andMoran’s I for similar hypothetical and empirical conditions. The empiricalwork includes studies of sudden infant death syndrome by county in North Carolina and dwelling unit prices in metropolitan San Diego by zip-code districts. Results indicate that G statistics should be used in conjunction with I in order to identify characteristics of patterns not revealed by the I statistic alone and, specifically, the Gi and G∗ i statistics enable us to detect local “pockets” of dependence that may not show up when using global statistics.
Rappelling down a flatiron, a climber is just ten minutes from his office in Boulder far below. A robust economy and swift access to mountains, canyons trails, trout streams, and ski slope have lured hordes of new suburbanites to the Front Range, all seeking their slice of the West's open spaces and blue skies.