# RECORD 1196e55c7401009f4e60d7c8db96469c1e13b00e
# TITLE: Capitalization of Open Spaces into Housing Values and the Residential Property Tax Revenue Impacts of Agricultural Easement Programs
# YEAR: 2003.0  VENUE: Agricultural and Resource Economics Review  DOI: 10.1017/s1068280500002483
# AUTHORS: J. Geoghegan; L. Lynch; S. Bucholtz
# PAGES: 13  CANDIDATE TABLE PAGES (1-based): [6, 8, 9, 10]
# ABSTRACT: nan


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Capitalization of Open Spaces into
Housing Values and the Residential
Property Tax Revenue Impacts of
Agricultural Easement Programs
Jacqueline Geoghegan, Lori Lynch, and Shawn Bucholtz
Using a unique spatial database, a hedonic model is developed to estimate the value to nearby
residents of open space purchased through agricultural preservation programs in three Maryland
counties. After correcting for endogeneity and spatial autocorrelation, the estimated coefficients are
used to calculate the potential changes in housing values for a given change in neighborhood open
space following an agricultural easement purchase. Then, using the current residential property tax
for each parcel, the expected increase in county tax revenue is computed and this revenue is
compared to the cost ofpreserving the lands.
Key Words: agricultural preservation, hedonic models, open space, spatial econometrics
The preservation of open space has become an
important policy issue in the United States as con-
version ofland in forestry and agricultural uses into
residential and commercial uses continues. From
1982 to 1992, approximately 6.2 million acres of
agricultural land and 5.1 million acres offorest land
were converted to urban and other developed uses
in the United States (Vesterby et aI., 1997). In the
Washington, DC, metropolitan area,therate atwhich
land is being consumed exceeds the population
Jacqueline Geoghegan is assistant professor, Department of Economics,
Clark University, Worcester, MA; Lori Lynch is associate professor and
Shawn Bucholtz is graduate student, both in the Department of Agricul-
tural and Resource Economics, University ofMaryland.
The authors thank Michael Fragkias, Laura Schneider, and Suchandra
Basu for their assistance with this research, as well as two anonymous
reviewers and participants at the NAREA 2002 Land Use Policy Work-
shop for helpful comments and suggestions. This research was supported
by U.S. EPA Grant No. R825309, NASA New Investigator Program in
Earth Sciences Grant No. NAG5-8559, Center for Agricultural and
Natural Resource Policy, and the National Center for Smart Growth
Research and Education at the University of Maryland. The geographic
data in the analysis are from a project coordinated by Professor Nancy
Bockstael, Department of Agricultural and Resource Economics, the
University of Maryland, with funding by EPA Cooperative Agreement
No. CR-821925010, EPA STAR Grant No. R-82801201, and a Maryland
Sea Grant.
This paper was presented at the Land Use Policy Workshop of the
Northeastern Agricultural and Resource Economics Association annual
meetings, Harrisburg (Camp Hill), PA, June 9-11, 2002.
growth rate by a factor of almost 2.5. If current
trends continue, 800,000 additional acres ofresource
lands will be developed by 2030 in the greater
Washington, DC, region (Chesapeake Bay Founda-
tion, 2002).
Open space provides many potential public goods
with aesthetic, recreation, and biodiversity values.
It also offers associated ecosystem services such as
flood control and waterpurification. Farmland pres-
ervation programs can help maintain a land base for
the agricultural economy, provide the amenities of
open space and rural character, slow suburban
sprawl, provide critical wildlife habitat, and reduce
pollution in areas where suburban development is
occurring (Bromley and Hodge, 1990;Fischel, 1985;
Gardner, 1977; McConnell, 1989; Wolfram, 1981).
Because the private land market does not
recognize these public goods (i.e., there is a market
failure), private and governmental entities have in-
troduced a variety ofmechanisms to preserve open
space or slow its conversion, including cluster and
exclusive agricultural zoning, purchase of and
transfer ofdevelopment rights programs (PDRs and
TDRs), use ofagricultural easements, and the out-
right purchase of open spaces for parks using tax
dollars or private donations. The general public
Agricultural and Resource Economics Review 32/1 (April 2003): 33-45
Copyright 2003 Northeastern Agricultural and Resource Economics Association
https://doi.org/10.1017/S1068280500002483 Published online by Cambridge University Press


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38 April 2003
Agricultural and Resource Economics Review
Table 2. Descriptive Statistics and Variable Definitions by County
Calvert Co.
Carroll Co.
Howard Co.
Mean
Mean
Mean
Variable Name
Variable Description
(Std. Dev.)
(Std. Dev.)
(Std. Dev.)
LN_PRICE
Dependent variable: Naturallogarithm offull
11.9423
11.9098
12.3072
transaction price (in dollars)
(0.4143)
(0.3471)
(0.3457)
(P_)LGPERMOPENa
Percent ofpermanent open space within large radius
0.0502
0.1214
0.1245
(1,600-meter radius buffer)
(0.0404)
(0.0777)
(0.0549)
(P_)SMPERMOPENa
Percent of permanent open space within small radius
0.0110
0.0419
0.0269
(100-meter radius buffer)
(0.0797)
(0.1650)
(0.1130)
(P_)LGAGFORESTa
Percent ofagricultural and forest land within large
0.5054
0.5303
0.3594
radius (1,600-meter radius buffer)
(0.1653)
(0.0838)
(0.0932)
(P_)SMAGFORESTa
Percent of agricultural and forest land within small
0.2017
0.1993
0.1375
radius (100-meter radius buffer)
(0.2724)
(0.2727)
(0.0780)
LN_ACRE
Natural logarithm ofparcel size (in acres)
-0.3632
-0.4869
-0.8688
(1.0826)
(1.0801)
(0.9082)
LN_AGE
Natural logarithm ofage ofstructure (in years)
1.6401
2.2654
1.7306
(1.5266)
(1.5149)
(1.3778)
LN_SQFT
Natural logarithm ofsize ofstructure (in square feet)
7.2793
7.3892
7.6759
(0.4341)
(0.3869)
(0.4302)
AVERAGE
Dummy variable for a structure ofaverage quality
0.5752
0.2831
0.5807
(0.4945)
(0.4506)
(0.4935)
GOOD_VGOOD
Dummy variable for a structure of good or very good
0.0227
0.0220
0.3894
quality
(0.1489)
(0.1468)
(0.4877)
AVSTORY
Number ofstories of structure
1.4657
1.6088
1.8098
(0.4733)
(0.4712)
(0.3862)
BASEMENT
Dummy variable for presence ofa basement in the
0.5931
0.9524
0.9326
structure
(0.4914)
(0.2129)
(0.2507)
FRAME
Dummy variable for framed structures
0.4612
0.0875
0.6842
(0.4986)
(0.2825)
(0.4649)
ALUM
Dummy variable for aluminum structures
0.4755
0.7038
0.2186
(0.4996)
(0.4567)
(0.4133)
LN_POPDEN
Natural logarithm ofpopulation density for census
4.7608
5.1915
5.8756
tract (in households per square mile)
(0.8077)
(0.9585)
(1.1733)
%EDUCB
Percent ofbachelor's degree education attainment
0.0697
0.0920
0.1753
(0.0247)
(0.0361)
(0.0543)
MEDHSINC
Median household income of census tract (in dollars)
47,665
43,645
62,181
(9,533)
(7,256)
(13,623)
LN_CITYDIST
Natural logarithm of distance to Washington, DC,
11.1133
10.6711
10.1559
or Baltimore,:MD (in 100 kilometers)
(0.2367)
(0.2233)
(0.2362)
LN_TOWNDIST
Natural logarithm of distance to nearest town
7.6665
7.8654
8.4256
(in 100 kilometers)
(0.7375)
(0.7486)
(0.8121)
a Either actual value or predicted value (see regression results, tables 3, 4, and 5).
agricultural or forest use, (b) are not factors that
explain housing prices, and (c) are not highly corre-
lated with the observed explanatory variables that
influence housing prices.
In addition to the soil attribute and slope variables
used in Irwin and Bockstael (2001), we also include
the distance to nearest transportation node and
whether the area is currently or is planned to be con-
nected to a sewer system. The physical attributes
measuring soil quality are clearly exogenous to land
values and to the other variables, as they are pre-
determined and do not change with a change in land
use. Transportation node and city distance variables
were tested for correlation, which was found to be
small (p ranged from 0.02 to 0.23).
Sewer service is expected to influence decisions
in the future for these currently undeveloped
land uses. Using the estimated coefficients from
these regressions, instruments for P_SMPERMOPEN,
P_SMAGFOREST, P_LGPERMOPEN, and P_LGAGFOREST
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Geoghegan. Lynch. and Bucholtz
Capitalization ofOpen Spaces into Housing Values 39
where u = AWU + e, and e ''IN(O, 0' 21). The weight
matrix W reflects the relationship between each i,j
pair of observations. For the models reported
below, a row-standardized inverse distance weight
matrix W was used, which assumes the spatial
effects decrease as the distance between observa-
tions increases. The elements ofW are defined such
that wij = 11dij if dij < C, and wij = 0 if i = j or if
d.> c, where dijis the distance between parcel i and
parcel j, C is the distance after which no spatial
correlation is expected, and c is set to 1,000 meters.
That is, only those "neighbors" within 1,000 meters
are assumed to have the potential for spatial effects.
Becausehedonic models arereduced-form models
of an equilibrium locus of offers and bids for
parcels ofland, economic theory cannot inform the
empirical specification [see Cropper, Deck, and
McConnell (1988) for further discussion]. There-
fore, following previous research (Bockstael, 1996;
Geoghegan, Wainger, and Bockstael, 1997), flexible
were created. If a Haussman test indicated the
presence of simultaneity in the price equation with
the land use variables, the predicted value from the
instrumental variable step, rather than the actual
value for the land use buffers, was used in the final
regression for each county.
In addition to the endogeneity issue involved
with estimating the hedonic model, the use of
spatial data leads to the potential for bias and
inefficiency if spatial effects are not taken into
account. One ofthe most relevant ofthese effects is
spatial autocorrelation, which results in inefficient
estimates and biased standard errors. Spatial auto-
correlation is likely to occur when important vari-
ables common to all observations in a neighborhood
are not observable by the researcher, and thus not
included in the model. Spatial autocorrelation in the
residuals was tested using Moran's I statistic, the
likelihood-ratio test, and a Lagrange multipliertest.
In each county, these tests were conducted to indi-
cate if there was spatial autocorrelation in the
residuals. Ifspatial autocorrelation existed, a spatial
model was estimated to address this issue. Similar
to serial correlation, corrections require specification
of a pattern to the errors. In this analysis, spatial
weight matrices based on the inverse distance are
employed.
Specifically, assume X is a matrix ofthe explan-
atory variables S, L, and G of equation (1). Then
the spatial model discussed above is specified as:
(2)
R=Xp+u,
function forms are used here, specifically a log-log
specification.
Estimation Results
The results from estimating equation (1) (the OLS
model) and equation (2) (the spatial model) for
Calvert, Carroll, and Howard counties are reported
in tables 3, 4, and 5, respectively. The test statistics
results ofthe spatial autocorrelation in the residuals
for all three counties are provided in table 6. While
for each county the results from the tests on spatial
autocorrelation in the residuals demonstrated the
need to use the spatial model, we found no major
differences when comparing the OLS results to the
spatial model results. Estimated coefficients are
generally ofthe same size, and the more efficient
test statistics from the spatial model do not result in
qualitative or statistical significance changes for the
estimatedcoefficients. Therefore, the following dis-
cussion of the parameter estimates is based on the
results from the spatial model estimations.3
As seen from tables 3, 4, and 5, the estimated
coefficients on the lot size and structural character-
istics all meet a priori expectations and are statisti-
cally significant for the three counties, except for
insignificant coefficients on NUSTORY for Carroll
County and BASEMENT for Calvert County. The
estimated coefficients for other characteristics varied
both qualitatively and quantitatively by county. For
example, the coefficient on LN_CITYDISTis negative
and statistically significant in the Calvert and
Carroll county regressions; the closer a parcel in
these counties is to eitherWashington or Baltimore,
the higher the sales price, as would be predicted by
urban economic theory. LN_CITYDIST is not statisti-
cally significant in Howard County. In explaining
this lack of significance, we argue that because
Howard County is situated between Washington
and Baltimore, it is possible Howard residents value
a location affording a dual-commute opportunity,
rather than simply being closer to one city versus
the other.
We originally hypothesized that the closer a par-
cel is to a town, the higher the price. However, our
findings show the distance to the nearest town
(LN_TOWNDIST) is not statistically significant in
Carroll or Howard counties, but is positive and
significant in Calvert County. Calvert residents
pay more to be farther away from towns, possibly
3 Semi-annual time dummy variables were included in the regressions
reported below and were all statistically significant.
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40 April 2003
Agricultural and Resource Economics Review
Table 3. Regression Results for Calvert County
OLS
Spatial Model
Asymptotic
Variable
Coefficient
t-Statistic
Coefficient
t-Statistic
CONSTANT
10.8992*
22.04
10.7624*
15.78
P_LGPERMOPEN
0.6303*
3.59
0.7118*
2.83
SMPERMOPEN
-0.1134
-1.72
-0.1117
-1.67
P_LGAGFOREST
-0.3492*
-7.42
-0.3907*
-6.03
SMAGFOREST
-0.0482
-2.38
-0.0496
-2.42
LN_ACRE
0.0982*
13.83
0.1002*
12.87
LN_AGE
-0.0226*
-5.31
-0.0287*
-6.28
LN_SQFT
0.3894*
20.46
0.3765*
19.49
AVERAGE
0.2106*
13.88
0.2020*
12.94
GOOD_VGOOD
0.4859*
12.29
0.4699*
11.56
NUSTORY
0.0790*
5.99
0.0655*
5.14
BASEMENT
-0.0126
-1.12
-0.0008
-0.07
FRAME
-0.0052
-0.22
-0.0311
-1.36
ALUM
-0.0422
-1.82
-0.0464
-2.07
LN_POPDEN
-0.0396*
-3.67
-0.0393*
-2.85
%EDUCB
0.9934*
3.94
1.0015*
2.83
MEDHSINC
0.0000
-0.30
0.0000
-0.18
LN_CITYDIST
-0.1669*
-4.27
-0.1432*
-2.59
LN_TOWNDIST
0.0241*
2.67
0.0265
2.02
Lambda
0.4067*
12.15
Adjusted R!
0.7549
0.7785
* Denotes statistical significance at the 5% level.
Table 4. Regression Results for Carroll County
OLS
Spatial Model
Asymptotic
Variable
Coefficient
t-Statistic
Coefficient
t-Statistic
CONSTANT
11.4720*
37.44
11.6311*
32.47
P_LGPERMOPEN
-0.1181
-1.86
-0.1171
-1.57
SMPERMOPEN
0.0443
2.01
0.0317
1.33
P_LGAGFOREST
-0.1932*
-3.39
-0.2074*
-3.26
SMAGFOREST
0.0027
0.20
0.0027
0.20
LN_ACRE
0.1334*
31.68
0.1338*
31.06
LN_AGE
-0.0641 *
-21.80
-0.0610*
-20.15
LN_SQFT
0.2692*
24.29
0.2629*
24.00
AVERAGE
0.1546*
17.51
0.1551*
16.76
GOOD_VGOOD
0.4036*
17.17
0.3973*
16.69
NUSTORY
-0.0212
-2.52
-0.0155
-1.83
BASEMENT
0.1324*
8.74
0.1330*
8.87
FRAME
-0.0528*
-4.05
-0.0488*
-3.79
ALUM
-0.0015
-0.16
-0.0007
-0.08
LN_POPDEN
0.0286*
4.75
0.0274*
4.02
%EDUCB
0.0814
0.62
0.0083
0.06
MEDHSINC
0.0000*
6.82
0.0000*
23.02
LN_CITYDIST
-0.1581 *
-6.45
-0.1687*
-5.80
LN_TOWNDIST
-0.0044
-0.76
-0.0048
-0.75
Lambda
0.2149*
21.15
Adjusted R"
0.7394
0.7458
* Denotes statistical significance at the 5% level.
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Geoghegan, Lynch, and Bucholtz
Capitalization ofOpen.Spaces into Housing Values 41
Table 5. Regression Results for Howard County
OLS
Spatial Model
Asymptotic
Variable
Coefficient
t-Statistic
Coefficient
t-Statistic
CONSTANT
9.8834*
59.98
10.1107*
1,661.01
P_LGPERMOPEN
0.4950*
6.40
0.5869*
3.91
SMPERMOPEN
0.0367
1.69
0.0489*
2.02
P_LGAGFOREST
-0.1612*
-2.89
-0.2035
-2.31
P_SMAGFOREST
0.2066*
3.18
0.1482
1.75
LN_ACRE
0.1045*
26.82
0.1114*
23.79
LN_AGE
-0.0559*
-20.79
-0.0566*
-18.56
LN_SQFT
0.2518
34.22
0.2031 *
29.46
AVERAGE
0.2258*
14.67
0.2300*
14.07
GOOD_VGOOD
0.4152*
24.96
0.4120*
23.06
NUSTORY
0.0675*
8.04
0.0734*
8.97
BASEMENT
0.0847*
8.13
0.0731 *
7.15
FRAME
-0.0736*
-7.85
-0.0538*
-6.00
ALUM
-0.0919*
-8.58
-0.0601 *
-5.65
LN_POPDEN
0.0089*
2.62
0.0041
0.74
%EDUCB
0.2816*
4.86
0.1601
1.62
MEDHSINC
0.0000*
13.31
0.0021 *
5.70
LN_CITYDIST
-0.0033
-0.24
0.0154
1.68
LN_TOWNDIST
-0.0059
-1.32
0.0004
0.05
Lambda
0.6339*
167.09
Adjusted R!
0.7390
0.7741
* Denotes statistical significance at the 5% level.
because not all of Calvert towns provide desirable
amenities or employment opportunities. Towns be-
tween and within these counties vary enormously,
For both Carroll and Howard, we find a high degree
of correlation between distance to towns and
distance to city (p ranges from -0.65 to -0.45), but
dropping this variable from the estimation did not
change any ofthe results appreciably.
Hypothesis tests on the estimated coefficients for
the block-group census variables resulted in some
unexpected outcomes. In Howard County, the esti-
mated coefficient on LN_POPDENis not statistically
significant, in Carroll it is positive and statistically
significant, and in Calvert it is negative and statis-
tically significant. However, population density
could have two opposite effects on housing prices.
Our a priori hypothesis was that population density
is a measure ofcongestion. Nevertheless, it is con-
ceivable that population density acts as a proxy for
the density ofother goods and services which might
attract people and bid up the housing prices-
although we hope to have captured these locational
amenities that are capitalized in the value of the
home through inclusion ofother right-hand-side
variables. MEDHSINchas the expectedpositive coef-
ficient in Carroll and Howard counties, but is not
statistically significant in Calvert County. The co-
efficient on %EDUCB is positive and statistically
significant only in Calvert County. These census
variables are averages over the block group rather
than for the micro-neighborhoods surrounding the
transaction parcels comprising the data set. Unfor-
tunately, these are the most disaggregated variables
available for these neighborhood attributes.
Thepolicy-relevant coefficientson the four open
space land use variables in each ofthe counties also
revealed mixed results. The Haussman test for
endogeneity indicated the predicted values for the
large buffer P_LGPERMOPEN and P_LGAGFOREST
measures shouldbe used for both Calvert and Carroll
counties, while in Howard County, predictedvalues
were used for these two variables as well as for the
small buffer P_SMAGFORESTmeasure. Given the
functional form used for the hedonic estimation, the
estimated coefficients on these variables can be
interpreted as elasticities. In Calvert County, the
estimated coefficient on the large permanent open
space (p_LGPERMOPEN) is positive and statistically
significant, with an estimated elasticity of 0.71. In
Howard County, the estimated coefficients for both
the large (p_LGPERMOPEN) and small (SMPERMOPEN)
open space variables are positive and statistically
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42
April 2003
Table 6. Error Term Spatial Dependence Test
Statistics, by County
County
Test Statistic a
Calvert
Carroll
Howard
Likelihood Ratio
89.74
58.34
606.39
(0.0000)
(0.0000)
(0.0000)
Lagrange Multiplier
109.84
77.54
1,093.29
(0.0000)
(0.0000)
(0.0000)
a The Moran's I test statistic was positive and significant for Calvert
and Carroll counties. It was not computationally feasible for Howard
County due to the large number ofobservations.
significant with estimated coefficients of 0.59 and
0.05, respectively. In Carroll County, these two esti-
mates are not statistically significant.
Carroll County residents could value open forest
and agricultural land less because there is a greater
amount of these lands in this county. However, in
the large buffer, Carroll County had a mean of 12%
permanently preserved open space and 53% unpre-
served agricultural and forest land around the parcels
examined. Howard County had a mean of 13%
permanently preserved open space and 36% unpre-
served agricultural and forest land. Calvert County
had only 5% permanently preserved open space,
but 51% unpreserved agricultural and forest land.
Similarly, in the small buffer, Carroll had a mean of
4% permanently preserved open space and 20%
agricultural and forest land, with corresponding
values for Howard County of3% and 14%, and for
Calvert County, 1% and 20%. Thus, while Carroll
has more open space around each parcel (65%
compared to 56% in Calvert and 49% in Howard),
it seems unlikely the differences in open space
immediately surrounding the parcels alone explain
the results.
If we consider open spaces beyond those sur-
rounding individual houses, we find there is more
agricultural land available in Carroll County. In
1997, Carroll had 178,000 acres ofagricultural land
compared to 40,000 acres in Howard and 45,000
acres in Calvert. In addition, while the rate ofhous-
ing development is increasing in Carroll County,
the county has not experiencedthe conversion rates
seen in Howard and Calvert counties during the last
25 years. Thus, Carroll County residents may have
a lower willingness to pay for open space due to a
perception that there is a sufficient quantity ofopen
space and they do not perceive it is disappearing
quickly.
Another possibility is that Carroll County fanners
employ different production practices than Howard
Agricultural and Resource Economics Review
or Calvert fanners. For example, they might start
farming earlier in the morning, have a higher density
ofanimals with the accompanying odors and insects,
spray insecticides more frequently, or employ some
other practice which neighboring residents find
objectionable. Therefore, although residents receive
some positive externalities from the presence of
nearby open space, they also receive negative exter-
nalities as well-negating the positive effects.
Additionally, we hypothesized that open space is
a luxury good and will have more value in counties
where incomes are higher. While Howard County's
median income is much higher on average than in
either of the two other counties, Carroll's average
median income of $44,000 is not much lower than
Calvert'smedianincome of$48,000. In conclusion,
further analysis is needed to explain the lack ofsig-
nificance in Carroll. While the results for Howard
County were robust, results for the Carroll and
Calvert county models show the estimated coeffi-
cients were sensitive to changes in the variables and
specification.
Interestingly, the agricultural and forest land
measures (AGFOREST) were either negative and sta-
tistically significantor not statisticallysignificant in
the three counties, suggesting individuals either do
not wish to live near these lands or are not willing
to pay a premium to do so. We remain perplexed by
this result. While permanent open space did have an
impact in two counties, open space in agricultural
or forest use that may be developed in the future was
not important or decreased the value of the prop-
erty. As mentioned above, perhaps these farms use
production practices that generate negative exter-
nalities to the neighbors. Perhaps the insecurity of
not knowing what changes might occur next to a
parcel some time in the future fosters a lack of
willingness to pay to be next to the open space.
In addition, the forests abutting these properties
might be unsightly or people might prefer to have
a view which the trees block. Deer have become an
issue in these counties as they eat homeowners'
landscape plants, cause car accidents, and increase
the incidence ofLyme disease-all ofwhich could
make forest land less desirable. However, we still
find it surprising that homeowners are not willing
to pay more to be near agricultural and forest land.
The central policy focus relates to the effect of
permanent open space on housing values. Based on
the regression results, Carroll County will have no
additional property taxes from increased preserved
agricultural land, as this land use variable had no
effect on the housing value. Consequently, we now
https://doi.org/10.1017/S1068280500002483 Published online by Cambridge University Press


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Geoghegan, Lynch, and Bucholtz
focus exclusively on Calvert and Howard counties
to conduct the simulations.
Simulations
How much additional land can agricultural easement
programs finance? To answer this question, we
used the estimated elasticities from the regression
models for Howard and Calvert counties to
simulate the increase in property taxes associated
only with houses located near current agricultural
easements for a 1% increase in the neighboring
agricultural easements. As both the large and small
permanent open space measures were statistically
significant for Howard County, the larger measure
is used for the simulations."
We increase permanent open space by 1% and
calculatethe increase in tax revenue associatedwith
the increase in housing values. Taking information
on land use around preserved agricultural parcels in
Howard and Calvert, the area of low density (0.4
units per acre) and medium/high density residential
land (8 units per acre) for a one-mile (approxi-
mately 1,600 meters) radius buffer was calculated.
This area was multiplied by the relevant housing
density to compute the expected number ofhouses
within this one-mile distance from each preserved
parcel.
Using the average value ofhouses in each ofthe
counties ($134,245 for Calvert and $227,963 for
Howard), the total value ofthe expected housing in
each buffer was calculated. The increase in the
property values was then calculated using the esti-
mated elasticityassuming currentpreserved agricul-
turalland has increased by 1%. Next, this increase
in the parcel value was multiplied by the county
property tax rate ($0.89 per $100 for Calvert and
$1.04 per $100 for Howard) to determine how much
tax revenue would be generated each year by the
simulated increase in preserved agricultural land.'
Finally, using the average county easement pay-
ment over 1993-1997 ($2,855 for Calvert and
4 The small open-space buffer is part of the large open-space buffer;
thus, using both would be double-counting.
S The increased value will not be immediately evident in the assessed
value of the property on which the property tax is based. However, in
Maryland, these assessments are updated at least once every three years.
The assessments are based on the fair market value using standard
appraisal approaches. A recent transaction will be used in computing the
fair market value of that property as well as the price of adjoining and
comparableproperties. Thus, weestimate the modelusing the mostrecent
transaction price, knowing that within 0-3 years of the transaction, the
property's assessment will be comparable. The property tax rate for
Howard County is $1.044 for each $100 of assessed value, so we used a
rate of 0.01044 for ease of exposition.
Capitalization ofOpen Spaces into Housing Values 43
Table 7. SimulationResults for One-Mile Buffer
of Each. Agricultural Easement: Howard and
Calvert Counties
Description
Howard County
Calvert County
[1] Sum oftotal housing with-
in 1 mile ofeach easement
41,631
29,526
[2] Average housing price
$227,963
$134,245
[3] Expected housing value
(= [1] *[2])
$9,490,251,998
$3,963,690,359
[4] I% increase in open space
181 acres
148 acres
[5] Estimated elasticity from
spatial model
0.5869
0.7118
[6] Expected housing value
increase for a 1% increase
in open space (= [3] *[5])
$55,695,442
$28,214,618
[7] Additional property tax
collected on increased value
$579,233
$251,674
[8] Average easement price
per acre
$5,274
$2,855
[9] Additional acres ofease-
ment that could be acquired
(= [7]+[8])
110 acres
88 acres
$5,274 for Howard), the total number ofnew acres
ofagricultural easement that could be purchased
with the increased tax revenue from just one year of
property tax revenue was computed. Simulation
results are presented in table 7.
For a 1% increase in preserved agricultural land
(148 acres) in Calvert County, the increase in hous-
ing values within a one-mile radius ofpreserved
parcels generated sufficienttax revenue to purchase
an additional 88 acres in the first year. Assuming
no real change in housing prices and no change in
the property tax rate, the county could increase the
number of acres preserved by 2,640 acres in 30
years. For a 1% increase in preserved agricultural
land in Howard County (181 acres), the increase in
housing values within a one-mile radius of these
preserved parcels generated sufficient tax revenue
to purchase an additional 110 acres in one year, or
in 30 years increased the preserved acreage by
3,300 acres.
Conclusions
Many agricultural land preservation programs have
inadequate resources to preserve all the parcels the
general public may wish protected and that agri-
cultural landowners would be willing to enroll.
Programs are looking for innovative funding mech-
anisms or new sources offunds. Beyond the social
benefits provided by permanently preserved agri-
cultural land to the region's residents as a whole,
https://doi.org/10.1017/S1068280500002483 Published online by Cambridge University Press