# RECORD 38bf59597966e99f31288b3b3bfbe1d0b22ac0b5
# TITLE: Spatial Discontinuities in Water Rights: Estimating Household Willingness-to-Pay for Unmetered Irrigation Water
# YEAR: 2024.0  VENUE: The Review of Regional Studies  DOI: 10.52324/001c.127962
# AUTHORS: Matthew K. Gnagey
# PAGES: 18  CANDIDATE TABLE PAGES (1-based): [10, 11, 12, 13, 14, 15, 16]
# ABSTRACT: House prices capture the value households place on environmental attributes and amenities. Access to an unlimited water right for irrigating lawns in an arid region is a prized environmental amenity. The distribution of this amenity varies across space, and often houses with these water rights are located in neighborhoods that differ substantially from other neighborhoods. This study aims to disentangle these neighborhood differences to identify the value that households place on unmetered water rights for lawn irrigation. Identification relies on a regression discontinuity design exploiting the variation in unmetered water access that occurs based on historical land use and geographic features. By examining houses on the border of the water rights zones, the analysis isolates houses that only differ by their access to unmetered irrigation water, thereby eliminating potentially correlated variation with this access, such as differences in socioeconomic attributes, local tax rates, school quality, and environmental amenities. The findings indicate that households are willing to pay a premium of 2% for access to unmetered irrigation water. This premium is not driven by factors such as lawn greenness or the curb appeal from more heavily irrigated landscapes.


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(2024) 54, 241–258
Spatial Discontinuities in Water Rights: Estimating
Household Willingness-to-Pay for Unmetered Irrigation
Water ∗
Matthew K. Gnageya
aDepartment of Economics, Weber State University, USA
Abstract: House prices capture the value households place on environmental attributes and amenities.
Access to an unlimited water right for irrigating lawns in an arid region is a prized environmental amenity.
The distribution of this amenity varies across space, and often houses with these water rights are located
in neighborhoods that differ substantially from other neighborhoods. This study aims to disentangle these
neighborhood differences to identify the value that households place on unmetered water rights for lawn
irrigation. Identification relies on a regression discontinuity design exploiting the variation in unmetered
water access that occurs based on historical land use and geographic features. By examining houses on the
border of the water rights zones, the analysis isolates houses that only differ by their access to unmetered
irrigation water, thereby eliminating potentially correlated variation with this access, such as differences in
socioeconomic attributes, local tax rates, school quality, and environmental amenities. The findings indicate
that households are willing to pay a premium of 2% for access to unmetered irrigation water. This premium
is not driven by factors such as lawn greenness or the curb appeal from more heavily irrigated landscapes.
Keywords: Regression discontinuity design, hedonic analysis, water conservation, irrigation
JEL Codes: D12, L95, Q21, 25
1.
INTRODUCTION
Environmental attributes are frequently capitalized into house prices. These amenities can
include external attributes at the regional or neighborhood level, but can also include the
actual environmental attributes on the property, such as low air pollution or an appealingly
landscaped lawn. The provision of these environmental attributes can result from local or
regional policy decisions, such as emission testing for air quality, or water rights to facilitate
green landscaping. In the arid American West, access to an unlimited water right for irri-
gating lawns is anecdotally a highly valued attribute. Because these water rights vary across
∗Gnagey is an Associate Professor of Economics at Weber State University, Ogden, UT 84403. Corresponding
Author: M. K. Gnagey, E-mail: mattgnagey@weber.edu
(c) Southern Regional Science Association 2024
ISSN 1553-0892, 0048-49X (online)
www.srsa.org/rrs


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The Review of Regional Studies 54(3)
Table 2: Regression Results Using Boundary Fixed Effects.
Dependent Variable is Natural Log of the Real House Price
Variable
All Houses
<1000ft
<500ft
<100ft
Secondary Water
0.0368***
0.0395***
0.0304***
0.0198**
(0.003)
(0.004)
(0.005)
(0.010)
House Size (’000 sq ft)
0.1510***
0.1060***
0.1007***
0.1576***
(0.007)
(0.011)
(0.015)
(0.027)
House Size Squared
0.0078***
0.0143***
0.0146***
0.0091
(0.001)
(0.002)
(0.003)
(0.006)
Acreage
0.0011*
0.0275
0.0877***
0.1766***
(0.001)
(0.018)
(0.027)
(0.036)
House Age
-0.0039***
-0.0037***
-0.0039***
-0.0038***
(0.000)
(0.000)
(0.000)
(0.000)
Bathrooms
0.0287***
0.0238***
0.0256***
0.0263***
(0.001)
(0.002)
(0.003)
(0.008)
Bedrooms
0.0697***
0.0603***
0.0570***
0.0492***
(0.002)
(0.003)
(0.004)
(0.009)
Downtown Ogden (min)
0.0074***
0.0087***
0.0114***
0.0095***
(0.000)
(0.001)
(0.001)
(0.002)
Air Conditioning
0.0796***
0.0703***
0.0813***
0.0763***
(0.002)
(0.004)
(0.005)
(0.012)
Deck
0.0214***
0.0160***
0.0067
0.0208**
(0.002)
(0.003)
(0.005)
(0.009)
Swimming Pool
-0.1137***
-0.0552***
-0.0477***
0.0418
(0.005)
(0.008)
(0.013)
(0.047)
Fireplace
0.0741***
0.0625***
0.0499***
0.0145
(0.002)
(0.004)
(0.005)
(0.013)
Elevation (’000ft)
0.0421***
0.1027***
0.1011***
0.0764*
(0.006)
(0.012)
(0.015)
(0.044)
View
0.0590***
0.0577***
0.0498***
0.0290**
(0.002)
(0.004)
(0.005)
(0.012)
Cul-de-sac
0.0152***
0.0180***
0.0246***
0.0487***
(0.003)
(0.005)
(0.006)
(0.014)
Corner Lot
0.0187***
0.0232***
0.0163***
-0.0100
(0.003)
(0.004)
(0.006)
(0.013)
Constant
11.3196***
11.0503***
11.0085***
10.8637***
(0.033)
(0.089)
(0.102)
(0.217)
Observations
74,375
22,454
11,385
1,906
R-squared
0.7417
0.7502
0.7462
0.7733
Boundary FE
Yes
Yes
Yes
Yes
Month/Year FE
Yes
Yes
Yes
Yes
Notes: Standard errors clustered at each boundary segment. *** p<0.01, ** p<0.05, * p<0.1.
©Southern Regional Science Association 2024.


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GNAGEY: SPATIAL DISCONTINUITIES IN WATER RIGHTS
251
Table 3: Regression Results Using Neighborhood Fixed Effects.
Dependent Variable is Natural Log of the Real House Price
Variable
All Houses
<1000ft
<500ft
<100ft
Secondary Water
0.0078***
0.0248***
0.0235***
0.0201**
(0.003)
(0.004)
(0.005)
(0.011)
House Size (’000 sq ft)
0.1632***
0.1200***
0.1090***
0.1617***
(0.007)
(0.010)
(0.015)
(0.025)
House Size Squared
0.0068***
0.0132***
0.0144***
0.0094
(0.001)
(0.002)
(0.003)
(0.006)
Acreage
0.0017**
0.0343
0.0975***
0.1895***
(0.001)
(0.021)
(0.027)
(0.033)
Age
-0.0038***
-0.0038***
-0.0039***
-0.0041***
(0.000)
(0.000)
(0.000)
(0.000)
Bathrooms
0.0246***
0.0206***
0.0226***
0.0231***
(0.001)
(0.002)
(0.003)
(0.008)
Bedrooms
0.0677***
0.0598***
0.0572***
0.0471***
(0.002)
(0.003)
(0.004)
(0.008)
Downtown Ogden (min)
0.0114***
0.0113***
0.0115***
0.0096***
(0.000)
(0.001)
(0.001)
(0.002)
Air Conditioning
0.0681***
0.0629***
0.0796***
0.0771***
(0.002)
(0.004)
(0.005)
(0.012)
Deck
0.0233***
0.0174***
0.0084*
0.0211**
(0.002)
(0.003)
(0.005)
(0.009)
Swimming Pool
-0.1057***
-0.0700***
-0.0723***
0.0417
(0.005)
(0.008)
(0.012)
(0.042)
Fireplace
0.0750***
0.0642***
0.0493***
0.0118
(0.002)
(0.004)
(0.006)
(0.012)
Elevation (’000ft)
0.0379***
0.0545***
0.0565***
0.0539
(0.007)
(0.014)
(0.018)
(0.046)
View
0.0615***
0.0555***
0.0496***
0.0355***
(0.002)
(0.004)
(0.005)
(0.011)
Cul-de-sac
0.0108***
0.0160***
0.0229***
0.0360**
(0.003)
(0.005)
(0.006)
(0.014)
Corner Lot
0.0159***
0.0239***
0.0186***
-0.0136
(0.003)
(0.004)
(0.006)
(0.014)
Constant
11.1422***
11.1068***
11.0899***
11.0645***
(0.033)
(0.062)
(0.082)
(0.202)
Observations
74,375
22,454
11,385
1,906
R-squared
0.7343
0.7431
0.7419
0.7741
Boundary FE
Yes
Yes
Yes
Yes
Month/Year FE
Yes
Yes
Yes
Yes
Notes: Standard errors clustered at each neighborhood. *** p<0.01, ** p<0.05, * p<0.1.
©Southern Regional Science Association 2024.


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252
The Review of Regional Studies 54(3)
Table 4: Regression Results Using Non-Summer Month Sales.
Dependent Variable is Natural Log of the Real House Price
Variable
All Houses
<1000ft
<500ft
<100ft
Secondary Water
0.0363***
0.0404***
0.0343***
0.0220*
(0.003)
(0.004)
(0.006)
(0.011)
Observations
53,027
16,041
8,103
1,379
R-squared
0.7430
0.7491
0.7489
0.7985
Control Variables
Yes
Yes
Yes
Yes
Boundary FE
Yes
Yes
Yes
Yes
Month/Year FE
Yes
Yes
Yes
Yes
Notes: Control variables include the complete set of variables presented in Table 2. Standard errors
clustered at each boundary segment. *** p<0.01, ** p<0.05, * p<0.1.
in curb appeal of the properties.
The regression models from Table 2 are re-run using this restricted dataset. If the price
premium observed earlier in the main analysis is primarily driven by greener lawns in summer
months, there would be a substantially reduced or non-existent premium in this model.
However, if the premium persists, this is evidence that the access to unmetered secondary
water is valuable, not merely the visual reflection of that access manifested through greener
landscapes.
The results are statistically and numerically similar using this restricted sample (Table 4).
Despite losing slightly more than 25% of the observations (unsurprisingly, a disproportion-
ately large number of sales happen in the summer months compared to the other seasons),
the point estimates and statistical significance of the estimates are similar across the entire
geographic region, and each of the three boundary restrictions, and the point estimates are
actually slightly larger in magnitude on this restricted sample, suggesting that the major-
ity of the premium is not driven by seasonal variation in landscape quality. Additionally,
restricting the sample to each individual season (spring, summer, autumn, winter) yields a
statistically significant and premium between 2-3% for each specification (results available
from the author upon request).
A second approach to model this phenomenon is through interacting the secondary water
variable with a summer dummy variable that indicates whether the property sale occurred
during a summer month. Estimates from this interaction in the RDD are similar to the
previous estimated premium for secondary water (Table 2). Houses sold during the summer
months on average sell for higher prices than houses sold during other times of year. This
reflects the well-known seasonality of the property market.
The estimated premium for
secondary water remains sizeable and statistically significant. Notably, there is no evidence
of an additional premium for secondary water during the summer months.
Even when
transactions are made outside of the peak irrigation months, buyers still are willing to pay
a substantial premium for properties that offer unmetered water access. Both sets of results
in this section support the main findings in this paper, that households place a substantial
©Southern Regional Science Association 2024.


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253
Table 5: Regression Results Testing for Higher Premiums for
Secondary Water During Summer Month Sales. Dependent
Variable is Natural Log of the Real House Price
Variable
All Houses
<1000ft
<500ft
<100ft
Secondary Water
0.0371***
0.0403***
0.0348***
0.0210**
(0.003)
(0.004)
(0.006)
(0.010)
Summer
0.0199***
0.0257***
0.0352***
0.0699***
(0.004)
(0.006)
(0.008)
(0.020)
Secondary*Summer
-0.0013
-0.0030
-0.0136
-0.0226
(0.005)
(0.007)
(0.010)
(0.025)
Observations
74,375
22,454
11,385
1,906
R-squared
0.7406
0.7483
0.7441
0.7688
Control Variables
Yes
Yes
Yes
Yes
Boundary FE
Yes
Yes
Yes
Yes
Month/Year FE
Yes
Yes
Yes
Yes
Notes: Control variables include the complete set of variables presented in Table 2. Standard errors
clustered at each boundary segment. *** p<0.01, ** p<0.05, * p<0.1.
value on access to unmetered secondary water, and this value is not merely due to immediate
aesthetic benefits such as differences in curb appeal that would be more prominent in the
summer than during other seasons.
6.2.
Heterogenous Effects
While the estimated premium of having access to secondary water is relatively consistent
across the various specifications, the valuation of secondary access may vary across prop-
erty types. Interaction effects are estimated between secondary water access and property
characteristics to identify differences in the premiums across different properties. Table 6
presents the results of interactions between secondary water access and lot size, as well as
secondary water access and house size. While findings are consistent for the full sample,
for conciseness Table 6 shows results using properties within 500 feet and 100 feet of the
boundary.
There is no statistically significant difference in premium for larger lots, indicating that
purchasers of larger properties do not value the premium differently than smaller properties,
despite the theoretical prediction that it would be less costly to irrigate smaller properties
using primary culinary water. This finding may reflect that larger lots are more expensive,
and thus these consumers may be less price sensitive to relatively small expenditures like
water bills. Consistent with this hypothesis, buyers of larger houses exhibit a diminished
premium for secondary access than those of smaller houses. These findings suggest that
further investigation into the heterogenous effects of secondary water access could yield
important insights into the relationship between individuals or property characteristics and
the valuation of water infrastructure.
©Southern Regional Science Association 2024.


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The Review of Regional Studies 54(3)
Table 6: Regression Results Examining Whether the Premium
for Secondary Water Varies Across Property Types. Dependent
Variable is Natural Log of the Real House Price
Impact of lot size
Impact of house size
Variable
<500ft
<100ft
<500ft
<100ft
Secondary Water
0.0275*
0.0223**
0.0750**
0.0756*
(0.012)
(0.011)
(0.016)
(0.032)
Secondary*Acres
0.0115
-0.0314
(0.047)
(0.065)
Secondary*Sqft
-0.0231**
-0.0327*
(0.008)
(0.015)
Observations
11,385
1,906
11,385
1,906
R-squared
0.7462
0.7734
0.7466
0.7741
Control Variables
Yes
Yes
Yes
Yes
Boundary FE
Yes
Yes
Yes
Yes
Month/Year FE
Yes
Yes
Yes
Yes
Notes: Control variables include the complete set of variables presented in Table 2. Standard errors
clustered at each boundary segment. *** p<0.01, ** p<0.05, * p<0.1.
6.3.
False Boundary Analysis
A key identifying assumption in this model is the absence of spatially varying unobservable
factors that are correlated with the boundary between areas with and without secondary
water access. These unobservables, which could range from historical land use patterns,
street layouts, distance to amenities, or socioeconomic sorting across space, could bias the
estimates of the premium for secondary water.
To test for the presence and potential impact of these spatially varying unobservables,
I employ a falsification test using artificial boundaries following a well-developed strategy
in analyses that rely on a geographical discontinuity RDD (Becker et al., 2016; Buggle,
2016; Conklin et al., 2020; Oto-Peral´ıas, 2020). If the main results are driven by the water
boundary itself, rather than an underlying spatial trend, then there should not be any effect
when false secondary water boundaries are used for the analysis.
False boundaries represent moving the actual boundary 750 and 2000 feet into the non-
secondary areas and also by moving the boundary 750 and 2000 feet into the area with
secondary water, respectively. These new boundaries are likely to be correlated with unob-
servable factors that vary across space in similar ways to the secondary water access points,
but properties across these false boundaries all have the same access (or non-access) to
secondary water.
For each of these false boundaries, the RDD analysis is replicated, treating houses on one
side of the boundary as if they have access to unmetered irrigation water and houses on the
other side of the boundary as if they do not have access. If the original results are robust,
there should not be any price discontinuities across these false boundaries.
©Southern Regional Science Association 2024.


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255
Table 7: Regression Results Using Artificial Boundaries Moved
Into the Secondary Water Areas. Dependent Variable is Natural
Log of the Real House Price
False Boundary 750ft
False Boundary 2000ft
Variable
<500ft
<100ft
<500ft
<100ft
Secondary Water - Artificial Boundary
0.0004
0.0081
0.0019
0.0076
(0.004)
(0.008)
(0.005)
(0.011)
Observations
13,669
2,824
8,019
1,616
R-squared
0.7473
0.7793
0.7366
0.7585
Control Variables
Yes
Yes
Yes
Yes
Boundary FE
Yes
Yes
Yes
Yes
Month/Year FE
Yes
Yes
Yes
Yes
Notes: Control variables include the complete set of variables presented in Table 2. Standard errors
clustered at each boundary segment. *** p<0.01, ** p<0.05, * p<0.1.
Results from all four artificial boundaries can be found in Tables 7 and 8. Columns 1
and 3 in both tables restrict the analysis to buffers within 500 feet on either side of the
artificial boundary.
Columns 2 and 4 present the analyses with 100-foot buffers of the
artificial boundaries. Table 7 presents results where the artificial boundary was moved 750
feet and 2000 feet into the secondary area, while Table 8 presents the results where the
artificial boundary was moved 750ft and 2000ft respectively into the non-secondary areas.
All regression discontinuities fail to identify a statistically significant premium associated
with differences in the artificial boundary.
7.
CONCLUSION
Environmental attributes are capitalized into the sale price of houses, allowing policymakers
the ability to understand the premium households are willing to pay for a specific environ-
mental good or service. Access to an unlimited water right for irrigating lawns is a highly
valued attribute in the arid American West.
I estimate the value of access to unmetered irrigation water through a regression disconti-
nuity design which isolates differences in house prices on either side of a boundary separating
those with access and those without. The remaining difference in mean house value across
this boundary after controlling for property attributes is the valuation of this access.
Households are willing to pay a premium of approximately 2% for access to secondary
irrigation water. This result indicates that access to secondary water is highly valued, ap-
proaching the value of an additional bathroom for houses in the sample. This finding is
indicative of the value of secondary water access in Northern Utah. While the context may
be similar to other regions in the arid American West, direct extrapolation outside the study
area must be done cautiously absent further research on a broader geographic region.
Several promising avenues exist for future research on this topic. These include, but
©Southern Regional Science Association 2024.


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The Review of Regional Studies 54(3)
Table 8: Regression Results Using Artificial Boundaries Moved
Into the Non-Secondary Water Areas. Dependent Variable is
Natural Log of the Real House Price
False Boundary 750ft
False Boundary 2000ft
Variable
<500ft
<100ft
<500ft
<100ft
Secondary Water - Artificial Boundary
0.0023
0.0024
0.0040
-0.0218
(0.006)
(0.013)
(0.007)
(0.015)
Observations
7,830
1,595
6,026
1,415
R-squared
0.7604
0.7892
0.7842
0.8007
Control Variables
Yes
Yes
Yes
Yes
Boundary FE
Yes
Yes
Yes
Yes
Month/Year FE
Yes
Yes
Yes
Yes
Notes: Control variables include the complete set of variables presented in Table 2. Standard errors
clustered at each boundary segment. *** p<0.01, ** p<0.05, * p<0.1.
are not limited to extending the analysis to other geographic regions, identifying temporal
changes in the premium through variation in drought periods, investigating the influence
of different demographic characteristics on the size of the premium, and exploring whether
access to secondary water influences landscaping decisions, such as xeriscaping vs traditional
lawns.
Such research directions are especially relevant given the ongoing policy debate about
effective measures to conserve water in dynamic regions that are experiencing demand pres-
sures from population growth, and supply pressures due to climate change reducing the
supply of water. As new technology facilitates better measurement of irrigation water using
the secondary water lines, households may expect to pay the full price of this previously
highly subsidized benefit to homeowners in certain neighborhoods. Furthermore, knowledge
of these values is critical to understanding potential pushback from households toward water
pricing policies based on use, as well as understanding the value that residents place on
irrigation water availability.
©Southern Regional Science Association 2024.


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