# RECORD d5fa1a30f0b446809e7d5129c56b30f298ed13b5
# TITLE: Land conservation policy and rental markets: evaluating unintended housing price effects
# YEAR: 2026.0  VENUE: The annals of regional science  DOI: 10.1007/s00168-026-01528-0
# AUTHORS: Corey Lang; Jarron VanCeylon
# PAGES: 19  CANDIDATE TABLE PAGES (1-based): [11, 13, 14, 15]
# ABSTRACT: Environmental policies can generate distributional impacts through the housing market, impacting homeowners and renters in unequal ways. While environmental improvements often benefit disadvantaged groups, one concern is that these improvements can increase the desirability of neighborhoods, which in turn can increase housing costs for renters and leaves the overall welfare effect ambiguous. In contrast, homeowners benefit from the additional amenities and the equity gain. This paper seeks to evaluate how rental prices respond to gains in nearby land conservation relative to owner-occupied home values using a panel of census and conservation data for the whole USA. We employ a first-difference regression model and use propensity score matching as a pre-processing method to improve the comparability of areas with and without new conservation. Our results suggest that local gains in conservation do not capitalize into rental prices but do capitalize into home values. Overall, our findings indicate that concerns for renters’ welfare and unintended consequences of land conservation are unsupported.


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Vol.:(0123456789)
The Annals of Regional Science (2026) 75:110
https://doi.org/10.1007/s00168-026-01528-0
ORIGINAL PAPER
Land conservation policy and rental markets: evaluating 
unintended housing price effects
Corey Lang1 · Jarron VanCeylon2
Received: 7 January 2026 / Accepted: 15 July 2026 / Published online: 18 August 2026 
© The Author(s) 2026
Abstract
Environmental policies can generate distributional impacts through the housing 
market, impacting homeowners and renters in unequal ways. While environmen-
tal improvements often benefit disadvantaged groups, one concern is that these 
improvements can increase the desirability of neighborhoods, which in turn can 
increase housing costs for renters and leaves the overall welfare effect ambiguous. In 
contrast, homeowners benefit from the additional amenities and the equity gain. This 
paper seeks to evaluate how rental prices respond to gains in nearby land conserva-
tion relative to owner-occupied home values using a panel of census and conserva-
tion data for the whole USA. We employ a first-difference regression model and use 
propensity score matching as a pre-processing method to improve the comparability 
of areas with and without new conservation. Our results suggest that local gains in 
conservation do not capitalize into rental prices but do capitalize into home values. 
Overall, our findings indicate that concerns for renters’ welfare and unintended con-
sequences of land conservation are unsupported.
JEL classification  Q24 · Q51 ·  Q52
1  Introduction
Environmental public goods and hazards are unequally distributed, with minority 
and low-income communities facing far worse conditions (Brooks and Sethi 1997; 
Christensen et al. 2022; Colmer et al. 2020; Depro et al. 2015; Godwyll and Buzinde 
2023; Jbaily et al. 2022). Due to unequal exposure, environmental improvements, 
 *	 Corey Lang 
	
clang@uri.edu
 *	 Jarron VanCeylon 
	
jvanceylon@uri.edu
1	
Department of Environmental and Natural Resource Economics, University of Rhode Island, 
Kingston, USA
2	
Department of Economics, St. John Fisher University, Rochester, USA


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Land conservation policy and rental markets: evaluating…
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Table 1   Summary statistics & sample comparison
Full sample means
Propensity score matched sample means
Variables: (Year 2000)
Conservation gain
No conservation gain
Standardized
difference
Conservation gain
No conservation gain
Standardized
difference
Median home value ($000 s)
174.54
161.93
0.115
178.42
180.02
− 0.014
(106.86)
(111.72)
(108.91)
(120.72)
Median monthly rent ($)
801.96
835.04
− 0.101
812.57
812.15
0.001
(328.04)
(328.33)
(338.76)
(342.70)
Median household income ($000 s)
63.65
55.81
0.335
63.67
63.69
− 0.001
(24.16)
(22.72)
(24.72)
(26.01)
Population density (Pop./Acres)
1.40
8.83
− 0.678
1.55
1.74
− 0.045
(3.86)
(15.01)
(4.11)
(4.05)
Renter population (%)
20.66
33.75
− 0.682
21.56
22.01
− 0.027
(15.51)
(22.28)
(16.23)
(16.46)
High school educated (%)
53.63
51.32
0.205
53.10
53.00
0.010
(10.73)
(11.85)
(10.86)
(11.22)
College educated (%)
27.78
26.03
0.109
27.97
28.04
− 0.004
(15.52)
(16.48)
(16.01)
(16.42)
Minority population (%)
14.83
32.33
− 0.640
16.62
17.38
− 0.033
(21.91)
(31.88)
(23.19)
(23.56)
Median rooms
5.86
5.31
0.619
5.79
5.78
0.023
(0.81)
(0.96)
(0.82)
(0.85)
Median year built
1970.23
1963.62
0.456
1971.40
1971.36
0.003
(13.36)
(15.56)
(13.07)
(13.36)
Distance to CBSA (mi.)
16.31
9.78
0.556
16.38
16.40
− 0.002
(12.63)
(10.76)
(13.19)
(14.74)
Conserved acres (%)
11.69
4.98
0.451
12.06
12.19
− 0.006


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C. Lang, J. VanCeylon 
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Standard deviations in parenthesis. We split the sample into block groups that experienced a gain in conservation between 2001 and 2009 and block groups that did not. 
All variables are in 2000 levels. A standardized difference greater than 0.1 in absolute value indicates imbalance between treatment and control
Table 1   (continued)
Full sample means
Propensity score matched sample means
Variables: (Year 2000)
Conservation gain
No conservation gain
Standardized
difference
Conservation gain
No conservation gain
Standardized
difference
(17.53)
(11.61)
(18.02)
(20.22)
Observations
14,223
148,036
11,841
47,091


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Land conservation policy and rental markets: evaluating…
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2018; Lang et al. 2023). Further, the owner findings indicate that the null results for 
renters are not a consequence of the research design nor data limitations.
4.1  Heterogeneous price impacts for renters
One concern in our study is that new conservation may financially burden vulner-
able households. While we see little evidence of price responses for the median 
monthly rents, there may be heterogeneous price impacts at different parts of the 
price distribution or for renters in low income or racially segregated neighborhoods. 
In Table  3, we estimate our preferred first-difference model (Table  2 Column 3) 
for median monthly rent and interact conservation with a series of socioeconomic 
indicator variables: below median rent, below median income, and above median 
minority population. Across all columns, none of the interaction coefficients are sta-
tistically significant, and all are small in magnitude, suggesting that a lack of rental 
response is common across these different types of neighborhoods.
We propose that conservation may impact rents at different points along the dis-
tribution of prices. Since our main analysis only uses median monthly rents, we 
may miss part of the story. Following the approach of Gamper-Rabindran and Tim-
mins (2013), we construct rent price quintiles for each block group and estimate 
Table 2   Homeowner & renter capitalization models
Observations are at the block group level and are weighted by total households. All specifications include 
full set of differenced housing & neighborhood characteristics including median household income, 
median rooms, median year built, housing units, % minority population, % high school educated, % col-
lege educated, % renter population, % over age 65, % under age 18, % employed population, vacancy 
rate, housing units, population density, % population employed in real estate, finance, insurance, scien-
tific management, or administrative fields, as well as base-year 2000 controls: % acres in 2000, block 
group neighbor acres in 2000, housing units in 2000, distance to nearest CBSA and distance to nearest 
CBSA squared. For region and state interactions, all housing, neighborhood, and base-year character-
istics are interacted with indicators for a specific region or state. All models include county-trend fixed 
effects. Robust standard errors are clustered at the tract level and shown in parentheses: *** p < 0.01, ** 
p 0.05, *  p < 0.1
Dependent variable: Δ Log 
median rent
Dependent variable: Δ Log Median value
Independent variables
(1)
(2)
(3)
(4)
(5)
(6)
ΔConserved acres (%)
0.0006
–0.0002
–0.0005
0.0035
0.0031
0.0027
(0.0009)
(0.0008)
(0.0008)
(0.0008)***
(0.0007)***
(0.0007)***
Controls:
Housing & neighbor-
hood characteristics
X
X
X
X
X
X
County-Trend FE
X
X
X
X
X
X
Region interactions
X
X
X
State interactions
X
X
Observations
58,932
58,932
58,932
58,932
58,932
58,932
R-squared
0.2800
0.4520
0.4844
0.4567
0.4910
0.5304


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C. Lang, J. VanCeylon 
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the effects of conservation changes on the 20th, 40th, 60th, and 80th price percen-
tiles. In Table 4, our results indicate that rental prices across the distribution do not 
respond to gains in conservation. The coefficient on conservation change in Column 
3 for the 60th percentile of rent prices has the largest magnitude of all percentiles 
at 0.0009, but there is no clear pattern across quintiles. All recovered estimates are 
not statistically different from zero and do not indicate a divergence from the main 
findings. We see this as a positive sign that vulnerable tenants at the bottom of the 
income distribution are not negatively impacted by local gains in conserved lands.
4.2  Alternative hypotheses
In the online appendix, we test several hypotheses that would undermine our conclu-
sion. First, if proximity is the primary avenue for valuation of conservation, rental 
units may be less likely to be proximate than owner-occupied homes. We subset 
block groups that are small, so all housing units would be relatively proximate, and 
still find no rent impact (Table A4). Second, it is possible that changes in the rental 
market lag the owner market (Lang 2015), in which case we may not have enough 
data to observe the eventual uptick in monthly rent. We test for lagged effects in 
rental prices and perform a placebo test using conservation from 1990–1999, simi-
larly finding no price response for either model (Table A5 and Table A6).
Table 3   Heterogeneous price impacts for renters
Observations are at the block group level and are weighted by total households. Binary interaction vari-
ables are defined as above or below the median for that variable. All specifications include the full set of 
controls as in Table 2 Column 3. Robust standard errors clustered at the tract level and shown in paren-
theses: *** p < 0.01, ** p < 0.05, * p < 0.1
Independent variables
Dependent variable: Δlog median rent
(1)
(2)
(3)
(4)
ΔConserved acres (%)
− 0.0007
− 0.0005
− 0.0008
− 0.0010
(0.0009)
(0.0012)
(0.0010)
(0.0013)
ΔConserved acres * Low rental prices (0/1)
0.0004
0.0001
(0.0016)
(0.0020)
ΔConserved acres * High minority population (0/1)
− 0.0001
0.0005
(0.0014)
(0.0015)
ΔConserved acres * Low-income area (0/1)
0.0006
0.0005
(0.0014)
(0.0017)
Controls:
Housing & neighborhood characteristics
X
X
X
X
County-trend FE
X
X
X
X
State interactions
X
X
X
X
Observations
58,932
58,932
58,932
58,932
R-squared
0.4863
0.4848
0.4918
0.4947


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5  Conclusion
This study establishes a novel relationship between rental prices and the conserva-
tion of open space at a national level. While land conservation can enhance the value 
of properties by offering valuable spatial amenities, there are documented environ-
mental justice and equitable distribution concerns like gentrification and access 
issues (Lang et al. 2023; Sims et al. 2022). We examine the housing market impacts 
of conservation efforts between 2001 and 2009 using national-scale census data and 
find positive effects on owner-occupied home values but no impact on monthly rent. 
These results differ from previous findings focused on different amenities where rent 
prices capitalize on amenity changes, just at a lower rate compared to owner values 
(Banzhaf and Farooque 2013; Bento et al. 2015; Davis 2011; Grainger 2012).
One concern about environmental improvements is that, while they provide 
benefits for many, some longstanding or vulnerable residents may be displaced 
due to rising housing prices, a process referred to as environmental gentrification 
or green gentrification (Black and Richards 2020; Immergluck and Balan 2018). 
Indeed, there are times when people may advocate against environmental improve-
ments, such as land conservation, but also cycling infrastructure and river restora-
tion, for fear of green gentrification (Pearsall and Anguelovski 2016; Harris et al. 
2020). Additionally, even when concerns about green gentrification or inequitable 
access to conserved land are present among practitioners, action can be limited due 
to resources, lack of necessary data, or being beyond their scope of mission (Nesbitt 
et al. 2023; Keller et al. 2024). Our findings may help relieve consternation among 
policymakers, NGOs, and community groups concerned about the negative impacts 
of local conservation on vulnerable communities. One possible explanation of our 
findings is that our sample primarily comprises small-scale investments instead of 
Table 4   Heterogeneous impacts 
on the distribution of rent prices
Observations are at the block group level and weighted by total 
households. All specifications include the full set of controls as in 
Table  2 Column 3. All models include county-trend fixed effects. 
Robust standard errors clustered at the tract level in parentheses: *** 
p < 0.01, ** p < 0.05, * p < 0.1
Dependent variable: Δ log median Rent 
(Percentile)
20th
40th
60th
80th
Independent variables
(1)
(2)
(3)
(4)
Δ Conserved acres (%)
0.0004
–0.0002
0.0009
0.0003
(0.0017)
(0.0011)
(0.0009)
(0.0009)
Controls:
Housing & neighbor-
hood characteristics
X
X
X
X
County-trend FE
X
X
X
X
State interactions
X
X
X
X
Observations
58,253
58,253
58,253
58,253
R-squared
0.2558
0.3548
0.3863
0.3419


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C. Lang, J. VanCeylon 
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transformative investments, such as Atlanta’s BeltLine or New York City’s High 
Line, which is somewhat consistent with “just green enough” practices that seek 
to improve environmental quality without displacing residents (Black and Richards 
2020; Curran and Hamilton 2012). Kim and Wu (2022) additionally find evidence 
that the characteristics of green space matter and not all types foster gentrification. 
While certainly more support is needed, these data points suggest that some of the 
concern about environmental gentrification is perceived and not actual.
A couple directions of future research would help bolster our findings or con-
textualize them. Clearly, utilizing individual, geocoded rental transactions instead 
of aggregated median monthly rent would improve the analysis, though we know 
of no publicly available source. Second, while we assume that preferences for con-
served land are the same for renters and owners, we are unable to verify that. Stated 
preference research reports mixed results comparing homeowner and renter prefer-
ences. Tu et al. (2016) find that renters (N = 180) are willing to pay more than own-
ers to live closer to parks and that homeowners with substitutes like private yards 
are willing to pay even less. In contrast, more general contingent valuation studies 
find that homeowners have a higher WTP than renters for neighborhood amenities 
(restaurants, public transportation options) (Caplan et al. 2021; Lindsey and Knaap 
1999). However, it is possible that homeowners exhibit a higher WTP due to cur-
rent scarcity; renters likely have more of these specific neighborhood amenities due 
to proximity to downtown. Thus, a large-scale survey that directly compares home-
owner and renter preferences for conserved open space would be valuable for future 
research.
Supplementary Information  The online version contains supplementary material available at https://​doi.​
org/​10.​1007/​s00168-​026-​01528-0.
Acknowledgements  For useful feedback, we thank Pengfei Liu and Ben Blachly.
Author contribution  C.L. and J.V. conceived the study, developed the theoretical framework, and co-
wrote the manuscript. J.V. conducted the primary data collection and analysis. C.L. secured funding and 
supervised the project. Together, J.V. and C.L. refined the analyses and finalized the manuscript. All 
authors reviewed and approved the final version.
Funding  National Institute of Food and Agriculture, USA, 2018-67024-27695
Data availability  Anonymous link for data replication package available at: [https://​osf.​io/​fmwun/​overv​
iew?​view_​only=​f882f​0338d​8b434​78168​b8783​55cfe​14].
Declarations 
Conflict of interest  The authors declare no conflict of interests.
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