# RECORD 7f45f70c2c2271b7539c0915321311343666b384
# TITLE: The Grass Isn’t Always Greener: How Crime Influences Property Values Near Urban Greenlines
# YEAR: 2026.0  VENUE: Journal of real estate finance and economics  DOI: 10.1007/s11146-025-10026-7
# AUTHORS: Jason De Freitas; Fatemeh Kamkar; Mark A. Sunderman; Velma Zahirovic-Herbert
# PAGES: 37  CANDIDATE TABLE PAGES (1-based): [12, 13, 20, 22, 23, 30, 31, 32, 33]
# ABSTRACT: 
 Green spaces, including greenlines and greenways, are widely regarded as amenities that enhance residential property values through aesthetic appeal, recreational opportunities, and improved community connectivity. However, these benefits may be offset by crime, which deters homebuyers and diminishes perceived safety. This study examines how proximity to greenlines affects property values in Memphis, Shelby County, Tennessee, and how this relationship is shaped by local crime conditions. Using a hedonic pricing model and a spatial Difference-in-Differences framework, we analyze property transactions alongside detailed measures of property, violent, and other crime types. We find that while greenlines are generally associated with increased property values, this effect is significantly moderated by crime—especially property crime. In high-crime neighborhoods, proximity to greenlines can decrease property values, suggesting that perceived safety concerns outweigh amenity benefits. Our findings suggest that perceived safety plays a central role in shaping the value residents place on green infrastructure. Although focused on Shelby County, the results offer broader insight into how crime can condition the amenity value of green infrastructure in other urban and suburban contexts.


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Vol.:(0123456789)
The Journal of Real Estate Finance and Economics
https://doi.org/10.1007/s11146-025-10026-7
The Grass Isn’t Always Greener: How Crime Influences 
Property Values Near Urban Greenlines
Jason De Freitas1   · Fatemeh Kamkar1 · Mark Sunderman1 · 
Velma Zahirovic‑Herbert1 
Accepted: 24 July 2025 
© The Author(s) 2026
Abstract
Green spaces, including greenlines and greenways, are widely regarded as ameni-
ties that enhance residential property values through aesthetic appeal, recreational 
opportunities, and improved community connectivity. However, these benefits may 
be offset by crime, which deters homebuyers and diminishes perceived safety. This 
study examines how proximity to greenlines affects property values in Memphis, 
Shelby County, Tennessee, and how this relationship is shaped by local crime condi-
tions. Using a hedonic pricing model and a spatial Difference-in-Differences frame-
work, we analyze property transactions alongside detailed measures of property, 
violent, and other crime types. We find that while greenlines are generally associ-
ated with increased property values, this effect is significantly moderated by crime—
especially property crime. In high-crime neighborhoods, proximity to greenlines can 
decrease property values, suggesting that perceived safety concerns outweigh amen-
ity benefits. Our findings suggest that perceived safety plays a central role in shap-
ing the value residents place on green infrastructure. Although focused on Shelby 
County, the results offer broader insight into how crime can condition the amenity 
value of green infrastructure in other urban and suburban contexts.
Keywords  Greenline · Property values · Crime · Neighborhood · Spatial 
externalities · Spatial difference-in-differences
 *	 Jason De Freitas 
	
jdefrets@memphis.edu
	
Fatemeh Kamkar 
	
fkamkar@memphis.edu
	
Mark Sunderman 
	
msndrman@memphis.edu
	
Velma Zahirovic‑Herbert 
	
velma.zahirovic-herbert@memphis.edu
1	
Department of Finance, Insurance, and Real Estate, Fogelman College of Business & 
Economics, University of Memphis, Memphis, TN 38152, USA


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J. De Freitas et al.
Table 3   Summary statistics for sales transactions and housing characteristics by greenline proximity, 
2006–2022
This table presents summary statistics for property sale prices and housing characteristics for the full 
sample and for properties located within three proximity bands (RINGs) surrounding the greenlines. 
RING1 includes properties within 0.25 miles of a greenline, RING2 includes properties between 0.26 
and 0.50 miles, and RING3 includes properties between 0.51 and 1 mile. Housing and transaction data 
are from the Memphis Area Association of Realtors (MAAR) MLS database. Proximity to greenlines is 
measured using spatial indicators derived from U.S. Census Bureau GIS data (Property transactions were 
geocoded to determine their distance from each greenline, which was used to construct proximity indica-
tors (RING1, RING2, RING3). Each transaction was also matched to a census tract using its geocoded 
location, allowing us to attach annual tract-level crime data. Census tract fixed effects are included in all 
regressions to control for time-invariant neighborhood characteristics). The final three columns report 
t-test results comparing each RING subgroup to the full sample. Statistical significance is denoted by 
***, **, and * at the 1%, 5%, and 10% levels, respectively
b The “t-test mean difference” columns report differences derived from two-sample t-tests comparing 
each RING group to the full sample mean, using unequal variances. These are not simple subtractions 
of meansbut reflect statistical differences accounting for pooled variance. Minor discrepancies between 
reported means and t-test values may occur due to rounding or standard error adjustments
t-test mean ­differenceb
Variable
Full sample mean
(SD)
RING 1 mean
(SD)
RING 2 mean
(SD)
RING 3 mean
(SD)
Full sample 
vs. ring 1
Full sample 
vs. Rin g 2
Full sample vs. 
RING 3
Sale price
220,443.800
(178,639.500)
217,284.400
(188,493.200)
214,866.800
(180,460.000)
227,297.700
(167,665.000)
4817.586*
7683.102***
−11,085.160***
Acreage
0.290
(0.450)
0.278
(0.365)
0.298
(0.562)
0.295
(0.429)
0.018***
−0.011
−0.008
Square feet
2033.153
(997.223)
2050.939
(1063.973)
2034.957
(999.278)
2015.821
(931.207)
−27.119*
−2.485
28.032*
Property age
40.902
(25.349)
39.696
(26.444)
37.738
(24.761)
44.260
(24.351)
1.838***
4.359***
−5.432***
Year built
1973.903
(24.705)
1975.141
(25.889)
1976.855
(23.973)
1970.668
(23.749)
−1.887 ***
−4.067***
5.233***
Bedrooms
3.238
(0.731)
3.273
(0.730)
3.260
(0.723)
3.191
(0.735)
−0.053***
−0.030**
0.076***
Full baths
2.084
(0.729)
2.125
(0.735)
2.108
(0.704)
2.031
(0.738)
−0.062***
−0.032***
0.086***
Half baths
0.335
(0.515)
0.337
(0.522)
0.379
(0.539)
0.302
(0.490)
−0.003
−0.061***
0.054***
Rooms
6.963
(1.673)
6.949
(1.747)
6.967
(1.699)
6.972
(1.583)
0.021
−0.006
−0.015
Fireplaces
0.937
(0.587)
0.983
(0.601)
0.948
(0.576)
0.887
(0.579)
−0.070***
−0.016*
0.080***
Obs
18,411
6337
5046
7028
18,411
18,411
18,411
and crime distributions across greenline proximities. These bands standardize meas-
urement zones across greenlines and allow for consistent visual comparisons. Full-
resolution figures enable clearer visualization of census tract boundaries and local 
neighborhood context. Figure 1 illustrates property sale densities near the greenlines 
within 0.25 miles, 0.50 miles and 1 mile from 2006 to 2022. The areas around the 
SFG and SFGE show a higher density of sales while the area around the WR shows 
a lower density of sales for the period.


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The Grass Isn’t Always Greener: How Crime Influences Property…
Table 4   Summary statistics for crime densities by proximity to greenlines, 2006–2022
This table reports summary statistics for log-transformed crime density measures—violent, property, and other crime—for properties located within three proximity bands 
(RINGs) around greenlines. RING1 includes properties within 0.25 miles of the greenlines, RING2 covers those between 0.26 and 0.50 miles, and RING3 includes prop-
erties between 0.51 and 1 mile. Crime density is defined as the number of crimes per square mile at the census tract level. Crime data are obtained from the Memphis Data 
Hub, while greenline proximity measures are constructed using GIS and spatial indicators derived from U.S. Census Bureau data (Property transactions were geocoded to 
determine their distance from each greenline, which was used to construct proximity indicators (RING1, RING2, RING3). Each transaction was also matched to a census 
tract using its geocoded location, allowing us to attach annual tract-level crime data. Census tract fixed effects are included in all regressions to control for time-invariant 
neighborhood characteristics). The final three columns report t-test results comparing the means of each RING group to the full sample. Statistical significance is denoted 
by ***, **, and * at the 1%, 5%, and 10% levels, respectively
b The “t-test mean difference” columns report differences derived from two-sample t-tests comparing each RING group to the full sample mean, using unequal variances. 
These are not simple subtractions of means but reflect statistical differences accounting for pooled variance. Minor discrepancies between reported means and t-test values 
may occur due to rounding or standard error adjustments
t-test mean ­differenceb
Variable
Full Sample Mean (SD)
RING 1 Mean (SD)
RING 2 Mean (SD)
RING 3 Mean (SD)
Full Sample 
vs. RING 1
Full Sample 
vs. RING 2
Full Sample vs. RING 3
Violent crime density
4.246 (1.915)
3.528 (1.870)
4.090 (1.929)
5.006 (1.649)
1.095***
0.215***
−1.229***
Property crime density
5.121 (2.051)
4.367 (2.072)
4.942 (2.099)
5.929 (1.673)
1.150***
0.247***
−1.307***
Other crime density
3.382 (1.806)
2.780 (1.690)
3.244 (1.791)
4.023 (1.708)
0.917***
0.189***
−1.037***
Obs
18,411
6337
5046
7028
18,411
18,411
18,411


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J. De Freitas et al.
Figure 1 shows the density of residential property sales across three standardized 
proximity bands—within 0.25 miles (RING1), 0.26–0.50 miles (RING2), and 0.51–1 
mile (RING3)—of the Shelby Farms Greenline (SFG), its western extension (SFGE), 
and the Wolf River Greenline (WR). We use proximity-based rectangles rather than 
individual points to enable consistent comparison across time and space, and to align 
with the structure of our empirical analysis. Apparent concentrations of sales near 
certain greenline segments reflect underlying patterns in housing supply and neigh-
borhood development. Our regression results control for these spatial and temporal 
differences, allowing for more accurate identification of greenline effects on property 
values.
We collected crime data from the Memphis Police Department and the FBI, 
encompassing information on crime type, location, and timing. Following the crime 
categorization in the economics literature (Lacoe et  al., 2018 and Palmer et  al., 
2019), the real estate literature (Harris et al., 2018) and the Uniform Crime Statistics 
guidelines from the FBI’s Uniform Crime Reporting (UCR) program, we aggregate 
our crime types into three specific categories: violent crime, property crime, and 
other crime. Violent crime includes homicide, robbery, assault, and kidnapping. In 
contrast, property crime includes property crime, breaking and entering, theft, arson, 
theft of vehicle, and theft from vehicle, and other crimes include weapons offense, 
family offense, traffic, disorder, vehicle recovery, drug-related offences, and liquor 
violations. Each property was matched to crime data corresponding to its census 
tract and year of sale, using tract boundaries defined by the U.S. Census Bureau’s 
2020 American Community Survey (ACS), the most recent delineations available at 
Fig. 1   Property sales density near the greenlines 2006–2022


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J. De Freitas et al.
that crime is a significant consideration in these neighborhoods and may impact 
property values. Although the RING and greenline variables are not explicitly 
reported in the table, their inclusion as binary indicators in the analysis is essen-
tial for examining how proximity to Greenlines might influence property values 
and crime rates. Overall, our summary statistics suggest that both proximity to 
greenlines and neighborhood crime rates play a role in shaping property values.
Table 5   Summary statistics of sales transactions, housing characteristics, and crime, 2006–2022
This table provides descriptive statistics for the key variables used in the analysis, covering the period 
from 2006 to 2022. Variables include property values, housing characteristics, neighborhood features, 
and local crime measures. Sale price, housing and neighborhood characteristics, and year of sale are 
sourced from MAAR MLS data. Crime density variables are calculated at the census tract level, using 
2020 tract definitions from the U.S. Census Bureau’s American Community Survey (ACS), and reflect 
average counts per square mile. Greenline proximity indicators, not shown here, are created using GIS-
based spatial analysis (Property transactions were geocoded to determine their distance from each green-
line, which was used to construct proximity indicators (RING1, RING2, RING3). Each transaction was 
also matched to a census tract using its geocoded location, allowing us to attach annual tract-level crime 
data. Census tract fixed effects are included in all regressions to control for time-invariant neighborhood 
characteristics). For each variable, the table reports the mean, standard deviation, minimum, and maxi-
mum values. All monetary values are in nominal U.S. dollars. The dependent variable used in regression 
analysis, LNSP, is the natural logarithm of the sale price
Variable
Mean
Standard deviation
Minimum
Maximum
LNSP
12.088
0.635
10.312
14.509
Sale price
220,443.800
178,639.500
30,100
2,000,000
Bedrooms
3.238
0.731
1
7
Full baths
2.084
0.729
1
7
Half baths
0.335
0.515
0
5
Rooms
6.963
1.673
3
16
Fireplaces
0.937
0.587
0
5
Acreage
0.290
0.450
0.050
8.450
Square feet
2033.153
997.223
572
10,689
Property age
40.902
25.349
0
134
Year built
1973.903
24.705
1875
2022
Townhouse
0.035
0.183
0
1
Condominium
0.006
0.079
0
1
Subdivision
0.153
0.361
0
1
Neighborhood
0.719
0.450
0
1
No pool
0.915
0.280
0
1
In-ground pool
0.069
0.254
0
1
Above ground pool
0.006
0.077
0
1
Carport
0.163
0.369
0
1
Covered garage
0.529
0.499
0
1
Violent crime density
4.246
1.915
0
7.085
Property crime density
5.121
2.051
0
8.139
Other crime density
3.382
1.806
0
6.390
Obs
18,411
18,411
18,411
18,411


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The Grass Isn’t Always Greener: How Crime Influences Property…
9  The coefficient results for all variables, including housing and neighborhood characteristics, are pro-
vided in the appendix in Table 8.
Results
The results for our hedonic regression models provide insight into the relationship 
between proximity to greenlines, crime, and property values. Using data on approxi-
mately 18,411 property transactions located within one mile of the Shelby Farms 
Greenline (SFG), Shelby Farms Greenline Extension (SFGE), and Wolf River 
Greenway (WR) between 2006 and 2022, we estimate the effects of greenline prox-
imity and crime rates on property values. Our analysis incorporates fixed effects at 
the year-by-census-tract level to control for unobserved heterogeneity and spatial 
differences.
Hedonic Regression with Difference‑in‑Differences (DiD) Approach Results
Table 6 presents the results of our baseline Difference-in-Differences (DiD) model, 
which extends the standard hedonic regression framework by incorporating interac-
tion terms to evaluate how proximity to greenlines affects property values before 
and after their respective openings.
These models estimate the relationship between greenline proximity, crime 
rates, and property values while controlling for property characteristics, neighbor-
hood characteristics, census tracts, and year fixed effects. The property and neigh-
borhood controls include the number of rooms, bedrooms, full and half bathrooms, 
fireplaces, acreage, square footage, property age, year built, presence of carports or 
garages, pool characteristics, property type (townhouse, condominium), subdivision, 
and neighborhood associations. However, for brevity, the coefficients for these con-
trols are not reported.9
The coefficients on RING1 and RING2 measure the general impact of proxim-
ity to greenlines on property values, independent of time. The results show a posi-
tive effect for properties near SFG, with values increasing by 11.0% in RING1 and 
12.6% in RING2, both statistically significant at the 1% level. The WR, however, 
experiences a more modest decline of 0.69%, which is only marginally significant.
The coefficients on RING1 and RING2 measure the general impact of proxim-
ity to greenlines on property values, independent of time. The results show a posi-
tive effect for properties near SFG, with values increasing by 11.0% in RING1 and 
12.6% in RING2, both statistically significant. This suggests that properties close to 
SFG benefited from greenline proximity before the treatment period. However, for 
WR and SFGE, no significant pre-treatment price premium is observed.
The RING × Post interaction terms capture how the post-treatment period modi-
fies the effect of greenline proximity on property values. In the SFG model, proper-
ties in RING1 experience an 8.2% decrease in value post-treatment, significant at the 
10% level, while properties in RING2 see a 9.9% decline, significant at the 1% level. 
These findings indicate that while proximity to SFG initially adds value, this effect 
diminished post-opening, possibly due to increased foot traffic, congestion, or crime 


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J. De Freitas et al.
concerns. In contrast, WR and SFGE do not exhibit statistically significant changes 
in property values for properties in RING1 or RING2 after the greenline openings.
Overall, these results suggest that the effects of greenlines on property values 
are nuanced and vary by location. While the SFG initially boosts property values, 
this effect declines after its opening. The WR shows a relatively stable trend, while 
SFGE exhibits a strong negative post-treatment effect. These findings highlight the 
importance of considering both spatial and temporal dynamics when evaluating 
greenline impacts on housing markets. Table 6 establishes the foundational proxim-
ity effects that are analyzed further in Tables 7, 8 and 9. The models in these later 
Table 6   Baseline difference-in-differences regression results, 2006–2022 for the greenline openings
This table shows the results of baseline Difference-in-Differences (DiD) regressions estimating the 
impact of greenline openings on property values between 2006 and 2022. The dependent variable is the 
natural log of sale price (LNSP). The models include fixed effects for census tracts (based on 2020 U.S. 
Census data) and year of sale (Property transactions were geocoded to determine their distance from each 
greenline, which was used to construct proximity indicators (RING1, RING2, RING3). Each transaction 
was also matched to a census tract using its geocoded location, allowing us to attach annual tract-level 
crime data. Census tract fixed effects are included in all regressions to control for time-invariant neigh-
borhood characteristics. Stata automatically omits the 2022 year fixed effect to avoid perfect multicol-
linearity. As a result, the Post indicator—equal to 1 for post-greenline-opening transactions—was not 
dropped and is included in the model by default. However, because Post is functionally redundant with 
the full set of year fixed effects, its coefficient does not represent a standalone treatment effect and is 
therefore not reported or interpreted in the table. All treatment effects are identified through the interac-
tion terms). Property and neighborhood characteristics from MLS data are controlled for but not dis-
played for brevity. Proximity to greenlines is captured using geocoded spatial buffers: RING1 includes 
properties within 0.25 miles, RING2 between 0.26 and 0.50 miles, and RING3 (0.51–1 mile) serves as 
the omitted baseline group. Treatment effects are identified through the interaction terms (RING1 × Post 
and RING2 × Post), which test for differential effects by proximity to greenlines after each greenline’s 
respective opening. Separate results are shown for the full sample and for properties near the Wolf River 
Greenline (WR), Shelby Farms Greenline (SFG), and Shelby Farms Greenway Extension (SFGE). Statis-
tical significance is denoted by ***, **, and * at the 1%, 5%, and 10% levels, respectively
Dependent variable: LNSP
Variable
Full sample mean 
(SD)
WR mean (SD)
SFG mean (SD)
SFGE mean (SD)
RING1
0.006
−0.008
0.110**
−0.030
(0.035)
(0.043)
(0.040)
(0.028)
RING1 x Post
−0.005
−0.002
−0.082*
0.019
(0.036)
(0.041)
(0.042)
(0.032)
RING2
0.031
−0.029
0.126***
−0.018
(0.027)
(0.039)
(0.025)
(0.010)
RING2 x Post
−0.014
0.085
−0.099***
0.014
(0.028)
(0.045)
(0.029)
(0.016)
Property controls
Yes
Yes
Yes
Yes
Neighborhood controls
Yes
Yes
Yes
Yes
Year fixed effects
Yes
Yes
Yes
Yes
Census tract fixed effects
Yes
Yes
Yes
Yes
R-squared
0.841
0.904
0.830
0.855
Obs
18,411
1806
11,438
5167


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The Grass Isn’t Always Greener: How Crime Influences Property…
Table 7   Extended difference-in-differences regression results: property crime, pre- and post-2010 and 
2016 for the greenline openings
This table presents results from extended Difference-in-Differences (DiD) regression models estimating the 
effect of property crime density on housing prices near greenlines from 2006 to 2022. The models extend the 
baseline specification by incorporating property crime measures and their interactions with greenline proximity 
and post-treatment periods (2010 and 2016). The dependent variable is the natural log of sale price (LNSP). 
Treatment effects are identified through interaction terms (RING1 × Post, RING2 × Post, Log Property Crime 
Density × Post, RING1 × Log Property Crime Density × Post, RING2 × Log Property Crime Density × Post), 
which test for how property crime influences property values across space and time. Separate regressions are 
reported for the full sample and for properties near the Wolf River Greenline (WR), Shelby Farms Greenline 
(SFG), and Shelby Farms Greenway Extension (SFGE). Fixed effects for year of sale and census tracts (2020 
U.S. Census data) are included (Property transactions were geocoded to determine their distance from each 
greenline, which was used to construct proximity indicators (RING1, RING2, RING3). Each transaction was 
also matched to a census tract using its geocoded location, allowing us to attach annual tract-level crime data. 
Census tract fixed effects are included in all regressions to control for time-invariant neighborhood character-
istics. Year fixed effects are also included; Stata automatically omits the 2022 year dummy to avoid perfect 
multicollinearity. As a result, the Post indicator—equal to 1 for post-greenline-opening transactions—was not 
dropped and is included in the model by default. However, because Post is fully determined by the year fixed 
effects, its coefficient is not reported or interpreted in the table. All treatment effects are captured through inter-
action terms involving Post). Structural housing and neighborhood characteristics from MLS data are also con-
trolled for but omitted from the table for brevity. Proximity to greenlines is measured by RING1 (≤ 0.25 miles) 
and RING2 (0.26–0.50 miles), with RING3 (0.51–1 mile) serving as the omitted reference group. Key interac-
tion terms between property crime, greenline proximity, and the post-treatment period allow us to assess how 
property values respond to crime across space and time. Statistical significance is denoted by ***, **, and * for 
the 1%, 5%, and 10% levels, respectively
Dependent variable: LNSP
Variable
Full Sample Mean 
(SD)
WR Mean 
(SD)
SFG Mean 
(SD)
SFGE Mean 
(SD)
RING1
−0.243***
0.164*
−0.235
−0.153*
(0.046)
(0.065)
(0.138)
(0.078)
RING1 x Post
0.179***
−0.159*
0.293*
0.136*
(0.046)
(0.063)
(0.146)
(0.060)
RING2
−0.287***
0.080*
−0.304**
−0.181***
(0.038)
(0.038)
(0.142)
(0.047)
RING2 x Post
0.229***
0.011
0.306**
0.139***
(0.047)
(0.050)
(0.131)
(0.018)
Log Property crime density
−0.057***
0.024
−0.108***
0.002
(0.014)
(0.015)
(0.031)
(0.016)
Log Property crime density x Post
0.042***
−0.029***
0.070***
0.025***
(0.008)
(0.007)
(0.018)
(0.002)
RING1 x Log Property crime density x Post
−0.030***
0.041
−0.047**
−0.024
(0.010)
(0.023)
(0.021)
(0.018)
RING2 x Log Property crime density x Post
−0.044***
0.010
−0.054***
−0.026***
(0.009)
(0.014)
(0.019)
(0.004)
Property controls
Yes
Yes
Yes
Yes
Neighborhood controls
Yes
Yes
Yes
Yes
Year fixed effects
Yes
Yes
Yes
Yes
Census tract fixed effects
Yes
Yes
Yes
Yes
Adj. R-squared
0.843
0.905
0.831
0.856
Obs
18,411
1806
11,438
5167


===== PAGE 24 (score 16, numeric-paren 0, table-words 5, var-words 6) =====

	
J. De Freitas et al.
tables explore how the baseline effects evolve post-greenline openings and examine 
the interaction between crime and these amenities. Thus, Table 6 provides a com-
parative context for evaluating shifts in property value dynamic over time.
Difference‑in‑Differences (DiD) Analysis
The DiD regression results, presented in Table  7 (as well as in Tables  8 and 9 
located in the appendix) examine the interaction between proximity to greenlines, 
crime, and the pre- and post-opening periods of the SFG, SFGE, and WR. These 
models allow us to assess how greenline opening influenced property values over 
time, while accounting for crime rate densities.
Table 7 presents the estimated coefficients and standard errors for the interaction terms 
between proximity to greenlines, property crime density, and the pre- and post-opening 
periods. The results provide key insights into how property crime influences housing val-
ues in different greenline areas over time. In the full sample, property crime density has a 
statistically significant negative effect on property values (−5.7% at the 1% level), indicat-
ing that higher crime rates are generally associated with lower home prices. However, this 
effect varies across greenline areas. While WR shows a small, positive but insignificant 
coefficient (2.4%), SFG exhibits the most pronounced negative effect (−10.8% at the 1% 
level), followed by SFGE, which has an insignificant coefficient (0.2%).
The interaction between property crime density and the post-treatment period 
reveals significant variations in how greenlines mediate crime effects. In the full 
sample, the interaction term is positive and significant (4.2% at the 1% level), sug-
gesting that after greenline openings, properties in high-crime areas experienced 
appreciation in value. However, WR shows a significant negative post-opening inter-
action (−2.9% at the 1% level), implying that crime had a worsening effect on prop-
erty values after WR’s opening. In contrast, SFG (7.0% at the 1% level) and SFGE 
(2.5% at the 1% level) exhibit significant positive effects, indicating that these green-
lines may have enhanced property values despite crime.
The key findings emerge from the triple interaction terms, which capture how 
crime influences property values near greenlines post-opening. In the full sample, 
the RING1 x Property Crime Density x Post interaction is negative and significant 
(−3.0% at the 1% level), implying that properties closest to greenlines in high-crime 
areas depreciated post-opening. This effect is particularly strong for SFG (−4.7% 
at the 5% level), suggesting that crime may have diminished the perceived amenity 
value of the greenline. WR and SFGE, however, show insignificant effects for this 
interaction, indicating less clear post-opening crime impacts in these areas.
For RING2, property crime density has a strong positive effect in the full sample 
(5.9% at the 1% level), suggesting that properties in the 0.26 to 0.50-mile range appreci-
ated in higher-crime areas. However, WR has a significant negative effect (−2.2% at the 
10% level), indicating crime’s more detrimental role in this region. The post-treatment 
interaction (RING2 x Property Crime Density x Post) is negative and significant in the 
full sample (−4.4% at the 1% level), implying that properties in this range were nega-
tively affected by crime after greenline openings. This effect is particularly strong for 


===== PAGE 30 (score 251, numeric-paren 209, table-words 2, var-words 14) =====

	
J. De Freitas et al.
Table 8   Baseline hedonic regression models of property values near greenlines for 2006 to 2022
Dependent variable: LNSP
Variable
(1)
(2)
(3)
(4)
(5)
(6)
Rooms
0.012***
0.012***
0.012***
0.012***
0.012***
0.012***
(0.004)
(0.004)
(0.004)
(0.004)
(0.004)
(0.004)
Bedrooms
0.006
0.006
0.006
0.006
0.006
0.007
(0.011)
(0.011)
(0.011)
(0.011)
(0.011)
(0.011)
Full baths
0.125***
0.125***
0.125***
0.125***
0.124***
0.124***
(0.021)
(0.021)
(0.021)
(0.021)
(0.021)
(0.021)
Half baths
0.034**
0.033**
0.033**
0.033**
0.033**
0.033**
(0.015)
(0.015)
(0.015)
(0.015)
(0.015)
(0.015)
Fireplaces
0.086***
0.086***
0.086***
0.086***
0.086***
0.086***
(0.012)
(0.012)
(0.012)
(0.012)
(0.012)
(0.012)
Acreage
0.007
0.006
0.007
0.007
0.007
0.007
(0.019)
(0.019)
(0.019)
(0.019)
(0.019)
(0.019)
Square feet
0.000***
0.000***
0.000***
0.000***
0.000***
0.000***
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
0.000
Property age
0.039***
0.039***
0.039***
0.039***
0.042***
0.042***
(0.001)
(0.001)
(0.001)
(0.001)
(0.001)
(0.001)
Year built
0.042***
0.042***
0.042***
0.042***
0.044***
0.044***
(0.001)
(0.001)
(0.001)
(0.001)
(0.002)
(0.001)
Carport
0.066***
0.066***
0.066***
0.066***
0.066***
0.065***
(0.017)
(0.017)
(0.017)
(0.017)
(0.017)
(0.017)
Covered garage
0.074***
0.073***
0.073***
0.073***
0.073***
0.073***
(0.008)
(0.008)
(0.008)
(0.008)
(0.008)
(0.008)
No pool
0.088**
0.089**
0.089**
0.089**
0.088**
0.089**
(0.040)
(0.040)
(0.040)
(0.040)
(0.040)
(0.040)
In-ground pool
0.105**
0.106**
0.106**
0.106**
0.106**
0.107**
(0.040)
(0.039)
(0.040)
(0.040)
(0.039)
(0.040)
Above ground pool
0.124***
0.122***
0.122***
0.123***
0.122***
0.122***
(0.044)
(0.044)
(0.044)
(0.044)
(0.044)
(0.044)
Townhouse
−0.213*** −0.210*** −0.210*** −0.210*** −0.210*** −0.210***
(0.022)
(0.022)
(0.022)
(0.022)
(0.022)
(0.022)
Condominium
−0.458**
−0.457**
−0.456**
−0.457**
−0.458**
−0.458**
(0.181)
(0.181)
(0.180)
(0.180)
(0.180)
(0.180)
Subdivision
0.010
0.010
0.010
0.009
0.009
0.009
(0.014)
(0.014)
(0.014)
(0.014)
(0.014)
(0.014)
Neighborhood
−0.041*
−0.040*
−0.040*
−0.040*
−0.040*
−0.040*
(0.058)
(0.021)
(0.021)
(0.021)
(0.021)
(0.021)
RING1
0.002
0.002
0.002
0.003
0.002
0.002
(0.021)
(0.021)
(0.021)
(0.021)
(0.021)
(0.021)
RING2
0.021
0.021
0.021
0.020
0.020
0.020
(0.014)
(0.014)
(0.014)
(0.014)
(0.014)
(0.014)
SFG
0.006
0.007
0.006
0.005
0.005


===== PAGE 31 (score 65, numeric-paren 18, table-words 14, var-words 15) =====

The Grass Isn’t Always Greener: How Crime Influences Property…
This table presents results from hedonic regressions estimating the relationship between property values 
and proximity to greenlines, housing characteristics, and neighborhood features (Property transactions 
were geocoded to determine their distance from each greenline, which was used to construct proximity 
indicators (RING1, RING2, RING3). Each transaction was also matched to a census tract using its geo-
coded location, allowing us to attach annual tract-level crime data. Census tract fixed effects are included 
in all regressions to control for time-invariant neighborhood characteristics). The dependent variable is 
the log of sale price (LNSP). Models include structural attributes, subdivision and property type indica-
tors, and fixed effects for year of sale and census tracts. Proximity to greenlines is represented by RING1 
(≤ 0.25 miles) and RING2 (0.26–0.50 miles), with RING3 (0.51–1 mile) as the reference. Greenline indi-
cators include SFG and WR; SFGE is excluded and serves as the base group. Crime variables (violent, 
property, and other) are added in columns (4) through (6). These baseline models inform the Difference-
in-Differences analysis presented in Table 6. Statistical significance is indicated by ***, **, and * at the 
1%, 5%, and 10% levels, respectively
Table 8   (continued)
Dependent variable: LNSP
Variable
(1)
(2)
(3)
(4)
(5)
(6)
(0.017)
(0.017)
(0.017)
(0.017)
(0.018)
WR
0.048
0.048
0.048
0.047
0.048
(0.037)
(0.038)
(0.037)
(0.037)
(0.038)
Log Violent crime density
0.007
0.026
(0.028)
(0.027)
Log Property crime 
density
−0.006
−0.004
(0.015)
(0.013)
Log Other crime density
−0.040**
−0.047***
(0.017)
(0.017)
Year fixed effects
Yes
Yes
Yes
Yes
Yes
Yes
Census tract fixed effects
Yes
Yes
Yes
Yes
Yes
Yes
Adj. R-squared
0.840
0.841
0.841
0.841
0.841
0.842
Obs
18,411
18,411
18,411
18,411
18,411
18,411
0.26–0.50 mi, and 0.51–1 mi) from the three greenlines (SFG, WR, SFGE). Vertical 
lines indicate the opening years of each greenline.
Table 8 presents the housing and neighborhood characteristics from the hedonic 
models estimated in Table 6, which were not reported there for brevity. This table 
includes coefficients for additional variables related to housing features, neighbor-
hood controls, RINGs, the greenlines and the various crimes reported in this study. 
The table spans the 2006–2022 period and includes variables such as the number of 
rooms, bedrooms, full and half baths, fireplaces, and property characteristics like 
acreage, square footage, and property age. The greenline indicators (SFG and WR) 
and RINGs (with RING3 as the baseline) are also included, as well as crime vari-
ables for violent, property, and other crimes. All other results remain consistent with 
those in Table 6. This supplemental table provides a detailed view of the estimated 
coefficients for the housing and neighborhood characteristics used in the analysis.
Table 9 presents the estimated coefficients and standard errors for the interac-
tion terms between proximity to greenlines, violent crime densities, and the pre- 
and post-opening periods. The results highlight significant triple interaction effects, 


===== PAGE 32 (score 81, numeric-paren 52, table-words 8, var-words 6) =====

	
J. De Freitas et al.
Table 9   Extended difference-in-differences regression results: violent crime, pre- and post-2010 and 
2016 for the greenline openings
This table presents results from extended Difference-in-Differences (DiD) regression models examining 
the relationship between violent crime density and housing prices near greenlines from 2006 to 2022. 
These models build on the baseline hedonic specification by incorporating interactions between violent 
crime density, greenline proximity, and post-treatment periods (post-2010 and post-2016). The depend-
ent variable is the natural log of sale price (LNSP). Treatment effects are identified through interaction 
terms (RING1 × Post, RING2 × Post, Log Violent Crime Density × Post, RING1 × Log Violent Crime 
Density × Post, RING2 × Log Violent Crime Density × Post), which test for how violent crime influences 
property values across space and time. Analyses are conducted for the full sample and separately by 
greenline: the Wolf River Greenline (WR), the Shelby Farms Greenline (SFG), and the Shelby Farms 
Greenway Extension (SFGE). Fixed effects for year of sale and census tracts (based on 2020 U.S. Census 
data) are included (Property transactions were geocoded to determine their distance from each greenline, 
which was used to construct proximity indicators (RING1, RING2, RING3). Each transaction was also 
matched to a census tract using its geocoded location, allowing us to attach annual tract-level crime data. 
Census tract fixed effects are included in all regressions to control for time-invariant neighborhood char-
acteristics. Year fixed effects are also included; Stata automatically omits the 2022 year dummy to avoid 
perfect multicollinearity. As a result, the Post indicator—equal to 1 for post-greenline-opening transac-
tions—was not dropped and is included in the model by default. However, because Post is fully deter-
mined by the year fixed effects, its coefficient is not reported or interpreted in the table. All treatment 
effects are captured through interaction terms involving Post.). Structural property features and neighbor-
hood characteristics from MLS data are also controlled for, but coefficients are omitted from the table for 
brevity. Greenline proximity is captured using RING1 (≤ 0.25 miles) and RING2 (0.26–0.50 miles), with 
RING3 (0.51–1 mile) serving as the omitted reference category. Key interaction terms measure how vio-
lent crime density affects property values in close proximity to greenlines before and after their openings. 
Statistical significance is indicated at the 1%, 5%, and 10% levels by ***, **, and *, respectively
Dependent variable: LNSP
Variable
Full Sample Mean 
(SD)
WR Mean 
(SD)
SFG Mean 
(SD)
SFGE Mean (SD)
RING1
−0.204***
0.151
−0.156
−0.146*
(0.034)
(0.094)
(0.147)
(0.075)
RING1 x Post
0.153***
−0.163
0.253*
0.128*
(0.047)
(0.097)
(0.138)
(0.055)
RING2
−0.234***
0.082
−0.218
−0.145**
(0.027)
(0.047)
(0.143)
(0.047)
RING2 x Post
0.184***
−0.008
0.252*
0.099***
(0.045)
(0.043)
(0.128)
(0.020)
Log Violent crime density
−0.044*
0.025
−0.014
0.008
(0.025)
(0.014)
(0.038)
(0.026)
Log Violent crime density x Post
0.041***
−0.044***
0.062***
0.027***
(0.008)
(0.007)
(0.019)
(0.002)
RING1 x Log Violent crime density x Post
−0.033***
0.054
−0.051**
−0.030
(0.012)
(0.049)
(0.024)
(0.021)
RING2 x Log Violent crime density x Post
−0.046***
0.020
−0.056**
−0.025***
(0.010)
(0.012)
(0.021)
(0.006)
Property controls
Yes
Yes
Yes
Yes
Neighborhood controls
Yes
Yes
Yes
Yes
Year fixed effects
Yes
Yes
Yes
Yes
Census tract fixed effects
Yes
Yes
Yes
Yes
Adj. R-squared
0.843
0.905
0.831
0.856
Obs
18,411
1806
11,438
5167


===== PAGE 33 (score 82, numeric-paren 52, table-words 9, var-words 5) =====

The Grass Isn’t Always Greener: How Crime Influences Property…
Table 10   Extended difference-in-differences regression results: other crime, pre- and post-2010 and 
2016 for the greenline openings
Dependent variable: LNSP
Variable
Full Sample 
Mean (SD)
WR Mean (SD)
SFG Mean (SD)
SFGE Mean (SD)
RING1
-0.123***
0.054
-0.144
-0.070
(0.021)
(0.039)
(0.113)
(0.050)
RING1 x Post
0.064**
-0.047
0.179**
0.049
(0.027)
(0.030)
(0.089)
(0.047)
RING2
-0.145***
0.006
-0.193
-0.086*
(0.019)
(0.057)
(0.112)
(0.039)
RING2 x Post
0.089***
0.054
0.167*
0.070***
(0.026)
(0.058)
(0.083)
(0.010)
Log Other crime density
-0.075***
0.016
-0.092***
-0.039*
(0.014)
(0.021)
(0.032)
(0.018)
Log Other crime density x Post
0.034***
-0.041***
0.063***
0.028**
(0.007)
(0.008)
(0.015)
(0.009)
RING1 x Log Other crime density x 
Post
-0.020*
0.016
-0.042**
-0.011
(0.011)
(0.028)
(0.019)
(0.031)
RING2 x Log Other crime density x 
Post
-0.036***
0.001
-0.049**
-0.025**
(0.009)
(0.024)
(0.017)
(0.008)
Property controls
Yes
Yes
Yes
Yes
Neighborhood controls
Yes
Yes
Yes
Yes
Year fixed effects
Yes
Yes
Yes
Yes
Census tract fixed effects
Yes
Yes
Yes
Yes
Adj. R-squared
0.843
0.905
0.831
0.856
Obs
18,411
1806
11,438
5167
This table results from extended Difference-in-Differences (DiD) regression models evaluating the relationship between 
other crime density and housing prices near greenlines from 2006 to 2022. These models include interaction terms 
between other crime density, greenline proximity, and post-treatment periods (post-2010 and post-2016) to assess how 
property values respond to less severe forms of crime over time. The dependent variable is the natural log of sale price 
(LNSP). Treatment effects are identified through interaction terms (RING1 × Post, RING2 × Post, Log Other Crime Den-
sity × Post, RING1 × Log Other Crime Density × Post, RING2 × Log Other Crime Density × Post), which test for how 
other crime influences property values across space and time. Separate regressions are estimated for the full sample as 
well as for properties near the Wolf River Greenline (WR), Shelby Farms Greenline (SFG), and Shelby Farms Green-
way Extension (SFGE). Fixed effects for year of sale and census tracts (based on 2020 U.S. Census data) are included 
(Property transactions were geocoded to determine their distance from each greenline, which was used to construct 
proximity indicators (RING1, RING2, RING3). Each transaction was also matched to a census tract using its geocoded 
location, allowing us to attach annual tract-level crime data. Census tract fixed effects are included in all regressions to 
control for time-invariant neighborhood characteristics. Year fixed effects are also included; Stata automatically omits 
the 2022 year dummy to avoid perfect multicollinearity. As a result, the Post indicator—equal to 1 for post-greenline-
opening transactions—was not dropped and is included in the model by default. However, because Post is fully deter-
mined by the year fixed effects, its coefficient is not reported or interpreted in the table. All treatment effects are cap-
tured through interaction terms involving Post.). Structural housing and neighborhood characteristics from MLS data 
are also controlled for, though omitted from the table for brevity. Greenline proximity is measured using RING1 (≤ 0.25 
miles) and RING2 (0.26–0.50 miles), with RING3 (0.51–1 mile) serving as the omitted reference group. Key interac-
tions examine how the effect of other crime density on housing prices varies by proximity and across post-treatment 
periods. Statistical significance is indicated at the 1%, 5%, and 10% levels by ***, **, and *, respectively


===== PAGE 34 (score 11, numeric-paren 0, table-words 2, var-words 7) =====

	
J. De Freitas et al.
particularly in RING1 and RING2, where higher violent crime density in post-treat-
ment periods is associated with diminished capitalization of greenlines. Specifically, 
the negative coefficient on the triple interaction term (RING1 x Violent crime density 
x Post) suggests that, in high-crime areas, the greenline effect on property prices is 
even more pronounced after the greenline’s opening, particularly in RING2. These 
results reinforce the main analysis findings and the broader relationship between 
crime and greenline proximity, though property crime is emphasized in the main 
discussion for its stronger link to homebuyer decision-making.
Table 10 presents the estimated coefficients and standard errors for the interaction 
terms between proximity to greenlines, other crime densities, and the pre- and post-
opening periods. The results show significant triple interaction effects, particularly 
in RING1 and RING2, where the negative interaction between other crime density 
and the post-treatment period suggests that areas with higher crime densities experi-
ence a more pronounced negative effect from greenline proximity. Specifically, the 
coefficients on the triple interaction terms (RING1 x Other crime density x Post) 
and (RING2 x Other crime density x Post) indicate that higher other crime density 
in post-treatment periods dampens the positive capitalization effect of the green-
lines, particularly in RING2. These findings align with the broader analysis on crime 
and greenline proximity, further emphasizing the nuanced impact of crime types on 
property values.
Authors Contributions  All authors contributed to the study’s conception and design. Material prepara-
tion, data collection, and analysis were performed by J. De Freitas, F. Kamkar, V. Zahirovic-Herbert, and 
M. Sunderman. The first draft of the manuscript was written by J. De Freitas, and all authors reviewed 
and provided input on previous versions of the manuscript. All authors read and approved the final 
manuscript.
Funding  The authors declare that no funds, grants, or other support were received during the preparation 
of this manuscript.
Data Availability  The data used in this study were obtained under a contract with Memphis Area Asso-
ciation of Realtors (MAAR) and are not publicly available due to restrictions on sharing. Access to the 
data is limited by the terms of our agreement with the provider.
Declarations 
We, the submitting authors, confirm that this manuscript is an original piece of empirical research, has not 
been previously published, and is not currently under evaluation by any other journal.
Ethical Approval  Not applicable.
Consent to Participate  Not applicable.
Consent to Publish  All authors give their explicit consent to publish this manuscript.
Competing interests  The authors have no relevant financial or non-financial interests to disclose.
Open Access  This article is licensed under a Creative Commons Attribution 4.0 International License, 
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long 
as you give appropriate credit to the original author(s) and the source, provide a link to the Creative 
Commons licence, and indicate if changes were made. The images or other third party material in this