# RECORD 14eecd7db66f7a02fa146490275ce97a19765a1a
# TITLE: The impact of a new light rail system on single-family property values in Charlotte, North Carolina
# YEAR: 2012.0  VENUE: nan  DOI: 10.5198/jtlu.v5i2.261
# AUTHORS: Si-Si Yan; Eric Delmelle; Michael Duncan
# PAGES: 8  CANDIDATE TABLE PAGES (1-based): [6]
# ABSTRACT: This paper examines the impact of a new light rail system on single-family housing values in Charlotte, North Carolina, from 1997 to 2008. We use a Hedonic Price Analysis (HPA) to estimate how proximity to light rail, housing characteristics, and spatial components (at a block group level) affect single-family housing values. The same method is applied to each of the four time periods (t1, t2, t3, t4) that coincide with the pre-planning, planning, construction, and operation phase of the light rail system. We observe a trend that suggests a greater desirability to live closer to a light rail station as the transit system becomes operational.


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          http://jtlu.org
. 5 . 2 [2012] pp. 60–67    doi: 10.5198/jtlu.v5i2.261
Sisi Yan
Environmental Systems Research 
Institute (ESRI)
Eric Delmelle
The University of North Carolina a
Michael Duncan
Florida State University b
Abstract:  This paper examines the impact of a new light rail system on single-family housing values in Charlotte, North Carolina, from 
1997 to 2008. We use a Hedonic Price Analysis (HPA) to estimate how proximity to light rail, housing characteristics, and spatial components 
(at a block group level) affect single-family housing values. The same method is applied to each of the four time periods (t1, t2, t3, t4) that 
coincide with the pre-planning, planning, construction, and operation phase of the light rail system. We observe a trend that suggests a greater 
desirability to live closer to a light rail station as the transit system becomes operational. 
Copyright 2012 Sisi Yan, Eric Delmelle and Michael Duncan.
Licensed under the Creative Commons Attribution – NonCommercial License 3.0.
1	
Introduction  
From the latter part of the 20th century until today, city and 
regional planners have promoted reliable public transportation 
as a sustainable element of their transportation systems. This 
can be attributed to an increasing awareness of sustainability 
among planners and the general public. Although personal au­
tomobiles are convenient and provide travel flexibility, they also 
cause congestion and accidents, increase energy consumption 
and emissions, generate sprawl, and exclude low-income and 
disabled populations (Richardson 1999). To reduce personal 
reliance on the automobile and address the social and environ­
mental issues mentioned above, most major European cities 
have invested in rail systems as an alternate mode of transporta­
tion. In the United States, cities such as Portland, Boston, and 
San Francisco have adopted light rail systems, which has influ­
enced their morphology over time (Muller 1995). The benefits 
public transit offer are relatively inexpensive transportation and 
the flexibility for workers to reside in suburban areas instead 
of the center city. Finally, public transit raises transportation 
equity standards for people with limited access to private trans­
portation (Dunbaugh 2008; Ong and Houstion 2002). 
Transit impact studies are generally based on hypotheses 
that assert that improved accessibility will increase land value. 
Alonso (1964) suggested that locating closer to the central busi­
ness district (CBD) reduces commuting costs at the expense of 
higher land rents. The underlying principle of Alonso’s work 
broadly applies to any location with a high level of accessibil­
ity, such as areas near highway interchanges and rail stations. 
It is generally acknowledged that transportation investments 
such as corridor development will improve the value of land 
adjacent to these developments, since commuting time may be 
reduced. In addition, increasing accessibility will attract more 
activities and spatial interaction to these places (Gatzlaff and 
Smith 1993). 
In this study, we are interested in evaluating whether in­
dividuals are willing to invest more money when purchasing a 
residential property located in proximity to a light rail station in 
Charlotte, North Carolina. What makes this study unique are 
the characteristics of the light rail line: (1) it is a short line; (2) 
it is located in a fairly low-density urban area; and (3) it uses the 
track of a former freight line. In addition, the light rail was con­
ceived and built during a period of rapid urban expansion. Re­
cent studies suggest that a rapid transit station is likely to affect 
the price of housing within a certain proximity, usually with­
in1600 meters (roughly one mile) of a station. (Gatzlaff 1993; 
Huang 1996; Ryan 1999). Many studies have found positive 
relationships between real estate values and proximity to fully 
developed light rail systems (Davis 1970; Lee 1973; Baldassare 
et al. 1979; Benjamin and Sirmins 1996; Lewis-Workman and 
Brod 1997; Weinstein and Clower 2002; Cervero and Duncan 
2002). Conversely, no relationship was found between home 
prices and proximity to the semi-developed rail system of Mi­
ami (Gatzlaff and Smith 1993). For a detailed analysis of this 
literature, we refer the reader to a handful of papers that have 
been dedicated to reviewing research about the impacts of rail 
on real estate values: Bartholomew and Ewing (in press), De­
brezion, Pels and Rietveld (2007), Ryan (1999), and Knight 
and Trigg (1977).
Most of the previous work in this area applies a cross-sec­
tional approach. In other words, there is no temporal compo­
nent to the analysis, and the implicit price of station proximity 
The impact of a new light rail system on single-family property values in Charlotte, 
North Carolina
a eric.delmelle@uncc.edu
b mdduncan@fsu.edu


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65
The impact of a new light rail system on single-family property values in Charlotte, North Carolina
time periods, which confirms the hypothesis that proximity to 
a light rail station at time period t4 contributes positively to 
house prices relative to earlier time periods. As t2 and t3 are not 
significantly different from t1, it appears that the rail invest­
ment did not affect Charlotte single-family home prices until 
the rail system actually began operation. This stands in contrast 
to other studies that have illustrated price increases during the 
planning stages of a rail system (Damm et al. 1980; McMillen 
and McDonald 2004). Possible reasons may be attributed to 
(1) the planning phase of the light rail with only part of the sys­
tem being implemented, (2) significantly less-congested traffic 
in Charlotte compared to Boston or Chicago, which reduces 
the attraction of living closer to light rail, and hence, (3) the 
limited awareness of public transportation.
Table 4: Z test for coefficients comparison among models
 
t1-2
t1-3
t1-4
t2-3
t2-4
t3-4
Z-test 
(blockgroup)
-1.29
-0.64
1.64
0.60
2.95
2.25
Sensitivity Analysis 
Using the coefficients of each variable from our hedonic model, 
a sample of adjusted price is computed holding other variables 
constant while changing the value of the network distance. We 
consider two similar 139.35-square-meter (1500-square-foot) 
houses in excellent condition (building grade = 5), 20 years old 
with air conditioning, one fireplace each and regular fuel, but 
with one located 402 meters (¼ mile) from the nearest station 
and the other 1609 meters (1 mile). Figure 3 indicates that, for 
each time period, as the network distance to the light rail in­
creases, so do housing prices. However, the difference in price 
for two houses becomes marginal in time t4, indicating that it 
is becoming more desirable to live closer to the light rail. This 
empirically shows that the light rail may be offsetting the nega­
tive influence that previously existed in this corridor. 
Figure 3: Variation in adjusted house price ($) at all four time periods at a 
distance of 402 meters (0.25 mile) and 1609 meters (1 mile). 
5	
Conclusion
In this paper, we apply a hedonic regression model (hedonic 
Table 3: Regression coefficients for the four time periods, block group level.
 
T1
T2
T3
T4
Variable
Coefficient
Coefficient
Coefficient
Coefficient
(constant)
8.196*
7.406*
8.123*
8.249*
Property characteristics
age
-0.004*
-0.001
-0.003
-0.006*
squared age 
4.37E-05*
2.59E-05
4.16E-05*
8.21E-05*
height
0.125*
0.062*
0.076*
0.053
no fuel
-0.796*
0.302
0.032
-0.794*
central air conditioning
0.045*
0.080*
0.090*
0.101*
building grade
0.034*
0.027*
0.032*
0.059*
# fireplaces
0.089*
0.064*
0.057*
0.004
ln (heated area)
0.337*
0.392*
0.338*
0.455*
Rail Impact
ln (network distance) 
(std. error)
0.123(.028)*
0.169(.022)*
0.148(.027)*
0.052(.033)
R2
0.779*
0.786*
0.811*
0.837*
Moran’s I (residuals)
0.097*
0.110*
0.167*
0.021*
*Note: significant at p < 0.05 


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66
JOURNAL OF TRANSPORT AND LAND USE ?.?
price analysis) at a block group scale to evaluate the impact of 
a newly added light rail system on single-family housing values 
in Charlotte, North Carolina. Most research efforts have been 
cross-sectional, and this study takes a longitudinal approach by 
repeating the same hedonic regression analysis at four different 
time periods, coinciding with the pre-planning, planning, con­
struction, and operation phase of the light rail system. 
Results from this study reveal that before the rail system 
began operation, proximity to the future rail corridor had a 
negative influence on home prices. This is likely due to the 
presence of industrial land use zones around existing stations. 
However, when compared across the four time periods, hous­
ing prices have started to react positively to light rail investment 
during the operational phase. This may suggest that accessibil­
ity to reliable transportation has improved the attractiveness of 
single-family houses in the vicinity of light rail stations, or that 
some of the unattractive industrial uses have disappeared, or 
that light rail investment has improved the image of the area. 
Locally, it is possible that after the light rail system went into 
operation, the accessibility provided by newly built light rail 
stations improved the attractiveness of these areas. Another 
explanation is that, due to the concept of transit-oriented de­
velopment of the Charlotte land planning commission, it is 
likely that multiple-family houses or commercial properties 
have started to concentrate in the vicinity of light rail stations. 
In this paper, we have used a distance of 1609 meters (1 
mile) around light rail stations to extract those single-family 
houses for which sale prices would potentially be influenced 
by the proximity to transit. O’Sullivan and Morrall (1996) in­
dicate that individuals living in suburban neighborhoods are 
willing to overcome a greater distance to get to a transit station 
than those individuals living in denser, urban areas. As future 
research, the distance buffer can be modified to be station-
specific, better reflecting the type and complexity of neighbor­
hoods surrounding them. 
In this study, we were limited in our ability to fully mea­
sure the effect on home values of the railway operating over a 
number of years, after it had matured. As a future study, we 
would like to extend our analysis over a wider period of time 
after the rail entered its operational phase. Spatially explicit re­
gression methods exist to test whether the price premium asso­
ciated with rail proximity will fluctuate from station to station, 
especially with varying distance from the CBD. 
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