# RECORD 8c46154fbaf164447e17b77d9874009c161a116a
# TITLE: The effect of light rail transit service on nearby property values: Quasi-experimental evidence from Seattle
# YEAR: 2018.0  VENUE: nan  DOI: 10.5198/jtlu.2018.1096
# AUTHORS: Michael R. Ransom
# PAGES: 18  CANDIDATE TABLE PAGES (1-based): [7, 10, 12, 15]
# ABSTRACT: This paper examined the effect of the construction of light rail transit stations on surrounding residential property values in Seattle, Washington. It studied sales of homes in the areas around the seven stations that serve primarily residential areas in the Rainier Valley, using a difference-in-differences regression technique to obtain plausibly causal estimates of the effect of new rail service. For these seven stations, the estimated impact of light rail service was positive for only one station and negative for two stations. Estimated impacts for the other stations were small and statistically insignificant. These results suggest that light rail service did not provide value to the neighborhoods in the Rainier Valley of Seattle. I speculate that the transport service provided by light rail was not a significant improvement relative to the bus lines that serviced the area before light rail was built.


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T J  T  L U    http://jtlu.org
V. 11 N. 1 [2018] pp. 387–404
Abstract:  This paper examined the effect of the construction of 
light rail transit stations on surrounding residential property values 
in Seattle, Washington. It studied sales of homes in the areas around 
the seven stations that serve primarily residential areas in the Rainier 
Valley, using a difference-in-differences regression technique to obtain 
plausibly causal estimates of the effect of new rail service. For these 
seven stations, the estimated impact of light rail service was positive 
for only one station and negative for two stations. Estimated impacts 
for the other stations were small and statistically insignificant. These 
results suggest that light rail service did not provide value to the neigh­
borhoods in the Rainier Valley of Seattle. I speculate that the transport 
service provided by light rail was not a significant improvement rela­
tive to the bus lines that serviced the area before light rail was built.
1	
Introduction
This paper analyzes the impact of new light rail service on the price of residential property in Seattle, 
Washington in the United States. Light rail has been popular with local transit agencies in North Amer­
ica. Since 1990, more than 20 cities in the United States have built new light rail lines or expanded ex­
isting lines. Planners generally believe that these systems will improve transit access, reduce dependency 
on automobiles, and that stations will provide a focal point for high density development. However, 
the systems are very expensive to build and to operate. For example, the construction cost of the system 
studied here, Seattle’s Central Link “starter,” was $2.7 billion (Lindblom, 2016). Furthermore, in the 
2014 fiscal year, operating costs for Seattle’s light rail exceeded fares by about $45.5 million (Federal 
Transit Authority, 2016), and fares covered only about 26 percent of operating costs.
Understanding the impact of new light rail service on housing prices is important for at least two 
reasons. First, it provides market based evidence of whether light rail provides improved transportation 
access compared to transit systems in place before light rail was built. If residents place value on the 
new transport option, this value should be capitalized into house prices where that access is available. 
Another reason to study the impact on property values is that many proponents of rail systems have 
proposed that new light rail systems might be financed with “value capture” schemes, whereby the tran­
The effect of light rail transit service on nearby property values: 
Quasi-experimental evidence from Seattle
Michael R Ransom
Brigham Young University
ransom@byu.edu
Article history:
Received: November 7, 2016
Received in revised form: August 
17, 2017
Accepted: December 28, 2017
Available online: May 16, 2018
Copyright 2018 Michael R Ransom
http://dx.doi.org/10.5198/jtlu.2018.1096
ISSN: 1938-7849 | Licensed under the Creative Commons Attribution – Noncommercial License 4.0 
The Journal of Transport and Land Use is the official journal of the World Society for Transport and Land Use (WSTLUR) 
and is published and sponsored by the University of Minnesota Center for Transportation Studies. This paper is also 
published with additional sponsorship from WSTLUR.


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393
The effect of light rail transit service on nearby property values
Table 1 presents summary statistics for the neighborhoods that are analyzed in this paper. Houses 
in most of these neighborhoods are generally comparable in terms of sale prices, size, and number of 
bathrooms. However, homes in the neighborhood around the Mount Baker stations sell for significantly 
more than those in other neighborhoods, and they are larger. Generally, houses near stations that are 
further from downtown Seattle are newer, have larger lots, and are less expensive.
Around 20 percent of sales took place after the opening of the light rail line, as indicated by the 
variable, “After.” The mean of the variable “Close” indicates the fraction of houses in the neighborhood 
that are within the 800-meter catchment basin of the corresponding rail station. In the Mt. Baker and 
Beacon Hill neighborhoods, a large fraction of houses (roughly half) is close to the station. On the other 
hand, in the SeaTac and Tukwila areas, only around 5 percent and 10 percent of houses in the neigh­
borhoods fall within the respective catchment areas. This difference has important implications for the 
analysis. One common approach in this type of analysis is to compare all the “close” houses to all the 
“distant” houses, combining all stations. In the Seattle case, the comparison would be disproportionately 
Table 1:  Summary statistics
Variable
Sea Tac 
Airport
Tukwila 
Int’l Blvd
Rainier 
Beach
Othello
Columbia 
City
Mount 
Baker
Beacon
Hill
Logarithm of 
Real Relative 
Sale Price
12.075
(0.305)
12.002
(0.293)
12.059
(0.338)
12.180
(0.401)
12.213
(0.379)
12.564
(0.470)
12.200
(0.315)
Sale Price 
223,408
(112,914)
206,744
(99,384)
224,515
(119,702)
268,564
(165,497)
270,963
(159,799)
394,869
(239,306)
261,237
(139,049)
Number 
Bathrooms
1.948
(0.85)
1.893
(0.878)
1.897
(0.872)
1.994
(0.945)
1.876
(0.896)
2.139
(0.949)
1.689
(0.818)
Size (ft2)
1,677
(641)
1,670
(669)
1,669
(642)
1,756
(717)
1,652
(636)
2,000
(761)
1,582
(594)
Age
39.077
(18.559)
43.262
(24.532)
48.325
(27.608)
50.391
(30.835)
57.325
(31.505)
65.668
(30.686)
67.305
(28.934)
Waterfront Lot
0.159
(0.366)
0.003
(0.055)
0.021
(0.144)
0.024
(0.154)
0.019
(0.137)
0.000
--
0.000
--
Outstanding 
View
0.070
(0.255)
0.058
(0.233)
0.161
(0.367)
0.142
(0.349)
0.152
(0.359)
0.301
(0.459)
0.120
(0.326)
Lot Size (acres)
0.241
(0.13)
0.253
(0.16)
0.174
(0.13)
0.146
(0.07)
0.125
(0.06)
0.114
(0.05)
0.106
(0.04)
Size Garage (ft2)
277.80 
(256.64)
221.11 
(251.83)
182.80 
(220.77)
168.15 
(211.12)
137.64 
(192.23)
121.01 
(175.96)
95.49 
(155.57)
Detached 
Garage?
0.161
(0.367)
0.250
(0.433)
0.205
(0.403)
0.193
(0.395)
0.234
(0.424)
0.289
(0.453)
0.286
(0.452)
Close (<1/2 
mile)
0.042 
(0.201)
0.103 
(0.304)
0.177 
(0.382)
0.293 
(0.455)
0.211 
(0.408)
0.416 
(0.493)
0.554 
(0.497)
After 2009
0.179 
(0.383)
0.170 
(0.375)
0.179 
(0.383)
0.210 
(0.407)
0.205 
(0.404)
0.229 
(0.420)
0.214 
(0.411)
After×Close
0.007 
(0.082)
0.018 
(0.134)
0.029 
(0.169)
0.067 
(0.251)
0.047 
(0.212)
0.088 
(0.283)
0.108 
(0.311)


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394
JOURNAL OF TRANSPORT AND LAND USE 11.1
between the “close” houses in the Beacon Hill and Mt. Baker neighborhoods with the “distant” houses 
in the Tukwila and Sea Tac neighborhoods. A comparison of the houses between those two groups is 
likely to provide a biased estimate of the effect of light rail on housing prices. Thus, I adopt the approach 
of analyzing each neighborhood separately. This approach is also consistent with the idea that the value 
of a new transit option in a neighborhood is likely based on the location of that neighborhood relative 
to employment and commercial centers, existing commuting routes and patterns that naturally differ 
across neighborhoods, as well as the characteristics of the individuals who choose to live there.
4	
The hedonic pricing model and difference-in-differences estimation
The hedonic approach is based on an economic theory developed by Rosen (1974). The basic idea, as 
applied to housing markets, is that housing prices in a neighborhood reflect a general equilibrium in 
the supply and demand of housing services. In the hedonic model, each of the characteristics of a house 
provides some benefit, and thus each of these components has a price. Some of these characteristics rep­
resent elements of the house itself, such as the size, age, or style of the house. Others represent locational 
characteristics, such as pleasant views, good neighborhood schools, or convenient access to downtown 
labor markets. To the extent that a new light rail line provides faster or more comfortable connections to 
areas of the city that residents want to visit, this should be reflected in housing prices. Those who most 
value the transit services provided by light rail will be willing to bid more for houses that are close to 
light rail stations. On the other hand, a rail line may also detract from housing values by creating noise, 
local traffic, or increasing neighborhood crime. In Seattle, light rail replaces or supplements bus services 
that were already available in these neighborhoods. It is possible that light rail service is inferior to exist­
ing bus services, as explained in Richmond (2001), who discusses how reconfiguring the transit system 
around a rail line may impose costs on riders. 
 	
The vast majority of the work that studies the impact of transit services on housing prices relies 
on the standard hedonic framework. Most of the work utilizes cross-sectional comparisons, which are 
likely to lead to bias because of the omission of important variables. The concern in the Seattle case 
focuses on the characteristics of the station neighborhoods. To the extent that the locations of the sta­
tions are different in ways that cannot be measured with available data on housing characteristics, simple 
cross-sectional comparison of prices will suffer from omitted variable bias. For example, the ability to 
economically acquire right-of-way for the rail line dictated its exact location. Were the areas thus chosen 
for stations less desirable that other locations in the neighborhoods that the line serves? 
One way to deal with the problem of omitted variables is to use a quasi-experimental approach, 
such as difference-in-differences estimation. In this case, difference-in-differences compares the changes 
in housing prices in the areas close to stations (within the catchment area) with other houses in the same 
neighborhoods that are further away. This is analogous to an experiment—it compares how initiation of 
rail service affects the price of houses that are well-served by rail (the “experimental group”) with houses 
in the same neighborhoods that are not well-served (the “control group”). This implicitly controls for 
unobserved characteristics, since the analysis focusses only on changes in the relative prices. In this pa­
per, the analysis follows closely the methods in Pope and Pope (2015) which are discussed in detail in 
Parmeter and Pope (2012).
I implement the above ideas in a simple regression model that describes the logarithm of the real 
relative sale price, as follows:
ln(pi* )= α0+α1 Closei+α2 Afteri+α3 Close×Afteri+β’ Xi+εi	 	
	
	
(1)
Where pi* represents the real relative price of the house that sold in transaction i. Close is an indi­


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396
JOURNAL OF TRANSPORT AND LAND USE 11.1
Table 2 presents estimates of the latter comparison. That is, it compares sales that took place after 
December 2009 relative to those that took place before January 2009. This table reports an estimate of 
the proximate effect of light rail service without considering the possible forward-looking responses to 
the announcement of the system by buyers and sellers. The hedonic variables included are: number of 
bathrooms, size of the house and its square (in square feet), age of the house and its square (in years), 
acreage of lot and its square, size of garage (in square feet), if the garage is detached, whether the lot is 
located on a waterfront, and whether the lot has outstanding views. 
The coefficient on Close indicates the relative value of parcels close to stations before 2009. Thus, 
except for the Beacon Hill catchment area, houses close to light rail stations sold at a discount compared 
Table 2:  Difference-in-Differences Regression Estimates of Effect of Opening of Light Rail Differences compare years before 
opening (1992-2008) with those after (2010-2016) (Dependent variable is logarithm of relative real sale price)
Variable
Beacon Hill
Mount
Baker
Columbia
City
Othello
Ranier
Beach
Tukwila/
Intnl Blvd
SeaTac
Airport
Close (within ½ 
mile of station)
0.0384**
(0.0136)
-0.1298***
(0.0148)
-0.1048***
(0.0096)
-0.0828***
(0.0095)
-0.0031
(0.0085)
-0.0106
(0.0096)
-0.0222
(0.0154)
After (sale after 
2009)
0.2031***
(0.0206)
0.1263***
(0.0175)
0.1645***
(0.0098)
0.1199***
(0.0111)
-0.0614***
(0.0085)
-0.1539***
(0.0077)
-0.1051***
(0.0082)
Close x After
-0.0256
(0.0285)
-0.0069
(0.0284)
0.1019***
(0.0203)
0.0932***
(0.0196)
-0.0116
(0.0203)
-0.0060
(0.0226)
-0.1347***
(0.0367)
Baths
0.0285**
(0.0107)
0.0565***
(0.0091)
0.0204**
(0.0064)
0.0347***
(0.0067)
0.0241***
(0.0055
0.0185***
(0.0053)
0.0192***
(0.0057)
Size (ft2)
0.00035***
(0.00004)
0.00048***
(0.00002)
0.00030***
(0.00002)
0.00031***
(0.00002)
0.000431***
(0.000020)
0.00030***
(0.00002)
0.00031***
(0.00002)
Size2
-5.23e-08***
(1.15e-08)
-3.44e-08***
(3.17e-09)
2.03e-08***
(5.53e-09)
-2.15e-08***
(2.77e-09)
-5.10e-08***
(4.76e-09)
-2.37e-08***
(3.13e-09)
-1.94e-08***
(3.68e-09)
Age
-0.00593***
(0.000890)
-0.00213**
(0.000821)
-0.00509***
(0.000474)
-0.00484***
(0.000497)
-0.004017***
(0.000390)
-0.006067***
(0.000376)
-0.006522***
(0.000495)
Age2
5.00e-05***
(7.49e-06)
3.40e-05***
(7.43e-06)
4.25e-05***
(4.07e-06)
3.81e-05***
(4.36e-06)
3.56e-05***
(3.48e-06)
3.86e-05***
(3.79e-06)
4.83e-05***
(6.92e-06)
Waterfront
--
0.6723***
(0.0712)
0.2902***
(0.0253)
0.2269***
(0.0234)
0.4724***
(0.0201)
0.0372
(0.0498)
0.0584***
(0.0077)
View
0.0509**
(0.0181)
0.1599***
(0.0142)
0.2325***
(0.0103)
0.2869***
(0.0124)
0.2198***
(0.0087)
0.0611***
(0.0119)
0.1484***
(0.0112)
Lot (acres)
0.0509**
(0.0181)
1.3753***
(0.1827)
0.8103***
(0.0983)
0.8986***
(0.0824)
-0.0818*
(0.0355)
0.2000***
(0.0267)
0.5928***
(0.0494)
Lot2
3.0950
(1.8734)
-0.8996***
(0.1645)
-0.2619**
(0.0904)
-0.1283*
(0.0597)
0.0322**
(0.0099)
-0.0064
(0.0114)
-0.2006***
(0.0319)
Garage (sf)
0.00033***
(0.00005)
0.00022***
(0.000045)
0.00021***
(0.00003)
0.00020***
(0.00002)
0.000193***
(0.000018)
0.000171***
(0.000014)
0.000172***
(0.000014)
Detached 
Garage
0.0775***
(0.0140)
0.1413***
(0.0149)
0.0701***
(0.0087)
0.0420***
(0.0102)
0.0784***
(0.0079)
0.0480***
(0.0069)
0.0730***
(0.0085)
Constant
11.7027***
(0.0537)
11.3914***
(0.0366)
11.6696***
(0.0278)
11.5490***
(0.0244)
11.4713***
(0.0236)
11.6384***
(0.0195)
11.5527***
(0.0210)
R2
0.38
0.69
0.52
0.66
0.57
0.63
0.70
N
1,851
2,871
6,015
4,506
5,921
4,673
3,817
Notes:  * p<0.05; ** p<0.01; *** p<0.001


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397
The effect of light rail transit service on nearby property values
houses further away. In some cases, these estimated differences are large and statistically significant. For 
example, in the Mt. Baker neighborhood, houses close the eventual location of the rail station sold for 
13 percent less than houses further away, on average. 
The coefficient on After indicates the relative growth rate of houses in the analysis neighborhoods, 
compared to houses in the Seattle metro area generally. (Recall that the dependent variable is the loga­
rithm of the sale price of a house deflated by the metro-wide Case-Schiller price index.) These estimates 
indicate that relative to prices in all of Seattle, housing prices have increased more rapidly in the analysis 
neighborhoods that are closer to downtown Seattle—Othello, Columbia City, Mt. Baker, and Beacon 
Hill. Generally, the increase has been larger for neighborhoods that are closer to downtown. For neigh­
borhoods that are most distant from downtown, the relative prices have fallen.
The crucial coefficient for the analysis is the interaction variable—Close×After. This shows how 
housing prices changed with the opening of the rail system, compared to the changes of prices for houses 
in the same neighborhood that are more distant from the stations. For all but one of the neighborhoods, 
the estimate of the effect of light rail is negative. For Sea Tac and Othello, these changes are also statisti­
cally significantly different from zero. However, for the Columbia City neighborhood, the estimated 
impact of the initiation of rail service is positive, quite large, and statistically significant. 


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398
JOURNAL OF TRANSPORT AND LAND USE 11.1
Table 3 reports estimates using the earlier date window, comparing sales before January 2004 with 
those that took place after December 2009. The estimates from this analysis are very similar, both quali­
tatively and quantitatively, to the previous analysis reported in Table 2. In this case, the sample sizes are 
somewhat smaller, since the analysis excludes sales that took place between January 2004 and December 
2009. 
As a test for sensitivity, I have also examined slightly different time periods. For example, I have 
estimated the model comparing the period between 2003 and 2007 with the period from 2011 through 
2015, as well as comparing the period from 1999-2003 with the period from 2011-2015. Results are 
qualitatively very similar. Thus, the results to not appear to be sensitive to the particular definitions of 
“before” and “after.” 
Table 3:  Difference-in-Differences Regression Estimates Difference compares years before construction (1992-2003) with 
those after opening (2010-2016) (Dependent variable is logarithm of relative real sale price)
Variable
Beacon
Mount
Baker
Columbia
City
Othello
Ranier
Beach
Tukwila/
Intnl Blvd
SeaTac
Airport
Close
0.0394*
(0.0159)
-0.1449***
(0.0174)
-0.1234***
(0.0113)
-0.0907***
(0.0119)
-0.0098
(0.0104)
-0.0050
(0.0119)
-0.0206
(0.0182)
After
0.2474***
(0.0217)
0.1514***
(0.0187)
0.2074***
(0.0103)
0.1696***
(0.0120)
-0.0273**
(0.0092)
-0.1298***
(0.0084)
-0.0985***
(0.0092)
Close x After
-0.0217
(0.0297)
0.0042
(0.0297)
0.1183***
(0.0213)
-0.0883***
(0.0211)
-0.0079
(0.0215)
-0.0101
(0.0243)
-0.1330***
(0.0384)
Baths
0.0282*
(0.0121)
0.0578***
(0.0101)
0.0154*
(0.0072)
0.0272***
(0.0078)
0.0198**
(0.0065)
0.0088
(0.0063)
0.0193**
(0.0067)
Size (ft2)
0.000352***
(0.000050)
0.000483***
(0.000024)
0.000322***
(0.000026)
0.000307***
(0.000017)
0.000447***
(0.000023)
0.000323***
(0.000017)
0.000297***
(0.000021)
Size2
-5.52e-08***
(1.33e-08)
-3.58e-08***
(3.73e-09)
-2.60e-08***
(6.23e-09)
-2.09e-08***
(3.03e-09)
-5.38e-08***
(5.60e-09)
-2.92e-08***
(3.68e-09)
-1.34e-08**
(4.59e-09)
Age
-0.005313***
(0.001009)
-0.001945*
(0.000932)
-0.004767***
(0.000535)
-0.004454***
(0.000592)
-0.004380***
(0.000465)
-0.006907***
(0.000462)
-0.006782***
(0.000571)
Age2
4.16e-05***
(8.55e-06)
3.22e-05***
(8.50e-06)
3.55e-05***
(4.62e-06)
3.02e-05***
(5.18e-06)
3.43e-05***
(4.17e-06)
4.13e-05***
(4.66e-06)
4.74e-05***
(7.97e-06)
Waterfront
--   
0.6472***
(0.0771)
0.2623***
(0.0283)
0.2040***
(0.0276)
0.4644***
(0.0227)
0.0394
(0.0584)
0.0471***
(0.0091)
View
0.0611**
(0.0200)
0.1537***
(0.0162)
0.2521***
(0.0115)
0.2952***
(0.0145)
0.2219***
(0.0102)
0.0620***
(0.0144)
0.1446***
(0.0130)
Lot (acres)
0.5880
(0.6628)
1.4153***
(0.2061)
0.8670***
(0.1091)
0.9429***
(0.0931)
-0.0913*
(0.0417)
0.2292***
(0.0313)
0.5780***
(0.0567)
Lot2
1.4301
(2.4902)
-0.9184***
(0.1765)
-0.2835**
(0.0937)
-0.1501*
(0.0636)
0.0345**
(0.0108)
-0.0061
(0.0123)
-0.2115***
(0.0349)
Garage (sf)
0.000354***
(0.000052)
0.000235***
(0.000051)
0.000214***
(0.000028)
0.000223***
(0.000028)
0.000200***
(0.000021)
0.000165***
(0.000017)
0.000191***
(0.000016)
Detached 
Garage
0.0829***
(0.0156)
0.1489***
(0.0168)
0.0660***
(0.0099)
0.0415***
(0.0120)
0.0823***
(0.0092)
0.0452***
(0.0082)
0.0729***
(0.0100)
Constant
11.6382***
(0.0636)
11.3575***
(0.0420)
11.6148***
(0.0315)
11.5228***
(0.0282)
11.4539***
(0.0278)
11.6385***
(0.0233)
11.5705***
(0.0249)
R2
0.42
0.70
0.55
0.67
0.57
0.61
0.70
N
1,451
2,264
4,697
3,356
4,454
3,484
2,905
Notes:  * p<0.05; ** p<0.01; *** p<0.001


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399
The effect of light rail transit service on nearby property values
6	
Residual analysis
An important underlying assumption for the analysis is that light rail stations are not sited in areas for 
which there may have been preexisting trends in housing prices. Although I do not expect this to be the 
case, it is possible to examine the question graphically. To do this, I estimate for each of the neighbor­
hoods a hedonic price regression model but leave out the indicators for the timing of the light rail, and 
the location of the house relative to the rail station. Thus, the residuals from these regressions “contain” 
the effects that are emphasized in the analysis. I then average these residuals for each year, separately for 
the “close” houses and the “not close” houses.
Figure 4:  Residual analysis by station neighborhoods.
-.4
-.2
0
.2
.4
Residual
1990
1995
2000
2005
2010
2015
Year
< 0.5 mi
0.5-2.0 mi
Beacon Hill
-.3
-.2
-.1
0
.1
.2
Residual
1990
1995
2000
2005
2010
2015
Year
< 0.5 mi
0.5-2.0 mi
Mount Baker
-.4
-.2
0
.2
.4
Residual
1990
1995
2000
2005
2010
2015
Year
< 0.5 mi
0.5-2.0 mi
Columbia City
-.3
-.2
-.1
0
.1
.2
Residual
1990
1995
2000
2005
2010
2015
Year
< 0.5 mi
0.5-2.0 mi
Othello
-.1
-.05
0
.05
.1
Residual
1990
1995
2000
2005
2010
2015
Year
< 0.5 mi
0.5-2.0 mi
Rainier Beach
-.1
-.05
0
.05
.1
Residual
1990
1995
2000
2005
2010
2015
Year
< 0.5 mi
0.5-2.0 mi
Rainier Beach
-.4
-.3
-.2
-.1
0
.1
residual
1990
1995
2000
2005
2010
2015
year
< 0.5 mi
0.5-2.0 mi
SeaTac Airport


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401
The effect of light rail transit service on nearby property values
Table 4:  Impact of Light Rail Opening on Characteristics of Houses Sold
Variables
Beacon
Hill
Mount
Baker
Columbia
City
Othello
Rainier
Beach
Tukwila/
Intnl Blvd
SeaTac
Airport
Panel A:  Size (ft2)
Close
46.57
-492.6***
15.76
-144.0***
-1.856
-21.10
-511.7***
(40.64)
(50.99)
(30.98)
(32.10)
(31.52)
(39.78)
(44.40)
After
49.18
165.2***
120.2***
198.2***
101.2***
128.4***
79.87***
(53.28)
(54.23)
(23.36)
(39.96)
(23.82)
(30.67)
(29.08)
Close x After
105.6
-6.211
-2.105
-72.64
9.026
19.14
213.6
(72.24)
(73.89)
(45.06)
(55.37)
(61.70)
(89.35)
(137.6)
Constant
1,535***
2,315***
1,636***
1,788***
1,659***
1,652***
1,687***
(30.28)
(38.88)
(14.16)
(20.22)
(13.61)
(14.52)
(14.87)
Observations
1,846
2,868
6,010
4,499
5,903
4,660
3,815
R-squared
0.009
0.068
0.006
0.018
0.004
0.005
0.025
Panel B:  Lot Size (Acres)
Close
-0.00540**
-0.0132***
-0.00161
-0.0171***
-0.0352***
0.0110
-0.0277***
(0.00233)
(0.00277)
(0.00291)
(0.00295)
(0.00354)
(0.00956)
(0.00441)
After
-0.00817**
0.00364
0.00115
0.0117**
0.00415
0.00599
0.00684
(0.00340)
(0.00437)
(0.00248)
(0.00558)
(0.00505)
(0.00665)
(0.00674)
Close x After
0.0105**
0.00491
-0.0164***
-0.0151**
0.00212
0.00977
0.0118
(0.00432)
(0.00812)
(0.00464)
(0.00666)
(0.00723)
(0.0205)
(0.0149)
Constant
0.110***
0.126***
0.126***
0.153***
0.179***
0.251***
0.242***
(0.00188)
(0.00229)
(0.000976)
(0.00217)
(0.00247)
(0.00349)
(0.00291)
Observations
1,846
2,868
6,010
4,499
5,903
4,660
3,815
R-squared
0.007
0.009
0.004
0.012
0.011
0.001
0.002
Panel C:  Age
Close
8.644***
6.734***
-3.293**
-6.230***
-7.467***
0.593
14.34***
(1.948)
(1.627)
(1.533)
(1.479)
(1.398)
(1.465)
(1.085)
After
-4.235
5.476***
6.416***
9.457***
11.53***
8.939***
11.37***
(3.250)
(1.880)
(1.194)
(1.358)
(1.004)
(1.131)
(0.855)
Close x After
12.26***
2.863
-14.78***
-5.728**
1.595
3.618
-7.104
(3.856)
(2.758)
(2.880)
(2.612)
(2.753)
(2.940)
(5.166)
Constant
62.08***
60.96***
57.20***
50.46***
47.40***
41.59***
36.47***
(1.704)
(1.175)
(0.645)
(0.769)
(0.555)
(0.524)
(0.406)
Observations
1,846
2,868
6,010
4,499
5,903
4,660
3,815
R-squared
0.046
0.021
0.015
0.023
0.037
0.021
0.073
Panel D:  Number of Bathrooms
Close
-0.152***
-0.607***
-0.0277
-0.0605
0.135***
0.0117
-0.636***
(0.0546)
(0.0575)
(0.0404)
(0.0439)
(0.0471)
(0.0559)
(0.0619)
After
0.293***
0.252***
0.214***
0.242***
0.138***
0.192***
0.119***
(0.0769)
(0.0577)
(0.0329)
(0.0454)
(0.0313)
(0.0396)
(0.0375)
Close x After
-0.00759
-0.0730
0.216***
0.0396
-0.0676
-0.0771
0.178
(0.104)
(0.0827)
(0.0681)
(0.0799)
(0.0891)
(0.119)
(0.167)
Constant
1.712***
2.466***
1.840***
1.978***
1.858***
1.862***
1.954***
(0.0440)
(0.0427)
(0.0195)
(0.0243)
(0.0177)
(0.0190)
(0.0204)
Observations
1,846
2,868
6,010
4,499
5,903
4,660
3,815
R-squared
0.031
0.088
0.016
0.012
0.006
0.006
0.024
Notes:  Standard errors in parenthesis.  Standard errors are heteroskeasticity robust and clustered to account for multiple sales 
of homes in the sample.  *** p<0.01, ** p<0.05, * p<0.1


===== PAGE 16 (score 13, numeric-paren 0, table-words 0, var-words 13) =====

402
JOURNAL OF TRANSPORT AND LAND USE 11.1
Panel B reports the estimates for lot size. In this case, the experimental effects estimates are small 
and not statistically significant for most of the stations. However, for Beacon Hill the estimate is positive 
and significant, and for Columbia City and Othello, it is negative and significant. The differences range 
from about 1/60th of an acre to about 1/100th of an acre, but in these neighborhoods the lots are typi­
cally quite small—around one-eighth of an acre for the Columbia City sample.
Panel C reports estimates for age. Again, the estimates for Beacon Hill are positive and statistically 
significant and for Columbia City and Othello they are negative and significant. Thus, the houses that 
are selling near Columbia City and Othello are disproportionately newer suggesting a relative influx of 
new housing around the Columbia City and Othello stations. The positive age effect for Beacon Hill 
suggests a relative influx of new housing in parts of the neighborhood that are not close to the station—
that the catchment area for the rail station is more stagnant with respect to new construction.
Panel D shows estimates for the number of bathrooms. The only statistically significant experimen­
tal effect is for Columbia City, and it is rather large at about .22. 
Taken together, these estimates indicate that there have been rather significant changes in the com­
position of housing near the Columbia City station. It is possible that at least some of the large positive 
estimated impact of rail access in this neighborhood is due to significant changes in the composition of 
the housing stock in that neighborhood. For most of the other stations, it appears that there has been 
little or no difference between the catchment areas and more distant parts of the neighborhood in terms 
of the composition of the housing stock. Clearly, some caution is warranted in the interpretation of the 
results for the Columbia City station. 
8	
Discussion
Although many studies report beneficial impacts of light rail on property values, the mostly null (or 
negative) findings of this study are not unique. For example, Kim and Lahr (2014) study the impact 
of the Hudson-Bergen light rail line in Northern New Jersey using a repeat sales method that is similar 
to the differences-in-differences method used here. They find different effects for different stations, 
arguing that those neighborhoods that experienced the largest housing price appreciation were in areas 
that experienced much improved transit service. Housing near stations that were already well-served by 
public transit options experienced little or no benefit. Chatman, Tulach, and Kim (2012) find neutral 
to negative impact on housing prices of the River Line in the New Jersey suburbs of Philadelphia using 
a repeat-sales method. Also, using a model of longitudinal changes in housing prices, Papon, Nguyen-
Luong, and Boucq (2015) found no beneficial impact of a new light rail line in Paris. Many of the cross-
sectional studies discussed by Higgins and Konaroglu (2016) also find negative proximity impacts of rail 
transit stations, but as explained earlier, it is difficult to interpret these results as causal as there may be 
reasons for light rail stations to be built in areas with depressed housing values. 
Taking the estimates of the present study at face value, it suggests that the transportation benefit 
of the new light rail line in Seattle is small, or that non-transportation externalities, such as noise and 
higher auto congestion near stations, outweigh the benefits. While the public perception of light rail is 
that it is superior to systems based on buses, this is not necessarily the case, as Richmond (2005) argues 
in his analysis of the development of Los Angeles’ light rail network. In particular, redesigning the bus 
transportation network to accommodate the fixed route of a rail line may significantly reduce the utility 
of public transit for many users (Richmond, 2001). 
There are limitations to using this study as a basis for evaluating the total benefit of the new light 
rail service in Seattle. The study makes no attempt to estimate the benefits to users that do not live in the 
residential neighborhoods of the Rainier Valley. Due to limitations in the data, this study is unable to 
evaluate the impact of light rail service on apartment dwellers. Commercial properties in downtown ar­