# RECORD b827a50a5b3fe735ba3475c0514ffecf35e91d7e
# TITLE: If you build it, they will change: Evaluating the impact of commuter rail stations on real estate values and neighborhood composition in the Rotterdam–The Hague metropolitan area, the Netherlands
# YEAR: 2021.0  VENUE: Journal of Transport and Land Use  DOI: 10.5198/jtlu.2021.1795
# AUTHORS: Amir Forouhar; D. van Lierop
# PAGES: 25  CANDIDATE TABLE PAGES (1-based): [10, 13, 15]
# ABSTRACT: In many metropolitan regions, transit-oriented developments are built to motivate the use of sustainable travel by promoting urban growth within walking distances of public transport stations. Changes in residential property values are a common way to assess the success of transit-oriented developments. However, studies that focus on property values alone have reported mixed effects. This paper attempts to evaluate the land value impact around commuter rail stations by analyzing the change in property values within the context of the transformation of socio-spatial neighborhood attributes. The study sets out to estimate the effect of Randstad Rail stations using real estate transaction data of residential properties and neighborhood socio-spatial attributes in the Rotterdam–the Hague metropolitan area of the Netherlands covering a period from 1985 to 2018. Adopting a quasi-experimental design, the effect is estimated for properties within different catchment zones around three commuter rail stations using a Difference-in-Differences Model and Multivariate Analysis of Variance. The results demonstrate the overall negative effect of the Randstad Rail on the value of residential properties at a distance equal to or less than 400 meters from the selected rail stations in the range of -18.8% to -11.5%. In contrast, a positive effect is observed for the residential properties located within a radius of 400 to 800 meters from the rail stations, which is estimated to be +15% to +33.2%. The findings also indicate a considerable socio-spatial transformation in the neighborhood composition after the opening of the rail stations in terms of neighborhood population density, land-use density, housing characteristics, and car ownership, which significantly affect the magnitude and direction of the impact.


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1	
Introduction
In many regions, the introduction of urban rail transit has been associated with reductions in air pol-
lution, noise, traffic accidents, and road congestion (Anderson, 2014; Baum-Snow, Kahn, & Voith, 
Article history:
Received: May 11, 2020
Received in revised form:  
January 15, 2021 
Accepted: May 2, 2021
Available online: September 8, 
2021
Copyright 2021 Amir Forouhar & Dea van Lierop
http://dx.doi.org/10.5198/jtlu.2021.1795
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. 
T J  T  L U    http://jtlu.org
V. 14 N. 1 [2021] pp. 949–973
Amir Forouhar
Art University of Isfahan
a.forouhar@aui.ac.ir
Dea van Lierop
Utrecht University
D.S.vanlierop@uu.nl
If you build it, they will change: Evaluating the impact of 
commuter rail stations on real estate values and neighborhood 
composition in the Rotterdam–The Hague metropolitan area,  
the Netherlands
Abstract: In many metropolitan regions, transit-oriented developments 
are built to motivate the use of sustainable travel by promoting urban 
growth within walking distances of public transport stations. Changes 
in residential property values are a common way to assess the success of 
transit-oriented developments. However, studies that focus on property 
values alone have reported mixed effects. This paper attempts to evaluate 
the land value impact around commuter rail stations by analyzing the 
change in property values within the context of the transformation of 
socio-spatial neighborhood attributes. The study sets out to estimate 
the effect of Randstad Rail stations using real estate transaction data of 
residential properties and neighborhood socio-spatial attributes in the 
Rotterdam–the Hague metropolitan area of the Netherlands covering 
a period from 1985 to 2018. Adopting a quasi-experimental design, 
the effect is estimated for properties within different catchment zones 
around three commuter rail stations using a Difference-in-Differences 
Model and Multivariate Analysis of Variance. The results demonstrate 
the overall negative effect of the Randstad Rail on the value of residential 
properties at a distance equal to or less than 400 meters from the selected 
rail stations in the range of -18.8% to -11.5%. In contrast, a positive 
effect is observed for the residential properties located within a radius 
of 400 to 800 meters from the rail stations, which is estimated to be 
+15% to +33.2%. The findings also indicate a considerable socio-spatial 
transformation in the neighborhood composition after the opening of 
the rail stations in terms of neighborhood population density, land-use 
density, housing characteristics, and car ownership, which significantly 
affect the magnitude and direction of the impact. 
Keywords: Urban rail transit, transit-oriented development, property 
value, socio-spatial transformation, Difference-in-Differences Model, 
Randstad Rail, the Netherlands


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JOURNAL OF TRANSPORT AND LAND USE 14.1
to match the CBS dataset with the housing sales data for each station at a given transaction year. The 
second limitation was due to the changes in the variables collected by the CBS and changes to variable 
definitions over time. For example, since 2006, the classification of companies and institutions was 
significantly changed, and thus, the number of companies and institutions in 2007 was no longer com-
parable with the previous data. In addition, some additional variables were also added to CBS reports 
such as the type of households, income of inhabitants, and social security conditions. Thus, we excluded 
the variables which were inconsistent in the CBS datasets between 1985 and 2018. The resulting dataset 
was exported into IBM SPSS Software (version 25) for each station and catchment zone. The ultimate 
dataset contains 12492, 8619, and 9961 valid transactions respectively for Barkel Westpolder, Pijnacker 
Zuid, and Nootdorp stations, covering a period from 1985 to 2018. The dataset encompasses informa-
tion related to housing structural characteristics, distance-based locational factors, land-use conditions, 
population factors, and transportation features which are further described in Table 1. 
Table 1. Descriptive statistics of contextual variables
Category
Variable Name
Variable 
Type
Minimum
Maximum
Mean
Std. 
Deviation
Source
Structural Factors
Transaction price (euro/m2)
scale
166
5899
2085.32
695.61
Brainbay
Construction date
nominal
before 1500
from 2001 
onwards
-
-
Brainbay
Insulation
nominal
without 
insulation
fully insulated
-
-
Brainbay
Surface area (m2)
scale
70
450
139.94
33.56
Brainbay
Number of balconies
scale
0
2
.08
.29
Brainbay
Number of floors
scale
1
5
2.79
.55
Brainbay
Number of toilets 
scale
1
4
1.38
.62
Brainbay
Garden area (m2)
scale
15
1925
78.85
78,17
Brainbay
Garage
dummy
without 
garage
with garage
-
-
Brainbay
Location Factors
Distance to nearest CBD (km)
scale
1.823
5.342
2.732
.453
CBS
Distance to nearest arterial 
road (Km)
scale
.143
2.331
.972
.260
CBS
Distance to built-up area
dummy
inside built-
up area
outside built-
up area
-
-
Brainbay
Land-use Factors
Available land (ha)
scale
10.00
1159.00
53.77
162.1
CBS
Land-use density (per km2)
scale
129.00
2518.00
877.41
300.94
CBS
Number of houses 
scale
10.00
7765.00
457.20
713.28
CBS
Number of business locations 
scale
1.00
870.00
33.51
56.04
CBS
Population Factors
Population density (per km2) 
scale
19.00
10752.00
4920.10
3360.88
CBS
Number of inhabitants 
scale
20.00
15940.00
1319.86
1935.99
CBS
Number of households 
scale
10.00
7830.00
459.62
714.43
CBS
Number of immigrants
scale
32.50
6376.00
255.41
422.93
CBS
Transportation 
Factors
Number of cars 
scale
15.00
5350.00
568.81
807.73
CBS
Number of cars per household
scale
.70
1.60
1.23
.14
CBS


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Evaluating the impact of commuter rail stations on real estate values and neighborhood composition
6	
Results 
The results obtained from the DID model are reported in Table 2 for the selected stations and differ-
ent catchment zones. The table not only demonstrates the impact of opening the selected rail stations 
on property values, but also specifies the effect of housing structural characteristics, location factors, 
land-use conditions, population factors, and transportation features on the results. The DID models for 
residential properties explain between 0.651 and 0.828 percent of the variation in property values for 
the selected stations. Broadly speaking, the DID estimator demonstrates a negative effect of Randstad-
Rail on the value of residential properties located close to the stations at a distance equal to or less than 
400 meters from the rail stations, while a significant positive impact on the residential properties located 
within a radius of 400 to 800 meters from the rail stations. The results of the DID models indicate that 
the treatment effects of the RandstadRail on housing values within a 400-meter radius from the selected 
stations are estimated to be -18.8%, -11.5%, and -15.3% respectively for Barkel Westpolder, Pijnacker 
Zuid, and Nootdorp stations. In other words, the average selling price of treated properties declined 
after the opening of the selected stations, compared to the control properties which were located within 
a radius of 1000 to 1600 meters of the rail stations. On the contrary, the DID estimator demonstrates a 
significant positive effect for properties located in the second catchment zone (400 to 800 meters), com-
pared to the control properties. The treatment effects on the housing values within a radius of 400 to 
800 meters from the rail stations indicate +33.2%, +15%, and +25.8% for Barkel Westpolder, Pijnacker 
Zuid, and Nootdorp stations, respectively. 
The DID models also yield that the attributes of the dwelling units and the neighborhood com-
position in terms of accessibility, land-use, population, and transportation features also played a con-
siderable role in changing housing prices in both catchment zones around the rail stations. The models 
reveal that an increase in housing prices is often associated with improvements in housing facilities such 
as insulation type, the number of toilets and balconies, and the presence of a garage. Furthermore, an 
increase in house size (surface area and garden area) and a decrease in the building age of the properties 
have a positive relationship with the dependent variable. In the case of the Barkel Westpolder station, 
the total surface area, insulation type, and construction date explain the highest percentage of change 
in housing prices near to the station, compared to other structural factors. However, the number of 
balconies does not show a significant relationship. The surface area also has the most explanatory power 
in the cases of Pijnacker Zuid and Nootdorp stations, as it explains 37.1 and 31.3 percent of the change 
in housing prices close to the stations, respectively.
 In addition, the models demonstrate that all selected locational variables have the expected signs. 
This means that when the distance from the nearest CBD and arterial roads increases, the housing value 
declines when holding all other variables constant. Distance to the nearest CBD explains 54.2, 23.1, and 
20.5 percent of the changes in housing prices close to Barkel Westpolder, Pijnacker Zuid, and Nootdorp 
stations, respectively. The location of residential properties in built-up areas can also positively uplift the 
housing prices, compared to the properties located in non-urban areas.
Land availability is also estimated to have a strong relationship with the housing values in the DID 
models. This is mostly an important factor in the context of the Netherlands in which land reclamation 
has a long history (Hoeksema, 2007). The models reveal that the land availability explains between 21.3 
and 61.8 percent of the changes in housing prices close to the selected stations, when holding all other 
variables constant. Moreover, land-use density—which refers to the number of houses, offices, schools, 
and shopping facilities per square kilometers—as well as the number of business locations are positively 
correlated with the price in the catchment areas around the rail stations. For example, in the case of 
Nootdorp station, the land-use density explains 30.9 percent of the change in housing prices in the first 


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Evaluating the impact of commuter rail stations on real estate values and neighborhood composition
Table 2. Estimating the effect of RandstadRail on residential property values using the difference-in-differences model
Berkel Westpolder
Pijnacker Zuid
Nootdorp
Catchment 
Zone 1
Catchment 
Zone 2
Catchment 
Zone 1
Catchment 
Zone 2
Catchment 
Zone 1
Catchment 
Zone 2
Category
Overall R Square
.828
.760
.698
.724
.664
.651
F-test
1101.552***
803.288***
413.835***
526.631***
320.725***
331.419***
Structural Factors
Construction Date
.114***
.118***
.147***
.054***
.144***
.080***
Insulation 
.136***
.158***
.136***
.156***
.014
.048***
Surface area (m2)
.529***
.006
.371***
.078***
.313***
.036**
Number of toilets
.092***
.036***
.097***
.032***
.054***
.019
Garden area (m2)
.004**
.006***
.008
.005**
.099***
.091***
Number of balconies
.003
.050***
.094***
.056***
.048***
.025**
Garage 
.107***
.141***
.243***
.176***
.016
.192***
Location Factors
Distance to nearest 
CBD (Km)
-.542***
-.583***
-.231***
-.050***
-.205***
-.222***
Distance to nearest 
arterial road (Km)
-.060***
-.061***
-.067***
-.095***
-.022***
-.011***
Distance to the built-
up area
-.551***
-.615***
-.195***
-.025***
-.241***
-.233***
Land-use Factors
Available Land (ha)
.638***
.441***
.064***
.209***
.213***
.037
Land-use density (per 
km2)
.485***
-.060
.404***
.095***
.309***
.346***
Number of houses
-.195***
-.099**
-.048***
-.123**
-.030***
-.096**
Number of business 
locations
.136***
.112***
.099***
.077***
.093***
.167***
Population  
Factors
Population density 
(per km2)
.142***
.158***
.294***
.160***
.010***
.041***
Number of Immi-
grants 
.058***
.035***
.132***
.097***
.038**
.127***
Transportation 
Factors
Number of cars
-.583***
-.488***
-.213***
-.088**
-.274***
-.095***
Time
Month
.019***
.001
.027***
030***
.091***
.090***
Year
.072***
.287***
.150***
.163***
.276***
.422***
Difference-in-
Differences 
Estimation
Interaction 
(Time*Group)
-.188***
.332***
-.115***
.150***
-.153**
.258***
 
Legend: *p = .10; **p = .05; ***p = .01. Values represent Standardized Coefficients Beta for each variable. All entered inde-
pendent variables satisfy a VIF of less than 2.5.
The results obtained from the Multivariate Analysis of Variance (MANOVA) suggest that the 
neighborhoods located near the selected stations (in both the treated catchment zones) experienced a 
considerable socio/spatial transformation after the opening of the rail stations compared to the control 
neighborhoods (Table 3). The findings indicate that the residential properties closest to the rail stations 
tended to become denser after the opening of the rail stations, since the average surface area of the 
residential properties decreased in the range of -6.29 to -75.25 square meters, and the number of floors 
significantly increased around the selected stations, compared to the control properties. For example, 
while the average surface area of the residential properties located in the control zone of Barkel Westpol-


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JOURNAL OF TRANSPORT AND LAND USE 14.1
der station increased from 136.86 to 144.10 square meters following the opening of the rail station, the 
treated properties close to the rail station experienced a decrease from 159.73 to 137.70 square meters. 
Regarding the changes in the number of floors, the changes near the selected station were more severe 
compared to the control neighborhoods. For instance, the residential properties located in the control 
zone of Nootdorp station experienced an increase in their number of floors from 2.53 to 2.92, however 
the treated properties experienced a more dramatic increase from 2.26 to 2.93 following the opening of 
the rail station.
 The results of the MANOVA also demonstrate a considerable increase in the number of houses, 
offices, schools, and shopping facilities (land-use density) in both the treated catchment zones following 
the opening of the selected rail stations, in such a way that the land-use density increased by 352.30, 
302.04, and 676.97 per square kilometer, respectively within a 400-meter radius from Barkel Westpol-
der, Pijnacker Zuid, and Nootdorp stations. Furthermore, in the neighborhoods close to the selected 
stations, the total number of houses increased by 500.81, 795.42, and 1466.21, and the number of busi-
ness locations rose by 100.89, 39.18, and 166.85, respectively for Barkel Westpolder, Pijnacker Zuid, 
and Nootdorp stations. The model also yields that the increase in land-use density significantly hap-
pened more intensely close to the rail stations compared to the control neighborhoods. As the land-use 
density increased by 243.86, 147.85, and 232.3 in the control neighborhoods of Barkel Westpolder, Pi-
jnacker Zuid, and Nootdorp stations respectively, the catchment zones close to the stations experienced 
a sharper increase in the land-use density by 596.16, 449.88, and 909.27. This means, that as expected, 
the areas near the new stations gained greater increases in land-use density. 
Population density also increased in both the treated catchment zones following the opening of the 
selected stations in the range of 1738 to 4638 inhabitants per square kilometer. For example, while the 
population density in the control zone of Pijnacker Zuid station increased from 6902.20 to 7512.66 per 
square kilometers following the opening of the rail station, the treated neighborhoods closer to the rail 
station experienced a sharper increase, changing from 2246.88 to 5185.00 per square kilometer. Subse-
quently, a significant increase in the number of inhabitants and households is observed around all three 
stations. In addition, the percentage of immigrant populations had a significant positive correlation 
with the introduction of the selected rail stations in the range of 3.05 to 18.17 percent. In this respect, 
the neighborhoods located within a 400-meter radius from the stations experienced a stronger wave of 
immigration, compared to the control neighborhoods and even the second catchment zones around 
the selected stations. Considering the change in the transportation features also revealed that opening 
the rail stations significantly decreased the number of cars per household in the range of 0.022 to 0.188 
in both the treated catchment zones around the selected stations even though the total number of cars 
increased considerably in both treated neighborhoods up to 2046. It should be mentioned that while 
the number of cars in the control neighborhoods around the selected stations often dropped during the 
period of the study, the treated neighborhoods close to the stations experienced a considerable increase 
in the number of cars, most likely due to the increases in population density. 


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963
Evaluating the impact of commuter rail stations on real estate values and neighborhood composition
Table 3. Socio-spatial transformation of the neighborhoods around the rail stations using Multivariate Analysis of Variance
Berkel Westpolder
Pijnacker Zuid
Nootdorp
Category
Catchment 
Zone 1
Catchment 
Zone 2
Catchment 
Zone 1
Catchment 
Zone 2
Catchment 
Zone 1
Catchment 
Zone 2
Partial Eta Squared
0.867
0.865
0.875
0.890
0.813
0.699
F-test
1252.142***
1310.622***
3109.525***
2165.279***
1985.660***
4256.494***
Structural 
Factors
Surface area (m2)
-29.273***
-24.543***
-6.292***
0.514 
-75.250***
-20.218***
Number of floors
0.238**
0.169***
0.290***
0.030 ***
0.289***
.133***
Land-use  
Factors
Land-use density (per 
km2)
352.299***
277.253***
302.036***
69.991***
676.974***
286.010***
Number of houses
500.811***
468.106***
795.419***
87.286***
1466.213***
729.485***
Number of business 
locations 
100.888***
-11.263 
39.176***
-38.242***
166.854***
17.804***
Population 
Factors
Population density (per 
km2)
4638.663***
3789.933***
2327.668***
1738.397***
2611.191***
2505.587***
Number of inhabitants
1674.402***
1505.673***
2287.514***
216.215**
4509.106***
2448.001***
Number of households
523.827***
480.425***
790.390***
113.011**
1458.585***
729.036***
Percentage of immi-
grants
18.168***
3.054***
8.578***
4. 017***
13.503***
3.330***
Transportation 
Factors 
Number of cars
602.496***
556.758***
934.350***
156.388**
2046.924***
1016.806***
Number of cars per 
household
-0.087***
-0.087***
-0.188***
-0.188***
-0.022 **
-0.116***
Legend: *p = .10; **p = .05; ***p = .01. Values represent Unstandardized Coefficients Beta for each variable.
7	
Discussion
The results of this study contribute to the existing literature by providing a possible explanation of how 
property values can be differently affected by the presence of commuter rail stations through the use of 
a context-sensitive approach that connects the change in property values to the socio-spatial transforma-
tion of neighborhoods around the rail stations. The results reveal that the introduction of the Rands-
tadRail brought a negative effect on the value of residential properties at a distance equal to or less than 
400 meters from the rail stations. However, the results indicate a significant positive effect for properties 
located within a radius of 400 to 800 meters around the stations. The findings are consistent with the 
literature in which housing prices located relatively close to rail stations are affected negatively, but those 
at an intermediate distance and which are beyond the externality effects, benefit from the transporta-
tion access provided by the rail stations. For example, in the context of the Netherlands, Debrezion 
et al. (2011) found that the neighborhoods located immediately next to new stations were affected 
negatively due to the externality effects as the houses located adjacent to the commuter rail stations (less 
than 250 meters) sold for lower prices compared to houses located only a few hundred meters from the 
station. Furthermore, the properties located between 250 and 500 meters of a station were on average 
about 25% more expensive than dwellings located at a distance of 15 kilometers or farther. Similarly, in 
Dubai, the United Arab Emirates, Mohammad et al. (2017) estimated a negative effect between -9% 
and -17.7% on the value of residential prices within 0.5 kilometer of the stations, but a positive impact 
of about 13% for properties located within 701 to 900 meters of the rail stations. AlQuhtani and Anjo-
mani (2019) also concluded that the residential properties that were closer to rail stations in the Dallas 


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JOURNAL OF TRANSPORT AND LAND USE 14.1
Fort-Worth metropolitan area experienced lower changes in housing value compared to the properties 
located farther away from stations. Since the present study captured the negative impacts close to the 
selected stations nearly a decade after the opening of the stations, the results contradict the findings by 
Pan (2019), Lee and Sener (2017), Cervero and Duncan (2002), and Chen et al. (1998) who reported 
that accessibility effects finally negate nuisance as a transit system matures or a significant positive effect 
would be expected to appear after a reasonable timespan.
The literature insinuates that factors such as noise, vibration, and pollution, which are associated 
with being too close to a rail line or station, particularly in the case of heavy or commuter rail, may be 
responsible for observed negative effects (AlQuhtani & Anjomani, 2019; Bowes & Ihlanfeldt, 2001; 
Debrezion et al., 2011; Forouhar & Hasankhani, 2018; Martínez & Viegas, 2009; Mohammad et al., 
2017). These are also important factors in the context of the Netherlands in which commuter rail lines 
and stations are often located above ground and near residential neighborhoods. A study on the impact 
of the nuisance and vibration caused by high-speed railways on housing prices in the Netherlands indi-
cated that the noise produced by Dutch railways negatively affected property prices located adjacent to 
the rail lines at up to 12 percent (Theebe, 2004). However, the results of the current study indicate that 
the nuisance effects could simply be a part of the reason behind the mixed estimated effects, and further 
explanation could lie in the transformation of the surrounding neighborhoods. The results of the cur-
rent study showed that the opening of the rail stations was often accompanied by a significant increase 
in population density as well as an increase in the percentage of immigrants residing in the adjacent 
neighborhoods. This observation is in line with empirical studies conducted by Wang and Chen (2018), 
Papa et al. (2008), and Transecon Consortium (2003) who reported that high-speed rail significantly 
contributes to a shift of residents from the city center to peripheral areas. It is worth mentioning that in 
the current study, the increases in population density and percentage of immigrants were higher in the 
immediate station area (≤400 meters) compared to the 400-800 meters zone. The observed increase in 
the percentage of immigrants could be related to the fact that newcomers may seek home locations that 
are within walking distance to transit stations to have good access to potential jobs, retail, education, and 
other opportunities. At the same time, the number of business locations considerably increased close to 
the selected rail stations compared to the farther neighborhoods. This finding is in line with those of Pan 
(2019) and Bowes and Ihlanfeldt (2001), who reported that increases in population density and im-
migration around rail stations could expand the retail activities and attract businesses and firms to locate 
around the station areas. However, Bowes and Ihlanfeldt (2001) explained that the population changes 
around rail stations may give rise to socio-cultural tensions like crime rates which would not be desir-
able for local residents (Bowes & Ihlanfeldt, 2001). A study by Forouhar and Hasankhani (2018) also 
reported that the local identity and privacy of the immediate neighborhoods can become profoundly 
damaged following the opening of a rail station due to increased population density and the presence 
of strangers. 
On the other hand, the current study also captured a significant transformation in the character-
istics of houses and built-up areas close to the case stations which could be related to the negative ex-
ternalities in the immediate neighborhoods. The findings showed that the residential properties tended 
to become denser after the opening of the rail stations, since the average surface area of the residential 
properties decreased and the number of floors in residential buildings increased around the selected sta-
tions, compared to the control properties. The results also demonstrated a considerable increase in the 
number of houses, offices, schools, and shopping facilities (land-use density) in both the treated catch-
ment zones following the opening of the selected rail stations. Thus, the overall findings imply that there 
is an increasing tendency toward a denser spatial structure around the selected rail stations. The results 
are consistent with the findings by Kasraian et al., (2016) who reported that rail infrastructure prompted 
further urban growth in the Randstad, the Netherlands, suggesting that urbanization is more likely to