L. Miguel Martínez and José Manuel Viegas
Effects of Transportation Accessibility on Residential Property Values: A Hedonic
Price Model in the Lisbon Metropolitan Area
L. Miguel Martínez
PhD Candidate
CESUR, Department of Civil Engineering
Instituto Superior Técnico
Lisbon Technical University
Av. Rovisco Pais 1049 - 001 Lisboa, Portugal.
Phone: +351-21-8418425
Fax: +351-21-840 9884
Email: [email protected]
José Manuel Viegas
Professor of Civil Engineering
CESUR, Department of Civil Engineering
Instituto Superior Técnico
Lisbon Technical University
Av. Rovisco Pais 1049 - 001 Lisboa, Portugal.
Phone: +351-21-8418413
Fax: +351-21-840 9884
Email: [email protected]
The total number of words is 8,389 (5,889 words + 4tables + 6figures)
Submitted to the 88th
Annual Meeting of the Transportation Research Board
15th
of November, 2008
L. Miguel Martínez and José Manuel Viegas 1
ABSTRACT
The aim of this paper is to examine the relationship between the availability of transportation
infrastructure and services and the pattern of house prices in an urban area, and to assess
whether public investment in transportation can really modify residential property values.
This study was developed for the Lisbon Metropolitan Area (LMA) as part of a broader study
that intends to develop new value capture financing schemes for public transportation in the
LMA. The paper focuses in three central municipalities (Amadora, Lisbon, Odivelas) where
these effects could be more easily measured due to the existence of a significant variability of
public transportation services.
The paper tries to determine, using different spatial hedonic pricing models, the extent to
which access to transportation infrastructure currently is capitalized into house prices,
isolating the influence of three different transportation infrastructures: metro, rail and road.
The results suggest that the proximity to one or two metro lines leads to significant property
value changes and that the classic hedonic price model (ordinary least squares estimation)
leads to similar coefficient values of the local accessibility dummy variables compared to the
spatial lag model, thus providing a steady basis to forecast the property values changes
derived from transportation investment for the study area in the absence of a significant
property values database.
L. Miguel Martínez and José Manuel Viegas 2
INTRODUCTION
For decades, there has been considerable discussion about the effects of transportation
accessibility on the housing prices. It is well known that a good public transport system
provides a high level of access to work and other activities for households, and to customers
and employees for businesses. The monetary value of this accessibility will be reflected in
the value of a home or a business, in addition to the value of other features such as the
specific physical attributes of the building and neighbourhood characteristics.
The impact of public transport on property values has been studied from many
perspectives, including analyses of different types of systems (e.g., rapid, commuter, light
rail), of residential versus commercial impacts, and studies that have attempted to isolate
both positive and negative effects. The varied approaches make it difficult to compare the
results of one study to another. Further, some of the contradictory results over the years have
often been due to differing methods of analysis, data quality, and regional differences.
This paper examines the relationship between the availability of transportation
infrastructure and services and the house prices in an urban area, trying to assess the impact
of public investment in transportation on residential property values. This study was
developed for the Lisbon Metropolitan Area (LMA) as part of a broader study that intends to
develop new value capture financing scheme for public transportation in the LMA.
The available data focuses in three central municipalities (Amadora, Lisbon, Odivelas) where
these effects could be more easily measured due to the existence of a significant variability of
public transportation services.
This study presents several hedonic pricing models to assess the relationship between
transportation accessibility and house values, ranging from the classic model to spatial
hedonic price models (spatial lag) and including local and systemwide accessibility
indicators. The results of the different models are assessed and compared having in mind the
need to forecast house prices in subsequent phases of the research project.
LITERATURE REVIEW
In the 1960s, economists like Alonso and Muth developed the theory for determining
residential location in the urban land market (1, 2). The theory illustrates a model where a
household chooses to locate at a point where its bid-rent curve intersects with the actual one,
in which the bid rent curves have a declining gradient with the distance from the residential
location to the central business district (CBD). However it might be necessary to consider the
effect of other variables such as neighborhood characteristics.
The introduction of the hedonic pricing methodology by Rosen (3) led to an easier
way of attributing value to different properties’ features. A number of studies have observed
the integration of physical, neighborhood and accessibility characteristics of the property in
models trying to explain the differences in property values or house prices (4-35). The
hedonic price model is a multivariate regression model for housing values, as well as a
common robust indirect approach to valuation in that its estimates represent the implied
prices that people place on obtaining desirable features of a property and avoiding
undesirable ones (20, 36).
Most commonly, hedonic price models have used ordinary least squares (OLS)
estimation (22, 33, 37-39), but more recently these models have been extended to incorporate
L. Miguel Martínez and José Manuel Viegas 3
spatial effects in multiple ways: feasible generalized least square estimation (34) and spatial
econometric models (spatial lag and spatial error models) (20, 40).
There are several empirical evidences relating the changes in commercial and
residential property market values and transport investment. Table 1 presents the information
from the Europe, whilst Table 2 does the same for North America.
As can be seen from the tables, the evidence is broadly positive with the widest
difference being found between the residential and commercial markets. Parsons Brinkerhoff
(41) concludes that proximity to rail systems is valued by property owners and there is little
support that this proximity can decrease property values.
Much of the European research (Table 1) has focused mainly on the residential
market, but in the US research (Table 2) where the commercial market has been the main
target. Almost uniformly, the impacts are seen as positive, with some very large percentage
increases particularly in commercial property values. The enormous variability in (positive)
impact points towards either the importance of other factors, or the specificity of results, or
the limitations of the methods used – or a combination of all these factors.
TABLE 1 Property value impacts of public transport proximity in European cities.
Case/Location Impact on Impact Source
Bremen Office rents +50% in most cases (42)
Croydon Tramlink Residential property Some localized positive
impacts (43)
Freiburg Office rent +15-20% (42)
Freiburg Residential rent +3% (42)
Greater Manchester Not stated +10% (42)
Hannover Residential rent +5% (42)
Helsinki Metro Property values +7.5-11% (44)
London Crossrail Residential and commercial
property Positive (45)
London Docklands LRT Residential and commercial
property Positive (44)
London JLE Residential and commercial
property Positive (46, 47)
Manchester Metrolink House Prices Unable to identify (48)
Montpellier Property values Positive (42)
Nantes LRT Commercial property Higher values (42)
Nantes LRT Not stated Small increase (42)
Nantes LRT Number of commercial premises +13% (44)
Nantes LRT Number of offices +25% (44)
Nantes LRT Number of residential dwellings +25% (44)
Newcastle upon Tyne House prices +20% (42)
Orléans Apartment rents None-initially negative due
to noise (42)
Rouen Rent and houses +10% most cases (42)
Saarbrűcken Not stated None (42)
Sheffield Supertram Property values Unable to identify (16, 48)
Strasbourg Office rent +10-15% (42)
Strasbourg Residential rent +7% (42)
Tyne and Wear Metro Property values +2% (49)
Vienna S-Bahn Housing units +18.7% (44)
L. Miguel Martínez and José Manuel Viegas 4
TABLE 2 Property value impacts of public transport proximity in North American cities.
Case/Location Impact on Impact Source
Atlanta Office rents Positive (8, 50)
Baltimore LRT Not stated Unable to identify (44)
Boston Residential property +6.7% (50, 51)
Buffalo, New York House prices +4-11% (23)
Chicago MTA House prices +20% (52)
Dallas DART Commercial rents +64.8% (53)
Dallas DART Property values +25% (53, 54)
Linden, New Jersey Residential property Positive (55)
Los Angeles Property values Higher values (56)
Miami House prices +5% (7)
New Jersey SEPTA rail House Prices +7.5-8% (57)
New Jersey PACTO rail House Prices +10% (57)
New York Not stated Positive (58)
Pennsylvania SEPTA rail House Prices +3.8% (57)
Portland House prices +10% (42)
Portland Gresham Residential rent >5% (42)
Portland Metropolitan
Express House prices +10.5% (17, 19)
San Diego Trolley Not stated Positive (44)
San Francisco Bay Area
BART Property values Positive (59)
San Francisco Bay Area
BART Residential rent
+10-15%
+15-26% (60, 61)
Santa Clara County Commercial and office property +23-120%% (62)
Santa Clara County House prices +45% (18)
Santa Clara County Residential rent +15% (25)
St. Louis Property values +32% (63)
Toronto Metro House prices +20% (29, 44)
Washington DC Residential rent Positive (50, 64)
DATA DESCRIPTION
The data used in this study are 2007 cross-sectional real estate data from an online
realtor’s database (Imokapa Vector) for Lisbon, Portugal. This database presents the asking
price of residential properties on sale during February of 2007 with a total of 12,488
complete records, 70% inside Lisbon’s Municipality. The real estate data contained
information on their asking sale price, structural attributes and address. The descriptive
statistics of the data is presented in Table 3.
The real estate data was geocoded and imported into geographic information system
(GIS) transportation analysis network map. All the properties were connected with the road
and public transport network in order to measure the several accessibility indicators used.
The spatial distribution of asking prices is presented in Figure 1, where it is easy to
denote that a great part of the most expensive properties are located near the metro and rail
lines.
The dependent variable is the advertised asking selling price at which the owner or
realtor is offering the property in the market. This can be a limitation to the model because
the dependent variable is not directly linked to an equilibrium price, where supply and
L. Miguel Martínez and José Manuel Viegas 5
demand have cleared the transaction (65). Other studies that relate public transport
accessibility to residential property or land values also have relied on asking prices (11, 16,
24, 64-66).
FIGURE 1 Spatial distribution of property asking prices for the study area
The properties of accessibility to public transport and to the road network were
developed using two different types of characterization: local accessibility and systemwide
accessibility.
Local accessibility indicators were calculated using the network distance to public
transport entry points (walking distance using 4 km/h) and to roads considered in Lisbon
Mobility Plan 2004 (67) road hierarchy at the various levels (distance in meters). The three
different levels of road hierarchy presented represent urban motorways for Network1, urban
arterials as Network2 and collector/distributor roads as Network3.
These accessibility measures were built using two different approaches. The first
approach considers an all-or-nothing influence of proximity to public transport entry points
and to the road network, resulting in a set of dummy variables for each public transport mode
or line and for each road hierarchy level.
L. Miguel Martínez and José Manuel Viegas 6
The second approach considers a continuous decreasing function of impedance of
proximity to public transport entry points and to the road network. To model this continuous
impedance it was used an inverse logistic function with different parameters for each public
transport line and road hierarchy. The inverse logistic function considered is:
( )( )XXbaY
−−+=
maxexp1
1 (1)
where Xmax is an specific parameter of each public transport line or road hierarchy and a and
b are calibrated by considering two different point in the curve (e.g. 5 minutes walking
distance – Y =0.90, and 15 minutes walking distance – Y =0.10).
An example of comparison between both measuring approaches can be assessed in
Figure 2, where the main differences are in the values observed for accessibility distances
greater than the threshold established for the all-or-nothing measuring approach.
0.0000
0.1000
0.2000
0.3000
0.4000
0.5000
0.6000
0.7000
0.8000
0.9000
1.0000
0 5 10 15 20 25 30
Accessibility time (min.)
Accessib
ility
indic
ato
r
Continuous Approach All-or-nothing Approach
FIGURE 2 Calibrated β values of the Gravitational model for Lisbon Mobility Plan 2004
These variables and their descriptive statistics are presented in the local accessibility
attributes of Table 3.
The systemwide accessibility indicators were calculated using a gravitational model
calibrated for Lisbon’s Mobility Plan survey of 2004. The model equation is (68):
)exp( ijjjiiij CBDAOF ⋅⋅⋅⋅⋅= β
∑ ⋅⋅⋅=
k
ikkk
iCBD
A)]exp([
1
β;
∑ ⋅⋅⋅=
m
mjmm
jCAO
B)]exp([
1
β (2)
L. Miguel Martínez and José Manuel Viegas 7
where ijF is the total flow between zone i and j for each mode, iO the total number of trips
with origin in i, jD the total number of trips with destination in j and ijC is the impedance
between zones i and j, measured in travel time between zones. iA and jB are calibration
variables that are needed in a doubly constrained gravitational model calibration.
The calibrated β’s for public transport (PT) and private car (PC) are presented in
Figure 3, where the difference between the calibrated β’s is very significant (approximately 4
times greater for the private car showing a much greater ease of displacement than in public
transport).
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 20 40 60 80 100 120 140 160 180 200
Accessibility time Cij (minutes)
Ex
p(-
be
ta.c
(k,j))
Beta PC = -0,03420 Beta PT = -0,00833
FIGURE 3 Calibrated β values of the Gravitational model for Lisbon Mobility Plan 2004
A land use database for the study area was then used for the calculation of the
accessibility indicator. The jD term of the gravitational model equation was replaced by the
land use surface ( jA ) and standardized using the land use surface of the whole study
area∑=
n
j
jA1
. The accessibility indicator results then in the following equation:
∑=
⋅⋅=n
j
jiji ApCAccGr1
)()exp(β ;
∑=
=n
j
j
j
j
A
AAp
1
)( (3)
The descriptive statistics of these accessibility indicators are presented in Table 3.
Some neighbourhood attributes for each property were also calculated in order to
improve to explanatory power of the models. These variables are an Educational Index that
calculates the percentage of undergraduate persons in the population over 20 years old in a
500 radius around the property, and the Entropy Index that measures the mixture of land use
types in a radius of 500 m (69, 70). These descriptive statistics of these neighbourhood
attributes are presented in Table 3.
L. Miguel Martínez and José Manuel Viegas 8
TABLE 3 Descriptive statistics of the variables (N= 12,488)
Variable Description Mean St Dev
Price Asking price (€) 223,123.11 145,408.15
Ln_Price Natural logarithm of the asking price 12.17 0.533
Structural Attributes
Bedrooms Number of bedrooms 2.393 1.068
House 1 if house 0.027 0.161
Floor Floor number 2.952 2.431
Area Area (sq. meters) 103.789 59.253
Age1 1 if Property age <= 10 years 0.351 0.477
Age2 1 if 10 years < Property age < 50 years 0.327 0.469
Age3 1 if Property age >= 50 years 0.322 0.467
Garage 1 if garage spaces >=1 0.470 0.499
Neighbourhood Attributes
Educational Index Number of undergraduate persons/Population over 20
years old (500 meters radius) 0.197 0.129
Entropy Index
Entropy Index within a walking distance of 500 meters
∑=
⋅=
k
i
ii
k
ppEI
1
500)ln(
)ln((69, 70)
0.220 0.103
Local Accessibility Attributes
Metro 2MAccess10 1 if walk time to access 2 metro lines <=10 minutes 0.048 0.214
2MAccess = 1/(1+exp(4.394 -0.439 *(20 –walking time))) 0.058 0.163
1MAccess10 1 if walk time to access 1 metro lines <=10 minutes 0.265 0.441
1MAccess = 1/(1+exp(6.812 -0.659 *(17 –walking time))) 0.123 0.240
Road
Network1_1000 1 if distance to Network1 <=1000 meters 0.425 0.494
Network1 = 1/(1+exp(8.789 -0.007 *(2000 –access distance))) 0.266 0.351
Network2_500 1 if distance to Network2 <=500 meters 0.438 0.496
Network2 = 1/(1+exp(8.789 -0.013 *(1000 –access distance))) 0.273 0.353
Network3_250 1 if distance to Network3 <=250 meters 0.558 0.500
Network3 = 1/(1+exp(8.789 -0.026 *(500 –access distance))) 0.345 0.367
Rail
Azambuja10 1 if walk time to Azambuja train station < 10 minutes and
less than 20% of the distance to CBD 0.006 0.078
Azambuja = 1/(1+exp(4.394 -0.439 *(20 –walking time))) 0.005 0.057
Lisboa10 1 if walk time to Lisbon train station < 10 minutes and less
than 10% of the distance to the CBD 0.014 0.119
Lisboa = 1/(1+exp(4.394 -0.439 *(20 –walking time))) 0.014 0.094
Nacional10 1 if walk time to Nacional train station < 10 minutes 0.013 0.114
Nacional = 1/(1+exp(4.394 -0.439 *(20 –walking time))) 0.010 0.082
Sintra10 1 if walk time to Sintra train station < 10 minutes and less
than 20% of the distance to CBD 0.028 0.164
Sintra = 1/(1+exp(4.394 -0.439 *(20 –walking time))) 0.029 0.131
Fertagus10 1 if walk time to Fertagus train station < 10 minutes and
less than 20% of the distance to CBD 0.001 0.037
Fertagus = 1/(1+exp(4.394 -0.439 *(20 –walking time))) 0.001 0.033
Cascais10 1 if walk time to Cascais train station < 10 minutes and
less than 20% of the distance to CBD 0.014 0.117
Cascais = 1/(1+exp(4.394 -0.439 *(20 –walking time))) 0.011 0.078
L. Miguel Martínez and José Manuel Viegas 9
Variable Description Mean St Dev
Systemwide Accessibility Attributes
Gravitational_PT
Gravitational model accessibility index with β calibrated
for public transport
0.708 0.058
Gravitational_PC
Gravitational model accessibility index with β calibrated
for private car
0.493 0.084
MODELING METHODOLOGY
Six different cross-sectional models were developed in this study. We used three
different specifications for the accessibility effect (local accessibility all-or-nothing, local
accessibility continuous and systemwide accessibility) and two modelling approaches
(ordinary least squares regression model (OLS) and spatial lag regression model). Both
present a semi logarithmic hedonic specification that is widely used in the property value
literature motivated by the fact that it usually produces robust estimates and enables
convenient coefficient interpretation. The general structure of the OLS model is:
iinniii XXXPLn εββββ +++++= '
2
'
21
'
10 ...)(
),0(~ 2IN σε
(4)
where iP is the price of house i , ini XX ...1 are the vectors of the explanatory variables for the
price of house i . The dependent variable is given in the natural logarithmic form; thus the
values of the coefficients represent percentage change. The specifications used for the OLS
models (for each type of accessibility indices) are given by:
ii
'
CSi
'
SNi
'
Ni
'
N
i
'
Ni
'
MAi
'
MA
i
'
EIi
'
LIi
'
GRi
'
AG
i
'
AGi
'
ARi
'
FLi
'
HSi
'
BDi
εCascais10βSintra10β_Networkβ_Networkβ
_NetworkβMAccessβMAccessβ
exEntropyIndβndexEducationIβGarageβAgeβ
AgeβAreaβFloorβHouseβBeedroomsβα)Ln(P
++++
+++
++++
++++++=
25035002
10001101102
3
2
32
112
3
2
(5)
iiCSiSNiNiN
iNiMAiMA
iEIiLIiGRiAG
iAGiARiFLiHSiBDi
CascaisSintraNetworkNetwork
NetworkMAccessMAccess
exEntropyIndndexEducationIGarageAge
AgeAreaFloorHouseBeedroomsPLn
εββββ
βββ
ββββ
βββββα
++++
+++
++++
++++++=
'''
3
'
2
'
1
'
1
'
2
''''
3
'
2
''''
32
112
3
2)(
(6)
iiPCiPT
iEIiLIiGRiAG
iAGiARiFLiHSiBDi
PCnalGravitatioPTnalGravitatio
exEntropyIndndexEducationIGarageAge
AgeAreaFloorHouseBeedroomsPLn
εββ
ββββ
βββββα
++
++++
++++++=
__
3
2)(
''
''''
3
'
2
''''
(7)
L. Miguel Martínez and José Manuel Viegas 10
The spatial lag models general structure is presented in Equation 8.
iinniiPLni XXXWPLni
εββββρ ++++++= '
2
'
21
'
1
'
0)( ...)(
),0(~ 2IN σε
(8)
where iP is the price of house i , ini XX ...1 are the vectors of the explanatory variables for the
price of house i , ρ is the autoregressive coefficient and )( iPLnW the spatial lagged variable in
order to a NN × spatial weight matrix.
The specifications used for the spatial lag models are given by:
iiCSiSNiNiN
iNiMAiMA
iEIiLIiGRiAGiAG
iARiFLiHSiBDPLni
Cascais10Sintra10NetworkNetwork
NetworkMAccessMAccess
exEntropyIndndexEducationIGarageAgeAge
AreaFloorHouseBeedroomsWPLni
εββββ
βββ
βββββ
ββββαρ
++++
+++
+++++
++++++=
'''
3
'
2
'
1
'
1
'
2
''''
3
'
2
''''
)(
250_3500_2
1000_1101102
32
)(
(9)
iiCSiSNiNiN
iNiMAiMA
iEIiLIiGRiAGiAG
iARiFLiHSiBDPLni
CascaisSintraNetworkNetwork
NetworkMAccessMAccess
exEntropyIndndexEducationIGarageAgeAge
AreaFloorHouseBeedroomsWPLni
εββββ
βββ
βββββ
ββββαρ
++++
+++
+++++
++++++=
'''
3
'
2
'
1
'
1
'
2
''''
3
'
2
''''
)(
32
112
32
)(
(10)
iiPCiPT
iEIiLIiGRiAGiAG
iARiFLiHSiBDPLni
PCnalGravitatioPTnalGravitatio
exEntropyIndndexEducationIGarageAgeAge
AreaFloorHouseBeedroomsWPLni
εββ
βββββ
ββββαρ
++
+++++
++++++=
__
32
)(
''
''''
3
'
2
''''
)(
(11)
The spatial weight matrix for both spatial lag models was developed assuming
constant spatial dependence between properties until a maximum established distance. The
maximum Euclidean distance used was 1000 m, resulting in a Moran’s I = 0.144 (P-value =
0.000)
MODELING RESULTS AND DISCUSSION
Estimation results from the six different models are presented in Table 4. Using the
Pseudo R2 as goodness-of-fit measure (squared correlation between the predicted and the
observed values of the dependent variable), we can observe a high explanation of the
dependent variable with values ranging from 0.75 and 0.80.
Langrange multiplier (LM) tests were also conducted to assess if the omission of the
spatial lag on the OLS model was erroneous (i.e 0:0 =ρH ). The LM test statistic is given
by (35):
L. Miguel Martínez and José Manuel Viegas 11
)'(/)'(
1'222
WWWtrMWXbWXb
WyeLM
++⋅
=σσ
(12)
where
=M common residual maker vector )( NN × in OLS estimation,
=e spherical OLS residual vector )1( ×N ,
Nee /'2 =σ ,
=b OLS coefficient vector )1( ×K ,
=tr trace of the matrix )( NN × .
The LM test is approximately 2χ distributed with one degree of freedom. As
presented in Table 4, the LM test for the local accessibility spatial lag models is significant
at 01.0=α , indicating a proper spatial lag specification, whilst the systemwide accessibility
spatial lag model is not significant, indicating that the OLS model in this case is more
appropriate.
Almost all the independent variables used in the six models are significant at
05.0=α (Sintra being the exception for both local accessibility spatial lag models),
independently of the estimation model and of the proxy accessibility variables used. In
addition, the models consistently demonstrate the impact of each independent variable on the
natural logarithm of the asking price and a similar magnitude of the coefficients of the
structural attributes along the different models.
The coefficient estimation for the structural attributes shows that the Area (floor
surface) is the attribute with greater impact in the dependent variable (approximately 0.07%
increase for a 1% square meter increase), followed by the Age and Bedrooms, which present
similar values in all the models.
The neighbourhood attributes are the ones that present higher coefficient variation
among the estimated models. As expected, the spatial lag term “replaces” some of the
explanatory power of the neighbourhood variables and of the constant of the model, although
it does not significantly affect the coefficients of the accessibility attributes, as can be seen in
the local accessibility models in Table 4.
This fact shows the stability of the local accessibility coefficients in the all-or-nothing
approach, the significance of the Sintra attribute being the only one affected. The metro
accessibility attributes coefficients range in the two models between 5.65% and 6.50% for
the accessibility to two metro lines and between 4.25% and 4.28% for the accessibility to a
single metro line, showing a significant impact of the metro proximity over the property
values.
The local accessibility coefficients in the continuous approach present less stability.
The metro accessibility attributes coefficients range in the two models between 7.28% and
11.27% for the accessibility to two metro lines and between 4.06% and 5.44% for the
accessibility to a single metro line, showing again a significant impact of the metro proximity
over the property values.
The rail accessibility attributes coefficients in all the local accessibility models
illustrate a positive impact for the proximity to the Cascais Line with coefficients ranging
L. Miguel Martínez and José Manuel Viegas 12
between 8.40% and 10.55% for the all-or-nothing accessibility measure approach and
between 10.16% and 18.83% for the continuous approach; and a negative impact for the
proximity to the Sintra Line with coefficients ranging between -4.45% and -1.06% (not
significant for the usual significance levels) for the all all-or-nothing approach and between
-10.16% and 1.04% (not significant for the usual significance levels) for the continuous
approach.
These effects might be explained by the perception of lack of security associated with
the Sintra Line, which prevents the properties of the nearby areas to take full advantage of
the proximity to this high capacity public transport system and the proximity of the Cascais
Line to a very expensive residential area in the Southeast area of Lisbon (Restelo
neighbourhood).
The road accessibility attributes coefficients range in the two models of the all-or-
nothing approach between -9.53% and -10.39% for the accessibility road hierarchy 1,
between 5.89% and 7.16% for the accessibility to road hierarchy 2 and between -3.77% and -
5.90% for the accessibility to the road hierarchy 3.
The road accessibility attributes presents similar impact for the two models of
continuous approach with coefficients ranging between -14.13% and -7.98% for the
accessibility road hierarchy 1, between 1.33% and 9.64% for the accessibility to road
hierarchy 2 and between -1.19% and -4.34% for the accessibility to the road hierarchy 3.
These estimations show that the proximity to the road hierarchy 2 (urban ring roads
and radial network) is the one that presents a positive impact on the property values, whilst
the proximity to the road hierarchy 3 (urban distribution network) and road hierarchy 1
(motorways) present a negative impact.
These results can derive from the congestion and noise externalities perceived by the
population near road hierarchy 1 and the switch to offices centres of buildings located near
road hierarchy 3 in the Lisbon’s city centre, reducing the residential supply in these areas.
The results of the local accessibility continuous spatial lag model show the existence
of a smaller impact of road network attributes on the dependent variable what might be
explained by the significant increase of the SP Lag coefficient of this model in comparison to
the all-or-nothing approach spatial lag model.
We cannot compare directly the coefficients resulting from the local accessibility
continuous models with the all-or nothing accessibility measure models due to fact that the
continuous indicators present continuous values between 0 and 1, not being possible to
derive a percentage of change directly from the coefficient. The percentage of change on the
property selling prices will result from the product between the value of the accessibility
indicator and the coefficient of the same indicator, resulting in a property selling prices
impact distribution rather than a single value.
The systemwide accessibility OLS model presents significant differences in
coefficients of the constant and neighbourhood attributes when compared with the local
accessibility OLS model. This might be due to the significant correlation of the systemwide
accessibility attributes with the neighbourhood attributes (i.e. correlation between
Gravitational_TC and Entropy Index is equal to 0.465 and to 0.235 with the Educational
Index), which can explain the reduction of the neighbourhood attributes coefficients.
This correlation results from the fact that the systemwide accessibility indicators
measure accessibility to activities scattered in the study area, which can be positively
L. Miguel Martínez and José Manuel Viegas 13
influenced by the presence of a high land use mixture around the property measured by the
Entropy Index.
We can see easily in Figure 4 that the Gravitational_TC indicator measures
simultaneously public transport accessibility and land use activity proximity and their
relation. This fact illustrates that is difficult to isolate with the systemwide accessibility
indicators the changes in property values derived from transportation infrastructures
investment from the neighbourhood land use characteristics.
FIGURE 4 Gravitational_TC indicator spatial distribution for the study area
The systemwide accessibility spatial lag model illustrates also the last statement due
to the non significance of the spatial lag model (see Table 4), because the systemwide
accessibility indicators can already explain part of the spatial dependence of the property
asking price.
Using the Akaike info criterion to rank the models, we can consider the local
accessibility continuous spatial lag model as the best prediction model followed by the
systemwide accessibility OLS model and the local accessibility all-or-nothing spatial lag
model.
We can thus conclude, from the estimates of the developed models, that the local
accessibility models can measure better the isolated effect of transportation investment on
L. Miguel Martínez and José Manuel Viegas 14
properties selling prices and that the estimates from the OLS models can be sufficiently
accurate in the absence of significant property values database for all the study area (needed
for the calculation of the spatial lag model).
The spatial distribution of the property prices estimates of the local accessibility
continuous spatial lag model is presented in Figure 5 where we can see a similar spatial
distribution to the database asking prices (see Figure 1). Figure 6 present the spatial
distribution of the estimated residuals, where we can denote sub-estimates and over-estimates
scattered along all the study area with some sub-estimates concentrated in the North part of
the study area and in the Expo area in the Northeast Lisbon’s border.
FIGURE 5 Property prices estimates of the local accessibility continuous spatial lag model
L. Miguel Martínez and José Manuel Viegas 15
FIGURE 6 Unstandardized residuals of the local accessibility continuous spatial lag model
L. Miguel Martínez and José Manuel Viegas 16
TABLE 4 Results of the OLS and Spatial Lag Models
Ordinary Least Squares (OLS) ML Spatial Lag
Local Accessibility
all-or-nothing Model
Local Accessibility
continuous Model
Systemwide
Accessibility Model
Local Accessibility
all-or-nothing Model
Local Accessibility
continuous Model
Systemwide
Accessibility Model
Coef. Std.
Error Coef.
Std.
Error Coef.
Std.
Error Coef.
Std.
Error Coef.
Std.
Error Coef.
Std.
Error
SP_LAG_LOGPRICE -- -- -- -- -- -- 0.4043 *** 0.0121 0.6264 ***
0.0085 0.2968 *** 0.0143
Constant 11.1146 *** 0.0099 11.1073 *** 0.0104 10.4106 *** 0.0369 6.2840 ***
0.1446 3.6777 *** 0.1010 6.8770 ***
0.1743
Structural attributes Bedrooms 0.0357 *** 0.0033 0.0600 ***
0.0032 0.0380 *** 0.0033 0.0396 ***
0.0032 0.0690 *** 0.0029 0.0410 ***
0.0032
House 0.2037 *** 0.0169 0.0838 *** 0.0165 0.2038 ***
0.0166 0.1738 *** 0.0162 0.0818 ***
0.0148 0.2017 *** 0.0163
Floor 0.0156 *** 0.0010 0.0172 *** 0.0010 0.0184 ***
0.0010 0.0169 *** 0.0010 0.0168 ***
0.0009 0.0183 *** 0.0010
Area 0.0069 *** 0.0000 0.0059 *** 0.0000 0.0070 ***
0.0000 0.0067 *** 0.0000 0.0056 ***
0.0000 0.0069 *** 0.0000
Age2 -0.1291 *** 0.0069 -0.1426 *** 0.0070 -0.1415 ***
0.0067 -0.1203 *** 0.0067 -0.1172 ***
0.0063 -0.1360 *** 0.0066
Age3 -0.0851 *** 0.0073 -0.0963 *** 0.0075 -0.0957 ***
0.0072 -0.0820 *** 0.0071 -0.0895 ***
0.0067 -0.0921 *** 0.0071
Garage 0.1205 *** 0.0064 0.1271 *** 0.0066 0.1279 ***
0.0063 0.1189 *** 0.0062 0.1235 ***
0.0059 0.1235 *** 0.0062
Neighbourhood attributes Educational Index 0.9811 *** 0.0202 1.0407 ***
0.0209 0.7638 *** 0.0193 0.7131 ***
0.0219 0.1972 *** 0.0220 0.5932 ***
0.0219
Entropy Index 0.5430 *** 0.0346 0.4231 *** 0.0257 0.2959 ***
0.0347 0.2466 *** 0.0340 0.2422 ***
0.0231 0.2014 *** 0.0344
Local Accessibility Attributes (all-or-nothing and continuous approach) 2MAccess10 (2MAccess) 0.0565 *** 0.0130 0.1127 ***
0.0171 -- -- 0.0650 *** 0.0126 0.0728 ***
0.0154 -- --
1Maccess10 (1MAccess) 0.0425 *** 0.0061 0.0406 *** 0.0114 -- -- 0.0428 ***
0.0059 0.0549 *** 0.0103 -- --
Network1_1000 (Network1) -0.0953 *** 0.0051 -0.1413 *** 0.0074 -- -- -0.1039 ***
0.0049 -0.0798 *** 0.0067 -- --
Network2_500 (Network2) 0.0716 *** 0.0048 0.0964 *** 0.0070 -- -- 0.0589 ***
0.0047 0.0133 ** 0.0063 -- --
Network3_250 (Network3) -0.0590 *** 0.0048 -0.0434 *** 0.0066 -- -- -0.0377 ***
0.0046 -0.0119 ** 0.0060 -- --
Sintra10 (Sintra) -0.0445 *** 0.0151 -0.1057 *** 0.0180 -- -- -0.0106 0.0146 0.0104 0.0162 -- --
Cascais10 (Cascais) 0.1055 *** 0.0245 0.1883 *** 0.0306 -- -- 0.0840 ***
0.0237 0.1016 *** 0.0274 -- --
Systemwide Accessibility Attributes
Gravitational_PT -- -- -- -- 0.4674 *** 0.0774 -- -- -- -- 0.6923 *** 0.0769
Gravitational_PC -- -- -- -- 0.8084 *** 0.0546 -- -- -- -- 0.4304 *** 0.0561
Pseudo R2 0.753 0.753 0.764 0.773 0.801 0.772
LM statistic 214.670 *** 2019.366 *** -203.435
Log likelihood -656.08 -886.031 -373.817 -426.742 123.652 -475.535
Akaike info criterion 1344.16 1806.06 771.635 889.483 -211.304 977.07
***, **
, and *
denote coefficient significantly different from zero at the 1%, 5%, and 10% level of significance (two-tailed test), respectively.
L. Miguel Martínez and José Manuel Viegas 17
SUMMARY AND CONCLUSIONS
This paper analyses the effect of transportation accessibility on the properties prices
as part of a broader study that intends to develop new value capture financing scheme for
public transportation in the LMA. Several cross-sectional hedonic price models are estimated
based on an online realtor’s database (Imokapa Vector) of property selling asking price. The
models account for structural, neighbourhood and accessibility attributes of residential
properties, the latest ones structured in two types: local accessibility attributes and
systemwide accessibility attributes.
The main focus of this study is to develop a framework to forecast house prices and
the influence of transportation infrastructure investment in further steps of the research
project.
The estimated models revealed that:
• The local accessibility hedonic price models developed showed the existence of
spatial interactions of sale prices, presenting a spatial autocorrelation with a
significant spatial lag.
• The local accessibility models present a stability of the local accessibility
coefficients estimated, the significance of the Sintra attribute being the only one
affected.
• The metro accessibility attributes coefficients range in the two all-or-nothing
models between 5.65% and 6.50% for the accessibility to two metro lines and
between 4.25% and 4.28% for the accessibility to a single metro line, showing a
significant impact of the metro proximity over the property values.
• The metro accessibility attributes coefficients range in the two continuous models
between 7.28% and 11.27% for the accessibility to two metro lines and between
4.06% and 5.49% for the accessibility to a single metro line.
• The rail accessibility attributes coefficients illustrate a positive impact for the
proximity to the Cascais Line with coefficients ranging between 8.40% and 10.55%,
and a negative impact for the proximity to the Sintra Line with coefficients ranging
between -4.45% and -1.06% (not significant for the usual significance levels) for the
all-or-nothing accessibility measure models.
• The rail accessibility attributes coefficients for the continuous accessibility
measure models present a positive impact for the proximity to the Cascais Line with
coefficients ranging between 10.16% and 18.83%, and a negative impact for the
proximity to the Sintra Line with coefficients ranging between -10.57% and 1.04%
(not significant for the usual significance levels).
• The road accessibility attributes coefficients range in the two all-or-nothing
accessibility measure models between -9.53% and -10.39% for the accessibility road
hierarchy 1, between 5.89% and 7.16% for the accessibility to road hierarchy 2 and
between -3.77% and -5.90% for the accessibility to the road hierarchy 3.
• The road accessibility attributes coefficients range in the continuous accessibility
measure models between -7.98% and -14.13% for the accessibility road hierarchy 1,
between 1.33% and 9.64% for the accessibility to road hierarchy 2 and between -
1.19% and -4.34% for the accessibility to the road hierarchy 3.
L. Miguel Martínez and José Manuel Viegas 18
• The systemwide accessibility models presents significant differences in
coefficients of the constant and neighbourhood attributes when compared with the
local accessibility models. This indicates the difficulty the isolate the accessibility
effects from the neighbourhood effects over house prices with these models.
• The systemwide accessibility spatial lag model developed is not significant,
which indicates that the systemwide accessibility indicators do also explain also part
of the spatial autocorrelation.
The coefficients resulting from the local accessibility continuous models cannot be
compared directly with the coefficients from the all-or nothing accessibility measure models
due to fact that the continuous indicators present continuous values between 0 and 1, not
being possible to derive a percentage of change directly from the coefficient. The percentage
of change on the property selling prices will result from the product between the value of the
accessibility indicator and the coefficient of the same indicator, resulting in a property selling
prices impact distribution rather than a single value.
Using the Akaike info criterion to rank the models, we can consider the local
accessibility continuous spatial lag model as the best prediction model followed by the
systemwide accessibility OLS model and the local accessibility all-or-nothing spatial lag
model.
The main conclusions that can be drawn from the estimates of the developed models are that
the local accessibility models can better measure the isolated effect of transportation
investment on properties selling prices and that the estimates from the OLS model can be
sufficiently accurate in the absence of a significant property values database for all the study
area (needed for the calculation of the spatial lag model).
ACKNOWLEDGMENTS
This research is being supported by the Portuguese National Science Foundation
(FCT) since 2006. The private company Imokapa Vector has provided support by making
available an online realtor’s database. TIS.pt has also provided support by making available
the LMA Mobility Survey from 2004, and to the software company INTERGRAPH for the
Geomedia Professional 5.2 license.
REFERENCE
1. Alonso, W., 1964. Location and Land Use: Towards a General Theory of Land Rent.
Cambridge, Massachusetts, Harvard University Press.
2. Muth, R. F., 1969. Cities and Housing: The Spatial Pattern of Urban Residential Land
Use. Chicago, Illinois, University of Chicago Press.
3. Rosen, S., 1974. Hedonic Prices and Implicit Markets - Product Differentiation in Pure
Competition. Journal of Political Economy, 82 (1), 34-55.
4. Cervero, R. and Duncan, M., 2001. Transit’s Value-Added: Effects of Light and
Commuter Rail Services on Commercial Land Values. Transit Resource Guides.
5. Bowes, D. R. and Ihlanfeldt, K. R., 2001. Identifying the impacts of rail transit stations
on residential property values. Journal of Urban Economics, 50 (1), 1-25.
L. Miguel Martínez and José Manuel Viegas 19
6. Cervero, R. and Duncan, M., 2004. Neighbourhood composition and residential land
prices: Does exclusion raise or lower values? Urban Studies, 41 (2), 299-315.
7. Gatzlaff, D. H. and Smith, M. T., 1993. The Impact of the Miami Metrorail on the Value
of Residences near Station Locations. Land Economics, 69 (1), 54-66.
8. Bollinger, C. R., Ihlanfeldt, K. R. and Bowes, D. R., 1998. Spatial Variation in Office
Rents Within the Atlanta Region. Urban Studies, 35 (7), 1097-1118.
9. Armstrong, R. J. and Rodriguez, D. A., 2006. An evaluation of the accessibility benefits
of commuter rail in Eastern Massachusetts using spatial hedonic price functions.
Transportation, 33 (1), 21-43.
10. Weinberger, R. R., 2001. Light rail proximity - Benefit or detriment in the case of Santa
Clara County, California? Transportation and Public Policy 2001(1747), 104-113.
11. Cheshire, P. and Sheppard, S., 2003. Capitalised in the Housing Market or How we Pay
for Free Schools: The Impact of Supply Constraints and Uncertainty. In: Proceedings of
the Royal Geographical Society Meeting.
12. Debrezion, G., 2006. The Impact of Rail Transport on Real Estate Prices: Empirical
Study of the Dutch Housing Market. In: Proceedings of the 85th
Transportation Research
Board Annual Meeting, Washington, D.C.
13. Lewis-Workman, S. and Brod, D., 1997. Measuring the Neighborhood Benefits of Rail
Transit Accessibility. Transportation Research Record - Financial, Economic, and Social
Topics in Transportation, 1576, 147-153.
14. Martins, C. and Bin, O., 2005. Estimation of hedonic price functions via additive
nonparametric regression. Empirical Economics, 30 (1), 93-114.
15. Epple, D., 1987. Hedonic Prices and Implicit Markets - Estimating Demand and Supply
Functions for Differentiated Products. Journal of Political Economy, 95 (1), 59-80.
16. Henneberry, J., 1998. Transport investment and house prices. Journal of Property
Valuation and Investment, 16 (2), 144-158.
17. Al-Mosaind, M. A., Dueker, K. J. and Strathman, J. G., 1993. Light-Rail Transit Stations
and Property Values: A Hedonic Price Approach. Transportation Research Record -
Planning and Programming, Land Use, Public Participation, and Computer Technology
in Transportation., 1400, 90-94.
18. Cervero, R. and Duncan, M., 2002. Benefits of Proximity to Rail on Housing Markets:
Experiences in Santa Clara County. Journal of Public Transportation, 5 (1).
19. Chen, H., Rufolo, A. and Dueker, K. J., 1998. Measuring the impact of light rail systems
on single-family home values - A hedonic approach with geographic information system
application. Transportation Research Record - Land Use and Transportation Planning
and Programming Applications, 1617, 38-43.
20. Kawamura, K. and Mahajan, S., 2005. Hedonic analysis of impacts of traffic volumes on
property values. Management and Public Policy 2005(1924), 69-75.
21. Cervero, R. and Duncan, M., 2002. Transit’s Value-Added Effects. Light and Commuter
Rail Services and Commercial Land Values. Transportation Research Record - Travel
Demand and Land Use 2002, 1805, 8-15.
22. Haider, M. and Miller, E. J., 2000. Effects of transportation infrastructure and location on
residential real estate values - Application of spatial autoregressive techniques.
Transportation Research Record - Transportation Land Use and Smart Growth(1722), 1-
8.
L. Miguel Martínez and José Manuel Viegas 20
23. Hess, D. B. and Almeida, T. M., 2006. Impact of Proximity to Light Rail Rapid Transit
on Station-Area Property Values in Buffalo. In: Proceedings of the 85th
Transportation
Research Board Annual Meeting, Washington, D.C.
24. Rodriguez, D. A. and Targa, F., 2004. Value of accessibility to Bogota's bus rapid transit
system. Transport Reviews, 24 (5), 587-610.
25. Weinberger, R. R., 2001. "Commercial Rents and Transportation Improvements : The
Case of Santa Clara County's Light Rail ", Lincoln Institute of Land Policy.
26. Bateman, I., Day, B., Lake, I. and Lovett, A., 2001. "The Effect of Road Traffic on
Residential Property Values: A Literature Review and Hedonic Pricing Study", Scottish
Executive Development Department.
27. Bartik, T. J., 1988. Measuring the Benefits of Amenity Improvements in Hedonic Price
Models. Land Economics, 64 (2), 172-183.
28. Kanemoto, Y., 1988. Hedonic Prices and the Benefits of Public Projects. Econometrica,
56 (4), 981-989.
29. Bajic, V., 1983. The Effects of a New Subway Line on Housing Prices in Metropolitan
Toronto. Urban Studies, 20 (2), 147-158.
30. Forrest, D., Glen, J. and Ward, R., 1996. The impact of a light rail system on the structure
of house prices - A hedonic longitudinal study. Journal of Transport Economics and
Policy, 30 (1), 15-29.
31. Adair, A., McGreal, S., Smyth, A., Cooper, J. and Ryley, T., 2000. House prices and
accessibility: The testing of relationships within the Belfast Urban Area. Housing Studies,
15 (5), 699-716.
32. Munoz-Raskin, R. C., 2007. Walking Accessibility to Bus Rapid Transit in Latin
America: Does It Affect Property Values? Case of Bogota, Colombia. In: Proceedings of
the 85th
Transportation Research Board Annual Meeting, Washington D.C.
33. Kockelman, K., 1997. Effects of Location Elements on Home Purchase Prices and Rents
in San Francisco Bay Area. Transportation Research Record: Journal of the
Transportation Research Board, 1606 (-1), 40-50.
34. Bae, C. H. C., Jun, M. J. and Park, H., 2003. The impact of Seoul's subway Line 5 on
residential property values. Transport Policy, 10, 85-94.
35. Shin, K., Washington, S. and Choi, K., 2007. Effects of transportation accessibility on
residential property values - Application of spatial hedonic price model in Seoul, South
Korea, metropolitan area. Transportation Research Record(1994), 66-73.
36. Forkenbrock, D. J., Benshoff, S. and Weisbrod, G. E., 2001. "Assessing the Social and
Economic Effects of Transportation Projects", National Cooperative Highway Research
Program, Transportation Research Board, National Research Council.
37. Carey, J. and Semmens, J., 2003. Impact of highways on property values - Case study of
superstition freeway corridor. Transportation Research Record - Transportation Finance,
Economics and Economic Development, 1839, 128-135.
38. Coulson, N. E. and Engle, R. F., 1987. Transportation Costs and the Rent Gradient.
Journal of Urban Economics, 21 (3), 287-297.
39. Tse, C. Y. and Chan, A. W. H., 2003. Estimating the commuting cost and commuting
time property price gradients. Regional Science and Urban Economics, 33 (6), 745-767.
40. Frazier, C. and Kockelman, K. M., 2005. Spatial econometric models for panel data -
Incorporating spatial and temporal data. Transportation and Land Development
2005(1902), 80-90.
L. Miguel Martínez and José Manuel Viegas 21
41. Parsons Brinckerhoff Quade & Douglas Inc., 2001. "The Effect of Rail Transit on
Property Values: A Summary of Studies", NEORail II, Cleveland, Ohio.
42. Hass-Klau, C., Crampton, G. and Benjari, R., 2004. "Economic Impact of Light Rail: The
Results of 15 Urban Areas in France, Germany, UK and North America", Environmental
& Transport Planning.
43. ATISREAL, Geofutures, UCL and Symonds-Group, 2004. "Land Value and Public
Transport. Stage Two – Testing the Methodology on the Croydon Tramlink", RICS,
London.
44. Hack, J., 2002. "Regeneration and Spatial Development: a Review of Research and
Current Practice", IBI Group, Toronto.
45. Hillier Parker, 2002. "Crossrail: Property Value Enhancement", Canary Wharf Group Plc.,
London.
46. Chesterton, 2000. "Property Market Study, Prepared for the Jubilee Line Extension
Impact Study", University of Westminster, London.
47. Pharoah, T., 2002. "Jubilee Line Extension Development Impact Study", University of
Westminster, London.
48. Dabinett, G., 1998. Realising Regeneration Benefits from Urban Infrastructure
Investment: Lesson from Sheffield in the 1990s. Town Planning Review, 62 (2), 171-189.
49. Pickett, M. W. and Perrett, K. E., 1984. "The Effect of the Tyne and Wear Metro on
Residential Property Values", Transport and Road Research Laboratory (TRL),
Crowthorne, Bershire, U.K.
50. APTA. 2002. "Rail Transit and Property Values." Retrieved 8th January 2006, from
http://www.apta.com/research/info/briefings/briefing_1.cfm.
51. Armstrong Jr, R. J., 1994. Impacts of Commuter Rail Service as Reflected in Single-
Family Residential Property Values. Transportation Research Record - Issues in Land
Use and Transportation Planning, Models, and Applications, 1466 88-98.
52. Gruen, A., 1997. "The Effect of CTA and Metra Stations on Residential Property Values",
Report to the Regional Transportation Authority.
53. Weinstein, B. L. and Clower, T. L., 1999. "The Initial Economic Impacts of the DART
LRT System", Center for Economic Development and Research, University of North
Texas.
54. Kay, J. H. and Haikalis, G., 2000. All Aboard. Planning, 66 (10), 14-19.
55. Diaz, R. B., 1999. Impacts Of Rail Transit On Property Values. In: Proceedings of the
American Public Transit Association (APTA) 1999 Rapid Transit Conference.
56. Fejarang, R., 1994. Impact on Property Values: A Study of the Los Angeles Metro Rail.
In: Proceedings of the Transportation Research Board 73rd
Annual Meeting, Washington
D.C.
57. Voith, R. P., 1991. Transportation, Sorting and Housing Values. Journal of the American
Real Estate and Urban Economics Association (AREUEA), 19 (2), 117-137.
58. Anas, A. and Armstrong, R. J., 1993. "Land Values and Transit Access: Modeling the
Relationship in the New York Metropolitan Area. An Implementation Handbook", U.S.
Federal Transit Administration, Office of Technical Assitance and Safety, Springfield
VA.
59. Cambridge Systematics Inc., Cervero, R. and Aschuer, D., 1998. "Economic Impact of
Transit Investments: Guidebook for Practitioners", Transit Cooperative Research
Program - The Federal Transit Administration, Washington D.C.
L. Miguel Martínez and José Manuel Viegas 22
60. Cevero, R., 1996. Transit based housing in the San Francisco Bay Area: Market profiles
and rent premiums. Transportation Quarterly, 50 (3), 33-49.
61. Sedway Group, 1999. "Regional Impact Study", Report commissioned by Bay Area
Rapid Transit District (BART).
62. Cervero, R., Ferrell, C. and S., M., 2002. "Transit-Oriented Development and Joint
Development in the United States: A Literature Review", Transit Cooperative Research
Program - The Federal Transit Administration, Washington D.C.
63. Garrett, T. A., 2004. "Light-Rail Transit in America: Policy Issues and Prospects for
Economic Development", Federal Reserve Bank of St. Louis.
64. Benjamin, J. D. and Sirmans, G. S., 1996. Mass Transportation, Apartment Rent and
Property Values. Journal of Real Estate Research, 12 (1), 1-8.
65. Rodríguez, D. A. and Mojica, C. H., 2007. "Capitalization of BRT network effects into
land prices", Lincoln Institute of Land Policy.
66. Du, H. B. and Mulley, C., 2007. The short-term land value impacts of urban rail transit:
Quantitative evidence from Sunderland, UK. Land Use Policy, 24 (1), 223-233.
67. Câmara Municipal de Lisboa, 2005. Lisboa: O desafio da mobilidade, Câmara Municipal
de Lisboa - Licenciamento Urbanístico e Planeamento Urbano.
68. Special Issue On Methodological Issues In Accessibility. Journal of Transportation and
Statistics M. L. Tischer. Washington, Bureau Of Transportation Statistics - United States
Department Of Transportation.
69. Cervero, R. and Kockelman, K., 1997. Travel demand and the 3Ds: Density, diversity,
and design. Transportation Research Part D-Transport and Environment, 2 (3), 199-219.
70. Potoglou, D. and Kanaroglou, P. S., 2008. Modelling car ownership in urban areas: a case
study of Hamilton, Canada. Journal of Transport Geography, 16 (1), 42-54.