# RECORD e7545aa69fd7e3b0b4fddbe8806b08519280a674
# TITLE: Analysis of House Prices to Assess Economic Impacts of New Public Transport Infrastructure
# YEAR: 2011.0  VENUE: nan  DOI: 10.3141/2245-16
# AUTHORS: Lucía Mejía Dorantes; A. Páez; J. M. Vassallo
# PAGES: 9  CANDIDATE TABLE PAGES (1-based): [7]
# ABSTRACT: Transportation infrastructure is known to affect the value of real estate property by virtue of changes in accessibility. The impact of transportation facilities is highly localized as well, and it is possible that spillover effects result from the capitalization of accessibility. The objective of this study was to review the theoretical background related to spatial hedonic models and the opportunities that they provided to evaluate the effect of new transportation infrastructure. An empirical case study is presented: the Madrid Metro Line 12, known as Metrosur, in the region of Madrid, Spain. The effect of proximity to metro stations on housing prices was evaluated. The analysis took into account a host of variables, including structure, location, and neighborhood and made use of three modeling approaches: linear regression estimation with ordinary least squares, spatial error, and spatial lag. The results indicated that better accessibility to Metrosur stations had a positive impact on real estate values and that the effect was marked in cases in which a house was for sale. The results also showed the presence of submarkets, which were well defined by geographic boundaries, and transport fares, which implied that the economic benefits differed across municipalities.


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Analysis of House Prices to Assess 
Economic Impacts of New Public 
Transport Infrastructure 
Madrid Metro Line 12 
Lucia Mejia Dorantes, Antonio Paez, and José Manuel Vassallo 
Transportation infrastructure is known to affect the valué of real estáte 
property by virtue of changes in accessibility. The hnpact of transporta-
tion facilities is highly localized as well, and it is possible that spillover 
effects result from the capitalization of accessibility. The objective of this 
study was to review the theoretical background related to spatial hedonic 
models and the opportunities that they provided to evalúate the effect of 
new transportation infrastructure. An empirical case study is presented: 
the Madrid Metro Line 12, known as Metrosur, in the región of Madrid, 
Spain. The effect of proximity to metro stations on housing prices was 
evaluated. The analysis took into account a host of variables, including 
structure, location, and neighborhood and made use of three modeling 
approaches: linear regression estimation with ordinary least squares, 
spatial error, and spatial lag. The results indicated that better accessibility 
to Metrosur stations had a positive hnpact on real estáte valúes and that 
the effect was marked in cases in which a house was for sale. The results 
also showed the presence of submarkets, which were well defined by 
geographic boundaries, and transport fares, which implied that the 
economic beneflts differed across municipalities. 
Public transportation infrastructure is known to affect urban áreas in 
a number of significant way s. The effects of public transportation were 
classifiedby Banister andBerechman (i) as follows: transportation-
related effects, land use effects, and effects on jobs and business 
activities. Researchers, including Mas andMaudos, have shownthat 
transportation infrastructure has significant, direct effects that also tend 
to spill over in the form of neighborhood effects (2). According to 
Boarnet (3), these effects can be classified as short term (i.e., related to 
reductions in travel time for the population that lives or works around 
the infrastructure), and long term [i.e., that stem from agglomera-
tion economies caused by positive externalities that improve the 
efficiency of firms as the result of economies of scale (4)]. 
Commuter rail and metro stations can have both positive 
(e.g., accessibility benefits) and negative (e.g., noise and congestión) 
L. M. Dorantes and J. M. Vassallo, Departamento de Transportes, Universidad 
Politécnica de Madrid, ETSI de Caminos, Canales y Puertos, Profesor Aranguren s/n. 
28040 Madrid, Spain. A. Paez, School of Geography and Earth Sciences, McMaster 
University, General Science Building, Room236, 1280 Main Street West, Hamilton. 
Ontario L8S 4L8, Canadá. Corresponding author: L. M. Dorantes, lmejia@caminos. 
upm.es 
effects. On the positive side, metro stations increase accessibility to 
public transportation for the people that live nearby and reduce their 
travel time and costs to other destinations in urban áreas, which 
produces a network externality. Similarly, business activities near 
the stations enjoy advantages. Jobs and shops are accessible to those 
that come from any destination. Business activities cióse to the 
stations—especially shops—benefit from the increase in passersby 
to and from the stations. Many of the negative effects associated 
with metro stations are spatial externalities, which include noise and 
changes to the urban landscape. The effects described abo ve depend on 
the type of transport infrastructure (2, 5), its location, and its specific 
characteristics. Researchers such as Maoh et al. have noted a strong 
interdependence between the locational patterns among residential 
and commercial land development (6). 
A problem of interest that arises whenever new transportation 
infrastructure, services, or both, are introduced is how to assess the net 
impact of the positive and negative effects. Two methods to assess 
real estáte markets are discussed in the literature: the repeated sales 
approach and the hedonic price analysis. Hybrid models, which 
combine those methodologies, also have been used. Bailey et al. first 
presented repeated sales deals with a regression of the difference 
in sale prices for the same set of homes and a set of time-of-sale 
indicators (7). Repeat sales can be difficult to implement as the result 
of small sample sizes. The sale of each property may be transacted 
infrequently, and the lengthy periods of time needed to collect a 
sufficiently large sample can be impractical or impossible in the 
case of new infrastructure. Compared with hedonic price analysis, 
repeat sales implementations are relatively rare (8). 
The hedonic models refer to the implicit characteristics of a good 
(9). In the case of real estáte, the idea is that property prices can be 
decomposed to evalúate the price of various structural, locational, 
and neighborhood characteristics. To estimate the implicit price for 
each attribute, the price is modeled as a function of various attributes 
and the use of a linear regression model with parameters estimated 
by the ordinary least squares (OLS) method. A house unit is fixed in 
its location and influenced by the characteristics of the neighborhood, 
which constitute a set of geographically distributed data. Data 
members may be related to each other as well, which is known as 
spatial autocorrelation (10,11). Spatial hedonic analysis has emerged 
as an approach that accounts for spatial autocorrelation in real estáte 
applications. 
The literature that accounts for transport effects in land valué 
through different approaches is extensive (12-16). As noted by 
Martínez and Viegas (17) andDu andMulley (18), however, studies 


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TABLE 2 
Hedonic Model Results 
if-squared 
Log likelihood 
Akaike info 
criterion 
Variable 
C 
m2 
lift 
bathr 
heating 
a a 
basem 
spool 
parking 
zone B2 
ms/pci 
cer/pci 
hosp/pci 
pop 
lambda 
W_price 
.7591 
-20,249.9 
40,527.8 
OLS 
65,921.5600** 
(3,975.429) 
1,402.8910** 
(42.63756) 
19,696.7800** 
(2,053.673) 
17,679.4900** 
(2,281.784) 
5,868.9630** 
(1,855.498) 
5,675.9520** 
(1,628.007) 
8,831.1460** 
(1,909.752) 
15,517.4800** 
(2,313.651) 
24,322.4000** 
(2,220.907) 
-31,553.56** 
(1,899.969) 
-45,862.7400* 
(18,787.84) 
-76,497.57** 
(13,989.91) 
61,411.55** 
(18,107.78) 
-0.1244867** 
(0.04346661) 
.7636 
-20,238.6 
40,505.3 
Spatial Error Model 
68,564.3** 
(4,265.663) 
1,400.5710** 
(43.10532) 
19,308.1700** 
(2,083.496) 
16,844.8700** 
(2,293.362) 
5,214.945** 
(1,889.629) 
5,603.8660** 
(1,601.317) 
8,139.1700** 
(1,907.114) 
15,329.5700** 
(2,406.694) 
23,780.6500** 
(2,252.612) 
-31,564.53** 
(2,246.84) 
-45,912.3700* 
(22,044.94) 
-74,584.5** 
(16,442.75) 
60,944.1** 
(21,288.32) 
-0.1474505** 
(0.05079898) 
0.1827401** 
(0.0379598) 
.7650 
-20,231.4 
40,492.8 
Spatial Lag Model 
42,584.1** 
(5,484.156) 
1,357.3570** 
(42.76402) 
18,207.8600** 
(2,035.105) 
15,889.0300** 
(2,260.322) 
5,113.653** 
(1,827.406) 
5,568.1830** 
(1,601.539) 
8,132.9380** 
(1,879.831) 
12,687.4200** 
(2,324.918) 
22,977.9400** 
(2,194.465) 
-28,817.85** 
(1,924.455) 
-42,145.6000* 
(18,492.35) 
-69,047.84** 
(13,819.2) 
47,469.47** 
(17,963.27) 
-0.07174224 
(0.04380104) 
0.1338048** 
(0.02199083) 
NOTE: Estimated coefficients are presented in euros; observations = 1,714. 
*p<.05;**p<.0l. 
€140,000.00 
€135,000.00 
€130,000.00 
€125,000.00 
€120,000.00 
€115,000.00 
€110,000.00 
€105,000.00 
€100,000.00 
-ALCORCON 
-GETAFE 
-LEGANES 
-MOSTOLES 
-FUENLABRADA 
1000 
Distan ce (m) 
(a) 
€150,000.00 
€140,000.00 
€130,000.00 
€120,000.00 
€110,000.00 
€100,000.00 
€90,000.00 
€80,000.00 
-LEGANES 
-ALCORCON 
-GETAFE 
-MOSTOLES 
-FUENLABRADA 
500 
1000 
Distance (m) 
( ) 
FIGURE 4 
Impact on valué of house related to its distance from (a) Metrosur and Ib) Cercanías stations. 


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and circular accessibility. Thus transfer stations appeared to have 
become valued the most, ceteris paribus, among all stations within 
the municipalities studied. 
CONCLUDING REMARKS AND DISCUSSION 
This paper presents an empirical analysis of the real estáte market 
in the south of Madrid to assess the economic benefits of a recent 
metro line infrastructure. The results of the study indicated that 
better accessibility to Metrosur stations had a positive impact on real 
estáte valúes and that the effect was particularly marked in cases in 
which a house was for sale. 
To avoid a discussion of pecuniary impact (46, 47), the results of 
the three models (OLS, SLM, and SEM), are presented, which were 
similar. According to Tsutsumi and Seya, some limitations exist in 
the spatial econometric approach to benefit evaluation because it uses 
a spatial weight matrix. Thus those parameters would be valid only 
for the given spatial weight matrix (54). In an empirical analysis, 
however, it is possible to observe that results in all cases are quite 
similar. It was concluded that the estimates were similar because the 
models accounted for housing submarkets and use was made of a 
detailed data set. This conclusión was consistent with Bourassa 
et al. (44, 45). Even without a spatial analysis, if submarkets are well 
controlled (in this case, market segmentations were accounted for 
by means of geographic boundaries and the transit zonal fare), and 
accurate information is used, OLS is a practical method that pro vides 
congruent estimates. 
The results successfully appraised the benefit that an urban mass 
transport infrastructure produces in house prices. The distance to 
Cercanias stations, however, was economically more important that 
the distance to the Metrosur stations. This result may be explained 
by the fact that the commuter rail fines took people directly to down-
town Madrid through a radial trip and therefore in less time. Metrosur 
stations took people to other municipalities or to the closest metro 
and commuter transfer station. Apparently, those commuters that 
used public transportation preferred transit that took them to Madrid 
(i.e., Cercanias, commuter rail fines). 
The municipalities that benefited the least from Metrosur had a more 
expensive transit fare (Zone B2), were located farthest away from the 
city of Madrid, and had the smallest per capita income. These obser-
vations revealed the trade-off between lower prices for housing and 
higher costs in time and transport fares. In other words, the results 
showed a higher willingness to pay for more accessible locations, 
especially in the municipalities closest to Madrid. 
Sometimes fare policies are intended to limit the location of people 
far from the city core. From the perspective of social equity, however, 
continued research might be needed to verify whether this policy 
fulfills the transportation goals at the local and regional levéis, or 
whether it limits the integration of outlying populations with the 
whole región. Researchers such as Cervero have found that transit 
fares have different effects in áreas with low population density and 
suburban settings: people are more reluctant to take public transport 
if they have a car available and at the same time are prone to accept 
an increase in the ticket fare when they make radial trips, which in this 
case would mean a higher appraisal of Cercanias than Metrosur (55). 
The results with regard to parking spaces also suggested that people in 
this área relied on cars to commute, which was in line with the findings 
of Jordá (24). The appraised valué of transfer stations increased in 
both modes, which related to an increase of vertical and horizontal 
accessibility. Further studies should focus on the advantages that 
populations perceive in these stations. 
If the reason to travel from one point to another many times is to 
engage in economic activities, such as shopping and working, the 
question that arises is if the increase in economic activities within 
the five municipalities is sufficient to cause people to increase their 
use of Metrosur around the región rather than go back and forth 
to the city of Madrid. A better job-housing balance would increase the 
use of Metrosur and therefore its economic appreciation. Rather 
than foster integration across all the municipalities, Fuenlabrada and 
Mosto les might pursue a sepárate, less accelerated track in the quest 
for more balanced growth. 
Urban planning and design play important roles to foster the use of 
public transport. A dense street network that puts public transit within 
easy reach encourages its use. A look at new urban development in 
the zones that were studied, however, showed that they were car-
oriented. Examples, such as the A-B-C policy in the Netherlands, 
should be borne in mind, which have succeeded in taking advantage 
of áreas cióse to transit stations. Not only is it important to have a 
public transit station near a neighborhood but also a combination 
of other factors in place, such as the optimization of resources and 
political agreements that foster the use of public transport. 
Finally, another application of these models for urban studies might 
be valué capture. Although the subject of much discussion, it is a com-
plicated concept to put it into practice. In Spain, certain fiscal mecha-
nisms already are in place that relate to property prices (56). The Urban 
Land Valué Increase Tax (commonly known as Impuesto sobre Plus-
valías) is one of them. Its main problem is that Spanish taxes are not 
earmarked. None of the benefits triggered by the transport infrastruc-
ture are returned to the Madrid regional transport authority, because it 
is the municipality that levies the rate of the tax and is the one that col-
lects it. These kinds of models open the possibility to account for real 
increases in land valué and any transport impaets in order to transfer to 
the transport authority the percentage increase produced by the public 
transportation sy stem. Such an approach would lead to more economic 
resources to maintain the public transport infrastructure with higher 
social acceptability, especially during periods of budgetary constraints. 
ACKNOWLEDGMENTS 
Financial support from the Ministry of Science and Innovation of 
Spain is gratefully acknowledged. The authors thank Javier Gutiérrez 
and Juan Carlos Garcia of the Department of Human Geography of 
the Universidad Complutense de Madrid for information that helped 
to carry out this research. 
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