# RECORD 036af7575382c2b07d715add3e591d6f0d1ad955
# TITLE: Modelling Real-Estate Values Around Railway Stations: Insights from an Italian Case
# YEAR: 2026.0  VENUE: Sustainability  DOI: 10.3390/su18052304
# AUTHORS: Francesco Guglielmi; T. Tabrizi; Francesco De Fabiis; Pierlugi Coppola
# PAGES: 18  CANDIDATE TABLE PAGES (1-based): [11, 12]
# ABSTRACT: This study investigates the Wider Economic Impacts (WEIs) of railway infrastructure in Italy by analysing how station characteristics and surrounding urban contexts are capitalized into residential property values. A nationwide cross-sectional dataset covering 985 railway stations is used to estimate a Hedonic Price Model (HPM) combining observed variables and latent constructs derived from Confirmatory Factor Analysis (CFA). Results show that railway centrality, long-distance service provision, and multimodal integration are positively associated with housing prices. In particular, shared mobility services generate significant value uplift effects, especially around Local and Local Plus stations. Conversely, car-oriented accessibility is negatively associated with residential values, reflecting the capitalization of traffic-related externalities. Socioeconomic and tourism-related characteristics further contribute to heterogeneous capitalization patterns across the national territory. The findings provide systemic empirical evidence to support investment prioritization, multimodal integration, and value uplift of station areas within the Italian railway network.


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Academic Editor: Maria Rosa Trovato
Received: 7 January 2026
Revised: 13 February 2026
Accepted: 25 February 2026
Published: 27 February 2026
Copyright: © 2026 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license.
Article
Modelling Real-Estate Values Around Railway Stations: Insights
from an Italian Case
Francesco Guglielmi *
, Tannaz Tabrizi, Francesco De Fabiis
and Pierluigi Coppola
Department of Mechanical Engineering, Politecnico di Milano, Via G. La Masa 1, 20156 Milan, Italy;
tannaz.tabrizi@mail.polimi.it (T.T.); francesco.defabiis@polimi.it (F.D.F.); pierluigi.coppola@polimi.it (P.C.)
* Correspondence: francesco.guglielmi@polimi.it
Abstract
This study investigates the Wider Economic Impacts (WEIs) of railway infrastructure in Italy
by analysing how station characteristics and surrounding urban contexts are capitalized
into residential property values. A nationwide cross-sectional dataset covering 985 railway
stations is used to estimate a Hedonic Price Model (HPM) combining observed variables
and latent constructs derived from Confirmatory Factor Analysis (CFA). Results show
that railway centrality, long-distance service provision, and multimodal integration are
positively associated with housing prices. In particular, shared mobility services generate
significant value uplift effects, especially around Local and Local Plus stations. Conversely,
car-oriented accessibility is negatively associated with residential values, reflecting the cap-
italization of traffic-related externalities. Socioeconomic and tourism-related characteristics
further contribute to heterogeneous capitalization patterns across the national territory.
The findings provide systemic empirical evidence to support investment prioritization,
multimodal integration, and value uplift of station areas within the Italian railway network.
Keywords: wider economic impacts (WEIs); suburban railway stations; accessibility;
property values; hedonic price modelling; value uplift
1. Introduction
The impact of transport infrastructure on residential property values is a key com-
ponent of land value–capture policies, which are emerging as an innovative form of in-
frastructure funding [1]. Accessibility improvements (e.g., via the opening of a new train
station, or via the enhancement of services provided) can generate a wide array of benefits
to transport and non-transport users. These issues are gaining attention with the advent
of new opportunities located in the area of the stations to accommodate new emerging
mobility solutions such as autonomous vehicles [2] and urban air taxis [3]. Interventions in
railways station, whether through the creation of new hubs or the revamping of existing
ones, can have significant impacts on passengers’ demand [4], travelers’ perceptions [5],
travel times [5], and accessibility [6,7], inducing an increase in passenger volumes and
fostering the modal shift from cars to public transport [8].
Additionally, investments in transportation hubs not only produces direct transport-
related benefits (e.g., travel time reductions) but also tends to have additional non-transport
benefits (e.g., increase in economic activity) that extend beyond the transportation system
itself. In fact, the improved accessibility of an area can stimulate economic activities,
increasing its attractiveness for residents and businesses, leading to increased property
values in the surroundings of the new or regenerated hub.
Sustainability 2026, 18, 2304
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3. Results
Table 7 reports the estimation results of the MLR model used to assess the impact of
transport-related, socioeconomic, and land use features on residential property prices in
station surroundings. Overall, the estimated model indicated a good fit to the observed
real estate values (Adjusted R2 = 0.888).
Table 7. Residential property price model (Note: *, **, and *** denote statistical significance at the
10%, 5%, and 1% levels, respectively).
Variable
Estimate
Std. Error
t Value
Significance
Residential Attractiveness
0.392
0.044
8.861
***
Railway Centrality & Connectivity
4.211
0.506
8.320
***
Long-distance Railway Supply
10.681
2.431
4.394
***
Private Parking
−0.003
0.001
−3.481
***
Distance to Attraction Poles
−0.004
0.001
−3.323
***
GDP per Capita
0.038
0.002
24.349
***
Car Catchment
−1.099
0.158
−6.951
***
Sharing Services (interaction w/Local & Local Plus Station dummy)
82.373
19.795
4.161
***
Private Parking (interaction w/Local Station dummy)
0.005
0.002
2.019
*
GDP per Capita (interaction w/Seaside Area dummy)
0.012
0.002
8.380
***
GDP per Capita (interaction w/Non-touristic City dummy)
−0.006
0.001
−5.489
***
Car Catchment (interaction w/Major and Hub Station dummy)
−1.324
0.336
−3.940
***
Model fit: Adjusted R2 = 0.888; F(12, 973) = 651.5, p < 0.001
Confirmatory Factor Analysis (CFA) was employed to identify and validate latent
dimensions underlying transport supply, accessibility, and urban context variables. The
CFA allowed assessing the internal coherence of each latent construct and the contribution
of the observed indicators to the corresponding latent dimension. Table 8 reports the
estimated CFA loadings, including both unstandardized and standardized coefficients.
Latent variable scales are identified by fixing one loading per construct, and all estimated
loadings are statistically significant (p < 0.01). Unstandardized loadings reflect the original
measurement scale of the observed indicators and are reported for transparency purposes
only. Standardized loadings are instead used to interpret the latent constructs and to
construct the CFA-based composite latent variables included in the regression model.
Model fit statistics from the CFA are reported for diagnostic purposes. As expected,
given the large sample size, the chi-square test rejects the null hypothesis of perfect fit.
Global fit indices indicate a moderate overall fit (CFI = 0.82; TLI = 0.80; RMSEA = 0.095;
SRMR = 0.085); however, it is worth mentioning that, in this research, the CFA is not
intended as a fully fledged structural equation model, but as a dimensionality-reduction
tool supporting only the construction of latent variables used in the regression analysis.
Table 8. Confirmatory Factor Analysis (CFA) results—Unstandardized and standardized loadings.
Latent Construct
Observed
Indicator
Loading
(Unstandardized)
Loading
(Standardized)
Residential Attractiveness
res
1.000
0.041
school
0.551
0.844
commerce
19.082
0.968
services
1.955
0.959
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Table 8. Cont.
Latent Construct
Observed
Indicator
Loading
(Unstandardized)
Loading
(Standardized)
Railway Centrality & Connectivity
train_reg
1.000
1.001
catchment_train
0.282
0.727
Long-distance Railway Supply
train_longhaul 1
1.000
0.686
train_regv
1.020
0.549
Sharing Services
taxi_parking
1.000
0.555
sharing_car
1.201
0.644
parking_bike
0.800
0.541
sharing_bike
0.441
0.434
bike_lanes
7.323
0.618
Distance to Attraction Poles
dist_to_hospital
1.000
0.548
dist_to_edu
1.922
0.680
1 This variable represents the total combined daily supply of high-speed (AV) and InterCity (IC) trains at a station.
Socioeconomic conditions emerge as one of the main drivers of residential property
values. GDP per capita exerts a strong and highly significant positive effect (β = 0.038,
t-value = 24.349), confirming that wealthier areas are systematically associated with higher
housing prices. This effect is further amplified in seaside municipalities through an interac-
tion effect (β = 0.012, t-value = 8.38). On the other hand, in non-touristic areas the positive
effect of GDP is slightly dampened by a negative coefficient (β = −0.006, t-value = −5.489).
The Residential Attractiveness latent feature represents a proxy of the quality of the
area by combining the presence of services, schools, commercial activities, and population
density. The estimated contribution to residential property prices is positive and highly
significant (β = 0.392, t-value = 8.861), confirming that the real estate market capitalizes
the availability of urban amenities. Distance to attraction poles, such as hospitals and
universities, shows that distance to key urban functions gives a negative contribution to
real estate prices (β = −0.004, t-value = −3.323).
Railway accessibility is shown to provide a substantial influence on residential prop-
erty prices. The Railway Centrality and Connectivity feature, constituted by both the
frequency of “Regionale” trains and the average 1 h spatial reach of the station’s daily
connections, has a positive effect on prices (β = 4.211, t-value = 8.32). This result suggests
that housing markets value not only the presence of railway services per se, but also the
degree to which a station is well integrated into the broader railway system. In addition,
the availability of long-distance railway services (Long Distance Railway Supply feature)
shows an even stronger positive impact (β = 10.681, t-value = 4.394), suggesting that there
is a demand for living near stations with high-tier service types.
Car catchment areas, a proxy of the incidence and performance of road infrastructure,
is associated with a negative coefficient (β = −1.099, t-value = −6.951). This effect is
further amplified in the vicinity of Major and Hub stations through an interaction term
(β = −1.324, t-value = −3.94), suggesting that in these predominantly dense contexts,
car-oriented accessibility may be perceived as a disamenity, possibly due to congestion
and noise negative perceptions. Private parking also shows a generally negative effect
(β = −0.003, t-value = −3.481), although this relationship varies by station class. The model
suggests that in lower-density contexts, parking remains a valued amenity, as in areas
surrounding Local stations, for instance, the availability of private parking exerts a small
but positive effect (β = 0.005, t-value = 2.019).
Multimodal mobility services, on the other hand, are deemed valuable in peripheral
and suburban real estate markets. The Sharing Services variable, in Local and Local Plus
station types, exhibits a positive coefficient (β = 82.373, t-value = 19.795). This interesting
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outcome suggests that even in low-density urban contexts, the presence of innovative
mobility services, such as car-sharing, bike-sharing, and taxi facilities, is positively capi-
talized, likely due to their role in providing convenient last-mile options in areas usually
characterized by low levels of public transport accessibility.
4. Discussion
Overall, the results highlight that residential property values around railway stations
in Italy reward railway connectivity, multimodality, availability of urban amenities, and
proximity to key urban functions, while penalizing car-dependent environments and
peripheral, poorly integrated station areas.
Higher levels of railway connectivity and service provision, particularly in terms of
network centrality and long-distance rail supply, are consistently associated with higher
housing prices, confirming that real estate markets capitalize the perceived benefits of
improved accessibility and reduced reliance on private car use.
Multimodal accessibility emerges as a complementary and highly relevant factor. The
availability of shared mobility services and last-mile facilities is positively capitalized by the
housing market, with particularly strong effects observed around Local and Local Plus sta-
tions. In these contexts, where transport options are typically more limited, multimodality
appears to substantially enhance perceived accessibility and neighborhood attractiveness,
generating price premiums comparable to, or even larger than, those observed in high-tier
urban stations.
Conversely, car-oriented accessibility, proxied by larger car catchment areas, is as-
sociated with lower residential property values. This negative relationship is especially
pronounced around major stations, suggesting that traffic-related externalities, and land
consumption tend to outweigh the benefits of road accessibility around major transport
hubs. Importantly, this relationship does not imply that road accessibility is intrinsically
undesirable. Rather, the negative association captured by the car catchment variable reflects
the broader characteristics of car-dependent urban environment, where extensive road
infrastructure, land take, and traffic-related externalities tend to dominate, particularly in
suburban contexts. This interpretation is consistent with evidence on perceived utility of
urban amenities in the context of residential location choices, showing that proximity to
major road infrastructures is often negatively valued in the immediate surroundings, with
utility increasing as distance grows within approximately 1 km [60].
When compared with the existing literature on Italian empirical analyses, the results
of this study both confirm and extend previous evidence on the relationship between rail
accessibility and residential property values. Consistent with early urban rail studies,
such as Pagliara and Papa [53], the analysis confirms that improved rail accessibility is
capitalized into higher residential prices, particularly within station catchment areas.
The findings are also aligned with more recent contributions focusing on High-Speed
Rail, which document measurable price premiums associated with proximity to HSR
stations and improved long-distance accessibility [55].
At the same time, this study expands the scope of existing evidence in several direc-
tions. First, by adopting a wider perspective that includes both regional rail and high-speed
services, the results show that capitalization effects are not limited to HSR corridors or
metropolitan hubs. Second, the analysis reveals that accessibility and multimodality are
often particularly pronounced around Local and Local Plus stations, suggesting that “or-
dinary” stations located in suburban or lower-density contexts can generate substantial
residential value gains when embedded in a well-integrated transport system.
Moreover, while previous studies primarily emphasize proximity effects to major
stations, the present results highlight the importance of service provision, network connec-
https://doi.org/10.3390/su18052304