1# RECORD 036af7575382c2b07d715add3e591d6f0d1ad9552# TITLE: Modelling Real-Estate Values Around Railway Stations: Insights from an Italian Case3# YEAR: 2026.0 VENUE: Sustainability DOI: 10.3390/su180523044# AUTHORS: Francesco Guglielmi; T. Tabrizi; Francesco De Fabiis; Pierlugi Coppola5# PAGES: 18 CANDIDATE TABLE PAGES (1-based): [11, 12]6# 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.789===== PAGE 1 (score 9, numeric-paren 0, table-words 0, var-words 9) =====1011Academic Editor: Maria Rosa Trovato12Received: 7 January 202613Revised: 13 February 202614Accepted: 25 February 202615Published: 27 February 202616Copyright: © 2026 by the authors.17Licensee MDPI, Basel, Switzerland.18This article is an open access article19distributed under the terms and20conditions of the Creative Commons21Attribution (CC BY) license.22Article23Modelling Real-Estate Values Around Railway Stations: Insights24from an Italian Case25Francesco Guglielmi *26, Tannaz Tabrizi, Francesco De Fabiis27and Pierluigi Coppola28Department of Mechanical Engineering, Politecnico di Milano, Via G. La Masa 1, 20156 Milan, Italy;29tannaz.tabrizi@mail.polimi.it (T.T.); francesco.defabiis@polimi.it (F.D.F.); pierluigi.coppola@polimi.it (P.C.)30* Correspondence: francesco.guglielmi@polimi.it31Abstract32This study investigates the Wider Economic Impacts (WEIs) of railway infrastructure in Italy33by analysing how station characteristics and surrounding urban contexts are capitalized34into residential property values. A nationwide cross-sectional dataset covering 985 railway35stations is used to estimate a Hedonic Price Model (HPM) combining observed variables36and latent constructs derived from Confirmatory Factor Analysis (CFA). Results show37that railway centrality, long-distance service provision, and multimodal integration are38positively associated with housing prices. In particular, shared mobility services generate39significant value uplift effects, especially around Local and Local Plus stations. Conversely,40car-oriented accessibility is negatively associated with residential values, reflecting the cap-41italization of traffic-related externalities. Socioeconomic and tourism-related characteristics42further contribute to heterogeneous capitalization patterns across the national territory.43The findings provide systemic empirical evidence to support investment prioritization,44multimodal integration, and value uplift of station areas within the Italian railway network.45Keywords: wider economic impacts (WEIs); suburban railway stations; accessibility;46property values; hedonic price modelling; value uplift471. Introduction48The impact of transport infrastructure on residential property values is a key com-49ponent of land value–capture policies, which are emerging as an innovative form of in-50frastructure funding [1]. Accessibility improvements (e.g., via the opening of a new train51station, or via the enhancement of services provided) can generate a wide array of benefits52to transport and non-transport users. These issues are gaining attention with the advent53of new opportunities located in the area of the stations to accommodate new emerging54mobility solutions such as autonomous vehicles [2] and urban air taxis [3]. Interventions in55railways station, whether through the creation of new hubs or the revamping of existing56ones, can have significant impacts on passengers’ demand [4], travelers’ perceptions [5],57travel times [5], and accessibility [6,7], inducing an increase in passenger volumes and58fostering the modal shift from cars to public transport [8].59Additionally, investments in transportation hubs not only produces direct transport-60related benefits (e.g., travel time reductions) but also tends to have additional non-transport61benefits (e.g., increase in economic activity) that extend beyond the transportation system62itself. In fact, the improved accessibility of an area can stimulate economic activities,63increasing its attractiveness for residents and businesses, leading to increased property64values in the surroundings of the new or regenerated hub.65Sustainability 2026, 18, 230466https://doi.org/10.3390/su18052304676869===== PAGE 11 (score 37, numeric-paren 12, table-words 6, var-words 8) =====7071Sustainability 2026, 18, 23047211 of 18733. Results74Table 7 reports the estimation results of the MLR model used to assess the impact of75transport-related, socioeconomic, and land use features on residential property prices in76station surroundings. Overall, the estimated model indicated a good fit to the observed77real estate values (Adjusted R2 = 0.888).78Table 7. Residential property price model (Note: *, **, and *** denote statistical significance at the7910%, 5%, and 1% levels, respectively).80Variable81Estimate82Std. Error83t Value84Significance85Residential Attractiveness860.392870.044888.86189***90Railway Centrality & Connectivity914.211920.506938.32094***95Long-distance Railway Supply9610.681972.431984.39499***100Private Parking101−0.0031020.001103−3.481104***105Distance to Attraction Poles106−0.0041070.001108−3.323109***110GDP per Capita1110.0381120.00211324.349114***115Car Catchment116−1.0991170.158118−6.951119***120Sharing Services (interaction w/Local & Local Plus Station dummy)12182.37312219.7951234.161124***125Private Parking (interaction w/Local Station dummy)1260.0051270.0021282.019129*130GDP per Capita (interaction w/Seaside Area dummy)1310.0121320.0021338.380134***135GDP per Capita (interaction w/Non-touristic City dummy)136−0.0061370.001138−5.489139***140Car Catchment (interaction w/Major and Hub Station dummy)141−1.3241420.336143−3.940144***145Model fit: Adjusted R2 = 0.888; F(12, 973) = 651.5, p < 0.001146Confirmatory Factor Analysis (CFA) was employed to identify and validate latent147dimensions underlying transport supply, accessibility, and urban context variables. The148CFA allowed assessing the internal coherence of each latent construct and the contribution149of the observed indicators to the corresponding latent dimension. Table 8 reports the150estimated CFA loadings, including both unstandardized and standardized coefficients.151Latent variable scales are identified by fixing one loading per construct, and all estimated152loadings are statistically significant (p < 0.01). Unstandardized loadings reflect the original153measurement scale of the observed indicators and are reported for transparency purposes154only. Standardized loadings are instead used to interpret the latent constructs and to155construct the CFA-based composite latent variables included in the regression model.156Model fit statistics from the CFA are reported for diagnostic purposes. As expected,157given the large sample size, the chi-square test rejects the null hypothesis of perfect fit.158Global fit indices indicate a moderate overall fit (CFI = 0.82; TLI = 0.80; RMSEA = 0.095;159SRMR = 0.085); however, it is worth mentioning that, in this research, the CFA is not160intended as a fully fledged structural equation model, but as a dimensionality-reduction161tool supporting only the construction of latent variables used in the regression analysis.162Table 8. Confirmatory Factor Analysis (CFA) results—Unstandardized and standardized loadings.163Latent Construct164Observed165Indicator166Loading167(Unstandardized)168Loading169(Standardized)170Residential Attractiveness171res1721.0001730.041174school1750.5511760.844177commerce17819.0821790.968180services1811.9551820.959183https://doi.org/10.3390/su18052304184185186===== PAGE 12 (score 52, numeric-paren 0, table-words 16, var-words 16) =====187188Sustainability 2026, 18, 230418912 of 18190Table 8. Cont.191Latent Construct192Observed193Indicator194Loading195(Unstandardized)196Loading197(Standardized)198Railway Centrality & Connectivity199train_reg2001.0002011.001202catchment_train2030.2822040.727205Long-distance Railway Supply206train_longhaul 12071.0002080.686209train_regv2101.0202110.549212Sharing Services213taxi_parking2141.0002150.555216sharing_car2171.2012180.644219parking_bike2200.8002210.541222sharing_bike2230.4412240.434225bike_lanes2267.3232270.618228Distance to Attraction Poles229dist_to_hospital2301.0002310.548232dist_to_edu2331.9222340.6802351 This variable represents the total combined daily supply of high-speed (AV) and InterCity (IC) trains at a station.236Socioeconomic conditions emerge as one of the main drivers of residential property237values. GDP per capita exerts a strong and highly significant positive effect (β = 0.038,238t-value = 24.349), confirming that wealthier areas are systematically associated with higher239housing prices. This effect is further amplified in seaside municipalities through an interac-240tion effect (β = 0.012, t-value = 8.38). On the other hand, in non-touristic areas the positive241effect of GDP is slightly dampened by a negative coefficient (β = −0.006, t-value = −5.489).242The Residential Attractiveness latent feature represents a proxy of the quality of the243area by combining the presence of services, schools, commercial activities, and population244density. The estimated contribution to residential property prices is positive and highly245significant (β = 0.392, t-value = 8.861), confirming that the real estate market capitalizes246the availability of urban amenities. Distance to attraction poles, such as hospitals and247universities, shows that distance to key urban functions gives a negative contribution to248real estate prices (β = −0.004, t-value = −3.323).249Railway accessibility is shown to provide a substantial influence on residential prop-250erty prices. The Railway Centrality and Connectivity feature, constituted by both the251frequency of “Regionale” trains and the average 1 h spatial reach of the station’s daily252connections, has a positive effect on prices (β = 4.211, t-value = 8.32). This result suggests253that housing markets value not only the presence of railway services per se, but also the254degree to which a station is well integrated into the broader railway system. In addition,255the availability of long-distance railway services (Long Distance Railway Supply feature)256shows an even stronger positive impact (β = 10.681, t-value = 4.394), suggesting that there257is a demand for living near stations with high-tier service types.258Car catchment areas, a proxy of the incidence and performance of road infrastructure,259is associated with a negative coefficient (β = −1.099, t-value = −6.951). This effect is260further amplified in the vicinity of Major and Hub stations through an interaction term261(β = −1.324, t-value = −3.94), suggesting that in these predominantly dense contexts,262car-oriented accessibility may be perceived as a disamenity, possibly due to congestion263and noise negative perceptions. Private parking also shows a generally negative effect264(β = −0.003, t-value = −3.481), although this relationship varies by station class. The model265suggests that in lower-density contexts, parking remains a valued amenity, as in areas266surrounding Local stations, for instance, the availability of private parking exerts a small267but positive effect (β = 0.005, t-value = 2.019).268Multimodal mobility services, on the other hand, are deemed valuable in peripheral269and suburban real estate markets. The Sharing Services variable, in Local and Local Plus270station types, exhibits a positive coefficient (β = 82.373, t-value = 19.795). This interesting271https://doi.org/10.3390/su18052304272273274===== PAGE 13 (score 11, numeric-paren 0, table-words 0, var-words 11) =====275276Sustainability 2026, 18, 230427713 of 18278outcome suggests that even in low-density urban contexts, the presence of innovative279mobility services, such as car-sharing, bike-sharing, and taxi facilities, is positively capi-280talized, likely due to their role in providing convenient last-mile options in areas usually281characterized by low levels of public transport accessibility.2824. Discussion283Overall, the results highlight that residential property values around railway stations284in Italy reward railway connectivity, multimodality, availability of urban amenities, and285proximity to key urban functions, while penalizing car-dependent environments and286peripheral, poorly integrated station areas.287Higher levels of railway connectivity and service provision, particularly in terms of288network centrality and long-distance rail supply, are consistently associated with higher289housing prices, confirming that real estate markets capitalize the perceived benefits of290improved accessibility and reduced reliance on private car use.291Multimodal accessibility emerges as a complementary and highly relevant factor. The292availability of shared mobility services and last-mile facilities is positively capitalized by the293housing market, with particularly strong effects observed around Local and Local Plus sta-294tions. In these contexts, where transport options are typically more limited, multimodality295appears to substantially enhance perceived accessibility and neighborhood attractiveness,296generating price premiums comparable to, or even larger than, those observed in high-tier297urban stations.298Conversely, car-oriented accessibility, proxied by larger car catchment areas, is as-299sociated with lower residential property values. This negative relationship is especially300pronounced around major stations, suggesting that traffic-related externalities, and land301consumption tend to outweigh the benefits of road accessibility around major transport302hubs. Importantly, this relationship does not imply that road accessibility is intrinsically303undesirable. Rather, the negative association captured by the car catchment variable reflects304the broader characteristics of car-dependent urban environment, where extensive road305infrastructure, land take, and traffic-related externalities tend to dominate, particularly in306suburban contexts. This interpretation is consistent with evidence on perceived utility of307urban amenities in the context of residential location choices, showing that proximity to308major road infrastructures is often negatively valued in the immediate surroundings, with309utility increasing as distance grows within approximately 1 km [60].310When compared with the existing literature on Italian empirical analyses, the results311of this study both confirm and extend previous evidence on the relationship between rail312accessibility and residential property values. Consistent with early urban rail studies,313such as Pagliara and Papa [53], the analysis confirms that improved rail accessibility is314capitalized into higher residential prices, particularly within station catchment areas.315The findings are also aligned with more recent contributions focusing on High-Speed316Rail, which document measurable price premiums associated with proximity to HSR317stations and improved long-distance accessibility [55].318At the same time, this study expands the scope of existing evidence in several direc-319tions. First, by adopting a wider perspective that includes both regional rail and high-speed320services, the results show that capitalization effects are not limited to HSR corridors or321metropolitan hubs. Second, the analysis reveals that accessibility and multimodality are322often particularly pronounced around Local and Local Plus stations, suggesting that “or-323dinary” stations located in suburban or lower-density contexts can generate substantial324residential value gains when embedded in a well-integrated transport system.325Moreover, while previous studies primarily emphasize proximity effects to major326stations, the present results highlight the importance of service provision, network connec-327https://doi.org/10.3390/su18052304328