Housing market impacts of urban air mobility in Seoul: evidence across district and parcel scales

Housing market impacts of urban air mobility in Seoul: evidence across district and parcel scales | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Housing market impacts of urban air mobility in Seoul: evidence across district and parcel scales Hanghun Jo, Gyuseong Kim, Minseok Kim, Hyunggeun Song, Yeolin Cho, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7621928/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Urban Air Mobility (UAM) is emerging as a new layer of urban transport, yet its effects on housing markets remain unclear. This study examines how UAM deployment could reshape apartment prices and support more balanced urban development in Seoul. A scenario-based framework is developed to link reductions in generalized travel cost with spatial variation in housing values. A hedonic pricing model is estimated using 35,408 individual apartment transactions across 466 legal subdistricts, accounting for building characteristics, neighborhood amenities, socio-demographics, and accessibility to transport hubs. Six UAM scenarios, differing in network coverage and service performance, are simulated. Results show that service quality improvements produce stronger housing price appreciation than network expansion alone, while integrated strategies generate the most widespread and equitable gains. Substantial increases appear in peripheral districts where accessibility is currently constrained, suggesting that UAM can mitigate spatial disparities and strengthen housing markets in underserved areas. By evaluating impacts at both the district and parcel levels, the study provides granular evidence of UAM’s influence on housing values. It delivers actionable insights for housing policy, urban planning, and sustainable urban development. Urban Air Mobility (UAM) Hedonic pricing model Accessibility Housing market Spatial equity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction Urban Air Mobility (UAM) is a transformative mode of urban transportation, offering a novel layer of mobility that extends beyond traditional surface-based infrastructure. By incorporating vertical takeoff and landing technologies, UAM has the potential to alleviate ground-level congestion, enhance urban accessibility, and even reshape spatial structures in metropolitan areas (Straubinger et al., 2020 ; Rothfeld et al., 2020 ; So et al., 2023 ; Ribeiro et al., 2023 ). Cities across the globe including those in the United States, Europe, and Asia are actively exploring UAM availability and investing in related infrastructure, supported by pilot programs and evolving regulatory frameworks (Sun et al., 2021 ). In this regard, the K-UAM Roadmap presented a vision for Korean commercialization by 2025 (MOLIT, 2020). As UAM transitions from concept to implementation, growing scholarly attention has been directed toward its potential effects on urban development structures (Zhao & Feng, 2024 ). Advanced UAM may redefine spatial accessibility by fundamentally altering how individuals perceive and interact with urban space. Decades of urban and transport research have shown that proximity to transportation infrastructure such as subways, airports, and bus rapid transit (BRT) systems significantly impacts residential property values (Tsutsumi & Seya, 2008 ; Garrow et al., 2020). However, UAM introduces a set of operational and spatial characteristics that differ from conventional modes. These characteristics, such as the distribution and location of vertiports, variation in travel speed and fare schedules, and novel vertical connectivity, are likely to produce new patterns in real estate valuation (Wei et al., 2021 ; Winter et al., 2020 ). As UAM is integrated into existing multimodal transit networks, its interaction effects could enhance accessibility while also motivating important considerations regarding equitable access and spatial planning outcomes (Mohri et al., 2021 ; Wild, 2024 ). Despite these potential transformations, few studies have explored the impact of UAM on real estate values. In particular, the relationship between UAM deployment and housing markets remains an underexplored dimension of urban mobility research. With global interest in UAM on the rise, there is a growing need to understand the effects of this new mobility on property values, urban spatial configurations, and broader sustainability goals (Straubinger et al., 2021a ). As property values are closely associated with various urban structures, transportation characteristics play a major role. In this regard, the present study contributes to this discussion by providing an empirical analysis of the effects of UAM deployment strategies on property values, accessibility, and proximity to transportation. This study is based on the theoretical foundation that UAM can significantly reduce travel times within urban areas, particularly in highly congested cities, potentially influencing residential location choices and housing values (Karami et al., 2024 ). Our research questions are: To what extent do different UAM deployment strategies—varying in vertiport density and operational plans—affect housing prices in the urban context? How do spatial and operational characteristics of UAM lead to heterogeneous housing market responses across districts in Seoul? Can UAM-induced accessibility improvements help mitigate spatial inequality in urban housing markets? To address these questions, we conducted a scenario-based empirical analysis that links generalized travel cost to observed housing prices, particularly focusing on apartment housing in Seoul, Korea. Six distinct UAM deployment scenarios were constructed to capture variations in vertiport location and operational strategies (i.e., differences in travel speed and fare structures). Multiple hedonic pricing models were developed by incorporating socio-economic demographics, built environment, and land use properties. Notably, we further define generalized travel costs to major urban transport hubs as a key variable in the models. This is based on our assumption that the role of UAM within the urban context in Seoul is limited to enhancing inter-regional trips or improving proximity to major transport hubs (e.g., express bus terminals, high-speed rail stations, or airports), which can be considered an extended first-/last-mile mode. Nevertheless, well-connected public transit services are already available in Seoul, and it is unlikely that many travelers would adopt UAM as their primary mode for intra-city travel. In this regard, the generalized cost variable can explain how mobility and accessibility can be improved after the provision of UAM. This allowed us to compare housing market responses across districts in Seoul and evaluate the relative significance of service quality and infrastructure density. Furthermore, we explored whether accessibility improvements driven by the adoption of UAM favor peripheral areas, resulting in higher spatial equity by overcoming spatial distance constraints. We believe that these academic efforts can contribute to the growing body of empirical research on UAM spatial and economic impacts on urban housing markets, providing both theoretical and policy-relevant insights. The present paper is structured as follows. Section 2 reviews prior research on UAM development, urban mobility transitions, and transportation-driven real estate dynamics. Section 3 outlines the study scope, data sources, scenario design, and analytical approach. In Section 4 , we present empirical results on UAM-driven changes in housing prices and spatial accessibility. Section 5 discusses the broader implications of these findings concerning spatial equity, policy development, and sustainable mobility. The final section provides conclusions with a brief summary of this study followed by limitations and directions for future research. 2 Literature review 2.1 UAM and urban mobility changes UAM has emerged as a disruptive innovation in the domain of urban transportation, offering a paradigm shift in access to and navigation of cities. By leveraging electric vertical takeoff and landing vehicles, UAM creates a third spatial layer of mobility, capable of bypassing the limitations of traditional surface-based transportation systems such as subways, highways, and railways (Yang et al., 2020 ; Cohen et al., 2021 ; Long et al., 2023 ; Mercan et al., 2025 ). This new dimension of mobility enables direct, point-to-point connections and is particularly relevant in densely populated megacities where land scarcity and traffic congestion pose chronic challenges to transportation planning. Unlike conventional transit systems that rely on fixed, linear infrastructures with long construction periods, UAM offers a modular, decentralized alternative with the flexibility to integrate vertiports into rooftops, parking structures, or existing multimodal transit nodes, significantly reducing the spatial footprint and associated infrastructure costs (Zhao & Feng, 2024 ). The strategic placement of vertiports within existing transportation networks enhances intermodal connectivity, enabling UAM to serve effectively as a feeder or connector service for airports, high-capacity transit hubs, and major activity centers (Rajendran & Srinivas, 2020 ; Garrow et al., 2021 ). Consequently, UAM reshapes urban accessibility by overcoming traditional barriers of distance and congestion through direct vertical mobility, significantly altering locational value and the hierarchy of urban sub-centers. Simulation-based studies further indicate substantial reductions in travel times, particularly for intra-urban commutes, potentially influencing residential location decisions and broader urban spatial structures (Long et al., 2023 ; Zhao & Feng, 2024 ; Karami et al., 2024 ). However, several recent studies asserted that the advantages of UAM may be most pronounced for long-distance trips or connections to major transport hubs, rather than short-distance urban travel. For example, simulations across metropolitan regions have shown that short aerial trips often fail to generate net time savings due to the detours required for first- and last-mile access to vertiports. This finding aligns with the view that UAM is functionally closer to commuter rail—optimized for medium- to long-distance travel—than to local bus services (Roy et al., 2020 ; Guo et al., 2025). A demand modeling study in Milan further supports this perspective, showing that UAM gained a higher mode share for airport shuttle-type services (2–5%) than for general intra-city trips (1–3%), highlighting its relative strength in longer or intermodal use cases (Coppola et al., 2025 ). Additional studies point to logistical burdens such as vertiport access, security procedures, and high fare levels as key barriers that reduce UAM competitiveness for short-distance mobility (Pons-Prats et al., 2022 ). Consequently, many researchers conceptualize UAM as a premium, high-speed connector between peripheral districts and core mobility hubs—such as airports, high-speed rail stations, or major transfer terminals—rather than as a ubiquitous replacement for surface-level public transit in dense urban cores (Straubinger et al., 2021b ; Coppola et al., 2025 ). In summary, UAM is positioned to redefine urban mobility by creating a vertically integrated, faster, and more flexible transportation system. While its transformative potential is widely recognized, recent evidence suggests that its greatest benefits are likely to emerge in longer intra-city trips or strategic connections to major transportation hubs, rather than as a universal substitute for short-distance urban travel. Its successful integration into the existing multimodal network depends on thoughtful vertiport placement, demand-responsive operations, and regulatory foresight and on a realistic understanding of its functional niche within complex urban transport ecosystems. Importantly, as cities consider incorporating UAM into their future plans, the spatial, social, and economic implications must be carefully evaluated to ensure that the resulting mobility transformation contributes to equitable and sustainable urban development. 2.2 Role of transportation in housing prices Housing prices are influenced by numerous factors, including property attributes, neighborhood conditions, economic environment, and transportation accessibility (Han et al., 2025 ). Among these factors, transportation infrastructure consistently exerts a strong influence by enhancing connectivity and reducing travel time, elevating property attractiveness (Debrezion et al., 2007 ; Mohammad et al., 2013 ; Mulley & Tsai, 2016 ). Proximity to transportation hubs such as rail stations, airports, and highways typically increases property values due to improved accessibility and convenience (Debrezion et al., 2007 ; Xu & Nakajima, 2017; Kanasugi & Ushijima, 2018). Empirical evidence has repeatedly shown that proximity to public transportation, especially rail stations, leads to a positive impact on residential property values. Studies focusing on a major metropolitan area indicate a clear price premium for properties near transit hubs due to reduced commuting times and improved inter-regional accessibility (Bowes & Ihlanfeldt, 2001 ; Cervero & Kang, 2011 ). Bus Rapid Transit (BRT) systems have enhanced land values by improving urban connectivity and reducing travel costs, demonstrating the broad influence of transit infrastructure on property valuation (Cervero & Kang, 2011 ; Mulley & Tsai, 2016 ). High-speed rail further underscores transportation’s significant role in shaping real estate values. The introduction of this service often transforms peripheral areas into attractive residential and commercial zones, substantially increasing local land values and driving urban development (Ahlfeldt & Feddersen, 2018 ). Likewise, airport proximity generally boosts commercial property values, although residential properties may face mixed outcomes due to negative externalities such as noise and pollution (Espey & López, 2000 ; Salvi, 2008 ; Trojanek et al., 2017 ; Winke, 2017 ). The emergence of UAM introduces novel dynamics into the relationship between traditional transportation and real estate. UAM systems, utilizing vertical take-off and landing technologies, promise efficient point-to-point travel by avoiding surface-level congestion, significantly enhancing urban accessibility (Cohen et al., 2021 ; Long et al., 2023 ). Their unique spatial flexibility allows vertiports to be established in dense urban contexts with minimal spatial footprints, potentially reshaping urban accessibility and land valuation (Zhao & Feng, 2024 ; Cohen et al., 2021 ). Recent research on UAM indicates potential real estate value impacts analogous to conventional transport hubs. Zhao and Feng ( 2024 ) showed that strategically placed vertiports integrated with existing multimodal networks can substantially enhance accessibility, likely driving up nearby real estate prices. However, due to UAM’s nascent stage, uncertainties regarding market acceptance, operational costs, regulatory frameworks, and system maturity remain significant. In this regard, scenario-based analyses accounting for diverse deployment conditions and configurations are crucial to accurately understanding potential real estate impacts (Cohen et al., 2021 ; Long et al., 2023 ). 2.3 Research gap and our contributions While prior studies have highlighted UAM's potential to enhance urban accessibility and influence real estate values, several limitations remain. Existing literature has focused largely on conceptual or simulation-based evaluations, with little empirical evidence demonstrates the effects of UAM deployment on housing markets under realistic spatial and operational constraints (Roy et al., 2023; Wang et al., 2023 ; Coppola et al., 2025 ). Moreover, previous research has primarily emphasized traditional transit modes such as subways and BRT in explaining property value formation, whereas UAM’s role, particularly under diverse deployment scenarios, remains underexplored in terms of both accessibility and equity impacts. Considering these aspects, we investigated how the introduction of UAM may affect apartment prices in Seoul, South Korea—a dense urban environment where real estate values are highly sensitive to transportation accessibility. Unlike traditional infrastructure, UAM introduces a flexible and rapidly deployable layer of mobility that can reshape urban accessibility patterns and value hierarchies. To capture these potential transformations, we adopted a scenario-based approach with various vertiport locations, operational characteristics (e.g., speed and fare), and generalized travel costs. In particular, we determined generalized cost by simulating UAM characteristics and attributes given the context of Seoul and then applying the concept of travel time valuation to calculations. 3 Empirical Framework 3.1 K-UAM plans and study area This study examines the impact of UAM deployment on real estate prices in Seoul, Korea, where the national K-UAM Roadmap aims to introduce commercial UAM operations by 2025. According to the official roadmap, the Korean government established a multi-phase development strategy for UAM services in the Seoul metropolitan area. The initial phase targets the launch of pilot programs and limited commercial operations by 2025, laying the foundation for early market entry. This will be followed by establishment of an urban-scale UAM network by around 2030, during which operational systems, regulatory frameworks, and vertiport infrastructure are expected to be expanded and standardized. In the longer term, full-scale nationwide deployment is planned for after 2035, with UAM envisioned as an integral part of the country’s future transportation system. The roadmap emphasizes the importance of public–private partnerships, safe and efficient airspace management, integration with existing transit modes, and sustainable urban mobility goals (MOLIT, 2020). The specific vertiport locations and planned routes for K-UAM are illustrated in Fig. 1 and Table 1 . Given this context, we selected Seoul as the study area due to its dense urban structure, high transportation demand, and dynamic real estate market. In examining the relationship between UAM deployment and housing prices, this research focuses on apartment-type housing. Apartments are the most common housing type in the Seoul metropolitan area, accounting for a significant share of the housing market. As of 2023, the share of apartment housing type was 53.3% (Seoul Institute, 2023 ). Their standardized valuation structure and relatively high prices, even in lower-priced districts, are particularly suitable for analyzing transportation-induced price changes. More importantly, Table 1 Phase-specific vertiport plans in the K-UAM roadmap Phase Vertiport location Characteristics Phase 1 V1: Drone test certification center V2: GyeYang new town Early-stage UAM test flight Operation in low-density suburban areas Phase 2 V3: Gimpo International Airport V4: Yeouido Park V5: Goyang KINTEX Connects business and transit hubs Tests intermodal efficiency with existing networks Phase 3 V6: Jamsil business and residential complex V7: Suseo Super Rapid Train Station Expands to high-density commercial & residential zones Note : “V” denotes a planned vertiport site proposed under the national K-UAM implementation framework. apartments tend to be highly sensitive to transportation infrastructure developments, responding more directly to changes in accessibility and urban expansion than other housing types such as detached houses or mixed-use buildings. This sensitivity is especially pronounced in Seoul, where public transportation accessibility is a major determinant of housing demand and price fluctuations. In sum, apartment-type housing prices serve as a reliable indicator for assessing the potential economic and spatial impact of UAM integration on urban real estate markets. 3.2 Scenario design To accurately assess the effects of UAM accessibility, we referred to the full K-UAM roadmap but narrowed its analytical focus to selected vertiport locations within the Seoul urban area. Phase 1 vertiports (V1, V2), which are located in suburban areas and primarily serve early-stage UAM trials, were excluded from the analysis due to their limited relevance to high-density urban real estate dynamics. Similarly, V5 (Goyang KINTEX), located at Phase 2 and beyond the Seoul boundary, was also excluded from the scenario list. Among these, only four vertiports located within Seoul’s urban area (V3, V4, V6, V7) were selected as target sites, as they are most relevant to high-density urban real estate dynamics. Figure 2 shows the geographic distribution of these target and non-target vertiports, highlighting the locations selected for the scenario design. Instead of estimating the effects based on a single UAM operation plan, we adopted a scenario-based framework to reflect the uncertainty and flexibility surrounding UAM deployment strategies. By simulating different spatial layouts and operational conditions, these scenarios served as an analytical tool to explore how UAM-driven accessibility changes may influence changes in apartment prices in Seoul. Importantly, these scenarios are not intended to forecast exact future prices but to provide a general overview of price changes and analyze spatial variations depending on UAM accessibility and mobility improvements. The detailed specifications of each scenario are summarized in Table 2 . The first scenario set (A Scenarios) examines changes in vertiport network density. Scenario A1 is the base scenario as proposed by the national K-UAM roadmap, consisting of four fixed vertiports located along the primary UAM route. Since this base scenario has limited access to UAM, we added 25 vertiports for each district in Seoul, representing a sizable network expansion. This setting allowed us to evaluate how improved proximity to UAM reshapes distribution of real estate values across urban subregions. The second set (B Scenarios) varies the operational efficiency of UAM services. Scenario B1 assumes a moderately improved operational plan featuring a speed of 260 km/h and a fare of $ 0.50/km. Scenario B2 further simulates an advanced deployment scenario compared to B1, achieving a higher speed of 320 km/h and a reduced fare of $ 0.36/km. These two specifications reflect different stages of UAM technological and market maturity. Since both fare and speed directly influence generalized travel cost and time, we expect that these updates will affect accessibility, leading to changes in property valuation. The third set (C Scenarios) introduces integrated configurations by combining the infrastructural expansion (Scenario A2) with the operational improvements (B Scenarios). Scenario C1 merges the A2 network with B1 service efficiency (moderate fare and speed), and Scenario C2 reflects the most optimistic configuration, featuring both full-size network coverage and the most advanced operational plan. These hybrid scenarios enable exploration of complicated interactions between spatial coverage and user cost, which are critical for understanding the transformative potential of UAM in reshaping urban spatial structures. Table 2 Specifications of UAM scenario settings Category Scenario Vertiport density Fare (USD a /km) Speed (km/h) Description Scenario A: Vertiport Configuration A1 Four fixed vertiports 1.43 200 Baseline: current national demonstration route A2 25 added vertiports for each district 1.43 200 Moderate network densification via additional vertiports Scenario B: Service efficiency B1 Four fixed vertiports 0.50 260 Improved operation with moderate speed B2 Four fixed vertiports 0.36 320 Advanced operation with high speed Scenario C: Integrated strategy C1 25 added vertiports for each district 0.50 260 Moderate speed and fare with full vertiport coverage (A2 + B1) C2 25 added vertiports for each district 0.36 320 High speed and low fare with full vertiport coverage (A2 + B2) a Based on the exchange rate of 1 USD = 1,400 KRW 3.3 Study data and variables In this study, we constructed a comprehensive set of variables based on public datasets, real transaction records, and spatial analysis using the Geographic Information System (GIS). The primary dataset consists of apartment transaction records collected from the Ministry of Land, Infrastructure, and Transport's Real Transaction Price Disclosure System. All transactions occurring between January 1, 2023, and January 1, 2024, within Seoul's 466 legal subdistricts were compiled, resulting in a total of 35,408 apartment units. To standardize the dependent variable, transaction prices were converted to price per unit area (㎡) to address skewness in distribution, allowing elasticity-based interpretation. The natural logarithm of unit price per square meter was applied, and all prices were converted into U.S. dollars (USD/m²). Independent variables were categorized into six groups: building characteristics, proximity to amenities, socio-economic traits, educational environment, accessibility to transport hubs (generalized cost variables), and regional characteristics. Building characteristics include apartment age, number of floors, and exclusive area per unit obtained from the national statistics of real estate. Proximity to amenities represents locational attributes of each apartment, which were defined using a GIS-based shortest-distance calculation method (the spatial information was sourced from the Seoul Big Data Portal and the Public Data Portal). This approach was applied consistently to variables such as distances to the nearest school, park, subway station, shopping facility, and general hospital (Yang et al., 2020 ). Hospitals included in the analysis were limited to general hospitals with at least 100 beds. Parks excluded small-scale facilities such as playgrounds to focus on significant urban green spaces. The shortest distances from each apartment to these facilities were calculated through spatial joining and nearest feature analysis in GIS, and the resulting values were included as continuous variables in the model. Socioeconomic traits were constructed at the legal subdistrict level to ensure consistency across spatial units when integrating demographic, economic, and geographic data. Income level was derived from the Environmental Big Data Platform and measured as the average annual household income in U.S. dollars for each legal subdistrict. These indicators reflect the underlying socioeconomic structure that shapes housing demand and price sensitivity. Population and employment density were calculated as number of residents or workers per square kilometer (1,000 persons/km²), based on official census data and subdistrict-level boundary shapefiles. The educational environment is treated as an important factor in the Korean housing market, where strong parental emphasis on children’s academic achievement significantly influences residential decisions, especially in Seoul. To capture this effect, we incorporated two variables. The first one is the admission rate to top 10 universities, defined as the proportion of high school graduates who enrolled at one of Korea’s top 10 universities in the two years after graduation. The other variable is the number of private educational institutes for each subdistrict, which indirectly represents the level of interest and engagement in education among residents. Both variables were aggregated at the legal subdistrict level using data from Seoul Education Statistics and the Open Data Portal. Last, we defined generalized travel cost to evaluate the impact of UAM deployment on housing prices. This indicates synthesized travel cost from each subdistrict to the three major transportation hubs Suseo Station (one subway and high-speed rail lines), Seoul Station (multiple subways and high-speed rail lines), and Gimpo Airport (a key domestic airport on the western edge of Seoul) using the optimum mode with the shortest travel time and lowest cost. The underlying assumption, grounded in conventional transport economics, is that individuals aim to minimize this total perceived cost when selecting travel routes and modes (Mackie et al., 2001 ). The generalized cost for a given transportation type i is calculated using the following Eq. ( 1 ): $$\:{GC}_{i}=\:{C}_{i}+{VOT}_{i}\times\:{T}_{i},$$ 1 where \(\:{GC}_{i}\) represents the generalized cost for mode \(\:i\) ; \(\:{C}_{i}\) and \(\:{T}_{i}\) denote the monetary travel cost and actual travel time of travel mode i , respectively; and \(\:{VOT}_{i}\) indicates the value of travel time for mode i . That is, generalized cost is defined as the sum of two monetary values consisting of monetary travel costs (e.g., fares, tolls, fuel) and the monetary value of travel time. The travel time valuation represents the total burden experienced by users during a trip and is a kind of opportunity cost (Small, 1992 ; Ortúzar & Willumsen, 2011 ). There are two approaches for determining the value of travel time savings (VOTTS): marginal rate of substitution and wage rate. We chose wage rate-based VOTTS, which is officially released by National Statistics (Park et al., 2025 ). It reflects the monetary worth travelers place on reducing their time in transit. According to the guideline, the time value for public transportation is 16.1 USD/hour, while the valuations for automobile and walking are 19.2 and 12.3 USD/hour, respectively. In UAM, we view this mode as a different type of public transportation and applied VOTTS for public transportation to UAM. For each scenario, generalized travel costs to the three key urban mobility hubs—Suseo Station, Seoul Station, and Gimpo Airport—were recalculated based on speed and fare assumptions. Travel time and cost data were obtained through a combination of public APIs and the automated scraping process. Vehicle routes were retrieved from an online API source (called Naver), and public transit and walking data were collected through structured web scraping under controlled conditions to avoid server overload. UAM routing was simulated using a custom Python-based model that combined access and egress paths with assumed aerial links, and all routing scenarios were generated for weekday evening peak hours to reflect typical commuter behavior. The updated cost estimates were integrated into the hedonic pricing model as key independent variables representing scenario-specific accessibility shifts. Table 3 summarizes the network structure and operational parameters of each scenario, including updated fare values in USD. To determine generalized cost, the four modes of car, bus, subway, and taxi (and UAM if available) were considered, and the lowest value among the four was selected to define this variable. Table 3 Description of incorporated variables Variable (unit) Description Dependent variable Apartment transaction prices (USD/m²) Log-transformed unit price of apartment sales Building characteristics Exclusive area (m²) Floor area in square meters of each apartment unit Number of floors Numbers of floors in the apartment building Apartment age Age of the building in years Proximity to amenities Distance to the nearest school (km) Euclidean distance to the nearest school Distance to the nearest park (km) Distance to the nearest public park Distance to the nearest subway station (km) Distance to the nearest subway station Distance to the nearest shopping facility (km) Distance to the nearest shopping/commercial center Distance to the nearest hospital (km) Distance to the nearest general hospital (≥ 100 beds) Socioeconomic traits Population density (per 1,000 person/km²) Number of residents per km² Employment density (per 1,000 person/km²) Number of jobs per km² Annual household income (USD) Average household income per year Educational environment Admission rate to top 10 universities (%) % of high school students entering top 10 universities Number of private education institutes Number of private education institutions in the travel area Accessibility to transport hubs Generalized cost to Suseo Station (USD) Time × value of travel time + fare to Suseo Station Generalized cost to Seoul Station (USD) Time × value of travel time + fare to Seoul Station Generalized cost to Gimpo Airport (USD) Time × value of travel time + fare to Gimpo Airport Regional characteristic Major district Dummy variable indicating subdistrict location in Gangnam, Seocho, Songpa, or Yongsan-gu We designated district-level centroids as hypothetical vertiport locations for scenario analysis. These centroids were calculated as floor-area–weighted points based on the spatial distribution of residential floor area across three housing types—apartments, single-family houses, and multiplex residences. In addition, we identified three major transportation hubs—Suseo Station, Seoul Station, and Gimpo Airport—as high-access nodes with strategic intermodal connectivity. Figure 3 illustrates the spatial arrangement of these hypothetical vertiports, major hubs, and administrative district boundaries. 3.4 Methodological approaches To quantify the impact of UAM accessibility on apartment prices, we employed a hedonic pricing model, a widely used econometric technique for analyzing the influence of product attributes on market values. The foundation of the hedonic pricing approach lies in the theory of implicit markets, first introduced by Ridker and Henning ( 1967 ) and later formalized by Rosen ( 1974 ) and Freeman ( 1979 ). The main premise is that the price of a heterogeneous good (e.g., housing) can be decomposed into the implicit values of its constituent characteristics. In the housing market context, property prices are assumed to be determined by a bundle of structural, locational, environmental, and accessibility attributes. The hedonic pricing model allows estimation of the marginal implicit price of each attribute based on observed variation in transaction prices across housing units that differ in these characteristics. This approach is particularly suitable for evaluating the capitalization effects of transportation infrastructure, including new and emerging technologies such as UAM (Debrezion et al., 2007 ; Ahlfeldt & Kavetsos, 2014 ). Formally, the hedonic pricing function can be expressed as follows (Eq. 2 ): where \(\:{P}_{i}\) represents the transaction price of apartment \(\:i\) ; \(\:{\beta\:}_{0}\) denotes the constant term; \(\:{X}_{ik}\) represents the k -th characteristic of apartment \(\:i\) ; \(\:{\beta\:}_{k}\) is the estimated coefficient of characteristic k , and \(\:{ϵ}_{i}\) is a stochastic error term. The model assumes a semi-log functional form, consistent with prior literature, which enables elastic interpretation of the coefficients and mitigates heteroscedasticity in transaction price data (Cropper et al., 1988 ; Sirmans et al., 2005 ). 4 Results 4.1 Descriptive statistics We analyzed a total of 35,408 apartment transactions recorded across all 466 legal districts in Seoul from January 1st, 2023, to December 31st, 2023. The dependent variable of the hedonic pricing model, as mentioned earlier, is the apartment transaction price per square meter (USD/m²), which is log-transformed to satisfy the assumptions of a semi-logarithmic regression structure. For interpretability and descriptive clarity, however, all price-related statistics are reported in their original (non-log) form in this section. Figure 4 presents the spatial distribution of apartment prices in Seoul using two complementary visualizations. The map on the left illustrates parcel-level transaction data, highlighting micro-scale differences and fine-grained clusters of high-value housing. The map on the right aggregates the same data at the district level, revealing clear regional gradients. Both maps confirm well-defined concentration values are observed in peripheral and northeastern districts. Spatial imbalances in housing demand, transport accessibility, infrastructure, and the built environment can lead to price inequities. Table 4 summarizes the descriptive statistics of all incorporated variables in the hedonic model, comprising apartment characteristics, proximity to amenities, socioeconomic traits of each subdistrict, educational environment, accessibility to transport hubs, and the indicator of the prime Seoul districts. To enhance general comparability, monetary values were converted from KRW to USD based on the exchange rate of 1,400 KRW/USD, and all distances were measured in kilometers. The results indicate that Seoul’s apartment stock is characterized by high-rise structures with mid-sized units and moderate building age. Locational accessibility varies substantially across subdistricts, with average distances to amenities such as schools, parks, subway stations, shopping centers, and hospitals reflecting spatial heterogeneity in urban infrastructure. Socioeconomic indicators and education-related variables also present substantial variation, suggesting disparities in income levels, population density, and educational resources. Travel accessibility, measured by generalized cost to major hubs, also exhibits spatial variation, with slightly lower average costs to Seoul Station (i.e., relatively better accessibility) compared to other hubs. Notably, more than 70% of the apartment transactions are concentrated in Gangnam, Seocho, Songpa, and Yongsan-gu, reinforcing the spatial concentration of high-value real estate in Seoul. Table 4 Descriptive statistics (n = 35,408) Variables Mean Max. Min. Std. dev. Dependent variable Apartment transaction prices (USD/m 2 ) 9,992.90 62,940.16 1,548.65 4,824.50 Building characteristics Number of floors 30.20 68.00 1.00 6.50 Exclusive area (m 2 ) 74.40 309.69 10.80 30.20 Building age (year) 21.38 64.00 2.00 11.45 Proximity to amenities Distance to the nearest school (km) 0.27 2.80 0.01 0.18 Distance to the nearest park (km) 0.59 2.89 0.02 0.39 Distance to the nearest subway station (km) 0.55 3.50 0.02 0.36 Distance to the nearest shopping facility (km) 0.45 3.74 0.01 0.33 Distance to the nearest hospital (km) 1.37 4.62 0.04 0.74 Socioeconomic traits Population density (1,000 people/km 2 ) 21.87 59.31 0.97 9.02 Employment density (1,000 people/km 2 ) 9.67 38.77 3.38 6.08 Annual household income (USD) 3958.40 11929.60 2144.60 1834.10 Educational environment Admission rate to top 10 universities (%) 0.19 0.37 0.10 0.07 Number of private education institutes 714.30 2,500.00 108.00 551.30 Accessibility to transport hubs Generalized cost to Suseo Station (USD) 13.17 23.04 3.03 4.65 Generalized cost to Seoul Station (USD) 10.49 18.63 3.36 2.83 Generalized cost to Gimpo Airport (USD) 15.62 25.94 4.19 4.67 Variables Cases Share (%) Regional characteristic Major district b 25,493 71.99 Others 9,915 28.01 a Gangnam, Seocho, Songpa, and Yongsan-gu districts. b This indicates whether a subdistrict belongs to Gangnam, Seocho, Songpa, or Yongsan-gu. 4.2 Estimation results of the hedonic model To assess the impact of UAM on housing prices in Seoul, we developed a hedonic price model using 35,408 apartment transactions. The model is the empirical basis for simulating price variations under different UAM scenarios after controlling relevant factors. Table 5 presents the estimation results with two kinds of estimates (unstandardized and standardized), showing how factors related to structural, locational, socioeconomic, educational, and accessibility factors influence apartment prices. The final model demonstrates that a substantial portion of price variation can be explained by the estimated model (adjusted R ² = 0.518). Among building characteristics, the number of floors has a significant positive impact on apartment prices (β = 0.097), indicating that higher-floor units tend to be valued more highly—possibly due to better views, privacy, or reduced noise levels. In contrast, exclusive areas showed a marginal negative impact (β = -0.043), suggesting that, when controlling for other factors, larger units may not always translate into higher unit prices per square meter. Building year exhibited a strong negative effect on housing prices (β = -0.126). This indicates that newer buildings tend to command a substantial premium, consistent with market preferences for more modern facilities, updated designs, and lower maintenance costs. All location characteristics except proximity to hospitals showed straightforward effects with the expected sign (negative). In particular, longer distances to a park (β = -0.104), school (β = -0.077), and subway station (β = -0.051) led to lower apartment values, indicating the importance of accessibility to green and transit infrastructure. Socioeconomic traits revealed more nuanced effects. Population and employment densities had negative coefficients of modest magnitude, while annual household income showed a strong positive effect (β = 0.221), consistent with our expectation. In the educational domain, both the admission rate to top 10 high schools (β = 0.095) and the number of private education institutes (β = 0.129) significantly increased housing values, highlighting the major role of education in shaping the real estate landscape in Seoul. A significant impact of premium spatial location was also observed: apartments located in one of the three Gangnam districts (i.e., Gangnam, Seocho, and Songpa) or Yongsan exhibited a higher contribution (β = 0.107), reinforcing the status of these areas as high-value residential zones within Seoul’s urban hierarchy. Notably, generalized travel cost to major transport hubs revealed differentiated impacts on housing prices. Better accessibility to Suseo Station and Seoul Station was strongly associated with higher apartment values (β = − 0.250 and − 0.244, respectively). From a built environment perspective, this finding shows that proximity to highly integrated transit nodes enhances residential desirability by reducing daily travel costs and embedding neighborhoods Table 5 Estimation results from the hedonic model Variables Estimate (B) Std. Estimate ( β ) Std. Error t-value Constant 9.617 *** 38.44 432.11 Building characteristics Number of floors 0.007 *** 0.097 0.0003 25.51 Exclusive area (m 2 ) -0.001 *** -0.043 0.0001 -10.99 Building age (year) -0.005 *** -0.126 0.0002 -32.31 Proximity to amenities Distance to the nearest school (km) -0.186 *** -0.077 0.010 -19.45 Distance to the nearest park (km) -0.120 *** -0.105 0.005 -25.49 Distance to the nearest subway station (km) -0.063 *** -0.051 0.006 -11.51 Distance to the nearest shopping facility (km) 0.098 *** 0.072 0.006 15.75 Distance to the nearest hospital (km) 0.002 0.003 0.002 0.69 Socioeconomic traits Population density (1,000 people/km 2 ) -0.001 *** -0.015 0.0002 -3.38 Employment density (1,000 people/km 2 ) -0.001 ** -0.013 0.0003 -2.83 Annual household income (USD) 0.052 *** 0.221 0.002 27.72 Educational environment Admission rate to top 10 high schools (%) 0.580 *** 0.095 0.038 15.36 Number of private education institutes 0.001 *** 0.129 0.00001 17.31 Accessibility to transport hubs Generalized cost to Suseo Station (USD) -0.024 *** -0.250 0.001 -34.94 Generalized cost to Seoul Station (USD) -0.038 *** -0.244 0.001 -42.04 Generalized cost to Gimpo Airport (USD) -0.0003 -0.004 0.001 -0.59 Prime regional characteristic Major district 0.117 *** 0.107 0.008 14.57 N 35,408 R-squared 0.519 Adjusted R-squared 0.518 F-statistic 2985.07 *** * p < 0.10, ** p < 0.05, *** p < 0.01. within broader metropolitan opportunity structures. Seoul Station functions as the city’s central multimodal hub, elevating the locational value of surrounding districts by concentrating jobs and inter-city linkages. Suseo Station, despite its peripheral position, demonstrates how strategic transport investments can reconfigure urban spatial hierarchies: its high-speed rail role and proximity to the Gangnam business district anchor the emergence of a subcenter that attracts both residential and commercial demand. These results suggest that accessibility premiums are most pronounced when transport nodes reinforce existing centers of activity or create new ones within the metropolitan fabric. In contrast, accessibility to Gimpo Airport did not significantly affect housing prices. While the airport provides regional connectivity, its peripheral location and weaker integration with Seoul’s primary employment corridors limit its capacity to generate residential premiums. Potential disamenities—such as aircraft noise, land-use constraints, and height restrictions—further reduce the attractiveness of nearby housing markets. This shows that transport infrastructure does not automatically translate into positive capitalization; rather, the effect depends on how accessibility interacts with urban form and neighborhood livability. From a housing and built environment standpoint, the contrast between central rail hubs and a peripheral airport highlights the importance of spatial positioning and functional integration within the city’s structure. 4.3 Scenario-based analysis To examine how different UAM implementation strategies would influence housing values, we applied the estimated model to six scenarios differentiated by vertiport density and service plan. For each scenario, generalized travel costs were updated only when the optimum travel mode was UAM, and the costs were incorporated into a hedonic pricing model to simulate apartment price changes in Seoul, whereas other explanatory variables were held constant. Table 6 summarizes the estimated average price change for each scenario, showing clear differences in magnitude depending on improvements to physical infrastructure, service performance, or both. Across all scenarios, service-related enhancements tended to generate larger price gains than network expansion alone, and combined strategies yielded the highest overall impacts. These results highlight the relative importance of reducing generalized travel costs—through faster speeds and lower fares—compared to merely increasing vertiport density. We examined the UAM impact at two spatial resolutions—the parcel level to capture localized accessibility effects (Fig. 5 ) and the district level to facilitate administrative comparisons and policy interpretation (Fig. 6 ). Table 6 Summary of housing price increases by UAM scenario Scenario Characteristics Apartment prices Average increase (%) No. of increased housing units Share of increased housing units Scenario A A1 Four vertiports 15.86 16,775 47.38 A2 A1 + additional vertiports (29 in total) 16.38 17,222 48.64 Scenario B B1 A1 + improved operation plan 27.40 23,684 66.89 B2 A1 + more improved operation plan 34.98 26,781 75.64 Scenario C C1 Combined scenario (A2 + B1) 38.53 27,340 77.21 C2 Combined scenario (A2 + B2) 52.42 30,661 86.59 As shown in Fig. 5 , scenario A was used to evaluate the effect of vertiport network expansion with the current operation plan. Scenario A1 assumes a minimal configuration with only four fixed vertiports, while A2 increases network coverage by adding a vertiport to each administrative district (25 in total). The average increase in apartment prices is modest in both scenarios, 15.86% in A1 and 16.38% in A2, indicating that improvements in physical network expansion have a limited impact. This suggests that adding more vertiports without improvements in travel speed or affordability yields only marginal additional benefit. Notably, the areas that benefited most in both A1 and A2 were those already located near the initial fixed four vertiports. This is because, under low-speed and high-fare conditions, UAM becomes the optimal mode primarily in southern core districts such as Gangnam and Seocho, where both short flight distances and short access distances are present. As a result, even with additional vertiports, price gains remain concentrated in these prime locations rather than being widely distributed. Scenario B focused only on the effects of service efficiency improvements while holding vertiport locations constant (four in total). Scenario B1 was used to simulate a service with a speed of 260 km/h and fare of $ 0.50/km, while B2 reflects a more advanced service plan with higher speed and reduced fare (320 km/h and $ 0.36/km). These scenarios produce substantial increases in price of 27.40% in B1 and 34.98% in B2, demonstrating that reduced generalized costs (via faster speed and affordable fares) have a stronger influence on housing values than infrastructure expansion alone. However, because vertiport locations remain fixed at the original four sites, the benefits still concentrate around existing hubs, particularly in the Gangnam area, while the spread to peripheral districts remains limited. Nevertheless, the spatial extent of areas influenced by UAM begins to visibly expand compared to Scenario A, reflecting the growing reach of the improved service. This pattern largely persists because, even with enhanced speed and lower fares, districts far from these few fixed vertiports still face substantial access times, limiting their shift toward UAM as the optimal mode. The C Scenarios combine the spatial expansion of vertiports (Scenario A2) with the operational enhancements (B Scenarios). This integrated strategy produced the most significant effects. Scenario C1 yielded an average increase of 38.53%, while C2 reported an increase of 52.42%. Notably, 30,661 housing units experienced a positive price change under the C2 scenario. Unlike Scenarios A and B, the price gains under C1 and C2 were more evenly distributed, with significant increases observed in northern and peripheral districts. This shift occurs because improving both vertiport coverage and service performance allows UAM to become the most attractive travel option in many districts that previously lacked nearby vertiports and had no competitive travel times. As a result, areas such as the northern and southwestern districts—historically underserved by premium transit—experienced relative price gains, closing much of the gap in accessibility-driven benefits between the traditionally high-priced Gangnam districts and the urban periphery. While absolute housing prices remained highest in Gangnam, the pattern of relative improvement became more evenly distributed across the city, indicating a substantial enhancement in spatial equity under Scenario C compared with Scenarios A and B. In contrast, intermediate districts showed smaller overall changes, likely because their geographic proximity to established core hubs and well-developed transport networks had already afforded them high accessibility, leaving less room for additional improvement compared with more remote peripheral zones. District-level visualizations (Fig. 6 ) support our interpretation. While A and B Scenarios showed pronounced price increases in southern districts of Gangnam, Seocho, and Songpa-gu, the benefits observed in C Scenarios extended further to peripheral areas including Nowon, Dobong, and Geumcheon-gu. Notably, districts that initially recorded the lowest apartment prices—primarily in the northern and southwestern areas of the city—exhibited the highest relative gains under the integrated UAM scenarios. This marked improvement suggests that accessibility gains triggered by UAM can help elevate undervalued or distant neighborhoods, potentially counteracting existing spatial disparities. Thus, we concluded that UAM can contribute, at least in part, to spatial equity by improving access in lower-priced, peripheral districts (a detailed discussion is presented in Chap. 5). 5 Discussion This study provides an early empirical understanding of how UAM deployment strategies influence housing markets in the context of the dense metropolitan area by improving air mobility. By linking changes in generalized travel costs to shifts in apartment prices, we offer one of the first quantitative assessments of the UAM urban impact beyond transportation outcomes. Our findings indicate that both the spatial configuration and operational performance of UAM systems can significantly affect property values, both in already-connected urban centers and in historically underserved peripheral districts. These results have meaningful implications for transport planning, land use policy, and efforts to promote equitable accessibility through next-generation mobility systems. First, our results consistently show that improvements in service quality—faster speeds and lower fares—have a stronger influence on housing prices than expanding the vertiport network. While increasing the number of vertiports could add more numerous access points (reducing first-/last-mile), the associated price gains were relatively modest. This suggests that we may need to further weight improving operation plans (speed, fare, headway, etc.) compared to enhancing physical proximity to UAM. Without upgrades to service performance, expanding infrastructure alone is unlikely to lead to a remarkable improvement in mobility—especially in comparison with existing transport options. Second, the most advanced scenario (C2) revealed a clear shift in housing value gains from central districts to more peripheral areas. Notably, districts with the lowest housing prices—such as those in the northern and southwestern parts of Seoul—are likely to experience relatively huge improvements. This finding underscores the importance of considering both service efficiency and spatial coverage when designing UAM systems, while also highlighting that integrated UAM strategies have strong potential to reduce persistent disparities in access to high-quality transportation. Although housing prices are an indirect indicator, their changes illustrate how improved accessibility is capitalized into local property markets. In the context of Scenario C2, this pattern signals of UAM can play a practical role in narrowing accessibility gaps and encouraging more balanced urban growth. Third, the scenario-driven differences in housing values demonstrate how alternative UAM configurations may influence future expectations in land markets. While our results are based on simulated assumptions (not on real-world specifications), they offer a window into the effects of mobility improvements on spatial patterns in terms of real estate value. Even in areas with historically poor connectivity, hypothetical provision of air mobility led to considerable price shifts, highlighting the importance of accessibility as a key factor in urban transformation. In addition, this study demonstrates how a scenario-based analytical framework incorporating changes in vertiport layout, travel speed, and fare structure can provide a structured and replicable method for assessing urban impacts of UAM deployment. This approach allows planners to explore multiple policy alternatives under different accessibility assumptions, offering flexibility for real-world applications. From a policy standpoint, the findings also emphasize the importance of strategic infrastructure investment and service design. Beyond network expansion, targeted siting of vertiports in underserved districts, combined with improvements in travel efficiency, may strengthen social values of UAM. Careful coordination with existing transport systems and land use patterns will be essential to maximize both market impact and equity outcomes. 6 Conclusion This study explored how UAM deployment strategies reshape housing markets in dense urban environments as exemplified by Seoul, Korea. By combining a scenario-based analytical framework with a hedonic pricing model, we estimated how variations in vertiport coverage, travel speed, and fare levels influence apartment-type housing prices. In particular, we introduced the concept of generalized travel cost as a measure of improved mobility in terms of travel cost and time, incorporating it into the pricing model as one key explanatory variable. Our findings offer forward-looking insights into the effects of UAM on urban accessibility, property value structures, and spatial equity. This work has provided four key contributions as summarized below: Empirical insight into the association between UAM and housing market A scenario-based framework for policy evaluation Policy guidance for urban planners and other practitioners Advances the discourse on spatial equity in future mobility Beyond housing price effects, such value shifts may also feed into broader regional economic development patterns, including land-use dynamics and local fiscal revenues. These contributions emphasize that UAM is not merely a technical advancement in transportation, but a spatial and economic force capable of reshaping urban development patterns. By quantifying how accessibility improvements under different operational and spatial configurations affect property values, this study provides an evidence-based perspective that addresses the current research gap. The scenario-based approach facilitates a structured comparison of policy alternatives, enabling more careful decisions on investment, pricing, and spatial coverage. Moreover, the analysis demonstrates that UAM has the potential to promote spatial equity by generating substantial value gains in previously underserved urban fringe areas, contributing to a more balanced and inclusive city structure. We believe that this study highlights the broader role of UAM in sustainable and equitable urban futures. While its promise lies in speed and innovation, its true value may depend on how well it is integrated into the social and spatial fabric of the city. Despite these contributions, we need to acknowledge several limitations. The analysis was based on estimated price changes derived from assumptions rather than on observed market responses to actual UAM deployment. Also, the study did not consider potential external factors such as land use regulations, noise, or community opposition, which could moderate real-world impacts. Moreover, the model focused on generalized cost variables without fully capturing behavioral or preference heterogeneity among households. In this regard, future research needs to examine further the interactions of UAM with existing modes of transport such as subways, buses, and shared mobility to assess its complementary or substitutive role within urban mobility systems. Integrating actual real estate market data into UAM demonstration projects would enhance the empirical robustness of future evaluations. Broader comparative studies across cities with different spatial forms and governance structures would also help to generalize the findings and inform more inclusive mobility planning. Declarations Author Contribution H.J. and S.C. conceptualized the study and designed the overall research framework. H.J. and G.K. developed the methodology. G.K. and M.K. collected and processed building, proximity, socioeconomic, and educational data, while H.S. and Y.C. collected and processed accessibility-related data. G.K., M.K., H.S., and Y.C. jointly conducted the baseline hedonic model estimation. H.J. carried out the scenario-based simulations and performed all visualizations, with assistance from G.K., M.K., H.S., and Y.C. H.J. drafted the original manuscript, and S.C. and H.J. revised and edited subsequent versions. S.C. supervised the research and acquired funding. All authors reviewed and approved the final version of the manuscript. Acknowledgement The authors thank eWorldEditing for providing professional English language editing services. This study builds on an undergraduate graduation research project conducted by four students under the supervision of Professor Sungtaek Choi, which was subsequently extended and developed into the present manuscript. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7621928","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":543431915,"identity":"cd634db0-2ecf-452d-a578-30a03956e7e6","order_by":0,"name":"Hanghun Jo","email":"","orcid":"","institution":"Hanyang University","correspondingAuthor":false,"prefix":"","firstName":"Hanghun","middleName":"","lastName":"Jo","suffix":""},{"id":543431916,"identity":"0d5c0486-d2cc-47ff-a33e-75e8dd2658de","order_by":1,"name":"Gyuseong Kim","email":"","orcid":"","institution":"Hanyang 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1","display":"","copyAsset":false,"role":"figure","size":488177,"visible":true,"origin":"","legend":"\u003cp\u003ePlanned routes and vertiport locations in the K-UAMroadmap\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7621928/v1/8fabe8599b36672149dc197f.png"},{"id":96428621,"identity":"09634525-7404-4485-be64-f5cb0918b684","added_by":"auto","created_at":"2025-11-21 03:13:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":562894,"visible":true,"origin":"","legend":"\u003cp\u003eTarget and non-target vertiport locations in Seoul based on the K-UAM roadmap\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7621928/v1/dc063cbf0fe65f2564f23f4d.png"},{"id":96454180,"identity":"78ac959f-5fd4-4cd3-bed7-5a3c35503507","added_by":"auto","created_at":"2025-11-21 10:02:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":651668,"visible":true,"origin":"","legend":"\u003cp\u003eSeoul district boundaries with hypothetical vertiports and major transport hubs\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7621928/v1/0a36a10b95e1cc934b0b08ec.png"},{"id":96428626,"identity":"f3f7e20a-5bc6-484a-a2e5-b5705831ba15","added_by":"auto","created_at":"2025-11-21 03:13:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":160774,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of apartment prices in Seoul (Left: parcel level; Right: district level)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7621928/v1/b16b26c4ca63dc544841034d.png"},{"id":96428622,"identity":"0039d0b7-167a-4d6a-afb7-b1959a20734f","added_by":"auto","created_at":"2025-11-21 03:13:33","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":572695,"visible":true,"origin":"","legend":"\u003cp\u003eParcel-level average price change by scenario\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7621928/v1/1c943939b6c4fb686ba72313.png"},{"id":96455010,"identity":"6e09cd77-91d6-4cbf-972d-d9e6e142e0b0","added_by":"auto","created_at":"2025-11-21 10:03:25","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":306807,"visible":true,"origin":"","legend":"\u003cp\u003eDistrict-level average price change by scenario\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7621928/v1/21c0b57f1401eab1300517a3.png"},{"id":96456886,"identity":"1cc2a3e1-c5ef-49fa-8d55-c3551cbbbd7f","added_by":"auto","created_at":"2025-11-21 10:08:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3434804,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7621928/v1/9d36ad82-19eb-4bf3-9d1f-963a6c49e4c9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Housing market impacts of urban air mobility in Seoul: evidence across district and parcel scales","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eUrban Air Mobility (UAM) is a transformative mode of urban transportation, offering a novel layer of mobility that extends beyond traditional surface-based infrastructure. By incorporating vertical takeoff and landing technologies, UAM has the potential to alleviate ground-level congestion, enhance urban accessibility, and even reshape spatial structures in metropolitan areas (Straubinger et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rothfeld et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; So et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ribeiro et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Cities across the globe including those in the United States, Europe, and Asia are actively exploring UAM availability and investing in related infrastructure, supported by pilot programs and evolving regulatory frameworks (Sun et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In this regard, the K-UAM Roadmap presented a vision for Korean commercialization by 2025 (MOLIT, 2020). As UAM transitions from concept to implementation, growing scholarly attention has been directed toward its potential effects on urban development structures (Zhao \u0026amp; Feng, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAdvanced UAM may redefine spatial accessibility by fundamentally altering how individuals perceive and interact with urban space. Decades of urban and transport research have shown that proximity to transportation infrastructure such as subways, airports, and bus rapid transit (BRT) systems significantly impacts residential property values (Tsutsumi \u0026amp; Seya, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Garrow et al., 2020). However, UAM introduces a set of operational and spatial characteristics that differ from conventional modes. These characteristics, such as the distribution and location of vertiports, variation in travel speed and fare schedules, and novel vertical connectivity, are likely to produce new patterns in real estate valuation (Wei et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Winter et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As UAM is integrated into existing multimodal transit networks, its interaction effects could enhance accessibility while also motivating important considerations regarding equitable access and spatial planning outcomes (Mohri et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wild, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite these potential transformations, few studies have explored the impact of UAM on real estate values. In particular, the relationship between UAM deployment and housing markets remains an underexplored dimension of urban mobility research. With global interest in UAM on the rise, there is a growing need to understand the effects of this new mobility on property values, urban spatial configurations, and broader sustainability goals (Straubinger et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e). As property values are closely associated with various urban structures, transportation characteristics play a major role.\u003c/p\u003e\u003cp\u003eIn this regard, the present study contributes to this discussion by providing an empirical analysis of the effects of UAM deployment strategies on property values, accessibility, and proximity to transportation. This study is based on the theoretical foundation that UAM can significantly reduce travel times within urban areas, particularly in highly congested cities, potentially influencing residential location choices and housing values (Karami et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Our research questions are:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo what extent do different UAM deployment strategies\u0026mdash;varying in vertiport density and operational plans\u0026mdash;affect housing prices in the urban context?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eHow do spatial and operational characteristics of UAM lead to heterogeneous housing market responses across districts in Seoul?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eCan UAM-induced accessibility improvements help mitigate spatial inequality in urban housing markets?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eTo address these questions, we conducted a scenario-based empirical analysis that links generalized travel cost to observed housing prices, particularly focusing on apartment housing in Seoul, Korea. Six distinct UAM deployment scenarios were constructed to capture variations in vertiport location and operational strategies (i.e., differences in travel speed and fare structures). Multiple hedonic pricing models were developed by incorporating socio-economic demographics, built environment, and land use properties. Notably, we further define generalized travel costs to major urban transport hubs as a key variable in the models. This is based on our assumption that the role of UAM within the urban context in Seoul is limited to enhancing inter-regional trips or improving proximity to major transport hubs (e.g., express bus terminals, high-speed rail stations, or airports), which can be considered an extended first-/last-mile mode.\u003c/p\u003e\u003cp\u003eNevertheless, well-connected public transit services are already available in Seoul, and it is unlikely that many travelers would adopt UAM as their primary mode for intra-city travel. In this regard, the generalized cost variable can explain how mobility and accessibility can be improved after the provision of UAM. This allowed us to compare housing market responses across districts in Seoul and evaluate the relative significance of service quality and infrastructure density. Furthermore, we explored whether accessibility improvements driven by the adoption of UAM favor peripheral areas, resulting in higher spatial equity by overcoming spatial distance constraints. We believe that these academic efforts can contribute to the growing body of empirical research on UAM spatial and economic impacts on urban housing markets, providing both theoretical and policy-relevant insights.\u003c/p\u003e\u003cp\u003eThe present paper is structured as follows. Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reviews prior research on UAM development, urban mobility transitions, and transportation-driven real estate dynamics. Section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e3\u003c/span\u003e outlines the study scope, data sources, scenario design, and analytical approach. In Section \u003cspan refid=\"Sec11\" class=\"InternalRef\"\u003e4\u003c/span\u003e, we present empirical results on UAM-driven changes in housing prices and spatial accessibility. Section \u003cspan refid=\"Sec15\" class=\"InternalRef\"\u003e5\u003c/span\u003e discusses the broader implications of these findings concerning spatial equity, policy development, and sustainable mobility. The final section provides conclusions with a brief summary of this study followed by limitations and directions for future research.\u003c/p\u003e"},{"header":"2 Literature review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 UAM and urban mobility changes\u003c/h2\u003e\u003cp\u003eUAM has emerged as a disruptive innovation in the domain of urban transportation, offering a paradigm shift in access to and navigation of cities. By leveraging electric vertical takeoff and landing vehicles, UAM creates a third spatial layer of mobility, capable of bypassing the limitations of traditional surface-based transportation systems such as subways, highways, and railways (Yang et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Cohen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Long et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mercan et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This new dimension of mobility enables direct, point-to-point connections and is particularly relevant in densely populated megacities where land scarcity and traffic congestion pose chronic challenges to transportation planning.\u003c/p\u003e\u003cp\u003eUnlike conventional transit systems that rely on fixed, linear infrastructures with long construction periods, UAM offers a modular, decentralized alternative with the flexibility to integrate vertiports into rooftops, parking structures, or existing multimodal transit nodes, significantly reducing the spatial footprint and associated infrastructure costs (Zhao \u0026amp; Feng, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The strategic placement of vertiports within existing transportation networks enhances intermodal connectivity, enabling UAM to serve effectively as a feeder or connector service for airports, high-capacity transit hubs, and major activity centers (Rajendran \u0026amp; Srinivas, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Garrow et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Consequently, UAM reshapes urban accessibility by overcoming traditional barriers of distance and congestion through direct vertical mobility, significantly altering locational value and the hierarchy of urban sub-centers. Simulation-based studies further indicate substantial reductions in travel times, particularly for intra-urban commutes, potentially influencing residential location decisions and broader urban spatial structures (Long et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhao \u0026amp; Feng, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Karami et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHowever, several recent studies asserted that the advantages of UAM may be most pronounced for long-distance trips or connections to major transport hubs, rather than short-distance urban travel. For example, simulations across metropolitan regions have shown that short aerial trips often fail to generate net time savings due to the detours required for first- and last-mile access to vertiports. This finding aligns with the view that UAM is functionally closer to commuter rail\u0026mdash;optimized for medium- to long-distance travel\u0026mdash;than to local bus services (Roy et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Guo et al., 2025). A demand modeling study in Milan further supports this perspective, showing that UAM gained a higher mode share for airport shuttle-type services (2\u0026ndash;5%) than for general intra-city trips (1\u0026ndash;3%), highlighting its relative strength in longer or intermodal use cases (Coppola et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Additional studies point to logistical burdens such as vertiport access, security procedures, and high fare levels as key barriers that reduce UAM competitiveness for short-distance mobility (Pons-Prats et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Consequently, many researchers conceptualize UAM as a premium, high-speed connector between peripheral districts and core mobility hubs\u0026mdash;such as airports, high-speed rail stations, or major transfer terminals\u0026mdash;rather than as a ubiquitous replacement for surface-level public transit in dense urban cores (Straubinger et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e; Coppola et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn summary, UAM is positioned to redefine urban mobility by creating a vertically integrated, faster, and more flexible transportation system. While its transformative potential is widely recognized, recent evidence suggests that its greatest benefits are likely to emerge in longer intra-city trips or strategic connections to major transportation hubs, rather than as a universal substitute for short-distance urban travel. Its successful integration into the existing multimodal network depends on thoughtful vertiport placement, demand-responsive operations, and regulatory foresight and on a realistic understanding of its functional niche within complex urban transport ecosystems. Importantly, as cities consider incorporating UAM into their future plans, the spatial, social, and economic implications must be carefully evaluated to ensure that the resulting mobility transformation contributes to equitable and sustainable urban development.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Role of transportation in housing prices\u003c/h2\u003e\u003cp\u003eHousing prices are influenced by numerous factors, including property attributes, neighborhood conditions, economic environment, and transportation accessibility (Han et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Among these factors, transportation infrastructure consistently exerts a strong influence by enhancing connectivity and reducing travel time, elevating property attractiveness (Debrezion et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Mohammad et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Mulley \u0026amp; Tsai, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eProximity to transportation hubs such as rail stations, airports, and highways typically increases property values due to improved accessibility and convenience (Debrezion et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Xu \u0026amp; Nakajima, 2017; Kanasugi \u0026amp; Ushijima, 2018). Empirical evidence has repeatedly shown that proximity to public transportation, especially rail stations, leads to a positive impact on residential property values. Studies focusing on a major metropolitan area indicate a clear price premium for properties near transit hubs due to reduced commuting times and improved inter-regional accessibility (Bowes \u0026amp; Ihlanfeldt, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Cervero \u0026amp; Kang, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBus Rapid Transit (BRT) systems have enhanced land values by improving urban connectivity and reducing travel costs, demonstrating the broad influence of transit infrastructure on property valuation (Cervero \u0026amp; Kang, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Mulley \u0026amp; Tsai, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). High-speed rail further underscores transportation\u0026rsquo;s significant role in shaping real estate values. The introduction of this service often transforms peripheral areas into attractive residential and commercial zones, substantially increasing local land values and driving urban development (Ahlfeldt \u0026amp; Feddersen, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Likewise, airport proximity generally boosts commercial property values, although residential properties may face mixed outcomes due to negative externalities such as noise and pollution (Espey \u0026amp; L\u0026oacute;pez, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Salvi, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Trojanek et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Winke, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe emergence of UAM introduces novel dynamics into the relationship between traditional transportation and real estate. UAM systems, utilizing vertical take-off and landing technologies, promise efficient point-to-point travel by avoiding surface-level congestion, significantly enhancing urban accessibility (Cohen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Long et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Their unique spatial flexibility allows vertiports to be established in dense urban contexts with minimal spatial footprints, potentially reshaping urban accessibility and land valuation (Zhao \u0026amp; Feng, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Cohen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Recent research on UAM indicates potential real estate value impacts analogous to conventional transport hubs. Zhao and Feng (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) showed that strategically placed vertiports integrated with existing multimodal networks can substantially enhance accessibility, likely driving up nearby real estate prices. However, due to UAM\u0026rsquo;s nascent stage, uncertainties regarding market acceptance, operational costs, regulatory frameworks, and system maturity remain significant. In this regard, scenario-based analyses accounting for diverse deployment conditions and configurations are crucial to accurately understanding potential real estate impacts (Cohen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Long et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Research gap and our contributions\u003c/h2\u003e\u003cp\u003eWhile prior studies have highlighted UAM's potential to enhance urban accessibility and influence real estate values, several limitations remain. Existing literature has focused largely on conceptual or simulation-based evaluations, with little empirical evidence demonstrates the effects of UAM deployment on housing markets under realistic spatial and operational constraints (Roy et al., 2023; Wang et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Coppola et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Moreover, previous research has primarily emphasized traditional transit modes such as subways and BRT in explaining property value formation, whereas UAM\u0026rsquo;s role, particularly under diverse deployment scenarios, remains underexplored in terms of both accessibility and equity impacts.\u003c/p\u003e\u003cp\u003eConsidering these aspects, we investigated how the introduction of UAM may affect apartment prices in Seoul, South Korea\u0026mdash;a dense urban environment where real estate values are highly sensitive to transportation accessibility. Unlike traditional infrastructure, UAM introduces a flexible and rapidly deployable layer of mobility that can reshape urban accessibility patterns and value hierarchies. To capture these potential transformations, we adopted a scenario-based approach with various vertiport locations, operational characteristics (e.g., speed and fare), and generalized travel costs. In particular, we determined generalized cost by simulating UAM characteristics and attributes given the context of Seoul and then applying the concept of travel time valuation to calculations.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Empirical Framework","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 K-UAM plans and study area\u003c/h2\u003e\n \u003cp\u003eThis study examines the impact of UAM deployment on real estate prices in Seoul, Korea, where the national K-UAM Roadmap aims to introduce commercial UAM operations by 2025. According to the official roadmap, the Korean government established a multi-phase development strategy for UAM services in the Seoul metropolitan area. The initial phase targets the launch of pilot programs and limited commercial operations by 2025, laying the foundation for early market entry. This will be followed by establishment of an urban-scale UAM network by around 2030, during which operational systems, regulatory frameworks, and vertiport infrastructure are expected to be expanded and standardized. In the longer term, full-scale nationwide deployment is planned for after 2035, with UAM envisioned as an integral part of the country\u0026rsquo;s future transportation system. The roadmap emphasizes the importance of public\u0026ndash;private partnerships, safe and efficient airspace management, integration with existing transit modes, and sustainable urban mobility goals (MOLIT, 2020). The specific vertiport locations and planned routes for K-UAM are illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eGiven this context, we selected Seoul as the study area due to its dense urban structure, high transportation demand, and dynamic real estate market. In examining the relationship between UAM deployment and housing prices, this research focuses on apartment-type housing. Apartments are the most common housing type in the Seoul metropolitan area, accounting for a significant share of the housing market. As of 2023, the share of apartment housing type was 53.3% (Seoul Institute,\u0026nbsp;\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Their standardized valuation structure and relatively high prices, even in lower-priced districts, are particularly suitable for analyzing transportation-induced price changes. More importantly,\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePhase-specific vertiport plans in the K-UAM roadmap\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePhase\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVertiport location\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhase 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eV1: Drone test certification center\u003c/p\u003e\n \u003cp\u003eV2: GyeYang new town\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEarly-stage UAM test flight\u003c/p\u003e\n \u003cp\u003eOperation in low-density suburban areas\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhase 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eV3: Gimpo International Airport\u003c/p\u003e\n \u003cp\u003eV4: Yeouido Park\u003c/p\u003e\n \u003cp\u003eV5: Goyang KINTEX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConnects business and transit hubs\u003c/p\u003e\n \u003cp\u003eTests intermodal efficiency with existing networks\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhase 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eV6: Jamsil business and residential complex\u003c/p\u003e\n \u003cp\u003eV7: Suseo Super Rapid Train Station\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExpands to high-density commercial \u0026amp; residential zones\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003eNote\u003c/em\u003e: \u0026ldquo;V\u0026rdquo; denotes a planned vertiport site proposed under the national K-UAM implementation framework.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eapartments tend to be highly sensitive to transportation infrastructure developments, responding more directly to changes in accessibility and urban expansion than other housing types such as detached houses or mixed-use buildings. This sensitivity is especially pronounced in Seoul, where public transportation accessibility is a major determinant of housing demand and price fluctuations. In sum, apartment-type housing prices serve as a reliable indicator for assessing the potential economic and spatial impact of UAM integration on urban real estate markets.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Scenario design\u003c/h2\u003e\n \u003cp\u003eTo accurately assess the effects of UAM accessibility, we referred to the full K-UAM roadmap but narrowed its analytical focus to selected vertiport locations within the Seoul urban area. Phase 1 vertiports (V1, V2), which are located in suburban areas and primarily serve early-stage UAM trials, were excluded from the analysis due to their limited relevance to high-density urban real estate dynamics. Similarly, V5 (Goyang KINTEX), located at Phase 2 and beyond the Seoul boundary, was also excluded from the scenario list. Among these, only four vertiports located within Seoul\u0026rsquo;s urban area (V3, V4, V6, V7) were selected as target sites, as they are most relevant to high-density urban real estate dynamics. Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows the geographic distribution of these target and non-target vertiports, highlighting the locations selected for the scenario design.\u003c/p\u003e\n \u003cp\u003eInstead of estimating the effects based on a single UAM operation plan, we adopted a scenario-based framework to reflect the uncertainty and flexibility surrounding UAM deployment strategies. By simulating different spatial layouts and operational conditions, these scenarios served as an analytical tool to explore how UAM-driven accessibility changes may influence changes in apartment prices in Seoul. Importantly, these scenarios are not intended to forecast exact future prices but to provide a general overview of price changes and analyze spatial variations depending on UAM accessibility and mobility improvements. The detailed specifications of each scenario are summarized in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eThe first scenario set (A Scenarios) examines changes in vertiport network density. Scenario A1 is the base scenario as proposed by the national K-UAM roadmap, consisting of four fixed vertiports located along the primary UAM route. Since this base scenario has limited access to UAM, we added 25 vertiports for each district in Seoul, representing a sizable network expansion. This setting allowed us to evaluate how improved proximity to UAM reshapes distribution of real estate values across urban subregions.\u003c/p\u003e\n \u003cp\u003eThe second set (B Scenarios) varies the operational efficiency of UAM services. Scenario B1 assumes a moderately improved operational plan featuring a speed of 260 km/h and a fare of \u003cspan\u003e$\u003c/span\u003e0.50/km. Scenario B2 further simulates an advanced deployment scenario compared to B1, achieving a higher speed of 320 km/h and a reduced fare of \u003cspan\u003e$\u003c/span\u003e0.36/km. These two specifications reflect different stages of UAM technological and market maturity. Since both\u003c/p\u003e\n \u003cp\u003efare and speed directly influence generalized travel cost and time, we expect that these updates will affect accessibility, leading to changes in property valuation.\u003c/p\u003e\n \u003cp\u003eThe third set (C Scenarios) introduces integrated configurations by combining the infrastructural expansion (Scenario A2) with the operational improvements (B Scenarios). Scenario C1 merges the A2 network with B1 service efficiency (moderate fare and speed), and Scenario C2 reflects the most optimistic configuration, featuring both full-size network coverage and the most advanced operational plan. These hybrid scenarios enable exploration of complicated interactions between spatial coverage and user cost, which are critical for understanding the transformative potential of UAM in reshaping urban spatial structures.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSpecifications of UAM scenario settings\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eScenario\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVertiport density\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFare\u003c/p\u003e\n \u003cp\u003e(USD\u003csup\u003ea\u003c/sup\u003e/km)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpeed\u003c/p\u003e\n \u003cp\u003e(km/h)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eScenario A: Vertiport Configuration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFour fixed vertiports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBaseline: current national demonstration route\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 added vertiports for each district\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate network densification via additional vertiports\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eScenario B:\u003c/p\u003e\n \u003cp\u003eService efficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFour fixed vertiports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImproved operation with moderate speed\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFour fixed vertiports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdvanced operation with high speed\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eScenario C:\u003c/p\u003e\n \u003cp\u003eIntegrated strategy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 added vertiports for each district\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate speed and fare with full vertiport coverage (A2\u0026thinsp;+\u0026thinsp;B1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 added vertiports for each district\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh speed and low fare with full vertiport coverage (A2\u0026thinsp;+\u0026thinsp;B2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Based on the exchange rate of 1 USD\u0026thinsp;=\u0026thinsp;1,400 KRW\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Study data and variables\u003c/h2\u003e\n \u003cp\u003eIn this study, we constructed a comprehensive set of variables based on public datasets, real transaction records, and spatial analysis using the Geographic Information System (GIS). The primary dataset consists of apartment transaction\u003c/p\u003e\n \u003cp\u003erecords collected from the Ministry of Land, Infrastructure, and Transport\u0026apos;s Real Transaction Price Disclosure System. All transactions occurring between January 1, 2023, and January 1, 2024, within Seoul\u0026apos;s 466 legal subdistricts were compiled, resulting in a total of 35,408 apartment units. To standardize the dependent variable, transaction prices were converted to price per unit area (㎡) to address skewness in distribution, allowing elasticity-based interpretation. The natural logarithm of unit price per square meter was applied, and all prices were converted into U.S. dollars (USD/m\u0026sup2;).\u003c/p\u003e\n \u003cp\u003eIndependent variables were categorized into six groups: building characteristics, proximity to amenities, socio-economic traits, educational environment, accessibility to transport hubs (generalized cost variables), and regional characteristics. Building characteristics include apartment age, number of floors, and exclusive area per unit obtained from the national statistics of real estate.\u003c/p\u003e\n \u003cp\u003eProximity to amenities represents locational attributes of each apartment, which were defined using a GIS-based shortest-distance calculation method (the spatial information was sourced from the Seoul Big Data Portal and the Public Data Portal). This approach was applied consistently to variables such as distances to the nearest school, park, subway station, shopping facility, and general hospital (Yang et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Hospitals included in the analysis were limited to general hospitals with at least 100 beds. Parks excluded small-scale facilities such as playgrounds to focus on significant urban green spaces. The shortest distances from each apartment to these facilities were calculated through spatial joining and nearest feature analysis in GIS, and the resulting values were included as continuous variables in the model.\u003c/p\u003e\n \u003cp\u003eSocioeconomic traits were constructed at the legal subdistrict level to ensure consistency across spatial units when integrating demographic, economic, and geographic data. Income level was derived from the Environmental Big Data Platform and measured as the average annual household income in U.S. dollars for each legal subdistrict. These indicators reflect the underlying socioeconomic structure that shapes housing demand and price sensitivity. Population and employment density were calculated as number of residents or workers per square kilometer (1,000 persons/km\u0026sup2;), based on official census data and subdistrict-level boundary shapefiles.\u003c/p\u003e\n \u003cp\u003eThe educational environment is treated as an important factor in the Korean housing market, where strong parental emphasis on children\u0026rsquo;s academic achievement significantly influences residential decisions, especially in Seoul. To capture this effect, we incorporated two variables. The first one is the admission rate to top 10 universities, defined as the proportion of high school graduates who enrolled at one of Korea\u0026rsquo;s top 10 universities in the two years after graduation. The other variable is the number of private educational institutes for each subdistrict, which indirectly represents the level of interest and engagement in education among residents. Both variables were aggregated at the legal subdistrict level using data from Seoul Education Statistics and the Open Data Portal.\u003c/p\u003e\n \u003cp\u003eLast, we defined generalized travel cost to evaluate the impact of UAM deployment on housing prices. This indicates synthesized travel cost from each subdistrict to the three major transportation hubs Suseo Station (one subway and high-speed rail lines), Seoul Station (multiple subways and high-speed rail lines), and Gimpo Airport (a key domestic airport on the western edge of Seoul) using the optimum mode with the shortest travel time and lowest cost. The underlying assumption, grounded in conventional transport economics, is that individuals aim to minimize this total perceived cost when selecting travel routes and modes (Mackie et al., \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e). The generalized cost for a given transportation type \u003cem\u003ei\u003c/em\u003e is calculated using the following Eq. (\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e):\u003c/p\u003e\n \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$\\:{GC}_{i}=\\:{C}_{i}+{VOT}_{i}\\times\\:{T}_{i},$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{GC}_{i}\\)\u003c/span\u003e\u003c/span\u003e represents the generalized cost for mode \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{i}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{T}_{i}\\)\u003c/span\u003e\u003c/span\u003e denote the monetary travel cost and actual travel time of travel mode \u003cem\u003ei\u003c/em\u003e, respectively; and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{VOT}_{i}\\)\u003c/span\u003e\u003c/span\u003e indicates the value of travel time for mode \u003cem\u003ei\u003c/em\u003e. That is, generalized cost is defined as the sum of two monetary values consisting of monetary travel costs (e.g., fares, tolls, fuel) and the monetary value of travel time.\u003c/p\u003e\n \u003cp\u003eThe travel time valuation represents the total burden experienced by users during a trip and is a kind of opportunity cost (Small, \u003cspan class=\"CitationRef\"\u003e1992\u003c/span\u003e; Ort\u0026uacute;zar \u0026amp; Willumsen, \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). There are two approaches for determining the value of travel time savings (VOTTS): marginal rate of substitution and wage rate. We chose wage rate-based VOTTS, which is officially released by National Statistics (Park et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). It reflects the monetary worth travelers place on reducing their time in transit. According to the guideline, the time value for public transportation is 16.1 USD/hour, while the valuations for automobile and walking are 19.2 and 12.3 USD/hour, respectively. In UAM, we view this mode as a different type of public transportation and applied VOTTS for public transportation to UAM. For each scenario, generalized travel costs to the three key urban mobility hubs\u0026mdash;Suseo Station, Seoul Station, and Gimpo Airport\u0026mdash;were recalculated based on speed and fare assumptions.\u003c/p\u003e\n \u003cp\u003eTravel time and cost data were obtained through a combination of public APIs and the automated scraping process. Vehicle routes were retrieved from an online API source (called Naver), and public transit and walking data were collected through structured web scraping under controlled conditions to avoid server overload. UAM routing was simulated using a custom Python-based model that combined access and egress paths with assumed aerial links, and all routing scenarios were generated for weekday evening peak hours to reflect typical commuter behavior. The updated cost estimates were integrated into the hedonic pricing model as key independent variables representing scenario-specific accessibility shifts.\u003c/p\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the network structure and operational parameters of each scenario, including updated fare values in USD. To determine generalized cost, the four modes of car, bus, subway, and taxi (and UAM if available) were considered, and the lowest value among the four was selected to define this variable.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescription of incorporated variables\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable (unit)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eDependent variable\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eApartment transaction prices (USD/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLog-transformed unit price of apartment sales\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBuilding characteristics\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExclusive area (m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFloor area in square meters of each apartment unit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of floors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumbers of floors in the apartment building\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eApartment age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge of the building in years\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eProximity to amenities\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistance to the nearest school (km)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEuclidean distance to the nearest school\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistance to the nearest park (km)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistance to the nearest public park\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistance to the nearest subway station (km)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistance to the nearest subway station\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistance to the nearest shopping facility (km)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistance to the nearest shopping/commercial center\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistance to the nearest hospital (km)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistance to the nearest general hospital (\u0026ge;\u0026thinsp;100 beds)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSocioeconomic traits\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePopulation density (per 1,000 person/km\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of residents per km\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmployment density (per 1,000 person/km\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of jobs per km\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnnual household income (USD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage household income per year\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEducational environment\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdmission rate to top 10 universities (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e% of high school students entering top 10 universities\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of private education institutes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of private education institutions in the travel area\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAccessibility to transport hubs\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeneralized cost to Suseo Station (USD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTime \u0026times; value of travel time\u0026thinsp;+\u0026thinsp;fare to Suseo Station\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeneralized cost to Seoul Station (USD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTime \u0026times; value of travel time\u0026thinsp;+\u0026thinsp;fare to Seoul Station\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeneralized cost to Gimpo Airport (USD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTime \u0026times; value of travel time\u0026thinsp;+\u0026thinsp;fare to Gimpo Airport\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRegional characteristic\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMajor district\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDummy variable indicating subdistrict location in Gangnam, Seocho, Songpa, or Yongsan-gu\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eWe designated district-level centroids as hypothetical vertiport locations for scenario analysis. These centroids were calculated as floor-area\u0026ndash;weighted points based on the spatial distribution of residential floor area across three housing types\u0026mdash;apartments, single-family houses, and multiplex residences. In addition, we identified three major transportation hubs\u0026mdash;Suseo Station, Seoul Station, and Gimpo Airport\u0026mdash;as high-access nodes with strategic intermodal connectivity. Figure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the spatial arrangement of these hypothetical vertiports, major hubs, and administrative district boundaries.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Methodological approaches\u003c/h2\u003e\n \u003cp\u003eTo quantify the impact of UAM accessibility on apartment prices, we employed a hedonic pricing model, a widely used econometric technique for analyzing the influence of product attributes on market values. The foundation of the hedonic pricing approach lies in the theory of implicit markets, first introduced by Ridker and Henning (\u003cspan class=\"CitationRef\"\u003e1967\u003c/span\u003e) and later formalized by Rosen (\u003cspan class=\"CitationRef\"\u003e1974\u003c/span\u003e) and Freeman (\u003cspan class=\"CitationRef\"\u003e1979\u003c/span\u003e). The main premise is that the price of a heterogeneous good (e.g., housing) can be decomposed into the implicit values of its constituent characteristics. In the housing market context, property prices are assumed to be determined by a bundle of structural, locational, environmental, and accessibility attributes. The hedonic pricing model allows estimation of the marginal implicit price of each attribute based on observed variation in transaction prices across housing units that differ in these characteristics. This approach is particularly suitable for evaluating the capitalization effects of transportation infrastructure, including new and emerging technologies such as UAM (Debrezion et al., \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e; Ahlfeldt \u0026amp; Kavetsos, \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). Formally, the hedonic pricing function can be expressed as follows (Eq. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e):\u003c/p\u003e\n \u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{P}_{i}\\)\u003c/span\u003e\u003c/span\u003e represents the transaction price of apartment \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{0}\\)\u003c/span\u003e\u003c/span\u003e denotes the constant term; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{ik}\\)\u003c/span\u003e\u003c/span\u003e represents the \u003cem\u003ek\u003c/em\u003e-th characteristic of apartment \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{k}\\)\u003c/span\u003e\u003c/span\u003e is the estimated coefficient of characteristic \u003cem\u003ek\u003c/em\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{ϵ}_{i}\\)\u003c/span\u003e\u003c/span\u003e is a stochastic error term. The model assumes a semi-log functional form, consistent with prior literature, which enables elastic interpretation of the coefficients and mitigates heteroscedasticity in transaction price data (Cropper et al., \u003cspan class=\"CitationRef\"\u003e1988\u003c/span\u003e; Sirmans et al., \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Descriptive statistics\u003c/h2\u003e\u003cp\u003eWe analyzed a total of 35,408 apartment transactions recorded across all 466 legal districts in Seoul from January 1st, 2023, to December 31st, 2023. The dependent variable of the hedonic pricing model, as mentioned earlier, is the apartment transaction price per square meter (USD/m\u0026sup2;), which is log-transformed to satisfy the assumptions of a semi-logarithmic regression structure. For interpretability and descriptive clarity, however, all price-related statistics are reported in their original (non-log) form in this section.\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the spatial distribution of apartment prices in Seoul using two complementary visualizations. The map on the left illustrates parcel-level transaction data, highlighting micro-scale differences and fine-grained clusters of high-value housing. The map on the right aggregates the same data at the district level, revealing clear regional gradients. Both maps confirm well-defined concentration values are observed in peripheral and northeastern\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003edistricts. Spatial imbalances in housing demand, transport accessibility, infrastructure, and the built environment can lead to price inequities.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e summarizes the descriptive statistics of all incorporated variables in the hedonic model, comprising apartment characteristics, proximity to amenities, socioeconomic traits of each subdistrict, educational environment, accessibility to transport hubs, and the indicator of the prime Seoul districts. To enhance general comparability, monetary values were converted from KRW to USD based on the exchange rate of 1,400 KRW/USD, and all distances were measured in kilometers. The results indicate that Seoul\u0026rsquo;s apartment stock is characterized by high-rise structures with mid-sized units and moderate building age. Locational accessibility varies substantially across subdistricts, with average distances to amenities such as schools, parks, subway stations, shopping centers, and hospitals reflecting spatial heterogeneity in urban infrastructure. Socioeconomic indicators and education-related variables also present substantial variation, suggesting disparities in income levels, population density, and educational resources. Travel accessibility, measured by generalized cost to major hubs, also exhibits spatial variation, with slightly lower average costs to Seoul Station (i.e., relatively better accessibility) compared to other hubs. Notably, more than 70% of the apartment transactions are concentrated in Gangnam, Seocho, Songpa, and Yongsan-gu, reinforcing the spatial concentration of high-value real estate in Seoul.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics (n\u0026thinsp;=\u0026thinsp;35,408)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMax.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMin.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStd. dev.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDependent variable\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApartment transaction prices (USD/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9,992.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62,940.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,548.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4,824.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBuilding characteristics\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of floors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExclusive area (m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e309.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBuilding age (year)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eProximity to amenities\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance to the nearest school (km)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance to the nearest park (km)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance to the nearest subway station (km)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance to the nearest shopping facility (km)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance to the nearest hospital (km)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSocioeconomic traits\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePopulation density (1,000 people/km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment density (1,000 people/km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnnual household income (USD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3958.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11929.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2144.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1834.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEducational environment\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdmission rate to top 10 universities (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of private education institutes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e714.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,500.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e108.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e551.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAccessibility to transport hubs\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeneralized cost to Suseo Station (USD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeneralized cost to Seoul Station (USD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeneralized cost to Gimpo Airport (USD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eShare (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRegional characteristic\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMajor district\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25,493\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9,915\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Gangnam, Seocho, Songpa, and Yongsan-gu districts.\u003c/p\u003e\u003cp\u003e\u003csup\u003eb\u003c/sup\u003e This indicates whether a subdistrict belongs to Gangnam, Seocho, Songpa, or Yongsan-gu.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Estimation results of the hedonic model\u003c/h2\u003e\u003cp\u003eTo assess the impact of UAM on housing prices in Seoul, we developed a hedonic price model using 35,408 apartment transactions. The model is the empirical basis for simulating price variations under different UAM scenarios after controlling relevant factors. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the estimation results with two kinds of estimates (unstandardized and standardized), showing how factors related to structural, locational, socioeconomic, educational, and accessibility factors influence apartment prices. The final model demonstrates that a substantial portion of price variation can be explained by the estimated model (adjusted \u003cem\u003eR\u003c/em\u003e\u0026sup2; = 0.518).\u003c/p\u003e\u003cp\u003eAmong building characteristics, the number of floors has a significant positive impact on apartment prices (β\u0026thinsp;=\u0026thinsp;0.097), indicating that higher-floor units tend to be valued more highly\u0026mdash;possibly due to better views, privacy, or reduced noise levels. In contrast, exclusive areas showed a marginal negative impact (β = -0.043), suggesting that, when controlling for other factors, larger units may not always translate into higher unit prices per square meter. Building year exhibited a strong negative effect on housing prices (β = -0.126). This indicates that newer buildings tend to command a substantial premium, consistent with market preferences for more modern facilities, updated designs, and lower maintenance costs. All location characteristics except proximity to hospitals showed straightforward effects with the expected sign (negative). In particular, longer distances to a park (β = -0.104), school (β = -0.077), and subway station (β = -0.051) led to lower apartment values, indicating the importance of accessibility to green and transit infrastructure. Socioeconomic traits revealed more nuanced effects. Population and employment densities had negative coefficients of modest magnitude, while annual household income showed a strong positive effect (β\u0026thinsp;=\u0026thinsp;0.221), consistent with our expectation. In the educational domain, both the admission rate to top 10 high schools (β\u0026thinsp;=\u0026thinsp;0.095) and the number of private education institutes (β\u0026thinsp;=\u0026thinsp;0.129) significantly increased housing values, highlighting the major role of education in shaping the real estate landscape in Seoul. A significant impact of premium spatial location was also observed: apartments located in one of the three Gangnam districts (i.e., Gangnam, Seocho, and Songpa) or Yongsan exhibited a higher contribution (β\u0026thinsp;=\u0026thinsp;0.107), reinforcing the status of these areas as high-value residential zones within Seoul\u0026rsquo;s urban hierarchy.\u003c/p\u003e\u003cp\u003eNotably, generalized travel cost to major transport hubs revealed differentiated impacts on housing prices. Better accessibility to Suseo Station and Seoul Station was strongly associated with higher apartment values (β = \u0026minus;\u0026thinsp;0.250 and \u0026minus;\u0026thinsp;0.244, respectively). From a built environment perspective, this finding shows that proximity to highly integrated transit nodes enhances residential desirability by reducing daily travel costs and embedding neighborhoods\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEstimation results from the hedonic model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003cp\u003e(B)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eStd. Estimate\u003c/p\u003e\u003cp\u003e(\u003cem\u003eβ\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStd. Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003et-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.617\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e38.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e432.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBuilding characteristics\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of floors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.007\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.0003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e25.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExclusive area (m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-10.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBuilding age (year)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.005\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.0002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-32.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eProximity to amenities\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance to the nearest school (km)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.186\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-19.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance to the nearest park (km)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.120\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-25.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance to the nearest subway station (km)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.063\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-11.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance to the nearest shopping facility (km)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.098\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance to the nearest hospital (km)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSocioeconomic traits\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePopulation density (1,000 people/km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.0002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-3.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment density (1,000 people/km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.0003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnnual household income (USD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.052\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e27.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEducational environment\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdmission rate to top 10 high schools (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.580\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of private education institutes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.00001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAccessibility to transport hubs\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeneralized cost to Suseo Station (USD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.024\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-34.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeneralized cost to Seoul Station (USD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.038\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.244\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-42.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeneralized cost to Gimpo Airport (USD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePrime regional characteristic\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMajor district\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.117\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35,408\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR-squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.519\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdjusted R-squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF-statistic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2985.07\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/p\u003e\u003cp\u003ewithin broader metropolitan opportunity structures. Seoul Station functions as the city\u0026rsquo;s central multimodal hub, elevating the locational value of surrounding districts by concentrating jobs and inter-city linkages. Suseo Station, despite its peripheral position, demonstrates how strategic transport investments can reconfigure urban spatial hierarchies: its high-speed rail role and proximity to the Gangnam business district anchor the emergence of a subcenter that attracts both residential and commercial demand. These results suggest that accessibility premiums are most pronounced when transport nodes reinforce existing centers of activity or create new ones within the metropolitan fabric.\u003c/p\u003e\u003cp\u003eIn contrast, accessibility to Gimpo Airport did not significantly affect housing prices. While the airport provides regional connectivity, its peripheral location and weaker integration with Seoul\u0026rsquo;s primary employment corridors limit its capacity to generate residential premiums. Potential disamenities\u0026mdash;such as aircraft noise, land-use constraints, and height restrictions\u0026mdash;further reduce the attractiveness of nearby housing markets. This shows that transport infrastructure does not automatically translate into positive capitalization; rather, the effect depends on how accessibility interacts with urban form and neighborhood livability. From a housing and built environment standpoint, the contrast between central rail hubs and a peripheral airport highlights the importance of spatial positioning and functional integration within the city\u0026rsquo;s structure.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Scenario-based analysis\u003c/h2\u003e\u003cp\u003eTo examine how different UAM implementation strategies would influence housing values, we applied the estimated model to six scenarios differentiated by vertiport density and service plan. For each scenario, generalized travel costs were updated only when the optimum travel mode was UAM, and the costs were incorporated into a hedonic pricing model to simulate apartment price changes in Seoul, whereas other explanatory variables were held constant. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e summarizes the estimated average price change for each scenario, showing clear differences in magnitude depending on improvements to physical infrastructure, service performance, or both. Across all scenarios, service-related enhancements tended to generate larger price gains than network expansion alone, and combined strategies yielded the highest overall impacts. These results highlight the relative importance of reducing generalized travel costs\u0026mdash;through faster speeds and lower fares\u0026mdash;compared to merely increasing vertiport density. We examined the UAM impact at two spatial resolutions\u0026mdash;the parcel level to capture localized accessibility effects (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) and the district level to facilitate administrative comparisons and policy interpretation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of housing price increases by UAM scenario\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u003cp\u003eScenario\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003eApartment prices\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAverage increase (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo. of increased housing units\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eShare of increased housing units\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eScenario A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFour vertiports\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16,775\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e47.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eA1\u0026thinsp;+\u0026thinsp;additional vertiports (29 in total)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17,222\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e48.64\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eScenario B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eA1\u0026thinsp;+\u0026thinsp;improved operation plan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23,684\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e66.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eA1\u0026thinsp;+\u0026thinsp;more improved operation plan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26,781\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e75.64\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eScenario C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCombined scenario\u003c/p\u003e\u003cp\u003e(A2\u0026thinsp;+\u0026thinsp;B1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27,340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e77.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCombined scenario\u003c/p\u003e\u003cp\u003e(A2\u0026thinsp;+\u0026thinsp;B2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30,661\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e86.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, scenario A was used to evaluate the effect of vertiport network expansion with the current operation plan. Scenario A1 assumes a minimal configuration with only four fixed vertiports, while A2 increases network coverage by adding a vertiport to each administrative district (25 in total). The average increase in apartment prices is modest in both scenarios, 15.86% in A1 and 16.38% in A2, indicating that improvements in physical network expansion have a limited impact. This suggests that adding more vertiports without improvements in travel speed or affordability yields only marginal additional benefit. Notably, the areas that benefited most in both A1 and A2 were those already located near the initial fixed four vertiports. This is because, under low-speed and high-fare conditions, UAM becomes the optimal mode primarily in southern core districts such as Gangnam and Seocho, where both short flight distances and short access distances are present. As a result, even with additional vertiports, price gains remain concentrated in these prime locations rather than being widely distributed.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eScenario B focused only on the effects of service efficiency improvements while holding vertiport locations constant (four in total). Scenario B1 was used to simulate a service with a speed of 260 km/h and fare of \u003cspan\u003e$\u003c/span\u003e0.50/km, while B2 reflects a more advanced service plan with higher speed and reduced fare (320 km/h and \u003cspan\u003e$\u003c/span\u003e0.36/km). These scenarios produce substantial increases in price of 27.40% in B1 and 34.98% in B2, demonstrating that reduced generalized costs (via faster speed and affordable fares) have a stronger influence on housing values than infrastructure expansion alone. However, because vertiport locations remain fixed at the original four sites, the benefits still concentrate around existing hubs, particularly in the Gangnam area, while the spread to peripheral districts remains limited. Nevertheless, the spatial extent of areas influenced by UAM begins to visibly expand compared to Scenario A, reflecting the growing reach of the improved service. This pattern largely persists because, even with enhanced speed and lower fares, districts far from these few fixed vertiports still face substantial access times, limiting their shift toward UAM as the optimal mode.\u003c/p\u003e\u003cp\u003eThe C Scenarios combine the spatial expansion of vertiports (Scenario A2) with the operational enhancements (B Scenarios). This integrated strategy produced the most significant effects. Scenario C1 yielded an average increase of 38.53%, while C2 reported an increase of 52.42%. Notably, 30,661 housing units experienced a positive price change under the C2 scenario. Unlike Scenarios A and B, the price gains under C1 and C2 were more evenly distributed, with significant increases observed in northern and peripheral districts. This shift occurs because improving both vertiport coverage and service performance allows UAM to become the most attractive travel option in many districts that previously lacked nearby vertiports and had no competitive travel times. As a result, areas such as the northern and southwestern districts\u0026mdash;historically underserved by premium transit\u0026mdash;experienced relative price gains, closing much of the gap in accessibility-driven benefits between the traditionally high-priced Gangnam districts and the urban periphery. While absolute housing prices remained highest in Gangnam, the pattern of relative improvement became more evenly distributed across the city, indicating a substantial enhancement in spatial equity under Scenario C compared with Scenarios A and B. In contrast, intermediate districts showed smaller overall changes, likely because their geographic proximity to established core hubs and well-developed transport networks had already afforded them high accessibility, leaving less room for additional improvement compared with more remote peripheral zones.\u003c/p\u003e\u003cp\u003eDistrict-level visualizations (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) support our interpretation. While A and B Scenarios showed pronounced price increases in southern districts of Gangnam, Seocho, and Songpa-gu, the benefits observed in C Scenarios extended further to peripheral areas including Nowon, Dobong, and Geumcheon-gu. Notably, districts that initially recorded the lowest apartment prices\u0026mdash;primarily in the northern and southwestern areas of the city\u0026mdash;exhibited the highest relative gains under the integrated UAM scenarios. This marked improvement suggests that accessibility gains triggered by UAM can help elevate undervalued or distant neighborhoods, potentially counteracting existing spatial disparities. Thus, we concluded that UAM can contribute, at least in part, to spatial equity by improving access in lower-priced, peripheral districts (a detailed discussion is presented in Chap.\u0026nbsp;5).\u003c/p\u003e\u003c/div\u003e"},{"header":"5 Discussion","content":"\u003cp\u003eThis study provides an early empirical understanding of how UAM deployment strategies influence housing markets in the context of the dense metropolitan area by improving air mobility. By linking changes in generalized travel costs to shifts in apartment prices, we offer one of the first quantitative assessments of the UAM urban impact beyond transportation outcomes. Our findings indicate that both the spatial configuration and operational performance of UAM systems can significantly affect property values, both in already-connected urban centers and in historically underserved peripheral districts. These results have meaningful implications for transport planning, land use policy, and efforts to promote equitable accessibility through next-generation mobility systems.\u003c/p\u003e\u003cp\u003eFirst, our results consistently show that improvements in service quality\u0026mdash;faster speeds and lower fares\u0026mdash;have a stronger influence on housing prices than expanding the vertiport network. While increasing the number of vertiports could add more numerous access points (reducing first-/last-mile), the associated price gains were relatively modest. This suggests that we may need to further weight improving operation plans (speed, fare, headway, etc.) compared to enhancing physical proximity to UAM. Without upgrades to service performance, expanding infrastructure alone is unlikely to lead to a remarkable improvement in mobility\u0026mdash;especially in comparison with existing transport options.\u003c/p\u003e\u003cp\u003eSecond, the most advanced scenario (C2) revealed a clear shift in housing value gains from central districts to more peripheral areas. Notably, districts with the lowest housing prices\u0026mdash;such as those in the northern and southwestern parts of Seoul\u0026mdash;are likely to experience relatively huge improvements. This finding underscores the importance of considering both service efficiency and spatial coverage when designing UAM systems, while also highlighting that integrated UAM strategies have strong potential to reduce persistent disparities in access to high-quality transportation. Although housing prices are an indirect indicator, their changes illustrate how improved accessibility is capitalized into local property markets. In the context of Scenario C2, this pattern signals of UAM can play a practical role in narrowing accessibility gaps and encouraging more balanced urban growth.\u003c/p\u003e\u003cp\u003eThird, the scenario-driven differences in housing values demonstrate how alternative UAM configurations may influence future expectations in land markets. While our results are based on simulated assumptions (not on real-world specifications), they offer a window into the effects of mobility improvements on spatial patterns in terms of real estate value. Even in areas with historically poor connectivity, hypothetical provision of air mobility led to considerable price shifts, highlighting the importance of accessibility as a key factor in urban transformation.\u003c/p\u003e\u003cp\u003eIn addition, this study demonstrates how a scenario-based analytical framework incorporating changes in vertiport layout, travel speed, and fare structure can provide a structured and replicable method for assessing urban impacts of UAM deployment. This approach allows planners to explore multiple policy alternatives under different accessibility assumptions, offering flexibility for real-world applications. From a policy standpoint, the findings also emphasize the importance of strategic infrastructure investment and service design. Beyond network expansion, targeted siting of vertiports in underserved districts, combined with improvements in travel efficiency, may strengthen social values of UAM. Careful coordination with existing transport systems and land use patterns will be essential to maximize both market impact and equity outcomes.\u003c/p\u003e"},{"header":"6 Conclusion","content":"\u003cp\u003eThis study explored how UAM deployment strategies reshape housing markets in dense urban environments as exemplified by Seoul, Korea. By combining a scenario-based analytical framework with a hedonic pricing model, we estimated how variations in vertiport coverage, travel speed, and fare levels influence apartment-type housing prices. In particular, we introduced the concept of generalized travel cost as a measure of improved mobility in terms of travel cost and time, incorporating it into the pricing model as one key explanatory variable. Our findings offer forward-looking insights into the effects of UAM on urban accessibility, property value structures, and spatial equity. This work has provided four key contributions as summarized below:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eEmpirical insight into the association between UAM and housing market\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eA scenario-based framework for policy evaluation\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePolicy guidance for urban planners and other practitioners\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eAdvances the discourse on spatial equity in future mobility\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eBeyond housing price effects, such value shifts may also feed into broader regional economic development patterns, including land-use dynamics and local fiscal revenues. These contributions emphasize that UAM is not merely a technical advancement in transportation, but a spatial and economic force capable of reshaping urban development patterns. By quantifying how accessibility improvements under different operational and spatial configurations affect property values, this study provides an evidence-based perspective that addresses the current research gap. The scenario-based approach facilitates a structured comparison of policy alternatives, enabling more careful decisions on investment, pricing, and spatial coverage. Moreover, the analysis demonstrates that UAM has the potential to promote spatial equity by generating substantial value gains in previously underserved urban fringe areas, contributing to a more balanced and inclusive city structure. We believe that this study highlights the broader role of UAM in sustainable and equitable urban futures. While its promise lies in speed and innovation, its true value may depend on how well it is integrated into the social and spatial fabric of the city.\u003c/p\u003e\u003cp\u003eDespite these contributions, we need to acknowledge several limitations. The analysis was based on estimated price changes derived from assumptions rather than on observed market responses to actual UAM deployment. Also, the study did not consider potential external factors such as land use regulations, noise, or community opposition, which could moderate real-world impacts. Moreover, the model focused on generalized cost variables without fully capturing behavioral or preference heterogeneity among households.\u003c/p\u003e\u003cp\u003eIn this regard, future research needs to examine further the interactions of UAM with existing modes of transport such as subways, buses, and shared mobility to assess its complementary or substitutive role within urban mobility systems. Integrating actual real estate market data into UAM demonstration projects would enhance the empirical robustness of future evaluations. Broader comparative studies across cities with different spatial forms and governance structures would also help to generalize the findings and inform more inclusive mobility planning.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eH.J. and S.C. conceptualized the study and designed the overall research framework. H.J. and G.K. developed the methodology. G.K. and M.K. collected and processed building, proximity, socioeconomic, and educational data, while H.S. and Y.C. collected and processed accessibility-related data. G.K., M.K., H.S., and Y.C. jointly conducted the baseline hedonic model estimation. H.J. carried out the scenario-based simulations and performed all visualizations, with assistance from G.K., M.K., H.S., and Y.C. H.J. drafted the original manuscript, and S.C. and H.J. revised and edited subsequent versions. S.C. supervised the research and acquired funding. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors thank eWorldEditing for providing professional English language editing services. This study builds on an undergraduate graduation research project conducted by four students under the supervision of Professor Sungtaek Choi, which was subsequently extended and developed into the present manuscript. This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korean Government (NRF-RS-2024-00415360), as part of the MaaS Basic Research Lab initiative.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData supporting the findings of this study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAhlfeldt, G. M., \u0026amp; Feddersen, A. (2018). 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This study examines how UAM deployment could reshape apartment prices and support more balanced urban development in Seoul. A scenario-based framework is developed to link reductions in generalized travel cost with spatial variation in housing values. A hedonic pricing model is estimated using 35,408 individual apartment transactions across 466 legal subdistricts, accounting for building characteristics, neighborhood amenities, socio-demographics, and accessibility to transport hubs. Six UAM scenarios, differing in network coverage and service performance, are simulated. Results show that service quality improvements produce stronger housing price appreciation than network expansion alone, while integrated strategies generate the most widespread and equitable gains. Substantial increases appear in peripheral districts where accessibility is currently constrained, suggesting that UAM can mitigate spatial disparities and strengthen housing markets in underserved areas. By evaluating impacts at both the district and parcel levels, the study provides granular evidence of UAM\u0026rsquo;s influence on housing values. It delivers actionable insights for housing policy, urban planning, and sustainable urban development.\u003c/p\u003e","manuscriptTitle":"Housing market impacts of urban air mobility in Seoul: evidence across district and parcel scales","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-21 03:13:28","doi":"10.21203/rs.3.rs-7621928/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"685ea295-f5ec-45d6-b7ea-5eaee7df08d1","owner":[],"postedDate":"November 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T06:55:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-21 03:13:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7621928","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7621928","identity":"rs-7621928","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}