1 Introduction

It has been abundantly shown that better accessibility to urban amenities can increase neighborhood satisfaction and well-being (Allen, 2015; Rekha et al., 2017). The recent active global discussion of the “n-minute city” or “15-min city” in which residents can easily access essential services including living, working, commerce, healthcare, education, and entertainment within a certain number of minutes of walking, reflects the increasing interest in urban amenity accessibility of most residents (Moreno et al., 2021). The South Korean (hereafter Korean) government introduced nationwide guidelines in the Special Act on Promotion and Increased Support for Urban Regeneration in 2018. These guidelines define the types of essential amenities for living, which the Act refers to as Living Social Overhead Capital (Living SOC), as well as the appropriate proximity to such amenities. For instance, pedestrian-oriented neighborhood facilities like libraries, kindergartens, and neighborhood parks should ideally be within a 15-min walk. On the other hand, facilities of regional significance, such as public libraries, sports facilities, or hospitals with emergency rooms, should be accessible within a 10 to 30-min drive. It is worth noting that the new term was coined by appending 'living' to SOC to specifically emphasize SOC's relevance to issues related to people's universal welfare and everyday quality of life (Jointly Related Ministries, 2018).

Despite the establishment of these standards, a lower level of satisfaction with the overall accessibility of Living SOC in rural areas has persisted (Koo, 2019). This disparity between rural and urban areas has become increasingly noticeable in recent new town developments and has been largely driven by the national policy that relocates governmental offices, national institutions, and public corporations to non-capital areas such as underdeveloped, medium-sized cities; this policy is also aimed at achieving regional and national growth balance in terms of population, development, and economy (Seo, 2009). In many cases, these relocated government offices and new towns are established in less developed areas with rural characteristics. As a result, the city takes on the form of an integrated urban–rural city, encompassing both urban and rural elements simultaneously. Although these relocations and newly built towns bring new facilities, populations, and services, the separation between new urban and existing rural (or less developed, in older portions of a city) has been growing more distinct, leading to the decline of rather than the growth of old towns, providing a more direct contrast in quality of life. The gap between income and the quality of the living environment is deepened due to the imbalanced supply of urban amenities such as medical hospitals, parks, and schools in Korea (Chen et al., 2014; Koo, 2019; Wen et al., 2013).

One of the significant challenges this type of city may face is the relative deprivation experienced by existing residents living in older towns, despite the fact that both existing and new residents share the same administrative area. Therefore, it is crucial to closely monitor ways to mitigate inequalities in Living SOC between urban and rural areas in integrated cities. This monitoring can provide valuable insights for future similar developments, especially for regions considering capital or institutional relocation due to highly imbalanced regional growth and capital or institutional relocation due to highly unbalanced regional growth.

In this regard, this study empirically explores the disparity in Living SOC between urban and rural areas in Sejong which is a textbook example of an integrated city in Korea. Moreover, this study identifies the associations between the types of Living SOC, their proximity, and the neighborhood satisfaction reflected in housing prices. The findings of this study help formulate strategies for creating a balanced and equitable physical environment between urban and rural areas in future new town developments and for fostering harmonious urban–rural integration.

2 Living SOC and urban–rural disparities

Numerous studies related to neighborhood amenities and housing prices have been widely researched, and several studies found that higher housing prices result from better neighborhood conditions (Blair & Larsen, 2010; Goodman, 1978). Jafari and Akhavian (2019) found that the physical conditions of housing units (e.g., size, number of bathrooms, building age) and locational characteristics significantly determine housing prices. For neighborhood characteristics, natural environments such as proximity to mountains, rivers, and central business districts (CBD) were found to significantly affect housing prices (Chica-Olmo et al., 2019; Herath & Jayasekare, 2021; Wen et al., 2017). Further, previous housing price studies have also found that the quality of urban amenities is a key influencing factor in promoting neighborhood satisfaction (Lee, 2009; Mohit & Azim, 2012; Yin & Miao, 2018). The number, proximity, and types of amenities, such as parks, medical facilities, and schools, were the most frequently mentioned factors (Choi et al., 2021; Rivas et al., 2019; Yuan et al., 2020). The impact of these factors (positive or negative influence) has, whether positive or negative, been mixed. For instance, Park et al. (2017) analyzed the level of accessibility to parks and found that housing prices decreased with greater distance to parks and lower walkability. Inversely, Crompton and Nicholls (2020) found that the proximity of parks showed a positive effect on houses closer to parks due to noise, congestion, and street parking. Similarly, the closer the hospital is located to a home, the higher the housing price (Cui et al., 2018); hospital proximity can also tend to depreciate housing prices due to the traffic congestion and increased pollution that can be caused by hospital visitors’ cars (Lan et al., 2018). A comprehensive positive effect of an elementary school and school bus availability on housing prices has also been found; regardless of the distance, houses with availability to use school buses tend to show higher prices (Metz, 2015). This, however, applies only to schools that operate school buses, and housing prices generally increase when a house is closer to an elementary school (Duan et al., 2021; Wen et al., 2018). Further, kindergarten facilities also showed significant positive effects on housing prices (Yuan et al., 2018). In addition, Yang et al. (2018) discovered that housing prices also rise when located closer to sports and cultural centers.

Some studies have revealed different impacts of urban amenities on housing prices by region, which are particularly distinct between urban and rural areas or urban and suburbs (Lee & Hong, 2013; Li et al., 2023; Logan & Burdick-Will, 2017; Wulandari & Laksono, 2019). Disparities in urban and rural areas are well-known topics that have been explored in various domains. For example, rural areas exhibited relatively lower income levels and greater economic growth inequalities compared to urban areas (Cao, 2010; Ding, 2002). Seong et al. (2021) found disparities in awarded grants and duration of policies, finding much lower figures in rural areas compared to urban ones. Regarding healthcare service utilization, urban areas were found to be better than rural areas due to barriers such as transportation difficulties, limited supply of care services, and social isolation (Goins et al., 2005; Marrone, 2007). Urban–rural disparities in amenity supplements, as well as their proximity, have also been routinely highlighted. For instance, Smith et al. (2010) found a disparity in the accessibility of grocery stores in rural areas, showing that rural or small towns had lower accessibility. Wen et al. (2013) analyzed the difference in spatial access to urban parks and found that distance to parks tended to increase when the area became more rural. Also, the proximity to educational and health amenities was found to be greater in urban areas (Abolhallaje et al., 2014; Borana & Yadav, 2017).

As uneven development and socio-economic disparities between urban and rural areas have become common problems in many countries (Gurrutxaga, 2013; Leibert et al., 2015), the concept of urban–rural integration or integrated development has emerged as a common approach to achieving balanced and sustainable development between these two areas (Ma et al., 2021; Yuzhe et al., 2022). However, after the merger of two cities into one, it is often observed that population migration from formerly rural areas and adjacent cities into newly built towns can lead to population imbalances. This influx usually continued for a while, resulting in unexpected challenges, prominently in income, education, living environments, social services, and policy benefits (Breau et al., 2018; Cao, 2010; Chen et al., 2013; Seong et al., 2021). Since it takes some time for rural areas to reach a similar level of infrastructure and public services as urban areas. This inevitably widens the already evident gap in living environments within urban–rural integrated cities (Kim & Kim, 2003). Although disparities in the living environment and the urban can commonly occur within the city, Borana and Yadav (2017) found disparities in education and health care facilities among wards within a city, not that there are insufficient studies that have analyzed disparities within the urban–rural integrated city. Further, there are a lack of studies that have compared the effects of each Living SOC type on housing prices between urban and rural areas in urban–rural integrated cities. Therefore, this study focuses on whether there are differences in the proximity of Living SOC between urban and rural areas and the effects of Living SOC by type in these cities.

3 Materials and methods

3.1 Study area

Sejong is a prime example of an urban–rural integrated city that has a clear difference in Living SOC between urban and rural areas (Fig. 1). In the 1970s, the government presented the first proposal of Sejong for relocating the governmental administrative offices outside of the capital city of Korea, Seoul in an effort to balance national development and distribute economic and cultural resources concentrated in the Seoul Metropolitan area, but it failed. The idea was revived in 2007 and proposed for the city of Sejong, approximately a two-hour drive from Seoul. Sejong was planned to be built on three municipalities such as Yeongi-gun and part of Gongju-si and Cheongwon-gun, which were predominantly rural settings at that time. One of the largest planned new towns is in the Southern part of Sejong in which a total of 43 central administrative agencies and 15 national research institutions were relocated from Seoul. As of 2019, Sejong covers a total area of 364.84 km2 with a population of approximately 800,000. The total size of Sejong is nearly three-quarters that of Seoul and the new town occupies 72.91 km2 (National Agency for Administrative City Construction, 2022).

Fig. 1
figure 1

Sejong urban and rural area division and the number of apartment sales units with apartment complex locations (A, B, C, D: Kakao map road view pictures (https://map.kakao.com/), A: Saerom-dong (urban area)/B:Jochiwon-eup (rural area once most developed before the new town development)/C:Geumnam-myeon(rural area close to urban area)/D: Janggun-myeon (rural area))

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Although this new town is still undergoing development, the rest of the region remained mostly rural. As a result, a significant amount of rural to new town migration occurred in Sejong (Lee, 2018). As shown in Fig. 1, the new town areas exhibit distinct urban characteristics in urban components; the building development was taller and denser. Also, it showed distinctive characteristics in terms of population density and industrial structure; higher population density (3415.11 people/km2) than surrounding rural areas and industrial structure concentrated in tertiary industry. Although the new town accounts for only about 20% of the total area, the population occupying the urban areas comprises approximately 80% of the total population (297,359 population in 2022 in the new town area) and the average age was 35.1 years old, much younger than that of the national average (44.2 years old in 2022) (National Agency for Administrative City Construction, 2022). In the industrial structure, the residents of the new town are mostly engaged in tertiary industry, while residents in the remaining areas are predominantly engaged in primary and secondary industries (e.g., agriculture, manufacturing, construction, etc.) (Sejong Statistics, 2022).

The population of the new town area has increased by more than 200 thousand, while that of other areas decreased by about 4000 from 2012 to 2019 (Ministry of Interior & Safety, 2022). Although Jochiwon-eup, once an activity core of the former Sejong (when it was part of Yeongi-gun, where 'gun' refers to a municipality with rural characteristics and a population of less than 100,000), had a relatively large population of 43,275 in 2019 (Ministry of Interior & Safety, 2022), it now exhibits a transitional appearance that falls between urban and rural characteristics, rather than having a purely urban form like the new town area. The names of administrative sub-districts or divisions differ in rural and urban areas in Korea: 'eup' and 'myeon' for rural areas, and 'dong' for urban areas. The new town only includes 'dong', while the rest have 'eup' and 'myeon'. Thus, this study categorizes the new town as urban and the rest as rural.

3.2 Data and measurements

Nearly 6,000 (5,678 in total) apartment sales in 2019 were retrieved from COMPAS data (https://compas.lh.or.kr/), collected by the Korea Land and Housing Corporation. This study only considered apartment sales because of data availability. Due to privacy issues, the full address of single-family houses was not opened to the public. While exploring the impacts of different housing types would be the best approach to take, using apartment sales is as a proxy, especially in cities where apartment is a main housing type as in Korea, has also been shown to be a valid and reasonable approach for following several reasons. First, apartment complexes are a mainstream housing type in Korea. In Sejong, more than 80% of residents live in apartments, 98% of the population lives in urban areas, and 36% of the rural population lives in apartments (Statistics Korea, 2019). This situation implies that most people in the study area live in apartment complexes. Conversely, in this study area, we cannot test the impact properly with single-family housing, as it constitutes only 1% of the residences in urban areas. Second, apartments, due to their higher population density, tend to have more amenities in their neighborhoods and residents in this type of housing would generally expect better accessibility to amenities (Sirmans et.al., 2020). If apartments in rural areas show relatively worse proximity to Living SOC, we could, therefore, easily assume that single-family housing within rural areas actually tend to have worse access to Living SOCs. As such, it not ideal to use apartment data than single family housing data in this circumstance, but more reasonable and valid. However, careful interpretation of the results is also necessary. Total sales data were converted into an average value per square meter for each apartment complex. Each apartment complex typically had unique names (e.g., Garak Maeul, Gaon Maeul, and Beomjigi Maeul) and showed a high level of homogeneity in terms of housing type, size, price, household characteristics (Hwang, 2006). The total number of apartment complexes for this analysis was 168.

The Special Act on Promotion and Increased Support for Urban Regeneration defined Living SOCs as two primary categories based on whether they serve the neighborhood or regional scale (Ministry of Land Infrastructure and Transport, 2019). According to this Act, on a daily basis, neighborhood facilities are primarily used by pedestrians that are classified by their function, including educational, learning, care, medical, exercise, rest, convenience, and transportation facilities, which should be reached within 15 min by walking. Meanwhile, regional facilities are often only available to access by car. Regional facilities are divided into learning, care, medical, cultural, exercise, and rest facilities, which should be reached within 10 to 30 min of driving. As shown in Table 1, this study considers ten types of neighborhood’s Living SOC and emphasized the proximity for walking. The 2018 Living SOC data were retrieved from the National Standard of Living SOC database and the distance to the ten types of Living SOC at the neighborhood level was measured. The shortest network distance from the center of each apartment complex to each facility through the road network was calculated, excluding highways because residents could not actually walk or bicycle on them. Road data as of 2019 was provided by the National Geographic Information Institute of Korea (https://www.ngii.go.kr/eng/main.do).

Table 1 Two categories of Living SOC in South Korea—Neighborhood facilities and regional facilities
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Factors known to affect apartment prices were also incorporated into the modeling, as guided by previous studies, including: building age (Keskin, 2008; Soltani et al., 2021), the total number of units in the apartment complex (Bae et al., 2018; Shimizu et al., 2010), floor level (Xiao et al., 2019), traffic facilities (Choi et al., 2021; Park et al., 2016), landscape (e.g., river, mountain) (Jim & Chen, 2009; Wen et al., 2017), and accessibility to the central city area (Baumont, 2009; Hyun & Milcheva, 2018). Further, the size of the apartment complex was considered since people prefer larger complexes in Korea (Kim & Lee, 2018). Following Korean small apartment complex construction guidelines and the 'Housing Construction Standard Regulation,' which require apartment complexes with more than 150 households to install senior care facilities and 200 households to have day care centers for residents, the apartment complexes were categorized as small or not-small (under 200 units (small) = 1, otherwise (not-small) = 0). This dummy variable counts the size of an apartment complex and controls the possible price premium embedded in the apartment complex that can be reconstructed and that has welfare facilities like senior care and daycare inside the complex. The influence of public transportation was calculated using the network distance to each bus stop. It is important to note that Sejong has no subway system yet. The location of the Government Complex, where all governmental administrative offices are located in the new town, was considered the center of the city. Other house-related data was retrieved from different sources such as the Ministry of Land, Infrastructure and Transport (building age and floor level) and the Naver Real Estate platform (total housing units of each apartment complex).

3.3 Analysis

Through a pairwise correlation test, multicollinearity (r = 0.7, p < 0.05) between dependent variables, in particular, distance to Living SOC was checked. Due to high collinearity between Living SOC items, ten items were grouped into three factors based on partial least square regression results as guided by Denham (2000). The newly created variables and their names are shown in Table 2 and listed below.

  • Learning & Care (Factor 1): Learning facilities (public libraries), and Care facilities (daycare centers, clinics, pharmacies)

  • Education & Leisure (Factor 2): Education facilities (kindergartens, elementary schools) and Leisure facilities (urban parks, sports centers)

  • Senior (Factor 3): Senior facilities (senior care facilities, senior education facilities)

Table 2 Factor Analysis Results
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Traditionally, housing prices have been analyzed using hedonic price models, but several recent studies have also considered the spatial dependence of housing prices. Due to the spatial dependency of each neighborhood (Moran’s I score = 0.614, p = 0.001), we assumed that there would be a spatial dependency. A Lagrange Multiplier (LM) test was conducted to determine the proper model between two spatial regression models. The results indicated the spatial lag model (SLM) was favorable. In this model, the influence of the surrounding area on the dependent variable is added to the regression model as a new explanatory variable. The result of ordinary least square regression (OLS) was also reported for simple comparison. The basic formula of SLM is:

$$y= \rho Wy+X\beta +\epsilon $$
(1)

where W is the spatial weight matrix, and ρ is the spatial autocorrelation coefficient.

4 Results

4.1 Descriptive statistics

The average housing price was about 4.3 million Korean won per square meter (≈ $3,400/m2). The gap between the highest and lowest prices was about 7 million Korean won/m2 won, with the most expensive houses in the urban area. The mean distance from apartment complexes to each facility was under 1 km, except for the senior education facility where the mean distance was larger than for other facilities, spanning up to 23 km. The minimum distances to Daycare, Senior care, and Clinics appeared to be zero, suggesting that these facilities are located within the apartment complexes themselves. As mentioned, the Housing Construction Standard Regulation mandates that large apartment complexes must have daycare and senior care facilities within their boundaries. Additionally, clinics and other small retail shops are often located within large apartment complexes in Korea. The average age of each apartment was relatively low due to new construction in a new town, and, on average, the bus stop can be reached within 1 km (Table 3).

Table 3 Descriptive statistics
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4.2 Welch’s t-test

Since the urban (n = 129) and rural (n = 39) had unequal sample sizes and variance (log-transformed housing price variance: \({\sigma }^{2}\)Urban = 0.057, \({\sigma }^{2}\)rural = 0.112), a Welch’s t-test was performed to assess the difference in proximity to Living SOC (Table 4.). The analysis revealed a statistically significant difference in network distance to Living SOC between urban and rural areas, with the exception of sports centers. In rural locations, the network distance to most Living SOC was found to be 0.67 km longer on average than in urban areas. Conversely, the distance to senior care (difference = − 0.14 km) and senior education (difference = − 4.65 km) facilities in rural areas was closer than in urban areas. For senior education facilities, the mean network distance in rural areas was approximately half that of urban areas. Besides daycare centers and senior care centers, which have regulations that are essential to be installed in large complexes of more than 200 households, parks were the closest facilities to apartments in urban areas. In rural areas, due to the few apartment complexes with over 200 households, the distance to daycare centers was a lot farther than in urban areas, and the closest facility was followed by senior care centers and sports centers. The facility that had the shortest distance gap between both regions was the senior care center. In addition to the Living SOC variables, housing prices and other variables, except for bus stop distance, exhibited a statistically significant difference between the two areas. Housing prices were significantly higher, while the average age of houses was lower in urban areas.

Table 4 Welch’s t-test of each variable
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4.3 Results of the spatial lag model

As shown in Table 5, the spatial autoregressive parameter (\(\rho \)= 0.248, p < 0.01), which reflects the spatial dependency of housing price, was highly significant. Compared with the OLS model, the explanatory power of SLM was higher (R2SLM = 0.899, R2OLS = 0.895) and the AIC score, in which a lower score means a better model, was lower (AICSLM = − 72.222, AICOLS = − 67.644). Therefore, the results of SLM are interpreted, and OLS is presented for simple reference (Table 5). Learning & Care facilities (b = − 0.039) and Education & Leisure facilities (b = − 0.057) were negatively associated. Meanwhile, Senior facilities (b = 0.057) were positively associated. This suggests that the closer the houses are to Learning & Care facilities and Education & Leisure facilities, the higher the value, while the opposite is true for elderly facilities. Building age (b = − 0.157) and the dummy variable of total units (b = − 0.094) were negatively associated with housing prices, while other control variables were positively associated. This refers to that newly built and larger apartment complexes and the higher apartments tend to have higher prices. Also, the closer to bus stops and Geum River the higher the price. As regional characteristics, apartments in urban areas tend to have higher prices than rural areas. The sign of each variable remained the same in both the SLM and OLS models, but the CBD was the only significant variable in the OLS model.

Table 5 SLM & OLS results
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Table 6 presents the results of the SLM model separated by urban and rural areas. Since the sample size of the rural area was small (39 apartment complexes with 832 sales cases), the interpretation of the rural model should be done cautiously. The spatial autoregressive parameter (\(\rho \)Urban = 0.877, \(\rho \)Rural = 0.436) was still highly statistically significant in both areas. The explanatory power of the SLM in urban areas was about 57%, and around 80% in rural areas. Comparing the R-squared value (R2SLM.Urban = 0.567, R2SLM.Rural = 0.802) and AIC score (AICSLM.Urban = − 88.899, AICSLM.Rural = − 14.990), the SLM showed better explanatory power in both areas. The signs of Living SOC variables remained the same, but Senior facilities were not statistically significant in rural areas. In urban areas, Learning & Care facilities (bUrban = − 0.085) and Education & Leisure facilities (bUrban = − 0.087) had negative effects on housing prices, while Senior facilities (bUrban = 0.103) had a positive effect. In rural areas, Senior facilities did not have a statistically significant effect on housing prices, while the other two Living SOCs had a significant negative effect (bRural.Learning&Care = − 0.034, bRural.Education&Leisure = − 0.050). This implies that the apartments located closer to both Learning & Care and Education & Leisure facilities had higher prices in both urban and rural areas, while, in the case of Senior facilities, it showed higher prices when the distance got farther only in urban areas.

Table 6 SLM & OLS results for each region
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5 Discussion

This study observed the disparities in Living SOC in urban and rural areas and their effects on neighborhood satisfaction that were reflected in housing prices in one sample of urban–rural integrated city, Sejong, Korea. Although this is exploratory work done with a single sample, the outcomes of this study illustrate a statistically significant difference in the proximity of each Living SOC between urban and rural areas and how they affect housing prices. As expected, most of Living SOC except senior care and education facilities were much closer to the apartments in urban areas, while only senior care and senior education facilities were closer to the apartments in rural areas. All three categories of Living SOC have significant effects on housing prices. The long-distance Learning & Care and Education & Leisure facilities tended to decrease housing prices in both urban and rural areas. In other words, the closer the Living SOC, the better the neighborhood satisfaction. These findings confirmed the previous findings that observed the positive impacts of urban amenities on neighborhood satisfaction (Choi et al., 2021; Wen et al., 2017; Yuan et al., 2020). In the case of Senior facilities, housing prices increased when the distance to facilities became farther, and there was no significant effect of proximity to facilities in rural areas.

The average distance disparities to Learning & Care and Education & Leisure facilities was 670 m. Based on this distance, adults in rural areas would need to walk eight minutes more, and the elderly would require an additional two minutes than that, based on the average walking speed provided by Bohannon and Andrews (2011); mean walking speed of the healthy elderly (60–69 years old) is approximately from 1.24 to 1.33 m/sec which could decline to 1 m/sec when gets older or sick. The average distance to senior care facilities was similar in urban and rural areas, and the distance to senior education facilities was much closer in rural areas. Like this, even with the expectation of positive spill-over development and infrastructure to rural areas through balanced development, natural spillover, on the other hand, was not detected in existing areas. Rather, it was reported that neighborhood satisfaction decreased and people claimed that relative deprivation occurred in nearby areas (Lee et al., 2018). Thus, new town plans should have consolidated urban amenity plans that cover adjacent existing areas.

As urban areas held a relatively low proportion of individuals over 60 in urban areas (5.8%) compared to rural areas (23.3%) (Ministry of Interior & Safety, 2022), Senior facilities for the young may not be necessary or could be located far away. Even in rural areas with many elderly persons, Senior facilities would not be considered necessary facilities. This situation in which Senior facilities become useless would be a unique Korean phenomenon. According to Kim (2019), the participation rate of the elderly in education was low compared to other age groups. Also, the elderly mostly use their community centers for interaction and relaxation in rural areas (Lee et al., 2012). So, there seems to be little need for official Senior facilities as community centers can work as Senior facilities. It is, therefore, necessary to update the procedures for providing official senior care and education facilities, as well as the division of responsibilities. Moreover, as urban areas also become older as time passes, a strategic approach supporting the lifestyle of the urban elderly should be implemented as senior facilities work as a core for social integration of the elderly and significantly contribute to their Living SOC (Aday et al., 2006; Yoo, 2015; Zarghami et al., 2019).

Senior education facilities observed in this study are facilities that are specifically designed to aid the elderly in their social adaptation and enhance their physical and mental well-being through various programs, including club activities and educational services such as self-development and social participation through computers (Han et al., 2011). In Sejong, a total of seven senior education facilities exist, each built before 2015, and all of them are located in rural areas. This is because the proportion of the elderly population is higher in rural areas, and also, more than half of the elderly in rural areas require literacy and other technological education for their daily lives (Ma et al., 2018). However, good accessibility to Senior facilities in rural areas where the elderly population continues to increase is beneficial, but the absence of care-related facilities (i.e., pharmacy and clinic) is a problem. In general, single-family houses in rural areas are further from the CBD than apartments, so the inconvenience due to the far distance of care facilities is much higher. This decrease in accessibility to essential facilities causes the population of existing rural areas to move to urban areas rather than the Naver Hood effect, as originally expected by urban and rural integrated cities. As evidence, the population of urban area in Sejong increased by 5% while the rural area decreased by 2.2% in 2022 compared to that of 2021 (National Agency for Administrative City Construction, 2022).

Factors other than Living SOC showed similar effects on housing prices to previous studies. Typically, bus stop distance showed a positive influence on housing prices. Usually, public transportation accessibility shows a positive effect on housing prices (Cordera et al., 2019; Dubé et al., 2011), but this was not the case in this study. Although further studies are necessary, we assume that this is due to the unique transportation conditions. According to a report from the Korea Transport Institute (2019), the mode share of the bus was only 9.7% in Sejong, while the national rate was 17.9%. Due to the low usability of the buses, bus stops may not always show a significant influence on neighborhood satisfaction in rural areas. Moreover, Cui et al. (2018) found that bus stops also showed a negative effect on neighborhood satisfaction because of the noise and confusion they cause, and some studies found no significant influence of buses on housing prices (Cervero & Duncan, 2002; Choi et al., 2021).

While it was not considered in this analysis, consideration of the needs of residents in single-family houses in rural areas may also merit future investigation to compare to these results, if new data emerges to conduct such an assessment. In the case of single houses, some people may live in such housing types as a result of self-selection by taking the inconvenience of proximity to Living SOCs (Pinjari et al., 2008). Even if they choose to live in rural areas voluntarily, they still have the right to enjoy the convenience of Living SOCs. Also, there may be people who are not allowed to self-select houses due to their work or financial limitations (Lin et al., 2017) and native residents who may feel relative deprivation due to far proximity to Living SOCs compared to urban areas in the aftermath of the development of new towns. Therefore, efforts are also needed to solve the problem of proximity for residents of single-family houses in these areas. It is impossible to build new facilities for individual residents of single-family houses since people do not live in one area as concentrated as an apartment. In such rural areas, increasing visiting social services can be a good alternative to fulfill rural residents’ demand for Living SOC. As such, the different supply and improvement policies of Living SOC by varying demographics, lifestyles, and residents’ (consumers) demands should be considered. It would be better to conduct a demand survey on Living SOC as a preliminary preparation for Living SOC supply.

6 Conclusion

The findings from this research illustrate the disparity in Living SOC in the urban–rural integrated city and how the effects of Living SOC can differ. Although the development of new towns is normally expected to benefit existing residents, a problem of disparity regarding the quality of the living environment is also normally present. Thus, it is necessary to continually identify and address the factors that drive the disparities between urban and rural areas, as the results of this study show. Inequalities in the quality of the living environment are one of the factors that deepen the disparities between urban and rural. Supplying Living SOC is shown to be one way to mitigate the issue, which eventually can also influence neighborhood satisfaction. Instead of creating a development plan that applies only to new towns, a comprehensive supply strategy for Living SOC for the entire region needs to be developed. As lifestyle and current and future demographic structures all differ by area, the establishment of a well-connected service network of Living SOC will support decreasing disparities across these differences.

Because this study analyzed only one city, future studies should consider additional analyses from other cities and regions for increased generalization of findings. Another limitation of this research is that, when processing data, the average housing price had to be used. If the individual location of each housing sales data was available, the outcomes and findings would tell a more detailed story. Also, as noted, although there is a multitude of single-family houses in the rural area of Sejong, only apartment sales data were used due to the unavailability of the location of each house. For the advanced study following, additional factors can be considered to examine the precise effect of Living SOC on house prices. Also, an additional study of the area with a high proportion of single-family houses to detect the effect of Living SOC on single-family houses may be possible as new data surfaces. While the model's explanatory power is sufficient to explain the effects of Living SOC and housing prices, the study was unable to take into account some neighborhood characteristics, particularly socio-economic and demographic factors, which have been found to have a significant impact on housing prices in previous studies (Agnew & Lyons, 2018; Lin et al., 2014). Specifically, an urban-only model could provide better explanations for these variables. Technically, socio-economic data is limited. Representative variables of socio-economic data, such as income, education, or poverty rates, are not available at the census tract level or sub-district level in Korea due to privacy protection issues; the census survey does not collect income information. With the growth of big data, Jung and Yoon (2023) and Jin and Hong (2022) have proposed using insurance premiums and credit card sales data as proxies for socio-economic status. However, obtaining these data requires purchasing them from private companies. Future studies may further the discussion using these proxy data. Further, using different units of analysis, such as neighborhoods and individual houses, in the OLS leads to the inevitable assumption of independence in OLS. Therefore, as suggested by Park and Kim (2017), multi-level modeling could be considered in future studies after obtaining neighborhood data to avoid this issue.