# RECORD bcc88aa6a74894b8030f7071e7fd929a9fa08668
# TITLE: Urban versus rural disparities in amenity proximity and housing price: the case of integrated urban–rural city, Sejong, South Korea
# YEAR: 2024.0  VENUE: Journal of Housing and the Built Environment  DOI: 10.1007/s10901-023-10098-y
# AUTHORS: Jiyeon Shin; Galen D. Newman; Y. Park
# PAGES: 21  CANDIDATE TABLE PAGES (1-based): [8, 12, 13, 14]
# ABSTRACT: Due to the growing importance of accessibility to urban amenities on quality of life and neighborhood satisfaction in South Korea, various efforts have been made to improve accessibility to urban amenities by establishing a minimum standard, an approach called Living Social Overhead Capital (Living SOC); for example, one such standards states that an elementary school should be reachable within a 15-min walk. However, these endeavors have not yet resulted in an equal distribution of Living SOC in urban and rural areas. Many residents feel the disparity of Living SOC is greatest in areas where the rural city was incorporated into an adjacent urban city—often called an integrated urban–rural city—or when new towns were developed in rural cities. This situation can lead to the creation of dissimilar landscapes and infrastructure within a single city. As such, this research observes how the resultant disparity in proximity between urban and rural areas impacts neighborhood satisfaction, as reflected in housing prices. The outcomes indicate that, with the exception of senior care and education, all observed amenities were found to be located closer to residences in urban areas. Additionally, the closer the Living SOC, the higher the housing prices. The findings of this study posit an urgent need for a comprehensive Living SOC plan for entire cities, not only for newly developed areas, to avoid widening the existing gap between urban and rural Living SOC.


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Vol.:(0123456789)
Journal of Housing and the Built Environment (2024) 39:727–747
https://doi.org/10.1007/s10901-023-10098-y
1 3
ARTICLE
Urban versus rural disparities in amenity proximity 
and housing price: the case of integrated urban–rural city, 
Sejong, South Korea
Jiyeon Shin1 · Galen D. Newman2 · Yunmi Park3 
Received: 27 May 2022 / Accepted: 11 December 2023 / Published online: 6 January 2024 
© The Author(s) 2024
Abstract
Due to the growing importance of accessibility to urban amenities on quality of life and 
neighborhood satisfaction in South Korea, various efforts have been made to improve 
accessibility to urban amenities by establishing a minimum standard, an approach called 
Living Social Overhead Capital (Living SOC); for example, one such standards states that 
an elementary school should be reachable within a 15-min walk. However, these endeav-
ors have not yet resulted in an equal distribution of Living SOC in urban and rural areas. 
Many residents feel the disparity of Living SOC is greatest in areas where the rural city 
was incorporated into an adjacent urban city—often called an integrated urban–rural 
city—or when new towns were developed in rural cities. This situation can lead to the crea-
tion of dissimilar landscapes and infrastructure within a single city. As such, this research 
observes how the resultant disparity in proximity between urban and rural areas impacts 
neighborhood satisfaction, as reflected in housing prices. The outcomes indicate that, with 
the exception of senior care and education, all observed amenities were found to be located 
closer to residences in urban areas. Additionally, the closer the Living SOC, the higher the 
housing prices. The findings of this study posit an urgent need for a comprehensive Living 
SOC plan for entire cities, not only for newly developed areas, to avoid widening the exist-
ing gap between urban and rural Living SOC.
Keywords  Urban amenities · Living social overhead capitals · Proximity · Urban–rural 
integrated city · Housing price · Urban–rural disparities
 *	 Yunmi Park 
	
ym.park@snu.ac.kr
	
Jiyeon Shin 
	
jys.urban@ewhain.net
	
Galen D. Newman 
	
gnewman@arch.tamu.edu
1	
Architectural and Urban Systems Engineering, College of Engineering, Ewha Womans University, 
Seoul 03760, South Korea
2	
Department Landscape Architecture and Urban Planning, Texas A&M University, College Station, 
TX 77843, USA
3	
Department of Civil and Environmental Engineering and Institute of Construction 
and Environmental Engineering, Seoul National University, Seoul 08826, South Korea


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•	 Senior (Factor 3): Senior facilities (senior care facilities, senior education facilities)
Traditionally, housing prices have been analyzed using hedonic price models, but sev-
eral 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:
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 mini-
mum 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 
(1)
y = 휌Wy + X훽+ 휖
Table 2   Factor Analysis Results
Variables
Factor1
Learning and Care
Factor2
Education 
and Leisure
Factor3
Senior
Pharmacy
0.8941
0.3742
0.1175
Clinic
0.8815
0.4346
0.0542
Daycare Center
0.8105
0.4465
0.0796
Public Library
0.7802
0.3496
0.3973
Kindergarten
0.4274
0.8696
0.3973
Elementary School
0.4372
0.8661
0.1282
Urban Park
0.7173
0.7221
0.1161
Sports Center
0.5031
0.6529
0.4624
Senior Care
0.1325
0.2451
0.6137
Senior Education
0.1761
0.0993
0.4953


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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).
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: 휎2
Urban = 0.057, 휎2
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 cent-
ers. 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.
4.3  Results of the spatial lag model
As shown in Table  5, the spatial autoregressive parameter ( 휌 = 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 ­(R2
SLM = 0.899, ­R2
OLS = 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 facili-
ties (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 vari-
able 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 apart-
ment 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 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 


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Table 4   Welch’s t-test of each variable
Urban (n = 129)
Rural (n = 39)
Variables
Mean (km)
SD
Mean (km)
SD
Urban–Rural (km)
t
Housing Price
507.911
122.506
157.951
66.683
349.96
− 23.058 ***
Public Library
0.748
0.654
1.244
1.782
− 0.496
1.703 *
Daycare Center
0.190
0.189
1.096
1.880
− 0.906
3.007 ***
Kindergarten
0.504
0.340
1.342
1.512
− 0.838
3.433 ***
Elementary School
0.538
0.352
1.390
1.534
− 0.852
3.442 ***
Senior Care
0.269
0.319
0.132
0.264
0.137
− 2.691 ***
Senior Education
9.365
1.876
4.717
6.427
4.648
− 4.459 ***
Urban Park
0.382
0.206
0.885
1.391
− 0.503
2.525 **
Clinic
0.499
0.394
1.309
1.882
− 0.810
2.669 **
Pharmacy
0.551
0.407
1.211
1.757
− 0.660
2.324 **
Sports Center
0.572
0.499
0.869
1.617
− 0.297
1.130
Age
4.333
1.954
21.205
7.001
− 16.872
14.876 ***
Max Floor
21.473
8.161
5.179
5.015
16.294
− 8.366 ***
Bus stop
0.205
0.142
0.222
0.111
− 0.017
0.769
CBD
3.029
1.419
14.544
6.446
− 11.515
11.075 ***
Freq
Percent
Freq
Percent
t
Total units 
(dummy)
0
107
82.950
24
61.540
2.500 **
1
22
17.050
15
38.460
Geum River 
(dummy)
0
115
89.150
38
97.440
− 2.205 *
1
14
10.850
1
2.560


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Urban versus rural disparities in amenity proximity and housing…
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parameter ( 휌Urban = 0.877, 휌Rural = 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 ­(R2
SLM.Urban = 0.567, ­R2
SLM.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, Learn-
ing & 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 posi-
tive 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 apart-
ments 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.
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 
Table 5   SLM & OLS results
***p < .01, ** < p < .05,*p < .1
SLM
OLS
Coef. (b)
z-value
Coef
t-value
Learning & Care
− 0.039**
− 2.504
− 0.040**
− 2.397
Education & Leisure
− 0.057***
− 3.304
− 0.053***
− 2.950
Senior
0.057**
2.378
0.062**
2.479
ln(Age)
− 0.157***
− 4.774
− 0.163***
− 4.687
Total Units (dummy)
− 0.094**
− 2.319
− 0.099**
− 2.320
ln(Max Floor)
0.117***
3.234
0.120***
3.139
Bus stop
0.323***
2.988
0.333***
2.918
CBD
− 0.007
− 1.348
− 0.012**
− 2.476
Urban (dummy)
0.383***
3.496
0.587***
7.054
Geum River (dummy)
0.090*
1.730
0.092*
1.674
Rho ( 휌)
0.248**
2.489
− 
CONSTANT
4.075***
7.105
5.421***
30.234
R-squared
0.899
0.895
Log-Likelihood/Adj.R-squared
48.111/–
44.822/0.888
Akaike info criterion (AIC)
− 72.222
− 67.644
Jarque–Bera test
71.539***
46.944***
Breusch-Pagan test
–
65.928***


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J. Shin et al.
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Table 6   SLM & OLS results for each region
***p < .01, ** < p < .05,*p < .1
Urban(N = 129)
Rural(N = 39)
SLM
OLS
SLM
OLS
Coef. (b)
z-value
Coef
t-value
Coef. (b)
z-value
Coef
t-value
Rho ( 휌)
0.877***
10.228
–
0.436***
3.117
–
Learning & Care
− 0.085*
− 1.871
− 0.089*
− 1.776
− 0.034*
− 1.907
− 0.040*
− 1.731
Education & Leisure
− 0.087*
− 1.943
− 0.078
− 1.592
− 0.050**
− 2.527
− 0.056**
− 2.183
Senior
0.103***
4.048
0.122***
4.362
0.057
1.385
0.029
0.560
ln(Age)
− 0.081**
− 2.169
− 0.076*
− 1.865
− 0.586***
− 8.638
− 0.564***
− 6.390
Total Units (dummy)
− 0.076
− 1.612
− 0.062
− 1.189
− 0.074
− 1.330
− 0.081
− 1.109
ln(Max Floor)
0.184***
4.526
0.191***
4.273
− 0.041
− 0616
− 0.011
− 0.121
Bus stop
0.284***
2.822
0.306***
2.767
− 0.164
− 0.644
0.018
0.055
CBD
− 0.035***
− 2.916
− 0.022
− 1.647
− 0.003
− 0.631
− 0.006
− 1.113
Geum River (dummy)
0.107**
2.125
0.117**
2.100
− 0.098
− 0.480
− 0.119
− 0.448
CONSTANT
0.319
0.580
5.686***
33.197
4.853***
6.563
6.900***
16.524
R-squared
0.567
0.516
0.802
0.748
Log-Likelihood/Adj. R-squared
55.449/–
49.511/0.480
18.495/–
14.686/0.670
Akaike info criterion (AIC)
− 88.899
− 79.022
− 14.990
− 9.371
Jarque–Bera test
–
1.664
–
0.501
Breusch-Pagan test
6.702
7.935
8.233
7.406


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the proximity of each Living SOC between urban and rural areas and how they affect hous-
ing 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 edu-
cation 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 facili-
ties 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 aver-
age 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 devel-
opment 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 neighbor-
hood 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 facili-
ties 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 phe-
nomenon. 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 lit-
tle 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 men-
tal 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