# RECORD 5cf2ca5773ec64d2d8be794899e97064def914d5
# TITLE: The effects of jobs, amenities, and locations on housing submarkets in Xiamen City, China
# YEAR: 2022.0  VENUE: Journal of Housing and the Built Environment  DOI: 10.1007/s10901-022-09984-8
# AUTHORS: Yong-Ling Li; Yanliu Lin; Jiaoe Wang; S. Geertman; P. Hooimeijer
# PAGES: 19  CANDIDATE TABLE PAGES (1-based): [12, 13]
# ABSTRACT: Numerous studies have found that jobs, amenities, and location influence housing prices in urban areas. However, there is still a lack of in-depth understanding of the impacts of these factors on various housing submarkets within a city. With the case study of Xiamen, this paper investigates the impacts of jobs, amenities, and location on four housing submarkets, classified by owner-occupied and rental housing, in inner and outer districts. The hedonic model, Chow test, and Tiao-Goldberger test are applied to analyze differentiation in the determinants of housing prices between four submarkets. The results show that all submarkets are influenced by blue-collar jobs (which have negative effects) and seascape (which has a positive impact). Besides, differentiated after submarkets show that school districts and public transportation have a greater influence on owner-occupied markets than on rental markets. A heterogeneity exists between inner-district and outer-district markets. For instance, bus rapid transit (BRT) has a positive effect on housing and rental prices in the outer districts but not in the inner districts. These differences are mainly caused by the disparities of spatial quality, economic development, and public facilities and amenities. The findings have profound implications for decision-making and planning practices.


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ARTICLE
Received: 8 November 2020 / Accepted: 26 September 2022 / Published online: 5 November 2022
© The Author(s) 2022
	
 Yanliu Lin
Y.Lin@uu.nl
1	
Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic 
Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
2	
Department of Human Geography and Spatial Planning, Utrecht University, Vening 
Meineszgebouw A, Princetonlaan 8a, 3584 CB, Utrecht, the Netherlands
3	
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 
China
The effects of jobs, amenities, and locations on housing 
submarkets in Xiamen City, China
Yongling Li1,2 · Yanliu Lin2 · Jiaoe Wang1,3 · Stan Geertman2 · Pieter Hooimeijer2
Journal of Housing and the Built Environment (2023) 38:1221–1239
https://doi.org/10.1007/s10901-022-09984-8
Abstract
Numerous studies have found that jobs, amenities, and location influence housing prices 
in urban areas. However, there is still a lack of in-depth understanding of the impacts of 
these factors on various housing submarkets within a city. With the case study of Xiamen, 
this paper investigates the impacts of jobs, amenities, and location on four housing sub­
markets, classified by owner-occupied and rental housing, in inner and outer districts. The 
hedonic model, Chow test, and Tiao-Goldberger test are applied to analyze differentiation 
in the determinants of housing prices between four submarkets. The results show that all 
submarkets are influenced by blue-collar jobs (which have negative effects) and seascape 
(which has a positive impact). Besides, differentiated after submarkets show that school 
districts and public transportation have a greater influence on owner-occupied markets 
than on rental markets. A heterogeneity exists between inner-district and outer-district 
markets. For instance, bus rapid transit (BRT) has a positive effect on housing and rental 
prices in the outer districts but not in the inner districts. These differences are mainly 
caused by the disparities of spatial quality, economic development, and public facilities 
and amenities. The findings have profound implications for decision-making and planning 
practices.
Keywords  Housing prices · Submarkets · Hedonic price model · Accessibility · Owner-
occupied housing · Rental housing
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Y. Li et al.
Table 2  Regression results of OLS, SEM, and SLM
Owner-occupied housing
Rental housing
Inner districts
Outer districts
Inner districts
Outer districts
OLS
SEM
OLS
SEM
OLS
SEM
OLS
SLM
Area
0.008*** 0.008*** 0.007*** 0.008*** 0.007*** 0.006*** 0.004***
0.004***
Interior (ref: simply furnished)
Well-furnished
0.044**
0.047*** 0.037**
0.043*** 0.131*** 0.117*** 0.121*
0.104*
Other
-0.017
0.018
0.016
0.021
0.034*
0.038**
0.005
0.003
Rough housing
0.023
0.065*
-0.013
-0.016
-0.135
-0.125
-0.810***
-
0.760***
Total floor (ref: Medium-high building)
High-rise 
buildings
0.080*** 0.104*** 0.162*** 0.188*** 0.047**
0.041
0.071
0.068
Multistory 
building
0.042*
0.049**
0.023
0.01
-0.007
-0.006
0.097*
0.083*
Low-rise 
building
-
0.450***
-
0.308***
-0.024
-0.075
0.041
0.059
0.168
0.133
Bedroom
0.097*** 0.091*** 0.094*** 0.065*** 0.073*** 0.084*** 0.092***
0.085***
Livingroom
0.156*** 0.146*** 0.131*** 0.137*** 0.179*** 0.175*** 0.151***
0.162***
Building Age
-
0.006***
-
0.007***
-
0.007***
-
0.006***
-
0.008***
-
0.008***
-0.0004
-0.001
Culture
0.016*** 0.010*
0.021*** 0.019*** 0.009
0.005
0.044***
0.041***
Sports
0.024
0.025
0.056**
0.063*** 0.007
-0.0002
-0.034
-0.018
Hospital
-
0.017***
-0.011**
-0.01
-0.012
-0.011**
-0.011*
0.009
0.007
Shopping
-
0.008***
-
0.006***
0.007*
0.001
-0.002
-0.002
0.0002
0.001
Park
0.01
-0.003
-
0.054***
-
0.056***
-0.015
-0.016
0.001
-0.001
Blue-collar 
jobs
-
0.046***
-
0.079***
-
0.109***
-
0.039***
-
0.039***
-0.081***
-0.053*
Pink-collar 
jobs
0.029**
0.011
0.083
0.169
-0.002
-0.004
0.258
0.198
White-collar 
jobs
0.051
0.107**
0.011
0.355
0.159*** 0.163*** -1.601**
-1.315**
Year 2017
0.268*** 0.198*** 0.252*** 0.144*** -0.054*
-0.060*
0.055
0.052
Bus stops
0.005*** 0.003
0.009*** 0.008**
-0.001
0.0002
-0.004
-0.006
BRT
-
0.066***
-
0.097***
0.071**
0.075*
-0.026
-0.029
0.288***
0.286***
Metro (ref: No)
0.138*** 0.090*** 0.090*** 0.156*** 0.060**
0.062**
-0.103**
-0.089**
River
-0.013
0.024
-0.08
-0.117
-0.017
-0.024
Seascape
0.190*** 0.135**
0.054*** 0.070*** 0.163**
0.148**
0.141***
0.125***
School district
0.096*** 0.102*** -0.048
-0.053*
-0.0002
-0.001
-0.005
0.017
Urban villages
0.02
0.028
-0.039*
-0.037*
-0.03
-0.047
-0.035
-0.031
Constant
4.317*** 4.444*** 4.000*** 4.006*** 7.182*** 7.169*** 6.746***
5.596***
R2
0.79
0.837
0.78
0.747
Adjusted R2
0.786
0.833
0.774
0.724
Log Likelihood
-215.568
286.676
9.151
4.344
AIC
624.544
489.135
-324.077
-515.351
91.645
39.699
53.971
47.311
Note: *p < 0.1; **p < 0.05; ***p < 0.01
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The effects of jobs, amenities, and locations on housing submarkets in…
4.2  Diversities: inner district and outer district
The results show that some variables including hospital and seascape have different effects 
on inner- and outer-district markets. In the inner districts, hospitals have a negative effect 
on housing markets, since they can cause nuisance (Huang, 2017), but have no impact on 
the housing market in the outer districts. A possible explanation is that in the outer-district 
hospitals are not located in close proximity to residential areas and will have less impact on 
living conditions. Seascape positively impact housing and rental prices in all submarkets, 
but the impact was greater in the inner districts than in the outer districts. The higher impact 
on the inner city may be due to the development of yacht marinas in the inner districts and 
the development of many high-end villas around the yacht marina.
Owner-occupied 
housing VS Rental 
housing
Inner 
districts 
VS Outer 
districts
Culture
4.661***
5.281***
8
Sports
3.926***
0.188
9
Hospital
0.077
3.231***
10
Shopping
15.711***
14.613***
11
Park
2.531***
14.349***
12
Blue-collar jobs
7.240***
1.106
13
Pink-collar jobs
4.341***
0.355
14
White-collar jobs
16.828***
4.655***
15
Year 2017
67.143***
7.813***
16
Bus stops
6.166***
0.107
17
BRT
1.893***
25.173***
Metro (ref: No)
13.626***
11.662***
River
0.834
0.041
Seascape
0.569
7.131***
School district
10.293***
7.954***
Urban villages
1.759**
2.707***
Constant
67.079***
7.759***
Table 4  Tiao-Goldberger test
Note: *p < 0.1; **p < 0.05; 
***p < 0.01
 
Segmentation
Physical
Chow
Owner-occupied 
housing
Inner districts with Outer 
districts
6.564 ***
Rental housing
Inner districts with Outer 
districts
6.665***
Inner districts
Owner-occupied housing 
with Rental housing
1220.639***
Outer districts
Owner-occupied housing 
with Rental housing
658.671***
Table 3  Chow test results
Note: *p < 0.1; **p < 0.05; 
***p < 0.01
 
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Y. Li et al.
4.3  Diversities: across all submarkets
The results show that the influence of most variables—including culture, shopping, park, 
year, white-collar jobs, BRT, metro, school district, and urban villages—is different in each 
submarket. Cultural facilities such as libraries and cultural centers have no impact on the 
inner-district rental market but have a positive impact on the other three submarkets. Con­
trary to our expectations, shopping facilities have a negative impact on housing prices in the 
inner districts. This may be related to our selection of variables, in which retail and conve­
nience stores are part of the shopping variable. Jang & Kang (2015) found that compared 
with shopping centers, the impact of supermarkets and convenience stores on housing prices 
is not significant. In addition, district-level parks have a negative impact on sale prices in 
the outer districts. Yang et al., (2016) found that there is a significant negative correlation 
between district-level green spaces and sale prices since the reservation value of district-
level public parks is higher than their use-value. In the outer districts, large parks tend 
to be located in areas with low population density and underdeveloped facilities, thereby 
having a negative impact on sale prices. In terms of transaction time, although housing 
prices were higher in 2017 than in 2016, rents were lower, especially in the inner districts. 
This suggests that sale prices and rental prices are inconsistent over time. White-collar jobs 
have a significant positive impact on housing sales and rental prices in the inner city, but 
do not affect sales prices in the outer districts, or even negatively affect rental prices in the 
outer districts. This negative effect may be caused by the fact that most white-collar jobs 
are mainly concentrated in the old areas of the outer districts, where the environment and 
buildings are relatively dilapidated which is lowering rental prices. BRT negatively impacts 
inner-district housing prices but positively impacts outer-district housing prices and rents. 
Regarding inner districts, public transportation facilities are already well developed, which 
leads to the lack of attractiveness of BRT. In contrast, BRT foremost enables people living in 
the outer districts to reach working places and services in the inner districts more efficiently. 
(Planned) metro stations negatively impact the outer-districts rental market, but positively 
impact the other three submarkets. This may be because, for many short-term tenants, metro 
Fig. 3  Similarity and diversity between housing submarkets
 
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