1# RECORD 5cf2ca5773ec64d2d8be794899e97064def914d52# TITLE: The effects of jobs, amenities, and locations on housing submarkets in Xiamen City, China3# YEAR: 2022.0 VENUE: Journal of Housing and the Built Environment DOI: 10.1007/s10901-022-09984-84# AUTHORS: Yong-Ling Li; Yanliu Lin; Jiaoe Wang; S. Geertman; P. Hooimeijer5# PAGES: 19 CANDIDATE TABLE PAGES (1-based): [12, 13]6# 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.789===== PAGE 1 (score 9, numeric-paren 0, table-words 0, var-words 9) =====1011ARTICLE12Received: 8 November 2020 / Accepted: 26 September 2022 / Published online: 5 November 202213© The Author(s) 202214 15 Yanliu Lin16Y.Lin@uu.nl171 18Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic 19Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China202 21Department of Human Geography and Spatial Planning, Utrecht University, Vening 22Meineszgebouw A, Princetonlaan 8a, 3584 CB, Utrecht, the Netherlands233 24College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 25China26The effects of jobs, amenities, and locations on housing 27submarkets in Xiamen City, China28Yongling Li1,2 · Yanliu Lin2 · Jiaoe Wang1,3 · Stan Geertman2 · Pieter Hooimeijer229Journal of Housing and the Built Environment (2023) 38:1221–123930https://doi.org/10.1007/s10901-022-09984-831Abstract32Numerous studies have found that jobs, amenities, and location influence housing prices 33in urban areas. However, there is still a lack of in-depth understanding of the impacts of 34these factors on various housing submarkets within a city. With the case study of Xiamen, 35this paper investigates the impacts of jobs, amenities, and location on four housing sub36markets, classified by owner-occupied and rental housing, in inner and outer districts. The 37hedonic model, Chow test, and Tiao-Goldberger test are applied to analyze differentiation 38in the determinants of housing prices between four submarkets. The results show that all 39submarkets are influenced by blue-collar jobs (which have negative effects) and seascape 40(which has a positive impact). Besides, differentiated after submarkets show that school 41districts and public transportation have a greater influence on owner-occupied markets 42than on rental markets. A heterogeneity exists between inner-district and outer-district 43markets. For instance, bus rapid transit (BRT) has a positive effect on housing and rental 44prices in the outer districts but not in the inner districts. These differences are mainly 45caused by the disparities of spatial quality, economic development, and public facilities 46and amenities. The findings have profound implications for decision-making and planning 47practices.48Keywords Housing prices · Submarkets · Hedonic price model · Accessibility · Owner-49occupied housing · Rental housing501 3515253===== PAGE 12 (score 168, numeric-paren 128, table-words 3, var-words 11) =====5455Y. Li et al.56Table 2 Regression results of OLS, SEM, and SLM57Owner-occupied housing58Rental housing59Inner districts60Outer districts61Inner districts62Outer districts63OLS64SEM65OLS66SEM67OLS68SEM69OLS70SLM71Area720.008*** 0.008*** 0.007*** 0.008*** 0.007*** 0.006*** 0.004***730.004***74Interior (ref: simply furnished)75Well-furnished760.044**770.047*** 0.037**780.043*** 0.131*** 0.117*** 0.121*790.104*80Other81-0.017820.018830.016840.021850.034*860.038**870.005880.00389Rough housing900.023910.065*92-0.01393-0.01694-0.13595-0.12596-0.810***97-980.760***99Total floor (ref: Medium-high building)100High-rise 101buildings1020.080*** 0.104*** 0.162*** 0.188*** 0.047**1030.0411040.0711050.068106Multistory 107building1080.042*1090.049**1100.0231110.01112-0.007113-0.0061140.097*1150.083*116Low-rise 117building118-1190.450***120-1210.308***122-0.024123-0.0751240.0411250.0591260.1681270.133128Bedroom1290.097*** 0.091*** 0.094*** 0.065*** 0.073*** 0.084*** 0.092***1300.085***131Livingroom1320.156*** 0.146*** 0.131*** 0.137*** 0.179*** 0.175*** 0.151***1330.162***134Building Age135-1360.006***137-1380.007***139-1400.007***141-1420.006***143-1440.008***145-1460.008***147-0.0004148-0.001149Culture1500.016*** 0.010*1510.021*** 0.019*** 0.0091520.0051530.044***1540.041***155Sports1560.0241570.0251580.056**1590.063*** 0.007160-0.0002161-0.034162-0.018163Hospital164-1650.017***166-0.011**167-0.01168-0.012169-0.011**170-0.011*1710.0091720.007173Shopping174-1750.008***176-1770.006***1780.007*1790.001180-0.002181-0.0021820.00021830.001184Park1850.01186-0.003187-1880.054***189-1900.056***191-0.015192-0.0161930.001194-0.001195Blue-collar 196jobs197-1980.046***199-2000.079***201-2020.109***203-2040.039***205-2060.039***207-0.081***208-0.053*209Pink-collar 210jobs2110.029**2120.0112130.0832140.169215-0.002216-0.0042170.2582180.198219White-collar 220jobs2210.0512220.107**2230.0112240.3552250.159*** 0.163*** -1.601**226-1.315**227Year 20172280.268*** 0.198*** 0.252*** 0.144*** -0.054*229-0.060*2300.0552310.052232Bus stops2330.005*** 0.0032340.009*** 0.008**235-0.0012360.0002237-0.004238-0.006239BRT240-2410.066***242-2430.097***2440.071**2450.075*246-0.026247-0.0292480.288***2490.286***250Metro (ref: No)2510.138*** 0.090*** 0.090*** 0.156*** 0.060**2520.062**253-0.103**254-0.089**255River256-0.0132570.024258-0.08259-0.117260-0.017261-0.024262Seascape2630.190*** 0.135**2640.054*** 0.070*** 0.163**2650.148**2660.141***2670.125***268School district2690.096*** 0.102*** -0.048270-0.053*271-0.0002272-0.001273-0.0052740.017275Urban villages2760.022770.028278-0.039*279-0.037*280-0.03281-0.047282-0.035283-0.031284Constant2854.317*** 4.444*** 4.000*** 4.006*** 7.182*** 7.169*** 6.746***2865.596***287R22880.792890.8372900.782910.747292Adjusted R22930.7862940.8332950.7742960.724297Log Likelihood298-215.568299286.6763009.1513014.344302AIC303624.544304489.135305-324.077306-515.35130791.64530839.69930953.97131047.311311Note: *p < 0.1; **p < 0.05; ***p < 0.013121 33131232314315316===== PAGE 13 (score 50, numeric-paren 30, table-words 2, var-words 12) =====317318The effects of jobs, amenities, and locations on housing submarkets in…3194.2 Diversities: inner district and outer district320The results show that some variables including hospital and seascape have different effects 321on inner- and outer-district markets. In the inner districts, hospitals have a negative effect 322on housing markets, since they can cause nuisance (Huang, 2017), but have no impact on 323the housing market in the outer districts. A possible explanation is that in the outer-district 324hospitals are not located in close proximity to residential areas and will have less impact on 325living conditions. Seascape positively impact housing and rental prices in all submarkets, 326but the impact was greater in the inner districts than in the outer districts. The higher impact 327on the inner city may be due to the development of yacht marinas in the inner districts and 328the development of many high-end villas around the yacht marina.329Owner-occupied 330housing VS Rental 331housing332Inner 333districts 334VS Outer 335districts336Culture3374.661***3385.281***3398340Sports3413.926***3420.1883439344Hospital3450.0773463.231***34710348Shopping34915.711***35014.613***35111352Park3532.531***35414.349***35512356Blue-collar jobs3577.240***3581.10635913360Pink-collar jobs3614.341***3620.35536314364White-collar jobs36516.828***3664.655***36715368Year 201736967.143***3707.813***37116372Bus stops3736.166***3740.10737517376BRT3771.893***37825.173***379Metro (ref: No)38013.626***38111.662***382River3830.8343840.041385Seascape3860.5693877.131***388School district38910.293***3907.954***391Urban villages3921.759**3932.707***394Constant39567.079***3967.759***397Table 4 Tiao-Goldberger test398Note: *p < 0.1; **p < 0.05; 399***p < 0.01400 401Segmentation402Physical403Chow404Owner-occupied 405housing406Inner districts with Outer 407districts4086.564 ***409Rental housing410Inner districts with Outer 411districts4126.665***413Inner districts414Owner-occupied housing 415with Rental housing4161220.639***417Outer districts418Owner-occupied housing 419with Rental housing420658.671***421Table 3 Chow test results422Note: *p < 0.1; **p < 0.05; 423***p < 0.01424 4251 34261233427428429===== PAGE 14 (score 18, numeric-paren 0, table-words 0, var-words 18) =====430431Y. Li et al.4324.3 Diversities: across all submarkets433The results show that the influence of most variables—including culture, shopping, park, 434year, white-collar jobs, BRT, metro, school district, and urban villages—is different in each 435submarket. Cultural facilities such as libraries and cultural centers have no impact on the 436inner-district rental market but have a positive impact on the other three submarkets. Con437trary to our expectations, shopping facilities have a negative impact on housing prices in the 438inner districts. This may be related to our selection of variables, in which retail and conve439nience stores are part of the shopping variable. Jang & Kang (2015) found that compared 440with shopping centers, the impact of supermarkets and convenience stores on housing prices 441is not significant. In addition, district-level parks have a negative impact on sale prices in 442the outer districts. Yang et al., (2016) found that there is a significant negative correlation 443between district-level green spaces and sale prices since the reservation value of district-444level public parks is higher than their use-value. In the outer districts, large parks tend 445to be located in areas with low population density and underdeveloped facilities, thereby 446having a negative impact on sale prices. In terms of transaction time, although housing 447prices were higher in 2017 than in 2016, rents were lower, especially in the inner districts. 448This suggests that sale prices and rental prices are inconsistent over time. White-collar jobs 449have a significant positive impact on housing sales and rental prices in the inner city, but 450do not affect sales prices in the outer districts, or even negatively affect rental prices in the 451outer districts. This negative effect may be caused by the fact that most white-collar jobs 452are mainly concentrated in the old areas of the outer districts, where the environment and 453buildings are relatively dilapidated which is lowering rental prices. BRT negatively impacts 454inner-district housing prices but positively impacts outer-district housing prices and rents. 455Regarding inner districts, public transportation facilities are already well developed, which 456leads to the lack of attractiveness of BRT. In contrast, BRT foremost enables people living in 457the outer districts to reach working places and services in the inner districts more efficiently. 458(Planned) metro stations negatively impact the outer-districts rental market, but positively 459impact the other three submarkets. This may be because, for many short-term tenants, metro 460Fig. 3 Similarity and diversity between housing submarkets461 4621 34631234464