Identifying the Spatial Heterogeneity in the Effects of the Social Environment on Housing Rents in Guangzhou, China

Wang, Yang; Wu, Kegong; Jin, Lixia; Huang, Guo‐Shing; Zhang, Yuling; Su, Yongxian; Zhang, Hongou; Qin, Jing

doi:10.1007/s12061-021-09383-6

Cited by 9 publications

(4 citation statements)

References 65 publications

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“…Thus, how determining the size of the adaptive bandwidth in the kernel function to obtain the optimal bandwidth is one of the key issues in determining the effectiveness of MGWR. We use the corrected Akaike information criterion (AICc), and its expression is shown in Equation (4) [ 73 , 74 ]:

where σ denotes the maximum likelihood estimate of the variance of the random error, and tr( S ) is the trace of matrix S .…”

Section: Methodsmentioning

confidence: 99%

Identifying the spatial heterogeneity of housing financialization in China: Insights from a multiscale geographically weighted regression

Wang,

Yue,

Wang

et al. 2024

Heliyon

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where σ denotes the maximum likelihood estimate of the variance of the random error, and tr( S ) is the trace of matrix S .…”

Section: Methodsmentioning

confidence: 99%

Identifying the spatial heterogeneity of housing financialization in China: Insights from a multiscale geographically weighted regression

Wang,

Yue,

Wang

et al. 2024

Heliyon

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“…This paper takes as its study area the Guangzhou Metropolitan Area, which is located within the Guangzhou Ring Expressway-Guangzhou-Gaoming Expressway-Guangzhou administrative boundary [5] . The residential housing quarters' sales data from the Net Ease data center in Guangzhou is used to calculate the housing vacancy rate.…”

Section: Methods and Data 21 Study Area And Datamentioning

confidence: 99%

Influencing Factors of Housing Sales Vacancy in the Guangzhou Metropolitan Area, China

Yue

Wang

Zhang

2021

JARD

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In this paper, nine indicators selected from three perspectives (convenience, environmental and location characteristics) and three regression models (OLS, SLM and SEM) are used to explore the influencing factors of housing sales vacancy in the Guangzhou Metropolitan Area, China. The results show that subway accessibility, peripheral aversion municipal facilities and distance from the CBD (Central Business District) are consistent with theoretical expectations. Subway accessibility is negatively correlated with the housing sales vacancy rates, while peripheral aversion municipal facilities and distance from the CBD are positively correlated with housing vacancy rates.

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“…The Marxist, structuralist school and its social space unity theory hold that the social environment affects individual choice, which is an important dimension in the process of living decision-making, and individual choice also affects social space [23,24]. For areas with poor social environment, residents mostly adopt an evasive attitude [25]. To some extent, residential location choice can be regarded as the residents' choice of their social environment [26,27].…”

Section: Introductionmentioning

confidence: 99%

“…According to the data of population sample survey in 2015, it is estimated that the floating population in Guangzhou accounts for 42.44% of its population. Guangzhou's physical environment [13], social environment [25,[30][31][32] and life convenience [33] are complex and diverse and are very suitable for case studies.…”

Section: Introductionmentioning

confidence: 99%

Relationship between Urban Floating Population Distribution and Livability Environment: Evidence from Guangzhou’s Urban District, China

et al. 2021

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The livability environment is an important aspect of urban sustainable development. The floating population refers to people without local hukou (also called ‘non-hukou migrants’). The floating population distribution is influenced by livability environment, but few studies have investigated this relationship. Especially, the influence of social environment on floating population distribution is rarely studied. Therefore, we study 1054 communities in Guangzhou’s urban district to explore the relationship between livability environment and floating population distribution. The purpose of this article is to study how livability environment affects floating population distribution. We develop a conceptual framework of livability environment, which consists of physical environment, social environment and life convenience. A cross-sectional dataset of the impact of livability environment on the floating population distribution is developed covering the proportion of floating population in the community as the dependent variable, eight factors of livability environment as the explanatory variables, and two factors of architectural characteristics and one factor of location characteristics as the control variables. We use spatial regression models to explore the degree of influence and direction of physical environment, social environment and life convenience on the floating population distribution in livability environment. The results show that the spatial error model is more effective than ordinary least squares and spatial lag model models. The five factors of the livability environment have statistical significance regarding floating population distribution, including four social environment factors (proportion of middle- and high-class occupation population, proportion of highly educated people in the population, proportion of rental households, and unemployment rate) and regarding life convenience factors (work and shopping convenience). The conclusion has value for understanding how the social environment affects the residential choice of the floating population. This study will help city administrators reasonably guide the residential pattern of the floating population and formulate reasonable management policies, thereby improving the city’s livability, attractiveness and sustainable development.

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Identifying the Spatial Heterogeneity in the Effects of the Social Environment on Housing Rents in Guangzhou, China

Cited by 9 publications

References 65 publications

Identifying the spatial heterogeneity of housing financialization in China: Insights from a multiscale geographically weighted regression

Identifying the spatial heterogeneity of housing financialization in China: Insights from a multiscale geographically weighted regression

Influencing Factors of Housing Sales Vacancy in the Guangzhou Metropolitan Area, China

Relationship between Urban Floating Population Distribution and Livability Environment: Evidence from Guangzhou’s Urban District, China

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