Nonlinear Influence of Public Services on Urban Housing Prices: A Case Study of China

Gan, Lei; Hong, Ren; Wei-min, Xiang; Wu, Kun‐Shan; Cai, Weiguang

doi:10.3390/land10101007

Cited by 12 publications

(4 citation statements)

References 37 publications

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“…Therefore, this study analyzed the influence of the digital economy on urban housing prices from a macro perspective, aiming to contribute innovative and distinctive insights. Moreover, while previous studies have explored the non-linear relationship between various factors, including investment demand (Chen et al, 2012), public services (Gan et al, 2021), and school quality (Mathur, 2022), and urban housing prices, there is no known research on the potential non-linear relationship between the digital economy and housing prices. To address this gap in the literature, this study considered the non-linear influence of digital economy growth on urban housing prices.…”

Section: Literature Reviewmentioning

confidence: 99%

The Impact of the Digital Economy on Urban House Prices: Comprehensive Explorations

Cong,

Chin,

Muhamad Senan

et al. 2024

International Journal of Strategic Property Management

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Internet technology and data-driven innovations are changing the way people live and work, which could have an impact on real estate supply and demand. Therefore, this study focuses on the relationship between urban digital economy growth and urban house prices. First, the empirical model shows that the digital economy has an inverted U-shaped impact on urban housing prices. The mechanisms underlying this relationship were then examined. The results indicated that fixed asset investment, government expenditure, and the urban environment influence the relationship between digital economy growth and urban house prices. Finally, a heterogeneity analysis revealed that the digital economy does not affect house prices in Tier 1, New Tier 1, and Tier 2 Chinese cities, but increases house prices in Tier 3 cities. In Tier 4 and Tier 5 cities, this relationship was found to be inverted U-shaped. These findings offer valuable insights to policymakers in China in balancing the growth of the digital economy and the stability of house prices.

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Section: Literature Reviewmentioning

confidence: 99%

The Impact of the Digital Economy on Urban House Prices: Comprehensive Explorations

Cong,

Chin,

Muhamad Senan

et al. 2024

International Journal of Strategic Property Management

View full text Add to dashboard Cite

show abstract

“…The differences in the surrounding environment, supporting resources, and neighborhood effects of the residential community are the reflections of the residential space differentiation and, in turn, play a role in the residential space differentiation. This research constructs a residential space indicator system containing nine indicators of the center, transportation, landscape, employment, commerce, medical care, leisure, housing price, and educational resources from four levels of location characteristics, supporting services, economic attributes, and school districts [11,35] (Table 2). After calculating and counting the values of corresponding indicators of each block unit, K-means clustering method is used to divide the residential space in the research area.…”

Section: K-means Residential Space Classification Based On Indicator ...mentioning

confidence: 99%

Heterogeneity, Differentiation Mechanisms and Social Effects of Urban Residential Space in China’s Large Cities: A Case Study of Wuhan

Cai,

Shen

2024

Land

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Different choices of living space for urban residents are concrete reflections of a social class divide. Emerging residential space differentiation is a critical issue in Chinese cities. This paper aims to explore the current situations and mechanisms of residential spatial differentiation, and reveal its social responses in Chinese cities. Taking Wuhan, the largest city in central China, as an example, this research divides the residential space based on social resource structures. It analyzes and compares spatial differentiation and influencing factors of different residential areas and houses with different prices in Wuhan by spatial differentiation indicators and geographic detector. The results show that residential areas are divergent due to differences in urban resource spatial structure, with few and concentrated high-quality residential areas. The spatial differentiation level of different residential areas also varies, with central location, landscape, educational resources, and other dominant scarce resources being the key to residential spatial differentiation in most of China’s big cities. In addition, residential spatial differentiation can lead to issues including housing wealth inequality, resource deprivation, and class identity solidification. In the end, this paper puts forward policy implications on alleviating residential spatial differentiation and optimizing allocation of residential resources.

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“…On the supply side, factors such as land, real estate investment, mortgage interest rates, and credit scale have always been main variables in studies on real estate prices [1][2][3]. On the demand side, factors such as demographic structure, migration, income, and industrial structure also have a significant impact on housing prices [4][5][6]. Additionally, real estate market speculation, government regulatory policies, and infrastructure service quality are key factors affecting the vitality of the urban real estate market [7,8].…”

Section: Introductionmentioning

confidence: 99%

Analysis on the Spatial Effect of Infrastructure Development on the Real Estate Price in the Yangtze River Delta

et al. 2022

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This study explores the spatial effect of infrastructure development on real estate prices in the Yangtze River Delta. It constructs an evaluation system of the infrastructure development level across five dimensions (i.e., transportation, water supply and drainage, energy and power, postal communication, and ecological environment), analyzes the development characteristics of urban infrastructure in the Yangtze River Delta, and uses a spatial panel model to explore how urban infrastructure development affects real estate prices. Results indicate that (1) the overall development level of urban infrastructure in the Yangtze River Delta region shows an upward trend. Significant regional differences exist as the development level of urban infrastructure in the eastern region is ahead of that in the central region; (2) Spatial autocorrelation and real estate prices in the Yangtze River Delta region in infrastructure development and overall levels, respectively, are high; (3) Infrastructure directly affects local real estate market demand and improves the vitality of the housing market in adjacent areas; and (4) Infrastructure construction can significantly promote the rise of urban real estate prices in the eastern region, while this driving effect is not significant in the central region. This research will help the government promote the coordinated development of urban infrastructure and formulate relevant policies for the macro-control of the real estate market in urban agglomerations.

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Nonlinear Influence of Public Services on Urban Housing Prices: A Case Study of China

Cited by 12 publications

References 37 publications

The Impact of the Digital Economy on Urban House Prices: Comprehensive Explorations

The Impact of the Digital Economy on Urban House Prices: Comprehensive Explorations

Heterogeneity, Differentiation Mechanisms and Social Effects of Urban Residential Space in China’s Large Cities: A Case Study of Wuhan

Analysis on the Spatial Effect of Infrastructure Development on the Real Estate Price in the Yangtze River Delta

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