Policy change, amenity, and spatiotemporal dynamics of housing prices in Nanjing, China

Yuan, Feng; Wu, Jiawei; Wei, Yehua Dennis; Wang, Lei

doi:10.1016/j.landusepol.2018.03.045

Cited by 75 publications

(44 citation statements)

References 55 publications

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“…Inequality in the housing market and residential segregation may limit the posssiblity of upward mobility (Ewing et al, 2016). Housing inequality has also risen in Chinese cities, and service amenities, especially education and subways, have become even more important factors affecting housing prices than in cities in the developed countries Yuan, Wu, Wei, & Wang, 2018).…”

Section: Intra-urban Dynamics Of Inequality: Housingmentioning

confidence: 99%

Urban inequalities in the 21st century economy

2020

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Section: Intra-urban Dynamics Of Inequality: Housingmentioning

confidence: 99%

Urban inequalities in the 21st century economy

2020

Self Cite

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“…Finally, Yuan et al [21] performed both an OLS model and a GWR model to study how a set of variables (including structure, neighborhood, accessibility, and amenity variables) influences the spatiotemporal dynamics of housing prices in Nanjing. The results showed that the presence of high-quality schools provided by governments, proximity to parks, the presence of (sub) Central Business Districts (CBDs) and government service centers, and the locations of new cities/towns are strongly associated with housing prices.…”

Section: Urban Vibrancymentioning

confidence: 99%

Urban Vibrancy: An Emerging Factor that Spatially Influences the Real Estate Market

2020

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Urban vibrancy is defined and measured differently in the literature. Originally, it was described as the number of people in and around streets or neighborhoods. Now, it is commonly associated with activity intensity, the diversity of land-use configurations, and the accessibility of a place. The aim of this paper is to study urban vibrancy, its relationship with neighborhood services, and the real estate market. Firstly, it is used a set of neighborhood service variables, and a Principal Component Analysis is performed in order to create a Neighborhood Services Index (NeSI) that is able to identify the most and least vibrant urban areas of a city. Secondly, the influence of urban vibrancy on the listing prices of existing housing is analyzed by performing spatial analyses. To achieve this, the presence of spatial autocorrelation is investigated and spatial clusters are identified. Therefore, spatial autoregressive models are applied to manage spatial effects and to identify the variables that significantly influence the process of housing price determination. The results confirm that housing prices are spatially autocorrelated and highlight that housing prices and NeSI are statistically associated with each other. The identification of the urban areas characterized by different levels of vibrancy and housing prices can effectively support the revision of the urban development plan and its regulatory act, as well as strategic urban policies and actions. Such data analyses support a deep knowledge of the current status quo, which is necessary to drive important changes to develop more efficient, sustainable, and competitive cities.

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“…The key role for conservation is deeply related to ''if you built, you will have to maintain it''. For achieving sustainable urbanization, it is critical to understand the spatiotemporal heterogeneity of housing market and formulating land/housing policies (Yuan et al 2018).…”

Section: Review Literaturementioning

confidence: 99%

The impact of neighborhood services on land values: an estimation through the hedonic pricing model

2020

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A strong need exists to increase the knowledge and recognize the values of neighborhood services. This paper makes an attempt to examine the impact of neighborhood services on land values, through analysis based on results obtained from multi linear regression analysis. This case study was done in area of dense urban settlement to check the consequence of neighborhood services through the hedonic pricing model base variables which include the structural, locational, community and neighborhood services. The regression coefficient was checked at (p \ 0.05) level of significance for each variable. The primary data was collect through the questionnaire filling by random sampling and Punjab Urban Gazette was used for the verification of land values in study area. The results found a valuable potential on land and property values of neighborhood services through the regression analysis results express through R (0.926), R 2 (0.856) and coefficient tables to represent the effect of each individual variable on property and land values. Every individual variable play role in defining the values of land and property based upon its utilization such as larger the structure of a property, more the covered area, larger size of a property more the land area higher price. Similarly, for locational factor study in geography, a piece of land adjacent to the road have high land values 50-70% compare to those located at distance. Very less attention has been paid on such issues in the world due to lack of effective urban planning and research on such crucial issues. If empirical studies on such issue has been done, effective way can be obtained for urban planning.

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Policy change, amenity, and spatiotemporal dynamics of housing prices in Nanjing, China

Cited by 75 publications

References 55 publications

Urban inequalities in the 21st century economy

Urban inequalities in the 21st century economy

Urban Vibrancy: An Emerging Factor that Spatially Influences the Real Estate Market

The impact of neighborhood services on land values: an estimation through the hedonic pricing model

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