The influence of Beijing rail transfer stations on surrounding housing prices

Xue-zhen, Dai; Bai, Xiaoqing; Xu, Min

doi:10.1016/j.habitatint.2016.02.008

Cited by 69 publications

(49 citation statements)

References 15 publications

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“…First, we did not consider the service capacity of amenities and variations of distance decay in the accessibility assessment; and the spatial separation to amenities can be network-based [62,73] or follows a power law [12], thus mediating the correlation [13]. Second, in the future, researchers should focus on communities located in a smaller urban sector rather than an entire city to minimize the impact of geographical heterogeneity.…”

Section: Discussionmentioning

confidence: 99%

“…In other words, housing units with relative proximity to urban centers, amenities, and transportation hubs tend to have considerably higher market value. For example, in a recent study on the effect of the proximity to Beijing railway stations on the values of surrounding properties, it was found that while both non-transfer and transfer stations positively affect the price, the impact of transfer stations nearby was more significant [13]. The vital role that public transit plays can also be extended to suburban regions, where the housing units located within the transit-oriented development zones are priced much higher than the units further away [29].…”

Section: Housing Development and Data Issues In Chinamentioning

confidence: 99%

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Exploring Determinants of Housing Prices in Beijing: An Enhanced Hedonic Regression with Open Access POI Data

Ying

Chen

et al. 2017

IJGI

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Abstract:The housing market in Chinese metropolises have become inflated significantly over the last decade. In addition to an economic upturn and housing policies that have potentially fueled the real estate bubble, factors that have contributed to the spatial heterogeneity of housing prices can be dictated by the amenity value in the proximity of communities, such as accessibility to business centers and transportation hubs. In the past, scholars have employed the hedonic pricing model to quantify the amenity value in relation to structural, locational, and environmental variables. These studies, however, are limited by two methodological obstacles that are relatively difficult to overcome. The first pertains to difficulty of data collection in regions where geospatial datasets are strictly controlled and limited. The second refers to the spatial autocorrelation effect inherent in the hedonic analysis. Using Beijing, China as a case study, we addressed these two issues by (1) collecting residential housing and urban amenity data in terms of Points of Interest (POIs) through web-crawling on open access platforms; and (2) eliminating the spatial autocorrelation effect using the Eigenvector Spatial Filtering (ESF) method. The results showed that the effects of nearby amenities on housing prices are mixed. In other words, while proximity to certain amenities, such as convenient parking, was positively correlated with housing prices, other amenity variables, such as supermarkets, showed negative correlations. This mixed finding is further discussed in relation to community planning strategies in Beijing. This paper provides an example of employing open access datasets to analyze the determinants of housing prices. Results derived from the model can offer insights into the reasons for housing segmentation in Chinese cities, eventually helping to formulate effective urban planning strategies and equitable housing policies.

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Section: Discussionmentioning

confidence: 99%

Section: Housing Development and Data Issues In Chinamentioning

confidence: 99%

Exploring Determinants of Housing Prices in Beijing: An Enhanced Hedonic Regression with Open Access POI Data

Ying

Chen

et al. 2017

IJGI

View full text Add to dashboard Cite

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“…Although extensive studies have been done to exam the effect of the subway system on the housing price, there still exist some shortages. First, most previous discussions were "context-free" in the sense that they only looked at one single city, especially mega-cities, such as Beijing, Shanghai, and Shenzhen [6,[11][12][13], and the housing data were collected within a neighborhood of one or a couple of subways lines. The "context-free" analysis did not pay enough attention to the complexity of the relationship between the subway system and the housing market and ignored the potential effect of many important hidden variables, such as the urban structure.…”

Section: Introductionmentioning

confidence: 99%

“…Although exceptions exist, such as [14,15], it is typical that only a simple mono-centric city with a prescribed CBD is considered, which is too simple and may not be realistic. Second, except for a few papers [1,[14][15][16], previous studies focused mainly on accessibility to metro stations, which is measured by the distance to metro stations and/or the dummy variables derived from distance, such as whether there is a metro station within the range of one mile [13,[17][18][19][20]. These studies ignore the heterogeneity among different metro stations and might over-estimate the positive effect of the subway system on housing prices.…”

Section: Introductionmentioning

confidence: 99%

“…This new finding also implies the importance of including both intra-urban and inter-urban housing data simultaneously. In most previous studies, either the city-level aggregated data across multiple cities or intra-city micro data within on single city were used to analyze the housing market [13,21,22]; they are rarely combined together as this paper does. This paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

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Urban Structure, Subway Systemand Housing Price: Evidence from Beijing and Hangzhou, China

et al. 2019

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Using housing market data of Beijing and Hangzhou, China, we conduct a case study to detect how the difference of urban structure can affect the relationship between the subway system and housing prices. To quantify the characteristics of urban structure, we propose a constrained clustering method, which can not only reveal the spatial heterogeneity of the housing market, but also provides a link between heterogeneity and the underlying urban structure. Applying constrained clustering to Beijing and Hangzhou, we find that the relationship between accessibility to metro stations and housing prices is weak and vulnerable, while the improvement of commuting efficiency, measured by a key variable, the metro index, does have a robust connection to metro premium on housing units. In particular, only a large metro index can be associated with a positive metro premium. Structural features, such as the size of urban core and the existence of multiple sub-centers, influence the metro premium by affecting the value and spatial distribution of the metro index. The evidence from Beijing and Hangzhou supports that in a mono-centric city, the size of the urban core is positively associated with the metro index and the metro premium, while in a poly-centric city with a small urban core, the metro index tends to be lower in the core region and higher in the satellite regions, which enforces the metro premium to be negative in the core while positive outside of the core.

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Place-Varying Impact of Metro Accessibility on Property Prices

Yang

2021

Property Price Impacts of Environment-Friendly Transport Accessibility in Chinese Cities

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The influence of Beijing rail transfer stations on surrounding housing prices

Cited by 69 publications

References 15 publications

Exploring Determinants of Housing Prices in Beijing: An Enhanced Hedonic Regression with Open Access POI Data

Exploring Determinants of Housing Prices in Beijing: An Enhanced Hedonic Regression with Open Access POI Data

Urban Structure, Subway Systemand Housing Price: Evidence from Beijing and Hangzhou, China

Place-Varying Impact of Metro Accessibility on Property Prices

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