Housing segregation in Chinese major cities

A K-nearest neighbor analysis of longitudinal big data

Bibliographic Data

ID7150618
AuthorsSebastian Kohl (0000-0002-8358-6021, Freie Universität Berlin), Bo Li (0000-0002-7801-0319, Freie Universität Berlin, corresponding author), Can Cui (0000-0002-5961-3958, East China Normal University)
Year2025
Volume121
Pages102326
Publication date2025-10-01
Open AccessYes
TypeARTICLE
VenueComputers Environment and Urban Systems (JOURNAL)
Journal identifiersISSN: 0198-9715 • E-ISSN: 1873-7587
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.compenvurbsys.2025.102326
OpenAlexW4411862486
LanguageEN
Citations received6
References cited45

Most studies on residential segregation in China have primarily relied on decennial population census data, which lacks the granularity and timeliness needed to capture segregation dynamics with higher frequency. Drawing on georeferenced housing market transaction data between 2012 and 2023 in Shanghai and Beijing, and employing fine-grained spatial segregation analysis techniques, including k-nearest neighbor approaches ( k −NN) and modifiable grids, we find that housing segregation by price and size increased between 2012 and 2018, followed by a decline thereafter, particularly in the larger-sized and higher-priced market segments. While segregation levels are generally comparable between the two cities, Shanghai exhibits higher segregation for the top 20 % of apartments, while Beijing shows greater segregation for the bottom 20 %. Segregation is highest for prices, followed by rents, with housing size showing the lowest segregation. Expanding the analysis to 11 major Chinese cities, we suggest that high and rising housing prices are associated with increasing segregation, particularly in cities with lower initial segregation. Methodologically, this paper demonstrates that leveraging big transaction and listing data, alongside utilizing fine-grained spatial analysis, can advance our understanding of urban inequalities. • We analyze housing segregation in Beijing and Shanghai while employing longitudinal real estate transaction data from 2012 to 2023, and include 9 additional major Chinese cities for robustness checks. • Housing segregation by price and size increased between 2012 and 2018, with a subsequent decline, especially in larger-sized and higher-priced housing segments. • Shanghai exhibits higher segregation for the top 20 % of apartments, while Beijing shows greater segregation for the bottom 20 %. • Housing price segregation is more pronounced than segregation by rent or housing size in both Shanghai and Beijing. • This paper demonstrates the use of big transaction data to provide real-time, fine-grained insights into urban housing inequalities

Big data · Cartography · Data mining · Economic geography · Geography · k-nearest neighbors algorithm · Regional science · Computer Science · Housing Market and Economics · Urban, Neighborhood, and Segregation Studies · Urbanization and City Planning · Artificial Intelligence

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