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<p>Since urban construction shifts from incremental development to stock redevelopment, shantytown redevelopment has gradually become an important driver of urban revitalization. This paper evaluates the heterogeneous spillover effects of shantytown redevelopment on neighborhood housing prices utilizing difference-in-differences model and quantile regression model. Using polygon data from 38 shantytown redevelopment projects in Beijing between 2014 and 2020, this paper explores both positive mechanisms (environmental improvement, infrastructure enhancement, signal transmission, and economic agglomeration) and negative mechanisms (supply competition and construction disruption) that impact neighborhood housing prices, by distinguishing three shantytown types and tracking complete lifecycle from announcement to completion. The results show that: (1) Shantytown redevelopment indeed increases neighborhood housing price. (2) Redevelopment of urban villages, collective dormitories, and self-constructed dwellings increase neighborhood housing prices by an average of 44.09%, 25.79%, and 24.44%, respectively. (3) Shantytown development increases neighborhood housing prices by an average of 34.99% during the announcement period and 28.04% during the completion period. (4) Spillover effect is more significant for low-priced housing compared to medium- and high-priced housing, displaying temporal persistence. Spillover effect in core areas is higher than in suburban areas; areas with more convenient transportation, a higher density of leisure services and shops, experience larger spillover effects. Refurbishment-type generate larger spillover effects than rebuilding-type. These findings provide critical guidance for policymakers to optimize redevelopment sequencing, formulate different strategies targeting distinct price segments and spatial locations, calibrate development intensity between core and suburban areas, select appropriate redevelopment approaches for different shantytown types, and coordinate supporting facilities.</p>

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Heterogeneous spillover effects of shantytown redevelopment on neighborhood housing prices: evidence from Beijing

  • 期刊论文

  • 发表日期：

    2026年2月19日

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  • Chao Zhang,
  • Hong-li He,
  • Jia-lin Xi,
  • Zong-min Lan

摘要

Since urban construction shifts from incremental development to stock redevelopment, shantytown redevelopment has gradually become an important driver of urban revitalization. This paper evaluates the heterogeneous spillover effects of shantytown redevelopment on neighborhood housing prices utilizing difference-in-differences model and quantile regression model. Using polygon data from 38 shantytown redevelopment projects in Beijing between 2014 and 2020, this paper explores both positive mechanisms (environmental improvement, infrastructure enhancement, signal transmission, and economic agglomeration) and negative mechanisms (supply competition and construction disruption) that impact neighborhood housing prices, by distinguishing three shantytown types and tracking complete lifecycle from announcement to completion. The results show that: (1) Shantytown redevelopment indeed increases neighborhood housing price. (2) Redevelopment of urban villages, collective dormitories, and self-constructed dwellings increase neighborhood housing prices by an average of 44.09%, 25.79%, and 24.44%, respectively. (3) Shantytown development increases neighborhood housing prices by an average of 34.99% during the announcement period and 28.04% during the completion period. (4) Spillover effect is more significant for low-priced housing compared to medium- and high-priced housing, displaying temporal persistence. Spillover effect in core areas is higher than in suburban areas; areas with more convenient transportation, a higher density of leisure services and shops, experience larger spillover effects. Refurbishment-type generate larger spillover effects than rebuilding-type. These findings provide critical guidance for policymakers to optimize redevelopment sequencing, formulate different strategies targeting distinct price segments and spatial locations, calibrate development intensity between core and suburban areas, select appropriate redevelopment approaches for different shantytown types, and coordinate supporting facilities.