What does this research mean for the field?

Social infrastructures, particularly public schools, are more decisively and consistently associated with housing prices than physical infrastructures, driving increasingly intensified intra-urban housing price disparities. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

Investigate intra-urban spatiotemporal disparities in housing price dynamics and evaluate how infrastructure capitalization shapes urban housing inequalities over time.

September 5, 2026Land Use PolicyOpen Access

Unravelling intra-urban spatiotemporal disparities in housing price dynamics: An infrastructural approach using interpretable machine-learning methods in Shenzhen, China

View Full Paper

Ask AI

Bookmark

Authors

LHLirong Hu SSShiliang Su SHShenjing He

Discussion

Member takes

Overview

Machine-learning analysis reveals widening housing price disparities linked to public school access in urban neighborhoods, highlighting how social infrastructure drives spatial inequality.

Key Points

Investigate intra-urban spatiotemporal disparities in housing price dynamics and evaluate how infrastructure capitalization shapes urban housing inequalities over time.
Analyzed 288,570 second-hand housing transactions linked with multi-source geographic data from 2010 to 2023 in Shenzhen, China.
Implemented an infrastructural approach using interpretable machine-learning methods to model spatiotemporal heterogeneity and capitalization effects.
Identified increasingly intensified housing price disparities that are especially pronounced in school catchment areas and 15-minute infrastructure-accessible neighborhoods.
Found that social infrastructures, notably public schools, are more decisively and consistently associated with housing prices than physical infrastructures, structural features, or locational attributes.
Demonstrated marked spatiotemporal heterogeneity and increasingly salient threshold effects in how social infrastructures capitalize into real estate values.

Cite This Study

Hu et al. (2026) studied this question.

synapsesocial.com/papers/6a9bd4126b95aff0620eb649 https://doi.org/10.1016/j.landusepol.2026.108314

View Full Paper

Ask AI

Bookmark

September 5, 2026Land Use PolicyOpen Access

Unravelling intra-urban spatiotemporal disparities in housing price dynamics: An infrastructural approach using interpretable machine-learning methods in Shenzhen, China

View Full Paper

Ask AI

Bookmark

Authors

LHLirong Hu SSShiliang Su SHShenjing He

Discussion

Member takes

Overview

Machine-learning analysis reveals widening housing price disparities linked to public school access in urban neighborhoods, highlighting how social infrastructure drives spatial inequality.

Key Points

Investigate intra-urban spatiotemporal disparities in housing price dynamics and evaluate how infrastructure capitalization shapes urban housing inequalities over time.
Analyzed 288,570 second-hand housing transactions linked with multi-source geographic data from 2010 to 2023 in Shenzhen, China.
Implemented an infrastructural approach using interpretable machine-learning methods to model spatiotemporal heterogeneity and capitalization effects.
Identified increasingly intensified housing price disparities that are especially pronounced in school catchment areas and 15-minute infrastructure-accessible neighborhoods.
Found that social infrastructures, notably public schools, are more decisively and consistently associated with housing prices than physical infrastructures, structural features, or locational attributes.
Demonstrated marked spatiotemporal heterogeneity and increasingly salient threshold effects in how social infrastructures capitalize into real estate values.

Cite This Study

Hu et al. (2026) studied this question.

synapsesocial.com/papers/6a9bd4126b95aff0620eb649 https://doi.org/10.1016/j.landusepol.2026.108314

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

View Full Paper

Ask AI

Bookmark