{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T17:05:45Z","timestamp":1779210345033,"version":"3.51.4"},"reference-count":53,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T00:00:00Z","timestamp":1778284800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Youth Project of the NSFC: Financial risk early warning based on high-dimensional supervised network","doi-asserted-by":"publisher","award":["12105178"],"award-info":[{"award-number":["12105178"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Mapping directed spillover pathways in urban housing prices is essential for monitoring the contagion of housing prices across cities. However, existing studies typically rely on either spatial gravity models or time-series models in isolation to analyze intercity connections, thus failing to simultaneously capture the spatiotemporal integration characteristics of housing price contagion. To address this, we embed a finite-length sequence correlation analysis (Correlation-Dependent Balanced Estimation of Diffusion Transfer Entropy, CBEDTE) into the gravity model, yielding the CBEDTE-GM integrated model. Using housing price data from 296 Chinese cities, we construct a spatiotemporal correlation matrix and employ the directed minimum spanning tree algorithm to extract core directed spillover pathways. Results reveal that China\u2019s urban housing price spillover network exhibits a hierarchical architecture with pronounced ripple effects, where eastern coastal cities and the national core city serve as dominant radiation hubs. The East China sub-network occupies a distinctive net spillover position. We identify heterogeneous structural evolution patterns across regional sub-networks: (1) North China evolved from a dispersed multi-centered configuration to a Beijing-dominated single-core structure; (2) East China developed a robust multi-centered architecture anchored by Shanghai; and (3) South China transitioned from a Guangzhou-centered single-core pattern to a tri-polar configuration co-driven by Guangzhou, Shenzhen, and Nanning.<\/jats:p>","DOI":"10.3390\/e28050537","type":"journal-article","created":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T16:07:26Z","timestamp":1779206846000},"page":"537","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Finite-Length Spatiotemporal Modelling for Housing Price Network Spillovers"],"prefix":"10.3390","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8630-4221","authenticated-orcid":false,"given":"Lu","family":"Qiu","sequence":"first","affiliation":[{"name":"School of Finance and Business, Shanghai Normal University, Shanghai 200234, China"},{"name":"Department of Finance, East China University of Science and Technology, Shanghai 200237, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5008-8794","authenticated-orcid":false,"given":"Yanzhe","family":"Jiao","sequence":"additional","affiliation":[{"name":"School of Finance and Business, Shanghai Normal University, Shanghai 200234, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gege","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Finance and Business, Shanghai Normal University, Shanghai 200234, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangcan","family":"Cui","sequence":"additional","affiliation":[{"name":"School of Finance and Business, Shanghai Normal University, Shanghai 200234, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cities.2017.01.006","article-title":"How does housing price affect consumption in China: Wealth effect or substitution effect?","volume":"64","author":"Dong","year":"2017","journal-title":"Cities"},{"key":"ref_2","first-page":"19","article-title":"The Impact of Real Estate Price Fluctuations on Systemic Financial Risk: Based on Nonlinear Effects and Spatial Spillover Effects","volume":"39","author":"Ba","year":"2025","journal-title":"Econ. 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