Article(id=1308453888596668824, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308453882472984927, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1672-6073.2026.03.006, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1751990400000, receivedDateStr=2025-07-09, revisedDate=1763654400000, revisedDateStr=2025-11-21, acceptedDate=null, acceptedDateStr=null, onlineDate=1789889316938, onlineDateStr=2026-09-20, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789889316938, onlineIssueDateStr=2026-09-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789889316938, creator=13701087609, updateTime=1789889316938, updator=13701087609, issue=Issue{id=1308453882472984927, tenantId=1146029695717560320, journalId=1146123302524792850, year='2026', volume='39', issue='3', pageStart='1', pageEnd='150', issueExtLink='null', onlineDate='null', pubDate='1781020800000', pubDateStr='2026-06-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1789889315479, creator='13701087609', updateTime=1789889814158, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1308455974147224139, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308453882472984927, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1308455974151418444, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308453882472984927, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=47, endPage=53, ext={EN=ArticleExt(id=1308453888831549849, articleId=1308453888596668824, tenantId=1146029695717560320, journalId=1146123302524792850, language=EN, title=The Spatially Heterogeneous Influence of Multiple Factors on Housing Prices Around Subway Stations: An MGWR-Based Empirical Analysis in Chaoyang District, Beijing, columnId=1153981622165689057, journalTitle=Urban Rapid Rail Transit, columnName=TOD, runingTitle=null, highlight=null, articleAbstract=

Based on a case study of Beijing's Chaoyang District, this research employs a multiscale geographically weighted regression (MGWR) model to systematically examine the spatial heterogeneity of housing prices around subway stations and the mechanisms through which multiple factors affect them. Using second-hand housing listing data from Lianjia in 2023, the study incorporates both internal estate characteristics (e.g., average housing age, property fees, greening rate, and plot ratio) and external location-based attributes (e.g., proximity to subway stations, bus stops, commercial centers, schools, hospitals, and parks) to construct the MGWR model, which is compared against ordinary least squares (OLS) model and the geographically weighted regression (GWR) model. The results show that the MGWR model achieves the highest goodness-of-fit (R²=0.691) and effectively captures the scale-varying effects of different factors. Distance to subway stations exhibits a significant negative correlation with housing prices, with a stronger effect in peripheral areas than in the urban core. Proximity to top-tier schools shows a heterogeneous pattern, positive in clustered areas but negative in peripheral zones, reflecting a localized saturation effect of educational resources. The influences of commercial, park, and hospital accessibility also demonstrate notable spatial variations, highlighting the complex interplay and contextual dominance of transportation, education, commerce, ecology, and medical services in the capitalization of housing prices. This study provides a multiscale analytical framework for understanding the formation mechanisms of housing prices in the context of urban rail transit, offering valuable insights for spatial governance and public service allocation.

, authors=Shiwei HU¹, Chang FU¹, Tianfu YU², Kechao CAI¹, Ziyi LI², authorsList=Shiwei HU, Chang FU, Tianfu YU, Kechao CAI, Ziyi LI, authorCompany=null, correspAuthors=Tianfu YU, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1308453889112568219, articleId=1308453888596668824, tenantId=1146029695717560320, journalId=1146123302524792850, language=CN, title=多因素对地铁站周边住宅价格的空间异质性影响——基于MGWR的北京市朝阳区实证分析, columnId=1308453888911241626, journalTitle=都市快轨交通, columnName=TOD专栏, runingTitle=null, highlight=null, articleAbstract=

为探讨地铁站周边住宅价格的空间异质性及其多因素影响机制，以北京市朝阳区为例，基于多尺度地理加权回归（multiscale geographically weighted regression, MGWR）模型进行研究。通过采集2023年链家网二手房挂牌数据，结合平均房龄、物业费用、绿化率和容积率等小区特征变量，同时纳入地铁站、公交站、商圈、学校、医院、公园等区位与邻里特征变量，构建MGWR模型，并与普通最小二乘模型（ordinary least squares, OLS）和地理加权回归（geographically weighted regression, GWR）模型进行对比。结果表明；MGWR模型拟合优度最高（R²=0.691），能够有效识别不同变量在不同空间尺度上的异质作用。地铁站距离与房价呈显著负相关，且外围区域敏感性高于中心区；重点中小学距离呈现“聚集区正向、外围负向”的异质格局，反映教育资源集聚的局部饱和效应；商圈、公园与医院距离的影响亦存在显著空间分异，体现了交通、教育、商业、生态与医疗要素在房价资本化过程中的复杂交互与区域主导性差异。本研究可为理解城市轨道交通背景下住宅价格的形成机制提供多尺度分析框架，对优化城市空间治理和公共服务配置具有参考价值。

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胡世伟，男，硕士，高级工程师，研究方向为轨道交通行业与投融资模式，hushiwei@henanric.com

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胡世伟，男，硕士，高级工程师，研究方向为轨道交通行业与投融资模式，hushiwei@henanric.com

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