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  • 华中科技大学

沈淑琳 副教授

沈淑琳,女,华中科技大学经济学院副教授。美国雪城大学经济学博士,研究方向为金融计量,房地产金融,城市与区域经济学。近年来主持国家自然科学(青年)项目,华中科技大学自主创新研究基金(人文社科)项目等。研究成果发表于Journal of Applied Econometrics,Canadian Journal of Economics, Journal of Economic Behavior & Organization,Journal of Real Estate Finance and Economics, Journal of Population Economics, Pacific-Basin Finance Journal, Resource and Energy Economics, Travel Beviour and Society等国际权威期刊。

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Local Housing Market Sentiments and Returns: Evidence from China

发布时间:2024-03-01 点击次数:

  • 论文类型:期刊论文
  • 第一作者:沈淑琳
  • 通讯作者:逄金栋
  • 合写作者:赵诣宜
  • 发表刊物:The Journal of Real Estate Finance and Economics
  • 收录刊物:SSCI
  • 学科门类:经济学
  • 一级学科:应用经济学
  • 文献类型:J
  • 期号:68
  • 页面范围:488-522
  • ISSN号:0895-5638
  • 关键字:Housing market · Sentiment · Market liquidity · Speculation · PLS
  • DOI码:10.1007/s11146-022-09933-w
  • 发表时间:2024-05-01
  • 摘要:This paper examines the impacts of local housing sentiments on the housing price dynamics of China. With a massive second-hand transaction dataset, we construct monthly local housing sentiment indices for 18 major cities in China from Janu ary 2016 to October 2020. We create three sentiment proxies representing the local housing market liquidity and speculative behaviors from the transaction dataset and then use partial least squares (PLS) to extract a recursive look-ahead-bias-free local housing sentiment index for each city considered. The local housing sentiments are shown to have robust predictive powers for future housing returns with a salient short-run underreaction and long-run overreaction pattern. Further analysis shows that local housing sentiment impacts are asymmetric, and housing returns in cities with relatively inelastic housing supply are more sensitive to local housing sentiments. We also document a significant feedback effect between housing returns and market sentiments, indicating the existence of a pricing-sentiment spiral which could potentially enhance the ongoing market fever of Chinese housing markets. The main estimation results are robust to alternative sentiment extraction methods and alternative sentiment proxies, and consistent for the sample period before COVID-19.