房地产数字化转型的现状与未来:智能房地产的系统回顾

Feng Wang
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引用次数: 1

摘要

房地产行业对全球和区域经济的贡献是显著的,但在数字技术和数字经济不断发展的今天,房地产行业的数字化转型滞后于其他行业。这一方面是由于房地产上下游行业的流程和体系固化,另一方面是由于数字技术颠覆了传统的经营方式,使行业充满了不确定性。房地产行业的数字化转型是一个广泛而新兴的概念。各种相关的研究领域都关注不同的创新技术在工业中的渗透和应用。本研究在PRISMA的指导下,运用文献计量学分析方法对智能房地产领域进行了系统综述。文献计量学分析在RStudio中进行,利用在Scopus和Web of Science中检索的2012年至2022年发表的22篇科学文献。研究结果表明:(1)智能房地产研究仍然是一个新兴但迅速崛起的领域;(ii)只有少数国家的有限学术机构,如澳大利亚的新南威尔士大学,做出了重大贡献;(3)研究呈现出特定的协同网络特征,作者和被引频次高度集中;(iv)数据驱动的主题,如“机器学习”、“信息管理”、“数据分析”和“大数据”,表明研究密度和中心性很高。
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The present and future of the digital transformation of real estate: A systematic review of smart real estate
The contribution of the real estate industry to the global and regional economy is remarkable, yet in today’s evolving digital technology and digital economy, the digital transformation of the real estate industry is lagging behind other industries. This is, on the one hand, due to the solidified processes and systems linked to the upstream and downstream real estate industries, and, on the other hand, due to the fact that digital technology disrupts traditional ways of doing business, making the industry full of uncertainty. The digital transformation of the real estate industry is a broad and emerging concept. Various related research fields are concerned with the penetration and application of different innovative technologies to the industry. This study provides a systematic review focusing on the field of smart real estate using the bibliometric analysis approach under the guidance of PRISMA. The bibliometric analyses were performed in RStudio by utilizing 22 scientific documents indexed in Scopus and Web of Science that were published from 2012 to 2022. The findings suggest that: (i) smart real estate research is still a new but rapidly emerging field; (ii) only limited academic institutions from a few countries, such as the University of New South Wales in Australia, have shown significant contributions; (iii) the research exhibits specific collaborative network characteristics, leading to a high concentration of authors and citations; and (iv) data-driven topics such as “machine learning,” “information management,” “data analytics” and “big data” indicate a high degree of research density and centrality.
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