This study aims to contribute to the relevant body of knowledge by examining the bibliometric studies related to big data and real estate indexed in the Scopus and Web of Science (WoS) database from a bibliometric perspective.
This study uses bibliometric analysis, collecting 1,692 big data in real estate bibliometric papers from the Scopus and Web of Science Core Collection from 1990s to 2024. The authors generated publication trend, keyword analysis, institution and countries analysis, and conclusions for journal development using ScientoPy and VOSviewer.
The analysis reveals significant growth in publications from 1990s to 2024, with China emerging as the global leader in research output. Machine Learning dominates recent research (43% of publications), followed by artificial intelligence and data analytics. Major companies worldwide, including Vanke, Fantasia Group (China), and Zillow, Redfin (US), are actively implementing big data solutions. The research landscape shows an increasingly interdisciplinary approach, combining data science, urban planning, economics, and investment.
This pioneering bibliometric analysis of big data and real estate papers aims to offer insights and projections for future research in the field. This research contributes to the literature by examining various aspects, including evaluating literature on trending topics, analyzing papers related to research areas and conducting content analysis of existing bibliometric studies in big data and real estate. It specifically groups these studies around fundamental topics, summarizes findings from contemporary research and identifies emerging research gaps.
