Big data approach as an institutional innovation to tackle Hong Kong's illegal subdivided unit problem

Yung YAU*, Wai Kin LAU

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

10 Citations (Scopus)


While applications of big data have been extensively studied, discussion is mostly made from the perspectives of computer science, Internet services, and informatics. Alternatively, this article takes the big data approach as an institutional innovation and uses the problem of illegal subdivided units (ISUs) in Hong Kong as a case study. High transaction costs incurred in identification of suspected ISUs and associated enforcement actions lead to a proliferation of ISUs in the city. We posit that the deployment of big data analytics can lower these transaction costs, enabling the government to tackle the problem of illegal accommodations. We propose a framework for big data collection, analysis, and feedback. As the findings of a structured questionnaire survey reveal, building professionals believed that the proposed framework could reduce transaction costs of ISU identification. Yet, concerns associated with the big data approach like privacy and predictive policing were also raised by the professionals.

Original languageEnglish
Article number2709
Number of pages17
JournalSustainability (Switzerland)
Issue number8
Publication statusPublished - 1 Aug 2018
Externally publishedYes

Bibliographical note

The work described in this article was fully supported by a grant under the Public Policy Research Funding Scheme administered by the Central Policy Unit of the Hong Kong Special Administrative Region, China (project no. 2014.A1.019.15B).

Publisher Copyright:
© 2018 by the authors.


  • Big data
  • Building stock management
  • Hong Kong
  • Housing problem
  • Illegal accommodation
  • Institutional innovation
  • Transaction costs


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