The big data regime shift in real estate

James R. DeLisle, Brent Never and Terry V. Grissom

Journal of Property Investment & Finance, 2020, vol. 38, issue 4, 363-395

Abstract: Purpose - The paper explores the emergence of the “big data regime” and the disruption that it is causing for the real estate industry. The paper defines big data and illustrates how an inductive, big data approach can help improve decision-making. Design/methodology/approach - The paper demonstrates how big data can support inductive reasoning that can lead to enhanced real estate decisions. To help readers understand the dynamics and drivers of the big data regime shift, an extensive list of hyperlinks is included. Findings - The paper concludes that it is possible to blend traditional and non-traditional data into a unified data environment to support enhanced decision-making. Through the application of design thinking, the paper illustrates how socially responsible development can be targeted to under-served urban areas and helps serve residents and the communities in which they live. Research limitations/implications - The paper demonstrates how big data can be harnessed to support decision-making using a hypothetical project. The paper does not present advanced analytics but focuses aggregating disparate longitudinal data that could support such analysis in future research. Practical implications - The paper focuses on the US market, but the methodology can be extended to other markets where big data is increasingly available. Social implications - The paper illustrates how big data analytics can be used to help serve the needs of marginalized residents and tenants, as well as blighted areas. Originality/value - This paper documents the big data movement and demonstrates how non-traditional data can support decision-making.

Keywords: Open data; Big data; Impact investing; Real estate development; Site selection; Spatiotemporal analysis (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eme:jpifpp:jpif-10-2019-0134

DOI: 10.1108/JPIF-10-2019-0134

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