The big data regime shift in real estate

DOIhttps://doi.org/10.1108/JPIF-10-2019-0134
Pages363-395
Publication Date03 May 2020
AuthorJames R. DeLisle,Brent Never,Terry V. Grissom
SubjectProperty management & built environment,Real estate & property,Property valuation & finance
The big data regime shift in
real estate
James R. DeLisle
Department of Global Entrepreneurship and Innovation,
University of Missouri - Kansas City,
Kansas City, Missouri, USA
Brent Never
Department of Public Affairs, University of Missouri - Kansas City,
Kansas City, Missouri, USA, and
Terry V. Grissom
Department of Economics and Econometrics, Ely Research Institute,
Fernandina Beach, Florida, USA
Abstract
Purpose The paper explores the emergence of the big data regimeand 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
Paper type Research paper
Introduction
Overthe past decade, the real-estateindustry has encountereda number of changing forcesthat
have affected industry practices. Some of these forces have led to incremental changes and
improvements in industry practices, which is typical of a maturing industry. Some of these
forces have been more dramatic and have pushed the frontier of industry practices. While
expanding the potential scope of services for industry professionals, most of the resultant
changes involvedmore efficient and effective ways of doingwhat professionals were already
doing. The relatively recent emergence of big data and the associated explosion in data
analyticsand data science is a more disruptive force,one that can appropriately be labeledthe
big data regimechange.Unlike prior changecycles, this regime changeis likely to put at risk
those who remain locked in current practices and try to depend on industry relationships to
support business as usual rather than embracing analytics (CapStack, 2017;Ledbetter, 2014;
The big data
regime shift in
real estate
363
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1463-578X.htm
Received 5 October 2019
Revised 18 December 2019
Accepted 19 December 2019
Journal of Property Investment &
Finance
Vol. 38 No. 4, 2020
pp. 363-395
© Emerald Publishing Limited
1463-578X
DOI 10.1108/JPIF-10-2019-0134
Lynn and McCarthy, 2012;McPherson, 2012;Mehok, 2014;Peters, 2016). This is particularly
true for an industry increasingly dependent on institutional funding to support transactions,
and the positive net income streams generated by tenants and occupiers of space to support
operations and provide a return on investment (Moodys, 2019). Thus, the players and the
industriesoperating in both the money-timeand space-time dimensionsof real estate are being
directly affected by the disruption caused by big data and data science (DeLisle, 2000). To
surviveand flourish in this increasinglydynamic, hypercompetitive environmentand serve the
needsof their core customers,the real estate industrymust rise to the challengeand harness the
power of big data and data science (Goldberg, 2014). The failure to embrace such changes
introduces therisk of being disintermediatedby a combination of technologicaladvances (e.g.
Artificial Intelligence, Machine learning), and the emergence of new players who have the
mindsets, resources and agility neededto establish a new norm which representsa quantum
leap from current practices.
The emergence of the big data regime can implicitly alter the foundational risk structure
or framing of real estate assets and decisions by decreasing the idiosyncratic expectations of
risk exposures. It can also help reduce the dominances of agency focus on local non-satiation
effects and an increasing frame of deductive analysis of systematic component of the base
risk exposures. Up to this point, analysis of real estate decisions and activity has generally
relied on traditional data that is readily available and, preferred and/or accepted by active or
controlling/dominant actors/agent in the industry. Such approaches must be supplanted by
more rigorous analytics that take advantage of the explosion of big data (Mejevitch, 2013;
Moreira, 2016). Those who embrace the changes can enhance decision making by taking a
more inductive approach, one that starts with exploring disparate data to discover and
identify relationships and causality that heretofore have been overlooked. Blending this more
holistic approach to analytics with the creativity and focus of design thinking has the promise
of revolutionizing the real estate industry. Design thinking is an enhancement of critical
thinking with the major distinction being the emphasis placed on the customer or user/
beneficiary of a product or service. One of the key components of design thinking is the
intellectual curiosity and willingness to take the scenic tourwhile remaining focused on the
destination. The combination of creativityand disciplined analyticsis also essential to
optimize solutions and capture the contribution of data science.
The objective of this paper is to demonstrate how harnessing big data can catapult the real
estate industry to a new age, one in which analytics becomes an essential component of real
estate decision-making. To achieve this goal, the paper begins with a review of some of the
calls for change, followed by a review of the convergence of forces that have set the stage for
the big data regime shift. Based on this foundation, a case study in big-data analytics in real
estate is presented. The article concludes with a discussion of the challenges the real estate
industry and educators face, as well as some recommendations for turning those challenges
into opportunities.
Before discussing the impact of big data on the real estate industry, it is useful to discuss
what is meant by the term big data.In some respects, the big-data moniker is something of a
misnomer. That is, it is not the sheer magnitude of data that is significant; academics and
analysts have long worked with large datasets. Indeed, one could argue that access to such
large, electronically available datasets on the capital markets side of the equation has been
one of the major reasons why some quantitative researchers have been drawn to the industry.
Rather than size of data, the key differentiating feature of big data is threefold: the disparate
sources of data and formats from which it emanates; the varied periodicity or time frames at
which such data are generated and available; and, the fact that the contents of these new data
have not previously been directly associated with real estate [1].
Despite the features that differentiate big data from big datasets and challenges
associated with the lack of domain primacy, it is becoming clear that harvesting these new
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data sets can improve our understanding of how the market operates. The application of
predictive analytics that benefit from an expanded set of data can also help us model the
decision-making and behavior of the key market participants. However, harnessing the
potential of such data can be a formidable task and represents a major shift in how
researchers approach research design and hypotheses testing. That is, rather than
assuming datasets are valid, those who operate in the big-data arena will find that
significant time is required to develop the data, to aggregate disparate data into a unified
dataset, and to draw from diverse datasets on an as-needed basis while maintaining the
integrity of the research process. The disparate sources of data also raise an additional
issue: how to deal with a lack of domain knowledge. While traditio nal real estate and capital
market datasets adopt standard definitions for individual variables, the same is not true for
blended datasetsthat are the essence of big data. In many cases,each source of data will have
its own set of concepts, with discipline-specific definitionsthat require some understandingof
the domain of knowledge from which they are drawn. An example is data on crime which,
despite theadoption of standardized definitionsand reporting requirements,cannot be validly
analyzed without drawing on insights from criminologists or other specialists who can
contextualize them so they can be incorporated in analysis that generates valid and reliable
results.
This same caveat applies to complex research questions that argue for interdisciplinary
teams of analysts and/or researchers. Such research will require a significant investment to
ensure members of the team understand what the data actually represents and, perhaps more
importantly, what they do not represent. This is somewhat analogous to the Garbage in,
Garbage Outmantra although in the case of big data, valid source data can become invalid
noise if the analyst fails to understand the data as well as how totransform or pre-process it to
feed into big data analysis. While this added step might be daunting, it is a necessary
condition to successful adaptation to the regime shift of big data. Unfortunately, it is not a
sufficient condition; researchers must also adopt new methods and inductive ways of
approaching research questions. To be successful in the big data regime, researchers and
industry professionals must embrace new, agile mindsets and invest in developing new
skillsets embedded in data science. At the same time, researchers must be able to apply
existing theory that emanates from various disciplinary perspectives, as well embrace the
development of new interdisciplinary theory and insights that may emerge as a result of the
big data regime change.
Calls for real-estate industry action on big data and analytics
The forces of change elevating interest in big data and analytics have been multifaceted and
have received significant attention. One of these calls to action was contained in a RICS
publication entitled, Our Changing World, Lets be Ready(Cook, 2015). This broad-based
report identified several major drivers of change: social and economic trends including
greater urbanization, inequality, scarcity and sustainability of resources; a new business
landscape including changing business models, adoption of technology, and potential of big
data; and, a changing role for the profession with a need for new skills, higher standards,
increased ethics, and a more comprehensive vision. On a related note but focused more on
cities, Deloitte published a report that explored how rapid advances in technology were
reshaping the economy and society. The report noted, Smart cities exist on the intersection of
digital technology, disruptive innovation and urban environments. They are an exciting
place to work and live and the breeding ground for new ideas(Dubbeldeman, 2015). In a
more recent report, Deloitte Insights pointed out the importance of embedding inclusion in
smart cities, embracing the need to ensure that digital solutions advance inclusion of citizens
rather than impeding it (Newman, 2019).
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