Smart buildings

Definition of a smart building
Whilst cities’ infrastructures are massively reshaped by the
fourth industrial revolution, buildings themselves are going
through a dramatic evolution, giving rise to a new generation of buildings called smart buildings.
Despite the instrumental role played by buildings in
smart urban environments, there is no standard definition
yet of what a smart building is. None of the numerous
articles published on smart buildings provides a definition
widely adopted by the academic community and industry
alike. Baum (2017) identifies over a dozen different definitions of smart building which are customarily created by
smart building system vendors to suit their own requirements. To add to the confusion, a lot of terms are used
concomitantly, for example, intelligent building, sentient
building, smart building among others (Ghaffarianhoseini
et al., 2016). As pointed out by Clements-Croome (2013),
the world of intelligent buildings and smart cities has a
‘‘wide vocabulary.’’
The concept of intelligent building has been around
since the 1980s. Initially, researchers defined intelligence in a building by its ability to have total control
over its environment. Wong et al. (2005) highlight that
the early definition of intelligent buildings supposed as
little human interaction with the building as possible.
The academic view is that smart systems are a subdivision of intelligent buildings since smart buildings are
developed upon intelligent building concepts (e.g.
Clements-Croome, 2013). However, smart buildings display radically distinctive traits.
Most importantly, adaptability separates smart buildings
from previous generations of buildings. As explained by
Buckman et al. (2014), smart buildings adapt to events by
utilising ‘‘information gathered internally and externally
from a range of sources to prepare [ … ] for a particular
event.’’ As a result, a smart building is ‘‘able to adapt its
operation and physical form for these events.’’ In a nutshell, a smart building is adaptive, whereas an intelligent
building is reactive.
Adaptability is also associated with the concept of
future-proofing, that is, smart buildings have built-in flexibility, sometimes called elasticity (RICS, 2017a). Embodied intelligence ultimately allows a biotechnological
approach to building performance (as opposed to an industrial one), conferring to buildings ‘‘a range of properties
that are traditionally associated with life’’ (Amstrong,
2016). A smart building should ‘‘learn from its inhabitants,
adapt to their life cycle and initiate decisions about changing states of engineering itself’’ (Volkov and Batov, 2015).
Bidirectional consciousness brings a new dimension of
intelligence to buildings. Some researchers even wonder
whether buildings are on their way to becoming ‘‘conscious’’ akin to living organisms (Warwick, 2013).
Consciousness between buildings and their occupants
encapsulates the unique nexus between physical and digital
in smart environments.
Smart buildings’ integrated adaptability is assessed both
short term (e.g. ability to change the number of people in a
room) and long term (e.g. adaptation to change in use).
Buckman et al. (2014) explain that long-term adaptability
will primarily depend on the materials and physical design
of the building. Scott Turner (2016) predicts that ‘‘buildings must now no longer be static structures and machines,
but dynamic, capable of re-building themselves to meet
unpredictable and shifting demands.’’ Materials and construction should allow for change in use and climate so that
future-proofed smart buildings will materialize as ‘‘flexible, loose-fit shells with easy access to rewire and retrofit’’
as new technologies become available (Charles Russell
Speechlys, 2016).
Buckman et al. (2014) identify five pillars underpinning
smart buildings’ adaptability: intelligence, enterprise,
materials, design, and control. Those pillars subsume integrated enterprise systems that are unique to smart buildings: ‘‘Enterprise is any method through which building use
information is collected.’’ Enterprise systems consist of
both hardware and software. As a by-product of enterprise,
buildings are increasingly evolving into repositories of data
sourced from building management systems. Thus, in smart
cities, buildings are turning into computing devices.
Smart buildings and the positioning of commercial
real estate in smart cities
Real estate as service provision. Smart buildings are constitutive of smart environments (McGlinn et al., 2010). Their
role is instrumental in activating urban smartness. By interacting with smart environments (i.e. smart grid, digital
skin, and other smart buildings), smart buildings are
enablers of smart cities.
Concretely, as mentioned in the section ‘‘Smart cities as
commercial real estate’s new urban environments,’’ smart
grids turn buildings into active participants in energy management and optimization to the point that grid-aware
buildings can become ‘‘zero energy’’ prosumers (Schibuola
et al., 2014). By the same token, the digital skin becomes
alive owing to buildings. Data used to feed analytics are
systematically collected within buildings by building management systems acting like neural networks of the built
space (Ratti and Haw, 2012). The ambient component of
smart cities’ intelligence originates in large part from buildings. Hence, the two layers of a smart city’s intelligence
(i.e. smart grid and digital skin) are intrinsically intertwined
with buildings.
With smart buildings, commercial real estate moves
from asset provision to service provision (RICS, 2017b).
As pointed out by Carvalho (2015), buildings in smart cities
are ‘‘ibuildings,’’ which allow commercial real estate players to broaden their value chains by linking the built environment with software applications. Indeed, smart
buildings have little in common with regular buildings,
leading real estate to shift from physical to digital
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(Lecomte, 2015). From architecture’s standpoint, smart
buildings are ‘‘living

bits and bricks’’ which embody ‘‘a
feed-back fuelled world where we don’t just inhabit our
architecture but integrate with it’’ (Ratti and Haw, 2012).
Architecture can connect rather than divide, ‘‘awakening
from the mute motionless matter it has always been into an
active state of being.’’
Access as factor of heterogeneity and value driver for smart
buildings. When space users integrate with buildings, there
are obviously implications for their relationship to space,
location, and property heterogeneity. Noticeably, commercial real estate’s long held belief about value deriving from
location is being challenged. What are the consequences for
commercial real estate?
Hyper-connectivity, efficiency, and differentiation powered by technology (such as IoT) are becoming more
important than location so much so that the old mantra
‘‘location, location, location’’ might be replaced by ‘‘location, information, analytics’’ (Kejriwal and Mahajan,
2016). Berman et al. (2016) explain: ‘‘[Commercial real
estate] has always believed that location, location, location
rules. But in the mobile world, where ‘location’ is mainly
virtual, [ …] assets are losing ground to access.’’
In smart urban environments, buildings will act as platforms to digital. While location is not going away (e.g.
augmented places), its relative role as a value driver for
commercial real estate lessens (RICS, 2017b). Under the
radical disruption brought upon the built environment by
Weiser’s pervasive computing, access is where bits and
atoms intersect. Access defines interactive loci or gateways
where human tasks are mediated and value created.
For commercial real estate players, the emergence of
smart buildings means success will depend on their ability
to leverage on synergies between bits and atoms, by being
positioned where as many services (i.e. interactions) as
possible can be provided to space users. Buildings will
become one of the tools in that process, the other being
As buildings move toward accrued standardization of
physical structure enabling a functionalist approach to the
built environment, heterogeneity in property markets traditionally defined by space (locations) and properties’ physical characteristics will have to be redefined in terms of
technology and the ability of buildings to fulfil a wide
range of functions. As a result, technology will become a
major factor of heterogeneity for commercial real estate in
smart cities both at the city and property scales.
Property heterogeneity should therefore be incorporated into real estate modeling with a new notion of
access (or digital positionality) instead of the traditional
physical location. This shift emphasizes the dominance
of digital space in future models of commercial real
estate in smart cities. Concretely, this implies that technological obsolescence will play a major role in buildings’ overall obsolescence, whereas the physical and
economic dimensions of obsolescence become relatively
less significant.
Assessing smartness in smart buildings
To overcome the challenges posed by the dominance of
technology in the built environment in the digital era, it
is important that tools enabling a precise measure of buildings’ smartness be readily available to real estate players
who, by training, are not technology experts. One such tool
is a scoring methodology for smart buildings which would
lay the foundations to a greater standardization of commercial real estate in smart cities.
Similar to green buildings’ Leadership in Energy and
Environmental Design (LEED) certification, smart buildings will need to come with standards to assess their performances and guide commercial real estate players.
Attempts to design evaluation frameworks and smartness
scores have been hindered by the lack of consensus stemming from multiple and diverse stakeholders (private sector, public authorities, technology companies, and real
estate specialists). Private companies have been the first
to develop their own sets of metrics, which are customarily
designed around their clients’ demands and/or product
As countries across the globe have adopted different
KPIs for smart buildings, the resulting indicators tend to
reflect deeply rooted interpretation of smart buildings’
essence and contributions to a smart environment. While
Europe’s ongoing Smart Readiness Index (SRI) (Stijn et al.,
2017) spearheaded by the European Commission
Directorate-General for Energy is geared toward sustainability, the United States (Building Intelligence Quotient
(BiQ)) emphasizes the performance and cost effectiveness
of smart buildings (Katz and Skopek, 2009). By the same
token, Asian countries have adopted a wide range of indicators with very different KPIs (Ghaffarianhoseini et al.,
Irrespective of their KPIs, most existing scores embody
an engineering view of smart buildings. An engineering
view tends to define smart buildings as highly sophisticated, self-contained ‘‘machines,’’ by stressing out their
technology rather than their interactive dimension. Noticeably, despite covering a wide array of elements, these
scores overwhelmingly ignore a building’s ability to interact with the smart environment.1 As identified by Alwaer
and Clements-Croome (2010), key performance indicators
(KPIs) for assessing sustainable intelligent buildings
should be based on ‘‘people, products and processes, and
their inter-relationships.’’ Therefore, in their current versions, existing scores of smart buildings are not sufficient
for commercial real estate. This article lists three directions
that future endeavors to build scores should follow.
Firstly, to be relevant to commercial real estate, scoring
methodologies should capture buildings’ ability to adapt to
technological changes. Assessing adaptability in the midst
of an industrial revolution with discontinuous innovations
is a challenge. As pointed out by Gaffarianhoseini et al.
(2016), the key to designing a relevant scoring methodology is to treat smart buildings as ‘‘dynamic and evolutionary entities rather than static and fixed ones’’. For instance,
Europe’s SRI stresses the importance of buildings’ future

proofing and establishes a difference between ‘‘smart
ready’’ and ‘‘smart now.’’ Smart ready describes a building
that is ‘‘itself smart but its potential to realise the benefits
from smart services may be constrained by limiting factors in the capability of the services it connects to as its
boundary’’ (Stijn et al., 2017). Smart now captures a
building’s operational smart capability. The SRI methodology focuses on smart ready, by allowing ‘‘relevant new
capabilities to be reflected as soon as possible and address
future proofing needs.’’
Secondly, in addition to setting industry standards,
smart building scores should reflect buildings’ value drivers in smart environments, that is, flexibility and versatility of the physical structure, smart readiness and future
proofing of the enterprise, as well as ability to interact
with digital space to meet their occupants’ changing
needs. The ‘‘omni-use’’ property type introduced in this
article would potentially allow maximum versatility of
physical structure.
Thirdly, smart buildings scores should be designed with
the concept of index in mind. In effect, real estate in smart
cities which is still in its infancy offers a unique opportunity to adopt a bottom-up approach to index construction,
by aggregating individual scores at the building level into
price indices of smart buildings at the neighborhood, city,
country, and region levels. Individual scores could be
aggregated by predefined ranges of smartness (e.g. derived
from scores or smart certifications).
The analysis presented in this article only focuses on the
fundamental principles that the scoring methodology
should follow. Further research is needed to develop a fully
blown methodology. Assessing buildings’ smartness with a
standardized framework is a crucial step toward setting up a
market for smart buildings in smart cities as no market can
properly function without widely agreed norms.
This article presents an introduction to cities and buildings
in the digital era. Smart technologies underpinned by ubiquitous computing will have a drastic impact on the way
space users interact with the built environment, ultimately
giving rise to ambient intelligence embodied by ‘‘senseable cities’’ and conscious buildings.
Commercial real estate will be massively affected by
these changes even though the urban form should not be
drastically altered. Hence, one should not expect a construction boom due to the technological disruption real
estate is currently facing.
More importantly, the growing role of digital space in
cities will challenge buildings’ traditional role as interfaces
to human activities. The new positioning of smart buildings
as platforms to digital means that access has become more
valuable than location. This focus on buildings’ digital
positionality has the potential to radically redefine city
maps in real estate analysis.
The fourth industrial revolution will also alter the way
real estate assets are being modeled, and ultimately valued.
While the quest for functional versatility comes to
dominate buildings’ physical structure, one can expect
technology to become the dominating factor of heterogeneity and the main value driver for commercial real estate
in smart cities. Technology’s impact will be felt at two
levels: firstly, at the urban level where each city’s idiosyncratic infrastructure (smart grid and digital skin) will affect
digital space, and secondly at the building level where
buildings’ smartness has to be normalized and assessed.
Mitchell’s ‘‘cities of bits and atoms’’ will represent a
new urban environment for corporations. With it will come
opportunities as well as challenges. The real estate sector
needs to adopt innovative business models, whereby commercial real estate players become service providers rather
than physical structure managers. New entrants in the sector (e.g. co-working space operators) are already showing
the way ahead.
For the other economic sectors, the role of real estate
in corporate strategies is expected to grow inasmuch as
commercial property moves from being a silent partner
to becoming an active component of corporate value
chains with the abilities to generate significant competitive advantages. For instance, tomorrow’s retailers will
choose to be located in ad hoc ‘‘augmented places’’
within smart cities. By doing so, they will maximize
synergies between physical and digital spaces while generating maximum returns from their real estate-related
costs. In their locational choices, companies will focus
on optimal access to the digital skin. A similar reasoning
applies to smart grids.
In many respects, smart technologies question the social
contract between corporations, public authorities, and citizens. There will be a myriad of ethical questions stemming
from the implementation of smart technologies in smart
cities, for example, privacy and security of data collected
in smart buildings, role of corporations in smart cities,
importance of technology-driven market forces in shaping
urban landscapes. Real estate companies can expect to be at
the forefront of these issues.
Beyond the search for efficiency in which artificial
intelligence will play an ever-increasing role, smart cities and smart buildings will force managers to ask themselves what role their companies should play in defining
human lives in smart urban environments while the
fourth industrial revolution keeps delivering on its promises for innovation.
Author’s note
This article was presented at the European Real Estate Society
25th annual conference held at the University of Reading, UK
(June 27–30, 2018).
The author would like to express his thanks to Professor Lee
(Hanbat National University, South Korea) and Professor Aurigi
(University of Plymouth, UK) for sharing their book and chapter,
respectively, on ‘‘Ubiquitous city: future of city, city of future.’’
The author also grateful to two anonymous referees for their very
useful comments. All errors and omissions are mine.
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Declaration of conflicting

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