MateMatiCanota

Smart cities as commercial real estate’s
new urban environments

Overview of the smart city concept
It might appear as a paradox that an idea as prevalent as
smart city does not come with a standard definition and
wide agreement about its meaning (RICS, 2017a). Batty
et al. (2015) acknowledging the broad, and noticeably
vague, scope of the smart city concept explain that ‘‘smart
cities is a label that is now being used generically to cover a
wide range of applications of computers, sensors, and
related computation and interaction that has any link whatsoever to the city.’’ According to Kitchin (2015), one of the
hurdles preventing a clear understanding of smart cities is
the lack of detailed genealogies of the concept, thus allowing global high-tech companies and smart city vendors,
such as Cisco and IBM, to promote their own vision of the
‘‘corporate smart city’’ (Hollands, 2015).
Smart cities find their roots in the 1970s when researchers first advocated urban cybernetics and later in the following two decades with the smart growth movement and
new urbanism (Townsend, 2013). Glasmeier and Christopherson (2015) explain that while the smart city concept is
linked to long-standing ideas about urban technological
utopias, contemporary smart cities are different insofar as
they put ‘‘the emphasis on places transformed by the
application of technologies rather than places where new
technologies are born such as Silicon Valley.’’ Smart cities are ‘‘the receptacles for technology, the target of its
applications.’’ The authors select two essential attributes
to identify a smart city: first, the use of technology to
facilitate the coordination of fragmented urban subsystems; second, ‘‘urban places where the lived experience
calls forth a new reality.’’
This new reality could materialize into ambient intelligence, giving rise to the concept of ‘‘sense-able city,’’ that
is, a city that can sense its inhabitants thanks to a myriad of
sensors (Ratti and Claudel, 2014a). Sensing through the
dissemination of electronic systems in smart cities allows
urban environments ‘‘to sense and respond to people.’’
Pervasive sensing creates a ‘‘feedback loop between the
city itself, the city management and the citizens’’ (Resch
et al., 2012). This process turns cities into ‘‘complex near
real-time control systems’’ dominated by data-driven
operational governance.
Broadly, there are two parallel perspectives on smart
cities. First, the smart city is a city enabling real-time monitoring, efficient management of urban services and utilities, the enforcement of public safety and security using an
extensive ICT infrastructure (Townsend, 2013). Second,
the smart city is a city fostering technically inspired innovation, creativity, and entrepreneurship by smart people,
that is, the epitome of the knowledge economy.
Kitchin (2015) explains that smart cities are often seen
as an urban panacea for business where ‘‘smart politics and
judicious investment in appropriate fiscal measures, human
capital and technological infrastructures and programmes
will attract businesses and jobs, create efficiencies and savings and raise the productivity and competitiveness of government and business.’’ This is especially the case in the
corporate vision of urban smartness where ‘‘IT can make
cities more economically prosperous and equal, more efficiently governed and less environmentally wasteful.’’
A key element of the smart city model is ‘‘the ability to
promote economic growth’’ (Shelton et al., 2015). Smart
cities’ growth relies on urban innovation ecosystems
which are green, smart, open, intelligent, and innovating
(Zygiaris, 2013). This leads smart cities to be frequently
identified with six dimensions: smart economy, smart
mobility, smart environment, smart people, smart living,
and smart governance (Caragliu et al., 2011). Many models of smart city, notably in Europe, put a special emphasis
on sustainability and quality of life, whereby the six
dimensions of urban smartness fuel sustainable economic
growth and high quality of life with a ‘‘wise management
of natural resources.’’
To make sense of the smart city concept, some researchers have developed taxonomies of smart cities. For
instance, Neirotti et al. (2014) researching European smart
cities propose a taxonomy built around application

domains. Smart cities are broken down into ‘‘hard’’
domains (office and residential buildings, energy grids,
natural resources, energy and water management, waste
management, environment, transport, mobility, and logistics) and ‘‘soft’’ domains (education, culture, policies promoting entrepreneurship, innovation and social inclusion,
and e-government enhancing communication between
local public administration and the citizens). Interestingly,
commercial real estate might play an active role in many, if
not all, subcategories of the hard domains.
The abundant academic literature on smart cities contains many attempts by geographers and planners to make
sense of ‘‘smart city imaginaries’’ promoted by purveyors
of smart city systems. While they acknowledge the power
of futuristic visions to capture the minds of corporate
actors, policymakers, and citizens, they also recommend
focusing on how cities use technology to deal with actual
urban issues. For instance, Shelton et al. (2015) assert that
the ‘‘actually existing smart city’’ is very far from the corporate smart city. ‘‘Rather than the construction of new
cities from scratch or wholesale importation of universal
ideals into existing cities, the smart city is assembled piecemeal, integrated awkwardly into existing configuration
of urban governance and the built environment.’’
From the viewpoint of commercial real estate, this
means that most smart cities will not require building
new urban environments. Instead, they will materialize
into renovation of existing urban environments and
infrastructures (Glasmeier and Christopherson, 2015).
Ratti and Claudel (2014a) assess that ambient intelligence and sensing networks will change ‘‘the contained,
not so much the container.’’
This is in contrast to the impact the first industrial revolution had on cities in the 19th century when railroads,
among other innovations, reshaped urban landscapes.
Markedly, infrastructures of urban smartness underpinning
the fourth industrial revolution have a radically different
impact on space than innovations that accompanied previous industrial revolutions. Although their footprint in physical space might be light, one can expect their impact on the
real estate sector to be extremely pervasive as demonstrated
in this article.
Infrastructures of urban smartness
The new intelligence of cities derives from two concomitant technological innovations: smart grids and ICT infrastructures overlaid on physical space.
The smart grid. Smart grids technologies embody a radical
enhancement to the basic structure of the electrical power
grid, which has remained fundamentally unchanged for 100
years. Gu¨ngor et al. (2011) assert that
experiences have shown that the hierarchical, centrally controlled grid of the 20th century is ill-suited to the needs of the
21st century. To address the challenges of the existing power
grid, the new concept of smart grid has emerged. The smart
grid can be considered as a modern electric power grid infrastructure for enhanced efficiency and reliability through
automated control, high-power converters, modern communications infrastructure, sending and metering technologies, and
modern energy management techniques based on the optimization of demand, energy, and network availability.
Furthermore, the smart grid relies on two-way flows
of electricity and information, which makes it highly
responsive to a wide array of conditions and events
(Fang et al., 2012).
Smart grids play a crucial role in enabling cities to be
smart. Indeed, it is widely accepted that smart grids serve as
‘‘the backbone of a smart city’’ and create the foundation
for smart city projects (Global Data, 2012). Incidentally,
smart grids are also vital to commercial real estate because
of buildings’ central role in their framework.
Considering that energy consumption for buildings
accounts for 40% of the energy used worldwide, Kolokatsa
(2016) explains that
buildings in the near future should be able to produce the
amount of energy they consume, i.e. become zero or nearly
zero energy buildings [ …] Zero energy buildings are buildings that work in synergy with the [smart] grid, avoiding putting additional stress on the power infrastructure.
With the smart grid, buildings can become energy prosumers (i.e. power producers and consumers).
In addition to fostering renewable energy sources, smart
metering, and zero energy buildings, smart grids that rely
on a smart information subsystem collecting real-time data
from end users (Fang et al., 2012) can also be viewed as
‘‘aggregators of buildings, consumers and communities’’
(Kolokasta, 2016). In that sense, they create a connection
between buildings, residential and non-residential, and the
broader urban environment where the six dimensions of a
smart city materialize.
Pervasive computing and the digital skin of smart cities. Smart
grids alone are not enough to make a city smart. Pervasive
networks are required. Smart cities are literally covered
with a myriad of wireless and connected sensors, mobile
devices, Radio Frequency Identification (RFID), and other
Internet of Things (IoT) systems. Gross (1999) was the first
to compare these pervasive electronic networks with a
‘‘digital skin donning planet Earth.’’ The concept that was
later widely adopted by academic researchers highlights the
ability of smart cities to generate ‘‘a vibrant understanding
of patterns and flows’’ (Ratti and Claudel, 2014b). Thanks
to the digital skin, smart cities have become sources of big
data, being turned in the process into ‘‘sensored and
metered cities’’ (Rabari and Storper, 2015). The skin also
turns urban landscapes into ‘‘info-scapes’’ where city
dwellers become ‘‘hyper-individualized’’ users.
Beyond the technological prowess of the digital skin
which increasingly involves artificial intelligence (RICS,
2017b), there are important sociological implications
from the implementation of such powerful networks in
cities. Batty (2007) stresses their radical impacts on city
dwellers’ lives:

Slowly, but surely, a skin [ …] is forming around the globe
which enables instantaneous transmission and access to digital
resources wirelessly from any place to everywhere at any time.
[ … ] Instantly accessible information is unprecedented and is
likely to have radical effects on the way we conduct our affairs
in every aspect of modern life. It is changing the nature of
markets, of retailing, of social contracts and relationships.
Such changes in human ways of life bear significant
implications for commercial real estate which traditionally
enclose these activities.
Concretely, as shown in Figure 1, commercial property
is positioned between the smart grid represented schematically underneath the urban surface and the digital skin
encompassing data analytics (e.g. cloud analytics) and covering all buildings and infrastructures alike.
Thus, the setting for real estate in smart cities is radically
different from the one real estate has been exposed to until
now. With smart cities’ digitally infused space, space is no
longer a passive component of real estate whose measurement is anchored in its physicality (Lecomte, 2018).
Space, place and location in smart cities
Cities of bits and atoms. Smart cities’ ambient intelligence
has led many researchers to question the spatiality of these
new cities especially at the point where physical space and
digital space intersect. Digital space is essentially the byproduct of the ever-growing digital skin. This issue matters
for our analysis insofar as it conditions our understanding
of space and place in commercial real estate and ultimately
property heterogeneity in smart cities.
The concept of location as a place in space has traditionally played a key role in defining heterogeneity in property markets. Indices of commercial real estate are
designed to be granular in terms of both location and property type. Therefore, how does the smart city model which
links buildings and their environments into a real time allencompassing system affect physical space (i.e. location
and buildings’ physical characteristics) as a factor of heterogeneity in real estate analysis?
To address this question, most notable are Mitchell’s
ideas captured in three seminal books published in the late
1990s and early 2000s. Mitchell’s argument focuses on the
nexus between physical and digital in smart cities, and
whether the digital domain overtakes the physical realm,
that is, bits over atoms.
Mitchell (1995) first envisioned that future cities would
be ‘‘cities of bits’’ where urban life is fundamentally
reduced to bits. In a city of bits, physical space, place, and
location do not matter. Life is unrooted to any physicality.
Over the years, Mitchell’s stance evolved, from his initial views about a city of bits and e-topia (1999) to smart
city as the ‘‘points where electronic information flows and
physical spaces intersect’’ (2003). One key factor to
explain his evolution stems from the emergence of wireless
technology which enables radically new relationships
between individuals and the environment. As the city itself
becomes ‘‘the spatial and material of the system,’’ Mitchell
deems that the separation of bits and atoms is over.
The corollary of this evolution is a continuous shift from
enclosures to networks. Boundaries in a traditional city
used to define a space of containers and places whereas
networks establish ‘‘a space of links and flows.’’ The proliferation of networks implies a gradual inversion of the
relationship between barriers and links. Mitchell (2003)
explains: ‘‘Network rather than enclosure is emerging as
the desired and contested object.’’ In other words, value of
commercial real estate in smart cities will derive less from
physical space but increasingly from real estate’s ability to
link with digital space, what industry analysts call access
(Bruelher, 2016).
Although not all researchers agree with Mitchell’s
analysis (e.g. Deakin, 2012), the overwhelming view is
that smart cities redefine the connection between physical
and digital. Ratti and Claudel (2016) talk about a ‘‘powerful collusion of physical and digital that augments
both,’’ by turning cities into ‘‘hybrid spaces of intersection of bits and atoms.’’
The crosslinkage of the physical and digital domains
transforms commercial real estate’s positioning in cities.
Buildings have long been the necessary interfaces between
humans and their environment (Ratti and Claudel, 2014b).
From the primitive hut to a place of worship, physical
structures produced space to enclose human activities and
fulfil needs, be it for protection or spirituality. In smart
cities, as noted by Ash et al. (2018), digital technologies
mediate ‘‘tasks such as work, travel, consumption and leisure,’’ thereby replacing buildings in providing the interface between humans and their every possible needs.
Increasingly, spaces are experienced through digital interfaces, thus generating new spatialities that are beyond the
traditional realm of real estate.
Ubiquitous city versus augmented city. The issue becomes
more complex when one considers that there are two types
of digital skin, each type linked to a different model of
smart city. In a ubiquitous city (or U-city), the skin is all
encompassing and ubiquitous. It delivers homogeneous
services all over the city through a centralized wireless
infrastructure. This is the case presented in Figure 1. Conversely, in an augmented city, the skin is uneven and peaks
at certain places where it produces augmented places,
also known as enhanced locations (Aurigi, 2009), as
shown in Figure 2.

Enhanced locations use space and physical location as a
platform to digital. This allows for a discrete and localized
coordination of digital and physical into a new type of
‘‘augmented places.’’
The concept of U-city derives from ubiquitous computing defined by Mark Weiser in a seminal article published
in 1991 (Lee, 2009). Weiser had the vision of computers
linked by wired and wireless networks, which are so ubiquitous that nobody notices their presence: ‘‘The most profound technologies are those that disappear. They weave
themselves into the fabric of everyday life until they are
undistinguishable from it.’’ In a visionary essay entitled
‘‘The Coming of Age of Calm Technology,’’ Weiser and
Brown (1996) propounded that given its radical social
impact, ubiquitous computing would be the third most
important innovation in the history of mankind, after the
invention of writing and that of electricity.
The interrelated conceptions of U-city and augmented
city produce radically different views with respect to space,
place, and location in smart cities. Anttiroiko (2013)
explains that the U-city model comes with a relative indifference to physical space inasmuch as urban services are
fully available regardless of location. The U-city as a whole
is a place. On the contrary, the augmented city supposes
spaces recombined by digital, thereby establishing a connection with the user’s specific location.
Arguably, this dichotomy oversimplifies ubiquitous life.
Aurigi (2009) identifies that a city will always be grounded
in its location. Encounters, events, and perceptions embody
the ‘‘power of place.’’ Hence, the two models do not have
to be exclusive but can actually be hybrid: ‘‘the U-city can
be an augmented city: global in reach, and very local, personal and ‘spatial’ in the experiences it proposes.’’
Commercial real estate’s heterogeneity in smart cities. This
ongoing debate has significant implications for commercial
real estate. If human life in U-city is truly placeless, commercial real estate’s focus would irremediably move from
physical to digital. As a result, one can envision that in a
smart city modeled after the U-city concept, digital will
ultimately shape physical space. Commercial real estate
markets might de facto become less heterogeneous with
an underlying trend toward property standardization driven
by what Peet (1998) designates as ‘‘existential outsiderness
in which all places assume the same meaningless identity’’
(Dodge and Kichin, 2001). Mitchell (2003:159) explains
that ‘‘in the emerging wireless era, our buildings and urban
environments need fewer specialized spaces built around
sites and resource availability and more versatile, hospitable, accommodating spaces that simply attract occupation
and can serve diverse purposes as required.’’ Hence, the
broader a digital place’s scope, the more commoditized
physical space is likely to become. This is a new take on
the functionalist approach to architecture: in a U-city, versatility in buildings’ function should override any concern
about their physical characteristics.
The lesser importance of physical space in smart cities
might have significant consequences for commercial real
estate in smart cities. U-city’s placelessness favors mixeduse developments and could trigger a new, highly versatile
‘‘omni-use’’ property type where physical characteristics
are mostly nonspecific and applicable to a wide range of
digital places, enabling very dynamic uses of properties
(e.g. co-working spaces together with co-living spaces).
In contrast, in a smart city modeled after the augmented
city concept, since spatial specification matters (i.e. digital
skin comes with locational characteristics), augmented
places will keep commercial real estate’s focus on properties’ heterogeneities (including their digital capabilities in
line with the model’s embedded segmentation of space).
Thinking about commercial real estate in terms of activities
linked to enhanced locations rather than property types per
se will be key. For instance, taxonomies of augmented
places could be established based on such fundamental
activities as work, shop, sleep, eat, travel, and commute.
In terms of urban structure, U-cities’ assemblage of
physical and digital spaces could be more diverse than
those in augmented cities where the digital skin naturally
results in a clustering of human activities. By being potentially fully random series of spatial events, U-cities will let
space users decide whether agglomeration economies make
sense in smart environments. Conversely, augmented cities
will have to be accompanied with maps of enhanced locations. The dichotomy between U-city and Augmented city
will address important questions about how space produced
at the intersection of physical and digital affects human
need for physical interactions, a need traditionally enabled
by real estate.
Moreover, the fact that not all cities will be equally
smart and/or ubiquitous, nor all places equally augmented
should not be overlooked. As explained by Shelton et al.
(2015), ‘‘it is important to recognize that smart cities are
also internally differentiated. That is, like any other phenomena, they are geographically uneven at a variety of
scales.’’ Consequently, to make sense of these differences,
digital places will have to be assessed, measured, and qualified with ad hoc metrics capturing this new spatiality’s
contribution to commercial real estate (Lecomte, 2018).
The next section of the article extends our analysis at the
property scale by describing the concept of smart buildings
and explaining how this new generation of building is

changing the positioning of commercial real estate in
smart cities.

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