Cushman & Wakefield - Capital Watch Issue 03 2016

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identify comparable areas or properties, characterise
urban dynamics and human mobility, guide site selection
and portfolio optimisation and benchmarking, generate
property valuations, and more.
Ripe for disruption
“The property industry is especially interesting as it
is extremely traditional. It is ripe for disruption.”
For all of its interest in understanding location, the
industry has not been as scientific as it could be. Chlump
believes that property professionals need to use and
share data and exploit scientific methods to improve
efficiency and focus on finding the real opportunities
and sources of value.
“PropTech is nascent. There are great companies out
there specialising in various elements of the supply chain
– for example, maintenance, property viewings, or lease
management. But as PropTech matures, you will start to
see these companies aim to offer complete end-to-end
solutions or break up and become incorporated into the
offerings of existing service providers”.
“Service providers that embrace or get ahead of this
disruption will flourish. I met Cushman & Wakefield’s
Juliette Morgan through a mutual contact at Google,
and she is a valued advisor to ”
What next for
“This is only the beginning. It is our hope that our
technologies will serve as the underlying infrastructure
for all location and mobility applications so that
companies such as Zoopla, Rightmove, and Uber could
build on top of a unified ecosystem and would no longer
need to maintain their own infrastructures.”
Chlump has been meeting with the great and good of
the property world as well as tech giants such as Google.
Investor interest in the current round is understandably
high. “They are excited and see great potential. There is
nothing out there like this at the moment.”
What does this mean
for the industry?
Service
providers
that embrace
or get ahead
of this dis-
ruption will
flourish
Big data is one the most important trends facing the
property industry. This isn’t a vogue; it is a paradigm shift
that will be felt in the near term and fundamentally change
the way that property owners and advisors do business.
An industry previously riddled with opacity is beginning to
open up, with far-reaching consequences for transparency,
liquidity, and decision making.
The explosion of data is not in doubt – more data have
been produced in the past two years than in all of previous
human existence. Fuelled in recent years by the pervasion
of search technology and mobile data collection, this will
only gather pace as the Internet of Things delivers on its
potential. The question should now be focussed on how
we use it. New standards, indices and certifications (such
as Wired Score) will undoubtedly emerge, and provide the
platform for new data analytics. gives us a
flavour for how powerful this analysis might become.
algorithms aggregate millions of data points
into objective indices, structures, and models that allow
us to understand the nature and dynamics of a location.
But more than that, they allow us very quickly to predict
change based on patterns and signals that previously were
identified through intuition or months of research.
One example is algorithmic comps analysis. By
identifying similarities across geographies and time, one can
spot the key markers in emergent locations that might help
us to establish mathematically growth potential and future
development and even suggest possible interventions.
A second example is occupier site selection. By
analysing existing retail store locations and a multitude of
key signals, it is possible to deconstruct a site-selection
strategy and even devise an optimal strategy for opening
new stores. Reportedly confirmed by the cofounder of
Caffè Nero, ’s algorithms successfully reverse
engineered, using only data, the divergent store-placement
strategies of both Caffè Nero and Starbucks. The value of
this approach to both retailers and property owners does
not take too much spelling out.
Richard Pickering, Head of Insight & Research,
UK & Ireland gives his view.
example of Indices
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