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PROPERTY FUNDS WORLD Special Report Nov 2016 www.propertyfundsworld.com | 10
SIEMENS MARKET INSIGHT
and benchmarked by Siemens to identify
continuous optimisation opportunities.
Having all this information at one’s
fingertips is leading to the use of predictive
analytics on what needs to be adjusted in
the future. For example, it could be used to
identify issues with boilers or HVAC systems
and replace them before they break down.
The benefits are not just measured in
terms of optimising maintenance activities
and reducing impact of equipment failure on
the business. Studies show that improving
the energy efficiency of the working
environment can improve productivity by
11% to 23% depending on the initiatives
undertaken.
“At the same time, globalisation has
changed the nature of the working
environment,” continues Huber. Proper
space management and utilisation is getting
complex. “The ability to predict occupancy
rates is something that is attracting more
attention as customers apply new office
concepts and want to see the improvements
in real-time.”
The case study – Credit Suisse Real
Estate Investment Management
Credit Suisse Real Estate Investment
Management has been a success story
since the launch of the first real estate
fund in 1938. It is ranked among the top 15
largest providers of real estate investments
worldwide, is the third largest in Europe and
the largest in Switzerland with USD45 billion
in AuM**.
Credit Suisse Real Estate Investment
Management looks after 1,300 properties
in 20 different countries with the majority
(approximately 1,200 buildings) located in
Switzerland.
In the past, there were few options for
systematically reviewing the savings and
optimisation potential of portfolio properties.
As such, back in 2009, Credit Suisse Real
Estate Investment Management sought
to define a sustainability strategy, built on
two pillars.
“The first pillar is based on a realisation
that the biggest potential to decarbonise the
portfolio is to focus on existing buildings in
the portfolio, for which we had no idea what
our carbon footprint was, or how energy
efficient the portfolio was,” explains Roger
developed Real Estate Cockpit and Siemens
Mindsphere,” confirms Huber.
There are various benefits to this
collaboration that help give customers
valuable information on their real estate
portfolios at the click of a button. For
example:
• Transparency on building types and
multiple KPI’s such as vacancy rates, cost
per tenant, cost per sqm.
• Leveraging internal and external data on
Siemens’ Navigator platform to benchmark
building performance and forecast
operational budgets.
• Predictive analytics can be applied for
fault detection and diagnosis so potential
issues can be addressed before anything
happens.
• Mobile applications can enable energy
audits and creation of audit reports from
anywhere.
Say the outside temperature is 33 degrees
Celsius and a particular building is running
both heating and cooling systems. In such
a situation, one could quickly shut down
the heating system on Navigator using
programming code.
Using Big Data to drive insights
This ability to connect buildings, collect
data, and monitor them from a single hub is
just the latest example of how the Internet
of Things is evolving. It is helping to drive
the application of Big Data within the real
estate space and underscore Huber’s belief
that the digitalisation of real estate is the
next evolution.
“The real benefit is to those who have
multiple buildings spread across multiple
locations,” says Huber, commenting on
the use of Big Data. “The management
overview afforded by Navigator gives real
estate investors a decision matrix on where
to invest to achieve better performance,
and to quickly identify poor performance in
the portfolio.”
Using predictive analytics to connect the dots
Data points are partly available but not
necessarily connected. With Navigator,
it’s about connecting those data points to
come up with predictive information on
the building’s cooling load, heating load,
etc. This information is then analysed