“JLL has got a lot of data, we probably have more data on commercial real estate than anyone,” says the real estate firm’s head of IT, Andrew Clowes.
But the challenge, according to Clowes, is convincing agents across Australia to use ‘locational intelligence’ to help them make decisions about the best locations for commercial properties rather than just rely on their experience.
“Locational intelligence is something I have been passionate about for many years. We [JLL] don’t use data that well, we are a real estate firm and we are all about space and decisions that people make based on location and revenue,” Clowes tells CIO Australia.
Over the past three years, the global organisation has moved to improve its data-driven intelligence by building a global data analysis platform using ESRI’s GIS technology and its own apps to solve what Clowes describes as “unique real estate problems.”
Over time, JLL has collected a lot of data through its property management and corporate solutions platforms. This information is now being combined with data the company has purchased from other providers.
One of these providers is Here, a developer of mapping and location technology that is backed by a consortium of automotive companies which includes Audi AG, BMW Group, and Daimler AG. JLL is also hoping to build APIs between its platforms and those provided by CoreLogic RP Data, a supplier of property information and analytics services.
The company is using its data analysis prowess to deliver results for its clients. One such customer is Moreton Hire, a national firm that in late 2015 wanted to relocate one of its industrial facilities to cut transport costs while minimising the impact on its staff.
JLL gathered data relating to traffic, where staff live and clients are located, and vehicle running costs, and ran this data through 248 test locations using multi-variant data analysis techniques to create ‘heat maps.’ This would help the firm determine the best location for the facility.
Tech staff selected sample locations – in this case the geographic centre of a postcode – before mapping each postcode to other variables. These included how long it would take each staff member to drive to each of these locations.
“To get that, we used the traffic/route analysis data, which we had previously purchased. This helped us build the drive time heat map,” says Clowes.
This ‘drive time’ heat map enabled Moreton Hire to determine the commute time from various locations for staff and clients. A ‘transport cost’ heat map also allowed the company to determine which routes would be the most cost-effective.
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