A strategic alliance helps Commercial Real Estate giants gain a competitive edge in acquisitions, property management, and sourcing new investors.
Real estate is the largest asset class is the world. A $200 trillion global industry, it's often lagged when it comes to innovation. But with AI analytics acting as a force multiplier, innovation is no longer just for start-ups.
To remain competitive in a rapidly changing market, real estate leaders are evolving quickly to unlock meaningful insights to provide a better user experience for tenants, investors and customers – or risk being left behind.
Taking this forward-thinking approach, Investa and Oxford Properties have formed a strategic alliance with SparkBeyond to build analytic solutions for the Commercial Real Estate industry.
The alliance recently completed Project Alpha, whereby teams sought to unpack the drivers of property value via data and AI, by comparing the impact of value-add and operational components against location and building attributes in Sydney, Boston and Toronto, Canada. The project goal was to answer one simple question - "What can we control in an office building that adds value – above the value of the dirt?"
The business and technical teams from Investa, Oxford and SparkBeyond came together as a virtual team across four countries and seven cities. Due to the time differences, the productivity was outstanding, enabling 20 hours of working time a day. The team dealt with more than 20 years of transactional data, 20 different external data sources and asked over 10 million questions using Artificial Intelligence.
Joanna Marsh, GM Innovation & Strategic Projects, said that while they are at the beginning of the journey, they have already identified some core insights that affect value that relate to amenities, external works and optimal tenant composition. The team are now taking this to the next level with build to rent and other asset classes.
"At Investa, we believe that this technology is the future team member that will handle the complexity of large data sets allowing us humans to focus on what we do best: being creative and thriving in an environment of ambiguity."
From a data perspective, commercial buildings vary widely in the amount of accessible data that is available. Newer buildings with state of-the-art sensors and technology allow real-time access to building data. This data is instrumental in developing insights into optimal building operations and preventive maintenance.
And although it's expensive and complicated, older buildings can be retrofitted with sensors and data can be obtained from siloed systems.
“Harnessing the combined power of digital twins and augmented reality allows us to provide a previously unexplored user experience through an interactive 3D model. This ensures owners, tenants and all users who interface with the building can enjoy a superior level of service and interaction with their environment,” said Shen Chiu, Investa's National Development Director.
“Utilizing the open data available in the building, a range of features are currently operational within the AR environment, including a BIM Overlay, IoT data, live temperature sensors and a timeline view of the building. These features offer incredible new management capabilities, specifically across commercial real estate, engineering and insurance industries.”
Investa and Oxford's active investment in data and decision intelligence offers a real competitive advantage. Forward-thinking property companies will progressively be able to see and realise value that the general property market cannot see.
Apply key dataset transformations through no/low-code workflows to clean, prep, and scope your datasets as needed for analysis
Apply key dataset transformations through no/low-code workflows to clean, prep, and scope your datasets as needed for analysis
Apply key dataset transformations through no/low-code workflows to clean, prep, and scope your datasets as needed for analysis
Apply key dataset transformations through no/low-code workflows to clean, prep, and scope your datasets as needed for analysis
Apply key dataset transformations through no/low-code workflows to clean, prep, and scope your datasets as needed for analysis
Apply key dataset transformations through no/low-code workflows to clean, prep, and scope your datasets as needed for analysis