The autumn edition of Urban Land by the ULI focused on "Big Data" and research conducted by Mckinsey & Co offers some compelling arguments for why developers and investors in US real estate should be harnessing its power.
Many may be sceptical of the latest buzz word to hit the real estate sector; after all, analysing data to predict trends is nothing new. However, what is new and exciting is that advances in A.I. and machine learning algorithms are making it easier to analyse vast volumes of unstructured data from disparate sources and use them to make better informed business decisions.
Here in the UK, with increasing governmental and social pressure on landlords to optimise energy efficiency, and millennials demanding flexible, connected, working environments, "Big Data" offers landlords the opportunity to meet these demands in a more cost effective way.
The key to "Big Data" is of course volume (it's big!). For it to work, companies will need to share data with each other. With a multi-let office building or shopping centre, key questions will include who owns the data and whether they have the right to share, trade and use it.
Leases of multi-let buildings and shopping centres will need to contain provisions to account for how data is processed, shared and utilised, with controls and permissions put in place to ensure data remains protected and anonymised, whilst also being useful.
If systems are to be fully integrated, with landlords and tenants across shopping centres, towns and even global networks all sharing and contributing, provision will need to be made to ensure that all parties meet minimum standards for the secure storage of data. Provisions will also be needed to allocate risk if these systems fail to identify who ultimately takes the risk for cyber breaches and as to how the risk will be allocated.
Also, when buildings are traded, parties will need to consider whether ownership of the historic data will follow the building or the seller, and how this will be negotiated and reflected in the document.
These are questions which are yet to be resolved in the market. But as buildings generate more data, and data becomes more valuable, perhaps the time is now to consider how its value is reflected in real estate transactions.
.... for an asset manager who wants to expand and optimize a portfolio of multifamily buildings, machine learning algorithms can rapidly combine macro and hyperlocal forecasts to prioritize cities and neighborhoods with the highest demand for multifamily housing. This allows the asset manager to identify buildings in areas that are undervalued but rising in popularity. Advanced analytics cannot serve as a crystal ball. In most cases, it should only support investment hypotheses, not generate them. But when it comes to these classic real estate conundrums, advanced analytics can rapidly yield powerful input that informs new hypotheses, challenges conventional intuition, and sifts through the noise to identify what matters most.