Is big data actionable in commercial Real Estate?
Last week, I had the opportunity to participate in a panel discussion at the RICS conference in Madrid. The topic focused on how current technology enables retailers or owners to make decisions. We have to take into account that the Real Estate industry amounts to 70% of the world’s wealth. In an environment of economic recovery and consumption increase, it is easy to think that such a solid industry is safe from disruption and doesn’t really need to worry about the future. They may be right, but are they missing a huge opportunity? Are they gambling stakeholders interests on intuition and experience?
Nowadays there are providers that focus on enabling everyone, regardless of their industry / expertise, to extract actionable insights from data. Data such as sociodemographic profiles, banks’ credit cards transactions data or telecoms’ customers’ location at a click. How can retailers and Real Estate owners leverage these emergent technologies?
It would seem that the retailers are the ones that can extract the most out this data. They are the ones that can focus on the pockets of target consumers among their catchment areas, or align the brand- and product-mix to the customers they want to attract. Regarding banks’ and telecoms’ data, retailers could easily predict consumers’ behaviour to reach a potential customer at the exact moment with the most relevant message. For example, they could reach a premium consumer through her smartphone the day before she drives past a given shopping center.
As expected, during the conference, the audience stated that this could be game-changing for retailers, but why would owners care? After all, their assets’ yields are solid and growing. I would love to share a couple of examples of how combining data from telecom providers with advanced gravitational models could help owners minimize risk and increase profits.
Let’s picture a Real Estate investment fund that is considering acquiring a regional shopping center. They could look at current performance and create an image of the target customer, the brand mix, etc. Or they could take into account (using telecom’s data) how the asset’s — and all of its nearest competitors’ — catchment areas fluctuate during the week, month or year. Therefore, they could identify those customers that during key events (e.g. Christmas) are willing to visit a competitor and calculate the refurbishments that would have to be carried out in the shopping center to attract those customers.
Advanced gravitational models would allow owners to simulate how their catchment areas would be modified by refurbishments in the asset or by changes in the brand mix. That way, they could calculate the total investment required to optimize the asset’s catchment area and negotiate leasing terms — for different premises — based on the potential customers attracted in each scenario.
If you have any questions or just want to know how Geoblink is solving these challenges for owners and retailers, don’t hesitate to contact me, I’ll be delighted to discuss how this data could help your organization.
