Today a Retail REIT Performance report came out that shows some important trends in retail real estate foot traffic. It contains some insightful (but hardly shocking) information about how many people are showing up to different types of stores. It found that the foot traffic growth that we have seen since late 2017 has peaked and has reversed course in recent months. Of all of the categories of retail real estate, it turns out that “grocery-anchored open-air malls” have had the best performance. This is likely due to the relatively low adoption of online grocery shopping compared to almost any other product category.
It also did a bit of debunking of some widely touted myths. It found that “experiential tenants” like Tesla, Apple or Eataly have yet to show any meaningful growth to a location when it comes to year-over-year visitation. It also showed that outlet malls, often seen as retail “destinations,” have had significant growth declines since the middle of last year.
While these stats are all very interesting, we recognize that any study is only as good as its methodology. Foot traffic has always been a bit difficult to fully understand. Without watching which people go into which stores it is easy to misrepresent a location’s foot traffic by including people just passing by and local commuters. But the company behind the study, The Thasos Group, uses cellphone data in a way that only includes the areas that actually provide foot traffic to a location, in correct proportion, as opposed to including all residents within a certain radius. They have also been selected as one of the first alternative data providers available via Bloomberg Enterprise Access Point, so we decided to ask a little more about how they were able to achieve a high level of accuracy on a normally rather fuzzy metric.
John Collins, Thasos Co-founder and Chief Product Officer told us that his company “collects data from apps running on phones (via agreements with the app providers), and puts that data through an extensive process including geofencing, large-scale processing, normalization, noise reduction (e.g., removing passers-by), and adding metadata (e.g., retail tenants at a mall property) so they can further segment foot traffic within the mall.”
Doing this has allowed them to predict sales to mall anchor tenants, including Macy’s, Nordstrom, JCPenney, and Sears, within plus or minus 2.5% of the reported number for the past 8 quarters. He says that a testament to their accuracy lies in the fact that the most discerning investors, hedge funds and REITs use this data every day to inform their investment decision making.
He also told me that it wasn’t just the amount of traffic that was important, but the type, “this level of detail produces incredibly powerful information: it identifies a property’s true customer profile and dramatically improves transparency into real customer demographics, a development that shakes up traditional REIT rankings, and the investment and business strategy decisions that go with that.” He thinks that the availability of this data will fundamentally change the way merchandising and leasing decisions are made at the REIT level.
Retail locations are valued based on the amount of potential customers that walk in front of it. Not being able to properly understand this number has left a lot of investors in the dark. But, as technology has created the online shopping phenomenon that is threatening the very existence of many retail locations, it is also able to help better understand them. While there might be no saving some doomed malls and shopping centers, there might still be a way to keep investors from making bad decisions about them.