We like to focus on companies that own assets that we believe are unique to the tenant or user and are difficult if not impossible to replicate. In the retail sector, one metric we focus on is what we call “income density.”
Retailers, especially large national retailers, have very sophisticated demographic data and models that allow them to predict sales per store or sales per square foot, often with a high degree of certainty. This in turn allows them to carefully construct expansion campaigns and manage their store networks in a way that gels with their branding goals and the needs of their specific demographic target market.
Whole Foods, for example, is perhaps the most sophisticated retailer in the country. They have an incredible amount of descriptive data about their shoppers, and based on this they choose locations where they have the highest density of potential Whole Foods customers. Specifically, they seek out education density (college or post graduate population within a given area). They believe post-graduates are more likely to seek out free trade, organic kale than high school dropouts are. As it turns out, education density and income density have a high degree of overlap and are highly correlated.
The primary demographic metric that upscale retailers are looking for is trade area income density: how many people are in the trade area and how much money they earn. This is the numerator in their sales model. The denominator is the number of other places in the trade area where customers can shop. The trade area is driven by the tenant draw for the specific center. For instance, a mall with Saks, Nordstrom, Bloomingdales and an Apple Store will have a trade area as large as 25 miles (even in a dense area with other shopping alternatives) while Sears and JC Penney will have a trade area as small as a few miles. Of course there are other drivers as well, some empirical and some subjective, and each location is unique.
This is the formula we use for our stock selections in retail:
Another popular way to measure income density is “Super Zips”. These are zip codes in which both education and income are in the 95th percentile. According to US Census data from 2010, there are 891 super zips (of 42,000 US zip codes and 32,000 zip code tabulation areas – which are zips in which the US Postal Service has calculated boundaries and which form the basis of census data), housing 9 million adults older than age 25, which compromise the top 5% of the US.
For the purposes of The Gap clothing store, just focusing on super zips is not a large enough population footprint to have thousands of stores. Most super-regional malls have a trade area larger than a zip code and zip codes are not like trade areas. They were established by the US Postal Service for delivering mail, not by economic demographers trying to figure out how to sell you kale. But for the purposes of Tiffany and Louis Vuitton it’s probably just right.
Mall Sales Density
Data prepared by AACA, complied from Green Street Advisers and SNL Financial
As shown above, Taubman (TCO) has 74% more income density than the average of the public companies – i.e., more wealthy people surrounding their malls. When we adjust this for trade area reach the numbers become even more compelling as Taubman’s malls have more exclusive tenants that will pull customers from a larger trade area than competitor’s assets. This shows in sales/square foot, which are almost 2x the mall peer group average. By comparison, CBL has only 26% as much income density surrounding its assets as Taubman – and since malls can’t be moved, in our opinion this becomes a permanent advantage for TCO and a permanent disadvantage for CBL.
In short, this is our basis for investing in the retail sector. We buy the retail assets with the highest concentration of wealthy people surrounding them.
By way of anecdote, I spent several hours stuck in a tour bus with the CFO of BMW USA and over the course of a crazy traffic jam he explained to me the ”psychographic profile” of their owner as compared to Audi and Mercedes. To the casual observer, these companies all compete for the same customer – in reality, not so much. There had to be twenty or thirty owner characteristics they measured and understood and tried to reflect in their cars. BMW’s owner profile is meaningfully different from Mercedes or Audi. They referred to qualities such as “adventure seeking”; this is why they don’t refer to their SUVs as SUVs. They call them SAVs – Sport Activities Vehicles.
BMW is ahead of most retailers in our opinion. For instance, Macy’s and Dillard’s make fewer distinctions between their customers than BMW and Audi, but as retailers get more sophisticated with massive amounts of customer-related “Big Data,” it allows them to further refine their offerings and store portfolios. We invest in retail property companies that pursue high income density locations because we believe they own properties that are and will be the most sought-after by rent-paying retailers even as retailers refine their target customers. We think this strategy will allow our portfolio to remain relevant over time.