The Real Questions Brokers Should Be Asking Data to Answer

The Real Questions Brokers Should Be Asking Data to Answer

Data is having its moment in commercial real estate as many are turning to transaction panels, mobile data, and other data signals to understand store performance and trade area health.

But as adoption grows, participants become more sophisticated. Eventually more and more people start to wonder --- Can I trust this?

It’s the most common question we hear from brokers and landlords exploring new data platforms — and in most cases, it’s the wrong one to ask.

The Problem with “Accuracy” in 3rd-Party data

Unless you are plugged into POS systems, its nearly impossible to get the exact sales figures for store. That said, it doesn't mean that data sources are useless. Every dataset can shed light on how different brands or locations perform relative to each other.

Is this a strong Dick's Sporting Good's relative to the fleet of stores nationwide?

How well does this Ulta Beauty do with Milenials and Gen-Z vs. the Target down the street. Is it 10% or sales? 30%? How does that compare to the average retailer across the nation or in the state?

All of these questions can be faithfully answered with transaction data. Relative insights can still make an impact in your due diligence process.

Similarly, A broker using mobile data to gauge foot traffic might get a clean read on visitation trends, but not necessarily on who’s spending. A landlord comparing store sales across markets might be drawing from datasets that overrepresent certain geographies or demographics, but if they know how to adjust for this they can still draw valuable conclusions from the data.

In other words, the brokers should more often ask "How do I use this dataset?" instead of "Is this a good dataset?"

Otherwise, they are leaving deals (and money) on the table.

Asking questions beyond "What are the sales?"

Instead of asking “Is this data accurate?”, brokers should be asking 2nd derivative questions like:

• Which consumers does this dataset actually represent?
• How consistent is that representation over time?
• What kinds of stores or categories might be over- or underrepresented?
• How does the source handle small-format stores, seasonal closures, or multi-tenant parcels?

These are questions that can’t be answered with old-school comps or anecdotal intel and separate brokers who use data to their advantage from those who are looking something to trust blindly.

They require new kinds of data — and with that, a new literacy around how to interpret it.

The future of retail real estate won’t be powered by a single dataset. It will come from connecting multiple sources — transaction data, mobility, and tenant-level reporting — into a unified, context-rich view of how stores and centers actually perform.