Retail Real Estate Still Can't See What Matters Most. Transaction Data Is Filling the Gap.

Written by:

Steve Marcinuk

Date:

18 Aug 2026

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Retail real estate underwriting has relied on foot traffic and mobile location data for the better part of a decade. The logic is straightforward: more visits should mean more sales. But that assumption breaks down more often than the industry acknowledges. The gap between visits and actual purchasing behavior is one that landlords, tenant representatives, and acquirers are only now beginning to address with harder evidence.

The core problem is structural. When an owner acquires a shopping center, they inherit leases negotiated by the previous owner, often with limited visibility into how individual tenants are actually performing. Many modern leases no longer require tenants to report sales figures. The result is a persistent information asymmetry: the retailer knows exactly how its store is doing, but the landlord frequently does not.

A handful of platforms have begun addressing this gap by aggregating card and debit transaction data to estimate store-level sales independent of what tenants choose to disclose. “The number one barometer for the success of a retail venture is: does the store perform?” says Carter Russ, Co-Founder & CEO of CenterCheck, one such platform, which estimates store-level sales across roughly 1,500 retail chains. “If people are buying from the store, it’s going well. If they’re not, they’re going to leave.”

Where Foot Traffic Fails

The distinction between visits and purchases sounds obvious in the abstract. In practice, the two metrics can point in opposite directions while both being technically correct.

Russ points to a case involving a Family Dollar store in Ely, Nevada – a town of roughly 800 people with no surrounding retail competition. Mobile data flagged the location as poorly trafficked. Transaction data told a different story: the store ranked in the 98th percentile of its chain across the state. Nearly every resident treated it as their primary shopping destination, visiting multiple times per month. The store captured virtually all local demand despite low absolute visitor counts.

“Mobile data was correct. It was very low-traffic,” Russ says. “However, everyone that lives there treats it like it’s their lifeblood.”

A broker evaluating that location on foot traffic alone would have advised against acquisition. The kind of transaction-level analysis platforms like CenterCheck perform revealed one of the chain’s strongest-performing stores, a gap that foot-traffic data alone, regardless of provider, is structurally unable to close.

The Lease Negotiation Problem

The information gap becomes most consequential at lease renewal. When a tenant approaches a landlord requesting a rent reduction, the landlord typically has no independent basis for evaluating the claim. Vacancy carries substantial risk, lost rent, tenant improvement costs for the next occupant, and potential complications with financing obligations.

“You’re more likely to acquiesce when you have no information,” Russ says. Knowing whether a tenant is genuinely struggling, holding steady, or operating one of their chain’s top-performing locations changes the negotiation entirely. Without that data, landlords negotiate blind against counterparties who know their own numbers precisely.

For tenants, the dynamic works in reverse. A retailer requesting legitimate relief at a genuinely underperforming location may face skepticism from a landlord who has no way to verify the claim, making concessions harder to secure even when warranted.

Adoption Friction in a Relationship-Driven Industry

Despite the utility, adoption of transaction-based analytics has been slow. Commercial real estate remains deeply relationship-driven, and many established brokers have built successful practices over decades without quantitative retail analytics.

“How do you tell a power broker who’s been doing something the same for 30 years that they should use this new thing and spend money on it?” Russ says. He describes the pace of change as “glacial,” compounded by subscription fatigue and skepticism bred by overpromising technology products more broadly. “No one wants to talk about AI anymore. There are 500 companies saying it’s an AI solution that’s going to change your life. And then it ends up being underwhelming.”

Remaining Gaps

Even with transaction data, market-capture analysis remains difficult. A children’s retailer doesn’t just compete with other children’s retailers; it competes with Target, Walmart, Costco, and Amazon for the same category spend. Understanding what percentage of a product category a given retailer owns in a specific market requires layering multiple data sources, including online transaction tracking and category-mix estimates for general merchandise stores. Russ acknowledges CenterCheck hasn’t fully solved this, and the limitation isn’t specific to any one platform.

His operating philosophy reflects the constraint directly: “All models are wrong, but some are useful.” The goal is triangulation: card data alongside foot traffic alongside any available retailer financials. “If one of the three disagree, it’s cause for further investigation.” The underlying need, a fuller, cross-validated picture of store performance, extends well beyond any single data provider.

The Stakes Are Rising

The broader retail environment is shifting in ways that increase demand for granular performance data. Russ points to Gen Z consumers returning to physical retail after growing up in an online-first era, gravitating toward experiential shopping, malls, and third spaces. At the same time, buy-online-pickup-in-store models are blurring the line between digital and physical revenue, making store-level performance harder to assess through any single metric.

Russ also says the stakes for getting site selection wrong have grown. “We’ve never been more squeezed,” he says of the current retail environment, estimating that store failure carries an average cost of roughly a million dollars when accounting for inventory write-downs, lease-break penalties, and employee separation costs. For small operators opening a second or third location, that cost can be existential.

That combination, rising failure costs and a retail landscape that no longer maps cleanly onto foot traffic alone, is pushing landlords, tenants, and acquirers toward the same conclusion: no single data source tells the whole story. The operators and landlords who can layer multiple sources, transactions, traffic, and retailer financials, will have the clearest picture of which locations are actually working and which are not.

About the Expert: Carter Russ is Co-Founder and CEO of CenterCheck, a platform that estimates store-level sales across approximately 1,500 retail chains using aggregated card and debit transaction data.

This article is based on information provided by the expert source cited above. It is intended for general informational purposes only and does not constitute legal, financial, or real estate advice. Readers should conduct their own research and consult qualified professionals before making any real estate or financial decisions.