The retail real estate sector has always possessed a wealth of data. What was previously missing was a reliable method to link this information to the performance of an individual store. A rent roll shows a tenant's contractual obligations. Demographic data describes the households in a property's catchment area. Footfall counts estimate the number of vehicles passing a location. However, none of these metrics alone answer the questions that are becoming increasingly relevant for shopping centre owners and brokers: Is this store productive? Do its customers actually shop in this catchment area? How does it compare to other stores in the chain? And if the tenant leaves, who can most successfully replace them?
This gap is increasingly closing. Retail landlords now have access to combinations of mobile location data, credit and debit card transactions, point-of-sale feeds, tenant credit information, and artificial intelligence. The crucial shift is not merely the existence of more data. It is the fact that information, once scattered across spreadsheets, reports, and separate platforms, can now be analysed together with increasing speed to influence leasing, acquisition, or merchandising decisions.
New analytical tools for informed decisions
Companies such as Placer.ai have brought location intelligence into the mainstream by analysing visits, catchment areas, and cross-shopping. CenterCheck approaches the market from a different angle, using anonymised card transactions to estimate store sales. Guesst connects directly to tenants' point-of-sale systems to automate sales reporting, while RetailStat combines retailers' financial health, credit risks, store locations, and market activity. SiteZeus applies AI and predictive modelling to site selection and sales forecasts. Individually, each tool answers a different question; together, they are beginning to form a comprehensive “Retail Underwriting Stack”.
For a neighbourhood or supermarket-anchored shopping centre, this fundamentally changes the valuation of a rent roll. An owner can go beyond the presence of a national anchor tenant and ask how that specific store performs, where its customers come from, which neighbouring tenants share these customers, and whether the broader financial health of a retailer supports the stability of its lease. A strong company name does not necessarily mean that every branch is strong. Conversely, a productive store can be more valuable to a centre than its pure corporate creditworthiness might suggest.
Strategic advantages in leasing and acquisitions
The impact on leasing could be even greater. Brokers traditionally advertised vacancies with floor areas, rental prices, footfall counts, demographics, and a site plan. These remain important but can now be supplemented by a tenant-specific argument. A leasing team can identify customers already visiting a centre, investigate where else they shop, and target retailers whose customer base overlaps with the property. This represents a fundamentally different sales process.
Instead of telling a retailer that space is available, the broker can argue why that retailer should succeed there. Sandy Sigal, CEO of NewMark Merrill, described this process to Commercial Observer earlier this year. His team used AI to rank potential tenants and create a more detailed leasing presentation for a retailer who had previously rejected a location. After the leasing team reviewed and refined the AI-generated analysis, the retailer ultimately agreed to the deal. The technology did not replace the broker's judgment but gave the team an additional way to test and communicate its thesis.
Major landlords are developing similar in-house capabilities. Brixmor has described how proprietary leasing information is combined with demographic and geospatial data to analyse co-tenancy and help their teams identify better-fitting retail opportunities. This indicates where AI might be most important: not as another standalone dashboard, but as an interface between a landlord's own operational data and external market information.
For owners and investors, this also changes the underwriting of acquisitions. The loss of an anchor or junior anchor tenant traditionally required an analyst to make assumptions about vacancy periods, replacement rents, and leasing costs. Better store and consumer data can add another layer: how much customer footfall the tenant generates, which other tenants depend on this footfall, how the store performs relative to its chain, and which replacement concepts fit the existing customer base.
None of these datasets should be considered definitive. Mobile data estimates visits. Card data represents observed transactions and requires extrapolation. Point-of-sale reporting depends on tenant participation. AI can accelerate analysis but can also make weak assumptions appear more convincing. Human judgment remains essential. The advantage will therefore not belong to the retail owner with the most data, but to the owner who can link sales, footfall, credit, customer behaviour, and internal property information to make better decisions. Retail has always been a merchandising business disguised as real estate. AI and better data are making this increasingly measurable.
