The Post-Anchor Era: What Makes a Mall Stable in Mexico?

Beyond major anchor stores, SiiLA data suggests that the stability of Mexican malls increasingly depends on a network of categories that can support and reinforce one another.

Óscar García Maceiras leads Inditex, owner of Zara Home, a brand focused on the home segment within Mexican malls. Photo: SiiLA.

By: SiiLA News

05/15/2026

In Mexico, shopping centers operate less like a collection of isolated stores and more like a network in which different categories sustain one another. An analysis of 26 quarters, 175 properties, and more than 270,000 real estate records shows that categories tied to everyday consumption—such as fast food, home, convenience, beauty, or fitness—rank among the most stable and synchronized segments within malls, alongside historically dominant categories such as department stores, fashion, or entertainment.

This implies that the stability of a shopping center no longer depends solely on stores capable of attracting visitors on their own; businesses that redistribute traffic throughout the mall also play a role. Viewed this way, a gym drives demand toward coffee shops, fast food or sporting goods stores, while a supermarket extends customer circulation toward services, convenience or entertainment, and ultimately reinforces activity across other categories.

The relationship between categories extends beyond the distribution of traffic within malls. It is also reflected in how different activities grow, slow down, or stabilize at the same time. The data, however, shows little evidence that one retail segment systematically anticipates another, suggesting the sector does not appear to organize itself sequentially—first consumption, then entertainment, then services—but rather as a system that reacts collectively to the broader economic environment.

Mall stability is distributed across multiple categories, not only major anchors.

Moreover, not all categories participate with the same intensity within that dynamic. While most segments associated with recurring consumption appear more integrated into the system, activities such as theme parks, pawn shops, and dry cleaners play a more peripheral, less connected role within the broader retail ecosystem.

Even so, not all categories fulfill the same function within the commercial system. The data indicate that segments linked to home and domestic life tend to exhibit more contained cycles and lower relative volatility, while categories associated with convenience and support services react more intensely to market expansions or slowdowns. The contrast is relevant because it suggests that part of everyday retail no longer merely cushions cycles but also amplifies the transmission of consumption throughout the mall.

Some categories cushion cycles; others react more intensely to the market.

The findings also show that if mall stability increasingly depends on activities that sustain dwell time and everyday traffic, the logic behind tenant mix strategies is no longer focused solely on maximizing gross leasable area or attracting isolated major brands. In that context, recurrence and connectivity between categories are becoming equally relevant for sustaining traffic, occupancy, and operational stability.

Under that logic, the position and complementarity of a store within the mall begin to matter as much as its individual ability to attract visitors.

Visit SiiLA Market Analytics or contact us at contacto@siila.com.mx to learn more about trends, performance, and strategies in Mexico’s commercial real estate market.

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Methodological Note: The analysis was conducted in R using 26 quarterly SiiLA files corresponding to the Q42019–Q12026 period, comprising 175 properties, 27 submarkets, 10 categories, 49 subcategories, 6,261 tenants, and 270,349 real estate records, consolidated into a panel of 68,605 observations by property, submarket, category, subcategory, and quarter. The dataset was cleaned by standardizing column names, cleaning strings, converting occupied area to numeric values, and excluding records without valid property, subcategory, or occupied area information; subsequent consistency checks and missing-value reviews were performed on critical variables. To measure market dynamics, quarterly and year-over-year logarithmic variations in occupied area and tenant counts by subcategory were calculated using quarterly and annual lags. Correlation matrices between subcategories were then constructed using Pearson coefficients applied to quarterly logarithmic growth in occupied area, using only valid observations and excluding non-finite values. The categories “Available,” “Government and Administration,” “Funeral Services,” and “Institutes, Foundations, Councils, Unions, Associations and Clubs” were excluded to preserve operational comparability within the analyzed retail universe. The series was also normalized using Z-scores to compare relative behavioral patterns across subcategories regardless of absolute scale. Based on this, correlation matrices and heat maps with hierarchical clustering were generated using Euclidean distance and the Ward.D2 method. The results describe statistical associations and co-movement patterns between subcategories observed during the analyzed period and do not imply direct causal relationships between categories.

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