The Bajío enters 2026 with momentum. In 2025, Guadalajara and Querétaro recorded the highest level of gross industrial absorption since SiiLA began tracking the market, with more than 1.6 million square meters occupied.

More significant than the volume itself was the low turnover in both markets, where five square meters were occupied for every one vacated. This balance helped contain vacancy rates—which have shown an upward trend since 2022–2023—despite a higher pace of new inventory deliveries than in previous years, keeping conditions consistent with a stable market environment.

This real estate performance is underpinned by a shared macroeconomic backdrop: state-level resilience amid weaker external momentum. Still, each market approaches 2026 from distinct productive structures.

Jalisco, whose capital is Guadalajara, does so from a position of scale and diversification. Its GDP now exceeds pre-pandemic levels by at least 7%, and in 2025 it posted export growth of more than 30%, cushioning an estimated manufacturing slowdown of -0.3%. Looking ahead to 2026, the tertiary sector is expected to continue acting as a stabilizer, supported by business services, logistics, and foreign trade. Its main vulnerability is sector-specific: high exposure to agri-exports, which are sensitive to regulatory and price shocks, without undermining overall state stability.

Querétaro, by contrast, enters 2026 with a smaller scale but greater operational and financial density. Between 2018 and 2024, the number of economic units grew by more than 20%, and nearly 19% gained access to formal financing—among the highest rates nationwide. This base has sustained growth in business services and above-average technology adoption, even amid a moderate manufacturing slowdown of around -0.4%. Its strength lies in the integration of industry, services, and capital; its primary vulnerability is greater sensitivity to the external manufacturing cycle.

These structural differences translate into a more predictable real estate dynamic, where absorption is driven mainly by operational continuity rather than rapid expansion. As a result, price trajectories in 2026 are expected to reflect gradual adjustments consistent with market normalization. Under this scenario, the expected annual change in rent levels¹ would range between 4% and 5.3% in Guadalajara—aligned with its greater scale and economic diversification—and between 3.5% and 4.4% in Querétaro, reflecting a solid operating base with higher exposure to external manufacturing conditions.

An additional factor helps explain this stability. Over the past year, roughly 95% of industrial occupancy in Guadalajara and Querétaro came from tenants already present in the market, while only about 5% corresponded to companies not previously registered. This indicates that recent absorption has been driven less by new entrants and more by internal expansions, operational reconfigurations, and consolidation of existing footprints.

This demand composition matters because it reduces cycle fragility. Markets that grow through continuity—rather than one-off entries—tend to reabsorb released space more quickly and with less volatility. In this context, low turnover is not merely a favorable outcome but a built-in adjustment mechanism: when space is vacated, an active tenant base can reallocate it without creating imbalances. For two of the Bajío’s most representative industrial markets, this means that real estate risk in 2026 will be tied less to the volume of new supply and more to the market’s capacity to recycle existing occupancy—a key dynamic for assessing income stability and asset resilience.

For more data, time series, and analysis of the industrial market, visit SiiLA Market Analytics or contact us at contacto@siila.com.mx.

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¹ Projections were estimated using quarterly rent price series starting in 2019, applying a set of univariate models (ETS without seasonality, automatic ARIMA, and ARIMA with drift), complemented by a conservative random-walk-with-drift scenario. The reported ranges reflect the interval of results consistent across models and capture the inherent variability of the process, assuming persistence of dynamics observed in the recent period rather than a point estimate.