Case Study: JLL (Jones Lang LaSalle) achieves 52% faster data refreshes and scalable, data-driven real-estate insights with CARTO

Using Location in Real Estate Market Analysis Applications

JLL, a global real estate services leader with 93,000 employees, developed Gea to bring together millions of spatial and non‑spatial data points for faster, data‑driven valuations and client presentations. The team needed a scalable, market‑localizable platform that could connect to their Big Data Cloud (JLL DataHub), handle billions of records, and deliver an intuitive experience consultants could use in day‑to‑day workflows.

By adopting CARTO for map‑based visualization and data pipelines (including Databricks connectivity and the CARTOframes Python package), JLL cut Gea data refresh time by about 50–52%, automated large‑scale updates, and reduced operational complexity. Today roughly 1,500 employees use Gea, it processes ~500 million data points per year, is rolling out across multiple countries, and has helped JLL deliver faster client insights, extend consultant expertise and strengthen its digital market positioning.

JLL

Isaac Pernas

Global Head of Application Engineering