Isaac Pernas JLL

“Many RE firms are still using PDF reports and Powerpoint. With Gea, we are moving beyond static information to provide digital solutions and a superior customer experience. By giving our consultants an unprecedented amount of information, we can enable our clients to achieve their goals faster - using data to move away from traditional intuition-based decision-making.”

Isaac Pernas JLL

Isaac Pernas,
Global Head of Application Engineering – Valuation Advisory, JLL

  • icon The Client


    JLL is a world leader in real estate services, powered by entrepreneurial spirit - with 92,000 employees globally. They buy, build, occupy and invest in a variety of assets including industrial, commercial, retail, residential and hotel real estate. From tech startups to global firms, their clients span industries including banking, energy, healthcare, law, life sciences, manufacturing and technology.

    Present in 80 countries, they manage 4.6 billion square feet of property and facilities, with 37,500 leasing transactions globally.

  • icon The Challenge

    JLL turned to CARTO to develop their Gea solution, which would be used by their consultants for market analysis and valuations. Challenges included market localization for multiple countries across the world; Big Data and Data Science connectivity (with Databricks) as well as UX being a key priority to ensure consultants would actually use the tool in their day-to-day.

  • icon Results

    The solution has led to faster deliveries on client mandates, extended consultant knowledge (beyond their traditional in-depth knowledge of their regions) and brand positioning for JLL as the most data-driven and location-aware firm in the Real Estate industry. Discover the specifics by downloading the case study.

    JLL Real Estate Market Analysis

  • icon Why CARTO?

    By using CARTO, JLL has been able to find first-class map-based visualizations and data pipeline solutions in one single platform - decreasing complexity, saving a significant amount of time (and therefore human resource) and avoiding mistakes in the dataops, devops and GISops process. See the solution first-hand in the full case study.

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