Artificial intelligence is playing an increasingly pivotal role in transforming real estate decision-making and data analysis. These two new case studies showcase how investment managers are leveraging advanced technologies to improve forecasting accuracy and enhance ESG integration—supporting better outcomes for investors and stakeholders.
a.s.r. real estate – Machine learning for market forecasting
To navigate uncertainty in volatile market conditions, a.s.r. real estate developed a machine learning-based forecasting framework that supports client-specific investment strategies. The tool predicts key market indicators such as yield gaps, rental value growth, and occupancy rates across multiple economic scenarios. Fully integrated into the firm’s data infrastructure, the model improves forecasting precision and enables investment teams to make smarter, faster decisions tailored to each client’s goals.
JLL – Using Large Language Models to close the ESG data gap
JLL explored the potential of Large Language Models (LLMs) to address persistent data challenges in ESG-informed valuations. By comparing various LLMs—including its proprietary JLLGPT—the firm assessed their effectiveness in extracting ESG data from diverse and multilingual sources. The study showed that JLLGPT delivered high levels of accuracy, underscoring AI’s promise in enhancing ESG reporting processes and strengthening valuation reliability.
These case studies demonstrate how real estate firms are harnessing AI to drive innovation—whether through smarter forecasting or more efficient ESG data integration. Check out our dedicated page on Technology for more related resources.
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a.s.r. real estate - Data science market forecasting framework
Published on 01 Jul 2025
a.s.r. real estate developed a machine learning-based forecasting framework to support client-specific investment decisions in turbulent market conditions. By predicting yield gaps, rental value growth, and occupancy rates under various economic scenarios, the tool enables tailored asset allocation strategies. Integrated into the firm’s data infrastructure, the model enhances forecasting accuracy and supports smarter, faster decision-making across investment teams.
JLL - How AI can bridge the ESG data gap in valuations
Published on 01 Jul 2025
Valuers face increasing pressure to incorporate ESG factors into valuations, yet gaps in data availability and quality remain a key obstacle. This case study explores how JLL tested Large Language Models (LLMs), including its proprietary JLLGPT, to enhance the extraction of ESG data from diverse sources. The findings reveal JLLGPT’s accuracy, particularly in processing multi-language documents, demonstrating the potential of AI to streamline ESG reporting and improve valuation robustness.