Library

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.

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.

This paper focuses primarily on how blockchain and tokenisation can impact nonlisted real estate investments, be it when making investments into funds or individual assets.

This paper addresses how technology can improve underwriting and investment decisions throughout the full cycle of a real estate investment.  It’s the second in a series of papers from the Technology Committee that aim to help INREV members understand how technology can be leveraged to address recurrent challenges in the real estate industry.

While some industries are constantly moving towards digital excellence, the real estate and construction industries still lag behind. This paper explores the diverse ways digital technologies may be applied in a digitally excellent real estate organisation, as well as the impact of technology on its business processes, structures and mindset.