Library

INREV welcomes the European Commission’s proposal on non-financial CRE statistics and supports its goal of improving data availability for macro-prudential oversight. To ensure the framework's effectiveness, INREV recommends sectoral differentiation, alignment with existing classifications, careful use of private data sources, and strong confidentiality safeguards.

Download INREV’s Position Paper on Commission’s Sustainable Finance Disclosure Regulation (SFDR) review

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.

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.

The first Global IRR Index provides data on the performance of value added and opportunity strategy closed end non-listed real estate funds in Asia Pacific, Europe and the US.