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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. 

• INREV submits comments to several EU and UK consultations
• Discussions of BEFIT, Unshelland Pillar II slowly progressing
• Spain: Residential Property Measures in Catalonia

• ESMA assesses the risks posed by the use of leverage in the fund sector
• EU Commission Call of Evidence -Revision of EU rules on sustainable finance disclosure
• Luxembourg: CSSF simplifies fund document approval process

• European Parliament State Aid Rules for affordable housing
• Omnibus I: the European Parliament and Council agree on the ‘Stop-the-clock’ mechanism
• United Kingdom: FCA and HMT Consultations on reviewing UK AIFMD rules

• Calculation of leverage for loan originating funds (LOFs)
• Omnibus I (Taxonomy, CSRD and CSDDD)
• The UK Unauthorised Co-ownership Alternative Investment Funds Regulations laid in Parliament