One of the largest green spaces for advanced analytics in Commercial Real Estate decision making is the ability to first forecast market movements and trends;
then, rapidly formulate and/or modify investment strategies to either exit a declining market or deploy capital in “the Next Austin, TX” faster than competition.
Today, this challenge is solved by fund and portfolio managers, primarily by calling up industry contacts (e.g. brokers) to be up to date on recent transactions or reading up on local news about new developments in target areas. There is a lot left to be desired in speed, quality of insights, and differentiation from competition.
One client compared his current experience to “guessing when to catch a falling knife”.
The following concepts tackle this opportunity, pushing boundaries for how a CRE tech company’s proprietary data can be be integrated into machine learning models, then turned into a report assembled by generative AI, equipping CRE investment managers with vale-generating insights and analytics.
