AI Flex Space Demand Analysis
The Problem
“Forecast flex space demand with higher confidence”
Organizations face these key challenges:
1
Fragmented demand signals and inconsistent data quality across brokers, listings, CRM, and market datasets
2
Slow, manual underwriting and comp selection that cannot update quickly as submarket conditions shift
3
High financial exposure to vacancy, mispricing, and incorrect unit mix decisions (bay depth, office %, clear height, loading, small-bay vs mid-bay)
Impact When Solved
Submarket-level demand forecasting with confidence intervals to support pricing, concessions, and lease-up strategyAutomated tenant/industry segmentation and lead scoring to prioritize outreach to highest-propensity demand sources (e.g., light manufacturing, contractors, last-mile, e-commerce support)Portfolio optimization recommendations (unit mix, capex, and acquisition/disposition timing) based on predicted demand-supply imbalance