AI Sublease Optimization
The Problem
“Sublease pricing and matching is slow, inaccurate”
Organizations face these key challenges:
1
Pricing sublease space is error-prone due to limited comps, rapidly shifting demand, and unique term constraints (remaining term, consent, use, furniture, TI).
2
High vacancy and long days-on-market driven by slow lead qualification, manual tenant matching, and inconsistent marketing across channels.
3
Negotiations frequently stall or fail because critical lease clauses and constraints are discovered late, increasing legal costs and time-to-close.
Impact When Solved
20–35% reduction in days-on-market via demand forecasting and dynamic pricing recommendations.2–5% improvement in net effective rent and 10–20% reduction in concessions through optimized incentive strategies.30–50% reduction in manual underwriting and screening time using automated term extraction, lead scoring, and constraint-aware matching.