AI Retail Vacancy Prediction
The Problem
“Predict Retail Vacancy Before Revenue Loss Occurs”
Organizations face these key challenges:
1
Vacancy is often discovered too late (after notice, delinquency, or closure), leaving insufficient lead time to retain tenants or line up replacements
2
Signals are fragmented across systems and vendors (rent roll, delinquency, foot traffic, demographics, competitor openings, sentiment), making consistent portfolio-level risk scoring difficult
3
Underwriting and leasing plans rely on static market vacancy/downtime assumptions that miss tenant-specific and location-specific risk, leading to mispricing and capital misallocation
Impact When Solved
30–90 days earlier identification of at-risk tenants and spaces, enabling proactive renewals, rent relief structuring, or pre-leasing10–25% reduction in vacancy-related revenue loss via shorter downtime and higher retention in priority segments