Placer.ai Launches Void Analysis Tool to Rapidly Identify the Ideal Tenants for Any Retail Space
Void Analysis tool immediately analyzes thousands of potential tenants, leveraging a variety of key metrics to improve leasing decision-making
Placer.ai, the leader in location analytics and foot traffic data, released its Void Analysis tool . Void Analysis is an easy-to-use interactive tool that empowers shopping center owners or leasing representatives to find the ideal tenants for any retail space. Prospective tenants are ranked and ordered based on a variety of factors, and a list of tenants is then automatically produced, quickly identifying the ideal candidates and candidate types for any vacant retail space.
“The retail real estate market is experiencing an unprecedented moment of change and the role of data in guiding decision-making is only increasing. Placer’s Void Analysis tool enables CRE professionals to rapidly identify the ideal fit for any retail space based on a variety of critical factors,” said Placer.ai Co-Founder and CEO Noam Ben-Zvi. “With this information, ideal candidates can be quickly identified and the pitch strengthened with objective, reliable location analytics. The result is a unique opportunity to help CRE professionals make better decisions, and faster than ever before.”
Void Analysis consists of two main elements:
- Analyze top tenants
- Top potential tenants for any shopping center vacancy based can be identified based on their Relative Fit Score (RFS).
- The Relative Fit Score is based on several parameters including demographic fit score (DFS), cannibalization rate, average monthly foot traffic, and co-tenancy fit.
- Learn more about potential tenants
- Dive deep into the match between your shopping center and a
- prospective tenant, including the breakdown of which factors suggest strong potential success.
- Gain a detailed look at key metrics like household income, gender, age, frequent co-tenants, and other parameters to help sharpen the focus and customize the search to account for more variables.
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