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 today. 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.
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“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.”
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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.
