How to Utilize AI for Effective Revenue Management
119 viewsAbout This Episode
The world of real estate investments has many challenges—lack of visibility and transparency across data sets; errors in data sets; potential loss of money on deals; and slow decision making.
Arunabh Dastidar of Leni joins the podcast to discuss how data should be conceived and consolidated through specific AI-driven strategies for effective revenue management including predictive analytics for rental pricing, identifying and reducing operational inefficiencies, and enhancing resident retention through personalized services.
Arunabh also explores how multifamily property owners and managers utilize AI capabilities to optimize cash flow, enhance property operations through AI applications in daily asset management, streamline marketing efforts and reduce costs by adjusting for seasonality, and analyze property-level trends to allocate resources efficiently—all of which can lead to significant cost savings and improved overall property operations.
Arunabh Dastidar of Leni joins the podcast to discuss how data should be conceived and consolidated through specific AI-driven strategies for effective revenue management including predictive analytics for rental pricing, identifying and reducing operational inefficiencies, and enhancing resident retention through personalized services.
Arunabh also explores how multifamily property owners and managers utilize AI capabilities to optimize cash flow, enhance property operations through AI applications in daily asset management, streamline marketing efforts and reduce costs by adjusting for seasonality, and analyze property-level trends to allocate resources efficiently—all of which can lead to significant cost savings and improved overall property operations.
About Our Guest
Arunabh Dastidar is the Co-Founder and CEO of Leni, which is a data intelligence and AI leader for the real estate industry. He identifies as an engineer turned into asset manager, and then realized there is large challenge that he can solve – the need for transparency across data sets.
