RICHARDSON, Texas--(BUSINESS WIRE)--
Machine learning-based solution delivers unprecedented predictability
The solution, developed by RealPage Resident Screening and Data Science teams, disrupts the paradigm of generic rules-based and statistical-based scoring models, enabling property management companies to identify high-risk renters with greater accuracy. This model is materially more effective than traditional screening solutions, with an average proven savings of $31 per apartment per year without negative impact to occupancy or revenue. RealPage AI Screening offers the potential to return hundreds of millions in financial losses back to property management companies across the industry.
“Our new outcome-driven, machine learning model sets the bar at an entirely new level for prospect risk management in our industry,” said Matt Davis, Senior Vice President of Financial Services at RealPage. “We are delivering innovation to our customers based on the massive dataset of move out experiences that exist within RealPage. Additionally, our clients won’t have to wait for innovation or improvements as this machine learning system will learn with each new move out experience. It’s a miraculous outcome from the marriage of machine learning and RealPage’s unique dataset. We are confident in our screening model’s capabilities because it was tested and piloted by several industry leaders over the past six months, spanning more than 100,000 apartments.”
Traditional screening models use credit score, rent-to-income, debt-to-income and generic financial data to determine renter risk. While these factors broadly measure an applicant’s capability to pay financial obligations, including rent, RealPage developed industry-specific insights to determine the willingness to pay rent. Together, analyzing an applicant’s capability and willingness to pay rent is a superior risk assessment model to predict a renter’s financial performance.
RealPage AI Screening is made possible with the pairing of data science and machine learning techniques utilizing more than thirty million actual lease outcomes to evaluate renter performance over the course of a lease. AI Screening also incorporates granular third-party consumer financial data to better predict applicant risk. RealPage’s massive, proprietary database of outcomes, augmented by consumer financial data, is the key driver of the screening algorithm’s success. By analyzing this vast repository of data, AI Screening exceeds the performance of all other models available in the industry, with proven reduction of bad debt and financial loss.
RealPage AI Screening confers additional advantages to property management companies. Screening results are available within seconds, not hours or days as with traditional models. Additionally, the scoring model continuously learns and is updated with financial data and outcomes to improve predictability regardless of economic conditions in the market.
For more information on RealPage® AI Screening, please visit https://www.realpage.com/apartment-marketing/ai-screening/.
RealPage is a leading global provider of software and data analytics to the real estate industry. Clients use its platform to improve operating performance and increase capital returns. Founded in 1998 and headquartered in Richardson, Texas, RealPage currently serves more than 12,100 clients worldwide from offices in North America, Europe and Asia. For more information about the company, visit https://www.realpage.com.