Matt Davis says the machine learning model sets the bar at a new level for prospect risk management.
RICHARDSON, TX—This week, RealPage Inc. introduces RealPage AI Screening, the first artificial intelligence-based screening algorithm built specifically for the multifamily industry. The solution, developed by RealPage resident screening and data science teams, disrupts generic rules-based and statistical-based scoring models, enabling property management companies to identify high-risk renters with greater accuracy.
“Our new outcome-driven machine learning model sets the bar at an entirely new level for prospect risk management in our industry,” says 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.”
This model purports to be 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.
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 desirable 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 30 million actual lease outcomes to evaluate renter performance during the course of a lease. AI Screening also incorporates granular third-party consumer financial data to better predict applicant risk. RealPage's large proprietary database of outcomes, augmented by consumer financial data, is the key driver of the screening algorithm's success. By analyzing this data repository, 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 is the multifamily industry's first and only AI-enabled scoring model, with unprecedented levels of predictive accuracy,” Davis tells GlobeSt.com. “This solution represents a monumental advance; hundreds of millions of dollars in value every year simply with the reduction in bad debt against the asset. By flipping the existing screening protocol on its head to identify low-risk renters, asset occupancy and revenue are not negatively impacted with AI Screening. This solution is possible only with the vast amount of multifamily-specific outcome data and advanced data science techniques only available through RealPage.”
RealPage AI Screening offers 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.
Process automation from AI does indeed offer an opportunity to lower costs associated with commercial real estate, especially those associated property management, according to Emerj Artificial Intelligence Research. In addition to property management, AI real estate applications present cost saving opportunities for businesses, including analytics in building automation systems and machine learning in real estate marketplaces.
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Lisa Brown
Lisa Brown is an editor for the south and west regions of GlobeSt.com. She has 25-plus years of real estate experience, with a regional PR role at Grubb & Ellis and a national communications position at MMI. Brown also spent 10 years as executive director at NAIOP San Francisco Bay Area chapter, where she led the organization to achieving its first national award honors and recognition on Capitol Hill. She has written extensively on commercial real estate topics and edited numerous pieces on the subject.
