Imagine a world where a property management system not only accurately tracked rental payment data, but also analyzed it to predict which payments have the highest likelihood of returning.
Actually, you don’t have to imagine. Thanks to advancements in predictive machine learning algorithms, this cutting-edge technology analyzes scores of data pertaining to payment history, timing and behavior. It can be used to more intuitively assess the ultimate result of the payment.
How do apartment operators get in on the action? By partnering with providers that invest in these sophisticated technologies.
While the payment analysis feature can be immensely valuable in helping reduce the amount of returned payments at properties and assisting with faster funding for lower-risk payments, it only scratches the surface of the possibilities created by predictive analytics.
To this point, the functionality of AI-based machine learning has arguably made its greatest impact in multifamily in the form of chatbots. Chatbots, which interact with rental prospects in online chats, have become increasingly adept at analyzing key words within conversations and providing real-time answers to a wide array of questions.
Again, a valuable feature, although not one that entirely taps the potential of predictive machine learning algorithms. From a payment standpoint, these algorithms will have the ability to decline risky payments before they go through. They’ll be able to diagnose residents who continually attempt payments that are likely to return and automatically switch them to certified funds. Additionally, they’ll provide reports and alerts for property managers to detect and combat fraud in the early stages.
On the broader scale, predictive modeling will likely make an impact in all facets of the leasing process. They’ll help create more intuitive screening processes, provide advanced insights for pricing, help predict residents’ likelihood to renew and assist with resident and prospect communications.
They have already started to reshape maintenance processes by offering predictive and preventative maintenance, and further advancements are on the way. This is exemplified in smart devices that can essentially submit their own service request ahead of time when a component is ready to fail. This is one of the ways predictive analytics will make a pronounced impact in the smart-home sector, as well.
As the tech evolution pushes forward, software providers that invest and prioritize in these types of technologies will remain on the forefront of innovation. Forward-thinking providers should always seek intuitive ways to implement predictive machine learning algorithms into their services—and test them. Those that ignore this highly relevant tech tool will fall behind the curve.