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Hot take: You might need machine learning, not AI

Hot take: You might need machine learning, not AI

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Hot take: You might need machine learning, not AI“Machine learning is where I would start, specifically around questions around underwriting and valuations.”That’s Garret Van Parys on why real estate companies may be overlooking one of the most practical applications of data science while rushing toward AI.Garret Van Parys is the Chief Strategy and Analytics Officer of Esusu, a billion-dollar fintech platform turning rental payments into credit-building outcomes. He previously spent more than thirteen years at Invitation Homes, most recently as Senior Vice President, Enterprise Analytics & Revenue Management, overseeing $3 billion in annual revenue while helping change how residential real estate investment management uses data and analytics to drive performance, strategy, and operational efficiency.Garret helped lead the two largest mergers and integrations in single-family rental history and was recognized as an IMN "Rising Star Under 35." He holds graduate degrees from Duke University's Fuqua School of Business in Quantitative Management and an MBA from the University of Arizona's Eller College of Management.Jonas and Garret go back to the early days of single-family rental, when the industry was scaling rapidly but the systems and data infrastructure needed to run it simply did not exist. Their conversation moves from those early days of Excel and Access databases to machine learning, AI, revenue management, customer retention, and what real estate companies should actually be doing with these technologies today.In this episodeBuilding the data infrastructure from scratch. Garret takes Jonas back to 2012, when he joined Colony American Homes as roughly employee number 30. The company had an Access database and some Excel files, but no ERP or established property management systems. With the business acquiring homes at an extraordinary pace, the team had to build the systems needed to track acquisitions, renovations, inspections, and readiness from the ground up.The operational challenge of scaling single-family rental. Managing hundreds or thousands of individual homes is fundamentally different from managing a large multifamily property. Garret explains how the distributed nature of single-family rental created enormous operational complexity, with homes in different locations and different stages of rehab. Even knowing where every home was in the process could become a major challenge.How data helped make massive mergers possible. Garret was involved in both the Colony and Starwood merger and the later Invitation Homes merger. He explains how data and analytics became essential to answering practical questions about how to run a combined portfolio, including staffing levels, market overlap, operational efficiency, and organizational structure. The company went from zero homes to roughly 80,000 homes in about five years, making the ability to model and understand the business critical.Why machine learning deserves more attention. Garret's central argument is that real estate companies may be moving too quickly toward AI without fully exploiting machine learning. He describes machine learning as more deterministic, less expensive, more straightforward, and generally more explainable. Once the underlying data is in place, he believes machine learning should often be the first step for solving fundamental problems in areas such as underwriting and valuation.The technology is only as good as the people using it. Garret pushes back on the idea that an executive can simply open Claude or another AI tool and ask it to build a machine learning algorithm. The tools are increasingly accessible, but knowing whether an approach is scalable, durable, and actually solving the right problem still requires expertise. He compares these technologies to a paintbrush: having the tool does not automatically make someone a great artist.Why business knowledge matters as much as technical expertise. Garret believes the strongest analytics teams combine technical capability with deep knowledge of the business. The people building models need to understand the real-world problem they are trying to solve, while also being curious enough to identify problems executives may not have even considered. He argues that companies should focus on finding and retaining people who can bring those capabilities together.AI can uncover customer signals that humans could never monitor manually. Garret sees one of the most promising applications for AI in understanding customer interactions at scale. Recorded customer calls can be transcribed, analyzed for sentiment and topics, and monitored continuously. That creates a new ability to understand whether customers are satisfied, whether a representative handled an interaction well, and whether a customer may be at risk of leaving.Retention may be the biggest ROI opportunity in rental real estate. Garret connects customer experience directly to economics. Every additional renewal can ...
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