Robert Kovalev
Published: 11/7/2024
In property management, data is abundant but often underutilized. Traditional methods, reliant on manually reviewed reports, can leave companies making critical decisions based on guesswork rather than data-driven insights. With RAG (Retrievable Augmented Generation) and Generative AI, we’re seeing a revolution in how property management firms approach their data—transforming vast amounts of raw information into clear, actionable insights that drive real results.
The Problem with Hand-Reviewed Reports
Imagine a property manager juggling hundreds of units, each with a constant stream of information logged in their Property Management System (PMS). The data—leasing trends, maintenance records, tenant satisfaction scores—is there, yet it remains locked away in cumbersome reports, largely untouched. This isn’t because the information is unimportant; it’s because accessing it is simply too time-consuming.
When property management companies rely on static reports, they’re forced to make quick, often surface-level assessments. When delinquency rises, for example, the gut reaction might be to blame poor screening. But what if the real cause lies elsewhere? Without a deep, data-driven dive, the true issues remain hidden, and every decision becomes a guessing game in the infamous “nickel-and-dime” trade-off.
RAG & Generative AI: Revealing the Truth in Data
Distilyze’s RAG and Generative AI approach changes the game by simplifying data access and empowering companies to use insights to act proactively. By bringing relevant data right to the forefront, property managers can understand not only “what” is happening but “why.”
Take one of our recent clients, for example. They initially assumed that rising delinquency rates were due to inadequate screening. But once we analyzed their entire portfolio through the RAG model, a different story emerged. Maintenance issues were rampant, with tenants facing frequent disruptions—broken fridges, misplaced keys, even cracked sinks left unattended. Tenant dissatisfaction skyrocketed, and it showed in the data. Maintenance issues led to lower CSAT scores, and frustrated residents were simply unwilling to pay rent.
The Shift to Proactive Property Management
By examining portfolio-wide data in real-time, we uncovered the real issues impacting delinquency rates. Rather than focusing solely on new tenants, we saw the importance of earning back the trust of current residents and addressing their concerns first.
At Distilyze, our goal is to shine a spotlight on real estate portfolios, especially when the reasons behind performance declines aren’t immediately obvious. As we continue training our datasets and advancing our AI and ML capabilities, we aim to become even more precise in identifying these critical patterns and transforming complex data into clear, actionable to-do lists for property managers.