A successful Texas-based real estate and property management company specializing in single family homes and rentals was looking to expand their reach and improve their pricing process. The company had been building homes and acquiring properties across the state for almost a decade but knew their pricing method was suboptimal and outdated, leaving a potentially substantial amount of revenue on the table. They wanted to evolve a new method of pricing beyond their old practice by taking advantage of advancements in Machine Learning and Artificial Intelligence to create a process both scalable and adaptive to the changing marketplace.
However, the client did not have the internal resources or the in-house expertise to create a workable Machine Learning process on their own. Moreover, their old pricing system was established in such an unresponsive fashion that the client couldn’t even be sure just how much additional revenue they were missing. The old pricing strategy was an entirely manual process and only updated once a year. It incorporated little to no data from the marketplace in general or any external competitors, and only included the client’s properties that happened to be on the market at the time. This resulted in a pricing process that relied more on brute force and gut feelings than any sort of concrete data, slow to adapt to an ever-quicker marketplace. To compete, they needed to build a better and more responsive pricing process but, like most companies their size, they did not have the resources to do so on their own. The client then reached out to CG Infinity.
CG Infinity brought our long years of experience assisting in the real estate industry, combined with the expertise in Machine Learning and Artificial Intelligence of many of our talented staff, to create a Proof of Concept for the client. The client accepted the POC and we are currently in the midst of implementing the new Machine Learning pricing process. This is the first phase of many working with the client going forward. CG Infinity overcame an initially small dataset to build a Machine Learning model that used the client’s property history, outside market forces plus online data such as website hits, and the local marketplace and competitors to adjust and predict the client’s pricing. This allowed the client’s new pricing process to go from slow annual updates to daily pricing updates that successfully adapt to the changing market while cutting many hours of now unnecessary labor costs. The new pricing process also supports the client in understanding how their properties are positioned compared to their competitors and to know which floorplans are most in demand for new construction. Both the client and CG Infinity estimate the new Machine Learning pricing process will generate nearly $1 million a month in additional revenue compared to the previous system.