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In 2007 the real estate market started showing clear trends of falling prices. After years of rapid growth of both sales and new homes, the market shifted. Falling prices coupled with high unemployment led to many homeowners being unable or unwilling to continue to pay their mortgages. Eventually, many of these homes went into default and became property of the lenders behind the mortgages (Financial Crisis Inquiry Commission, 2011; Holt, J., 2009). These properties are known as Real Estate Owned.
The sale of real estate owned (REO) properties, known among those not in the industry as foreclosures, has little in common with a traditional sale. An activity typically associated with subjectivity and things like charm—the selling and purchase of a home—becomes much more methodical with considerations made by individuals with only a financial, not an emotional, stake in the property.
Listing Agents have responded to this change from the standard client by developing more methodical and systematic approaches themselves. Some have turned to technology to assist in the process, from record keeping to workflow management.
In my own experience, existing solutions were lacking. While there were industry-specific software options available to REO Listing Agents that were advertised as workflow/task managers, many came up short as they sought to solve the problem of variance within the industry with simple tools that typically served the same function as a calendar reminder.
Motivated by my background in computer science, we sought to develop a better solution that went the other direction. It would be one based on an abundance of data points, with capturing these data points being the first goal of the software. The data would be structured by a workflow designed to tackle every step of the process with tasks that required only as much as needed to advance the process but capable of capturing much more. And, to address that problem of variance, the workflows and tasks would be adaptable to an office’s clients or own preferred practice through logic rules and customized orders (that is, the order in which tasks occurred). We called it WinREO.
In developing WinREO, my team and I took a data-centric approach to the REO process. We believed that focusing on the data and not just a prescribed series of actions, we’d have a product that could support many brokerages. This followed the realization that while the Listing Agent was seeking to accomplish certain tasks, their primary goal was to capture certain data points that could be conveyed to their client or used to facilitate the next action. We did understand that capturing that data required a framework that was user-friendly and staggered. So while the end result is a workflow of tasks, what we feel makes our approach different is that we developed our software by seeking out the data points, then worked backward again to shape the tasks to be, first-and-foremost, aggregators for these data.
To create WinREO, the end-to-end REO process was deconstructed into a series of tasks collecting a specified array of data with distinct stakeholders invested in its completion. This paper reviews that deconstruction and the approach taken in developing the software.