Diy Zillow

This case examines a realtor&apos;s attempt to improve an online residential real estate website&apos;s mapping and home estimation features. With the help of a data scientist, the two professionals work to create an interactive map that provides greater filtering capabilities and improved school boundary views, allowing clients to easily locate homes in their desired school districts. In addition to the visualization enhancements, they also build a simple predictive model for forecasting a home&apos;s current value. These forecasts can be used to evaluate properties on the market by comparing a home&apos;s listed price to the estimated value from the model.<p>Excerpt</p><p>UVA-QA-0931</p><p>Rev. May 24, 2022</p><p>DIY Zillow</p><p>Escape from New York</p><p>During the 2020–21 housing boom, Charlottesville, Virginia, realtor Reidar Stiernstrand was busy assisting home buyers navigate the real estate market. One of his prospective clients, Marion Sabor, was planning to join the droves of people moving away from New York City in search of more residential communities. Instead of the neighboring New York City suburbs, Sabor was interested in relocating her family to Charlottesville.</p><p>The Sabors had already spent some time researching the Charlottesville-area housing market using Zillow, a popular online real estate website. Because of the couple&apos;s frustration with Zillow&apos;s views of homes within the various school districts, Stiernstrand planned a “Do It Yourself” (DIY) project to build a tool that enhanced the views of new home listings. Stiernstrand&apos;s goal was to create a customized map to guide the search for the perfect home, not only for the Sabors but also for future clients and their families.</p><p>. . .</p>