Diy Zillow

9 Pages Posted: 7 Mar 2022 Last revised: 8 Mar 2022

See all articles by Kenneth C. Lichtendahl

Kenneth C. Lichtendahl

University of Virginia - Darden School of Business

Benjamin Boatright

Independent

Joe Andrasko

University of Virginia - Darden School of Business

Abstract

This case examines a realtor's attempt to improve an online residential real estate website'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's current value. These forecasts can be used to evaluate properties on the market by comparing a home's listed price to the estimated value from the model.

Excerpt

UVA-QA-0931

Rev. May 24, 2022

DIY Zillow

Escape from New York

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.

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's frustration with Zillow'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'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.

. . .

Keywords: predictive modeling, data preparation, model scoring, SQL, data visualization, regression, geographical data

Suggested Citation

Lichtendahl, Kenneth C. and Boatright, Benjamin and Andrasko, Joe, Diy Zillow. Darden Case No. UVA-QA-0931, Available at SSRN: https://ssrn.com/abstract=4050083

Kenneth C. Lichtendahl (Contact Author)

University of Virginia - Darden School of Business ( email )

P.O. Box 6550
Charlottesville, VA 22906-6550
United States

Benjamin Boatright

Independent

Joe Andrasko

University of Virginia - Darden School of Business ( email )

P.O. Box 6550
Charlottesville, VA 22906-6550
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
3
Abstract Views
96
PlumX Metrics