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DIY Zillow
Lichtendahl, Kenneth C. Jr.; Andrasko, Joe; Boatright, Benjamin Case QA-0931 / Published December 14, 2021 / 9 pages.
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Product Overview

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.


Learning Objectives

This case can be used to (1) demonstrate data collection and data processing; (2) introduce the “unit of observation” and the “unit of analysis” concepts; (3) teach the training and deploying predictive models; and (4) discuss how to build visualizations that utilize geographic information such as shapefiles.

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  • Overview

    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.

  • Learning Objectives

    Learning Objectives

    This case can be used to (1) demonstrate data collection and data processing; (2) introduce the “unit of observation” and the “unit of analysis” concepts; (3) teach the training and deploying predictive models; and (4) discuss how to build visualizations that utilize geographic information such as shapefiles.