A Seller's Market: Leveraging Analytics to Predict Winning Bids for Bay Area Homes

by: Hyun-Soo Ahn

Publication Date: October 18, 2017
Length: 4 pages
Product ID#: 7-917-031

Core Disciplines: Accounting/Finance, Economics, Operations Management/Supply Chain

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Teaching Note

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Description

Bruce, a Silicon Valley native, bought a duplex in Palo Alto, California in 2011 and it was a huge success. The property increased in value and the monthly rent skyrocketed from $4,000 to $11,000. As the economic boom continued in that region, and young talent flocked to the area to work at the hottest tech companies, the real estate market became a seller’s market. So, Bruce decides to buy a home in downtown Mountain View, considered the heart of Silicon Valley. He finds a property with a great location and other desirable amenities but needs to act fast and provide the right bid.

Combining data from multiple sources, Bruce creates a spreadsheet of recently purchased homes in the area. Students analyze Bruce’s data to determine an appropriate bid for the property based on variables such as home size, lot size, school district, and home expansion possibilities.

Teaching Objectives

After reading and discussing the material, students should:

  • Select and build a regression model.
  • Interpret statistical output to generate recommendations.
  • Use regression models as a prediction device.
  • Understand how to use regression output to develop a bidding strategy.