In this project we identified and analyzed various trends within the housing market in the United States to assist an alternative investment firm in identifying opportunities for investment. Using data science technologies–Databricks, Apache Spark, and pandas–we wrote software and queries to parse the various datasets at our disposal and create important interactive visualizations to be used by the firm in the future. We looked at hurricanes and other influential factors as we developed and implemented a forecasting model for median sale price of some of the top housing markets in the United States. The combination of our interactive visualizations and the forecasting model is significant for our sponsors in their pursuit of investments and returns.

• This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.

Creator

• Proko, Aleksander
• Zhu, Yueting
• Bolduc, Alex
• Brunelle, Reagan

Subject

• Computing
• Innovation
• Project-Based Learning
• Industry

Publisher

• Worcester Polytechnic Institute

Identifier

• 82866
• E-project-121422-125829

Keyword

• Databricks
• Seasonal-Trend Decomposition with LOESS (STL)
• Power BI
• ARIMA
• Real Estate
• Fintech

Advisor

• Blais, Marcel Y.
• Sarnie, Robert
• Elmes, Michael B.
• Wong, Wilson

Year

• 2022

Sponsor

• Alternative Investment Company

UN Sustainable Development Goals

• Not Specified

Date created

• 2022-12-14

Resource type

• Major Qualifying Project

Major

• Computer Science
• Data Science
• Business

Source

• E-project-121422-125829

Rights statement

• In Copyright

Last modified

• 2023-01-13