SF-home-price-prediction is a Python codebase that explores the effect of IPO's on Silicon Valley Home Prices in the near future. The findins and conclusions will be stored in the /conclusions folder.
The datasets and graphics will be updated monthly as new IPO's are filed to create a real time estimate of future home prices by zipcode in Silicon Valley.
"Everybody is getting pumped about the new wave of tech IPOs in 2019 and 2020. These SF Bay Area tech IPOs include Uber, Lyft, Airbnb, Slack, Pinterest, and more.
The default assumption is that these tech IPOs will bring in huge demand and drive SF Bay Area real estate prices up. After all, these soon-to-be public companies are worth billions and employ tens of thousands of sub-35 year-old-employees looking to establish roots." - Financial Samurai
Most real estate agents in the Bay Area mention the IPO's as the single biggest reason to buy property NOW. I've been told "Buy now or you might never be able to" numerous times.
Given this large expectation of an expected surge in demand, I am modeling the change in Home Prices by Zipcode. In the Bay Area there exist great variations in median income, demographic information, and home prices across zipcodes. Even zipcodes that are right next to eachother. These new millionaires will be starting families and investing in locations where they can build their lives and futures, so the variance in home prices across zip codes will grow. This effect is what I will be modeling and predicting.
- Data of All US Filed IPO's from 1996 to present day
- IPO Data compiled by John Ritter between 1990 - 2018
- 2017 US Census Data
- Zillow Median All Home Prices Data by Zipcode
- Silicon Valley + San Francisco Zip Code Information
Built with
- Python3
- Pandas
- SciKit - Learn
- Seaborn
This is the first and currently the only ML approach to Predicting of Housing Prices as a function of IPO's filed.
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If you would like to use the project or contribute please contact [email protected].
MIT © Aaron P Thomas