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Real Estate ML Model with Data Scraper

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Getting Started

  • Must fill-in username in password in data/resources/redfin_login.py
  • Create zipcodes.csv in data/resources with headers as [Region, City, Zip Code], Zip Code being the main values for the scraper.
  • python run.py to start the webapp. Access through http://localhost:45513/

Predictions

http://localhost:{port}/predictions

  • MLS Data is pulled through searching Redfin using the 'Search Redfin' Button. Do not spam or a ban might occur.
    • The app also uses the address to find latitude and longitude (inputs for the model). Searching for the same address breaks this.
  • Any current data entered, creates live updates to the predicted sales price at the bottom.
  • Current model uses the XGBoost algorithm and has and RMSE of ~45k
  • Model is built in ml-model.py and saved as .joblib files for use in the predictions.

prediction

Update/Scraper

  • To update the data navigate to /data and run python updater.py
  • Options will appear to scrape both sales data, MLS data, and Model (combined) data.
  • NOTE: Scraping MLS data is very slow (~ 1-2 seconds per address)

Future Plans

  • Include more features and switch to neural network
  • List Prices would significantly improve the model

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Real Estate ML Model with Redfin Data Scraper

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