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Housing Market Prediction Model

The effects of the COVID 19 pandemic have had a major effect on the housing market and home values/listing prices. Using zillow to determine average home values per month for multiple neighborhood, and combining that with Federal Reserve Interest Rates and data from the US Census, we train a machine learning model to predict future home values.

The final presentation can be viewed in the included powerpoint deck.

Team Members: Bonnie Bailey, Diane Tiblin, Joseph March

Data Sources:

Resources:

  • AWS S3 Buckets
  • Census and json
  • Google Colab
  • Scikit-learn
  • PySpark
  • Pandas

Coding:

  • Pulled API data from US Census.
  • Cleaned the data by modifying headers, formatting data and converting to rates.
  • Uploaded Zillow and Federal Funding data to AWS in S3 buckets.
  • Pulled Zillow and Federal Funding data using PySpark.
  • Cleaned MSA columns so Census data would match Zillow in order to join data frames together.
  • Converted Federal Funding data into lists to join to the merged data frame with date columns.
  • Removed duplicate and non-zero values from the merged data frame. Performed one-hot encoding on the categorical data.
  • Scaled the data.
  • Created and tested linear regression model.

Model Performance

We tested several versions of the model before coming to our final version.

modeldata

Challenges

  • Difficulty finding free data on property sales.
  • Slight narrowing of our data source with the elimination of fields with null data or duplications.
  • Data was limited to metropolitan areas and the suburbs surrounding.

Results/Findings

A model can be created to predict the market value to within 99.99% accuracy within a metropolitan area.

Note: The housing market is naturally unpredictable as a result of human behavior.

How Could We Expand The Project?

  • Currently the model predicts one month ahead. With additional work the model could be expanded to predict further into the future.
  • Currently the model predicts averages within a metropolitan area. With funding, data could be pulled for individual properties.

Contacts