This group project uses Kaggle data on Ames (Iowa) housing prices and various machine learning techniques to build a predictive model.

Besides original Kaggle data we add several more features:

• Dow Jones US Real Estate Index
• Corn prices
• Labor force in Ames
• Unemployment rate in Ames
• Fannie Mae mortgage rates.

All these variables are treated as lagged variables compareg to the date of house sale.

We use 4 linear models, a Random Forest Regressor and an XGB model to make predictions.

Finally, we stack the models using the inverse of their error rate on a test set as weights.

The resulting RMSLE is 0.119