Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence.

With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.

The potential for creative feature engineering provides a rich opportunity for fun and learning. This dataset lends itself to advanced regression techniques like random forests and gradient boosting with the popular XGBoost library. We encourage Kagglers to create benchmark code and tutorials on Kernels for community learning. Top kernels will be awarded swag prizes at the competition close.