Welcome to this Kaggle InClass competition as part of the "Econometrics 2" course at WU Vienna.

In this competition you are provided with a dataset on house prices and potential explanatory variables. This dataset is split in a training subset with and a testing subset without house prices. Your goal is to predict these missing prices using the data provided to you.

The data has been cleaned and prepared for you, so you can focus on data transformation, model specification, model selection, and actual estimation. There is a relatively large number of potential explanatory variables, so be wary of overfitting and poor out-of-sample performance.

Your predictions are evaluated in two steps. During the competition, your submissions receive a preliminary score based on a part of the testing dataset, which will appear on the public leaderboard. Your final score is based on the other part of the testing dataset and will only be visible once the competition has finished - making good out-of-sample performance crucial. As a result, your final score and rank may change considerably from the public leaderboard.
Acknowledgements

I thank the creators and provider of the dataset and Prof. Geyer for the idea and his highly valued inputs.