Stacking models for improved predictions: A case study for housing prices

If you have ever competed in a Kaggle competition, you are probably familiar with the use of combining different predictive models for improved accuracy which will creep your score up in the leader board. While it is widely used, there are only a few resources that I am aware of where a clear description is available (One that I know of is here, and there is also a caret package extension for it). Therefore, I will try to workout a simple example here to illustrate how different models can be combined. The example I have chosen is the House Prices competition from Kaggle. This is a regression problem and given lots of features about houses, one is expected to predict their prices on a test set.