Constructing Residential Price Property Indices Using Robust and Shrinkage Regression Modelling

Sammanfattning: This thesis intends to construct and compare multiple Residential Price Property Indices (RPPI) with the aim to express the price development of houses in Stockholm county from January 2013 to September 2018. The index method used is the hedonic time dummy variable method. Different methods of imputation of missing data will be applied and new variables will be derived from the available data in order to develop various regression models. Observations judged as not part of the index's target population will be excluded to improve the quality of the training data. The indices will be computed by fitting the final model with OLS regression (as a benchmark), Huber regression, Tukey regression, Ridge regression as well as least-angle regression. Lastly, the obtained indices will be assessed by analyzing different measures of performance when included in \textit{Booli}'s valuation engine. The main result of this thesis is that a specific regression model is produced and that it is concluded that Huber regression slightly outperforms the other methods.