Document Type

Theses, Masters


This item is available under a Creative Commons License for non-commercial use only


Computer Sciences

Publication Details

A dissertation submitted in partial fulfilment of MSc in Computing (Data Analytics), Technological University Dublin, March 2016.


Housing is a fundamental human right. Increasing rents and rising unemployment contribute to increased rates of homelessness. Traditionally housing prices are determined by supply and demand. This project will investigate the relationship between hedonic features and domestic rental prices in California and New York, using multivariate regression models. The literature outlines a number of approaches taken to model real estate pricing using hedonic regression.

Two models were created to analyse the difference between California and New York. Features were selected using correlation analysis. Some features were derived using logarithmic and dummy feature transformations. The models themselves were evaluated by assessing the root mean square error (RMSE) and by visually inspecting the residual plots.

Despite the models not providing a high degree of accuracy in predicting rental prices, a number of valuable insights were gathered by analysing the difference between the regional models. Also, a Tableau dashboard was created to show how such models could be visualised for a data analytics novice.

Areas for future work have also been identified for those interested in expanding upon the work within this project.