The criteria for mortgage lending, set by governmental regulations and the Swiss financial market supervisory authority, were last tightened in 2014. As a consequence of this stricter framework, Swiss financial houses had to reassess their mortgage portfolios. In order to monitor real estate price developments and, connected thereto, the compliance of their customers with the stricter regulations, some Swiss financial houses have recently developed real estate price indices or real estate bubble indices for their areas of business. However, as no monitoring tool of this type exists specifically for the canton of Aargau, the aim of this bachelor's thesis is to develop a prototype real estate bubble index for this area.

The secondary data utilised for the purpose of this research was obtained from a Swiss regional bank, the Swiss national bank, Comparis.ch and the governmental statistics department of the canton of Aargau. The data obtained, after the required data transformation, was analysed using exploratory, and confirmatory multivariate data analysis.

Due to limitations emerging from the data utilised, an index based on factor analysis could not be created. Nevertheless, a statistically valid multiple regression model was developed, which allows predictions to be made on future real estate price developments. Furthermore, the multiple regression model allows for the comparison of the recently observed real estate price increase to the predicted upper boundary, calculated from the model. Although the difference between the predicted upper boundary and the observed real estate price increase is very small for the years 2010, 2014 and 2016, it can still be concluded that real estate prices have not experienced an extraordinarily large price increase. Hence, the likelihood of an upcoming real estate price bubble has not increased dramatically in recent years. However, because the multiple regression model is still considered a prototype, and, because of the limited availability of the needed data, the results are subject to both, a availabaility and time period bias. Nevertheless, the model can be regarded as a solid base for further research, and the conducted analysis contains useful information for the further development of a real estate bubble index.