Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Most European economies will experience significant demographic changes in the decades ahead. Due to low birth rates, populations are shrinking and ageing at the same time. This paper explores the impact of demographic change on the banking industry. A unique data set, which contains detailed information on almost 2.5 million accounts in 11 German savings banks, allows us estimating the socio-demographic determinants of retail profitability. Using a simulation model, we are able to predict the development of bank profitability due to demographic shifts up to 2025. One of the main findings is that the effects of population ageing will partially offset the impact of shrinking customer bases. While the decline in the size of the population reduces the customer base, ageing per se increases profitability as older customers typically generate higher profits for their banks. JEL-Code: G21, J10, L10, O16. Terms of use: Documents in

There is a broad literature on determinants of house price dynamics, which received increasing attention in the aftermath of the subprime crisis. Additional to macroeconomic standard variables, there might be other hard to measure or even unobservable factors influencing real estate prices. Using quarterly data, we try to increase the informational input of conventional models and capture such effects by including Google search engine query information into a set of standard fundamental variables explaining house prices. We use the house price index (HPI) published by Eurostat to perform fixed-effects regressions for a panel of 14 EU-countries comprising the years 2005-2013. We find that Google data as a single aggregate measure plays a prominent role in explaining house price developments.

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