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Title: Modeling the UK mortgage demand using online searches Authors:  Jaroslav Pavlicek - Institute of Economic Studies, Charles University (Czech Republic) [presenting]
Ladislav Kristoufek - Institute of Information Theory and Automation, Czech Academy of Sciences (Czech Republic)
Abstract: Internet has become the primary source of information for most of the population in modern economies and as such it provides enormous amount of readily available data and the data on the internet search queries have been shown to improve forecasting models for various economic and financial series. In the aftermath of the global financial crisis, modeling and forecasting mortgage demand has become a central issue in the banking sector as well as for governments and regulators. In the UK, the mortgage market dynamics is could be measured by new mortgage approvals. As the online searches are expected to be one of the last steps before the actual customer application for a large share of population, the intuitive utility of utilizing the intensity of specific online search queries to model them is appealing. When comparing two baseline models - an autoregressive model and a structural model with relevant macroeconomic variables - with their extensions utilizing online searches on Google, the extended models show to better explain the number of new mortgage approvals and improve their nowcasting and forecasting performances markedly. Moreover, utilizing machine learning techniques, the data on Google searches are preferred and, to a certain extent, able to replace the macroeconomic indicators.