Abstract
The internet has become the primary source of information for most of the population in modern economies, and as such, it provides an enormous amount of readily available data. Among these are the data on the internet search queries, which 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 and subsequent approvals have become a central issue in the banking sector as well as for governments and regulators. Here, we provide new insights into the dynamics of the UK mortgage market, specifically the demand for mortgages measured by new mortgage approvals, and whether or how models of this market can be improved by incorporating the online searches of potential mortgage applicants.
Because online searches are expected to be one of the last steps before a customer's actual application for a large share of the population, intuitive utility is an appealing approach. We compare two baseline models - an autoregressive model and a structural model with relevant macroeconomic variables - with their extensions utilizing online searches on Google.
We find that the extended models better explain the number of new mortgage approvals and markedly improve their nowcasting and forecasting performance.