Analysis of the Relationship Between COVID-19 Infections and Web-Based Housing Searches

The study used Google search query data on real estate interest for several countries in the Baltic area. The dynamics of public interest in housing have been compared to the dynamics of the COVID-19 infections in Lithuania, Latvia, Poland, and Sweden. This study uses the Vector autoregressive (VAR) model to forecast such time series. VAR is a multivariate linear time series model in which the endogenous variables in the system are lagged functions of the values of all endogenous variables. The increase in COVID-19 infections negatively affected society's interest in housing. The study used Google Trends and R software. [ FROM AUTHOR]

Does Google Trends Show the Strength of Social Interest as a Predictor of Housing Price Dynamics?

A recently emerged sustainable information society has ceased to be only a consumer and has become a web-based information source. Society’s online behaviour is tracked, recorded, processed, aggregated, and monetised. As a society, we are becoming a subject of research, and our web behaviour is a source of information for decision-makers (currently mainly business). The research aims to measure the strength of social interest in the housing market (Google Trends), which will then be correlated with the dynamics of housing prices in Poland in the years 2010–2021. The vector autoregressive model was used to diagnose the interrelationships (including Granger causality) and to forecast housing prices. The research showed that web searching for the keyword “dwelling” causes the dynamics of dwelling prices and is an attractive alternative to the classical variables used in forecasting housing market prices.