[EN] Most people use web search tools to collect information on goods or services
they intend to buy. Given the prominence of Google among the search engines
and the availability of Google trends (GT) as a tool packaging some
characteristics of those searches (geography, topic, categories, among others)
it is only natural to use this instrument in order assess trends in the market.
In this paper we build indicators reflecting the real estate market stance. To
do so we rely GT’s TOPIC´s option that approximates the concept (housing,
purchase, sale ...) instead of the exact wordings used by searchers. This
approach is particularly useful in a country with several official languages and
an important foreign market.
The baseline quarterly model describes house sales (measured by its year-onyear growth rate) as an autoregressive AR (1/4) model and unemployment rate
as a covariate. The alternative augments the baseline with contemporary a
Google indicator. The models are estimated for 2004Q1-2014Q4 and
recursive one period ahead forecasts are made for 2015Q1-2018Q4. The
inclusion of Google indicator reduces the EAM of prediction errors (outside
the sample) from 0.077 to 0.034. The forecasts also have greater accuracy and
lower bias. The same procedure has been replicated for regions with very
similar results for the main regional markets (Madrid and Catalonia) and
more unequal in other regions.Artola, C.; Herrera De La Cruz, J. (2020). Internet searches as a leading indicator of house purchases in a subnational framework: the case of Spain. Editorial Universitat Politècnica de València. 338-338. http://hdl.handle.net/10251/148776OCS33833