Google-haut Suomen asuntojen hintojen ennustajana

This report examines whether Google search queries can be used to predict the present and the near future house prices in Finland. Compared to a simple benchmark model, Google searches improve the prediction of the present house price index by 7.5 % measured by mean absolute error. In addition, search queries improve the forecast of near future house prices. Predicting the present and near future house prices is relevant information to many agents, such as realtors and political decision makers.

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Alternative title:	Predicting Housing Prices with Google Searches in Finland
Year of publication:	2016
Authors:	Widgrén, Joona
Publisher:	Helsinki : The Research Institute of the Finnish Economy (ETLA)
Subject:	Prognoseverfahren \| Online-Recherche \| Immobilienpreis \| Zeitreihenanalyse \| Finnland \| Google Trends \| Internet \| Nowcasting \| Forecasting \| Housing market \| Time series

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Series:	ETLA Report ; 63
Type of publication:	Book / Working Paper
Type of publication (narrower categories):	Research Report
Language:	Finnish
Other identifiers:	hdl:10419/201345 [Handle] RePEc:rif:report:63 [RePEc]
Classification:	C1 - Econometric and Statistical Methods: General ; C22 - Time-Series Models ; C43 - Index Numbers and Aggregation ; C53 - Forecasting and Other Model Applications ; C82 - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data ; E27 - Forecasting and Simulation
Source:	EconStor

Persistent link: https://www.econbiz.de/10012037683