IDEAS home Printed from https://ideas.repec.org/p/rif/report/63.html
   My bibliography  Save this paper

Predicting Housing Prices with Google Searches in Finland

Author

Listed:
  • Widgrén, Joona

Abstract

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.

Suggested Citation

  • Widgrén, Joona, 2016. "Predicting Housing Prices with Google Searches in Finland," ETLA Reports 63, The Research Institute of the Finnish Economy.
    • Handle: RePEc:rif:report:63
    as

    Download full text from publisher

    File URL: http://www.etla.fi/wp-content/uploads/ETLA-Raportit-Reports-63.pdf
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    Google Trends; Internet; Nowcasting; Forecasting; Housing market; Time series;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:rif:report:63. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Kaija Hyvönen-Rajecki (email available below). General contact details of provider: https://edirc.repec.org/data/etlaafi.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.