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A Primer on Slovene House Prices Forecast

Author

Listed:
  • Lenarčič, Črt
  • Zorko, Robert
  • Herman, Uroš
  • Savšek, Simon

Abstract

This paper presents a basic econometric model for house prices forecasting in Slovenia. The model is based on Bayesian Vector Autoregression model (BVAR), which is a common econometric tool used in the forecasting processes. The key �findings of the paper are that the house prices in Slovenia are driven by the following 6 variables: volume of housing investment, personal disposable income, unemployment rate, construction costs, economic growth, measured with a real gross domestic product, and real housing interest rate. The applied econometric framework predicts that the declining trends in Slovene housing market will probably come to a standstill. As the solid economic growth in Slovenia continues, presented econometric model predicts a positive but only gradual growth of house prices over the projection horizon. In addition, forecasting housing prices can become a necessary tool for a prudent implementation of macroprudential regulation. Further development of econometric tools for predicting developments in Slovene housing market is necessary irrespective to the limitations posed by relatively short Slovene house-price time-series data.

Suggested Citation

  • Lenarčič, Črt & Zorko, Robert & Herman, Uroš & Savšek, Simon, 2016. "A Primer on Slovene House Prices Forecast," MPRA Paper 103552, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:103552
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    References listed on IDEAS

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    More about this item

    Keywords

    house prices forecast; Bayesian VAR;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E22 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Investment; Capital; Intangible Capital; Capacity
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

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