A Primer on Slovene House Prices Forecast

Črt Lenarčič, Robert Zorko, Uros Herman () and Simon Savšek

MPRA Paper from University Library of Munich, Germany

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.

Keywords: house prices forecast; Bayesian VAR (search for similar items in EconPapers)
JEL-codes: C53 E22 E27 (search for similar items in EconPapers)
Date: 2016-05
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Published in SIR*IUS 3.2017(2017): pp. 1-18

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