Soylemez, IsmetNalici, Mehmet ErenUnlu, Ramazan01. Abdullah Gül University02.02. Endüstri Mühendisliği02. Mühendislik Fakültesi07. Fen Bilimleri Enstitüsü07.03. Endüstri Mühendisliği Anabilim Dalı2025-10-202025-10-2020251302-09002147-9429https://doi.org/10.2339/politeknik.1724043https://hdl.handle.net/20.500.12573/5146This study presents a comparative analysis of a time series models for forecasting changes in the Housing Price Index (HPI) in 27 European countries. Accurate HPI forecasting is essential for the development of effective policies and investment strategies. The study uses quarterly data from Q4 2013 to Q3 2024. Methodologically, the stationarity of the data is tested using the Dickey-Fuller test and differencing is applied to non-stationary series. The ARIMA, Holt Linear Trend, Additive Damped Trend and Exponential Smoothing models are evaluated based on the lowest mean squared error (MSE) value for each country. The findings confirmed the heterogeneous structure of the European housing market, showing that no single model is suitable for all countries. The ARIMA model provided the most accurate results for nine countries, while the Holt Linear Trend and Additive Damped Trend models performed best in seven countries each. Forecasts for the period 2025-2026 are generated based on these results. This study highlights the importance of adopting country-specific and adaptable forecasting approaches to accommodate the varying dynamics of European housing markets.eninfo:eu-repo/semantics/closedAccessHouse Price Index ChangeTime Series ForecastingARIMAHousing Market DynamicsArticle10.2339/politeknik.1724043