Avrupa Konut Piyasasındaki Dalgalanmalar: Zaman Serisi Modelleriyle Konut Fiyat Endeksi Değişiminin Tahmini

Year 2025, EARLY VIEW, 1 - 1

İsmet Soylemez , Mehmet Eren Nalici , Ramazan Ünlü

Abstract

Bu çalışma, 27 Avrupa ülkesinde Konut Fiyat Endeksi değişikliklerini tahmin etmek için zaman serisi modellerinin karşılaştırmalı bir analizini sunmaktadır. Doğru Konut Fiyat Endeksi tahmini, etkili politikalar ve yatırım stratejileri geliştirmek için gereklidir. Çalışma, 2013 yılının 4. çeyreğinden 2024 yılının 3. çeyreğine kadar olan veriler kullanmaktadır. Metodolojik olarak, verilerin durağanlığı Dickey–Fuller testi ile test edilmiş ve durağan olmayan serilere fark alma uygulanmıştır. ARIMA, Holt Doğrusal Trend, Katkılı Sönümlü Trend ve Üstel Düzeltme modelleri, her ülke için en düşük ortalama karesel hata (MSE) değerine göre değerlendirilmiştir. Bulgular, Avrupa konut piyasasının heterojen yapısını doğrulamış ve tek bir modelin tüm ülkeler için uygun olmadığını göstermiştir. ARIMA modeli dokuz ülke için en doğru sonuçları verirken, Holt Doğrusal Trend ve Katkılı Sönümlü Trend modelleri yedi ülkede en iyi performansı gösterdi. 2025–2026 dönemi için tahminler bu sonuçlara dayalı olarak oluşturulmuştur. Bu çalışma, Avrupa konut piyasalarının değişen dinamiklerine uyum sağlamak için ülkeye özgü ve uyarlanabilir tahmin yaklaşımlarının benimsenmesinin önemini vurgulamaktadır.

Keywords

Ev Fiyat Endeksi Değişimi , Zaman Serisi Tahminleme , ARIMA , Konut Piyasası Dinamikleri

Fluctuations in the European Housing Market: Forecasting the House Price Index Change with Time-Series Models

Abstract

This 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.

Keywords

House Price Index Change , Time Series Forecasting , ARIMA , Housing Market Dynamics

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