Deciphering Housing Market Trends: Forecasting Total Housing Sales in Turkey

Abstract

This paper uses the SARIMA (Seasonal Autoregressive Integrated Moving Average) model to analyze monthly real estate sales in Turkey and forecast future sales. Monthly real estate sales data provided by the Turkish Statistical Institute (TurkStat) between 01/2013 and 03/2023 are used. The data set is divided into two phases: the training phase (85%) and the estimation phase (15%). Data analysis is performed using differencing and z-score normalization. Seasonal decomposition techniques are used to identify seasonal, trend and residual components in the data set and the ADF test is used to verify that the data are stationary. The SARIMAX model is evaluated using information criteria (AIC, BIC, HQIC) and estimation accuracy (MAPE, MSE, MAE) and is determined as the most appropriate model. The AIC, BIC and MSE values of the model indicate that the model has high predictive ability. The fit between the model's forecasting results and actual home sales is evaluated graphically and statistically, and it is found that the model accurately captures seasonal effects and reliably predicts future sales. This study provides an important source of information for stakeholders in the real estate market, economists, policy makers and investors.

Keywords

• Housing Sales
• Machine Learning
• Financial Modeling
• Financial Forecasting
• Seasonal Disaggregation

Konut Piyasası Trendlerinin Çözümlenmesi: Türkiye'de Toplam Konut Satışlarının Tahmini

Abstract

Bu çalışma, Türkiye'deki aylık gayrimenkul satışlarını analiz etmek ve gelecekteki satışları tahmin etmek amacıyla SARIMA (Sezonluk Otoregresif Entegre Hareketli Ortalama) modelini kullanmaktadır. Türkiye İstatistik Kurumu (TÜİK) tarafından sağlanan 01/2013-03/2023 tarihleri arasındaki aylık konut satış verileri kullanılmıştır. Veri seti, modelin eğitim aşaması (%85) ve tahmin aşaması (%15) olmak üzere ikiye ayrılmıştır. Fark alma ve z- score normalizasyonu kullanılarak veri analizi yapılmıştır. Mevsimsel ayrıştırma teknikleriyle veri setindeki mevsimsel, trend ve artık bileşenler belirlenmiş ve ADF testi ile verinin durağan hale geldiği doğrulanmıştır. SARIMAX modeli, bilgi kriterleri (AIC, BIC, HQIC) ve tahmin doğruluğu (MAPE, MSE, MAE) kullanılarak değerlendirilmiş ve en uygun model olarak belirlenmiştir. Modelin AIC, BIC ve MSE değerleri, modelin tahmin yeteneğinin yüksek olduğunu göstermektedir. Modelin tahmin sonuçları ile gerçekleşen konut satışları arasındaki uyum, grafiksel ve istatistiksel olarak değerlendirilmiş ve modelin mevsimsel etkileri doğru bir şekilde yakaladığı ve gelecekteki satışları güvenilir bir şekilde tahmin edebildiği görülmüştür. Bu çalışma, gayrimenkul piyasasındaki paydaşlar, ekonomistler, politika yapıcılar ve yatırımcılar için önemli bir bilgi kaynağı sunmaktadır.

Keywords

• Konut Satışları
• Makine Öğrenmesi
• Finansal Modelleme
• Finansal Tahmin
• Mevsimsel Ayrıştırma

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