BÖLGESEL KONUT PİYASASI VERİLERİ İLE YENİ KİRACI KİRA ENDEKSİ TAHMİNİ: TOPLULUK ÖĞRENME ALGORİTMALARI YAKLAŞIMI

Abstract

Küreselleşme ve artan şehirleşme hızıyla birlikte konut piyasaları, barınma ihtiyacının ötesinde stratejik bir yatırım aracı ve ekonomik dengenin temel belirleyicisi haline gelmiştir. Türkiye özelinde pandemi, 6 Şubat depremleri ve döviz kuru hareketliliği gibi dışsal şoklar, mevcut kira sözleşmeleri ile piyasadaki gerçek değerler arasında ciddi farklar oluşturarak ‘yeni kiracı kira endeksi’ verilerinin analizini kritik hale getirmiştir. Bu çalışmanın amacı, Türkiye’deki yeni kiracı kira endeksi değerlerini topluluk öğrenme algoritmalarıyla analiz etmek ve 2026 yılının ilk altı ayına yönelik tahminler üretmektir. Araştırma kapsamında, Türkiye Cumhuriyet Merkez Bankası (TCMB) veri sisteminden alınan Ocak 2018- Aralık 2025 dönemine ait aylık veriler kullanılmıştır. Analizlerde Artımlı Öğrenme Tabanlı Destek Vektör Regresyonu (AR-DVR) ve Tarafsız Öznitelik Seçilmiş Sınıflandırıcı Destek Vektör Regresyonu (UAS-DVR) algoritmalarından yararlanılmıştır. Bulgular, AR-DVR modelinin %95.85 yönsel doğruluk ve 1.06 MAPE değeri ile başarılı performans sergilediğini göstermiştir. İkili Korelasyon Tabanlı Öznitelik Seçim Algoritması sonuçlarına göre, İstanbul, İzmir, Adana-Mersin, Kocaeli havzası ve Bursa-Eskişehir gruplarının yeni kiracı kira endeksi üzerinde en yüksek belirleyiciliğe sahip merkezler olduğu saptanmıştır. Çalışma, veri sadeleştirilmesine rağmen algoritmaların yüksek başarı oranlarını koruduğunu ve geliştirilen hibrit yaklaşımların kira piyasası tahminlerinde sürdürülebilir bir karar destek aracı olarak kullanılabileceğini ortaya koymaktadır.

Keywords

• Yeni Kiracı Kira Endeksi
• Topluluk öğrenmesi
• Tahminleme
• Türkiye

PREDICTING NEW TENANT RENT INDEX WITH REGIONAL HOUSING MARKET DATA: AN ENSEMBLE LEARNING ALGORITHMS APPROACH

Abstract

As globalization and rapid urbanization accelerate, housing markets have evolved beyond mere shelter into strategic investment vehicles and key determinants of economic stability. In the specific context of Turkey, external shocks such as the pandemic, the February 6 earthquakes, and exchange rate volatility have created a significant gap between existing lease agreements and actual market values, making the analysis of 'new tenant rent index' data critical. This study aims to analyze Turkey's new tenant rent index values using ensemble learning algorithms and to generate predicting for the first six months of 2026. The research utilizes monthly data from January 2018 to December 2025, sourced from the Central Bank of the Republic of Turkey (CBRT). The analysis employs Additive Regression-based Support Vector Regression (AR-SVR) and Unbiased Attribute Selected Classifier Support Vector Regression (UAS-SVR) algorithms. Findings indicate that the AR-SVR model performed successfully, achieving 95.85% directional accuracy and a MAPE value of 1.06. According to the results of the Pairwise Correlation Feature Selection Algorithm, Istanbul, Izmir, the Adana-Mersin region, the Kocaeli basin, and the Bursa-Eskisehir groups were identified as the most influential centers for the new tenant rent index. The study reveals that the algorithms maintain high success rates despite data reduction, demonstrating that the developed hybrid approaches can serve as sustainable decision-support tools for rental market predicting.

Keywords

• New Tenant Rent Index
• Ensemble Learning
• Predicting
• Türkiye

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