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Journals / Kamu Ekonomisi ve Kamu Mali Yönetimi Dergisi / 2026 / Cilt: 6 Sayı: 2

INVESTIGATION OF HOUSING PRICE CHANGES IN ANKARA THROUGH RESIDENTIAL FEATURES

Başkent Ankara’da Konut Fiyatları Değişiminin Konut Özellikleri ile İncelenmesi

Enes Filiz(BALIKESİR ÜNİVERSİTESİ)0000-0002-8006-9467
Pages
557–572
Publication Date
2026-10-28
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results of the study revealed that the performance of all algorithms improved after feature selection, and the FT algorithm provided the most successful classification model for Ankara housing market data with an accuracy rate of 83.7%.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Housing is recognized as a fundamental indicator of economic growth, consumer confidence, and financial stability, beyond its primary role of providing shelter.
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Key Findings & Data:Within the scope of the research, classification models were established using 510 housing data points listed in the central districts of Ankara in 4-5-6 May 2026, employing Locally Weighted Learning (LWL), Stochastic Gradient Descent (SGD), Fuzzy Unordered Rule Induction Algorithm (FURIA), and Functional Trees (FT) algorithms.
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Conclusion & Impact:The results of the study revealed that the performance of all algorithms improved after feature selection, and the FT algorithm provided the most successful classification model for Ankara housing market data with an accuracy rate of 83.7%.
🔬Design:Modeling & Simulation
📍Scope:2026 Verileri
⚙️Variables:Ankara · Sınıflandırma · Makine öğrenmesi

Abstract

Housing is recognized as a fundamental indicator of economic growth, consumer confidence, and financial stability, beyond its primary role of providing shelter. In countries experiencing rapid urbanization like Türkiye, the housing market exhibits a complex structure due to land supply, construction costs, and evolving consumer preferences. The primary objective of this study is to analyze price changes in the Ankara housing market and the parameters influencing these changes using modern machine learning algorithms. Within the scope of the research, classification models were established using 510 housing data points listed in the central districts of Ankara in 4-5-6 May 2026, employing Locally Weighted Learning (LWL), Stochastic Gradient Descent (SGD), Fuzzy Unordered Rule Induction Algorithm (FURIA), and Functional Trees (FT) algorithms. As a result of the Chi-squared feature selection algorithm applied to enhance model performance; variables such as the ‘number of bathrooms’, ‘gross m2’, ‘net m2’, and ‘floor level’ were identified as the most effective variables on price. The results of the study revealed that the performance of all algorithms improved after feature selection, and the FT algorithm provided the most successful classification model for Ankara housing market data with an accuracy rate of 83.7%.

Özet

Konut, barınma ihtiyacını karşılamasının yanı sıra ekonomik büyüme, tüketici güveni ve finansal istikrarın en temel göstergelerinden biri olarak kabul edilmektedir. Türkiye gibi hızlı kentleşme sürecindeki ülkelerde konut piyasası; arsa arzı, inşaat maliyetleri ve değişen tüketici tercihleri nedeniyle oldukça karmaşık bir yapı sergilemektedir. Bu çalışmanın temel amacı, Ankara konut piyasasındaki fiyat sınıflandırmasını ve bu değişimlere etki eden değişkenleri makine öğrenmesi algoritmalarıyla analiz etmektir. Araştırma kapsamında, Ankara'nın merkez ilçelerinde 4-5-6 Mayıs 2026'da ilana çıkan 510 konut verisi üzerinden; Yerel Ağırlıklı Öğrenme (LWL), Stokastik Gradyan İnişi (SGD), Bulanık Sıralanmamış Kural Çıkarım Algoritması (FURIA) ve Fonksiyonel Karar Ağacı (FT) algoritmaları kullanılarak sınıflandırma başarıları incelenmiştir. Algoritmaların performansını artırmak amacıyla uygulanan Ki-Kare öznitelik seçim algoritması sonucunda; ‘Banyo sayısı’, ‘m2 brüt’, ‘m2 net’ ve ‘Bulunduğu kat’ gibi değişkenlerin fiyat sınıflandırmasında en etkili değişkenler olduğu tespit edilmiştir. Öznitelik seçimi sonrası tüm algoritmaların performansının arttığını ve %83.7 doğruluk oranı ile FT algoritmasının Ankara konut piyasası verileri için en başarılı sınıflandırma oranını sunduğunu ortaya koymuştur.

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Cite This Article

Filiz, E. (2026). INVESTIGATION OF HOUSING PRICE CHANGES IN ANKARA THROUGH RESIDENTIAL FEATURES. Kamu Ekonomisi ve Kamu Mali Yönetimi Dergisi, Cilt: 6 Sayı: 2, 557–572. https://doi.org/10.71284/jpepfm.20266215

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