KONUT PİYASASINDA MEKANSAL HETEROJENLİK: ANKARA METROPOLİTEN ALANI

Year 2022, Issue: 63, 9 - 15, 30.12.2022

Tuğba Güneş Ayşen Apaydın

https://doi.org/10.18070/erciyesiibd.1122568

Abstract

İleri istatistiksel modeller, çeşitli amaçlarla gerçekleştirilen gayrimenkul değerleme çalışmalarında son elli yıldır yaygın olarak kullanılmakta olup, hedonik yaklaşımlar basit ve kolay yorumlanabilir özellikleri sebebiyle bu modeller arasında popüler hale gelmiştir. Ancak konut piyasalarında mekânsal heterojenlik ve mekânsal otokorelasyon durumları söz konusu olabilmektedir ve geleneksel regresyon analizinde bu konumsal etkiler modele yeterince yansıtılamamaktadır. Bu çalışmada Coğrafi Ağırlıklandırılmış Regresyon (CAR) analizi kullanılarak, Ankara ilinin metropoliten alanındaki konut piyasasında mekânsal heterojenlik incelenmiştir. Konut fiyatları ve özelliklerinden oluşan veri seti üzerinde Gauss kernel ağırlık fonksiyonu ve çapraz doğrulama yöntemine dayalı olarak belirlenen değişken (adaptif) bant genişliği kullanılmış, mekânsal etkileri çoğunlukla göz ardı eden en küçük kareler yöntemine dayalı geleneksel regresyon modeline kıyasla CAR modelinin daha başarılı sonuçlar elde ettiği ve konut piyasasında mekânsal heterojenlik olduğu görülmüştür. CAR modelinin konut fiyatlarını açıklama gücünün ve parametre tahminlerinin coğrafi olarak durağan olmadığı anlaşılmıştır. Parametrelerdeki bu değişimler harita üzerinde gösterilerek açıklanmış ve konut fiyatları ile özellikleri arasındaki mekânsal korelasyonlar yardımıyla bu sonuçlar desteklenmiştir.

Keywords

mekânsal heterojenlik, konut piyasası, coğrafi ağırlıklandırılmış regresyon

SPATIAL HETEROGENEITY IN HOUSING MARKET: ANKARA METROPOLITAN AREA

Abstract

Advanced statistical models have been widely used in real estate valuations for various purposes over the last fifty years, and hedonic approaches with their simple and easy interpretable features are still the most popular among these models. However, spatial heterogeneity and spatial autocorrelation are the two major features of the housing markets, and traditional regression cannot reflect these locational effects into the model sufficiently. This study employs a Geographically Weighted Regression (GWR) model to explore the spatial heterogeneity in the metropolitan area housing market in the city of Ankara. By applying a Gaussian kernel weighting function with adaptive bandwidth based on cross-validation approach on a house listing dataset, it is found that the GWR fit the data better than the traditional ordinary least squares regression which mostly ignore the spatial effects, and there is spatial heterogeneity in the housing market. Explanatory power of the GWR model and parameter estimations are non-stationary over the geographical area. The variations in the coefficients of the variables are depicted on the map and is supported with the spatial correlations between the housing prices and attributes as well.

Keywords

spatial heterogeneity, housing market, geographically weighted regression

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