Araştırma Makalesi
BibTex RIS Kaynak Göster

Investigating Housing Price Differences in Adjacent Neighborhoods Using Spatial Analysis: The Case of Ankara’s Çankaya District

Yıl 2025, Cilt: 6 Sayı: 2, 343 - 359, 27.09.2025
https://doi.org/10.48123/rsgis.1731127

Öz

This study investigates the differences in asking prices between nearby areas in the housing market and examines this situation through spatial analyses. The district of Çankaya in Ankara is selected as the study area, and data on unit sale prices per square meter are obtained from the Endeksa website. Neighborhood-based housing price changes for the years 2020–2024 are analyzed using the spatial analysis techniques of Getis-Ord and Local Moran's I. While the Getis-Ord analysis revealed clusters of high or low pricing during the examined years, the Local Moran's I method identified the spatial autocorrelation of prices between neighboring areas. This study reveals the spatial distribution of housing sale prices in Çankaya district over the years, identifying consistent clustering patterns as well as diverging areas. The findings contribute to understanding regional dynamics in the housing market and offer a spatially grounded analysis to inform investment and planning decisions.

Kaynakça

  • Alkan, T., Dokuz, Y., Ecemiş, A., Bozdağ, A., & Durduran, S. S. (2023). Using machine learning algorithms for predicting real estate values in tourism centers. Soft Computing, 27(5), 2601–2613. https://doi.org/10.1007/s00500-022-07579-7
  • Anselin, L. (1995). Local Indicators of Spatial Association—LISA. Geographical Analysis, 27(2), 93–115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
  • Aydınoğlu, A. Ç., Şişman, S., & Yılmaz, Y. (2021). Taşınmaz Yönetiminde Arsa Rayiç Değerindeki Zamansal Değişimin Konumsal Analiz Teknikleri ile İncelenmesi. Afyon Kocatepe University Journal of Sciences and Engineering, 21(3), 606–619. https://doi.org/10.35414/akufemubid.892510
  • Bagheri, B., & Shaykh-Baygloo, R. (2021). Spatial analysis of urban smart growth and its effects on housing price: The case of Isfahan, Iran. Sustainable Cities and Society, 68, Article 102769. https://doi.org/10.1016/j.scs.2021.102769
  • Barreca, A., Curto, R., & Rolando, D. (2018). Housing vulnerability and property prices: Spatial analyses in the turin real estate market. Sustainability, 10(9), Article 30368. https://doi.org/10.3390/su10093068
  • Basu, S., & Thibodeau, T. G. (1998). Analysis of Spatial Autocorrelation in House Prices. Journal of Real Estate Finance and Economics, 17(1), 61–85. https://doi.org/10.1023/A:1007703229507
  • Bitter, C., Mulligan, G. F., & Dall’erba, S. (2007). Incorporating spatial variation in housing attribute prices: A comparison of geographically weighted regression and the spatial expansion method. Journal of Geographical Systems, 9(1), 7–27. https://doi.org/10.1007/s10109-006-0028-7
  • Boza, E. (2015). Investigation of housing valuation models based on spatial and non-spatial techniques [PhD thesis, Middle East Technical University]. YÖK Ulusal Tez Merkezi. https://tez.yok.gov.tr/UlusalTezMerkezi
  • Cao, K., Diao, M., & Wu, B. (2019). A Big Data–Based Geographically Weighted Regression Model for Public Housing Prices: A Case Study in Singapore. Annals of the American Association of Geographers, 109(1), 173–186. https://doi.org/10.1080/24694452.2018.1470925
  • Cellmer, R., Bełej, M., & Konowalczuk, J. (2019). Impact of a vicinity of airport on the prices of single-family houses with the use of geospatial analysis. ISPRS International Journal of Geo-Information, 8(11), Article 471. https://doi.org/10.3390/ijgi8110471
  • de Bruyne, K., & van Hove, J. (2013). Explaining the spatial variation in housing prices: An economic geography approach. Applied Economics, 45(13), 1673–1689. https://doi.org/10.1080/00036846.2011.636021
  • Eğdemir, F. G. (2001). İstanbul’da konut fiyatlarının mekansal analizi [Doktora tezi, İstanbul Teknik Üniversitesi]. YÖK Ulusal Tez Merkezi. https://tez.yok.gov.tr/UlusalTezMerkezi
  • Endeksa. (2025). Ankara ili Çankaya ilçesi konut satış fiyatları. 17 Ocak 2025’te https://www.endeksa.com/tr/ adresinden alındı.
  • Erçetin, C. (2022). Konut sorununa geçmişten bir bakış: yerel yönetimler ve konut sunumu. İDEALKENT, 13(37), 1410–1429. https://doi.org/10.31198/idealkent.1177401
  • ESRI. (2025a). How Spatial Autocorrelation (Global Moran's I) works. 11 Nisan 2025’te https://pro.arcgis.com/en/pro-app/latest/tool-reference/ spatial-statistics/h-how-spatial-autocorrelation-moran-s-i-spatial-st.htm adresinden alındı.
  • ESRI. (2025b). How Cluster and Outlier Analysis (Anselin Local Moran's I) works. 11 Nisan 2025’te https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-statistics/h-how-cluster-and-outlier-analysis-anselin-local-m.htm adresinden alındı.
  • ESRI. (2025c). How Hot Spot Analysis (Getis-Ord Gi*) works. 11 Nisan 2025’te https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-statistics/ h-how-hot-spot-analysis-getis-ord-gi-spatial-stati.htm adresinden alındı.
  • Genç, N. (2021). Konut taşınmazların değerini etkileyen faktörlerin coğrafi ağırlıklandırılmış regresyon analizi ile CBS tabanlı irdelenmesi [Yüksek lisans tezi, Karadeniz Teknik Üniversitesi]. YÖK Ulusal Tez Merkezi. https://tez.yok.gov.tr/UlusalTezMerkezi
  • Getis, A., & Ord, J. K. (1992). The Analysis of Spatial Association by Use of Distance Statistics. Geographical Analysis, 24(3), 189–206. https://doi.org/10.1111/j.1538-4632.1992.tb00261.x
  • Güneş, T., & Apaydın, A. (2022). Spatial Heterogeneity in Housing Market: Ankara Metropolitan Area. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 63, 9–15. https://doi.org/10.18070/erciyesiibd.1122568
  • Habash, A. Al, & Unanoğlu, M. (2022). Factors Influencing Prices of Residential Real Estate in Turkey. Florya Chronicles of Political Economy, 8(2), 153–172. https://doi.org/10.17932/iau.fcpe.2015.010/fcpe_v08i2002
  • Hanink, D. M., Cromley, R. G., & Ebenstein, A. Y. (2012). Spatial Variation in the Determinants of House Prices and Apartment Rents in China. Journal of Real Estate Finance and Economics, 45(2), 347–363. https://doi.org/10.1007/s11146-010-9262-3
  • Hazer, A., Bozdağ, A., & Atasever, Ü. H. (2024). Hiper-optimize edilmiş makine öğrenim teknikleri ile taşınmaz değerlemesi, Yozgat Kenti örneği. Geomatik, 9(3), 299–312 https://doi.org/10.29128/geomatik.1454915
  • Hu, S., Yang, S., Li, W., Zhang, C., & Xu, F. (2016). Spatially non-stationary relationships between urban residential land price and impact factors in Wuhan city, China. Applied Geography, 68, 48–56. https://doi.org/10.1016/j.apgeog.2016.01.006
  • Huang, B., Wu, B., & Barry, M. (2010). Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices. International Journal of Geographical Information Science, 24(3), 383–401. https://doi.org/10.1080/13658810802672469
  • Iban, M. C. (2022). An explainable model for the mass appraisal of residences: The application of tree-based Machine Learning algorithms and interpretation of value determinants. Habitat International, 128, Article 102660. https://doi.org/10.1016/j.habitatint.2022.102660
  • Iliopoulou, P., & Feloni, E. (2022). Spatial Modelling and Geovisualization of House Prices in the Greater Athens Region, Greece. Geographies, 2(1), 111–131. https://doi.org/10.3390/geographies2010008
  • Kok, S. H., Ismail, N. W., & Lee, C. (2018). The sources of house price changes in Malaysia. International Journal of Housing Markets and Analysis, 11(2), 335–355. https://doi.org/10.1108/IJHMA-04-2017-0039
  • Kutsal, S., & Polatoğlu, Ç. (2023). Türkiye’de artan konut ihtiyacı ve konut sorununun dinamikleri. Topkapı Sosyal Bilimler Dergisi, 2(1), 133–158.
  • Li, S., Ye, X., Lee, J., Gong, J., & Qin, C. (2017). Spatiotemporal Analysis of Housing Prices in China: A Big Data Perspective. Applied Spatial Analysis and Policy, 10(3), 421–433. https://doi.org/10.1007/s12061-016-9185-3
  • Liu, F., Min, M., Zhao, K., & Hu, W. (2020). Spatial-Temporal variation in the impacts of urban infrastructure on housing prices in Wuhan, China. Sustainability, 12(3), Article 1281. https://doi.org/10.3390/su12031281
  • Liu, N., & Strobl, J. (2023). Impact of neighborhood features on housing resale prices in Zhuhai (China) based on an (M)GWR model. Big Earth Data, 7(1), 146–169. https://doi.org/10.1080/20964471.2022.2031543
  • Manganelli, B., Pontrandolfi, P., Azzato, A., & Murgante, B. (2014). Using geographically weighted regression for housing market segmentation. International Journal of Business Intelligence and Data Mining, 9(2), 161–177. https://doi.org/10.1504/IJBIDM.2014.065100
  • Memiş, S. (2019). Tüketicilerin Konut Tercihini Etkileyen Faktörlerin AHP İle Ölçülmesi: Giresun İli Örneği. Avrasya Uluslararası Araştırmalar Dergisi, 7(16), 783–796. https://doi.org/10.33692/avrasyad.543867
  • Moran, P. A. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1–2), 17–23. https://doi.org/10.1093/biomet/37.1-2.17
  • Ord, J. K., & Getis, A. (1995). Local Spatial Autocorrelation Statistics: Distributional Issues and an Application. Geographical Analysis, 27(4), 286–306. https://doi.org/10.1111/j.1538-4632.1995.tb00912.x
  • Raifer, M. (2025). Overpass API. 11 Nisan 2025’te https://overpass-turbo.eu/index.html adresinden alındı.
  • Sadaa, D. A. (2024). Developing a GIS tool to analyze housing price variability in urban regions case study: Ankara [Master thesis, Middle East Technical University]. YÖK Ulusal Tez Merkezi. https://tez.yok.gov.tr/UlusalTezMerkezi
  • Sarkar, A., & Jana, A. (2023). Interpreting the energy choices and environmental satisfaction determinants in low-income housing typologies: Cases from slums and slum rehabilitation housing of Mumbai, India. Cities, 143, Article 104576. https://doi.org/10.1016/j.cities.2023.104576
  • Şişman, S., & Aydınoğlu, A. C. (2022). Improving performance of mass real estate valuation through application of the dataset optimization and Spatially Constrained Multivariate Clustering Analysis. Land Use Policy, 119, Article 106167. https://doi.org/10.1016/j.landusepol.2022.106167
  • Souza, T. G. D., Fonseca, F. D. R., Fernandes, V. D. O., & Pedrassoli, J. C. (2021). Exploratory spatial analysis of housing prices obtained from web scraping technique. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 43(B4-2021), 135–140. https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-135-2021
  • Soylu, A. T., & Kaynak, S. (2024). Konut Fiyatları ve Değişkenliğinin Modellenmesi: Türkiye Örneği. Uluslararası Ekonomi ve Yenilik Dergisi, 10(2), 195–214.
  • Tobler, W. (2004). On the first law of geography: A reply. Annals of the Association of American Geographers, 94(2), 304–310. https://doi.org/10.1111/j.1467-8306.2004.09402009.x
  • Toprak, M. F., & Güngör, O. (2023). Kayseri’de çoklu regresyon ve coğrafi ağırlıklı regresyon yöntemleri ile konutların toplu değerlemesi. Turkish Journal of Remote Sensing and GIS, 4(1), 114–124.
  • Türkiye İstatistik Kurumu. (2025a). Adrese Dayalı Nüfus Kayıt Sistemi (ADNKS). 11 Nisan 2025’te https://biruni.tuik.gov.tr/medas/?kn=95&locale=tr adresinden alındı.
  • Türkiye İstatistik Kurumu. (2025b). Konut Satış İstatistikleri. 11 Nisan 2025’te https://biruni.tuik.gov.tr/medas/?kn=95&locale=tr adresinden alındı.
  • Tuna, M. F., Türk, T., & Kitapçı, O. (2015, 25–28 Mart). Lineer Regresyon ve Coğrafi Bilgi Sistemleri Yardımıyla Ev Fiyatlarının Tahmin Edilmesi: Ankara Örneği [Bildiri sunumu]. TMMOB Harita ve Kadastro Mühendisleri Odası 15. Türkiye Harita Bilimsel ve Teknik Kurultayı, Ankara, Türkiye.
  • UN-Habitat. (2009). The Right to Adequate Housing. Fact Sheet No. 21/Rev.1. Office of the United Nations High Commissioner for Human Rights (OHCHR). https://unhabitat.org/the-right-to-adequate-housing-fact-sheet-no-21rev1
  • Ulucan, A. G., Bozdağ, A., Karakoyun, M., & Alkan, T. (2025). Forecasting pandemic-induced changes in real estate market values through machine learning approaches. International Journal of Strategic Property Management, 29(3), 196–214. https://doi.org/10.3846/ijspm.2025.24063
  • United Nations. (1948, December 10). Universal declaration of human rights, article 25. https://www.un.org/en/about-us/universal-declaration-of-human-rights
  • Vui, C. C. (2006). Using geographical information system - multiple regression analysis - generated location value response surface approach to model locational factor in the prediction of residential property values [Master thesis, Universiti Teknologi Malaysia]. Universiti Teknologi Malaysia Institutional Repository. http://eprints.utm.my/5144/
  • Wang, W. C., Chang, Y. J., & Wang, H. C. (2019). An application of the spatial autocorrelation method on the change of real estate prices in Taitung city. ISPRS International Journal of Geo-Information, 8(6), Article 249. https://doi.org/10.3390/ijgi8060249
  • Wang, L., Wang, G., Yu, H., & Wang, F. (2022). Prediction and analysis of residential house price using a flexible spatiotemporal model. Journal of Applied Economics, 25(1), 503–522. https://doi.org/10.1080/15140326.2022.2045466
  • Yang, J., Bao, Y., Zhang, Y., Li, X., & Ge, Q. (2018). Impact of Accessibility on Housing Prices in Dalian City of China Based on a Geographically Weighted Regression Model. Chinese Geographical Science, 28(3), 505–515. https://doi.org/10.1007/s11769-018-0954-6

Komşu Mahallelerdeki Konut Fiyat Farklılıklarının Mekansal Analizlerle İncelenmesi: Ankara Çankaya İlçesi Örneği

Yıl 2025, Cilt: 6 Sayı: 2, 343 - 359, 27.09.2025
https://doi.org/10.48123/rsgis.1731127

Öz

Bu çalışma, konut piyasasında yakın bölgeler arasındaki satılık fiyat farklılıklarını araştırarak bu durumu mekânsal analizlerle incelemektedir. Ankara'nın Çankaya ilçesi çalışma alanı olarak belirlenmiş ve konut satış birim (m²) fiyatları için Endeksa web sayfası veri kaynağı olarak kullanılmıştır. 2020-2024 yıllarına ait mahalle bazlı konut fiyat değişimleri incelenirken, Getis-Ord ve Lokal Moran's I mekânsal analiz teknikleri kullanılmıştır. Getis-Ord analizi ile incelenen yıllarda konut fiyatlarındaki yüksek ya da düşük fiyat kümelenmeleri ortaya çıkarılmış, Lokal Moran's I yöntemiyle ise yakın bölgeler arasındaki fiyatların mekânsal otokorelasyonu tespit edilmiştir. Bu çalışma, Çankaya ilçesindeki konut satış fiyatlarının yıllara göre mekânsal dağılımını ortaya koyarak tutarlı kümelenme örüntülerini ve ayrışan bölgeleri belirlemiştir. Bulgular, konut piyasasındaki bölgesel dinamiklerin anlaşılmasına katkı sağlayarak yatırım ve planlama kararları için mekânsal temelli bir analiz sunmaktadır.

Kaynakça

  • Alkan, T., Dokuz, Y., Ecemiş, A., Bozdağ, A., & Durduran, S. S. (2023). Using machine learning algorithms for predicting real estate values in tourism centers. Soft Computing, 27(5), 2601–2613. https://doi.org/10.1007/s00500-022-07579-7
  • Anselin, L. (1995). Local Indicators of Spatial Association—LISA. Geographical Analysis, 27(2), 93–115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
  • Aydınoğlu, A. Ç., Şişman, S., & Yılmaz, Y. (2021). Taşınmaz Yönetiminde Arsa Rayiç Değerindeki Zamansal Değişimin Konumsal Analiz Teknikleri ile İncelenmesi. Afyon Kocatepe University Journal of Sciences and Engineering, 21(3), 606–619. https://doi.org/10.35414/akufemubid.892510
  • Bagheri, B., & Shaykh-Baygloo, R. (2021). Spatial analysis of urban smart growth and its effects on housing price: The case of Isfahan, Iran. Sustainable Cities and Society, 68, Article 102769. https://doi.org/10.1016/j.scs.2021.102769
  • Barreca, A., Curto, R., & Rolando, D. (2018). Housing vulnerability and property prices: Spatial analyses in the turin real estate market. Sustainability, 10(9), Article 30368. https://doi.org/10.3390/su10093068
  • Basu, S., & Thibodeau, T. G. (1998). Analysis of Spatial Autocorrelation in House Prices. Journal of Real Estate Finance and Economics, 17(1), 61–85. https://doi.org/10.1023/A:1007703229507
  • Bitter, C., Mulligan, G. F., & Dall’erba, S. (2007). Incorporating spatial variation in housing attribute prices: A comparison of geographically weighted regression and the spatial expansion method. Journal of Geographical Systems, 9(1), 7–27. https://doi.org/10.1007/s10109-006-0028-7
  • Boza, E. (2015). Investigation of housing valuation models based on spatial and non-spatial techniques [PhD thesis, Middle East Technical University]. YÖK Ulusal Tez Merkezi. https://tez.yok.gov.tr/UlusalTezMerkezi
  • Cao, K., Diao, M., & Wu, B. (2019). A Big Data–Based Geographically Weighted Regression Model for Public Housing Prices: A Case Study in Singapore. Annals of the American Association of Geographers, 109(1), 173–186. https://doi.org/10.1080/24694452.2018.1470925
  • Cellmer, R., Bełej, M., & Konowalczuk, J. (2019). Impact of a vicinity of airport on the prices of single-family houses with the use of geospatial analysis. ISPRS International Journal of Geo-Information, 8(11), Article 471. https://doi.org/10.3390/ijgi8110471
  • de Bruyne, K., & van Hove, J. (2013). Explaining the spatial variation in housing prices: An economic geography approach. Applied Economics, 45(13), 1673–1689. https://doi.org/10.1080/00036846.2011.636021
  • Eğdemir, F. G. (2001). İstanbul’da konut fiyatlarının mekansal analizi [Doktora tezi, İstanbul Teknik Üniversitesi]. YÖK Ulusal Tez Merkezi. https://tez.yok.gov.tr/UlusalTezMerkezi
  • Endeksa. (2025). Ankara ili Çankaya ilçesi konut satış fiyatları. 17 Ocak 2025’te https://www.endeksa.com/tr/ adresinden alındı.
  • Erçetin, C. (2022). Konut sorununa geçmişten bir bakış: yerel yönetimler ve konut sunumu. İDEALKENT, 13(37), 1410–1429. https://doi.org/10.31198/idealkent.1177401
  • ESRI. (2025a). How Spatial Autocorrelation (Global Moran's I) works. 11 Nisan 2025’te https://pro.arcgis.com/en/pro-app/latest/tool-reference/ spatial-statistics/h-how-spatial-autocorrelation-moran-s-i-spatial-st.htm adresinden alındı.
  • ESRI. (2025b). How Cluster and Outlier Analysis (Anselin Local Moran's I) works. 11 Nisan 2025’te https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-statistics/h-how-cluster-and-outlier-analysis-anselin-local-m.htm adresinden alındı.
  • ESRI. (2025c). How Hot Spot Analysis (Getis-Ord Gi*) works. 11 Nisan 2025’te https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-statistics/ h-how-hot-spot-analysis-getis-ord-gi-spatial-stati.htm adresinden alındı.
  • Genç, N. (2021). Konut taşınmazların değerini etkileyen faktörlerin coğrafi ağırlıklandırılmış regresyon analizi ile CBS tabanlı irdelenmesi [Yüksek lisans tezi, Karadeniz Teknik Üniversitesi]. YÖK Ulusal Tez Merkezi. https://tez.yok.gov.tr/UlusalTezMerkezi
  • Getis, A., & Ord, J. K. (1992). The Analysis of Spatial Association by Use of Distance Statistics. Geographical Analysis, 24(3), 189–206. https://doi.org/10.1111/j.1538-4632.1992.tb00261.x
  • Güneş, T., & Apaydın, A. (2022). Spatial Heterogeneity in Housing Market: Ankara Metropolitan Area. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 63, 9–15. https://doi.org/10.18070/erciyesiibd.1122568
  • Habash, A. Al, & Unanoğlu, M. (2022). Factors Influencing Prices of Residential Real Estate in Turkey. Florya Chronicles of Political Economy, 8(2), 153–172. https://doi.org/10.17932/iau.fcpe.2015.010/fcpe_v08i2002
  • Hanink, D. M., Cromley, R. G., & Ebenstein, A. Y. (2012). Spatial Variation in the Determinants of House Prices and Apartment Rents in China. Journal of Real Estate Finance and Economics, 45(2), 347–363. https://doi.org/10.1007/s11146-010-9262-3
  • Hazer, A., Bozdağ, A., & Atasever, Ü. H. (2024). Hiper-optimize edilmiş makine öğrenim teknikleri ile taşınmaz değerlemesi, Yozgat Kenti örneği. Geomatik, 9(3), 299–312 https://doi.org/10.29128/geomatik.1454915
  • Hu, S., Yang, S., Li, W., Zhang, C., & Xu, F. (2016). Spatially non-stationary relationships between urban residential land price and impact factors in Wuhan city, China. Applied Geography, 68, 48–56. https://doi.org/10.1016/j.apgeog.2016.01.006
  • Huang, B., Wu, B., & Barry, M. (2010). Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices. International Journal of Geographical Information Science, 24(3), 383–401. https://doi.org/10.1080/13658810802672469
  • Iban, M. C. (2022). An explainable model for the mass appraisal of residences: The application of tree-based Machine Learning algorithms and interpretation of value determinants. Habitat International, 128, Article 102660. https://doi.org/10.1016/j.habitatint.2022.102660
  • Iliopoulou, P., & Feloni, E. (2022). Spatial Modelling and Geovisualization of House Prices in the Greater Athens Region, Greece. Geographies, 2(1), 111–131. https://doi.org/10.3390/geographies2010008
  • Kok, S. H., Ismail, N. W., & Lee, C. (2018). The sources of house price changes in Malaysia. International Journal of Housing Markets and Analysis, 11(2), 335–355. https://doi.org/10.1108/IJHMA-04-2017-0039
  • Kutsal, S., & Polatoğlu, Ç. (2023). Türkiye’de artan konut ihtiyacı ve konut sorununun dinamikleri. Topkapı Sosyal Bilimler Dergisi, 2(1), 133–158.
  • Li, S., Ye, X., Lee, J., Gong, J., & Qin, C. (2017). Spatiotemporal Analysis of Housing Prices in China: A Big Data Perspective. Applied Spatial Analysis and Policy, 10(3), 421–433. https://doi.org/10.1007/s12061-016-9185-3
  • Liu, F., Min, M., Zhao, K., & Hu, W. (2020). Spatial-Temporal variation in the impacts of urban infrastructure on housing prices in Wuhan, China. Sustainability, 12(3), Article 1281. https://doi.org/10.3390/su12031281
  • Liu, N., & Strobl, J. (2023). Impact of neighborhood features on housing resale prices in Zhuhai (China) based on an (M)GWR model. Big Earth Data, 7(1), 146–169. https://doi.org/10.1080/20964471.2022.2031543
  • Manganelli, B., Pontrandolfi, P., Azzato, A., & Murgante, B. (2014). Using geographically weighted regression for housing market segmentation. International Journal of Business Intelligence and Data Mining, 9(2), 161–177. https://doi.org/10.1504/IJBIDM.2014.065100
  • Memiş, S. (2019). Tüketicilerin Konut Tercihini Etkileyen Faktörlerin AHP İle Ölçülmesi: Giresun İli Örneği. Avrasya Uluslararası Araştırmalar Dergisi, 7(16), 783–796. https://doi.org/10.33692/avrasyad.543867
  • Moran, P. A. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1–2), 17–23. https://doi.org/10.1093/biomet/37.1-2.17
  • Ord, J. K., & Getis, A. (1995). Local Spatial Autocorrelation Statistics: Distributional Issues and an Application. Geographical Analysis, 27(4), 286–306. https://doi.org/10.1111/j.1538-4632.1995.tb00912.x
  • Raifer, M. (2025). Overpass API. 11 Nisan 2025’te https://overpass-turbo.eu/index.html adresinden alındı.
  • Sadaa, D. A. (2024). Developing a GIS tool to analyze housing price variability in urban regions case study: Ankara [Master thesis, Middle East Technical University]. YÖK Ulusal Tez Merkezi. https://tez.yok.gov.tr/UlusalTezMerkezi
  • Sarkar, A., & Jana, A. (2023). Interpreting the energy choices and environmental satisfaction determinants in low-income housing typologies: Cases from slums and slum rehabilitation housing of Mumbai, India. Cities, 143, Article 104576. https://doi.org/10.1016/j.cities.2023.104576
  • Şişman, S., & Aydınoğlu, A. C. (2022). Improving performance of mass real estate valuation through application of the dataset optimization and Spatially Constrained Multivariate Clustering Analysis. Land Use Policy, 119, Article 106167. https://doi.org/10.1016/j.landusepol.2022.106167
  • Souza, T. G. D., Fonseca, F. D. R., Fernandes, V. D. O., & Pedrassoli, J. C. (2021). Exploratory spatial analysis of housing prices obtained from web scraping technique. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 43(B4-2021), 135–140. https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-135-2021
  • Soylu, A. T., & Kaynak, S. (2024). Konut Fiyatları ve Değişkenliğinin Modellenmesi: Türkiye Örneği. Uluslararası Ekonomi ve Yenilik Dergisi, 10(2), 195–214.
  • Tobler, W. (2004). On the first law of geography: A reply. Annals of the Association of American Geographers, 94(2), 304–310. https://doi.org/10.1111/j.1467-8306.2004.09402009.x
  • Toprak, M. F., & Güngör, O. (2023). Kayseri’de çoklu regresyon ve coğrafi ağırlıklı regresyon yöntemleri ile konutların toplu değerlemesi. Turkish Journal of Remote Sensing and GIS, 4(1), 114–124.
  • Türkiye İstatistik Kurumu. (2025a). Adrese Dayalı Nüfus Kayıt Sistemi (ADNKS). 11 Nisan 2025’te https://biruni.tuik.gov.tr/medas/?kn=95&locale=tr adresinden alındı.
  • Türkiye İstatistik Kurumu. (2025b). Konut Satış İstatistikleri. 11 Nisan 2025’te https://biruni.tuik.gov.tr/medas/?kn=95&locale=tr adresinden alındı.
  • Tuna, M. F., Türk, T., & Kitapçı, O. (2015, 25–28 Mart). Lineer Regresyon ve Coğrafi Bilgi Sistemleri Yardımıyla Ev Fiyatlarının Tahmin Edilmesi: Ankara Örneği [Bildiri sunumu]. TMMOB Harita ve Kadastro Mühendisleri Odası 15. Türkiye Harita Bilimsel ve Teknik Kurultayı, Ankara, Türkiye.
  • UN-Habitat. (2009). The Right to Adequate Housing. Fact Sheet No. 21/Rev.1. Office of the United Nations High Commissioner for Human Rights (OHCHR). https://unhabitat.org/the-right-to-adequate-housing-fact-sheet-no-21rev1
  • Ulucan, A. G., Bozdağ, A., Karakoyun, M., & Alkan, T. (2025). Forecasting pandemic-induced changes in real estate market values through machine learning approaches. International Journal of Strategic Property Management, 29(3), 196–214. https://doi.org/10.3846/ijspm.2025.24063
  • United Nations. (1948, December 10). Universal declaration of human rights, article 25. https://www.un.org/en/about-us/universal-declaration-of-human-rights
  • Vui, C. C. (2006). Using geographical information system - multiple regression analysis - generated location value response surface approach to model locational factor in the prediction of residential property values [Master thesis, Universiti Teknologi Malaysia]. Universiti Teknologi Malaysia Institutional Repository. http://eprints.utm.my/5144/
  • Wang, W. C., Chang, Y. J., & Wang, H. C. (2019). An application of the spatial autocorrelation method on the change of real estate prices in Taitung city. ISPRS International Journal of Geo-Information, 8(6), Article 249. https://doi.org/10.3390/ijgi8060249
  • Wang, L., Wang, G., Yu, H., & Wang, F. (2022). Prediction and analysis of residential house price using a flexible spatiotemporal model. Journal of Applied Economics, 25(1), 503–522. https://doi.org/10.1080/15140326.2022.2045466
  • Yang, J., Bao, Y., Zhang, Y., Li, X., & Ge, Q. (2018). Impact of Accessibility on Housing Prices in Dalian City of China Based on a Geographically Weighted Regression Model. Chinese Geographical Science, 28(3), 505–515. https://doi.org/10.1007/s11769-018-0954-6
Toplam 54 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Coğrafi Bilgi Sistemleri ve Mekansal Veri Modelleme
Bölüm Araştırma Makaleleri
Yazarlar

Ayşe Giz Gülnerman Gengeç 0000-0002-9163-6068

Tuğba Memişoğlu Baykal 0000-0003-3548-6795

Yayımlanma Tarihi 27 Eylül 2025
Gönderilme Tarihi 30 Haziran 2025
Kabul Tarihi 20 Eylül 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 6 Sayı: 2

Kaynak Göster

APA Gülnerman Gengeç, A. G., & Memişoğlu Baykal, T. (2025). Komşu Mahallelerdeki Konut Fiyat Farklılıklarının Mekansal Analizlerle İncelenmesi: Ankara Çankaya İlçesi Örneği. Türk Uzaktan Algılama ve CBS Dergisi, 6(2), 343-359. https://doi.org/10.48123/rsgis.1731127

Creative Commons License
Turkish Journal of Remote Sensing and GIS (Türk Uzaktan Algılama ve CBS Dergisi), Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License ile lisanlanmıştır.