Research Article
BibTex RIS Cite

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

References

  • Akkoç, U., Akçağlayan, A., & Kargın Akkoç, G. (2021). The impacts of oil price shocks in Turkey: sectoral evidence from the FAVAR approach. Economic Change and Restructuring, 54(4), 1147-1171.
  • Bidanset, P., McCord, M., Lombard, J. A., Davis, P., & McCluskey, W. (2018). Accounting for locational, temporal, and physical similarity of residential sales in mass appraisal modeling: the development and application of geographically, temporally, and characteristically weighted regression. Journal of Property Tax Assessment and Administration, 14(2), 5-13.
  • Bidanset, P. E., & Lombard, J. R. (2014). Evaluating spatial model accuracy in mass real estate appraisal: A comparison of geographically weighted regression and the spatial lag model. Cityscape, 16(3), 169-182.
  • Bidanset, P. E., Lombard, J. R., Davis, P., McCord, M., & McCluskey, W. J. (2017). Further evaluating the impact of kernel and bandwidth specifications of geographically weighted regression on the equity and uniformity of mass appraisal models. In Advances in Automated Valuation Modeling (pp. 191-199): Springer.
  • 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.
  • Brunsdon, C., Fotheringham, A. S., & Charlton, M. E. (1996). Geographically weighted regression: a method for exploring spatial nonstationarity. Geographical analysis, 28(4), 281-298.
  • Bujanda, A., & Fullerton, T. M. (2017). Impacts of transportation infrastructure on single-family property values. Applied Economics, 49(51), 5183-5199.
  • Chica-Olmo, J. (2007). Prediction of housing location price by a multivariate spatial method: Cokriging. Journal of Real Estate Research, 29(1), 91-114.
  • Crawford, T. W. (2009). Scale Analytical. In R. Kitchin & N. Thrift (Eds.), International encyclopedia of human geography (pp. 29-36): Elsevier.
  • Davis, P., Bidanset, P., McCord, M., & Cusack, M. (2019). Nationwide Mass Appraisal Modeling in China: Feasibility Analysis for Scalability Given Ad Valorem Property Tax Reform. Working Paper WP20PD1, Lincoln Institute of Land Policy.
  • Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2000). Quantitative geography: perspectives on spatial data analysis: Sage.
  • Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2003). Geographically weighted regression: the analysis of spatially varying relationships: John Wiley & Sons.
  • Fotheringham, A. S., Charlton, M. E., & Brunsdon, C. (1998). Geographically weighted regression: a natural evolution of the expansion method for spatial data analysis. Environment and planning A, 30(11), 1905-1927.
  • Fotheringham, A. S., Crespo, R., & Yao, J. (2015). Exploring, modelling and predicting spatiotemporal variations in house prices. The Annals of Regional Science, 54(2), 417-436.
  • Fullerton, T. M., & Bujanda, A. (2018). Commercial property values in a border metropolitan economy. Asia-Pacific Journal of Regional Science, 2(2), 337-360.
  • Gloudemans, R. J., & Almy, R. R. (2011). Fundamentals of mass appraisal: International Association of Assessing Officers.
  • Grover, R. (2016). Mass valuations. Journal of Property Investment & Finance, Emerald Group Publishing, Vol. 34(2), pages 191-204.
  • Guo, L., Ma, Z., & Zhang, L. (2008). Comparison of bandwidth selection in application of geographically weighted regression: a case study. Canadian Journal of Forest Research, 38(9), 2526-2534.
  • Hanink, D. M., Cromley, R. G., & Ebenstein, A. Y. (2012). Spatial variation in the determinants of house prices and apartment rents in China. The Journal of Real Estate Finance and Economics, 45(2), 347-363.
  • Hu, L., He, S., Han, Z., Xiao, H., Su, S., Weng, M., & Cai, Z. (2019). Monitoring housing rental prices based on social media: An integrated approach of machine-learning algorithms and hedonic modeling to inform equitable housing policies. Land Use Policy, 82, 657-673.
  • 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.
  • Matysiak, G. A. (2017). The accuracy of automated valuation models (AVMs). Report for TEGoVA.
  • Militino, A. F., Ugarte, M. D., & Garcia-Reinaldos, L. (2004). Alternative models for describing spatial dependence among dwelling selling prices. The Journal of Real Estate Finance and Economics, 29(2), 193-209.
  • Mooya, M. M. (2016). Real Estate Valuation Theory: Springer.
  • Morali, O., & Yilmaz, N. (2020). Spatial Heterogeneity in Istanbul Housing Market: A Geographically Weighted Approach. Journal of Economics Finance and Accounting, 7(4), 298-307.
  • Páez, A., Long, F., & Farber, S. (2008). Moving window approaches for hedonic price estimation: an empirical comparison of modelling techniques. Urban Studies, 45(8), 1565-1581.
  • RICS. (2017). The Future of Valuations: The relevance of real estate valuations for institutional investors and banks – views from a European expert group, London, UK. Retrieved from RICS.
  • Sayın, Z. M. (2021). House price dynamics in izmir's neighborhoods: A comprehensive approach, Unpublished master's thesis,. Izmir Institute of Technology,
  • Schulz, R., Wersing, M., & Werwatz, A. (2014). Automated valuation modelling: a specification exercise. Journal of Property Research, 31(2), 131-153.
  • Sirmans, S., Macpherson, D., & Zietz, E. (2005). The composition of hedonic pricing models. Journal of Real Estate Literature, 13(1), 1-44.
  • Sisman, S., & Aydinoglu, A. (2022). A modelling approach with geographically weighted regression methods for determining geographic variation and influencing factors in housing price: A case in Istanbul. Land Use Policy, 119, 106183.
  • Soltani, A., Pettit, C. J., Heydari, M., & Aghaei, F. (2021). Housing price variations using spatio-temporal data mining techniques. Journal of Housing and the Built Environment, 36(3), 1199-1227.
  • Steurer, M., Hill, R. J., & Pfeifer, N. (2021). Metrics for evaluating the performance of machine learning based automated valuation models. Journal of Property Research, 1-31.
  • Tchuente, D., & Nyawa, S. (2021). Real estate price estimation in French cities using geocoding and machine learning. Annals of Operations Research, 1-38.
  • Tobler, W. R. (1970). A computer movie simulating urban growth in the Detroit region. Economic geography, 46(sup1), 234-240.
  • Wang, C.-H., & Chen, N. (2020). A geographically weighted regression approach to investigating local built-environment effects on home prices in the housing downturn, recovery, and subsequent increases. Journal of Housing and the Built Environment, 35(4), 1283-1302.
  • Yu, D. (2007). Modeling owner-occupied single-family house values in the city of Milwaukee: A geographically weighted regression approach. GIScience & Remote Sensing, 44(3), 267-282.

SPATIAL HETEROGENEITY IN HOUSING MARKET: ANKARA METROPOLITAN AREA

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

Tuğba Güneş , Ayşen Apaydın

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

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

References

  • Akkoç, U., Akçağlayan, A., & Kargın Akkoç, G. (2021). The impacts of oil price shocks in Turkey: sectoral evidence from the FAVAR approach. Economic Change and Restructuring, 54(4), 1147-1171.
  • Bidanset, P., McCord, M., Lombard, J. A., Davis, P., & McCluskey, W. (2018). Accounting for locational, temporal, and physical similarity of residential sales in mass appraisal modeling: the development and application of geographically, temporally, and characteristically weighted regression. Journal of Property Tax Assessment and Administration, 14(2), 5-13.
  • Bidanset, P. E., & Lombard, J. R. (2014). Evaluating spatial model accuracy in mass real estate appraisal: A comparison of geographically weighted regression and the spatial lag model. Cityscape, 16(3), 169-182.
  • Bidanset, P. E., Lombard, J. R., Davis, P., McCord, M., & McCluskey, W. J. (2017). Further evaluating the impact of kernel and bandwidth specifications of geographically weighted regression on the equity and uniformity of mass appraisal models. In Advances in Automated Valuation Modeling (pp. 191-199): Springer.
  • 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.
  • Brunsdon, C., Fotheringham, A. S., & Charlton, M. E. (1996). Geographically weighted regression: a method for exploring spatial nonstationarity. Geographical analysis, 28(4), 281-298.
  • Bujanda, A., & Fullerton, T. M. (2017). Impacts of transportation infrastructure on single-family property values. Applied Economics, 49(51), 5183-5199.
  • Chica-Olmo, J. (2007). Prediction of housing location price by a multivariate spatial method: Cokriging. Journal of Real Estate Research, 29(1), 91-114.
  • Crawford, T. W. (2009). Scale Analytical. In R. Kitchin & N. Thrift (Eds.), International encyclopedia of human geography (pp. 29-36): Elsevier.
  • Davis, P., Bidanset, P., McCord, M., & Cusack, M. (2019). Nationwide Mass Appraisal Modeling in China: Feasibility Analysis for Scalability Given Ad Valorem Property Tax Reform. Working Paper WP20PD1, Lincoln Institute of Land Policy.
  • Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2000). Quantitative geography: perspectives on spatial data analysis: Sage.
  • Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2003). Geographically weighted regression: the analysis of spatially varying relationships: John Wiley & Sons.
  • Fotheringham, A. S., Charlton, M. E., & Brunsdon, C. (1998). Geographically weighted regression: a natural evolution of the expansion method for spatial data analysis. Environment and planning A, 30(11), 1905-1927.
  • Fotheringham, A. S., Crespo, R., & Yao, J. (2015). Exploring, modelling and predicting spatiotemporal variations in house prices. The Annals of Regional Science, 54(2), 417-436.
  • Fullerton, T. M., & Bujanda, A. (2018). Commercial property values in a border metropolitan economy. Asia-Pacific Journal of Regional Science, 2(2), 337-360.
  • Gloudemans, R. J., & Almy, R. R. (2011). Fundamentals of mass appraisal: International Association of Assessing Officers.
  • Grover, R. (2016). Mass valuations. Journal of Property Investment & Finance, Emerald Group Publishing, Vol. 34(2), pages 191-204.
  • Guo, L., Ma, Z., & Zhang, L. (2008). Comparison of bandwidth selection in application of geographically weighted regression: a case study. Canadian Journal of Forest Research, 38(9), 2526-2534.
  • Hanink, D. M., Cromley, R. G., & Ebenstein, A. Y. (2012). Spatial variation in the determinants of house prices and apartment rents in China. The Journal of Real Estate Finance and Economics, 45(2), 347-363.
  • Hu, L., He, S., Han, Z., Xiao, H., Su, S., Weng, M., & Cai, Z. (2019). Monitoring housing rental prices based on social media: An integrated approach of machine-learning algorithms and hedonic modeling to inform equitable housing policies. Land Use Policy, 82, 657-673.
  • 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.
  • Matysiak, G. A. (2017). The accuracy of automated valuation models (AVMs). Report for TEGoVA.
  • Militino, A. F., Ugarte, M. D., & Garcia-Reinaldos, L. (2004). Alternative models for describing spatial dependence among dwelling selling prices. The Journal of Real Estate Finance and Economics, 29(2), 193-209.
  • Mooya, M. M. (2016). Real Estate Valuation Theory: Springer.
  • Morali, O., & Yilmaz, N. (2020). Spatial Heterogeneity in Istanbul Housing Market: A Geographically Weighted Approach. Journal of Economics Finance and Accounting, 7(4), 298-307.
  • Páez, A., Long, F., & Farber, S. (2008). Moving window approaches for hedonic price estimation: an empirical comparison of modelling techniques. Urban Studies, 45(8), 1565-1581.
  • RICS. (2017). The Future of Valuations: The relevance of real estate valuations for institutional investors and banks – views from a European expert group, London, UK. Retrieved from RICS.
  • Sayın, Z. M. (2021). House price dynamics in izmir's neighborhoods: A comprehensive approach, Unpublished master's thesis,. Izmir Institute of Technology,
  • Schulz, R., Wersing, M., & Werwatz, A. (2014). Automated valuation modelling: a specification exercise. Journal of Property Research, 31(2), 131-153.
  • Sirmans, S., Macpherson, D., & Zietz, E. (2005). The composition of hedonic pricing models. Journal of Real Estate Literature, 13(1), 1-44.
  • Sisman, S., & Aydinoglu, A. (2022). A modelling approach with geographically weighted regression methods for determining geographic variation and influencing factors in housing price: A case in Istanbul. Land Use Policy, 119, 106183.
  • Soltani, A., Pettit, C. J., Heydari, M., & Aghaei, F. (2021). Housing price variations using spatio-temporal data mining techniques. Journal of Housing and the Built Environment, 36(3), 1199-1227.
  • Steurer, M., Hill, R. J., & Pfeifer, N. (2021). Metrics for evaluating the performance of machine learning based automated valuation models. Journal of Property Research, 1-31.
  • Tchuente, D., & Nyawa, S. (2021). Real estate price estimation in French cities using geocoding and machine learning. Annals of Operations Research, 1-38.
  • Tobler, W. R. (1970). A computer movie simulating urban growth in the Detroit region. Economic geography, 46(sup1), 234-240.
  • Wang, C.-H., & Chen, N. (2020). A geographically weighted regression approach to investigating local built-environment effects on home prices in the housing downturn, recovery, and subsequent increases. Journal of Housing and the Built Environment, 35(4), 1283-1302.
  • Yu, D. (2007). Modeling owner-occupied single-family house values in the city of Milwaukee: A geographically weighted regression approach. GIScience & Remote Sensing, 44(3), 267-282.
There are 37 citations in total.

Details

Primary Language English
Journal Section Makaleler
Authors

Tuğba Güneş 0000-0002-7472-1017

Ayşen Apaydın 0000-0003-4683-0459

Early Pub Date December 29, 2022
Publication Date December 30, 2022
Acceptance Date October 8, 2022
Published in Issue Year 2022 Issue: 63

Cite

APA 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
 Download Cover Image

Ethical Principles and Ethical Guidelines

The Journal of Erciyes University Faculty of Economics and Administrative Sciences places great emphasis on publication ethics, which serve as a foundation for the impartial and reputable advancement of scientific knowledge. In this context, the journal adopts a publishing approach aligned with the ethical standards set by the Committee on Publication Ethics (COPE) and is committed to preventing potential malpractice. The following ethical responsibilities, established based on COPE’s principles, are expected to be upheld by all stakeholders involved in the publication process (authors, readers and researchers, publishers, reviewers, and editors).

Ethical Responsibilities of Editors
Make decisions on submissions based on the quality and originality of the work, its alignment with the journal's aims and scope, and the reviewers’ evaluations, regardless of the authors' religion, language, race, ethnicity, political views, or gender.
Respond to information requests from readers, authors, and reviewers regarding the publication and evaluation processes.
Conduct all processes without compromising ethical standards and intellectual property rights.
Support freedom of thought and protect human and animal rights.
Ensure the peer review process adheres to the principle of double-blind peer review.
Take full responsibility for accepting, rejecting, or requesting changes to a manuscript and ensure that conflicts of interest among stakeholders do not influence these decisions.
Ethical Responsibilities of Authors
Submitted works must be original. When utilizing other works, proper and complete citations and/or references must be provided.
A manuscript must not be under review by another journal simultaneously.
Individuals who have not contributed to the experimental design, implementation, data analysis, or interpretation should not be listed as authors.
If requested during the review process, datasets used in the manuscript must be provided to the editorial board.
If a significant error or mistake is discovered in the manuscript, the journal’s editorial office must be notified.
For studies requiring ethical committee approval, the relevant document must be submitted to the journal. Details regarding the ethical approval (name of the ethics committee, approval document number, and date) must be included in the manuscript.
Changes to authorship (e.g., adding or removing authors, altering the order of authors) cannot be proposed after the review process has commenced.
Ethical Responsibilities of Reviewers
Accept review assignments only in areas where they have sufficient expertise.
Agree to review manuscripts in a timely and unbiased manner.
Ensure confidentiality of the reviewed manuscript and not disclose any information about it, during or after the review process, beyond what is already published.
Refrain from using information obtained during the review process for personal or third-party benefit.
Notify the journal editor if plagiarism or other ethical violations are suspected in the manuscript.
Conduct reviews objectively and avoid conflicts of interest. If a conflict exists, the reviewer should decline the review.
Use polite and constructive language during the review process and avoid personal comments.
Publication Policy
The Journal of Erciyes University Faculty of Economics and Administrative Sciences is a free, open-access, peer-reviewed academic journal that has been in publication since 1981. The journal welcomes submissions in Turkish and English within the fields of economics, business administration, public finance, political science, public administration, and international relations.

No submission or publication fees are charged by the journal.
Every submitted manuscript undergoes a double-blind peer review process and similarity/plagiarism checks via iThenticate.
Submissions must be original and not previously published, accepted for publication, or under review elsewhere.
Articles published in the journal can be cited under the Open Access Policy and Creative Commons license, provided proper attribution is given.
The journal is published three times a year, in April, August, and December. It includes original, high-quality, and scientifically supported research articles and reviews in its listed fields. Academic studies unrelated to these disciplines or their theoretical and empirical foundations are not accepted. The journal's languages are Turkish and English.

Submissions are first subject to a preliminary review for format and content. Manuscripts not meeting the journal's standards are rejected by the editorial board. Manuscripts deemed suitable proceed to the peer review stage.

Each submission is sent to at least two expert reviewers. If both reviews are favorable, the article is approved for publication. In cases where one review is positive and the other negative, the editorial board decides based on the reviews or may send the manuscript to a third reviewer.

Articles published in the journal are open access and can be cited under the Creative Commons license, provided proper attribution is made.