Residential Market Dynamics and Micro-Scale Environmental Quality: A Hedonic Assessment in Transforming Neighborhoods

Abstract

This paper analyses the links between structural, locational, and micro-scale environmental features and housing prices in two transforming neighborhoods in İzmir, Türkiye: Mansuroğlu (Bayraklı) and Kazımdirik (Bornova). Neighborhood-specific hedonic price models were applied to a dataset of 120 geolocated housing listings, allowing for a comparative assessment of price determinants under different redevelopment dynamics. Micro-scale environmental quality (MSEQ) was evaluated through expert-based street audits using Google Street View and incorporated into the models as a composite indicator reflecting pedestrian-level environmental conditions. The findings indicate that housing prices are primarily influenced by structural characteristics such as floor area, building age, and number of bathrooms, as well as locational attributes, particularly being located within a gated residential complex. These results are consistent across both neighborhood models, highlighting the dominant role of physical housing attributes and redevelopment patterns in shaping market values. Although the MSEQ coefficient is positive in both cases, it is not statistically significant at conventional levels. This suggests that, in rapidly transforming housing markets, structural and redevelopment-related factors outweigh the influence of micro-scale environmental quality in price formation. Nevertheless, the inclusion of environmental indicators provides a more comprehensive understanding of neighborhood dynamics and urban transformation processes. The findings also point to the potential indirect or context-dependent role of environmental quality, indicating the need for further research incorporating larger datasets and alternative methodological approaches.

Keywords

• Housing values
• hedonic model
• Urban transformation
• Neighborhood characteristics
• Spatial and locational factors

Dönüşüm Sürecindeki Mahallelerde Konut Piyasası Dinamikleri ve Mikro-Ölçekli Çevresel Kalite: Hedonik Bir Değerlendirme

Öz

Bu çalışma, İzmir’de dönüşüm sürecindeki iki mahallede—Mansuroğlu (Bayraklı) ve Kazımdirik (Bornova)—yapısal, konumsal ve mikro-ölçekli çevresel özellikler ile konut fiyatları arasındaki ilişkileri incelemektedir. Farklı yeniden gelişim dinamikleri altında fiyat belirleyicilerini karşılaştırmalı olarak değerlendirebilmek amacıyla, 120 adet coğrafi konumu belirlenmiş konut ilanından oluşan veri setine mahalleye özgü hedonik fiyat modelleri uygulanmıştır. Mikro-ölçekli çevresel kalite (MESQ), Google Street View üzerinden gerçekleştirilen uzman temelli sokak gözlemleriyle ölçülmüş ve yaya düzeyindeki çevresel koşulları temsil eden bileşik bir gösterge olarak modellere dâhil edilmiştir. Bulgular, konut fiyatlarının büyük ölçüde metrekare, bina yaşı ve banyo sayısı gibi yapısal özellikler ile site içinde yer alma gibi konumsal nitelikler tarafından belirlendiğini göstermektedir. Bu sonuçlar her iki mahalle modeli için de tutarlıdır ve fiziksel konut özellikleri ile yeniden gelişim süreçlerinin piyasa değerleri üzerindeki baskın rolünü ortaya koymaktadır. MESQ değişkeninin katsayısı her iki modelde de pozitif olmakla birlikte, geleneksel anlamlılık düzeylerinde istatistiksel olarak anlamlı bulunmamıştır. Bu durum, hızlı dönüşüm geçiren konut piyasalarında fiyat oluşumunda mikro-ölçekli çevresel kaliteden ziyade yapısal ve yeniden gelişim odaklı faktörlerin daha belirleyici olduğunu göstermektedir. Bununla birlikte, çevresel göstergelerin modele dâhil edilmesi, mahalle dinamiklerinin ve kentsel dönüşüm süreçlerinin daha bütüncül bir şekilde anlaşılmasına katkı sağlamaktadır. Bulgular ayrıca, çevresel kalitenin dolaylı veya bağlama özgü etkilerine işaret etmekte olup, daha geniş veri setleri ve alternatif yöntemler kullanılarak yapılacak ileri çalışmaların gerekliliğini ortaya koymaktadır.

Anahtar Kelimeler

• Konut değerleri
• Kentsel dönüşüm
• Mahalle özellikleri
• Mekansal ve konumsal faktörler

Kaynakça

1. Ağır, H., & Önder, F. (2025). Türkiye Ekonomisinin Yapisal Dönüşümü Üzerine Bir Değerlendirme. Kahramanmaraş Sütçü İmam Üniversitesi Sosyal Bilimler Dergisi, 22(3), 1455-1468. https://dergipark.org.tr/tr/pub/ksusbd/article/1809722
2. Altun, Ö. (2022). Kamu Konutu Fiyatlarını Belirleyen Faktörlerin Hedonik Fiyat Modeliyle Analizi. Sosyoekonomi, 30(52), 349-378. https://doi.org/10.17233/sosyoekonomi.2022.02.19
3. Arslanlı, K. Y. (2020). Analysis of house prices: a hedonic model proposal for Istanbul metropolitan area. Journal of Design for Resilience in Architecture and Planning, 1(1), 57-68. https://doi.org/10.47818/DRArch.2020.v1i1004
4. Atkinson, R., & Blandy, S. (2005). Introduction: International perspectives on the new enclavism and the rise of gated communities. Housing Studies, 20(2), 177–186. https://doi.org/10.1080/0267303042000331718
5. Balaban, O. (2012). The negative effects of construction boom on urban planning and the environment in Turkey: Unraveling the role of the public sector. Habitat International, 36(1), 26–35. https://doi.org/10.1016/j.habitatint.2011.05.003
6. Bartholomew, K., & Ewing, R. (2011). Hedonic price effects of pedestrian- and transit-oriented development. Journal of Planning Literature, 26(1), 18–34. https://doi.org/10.1177/0885412210386540
7. Ben, S., Zhu, H., Lu, J., & Wang, R. (2023). Valuing the accessibility of green spaces in the housing market: A spatial hedonic analysis in Shanghai, China. Land, 12(9), 1660. https://doi.org/10.3390/land12091660
8. Debrezion, G., Pels, E., & Rietveld, P. (2007). The impact of railway stations on residential and commercial property value: A meta-analysis. Journal of Real Estate Finance and Economics, 35(2), 161–180. https://doi.org/10.1007/s11146-007-9032-z

9. Deng, T., Ma, M., & Nelson, J. D. (2015). Measuring the impacts of bus rapid transit on residential property values: The Beijing case. Transport Policy, 39, 54–66. https://doi.org/10.1016/j.retrec.2016.08.005
10. Dökmeci, V., & Erdoğan, N. (2021). Spatial analysis of 2000–2018 residential prices in Istanbul. KAPU Trakya Mimarlık ve Tasarım Dergisi, 1(1), 61–77.
11. Ebru, Ç., & Eban, A. (2011). Determinants of house prices in Istanbul: A quantile regression approach. Quality & Quantity, 45, 305–317. https://doi.org/10.1007/s11135-009-9296-x
12. Erkip, F. (2010). The rise of the shopping mall in Turkey: The use and appeal of a mall in Ankara. Cities, 27(3), 179–186. https://doi.org/10.1016/j.cities.2004.10.001
13. Franzese, R. J. (2017). Empirical Strategies for Various Manifestations of Multilevel Data. Political Analysis, 13(4), 430-446. doi:10.1093/pan/mpi024
14. Ganioğlu, A., & Seven, Ü. (2021). Do regional house prices converge? Evidence from a major developing economy. Central Bank Review, 21(1), 17-24. https://doi.org/10.1016/j.cbrev.2021.03.001
15. Geniş, Ş. (2007). Producing elite localities: The rise of gated communities in Istanbul. Urban Studies, 44(4), 771–798. https://doi.org/10.1080/00420980601185684
16. Gujarati, D. N., & Porter, D. C. (2009). Basic econometrics (5th ed.). McGraw-Hill.
17. Güller, C., & Varol Özden, Ç. (2022). Determining the factors affecting house prices in Erzurum using hedonic analysis. Sosyoekonomi, 30(54). https://doi.org/10.17233/sosyoekonomi.2022.04.20
18. Hayrullahoğlu, G., Aliefendioğlu, Y., Tanrıvermiş, H., & Hayrullahoğlu, A. C. (2018). Estimation of the hedonic valuation model in housing markets: The case of Cukurambar region in Cankaya District of Ankara Province. Ecoforum Journal, 7(1). https://www.ceeol.com/search/article-detail?id=1048042
19. IMF. (2022). Global financial stability report. International Monetary Fund. https://www.imf.org/en/publications/gfsr/issues/2022/10/11/global-financial-stability-report-october-2022
20. Jim, C. Y., & Chen, W. Y. (2006). Impacts of urban environmental elements on residential housing prices in Guangzhou (China). Landscape and Urban Planning, 78(4), 422–434. https://doi.org/10.1016/j.landurbplan.2005.12.003
21. Kangallı-Uyar, S., & Keten, N. (2020). Konut fiyatlarının mekânsal kantil regresyon yaklaşımına göre modellenmesi: Denizli ili örneği. Business and Economics Research Journal, 11. https://www.ceeol.com/search/article-detail?id=893182
22. Kartal, M. T., Kılıç Depren, S., & Depren, Ö. (2023). Housing prices in emerging countries during COVID-19: evidence from Turkey. International Journal of Housing Markets and Analysis, 16(3), 598-615. https://doi.org/10.1108/IJHMA-07-2021-0083
23. Kaya, A., & Atan, M. (2014). Determination of the factors that affect house prices in Turkey by using hedonic pricing model. Journal of Business, Economics and Finance, 3, 313–327. https://dergipark.org.tr/en/pub/jbef/article/360453
24. Keskin, B. (2008). Hedonic analysis of price in the Istanbul housing market. International Journal of Strategic Property Management, 12(2), 125–138. https://doi.org/10.3846/1648-715X.2008.12.125-138
25. Kuyucu, T., & Ünsal, Ö. (2010). Urban transformation as state-led property transfer: An analysis of two cases of urban renewal in Istanbul. Urban Studies, 47(7), 1479–1499. https://doi.org/10.1177/0042098009353629
26. Lovering, J., & Türkmen, H. (2011). Bulldozer neo-liberalism in Istanbul: The state-led construction of property markets, and the displacement of the urban poor. International Planning Studies, 16(1), 73-96. https://doi.org/10.1080/13563475.2011.552477
27. Mağden, B. (2022). Ordu İlinde Konut Fiyatlarina Etki Eden Faktörlerin Yatay Kesit Analizi ile İncelenmesi. Giresun Üniversitesi İktisadi ve İdari Bilimler Dergisi, 8(1), 16-29. https://doi.org/10.46849/guiibd.1060105
28. Malpezzi, S. (2002). Hedonic pricing models: A selective and applied review. In A. O’Sullivan (Ed.), Housing economics and public policy (pp. 67–89). Blackwell.
29. Pesaran, M. H., Shin, Y., & Smith, R. P. (1999). Pooled Mean Group Estimation of Dynamic Heterogeneous Panels. Journal of the American Statistical Association, 94(446), 621–634. https://doi.org/10.1080/01621459.1999.10474156
30. Piaggio, M. (2021). The value of public urban green spaces: Measuring the effects of proximity to and size of urban green spaces on housing market values in San José, Costa Rica. Land Use Policy, 109, 105656. https://doi.org/10.1016/j.landusepol.2021.105656
31. Pivo, G., & Fisher, J. D. (2011). The walkability premium in commercial real estate investments. Real Estate Economics, 39(2), 185–219. https://doi.org/10.1111/j.1540-6229.2010.00296.x
32. Qiu, W., Zhang, Z., Liu, X., Li, W., Li, X., Xu, X., & Huang, X. (2022). Subjective or objective measures of street environment: Which are more effective in explaining housing prices? Landscape and Urban Planning, 221, 104358. https://doi.org/10.1016/j.landurbplan.2022.104358
33. Rosen, S. (1974). Hedonic prices and implicit markets: Product differentiation in pure competition. Journal of Political Economy, 82(1), 34–55. https://doi.org/10.1086/260169
34. Sayın, Z. M., Elburz, Z., & Duran, H. E. (2022). Analyzing housing price determinants in Izmir using spatial models. Habitat international, 130, 102712. https://doi.org/10.1016/j.habitatint.2022.102712
35. Selim, H. (2009). Determinants of house prices in Turkey: Hedonic regression versus artificial neural network. Expert Systems with Applications, 36, 2843–2852. https://doi.org/10.1016/j.eswa.2008.01.044
36. Şahin, E. (2023). Factors affecting housing prices: The case of Istanbul-Ataköy. Firat University Journal of Social Sciences, 33(3), 1251–1266. https://doi.org/10.18069/firatsbed.1285789
37. Song, Y., & Knaap, G. J. (2004). Measuring the effects of mixed land uses on housing values. Regional Science and Urban Economics, 34(6), 663–680. https://doi.org/10.1016/j.regsciurbeco.2004.02.003
38. TÜİK (Türkiye İstatistik Kurumu). (2025). Yıllık Gayrisafi Yurt İçi Hasıla, 2024 https://veriportali.tuik.gov.tr/tr/press/54187 (Acessed date: 13.02.2025)
39. Türkün, A. (2011). Urban regeneration and hegemonic power relationships. International Planning Studies, 16(1), 61–72. https://doi.org/10.1080/13563475.2011.552473
40. Wang, R., Zhang, G., Zhang, Y., & Lan, Y. (2024). Exploring the spatially heterogeneous effects of street-level perceived qualities on listed real estate prices using geographically weighted regression (GWR) modeling. Buildings, 14(7), 1982. https://doi.org/10.3390/buildings14071982
41. World Bank. (2021). Global economic prospects. https://thedocs.worldbank.org/en/doc/600223300a3685fe68016a484ee867fb-0350012021/original/Global-Economic-Prospects-June-2021.pdf
42. Yıldırım, Ö. C., Sungur, A., & Guleç Özer, D. (2024). Understanding measurement of walkability in urban environments: A systematic review and research agenda. International Journal of Urban Sciences. https://doi.org/10.1080/12265934.2024.2438189