Spatial distribution and clustering patterns of short-term rental housing (Airbnb): the case of Istanbul

Öz

In this study, the distribution of Airbnb supply is evaluated specifically for Istanbul. The areas with a high concentration of Airbnb listings are primarily located in the city center and around major tourist attractions. Listings are notably clustered around historical sites, the Bosphorus, and coastal zones, gradually diminishing towards the urban periphery. At the district level, Beyoğlu, Fatih, Şişli, and Kadıköy exhibit the highest density of listings, whereas Sultangazi, Sultanbeyli, Esenler, and Sancaktepe have the fewest.Following the examination of spatial distribution, spatial statistical methods were employed for further analysis. These include Moran’s I, Local Indicators of Spatial Association (LISA), and Hot Spot Analysis (Getis- Ord Gi*). Listings were analyzed at the neighborhood scale, taking into account factors such as accessibility, urban green spaces, forest areas, historical and cultural sites, social facilities, education, public services, health, sports, and commercial areas. This approach enabled the identification of correlations between Airbnb distribution and various geographical features. The findings reveal that historical and cultural resources, as well as ease of access, are the most influential factors in shaping the spatial distribution of Airbnb listings. Locations with proximity to key destinations, good accessibility, or robust transportation infrastructure tend to stand out in terms of Airbnb clustering. Similar results have been reported in existing academic literature, and this pattern holds true for the case of Istanbul as well.

Anahtar Kelimeler

• Airbnb
• Istanbul
• Spatial Statistics
• Tourism Geography
• Short Term Rental Platform

İstanbul’daki kısa süreli konut kiralamalarının (Airbnb) mekânsal dağılışı ve kümelenme desenleri

Öz

Bu çalışmada Airbnb arzının dağılışı İstanbul özelinde değerlendirilmektedir. Çalışma alanında Airbnb ilanlarının yüksek olduğu kısımlar özellikle kent merkezi ve turistik noktalardır. Tarihi alanlar, boğaz ve kıyı boyunca yoğunlaşan ilanlar çeperlere doğru seyrelmektedir. İlanların dağılışı ilçe ölçeğinde Beyoğlu, Fatih, Şişli ve Kadıköy olarak sıralanmaktadır. En az ilan ise Sultangazi, Sultanbeyli, Esenler ve Sancaktepe’de yer almaktadır. Dağılışın incelenmesinin ardından mekânsal istatistik yöntemleri ile değerlendirme yapılmıştır. Moran’s I, LISA ve Sıcak Nokta Analizi (Getis-Ord-Gi*) yapılmıştır. İlanlar mahalle bazında incelenmiş; ulaşım, kentsel yeşil alan, orman alanları, tarihi ve kültürel alanlar, sosyal alanlar, eğitim, kamu, sağlık, spor ve ticaret alanları baz alınarak değerlendirme yapılmıştır. Böylelikle Airbnb ile mekânsal çevre özellikleri arasındaki bağlantı ortaya konulmuştur. Çalışmamızda tarihi ve kültürel kaynaklar ile ulaşım kolaylığının dağılışı etkileyen en önemli faktörler olduğu tespit edilmiştir. Airbnb kümelenmesinde destinasyon noktalarına yakın ve erişim kolaylığı olan veya ulaşım altyapısının güçlü olduğu noktalar ön plana çıkmaktadır. Akademik çalışmalarda da benzer sonuçlara ulaşılmıştır. İstanbul özelinde de durum benzerdir.

Anahtar Kelimeler

• Airbnb
• İstanbul
• Mekânsal İstatistik
• Turizm Coğrafyası
• Kısa Süreli Kiralama Platformu

Kaynakça

1. Adamiak, C. (2018). Mapping airbnb supply in European cities. Annals of Tourism Research, 71, 67-71. Adamiak, C., Szyda, B., Dubownik, A., & García-Álvarez, D. (2019). Airbnb offer in Spain—Spatial analysis of the pattern and determinants of its distribution. ISPRS International Journal of Geo-Information, 8(3), 1-26. 10.3390/ijgi8030155
2. Airbnb. (2024). Airbnb about us. Airbnb Newsroom. https://news.Airbnb.com/about-us/
3. Alvarez, M. D. (2010). Creative cities and cultural spaces: New perspectives for city tourism. International Journal of Culture, Tourism and Hospitality Research, 4(3), 171-175. https://doi.org/10.1108/175.061.81011067565
4. Andreu, L., Bigne, E., Amaro, S., & Palomo, J. (2020). Airbnb research: An analysis in tourism and hospitality journals. International Journal of Culture, Tourism and Hospitality Research, 14(1), 2-20. https://doi.org/10.1108/IJCTHR-06-2019-0113
5. Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93-115.
6. Aytar, V. (2006). Caterers of the consumed metropolis: Ethnicized tourism and entertainment labourscapes in Istanbul. İçinde Tourism, Ethnic Diversity and the City. Routledge.
7. Curto, R. A., Rubino, I., & Verderosa, A. (2022). Investigating airbnb evolution in an urban tourism context: The application of mathematical modelling and spatial analysis. Current Issues in Tourism, 25(10), 1666-1681. https://doi.org/10.1080/13683.500.2021.1932767
8. Decrop, A., & Zidda, P. (2008). Tatil kararı verme tarzına dayalı bir tipoloji. Anatolia: Turizm Araştırmaları Dergisi, 19(1), 84-92. https:// izlik.org/JA28EP38ZZ

9. Deliormanlı, A., Özdemir, C., & Düşmezkalender, E. (2023). Turizmin dönüştürücü gücü: Airbnb deneyimi. Turizm Akademik Dergisi, 10(1), 85-100. https://izlik.org/JA85GA64TA
10. Demir, E., & Emekli, G. (2021). Is airbnb no longer a sharing economy platform? Evidence from Europe’s top 10 airbnb destinations. Anatolia, 32(3), 470-488. https://doi.org/10.1080/13032.917.2021.1890626
11. Dolnicar, S. (2019). A review of research into paid online peer-to-peer accommodation: Launching the annals of tourism research curated collection on peer-to-peer accommodation. Annals of Tourism Research, 75, 248-264. https://doi.org/10.1016/j.annals.2019.02.003
12. Duman, S., Ünsal, Ö., & Zaman, S. (2024). The clustering of the population at building scale in Bursa City (Türkiye). Sustainability, 16(19), 1-29. https://doi.org/10.3390/su16198615
13. Ert, E., Fleischer, A., & Magen, N. (2016). Trust and reputation in the sharing economy: The role of personal photos in airbnb. Tourism Management, 55, 62-73. https://doi.org/10.1016/j.tourman.2016.01.013
14. Eugenio-Martin, J. L., Cazorla-Artiles, J. M., & González-Martel, C. (2019). On the determinants of airbnb location and its spatial distribution. Tourism Economics, 25(8), 1224-1244. https://doi.org/10.1177/135.481.6618825415
15. Fang, B., Ye, Q., & Law, R. (2016). Effect of sharing economy on tourism industry employment. Annals of Tourism Research, 57(C), 264-267. 10.1016/j.annals.2015.11.018
16. Fischer, D., Jenssen, S., & Tappeser, V. (2015). Getting an empirical hold of the sustainable university: A comparative analysis of evaluation frameworks across 12 contemporary sustainability assessment tools. Assessment & Evaluation in Higher Education, 40(6), 785-800. https://doi.org/10.1080/02602.938.2015.1043234
17. 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
18. Gezici, F., & Kerimoglu, E. (2010). Culture, tourism and regeneration process in Istanbul. International Journal of Culture, Tourism and Hospitality Research, 4(3), 252-265. https://doi.org/10.1108/175.061.81011067637
19. Grekousis, G. (2020). Spatial analysis methods and practice: Describe – explore – explain through GIS. Cambridge University Press.
20. Gutiérrez, J., García-Palomares, J. C., Romanillos, G., & Salas-Olmedo, M. H. (2017). The eruption of airbnb in tourist cities: Comparing spatial patterns of hotels and peer-to-peer accommodation in Barcelona. Tourism Management, 62, 278-291. https://doi.org/10.1016/j. tourman.2017.05.003
21. Guttentag, D., Smith, S., Potwarka, L., & Havitz, M. (2018). Why tourists choose airbnb: A motivation-based segmentation study. Journal of Travel Research, 57(3), 342-359. https://doi.org/10.1177/004.728.7517696980
22. Gyódi, K., & Nawaro, Ł. (2021). Determinants of airbnb prices in European cities: A spatial econometrics approach. Tourism Management, 86, 104319. https://doi.org/10.1016/j.tourman.2021.104319
23. Haining, R. P. (2003). Spatial data analysis: Theory and practice. Cambridge University Press.
24. Ki, D., & Lee, S. (2019). Spatial distribution and location characteristics of airbnb in Seoul, Korea. Sustainability, 11(15), 1-16. https://doi. org/10.3390/su11154108
25. KTB. (2024). Kültür Turizm Bakanlığı İstatistikleri.
26. La, L., Xu, F., Hu, M., & Xiao, C. (2021). Location of airbnb and hotels: The spatial distribution and relationships. Tourism Review, 77(1), 209-224. https://doi.org/10.1108/TR-10-2020-0476
27. Lagonigro, R., Martori, J. C., & Apparicio, P. (2020). Understanding airbnb spatial distribution in a Southern European city: The case of Barcelona. Applied Geography, 115, 1-11. https://doi.org/10.1016/j.apgeog.2019.102136
28. Liang, C., Yeung, M. C. H., & Au, A. K. M. (2022). The impact of airbnb on housing affordability: Evidence from Hong Kong. Environment and Planning B: Urban Analytics and City Science, 49(3), 1048-1066. https://doi.org/10.1177/239.980.83211043123
29. Morales-Pérez, S., Garay, L., & Wilson, J. (2022). Airbnb’s contribution to socio-spatial inequalities and geographies of resistance in Barcelona. Tourism Geographies, 24(6-7), 978-1001. https://doi.org/10.1080/14616.688.2020.1795712
30. Nieto, J., Hernández-Maestro, R. M., & Muñoz-Gallego, P. A. (2014). Marketing decisions, customer reviews, and business performance: The use of the Toprural website by Spanish rural lodging establishments. Tourism Management, 45, 115-123. https://doi.org/10.1016/j. tourman.2014.03.009
31. Rabiei-Dastjerdi, H., McArdle, G., & Hynes, W. (2022). Which came first, the gentrification or the airbnb? Identifying spatial patterns of neighbourhood change using airbnb data. Habitat International, 125, 1-12. https://doi.org/10.1016/j.habitatint.2022.102582
32. Sahin, S., & Baloglu, S. (2011). Brand personality and destination image of Istanbul. Anatolia, 22(1), 69-88. https://doi.org/10.1080/13032. 917.2011.556222
33. Stillwell, J., & Clarke, G. (2003). Applied GIS and spatial analysis. John Wiley & Sons.
34. Sun, S., Wang, X., & Hu, M. (2022). Spatial distribution of airbnb and its influencing factors: A case study of Suzhou, China. Applied Geography, 139, 1-9. https://doi.org/10.1016/j.apgeog.2022.102641
35. Tobler, W. R. (1970). A computer movie simulating urban growth in the Detroit Region. Economic Geography. 46, 234-240
36. Türk, U., Östh, J., Kourtit, K., & Nijkamp, P. (2021). The path of least resistance explaining tourist mobility patterns in destination areas using airbnb data. Journal of Transport Geography, 94, 1-15. https://doi.org/10.1016/j.jtrangeo.2021.103130
37. Voltes-Dorta, A., & Inchausti-Sintes, F. (2021). The spatial and quality dimensions of airbnb markets. Tourism Economics, 27(4), 688-702. https://doi.org/10.1177/135.481.6619898075
38. Wilson, J., Garay-Tamajon, L., & Morales-Perez, S. (2022). Politicising platform-mediated tourism rentals in the digital sphere: Airbnb in Madrid and Barcelona. İçinde Platform-Mediated Tourism. Routledge.
39. Xu, F., Hu, M., La, L., Wang, J., & Huang, C. (2020). The influence of neighbourhood environment on airbnb: A geographically weighed regression analysis. Tourism Geographies, 22(1), 192-209. https://doi.org/10.1080/14616.688.2019.1586987
40. Yamada, I., & Thill, J.-C. (2007). Local indicators of network-constrained clusters in spatial point patterns. Geographical Analysis, 39(3), 268- 292. https://doi.org/10.1111/j.1538-4632.2007.00704.x
41. Zaman, S., Erdoğan, H., & Eşim, İ. (2024). İç turizmdeki pandemi etkisinin kümelenme analizi kullanılarak incelenmesi: Türkiye örneği. Anatolia: Turizm Araştırmaları Dergisi, 35(1), 60-73. https://doi.org/10.17123/atad.1427640
42. Zervas, G., Proserpio, D., & Byers, J. (2016). The rise of the sharing economy: Estimating the impact of airbnb on the hotel industry (SSRN Scholarly Paper 2366898). Social Science Research Network.
43. Zhang, Z., & Chen, R. J. C. (2019). Assessing airbnb logistics in cities: Geographic information system and convenience theory. Sustainability, 11(9), 1-11. https://doi.org/10.3390/su11092462
44. Zoğal, V., & Emekli, G. (2023). Kısa süreli konut kiralamalarının profesyonelleşmesi ve etkileri: Muğla’da airbnb örneği. Ege Coğrafya Dergisi, 32(2), 289-312. https://doi.org/10.51800/ecd.1399416