Abstract
When choosing a housing, the region in which it is located is as important as the quality of the housing. Depending on a number of factors such as the socio-cultural structure of that region, the services offered, and the opportunities in the surrounding area, the choice of housing location may change. In this context, the aim is to investigate which district is the most suitable when buying a house by evaluating customers' preferences for housing location in Erzurum province. In the case study, 3 alternative regions (Palandoken, Yakutiye ve Aziziye) and 6 criteria (transportation accessibility, housing price, population density, noise and air pollution, infrastructure safety, social and cultural activity areas) were defined and the criteria weights were calculated using the Fuzzy Full Consistency Method (F-FUCOM). Then, the Fuzzy Measurement Alternatives and Ranking According to Compromise Solution (F- MARCOS) method was used to evaluate the alternatives. The results of the research have shown that the most important criterion is the price of the house, while the least important criterion is noise and air pollution for customers to buy a house. In addition, the results have shown that Yakutiye district is the best alternative for choosing housing districts in Erzurum province. The other alternatives are Palandoken and Aziziye respectively.
Publisher
ACADEMY Saglik Hiz. Muh. Ins. Taah. Elekt. Yay. Tic. Ltd. Sti.
Reference28 articles.
1. Memiş, S., Tüketicilerin konut seçimini etkileyen faktörlerin belirlenmesine yönelik bir araştırma. International Journal of Academic Value Studies. (2018) 4(20): p. 652-665.
2. TSI. 2022; Available from: https://www.tuik.gov.tr/.
3. Palandoken, T.M.o. 2022; Available from: https://www.palandoken.bel.tr/hakkinda.
4. Aziziye, T.M.o. 2022; Available from: http://www.erzurumaziziye.bel.tr/.
5. Erzurum, T.G.o. 2022; Available from: http://www.erzurum.gov.tr/Yakutiye.
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