Exploring the perspectives of university students on post-COVID-19 rental housing demands: a case study of Çanakkale, Türkiye

Usakli M., YÜCEBAŞ S. C., GENÇ L.

Journal of Housing and the Built Environment, vol.40, no.2, pp.803-817, 2025 (SSCI) identifier

  • Publication Type: Article / Article
  • Volume: 40 Issue: 2
  • Publication Date: 2025
  • Doi Number: 10.1007/s10901-025-10182-5
  • Journal Name: Journal of Housing and the Built Environment
  • Journal Indexes: Social Sciences Citation Index (SSCI), Scopus, International Bibliography of Social Sciences, ABI/INFORM, Agricultural & Environmental Science Database, Environment Index, Geobase, Civil Engineering Abstracts
  • Page Numbers: pp.803-817
  • Keywords: C4.5, Covid-19, Decision tree, Machine learning, University students
  • Çanakkale Onsekiz Mart University Affiliated: Yes

Abstract

This study focused on utilizing Machine Learning (ML) to examine the housing preferences of students. Employing a survey method, the study utilized decision tree, a widely favored ML approach, to present findings. The analysis focused on the post-COVID-19 rental housing preferences of students and their impact on rental prices. Furthermore, the research identified the number of rooms as a crucial factor for male students, particularly for first-year students, with gender becoming significant for second-, third-, and fourth-year students in Barbaros neighborhood. A noteworthy post-COVID-19 trend was the observation that students, in general, preferred communal living arrangements, sharing rental costs. Additionally, the study found that under different circumstances, male students were more inclined to lease housing in Çanakkale province compared to their female counterparts.