Abstract
The aim of this article is to attain a better understanding of which aspects influence students’ housing satisfaction in Trondheim, Norway. Due to rising student numbers in the last decade in Norway, there is a distinct need for new student housing. It has been stated previously that students prefer specific, often central locations in university cities and that they have become more demanding when it comes to the standard of accommodation. Questions related to how and where to accommodate students have become an issue in both public and professional discussions. This study adds to the knowledge on different aspects that influence student housing satisfaction and thus offers background information for further discussion on the student housing situation in medium-sized university cities. Data were collected through a quantitative survey, which emphasized the following five aspects: (1) Type of tenancy/ownership, (2) The impact of demographic variables, (3) Housing location, (4) Different housing characteristics, and (5) Individual facilities (kitchen/bathroom). The survey data indicate that the most important variables for student residential satisfaction were, first, the type of tenancy/ownership; second, the quality of different housing characteristics; and third, the location. In this study, individual facilities and demographic variables did not have a significant effect on housing satisfaction.
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Notes
Samskipnaden, founded in Norway in 1948, is a non-profit organization with a majority of students sitting on its board (www.sit.no; www.sio.no). Samskipnaden operates independently in each university city, providing a wide range of services in the areas of health, sports, bookshops and also student housing.
600,000 Norwegian Kroner is approximately 75,000 Euro (8 NOK = 1 €).
Standard deviation is a measure of the dispersion. A high standard deviation indicates that the data are spread out over a large range of values, while a low standard deviation indicates that the answers tend to be very close to the mean.
Beta coefficients have a variance of 1. This is usually reported to answer the question of which of the independent variables has a greater effect on the dependent variable in a multiple regression analysis.
The adjusted R 2 is a statistical measure of how well the regression line approximates the real data points (adjusted for the number of regression coefficients). An adjusted R 2 of 1.0 indicates that the regression line perfectly fits the data. It may be interpreted as how much (in percent) of the variation of the dependent variable is attributable to the independent variables.
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Thomsen, J., Eikemo, T.A. Aspects of student housing satisfaction: a quantitative study. J Hous and the Built Environ 25, 273–293 (2010). https://doi.org/10.1007/s10901-010-9188-3
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DOI: https://doi.org/10.1007/s10901-010-9188-3