Reza Asadi; Atoosa Modiri; Farhad Hosseynali; AliAkbar Gholi Zadeh
Abstract
Families meet different needs that force them to relocate in the city, which includes changes in the housing unit and the socio-economic context of the residential environment. Identifying ...
Read More
Families meet different needs that force them to relocate in the city, which includes changes in the housing unit and the socio-economic context of the residential environment. Identifying the pattern of housing selection and differentiation of household preferences is explained based on the theory of life cycle. The theory is explained, the general cycle of life changes and housing needs for all households, and in progress of developing the theory, urban households are divided into distinct categories with socio-economic characteristics that is the source of various housing choices. Identifying the relationships between the socio-economic characteristics of the household and housing helps urban planners and policymakers to plan more accurately for city.Residential location modes were performed in a discrete manner using the "quantitative multivariate regression" method, which allows the analysis of multiple dependent variables. The input data of the model were obtained from the population and housing census of Tehran in 2011 and Tehran urban facilities’ data gained from a detailed plan. Dependent variables are "housing", which are divided into two groups: housing unit and the socio-economic context of the residents. The variables of the residential unit group include the area of house unite and the stability of the building. The variables of the socio-economic context of the residents include the literacy rate in the women's group, the female employment rate, the attendance rate of highly educated residents, the per capita personal vehicle and the student attendance rate.Finding shows that the variables of "housing unit" group play the most important role in housing selection and among the variables of socio-economic context of households, all variables have a significant relationship that indicates the validity of the model and two variables "per capita personal car" and "Women's literacy rates" play the largest percentage of explanation in this group.
