Investigation of the relationship between quantitative indicators of housing and the citizens’ mental health Case study: Mardavij neighborhood- Isfahan

In the present world and with the development of cities and urbanization, citizens’ mental health is at risk. In other words, rapid growth of the cities can be considered as one of the threats to the environment which influence the residents of the cities i.e. humans by devastating effects on their soul and body. Studies have shown that the quality of the constructed life environments and urban neighborhoods affects the citizens’ mental health. If we accept that the quality of the constructed environments affects the mental health, in this physical environment housing plays an important role in the indicators of mental health, because people spend much of their time at home and their residential environment. With this approach, this research is mainly aimed at investigating the mutual relationship of different patterns of residential densities on the indicators of the citizens’ mental health in Mardavich neighborhood, Isfahan.

The main goal of this research is investigating the mutual relationship between quantitative indicators of housing and the citizens’ mental health. In this regards, independent variables include different types of housing patterns, household density in the residential unit, and density of peoples living in a room. Depression and perceived stress are dependant variables of the research. For measuring people’s stress, perceived stress scale by Cohen et al. (1983) was used and for measuring depression, the second edition of depression questionnaire by Beck et al. (BDI-II) was used. The research population includes the households living in Mardavich neighborhood. Regarding the fact that no precise statistics were available, for filling the questionnaires, 250 questionnaires were distributed among the households and at the end, 231 correctly filled questionnaires were gathered.

One of the independent variables of the research is housing pattern. In this research, the respondents’ homes were analyzed in three classes including house, apartment, and high-rise residential complexes. The results showed that the average depression and perceived stress in houses and apartments are much lower than high-rise residential complexes. Statistically, there is an almost strong relationship between different housing patterns and mental health indicators. The second independent variable of the research is building density. The results of the present research showed that with the increase of building density, the average scores of depression and perceived stress also increase. Statistically, building density has a relationship with the variable of depression with the correlation coefficient of 0.518 and also, it has a relationship with perceived stress with the correlation coefficient of 0.464. The other independent variable of the research is the per capita residential land. The results of the present research showed that there is a negative relationship between the per capita housing and mental health indicators. Actually, with the increase of per capita housing and allocation of more land area to every individual, stress and depression will be decreased. Statistically, the correlations between the indicator of per capita housing and the dependant variables i.e. depression and perceived stress are respectively equal to 0.447 and 0.373. The forth independent variable of the research is the density of people living in a room. According to the obtained results, with the increase of people density in a room, depression and perceived stress will also increase. Statistically, people density in a room has a significant and positive relationship with depression and perceived stress with the respective correlation coefficients of 0.405 and 0.380. In the following, for investigating the fact that which of the independent variables has a stronger effect on perceived stress and depression, modeling of the changes of depression and perceived stress levels is done based on quantitative indicators of housing by stepwise linear regression. According to the results, significance level of F statistics for the indicators of perceived stress and depression is equal to 0.000 in the proposed models. This finding which is the result of regression analysis by variance analysis suggests that the research conceptual model has an appropriate goodness of fit. The results of perceived stress showed that for the changes of this indicator, we can propose a model based on the indicator of building density. It means that of the four independent variables, building density has the strongest effect on perceived stress. Finally based on the proposed model, building density can predict 47 percent of the changes of this indicator. According to the proposed models, the two indicators of building density and people density in a room are introduced as the predictive indicators of the variable of depression. Actually, the results of stepwise linear regression analysis for the variable of depression showed that two models can be proposed for the changes of this variable. In the first model, building density is the only predictive indicator which predicts 51.9 percent of the changes of depression. In the second model, the two indicators of building density and people density in room can respectively predict 42.6 and 16.7 percent of the changes of depression. Conclusion In general, it can be stated that all the independent variables of the research i.e. the quantitative indicators of housing have a significant relationship with mental health indicators i.e. depression and perceived stress. So, this research hypothesis which is consistent with many other works is approved and it suggests that there is a significant relationship between quantitative indicators of housing and mental health indicators. Therefore, by improving the quantitative indicators of housing, people’s mental health can be improved. This fact suggests that the communication between housing planning experts and psychologists should be strengthened more than the past in order to be able to control the negative effect of increased density on mental health indicators.