Understanding Neighborhood Well-Being: The Case of Metropolitan Baltimore
 
Matthew Wilson, Amanda Vemuri and J. Morgan Grove

 
Using data from a telephone survey of 1508 respondents across the metropolitan Baltimore region and objective environmental data, we investigate what types of variables are most significant for neighborhood satisfaction and life satisfaction.
 
Bivariate correlations show that social capital, environment satisfaction, and move away have substantial and highly significant correlations with neighborhood satisfaction. Life satisfaction has substantial and significant correlations with environment satisfaction, income, neighborhood satisfaction, and education. We also conducted logistic regression analyses of both life and neighborhood satisfaction. For both life and neighborhood satisfaction, regression models including a variety of variables, covering the four basic types of capital, were more successful in explaining variation in the dependent variable than regression models using only socioeconomic and demographic variables. Logistic regression analysis did not find the objective environmental variables to be significant predictors of either neighborhood or life satisfaction but the environment satisfaction variable was a significant factor for both. In the main neighborhood logistic regression model, variables representing human, social, and natural capital were all found to be significant factors.
 
Similarly in the main life satisfaction logistic regression model, variables representing built, social, and natural capital were found to be significant factors. The analyses presented here highlight the differences between life and neighborhood satisfaction and show that variables other than traditional socioeconomic and demographic variables are important to satisfaction.
 

 
Keywords: social capital, well-being, neighborhood satisfaction
 

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