Tion, low income, or lack of information (e.g., Pager and

Tion, low income, or lack of information (e.g., Pager and Shepherd 2008). Mobility studies can combine information on CPI-455 supplier residential choices of individuals with population data on neighborhoods to infer the population dynamics and residential patterns that are implied by the residential preferences and choices of individuals. Such studies may focus on the processes that underpin segregation and population dynamics (e.g., Schelling 1969, 2006; Bruch and Mare 2006, 2009) or examine how housing policies, natural disasters, and other exogenous factors affect mobility behavior and population redistribution (e.g., Kingsley and Johnson 2003; Basolo and Nguyen 2005; Clark 2005; Groen and Polivka 2009; Fussell et al. 2010). In reviewing methodological issues in the analysis of residential preference and residential mobility, we focus on how individuals respond to the race-ethnic composition of their neighborhoods, although the methods discussed here may be used to model choices based on any dimension of neighborhoods. For the purposes of discussion, we will refer to choices by “individuals,” but, with suitable modification, these methods can take account of the fact that households, families, or other social units may make mobility decisions. We review a variety of types of data on residential preferences and mobility and discuss appropriate statistical models for these data. We discuss the analysis of ranked and other types of clustered data; functional form issues; problems of unobserved heterogeneity in individuals and in neighborhoods; and strengths and weaknesses of stated preference data versus observations of actual mobility behavior. We also discuss specific problems with residential mobility data, including the treatment of one’s current location as a potential choice, how to specify the choice set of potential movers, the aggregation of units (such as dwelling units into neighborhoods) and the need to take account of variations in neighborhood size, the problem of very large choice sets and possible sampling solutions; and the link between residential mobility and patterns of neighborhood change. This paper makes several contributions to the existing literature. First, although the basic discrete choice model is a well-known social science tool, sociological studies of residential preferences and residential mobility have made Beclabuvir web little use of these models. Scholars working in these areas typically employ regression models for the effects of individuals’ characteristics on their probabilities of moving to/from a neighborhood or focus only on a single dimension of neighborhoods. These models do not naturally represent residential mobility as a choice that is constrained by available options and motivated by the differential attractiveness of destinations across multiple dimensions. The analysis of residential mobility requires a number of specific adaptations to the basic choice model that we discuss below. Second, we suggest how the discrete choice framework may be used to develop more behaviorally sophisticated models of residential choice behavior, including how people respond to past experience and neighborhood change. Third, the models discussed in this paper provide a common analytic framework for both actual mobility behavior and stated residential preferences (as typically elicited through vignettes). Finally, we show how statistical models of individual preference and choice provide a foundation for the analysis of aggregate patterns.Tion, low income, or lack of information (e.g., Pager and Shepherd 2008). Mobility studies can combine information on residential choices of individuals with population data on neighborhoods to infer the population dynamics and residential patterns that are implied by the residential preferences and choices of individuals. Such studies may focus on the processes that underpin segregation and population dynamics (e.g., Schelling 1969, 2006; Bruch and Mare 2006, 2009) or examine how housing policies, natural disasters, and other exogenous factors affect mobility behavior and population redistribution (e.g., Kingsley and Johnson 2003; Basolo and Nguyen 2005; Clark 2005; Groen and Polivka 2009; Fussell et al. 2010). In reviewing methodological issues in the analysis of residential preference and residential mobility, we focus on how individuals respond to the race-ethnic composition of their neighborhoods, although the methods discussed here may be used to model choices based on any dimension of neighborhoods. For the purposes of discussion, we will refer to choices by “individuals,” but, with suitable modification, these methods can take account of the fact that households, families, or other social units may make mobility decisions. We review a variety of types of data on residential preferences and mobility and discuss appropriate statistical models for these data. We discuss the analysis of ranked and other types of clustered data; functional form issues; problems of unobserved heterogeneity in individuals and in neighborhoods; and strengths and weaknesses of stated preference data versus observations of actual mobility behavior. We also discuss specific problems with residential mobility data, including the treatment of one’s current location as a potential choice, how to specify the choice set of potential movers, the aggregation of units (such as dwelling units into neighborhoods) and the need to take account of variations in neighborhood size, the problem of very large choice sets and possible sampling solutions; and the link between residential mobility and patterns of neighborhood change. This paper makes several contributions to the existing literature. First, although the basic discrete choice model is a well-known social science tool, sociological studies of residential preferences and residential mobility have made little use of these models. Scholars working in these areas typically employ regression models for the effects of individuals’ characteristics on their probabilities of moving to/from a neighborhood or focus only on a single dimension of neighborhoods. These models do not naturally represent residential mobility as a choice that is constrained by available options and motivated by the differential attractiveness of destinations across multiple dimensions. The analysis of residential mobility requires a number of specific adaptations to the basic choice model that we discuss below. Second, we suggest how the discrete choice framework may be used to develop more behaviorally sophisticated models of residential choice behavior, including how people respond to past experience and neighborhood change. Third, the models discussed in this paper provide a common analytic framework for both actual mobility behavior and stated residential preferences (as typically elicited through vignettes). Finally, we show how statistical models of individual preference and choice provide a foundation for the analysis of aggregate patterns.