Reexamining the Influence of Work and Nonwork Accessibility on Residential Location Choices with a Microanalytic Framework

Abstract

IntroductionGenerally defined as the ease of access between entities in different locations (Harris, 2001), the concept of accessibility reflects the possibilities for activities, such as employment and shopping, and has long been theorized as a principal determinant of residential choice behavior. The classic monocentric city model in urban economics held central the trade-off relationship between commuting and land costs in explaining household locations (Alonso, 1964;Muth, 1969).Research on the influence of accessibility on residential choice is extensive but the empirical results have been mixed. Recent reviews of this literature have found, in general, accessibility to have modestly positive explanatory variables (Blijie, 2005; Zondag and Pieters, 2005), but their impacts on residential choice behavior are secondary to and confounded by other household, dwelling, and neighborhood characteristics

The role of location in residential location choice models: a review of literature

“…If dwelling is a choice alternative, it is possible to interact household attributes with residential units attributes, e.g., household size in relation to apartment size. Lee and Waddell (2010a) conclude that these dwelling characteristics tend to dominate accessibility indicators. Our own study in the Zurich area also showed that including dwelling characteristics strongly enhanced the models (Schirmer et al 2013).…”

“…Zolfaghari et al (2012) see a positive price impact when constraining the choice set based on price, indicating that households choose the best available alternative within a certain range. Ratio of price to household income is also significant when it is the only price-related variable in the model specification (Belart 2011;Bürgle 2006;Lee and Waddell 2010a;Waddell 2006;Weisbrod et al 1980;Zhou and Kockelman 2008). Walker and Li (2007) observe that price sensitivity decreases with rising income; integration of the ratio and, ultimately, the logarithmic transformation seems reasonable.…”

“…Only non-single households with employees are unaffected; however, this group reacts to accessibility for all travel modes, assumed to be partly correlated. Lee and Waddell (2010a) use the neighborhood jobs logarithm as a variable in their model and this has a positive effect, while Guo and Bhat (2007) find that lower-income households and single households are more e role of location in residential location choice models  likely to be located near employment centers. We expect that service density and retail density have a common positive influence on residential choice.…”

“…Also, a further differentiation of household types should be attempted, as young households tend to favor urban areas. As a side note, not only does location choice behavior differ, but also relocation probability changes over a life span and is additionally influenced by life-cycle events such as a change of marital status, a job change, or the birth of a child (Andrew and Meen 2006;Beige 2008;Eluru et al 2009;Kim et al 2005;Lee and Waddell 2010a).        .…”

“…Geo-databases allow combinations of various datasets, as well as adding a spatial reference point. With the increasing availability of such data, ways to represent residential alternatives and their environment have changed; recent models consider buildings or units as choice alternatives and include building-and location-specific attributes (Habib and Miller 2009;Lee and Waddell 2010a).…”