Joint Model of Residential Relocation Choice and Underlying Causal Factors

Abstract

Residential location choice is a key determinant of activity-travel behavior and yet, little is known about the underlying reasons why people choose to move, or not move, residences. Such understanding is critical to being able to model residential location choices over time, and design built environments that people find appealing. This paper attempts to fill this gap by developing a joint model of the choice to move residence and the primary reason for moving (or not moving). The model is estimated on the Florida subsample of the 2009 National Household Travel Survey. Model results shed considerable light on the socio-economic and demographic variables that impact household decision whether to move residence and the primary reason underlying that decision. Kortum, Paleti, Bhat and Pendyala 1 INTRODUCTIONResidential location choice is a topic of much interest because decisions about where to work, shop, go to school, or pursue recreational activities are all inextricably tied to people's residential location (1). Although there is considerable literature devoted to modeling and describing residential location choice behavior, an understanding of the underlying factors that contribute to a household decision to relocate residence (or not) continues to be challenging and in need of further enquiry. To set the context, we first briefly identify the factors that have been identified as determinants of residential relocation in the next section, followed by an overview of the methods used for residential relocation modeling in the subsequent section. Within each of these two sections, we position and highlight the salient aspects of this study.

Modelling residential mobility decision and its impact on car ownership and travel mode

“…Presence of senior adults (more than 75 years of old) in the household is found to reduce the likelihood of moving. (see Kortum et al (2012) for similar finding). This may be due to the fact that most of the elderly people are more settled in their place and their physical condition constrain to move frequently.…”

“…Highly educated people are also found to have higher propensity to move. This phenomenon (also observed by Kortum et al (2012)), may be due to their higher access to opportunities (specifically in the job market). Presence of senior adults (more than 75 years of old) in the household is found to reduce the likelihood of moving.…”

Keynote — Total Design Data Needs for the New Generation Large-Scale Activity Microsimulation Models

“…Residence, Workplace, and School Location Choice: This is a critical survey component for behaviorally integrated land use travel demand models. We expect this component to be an indepth survey to identify the determinants for each of the residential, workplace, and school choices (see Kortum et al, 2012). Both primary locations and secondary locations should be examined in more detail than typical household surveys and data collected to estimate choice models for each facet.…”

Toll Roads in Indonesia: Transport System, Accessibility, Spatial and Equity Impacts

“…Changes in accessibility that result from the interaction of land use and new transportation infrastructures can influence preferences for residential location (Wegener & Fürst, 2004). Several studies have found a relationship between housing choices and accessibility indicators, for example, travel time to work (Kim, Pagliara, & Preston, 2005;Vega & Reynolds-Feighan, 2009), (monetary) cost of travel to work (Olaru et al, 2011;Tillema et al, 2010), and accessibility of various urban facilities (Chen, Chen, & Timmermans, 2008;Haugen, Holm, Strömgren, Vilhelmson, & Westin, 2012;Kortum, Paleti, Bhat, & Pendyala, 2012).…”

“…Figure 4.1 shows the conceptual model that accommodates residential location choice, toll road construction and other influencing factors; the ellipse with a dashed line represents unobserved (latent) variables. Many studies have found a relationship between accessibility indicators and housing choices, for example travel time to work (Kim et al, 2005;Vega & Reynolds-Feighan, 2009), travel (monetary) cost to work (Olaru et al, 2011;Tillema et al, 2010), accessibility of various urban facilities (Chen et al, 2008;Haugen et al, 2012;Kortum et al, 2012) and distance to main roads (de Palma, Motamedi, Picard, & Waddell, 2005;Huu Phe & Wakely, 2000). Tillema et al (2010), moreover, found that people are more sensitive to travel costs, including toll and fuel costs, than to housing costs, to choose residential location.…”