Residential mobility and location choice: a nested logit model with sampling of alternatives

Abstract

Residential mobility and relocation choice are essential parts of integrated transportation and land use models. These decision processes have been examined and modeled individually to a great extent but there remain gaps in the literature on the underlying behaviors that connect them. Households may partly base their decision to move from or stay at a current location on the price and quality of the available alternatives. Conversely, households that are on the market for a new location may evaluate housing choices relative to their previous residence. How and the degree to which these decisions relate to each other are, however, not completely understood. This research is intended to contribute to the body of empirical evidence that will help answer these questions. It is hypothesized that residential mobility and location choice are related household decisions that can be modeled together using a two-tier hierarchical structure. This paper presents a novel nested logit (NL) model with sampling of alternatives and a proposed procedure for sampling bias correction. The model was estimated using fullinformation maximum likelihood estimation methods. The results confirm the applicability of this NL model and support similar findings from other empirical studies in the residential mobility and location choice literature.

Modelling the spatial interactions between workplace and residential location

“…1 to compare the structure of the models). Previous research with large choice groups has used the random sampling of alternatives technique proposed by McFadden, even though this can only be used in the case of MNL models unless additional corrections are used (Lee and Waddell, 2010). However, the current software available for estimations (e.g.…”

Joint Model of Residential Relocation Choice and Underlying Causal Factors

“…The first category corresponds to household demographic and socio-economic factors. Previous research has indicated that relocation is highest among younger adults (2) and lowest among older households (3). Many research efforts have found that residential relocations are also high among households who have experienced life course changes in household structure, lifecycle stage, and employment, say due to marriage, child birth, divorce, retirement, or an occupation change (4)(5)(6).…”

“…Besides demographic and socio-economic attributes, a second category of attributes influencing residential relocation are the characteristics/tenure type of the housing unit itself and the housing market conditions, including considerations of housing price (2), home size (8), and the age of the unit (9). In terms of tenure type, previous research suggests that renters are about twice as likely to move as home owners, a reflection of the high transaction costs of getting out of a currently owned home relative to getting out of a rented unit (10).…”

De Montréal vers la banlieue : déterminants du choix du lieu de résidence

“…Généralement, cette hypothèse doit être rejetée lorsque le choix de rester sur place fait partie des possibilités de la variable réponse, ou lorsqu'il y a plus d'un type de déplacement (par exemple, intrarégional et interrégional). Dans ces cas, les modèles de type logit emboîté (nestedlogit) sont plus appropriés (Lee et Waddell, 2010), ceux-ci divisant l'analyse en plusieurs niveaux afin de regrouper les possibilités similaires en noeuds. Dans le cadre de l'analyse des déterminants de la localisation rési-dentielle à l'échelle des collectivités, des comparaisons avec d'autres types de modèles à choix discrets qui n'impliquent pas cette hypothèse ont montré qu'un modèle logit conditionnel standard est approprié pour ce genre d'études, ce qui signifie que l'hypothèse IIA est valide (Dahlberg et Eklöf, 2003).…”

“…Par exemple, un pourcentage élevé de francophones dans une municipalité devrait augmenter la probabilité qu'un francophone la choisisse comme destination, mais diminuer celle d'un anglophone. L'on retrouve également les variables se rattachant à la structure familiale, dont l'une des variables importantes concerne la présence d'enfants de 5 ans ou moins, car les familles avec jeunes enfants cherchent souvent à vivre dans des secteurs où y a d'autres familles avec jeunes enfants (Karsten, 2007 ;Lee et Waddell, 2010). À cet égard, l'inclusion d'une variable d'interaction n'est pas requise puisque le modèle est déjà stratifié selon la structure familiale.…”