A data-driven predictive model for residential mobility in Australia – A generalised linear mixed model for repeated measured binary data

Abstract

Household relocation modelling is an integral part of the Government planning process as residential movements influence the demand for community facilities and services. This study will address the problem of modelling residential relocation choice by estimating a logit-link class model. The proposed model estimates the probability of an event which triggers household relocation. The attributes considered in this study are: requirement for bedrooms, employment status, income status, household characteristics, and tenure (i.e. duration living at the current location). Accurate prediction of household relocations for population units should rely on real world observations. In this study, a longitudinal survey data gathered in the Household, Income and Labour Dynamics in Australia (HILDA) program is used for modelling purposes. The HILDA dataset includes socio-demographic information such general health situation and well-being, lifestyle changes, residential mobility, income and welfare dynamics, and labour market dynamics collected from the sampled individuals and households. The technique presented in this paper links possible changes in households' socio-demographic characteristics to the probability of residential relocation by developing a mixed effects discrete-choice logit model (MEDCLM) for longitudinal binary data using the HILDA dataset. The proposed model captures the effect of repeated measurements together with the area-specific random effects. AbstractHousehold relocation modelling is an integral part of the Government planning process as residential movements influence the demand for community facilities and services. This study will address the problem of modelling residential relocation choice by estimating a logit-link class model. The proposed model estimates the probability of an event which triggers household relocation. The attributes considered in this study are: requirement for bedrooms, employment status, income status, household characteristics, and tenure (i.e. duration living at the current location). Accurate prediction of household relocations for population units should rely on real world observations. In this study, a longitudinal survey data gathered in the Household, Income and Labour Dynamics in Australia (HILDA) program is used for modelling purposes. The HILDA dataset includes socio-demographic information such general health situation and wellbeing, lifestyle changes, residential mobility, income and welfare dynamics, and labour market dynamics collected from the sampled individuals and households. The technique presented in this paper links possible changes in households' socio-demographic characteristics to the probability of residential relocation by developing a mixed effects discrete-choice logit model (MEDCLM) for longitudinal binary data using the HILDA dataset. The proposed model captures the effect of repeated measurements together with the area-specific random effects.

A generalized linear mixed model for understanding determinant factors of student's interest in pursuing bachelor's degree at Universitas Syiah Kuala

“…We can also find the application of GLMM to identify triggers for resettlement to Australia with repeated measurement in the data. In the research, Generalized Estimating Equations (GEEs) were used to estimate the parameters of the model [12]. The GLMM with the Penalized Quasi-Likelihood, the maximum likelihood with Laplace and Adaptive Gaussian Quadrature (AGQ) approximations for estimating model has been applied to simulation data with high dimension in 2017.…”

Situating individuals’ subjective needs and aspirations within the wider context of the housing market

“…In reality, individuals' housing trajectory in the Australian housing market is increasingly irregular. It is progressively difficult to access the housing market, housing tenure is uncertain and residential mobility, which partially signals disruptions in the housing trajectory has been consistently high (Namazi-Rad, Mokhtarian, Shukla, & Munoz, 2016 ;Sánchez & Andrews, 2011). Consequently, this research identifies the erratic homeownership and rental trajectory of individuals in the Brisbane GCCSA Region as a problem worth investigating.…”