Small area housing stress estimation in Australia: Calculating confidence intervals for a spatial microsimulation model

Azizur Rahman

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

This study provides small area housing stress estimates by tenure type in Australia with a way of calculating confidence intervals for a spatial microsimulation model. Findings reveal that prevalence of housing stress for private-renter, buyer, public-renter and owner households are 59.6%, 33.2%, 6.9%, and 0.3%, respectively. Almost two-thirds of these households are located in statistical local areas (SLAs) in eight capital cities, and a large number of them are in Sydney and Melbourne. Estimates for private renters and buyers are significantly high in some capitals and southeast coastal regions. About 95.7% of SLAs show accurate estimates with narrow confidence intervals.

Original languageEnglish
Pages (from-to)7466-7484
Number of pages19
JournalCommunications in Statistics: Simulation and Computation
Volume46
Issue number9
DOIs
Publication statusPublished - 1 Jan 2017

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Microsimulation
Confidence interval
Estimate
Model

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abstract = "This study provides small area housing stress estimates by tenure type in Australia with a way of calculating confidence intervals for a spatial microsimulation model. Findings reveal that prevalence of housing stress for private-renter, buyer, public-renter and owner households are 59.6{\%}, 33.2{\%}, 6.9{\%}, and 0.3{\%}, respectively. Almost two-thirds of these households are located in statistical local areas (SLAs) in eight capital cities, and a large number of them are in Sydney and Melbourne. Estimates for private renters and buyers are significantly high in some capitals and southeast coastal regions. About 95.7{\%} of SLAs show accurate estimates with narrow confidence intervals.",
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Small area housing stress estimation in Australia: Calculating confidence intervals for a spatial microsimulation model. / Rahman, Azizur.

In: Communications in Statistics: Simulation and Computation, Vol. 46, No. 9, 01.01.2017, p. 7466-7484.

Research output: Contribution to journalArticle

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