House Price Determinants in Sydney

Ge, X; Williams, B

House Price Determinants in Sydney

Ge, X

Williams, B

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Publisher:: Urban and Environmental Planning and Research Center, Istanbul Technical University
Publication Type:: Conference Proceeding
Citation:: Proceedings of the 22nd Annual European Real Estate Society Conference ERES 2015 Istanbul, 2015, (ERES ITU), pp. 135 - 148 (14)
Issue Date:: 2015-07-24

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Field	Value	Language
dc.contributor.author	Ge, X https://orcid.org/0000-0002-2252-7829	en_US
dc.contributor.author	Williams, B	en_US
dc.contributor.editor	Arslani, KY	en_US
dc.date	2015-06-24	en_US
dc.date.issued	2015-07-24	en_US
dc.identifier.citation	Proceedings of the 22nd Annual European Real Estate Society Conference ERES 2015 Istanbul, 2015, (ERES ITU), pp. 135 - 148 (14)	en_US
dc.identifier.isbn	978-975-561-465-6	en_US
dc.identifier.uri	http://hdl.handle.net/10453/36401
dc.description.abstract	Purpose – This paper aims to study the main determinants of house price increase in Sydney using quarterly data for the period of March 1994 to June 2014. The paper examines whether variables such as population growth, family income, mortgage rate and supply factors that contribute to the changes of Sydney house prices. Design/methodology/approach - The paper uses reduced-form equation for house price function derived on the supply and demand functions for owner-occupied housing (DiPasquale and Wheaton, 1994). Multiple regression analysis is applied to derive the significant variables effect on house prices. Collected time series variables are tested stationary using Augmented Dickey-Fuller test. Findings – Statistical results suggest that the lack of house supply, mortgage rate, and net overseas migration are the main attributes of house price appreciation in Sydney. House prices are largely affected by the price movements from the previous periods. Multiple regression analysis is one of the methods to test causal relationship of house prices with other variables. Research limitations/implications – There are limitations using multiple regression analysis (MRA) One of the difficulties of using MRA is to handle problems with multicollinearity and non-linearity among variables. It is worthwhile to try Vector Autoregressive (VAR) or nonlinear models that may help to solve the problem of building in regression statistics. Practical implications - The findings would suggest that measures to increase of housing supply may help to prevent house price bubble. Originality/value – The research updates the investigation of house price determinants and tests the using of MRA method on the nonlinear variables.	en_US
dc.publisher	Urban and Environmental Planning and Research Center, Istanbul Technical University	en_US
dc.relation.ispartof	Proceedings of the 22nd Annual European Real Estate Society Conference ERES 2015 Istanbul	en_US
dc.relation.ispartof	Annual Conference of European Real Estate Society	en_US
dc.title	House Price Determinants in Sydney	en_US
dc.type	Conference Proceeding
utslib.citation.volume	ERES ITU	en_US
utslib.location	Istanbul	en_US
utslib.location.activity	Istanbul, Turkey	en_US
utslib.for	120503 Housing Markets, Development, Management	en_US
dc.location.activity	Istanbul Turkey
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building/School of Built Environment
utslib.copyright.status	open_access
pubs.consider-herdc	true	en_US
pubs.finish-date	2015-06-27	en_US
pubs.issue	ERES ITU	en_US
pubs.place-of-publication	Istanbul	en_US
pubs.publication-status	Published	en_US
pubs.start-date	2015-06-24	en_US

Abstract:

Purpose – This paper aims to study the main determinants of house price increase in Sydney using quarterly data for the period of March 1994 to June 2014. The paper examines whether variables such as population growth, family income, mortgage rate and supply factors that contribute to the changes of Sydney house prices. Design/methodology/approach - The paper uses reduced-form equation for house price function derived on the supply and demand functions for owner-occupied housing (DiPasquale and Wheaton, 1994). Multiple regression analysis is applied to derive the significant variables effect on house prices. Collected time series variables are tested stationary using Augmented Dickey-Fuller test. Findings – Statistical results suggest that the lack of house supply, mortgage rate, and net overseas migration are the main attributes of house price appreciation in Sydney. House prices are largely affected by the price movements from the previous periods. Multiple regression analysis is one of the methods to test causal relationship of house prices with other variables. Research limitations/implications – There are limitations using multiple regression analysis (MRA) One of the difficulties of using MRA is to handle problems with multicollinearity and non-linearity among variables. It is worthwhile to try Vector Autoregressive (VAR) or nonlinear models that may help to solve the problem of building in regression statistics. Practical implications - The findings would suggest that measures to increase of housing supply may help to prevent house price bubble. Originality/value – The research updates the investigation of house price determinants and tests the using of MRA method on the nonlinear variables.

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http://hdl.handle.net/10453/36401