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NILS Working paper no 181. Modelling house prices across Sydney with estimates for access, property size, public transport, urban density and crime


  • Abelson, Peter
  • Joyeux, Roselyne
  • Mahuteau, Stephane


This paper examines the structure of house prices across the city, in this case Sydney, as an aid to urban development strategy and in particular to determine the potentially positive effects of public transport and negative effects of residential density on property prices. We model median house prices in 626 suburbs and achieve a high level of explanation. Distances from the CBD and from the coast are dominant factors in explaining house prices in Sydney. Predictably house and lot size are also highly significant factors. On the other hand a high propensity for violent crime significantly reduces property values. Over the whole city distance to rail station is not a statistically significant variable, but in suburb groups that are poorly served by other modes, median house prices fall significantly with increased distances to station. We found a similar but weaker result for access to high frequency buses. Contrary to expectation we found that higher density is marginally associated with higher median prices. However as the density variable is correlated (negatively) with median land area and, to a lesser extent, with distance to CBD, we would be cautious about concluding that density has no negative effect on house prices.

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  • Abelson, Peter & Joyeux, Roselyne & Mahuteau, Stephane, 2012. "NILS Working paper no 181. Modelling house prices across Sydney with estimates for access, property size, public transport, urban density and crime," NILS Working Papers 26086, National Institute of Labour Studies.
  • Handle: RePEc:fli:wpaper:26086 Note: Abelson, P.; Joyeux, R.; Mahuteau, S. 2012. Modelling House Prices across Sydney with Estimates for Access, Property Size, Public Transport, Urban Density and Crime. Working Paper No.181

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    1. Kathleen P. Bell & Nancy E. Bockstael, 2000. "Applying the Generalized-Moments Estimation Approach to Spatial Problems Involving Microlevel Data," The Review of Economics and Statistics, MIT Press, vol. 82(1), pages 72-82, February.
    2. Ben McNair & Peter Abelson, 2010. "Estimating the Value of Undergrounding Electricity and Telecommunications Networks," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 43(4), pages 376-388, December.
    3. Mariano Kulish & Anthony Richards & Christian Gillitzer, 2012. "Urban Structure and Housing Prices: Some Evidence from Australian Cities," The Economic Record, The Economic Society of Australia, vol. 88(282), pages 303-322, September.
    4. Du, Hongbo & Mulley, Corinne, 2012. "Understanding spatial variations in the impact of accessibility on land value using geographically weighted regression," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 5(2), pages 46-59.
    5. Kuminoff, Nicolai V. & Parmeter, Christopher F. & Pope, Jaren C., 2010. "Which hedonic models can we trust to recover the marginal willingness to pay for environmental amenities?," Journal of Environmental Economics and Management, Elsevier, vol. 60(3), pages 145-160, November.
    6. Maurizio Pisati, 2001. "Tools for spatiel data analysis," Stata Technical Bulletin, StataCorp LP, vol. 10(60).
    7. Brueckner, Jan K., 1987. "The structure of urban equilibria: A unified treatment of the muth-mills model," Handbook of Regional and Urban Economics,in: E. S. Mills (ed.), Handbook of Regional and Urban Economics, edition 1, volume 2, chapter 20, pages 821-845 Elsevier.
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    House prices; Spatial modelling; Public transport; Urban density;

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