Estimating the Excess Returns to Housing at a Disaggregated Level: An Application to Sydney 2003-11

Real Estate Economics, Forthcoming

Posted: 7 Feb 2013

See all articles by Daniel Melser

Daniel Melser

UNSW Australia Business School, School of Economics

Adrian D. Lee

Deakin University - Department of Finance (Property and Real Estate)

Multiple version iconThere are 2 versions of this paper

Date Written: February 6, 2013

Abstract

The returns to housing are particularly important because this asset class makes up such a large fraction of household wealth. Yet they are not straightforward to calculate given both the heterogeneity in homes and the fact they sell only infrequently. We outline a methodology for constructing the excess returns to housing at a disaggregated level, essentially that of the individual home. Our approach explicitly takes account of the inherent risk in homeownership with regard to the capital gain or loss component of housing returns. This approach is applied to a rich data set for Sydney, Australia, from 2003Q1 to 2011Q2. Our findings indicate that the returns to housing are on average quite weak though they exhibit significant diversity across dwelling types and regions. Excess returns are also strongly influenced by assumptions regarding the level of risk aversion.

Keywords: Hedonic regression, housing, excess returns, risk

Suggested Citation

Melser, Daniel and Lee, Adrian D., Estimating the Excess Returns to Housing at a Disaggregated Level: An Application to Sydney 2003-11 (February 6, 2013). Real Estate Economics, Forthcoming, Available at SSRN: https://ssrn.com/abstract=2212769

Daniel Melser (Contact Author)

UNSW Australia Business School, School of Economics ( email )

High Street
Sydney, NSW 2052
Australia
+61 2 9385 1567 (Phone)
+61 2 9313 6337 (Fax)

Adrian D. Lee

Deakin University - Department of Finance (Property and Real Estate) ( email )

70 Elgar Road
Melbourne, VIC 3125
Australia

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Abstract Views
416
PlumX Metrics