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Improving Median Housing Price Indexes through Stratification

Author

Listed:
  • Nalini Prasad

    () (Reserve Bank of Australia NSW, Australia 2001)

  • Anthony Richards

    () (Reserve Bank of Australia NSW, Australia 2001)

Abstract

There is a trade-off between how easy a housing price series is to construct and the extent to which it adjusts for changes in the mix of dwellings sold. Median house price measures are easily calculated, frequently used by industry bodies, and quoted in the media. However, such measures provide poor estimates of shortterm changes in prices because they reflect changes in the composition of transactions, as well as changes in demand and supply conditions. This study uses a database of 3.5 million transactions in the six largest Australian cities to demonstrate that compositional shifts between higher- and lower-priced parts of cities can account for much of the noise in median price measures. Accordingly, a simple method of adjusting for compositional change through stratification is proposed. The measure differs from those commonly used internationally, as neighborhoods or small geographic regions are grouped according to the long-term average price level of dwellings in those regions. The measure of price growth produced improves substantially upon a median and is very highly correlated with regression-based measures.

Suggested Citation

  • Nalini Prasad & Anthony Richards, 2008. "Improving Median Housing Price Indexes through Stratification," Journal of Real Estate Research, American Real Estate Society, vol. 30(1), pages 45-72.
  • Handle: RePEc:jre:issued:v:30:n:1:2008:p:45-72
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    Cited by:

    1. David Genesove & James Hansen, 2014. "Predicting Dwelling Prices with Consideration of the Sales Mechanism," RBA Research Discussion Papers rdp2014-09, Reserve Bank of Australia.
    2. Steven C. Bourassa & Martin Hoesli & Donato Scognamiglio & Philippe Sormani, 2008. "Constant-Quality House Price Indexes for Switzerland," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 144(IV), pages 561-575, December.
    3. Ghysels, Eric & Plazzi, Alberto & Valkanov, Rossen & Torous, Walter, 2013. "Forecasting Real Estate Prices," Handbook of Economic Forecasting, Elsevier.
    4. Katja Hanewald & Michael Sherris, 2013. "Postcode-Level House Price Models for Banking and Insurance Applications," The Economic Record, The Economic Society of Australia, vol. 89(286), pages 411-425, September.
    5. Genesove, David & Hansen, James, 2016. "The Role of Auctions and Negotiation in Housing Prices," CEPR Discussion Papers 11392, C.E.P.R. Discussion Papers.
    6. Asli Kaya & Ayça Topaloglu Bozkurt & Emine Meltem Bastan & Özgül Atilgan Ayanoglu, 2013. "Constructing a house price index for Turkey," IFC Bulletins chapters,in: Bank for International Settlements (ed.), Proceedings of the Sixth IFC Conference on "Statistical issues and activities in a changing environment", Basel, 28-29 August 2012., volume 36, pages 153-171 Bank for International Settlements.

    More about this item

    JEL classification:

    • L85 - Industrial Organization - - Industry Studies: Services - - - Real Estate Services

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