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Price Discovery in Time and Space: The Course of Condominium Prices in Singapore

Author

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  • Hwang, Min
  • Quigley, John M.

Abstract

Despite evidence that aggregate housing price are predictable, a random walk in time and independence in space are two maintained hypotheses in the empirical models for housing price measurement used by government and commercial companies. This paper examines the price discovery process in individual dwellings over time and space by relaxing both assumptions, using data from the Singapore private condominium market. We develop a model that tests directly the hypotheses that the prices of individual dwellings follow a random walk over time and that the price of an individual dwelling is independent of the price of a neighboring dwelling. The model is general enough to include other widely used models of housing price determination, such as Bailey, Muth, and Nourse (1963), Case and Shiller (1987) and Redfearn and Quigley (2000), as special cases. The empirical results clearly support mean reversion in housing prices and also diffusion of innovations over space. Our estimates of the level of housing prices, derived from a generalized repeat sales model, suggest that serial and spatial correlation matters in the computation of price indices and the estimation of price levels. investment returns is completely absent.
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Suggested Citation

  • Hwang, Min & Quigley, John M., 2002. "Price Discovery in Time and Space: The Course of Condominium Prices in Singapore," Department of Economics, Working Paper Series qt7ph788mn, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    • Handle: RePEc:cdl:econwp:qt7ph788mn
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    Cited by:

    1. Charles Leung, 2007. "Equilibrium Correlations of Asset Price and Return," The Journal of Real Estate Finance and Economics, Springer, vol. 34(2), pages 233-256, February.
    2. Hjalmarsson, Erik & Hjalmarsson, Randi, 2006. "Efficiency In Housing Markets: Do Home Buyers Know How To Discount?," Working Papers in Economics 232, University of Gothenburg, Department of Economics.
    3. Hjalmarsson, Erik & Hjalmarsson, Randi, 2009. "Efficiency in housing markets: Which home buyers know how to discount?," Journal of Banking & Finance, Elsevier, vol. 33(11), pages 2150-2163, November.
    4. Min Hwang & John M. Quigley, 2004. "Selectivity, Quality Adjustment and Mean Reversion in the Measurement of House Values," The Journal of Real Estate Finance and Economics, Springer, vol. 28(2_3), pages 161-178, March.
    5. Raymond J. G. M. Florax & Arno J. Van der Vlist, 2003. "Spatial Econometric Data Analysis: Moving Beyond Traditional Models," International Regional Science Review, , vol. 26(3), pages 223-243, July.
    6. Hua Sun & Seow Ong, 2014. "Bidding Heterogeneity, Signaling Effect and its Implications on House Seller’s Pricing Strategy," The Journal of Real Estate Finance and Economics, Springer, vol. 49(4), pages 568-597, November.
    7. Hua Sun & Yong Tu & Shi-Ming Yu, 2005. "A Spatio-Temporal Autoregressive Model for Multi-Unit Residential Market Analysis," The Journal of Real Estate Finance and Economics, Springer, vol. 31(2), pages 155-187, September.

    More about this item

    Keywords

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    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • R32 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Other Spatial Production and Pricing Analysis

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