Housing Price Dynamics in Time and Space: Predictability, Liquidity and Investor Returns
AbstractIt is widely accepted that aggregate housing prices are predictable, but that excess returns to investors are precluded by the transactions costs of buying and selling property. We examine this issue using a unique data set -- all private condominium transactions in Singapore during an eleven-year period. We model directly the price discovery process for individual dwellings. Our empirical results clearly reject a random walk in prices, supporting mean reversion in housing prices and diffusion of innovations over space. We find that, when house prices and aggregate returns are computed from models that erroneously assume a random walk and spatial independence, they are strongly autocorrelated. However, when they are calculated from the appropriate model, predictability in prices and in investment returns is completely absent. We show that this is due to the illiquid nature of housing transactions. We also conduct extensive simulations, over different time horizons and with different investment rules, testing whether better information on housing price dynamics leads to superior investment performance.
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Bibliographic InfoArticle provided by Springer in its journal The Journal of Real Estate Finance and Economics.
Volume (Year): 41 (2010)
Issue (Month): 1 (July)
Contact details of provider:
Web page: http://www.springerlink.com/link.asp?id=102945
Housing market liquidity; Price discovery; Spatial correlation; E31; C23; R32;
Other versions of this item:
- Hwang, Min & Quigley, John M., 2010. "Housing Price Dynamics in Time and Space: Predictability, Liquidity and Investor Returns," Berkeley Program on Housing and Urban Policy, Working Paper Series qt41k6c76w, Berkeley Program on Housing and Urban Policy.
- 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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