Relationship between volatility and spatial autocorrelation in real estate prices

1. Relationship between volatility and spatial autocorrelation in real estate prices Lo Y.F. Daniel Department of Real Estate and Construction The University of Hong Kongdaniello@hku.hk

2. Spatial Autocorrelation in Real Estate Prices • Similar to Serial Autocorrelation in Time Series • Housing prices show regular pattern over space, despite detailed hedonic specification. • Consequences: • OLS estimates of the t-test values no long reliable • OLS estimates are no longer relatively efficient • Research Foci: • Detect spatial autocorrelation • Improving the estimation reliability by different “correction models”

3. Take it for granted • The underlying cause(s) remain unknown

4. Possible Causes of Spatial Autocorrelation of Real Estate Prices • Omitted Variable(s) in Hedonic Equation • Ignorance of the researchers. • Some hedonic variables are not easily observable/quantifiable, e.g. noise pollution, air pollution • But they are likely be spatially correlated. • Resulting in spatial autocorrelation of housing prices!

5. Possible Causes of Spatial Autocorrelation of Real Estate Prices • Building/Construction Characteristics • Building in close proximity tends to be developed at the same time • Share similar architecture designs, structural features, age, height, facilities, amenities etc. • Compatibility Law: ensure communities remain environmentally intact over time. • ->>>>Spatially autocorrelation

6. Possible Causes of Spatial Autocorrelation of Real Estate Prices • Information Search Conjecture • Real Estate is inefficient • Heterogeneous • Traders have incomplete and imperfect information • Traded in decentralized market • Search around the neighborhood for recent transaction information (i.e. comparables) spatial autocorrelation of housing prices. • In addition, when market is more volatile, traders would rely less on comparables weakening spatial linkages of housing prices.

7. Our Empirical Tests Equation 1: P: log of transaction price S: structural characteristics N: Neighborhood characteristics S*N: Interaction term of S and N T: Time Dummies

8. Equation 2: • Pit: Property i transacted at time t • PJ, t-m: Property j transacted at time t-m • W is a spatial weight measuring the spatio-temporal “closeness” of each pair of transaction data. • D: distance between property i and j • Mi,j: time between Pi and Pj

9. Equation 3: • V: Volatility of housing prices

10. Map of Hong Kong Island

11. Property Price Index of Hong Kong

12. Over 160 000 geo-referenced transaction data • Approx. 1.29 M population • Approx. 5000 residential buildings • 1997 to 2008 • Hong Kong Island • Relatively volatile • Information availability, highly efficient!

13. Regression Results

14. Conclusion • Real Estate prices are spatially autocorrelated • The degree of spatial autocorrelation is dependent on market volatility • When the market is more volatile> smaller the spatial autocorrelation!

15. Implications • A better theoretical understanding • Should include volatility into the hedonic equation • Improve valuation accuracy and efficiency

16. The End • Thank you! • Please send comments to daniello@hku.hk