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European Real Estate Society Conference Stockholm, Sweden, 24-27 June 2009

European Real Estate Society Conference Stockholm, Sweden, 24-27 June 2009. The Accuracy and Robustness of Real Estate Price Index Methods. Greg Costello Curtin University of Technology Perth, Western Australia Yen Min Goh Department of Finance The University of Melbourne Greg Schwann

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European Real Estate Society Conference Stockholm, Sweden, 24-27 June 2009

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  1. European Real Estate Society ConferenceStockholm, Sweden, 24-27 June 2009 The Accuracy and Robustness of Real Estate Price Index Methods Greg Costello Curtin University of Technology Perth, Western Australia Yen Min Goh Department of Finance The University of Melbourne Greg Schwann Department of Finance The University of Melbourne

  2. Research Question • How accurate and robust are different house price index methods when subjected to finer levels of aggregation: • Temporal – monthly time intervals • Geographic – suburb specifications

  3. Chart 1:transaction volume by month

  4. Chart 2: Transaction volume by suburb

  5. Two important issues • Why do we need to pool data? • Sample size • Does the pooled sample represent the equivalent subsamples? • What is the “true” price trend? • It is unobservable • How do we proxy a true price trend to compare relative performance of indexes?

  6. Two important papers Englund Quigley Redfearn (1999) “The choice of methodology for computing housing price indexes: Comparisons of temporal aggregation and sample definition” (JREFE) Diewert Heravi Silver (2007) “Hedonic imputation versus time dummy hedonic indexes” International Monetary Fund Working Paper No. 07/234.

  7. Our approach • Rigorously test different indexes with different aggregation formats • Use out of sample technique 75% of data to estimate, then 25% to forecast in order to overcome unobservable “true” price trend

  8. Table 1: Data

  9. Testing the influence of Geographic and Temporal Aggregation

  10. Table 2: Framework for Testing Geographic and Temporal Aggregation Base case is the highest level of aggregation, monthly-suburb, “unrestricted”

  11. Table 2: Framework for Testing Geographic and Temporal Aggregation Case 16 is the lowest level of aggregation, annual-region, most restricted

  12. Table 3: F Statistics for Different Model Restrictions Panel (a): F-ratios comparing different models of decreasing geographic and temporal aggregation to the most disaggregated (monthly-suburb) model

  13. Table 3: F Statistics for Different Model Restrictions Note pronounced influence of geographic disaggregation

  14. Five house price index methods • The hedonic imputation model • The longitudinal hedonic approach • An augmented weighted repeat-sales (WRS) model • The Quigley (1995) hybrid model • The mix-adjusted median method

  15. Table 4: Index Accuracy - Mean Squared Errors

  16. Chart 3A: MSE Surface Plot-The Hedonic Imputation Model

  17. Table 4: Index Accuracy - Mean Squared Errors

  18. Chart 3D: MSE Surface Plot - The Augmented Repeat-sales Model

  19. Conclusions • The aggregation of data, whether along temporal or geographic definitions, is generally unwarranted • Price indexes should be estimated using the most disaggregated dataset available • Convincing evidence that the hedonic imputation method performs significantly better than four other methods considered on all measures of accuracy and robustness **assuming that high-quality data is available**

  20. Further research? • The economic significance of differences in various index methods, does it really matter? • Important within the context of developing derivative products applied to property markets • The values of derivative contracts in housing markets would be sensitive to any underlying house price index?

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