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1 European Real Estate Society Conference Stockholm, Sweden, 24-27 June 2009 Greg Costello Curtin University of Technology Perth, Western Australia Yen.

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Presentation on theme: "1 European Real Estate Society Conference Stockholm, Sweden, 24-27 June 2009 Greg Costello Curtin University of Technology Perth, Western Australia Yen."— Presentation transcript:

1 1 European Real Estate Society Conference Stockholm, Sweden, June 2009 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 The Accuracy and Robustness of Real Estate Price Index Methods

2 2 Research Question ► How accurate and robust are different house price index methods when subjected to finer levels of aggregation: 1. Temporal – monthly time intervals 2. Geographic – suburb specifications

3 Chart 1:transaction volume by month

4 Chart 2: Transaction volume by suburb

5 5 Two important issues 1. Why do we need to pool data?  Sample size  Does the pooled sample represent the equivalent subsamples? 2. What is the “true” price trend?  It is unobservable  How do we proxy a true price trend to compare relative performance of indexes?

6 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 7 Our approach 1. Rigorously test different indexes with different aggregation formats 2. 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)modelPanel (a):F-ratios comparing different models of decreasing geographic and temporal aggregation to the most disaggregated (monthly-suburb)model

13 Note pronounced influence of geographic disaggregation

14 14 Five house price index methods i. The hedonic imputation model ii. The longitudinal hedonic approach iii. An augmented weighted repeat-sales (WRS) model iv. The Quigley (1995) hybrid model v. 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 19 Conclusions 1. The aggregation of data, whether along temporal or geographic definitions, is generally unwarranted 2. Price indexes should be estimated using the most disaggregated dataset available 3. 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** **assuming that high-quality data is available**

20 20 Further research? 1. The economic significance of differences in various index methods, does it really matter? 2. Important within the context of developing derivative products applied to property markets 3. The values of derivative contracts in housing markets would be sensitive to any underlying house price index?


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