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APPENDIX CHANGES TO THE WEIGHTS AND STRATIFICATION METHOD USED IN HPI


INTRODUCTION

1 The purpose of this appendix is to describe changes to the weights and stratification method used in the construction of the Established House Price Index (HPI) which take effect in this issue of 6416.0.

2 The ABS undertakes periodic reviews of its price indexes to ensure that they continue to meet users' needs and reflect current economic conditions. The main objective of a review is to update the quantities of goods and services that underpin the weights of the index, but it also provides an opportunity to reassess the structure and compilation methodology of the index.

3 During 2007 and 2008, the ABS undertook a review of the HPI. As a result, the housing stock weights have been updated using quantity data from the 2006 Census of Population and Housing and the method of stratification used to compile the index has been refined.


STRATIFICATION IN THE HPI

4 The HPI uses the change in the prices of detached houses sold each quarter to represent quarterly changes in the value of the stock of detached houses. The stock of houses is a heterogenous set of dwellings, as is the subset of houses sold every quarter. Not only will the houses sold in any quarter vary in terms of characteristics such as location, size and price, but from one quarter to the next there will be a change, in terms of these characteristics, in the composition of the set of houses sold. Stratification is used to control for this 'compositional' effect by grouping (or 'clustering') houses according to a set of price determining characteristics.

5 A major review of the HPI was undertaken in 2004, with the changes implemented in the September quarter 2005 issue of 6416.0 and backcast to March quarter 2002. The main outcomes of the review were the introduction of stock weights (see the section on New Weights, below, for more information on this method of weighting) and improvements to the timeliness of the index and the method of stratification. Improvements to timeliness were achieved by changing the timing of transactions from settlement date to exchange date, and the use of the more up-to-date data from lending institutions to supplement valuers' general data in the two most recent quarters. The 2004 review found that given the absence of the detailed data required for hedonic and repeat sales approaches, the most feasible option for controlling compositional effects remained the stratification approach.

6 A stratification was developed based on attributes that can be broadly defined as the structural, locational and 'neighbourhood' characteristics of suburbs. An analysis determined that four structural variables, four locational variables and one neighbourhood variable were the most relevant in determining the similarity of suburbs for stratification purposes. The structural variables were determined from 2001 Census of Population and Housing data and described the percentage of dwellings in a suburb with particular characteristics, such as number of bedrooms. The locational variables were determined from geographic data and described average distance to facilities, such as the nearest shops, by suburb. The neighbourhood variable was represented by the ABS Socio-Economic Index for Areas (SEIFA(footnote 1) ), which is a measure, derived from Census data, summarising different aspects of the socio-economic conditions of people living in an area. The number of non-SEIFA variables were reduced into two principal components, one each for the structural variables and the locational variables. A process of cluster analysis was then undertaken using these two principal components and SEIFA as variables to select the optimal number of clusters. As there was an aim at the time to publish the HPI at lower levels than the city, this analysis was applied with a constraint to ensure that only suburbs within the same statistical subdivision(footnote 2) (SSD) could be grouped together and clusters could not cross SSD boundaries. A detailed description of the changes to the index resulting from the 2004 review is provided in Information Paper: Renovating the Established House Price Index (cat. no. 6417.0).

7 As part of ongoing improvements to the quality of the indexes, in 2007 and 2008 a stratification review was undertaken. Its purpose was to take account of economic, social and demographic changes since the 2001 Census; to identify improved methods of managing compositional change; and to examine the current SSD constraint, to determine whether its relaxation might enable clusters to comprise broader groupings of similar suburbs. The review also considered work by the Reserve Bank of Australia which suggested that a stratification based solely on long-term median price would produce a robust measure of the movement in house prices (Prasad and Richards, Measuring Housing Price Growth: Using Stratification to Improve Median-based Measures, RDP2006-04). With the SSD constraint removed an analysis with updated data was undertaken of alternative stratification methods using combinations of the variables used in the 2004 review, plus a further variable: that of long-term median price. The outcome of this analysis is the selection of a stratification method based on SEIFA and long-term median price which balances the homogeneity of suburbs within the same cluster with sufficient sales observations to construct reliable measurements of price movement. For more information on the analysis, refer to the ABS Research Paper: Refining the Stratification for the Established House Price Index (cat. no. 1352.0.55.093).


IMPACT OF CHANGES TO THE STRATIFICATION METHOD

8 The refined stratification method has resulted in fewer clusters. They are more homogenous and there should be a smaller number that suffer from volatile movements in median price. This should result in an improved measurement of the quarter-to-quarter change in established house prices. The following table shows the number of clusters now used for each city (Series 2), compared to previous series (Series 1):

Number of clusters
	Series 2 (from June quarter 2008)(a)	Series 1 (from March quarter 2002)
Sydney	22	55
Melbourne	20	39
Brisbane	20	51
Adelaide	11	27
Perth	10	14
Hobart	5	8
Darwin	6	5
Canberra	7	14
(a) Refer to paragraphs 12 and 13, below, for details on the introduction of the new series.

Percentage contribution to eight capital cities(a)
	Series 2 (at March quarter 2008)(b)	Series 1 (at March quarter 2002)
	%	%
Sydney	33.5	43.5
Melbourne	27.3	27.5
Brisbane	13.2	9.8
Adelaide	7.6	6.3
Perth	14.2	9.2
Hobart	1.2	0.9
Darwin	0.6	0.5
Canberra	2.5	2.3
8 capital cities	100.0	100.0
(a) Percentages may not add due to rounding.
(b) Refer to paragraphs 12 and 13, below, for details on the introduction of the new series.