RDP 2006-04: Measuring Housing Price Growth – Using Stratification to Improve Median-based Measures 6. Conclusion

One of the problems inherent in measuring housing price growth is that the sample of dwellings transacted in any period may be far from random and the characteristics of the sample may change from period to period. As a result, simple measures of growth in mean or median housing prices will reflect changes in the composition of dwellings sold as well as pure price changes. In this paper, we have proposed a simple non-regression-based measure of house price growth that addresses the problem of compositional change by stratifying individual transactions into different groups. Our measure differs from those commonly used internationally in that we group small geographic regions (suburbs) according to the long-term average price of dwellings in those regions, rather than simply clustering smaller geographic regions into larger geographic regions. That is, our method of stratification is specifically designed to control for what appears to be the most important form of compositional change, namely changes in the proportion of houses sold in higher-and lower-priced regions in any period.

We find that stratifying sales in this manner produces a mix-adjusted measure of price growth that substantially improves upon standard unstratified median measures. In particular, when compared with a median measure, our mix-adjusted measure of price growth is considerably less volatile, is not subject to seasonality, and performs better in real time with limited data samples. Our results suggest that seasonal adjustment should be considered a ‘bare minimum’ response to such compositional effects. However, house prices are not truly seasonal: seasonality in median prices arises because of seasonality in the composition of transactions that occur. Our measure improves significantly upon seasonally adjusted medians, because we are also able to account for compositional effects that are non-seasonal in nature. In addition, our mix-adjusted growth rate lines up quite closely with more advanced regression-based measures of price growth. Overall, this indicates that it is possible to develop computationally simple estimates of price growth that control for compositional change.

Given the recent run-up in house prices in many other countries and the macroeconomic effects associated with this, developments in house prices are now of significant interest to policy-makers. Therefore, the methodology outlined in this paper may be applicable for measuring price growth in a number of countries.

Furthermore, the stratification techniques contained in this paper have broader applications than just the measurement of house prices. Many industry bodies, not just in the housing industry, use simple means or medians as a summary measure because they are simple to compute. However, if samples are not random, compositional change may be a major issue. This paper shows that if a sample is stratified appropriately (by the variable that is most related to what is obscuring the underlying movements of interest) substantial benefits can be achieved over a median measure.