Victoria’s Economic Bulletin: Understanding the impact of settlement lags and data revisions on Victoria’s property market projections

Madeleine Tan^{1 2}

<sup>1. Department of Treasury and Finance
2. The author would like to thank Xinyi Tang, Jeffrey H Wong and Rebecca Valenzuela for their assistance. The paper has greatly benefitted from comments from Prof. Alicia Rambaldi and Andrew O’Keefe.</sup>

_{Author contact details: veb@dtf.vic.gov.au.
Disclaimer: The views expressed are those of the author and do not necessarily reflect the views of the Victorian Department of Treasury and Finance (DTF).
Suggested Citation: Madeleine Tan (2024), Understanding the impact of settlement lags and data revisions on Victoria’s property market projections. Victoria’s Economic Bulletin, March 2024, vol 8, no 1. DTF.}

Abstract

Developing forecasts and making predictions can prove challenging in an environment of constantly changing information. An essential component of making good decisions is understanding what is not yet known. Property market data is a good example of this. Transactions data, specifically, can present issues because data on transacted properties is available only at the point of settlement rather than at contract signing. This delay, also known as the settlement lag, can range from several weeks to months. This means that only a partial view of the market is available to forecasters and modellers when decisions need to be made and can differ from the choice that would have been selected with a full view of the market, only available at a much later date.

In this paper, we systematically examine property data to understand the impact of settled sales data revisions on property market estimates in Victoria. Findings from this study will either affirm our current approach of using real-time data for forecasting or serve as an impetus for rethinking our approach in forecasting in the property space.

The analysis relies on a real-time data approach to assess the differences in forecasts across data releases. First, it looks at identifying segments of the market (e.g. by location, property type and contract price) that make up a larger share of earlier releases of the data. As the first few data releases often significantly influence decisions, these segments hold more sway in the decision‑making process. Next, the impacts of each additional data release are explored. This is done by quantifying the changes observed in a hedonic housing price index with each data release.

We find that higher value properties make up a bigger share of the early data received on transacted properties, while the share of properties by location and type appeared to be stable over vintages. With approximately 60 per cent of transactions received in the first data release on average, this appears to hold enough information to indicate that results from a hedonic price index do not change significantly. The DTF hedonic price index (HPI) was revised up by about 0.6 percentage points over the data releases over a 12-month period.

As the data is currently quite limited in span of time, it is worthwhile revisiting the analysis as more data releases become available under varying property market conditions, to see whether the results change.