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Extrapolative time-series modelling of house prices: a case study from Sydney, Australia

Shanaka Herath (Faculty of Design, Architecture and Building, University of Technology Sydney, Sydney, Australia)
Vince Mangioni (Faculty of Design, Architecture and Building, University of Technology Sydney, Sydney, Australia)
Song Shi (Faculty of Design, Architecture and Building, University of Technology Sydney, Sydney, Australia)
Xin Janet Ge (Faculty of Design, Architecture and Building, University of Technology Sydney, Sydney, Australia)

International Journal of Housing Markets and Analysis

ISSN: 1753-8270

Article publication date: 19 April 2023

Issue publication date: 13 August 2024

217

Abstract

Purpose

House price fluctuations send vital signals to many parts of the economy, and long-term predictions of house prices are of great interest to governments and property developers. Although predictive models based on economic fundamentals are widely used, the common requirement for such studies is that underlying data are stationary. This paper aims to demonstrate the usefulness of alternative filtering methods for forecasting house prices.

Design/methodology/approach

We specifically focus on exponential smoothing with trend adjustment and multiplicative decomposition using median house prices for Sydney from Q3 1994 to Q1 2017. The model performance is evaluated using out-of-sample forecasting techniques and a robustness check against secondary data sources.

Findings

Multiplicative decomposition outperforms exponential smoothing at forecasting accuracy. The superior decomposition model suggests that seasonal and cyclical components provide important additional information for predicting house prices. The forecasts for 2017–2028 suggest that prices will slowly increase, going past 2016 levels by 2020 in the apartment market and by 2022/2023 in the detached housing market.

Research limitations/implications

We demonstrate that filtering models are simple (univariate models that only require historical house prices), easy to implement (with no condition of stationarity) and widely used in financial trading, sports betting and other fields where producing accurate forecasts is more important than explaining the drivers of change. The paper puts forward a case for the inclusion of filtering models within the forecasting toolkit as a useful reference point for comparing forecasts from alternative models.

Originality/value

To the best of the authors’ knowledge, this paper undertakes the first systematic comparison of two filtering models for the Sydney housing market.

Keywords

Acknowledgements

The authors thank Asiri Nawarathna for research assistance with this research. The authors acknowledge Landcom, the NSW Government’s land and property development organisation, for funding this project.

Citation

Herath, S., Mangioni, V., Shi, S. and Ge, X.J. (2024), "Extrapolative time-series modelling of house prices: a case study from Sydney, Australia", International Journal of Housing Markets and Analysis, Vol. 17 No. 5, pp. 1157-1175. https://doi.org/10.1108/IJHMA-02-2023-0018

Publisher

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Emerald Publishing Limited

Copyright © 2023, Emerald Publishing Limited

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