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ARIMA modelling of Lithuanian house price index

Author

Listed:
  • Arvydas Jadevicius
  • Simon Huston

Abstract

Purpose - – This paper aims to investigate Lithuanian house price changes. Its twin motivations are the importance of information on future house price movements to sector stakeholders and the limited number of related Lithuanian property market studies. Design/methodology/approach - – The study employs ARIMA modelling approach. It assesses whether past is a good predictor of the future. It then examines issues relating to an application of this univariate time-series modelling technique in a forecasting context. Findings - – As the results of the study suggest, ARIMA is a useful technique to assess broad market price changes. Government and central bank can use ARIMA modelling approach to forecast national house price inflation. Developers can employ this methodology to drive successful house-building programme. Investor can incorporate forecasts from ARIMA models into investment strategy for timing purposes. Research limitations/implications - – Certainly, there are number of limitations attached to this particular modelling approach. Firm predictions about house price movements are also a challenge, as well as more research needs to be done in establishing a dynamic interrelationship between macro variables and the Lithuanian housing market. Originality/value - – Although the research focused on Lithuania, the findings extend to global housing market. ARIMA house price modelling provides insights for a spectrum of stakeholders. The use of this modelling approach can be employed to improve monetary policy oversight, facilitate planning for infrastructure or social housing as a countercyclical policy and mitigate risk for investors. What is more, a greater appreciation of Lithuania housing market can act as a bellwether for real estate markets in other trade-exposed small country economies.

Suggested Citation

  • Arvydas Jadevicius & Simon Huston, 2015. "ARIMA modelling of Lithuanian house price index," International Journal of Housing Markets and Analysis, Emerald Group Publishing Limited, vol. 8(1), pages 135-147, March.
    • Handle: RePEc:eme:ijhmap:v:8:y:2015:i:1:p:135-147
    DOI: 10.1108/IJHMA-04-2014-0010
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    Citations

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    Cited by:

    1. Marco Locurcio & Pierluigi Morano & Francesco Tajani & Felicia Di Liddo, 2020. "An Innovative GIS-Based Territorial Information Tool for the Evaluation of Corporate Properties: An Application to the Italian Context," Sustainability, MDPI, vol. 12(14), pages 1-29, July.
    2. Jaekyung Lee & Hyunwoo Kim & Hyungkyoo Kim, 2021. "Commercial Vacancy Prediction Using LSTM Neural Networks," Sustainability, MDPI, vol. 13(10), pages 1-17, May.
    3. Yener Coskun & Unal Seven & H. Murat Ertugrul & Ali Alp, 2020. "Housing price dynamics and bubble risk: the case of Turkey," Housing Studies, Taylor & Francis Journals, vol. 35(1), pages 50-86, January.

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