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Modelling and forecasting office returns in the Helsinki area

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  • Olga Karakozova

Abstract

This paper presents an econometric study of office returns determination in the Helsinki area, a small European market, over a 30-year period from 1971 to 2001. Particularly, the study investigates the variation in office capital growth, which is the most volatile component of office total return in the Helsinki market, using three alternative models: a regression model, an error correction model (ECM), and an integrated autoregressive-moving average model with exogenous explanatory variables (ARIMAX). The study also evaluates the forecasting performance of the alternative specifications. The results indicate that the ARIMAX models, incorporating past values of capital growth and growth in service sector employment and in the gross domestic product, are able to pick up shocks present in the data, and thus provide the best forecasting tool for office returns in Helsinki. The ECM models, which incorporate long-run information, cannot satisfactorily model the irregularities in the Helsinki office market, such as the boom-bust cycle of the 1980s and 1990s, and thus are suitable for modelling and forecasting only part of the Helsinki market. It is predicted that office capital returns will continue to grow in real terms by 0.1% on average in the 2002-2005 period. This implies that real office total returns will grow on average at the rate of 5.7% over the same time period.

Suggested Citation

  • Olga Karakozova, 2004. "Modelling and forecasting office returns in the Helsinki area," Journal of Property Research, Taylor & Francis Journals, vol. 21(1), pages 51-73, January.
  • Handle: RePEc:taf:jpropr:v:21:y:2004:i:1:p:51-73
    DOI: 10.1080/0959991042000254579
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    Cited by:

    1. Kwabena Mintah & Woon-Weng Wong & Peng Yew Wong, 2020. "Cross Border Real Estate Investments and Commercial Office Property Market Performance: Evidence from Australia," International Real Estate Review, Asian Real Estate Society, vol. 23(2), pages 837-860.
    2. Oikarinen, Elias, 2009. "Household borrowing and metropolitan housing price dynamics - Empirical evidence from Helsinki," Journal of Housing Economics, Elsevier, vol. 18(2), pages 126-139, June.
    3. Kwabena Mintah & Woon-Weng Wong & Peng Yew Wong, 2020. "Cross Border Real Estate Investments and Commercial Office Property Market Performance: Evidence from Australia," International Real Estate Review, Global Social Science Institute, vol. 23(2), pages 211-234.
    4. Sophia L. Zhou, 2022. "Real Estate Trend Prediction Using Linear Regression And Artificial Neural Network Techniques," Global Journal of Business Research, The Institute for Business and Finance Research, vol. 16(1), pages 1-16.
    5. Ekholm, Bo-Göran & Wallin, Jan, 2004. "Strategic Priorities, Company Performance and Attitudes Towards Management Accounting Techniques: an Empirical Study," Working Papers 507, Hanken School of Economics.
    6. Arvydas Jadevicius & Brian Sloan & Andrew Brown, 2012. "Examination of property forecasting models - accuracy and its improvement through combination forecasting," ERES eres2012_082, European Real Estate Society (ERES).

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