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Forecasting Real Estate Prices in Romania: A Lag Optimized Linear Approach

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  • Chirilus Alexandru I.

    (1 Cybernetics and Statistics Doctoral School, Bucharest University of Economic Studies)

Abstract

This study focuses on the real estate market in Romania and presents a forecasting model for predicting future real estate prices. The model, based on multiple linear regression, provides a comprehensive understanding of the market and enables real estate analysts to devise more efficient investment strategies. By enhancing investment efficiency, the model contributes to the overall efficiency of financial markets and supports sustained economic benefits for stakeholders. Although limited to a specific timeframe and apartment auction markets in Romania, future research can expand the model’s scope, improve accuracy through diverse data sets, and explore key factors for enhanced performance. The study’s contribution lies in its valuable insights for real estate analysts, enhancing investment efficiency, and fostering sustained economic benefits for stakeholders.

Suggested Citation

  • Chirilus Alexandru I., 2023. "Forecasting Real Estate Prices in Romania: A Lag Optimized Linear Approach," Baltic Journal of Real Estate Economics and Construction Management, Sciendo, vol. 11(1), pages 120-132, January.
    • Handle: RePEc:vrs:bjrecm:v:11:y:2023:i:1:p:120-132:n:4
    DOI: 10.2478/bjreecm-2023-0008
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    References listed on IDEAS

    as
    1. Fan, Ying & Yang, Zan & Yavas, Abdullah, 2019. "Understanding real estate price dynamics: The case of housing prices in five major cities of China✰," Journal of Housing Economics, Elsevier, vol. 43(C), pages 37-55.
    2. Jorge Iván Pérez-Rave & Juan Carlos Correa-Morales & Favián González-Echavarría, 2019. "A machine learning approach to big data regression analysis of real estate prices for inferential and predictive purposes," Journal of Property Research, Taylor & Francis Journals, vol. 36(1), pages 59-96, January.
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