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Modeling Building Stock Development

Author

Listed:
  • Antti Kurvinen

    (Faculty of Built Environment, Tampere University, Korkeakoulunkatu 5, FI-33720 Tampere, Finland)

  • Arto Saari

    (Faculty of Built Environment, Tampere University, Korkeakoulunkatu 5, FI-33720 Tampere, Finland)

  • Juhani Heljo

    (Faculty of Built Environment, Tampere University, Korkeakoulunkatu 5, FI-33720 Tampere, Finland)

  • Eero Nippala

    (School of Built Environment and Bioeconomy, Tampere University of Applied Sciences, Kuntokatu 3, FI-33520 Tampere, Finland)

Abstract

It is widely agreed that dynamics of building stocks are relatively poorly known even if it is recognized to be an important research topic. Better understanding of building stock dynamics and future development is crucial, e.g., for sustainable management of the built environment as various analyses require long-term projections of building stock development. Recognizing the uncertainty in relation to long-term modeling, we propose a transparent calculation-based QuantiSTOCK model for modeling building stock development. Our approach not only provides a tangible tool for understanding development when selected assumptions are valid but also, most importantly, allows for studying the sensitivity of results to alternative developments of the key variables. Therefore, this relatively simple modeling approach provides fruitful grounds for understanding the impact of different key variables, which is needed to facilitate meaningful debate on different housing, land use, and environment-related policies. The QuantiSTOCK model may be extended in numerous ways and lays the groundwork for modeling the future developments of building stocks. The presented model may be used in a wide range of analyses ranging from assessing housing demand at the regional level to providing input for defining sustainable pathways towards climate targets. Due to the availability of high-quality data, the Finnish building stock provided a great test arena for the model development.

Suggested Citation

  • Antti Kurvinen & Arto Saari & Juhani Heljo & Eero Nippala, 2021. "Modeling Building Stock Development," Sustainability, MDPI, vol. 13(2), pages 1-17, January.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:2:p:723-:d:479701
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    References listed on IDEAS

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    3. Swan, Lukas G. & Ugursal, V. Ismet, 2009. "Modeling of end-use energy consumption in the residential sector: A review of modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1819-1835, October.
    4. Ryan Fan & S. Thomas Ng & James Wong, 2010. "Reliability of the Box-Jenkins model for forecasting construction demand covering times of economic austerity," Construction Management and Economics, Taylor & Francis Journals, vol. 28(3), pages 241-254.
    5. Goh Bee-Hua, 2000. "Evaluating the performance of combining neural networks and genetic algorithms to forecast construction demand: the case of the Singapore residential sector," Construction Management and Economics, Taylor & Francis Journals, vol. 18(2), pages 209-217.
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    Cited by:

    1. Sunkuk Kim, 2021. "Technology and Management for Sustainable Buildings and Infrastructures," Sustainability, MDPI, vol. 13(16), pages 1-3, August.
    2. Mario Kolkwitz & Elina Luotonen & Satu Huuhka, 2023. "How changes in urban morphology translate into urban metabolisms of building stocks: A framework for spatiotemporal material flow analysis and a case study," Environment and Planning B, , vol. 50(6), pages 1559-1576, July.

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