IDEAS home Printed from https://ideas.repec.org/p/hhs/kthrec/2020_018.html

What determines the supply of housing for the elderly, and how is it related to the spread of Covid-19 and future demographic changes?

Author

Listed:
  • Kulander, Maria

    (University of Gävle, Sweden)

  • Wilhelmsson, Mats

    (Department of Real Estate and Construction Management, Royal Institute of Technology)

Abstract

As in many other countries, the population in Sweden is getting older. It means that the number of older people in society increases in absolute numbers and relative terms. Consequently, this will mean that the need for elderly housing will increase and the cost of these investments will be high. The following study aims to quantitatively analyse the spatial distribution of the number and size of housing for the elderly in Sweden over 2013-2018. The number of elderly housing per capita is not evenly distributed, and a large part of the explanation is, of course, that the number of older people is not evenly distributed between municipalities. Nevertheless, we can also state that the municipality's income level and tax base, as well as the geographical size and degree of urbanisation, play a role. If the municipality has a surplus or deficit in the supply of special housing for the elderly, it has no correlation with the distribution of Covid-19 cases or with the forecast number of older people in the future.

Suggested Citation

  • Kulander, Maria & Wilhelmsson, Mats, 2020. "What determines the supply of housing for the elderly, and how is it related to the spread of Covid-19 and future demographic changes?," Working Paper Series 20/18, Royal Institute of Technology, Department of Real Estate and Construction Management & Banking and Finance.
    • Handle: RePEc:hhs:kthrec:2020_018
    as

    Download full text from publisher

    File URL: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-288129
    File Function: Full text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    2. Joshua D. Angrist & Jörn-Steffen Pischke, 2009. "Mostly Harmless Econometrics: An Empiricist's Companion," Economics Books, Princeton University Press, edition 1, number 8769, December.
    3. Engelhardt, Gary V. & Greenhalgh-Stanley, Nadia, 2010. "Home health care and the housing and living arrangements of the elderly," Journal of Urban Economics, Elsevier, vol. 67(2), pages 226-238, March.
    4. Im, Kyung So & Pesaran, M. Hashem & Shin, Yongcheol, 2003. "Testing for unit roots in heterogeneous panels," Journal of Econometrics, Elsevier, vol. 115(1), pages 53-74, July.
    5. Levin, Andrew & Lin, Chien-Fu & James Chu, Chia-Shang, 2002. "Unit root tests in panel data: asymptotic and finite-sample properties," Journal of Econometrics, Elsevier, vol. 108(1), pages 1-24, May.
    6. Charles Ka Yui Leung & Wei Wang, 2007. "An Examination of the Chinese Housing Market through the Lens of the DiPasquale- Wheaton Model: a Graphical Attempt," International Real Estate Review, Global Social Science Institute, vol. 10(2), pages 131-165.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Eicher, Theo S. & Schreiber, Till, 2010. "Structural policies and growth: Time series evidence from a natural experiment," Journal of Development Economics, Elsevier, vol. 91(1), pages 169-179, January.
    2. Seung C. Ahn & Gareth M. Thomas, 2023. "Likelihood-based inference for dynamic panel data models," Empirical Economics, Springer, vol. 64(6), pages 2859-2909, June.
    3. Na Zhang & Jinqian Deng & Fayyaz Ahmad & Muhammad Umar Draz & Nabila Abid, 2023. "The dynamic association between public environmental demands, government environmental governance, and green technology innovation in China: evidence from panel VAR model," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(9), pages 9851-9875, September.
    4. Roberto DELL'ANNO & Stefania VILLA, 2012. "Growth in Transition Countries: Big Bang versus Gradualism," CELPE Discussion Papers 122, CELPE - CEnter for Labor and Political Economics, University of Salerno, Italy.
    5. Jacobo Campo & Henry Mendoza, 2018. "Gasto público y crecimiento económico: un análisis regional para Colombia, 1984-2012," Revista Lecturas de Economía, Universidad de Antioquia, CIE, issue 88, pages 77-108.
    6. Eric S. Lin & Hamid E. Ali, 2009. "Military Spending and Inequality: Panel Granger Causality Test," Journal of Peace Research, Peace Research Institute Oslo, vol. 46(5), pages 671-685, September.
    7. Gautam, Tej K. & Paudel, Krishna P., 2018. "The demand for natural gas in the Northeastern United States," Energy, Elsevier, vol. 158(C), pages 890-898.
    8. MAÏ ASSAN CHEDI, Maman, 2022. "Does Defence Expenditure Affect Education and Health expenditures in Saharan Africa?," African Journal of Economic Review, African Journal of Economic Review, vol. 10(4), September.
    9. António Afonso & Catarina Farinha Miranda, 2025. "Compliance With Fiscal Sustainability And The Euro," Working Papers REM 2025/0371, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    10. Töngür, Ünal & Elveren, Adem Yavuz, 2014. "Deunionization and pay inequality in OECD Countries: A panel Granger causality approach," Economic Modelling, Elsevier, vol. 38(C), pages 417-425.
    11. Shreya Pal, 2024. "The International Capital Flows and Domestic Savings–domestic Investment Nexus: A Comparative Evidence Between Heterogeneous Developing Regions," South Asian Journal of Macroeconomics and Public Finance, , vol. 13(2), pages 169-212, December.
    12. Fratzscher, Marcel & Müller, Gernot J. & Bussière, Matthieu, 2004. "Current accounts dynamics in OECD and EU acceding countries - an intertemporal approach," Working Paper Series 311, European Central Bank.
    13. Jonida Bollano & Delina Ibrahimaj, 2015. "Current Account Determinats in Central Eastern European Countries," IHEID Working Papers 22-2015, Economics Section, The Graduate Institute of International Studies.
    14. Philipp Otto & Wolfgang Schmid, 2018. "Spatiotemporal analysis of German real-estate prices," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 60(1), pages 41-72, January.
    15. Binder, Michael & Hsiao, Cheng & Pesaran, M. Hashem, 2005. "Estimation And Inference In Short Panel Vector Autoregressions With Unit Roots And Cointegration," Econometric Theory, Cambridge University Press, vol. 21(4), pages 795-837, August.
    16. Chris Belmert Milindi & Roula Inglesi-Lotz, 2023. "Impact of technological progress on carbon emissions in different country income groups," Energy & Environment, , vol. 34(5), pages 1348-1382, August.
    17. Robert Baumann & Bryan Engelhardt & Victor A. Matheson, 2012. "Labor Market Effects of the World Cup: A Sectoral Analysis," Chapters, in: Wolfgang Maennig & Andrew Zimbalist (ed.), International Handbook on the Economics of Mega Sporting Events, chapter 22, Edward Elgar Publishing.
    18. Md zulquar Nain & Sai sailaja Bharatam & Bandi Kamaiah, 2017. "Electricity consumption and NSDP nexus in Indian states: a panel analysis with structural breaks," Economics Bulletin, AccessEcon, vol. 37(3), pages 1581-1601.
    19. Gharehgozli, Orkideh, 2021. "An empirical comparison between a regression framework and the Synthetic Control Method," The Quarterly Review of Economics and Finance, Elsevier, vol. 81(C), pages 70-81.
    20. Christos Alexakis & Theophano Patra & Sunil Poshakwale, 2010. "Predictability of stock returns using financial statement information: evidence on semi-strong efficiency of emerging Greek stock market," Applied Financial Economics, Taylor & Francis Journals, vol. 20(16), pages 1321-1326.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • J11 - Labor and Demographic Economics - - Demographic Economics - - - Demographic Trends, Macroeconomic Effects, and Forecasts
    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hhs:kthrec:2020_018. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Cecilia Hermansson (email available below). General contact details of provider: https://edirc.repec.org/data/ifkthse.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.