Article
The Swedish Production of Apartments as a Function of GNP, Building Costs and Population Changes: Generation of Intelligent Media Content via Big Data Analytics
Downloads
Download
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright
The authors shall retain the copyright of their work but allow the Publisher to publish, copy, distribute, and convey the work.
License
Journal of Intelligent Communication (JIC) publishes accepted manuscripts under Creative Commons Attribution 4.0 International (CC BY 4.0). Authors who submit their papers for publication by Journal of Intelligent Communication (JIC) agree to have the CC BY 4.0 license applied to their work, and that anyone is allowed to reuse the article or part of it free of charge for any purpose, including commercial use. As long as the author and original source is properly cited, anyone may copy, redistribute, reuse and transform the content.
The production of new apartments in Sweden has varied strongly during the period from 1975 to 2021. A new statistical function, which explains these production changes, has been developed. This function is designed, based on a set of hypotheses of how the production level should be affected by different explaining factors, such as the GNP, the size of the population, the growth of the population, and the cost of construction. The following hypotheses could not be rejected: the apartment production is a strictly increasing and strictly convex function of GNP, and a strictly increasing function of the size of the population and the growth of the population, and a strictly decreasing function of the cost of construction. The parameters of the statistical function have been estimated with high precision, via multiple regression analysis. It was not possible to detect heteroscedasticity via residual analysis. Furthermore, no indications that nonlinear transformations would improve the selected model were found. The apartment production model contains a strongly significant negative time trend. The estimated function is used to predict the future apartment production until the year 2050. The predictions are based on assumed growth levels of GNP and the population, and on alternative future time trends of the construction cost index. If the real construction cost index continues to grow with the same average trend as from the year 1993 to 2021, the future apartment construction level will stay almost constant at 40,000 apartments per year until 2050. If the future real construction cost index stays constant at the level in 2022, the production of new apartments will grow to almost 90,000 apartments per year in 2050. If the real construction cost index can be decreased to the level in 1993, the production of new apartments will grow to almost 130,000 apartments per year in the year 2050.
Keywords:
construction industry apartments statistical analysis predictionsAuthor Biography
Professor Dr. Peter Lohmander focuses his research on optimization, optimal control and applications to decision problems in different sectors. Since 2015, Peter Lohmander is president of his own research company, Peter Lohmander Optimal Solutions. Optimization of real dynamic and stochastic decision problems, in particular via stochastic dynamic programming and stochastic dynamic control theory, has gained considerable attention in the research projects. Application areas are economics, natural resource management, forestry, logistics, global warming, bioenergy, the military and other areas. Peter Lohmander was full professor of forest management and economic optimization, Swedish University of Agricultural Sciences, Umea, Sweden, 2000 - 2015.
Contact and References:
E-mail: Peter@Lohmander.com
Alternative E-mail: peter.lohmander@icloud.com
Cell Phone: +46-738-288 294
Web References: http://www.lohmander.com/Information/Ref.htm
Author: Professor Dr Peter Lohmander.
Owner of the research and consultancy company:
Peter Lohmander Optimal Solutions
ORCID: https://orcid.org/0000-0003-2013-2580
Education Background and Positions:
High school exam, in Science, Rinnmanskolan, Eskilstuna, Sweden, 1975.
Army ranger officer exam, The Cavalry Special Forces, K4, Sweden, 1979.
Master of Science, with master thesis in mathematical statistics, Swedish University of Agricultural Sciences, Umea, Sweden, 1981.
Lecturer, Planning, full time, permanent position, Royal Veterinary and Agricultural University, Copenhagen, Denmark, 1986.
Lecturer, Management and Business Administration, full time, permanent position, Swedish University of Agricultural Sciences, Umea, Sweden, 1986.
PhD, with thesis title “The Economics of Forest Management Under Risk”, Swedish University of Agricultural Sciences, Umea, Sweden, 1987.
Senior lecturer of management and business administration, full time, permanent position, Swedish University of Agricultural Sciences, Umea, Sweden, 1988.
Acting professor, Forest Economics, Swedish University of Agricultural Sciences, Umea, Sweden, 1990.
Declared as competent, and as the most competent of three competent applicants, for professorship (by all the three referees) in Forest Economics, Swedish University of Agricultural Sciences, Umea, Sweden, 1990.
Associate professor of forest economics, Swedish University of Agricultural Sciences, Umea, Sweden, 1995.
Professor (full time, permanent position) of forest management and economic optimization, Swedish University of Agricultural Sciences, Umea, Sweden, 2000.
Declared competent (by all the three referees) for professorship in Forest Industrial Economics, Swedish University of Agricultural Sciences, Uppsala, Sweden, 2009.
Professor Dr Peter Lohmander starts his research and consultancy company Peter Lohmander Optimal Solutions, 2015.
Cooperation with several universities in several countries, 2015 – Cont.
References
- Barr, J. Skyscraper Height. J. R. Estate Finance Econ. 2012, 45, 723–753.
- Barr, J.; Luo, J. Growing Skylines: The Economic Determinants of Skyscrapers in China. J. R. Estate Finance Econ. 2021, 63, 210–248.
- Batrancea, L.; Rathnaswamy, M.M.; Batrancea, I. A Panel Data Analysis of Economic Growth Determinants in 34 African Countries. J. Risk Finance Manage. 2021, 14, 260.
- Dipasquale, D. Why Don't We Know More about Housing Supply? J. Real Estate Finance Econ. 1999, 18, 9–23.
- Gat, D. Optimal Development of a Building Site. J. Real Estate Finance Econ. 1995, 11, 77–84.
- Guthrie, G. Land Hoarding and Urban Development. J. Real Estate Finance Econ. 2023, 67, 753–793.
- Koblyakova, A.; Fleishman, L.; Furman, O. Accuracy of Households’ Dwelling Valuations, Housing Demand and Mortgage Decisions: Israeli Case. J. Real Estate Finance Econ. 2022, 65, 48–74.
- Lohmander, P. Continuous Extraction Under Risk. Syst. Anal.-Modell.-Semin. 1988, 5, 131–151.
- Lohmander, P. Applications and Mathematical Modeling in Operations Research. In Proceedings of the International Workshop on Mathematics and Decision Science, Guangzhou, China, 12–15 September, 2016.
- Ott, S.H.; Hughen, W.K.; Read, D.C. Optimal Phasing and Inventory Decisions for Large-Scale Residential Development Projects. J. Real Estate Finance Econ. 2012, 45, 888–918.
- Pyhrr, S.; Roulac, S.; Born, W. Real Estate Cycles and Their Strategic Implications for Investors and Portfolio Managers in the Global Economy. J. Real Estate Res. 1999, 18, 7–68.
- Statistics Sweden: Available online at: https://www.scb.se/en/ (accessed on 14 June 2024).
- Wigren, R.; Wilhelmsson, M. Construction Investments and Economic Growth in Western Europe. J. Policy Modell. 2007, 29, 439–451.
- Witkiewicz, W. The Use of the HP-Filter in Constructing Real Estate Cycle Indicators. J. Real Estate Res. 2002, 23, 65–88.