Ranking residential neighborhoods based on their sustainability: a CM-BWM approach
Abstract
Population growth and rapid urbanization have consequences that are reflected in the economic, environmental, and social stability of city-residential neighborhoods. These impacts directly affect not only residents but also real estate markets and local governments. The professionals working in the latter entities have become increasingly concerned about urban sustainability and its strategic integration into their plans. Strategies have been implemented that focus on both addressing negative aspects of residential neighborhoods and enhancing positive features that can contribute to the continuous improvement of locals’ living conditions. This study applies the multiple-criteria decision analysis approach and a combination of cognitive mapping and the best-worst method (BWM) to identify the most relevant criteria and use these to rank residential neighborhoods according to their sustainability. To apply the selected techniques, two group meetings were held with a panel of decision makers. The results were validated by the panel members and the Funchal City Council councilor for urbanism, who concurred that the proposed ranking system facilitates the identification of the most sustainable residential neighborhoods. The contributions and limitations of the methodological approach are also discussed.
Keyword : best-worst method (BWM), cognitive mapping, multiple criteria decision analysis (MCDA), real estate market, residential neighborhood, sustainability
How to Cite
Andrade, F. M. C., Ferreira, F. A. F., & Correia, R. J. C. (2022). Ranking residential neighborhoods based on their sustainability: a CM-BWM approach. International Journal of Strategic Property Management, 26(6), 410–423. https://doi.org/10.3846/ijspm.2022.18310
This work is licensed under a Creative Commons Attribution 4.0 International License.
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Bell, S., & Morse, S. (2013). Groups and facilitators within problem structuring processes. Journal of the Operational Research Society, 64(7), 959–972. https://doi.org/10.1057/jors.2012.110
Belton, V., & Stewart, T. (2002). Multiple criteria decision analysis: an integrated approach. Kluwer Academic Publishers. https://doi.org/10.1007/978-1-4615-1495-4
Ciampalini, A., Raspini, F., Lagomarsino, D., Catani, F., & Casagli, N. (2016). Landslide susceptibility map refinement using PSInSAR data. Remote Sensing of Environment, 184, 302–315. https://doi.org/10.1016/j.rse.2016.07.018
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Delmelle, E. (2015). Five decades of neighbourhood classifications and their transitions: a comparison of four US cities, 1970–2010. Applied Geography, 57, 1–11. https://doi.org/10.1016/j.apgeog.2014.12.002
Droj, L., & Droj, G. (2015). Usage of location analysis software in the evaluation of commercial real estate properties. Procedia Economics and Finance, 32, 826–832. https://doi.org/10.1016/S2212-5671(15)01525-7
Eden, C. (2004). Analyzing cognitive maps to help structure issues or problems. European Journal of Operational Research, 159(3), 673–686. https://doi.org/10.1016/S0377-2217(03)00431-4
Eden, C., & Ackermann, F. (2001). SODA – The principles. In J. Rosenhead & J. Mingers (Eds.), Rational analysis for a problematic world revisited: problem structuring methods for complexity, uncertainty and conflict (pp. 21–41). John Wiley & Sons.
Eurostat. (2021). Sustainable development in the European Union: overview of progress towards the SDGs in an EU context. Eurostat.
Faria, P., Ferreira, F., Jalali, M., Bento, P., & António, N. (2018). Combining cognitive mapping and MCDA for improving quality of life in urban areas. Cities, 78, 116–127. https://doi.org/10.1016/j.cities.2018.02.006
Ferreira, F. (2016). Are you pleased with your neighborhood? A fuzzy cognitive mapping-based approach for measuring residential neighborhood satisfaction in urban communities. International Journal of Strategic Property Management, 20(2), 130–141. https://doi.org/10.3846/1648715X.2015.1121169
Ferreira, F., Jalali, M., & Ferreira, J. (2016). Experience-focused thinking and cognitive mapping in ethical banking practices: from practical intuition to theory. Journal of Business Research, 69(11), 4953–4958. https://doi.org/10.1016/j.jbusres.2016.04.058
Ferreira, F., Jalali, M., Zavadskas, E., & Meidutė-Kavaliauskienė, I. (2017). Assessing payment instrument alternatives using cognitive mapping and the Choquet integral. Transformations in Business & Economics, 16(2/41), 170–187.
Ferreira, F., Spahr, R., Sunderman, M., & Jalali, M. (2018). A prioritisation index for blight intervention strategies in residential real estate. Journal of the Operational Research Society, 69(8), 1269–1285. https://doi.org/10.1080/01605682.2017.1390535
Ferreira, F., Spahr, R., Sunderman, M., Govindan, K., & Meidutė-Kavaliauskienė, I. (2022). Urban blight remediation strategies subject to seasonal constraints. European Journal of Operational Research, 296(1), 277–288. https://doi.org/10.1016/j.ejor.2021.03.045
Freire, C., Ferreira, F., Carayannis, E., & Ferreira, J. (2021). Artificial intelligence and smart cities: a DEMATEL approach to adaptation challenges and initiatives. IEEE Transactions on Engineering Management. https://doi.org/10.1109/TEM.2021.3098665
Gagnon, M. (2012). Sustainable minded entrepreneurs: developing and testing a values-based framework. Journal of Strategic Innovation and Sustainability, 8(1), 9–25.
Haybatollahi, M., Czepkiewicz, M., Laatikainen, T., & Kyttä, M. (2015). Neighbourhood preferences, active travel behaviour, and built environment: an exploratory study. Transportation Research Part F, 29, 57–69. https://doi.org/10.1016/j.trf.2015.01.001
Jones Lang LaSalle. (2015). Mercado Imobiliário em Portugal 2015 - Perspetivas 2016. https://www.jll.pt/pt/estudos-e-tendencias/estudos-de-mercado/mercado-imobiliario-em-portugal-2015-perspetivas-2016
Komeily, A., & Srinivasan, R. (2016). What is neighbourhood context and why does it matter in sustainability assessment? Procedia Engineering, 145, 876–883. https://doi.org/10.1016/j.proeng.2016.04.114
Krivo, L., Byron, R., Calder, C., Peterson, R., Browning, C., Kwan, M., & Lee, J. (2015). Patterns of local segregation: do they matter for neighbourhood crime? Social Science Research, 54, 303–318. https://doi.org/10.1016/j.ssresearch.2015.08.005
Laboratório Nacional de Engenharia Civil. (2010). Sustentabilidade ambiental da habitação (Proc. 0806/11/17779, Report 239/2010). Departamento de Edifícios, Núcleo de Arquitetura e Urbanismo. http://repositorio.lnec.pt:8080/xmlui/handle/123456789/1000460?show=full
Lousada, A., Ferreira, F., Meidutė-Kavaliauskienė, I., Spahr, R., Sunderman, M., & Pereira, L. (2021). A sociotechnical approach to causes of urban blight using fuzzy cognitive mapping and system dynamics. Cities, 108, 102963. https://doi.org/10.1016/j.cities.2020.102963
Maghsoodi, A., Riahi, D., Herrera-Viedma, E., & Zavadskas, E. (2020). An integrated parallel big data decision support tool using the W-CLUS-MCDA: a multi-scenario personnel assessment. Knowledge-Based System, 195, 1–19. https://doi.org/10.1016/j.knosys.2020.105749
Malek, J., & Desai, T. (2019). Prioritization of sustainable manufacturing barriers using Best Worst Method. Journal of Cleaner Production, 226, 589–600. https://doi.org/10.1016/j.jclepro.2019.04.056
Marques, S., Ferreira, F., Meidutė-Kavaliauskienė, I., & Banaitis, A. (2018). Classifying urban residential areas based on their exposure to crime: a constructivist approach. Sustainable Cities and Societies, 39, 418–429. https://doi.org/10.1016/j.scs.2018.03.005
Marvi, L., & Behzadfar, M. (2015). Local sustainability with emphasis on CPTED approach: the case of Ab-Kooh neighborhood in Mash-Had. Procedia – Social and Behavioral Sciences, 201, 409–417. https://doi.org/10.1016/j.sbspro.2015.08.194
Mendes, A., Ferreira, F., Kannan, D., Ferreira, N., & Correia, R. (2022). A BWM approach to determinants of sustainable entrepreneurship in small and medium-sized enterprises. Journal of Cleaner Production, 371, 133300. https://doi.org/10.1016/j.jclepro.2022.133300
Miguel, B., Ferreira, F., Banaitis, A., Banaitienė, N., Meidutė-Kavaliauskienė, I., & Falcão, P. (2019). An expanded conceptualization of “smart” cities: adding value with fuzzy cognitive maps. E+M: Ekonomie a Management, 22(1), 4–21. https://doi.org/10.15240/tul/001/2019-1-001
Mingers, J., & Rosenhead, J. (2004). Problem structuring methods in action. European Journal of Operational Research, 152(3), 530–554. https://doi.org/10.1016/S0377-2217(03)00056-0
Mohammadi, M., & Rezaei, J. (2020). Bayesian best-worst method: a probabilistic group decision making model. Omega, 96, 102075. https://doi.org/10.1016/j.omega.2019.06.001
Nesticò, A., & Bencardino, M. (2016). Urban real estate values on vast area and macroeconomic parameters. Procedia – Social and Behavioral Sciences, 223, 410–415. https://doi.org/10.1016/j.sbspro.2016.05.256
Nunes, S., Ferreira, F., Govindan, K., & Pereira, L. (2021). “Cities go smart!”: a system dynamics-based approach to smart city conceptualization. Journal of Cleaner Production, 313, 127683. https://doi.org/10.1016/j.jclepro.2021.127683
Pearson, A., Rzotkiewicz, A., Pechal, J., Schmidt, C., Jordan, H., Zwickle, A., & Benbow, M. (2019). Initial evidence of the relationships between the human postmortem microbiome and neighborhood blight and greening efforts. Annals of the American Association of Geographers, 109(3), 958–978. https://doi.org/10.1080/24694452.2018.1519407
Pinto, A., Ferreira, F., Spahr, R., Sunderman, M., Govindan, K., & Meidutė-Kavaliauskienė, I. (2021). Analyzing blight impacts on urban areas: a multi-criteria approach. Land Use Policy, 108, 105661. https://doi.org/10.1016/j.landusepol.2021.105661
Pinto, B., Ferreira, F., Spahr, R., Sunderman, M., & Pereira, L. (2022). Analyzing causes of urban blight using cognitive mapping and DEMATEL. Annals of Operations Research. https://doi.org/10.1007/s10479-022-04614-6
Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49–57. https://doi.org/10.1016/j.omega.2014.11.009
Rezaei, J. (2016). Best-worst multi-criteria decision-making method: some properties and a linear model. Omega, 64, 126–130. https://doi.org/10.1016/j.omega.2015.12.001
Rezaei, J. (2020). A concentration ratio for nonlinear best worst method. International Journal of Information Technology & Decision Making, 19(3), 891–907. https://doi.org/10.1142/S0219622020500170
Rezaei, J., Wang, J., & Tavasszy, L. (2015). Linking supplier development to supplier segmentation using Best Worst Method. Expert Systems with Applications, 42, 9152–9164. https://doi.org/10.1016/j.eswa.2015.07.073
Rosenhead, J. (1996). What’s the problem? An introduction to problem structuring methods. Interfaces, 26(6), 117–131. https://doi.org/10.1287/inte.26.6.117
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