Barriers to real estate investments for residential rental purposes: mapping out the problem
Abstract
The recent economic crisis led to significant changes in the real estate market; one of which was a shift toward home rental (rather than buying). Real estate investors have an important role in the growth of the rental market. However, there are often hindrances to investing for residential rental purposes. In order to overcome these barriers, they first need to be identified and understood. With this in mind, the main focus of this investigation was the creation of a conceptual model, through fuzzy cognitive mapping, to identify and understand the cause-and-effect relationships between the factors that represent an obstacle to real estate investments for residential rental purposes. The results show that cognitive maps can be of great use for the structuring of complex decision problems, minimizing the number of factors left out of the decision making process. In particular, the tenant risk behavior, property location and associated costs (for the owner) were identified as the main obstacles to real estate investment rental propose. The practical implications of the model, as well as the advantages and limitations of the process followed, are also discussed.
Keyword : cognitive mapping, decision aid, investment, renting, residential real estate
How to Cite
Pires, A. S., Ferreira, F. A., Jalali, M. S., & Chang, H.-C. (2018). Barriers to real estate investments for residential rental purposes: mapping out the problem. International Journal of Strategic Property Management, 22(3), 168-178. https://doi.org/10.3846/ijspm.2018.1541
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Adams, K., & Ntuen, C. (2006, December). An overview of cognitive maps and their applications. Proceedings of the 2006 IIE Annual Conference and Exhibition (pp. 1-7). Orlando FL, USA.
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. Dordrecht: Kluwer Academic Publishers. https://doi.org/10.1007/978-1-4615-1495-4
Brown, R., & Young, M. (2011). Coherent risk measures in real estate investment. Journal of Property Investment & Finance, 29(4/5), 479-493. https://doi.org/10.1108/14635781111150358
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Carlucci, D., Schiuma, G., Gavrilova, T., & Linzalone, R. (2013, June). A fuzzy cognitive map based approach to disclose value creation dynamics of ABIs. Proceedings of the 8th International Forum on Knowledge Asset Dynamics (IFKAD-2013) (pp. 207-219). Zagreb, Croatia.
Cartwright, D., & Harary, F. (1956). Structural balance: a generalization of Heider’s theory. Psychological Review, 63(5), 277-293. https://doi.org/10.1037/h0046049
Carvalho, J. (2013). On the semantics and the use of fuzzy cognitive maps and dynamic cognitive maps in social sciences. Fuzzy Sets and Systems, 214(1), 6-19. https://doi.org/10.1016/j.fss.2011.12.009
Carvalho, J., & Tomé, J. (1999, August). Fuzzy mechanisms for causal relations. Proceedings of the 8th International Fuzzy Systems Association World Congress (IFSA 99) (pp. 1-5). Taipei, Taiwan.
Cheong, C., Olshansky, A., & Zurbruegg, R. (2011). The influence of real estate risk on market volatility. Journal of Property Investment & Finance, 29(2), 145-166. https://doi.org/10.1108/14635781111112774
Devaney, M., & Weber, W. (2005). Efficiency, scale economies, and the risk/return performance of real estate investment trusts. Journal of Real Estate Finance and Economics, 31(3), 301-317. https://doi.org/10.1007/s11146-005-2791-5
Eden, C. (2004). Analyzing cognitive maps to help structure is-sues or problems. European Journal of Operational Research, 159(3), 673-686. https://doi.org/10.1016/S0377-2217(03)00431-4
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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. (2015). Identifying key determinants of housing sales and time-on-the-market (TOM) using fuzzy cognitive mapping. International Journal of Strategic Property Management, 19(3), 235-244. https://doi.org/10.3846/1648715X.2015.1052587
Ferreira, F., Jalali, M., & Ferreira, J. (2016a). Integrating qualitative comparative analysis (QCA) and fuzzy cognitive maps (FCM) to enhance the selection of independent variables. Journal of Business Research, 69(4), 1471-1478. https://doi.org/10.1016/j.jbusres.2015.10.127
Ferreira, F., Jalali, M., Ferreira, J., Stankevičienė, J., & Marques, C. (2016b). Understanding the dynamics behind bank branch service quality in Portugal: pursuing a holistic view using fuzzy cognitive mapping. Service Business, 10(3), 469-487. https://doi.org/10.1007/s11628-015-0278-x
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., Santos, S., & Rodrigues, P. (2011). Adding value to bank branch performance evaluation using cognitive maps and MCDA: a case study. Journal of the Operational Research Society, 62(7), 1320-1333. https://doi.org/10.1057/jors.2010.111
Ferreira, F., Santos, S., Rodrigues, P., & Spahr, R. (2014). How to create indices for bank branch financial performance measurement using MCDA techniques: an illustrative example. Journal of Business Economics and Management, 15(4), 708-728. https://doi.org/10.3846/16111699.2012.701230
Ferreira, F., Spahr, R., Gavancha, I., & Çipi, A. (2013). Readjusting trade-offs among criteria in internal ratings of credit-scoring: an empirical essay of risk analysis in mortgage loans. Journal of Business Economics and Management, 14(4), 715-740. https://doi.org/10.3846/16111699.2012.666999
Filipe, M., Ferreira, F., & Santos, S. (2015). A multiple criteria information system for pedagogical evaluation and professional development of teachers. Journal of the Operational Research Society, 66(11), 1769-1782. https://doi.org/10.1057/jors.2014.129
Financial Times. (2017). Retrieved from https://www.ft.com/content/a49f47c4-3041-11e7-9555-23ef563ecf9a
Glykas, M. (2013). Fuzzy cognitive strategic maps in business process performance measurement. Expert Systems with Applications, 40(1), 1-14. https://doi.org/10.1016/j.eswa.2012.01.078
Gonçalves, T., Ferreira, F., Jalali, M., & Meidutė-Kavaliauskienė, I. (2016). An idiosyncratic decision support system for credit risk analysis of small and medium-sized enterprises. Technological and Economic Development of Economy, 22(4), 598-616.
Guo, J., Xu, S., & Bi, Z. (2014). An integrated cost-based approach for real estate appraisals. Information Technology and Management, 15(2), 131-139.
Hill, R. (2011). Hedonic price indexes for housing. OECD Statistics Working Papers, 2011(1), 1-61.
Hui, E., Wang, Z., & Wong, H. (2014). Risk and credit change in Asian securitized real estate market. Habitat International, 43(1), 221-230. https://doi.org/10.1016/j.habitatint.2014.03.008
Jalali, M., Ferreira, F., Ferreira, J., & Meidutė-Kavaliauskienė, I. (2016). Integrating metacognitive and psychometric decision-making approaches for bank customer loyalty measurement. International Journal of Information Technology & Decision Making, 15(4), 815-837. https://doi.org/10.1142/S0219622015500236
Kang, B., Deng, Y., Sadiq, R., & Mahadevan, S. (2012). Evidential cognitive maps. Knowledge-Based Systems, 35(15), 77-86. https://doi.org/10.1016/j.knosys.2012.04.007
Kim, H., & Lee, K. (1998). Fuzzy implications of fuzzy cognitive map with emphasis on fuzzy causal relationship and fuzzy partially causal relationship. Fuzzy Sets and Systems, 97(3), 303-313. https://doi.org/10.1016/S0165-0114(96)00349-1
Kok, K. (2009). The potential of fuzzy cognitive maps for semi-quantitative scenario development, with an example from Brazil. Global Environmental Change, 19(1), 122-133. https://doi.org/10.1016/j.gloenvcha.2008.08.003
Kosko, B. (1986). Fuzzy cognitive maps. International Journal of Man-Machine Studies, 24(1), 65-75. https://doi.org/10.1016/S0020-7373(86)80040-2
Kosko, B. (1992). Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence. New Jersey: Prentice Hall.
La, L., & Mei, B. (2014). Portfolio diversification through timber real estate investment trusts: a cointegration analysis. Forest Policy and Economics, 50(1), 269-274.
Lee, M., Lee, M., & Chiang, K. (2008). Real estate risk exposure of equity real estate investment trusts. Journal of Real Estate Finance & Economics, 36(2), 165-181. https://doi.org/10.1007/s11146-007-9058-2
Lee, S., & Stevenson, S. (2005). Real estate portfolio construction and estimation risk. Journal of Real Estate Finance and Economics, 23(3), 234-253. https://doi.org/10.1108/14635780510599458
Maier, G., & Herath, S. (2009). Real estate market efficiency: a survey of literature, SRE – Discussion Papers, 2009(7), 1-46.
Mao, Y., & Wu, W. (2011). Fuzzy real option evaluation of real estate project. Systems Engineering Procedia, 1(1), 228-235. https://doi.org/10.1016/j.sepro.2011.08.036
Mazlack, L. (2009, June). Representing causality using fuzzy cognitive maps. Proceedings of the Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS 2009) (pp. 1-6). Cincinnati, USA. https://doi.org/10.1109/NAFIPS.2009.5156434
Minli, Z., & Wenpo, Y. (2012). Fuzzy comprehensive evaluation method applied in the real estate investment risks research. Physics Procedia, 24(C), 1815-1821.
Nicolini, D. (1999). Comparing methods for mapping organizational cognition. Organizations Studies, 20(5), 833-860. https://doi.org/10.1177/0170840699205006
Olazabal, M., & Pascual, U. (2016). Use of fuzzy cognitive maps to study urban resilience and transformation. Environmental Innovation and Societal Transitions, 18(1), 18-40. https://doi.org/10.1016/j.eist.2015.06.006
Papageorgiou, E., Roo, J., Huszka, C., & Colaert, D. (2012). Formalization of treatment guidelines using fuzzy cognitive maps and semantic web tools. Journal of Biomedical Informatics, 45(1), 45-60. https://doi.org/10.1016/j.jbi.2011.08.018
Peng, Z., Peng, J., Zhao, W., & Chen, Z. (2015). Research on FCM and NHL based high order mining driven by big data. Mathematical Problems in Engineering, 2015(4), 1-7. https://doi.org/10.1155/2015/802505
Ribeiro, M., Ferreira, F., Jalali, M., & Meidutė-Kavaliauskienė, I. (2017). A fuzzy knowledge-based framework for risk assessment of residential real estate investments. Technological and Economic Development of Economy, 23(1), 140-156. https://doi.org/10.3846/20294913.2016.1212742
Rybak, J., & Shapoval, V. (2011). Industries and sectors: Issues and policies, perspectives of innovations. Economics & Business, 8(2), 17-22.
Salmeron, J. (2009). Augmented fuzzy cognitive maps for modelling LMS critical success facts. Knowledge-Based Systems, 22(4), 275-278. https://doi.org/10.1016/j.knosys.2009.01.002
Stach, W., Kurgan, L., & Pedrycz, W. (2010). A divide and conquer method for learning large fuzzy cognitive maps. Fuzzy Sets and Systems, 161(19), 2515-2532. https://doi.org/10.1016/j.fss.2010.04.008
Stach, W., Kurgan, L., Pedrycz, W., & Reformat, M. (2005). Genetic learning of fuzzy cognitive maps. Fuzzy Sets and Systems, 153(3), 371-401. https://doi.org/10.1016/j.fss.2005.01.009
Tolman, E. (1948). Cognitive maps in rats and men. Psychological Review, 55(4), 189-208. https://doi.org/10.1037/h0061626
Tsadiras, A. (2008). Comparing the inference capabilities of binary, trivalent and sigmoid fuzzy cognitive maps. Information Sciences, 178(20), 3880-3894. https://doi.org/10.1016/j.ins.2008.05.015
Wheaton, W., Torto, R., Sivitanides, P., & Southard, J. (1999). Evaluating risk in real estate. Real Estate Finance, 16(2), 15-22.
Winger, A. (2002). Why the emerging economy will mean more systemic risk in real estate lending. Real Estate Issues, 27(1), 28-35.
Yunus, N. (2013). Dynamic interactions among property types. Journal of Property Investment & Finance, 31(2), 135-159. https://doi.org/10.1108/14635781311305372
Zadeh, L. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X
Adair, A., & Hutchison, N. (2005). The reporting of risk in real estate appraisal property risk scoring. Journal of Real Estate Finance & Economics, 23(3), 254-268. https://doi.org/10.1108/14635780510599467
Adams, K., & Ntuen, C. (2006, December). An overview of cognitive maps and their applications. Proceedings of the 2006 IIE Annual Conference and Exhibition (pp. 1-7). Orlando FL, USA.
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. Dordrecht: Kluwer Academic Publishers. https://doi.org/10.1007/978-1-4615-1495-4
Brown, R., & Young, M. (2011). Coherent risk measures in real estate investment. Journal of Property Investment & Finance, 29(4/5), 479-493. https://doi.org/10.1108/14635781111150358
Canas, S., Ferreira, F., & Meidutė-Kavaliauskienė, I. (2015). Setting rents in residential real estate: a methodological proposal using multiple criteria decision analysis. International Journal of Strategic Property Management, 19(4), 368-380. https://doi.org/10.3846/1648715X.2015.1093562
Carlucci, D., Schiuma, G., Gavrilova, T., & Linzalone, R. (2013, June). A fuzzy cognitive map based approach to disclose value creation dynamics of ABIs. Proceedings of the 8th International Forum on Knowledge Asset Dynamics (IFKAD-2013) (pp. 207-219). Zagreb, Croatia.
Cartwright, D., & Harary, F. (1956). Structural balance: a generalization of Heider’s theory. Psychological Review, 63(5), 277-293. https://doi.org/10.1037/h0046049
Carvalho, J. (2013). On the semantics and the use of fuzzy cognitive maps and dynamic cognitive maps in social sciences. Fuzzy Sets and Systems, 214(1), 6-19. https://doi.org/10.1016/j.fss.2011.12.009
Carvalho, J., & Tomé, J. (1999, August). Fuzzy mechanisms for causal relations. Proceedings of the 8th International Fuzzy Systems Association World Congress (IFSA 99) (pp. 1-5). Taipei, Taiwan.
Cheong, C., Olshansky, A., & Zurbruegg, R. (2011). The influence of real estate risk on market volatility. Journal of Property Investment & Finance, 29(2), 145-166. https://doi.org/10.1108/14635781111112774
Devaney, M., & Weber, W. (2005). Efficiency, scale economies, and the risk/return performance of real estate investment trusts. Journal of Real Estate Finance and Economics, 31(3), 301-317. https://doi.org/10.1007/s11146-005-2791-5
Eden, C. (2004). Analyzing cognitive maps to help structure is-sues 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) (2nd ed.). Chichester: John Wiley & Sons.
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. (2015). Identifying key determinants of housing sales and time-on-the-market (TOM) using fuzzy cognitive mapping. International Journal of Strategic Property Management, 19(3), 235-244. https://doi.org/10.3846/1648715X.2015.1052587
Ferreira, F., Jalali, M., & Ferreira, J. (2016a). Integrating qualitative comparative analysis (QCA) and fuzzy cognitive maps (FCM) to enhance the selection of independent variables. Journal of Business Research, 69(4), 1471-1478. https://doi.org/10.1016/j.jbusres.2015.10.127
Ferreira, F., Jalali, M., Ferreira, J., Stankevičienė, J., & Marques, C. (2016b). Understanding the dynamics behind bank branch service quality in Portugal: pursuing a holistic view using fuzzy cognitive mapping. Service Business, 10(3), 469-487. https://doi.org/10.1007/s11628-015-0278-x
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., Santos, S., & Rodrigues, P. (2011). Adding value to bank branch performance evaluation using cognitive maps and MCDA: a case study. Journal of the Operational Research Society, 62(7), 1320-1333. https://doi.org/10.1057/jors.2010.111
Ferreira, F., Santos, S., Rodrigues, P., & Spahr, R. (2014). How to create indices for bank branch financial performance measurement using MCDA techniques: an illustrative example. Journal of Business Economics and Management, 15(4), 708-728. https://doi.org/10.3846/16111699.2012.701230
Ferreira, F., Spahr, R., Gavancha, I., & Çipi, A. (2013). Readjusting trade-offs among criteria in internal ratings of credit-scoring: an empirical essay of risk analysis in mortgage loans. Journal of Business Economics and Management, 14(4), 715-740. https://doi.org/10.3846/16111699.2012.666999
Filipe, M., Ferreira, F., & Santos, S. (2015). A multiple criteria information system for pedagogical evaluation and professional development of teachers. Journal of the Operational Research Society, 66(11), 1769-1782. https://doi.org/10.1057/jors.2014.129
Financial Times. (2017). Retrieved from https://www.ft.com/content/a49f47c4-3041-11e7-9555-23ef563ecf9a
Glykas, M. (2013). Fuzzy cognitive strategic maps in business process performance measurement. Expert Systems with Applications, 40(1), 1-14. https://doi.org/10.1016/j.eswa.2012.01.078
Gonçalves, T., Ferreira, F., Jalali, M., & Meidutė-Kavaliauskienė, I. (2016). An idiosyncratic decision support system for credit risk analysis of small and medium-sized enterprises. Technological and Economic Development of Economy, 22(4), 598-616.
Guo, J., Xu, S., & Bi, Z. (2014). An integrated cost-based approach for real estate appraisals. Information Technology and Management, 15(2), 131-139.
Hill, R. (2011). Hedonic price indexes for housing. OECD Statistics Working Papers, 2011(1), 1-61.
Hui, E., Wang, Z., & Wong, H. (2014). Risk and credit change in Asian securitized real estate market. Habitat International, 43(1), 221-230. https://doi.org/10.1016/j.habitatint.2014.03.008
Jalali, M., Ferreira, F., Ferreira, J., & Meidutė-Kavaliauskienė, I. (2016). Integrating metacognitive and psychometric decision-making approaches for bank customer loyalty measurement. International Journal of Information Technology & Decision Making, 15(4), 815-837. https://doi.org/10.1142/S0219622015500236
Kang, B., Deng, Y., Sadiq, R., & Mahadevan, S. (2012). Evidential cognitive maps. Knowledge-Based Systems, 35(15), 77-86. https://doi.org/10.1016/j.knosys.2012.04.007
Kim, H., & Lee, K. (1998). Fuzzy implications of fuzzy cognitive map with emphasis on fuzzy causal relationship and fuzzy partially causal relationship. Fuzzy Sets and Systems, 97(3), 303-313. https://doi.org/10.1016/S0165-0114(96)00349-1
Kok, K. (2009). The potential of fuzzy cognitive maps for semi-quantitative scenario development, with an example from Brazil. Global Environmental Change, 19(1), 122-133. https://doi.org/10.1016/j.gloenvcha.2008.08.003
Kosko, B. (1986). Fuzzy cognitive maps. International Journal of Man-Machine Studies, 24(1), 65-75. https://doi.org/10.1016/S0020-7373(86)80040-2
Kosko, B. (1992). Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence. New Jersey: Prentice Hall.
La, L., & Mei, B. (2014). Portfolio diversification through timber real estate investment trusts: a cointegration analysis. Forest Policy and Economics, 50(1), 269-274.
Lee, M., Lee, M., & Chiang, K. (2008). Real estate risk exposure of equity real estate investment trusts. Journal of Real Estate Finance & Economics, 36(2), 165-181. https://doi.org/10.1007/s11146-007-9058-2
Lee, S., & Stevenson, S. (2005). Real estate portfolio construction and estimation risk. Journal of Real Estate Finance and Economics, 23(3), 234-253. https://doi.org/10.1108/14635780510599458
Maier, G., & Herath, S. (2009). Real estate market efficiency: a survey of literature, SRE – Discussion Papers, 2009(7), 1-46.
Mao, Y., & Wu, W. (2011). Fuzzy real option evaluation of real estate project. Systems Engineering Procedia, 1(1), 228-235. https://doi.org/10.1016/j.sepro.2011.08.036
Mazlack, L. (2009, June). Representing causality using fuzzy cognitive maps. Proceedings of the Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS 2009) (pp. 1-6). Cincinnati, USA. https://doi.org/10.1109/NAFIPS.2009.5156434
Minli, Z., & Wenpo, Y. (2012). Fuzzy comprehensive evaluation method applied in the real estate investment risks research. Physics Procedia, 24(C), 1815-1821.
Nicolini, D. (1999). Comparing methods for mapping organizational cognition. Organizations Studies, 20(5), 833-860. https://doi.org/10.1177/0170840699205006
Olazabal, M., & Pascual, U. (2016). Use of fuzzy cognitive maps to study urban resilience and transformation. Environmental Innovation and Societal Transitions, 18(1), 18-40. https://doi.org/10.1016/j.eist.2015.06.006
Papageorgiou, E., Roo, J., Huszka, C., & Colaert, D. (2012). Formalization of treatment guidelines using fuzzy cognitive maps and semantic web tools. Journal of Biomedical Informatics, 45(1), 45-60. https://doi.org/10.1016/j.jbi.2011.08.018
Peng, Z., Peng, J., Zhao, W., & Chen, Z. (2015). Research on FCM and NHL based high order mining driven by big data. Mathematical Problems in Engineering, 2015(4), 1-7. https://doi.org/10.1155/2015/802505
Ribeiro, M., Ferreira, F., Jalali, M., & Meidutė-Kavaliauskienė, I. (2017). A fuzzy knowledge-based framework for risk assessment of residential real estate investments. Technological and Economic Development of Economy, 23(1), 140-156. https://doi.org/10.3846/20294913.2016.1212742
Rybak, J., & Shapoval, V. (2011). Industries and sectors: Issues and policies, perspectives of innovations. Economics & Business, 8(2), 17-22.
Salmeron, J. (2009). Augmented fuzzy cognitive maps for modelling LMS critical success facts. Knowledge-Based Systems, 22(4), 275-278. https://doi.org/10.1016/j.knosys.2009.01.002
Stach, W., Kurgan, L., & Pedrycz, W. (2010). A divide and conquer method for learning large fuzzy cognitive maps. Fuzzy Sets and Systems, 161(19), 2515-2532. https://doi.org/10.1016/j.fss.2010.04.008
Stach, W., Kurgan, L., Pedrycz, W., & Reformat, M. (2005). Genetic learning of fuzzy cognitive maps. Fuzzy Sets and Systems, 153(3), 371-401. https://doi.org/10.1016/j.fss.2005.01.009
Tolman, E. (1948). Cognitive maps in rats and men. Psychological Review, 55(4), 189-208. https://doi.org/10.1037/h0061626
Tsadiras, A. (2008). Comparing the inference capabilities of binary, trivalent and sigmoid fuzzy cognitive maps. Information Sciences, 178(20), 3880-3894. https://doi.org/10.1016/j.ins.2008.05.015
Wheaton, W., Torto, R., Sivitanides, P., & Southard, J. (1999). Evaluating risk in real estate. Real Estate Finance, 16(2), 15-22.
Winger, A. (2002). Why the emerging economy will mean more systemic risk in real estate lending. Real Estate Issues, 27(1), 28-35.
Yunus, N. (2013). Dynamic interactions among property types. Journal of Property Investment & Finance, 31(2), 135-159. https://doi.org/10.1108/14635781311305372
Zadeh, L. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X