Abstract
The increasing concentration of populations in urban areas in recent decades has strengthened the interest in – and the importance given to – these zones. Cities have become quite attractive from investors’ point of view because of the wide array of opportunities and growing need for investment in urban areas. Thus, city strategic planning quite often requires an understanding of the determinants that attract investment to urban zones. This study sought to identify the factors that strengthen urban investment based on the knowledge of a panel of experts. Fuzzy cognitive mapping techniques were applied to understand the concepts and decision criteria included in the decision-support model and their cause-and-effect relationships. The results provide insights into which determinants most strongly influence urban investment, namely, infrastructure, supporting services, and political-administrative factors. Diverse scenarios at the intra- and inter-cluster levels were created to clarify the impacts of variable changes on the model developed. The findings were validated by both the expert panel members and the vice-president of the Portuguese Association of Real Estate Developers and Investors. Advantages and limitations of the proposed framework are presented, as well as recommendations for future research.
Highlights
Awareness of urban investment importance has been growing due to its well-known social and economic impacts at different societal levels
The fifth objective was to develop a fuzzy cognitive map (FCM) and a stock-and-flow diagram (SFD), while the last was to create scenarios and simulations to understand the impact that changes in determinants have on urban investment
Decision makers can have a better idea of the impact of their decisions on urban investment
Summary
Awareness of urban investment importance has been growing due to its well-known social and economic impacts at different societal levels. The main objectives were to identify determinants that attract urban investment and analyze their cause-and-effect relationships, as well as these variables’ behavior over time when any change occurs. In this type of research, statistical models have been by far the most popular approach. The second objective was to conduct an initial session with this group in which the factors attracting urban investment were identified, as well as their cause-and-effect relationships and internal hierarchy, thereby constructing a basic cognitive structure of the decision problem. The fifth objective was to develop a fuzzy cognitive map (FCM) and a stock-and-flow diagram (SFD), while the last was to create scenarios and simulations to understand the impact that changes in determinants have on urban investment. The last section details the contributions and limitations of our methodological framework, and suggests avenues for future research
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