Monte Carlo simulation in a financial strategy for urban development using land value capture

Abstract

Purpose The purpose of this paper is to explore how Monte Carlo Simulation (MCS) could have enhanced understanding of the risks involved in the financial strategy for revitalization of Rio de Janeiro’s central city based on the capture of value generated by government interventions. Design/methodology/approach The study first describes the process involved in developing the financial strategy and model without MCS. Then, it shows how the MCS could have been integrated into this process and evaluates its potential impacts on the quality of risk analysis. Findings If MCS is fully integrated into the decision-making process, it can serve as a heuristic tool that helps team members to better understand risks by generating forecasts of land value and other variables as a probability distributions. By showing the variance of the forecasted variable, MCS integrates elements of modern risk analysis into financial model development in a cost-effective manner. Research limitations/implications MCS covers only the risks associated with the variables in the financial model. Events that seem extremely unlikely (i.e. “black swans”) can occur and must be assessed separately. Practical implications MCS can help analysts to understand the financial risks of large-scale development projects involving value capture, even in the prefeasibility stage. Social implications By facilitating value capture, MCS could help close the financing gap for sustainable urban development and subsidies for lower income families. Originality/value The study “retrofits” MCS on a successfully completed financial prefeasibility study to assess its usefulness as a heuristic tool.

Black swan: bibliometric analysis and development of research agenda

“…A bibliometric analysis would provide a snapshot of the literature and performing the analysis would be the starting point to develop a research agenda to drive future research. 2014;Jebari, 2015;Jore et al, 2018;Lakshmi, 2018;Lee and Su, 2010;Lindaas and Pettersen, 2016;Loizzo et al, 2015;de Loo and Davis, 2003;Luria et al, 2017;Makridakis and Taleb, 2009;Maslen and Hayes, 2016;Mefford et al, 2017;Millett, 2010;Mueller and Stewart, 2016;Nafday, 2009;Ndubisi et al, 2016;Oh and Lee, 2017;Olivares-Aguila and ElMaraghy, 2020;de Palma et al, 2014;Payzan-LeNestour, 2018;Perera et al, 2018;Pilkington and Meredith, 2009;Reut et al, 2019;van Rijmenam et al, 2019;Schurr et al, 2014;Sengar et al, 2017;Sniedovich, 2012;Taleb and Dearlove, 2009;Vetter and Vetter, 2019;Wardman and Mythen, 2016;Wells et al, 2017;Werther, 2013;Zafft, 2012;Zaman et al, 2019) falling under the timespan from 1997 to 2020 (articles published till 30 April 2020). The full record of the articles bibliographical information was downloaded in BibTex format for further use in analysis using R package and VOSviewer.…”

Cognitive Computational Model Using Machine Learning Algorithm in Artificial Intelligence Environment

“…Then Monte Carlo simulation is adopted to simulate the value at risk (VaR) of the investment portfolio [21].…”