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

Monte Carlo simulation in a financial strategy for urban development using land value capture 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/approachThe 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.FindingsIf 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/implicationsMCS 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 implicationsMCS can help analysts to understand the financial risks of large-scale development projects involving value capture, even in the prefeasibility stage.Social implicationsBy facilitating value capture, MCS could help close the financing gap for sustainable urban development and subsidies for lower income families.Originality/valueThe study “retrofits” MCS on a successfully completed financial prefeasibility study to assess its usefulness as a heuristic tool. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Property Investment & Finance Emerald Publishing

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

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References (8)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1463-578X
DOI
10.1108/jpif-03-2019-0032
Publisher site
See Article on Publisher Site

Abstract

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/approachThe 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.FindingsIf 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/implicationsMCS 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 implicationsMCS can help analysts to understand the financial risks of large-scale development projects involving value capture, even in the prefeasibility stage.Social implicationsBy facilitating value capture, MCS could help close the financing gap for sustainable urban development and subsidies for lower income families.Originality/valueThe study “retrofits” MCS on a successfully completed financial prefeasibility study to assess its usefulness as a heuristic tool.

Journal

Journal of Property Investment & Finance – Emerald Publishing

Published: Jun 10, 2019

Keywords: Monte Carlo simulation; Financial model; Risk assessment; Urban development; Land value capture; Scenario analysis; Rio de Janeiro; Brazil

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