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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. 1 Abstract * Using a hedonic residential rent model for Brazil's metropolitan areas calibrated with microdata from Brazil's annual household survey, this study estimates that increasing the sense of security in the home by one standard deviation would increase average home values by R$1,513 (US$757), or about US$13.6 billion if applied to all 18.0 million households in the study area. The principal components analysis of sense of security and crime victimization variables indicates that higher-income households feel more secure from crime in the home, even though theft and robbery victimization rise with household income and rent level. Higher levels of home protection measures by higher-income households partially explain this result. Terms of use: Documents inJEL classifications: C83, R23, R31

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.

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