Risk and uncertainty in development

Abstract

Purpose -The purpose of this paper is to deal with the appropriateness of using the Monte Carlo simulation as a technique to calculate risk in real estate development. Design/methodology/approach -The paper is divided into two interlinked segments. The first segment examines the general definition of risk and Monte Carlo simulation methodology as a tool to estimate risk. The second outlines the appropriateness of using Monte Carlo as a tool to model real estate development, given the lack of data quality and its inability to account for human relationships in the development process. Findings -It is important that the Monte Carlo Simulation model is used as prescriptive model that builds on the original elicitation procedures; produces initial results; allows for detailed sensitivity analysis and then remodels as required. In short, to be fully effective, the Monte Carlo Simulation model needs to be used in a complementary fashion with an understanding of human judgement and decision making. Research limitations/implications -A fuller analysis may include an examination of the uncertainties in each of the components of the appraisal and account for the appropriate distributions of each of these variables. This is generally referred to as a Monte Carlo simulation. The argument in favour of a Monte Carlo simulation is that it helps the appraiser have a better understanding of the possible outcomes for the development and the relative impact of each input in the pricing of the project. Practical implications -A lot of work has been done looking at scenario modelling with probabilities and the results that ensue. However, it is important that these quantitative results are placed in the context of the heuristic and cognitive approaches adopted by the decision maker. In other words, the behaviour of the decision maker is as influential in the interpretation of the results as the numbers themselves. This paper looks at the advantages and disadvantages of using Monte Carlo simulation in this context. Originality/value -This study contributes significantly to the practical application of probability-based models to development appraisal. The findings of the study are useful for users of feasibility studies to understand the context in which a development feasibility is carried out, and for appraisers to extend the scope of their analysis when carrying them out.

Monte Carlo Cash Flows and Sustainability: How to Decide on Going Green

“…In general, we use a cash flow model that is based on several inputs for which stochastic processes are defined, and where the random outcomes define the resulting cash flows. With our simulation approach, we are in line with many studies that apply Monte Carlo methods for cash flows in the real estate area (e.g., Pyhrr, 1973;Atherton, French, and Gabrielli, 2008;and Loizou and French, 2012), which mainly focus on risks. The application of Monte Carlo methods both in static and dynamic approaches is discussed by Pfnür and Armonat (2013).…”

Energy Retrofit in European Building Portfolios: A Review of Five Key Aspects

“…The latter is among the most useful techniques in the field of building energy efficiency because of its versatility [215,252]. The MC simulation can be employed in its simple version [253] or in three other extensions: the two-dimensional MC simulation (2D MC) [254,255], the incremental sampling method [256] and the surrogate model based MC method [257].…”

Assessing Real Estate Investment Alternatives: A Multi-Criteria and Multi-Stakeholder Decision Aid Tool

“…Therefore, it is important to incorporate a method in the financial modeling stage to model the uncertainty occurring from these factors and provide more robust input estimates. Loizou and French (2012) proposed the use of a Monte Carlo simulation as the best method to address risk and uncertainty in real estate decisions. In addition to that, the effectiveness of Monte Carlo in three of the decision models described in the literature review renders it a suitable method to address this issue in the financial modeling stage (Jiang et al 2007;Atherton et al, 2008;Ginevicius and Zubrecovas, 2009).…”