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Cited by 3Construction Project Management and PerformanceCapital Investment and Risk AnalysisReal estate and construction management

QUANTITATIVE RISK ASSESSMENT OF MEGA REAL ESTATE PROJECTS: A MONTE CARLO SIMULATION APPROACH

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Chief Executive Officer (CEO), GM Holdings Limited (Deshbandhu Group), Dhaka, Bangladesh, Jahid, MKA Shahinoor Rahman·2022·Journal of Sustainable Development and Policy
DOI 10.63125/nh269421
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Abstract

This systematic literature review synthesizes how Monte Carlo simulation is designed, implemented, and interpreted for quantitative risk assessment in mega real estate projects. Following a PRISMA-aligned protocol, we searched Scopus, Web of Science, ASCE Library, ScienceDirect, IEEE Xplore, and Emerald Insight, complemented by Taylor & Francis Online and snowballing, screened records in two stages, and extracted standardized methodological variables. The final sample comprises 115 studies, analyzed across four decision-shaping themes: input distributions, dependence modeling, sampling discipline, and integration of cost, schedule, and finance. Most studies adopt triangular or PERT-style inputs, with lognormal alternatives common, while heavy-tailed forms remain underused despite escalation and long-lead exposures. Explicit dependence is unevenly treated, many models assume independence, whereas rank correlation, common drivers, or copulas reveal wider tails. Latin hypercube sampling dominates, quasi Monte Carlo appears in a minority, and only a subset justifies run size against precision targets. Where models link schedule to cost and propagate into cash flow and financing metrics such as debt service coverage ratio, credible bands widen and breach probabilities rise, improving alignment with lender and board thresholds. Sensitivity analysis is frequent yet mixed in rigor, with global and tail-focused measures offering clearer mitigation leverage than tornado charts, and validation by hindcasting or exceedance tests remains rare but decisive for credibility. We conclude that governance-ready practice requires tail-aware input fitting, co-movement, variance-efficient designs with accuracy goals, integrated cost, schedule, and finance modeling, and transparent sensitivity and validation protocols that yield auditable percentile contingencies and covenant risk statements for stage-gate decisions.

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  • Over 60 percent of industrial cyber intrusions exploit insecure OT protocols and unauthorized remote access services.

    “International cybersecurity incident reports indicate that over 60 percent of industrial cyber intrusions exploit insecure OT protocols and unauthorized remote access services (Jahid, 2022)”

    From ADAPTING PLC/SCADA SYSTEMS TO MITIGATE INDUSTRIAL IOT CYBERSECURITY RISKS IN GLOBAL MANUFACTURING (2024) · cites this paper as Jahid, 2022

    The Data Synthesis and Analytical Approach section discusses cost, schedule, and financing risks in real estate projects, not industrial cyber intrusions. Searches for OT protocols, unauthorized remote access, and the claimed 60-percent figure found no passage addressing the claim; the cybersecurity-related reference entry does not establish it. (LLM, conf=0.76)

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Cited by

Sorted by citations

Authors
Chief Executive Officer (CEO), G. H. L. (. G., & Jahid, M. S. R.
Venue
Journal of Sustainable Development and Policy
Publisher
American Scholarly Publishing Group Inc
Published
2022-04-30
Volume
01
Issue
02
Pages
01-34
DOI
10.63125/nh269421
OpenAlex
W7084033432
Cited by
3
References
2
Citations by year
2026: 2 · 2025: 1
FWCI
0.15
Type
journal article
Language
en
Open access
Licence
cc-by
Topics
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Construction Project Management and Performance
  • Social Sciences › Economics, Econometrics and Finance › Finance › Capital Investment and Risk Analysis
  • Physical Sciences › Engineering › Architecture › Real estate and construction management
Keywords
quantitative risk assessment, Monte Carlo simulation, input distribution, dependence modeling, copulas, Latin hypercube sampling, quasi-Monte Carlo, sensitivity analysis, model validation, debt service coverage ratio, heavy-tailed distributions

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