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A unified risk management framework for cost and resource optimization
in housing development projects
Tosin Samuel Oyetunji1, Fasasi Lanre Erinjogunola2, Rasheed O. Ajirotutu3,
Abiodun Benedict Adeyemi4, Tochi Chimaobi Ohakawa5, & Saliu Alani Adio6
1Independent Researcher, Richmond, Texas, USA
2Al Sarh Alqema Consultancy & Contracting, Doha, Qatar
3Vanderlande Industries, USA
4Independent Researcher, Lagos, Nigeria
5Independent Researcher, UK
6Khatib & Alami (Consolidated Engineering Co.), Doha, Qatar
Corresponding Author: Tosin Samuel Oyetunji
Corresponding Author Email: olutosin.project@gmail.com
Article Info
Volume No: 3 Issue No: 4 Page No: 985-997
Received: 25-11-24 Accepted: 30-01-25 Published: 08-04-25
DOI: 10.51594/gjabr.v3i4.130
DOI URL: https://doi.org/10.51594/gjabr.v3i4.130
___________________________________________________________________________
Abstract
The increasing demand for affordable housing has highlighted the need for more efficient
project management strategies, especially in the face of rising costs, resource constraints, and
regulatory complexities. This paper proposes a unified risk management framework designed
to optimize cost and resource allocation in housing development projects. The framework
integrates various risk management techniques, including risk identification, assessment, and
mitigation, with cost control methods and resource optimization strategies. It emphasizes a
dynamic, data-driven approach to evaluating and managing risks throughout the lifecycle of
housing projects, from planning and execution to monitoring and completion. By embedding
risk management practices into all project phases, this framework enhances project efficiency,
reduces the likelihood of cost overruns, and ensures timely delivery of housing projects. Real-
world case studies demonstrate the framework's potential to improve housing development
outcomes, though challenges such as data limitations, stakeholder alignment, and resistance to
change must be addressed for successful implementation. The paper also identifies promising
areas for future research, including the integration of AI and machine learning for predictive
risk assessment, real-time monitoring systems, and further exploration of strategies for large-
Gulf Journal of Advance Business Research
ISSN 3078-5294 (Online), ISSN 3078-5286 (Print)
FE Gulf Publishers
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scale housing developments. Overall, the unified risk management framework offers a holistic
solution to the persistent issues of cost and resource optimization in housing development.
Keywords: Risk Management, Housing Development, Cost Optimization, Resource
Allocation, Predictive Analytics, Project Management.
___________________________________________________________________________
INTRODUCTION
Context and Importance of Housing Development Projects
Housing development plays a pivotal role in shaping the socio-economic fabric of
communities, influencing economic growth, and providing shelter as a fundamental human
need. Particularly, affordable housing is critical to addressing the growing issue of
urbanization and the increasing population in cities worldwide (Onukwulu, Fiemotongha,
Igwe, & Ewim, 2023). In many regions, the gap between housing demand and supply
continues to widen, exacerbating issues such as overcrowding, rising rent prices, and slum
development. The successful execution of large-scale housing development projects is
essential in addressing these problems while ensuring that the housing provided is accessible,
livable, and sustainable (Abisoye et al.; Gil-Ozoudeh, Iwuanyanwu, Okwandu, & Ike).
However, housing development projects, especially on a large scale, often face significant
challenges that hinder their success. Resource constraints, such as limited land availability,
financing, and skilled labor, can greatly affect the project’s timeline and quality. Additionally,
cost overruns and delays are common problems, with projects frequently exceeding budgets
and timeframes, which impacts the affordability and availability of housing (Eyo-Udo et al.,
2024; Onukwulu, Agho, Eyo-Udo, Sule, & Azubuike, 2024b). These inefficiencies not only
strain financial resources but can also result in missed opportunities to provide housing for
those most in need. The urgency of addressing these challenges has made effective project
management and resource optimization paramount in housing development, especially in
times of economic uncertainty and rising demand for affordable housing (Afolabi &
Akinsooto, 2021; Igwe, Eyo-Udo, & Stephen, 2024b).
Given the global and regional housing challenges, the integration of a comprehensive and
structured approach to managing risks—particularly those related to cost and resource
optimization—becomes increasingly important. Without the proper strategies in place to
manage risks and optimize the use of resources, housing projects are at risk of failing to meet
their goals, which can lead to even larger social and economic problems (Paul, Ogugua, &
Eyo-Udo, 2024b).
Overview of Risk Management in Housing Projects
Risk management in housing projects is essential for navigating the complexities and
uncertainties that arise during their planning and execution. Housing development projects
often involve a multitude of stakeholders, including developers, contractors, governmental
agencies, and the communities who will inhabit the spaces. Each of these entities may
introduce various risks into the project that, if not properly mitigated, could cause disruptions.
These risks can be classified into different categories: financial risks, operational risks,
regulatory risks, and environmental risks (Adewoyin, 2022; Akhigbe, 2025).
Financial risks are among the most critical in housing development, as they directly affect the
project’s budget and overall viability. Fluctuations in construction costs, financing issues, and
changes in market demand can jeopardize the financial stability of the project. Operational
risks, on the other hand, involve issues like construction delays, resource shortages, and poor
project coordination, which can all lead to project delays or compromised quality (Olufemi-
Phillips, Ofodile, Toromade, Igwe, & Adewale, 2024).
Regulatory risks are related to non-compliance with local and international building codes,
zoning laws, and other regulations that govern the construction process. Failure to meet these
legal requirements can result in project halts, fines, or even project abandonment.
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Environmental risks involve issues such as unexpected site conditions, adverse weather, and
ecological concerns that can disrupt the construction process and escalate costs (Basiru,
Ejiofor, Onukwulu, & Attah, 2022; Daramola, Apeh, Basiru, Onukwulu, & Paul, 2025).
Given the multifaceted nature of these risks, a holistic and integrated approach to risk
management is crucial. This approach should identify and assess potential risks at every stage
of the project, from planning and design to construction and post-completion. By effectively
addressing these risks through preventive and corrective measures, housing developers can
reduce the likelihood of negative impacts on cost and resource allocation, ensuring a smoother
project execution process (Oluokun, Akinsooto, Ogundipe, & Ikemba, 2025; Onukwulu,
Fiemotongha, Igwe, & Ewin, 2024).
Objectives of the Paper
The primary objective of this paper is to develop a unified risk management framework that
focuses on the critical aspects of cost control and resource optimization in housing
development projects. This framework aims to identify and mitigate the various risks that can
affect the success of large-scale housing initiatives. By integrating risk management
techniques into each phase of a housing project, the framework seeks to improve decision-
making, enhance resource utilization, and prevent cost overruns and delays that are commonly
associated with such projects.
Additionally, the paper aims to explore how a unified risk management approach can lead to
better stakeholder coordination and more effective implementation of housing policies. The
integration of such a framework can provide valuable insights for developers, project
managers, and policymakers, helping them to optimize resource allocation, improve project
planning, and execute projects within their defined budgets and timelines. Ultimately, the
unified framework should contribute to the creation of more efficient, cost-effective, and
sustainable housing development projects that are capable of addressing the global housing
crisis.
Furthermore, the paper will explore the potential applications of the proposed framework
across different housing projects, from small-scale residential developments to large urban
housing initiatives. The potential benefits of this approach will be discussed, along with
recommendations for further research into the refinement and scaling of the model. By
offering a practical solution to managing the inherent risks in housing projects, this paper
aims to contribute to the body of knowledge on project management and provide a foundation
for future studies in housing development and risk management.
LITERATURE REVIEW
Existing Risk Management Frameworks in Housing Projects
Risk management in housing projects is a widely studied area, with various frameworks
proposed over the years to address the unique challenges faced during development.
Traditional risk management techniques have often focused on identifying, assessing, and
mitigating risks through structured processes such as risk registers, qualitative and
quantitative risk analysis, and contingency planning (J. O. Basiru, C. L. Ejiofor, E. C.
Onukwulu, & R. U. Attah, 2023c). These methods, while effective in certain contexts, tend to
be linear and reactive, offering limited flexibility in the face of dynamic project conditions.
The risk identification process usually begins at the project initiation phase and progresses
through planning, execution, and delivery. However, such traditional frameworks have been
criticized for their inability to adapt to the complexity and unpredictability of modern housing
projects, particularly those involving large-scale developments or a high number of
stakeholders (Abisoye & Akerele, 2022; Chisom Elizabeth Alozie, Olanrewaju Oluwaseun
Ajayi, Joshua Idowu Akerele, Eunice Kamau, & Teemu Myllynen).
In response to the limitations of traditional methods, more contemporary frameworks have
emerged that emphasize flexibility, continuous monitoring, and real-time risk assessment.
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These modern approaches incorporate new tools and technologies, including risk simulation
models, advanced software for risk tracking, and integrated communication platforms for real-
time decision-making (Oluokun, Akinsooto, Ogundipe, & Ikemba, 2024d). The incorporation
of these advanced methodologies has allowed housing developers to better anticipate risks
such as market fluctuations, labor shortages, and regulatory changes, with a more proactive
approach.
Furthermore, these frameworks have increasingly incorporated the principles of lean
management, systems thinking, and the integration of stakeholder perspectives into risk
management processes. This shift has been aimed at addressing the limitations of traditional
frameworks, which often overlooked the holistic nature of risk in housing projects (Chisom
Elizabeth Alozie, Olarewaju Oluwaseun Ajayi, Joshua Idowu Akerele, Eunice Kamau, &
Teemu Myllynen; Paul, Ogugua, & Eyo-Udo, 2024a).
Despite these advancements, existing frameworks still struggle with fully integrating various
types of risks—financial, operational, environmental, and social—into a cohesive, dynamic
model that adapts to the full range of challenges faced in housing development. The evolving
complexity of housing projects requires more comprehensive frameworks that can account for
both internal and external factors and facilitate real-time decision-making to optimize
outcomes (Egbuhuzor, Ajayi, Akhigbe, & Agbede, 2024).
Cost and Resource Optimization in Housing Projects
The literature on cost and resource optimization in housing projects is extensive, with
numerous studies exploring various strategies to minimize costs, reduce waste, and enhance
resource allocation. One of the key challenges in housing development is balancing the desire
for affordable housing with the escalating costs of land, materials, labor, and financing.
Effective cost management is essential to ensuring that projects remain within budget, meet
quality standards, and are completed on time (Egbuhuzor et al., 2025).
Resource optimization in housing projects typically focuses on improving the efficiency of
resource usage across various stages of development. This includes strategies for better
managing construction materials, reducing material wastage, improving labor productivity,
and utilizing machinery and equipment effectively. Previous research has demonstrated the
potential benefits of adopting lean construction techniques to streamline processes and reduce
inefficiencies. Lean principles, which originated in manufacturing, emphasize minimizing
waste—whether it is time, materials, or labor—by focusing on value creation and eliminating
non-value-adding activities (ADENIYI & ADELUGBA, 2024; Egbuhuzor, Ajayi, Akhigbe, &
Agbede, 2022).
Another important focus in the literature has been the role of technology in optimizing
resource use. The advent of digital tools such as Building Information Modeling (BIM),
advanced project management software, and automated scheduling tools has allowed
developers to model and track the allocation of resources in real-time, leading to better
planning and decision-making. These tools help project managers to optimize construction
timelines, minimize costs, and avoid delays caused by resource shortages or misallocation.
BIM, for example, enables precise calculations of material requirements, reducing excess
procurement and the risk of errors during construction (Ajayi, Akhigbe, Egbuhuzor, &
Agbede, 2022; Onukwulu, Fiemotongha, Igwe, & Ewim, 2022).
Moreover, studies have shown that effective cost management involves both short-term cost
reduction strategies and long-term investment in sustainable practices. Sustainable building
practices, such as energy-efficient designs and the use of environmentally friendly materials,
may initially increase project costs but have the potential to yield long-term savings in terms
of reduced energy consumption and maintenance costs. Therefore, a comprehensive cost and
resource optimization strategy in housing projects should consider both the immediate
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financial implications and the long-term economic benefits (Ajayi, Agbede, Akhigbe, &
Egbuhuzor, 2023; Fiemotongha, Igwe, Ewim, & Onukwulu, 2023b).
Gaps in Existing Literature and Research
While the literature on risk management and cost optimization in housing projects is rich,
there are several significant gaps that need to be addressed. One of the key shortcomings is the
lack of integration between cost control and resource management. Although both cost and
resource optimization are critical for the success of housing development, existing
frameworks often treat them as separate entities. This siloed approach can lead to
inefficiencies, as the allocation of resources may not be aligned with the project’s financial
constraints, and vice versa. There is a growing recognition in the literature that a more
integrated approach is necessary, one that considers cost, resource use, and risk management
as interconnected elements in a unified framework (Okeke, Alabi, Igwe, Ofodile, & Ewim,
2024a, 2024b).
Additionally, many of the existing frameworks remain static and rely on historical data, which
can quickly become outdated or fail to account for dynamic market conditions, regulatory
changes, or unforeseen disruptions. The need for more dynamic, data-driven approaches to
risk management and resource optimization is becoming increasingly evident. Real-time data
analytics, machine learning algorithms, and predictive modeling techniques are emerging as
promising tools to address these challenges. However, their application in housing projects
has been limited, and further research is needed to explore how these advanced technologies
can be integrated into existing frameworks (Ajayi et al., 2023).
Furthermore, while there is an emphasis on cost and resource optimization, there is a lack of
attention given to the human factors in housing projects, such as the skills and capabilities of
the project team, the role of collaboration among stakeholders, and the importance of
stakeholder engagement. These human elements are often critical in ensuring the successful
execution of housing projects but are frequently overlooked in the existing literature on cost
and resource optimization (Fiemotongha, Igwe, Ewim, & Onukwulu, 2023a).
Finally, the existing research on risk management in housing development has largely focused
on large-scale projects in developed countries, with limited attention given to the challenges
faced in developing regions. Housing projects in emerging economies often face different sets
of risks, such as political instability, informal markets, and inadequate infrastructure. More
research is needed to develop frameworks that are tailored to the specific risks and conditions
in these contexts, ensuring that housing projects are both sustainable and adaptable to local
conditions (Odio et al., 2021).
These gaps indicate that there is a need for more holistic, data-driven, and context-sensitive
approaches to risk management and resource optimization in housing development. Bridging
these gaps will not only improve project success rates but also ensure that affordable housing
projects meet the growing demand for shelter across diverse regions and socio-economic
contexts (Oluokun, Akinsooto, Ogundipe, & Ikemba, 2024c; Paul, Abbey, Onukwulu, Agho,
& Louis, 2021).
METHODOLOGY FOR DEVELOPING THE UNIFIED FRAMEWORK
Approach to Risk Identification and Assessment
The first step in developing a unified risk management framework for housing development
projects is the identification and assessment of potential risks. Risk identification is typically
conducted through a combination of qualitative and quantitative methods. The qualitative
approach includes brainstorming sessions, expert interviews, and workshops with
stakeholders such as project managers, contractors, and architects. This approach helps to
uncover risks that may not be immediately apparent from data alone, such as social, political,
and regulatory risks. Experts can offer insights into the operational challenges that might not
have been fully anticipated during the planning phase, such as labor strikes, material
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shortages, or unexpected regulatory changes (Achumie, Oyegbade, Igwe, Ofodile, &
Azubuike, 2022; Kokogho, Odio, Ogunsola, & Nwaozomudoh, 2024a).
On the quantitative side, historical data from previous housing projects serves as a valuable
source for risk assessment. For instance, financial records and past project outcomes provide
concrete information on cost overruns, delays, and resource misallocations. Statistical analysis
of these data points, including regression models or Monte Carlo simulations, can help project
managers estimate the probability and potential impact of similar risks in future projects. This
method allows for the calculation of risk exposure, which is essential for developing strategies
that effectively mitigate or control these risks. The combination of both qualitative and
quantitative approaches provides a holistic view of potential risks, ensuring that the
framework is comprehensive and adaptable to various project conditions (Adeniyi & Adeeko,
2024; Oyekunle, Adeniyi, & Adeeko, 2024).
Framework Development Process
The process of developing the unified risk management framework for housing development
projects involves several key steps. Initially, it is crucial to categorize and define the types of
risks that typically affect housing projects (Onukwulu, Agho, Eyo-Udo, Sule, & Azubuike,
2024a). These risks may be classified into several categories, including financial, operational,
environmental, regulatory, and social risks. Financial risks include issues such as cost
overruns, funding delays, and fluctuating market conditions, while operational risks may
involve delays in construction, labor shortages, or supply chain disruptions. Environmental
risks might include adverse weather conditions or environmental regulations, and regulatory
risks could involve changes in zoning laws, permits, or safety standards. Social risks refer to
the potential impacts of public perception, community opposition, or stakeholder
disengagement (Igwe, Eyo-Udo, & Stephen, 2024a).
Once the risk categories have been established, the next step is to integrate cost control
techniques and resource optimization tools into the framework. Cost control techniques such
as Earned Value Management (EVM) and budgeting techniques, alongside resource
optimization tools like resource leveling and critical path method analysis, help ensure that
risks are quantified and managed effectively. The framework also incorporates stakeholder
analysis to understand how different parties—ranging from project developers and contractors
to local communities and regulatory bodies—may influence the project. This multi-faceted
approach ensures that risk management is not limited to financial and operational concerns but
is inclusive of social and environmental considerations, which are essential for the long-term
success of housing projects (Eyieyien, Idemudia, Paul, & Ijomah, 2024b; Sule, Eyo-Udo,
Onukwulu, Agho, & Azubuike, 2024).
Data Sources and Tools Used
A critical component of developing the unified risk management framework is the collection
and analysis of relevant data. The data sources used for framework development primarily
include historical project data, financial records, resource consumption patterns, and real-time
project performance metrics. Historical data from previous housing projects is particularly
valuable, as it provides insight into common risk factors, project timelines, cost estimations,
and outcomes. Financial records, such as budget reports and cost breakdowns, offer detailed
information on how risks, such as unforeseen expenses or funding issues, have impacted past
projects (Agbede, Akhigbe, Ajayi, & Egbuhuzor; Olufemi-Phillips, Igwe, Ofodile, & Louis,
2024).
In addition to historical data, real-time project data collected from ongoing housing
developments can be used to refine risk assessments. This data includes real-time progress
reports, labor hours, material usage rates, and any operational bottlenecks encountered during
construction. The integration of this data into the framework allows for continuous monitoring
and real-time updates, ensuring that risks are mitigated as they arise rather than being
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addressed reactively (Abisoye & Akerele; J. O. Basiru, C. L. Ejiofor, E. C. Onukwulu, & R.
U. Attah, 2023b).
To process and analyze this data, a range of analytical tools and software are employed.
Project management software such as Microsoft Project or Primavera P6 is used for
scheduling and resource allocation, while data analytics platforms like R, Python, or
specialized risk management software (e.g., RiskWatch or Risk+), which use advanced
statistical methods and machine learning algorithms, enable more precise forecasting of risks
and their impacts on cost and resources. These tools facilitate the identification of patterns,
correlations, and trends that would be difficult to detect manually, thus providing more
accurate risk assessments and optimization recommendations. By leveraging a combination of
data sources and analytical tools, the framework is able to offer a dynamic, data-driven
approach to risk management in housing projects (J. O. Basiru, C. L. Ejiofor, E. C. Onukwulu,
& R. U. Attah, 2023a; Otokiti, Igwe, Ewim, Ibeh, & Sikhakhane-Nwokediegwu, 2022).
IMPLEMENTATION OF THE UNIFIED RISK MANAGEMENT FRAMEWORK
Application to Housing Projects
The unified risk management framework can be applied to various phases of housing
development projects, from the initial planning stages to project execution and monitoring.
During the planning phase, the framework is instrumental in identifying potential risks that
may affect the project’s scope, schedule, and budget. At this stage, the risk identification
process, as outlined in the framework, helps project managers identify both internal and
external risks, such as financial instability, regulatory changes, or environmental factors. The
development of a risk register based on these identified risks ensures that the project team can
allocate appropriate resources for mitigation measures (Afolabi & Akinsooto, 2023; Kokogho,
Odio, Ogunsola, & Nwaozomudoh, 2024b).
In the execution phase, the framework’s integration with cost control techniques and resource
optimization strategies becomes crucial. Tools such as earned value management (EVM) can
be used to track project performance in terms of time and cost. This enables real-time
assessment of project deviations and ensures that risks related to cost overruns and delays are
immediately addressed. Furthermore, the inclusion of resource optimization techniques, such
as resource leveling, ensures that resources—whether human, financial, or material—are
efficiently distributed across the project. This helps avoid bottlenecks and ensures that the
project progresses according to the timeline and budget (Ajayi, Agbede, Akhigbe, &
Egbuhuzor, 2024; Daramola, Apeh, Basiru, Onukwulu, & Paul, 2024; Umoga et al., 2024).
In the monitoring phase, the framework’s reliance on real-time data integration and
continuous risk assessment allows for ongoing risk evaluation. Project managers can use
advanced analytics to track any emerging risks or project deviations. By doing so, corrective
actions can be implemented promptly to avoid any escalation. The dynamic nature of the
framework ensures that risks are continuously assessed, providing an adaptive approach to
project management (J. O. Basiru, C. L. Ejiofor, E. C. Onukwulu, & R. Attah, 2023).
The application of the unified risk management framework can be illustrated through case
studies of housing development projects, both successful and those where the framework
could have improved outcomes. A key example is the development of affordable housing
projects in urban areas, where large-scale developments often face significant risks, such as
budget overruns and delays due to resource mismanagement or unforeseen environmental
challenges (Oluokun, Akinsooto, Ogundipe, & Ikemba, 2024a, 2024b).
For instance, the construction of affordable housing in major cities like New York or London
often deals with cost and time-related risks, particularly because of the high demand for labor,
building materials, and the complexity of urban construction. In a project such as this, the
unified risk management framework would allow stakeholders to identify early-stage financial
risks and resource allocation issues through historical data and predictive analytics. By
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implementing risk control strategies such as resource optimization and predictive scheduling,
the project could have avoided several delays or cost overruns, ensuring it stays on track and
within budget (Agho, Eyo-Udo, Onukwulu, Sule, & Azubuike, 2024; Eyieyien, Idemudia,
Paul, & Ijomah, 2024a).
A notable real-world application of a similar approach is seen in the Singapore Housing
Development Board (HDB), where project management frameworks are integrated with risk
management practices to minimize construction delays and budget issues. By utilizing
advanced data analytics and stakeholder collaboration, HDB has been able to deliver large-
scale public housing projects on time and within budget. This case exemplifies how a unified
risk management framework can be beneficial in achieving cost and resource optimization
goals (Adewoyin, 2021; Ajiga, Hamza, Eweje, Kokogho, & Odio).
Challenges and Barriers to Implementation
Despite the clear advantages of the unified risk management framework, several challenges
and barriers can hinder its successful implementation in real-world housing projects. One of
the primary challenges is resistance to change from stakeholders who are accustomed to
traditional risk management methods. The integration of new technologies and methodologies
requires training and adaptation, which can face pushback from senior management,
contractors, or local regulatory bodies who may be reluctant to adopt a new system
(Durojaiye, Ewim, & Igwe, 2024; Otokiti, Igwe, Ewim, & Ibeh, 2021).
Additionally, the successful implementation of the framework depends on the availability and
quality of data. Housing development projects often face challenges in gathering accurate and
comprehensive data, especially in resource-constrained environments. Insufficient or
inaccurate data can undermine the framework’s predictive analytics capabilities and lead to
suboptimal risk assessments (J. O. Basiru, L. Ejiofor, C. Onukwulu, & R. U. Attah, 2023;
EZEANOCHIE, AFOLABI, & AKINSOOTO, 2021).
Another barrier to implementation is the difficulty in aligning stakeholders’ interests and
ensuring clear communication across various project teams. Housing projects often involve
multiple parties, including government agencies, private developers, contractors, architects,
and the local community. Coordination between these stakeholders can be challenging,
particularly when risk management strategies differ across parties. Without proper alignment,
it becomes difficult to ensure that the framework is consistently applied across all stages of
the project. Finally, the complexity of integrating advanced risk management tools with
existing project management software can also pose a technical barrier. For instance, if the
tools require significant customization or data from disparate sources, project managers may
face difficulties in synchronizing these systems effectively. This may increase the overall
implementation time and cost (Ajiga, Hamza, Eweje, Kokogho, & Odio; Ezeanochie, Afolabi,
& Akinsooto, 2024). CONCLUSION AND FUTURE DIRECTIONS
This paper has outlined the critical importance of developing a unified risk management
framework for housing development projects, particularly in addressing the challenges of cost
and resource optimization. The framework, as presented, offers a comprehensive approach to
identifying, assessing, and mitigating the diverse risks inherent in housing projects, such as
financial, operational, and regulatory risks. By integrating these risk management techniques
with cost control measures and resource optimization strategies, the framework ensures that
projects can be executed efficiently, on time, and within budget.
A key takeaway is that risk management is not a standalone practice but must be embedded
throughout all stages of housing development, from planning and execution to monitoring and
closure. The framework’s dynamic, data-driven nature allows for real-time risk evaluation and
provides project managers with the tools needed to make informed decisions, ensuring that
risks do not negatively impact project outcomes. Furthermore, the integration of predictive
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analytics and resource optimization techniques significantly improves the likelihood of
achieving cost-effective and timely delivery of housing projects.
Additionally, the application of the unified framework in case studies highlights its potential
to enhance the execution of large-scale housing initiatives. However, successful
implementation hinges on overcoming challenges such as stakeholder resistance, data
limitations, and coordination difficulties. These insights demonstrate that while the framework
holds considerable promise, its adoption requires careful planning, effective communication,
and robust data infrastructure.
Given the evolving landscape of housing development and risk management, several areas
remain open for further exploration. One promising direction for future research is the
integration of Artificial Intelligence (AI) and machine learning algorithms into the risk
assessment process. These technologies could significantly enhance the predictive capabilities
of risk management frameworks, enabling more accurate identification of potential risks and
more effective mitigation strategies. AI-driven models could continuously analyze data from
past projects, monitor real-time conditions, and offer insights into emerging risks.
Another potential area for future research is the development of real-time monitoring systems
that integrate with the unified risk management framework. Such systems could leverage
Internet of Things (IoT) sensors and digital project management tools to provide continuous,
real-time data on project performance, resource usage, and environmental factors. This would
allow for quicker identification and response to issues, minimizing the chances of project
delays or cost overruns. Lastly, further exploration of risk mitigation strategies in large-scale
housing developments is essential. Research could focus on identifying best practices for
managing risks related to stakeholder coordination, environmental factors, and the impact of
regulatory changes. Developing new strategies to tackle these complex challenges could help
scale housing projects more effectively while minimizing adverse effects on cost and resource
use.
References
Abisoye, A., & Akerele, J. I. A High-Impact Data-Driven Decision-Making Model for
Integrating Cutting-Edge Cybersecurity Strategies into Public Policy, Governance,
and Organizational Frameworks.
Abisoye, A., & Akerele, J. I. (2022). A Scalable and Impactful Model for Harnessing Artificial
Intelligence and Cybersecurity to Revolutionize Workforce Development and
Empower Marginalized Youth.
Abisoye, A., Akerele, J. I., Odio, P. E., Collins, A., Babatunde, G. O., & Mustapha, S. D.
Using AI and Machine Learning to Predict and Mitigate Cybersecurity Risks in
Critical Infrastructure.
Achumie, G. O., Oyegbade, I. K., Igwe, A. N., Ofodile, O. C., & Azubuike, C. (2022). AI-
driven predictive analytics model for strategic business development and market
growth in competitive industries.
Adeniyi, B. C., & Adeeko, J. (2024). Industry 4.0 technology adoption and market scalability
of small and medium enterprises in Southwest, Nigeria. International Journal of
Business and Management Review, 12(4), 70-84.
Adeniyi, B. C., & Adelugba, I. A. (2024). Knowledge management adoption and enhancement
of competitive advantages of micro, small and medium enterprises in South-West,
Nigeria. Nigerian Journal of Management Studies, 25(2), 35-49.
Adewoyin, M. A. (2021). Developing frameworks for managing low-carbon energy
transitions: overcoming barriers to implementation in the oil and gas industry.
Adewoyin, M. A. (2022). Advances in risk-based inspection technologies: Mitigating asset
integrity challenges in aging oil and gas infrastructure.
Gulf Journal of Advance Business Research, Vol. 3, Issue 4, April 2025
994 | P a g e
Afolabi, S. O., & Akinsooto, O. (2021). Theoretical framework for dynamic mechanical
analysis in material selection for high-performance engineering applications. Noûs, 3.
Afolabi, S. O., & Akinsooto, O. (2023). Conceptual framework for mitigating cracking in
superalloy structures during wire arc additive manufacturing (WAAM). Available
from:
https://www.allmultidisciplinaryjournal.com/uploads/archives/20250123172459_MG
E-2025-1-190.1. pdf
Agbede, O. O., Akhigbe, E. E., Ajayi, A. J., & Egbuhuzor, N. S. Assessing economic risks and
returns of energy transitions with quantitative financial approaches.
Agho, M. O., Eyo-Udo, N. L., Onukwulu, E. C., Sule, A. K., & Azubuike, C. (2024). Digital
Twin Technology for Real-Time Monitoring of Energy Supply Chains. International
Journal of Research and Innovation in Applied Science, 9(12), 564-592.
Ajayi, A. J., Agbede, O. O., Akhigbe, E. E., & Egbuhuzor, N. S. (2024). Enhancing Public
Sector Productivity with AI-Powered SaaS in E-Governance Systems.
Ajiga, D. I., Hamza, O., Eweje, A., Kokogho, E., & Odio, P. E. (2024). Developing
Interdisciplinary Curriculum Models for Sustainability in Higher Education: A Focus
on Critical Thinking and Problem Solving.
Akhigbe, E. E. (2025). Advancing geothermal energy: A review of technological
developments and environmental impacts. Gulf Journal of Advance Business
Research.
Alozie, C. E., Ajayi, O. O., Akerele, J. I., Kamau, E., & Myllynen, T. (2024). The Role of
Automation in Site Reliability Engineering: Enhancing Efficiency and Reducing
Downtime in Cloud Operations.
Alozie, C. E., Ajayi, O. O., Akerele, J. I., Kamau, E., & Myllynen, T. (2024). Standardization
in Cloud Services: Ensuring Compliance and Supportability through Site Reliability
Engineering Practices.
Basiru, J. O., Ejiofor, C. L., Onukwulu, E. C., & Attah, R. (2023). Enhancing financial
reporting systems: A conceptual framework for integrating data analytics in business
decision-making. IRE Journals,[online], 7(4), 587-606.
Basiru, J. O., Ejiofor, C. L., Onukwulu, E. C., & Attah, R. U. (2022). Streamlining
procurement processes in engineering and construction companies: a comparative
analysis of best practices.
Basiru, J. O., Ejiofor, C. L., Onukwulu, E. C., & Attah, R. U. (2023a). Financial management
strategies in emerging markets: a review of theoretical models and practical
applications.
Basiru, J. O., Ejiofor, C. L., Onukwulu, E. C., & Attah, R. U. (2023b). The impact of contract
negotiations on supplier relationships: A review of key theories and frameworks for
organizational efficiency.
Basiru, J. O., Ejiofor, C. L., Onukwulu, E. C., & Attah, R. U. (2023c). Optimizing
administrative operations: A conceptual framework for strategic resource management
in corporate settings.
Basiru, J. O., Ejiofor, L., Onukwulu, C., & Attah, R. U. (2023). Corporate health and safety
protocols: A conceptual model for ensuring sustainability in global operations. Iconic
Research and Engineering Journals, 6(8), 324-343.
Daramola, O. M., Apeh, C. E., Basiru, J. O., Onukwulu, E. C., & Paul, P. O. (2024).
Environmental Law and Corporate Social Responsibility: Assessing the Impact of
Legal Frameworks on Circular Economy Practices.
Daramola, O. M., Apeh, C. E., Basiru, J. O., Onukwulu, E. C., & Paul, P. O. (2025).
Sustainable packaging operations: Balancing cost, functionality, and environmental
concerns.
Gulf Journal of Advance Business Research, Vol. 3, Issue 4, April 2025
995 | P a g e
Durojaiye, A. T., Ewim, C. P.-M., & Igwe, A. N. (2024). Designing a machine learning-based
lending model to enhance access to capital for small and medium enterprises.
Egbuhuzor, N. S., Ajayi, A. J., Akhigbe, E. E., & Agbede, O. O. (2024). Leveraging AI and
cloud solutions for energy efficiency in large-scale manufacturing.
Egbuhuzor, N. S., Ajayi, A. J., Akhigbe, E. E., Agbede, O. O., Ewim, C. P.-M., & Ajiga, D. I.
(2025). AI and data-driven insights: Transforming customer relationship management
(CRM) in financial services. Gulf Journal of Advance Business Research, 3(2), 483-
511.
Eyieyien, O. G., Idemudia, C., Paul, P. O., & Ijomah, T. I. (2024a). Effective stakeholder and
risk management strategies for large-scale international project success.
Eyieyien, O. G., Idemudia, C., Paul, P. O., & Ijomah, T. I. (2024b). The Impact of ICT
Projects on Community Development and Promoting Social Inclusion.
Eyo-Udo, N. L., Agho, M. O., Onukwulu, E. C., Sule, A. K., Azubuike, C., Nigeria, L., &
Nigeria, P. (2024). Advances in Blockchain Solutions for Secure and Efficient Cross-
Border Payment Systems. International Journal of Research and Innovation in
Applied Science, 9(12), 536-563.
Ezeanochie, C. C., Afolabi, S. O., & Akinsooto, O. (2021). A Conceptual Model for Industry
4.0 Integration to Drive Digital Transformation in Renewable Energy Manufacturing.
Ezeanochie, C. C., Afolabi, S. O., & Akinsooto, O. (2024). Designing a Framework to
Enhance Workforce Productivity Using Digital Gemba Audits and Signage Solutions.
Fiemotongha, J. E., Igwe, A. N., Ewim, C. P.-M., & Onukwulu, E. C. (2023a). Innovative
trading strategies for optimizing profitability and reducing risk in global oil and gas
markets. Journal of Advance Multidisciplinary Research, 2(1), 48-65.
Gil-Ozoudeh, I., Iwuanyanwu, O., Okwandu, A. C., & Ike, C. S. Water conservation strategies
in green buildings: Innovations and best practices.
Igwe, A. N., Eyo-Udo, N. L., & Stephen, A. (2024a). The Impact of Fourth Industrial
Revolution (4IR) Technologies on Food Pricing and Inflation.
Igwe, A. N., Eyo-Udo, N. L., & Stephen, A. (2024b). Synergizing AI and Blockchain to
Enhance Cost-Effectiveness and Sustainability in Food and FMCG Supply Chains.
Kokogho, E., Odio, P. E., Ogunsola, O. Y., & Nwaozomudoh, M. O. (2024a). AI-Powered
Economic Forecasting: Challenges and Opportunities in a Data-Driven World.
Kokogho, E., Odio, P. E., Ogunsola, O. Y., & Nwaozomudoh, M. O. (2024b). Conceptual
Analysis of Strategic Historical Perspectives: Informing Better Decision Making and
Planning for SMEs.
Odio, P. E., Kokogho, E., Olorunfemi, T. A., Nwaozomudoh, M. O., Adeniji, I. E., &
Sobowale, A. (2021). Innovative financial solutions: A conceptual framework for
expanding SME portfolios in Nigeria's banking sector. International Journal of
Multidisciplinary Research and Growth Evaluation, 2(1), 495-507.
Okeke, N. I., Alabi, O. A., Igwe, A. N., Ofodile, O. C., & Ewim, C. P.-M. (2024a). AI-driven
personalization framework for SMES: Revolutionizing customer engagement and
retention.
Okeke, N. I., Alabi, O. A., Igwe, A. N., Ofodile, O. C., & Ewim, C. P.-M. (2024b). AI in
customer feedback integration: A data-driven framework for enhancing business
strategy.
Olufemi-Phillips, A. Q., Igwe, A. N., Ofodile, O. C., & Louis, N. (2024). Analyzing economic
inflation's impact on food security and accessibility through econometric modeling.
International Journal of Green Economics.
Olufemi-Phillips, A. Q., Ofodile, O. C., Toromade, A. S., Igwe, A. N., & Adewale, T. T.
(2024). Strategies for adapting food supply chains to climate change using simulation
models. Strategies, 20(11), 1021-1040.
Gulf Journal of Advance Business Research, Vol. 3, Issue 4, April 2025
996 | P a g e
Oluokun, O. A., Akinsooto, O., Ogundipe, O. B., & Ikemba, S. (2024a). Energy efficiency in
mining operations: Policy and technological innovations.
Oluokun, O. A., Akinsooto, O., Ogundipe, O. B., & Ikemba, S. (2024b). Enhancing energy
efficiency in retail through policy-driven energy audits and conservation measures.
Oluokun, O. A., Akinsooto, O., Ogundipe, O. B., & Ikemba, S. (2024c). Integrating renewable
energy solutions in urban infrastructure: A policy framework for sustainable
development.
Oluokun, O. A., Akinsooto, O., Ogundipe, O. B., & Ikemba, S. (2024d). Leveraging Cloud
Computing and Big Data analytics for policy-driven energy optimization in smart
cities.
Oluokun, O. A., Akinsooto, O., Ogundipe, O. B., & Ikemba, S. (2025). Strategic policy
implementation for enhanced energy efficiency in commercial buildings through
Energy Performance Certificates (EPCS).
Onukwulu, E. C., Agho, M. O., Eyo-Udo, N. L., Sule, A. K., & Azubuike, C. (2024a).
Advances in automation and AI for enhancing supply chain productivity in oil and
gas. International Journal of Research and Innovation in Applied Science, 9(12), 654-
687.
Onukwulu, E. C., Agho, M. O., Eyo-Udo, N. L., Sule, A. K., & Azubuike, C. (2024b).
Advances in blockchain integration for transparent renewable energy supply chains.
International Journal of Research and Innovation in Applied Science, 9(12), 688-714.
Onukwulu, E. C., Fiemotongha, J. E., Igwe, A. N., & Ewim, C. P.-M. (2022). International
Journal of Management and Organizational Research.
Onukwulu, E. C., Fiemotongha, J. E., Igwe, A. N., & Ewim, C. P.-M. (2023). Transforming
supply chain logistics in oil and gas: best practices for optimizing efficiency and
reducing operational costs. Journal of Advance Multidisciplinary Research, 2(2), 59-
76.
Onukwulu, E. C., Fiemotongha, J. E., Igwe, A. N., & Ewin, C. P.-M. (2024). Strategic contract
negotiation in the oil and gas sector: approaches to securing high-value deals and
long-term partnerships. Journal of Advance Multidisciplinary Research, 3(2), 44-61.
Otokiti, B. O., Igwe, A. N., Ewim, C., Ibeh, A. I., & Sikhakhane-Nwokediegwu, Z. (2022). A
framework for developing resilient business models for Nigerian SMEs in response to
economic disruptions.
Otokiti, B. O., Igwe, A. N., Ewim, C. P.-M., & Ibeh, A. I. (2021). Developing a framework for
leveraging social media as a strategic tool for growth in Nigerian women
entrepreneurs. .
Oyekunle, O. B., Adeniyi, B. C., & Adeeko, J. D. (2024). The Influence of Marketing
Orientation on the Formation of Unique Value Propositions of Food Processing
Companies in Southwest, Nigeria. British Journal of Marketing Studies, 12(2), 27-42.
Paul, P. O., Abbey, A. B. N., Onukwulu, E. C., Agho, M. O., & Louis, N. (2021). Integrating
procurement strategies for infectious disease control: Best practices from global
programs. Prevention, 7, 9.
Paul, P. O., Ogugua, J. O., & Eyo-Udo, N. L. (2024a). Procurement in healthcare: Ensuring
efficiency and compliance in medical supplies and equipment management.
Paul, P. O., Ogugua, J. O., & Eyo-Udo, N. L. (2024b). Sustainable procurement practices:
Balancing compliance, ethics, and cost-effectiveness. International Journal of
Scientific Research Updates, 8(1), 027-036.
Sule, A. K., Eyo-Udo, N. L., Onukwulu, E. C., Agho, M. O., & Azubuike, C. (2024).
Implementing blockchain for secure and efficient cross-border payment systems.
International Journal of Research and Innovation in Applied Science, 9(12), 508-535.
Umoga, U. J., Sodiya, E. O., Ugwuanyi, E. D., Jacks, B. S., Lottu, O. A., Daraojimba, O. D.,
Gulf Journal of Advance Business Research, Vol. 3, Issue 4, April 2025
997 | P a g e
& Obaigbena, A. (2024). Exploring the potential of AI-driven optimization in
enhancing network performance and efficiency. Magna Scientia Advanced Research
and Reviews, 10(1), 368–378. In.
