Housing Real Estate Economics and Finance

Li, Rita Yi Man

doi:10.3390/jrfm15030121

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…Machine learning algorithms can analyze historical housing market data, predict future trends, and simulate various scenarios with higher accuracy, thereby providing more reliable insights for policymakers (Chen et al, 2020). AI-driven models can also optimize financial structures by assessing numerous variables and their interactions in real time, improving the decision-making process for investment and policy formulation (Li et al, 2022). Another significant development is the application of real-time data analytics to financial modeling.…”

Section: Future Directionsmentioning

confidence: 99%

“…Advanced financial modeling techniques can lead to more accurate predictions of project viability, better resource allocation, and improved investment strategies. By incorporating real-time data and AI-driven insights, policymakers can make more informed decisions and adapt to changing conditions more effectively (Li et al, 2022). Similarly, innovations in feasibility analysis, such as GIS and sustainability metrics, can help ensure that affordable housing projects are not only financially sound but also environmentally and socially beneficial (Elwood, 2020).…”

Section: Future Directionsmentioning

confidence: 99%

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Dynamic financial modeling and feasibility studies for affordable housing policies: A conceptual synthesis

Adetola Adewale Akinsulire,

Courage Idemudia,

Azubuike Chukwudi Okwandu

et al. 2024

Int. j. adv. economics

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The escalating demand for affordable housing necessitates innovative approaches to financial modeling and feasibility analysis to ensure the success and sustainability of housing policies. This abstract presents a conceptual synthesis of dynamic financial modeling and feasibility studies, exploring their pivotal role in shaping effective affordable housing policies. Dynamic financial modeling involves creating flexible and adaptive financial models that account for varying economic conditions, policy changes, and market fluctuations. These models are essential for predicting financial outcomes, assessing the viability of housing projects, and guiding investment decisions. By incorporating real-time data and scenario analysis, dynamic financial models help stakeholders understand potential risks and opportunities, enabling more informed decision-making. Key components of dynamic financial modeling include cash flow analysis, sensitivity testing, and scenario planning, which together provide a comprehensive view of a project's financial viability over time. Feasibility studies complement financial modeling by evaluating the practical aspects of affordable housing projects, such as site selection, regulatory compliance, and market demand. These studies assess the alignment of housing projects with community needs and regulatory requirements, ensuring that proposed developments are both feasible and beneficial. Feasibility studies often include market analysis, cost estimates, and risk assessments, providing a detailed understanding of project constraints and opportunities. The synthesis of dynamic financial modeling and feasibility studies offers a robust framework for evaluating affordable housing policies. It allows for the integration of financial projections with practical considerations, ensuring that policies are not only economically viable but also aligned with social and regulatory objectives. Case studies from various regions demonstrate the effectiveness of this approach in identifying successful strategies and addressing common challenges. In conclusion, dynamic financial modeling and feasibility studies are crucial tools for developing and implementing effective affordable housing policies. They provide a comprehensive understanding of financial and practical dimensions, facilitating informed decision-making and enhancing the sustainability and impact of affordable housing initiatives. This conceptual synthesis underscores the importance of integrating these methodologies to achieve long-term success in addressing housing affordability challenges. Keywords: Policies, Affordable Housing, Feasibility Studies, Dynamic Financial Modeling, conceptual Synthesis.

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Section: Future Directionsmentioning

confidence: 99%

Section: Future Directionsmentioning

confidence: 99%

Dynamic financial modeling and feasibility studies for affordable housing policies: A conceptual synthesis

Adetola Adewale Akinsulire,

Courage Idemudia,

Azubuike Chukwudi Okwandu

et al. 2024

Int. j. adv. economics

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Managers’ Lived Experience with Technology in the Mortgage Industry, During the COVID-19 Pandemic

Gerbersgagen,

Alexander SPATH I

2023

IJMSBA

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This research paper aimed to explore how managers describe their experiences of technology usage within the mortgage industry during the COVID-19 pandemic. The study used a purposive sampling technique with eight participants who were mortgage industry managers over the age of 25 and had at least five years of experience managing during and after the pandemic. Data collection involved 60-90 minute semi-structured interviews, and Leavitt’s Diamond, a system model developed by Harold Leavitt in 1965, was used to create the four central themes of Structure, Technology, Tasks, and People. The Giorgi five-step descriptive phenomenological approach was used for eidetic analysis, which allowed for a reduction of the main constituents and their connections. The study’s findings can contribute to mortgage management with technology and offer insight into how managers, structures, technologies, and tasks integrate to increase sustainability.

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Housing Real Estate Economics and Finance

Cited by 2 publications

References 12 publications

Dynamic financial modeling and feasibility studies for affordable housing policies: A conceptual synthesis

Dynamic financial modeling and feasibility studies for affordable housing policies: A conceptual synthesis

Managers’ Lived Experience with Technology in the Mortgage Industry, During the COVID-19 Pandemic

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