Case Study: Mortgage Approval Time Study Problem Statement

Case Study 2mortgage Approval Time Studyproblem Statementa Major Finan

A major financial services company wishes to better understand its mortgage approval process. In particular, the company is interested in learning about the effects of credit history (good versus fair), the size of the mortgage ($500,000), and the region of the United States (western versus eastern) on the amount of time it takes to get a mortgage approved. The database of mortgages approved in the last year is accessed, and a random sample of five approved mortgages is selected for each of the eight combinations of the three variables. The data are shown in the table.

Mortgage Approval Time Study Credit History Mortgage Size Region Approval Times (Days) Good $500,000 Western Fair >$500,000 Western Good $500,000 Eastern Fair >$500,000 Eastern. Using this data, conduct a design of experiment (DOE) analysis in MS Excel to identify key factors affecting mortgage approval time. Determine the effects and interactions of credit history, mortgage size, and region on approval times. Select appropriate graphical tools, such as interaction effect charts, to visualize the results, and justify your choice.

Assess whether the sample size of five per combination is adequate for the analysis, and recommend an appropriate sample size for future studies to accurately measure the effects of each variable. Consider the circumstances in which larger samples are necessary and discuss what additional variables might influence mortgage approval times.

Finally, propose practical recommendations for the financial services company that could help to reduce mortgage approval duration based on your DOE findings. Support your analysis with credible references and relevant concepts from experimental design and process improvement.

Paper For Above instruction

The mortgage approval process is a critical component within financial services, directly impacting customer satisfaction, operational efficiency, and competitive advantage. Understanding the factors influencing approval times can enable institutions to streamline workflows, optimize resource allocation, and improve overall service quality. The present analysis leverages experimental design principles to identify the key drivers affecting mortgage approval durations, focusing on credit history, mortgage size, and geographic region. By applying structured statistical methods, the aim is to develop actionable insights that support process optimization.

Background research indicates that mortgage approval times are affected by multiple intertwined factors, including creditworthiness, loan amount, and regional procedural discrepancies. For example, credit history is a primary indicator of borrower risk; borrowers with good credit generally experience faster approvals due to fewer verification requirements. Mortgage size often correlates with complexity, with larger loans requiring more extensive documentation and appraisal processes. Regional differences may reflect variations in local regulatory environments, staff expertise, and operational policies. Understanding these variables' interplay offers a pathway to targeted improvements.

The specific objective of this study is to analyze how credit history, mortgage size, and geographic region influence approval times using experimental design techniques. This involves conducting a factorial experiment, with each factor at two levels, resulting in eight combinations. Data collection comprises a representative sample—five mortgages per combination—yielding data sufficient for preliminary analysis, but potentially limited in detecting subtle effects. Using Microsoft Excel, the data is organized into a factorial design matrix, enabling the calculation of main effects, interactions, and error estimates.

Applying the factorial analysis involves calculating the effects of each factor by comparing average approval times across levels, as well as examining two-way and three-way interactions. Results from these calculations indicate whether certain combinations significantly prolong the approval process. For example, the interaction between credit history and region may reveal regional variations in credit assessment speed. To visualize these findings, interaction effect plots are ideal because they facilitate understanding the interplay between factors, illustrating how approval times change across different variable levels.

Assessing the adequacy of the sample size involves considering statistical power and the ability to detect effects of practical significance. With only five observations per cell, the current sample provides initial insights yet may not fully capture variability—particularly for subtle interactions. Larger samples, such as 10-20 per cell, would enhance the reliability and generalizability of conclusions. Future studies should incorporate these larger samples, enabling more robust statistical testing, including analysis of variance (ANOVA) with higher confidence levels. Moreover, increased sample sizes facilitate subgroup analyses, such as differentiating by borrower demographics or loan purpose.

Additional variables worth exploring include borrower income levels, employment status, or even online application processing times. These factors could add further depth to understanding approval delays and help refine process improvements. Moreover, integrating data from different operational units could identify regional disparities, leading to targeted staff training or procedural standardization.

Based on the experimental outcomes, a key recommendation for reducing mortgage approval times involves streamlining credit assessment procedures for regions exhibiting prolonged approval durations. Implementing automated credit scoring systems, integrated with the loan origination platform, could expedite decision-making, especially for borrowers with good credit histories. Additionally, developing region-specific process protocols, aligned with local regulatory requirements, could minimize procedural delays. Regular monitoring of approval metrics, coupled with continuous process improvement initiatives like Lean or Six Sigma, could sustain efficiency gains.

In conclusion, employing a systematic experimental design approach enables financial institutions to identify and quantify the effects of critical variables on mortgage approval timeframes. These insights facilitate targeted interventions, such as automation and process reengineering, to improve operational performance. Such data-driven strategies are vital in a competitive environment where customer experience and operational agility determine long-term success.

References

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