Case Study 2: Mortgage Approval Time Study

Case Study 2 Mortgage Approval Time Studyread The Following Case Stud

Analyze the mortgage approval process by conducting a design of experiment (DOE) in Microsoft Excel using data on credit history, mortgage size, and region to determine their effects on approval times. Evaluate the sampling method, recommend appropriate sample sizes, identify other variables of interest, and propose improvements based on the analysis. Create a PowerPoint presentation summarizing your findings with at least 10 slides, including a title and references in APA format.

Paper For Above instruction

The mortgage approval process is a critical component of financial services, directly impacting customer satisfaction and operational efficiency. Understanding the key factors that influence approval times can help institutions streamline their processes, reduce delays, and enhance overall service quality. This paper presents an analytical approach to evaluating the effects of credit history, mortgage size, and regional differences on approval times using design of experiments (DOE), sampling assessment, and strategic recommendations.

Introduction

Mortgage approvals are inherently complex, influenced by various applicant and regional factors. To optimize this process, financial institutions must identify factors significantly affecting approval duration and develop strategies to address delays. The case study involves analyzing data from a sample of eight combinations of three variables: credit history (good vs. fair), mortgage size ($500,000), and region (western vs. eastern). The goal is to understand the magnitude and nature of these effects and recommend process improvements.

Design of Experiment (DOE) Analysis

The DOE approach allows for systematic assessment of the influence of multiple factors and their interactions on approval times. In this context, the data collected from five mortgages per combination (totaling 40 mortgage cases) serves as the basis for this analysis. Using Microsoft Excel, one can perform a factorial ANOVA to evaluate main effects and interactions among credit history, mortgage size, and region.

The primary steps include organizing the data in a structured format, coding categorical variables, and utilizing Excel’s Data Analysis Toolpak to conduct an ANOVA. This process will elucidate which variables significantly impact approval times and whether there are interaction effects that intensify or mitigate these impacts.

Graphical Display and Key Drivers

Interaction Effect Charts or Surface Plots are effective tools for visualizing the relationships and interactions between variables. An Interaction Effect Plot, for instance, displays approval times across levels of one variable grouped by levels of another, highlighting combined effects. Such visualization helps in pinpointing the key drivers—variables that notably influence approval times individually or through their interactions.

The rationale for choosing an interaction plot is its clarity in depicting how two variables jointly affect the response variable, aiding decision-makers in understanding complex relationships. For example, it can reveal if credit quality impacts approval times differently in eastern versus western regions or if larger mortgages tend to take longer in certain regions.

Sampling Method Assessment

The sample size of five mortgages per combination is relatively small, raising concerns about the statistical power of the analysis. Small samples increase variability and reduce confidence in the results, potentially obscuring true effects. A larger sample size would enhance the robustness of the findings, allowing for more precise estimates of effect sizes and interactions.

In circumstances where resource constraints exist, a larger sample—such as 15 to 30 mortgages per combination—would provide more reliable data, reduce sampling error, and improve the generalizability of conclusions. A bigger sample would also facilitate the detection of smaller effects and interactions that might be missed with limited data.

From a sampling perspective, a larger sample can help disentangle the effects of variables, account for variability in applicant profiles, and support modeling more complex relationships or additional variables of interest.

Additional Variables of Interest

Beyond credit history, mortgage size, and regional factors, other variables may influence approval times. These include:

• Loan-to-Value (LTV) Ratio: Higher LTV ratios may involve additional documentation or scrutiny, affecting approval duration.
• Income Verification Process: Variability in verifying borrower income can impact processing time.

Measuring these variables provides a more comprehensive understanding of the mortgage approval pipeline and potential bottlenecks.

Recommendations for Process Improvement

Based on the DOE analysis, the financial services company should focus on streamlining processes for applications with larger mortgage amounts and those with fair credit histories, especially in regions identified as having longer approval times. Implementing automated credit checks, standardized documentation requirements, and staff training could significantly reduce processing delays.

Furthermore, adopting a targeted approach to expedite applications with high LTV ratios or complex income verification might improve throughput without compromising due diligence. The company should also invest in increasing sample sizes for ongoing monitoring to continuously refine their understanding of factors affecting approval times.

Supporting literature emphasizes the importance of data-driven process improvements. For instance, Montgomery (2017) advocates for the use of factorial designs in process optimization, enabling organizations to identify and control critical variables. Additionally, customer-centric initiatives, such as transparent communication about processing times, can improve perceived service quality (Zeithaml et al., 2018).

Conclusion

The application of DOE in evaluating mortgage approval times offers clear insights into influential factors. Larger sample sizes, additional variables, and graphical visualization tools such as interaction plots enhance the understanding of complex relationships. By focusing on key drivers—such as mortgage size and credit quality—and streamlining procedures through technological and process innovations, the financial institution can effectively reduce approval times, leading to increased customer satisfaction and operational efficiency. Continuous data collection and analysis are essential for sustaining improvements and adapting to changing market conditions.

References

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