Free Custom «Mortgages» Essay Paper

Free Custom «Mortgages» Essay Paper

Background

The mortgage industry is a key financial sector in the US. The mortgage market has witnessed numerous developments to attain its present state as the most complex, innovative, and the largest home-financing sector globally. Currently, the number of housing starts annually in the US is estimated to be 1.4 million and has been on an upward trend (Campbell, 2013). Despite being a core financial sector, the mortgage industry is facing a myriad of problems that require an urgent attention.

Research Questions

The research question for this study is:

What are the problems encountered within the mortgage sector?

Problem Statement

The health of the mortgage sector is crucial in terms of the economy of the US with respect to the employment and the Gross Domestic Product (GDP) contribution. However, the mortgage market in the US and across the globe has contracted significantly and is yet to recover fully, following the 2008 financial crisis (Barth, 2009). Property sales are still at low levels. Moreover, there is no adequate inventory in the property market. Moreover, people are skeptical about purchasing the property through credit, because of the lack of confidence in the labor market. All these factors are having a significant impact on property purchases (Campbell, 2013). Nevertheless, these are the factors outside the mortgage industry. Still, there are factors inherent to the industry that are presenting substantial challenges for the sector. It is imperative for mortgage providers to have an understanding of these factors for mortgage lenders to devise appropriate measures to respond to them. To this end, the purpose of this research is to explore the problems encountered in the mortgage sector.

Methodology

The methodological choice for this research is the qualitative method, which is characterized by undertaking a critical analysis of texts as well as reflecting on the theoretical themes instead of using numbers and statistics to present the findings of the research. The qualitative research is best suited for this research because of its explorative nature – the goal was to research various problems encountered in the mortgage sector. In addition, the methodology entailed performing a critical evaluation of findings from the existing researches and different forms of the literature. Thus, the qualitative secondary research entailed searching through the previous theoretical and empirical literature, after which a critical evaluation of these sources was performed.

The secondary data utilized in this research was obtained using an electronic literature search in citation indices and electronic databases focusing on the topic under the investigation. Some of the databases that were searched through included Google Scholar, JSTOR, SpringerLink, Ebscohost, and Emerald among others. The sources considered for the review were those circulated from 2005 onwards. The utilized search strategy entailed using various search terms such as “mortgage sector,” “issues and trends in the mortgage sector,” and “problems encountered in the mortgage sector.” The collected data was analyzed using the content analysis that focused on the identification of the recurring and dominant themes expressed in the secondary sources. The focus of the analysis was to delineate the challenges facing the mortgage industry.

Analysis and Discussion

The first problem in the mortgage sector cited by numerous authors relates to the information quality (IQ) issues (Wigand, Wood, & Yiliyasi, 2009). Mortgage lending is an industry that is information sensitive and utilizes both unstructured and structured data; therefore, IQ during the mortgage lending process is a crucial success factor for the mortgage sector. Many mortgage researchers and commentators emphasize the need for standardizing the mortgage lending system. This is because the mortgage sector comprises of a complex and vast system of interlinked mortgage brokers and banks collaborating to offer diverse loan products to businesses and residential home buyers. Therefore, the mortgage lending process right from its initiation to the finalization is a tedious one and presents the likelihood of IQ errors (Wigand, Wood, & Yiliyasi, 2009). An example of the most common IQ error is the entry of the mortgage loan applications averagely seven times between the stages of initial application and final approval. It is unfortunate that this is a necessity because of the lack of standards and software incompatibilities. Thus, IQ errors are inherent in the mortgage lending system and might be extremely difficult to recognize and rectify (Bernanke, 2009). Human errors in the mortgage lending process have been addressed by automating the process using eMortgage and automated underwriting; nevertheless, the automation is yet to be widely adopted in the sector because of the complexities associated with providing a credit to consumers deemed credit-worthy. Another example of IQ issues in the mortgage industry stems from the use of at least 3000 proprietary forms by recording companies, insurers, vendors, servicers, underwriters, evaluators, realtors, brokers and banks among others (Wigand, Wood, & Yiliyasi, 2009). These numerous forms result in the incompatibility and errors in terms of the transfer and data capture. Wigand, Wood, and Yiliyasi (2009) pointed out that the IQ problems encountered in the mortgage industry relate to accuracy, completeness, consistency, quantity of data, accessibility, and timeliness. Accuracy denotes the reliability and correctness of data. The inaccurate loan data can be deleterious to the lending process and lead to system-wide errors. Completeness refers to the degree, at which there is no missing data, and it is adequate. The mortgage sector cannot work properly in case of the absence of the complete loan data (Leece, 2008). Consistency entails presenting the loan data in the same format using common data formats; however, in the mortgage sector, different mortgage companies are utilizing different credit models for determining the credit worthiness. Timeliness denotes the degree, to which the data is updated (Campbell, 2013). The issue of timeliness is crucial in the mortgage sector, since customers can switch banks or stop pursuing the mortgage process as a whole, which subsequently translates to losses in sales.

 

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