Impact of Mortgage Characteristics on Retail Mortgage Transaction Completion Time

Section 1: Foundation of the Study

In the mortgage profession, managers identify procedural problems to make

educated decisions to improve business processes (Agarwal, Ben-David, & Yao, 2017;

Bapat, 2015). Managerial decision-making, whether positive or negative, may influence a

firm’s profitability (Persson & Ryals, 2014). The goal of this study was to examine a

potential impact of mortgage loan purpose (MLP), mortgage loan type (MLT), and subject

property type (SPT) on average turnaround time (ATT) to complete a retail mortgage

transaction (RMT) from origination to funding.

Background of the Problem

The mortgage trade is a vast, sophisticated group of financial transactions that

deliver intangible services with a possibility of financial risk at every link of the value

chain (Muthusamy & Dass, 2014; Zhang & Tang, 2014). Mortgage loan originators (LOs)

provide consumers with retail mortgage products in the primary market, while private and

public investors supply capital to the finance and housing industries in the secondary

market (Ametefe, Devaney, & Marcato, 2016; Petersen, Waal, MukuddemPetersen, &

Mulaudzi, 2014; Roulac, 2014). Eckstein (2015) noted that understanding complex

mortgage investments requires a sophisticated knowledge of the extensive guidelines and

complicated regulations of the primary and secondary mortgage sectors. Hall (2015)

found that an LOs inefficient processes with complex mortgage procedures increase ATT

to complete an RMT. A better understanding of the impact of MLP, MLT, and SPT on ATT

may help managers to perfect the retail mortgage lending process.

The formation of the Consumer Financial Protection Bureau (CFPB) spurred

consumer expertise in mortgage products and guidelines while adding to the competitive

nature of the business for service quality and efficiency (Ambrose & Conklin, 2014;

Eckstein, 2015). Hall (2015) stated that a firm’s leaders would need quantifiable data and

research to refine a business or process. Eckstein (2015) also mentioned that stricter

mortgage regulations and credit standards cause competitors to rethink consumer

acquisition strategies and to revise mortgage-processing techniques. Examination of the

impact of MLP, MLT, and SPT on ATT may offer mortgage managers insight for retail

mortgage process improvements to reduce ATT, thus increasing efficiency. Furthermore,

these results may offer managers ways to reduce transaction costs, improve organizational

reputation, and hence increase profitability and intrinsic value.

The consolidation and loss of financial institutions, which resulted from the 2008-

2009 financial crisis, caused customer acquisition costs to increase (Harrison & Seiler,

2015). Abosag, Yen, and Barnes (2016) showed that LOs develop mortgage business

through proven marketing strategies to attract consumers to purchase or refinance

residential real estate. Carswell and Babiarz (2016) noted that the increase in overall

transaction costs and process turnaround times should alert managers to consider

functional relationships between consumers and employees. Customer acquisition

happens through networking with realtors and builders, and through direct consumer

advertising.

Problem Statement

Above-industry ATT to complete an RMT from origination to funding results in

revenue losses (Hall, 2015). On average, only 20% of mortgage applicants continue to

closing; however, mortgage origination costs represent 33% of the total closing costs of an

RMT (Ambrose & Conklin, 2014; Carswell & Babiarz, 2016). The general business

problem is that retail mortgage institutions incur escalating transaction costs as ATT

increases to complete an RMT (Bakare, 2016). The specific business problem is that some

mortgage managers do not know the impact of MLP, MLT, and SPT on ATT to complete

an RMT from origination to funding.

Purpose Statement

The purpose of this quantitative, causal-comparative study was to examine the

impact of MLP, MLT, and SPT on ATT. The independent variables were MLP, MLT, and

SPT. The dependent variable was ATT to complete an RMT from origination to funding.

The archival population data included a selected mortgage institution’s retail originations

data from the state of Florida. Mortgage managers may increase the firm’s intrinsic value

through more efficient ways to minimize ATT by reducing transaction costs and lessening

risks to complete an RMT. Therefore, the social change implication of this doctoral study

includes the potential increase in the firm’s intrinsic value for organizational stakeholders.

Nature of the Study

Dobbin and Ionan (2015) mentioned that a quantitative method, as I used in this

study, is valid when a researcher collects numerical data for hypotheses testing. A

quantitative method was suitable for this doctoral study because I collected numerical data

and tested the study hypotheses. Marshall and Rossman (2015) stated that a qualitative

research method is appropriate when a scholar seeks to explore a phenomenon through

participant interviews. The qualitative method would not have been conducive to my

research goals because I did not conduct interviews to explore a phenomenon. Finally,

researchers use the mixed method to explore phenomena and examine numerical data,

concurrently, through interviews and surveys (Stockman, 2015). Again, the mixedmethod

approach was not appropriate because I did not conduct interviews to research a

phenomenon.

Explicitly, the use of a causal-comparative design can indicate statistical

relationships between three categorical independent variables and one continuous

dependent variable (Huck, 2012). Dobbin and Ionan (2015) mentioned that other

quantitative designs indicate correlations between research variables. The objective of this

doctoral study was not to show a correlation but to examine a potential impact between

the independent variables on a dependent variable. Koskey and Stewart (2014) offered

that an exact experimental design requires four elements that include manipulation,

control, random assignment, and random selection. In this doctoral study, I did not show

any experimental manipulation, control, nor selection. Therefore, an appropriate design

for this doctoral research is a causal-comparative design.

Research Question and Hypotheses

Research Question: What is the impact of MLP, MLT, and SPT on ATT to

complete an RMT from origination to funding?

H0: MLP, MLT, and SPT do not have an impact on ATT to complete an RMT from

origination to funding.

H1: MLP, MLT, and SPT do have an impact on ATT to complete an RMT from

origination to funding.

Theoretical Framework

Previous scholars have used theoretical propositions as lenses to view a business

problem or phenomenon (Krahmer & Strausz, 2015; Lahav & Zimand-Sheiner, 2016). I

developed a proposition regarding a possible theoretical explanation for the impact of

MLP, MLT, and SPT on ATT in an RMT, which I justified using findings in extant

literature. Al-Bahrani and Su (2015) demonstrated that MLP is a significant variable to

determine mortgage pricing. Park (2016) proved that MLT is an influential variable to

assess risk performance between insured and uninsured MLTs in RMTs. Gallagher

(2016) verified SPT as a significant indicator of tax revenues in local governments. Feng,

Fan, and Yoon (2015) measured loan popularity and proved ATT is a critical variable in

the assessment of peer-to-peer (P2P) lending strategies for borrowers and lenders. The

review of concurrent and previous studies assisted me in evaluating potential impacts of

the developed research variables.

In this doctoral study, the independent variables may indicate an impact on the

dependent variable based on previous scholars’ research. Bhutta, Ringo, and Kelliher

(2016) examined MLP and MLT data retrieved from the Home Mortgage Disclosure Act

(HMDA aka Humda) database and reported that an increase in MLP and MLT data

indicated industry growth. McCormick and Calahan (2013) used SPT as the foundation

for a unique loan identifier in an RMT to track performance throughout primary and

secondary market transactions. Li and Goodman (2015) also mentioned that many indices

include MLP, MLT, and SPT as factors to investigate critical mortgage problems.

Therefore, drawing on existing literature, I proposed that a linear combination of MLP,

MLT, and SPT may predict ATT when using a selected institution’s RMT data.

Operational Definitions

Average turnaround time: The average time, in days, to complete the RMT process

from mortgage origination to funding (Gupta, Yadav, & Goyal, 2017).

Closing: Closing is the finalizing stage for an approved RMT in the process

(Barrutia & Espinosa, 2014).

Funding: Funding is the disbursement of funds that consummates an RMT (Papin

& Turinici, 2014).

Mortgage loan purpose: Consumers us a mortgage loan to purchase or refinance

real estate (Mason, Imerman, & Lee, 2014).

Mortgage loan type: Mortgage loan type is the type of insured or securitized loan

provided by retail mortgage companies (Rose, 2016). For the proposed study, loan types

include government-insured loans, such as Federal Housing Administration (FHA) and

Veterans Administration (VA) mortgages, and conventional loans, securitized by private

investors.

Mortgage process: LOs workflow for transaction processing retail mortgages

(Hall, 2015). For the proposed study, the mortgage process includes origination,

processing, underwriting, closing, and funding.

Origination: The consumer or LO initiate the mortgage process by completing a

mortgage application with the intent to proceed (Ambrose & Conklin, 2014).

Processing: The accumulation of all stipulated borrower, LO, title, insurance,

home association, taxes, inspection, and appraisal documents to support underwriter

approval (Hall, 2015).

Subject property type: Mortgage applications indicate the subject property, or the

type of real estate collateral, in an RMT (Thode & Levine, 2017). The SPT for the

proposed study is owner-occupied and non-owner-occupied property types.

Underwriting: The overall assessment of borrower and lender risks associated with

the approval or denial of an RMT (Bakare, 2016).

Assumptions, Limitations, and Delimitations

Scholars acquire knowledge and information from research to

help with developing expectations, boundaries, and omissions in a study (Marshall &

Rossman, 2015). A scholar demonstrates the range of intensity of the research by

identifying assumptions, limitations, and delimitations (Baumeister, 2013; Riffe,

Lacy, & Fico,

2014). In the following literature, I outline my assumptions and the study’s limitations and

omissions to provide an opening for future scholars to fill any research gaps.

Assumptions

An assumption is a non-testable claim that a researcher takes as true without

knowing if the statement is factual (Riffe et al., 2014). My first assumption was that the

independent variables reflected an accurate representation of loan characteristics within an

RMT. The second assumption was that the dependent variable was an appropriate measure

of efficiency in all mortgage institutions. The third assumption was that the selected data

was indicative of all mortgage company data compliant with HMDA and respective state

statutes. My final assumption was that there was consistency in the selected data across

the mortgage industry.

Limitations

Stockman (2015) mentioned that the shortcomings in research design and

methodology indicate the potential boundaries of a study. Specifically, in this study, the

limitations were the ex post facto study design and archival data. Koskey and Stewart

(2014) stated that a study might be limited owing to the researcher’s inability to generalize

the research findings and establish study validity. Reio (2016) identified a limitation to the

causal-comparative research design noting that it lacks randomization and control and

increases the chance of researcher bias. Cotteleer and Wan (2016) experienced limitations

in research design and methodology when using archival data to analyze and interpret a

phenomenon or to ascertain potential relationships between industry and research. Turiano

(2014) stated that the main limitation to archival data is the lack of reciprocity among

previous research studies for present scholars to use. In this doctoral study, I sought to

define the boundaries and minimize limitations by developing constraints such as

controlling data sets and limiting the range of the selected data.

Delimitations

Delimitations are the constraints or controls in research (Atencia, 2014; Wang J,

2015). In this doctoral study, I did not include retail mortgage data from outside the

United States. Similarly, I did not include retail mortgage data outside of Florida in the

procured study sample. Furthermore, I did not include retail home improvement mortgage

data. Amadi-Echendu and Pellissier (2014) indicated that personal employee data, such as

marital status and age, have minimal significance on turnaround times.

Therefore, I omitted personal employee data from this examination.

Significance of the Study

Findings from this study may extend mortgage knowledge in scholarly literature

and be used to improve retail mortgage processes. Mortgage managers may gain

additional perspectives for procedural improvements to increase efficiency, decrease

transaction costs, and diminish moral hazard. Bakare (2016) determined that significance

in scholarly research depends on the application of the research to a real-time business

environment. For bank owners, mortgage lenders, and managers, understanding the value,

contribution, and social change factors in this study may lead them to develop vital

procedural improvements in RMTs (Adeleye, 2015; Barrutia & Espinosa, 2014).

Mortgage managers may choose to apply the findings, whether significant or not, to

improve or modify overall loan processes while seeking to improve efficiency, reduce

transaction costs, and curb moral hazard.

Contribution to Business Practice

This study has four identifiable contributions to mortgage business practices to

help mortgage leaders and managers improve RMT processes and profits. Mortgage

managers who can pinpoint and attract applicants with the lowest acquisition costs tend to

profit the most (Van Rensselaer, Blackstone, Crabb, & Gordon, 2014). Adeleye

(2015), Bapat (2015), and Ertürk (2014) showed that theoretical research applied in

business practices has included significant findings, which have provided managers with

applicable results to contribute knowledge and innovate business practices. Hence, the

first benefit of this study’s findings is the reduction of RMT costs. A second benefit from

my findings is the potential for a more efficient mortgage process. A third benefit is that

mortgage managers may use the findings to better understand the impact relationships of

RMT characteristics have on ATT to achieve organizational goals. A final benefit of this

study is that it provides significant data and results to mortgage managers seeking to help

increase their firms’ intrinsic value. Therefore, my evaluation of MLP, MLT, SPT, and

ATT in this study may help mortgage leaders and managers reduce transaction costs,

improve RMT efficiencies, and increase their firms’ intrinsic value for stakeholders.

Implications for Social Change

This study’s positive social change implications include the potential to assist

mortgage stakeholders (customers, employees, managers, government agencies,

shareholders, and third-party vendors) with process improvements to boost industry

reputation. Since 2008, real estate, mortgage, and finance professionals have experienced

an increased level of consumer animosity while completing an RMT (Abosag et al., 2016;

Hall, 2015). Hall (2015) further stated that the mortgage process needs an overhaul to

improve industry reputation to decrease moral hazard and consumer animosity.

Understanding among mortgage leaders and managers regarding consumer and employee

experiences is vital to achieving a firm’s social objectives in the mortgage industry

(Bordo, 2014; Pelser, 2014). Markovitch and Willmott (2014) also mentioned that

improving the mortgage process may mitigate the moral hazard felt by consumers during

the 2008-2009 financial crisis. My analysis of the impact of MLP, MLT, and SPT on ATT

may assist managers in simplifying the loan process to curb consumer animosity and

moral hazard and improve industry reputation while increasing the firm’s intrinsic value.

A Review of the Professional and Academic Literature

A review of the academic and professional literature shows the various sources a

researcher utilized in a study, including peer-reviewed journal articles, government

sources, and seminal books (Baumeister, 2013). Cotteleer and Wan (2016) suggested that

literature reviews should adopt a phenomenon-driven approach as an alternative approach

to literature-driven reviews. Irrespective of the literature review approach, the content

analysis must be succinct and show synthesis between professional application, academic

research, and results (Larsen & How Bong, 2016). In the literature review, I offer a critical

analysis of the extant sources associated with the developed independent variables

supporting the refined theoretical proposition while including a critical analysis of the

extant sources related to the dependent variable.

Analyzing the literature and synthesizing the research indicated the following

topics, (a) theoretical framework; and (b) supportive theoretical reviews, complete with a

transitory synopsis of the literature. The following tables represent the frequencies of

peer-reviewed and non-peer-reviewed sources organized by year, percentages, and

sections. Table 1 represents the number and frequency of peer-reviewed and non-

peerreviewed sources by year. Table 2 indicates the number and frequency of literature

review sources by variables. Table 3 displays the number and frequency of unique sources

by literature review section. Table 4 shows the reference type by peer-reviewed and non-

peer-reviewed frequencies.

Table 1

Number and Frequency of Peer-Reviewed and Non-Peer-Reviewed Sources by Year

Source year

No. of sources

% of total

Non-PR

<2014

5.03%

2014

16.20%

2015

24.58%

2016

26.82%

2017

23.46%

2018

3.91%

Total sources

179

100.00%

170

Table 2

Number and Frequency of Literature Review Sources by Variable

Research variables

No. of sources

% of total

Non-PR

MLP - Independent variable 1

32.89%

MLT - Independent variable 2

26.32%

SPT - Independent variable 3

25.00%

ATT - Dependent variable

15.79%

Totals

100%

Table 3

Number and Frequency of Unique Sources by Literature Review Section

Literature review section

No. of sources

% of total

Non-PR

Search database & terms

3.61%

Theoretical framework

84.34%

Supportive theories

12.05%

Totals

100%

Table 4

Number and Frequency of Reference Type by Peer-Reviewed and Non-Peer-Reviewed

Reference type

Peer-reviewed

Non-PR

Totals

% of total

Book

1.12%

Conference

0.56%

Government

3.35%

Journal article

164

166

92.74%

Report

1.68%

Website

0.56%

Totals

170

179

100.00%

Search Databases and Terms

I used Google Scholar, EBSCO Host, ProQuest, Emerald Insight, SAGE Journals,

and Science Direct databases to gather materials for this literature review. The search

strategy included using multiple search windows, copy and pasting search terms into

different databases, using keyboard shortcuts, and brainstorming. An additional strategy

involved combining search terms and quoted phrases to narrow down sources. The

following were the significant terms and combined quoted phrases I used in searches:

mortgage operations, mortgage turnaround, average turnaround time, mortgage loan

types, mortgage loan purposes, subject property types, and theoretical propositions.

Additional search terms and quoted phrases included: loan originator, home loan,

mortgage process, mortgage originations, owner occupied, non-owner occupied, 1-4

units, multifamily, purchase, refinance, FHA, VA, and conventional mortgages. Other

search terms and quoted phrases included: theory of constraints, thinking process,

qualitative, quantitative, mixed methods, turnover, and experience.

Theoretical Framework

In this research, my goal was to show that MLP, MLT, and SPT have a significant

impact on the ATT to complete an RMT from origination to funding. Scholars have used

MLP, MLT, and SPT as independent or predictor variables in different studies that

determined significance on corresponding dependent variables (Al-Bahrani & Su, 2015;

Feng et al., 2015; Gallagher, 2016; Park K. A., 2016). Al-Bahrani and Su (2015) applied

ordinary least squares (OLS) in a quantile regression (N = 5,013) to predict the

significance of purchase MLPs on mortgage pricing. The regression results were

significant, (OLS = .1174; df = 33; p < .05). Park (2016) examined the risk performance of

insured MLTs relative to uninsured MLTs. Park used a propensity score matching

technique on observed credit risk scenarios (N = 2,325,945) that yielded notable results

that showed MLT as a significant predictor in the model, (t = 1.607; p = .01). Gallagher

(2016) examined municipalities and school district data (N = 5,280) from 1980 to 2010 to

correlate SPT and property taxes as a funding source for local governments. First-stage

regression results were significant for multifamily SPTs in suburban municipalities,

(F[3,388] = -.876; p ≤ .01; R2 = .42), meaning multifamily SPTs indirectly related to

funding for suburban municipalities. Feng et al. (2015) examined loan characteristics,

such as rate, amount, and period correlated to predict loan popularity, measured by

funding success rates, the number of bids, and funding times, among potential P2P

investors. Feng et al. observed transactions (N = 1,057) and verified, with statistical

significance, that loan period had an impact on funding time as a predictor in the model,

(t[3] = .866; p < .01). The research findings for the independent variables, MLP, MLT, and

SPT, may show mortgage managers a potential impact on the dependent variable, ATT. I

will further discuss the independent variables along with the dependent variable in the

remaining portion of the theoretical framework.

Mortgage Loan Purposes

A consumer uses a retail mortgage to purchase or refinance residential real estate

(Mason et al., 2014). There are similarities and differences in time-based procedures

between the two loan purposes, although completion times are almost identical (Bhutta et

al., 2016). A purchase MLP requires additional inspectors whereas a refinance MLP may

not require an appraiser (Wilcox, 2015). Comparing and contrasting MLP characteristics

may offer mortgage managers viable solutions to improve processes to reduce ATT. An in-

depth examination of MLP attributes’ may provide managers with the data regarding RMT

outcomes; however, it will help me to provide a succinct and critical analysis for the

inclusion of MLP as a study variable.

Purchase. The purpose of a primary loan is to facilitate a consumer’s procurement

of residential real estate (Agarwal, Chomsisengphet, & Zhang, 2017).

Quantitative and qualitative researchers have found significant relationships between

MLP characteristics and the impacts on RMT outcomes (Berg, 2015; Neuhauser, 2015;

Serrano-Cinca, Gutiérrez-Nieto, & López-Palacios, 2015). Berg’s (2015) regression

discontinuity analysis of MLP originations (N = 4,013) showed a 50% reduction in default

rates because of the involvement of a risk manager in an RMT approval process. The

regression results were significant, (OR = .313; p = .01; R2 = .08). Serrano-Cinca et al.

(2015) analyzed P2P lending applications (N = 24,449) with 14 loan purposes and found,

with statistical significance (p < .001), that the P2P borrower lending grade is an accurate

variable for default prediction for 10 of the 14 loan purposes. More interestingly and

specifically, a borrower’s home purchase did not have any significant impact on P2P

lending grades or mortgage defaults in the sampled data. Conversely, in a qualitative

review of post-2008 recession articles on financial crisis, Neuhauser (2015) observed that

the relaxed rating agency guidelines on MLP, particularly with subprime mortgage loans,

increased default rates in securitized RMTs. Collectively, previous research has shown

that MLP is an important and required research variable to assess default risk and loan

performance. A mortgage manager’s analysis of MLP characteristic impacts in an RMT

may lead to improved processes to reduce ATT and minimize transaction costs.

Scholars have found that purchase MLPs have a substantial role in estimating risk

and capacity metrics to improve efficiencies and reduce costs (Li & Goodman, 2015;

Serrano-Cinca et al., 2015; Sharpe & Sherlund, 2015). Li and Goodman (2015) stated and

measured probability factors of borrower default, and calculated credit accessibility and

risk, simultaneously. Li and Goodman’s risk index showed that government-insured

purchase MLPs increased by 48% after the 2008 housing crisis. Serrano-Cinca et al.

(2015) stated that loan purpose is a risk assessment factor associated with the probability

of loan defaults among all P2P lending grades, A through G. For the highest P2P lending

grade (A), chi-square test results were statistically significant, (χ2[6, N = 7,901] =

342.041; p = .01), whereas the chi-square test results for the lowest grade (G) were also

statistically significant (χ2[6, N = 55] = 42.218; p = .01). Sharpe and Sherlund (2015)

completed a regression analysis of HMDA data applications and originations between

2003 and 2014, to determine if mortgage employees, credit scores, and MLPs predicted

capacity utilization. Purchase MLPs (N = 4) per mortgage employee was a significant

predictor in the model, (t = .196; p < .001). An LOs efficient ATT, or capacity utilization,

of RMTs, is critical to delivering an intangible product in consumer lending; therefore,

managers’ capabilities to utilize the metrics to make decisions may assist them in meeting

company goals to optimize capacity, thus reducing ATT and RMT costs.

Process optimization to manage capacity is an organizational goal for mortgage

managers to increase mortgage-backed securities (MBSs) investments influencing market

activity (Downs & Shi, 2015; Teye, Teye, & Asiedu, 2015). Downs and Shi (2015)

conducted a difference-in-differences estimate of HMDA data between 2003 and 2010 to

compare pre-2008 and post-2008 securitization regulations on RMT denial rates between

various lender types. RMT denial rates influenced purchase MLP capacities in subprime

lending within bank holding companies (BHCs) and respective affiliates, unaffiliates, and

independent mortgage companies. Downs and Shi assessed the association, with statistical

significance, between lender type and MLP denial rates, where lender type, MBSs of

BHCs showed above average purchase MLP denial rates that resulted in chisquare test

results, (χ2[3, N = 6,364,421] = .48; p = .01). In the rural housing sector of

Ireland, Teye et al. (2015) surveyed two housing communities (N = 380) to explore the

systematic relationship between new housing finance and supply and demand capacities

in the Ghanaian mortgage market. The chi-square test results showed with statistical

significance, (χ2[4, N = 196] = .148; p = .05), that the relationship between purchase

MLPs and housing supply capacities signals strength in industry activities. A successful

mortgage manager’s ability to manage RMT capacity through purchase MLPs will require

minimization of ATT to maximize profitability on the sale of MBSs in the secondary

mortgage market. The significance between purchase MLPs and capacity management in

various mortgage systems has stalled efficient operations in multiple real estate sectors;

therefore, is a justified reason for me to include MLP as a study variable to predict a

potential impact on ATT in completed RMTs.

Researchers have presented additional justification for the inclusion of MLP as a

research variable and have shown various relationships and phenomena between purchase

MLPs and market activity (Bhutta et al., 2016; Curtis, 2014; Zou, 2016). Bhutta et al.

(2016) examined 2012 HMDA RMT data (N = 18,691,550) to analyze market activity by

lending institution segmented by MLP. The scholars calculated a 10% increase in

purchase MLPs from 2011 to 2012, which resulted in a 26% increase in total RMT

activity for the same period. This statistic is important as it indicated the first increase in

purchase MLPs since 2004. Curtis (2014) observed mortgage transactions (N = 31,971)

and created a state-specific index that showed which state foreclosure laws favored

lenders, and that predicted the impact of the laws to mortgage origination activity in prime

and subprime markets. Lender-favored state foreclosure law contained mixed results of

prime purchase MLP activity as a predictor variable being insignificant, (t[12]

= .48; p > .10; R2 = .361), whereas the scholar’s results also exhibited with statistical

significance, (t[12] = 1.90; p < .10; R2 = .431), subprime purchase MLP activity as a

predictor variable influenced LOs to originate in states with lender-favored foreclosure

laws. In contrast, Zou (2016) analyzed the challenges and changes that arose from the

evolution of Chinese housing operations through a combination of financial liberalization

regulations to increase affordable housing. Zou found that a location-specific mix of

financial liberalization regulations assisted borrowers with alternative purchase MLPs,

which contributed to a 10% increase in location-specific economic development.

Mortgage managers who can optimize purchase MLP activity may produce an increase in

overall market activity as indicated in the research above; therefore, improving ATT to

meet demand validates the inclusion of MLP as a study variable.

Financiers, private and public, moderate investing behaviors by evaluating market

activity, examining borrower creditworthiness, and utilizing available information (Kim,

Tunas, & Green, 2016; Prystav, 2016; Zhang & Tang, 2014). Zhang and Tang (2014)

showed government-sponsored enterprises (GSEs) investor performance through 15

different credit standards segmented by MLP among RMTs (N = 18,700,000) and

calculated the modification rates between 2000 and 2010. The GSE, Federal Home

Mortgage Loan Corporation (FHMLC aka Freddie Mac) had higher modification rates

eight out of 10 years compared to GSE, Federal National Mortgage Association (FNMA

aka Fannie Mae). Kim et al. (2016) stated that MLP is the second most significant factor

to predict loan delinquency but did not provide statistical findings to support the claim.

Prystav (2016) experimented with German business students (N = 432) to evaluate the

relationship between loan purpose, information availability, investor behaviors, and loan

project investment amount. The loan purpose results of the one-way ANOVA were

significant, (F[3,235.653] = 3.789; p < .01). The challenge is for investors to moderate

financing behaviors through market analysis, creditworthiness, and information

availability; therefore, analyzing MLPs to determine an impact on ATT in an RMT may

also provide mortgage investors with additional, yet critical information to moderate

investing behaviors.

Refinance. An alternative loan purpose for consumers in an RMT is to refinance

real estate to receive cash or reduce interest rates and maturity terms (Mason et al., 2014).

Researchers showed that refinance MLPs had a significant impact on origination capacity

(Bhutta et al., 2016; Downs & Shi, 2015; Sharpe & Sherlund, 2015). Sharpe and Sherlund

(2015) found an inverse relationship between refinance MLPs and interest rate

adjustments controlled by the LOs to manage refinancing demand. Downs and Shi (2015)

proved those capacity limitations were significant between 2003 and 2010 as the research

indicated LOs originated 11% less refinance MLPs. Bhutta et al. (2016) confirmed

Downs and Shi’s research through the analysis of purchase and refinanced MLPs of

HMDA data between 2004 and 2015, which indicated that LOs originated 14% less

refinance MLPs. An LOs ability to control refinance MLP capacity with interest rate

adjustments is more reason for the inclusion of MLP as a study variable to examine the

impact on ATT in an RMT.

Scholars also examined significant relationships between risk, default,

prepayment, and refinance MLPs, respectively (Bylander & Hamilton, 2015; Caplin,

Cororaton, & Tracy, 2015; Gibilaro & Mattarocci, 2016). Gibilaro and Mattarocci (2016)

completed a regression analysis of property market trends and bank risk exposure (N =

2,798) while controlling for type of bank and loan purpose. Loan purpose was a

significant predictor in the model, (t = .16; p = .05), as mortgage managers determined

default risk in an RMT. In the Cambodian microfinance industry, Bylander and Hamilton

(2015) examined socio-economic relationships (N = 11,662) between credit risk and

migration and used loan purpose as a control variable. The logistic regression results were

significant for 2 of 3 loan purposes, (OR = 1.39; p < .01) and (OR = 1.38; p < .01),

investment and consumption loan purposes, respectively. Among borrowers with Federal

Housing Administration (FHA) mortgages, Caplin et al. (2015) defined sustainable

homeownership as paying off a current loan by refinancing or held to maturity by default.

The prepayment (N = 188,789) and default (N = 191,393) results indicated statistical

significance for cash-out and no cash-out refinance MLPs. The scholars found statistically

significant findings for cash-out refinance MLPs was, (F[1,3] = 1.30; p = .01;

R2 = .024), whereas no cash-out refinance MLPs was, (F[1,3] = 1.44; p = .01; R2 = .023).

The prepayment result for cash-out refinance MLPs was (F[1,3] = 1.38; p = .01; R2 =

.019), whereas no cash-out refinance MLPs was (F[1,3] = .78; p = .01; R2 = .012).

Migration, sustainable homeownership, and capital necessities for different borrowers

proved that loan purpose, whether in retail banking or microfinance, significantly impacts

socio-economic consequences as well as transaction outcomes. Therefore, previous

researchers justified my decision for the inclusion of MLP as an independent study

variable.

Additional justification for the inclusion of the MLP variable was because of the

various relationships between decision-making, costs, competition, and refinance MLPs.

Ambrose and Conklin (2014) used the refinance MLP characteristic, cash-out loans (N =

269,573), as a predictor in an OLS regression to examine the relationship between

mortgage broker competition and costs. The scholars found MLP predictor variable results

statistically significant, (OLS = 443.00; df = 18; p = .01). Al-Bahrani and Su (2015) also

used OLS, although in a quantile regression to indicate the significance in refinance

MLPs, specifically cash-out loans (N = 5,013), on mortgage pricing. The results were

significant, (OLS = .3011; df = 33; p < .01). Agarwal, Ben-David et al. (2017) assessed

borrower decision-making and measured the net present value (NPV) of investing in one

percentage point to reduce the note rate and exiting a mortgage within a specified time.

Agarwal, Ben-David et al. used the Cox model hazard regression and yielded results, with

statistical significance, for refinance MLP types (N = 17,204,751), rate-term and cash-out,

(t[-46.84] = -.027; p = .01) and (t[-78.92] = -.045; p = .01), respectively. Refinance MLPs,

specifically cash-out, had a significant impact on mortgage pricing, costs, and

competition; therefore, may offer mortgage managers additional data in decision-making

processes and provides further justification for the inclusion of MLP as a variable in this

study.

Mortgage Loan Types

LOs sell bundles of RMTs by MLT to secure private or government-insured

investors in the secondary market to replenish primary market capital (Cao & Liu, 2016).

In this study, MLT characteristics in RMTs included conventional and government

purchase and refinance mortgages (Rose, 2016). Conventional, FHA, and VA mortgages

differ in lending institutions, guidelines, costs, and turnaround times (Harrison & Seiler,

2015). In this study, assessing similarities and differences between MLT characteristics in

RMTs may offer significant data for industry stakeholders to make effective decisions to

reduce costs, minimize turnaround and achieve socio-economic goals.

Conventional. A conventional mortgage is a residential, non-government real

estate loan provided by LOs to borrowers with a specific risk profile (Cao & Liu, 2016).

Previous quantitative and qualitative researchers analyzed primary and secondary data and

found various relationships between borrower decisions, MLT characteristics and RMT

purposes (Lang & Hurst, 2014; Park K. A., 2016; Rose, 2016). Park (2016) analyzed a

scenario after borrowers decided to default on conventional and government loans. Park’s

results indicated statistical significance for borrowers (N = 6,625) that decided to default

on conventional loans, (t[10] = 1.607; p = .01) compared to government loans. Rose

(2016) showed that MLT guidance regarding debt structure and utilization choices for

people with homeownership as an objective. Rose advised homeownership-seeking

decision-makers that effective debt management and utilization strategies will lead to

lower cost mortgages, such as conventional MLTs. Lang and Hurst (2014) assessed the

decision-making of state grant recipients (N = 35,161) through observation of MLT

selection in RMTs. Lang and Hurst proved with statistical significance, (t[16] = .096; p <

.01), (t[16] = .217, p < .01), (t[16] = .123; p < .01), for 2007, 2008, and 2009, respectively,

state grant recipients made correct decisions to choose conventional MLTs over

government MLTs as related to loan size and down payment options in RMTs. Mortgage

borrowers’ individual decisions influenced MLT choice in a purchase RMT; therefore,

including MLT as a variable to examine the impact on ATT may offer mortgage managers

significant data for profitability and efficiency metrics to make effective decisions to meet

organizational goals.

Prior researchers used MLT data to calculate industry performance metrics, such

as changes in the federal funds rate (FFR), regulation, and foreclosure rates (Bhutta et al.,

2016; Orzechowski, 2017; Shindelar, 2015). Bhutta et al. (2016) analyzed HMDA data

and showed that conventional MLT originations decreased by 14% between 2009 and

2015 because of the increased role of government in consumer lending. Orzechowski

(2017) estimated loan growth rates by MLTs as grouped into high and low capitalintensive

banks to determine significant changes in the FFR. The generalized least square (GLS)

regression of MLT to predict the changes in the FFR indicated with statistical significance

in both high (N = 1,008) and low (N = 1,440) capital intensive banks, (t = .095; p < .001)

and (t = -.102; p < .001), respectively. Shindelar (2015) showed that there was no

significance in the foreclosure rates among prime, conventional MLTs (4%) measured

against sub-prime conventional (15%) and FHA (4%) government loans. MLT as a

variable to explore industry performance metrics provided mortgage managers with

quantified performance data to make valuable decisions to minimize foreclosure rates,

improve regulations, and capitalize on FFR changes.

Mortgage consumers use credit availability, risk, and financial literacy

fundamentals as crucial decision variables for MLT choice in mortgage markets (Cox,

Brounen, & Neuteboom, 2015; Li & Goodman, 2015; Lucas, 2016). In the Dutch

mortgage market, loan types are amortizing, deferred amortization, and interest-only

loans. Cox et al. (2015) found that risk aversion and financial literacy decision variables

were only statistically significant in relationship, both indirect and direct, to interest-only

loan types, (t = -2.33; p = .05) and (t = 2.11; p = .05), respectively. In the U.S. market, Li

and Goodman (2015) measured the expected GSEs default risk versus origination quarter

between 1998 and 2013 that yielded a credit availability trend. The scholars used the trend

line to show that the GSEs parallel industry credit trends, reducing conventional MLT

credit accessibility during the 2008 financial crisis. Lucas (2016) grouped loan type data

between 1998 and 2010 and stated that by 2010, GSEs financed 63% of new home

purchase MLPs with conventional MLTs. Credit, risk, and financial literacy are essential

variables in borrowers’ decisions to choose loan type; therefore, verified the inclusion of

MLT as an independent variable to determine an impact on ATT to complete RMTs from

origination to funding.

Government. Government MLTs, for the purposes this study, included

FHAinsured and VA-guaranteed mortgages (Reiss, 2016). In previous quantitative and

qualitative research studies, scholars examined different relationships between

decisionmaking, sustainability, and government MLTs (Aksoy, Keiningham, Buoye, &

Ball,

2016; Caplin et al., 2015; Reiss, 2016). Reiss (2016) assessed the sustainability of the

FHA and the impact in the secondary mortgage market during the Great Recession. Reiss

stated that it benefits the FHA to ensure sustainable mortgages to limit default risk, which

leads to increased transaction costs. Conversely, Caplin et al. (2015) defined and analyzed

homeownership sustainability through FHA MLTs paid in full or refinanced to non-FHA

mortgages. Caplin et al. found that prepayment risk of FHA MLTs (N = 159,169) at a

loan-to-value (LTV) level of 80-84%, (t = .96; p = .05), are twice as likely than LTV level

of 100-104%, (t = .64; p = .01), to pay off FHA mortgages. Aksoy et al.

(2016) surveyed credit union members (N = 642) and applied the wallet allocation rule

(WAR) to members’ decisions to use a credit union or bank for a purchase MLP controlled

by seven loan types. Aksoy et al. revealed that most credit union members surveyed

(70%), used the WAR to choose a bank over a credit union regarding competition in value

and loan type offerings and yielded a 55% bank application rate. Borrowers that sought

funds obtained competitive government MLT offerings compared to conventional MLT

offerings in down payment and RMT costs. Therefore, my assertion is correct to include

MLT as an independent variable in this study to examine the impact on ATT in RMTs

between origination and funding.

Scholars further analyzed the lending decisions in primary and secondary markets

to determine MLT characteristics impact on credit supply and borrower decision-making

to attain company goals (Dettling & Hsu, 2017; Ivanov & Wang, 2017; Metawa, Hassan,

& Elhoseny, 2017). In the primary market at the state level, Dettling and Hsu (2017)

stated loan types (N = 3,555,612) are credit cards, auto loans, unsecured loans, and

mortgages and examined consumer offers received by census tract income. Dettling and

Hsu’s results, (t[3] = -.0349; p = .01), proved, with statistical significance, that changes in

the minimum wage rate influenced loan type decisions as income levels increased. Ivanov

and Wang (2017) defined loan types as revolving or term loans (N = 6,190) and found

with statistical significance, in an OLS regression, results, (OLS = .4016; p < .01), as

lender credit ratings declined, borrowers loan type options to access credit also declined.

Metawa et al. (2017) defined mortgage, personal, and auto loans as loan types. Metawa et

al. provided a genetic algorithm (GA) assisting mortgage managers to make effective

decisions based on loan types during periods of illiquidity as compared to other

algorithms, such as two-level partitioning (TLP) algorithm and the multi-objective

evolutionary algorithm (MODE-GL). The scholars’ results indicated that MLT was a

significant variable (M = .43, SD = .011) in GA lending decisions as compared to lending

decisions based on TLP and MODE-GL algorithms. The impact of MLT characteristics on

credit supply and borrower decision-making is noteworthy because mortgage managers

used MLT attributes that derived the information to make effective decisions to achieve

company objectives.

Further justification for MLT inclusion as a variable in this study is because in

prior studies scholars assessed MLT attributes and various RMT metrics (Akins, Li, Ng, &

Rusticus, 2016; Dodson, 2014; Ellie Mae, Inc, 2016). Akins et al. (2016) examined the

relationships (N = 21,454,463) between competition and loan application rejection,

controlling for MLT among other predictors. Akins et al. proved that MLT is a significant

predictor in the model, (t[17] = -.83; p =.01), where competition decreases, and rejections

increase. Dodson (2014) performed tests (N = 370) that assessed the comparative

advantages between large and small banks government MLT offerings in competition for

fixed-asset lending in rural areas. Borrower MLT selection and property equity were

significant indicators for large banks to lend in rural areas for fixed assets. The scholar’s

results were statistically significant, (t[13] = -.88; p = .10) and (t[13] = -.0375; p = .10),

respectively. In 2016, Ellie Mae analysts assessed the time to close mortgage loans in

days, segmented by MLT. The analysts found as of December 2016, the FHA averaged

one business day better than conventional lenders, and three days better than VA lenders.

The results indicated the FHA time to close was 49 days, 50 days for conventional, and 52

days for VA mortgages. The scholars above showed that MLT was a significant indicator

for RMT metrics; therefore, qualifying my decision to include MLT as an independent

variable to examine the impact on ATT in RMTs.

Subject Property Types

In RMTs, the SPT refers to housing units that are single family residences (SFRs),

both detached and attached, condominiums, and multifamily properties (DeLisle, 2015).

LOs classify owner-occupied SFRs and condominiums as one detached to four attached

residential housing units whereas non-owner occupied multifamily properties, such as

apartments, are greater than five units (McCormick & Calahan, 2013). Private and public

investors use specific guidelines, procedures, and valuation models for SPT attributes

(Bellotti, 2017). Mortgage managers’ assessments of SPT characteristics may help to

decide on process improvements to reduce ATT and increase intrinsic value for

stakeholders.

Owner-occupied. Much of residential real estate includes owner-occupied

properties appraised as detached SFRs to attached SFRs, such as a townhome or a

quadplex, and condominium (Thode & Levine, 2017). Quantitative and qualitative

researchers used SPT characteristics to examine various industry metrics, such as

liquidity, homeownership rates, and market shifts (Blau, Nguyen, & Whitby, 2015;

Glascock & Lu-Andrews, 2015; Grover & Grover, 2014). Glascock and Lu-Andrews

(2015) showed that size and liquidity have a significant impact on real estate investment

trusts (REITs) stock price behaviors in extreme stock market declines. The scholars

measured REITs size by market capitalization and REITs liquidity by turnover and bidask

spread. Glascock and Lu-Andrews controlled for SPTs and examined beta statistics of

REIT stocks (N = 5,652) before an extreme stock market decline to predict stock pricing

behaviors that affected abnormal returns. The scholars’ findings were statistically

significant. Glascock and Lu-Andrews found that liquid REITs holding residential SPTs

were an influential predictor in the pricing behavior model, (t[10] = 1.04; p = .01) and

(t[10] = -.6215; p = .01), turnover and bid-ask spread, respectively, to maximize abnormal

returns during extreme market declines. Blau et al. (2015) performed a multivariate

analysis that observed (N = 8,848) effects of the distribution of market liquidity between

REITs and non-REITs, which used seven SPTs as indicator variables of the REITs bid-ask

spread. The scholars’ results for residential SPTs as a predictor in the model showed

statistical significance on REITs liquidity distribution, (t[7] = -4.36; p = .01). In the

European Union (EU), Grover and Grover (2014) explored the phenomena of residential

property prices indices (RPPI) on integrated financial systems to develop an aggregation

of EU financial systems into a single index. Grover and Grover did not provide statistics

but stated that Central and Eastern EU members have higher owner occupancy rates

although a low mortgage usage rate, which indicated less cohesion among EU financial

systems that contributed to market declines. Although Grover and Grover showed that the

EU members had substantial homeownership rates, instability remained in respective

financial systems whereas Glascock and Lu-Andrews and Blau et al. indicated that REITs

with high liquidity, holding specific rather than diverse SPTs, capitalized on financial

instabilities during extreme market declines, maximizing abnormal returns. Maintaining

liquidity in the primary mortgage market from the secondary market allows for LOs to

take additional risks on SPTs to maximize homeownership rates and minimize default

rates.

Additional researchers showed that market declines accounted for defaulted

mortgages and assessed loan performance metrics controlling for SPT attributes

(Adelino, Gerardi, & Hartman-Glaser, 2016; Agarwal, Chomsisengphet, et al., 2017;

Brueckner, Calem, & Nakamura, 2016). Adelino et al. (2016) examined mortgages (N =

5,313,951) from Lender Processing Services (LPS) and Core Logic (CL) and assessed the

relationship between default and the time to sell mortgages to private investors,

controlling for SPT. The scholars’ results showed SPT as an indicator variable that did not

have any statistical significance on defaulted loans, (t[32] = -.0012; p = .01). Conversely,

Brueckner et al. (2016) used SPT attributes and created a model to predict default among

private (N = 401,017), public (N = 1,100,972), and portfolio (N = 151,058) investors in

securitized mortgages originated with alternative mortgage products (AMPs). Brueckner

et al. conducted a proportional hazard regression and verified, with statistical significance,

that owner-occupied residences defaulted on AMPs regardless of investor types, (t[44] =

.158; p = .001), (t[44] = .253; p = .001), (t[44] = .255; p = .001), respectively. Agarwal,

Chomsisengphet et al. (2017) examined delinquency rates among professionals in the

finance industry, controlling for SPT. Agarwal, Chomsisengphet et

al. found with statistical significance, (OR = .84; p = .01), that SPT was a noteworthy

indicator in the model that determined delinquency rates among financial professionals

that owned and resided at their primary residence. Even though Adelino et al. did not find

any significance in the results, Agarwal, Chomsisengphet et al. and Brueckner et al.

proved that SPT characteristics are significant predictors for default. Therefore, mortgage

managers may use SPT characteristics to determine an impact on ATT in an RMT, as

considered for this study.

Prior scholars showed that information and feedback influenced profitability and

value, respectively, among private and public investors (Freybote, Ziobrowski, &

Gallimore, 2014; Gokkaya, Highfield, Roskelley, & Steele, 2015; Ribeiro-Ferreira, 2016).

Ribeiro-Ferreira (2016) stated that the Australian government put profits before people

during the financial reform that led to exclusionary policies for potential homeowners. To

determine the association between profitability and SPT attributes, Gokkaya et al. (2015)

examined REITs with single and multiple property types (N = 126) between 1993 and

2007 and correlated information asymmetry impacting IPO returns between specific REIT

holdings. Gokkaya et al. found, with statistical significance, that IPO returns for REITs

holding a particular property type realized fewer returns than REITs holding multiple

property types at IPO. The ANOVA results were significant for single and multiple SPTs,

(F[2, 90] = 2.701; p = .01) and (F[2, 36] = 11.491; p = .01), respectively. Freybote et al.

(2014) created a controlled experiment of residential appraisers (N = 30) that determined a

relationship between feedback and appraisers’ SPT value judgments since Dodd-Frank

required LOs to use appraisal management companies (AMCs). Freybote et al. results,

(t[3] = .668; p = .05), proved that feedback had not influenced appraisers’ SPT value

judgments since the start of AMCs. Although RibeiroFerreira did not show any

significance with SPT valuations, which could have been from the small sample, Gokkaya

et al. and Freybote et al. provided noteworthy evidence of the relationships and

associations between SPT attributes and RMT metrics, such as valuations and

profitability. Therefore, the decision to include SPT as an independent research variable is

correct.

Non-owner occupied. Many homeowners purchase additional real estate in

various locations for use as a second home or an investment property (McCormick &

Calahan, 2013). Qualitative and quantitative scholars explored phenomenon and examined

stock prices to determine effects and impacts on the mortgage and real estate industries

(Bokhari & Geltner, 2016; Glascock & Lu-Andrews, 2015; Ribeiro-Ferreira,

2016). With a qualitative lens, Ribeiro-Ferreira (2016) analyzed the change in the

Australian residential real estate market through the evolution of the mortgage industry.

The scholar found that the evolution of the mortgage industry caused a decline in

owneroccupied properties and a corresponding rise in non-owner-occupied properties.

Glascock and Lu-Andrews (2015) created a model, controlling for REIT SPTs. Glascock

and LuAndrews examined beta metrics of REIT stocks before an extreme stock market

decline to predict stock pricing behaviors impacting abnormal returns on the day of (Day

0), the day after (Day 1), and up to three days (Days 1-3) after an extreme stock market

decline.

The scholars’ results indicated with statistical significance, (t[10] = -.8479; p = .01),

(t[10] = .9058; p = .01), and (t[10] = .4936; p = .01), respectively, that industrial/office

SPT is another influential predictor in the pricing behavior model. Bokhari and Geltner

(2016) used SPT characteristics and predicted the nature and magnitude of depreciation in

multifamily and commercial properties (N = 107,805) and the impact on valuations over

time, from an investment perspective. The scholars OLS regression predicted, with

statistical significance, (OLS = 50.46; p < .01) and (OLS = 52.99; p < .01), office and

retail SPTs, respectively, depreciated at a faster rate than industrial SPTs, (OLS = 73.75; p

< .01). Bokhari and Geltner’s results proved that office and retail SPTs required

redevelopment at least 21 years earlier, impacting depreciation values over time. Property

valuations affected by defaults impacted corporate profits; therefore, a review of default,

trends, and profitability associated with SPTs may assist managers to limit default,

minimize ATT, and reduce transaction costs.

In previous research, scholars used SPT characteristics as predictors to determine

default and profitability; and indicators to explore trends in laws and regulations (Aznar,

Sayeras, Rocafort, & Galiana, 2017; Griffin & Maturana, 2016; Sklar & Edwards, 2017).

Griffin and Maturana (2016) examined MBS loan data from 2002 to 2007 that determined

the effects of occupancy misreporting predicted delinquencies that resulted in MBS loan

losses. In a logit regression, the researchers evidenced, with statistical significance,

occupancy misreporting influenced delinquencies, (OR = 1.08; p < .01). In

Barcelona, Spain, Aznar et al. (2017) found that new non-owner-occupied property types

(N = 43), such as properties listed on Airbnb, impacted the profitability of area hotels. At

the p = .05 significance level, the scholars’ results exhibited ten hotels with negative

profitability, ten hotels showed positive profitability levels less than a 5% return, and 12

hotels stated profitability above a 10% return, an indication of variability of profitability

among hotels within one kilometer of an Airbnb. Conversely, Sklar and Edwards (2017)

explored Florida community deeds, covenants, and restrictions regarding non-residential

use of residential properties, such as Airbnb and vacation rentals by owner (VRBO). Sklar

and Edwards stated that local, state, and federal governments ruled for the community

association laws that disallow the use of residential properties for Airbnb and VRBO

businesses. The SPTs as indicators and predictors may help to assist mortgage managers

in making decisions on SPTs impact on ATT in an RMT; therefore, further justified for

inclusion as an independent variable in this study.

Average Turnaround Time

Mortgage managers use ATT as a critical industry metric to monitor the efficiency

of sales and operations employees who execute the retail mortgage process

(Markovitch & Willmott, 2014). The time begins with the borrower’s signature on the

intent to proceed document and ends with the disbursement of funds, typically measured

in days (McCormick & Calahan, 2013). The industry ATT is 50 days from mortgage

origination to funding (Bhutta et al., 2016). Analysis of the ATT metric in various

industries may offer mortgage managers meaningful results to maximize process

efficiencies, thus reducing costs, and increasing profitability and intrinsic value for

stakeholders.

Most of the previous research about ATT was outside the mortgage industry,

although scholars found that consumer preferences indicated that process efficiencies, or

efficient ATT, increases intrinsic value (Amadi-Echendu & Pellissier, 2014; Coletti,

Gosselin, & MacDonald, 2016; Lanzarini, Monte, Bariviera, & Santana, 2017).

Conducting interviews and distributing questionnaires, Amadi-Echendu and Pellissier

(2014) analyzed conveyancing transactions in South Africa and found that because of the

number of role players and regulations, buyers and sellers had similar research results that

concerned process impediments that hindered ATT in transactions. Amadi-Echendu and

Pellissier did not provide statistical data but provided a framework to reduce delays and

improve ATT in conveyancing transactions in South Africa. Coletti et al. (2016) discussed

the benefits of non-depository mortgage finance companies (MFCs) in the Canadian

mortgage market that delayed ATT. Coletti et al. stated that the benefits of technological

enhancements improved efficiency, which reduced ATT in Canadian mortgage processes

but did not provide statistical data for verification. In Ecuador, Lanzarini et al. (2017)

created a decision tree model and examined credit records to analyze time-consuming

processes in credit scoring applications, or ATT, and two previous models, C4.5 and

PART, that showed the same metrics with different attributes. Lanzarini et al. compared

the research credit model to two previous models, individually, and found that alpha

levels for the two previous models were lower than the research model method by α = .03

as well as model precision 2% more accurate. Technology, process optimization, and

improved credit assessment techniques improved credit granting processes thus reducing

ATT and increasing the firm’s intrinsic value for stakeholders.

Previous researchers examined the maritime industry, focusing on industry metrics, such

as occupancy rates and turnaround time of seaports in various locations that influenced

competition (Omoke, Diugwu, Nwaogbe, Ibe, & Ekpe, 2015; Santos, Mendes, & Soares,

2016; Schepler, Balev, Michel, & Sanlaville, 2017). In Portugal, Santos et al. (2016)

created a marginal cost pricing model for European maritime seaport throughput

analysis. In the dynamic model, Santos et al. measured throughput as the number of

containers processed per unit of time, or the ATT of seaport employees handling

containers. The scholars model specified increases in marginal costs, which increased

capacity and occupancy rates, increased ATT, thus reducing seaport efficiency. Schepler

et al. (2017) created an optimization model for competing European seaports based on

the scheduling of trucks, trains, and ships on port terminals that determined ATT, or port

efficiencies. The unique optimization model from Schepler et al. provided multi-terminal

and multi-modal container ports optimal configurations to reduce ATT and improve port

services to increase profitability and market share among competitors for maritime ports.

Omoke et al. (2015) investigated privatization effects of the Nigerian transportation

industry, specifically maritime performance, which used average berth occupancy and

ATT data. The scholars used non-parametric tests, specifically Mann-Whitney U and

Wilcoxon W tests, and rejected the null in both scenarios, meaning that privatization had

a positive and significant impact on average berth occupancy, (MWU = 6; WW = 72; .02

< p < .05), and ATT, (MWU = 5; WW = 33; .001 < p < .05), in Nigerian ports.

In addition, prior researchers showed that scheduling algorithms influenced ATT in

automated and digital queuing processes (Gupta et al., 2017; Tani & El-Amrani, 2017;

Wang, Huang, & Wang, 2016). Wang et al. (2016) devised a scheduling framework that

divides processes into four phases to reduce ATT in workflow systems. Wang et al. found

that the developed scheduling framework improved performance, or ATT, by over 20%.

Tani and El-Amrani (2017) showed the importance of developing efficient scheduling

algorithms that dealt with big data and cloud computing efficiency to complete tasks. Tani

and El-Amrani examined five algorithms and proved that ATT decreases, significantly,

with higher-order algorithms that are more complex and advanced to accomplish tasks

with swiftness. Moreover, Gupta et al. (2017) assessed scheduling algorithms between

several queues by developing a unique model. The scholars’ models stated that a

combination of prior scheduling algorithms could improve ATT in multilevel queuing

processes. Scheduling algorithms can transpose into the mortgage industry by

streamlining departmental processes and wait times to reduce ATT, thus increasing

profitability and value. The previous scholars’ research provided excellent evidence and

support for utilizing ATT as the dependent research variable.

Supportive Theoretical Reviews

The highest level of scholarly research requires that the researcher provide an

opposing or supporting view of a phenomenon to substantiate a literature analysis

(Baumeister, 2013). Also, a scholar’s critical analysis of peer-reviewed literature increases

research synthesis and construct identity (Larsen & How Bong, 2016). Moreover, the

development of literature reviews through a different lens further shows breadth and depth

to scholarly research (Stockman, 2015). I confirmed that the theoretical propositions of

the causal-comparative relationships between MLP, MLT, SPT, and the corresponding

impact on ATT through a supportive lens with two theories, the theory of constraints and

the thinking process theory.

Theory of Constraints

The objective for any organization is the principle of continuous improvement

through lean operations (Goldratt & Cox, 1984; Golmohammadi & Mansouri, 2015;

Taylor & Mead, 2015). Goldratt and Cox (1984) defined the theory of constraints (TOC)

as the development of manufacturing processes around the point of congestion to improve

throughput or ATT. As defined previously in this study, the RMT process begins with

origination, continues through processing, underwriting, closing, and concludes with

funding. Golmohammadi and Mansouri (2015) stated that managers created production

strategies based on the TOC. The complexity of the retail mortgage process includes

production phases that may require improvement centered around TOC. Identifying

constraints is a challenging goal, although Taylor and Mead (2015) mentioned that

undesired effects, or constraints, arise during competitive times along the process.

Therefore, application of TOC in the mortgage process may assist mortgage managers

with identifying and eliminating constraints that delay ATT and reduce costs to increase

profitability and intrinsic value for stakeholders.

Thinking Process

Previous scholars identified theoretical concepts that offered managers analytical

data to determine process optimization and improvements in various industries (Santos,

2013; Taylor & Asthana, 2016; Taylor, Hailey, & Parajuli, 2015). Fulfilling mortgage

consumer demand requires lean operations to deliver intangible services. Taylor, Hailey et

al. (2015) used Goldratt’s thinking process theory, which is identifying what to change,

knowing what to change it to, and how to implement the change, to help eliminate

constraints and improve lending processes in the rural Nepalese microfinance industry.

The current mortgage process is factory-like from the perspective of process fulfillment.

Santos (2013) stated that process perfection was the product of identifying process

inadequacies to re-prioritize competencies in real time. Taylor and Asthana (2016)

examined the U.S. electrical industry and applied the TOC and the thinking process to

inventory control problems to minimize cost and to meet delivery dates. Although

managers implement TOC and thinking process concepts in manufacturing, the principles

of lean production may convey into the consumer lending industry. Therefore, coupling

the thinking process concept with TOC in this study may allow for constraint

identification and throughput improvements in the mortgage process to reduce costs,

ATT, and to maximize profitability and intrinsic value.

Literature Synopsis

The potential results of this study may provide mortgage managers with credible

data to make valuable decisions regarding process optimization, cost reduction, and value

maximization. The purpose of this study was to analyze the impact of MLP, MLT, and

SPT on ATT to reduce customer wait times and transaction costs in an RMT. Moreover,

another purpose of this study was to improve the retail mortgage process to increase

profitability and intrinsic value for organizational stakeholders. Furthermore, this study

may offer scholars additional data to fill knowledge gaps in future studies.

Transition

The background of this study showed the procedural problems with the

consumers, employees, and management’s inabilities to complete an RMT in an efficient

time. Identification of the general and specific business problems identified the purpose of

the study, which led to the articulation of research questions and hypotheses from the

extensive literature review. Additionally, the first section of the proposal indicated the

importance of justifying theoretical propositions as a lens to view the business problem.

The appropriate terminology of the established operational definitions justified the

assumptions, limitations, and delimitations. Furthermore, Section 1 indicated the

importance of the study, which benefits mortgage institutions through business

contributions and social change.

In Section 2, I discussed my role as the researcher, the reason for the lack of

participants along with the procured archival research data. This section also included the

research methods and design chosen for this study as well as the determination of the

population and sampling, which will show the appropriate sample size from a target

population for the evaluated archival data records. Also, in Section 2, I indicated that the

data instruments and techniques for this study included data analysis and research

validation. The concluding section, Section 3, showed the findings and an applicable

presentation of the completed study into mortgage practices. Moreover, Section 3 showed

that the findings applied to professional practice while creating social action in the

mortgage industry. Additionally, in Section 3, I offered recommendations for action and

future studies. Furthermore, I concluded this study with a reflection on the process and

experience.

Section 2: The Project

In applied research, identifying the purpose of the study, the researcher’s role, the

participants, and the research information may improve academics and businesses.

Scholarly research information includes a method, design, instrumentation, data

collection, analysis, and validity, which provides the academic and business communities

with numerical data to make effective decisions based on statistical evaluation and

interpretation. In the following section, I present the methodology I used for the study and

describe the significance of the research.

Purpose Statement

The purpose of this quantitative, causal-comparative study was to examine the

impact of MLP, MLT, and SPT on ATT. The independent variables were MLP, MLT, and

SPT. The dependent variable was ATT to complete an RMT from origination to funding.

The archival population data included a selected mortgage institution’s retail originations

data from the state of Florida. Mortgage managers may increase the firm’s intrinsic value

through more efficient ways to minimize ATT by reducing transaction costs and lessening

risks to complete an RMT. Therefore, the social change implication of this doctoral study

includes the potential increase in the firm’s intrinsic value for

organizational stakeholders.

Role of the Researcher

A researcher’s role in a scholarly study depends on the selected research

methodology and design (Call-Cummings, 2017; Metcalf, 2016; Stockman, 2015). My

role as the researcher was to contact a targeted retail mortgage company in order to

procure, analyze, and interpret the archival data; in doing so, I ensured ethical compliance

under the purview of the Belmont Report. The Belmont Report contains the guidelines

and ethical principles for the protection of human subjects during behavioral and

biomedical research (The National Commission for the Protection of Human Subjects of

Biomedical and Behavioral Research, 1979). The Belmont Report consists of three

primary principles that indicate (a) respect for persons, (b) beneficence, and (c) justice

(The National Commission for the Protection of Human Subjects of Biomedical and

Behavioral Research, 1979). In what follows, I discuss these principles in detail as they

applied to this study.

Respect for Persons

In this study, I did not use human participants, thus eliminating the need for any

biomedical or behavioral tests as indicated in the Belmont Report (Clayton, Supiano,

Wilson, Lassche, & Latendresse, 2017; Guillemin et al., 2017). I collected and

incorporated primary archival data records into this study from a selected mortgage

institution for analysis and interpretation. Acemoglu, Johnson, Kermani, Kwak, and

Mitton (2016) stated that personal connections are biased towards a given side of an issue,

which reduces beneficial outcomes. As I procured the archival data to complete this study,

I showed respect and professionalism towards my professional partners within the

selected mortgage institution. Obeying the Belmont Report guidelines required meticulous

attention to the line between academic research and professional practice. While not using

human participants, abiding by the guidelines, with beneficence and justice, exudes

respect for people in academic and professional research.

Beneficence

Beneficence is the act of doing good or providing a benefit in research, which

leads to favorable societal outcomes (Borgia, 2013; Hales, 2016; McCann & Sweet,

2014). Hales (2016) showed that research requires an objective perspective, which

benefits academic and professional research because a subjective view indicates

researcher bias. Bradley, Gokkaya, and Liu (2017) gathered 28 years of employment

history from the financial analyst industry that indicated increased knowledge and

expertise benefits personal and professional development among employees and leaders in

an organization. Ertürk (2014) proved that managers gained tacit knowledge from a

hands-on approach throughout organizational processes benefits further scholarly research

to improve business procedures. The breadth and depth of this study provided me the

appropriate materials to complete this study commensurate with my education and

experience and to benefit academic research and professional practice.

Moreover, ethical consideration is mandatory in scholarly research; it must be

upheld and continued by all researchers, academic and professional (Borgia, 2013;

Clayton et al., 2017; Hales, 2016). Borgia (2013) stated that ethical governance in banking

requires a social report to maintain the organization’s success at implementing and

assessing corporate social responsibility activities. Also, Hales (2016) showed that a

researcher’s uses of the appropriate tests for proposed research would reduce intentional

and unintentional personal biases in cultural research. Therefore, I approached this study

with objectivity to minimize any researcher bias and maximize research benefits.

Justice

The concept of justice in research is synonymous with respect for people regarding

their equality and rationality (Call-Cummings, 2017; Hales, 2016; Lahav & Zimand-

Sheiner, 2016). Ertürk (2014) mentioned that managerial rationality supports the ability to

innovate processes from experience whereas Bradley et al. (2017) showed that the

employee equality gained over time benefited the organization with rational

decisionmakers. Rational and equitable working relationships develop over an employee’s

tenure in any industry (Acemoglu et al., 2016; McCann & Sweet, 2014). Even though I

sustained a career in the mortgage industry for over 25 years, no professional relationship

existed between the mortgage institution, the employees, or myself. However, I do have a

relationship with the topic because of my career in the industry. In Florida, I am a licensed

real estate agent and mortgage loan originator, and I actively work in the industry. I

previously worked at many large banks and owned my own mortgage business during my

professional tenure. Sustaining the justice principle of the Belmont Report required me to

draw from previous working experiences to ensure equitable benefits for all vested

stakeholders.

Participants

In this study, I did not include any human participants because I used recent

archival data from a selected retail mortgage company. Previous researchers stated that

successful research includes and excludes specific data determined by eligibility criteria

(Bailey, 2014; LaBonte & Kilpatrick, 2017; Reio, 2016). My criteria for data included in

this study was that it was archival data from a selected retail mortgage company doing

business in the state of Florida for at least 5 years. Excluding archival data from outside

the state of Florida allowed me to generalize the findings to a national scale. Additionally,

I did not include home improvement data in the research. Moreover, the mortgage

characteristics of the archival data were specific to MLP, MLT, SPT, application dates, and

funding dates. Furthermore, I excluded employee demographic information because of the

lack of human participants.

Previous scholars have justified the use of archival data in research to analyze,

predict, and interpret observable facts in research (Barraclough, af Wahlberg, Freeman,

Watson, & Watson, 2016; Gligor, Esmark, & Holcomb, 2015; Szabó, Müllerová,

Suchánková, & Kotačka, 2015). Acemoglu et al. (2016) used a relationship network to

obtain archival data to analyze unusual returns from firms that have employees nominated

for high public office positions. Similarly, to procure the raw archival data I contacted

decision-making, mortgage professionals, via networking, phone, and email, whom

originated and funded retail mortgages in the state of Florida.

Creating and sustaining effective working relationships requires a scholar to use a

robust professional network within the targeted industry, which is important to further

reinforce academic and professional research applications (de Sivatte, Gordon, Rojo, &

Olmos, 2015; Houghton, 2016; Malone & Issa, 2013). My strategy was to reach out to

affiliates in my professional network to obtain contact information of decision-makers in

retail mortgage companies to procure archival RMT data for analysis and interpretation.

Moreover, I planned and organized virtual meetings that developed and maintained a

working relationship with mortgage professionals in various departments, such as IT,

sales, and operations to complete this study.

Research Method and Design

There are three research methodologies used in conducting social scientific

studies: qualitative, quantitative, and mixed methods (Koskey & Stewart, 2014; Park &

Park, 2016; Runfola, Perna, Baraldi, & Gregori, 2017). Specific research methodologies

parallel scholars’ philosophical assumptions of worldviews. My selection of a specific

method resulted from careful consideration of all three methodologies.

Research Method

Scholars’ worldviews are the set of principles that influence their actions in

research (Cairney & St Denny, 2015; Cap, 2017; Shahadan & Oliver, 2016). Worldviews

derive from scholars’ research disciplines, mentorships, and previous research experiences

(Guo, 2015; Kaplan, 2015; Neuman & Guterman, 2016). The three worldviews that

influence researchers when choosing a methodology are pragmatic, constructivist, and

post-positivist.

Pragmatic. Pragmatic researchers will apply all sensible and realistic research

methods to analyze variable relationships and explore phenomena (Cap, 2017; Ralph,

Birks, & Chapman, 2015; Snelgrove, 2017). Scholars with commonsensical worldviews

will complete mixed methods research, which is the combination of qualitative and

quantitative methods (Cairney & St Denny, 2015; Hales, 2016; Shahadan & Oliver, 2016).

Further, pragmatic researchers implement all research approaches to solve theoretical

problems with rational principles (Shahadan & Oliver, 2016; Snelgrove, 2017; Stockman,

2015). The pragmatic worldview offers scholars the most advantageous methods to

analyze and interpret data (Hales, 2016; Koskey & Stewart, 2014; Stockman, 2015).

Because the formulation of my research questions and hypotheses were quantitative, I

omitted qualitative research scenarios, and thus also omitted the use of a mixed-methods

approach. Even though I approached the research study with practical knowledge from

education and experience, there were not any immeasurable data characterizations

involved in this research. Although a pragmatic worldview is essential to complete a

mixed-method research approach, it was not suitable because of the lack of qualitative

research methods for the research study.

Constructivist. Scholars with a constructivist worldview use subjectivity to

explore and assess phenomena in a qualitative approach (Jakobsen, 2016; Neuman &

Guterman, 2016; Raza, Murad, & Zakar, 2016). Constructivist researchers analyze and

interpret study participants’ views with qualitative research methods (Neuman &

Guterman, 2016; Ralph et al., 2015; Snelgrove, 2017). Researchers with constructivist

philosophies also work to understand study participants’ backgrounds to gain cultural and

historical perspectives (Jakobsen, 2016; Raza et al., 2016; Snelgrove, 2017). Researchers

who use a qualitative methodology to describe the accounts of participants’ views or

observations of a phenomenon articulated a conceptual basis for a study (Bailey, 2014;

Cairney & St Denny, 2015; Runfola et al., 2017). Again, I simplified the research study by

using quantitative archival data, so a constructivist approach was not suitable for this

research.

Post-positivist. Post-positivist researchers analyze data through quantitative

methods to predict relationships between variables and then use the assessments to test

projections stemming from the theories (Guo, 2015; Kaplan, 2015; Snelgrove, 2017).

Post-positivist researchers employ a deterministic worldview to resolve possible outcomes

and effects of research studies (Guo, 2015; Hales, 2016; Kaplan, 2015). Scholars will be

more inclined to select a quantitative method with a post-positivist emphasis in research

studies with testable theories (Bailey, 2014; Hales, 2016; Snelgrove, 2017). Therefore, I

drew upon post-positivist philosophical assumptions to shape the research technique for

this study.

In quantitative research, scholars created a hypothetical framework for data

acquisition from primary and secondary data sources, which requires a statistical lens to

formulate an analysis of the findings (Onen, 2016; Park & Park, 2016; Reio, 2016). An

appropriate quantitative evaluation technique is a factorial analysis of variance (ANOVA)

F test to measure three categorical independent variables and one continuous dependent

variable (Dobbin & Ionan, 2015; Koskey & Stewart, 2014; Onen, 2016). After

consideration of all three methods, I determined that a quantitative approach would be the

most effective method for this current study. I analyzed an impact relationship between

independent and dependent variables that yielded numerical results for statistical analysis

and interpretation. For the reasons above, application of the quantitative research

methodology to the study is reasonable and justified.

Research Design

Three quantitative designs in scholarly research include correlation, experimental,

and quasi-experimental designs (Dobbin & Ionan, 2015; Hales, 2016; Park & Park, 2016).

Researchers use quantitative designs to analyze scientific theories by investigating

relationships between independent and dependent variables (Al-Thani & Semmar, 2017;

Cotteleer & Wan, 2016; Guo, 2015). Moreover, scholars use quantitative research designs

to answer research questions by evaluating hypotheses with statistical procedures, which

yields numerical results to interpret and generalize to a larger population. I used a causal-

comparative design to analyze the impact of MLP, MLT, and SPT on ATT to complete an

RMT from origination to funding.

Correlational. Quantitative researchers use correlational designs to examine a

relationship between continuous independent and continuous dependent variables (Bosco,

Aguinis, Singh, Field, & Pierce, 2015; Dobbin & Ionan, 2015; Kaplan, 2015). Although a

correlational design helps researchers to relate variables, researchers are unable to imply

causality by using this design (Dobbin & Ionan, 2015; Guo, 2015; McCarthy, Whittaker,

Boyle, & Eyal, 2017). Thus, scholars will use regression analyses to analyze and interpret

the results to the general population (Guo, 2015; McCarthy et al., 2017; Park & Park,

2016). The primary objective of this study was to determine an impact of categorical

independent variables on a continuous dependent variable; therefore, a correlational

design was not suitable.

Experimental. Quantitative researchers use experimental designs to analyze a

cause and effect association between categorical independent variables and categorical

dependent variables (Al-Thani & Semmar, 2017; Guo, 2015; Marshall & Rossman, 2015).

In an experimental design, researchers randomly assign variables for manipulation to

make causal inferences (Koskey & Stewart, 2014; Marshall & Rossman, 2015; Park &

Park, 2016). Scholars use an experimental design to test variances with statistical

procedures such as ANOVA, analysis of covariance (ANCOVA), and multivariate analysis

of variance (MANOVA) to interpret and generalize study results (Guo, 2015; McCarthy et

al., 2017; Runfola et al., 2017). In this study, I sought to examine a causalcomparative

impact of independent variables on a dependent variable; thus, a cause and effect

relationship was not sufficient. Instead, I used an ANOVA to examine the impact of three

categorical independent variables on a continuous dependent variable.

Quasi-experimental. Quantitative researchers also assess causal relationships in

quasi-experimental designs, although there is a lack of randomization for study variables

(Cotteleer & Wan, 2016; Park & Park, 2016; Reio, 2016). Scholars may also influence

independent variables but have a lessened ability to make causal inferences (Guo, 2015;

Koskey & Stewart, 2014; McCarthy et al., 2017). A causal-comparative design provides

future scholars a pre-established relationship of variables for future research (Al-Thani &

Semmar, 2017; Park & Park, 2016; Riffe et al., 2014). Although scholars will use

ANOVAs, ANCOVAs, and MANOVAs in quasi-experimental research, a

quasiexperimental design was not suitable for this study.

Population and Sampling

Scholarly research requires a targeted sample from a population to obtain

necessary data for statistical analysis and interpretation (Gibson, 2017; Koskey & Stewart,

2014; Onen, 2016). Researchers segment quantitative data collection by demographics

and psychographics in surveys whereas with the use of archival data; appropriate

sampling methods could be randomized (Aziz & Hassan, 2017; Ciabuschi, Forsgren, &

Martín, 2017; Sage, Blalock, & Carpenter, 2017). A selected mortgage institution

provided the population with archival data from which to retrieve the sample for this

study.

Population

I used lender-provided data to select a sample from the population of originated

and funded retail mortgage data in the state of Florida within the previous 12 months of

this study. The procured archival data contained in the population included mortgage

characteristics such as loan purpose, loan type, and property type. Moreover, the primary

archival data included loan application dates and loan funding dates for me to compute the

difference in days between the dates, or ATT. Furthermore, I derived the research sample

from the population for analysis and interpretation.

Sampling

Researchers group sampling in two categories, probabilistic and non-probabilistic

methods, which include specific methods for each sampling category (Aziz & Hassan,

2017; Ciabuschi et al., 2017; Sage et al., 2017). I used a probabilistic, simple random

sampling approach in this study. The reason I included the research variables stems from

literature investigation and professional experience. Prior scholars showed that the

propositioned independent variables, MLP, MLT, and SPT, influenced various mortgage

industry outcomes, respectively (Al-Bahrani & Su, 2015; Gallagher, 2016; Park K. A.,

2016); therefore, these were necessary for inclusion as research variables. Moreover, the

independent research variables are necessary mortgage application inputs to determine

underwriting approvals and successful disbursements of funds in RMTs.

A probabilistic technique involves a random sampling procedure, whereas

nonprobabilistic is a specific sampling method (Dobbin & Ionan, 2015; Koskey &

Stewart, 2014; Turiano, 2014). As in all research, there are advantages and disadvantages

to a proposed sampling approach and technique. The strengths of a simple random,

probabilistic approach are that the random selection of the sample data from the target

population creates an equal chance to minimize data variabilities along with an unbiased

selection process (Aziz & Hassan, 2017; Ciabuschi et al., 2017; Sage et al., 2017). The

weaknesses of a simple random, probabilistic technique are the increased chances of

sampling errors, wider dispersion of the sample, and heterogeneity for a scholar to

generalize research findings (Aziz & Hassan, 2017; Ciabuschi et al., 2017; Sage et al.,

2017).

There are four broad probabilistic sampling techniques used by scholars in

quantitative research, which are simple random, stratified, systematic, and cluster

sampling. I selected a simple random sampling procedure because the independent

research variables in this study are not mutually exclusive. Researchers must define the

sampling technique in the proposed study to assist with data collection, analyses,

interpretation, and generalization (Çankaya, 2016; Khan M, 2016; Scealy & Welsh, 2017).

Scholars design research around a simple random sampling technique, giving each

population data point and an equal chance for selection to limit uncertainty, decrease

variability and simulate patterns (Endo, Watanabe, & Yamamoto, 2015; McCormick,

Jackson, Carr, & Meyer, 2015; Xu & Yan, 2017). Scholars use stratified sampling to

organize the population into mutually exclusive groups, then apply simple random

procedures to reduce dispersal among a sample of predicted and observed trials

(Bensadoun, Monod, Makowski, & Messean, 2016; Hu et al., 2016; Lafontaine, Sawada,

& Kristjansson, 2017). A mortgage loan originator can have owner-occupied, government-

refinance transactions or non-owner occupied, conventional-purchase transactions. Also,

my use of a simple random sampling technique was because the developed independent

research variables are not subsequent variables of the other; thus, eliminating a systematic

sampling method. Systematic sampling has a multitude of variations dating back to 1948,

although researchers use a systematic sampling procedure when the population sizes, N,

are a multiple of sample sizes, n, and intervals, k (Çankaya, 2016; Khan M., 2016; Khan,

Shabbir, & Gupta, 2015). In research, where variables occur naturally, scholars use a

cluster sampling procedure to select a sample from the target population groups (Firoozi,

Kazemi, & Jokar, 2017; Momeni, Mohammadreza, & Amini, 2017; Scealy & Welsh,

2017). Moreover, the justification for my selection of a simple random sampling

technique was because the developed independent research variables do not occur in

natural groups.

Evaluating the sample size requires computer applications to assess mathematical

relationships. MS Excel 2016, G*Power version 3.1.9.2, and Statistical Package for Social

Sciences (SPSS) version 24 are software packages used to analyze statistical significance

of a potential impact of independent variables on a dependent variable (Faul, Erdfelder,

Buchner, & Lang, 2009). Effect sizes and power analyses are essential to empirical study

scholars who seek relationships between categorical and continuous variables using

statistical tests (Bosco et al., 2015). Additionally, effect sizes and power analyses indicate

the magnitude of a relationship in an empirical study (Bosco et al., 2015). Therefore,

conducting a priori power analyses will identify the sample size range for an empirical

study. In this study, I conducted a 2 x 2 x 2 factorial ANOVA, as shown in a G*Power F-

test selection, applying a medium effect size f = .25, α = .05, and df = 1, indicating a

minimum sample size of 128 archival data records required to achieve a power analysis of

.80. Increasing the sample size to 296 will increase the power level to .99. Further

increasing the sample size to 410 will increase the power level to .999. Therefore, an

appropriate sample size for the proposed study will be between 128 and 410 archival data

records graphed in Figures 1 and 2, respectively.

Figure 1. Total range of archival data records between .80 and .99 power analyses.

Figure 2. Total range of archival data records between .99 and .999 power analyses.

Ethical Research

I sampled non-identifiable, archival data records from a selected mortgage

institution for analysis and interpretation. To maintain ethical standards and

professionalism, the Walden University Institutional Review Board reviewed and

approved the request for collected data (approval number 11-13-17-0426588) for this

study. Also, I sent an initial request to participate in the study to the company

decisionmaker (see Appendix A). Also, I needed the company’s decision-maker to

authorize an official letter of cooperation to begin data collection of archival data within

the firm (see Appendix B). Moreover, both parties will agree to a mutual confidentiality

agreement limited to the proposed study (see Appendix C). Furthermore, both parties will

consent to a mutual limited dataset user agreement limited to the scope of the current

research (see

Appendix D).

The selected mortgage institution, nor the employees received any compensation

for participation in this research study. I did not receive any compensation, nor incentives

limited to the facilitation of the developed research. Also, if the decision-maker decided to

withdraw the selected mortgage institution from the study, there are no penalties for doing

so. The requirement for withdrawal from the study is a written correspondence from the

decision-maker explaining the reason(s) for withdrawal. Also, upon confirmed withdrawal

from the study, I would appropriately dispose of all raw data, following all federal, state,

and local electronic disposal laws.

An affirmative response showed that the decision-maker acquiesced to my

professional request and agreed to participate in this research study. I will store the

archival data in an encrypted and password-protected storage drive for at least five years

by all research compliance regulations. DeSimone, Harms, and DeSimone (2015) stated

that data screening and storage in scholarly research requires techniques to minimize

weak data points. Although I took precaution with encryption and password protective

security measures, screening the archival data confirmed that the data did not contain any

private identifying information specific to any borrower in any retail mortgage

transaction. Furthermore, the appendices contain specific details regarding all consent,

compliance, and confidentiality forms for this study.

Data Collection Instruments

Researchers gathered data to correlate the significance of various data collection

methods, including archival data, to provide an analysis of the findings and to generalize

to the population (Barraclough et al., 2016; Ma, Zhang, Lin, & Li, 2017; Reio, 2016).

Upon gathering the data, scholars begin to screen, analyze, and organize the data for

outliers, anomalies, and incomplete data points (LaBonte & Kilpatrick, 2017; Sleeper et

al., 2017; Zhang et al., 2017). I did not require the use of standardized data collection

instruments such as surveys because the population of data is archival; therefore, once I

received the archival data, I processed the data to ensure that the requested variable

information was correct in the electronic source document. The independent variables,

MLP, MLT, and SPT, reflect a nominal scale of measurement with two levels per

independent variable. The dependent variable, ATT, is continuous with a ratio scale of

measurement.

The archival data consisted of mortgage characteristics such as loan purpose, loan

type, and property type, which defines the independent variables, respectively. The first

independent variable, loan purpose or MLP, consisted of two levels, purchase and

refinance mortgage transactions. The second independent variable, loan type or MLT,

consisted of two levels, conventional and government mortgage transactions. The final

independent variable, property type or SPT, consisted of two levels, owner-occupied and

non-owner-occupied mortgage transactions. Also, the archival data contained continuous

dependent variable information, such as application and funding dates, to determine ATTs

to complete RMTs from the selected random sample.

Using SPSS, I measured the nominal independent variables in a 0/1 scoring

system. The MLP variable, 0 = purchase, 1 = refinance. The MLT variable, 0 =

conventional, 1 = government. The SPT variable, 0 = owner-occupied, 1 = non-

owneroccupied. Moreover, I measured the continuous dependent variable, ATT, through

calculating the difference between the funding date and the application date to derive the

turnaround time, or ATT, to complete an RMT from origination to funding. Furthermore,

the raw archival data records may be provided upon request.

In prior research, many scholars collected data through different methods and

means to produce raw data for analysis and interpretation (LaBonte & Kilpatrick, 2017;

Ma et al., 2017; Sleeper et al., 2017). Previous researchers used technology to improve

data collection methods to enhance data screening and project efficiencies (Huang &

Savkin, 2017; Read, LaPolla, Tolea, Galvin, & Surkis, 2017; Zhang et al., 2017). A variety

of researchers in different industries used electronic data capture methods to provide

accurate and precise data analysis for reliable data interpretation (Huang & Savkin, 2017;

LaBonte & Kilpatrick, 2017; Zhang et al., 2017). In this study, I was the primary source

for data collection; although, I did retrieve the data set from the selected mortgage

institution, electronically.

Data Collection Techniques

Researchers use various data collection techniques, such as surveys, archival

databases, and structured observations, to acquire data from reliable and valid collection

instruments (Eisenhardt, Graebner, & Sonenshein, 2016; George, Haas, & Pentland, 2014;

Gibson, 2017). For this study, the decision to select archival data required careful thought

and scrutiny, to ensure accurate and precise data records for results interpretation and

generalization (Gibson, 2017; Houghton, 2016; LaBonte & Kilpatrick, 2017). My

decision to procure the archival data was to utilize professional affiliations, which

provided a benefit for me with minimal travel costs, maximum data availability, and

convenience. Surveys and observations may require additional levels of cost, security, and

privacy factors exceeding the scope of this study. The deficiency in the professional

affiliation method was the time needed to contact decision-makers of the selected firm and

secure a time to speak about the research and set up appropriate security and privacy

credentials to obtain the archival dataset.

Data Analysis

Research Question

What is the impact of MLP, MLT, and SPT on ATT to complete an RMT from

origination to funding?

Hypotheses

Null Hypothesis (H0). MLP, MLT, and SPT do not have an impact on ATT to

complete an RMT from origination to funding.

Alternative Hypothesis (H1). MLP, MLT, and SPT do have an impact on ATT to

complete an RMT from origination to funding.

For the completion of this study, I analyzed and interpreted the archival data

records through a statistical ANOVA F-test. A correlation design requires that both,

independent and dependent variables use scale measurements whereas a chi-square design

requires independent and dependent variables in nominal measurements, or levels

(Dobbin & Ionan, 2015; Huck, 2012; Park & Park, 2016). Independent-samples t-tests

mandates for a single independent variable, which have two or more levels on a nominal

scale of measurement whereas an ANOVA F-test better fits the statistical analyses for the

research study because of the multiple, two level, categorical independent variables, and

the scale, continuous, dependent variable.

Different data collection methods require different data scrubbing and screening

techniques although all approaches require security measures to maintain data integrity

(DeSimone et al., 2015; Lin, Shen, Chen, & Sehldon, 2017; Sleeper et al., 2017). I

collected the archival data from a lone source, thus allowing me to screen and clean the

data efficiently. Even though there was not an issue with missing data, there was a

minimal opportunity for missing data from data retrieval and through electronic transfer.

However, missing data was not as likely as it would have been with survey and

observation techniques because of the subjective variability of participants in the survey

and observation approaches (Eisenhardt et al., 2016; George et al., 2014; Slater,

Joksimović, Kovanovic, Baker, & Gasevic, 2017). In the case of missing archival data, I

contacted the appropriate company liaison to retrieve the missing information to complete

this study.

Previous scholars stated that ordinary factorial ANOVA assumptions include

random sampling, an adequate sample size, appropriate scales of measurement, and

regular data distribution (McCarthy et al., 2017; Serbic & Pincus, 2017; Tacikowski,

Freiburghaus, & Ehrsson, 2017). Researchers test hypotheses by utilizing various

statistical measures such as ANOVA F-tests and independent samples t-tests (McCarthy et

al., 2017; Serbic & Pincus, 2017; Tacikowski et al., 2017). I tested and assessed

assumptions in the evaluated data research through factorial design ANOVA to interpret

the findings and make recommendations for future research. Although I am utilizing a

probabilistic, simple random sampling method, which is an assumption violation,

bootstrapping the archival data may offer an indication of a normal distribution of data

within the acceptable sample size range shown in Figures 1 and 2, respectively.

Scholars who conduct empirical studies must consider descriptive and inferential

statistics to analyze, interpret, and generalize significant findings (van Schaik & Weston,

2016). The Delphi Method is a statistical procedure that allows a researcher to infer

results that will help solve business problems (Ertürk, 2014; van Schaik & Weston, 2016).

Furthermore, inferring results from selected categorical and continuous variable

relationships to require the appropriate data analysis instruments, such as SPSS, to prove

strong numerical relationships for analysis and interpretation (Larsen & How Bong, 2016;

van Schaik & Weston, 2016). Interpretation of the statistical results will use effect sizes

and confidence intervals to determine a significant impact of the independent variables on

the dependent variable.

Study Validity

In this study, I used a probabilistic, simple random sampling method to ensure the

external validity of the research. Also, the selection, uniqueness, and timing of data can

threaten external validity (Call-Cummings, 2017; Gibson, 2017; van Duijn & Post, 2014).

External validity determines the scholar’s opportunity for generalizing the findings to the

population (Gibson, 2017; van Duijn & Post, 2014; Wacker, Hershauer, Walsh, & Sheu,

2014). Moreover, a correlation design would threaten the external validity of the study;

thus, further justifying my decision for a causal-comparative design.

Scholars threaten internal validity in research by the data selection to make

inferences, experimental data manipulation, and testing procedures such as data collection

instruments (Cotteleer & Wan, 2016; Ma et al., 2017; Onen, 2016). I used archival data in

this study; therefore, mitigating any internal validity threats. Scholars can lessen internal

validity threats by creating control groups, developing equality among data points, and

increased data randomization (Call-Cummings, 2017; Onen, 2016; van Duijn & Post,

2014). Thus, I integrated the present archival data records required to minimize any

internal validity threats and to examine, interpret, and generalize the findings to a larger

population.

Statistical conclusion validity ensures that the inferences made regarding the

research questions, and models are correct (Huck, 2012; van Duijn & Post, 2014; Wacker

et al., 2014). Also, construct validity ensures that the variables are appropriate for the

proposed measurements and analysis (Call-Cummings, 2017; van Duijn & Post, 2014;

Wacker et al., 2014). The threats to statistical conclusion validity consist of the reliability

of the instrument, the assumptions made from the data, and the sample size

(CallCummings, 2017; Gibson, 2017; Huck, 2012). These three validity threats magnify

the Type I error rate, which causes the rejection of the null hypothesis when, in fact, the

null hypothesis is correct.

Seeking an acceptable coefficient value higher than 0.7, I conducted an internal

consistency reliability check against the sample size that showed the relative association

through Cronbach’s alpha (Huck, 2012; Slater et al., 2017; van Duijn & Post, 2014).

Moreover, the appropriate sample size will improve the validity of a proposed study

whereas a sample size below the optimal study requirements may limit or nullify the

findings causing improper inferences (Dobbin & Ionan, 2015; Huck, 2012; van Duijn &

Post, 2014). Furthermore, ensuring the use of an appropriate sample size, by conducting a

power analysis through the G*Power tool sufficed as evidence of an appropriate sample

size.

Summary and Transition

Above-industry average turnaround time (ATT) to complete an RMT from

origination to funding results in revenue losses. I grounded this study based on the

hypothesis that MLP, MLT, and SPT could have a significant impact on ATT to complete

an RMT from origination to funding. To test this hypothesis, I implemented a 2 x 2 x 2

factorial ANOVA to examine the impact of the categorical independent variables on the

continuous dependent variable. My role as the researcher was to contact and acquire a

targeted retail mortgage company followed up with procuring, analyzing, and interpreting

the archival data, and in doing so, ensure the ethical compliance under the purview of the

Belmont Report. I did not include any human participants because the data comprised

archival data records for the last 12 months from a selected retail mortgage company

operating in the state of Florida. Based on a sample size calculation, I selected, randomly,

between 128 and 410 archival records to complete the statistical analysis.

In Section 3, I introduce the findings of the statistical analysis and any

professional applications related to the mortgage industry. Also, the next section included

implications for social change along with my recommendations for action and future

studies. Furthermore, I concluded with a reflection on my experiences with the doctoral

process and completed the study.

Section 3: Application to Professional Practice and Implications for Change

Research Study Overview

The purpose of this quantitative, causal-comparative study was to examine the

impact of MLP, MLT, and SPT on ATT. The independent variables were MLP, MLT, and

SPT. The dependent variable was ATT to complete an RMT from origination to funding.

The null hypothesis was that MLP, MLT, and SPT did not have an impact on ATT to

complete an RMT from origination to funding. The alternative hypothesis was that MLP,

MLT, and SPT did have an impact on ATT to complete an RMT from origination to

funding. The results of the 2 x 2 x 2 ANOVA indicated non-significant results; I found no

main or interaction effects. Therefore, I failed to reject the null hypothesis that MLP, MLT,

and SPT do not have an impact on ATT to complete an RMT from origination to funding.

Presentation of the Findings

Descriptive Statistics

The research analysis included 146 archival data records from a selected mortgage

institution in the state of Florida. The analysis comprised three nominal variables and one

ratio variable. Table 5 shows the descriptive statistics for the study nominal variables.

Table 6 depicts the descriptive statistics for the doctoral study scale variable.

Table 5

Frequencies (f) & Percentages (%) by Nominal Level

Independent variable

Nominal level

Mortgage loan purpose

Purchase

137

93.8%

Refinance

6.2%

Total

146

100.0%

Mortgage loan type

Conventional

43.8%

Government

56.2%

Total

146

100.0%

Subject property type

Owner-occupied

136

93.2%

Non-owner-occupied

6.8%

Total

146

100.0%

Table 6

ATT Mean (M) and Standard Deviation (SD) by Factor Nominal Level

Independent variable

Nominal level

Mortgage loan purpose

Purchase

41.41

20.57

Refinance

45.78

20.64

Mortgage loan type

Conventional

39.28

17.99

Government

43.55

22.25

Subject property type

Owner-occupied

41.93

20.32

Non-owner-occupied

38.30

24.25

Assumptions Testing

The ANOVA assumptions tested normality and equality of variances; however, the

Levene’s test of equality of variances was not significant (F = .982; p = .43), showing the

assumption of equal variances was not in violation. The normality assumption as violated

is depicted in Figure 3.

Figure 3. Histogram of average turnaround time frequency

Inferential Statistics

The results of the 2 x 2 x 2 ANOVA indicated that there was no significant main

effect (F[5,140] = 0.42; p = .83); MLP, MLT, and SPT did not significantly impact ATT to

complete an RMT. There were no significant interaction effects found either. Table 7

depicts the results of the 2 x 2 x 2 ANOVA.

Table 7

2 X 2 X 2 ANOVA Results (N = 146)

Values could not be computed due to small cell sample size

Analysis of Findings

The theoretical framework I used in

this study was a proposition supported with

theories such as TOC and thinking process to

determine if MLP, MLT, and SPT impacted ATT during the completion of an RMT from

origination to funding. The theoretical proposition required me to develop constructs I

could use to determine the significance of any impacts between MLP, MLT, SPT, and ATT

in an RMT. The first independent variable, MLP, was not significant in this study,

although other researchers have determined it to be significant in relation to mortgage

pricing, denial rates, and loan performance (Al-Bahrani & Su, 2015; Downs & Shi, 2015;

Li & Goodman, 2015). Also, previous scholars have identified statistical significance

between purchase MLPs and regulations, risk, and capacity (Neuhauser, 2015; Serrano-

Variable

MLP

.135

.001

.714

MLT

.837

.006

.362

SPT

.016

.000

.900

MLP*MLT

.257

.002

.613

MLP*SPT

.063

.000

.802

MLT*SPTa

MLP*MLT*SPTa

Cinca et al., 2015; Sharpe & Sherlund, 2015). However, in this study, purchase MLPs did

not indicate statistical significance as an interaction effect with the dependent variable,

ATT. Moreover, previous scholars found that MLP and market activity relationships were

statistically significant (Bhutta et al., 2016; Curtis, 2014; Zou, 2016). Although in this

study, purchase MLPs indicated greater market activity than refinance MLPs, there was no

statistical significance to market activity regarding ATT.

Analysis from earlier researchers also showed that significant statistical

relationships existed between MLP and origination activity, costs, and decision-making

(Agarwal, Ben-David, et al., 2017; Al-Bahrani & Su, 2015; Downs & Shi, 2015). Downs

and Shi (2015) found a significant difference between purchase and refinance MLPs,

favoring purchase MLPs. My findings confirmed this statistic in that the selected

mortgage organization data showed a significant difference in nominal MLP levels (see

Table 5). Al-Bahrani and Su (2015) found that MLP influenced mortgage pricing at the

refinance level, while Agarwal, Ben-David et al. (2017) assessed the NPV of costs. Both

prior studies resulted in statistically significant findings among the relationships.

Understanding the relationship between MLP and RMT outcomes may allow mortgage

managers additional data to make improved decisions regarding capacity, risk, and

regulation while working towards continuous process improvements.

Prior researchers have evaluated MLT (the second independent variable in this

study) and various RMT characteristics and have found statistical significance in their

respective findings (Lang & Hurst, 2014; Park K. A., 2016; Rose, 2016). The statistical

significance in the earlier scholars’ research showed that managers’ decisions pertaining to

loan type offered effective debt management strategies and lower costs when borrowers

chose conventional MLTs rather than government MLTs. This borrower decision increased

risk and pricing of government MLTs for down payments less than 20%. In this study,

MLT, on either nominal level, did not indicate any statistical significance in regard to

completing an RMT from origination to funding. However, the frequencies of MLT

nominal levels were the closest among the three independent variables with government

MLTs greater than conventional MLTs (see Table 5). My study data supports the MLT

frequency statistic because the higher percentage of government MLTs indicates the

inability of potential mortgage consumers to realize a 20% down payment as needed for

conventional MLTs.

Scholars also have examined MLT nominal levels in conjunction with industry

performance metrics, such as foreclosure rates and regulations (Bhutta et al., 2016;

Orzechowski, 2017; Shindelar, 2015). These scholars analyzed data between 2009 and

2015 and determined that increase in government MLTs had not influenced the rate of

foreclosures; however, the ability of MLT characteristics to predict regulatory changes,

such as the FFR, showed significance. In this study, MLT as an independent variable did

not indicate statistical significance in RMT performance.

The increase in mortgage costs is a concern for all parties involved in an RMT,

especially purchase customers who seek to limit cash outflows for physical real estate

investments (Aksoy et al., 2016; Caplin et al., 2015; Reiss, 2016). Prior scholars’ have

found that the greater the LTV of government MLTs, the greater the risk of default; hence,

the stringent decision-making necessary to maintain homeownership sustainability leading

to lower mortgage costs. Moreover, previous researchers showed how minimum wage

changes impacted MLT choice from mortgage customers (Dettling & Hsu, 2017).

Thus, I found that a borrower’s ability to afford a conventional MLT was limited and that

conventional MLTs are not as frequent as government MLTs (see Table 5).

Previous researchers indicated that there was a statistically significant relationship

between SPT nominal levels and various industry metrics, which included liquidity,

homeownership rates, and market shifts (Blau et al., 2015; Glascock & Lu-Andrews,

2015; Grover & Grover, 2014). These scholars found that liquidity, ownership rates, and

market changes determined specific investment behaviors of real estate investors.

However, SPT characteristics in my study did not indicate any statistical significance to

complete an RMT. In contrast to the analysis of Grover and Grover (2014), who indicated

that Eastern EU members had high homeownership rates, yet low financing rates, my data

analysis yielded a more significant percentage of owner-occupied SPT transactions (see

Table 5).

Many scholars have associated and evaluated real estate investment trends and the

impact on SPT characteristics. My findings indicated a higher percentage of

owneroccupied SPTs, whereas Ribeiro-Ferreira (2016) showed that, in the Australian real

estate market, non-owner-occupied property investments rose with the evolution of the

mortgage industry. Additionally, prior scholars found that specific non-owner-occupied

SPT characteristics predicted, with statistical significance, depreciation in the several

types of non-owner-occupied SPTs, such as office/retail, industrial, and multifamily

properties.

In this study, the findings that resulted for ATT indicated consistency in the

mortgage industry as related to prior research. In previous research, Bhutta et al. (2016)

yielded a 50-day turnaround time to complete an RMT. My findings showed improvement

of this metric, yielding a 42-day ATT. However, Bhutta et al. included a more substantial

amount of archival data records from the HMDA database. Also, previous scholars

indicated that automated scheduling and queueing algorithms helped to reduce ATT in

workflow processes (Tani & El-Amrani, 2017; Wang et al., 2016). Also, Gupta et al.

(2017) evaluated the scheduling algorithms between a multi-queue workflow system and

found that algorithms could improve ATT. Although the selected categorical, independent

variables did not yield results of statistical significance to ATT in an RMT, process

improvements should remain as priorities for organizations to meet consumer demand.

Applications to Professional Practice

Since the advent of mobile technology and as society progresses into the future,

consumers will demand more user-friendly technology for more efficient mortgage

transactions, continually, while LOs seek to mitigate financial risks (Asal, 2018;

Blackwell & Kohl, 2018; Li, Skouri, Teng, & Yang, 2018). In 2018, Blackwell and Kohl

studied the typology of housing financing systems. Blackwell and Kohl determined four

ideal systems that included person-to-person, state, deposit-based, and bond-based

financing systems. Also, in 2018, Li et al. analyzed various trade credit payment options

to mitigate default risks by the buyers and the impact on seller profitability. Li et al. found

a minimal profitability impact to demand from sellers who requested advanced payments

versus credit payments using trade credit financing. Moreover, in 2018, Asal analyzed

Swedish housing price data between the first quarter of 1986 and the last quarter of 2016,

controlled with affordability, demographics, and price effects. Asal found a direct

statistical relationship between a long-run indicator, housing prices, and a shortterm

dynamic metric, disposable income, affecting financing decisions. Thus, researching the

alternative mortgage characteristics that impact ATT while investing in innovative

technologies to improve customer relationships will provide LOs with more efficient

processes to reduce costs and to increase stakeholder value.

Mortgage managers can apply the findings of this study to make operational and

technological advancements to the current mortgage process, in respective organizations.

Based on the results of this study, I found that not all theoretical propositions accurately

gave an analysis of the impact on a specific outcome. The relevance of these study

findings as related to mortgage business practices offered a gap in knowledge between

research and practice. The association between research, such as TOC and thinking

process, and mortgage lending using MLP, MLT, and SPT to evaluate the impact on ATT

to complete an RMT from origination to funding offers managers applicable business and

research findings in areas of risk, capacity, efficiency, and value.

Additionally, alternative research characteristics such as communication between

borrower and lender will offer LOs increased knowledge of respective mortgage

consumers. Xu and Chau (2018) researched the impact of communications between

borrower and lender in successful peer-to-peer lending transactions. Xu and Chau found

an indirect relationship between lender comments and funding success while a direct

relationship between borrower responses to lender comments and funding success. Hence,

efficient communications between borrower and lender impact funding rates, or ATT.

The mortgage industry is evolving as technological tools expand organizational

reach to the market. A manager’s ability to minimize risk and maximize resources to

capitalize on technological advances may provide an increase in profitability and intrinsic

value. Understanding the relationship between various RMT characteristics may also

provide mortgage managers with data to make necessary changes at the right time as

previous research showed. Moreover, comprehending when and how to implement the

change is critical to successful and efficient operations. Therefore, making necessary

adjustments, knowing how to adjust, and implementing the adjustment to mortgage

operations may deliver improved results in RMT outcomes.

Implications for Social Change

Social change in the mortgage industry derives from geopolitical, socioeconomic,

and environmental factors in which lawmakers react via policy-making; and most often,

too slow to impede crises (Emin, 2018; Graff, 2018). In 2018, Graff examined and

measured the performance metrics of homeownership policies, namely Roosevelt-era

housing policies and Clinton-era housing policies. Graff defined two metrics being

government homeownership rates and mortgage market financial frictions. Furthermore,

in 2018, Emin researched the effects of crisis origin countries on global finance and trade.

Emin used statistical relationships between correlation data that found investors liquidate

holdings in crisis originated countries, which cause financial shocks in the investor’s

home country. The researchers showed that geopolitical, socioeconomic, and

environmental factors impact global financing; thus, impacting retail financing.

Mitigating crises risk and market frictions will offer LOs incentives to provide mortgage

customers with most effective technologies to complete RMTs more efficiently,

considering mortgage debt to be the most significant percentage of consumer investment

portfolios.

A real estate transaction is the most valuable asset an individual will have in their

respective investment portfolios, and the ability to transact real estate efficiently supports

socio-economic improvements. In 2018, Shichor investigated the social impacts of the

unethical and illegal activities and the lack of criminal sanctions against guilty parties.

Shichor found that judges and lawmakers imposed minimal penalties against guilty

parties. The propositions for positive social change included the possibility to provide

value to mortgage stakeholders, who include customers, employees, managers,

government agencies, shareholders, and third-party vendors, with process improvements

to enhance mortgage business attractiveness and reduce costs. Mortgage managers

application of effective processes may lead to improved industry reputation, technological

process advancements, reduced employee moral hazard, and increased intrinsic value.

Realistic and logical implications are that mortgage leaders can apply this study to obtain

a better understanding of ways to improve the overall retail mortgage transaction

processes from origination to funding.

Recommendations for Action

Continuous improvement is the most common goal in all production-based

organizations. The results from this study are relevant to mortgage lenders, and I

recommend that industry decision-makers research and implement technological process

improvements to the mortgage process that focuses on reducing ATT to complete an RMT

from origination to funding. The independent variables, MLP, MLT, and SPT, are

significant about decision-making, capacity constraints, and profitability while the

dependent variable is significant to improve efficiency and reduce costs.

The successful implementation of process advancements may help lending

managers to enhance employee-customer relationships. Mortgage institutional

decisionmakers should apply findings of this study to business processes to improve on

ATT to complete RMTs from origination to funding. The earlier research and current

findings support the need for industry leaders to consider technological advancements in

information processing and verification to complete RMTs more efficiently and

effectively to increase intrinsic value.

I will share my study results with other industry colleagues and professionals

through scholarly journal publications. Additionally, I will share the results through

training courses and seminars regarding technological RMT process enhancements. My

focus will be on assisting institutional leaders to reduce costs, improve ATT, and increase

intrinsic value for organizational stakeholders.

Recommendations for Further Research

I offer the following areas for future research on the topic of mortgage

characteristics impacting completion times. Recommendations for further research include

identifying additional mortgage characteristics, such as default rates, regulatory controls,

and secondary market investors, which will allow for other statistical analyses, such as

multivariate regression analysis. Moreover, a future scholar could use interest rates as an

independent variable or a control variable. Further recommendations include a more

extensive dataset and multiple organizations to analyze and interpret results more

significantly that limited the statistical findings of this study.

Also, future scholars should conduct further research to examine ATT and the

relationship to alternative mortgage metrics not covered in the scope of this study.

Performing future studies about ATT could help mortgage leaders with the data necessary

to make the proper adjustments to workflow processes. Moreover, considering this study

focused on a single mortgage institution in the state of Florida, future scholars should

acquire data outside the state to compare any relevance or statistical significances.

Reflections

The DBA Doctoral Study process was a motivating and humbling experience for

me. This DBA Doctoral Study process was a challenge for me to balance work, home, and

school while recovering from an economic disaster. Each procedure required of me to be

meticulous to ensure that I met and exceeded the Walden University requirements of a

rigorous process. Attention to detail is necessary to complete the various steps along the

process, including meeting the DBA rubric requirements from committee members, the

IRB process, and conforming to APA guidelines. Exceeding the requirements offered an

implication of higher academic recognition by writing and communicating with peers and

professionals.

Investing the time to complete a retail mortgage transaction from origination to

funding is a personal objective of mine, considering my employment history in the

mortgage business. As a licensed mortgage loan originator for over 25 years, this topic is

essential to determine expectations for your customers and improve efficiency metrics for

the organization. The results pertaining to the independent variables of this research study

were typical to my personal experiences in the industry as the majority of my clients were

owner-occupant borrowers seeking either conventional or government financing. The

findings for ATT were also better than average from my experiences in the mortgage

industry and examined research.

Conclusion

The explicit goal of this study was to determine if specific mortgage

characteristics, such as MLP, MLT, and SPT impacted ATT to complete an RMT from

origination to funding. The results of this study showed that I fail to reject the null

hypothesis because of the lack of significance between the independent, nominal variables

and the dependent, interval variable. Although, the data conformed to industry averages as

the frequencies of the independent variable confirmed the economic lending environment

in the state of Florida. Owner-occupant, government-insured, purchase mortgages are still

dominating the industry and maintaining market share since the 2008-

2009 financial crisis.

Moreover, the results of this study substantiate and reinforce the theoretical

framework and supportive theories of throughput (TOC) and process improvements

(Thinking Process). Even though the data yielded insignificance, the data results may lead

to further research to improve upon workflow completion times in the mortgage industry.

The general idea to improve ATT was to improve the overall intrinsic value of the

organization for all stakeholders.

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