Subsidized Rental Income, Nonrental Income, and Profitability in Tax Credit Development Companies

Section 1: Foundation of the Study

The success of the low-income housing tax credit (LIHTC) program has been

demonstrated in the creation of nearly 2.9 million housing units since its inception in

1986 (Novogradac, 2016). The LIHTC program has become the primary vehicle to

finance affordable rental housing in the United States (Hammett, 2015), with nearly 8

billion tax credits awarded (Kalugina, 2016). Emerging from concerns that the existing

system of subsidized housing was ineffective, the LIHTC program was launched as a

housing system that was financially sustainable and politically popular, with the infusion

of tax credit allocations to commercial enterprises (Johnson, 2014). One of the driving

forces for implementing the LIHTC program was to end tax shelters (Stanhope, 2016).

The problem facing tax credit developers, however, is of a growing reliance on the

housing choice voucher (HCV) program and other means to sustain profit margins. The

HCV program represents subsidized rental income (SRI). In addition to SRI, tax credit

developers seek nonrental income (NRI) means to support profitable operations. With

continued Congressional budget cuts of the HCV program, the profit margins of tax credit

developments are often at risk. The purpose of this quantitative correlational study was to

examine the relationship between the independent variables SRI and NRI and the

dependent variable of the profitability of tax credit development companies. Quantitative

designs are used by researchers to examine the relationship between independent and

dependent variables (Al-Thani & Semmar, 2017; Guo, 2015; Onen, 2016), which was the

aim of this study.

Background of the Problem

LIHTC developments are made affordable because commercial enterprises use

federal subsidies to pay a significant portion of the construction costs, thereby

minimizing debt service payments. Tax credit developers create commercial enterprises

and must agree to set aside a certain percentage of units at reduced rates for low- and

moderate-income tenants (Johnson, 2014). Subsidy infusion is exchanged for affordable

units within the tax credit developments (Schwartz, 2015). The exchange forms the basis

for the public-private partnership between the government and tax credit real estate

developers (Novogradac, 2014).

Low-income housing tax credits represent a supply-side program, which means

that the federal subsidy is delivered directly to construct or rehabilitate buildings and is

not distributed to households (Schwartz, 2015). The HCV program, however, is a

demand-side program in which federal assistance is provided directly to tenants in the

form of vouchers to find available housing (Hsu, 2015). Public and private funds are used

to construct and operate tax credit developments. The financing mechanism was designed

so that the developments can sustain favorable profit margins (Johnson, 2014). In

addition to housing tenants that meet LIHTC criteria of 60% or below median income,

the program can also house persons who receive federal assistance from the HCV

program (Novogradac, 2014), which is the demand-side program. An overreliance on

voucher holders by tax credit developers poses a risk to profitable operations as the HCV

program is subject to Congressional budget cuts (Affordablehousing, 2017).

Hsu (2015) found that there has been a heavy reliance on the HCV program to fill

LIHTC developments. Researchers have recently discovered that the LIHTC program

may be considered more of a hybrid of supply-side and demand-side programs, with

voucher holder participation estimated between 16% and 40% (Hsu, 2015). Many tenants

use housing choice vouchers to rent from tax credit developments (Kingsley, 2017). Tax

credit developers tend to target HCV households for two reasons. First, voucher holders

are sought in areas where the tax credit rents are competitive with fair market rents.

Second, surplus income is derived from housing voucher holders, which is not the case

with housing nonvoucher holders. Tenant voucher holders pay above maximum rents

because their payment standards are higher than LIHTC rents, creating a windfall for the

developers of tax credit units.

Problem Statement

Many tax credit development companies that build affordable housing fail to

operate profitably as commercial enterprises without assistance from government

subsidies (Hsu, 2015). Nearly 35% of tax credit development companies were operating

below break-even (Hammett, 2015), and have required additional streams of income to

sustain operations. The general business problem was that some tax credit housing

developers fail to understand the negative impact of dwindling income sources on

profitable operations. The specific business problem was that some tax credit developers

do not know the relationship among SRI, NRI, and profitability.

Purpose Statement

The purpose of this quantitative correlational study was to examine the

relationship between SRI, NRI, and profitability as this pertains to tax credit housing

developers. The independent variables were SRI and NRI, and the dependent variable

was profitability. The targeted population consisted of tax credit housing developments

with 200 or more units in the state of Florida, from which a random sample was drawn.

The implications for positive social change from the study findings may lie in the

possible contribution to knowledge, which could advance the ability to offer affordable

housing to the economically disadvantaged in society. The study findings may provide tax

credit housing developers with an improved understanding of the association between

SRI, NRI, and profitability, and may thereby benefit low-income tenants. With an

improved understanding of the relationship between SRI, NRI, and profitability, the

potential for positive social change could include the development of more affordable

housing by developers to meet the needs of low-income tenants.

Nature of the Study

The three types of research methods include quantitative, qualitative, and mixed

methods (Yin, 2017). For this study, a quantitative method was chosen to examine the

relationships between variables (see Lach, 2014). The purpose of the quantitative method

was to examine the relationship between SRI, NRI, and profitability. Qualitative methods

are used by researchers who seek to understand a phenomenon by seeking answers to how

and why questions rather than using statistical results (Yin, 2017); the latter was the aim

of the current study. The need for results based on statistical testing rendered the

qualitative method inappropriate for the study. Mixed methods, a combination of

quantitative and qualitative approaches, is advantageous when addressing research

questions that require both types of data in a single study (Zhang & Watanabe-Galloway,

2014). The mixed-methods approach was not chosen for this study because there was no

need to combine objective measurements with a subjective inquiry. The mixed-methods

approach is often cumbersome and may require additional time in executing the research

(McKim, 2015).

Nonexperimental, experimental, and quasi-experimental designs were reviewed to

determine the appropriate design for this study. Based on the objective of the study, which

was to examine the relationship between SRI, NRI, and profitability within the tax credit

housing industry, a nonexperimental correlational design was appropriate. A correlational

design is used to determine the degree and strength of the relationship between the

independent and dependent variables from data generated using appropriate correlational

techniques and analyzed using appropriate statistical techniques (Arkkelin, 2014). The

purpose of the correlational design was to examine the relationship between two

independent variables (SRI and NRI) and the dependent variable (profitability).

Experimental and quasi-experimental designs involve controlled environments, which

may entail the manipulation of variables and interventions as the aim is to examine

causeand-effect relationships (Walter, Dunsmuir, & Westbrook, 2015). The aim of the

current study was to examine the correlational relationship between variables, not cause

and effect; therefore, quasi-experimental and experimental designs were inappropriate.

Research Question

The research question was the following: What is the relationship between

subsidized rental income, nonrental income, and profitability?

Hypotheses

H0: There is no relationship between subsidized rental income, nonrental income,

and profitability.

H1: There is a relationship between subsidized rental income, nonrental income,

and profitability.

Theoretical Framework

The supply-side theory (Laffer, 1983) and the demand-side theory (Keynes, 1936)

underpinned the study and constituted the theoretical framework. The aim was to

understand how tax credit housing developers apply SRI and NRI to profitability. The

two categories of federal housing programs, supply-side and demand-side, aligned with

the economic theories chosen to underpin the study. Canto, Joines, and Laffer (1982)

argued that reduced taxes spur economic growth. Economic growth is driven by the

supply of goods and services (Laffer, 1983). The constructs underlying the supply-side

theory include reduced taxes, increased private sector investments, and increased capital

production, pertain to the tax credit housing program. The supply-side theory was

relevant to this study because the federal government created a supply of goods and

services through the LITHC program, which includes tenants who provide SRI and NRI

to support the profitability of tax credit housing companies. SRI and NRI are sources that

may negatively affect profitability.

Keynes (1936) developed the demand-side economic theory. Keynesian theorists

have argued that economic growth is driven by the demand for goods and services

(Prizio, 2015). One of the constructs underlying the demand-side theory is the infusion of

spending to create consumer demand (Keynes, 1936). The demand-side theory was

relevant to this study because the federal government has created a demand for goods and

services through the HCV program. Tenant voucher holders provide income streams to

tax credit developments that include SRI and NRI to sustain profitability (Hsu, 2015).

Operational Definitions

The following operational definitions provide clarity regarding the meaning of

terms as they were used in this study:

Affordable housing: Rental dwellings within reach of individuals and families

who pay no more than 30% of annual income for rent and utilities (Hammett, 2015).

Area median income: The midpoint household income from a metropolitan area or

a nonmetropolitan county used in determining eligibility for housing programs (U.S.

Department of Housing and Urban Development [HUD], 2015b).

Developers: Professionals involved in the production of LIHTC multifamily

housing, including syndicators, bankers, builders, managers, and investors (Hammett,

2015).

Gross operating profit: The difference between the cost of goods sold and total

sales divided by total assets minus financial assets (Ukaegbu, 2014).

Low-income households: Households with incomes between 50.1% and 80% area

median income (HUD, 2015b).

Low income housing tax credits (LIHTC): The largest subsidy that provides tax

credits as an incentive for private investors participating in low-income housing

production (Schwartz, 2015).

Public-private partnership: Partnerships between governments and the private

sector that spur the development of affordable housing by adding resources (Johnson,

2014).

Qualified allocation plan (QAP): A federally required annual planning tool used

by state housing finance agencies to explain how tax credits were allocated (HUD,

2015b).

Return on assets (ROA): The earnings before interest, taxes, depreciation, and

amortization divided by the total assets (Kara, Erdur, & Karabiyik, 2015).Assumptions,

Limitations, and Delimitations

Assumptions, limitations, and delimitations provide critical information about the

methodology, findings, and scope of a study (Leedy & Ormrod, 2015). Assumptions are

assertions not confirmed in the study (Marshall & Rossman, 2016). Limitations are

potential weaknesses of a study (Leedy & Ormrod, 2015), while the scope and

delimitations indicate the boundaries of a study (Leedy & Ormrod, 2015).

Assumptions

Assumptions in a study are assertions that the researcher takes to be true in the

absence of confirmation (Marshall & Rossman, 2016). The first assumption was that I

would be able to locate tax credit developers who rely on households receiving federal

rent assistance to bolster favorable profit margins of the company. The second assumption

was that I would be able to retrieve archival financial data from the Florida State Housing

Corporation and public housing authorities to gather information for the independent and

dependent variables. The last assumption was that there would be an alignment between

the study and the quantitative correlational design. The purpose of the study was to

examine the relationship between the independent variables (SRI and NRO) and the

dependent variable (profitability).

Limitations

Limitations are potential weaknesses that represent important components of a

study (Leedy & Ormrod, 2015). First, there was a lack of data to identify the percentage

of HCV households who occupy tax credit developments. Second, there appeared to be a

dearth of research as to how tax credit developers apply surplus income from voucher

households to their developments. Finally, the findings from the study may not be

generalizable to all tax credit housing developments in the United States because different

locations can affect rents and profitability.

Delimitations

Delimitations indicate boundaries regarding what the researcher will cover in the

study (Leedy & Ormrod, 2015). The first delimitation was that only the strength and

direction of the association were examined in the study, not the cause-and-effect

relationship between SRI, NRI, and profitability. The second delimitation was that the

focus of the study was on developments that contain 200 or more units and excluded

smaller properties. The last delimitation was that the study was restricted to the state of

Florida.

Significance of the Study

The findings from this study may provide tax credit housing developers and

affordable housing researchers with a better understanding of the possible risks and

opportunities to profitable operations from the use of different income streams. The study

may also fill a gap in the literature regarding the impact of SRI on the tax credit housing

development industry. The study may also contribute to the improvement of business

practices in the tax credit housing industry and to the advancement of positive social

change.

Contribution to Business Practice

The findings from the study may contribute to business practice by providing new

knowledge on the for-profit concept of maximization of profits and minimization of risks.

Hammett (2015) posited that the goal of commercial businesses is to maximize profits

and reduce risks. The findings may also lead to a better understanding of the effect of SRI

and NRI on the profitability of tax credit developments. Tax credit housing developers

may use the findings to improve their understanding of the connection between SRI, NRI,

and profitability within operations (see Bratt & Lew, 2016). Second, the results could

help tax credit developers avoid possible risks to profitable operations (see Hammett,

2015). Third, the findings may help public policy officials implement regulations

designed to increase the population of eligible tax credit tenants. Finally, public policy

officials may use the findings to fund additional solutions to the affordable housing

dilemma (see Hsu, 2015).

Implications for Social Change

The findings from this study may contribute to positive social change by

increasing the understanding of developers with respect to the association between SRI,

NRI, and profitability. The findings of the study may contribute to existing knowledge in

this domain and may be used to increase affordable housing for the economically

underserved. Tax credit developers and public housing officials may be informed of ways

to decrease rent burdens (see Bratt & Lew, 2016), which could benefit low-income

tenants and communities that address homelessness (see Hsu, 2015).. Society may benefit

as additional renters move from substandard living conditions to affordable housing (see

Hsu, 2015).

A Review of the Professional and Academic Literature

Researchers often seek to gain an intimate understanding of themes related to the

objectives of a study through an exhaustive review of articles, books, and other literature.

With a thorough understanding of themes, researchers can convey the intent of the study.

The purpose of this quantitative correlational study was to examine the relationship

between the dependent variable of profitability and the independent variables of SRI and

NRI in LIHTC developments. The following research question guided this study: What is

the relationship between SRI, NRI, and profitability? The hypotheses for this study were

the following:

H0: There is no relationship between SRI, NRI, and profitability.

H1: There is a relationship between SRI, NRI, and profitability.

The supply-side theory (Laffer, 1983) and the demand-side theory (Keynes, 1936)

underpinned the study. The theoretical framework was used to guide the examination of

the research variables. Information in this section includes supply-side theory,

demandside theory, and profitability as the main topics of review. The constructs

underlying the supply-side theory include reduced taxes, increased private sector

investments, and increased capital production, pertain to the tax credit housing program.

A major construct underlying the demand-side theory is government intervention to

promote consumer spending. The demand-side theory was relevant to this study because

the federal government created a demand for goods and services through the HCV

program from which tax credit housing developers receive SRI, which is relied on for

profitable operations. The two economic theories contain opposing perspectives regarding

affordable housing, which may be relevant when examining profitability within the tax

credit housing arena.

Strategy for Sourcing Literature

Dissertations, industry websites, journal articles, and scholarly texts were primary

sources for the literature review for this study. The Walden University online library was

the primary source for finding and selecting pertinent peer-reviewed and other journal

articles. Access to online library databases was based on enrollment status at Walden

University. The academic journal repositories proved beneficial for relevant searches.

Keywords were used in the following databases: Thoreau, Academic Search Complete,

ProQuest Central, SAGE Premier, and Google Scholar. Keywords and phrases queried in

the databases included LIHTC, supply-side theory, demand-side theory, affordable

housing, quantitative correlation, qualified action plan, profitability measures, Housing

Choice Vouchers, public-private partnerships, and subsidized housing.

Table 1 provides a summary of the references used in this doctoral study. Queries

from databases resulted in 130 research items. The appropriateness for retrieval of

scholarly and practitioner literature was determined by reviews of the abstracts,

introductions, and conclusions. The literature review for this study included 130

references, and 112 were published between 2015 and 2018. Over 85% of the references

in this study were peer-reviewed and published on or after 2015. The literature review

contains 82 in-text citations with 70 from peer-reviewed references. The results reflect

compliance with the 85% guide.

Table 1

Detailed List of References

Description

Value

Total number of references used in this doctoral study

130

Total number of references within the last 5 years

112

Percentage of references within the last 5 years

86%

Total number of peer-reviewed references

111

Percentage of peer-reviewed references

85%

Total number of references used in the literature review

Number of literature review references within the last 5 years

Percentage of literature review references within the last 5 years

87%

Five topics are covered in the literature review. The first and second topics include

a review of studies on the theoretical frameworks: the supply-side economic theory and

the demand-side economic theory. Review of the literature addressing the SRI

independent variable is covered under the demand-side housing topic. The NRI

independent variable is addressed under the supply-side housing topic. The third topic is

the dependent variable (profitability). Construct measurements and profitability

methodologies are covered in the last two topics. The section concludes with a summary

and transition.

Supply-Side Housing

The LIHTC program is an $8 billion industry that was created by Congress to

build affordable housing for low-income families (Gramlich, 2016; Sullivan & Anderson,

2017). Supply-side theorists have argued that economic growth is driven by the supply of

goods and services (Prizio, 2015). The constructs underlying the supply-side theory

include lower taxes, increased private sector investments, and increased capital

production. The supply-side theory was relevant to this study because the LIHTC

program is a supply-side government subsidy derived from tax credits. Shaw (2015)

posited that tax credit subsidies are the only way affordable housing can be built

effectively. One of the weaknesses identified in the LIHTC program was sensitivity to

shifts in the economy (Schwartz, 2015). The market for tax credits showed that as the

economy slows, the demand for tax credits may also decline (Schwartz, 2015).

Supply-side theorists are often at odds with demand-side theorists who support the

Keynesian economic theory (Phelps, 2016). Keynesian theorists have argued that

economic growth is based on the demand for goods and services (Prizio, 2015).

Incorporated in both theories is the reliance on government intervention. Demand-side

economists argue that placing money in the hands of lower- and middle-income renters

increases aggregate demand and thereby increases economic activity (Prizio, 2015).

Within the demand-side housing program, vouchers are placed in the hands of lowincome

persons who use them to rent from private landlords or tax credit companies (Hsu, 2015).

Within the supply-side housing program, subsidies are provided to private real estate

companies to build and rehabilitate affordable housing (Shaw, 2015).

The U.S. government categorizes housing subsidies into supply-side or

demandside programs. The categories align with the supply-side and demand-side

economic theories. A subsidy for affordable housing tenants delivered indirectly for the

construction or rehabilitation of buildings is characterized as supply-side and has

produced 2.6 million units of affordable housing (Hsu, 2016). The tax credit program has

been criticized for higher costs and lower numbers of units produced in the past 20 years

(Sullivan & Anderson, 2017). Sullivan and Anderson (2017) found that tax credits cost

66% more during that period, with industry officials citing increased construction costs,

lower grant revenue, and increased targeting of low-income renters as the reasons.

Federal government officials adopted the belief that all citizens should be

provided access to decent, safe, and affordable housing (Johnson, 2014). Housing needs

fall into Maslow’s (1943) theory Layer 2: hierarchy of needs. Maslow (as cited in

Henwood, Derejko, Couture, & Padgett, 2015) described safety needs as a primary

motivation for behavior if they go unmet. Maslow noted that housing is one of the

measures for an individual or family to feel secure. Within Maslow’s levels of

progression to an individual’s self-actualization, the need for housing must be met after

the basic needs are met. Criticism of Maslow’s theory and hierarchy has arisen from

many scholars (Bouzenita & Boulanouar, 2016). According to Harrigan and Commons

(2015), Maslow did not explain elements of behavior that cause individuals to move from

one stage to another. With the application of Maslow’s theory of the hierarchy of needs to

explain renter motivations, it may be evident from the literature review that when people

are provided affordable housing, a level of behavioral motivation may have been

experienced for them to become productive citizens. Henwood et al. (2015) posited that

the identification of self-actualization goals is associated with the lack of having basic

needs met instead of the fulfillment of basic needs.

The housing goal, along with the demand for increased accountability from the

government in resource distribution, became the basis for resolving housing issues and

led to the implementation of the LIHTC program (Johnson, 2014). Greater accountability

of government resources was one of the reasons for creating the tax credit housing

program. However, in recent years public accounting of the program costs among

government officials and regulators responsible for monitoring has been insufficient, with

some calling for changes (Sullivan & Andersons, 2017). Sullivan and Anderson (2017)

also found insignificant data pertaining to the oversight and monitoring efforts of the tax

credit housing program, with the possible implication that the IRS is not properly

performing its oversight responsibilities. Advocates have argued that the tax credit

housing program has an effective built-in self-monitoring mechanism (Di & Murdoch,

2013). Bratt and Lew (2016) noted that monitoring is performed by the developer and

owner who require exemplary management for the development to remain in compliance.

The LIHTC program aligns with the supply-side economic theory. Supply-side

theorists have supported the use of tax cuts to spur private investment in the production of

capital projects (Prizio, 2015). Although broad support for the LIHTC program has been

expressed, skepticism has arisen about the ability of the program to have a significant

national impact on affordable housing needs (Johnson, 2014). The tax credit housing

program gained widespread popularity with developers who saw the benefits of building

housing complexes in which a significant portion of the construction was funded by the

government (Schwartz, 2015).

The LIHTC program was initially criticized because of the assertion that

underwriting was unnecessarily burdensome (Hammett, 2015). The program has also

been criticized because of complex rules and inefficiency (Sullivan & Anderson, 2017).

Critics have complained about high transaction costs and inefficiency within the LIHTC

(Edwards & Calder, 2017). Hammett (2015) highlighted the efficiency with which tax

credit dollars have been applied to construction development rather than administrative

costs. Tax credit housing efficiency production has declined in recent years with the

production of fewer units and the use of more housing tax credits (Sullivan & Anderson,

2017).

Ihlanfeldt and Mayock (2017) advocated for the need to locate affordable housing

near high-quality schools as a means of improving academic achievement for students of

low-income households. Perceptions of deterioration in the quality of schools and

neighborhoods loom as barriers to establishing tax credit housing developments. Tax

credit developers, as well as other affordable housing developers, must address the notin-

my-backyard perception of residents when planning housing developments (Scally &

Tighe, 2015). Dillman, Horn, and Verrilli (2017) noted that the production of LIHTC

housing in economically distressed communities has a positive impact on surrounding

neighborhoods in terms of modest increases in property values and enhanced safety.

Higher opportunity neighborhoods that receive LIHTC housing experience modest

property value reductions and no effect on crime (Dillman et al., 2017).

Additional operating income is often required for tax credit developments to

operate profitably (Hsu, 2015). Thayer (2018) described subsidy layering as a

gapfinancing mechanism that has been used for over 30 years. Tax credit projects are

complicated due to the layering of grants, tax credits, bonds, and municipal assistance.

Funding mechanisms from states and local governments are sought by developers. Bratt

(2016) suggested that tenant incomes and the ability to pay should drive the affordability

of rental apartments, but such a mechanism requires additional subsidies beyond the

LIHTC funding. Weisbach (2006) argued that the LIHTC program should be replaced

with vouchers because the voucher program is less expensive. Although less expensive,

the voucher program is subject to annual funding cuts from Congress, which is not the

case with the LIHTC program. Housing advocates promoted the view that the LIHTC

program was more geared toward benefiting wealthy investors than low-income

households (Johnson, 2014) because upper-income investors and big corporations

benefited from poor households. Novogradac (2016) noted that more than 13.3 million

Americans have benefited from the LIHTC program over a 30-year period. Freedman and

McGavock (2015) argued that LIHTC crowds out private development, and that nearly

half of existing tax credit housing units would have been constructed without a subsidy.

Overall, the tax credit program has been viewed as a win-win process because the

benefits accrue to all parties within the affordable housing arena. An affordability

problem exists among a growing number of renter households in the United States

(Johnson, 2014). An important housing policy question to ask would be the following:

Are LIHTC developers doing a disservice to eligible nonvoucher holders by limiting

available units when they rent to voucher holders? Demand subsidies in the form of

vouchers can harm nonvoucher holders who seek housing in tax credit developments.

Supply-side operations include the application process, compliance, tax credit developers,

and public-private partnerships.

Application process. The profitability of tax credit housing developments is

affected by the application process, which includes the developer identifying levels of

rents and the location of the development. The application process is a competitive

mechanism that is geared toward ensuring the best housing is produced, and the process

begins with a project owner submitting a competitive application to the state housing

agency (Hsu, 2015). The intense competition for tax credits has come under scrutiny as

the process hampers development and serves to frustrate developers who would rather

spend the time working additional tax credit applications (Bratt, 2016).

Federal law requires that states give preference to developments serving the

lowest income renters and within poverty areas (HUD, 2015a). However, market forces

govern income levels and profit margins of tax credit developments, as with other real

estate markets (Johnson, 2014). Developers have targeted the higher income group at or

below 60% of area median income for 88% of tax-credit projects placed in service

between 1987 and 2014 (Lew, 2015).

The application process includes developers’ attentiveness to the Qualified

Allocation Plan, which is used by states to award the tax credits to developers (Skuzinski

et al., 2016). Section 42 of the Internal Revenue Code requires that states create an annual

QAP, which sets the criteria for how tax credit projects are to be selected. Preferences are

set for projects serving the lowest income tenants (Ellen, Horn, Kuai, Pazuniak, &

Williams, 2015). The building locations used for affordable housing development are often

less than ideal for market-rate developers, who seek the best sites (Davis, 2016).

Affordable housing developers choose to maximize the amount of LIHTCs

received by targeting neighborhoods designated as Difficult Development Areas or

Qualified Census Tracts, which are frequently characterized as poverty areas (Eriksen,

2017; Niver, 2016). Awarding of tax credits by state agencies is a process of chasing

points by developers. To ensure that LIHTC developments are built in high-opportunity

areas, Spotts (2016) argued that housing finance agencies (HFA) should adopt

locationsensitive cost evaluations.

High-opportunity areas are described as geographic locations that are served by

high-quality schools and have such amenities as access to quality transportation, jobs,

superior health care, and public services (Neville, 2017). Higher opportunity

neighborhoods that are occupied by LIHTCs experience modest property value reductions

with little to no effect on crime (Dillman et al., 2017). Although additional points are

awarded based on the location of developments in high poverty areas, the negative effect

on profit margins could cause developers to seek rental assistance from the HCV program

as well as other streams of income.

Currently, developers are reluctant to target high-opportunity areas because QAPs

contains threshold requirements such as land and per unit development costs that are not

conducive to high opportunity areas (Spotts, 2016). Progress to deconcentrate poverty has

been made within the LIHTC program (Niver, 2016). Affordable housing is more

regionally distributed within the LIHTC, providing low-income households with greater

access to high-opportunity neighborhoods. Tax credit developers locate more than

onethird of units in the suburbs (Niver, 2016). Profitability is ultimately at risk when

developers chase points by locating their developments in high poverty areas, leading to

the need for greater rental assistance in the form of SRI.

Compliance. The Internal Revenue Service (IRS) mandates compliance with rent

structures and unit restrictions that affect the profit margins of tax credit housing

developments as specific percentages of rents and units must be maintained during a

15year period (Schwartz, 2015). Compliance monitoring for a subsequent 15-year period

is provided by state housing agencies (Bratt, 2016). The monitoring efforts by the IRS

have been criticized in recent years, as Sullivan and Anderson (2017) noted that only

seven audits had been performed of the 58 housing agencies assigned by the IRS to

monitor the program since its inception in 1986. Lipschultz (2016) noted the Internal

Revenue Service (IRS) has generally found that program requirements are met by

developers of LIHTC properties.

The quality of the LIHTC project is maintained by private for-profit investors who

provide oversight and discipline (Bratt, 2016). The LIHTC program is an IRS

responsibility, included in the 1986 Tax Reform Act; however, enforcement and

assessment rest with the Department of Housing and Urban Development (Schwartz,

2015). The oversight for the first fifteen years of a LIHTC project is provided by the

federal government, which includes the threat that tax credits could be rescinded if the

project fails to comply with the proper level of income and units (Bratt, 2016).

Proponents have argued that less than 1% foreclosure rate occurs within the LIHTC

program (Johnson, 2014). Compliance with reduced rents and a target number of units set

aside for low-income use means that the developer has agreed to forego maximum rents,

resulting in the need for additional income streams such as the HCV program. Developers

seek rental assistance from the HCV program to bolster their profit margins (Hsu, 2015).

Affordable housing advocates have expressed concern that the affordability

component would cease after the compliance period ended (Sullivan and Anderson,

2017). The shortage of affordable housing fuels the concerns when considering which

direction tax credit developments will move after the compliance period. Weiss (2016)

predicted by 2043, two million apartments in the U.S. will have lost the affordability

component attached to federal restrictions. Researchers, however, found that only 6% of

projects whose compliance period ended reverted to market-rate housing, with the

remainder of the projects either having their tax credits renewed or remaining affordable

without tax credits (Schwartz, 2015). Affordability of developments in the absence of tax

credits places an additional strain on profit margins and a greater reliance on voucher

holder income to fill the financial gap.

Tax credit developers. Tax credit developers exchange the opportunity to charge

market-rate rents for the subsidy provided through the tax credit program (Johnson,

2014). The process effectively lowers the debt service costs for the development

(Schwartz, 2015). Maximization of profits and minimization of risks are established goals

within real estate development (Hammett, 2015). However, maximization of profits is not

realized for developers within the LIHTC industry as rental income is sometimes reduced

for the sake of affordability. With nearly one-fourth of all tax credit developments

operating below breakeven, as found in several publications, the implications may be that

the tax credit subsidy is not enough to sustain profitable operations. Invariably,

developers turn to SRI and other means to sustain profitable operations.

Tax credit developments receive higher rent payments from voucher holders than

from eligible non-voucher holders. HUD provides a higher payment standard for voucher

holders than the 60% LIHTC payments (Novogradac, 2014). Novogradac further posited

that researchers have recently discovered that the LIHTC program may be considered a

hybrid of supply-side and demand-side programs as development managers use HCV

households to fill their apartment complexes. However, the risk is posed to tax credit

developers by overly relying on income from the HCV program as it is subject to annual

Congressional budgetary cuts (Hammett, 2015).

Tax credit housing developers have grown dependent on the voucher program to

fill units and to gain incremental income to sustain operations and profit margins (Hsu,

2015). The HCV program is subject to annual Congressional review and budget

reductions. Unlike the HCV program, the LIHTC program is not subject to annual review

for spending authorization because the program has been established by the 1986 Tax

Reform Act within the IRS tax code and is therefore not subject to annual funding cuts

(Hammett, 2015).

Developers use tax credit subsidies to pay a significant portion of construction

costs for erecting affordable developments, with the intent of reducing debt service costs

and creating sustainable operations (Schwartz, 2015). Sustainable operations within the

tax credit development industry have been realized through additional funding sources

(Hsu, 2015). Although SRI increases the profit margins of tax credit developments,

shrinking federal dollars within the HCV program should serve to motivate tax credit

developers to more aggressively market the vast pool of non-voucher renters who meet

the 60 percent AMI eligibility.

Many of the extremely-low-income renters experience rent burdens while housed

in tax credit housing programs (HUD, 2015a). JCHS (2015) reported a shortage of

affordable housing in the U.S., which results in cost burdens to renter households. HUD

defines extremely-low-income households as earning 30% or less of the area median

income compared to 60% or less for the LIHTC program (HUD, 2015b). Housing

extremely low-income renters negatively affect the profitability of tax credit

developments. Bratt (2016) observed that tenant income and the ability to pay should

drive the affordability of rental apartments, but such a mechanism requires additional

subsidies beyond the LIHTC funding.

Developers who are committed to serving extremely low-income households tend

to employ creative strategies in mixed-income complexes (Johnson, 2014). Some tax

credit developers use incremental income to assist extremely-low-income households

through a strategy termed cross-subsidization (Hsu, 2015). Hsu also noted that the

crosssubsidization strategy implemented by some development firms in utilizing income

from higher-earning households might help offset lower rents to meet the overall

operating costs of the development.

Most of the criticism of the tax credit program was based on earlier observations

such as cumbersomeness of the paperwork and syndication costs (Hammett, 2015). A

reduction in syndication costs has occurred as well as a willingness on the part of

investors to accept lower returns (Niver, 2016). Throughout the years, the tax credit

housing program has been demonstrated to be a stable investment that attracts developers

and investors (Schwartz, 2015). The program is described as beneficial to all parties:

lowincome families receive modern, well-built, affordable dwellings; and developers,

syndicators, and investors profit from their roles.

Investments in LIHTC developments are viewed as low risk because of the high

demand and low foreclosure rates associated with the program. Sullivan and Anderson

(2017) noted that tax credit housing is perceived by investors as a safe investment

because the developments are often filled with tenants and long waiting lists. Incentives

within the tax credit housing industry are crucial for attracting private sector investment.

Schwartz (2015) suggested that the tax credit housing program was vulnerable to

economic shifts as there was a reticence on the part of developers to purchase tax credits

during the great recession. The susceptibility of the LIHTC market to shifts in investor

demand was evidenced during the financial crisis when revenues were reduced across the

United States, slashing the demand for tax credits.

Surplus income from voucher holders is used by tax credit housing developers in

the operations of the developments (Hsu, 2015). Other developers fill the gap in operating

tax credit developments by applying traditional financial housing mechanisms (Johnson,

2014). A gap in literature may exist regarding how tax credit developers use the

incremental income from voucher holders, which could serve as an additional stream of

income to sustain operations. HUD does not mandate how the surplus income derived

from voucher holders should be used by tax credit developers (Hammet, 2015). An

important question is raised based on developers receiving incremental income from

voucher holders: Should all developers use the surplus to cross-subsidize, reduce rents for

other extremely low-income households as some developers are doing? Additional

research may be needed to determine how tax credit developers use surplus funds from

SRI within the housing developments.

Public-private partnerships within tax credits. The low-income housing tax

credit (LIHTC) is a public–private partnership that has become an integral part of

affordable housing. A public-private partnership is often described as a mutually

beneficial relationship between the public and private sectors (Du, Wu, & Zhu, 2018;

Wang, Cui, Liu, 2018). The entrance of private sector developers into affordable housing

production has proven to be a successful strategy (Woo, Joh, & Van Zandt, 2016; Woo & Yu, 2017).

The LIHTC program represents an evolution from government conventional

lowincome housing programs, to a gradual shift in responsibility to private landlords,

developers, investors, and public-private partnerships (Johnson, 2014). Banks have

recently become the largest investors in LIHTCs. The bankers are statutorily motivated

by the Community Reinvestment Act, which was enacted in 1977 to meet the credit needs

within a community. Investors are incentivized to partner with developers to build

affordable housing apartments by providing a portion of the capital for construction and

in turn receiving tax credits for a 10-year period.

The federal government has become dependent on for-profit and non-profit

organizations to deliver affordable housing, which has brought about decentralization of

the affordable housing program (Bratt & Lew, 2016). Concerns have been raised

regarding the seemingly over-reliance on private actors by the federal government (Niver,

2015). The question of accountability was raised with government dependence on

private-sector production of affordable housing. The profitability of tax credit

developments can be attributed to the private sector partnerships as developers seek

various financial means, including the HCV program, to sustain operations.

Shifting the responsibility of affordable housing production to the private sector,

the federal government additionally devolved its responsibility for affordable housing to

regional, state, and local government for creating greater productivity and efficiency

(Bratt & Lew, 2016). Some criticism has arisen concerning the degree that the

government relies on the private sector to perform functions formerly handled by the

government (Sullivan & Anderson, 2017). The role of the private sector in performing

public functions has been valued at a trillion dollars annually (Niver, 2015).

The tax credit program was designed as a catalyst for private investment in

affordable housing (Schwartz, 2015). A paradigm shift occurred within the federal

housing policy that moved the responsibility of resolving housing issues from the federal

level to public-private partnerships. Efficiency and profitability drivers of private

business coupled with governmental goals account for the astounding production of

affordable housing. Public-private partnerships have been widely used in the operations

and responsibilities of public service (Liu et al., 2017). Although the public-private

partnership has been instrumental in the success of affordable housing production within

the LIHTC program, goal congruence has become a concern as the two entities

collaborate to address housing needs (Bratt & Lew, 2016). Higgins and Huque (2015)

found differences in how the two sectors measured accountability, with the public partner

requiring greater visibility of its investment than the private partner.

Within the affordable housing realm, the goal of the public sector is to meet the

demands of public policy in providing affordable housing for its citizenry (Johnson,

2014). Tax incentives were the motivation for private developers to take on the role of

affordable housing production (Schwartz, 2015). However, the goal of commercial

businesses is to maximize profits and reduce risks (Hammett, 2015). The goals of public

and private sector representatives may not always intersect perfectly because of their

respective motives. However, despite ambiguity, conflicts, and complexities, public and

private sectors have been extremely successful in merging to produce nearly 2.9 million

affordable housing units (Novogradac, 2016). Inherent within goal congruence are issues

such as goal ambiguity, goal conflicts, and goal complexity.

One of the chief goals of the tax credit program was to encourage private sector

support of affordable housing (Johnson, 2014). Nearly $8 billion tax credits are awarded

on an annual basis within the tax credit program (Hammett, 2015). The exchange of

subsidy for affordable housing units forged the relationship upon which the publicprivate-

partnership has been based between the federal government and residential real estate

developers (Johnson, 2014). As private sector investors benefit from the cost reduction of

government subsidy, they must balance the need to sustain profitability by providing the

required number of affordable units for low-income tenants (Shaw, 2015).

The opportunity to claim a federal tax credit is the main economic incentive

derived from tax credit investment by the private sector (Hsu, 2015). Equity for the

development is generated from private investors who then use tax credits to offset their

tax liability. Schwartz (2015) noted that private investment in affordable housing was

spurred by the tax credit program. Corporations and individuals are provided incentives

to invest in the construction or rehabilitation of affordable rental housing (Johnson,

2014). Although the investment component is largely responsible for attracting investors

into the affordable housing market, public officials are incentivized by the creation of

affordable housing (Schwartz, 2015).

Public-private partnership agreements abound in communities across the United

States. The city of Buffalo, NY, represents an example of a private-partnership agreement in which

city officials demonstrated partnership cooperation by offering 17 acres to developers for the

purpose of building affordable housing (Popiolkowski, 2015). Success stories abound where

partnerships have formed between public and private sectors to achieve affordable housing.

Municipalities have demonstrated a willingness to donate or sell land at a low cost. Additionally,

local governments, through their Community Redevelopment Agency entities, have demonstrated

a willingness to fund infrastructure for LIHTC projects that benefit the housing community (Hsu,

2015). Although public officials are influenced to merge with private developers in providing

affordable housing, private developers are generally influenced by economic reasons (Fyall, 2014).

Additional funding is commonly required for profitable operations of tax credit

developments (Hsu, 2015). Tax credit housing developments generally require multiple

layers of funding sources, in addition to the tax credit housing subsidy (Bratt, 2016). The

need for additional funding is indicative of the higher cost of the tax credit program

compared to the voucher program. Replacement of the tax credit housing program with

the voucher program was advocated by Weisbach (2006) because the voucher program

has been deemed to be less expensive and complicated.

Demand-Side Housing

The demand-side housing program aligns with the theory developed by economist

Keynes (1936), which is termed Keynesian economics or demand-side economics.

Keynesian economics is based on the premise that economic activity is driven by the

demand for goods and services (Backhouse, 2015). Demand-side economics is often at

odds with the supply-side theory, which is based on the presumption that the supply of

goods and services is the main driver of economic growth (Prizio, 2015). Government

intervention is a common theme in demand-side and supply-side housing. Vouchers

provided to low-income persons to find available housing is an example of government

intervention in demand-side economics. Money allocated to businesses through tax

credits to build affordable housing is an example of government intervention in

supplyside economics (Laffer, 1983).

Governmental interaction to boost consumer spending is a key construct of the

demand-side theory (Keynes, 1936). The HCV program, implemented in 1998 with the

Quality Housing and Work Responsibility Act, is a demand-side housing program (Niver,

2016). The HCV program was designed to pay the rent of eligible households with

government vouchers. The program provides affordable housing for renters who earn

60% or less of the area median income (Johnson, 2014). Tenant-based vouchers are

currently held by approximately two million families. Renters who have vouchers are

provided the option to rent from private landlords and tax credit developments (Hsu,

2015). Private landlords have the option of accepting or rejecting voucher holders, but tax

credit developers are prohibited from rejecting voucher holders (Schwartz, 2015).

Hsu (2015) found that there has been a growing dependence on the HCV program

by tax credit developers. Researchers have discovered that the LIHTC program may be

considered more of a hybrid of supply-side and demand-side programs as tax credit

developers use voucher holders to fill their complexes (Novogradac, 2014: Edwards and

Calder, 2017). Based on studies by HUD and others that demand-side programs are more

cost-effective than the supply-side programs, Weisbach (2006) argued that the LIHTC

should have been replaced by demand-side rent vouchers. Shaw (2015) posited that tax

credit subsidies are the only way affordable housing can be built effectively.

The HCV program is a demand-side mechanism in which federal assistance is

provided directly to tenants in the form of vouchers to find available housing

(Novogradac, 2014). Quite frequently, developers of tax credit apartments seek out

voucher holders to increase their profit margins (Hsu, 2015). Hsu noted that SRI from the

demand-side program is known to be present in some of the tax credit developments, but

there is a lack of academic literature on the number of voucher holders who comprise the

developments. Congressional budget cuts within the HCV program are threats to

sustained profitability of tax credit developments. The funding for housing assistance in

2016 was 4.6% or $2.1 billion below the level for 2010 adjusted for inflation (Rice,

2016).

Johnson (2014) observed that the LIHTC program was launched as a housing

system that would be both financially sustainable and politically popular. However, the

problem facing tax credit developers is a growing reliance on demand-side subsidy from

voucher holders to sustain profit margins. Many tax credit development companies that

develop affordable housing do not operate profitably without government subsidies (Hsu,

2015). The HCV program continues to shrink with Congressional cuts

(Affordablehousing, 2017). The aim of the study is to determine to what extent, if any,

developers rely on these demand-side subsidies to increase profitability. A gap in

knowledge exists about the financial impact of the incremental income derived from

vouchers on tax credit developments. Further research is needed to generate answers in

these areas. The proposed cuts to demand-side housing programs are indicative of the

risks to profitability faced by tax credit developers who may rely on SRI. To avoid the

risk to profitability, tax credit developers should apply greater efforts to attract eligible

non-voucher holders within supply-side housing because LIHTC funding is not subject to

annual budget cuts (Affordablehousing, 2017).

Residents of LIHTC-funded units often receive other forms of housing support.

The exchange of reduced debt for low-income units was intended to improve the cash

flow of tax credit developments (Hsu, 2015). Indicated within the review of literature is

that the tax credit subsidy does not provide adequate funding to support operating

expenses. Quite often, developers seek additional subsidies, including SRI from HCV

holders. In addition to voucher holder income, state and local governments provide

income to fill the financial gap in operating costs (Johnson, 2014). The need for

additional subsidy is demonstrated as nearly 35% of tax credit properties operated below

the break-even point (Hammett, 2015).

The findings from several researchers indicated that all production subsidy

programs are more expensive than vouchers when considering both government and

tenant costs (Niver, 2017). Although more expensive, the tax credit program is not subject

to annual Congressional cuts faced by the HCV program (Affordablehousing, 2017). As

the demand-side subsidies undergo Congressional cuts, tax credit developers will need to

operate with greater efficiency and less reliance on voucher holders. Greater marketing

efforts should be applied by tax credit developers to rent their units to a greater number of

tax credit eligible tenants and rent less to voucher holders to break what could be an

increasing dependency on the declining voucher program. With the recently proposed

funding cuts to the HCV program, the argument that Weisbach (2006) raised for replacing

the tax credit housing program with the voucher program could prove questionable.

Revealed in a recent analysis of the LIHTC database was that developers targeted

the higher income group more frequently over the past twenty years (Lew, 2016).

Sustained profitability within tax credit housing requires a delicate balance between

housing low-income tenants and generating adequate income. Bratt (2016) posited that

the affordability of rental units should be driven by tenant incomes and the ability to pay.

Johnson (2014) argued that rent levels are essential for maintaining the affordability of

housing. The affordability component within the LIHTC program caused a strain on the

profit margins of tax credit developments, resulting in the possible reliance on the

voucher program and other additional funding streams in order to sustain profit margins

(Hsu, 2015).

Profitability

Profitability is a measure of performance, which includes a level of financial

knowledge. Financial performance has long been an indicator of a firm’s success

(Edgeman, Eskildsen, & Neely, 2015). Secondary data was used to examine firm

profitability. Arguably however, calculations using financial ratios cannot address

nonfinancial issues. Ahmad and Zabri (2016) used nonfinancial performance measures to

investigate 102 Malaysian manufacturing firms. Edgeman et al. (2015) explained that the

occurrence of nonfinancial issues is commonly due to organizational failure to implement

appropriate policies. Weaknesses due to nonfinancial issues should be addressed with

organizational policies. Profitability is the dependent variable in this study and was

measured by the ROA and gross operating profits financial ratios. Tan (2016) used ROA

as a profitability indicator to successfully study 41 Chinese banks over a period of 8

years.

A non-experimental design was used in the study to examine profitability from

secondary data as the dependent variable. With the use of a non-experimental design as a

theoretical framework, profitability was used as a dependent variable by Temtime (2016)

to examine the working capital of small and medium-sized enterprises. Multiple measures

of profitability are needed to effectively gage firm performance (Abu-Shanab,

AbuShanab, & Khairallah, 2015; Mas’adeh, Tayeh, Al-Jarrah, & Tarhini, 2015; Wang,

Feng, & Lawton, 2016). The researchers noted that a more comprehensive perspective of

firm performance is gained through the application of a multi-dimensional measurement

approach versus the use of a single measurement. Multiple measures of profitability have

been advocated to convey the diversity of stakeholder’s interests (Falavi & Abdoli, 2015;

Feng, Morgan, & Rego, 2015). Firm performance includes business financial and

operational performance.

Edgeman et al. (2015) argued the traditional use of financial measures to

determine firm success. Firm performance is also determined by means other than

financial measures. Lazăr (2016) found that sales growth and value-added resulted in a

positive effect on firm performance. Bhatia and Srivastava (2016) advocated the use of

market-based measures in addition to accounting measures to gauge firm performance.

SRI and NRI are the independent variables in the study. These variables were examined

to determine the degree of relationship between profitability, the dependent variable. SRI

and non-rental income were also examined to determine whether the use of these

indicators can predict profitability.

Construct Measurement

Measurement accuracy is often achieved with quantitative research, using

statistical and mathematical approaches to develop and project experimental research

findings (Campbell & Stanley, 2015; Gorard, 2015; McCusker & Gunyadin, 2015). The

confidence in quality may be built in the discerning reader through the execution of a

quality and rigorous study. The objective for researchers using qualitative research is to

probe for why and how answers pertaining to the phenomenon (Yin, 2017). Measurement

and discernment of quality in quantitative research is often evident and achieved rigorous

construct measurement. The next section covers the review of the measurement variables

for the constructs of SRI, NRI, and profitability.

Measures of subsidized rental income. SRI is a demand-side subsidy directly

provided in the HCV program to low-income renters (Novogradac, 2014). The LIHTC is

a supply-side subsidy that is indirectly provided for eligible renters for tax credit units

(Hammett, 2015). Renters must meet 60% or below area median income (AMI) to qualify

for the LIHTC program (Schwartz (2015).

For the study, SRI is rental assistance received by tax credit developers who house

voucher holders. The rental assistance is provided in the form of HCV payments to the

tax credit companies from local housing authorities. The income from voucher holders is

targeted by developers because higher payments are received from voucher holders and

have the effect of increasing profitability (Hsu, 2015). When voucher holders are re-

directed from the private market to tax credit housing, the effect could be that fewer

LIHTC units are available for non-voucher holders. A primary focus of the study was to

determine the degree of the financial impact that the HCV program (SRI) has on the

LIHTC program.

The value for SRI is mined from 1099-Misc documents, secondary financial data

sources. The reliability of secondary data sources was argued by Johnston (2014), who

posited that researchers could collect data in a less obtrusive manner. Dunn et al. (2015)

posited that secondary data was an efficient and effective approach in research. Tasic and

Feruh (2012) noted that secondary data might not be appropriate to address a research

question under study because the purpose of the original data collection may be different.

The secondary data sources in this study aligned with the intended purpose. SRI was

calculated as a percentage of total income, to potentially determine the degree of the

financial impact that the voucher program has on the tax credit housing program.

Measures of nonrental income. Although rent restrictions lower development

income, subsidy mechanisms have been put in place to support facility operations and

debt service (Schwartz, 2015). NRI represents non-rental income that is generated by the

tax credit development entity and is an independent variable. Nearly one-fourth of

LIHTC developments operate below the break-even point (Hammett, 2015). The

construct was calculated as a percentage of total revenue, and that percentage was

converted as a value. The NRI variable was mined from the income section of the

independently audited financial statements. Rents and subsidies are frequently

supplemented with NRI to sustain tax credit housing operations. Rent levels are essential

for maintaining the affordability of housing (Johnson, 2014). NRI represents additional

services and means on the part of tax credit developments to generate supplemental

income with the intent of bolstering profitability.

Measures of profitability. The accounting measures of profitability in the study

include ROA and GOP. These proxies were used to determine the degree of profitability

for tax credit development companies (Kara et al., 2015), and posited that profitability

could be measured from the accounting ratio and measure of ROA. Temtime (2015)

argued the use of GOP as an effective accounting measure. The ROA is an accounting

financial performance measurement (Kara et al., 2015). The ROA is a familiar

measurement of profitability within accounting (Faello, 2015; Kara et al., 2015; Masadeh,

2015).

Wang et al. (2016) and Margaretha and Supartika (2016) argued that a more

comprehensive perspective of firm performance is gained through the application of a

multi-dimensional measurement approach versus the use of a single measurement. Falavi

and Abdoli (2015) supported the use of multiple measures of profitability in conveying

the diversity of stakeholder’s interests. For the study, ratio analyses for the ROA and the

GOP were applied to measure the profitability of low-income housing tax credit

developments.

The ROA and GOP ratios are standard measures of profitability used in

accounting. The ratios are used in this study to answer the research question: What is the

relationship between SRI, NRI, and profitability of the tax credit development industry?

Both U.S. companies and non-U.S. companies use ROA as a measure of profitability.

ROA is often used as a measure of profitability by non-U.S. firms (Enqvist, Graham, &

Nikkinen, 2014). The fact that the ROA was not affected by various degrees of financial

leverage was pointed out as a weakness (Enqvist et al., 2014).

Within this study, gross operating profit (GOP) was also used as a proxy for

profitability. The degree of profitability for tax credit development companies was

determined by the GOP ratio. Temtime (2016) used GOP as a proxy for profitability to

examine the working capital of small and medium-sized enterprises. The ratio of GOP is

derived from the formula of sales minus costs of goods sold to total assets minus financial

assets, where financial assets are characterized as non-physical in nature (Enqvist et al.,

2014). The GOP reflects the measure of a firm’s operating efficiency to physical assets.

Reliability and validity properties of measurements. Reliability and validity

are concepts of measurement and affect the rigor of research instruments and the

trustworthiness of findings within a study (Sing, 2017). Researchers in economics and

finance have experienced a proliferation of governmental and private databases, which

has spurred the use of archival data. A decline in the use of laboratory studies and surveys

has occurred while an increase has occurred in the use of archival data (Boyd, Bergh,

Ireland, & Ketchen, 2013). Archival data for the independent variables of SRI and NRI

and the dependent variable of profitability were used in this study to answer the research

question: What is the relationship between SRI, NRI, and Profitability in the low-income

housing tax credit industry?

The use of secondary archival data sources has increased because of the expansion

of electronic databases in the fields of finance and economics (Tasic & Feruh,

2012). Researchers used fewer surveys and laboratory studies from the 1980s to the

2000s, during which an increase occurred in the use of archival data (Boyd et al., 2012).

The limitations in research design and methodology were experienced by Cotteleer and

Wan (2016) in the use of archival data to examine a phenomenon. The reliability and

validity of archival data must be ensured as greater reliance is placed on these sources. A

detailed account of reliability and validity is provided in the following section.

Reliability is characterized by dependability and consistency in the measurement

of variables (Du & Zhou, 2012). Researchers have a variety of ways to test reliability.

The test-retest reliability is used to demonstrate the stability of a test in different sessions

(Bolarinwa, 2015). Inter-rater reliability is used to evaluate different observers scoring an

event using the same instrument (Bolarinwa, 2015). The split-half reliability analysis is a

process of dividing question items into two groups in which scores are computed for each

half and then examining their correlation (Du & Zhou, 2012). Other conventional

reliability methods include the Cronbach’s alpha coefficient and Confirmatory Factor

Analysis (Sing, 2017). No objective statistical tests exist to evaluate the reliability and

validity of archival data sources (Du & Zhou, 2012).

Measurement validity is described as the accuracy of the instrument to perform

the intended measurements (Singh, 2017). Internal validity analysis is based on the

premise that variations in the dependent variable result from changes in the independent

variables and refer to the accuracy of measurement obtained from the research

(Bolarinwa, 2015). Internal validity is supported by analysis procedures that are credible.

Construct validity refers to the extent to which an instrument measures the constructs that

are intended to be measured (Aravamudhan, & Krishnaveni, 2016). A lower degree of

measurement credibility is ascribed to an analysis that may indicate a lower construct

validity (Tasic & Feruh, 2012).

Researchers use convergent and discriminant validity to demonstrate the

correlation of scores on one measure with other measures of the same construct

(Bolarinwa, 2015). Indicated within discriminant validity is the degree to which the test

assesses the construct to be examined (Singh, 2017). Demonstrated within convergent

validity is the extent to which a construct measure and other measures of the same

construct are consistent (Duppong Hurley, Lambert, Epstein, & Stevens, 2015).

External validity may reflect how well the results of the research can be applied to

groups and environments outside of the study (Bolarinwa, 2015). External validity often

depicts a broader application of the results of the study. The reliability of secondary data

sources is gauged from the credibility ascribed to the reports (Tasic & Feruh, 2012).

Financial reports, as secondary data sources, were considered reliable by Du and Zhou

(2012). Independent auditing firms verify and attest to the credibility of the financial

statements. Quality indicators for financial statements include the process of independent

auditing, attesting, and verification (Boyd et al., 2013). Although audited financial reports

are reliable sources, the reliability of the reports can be affected by computational errors,

sample errors, and missing data (Du & Zhou, 2012).

Potential errors can occur with the use of secondary data. The occurrence of

sampling error means that an equal chance of selection is not afforded to each element of

the population (Tasic & Feruh, 2012). The purpose of the original data could have been

reorganized to meet a purpose that is different from the current study (Johnston, 2017).

Threats from secondary data sources can be mitigated when researchers use a random

sampling technique and by confirming that the same measurements and units of analysis

were used in the archival data sources (Tasic & Feruh, 2012).

Boyd et al. (2013) posited that the selection of secondary data proxies should be

based on clear theoretical specifications of the constructs and variables. Boyd et al.

further stated that the literature review should contain an explanation of the fit between

the secondary data proxies and the theoretical constructs. The occurrence of missing data

is less likely as it would be with survey and observation approaches because of the

subjectivity of participants in the survey and observation techniques (Eisenhardt,

Graebner, & Sonenshein, 2016; Slater, Joksimović, Kovanovic, Baker, & Gasevic, 2017).

Encounters with missing data should result in the removal of the entire data record from

consideration within the study and replacing the missing record with a new complete data

record (Xu, Baldea, Edgar, Wojsznis, Blevins, & Nixon, 2015).

The use of secondary data sources, such as financial statements, has been

questioned by some scholars (Du & Zhou, 2012). Financial reports were shown as

reliable and dependable sources through literature review. Miller (2016) and Temtime

(2016) used archival financial data to conduct their doctoral studies. Johnston (2017)

noted that the reliability of financial reports is often based on that external and

independent entities have already audited, verified, and attested to the accuracy of the

financial data.

Profitability Study Methodologies

Creditors, investors, and financial analysts make decisions based on the reliability

of audited financial reports (Johnston, 2017). Researchers use a variety of methods to

measure firm performance. Profitability has been commonly examined through the lens

of quantitative research. However, few researchers appear to have used qualitative

research to explore profitability. Qualitative research was used by Gandy (2015) to

explore the strategies small business owners used to achieve profitability by the end of

the first 5 years from business establishment. However, based on an extensive review of

literature, the standard in the study of profitability generally favored by researchers

included quantitative research. Temtime (2016) used a quantitative correlation design to

examine the relationship between working capital management, policies, and profitability.

Researchers, therefore, predominantly used quantitative research, invariably using a non-

experimental design to examine profitability.

Transition

Section 1 reflects descriptions of the general and specific business problems, as

well as the purpose, nature, and significance of the study to provide a foundational basis

for the research. The review of prior study models may show that a quantitative research

method, correlation design, and archival data available through public entities are

appropriate to examine the relationship between SRI, NRI, and profitability. Also

included in Section 1 was a review of academic and professional literature on the topic of

inquiry and the theoretical framework, study constructs and variables, research

measurements, and methods and designs. Assumptions, limitations, and delimitations

related to the study were also discussed in Section 1.

Section 2 includes discussions regarding the role of the researcher, the rationale

and justification of research data source mining, and the research method and design

selection. Section 2 also includes a restatement of the purpose statement, specific details

on population and sampling, ethical research instrumentation, data collection, and

analysis techniques, and the validity of the study. Section 3 includes the results and

findings of the research, the application to professional practice and the implication for

social change from the possible contribution of knowledge that the study may hold for the

community of renters and the providers of low-cost housing.

Section 2: The Project

The purpose of this quantitative correlational study was to examine the

relationship between SRI, NRI, and profitability of the tax credit development industry.

In Section I, I provided an explanation of the general business problem and the specific

business problem. Section 2 includes details on conducting the study: (a) the role of the

researcher, (b) qualifications of participants, (c) methodology, (d) tools used in data

collection, and (e) study validity.

Purpose Statement

The purpose of this quantitative correlational study was to examine the

relationship between the independent variables of SRI and NRI and the dependent

variable of profitability as it pertained to tax credit housing developers. The targeted

population consisted of tax credit housing companies in the state of Florida. The

implications for positive social change include the contribution to knowledge, which

could advance the ability to offer affordable housing to the economically disadvantaged

in society. The study findings may also provide tax credit housing developers with an

improved understanding of the association between SRI, NRI, and profitability, which

may benefit low-income tenants. With an improved understanding of the relationship

between SRI, NRI, and profitability, the potential for positive social change could include

the development of more affordable housing by developers to meet the needs of

lowincome tenants.

Role of the Researcher

This quantitative correlational study was undertaken by analyzing archival data

from financial statements and 1099 documents. The financial statements of tax credit

documents were requested from the Florida Housing Finance Corporation, which

administers tax credit developments within the state of Florida. The 1099 documents,

which reflect annual payments to these entities, were requested from public housing

authorities in the state of Florida. These agencies are required by law to honor a public

records request; therefore, data collection for this study did not pose difficulties. Email

was used to request and receive financial documents.

Multiple regression analysis was used to examine the data and determine the

degree and extent of any correlations between SRI, NRI, and profitability. I (a) collected

the data for the variables, (b) organized the data on an Excel spreadsheet for the year

2014, (c) and imported the data into Statistical Package for the Social Sciences (SPSS)

quantitative analytical software. The data collection efforts involved retrieving secondary

data for the independent variables of SRI and NRI, and the dependent variable of

profitability for the 31 tax credit developments that constituted the sample population.

I participated in all facets of the study, which included (a) ensuring appropriate

storage of data, (b) ensuring data integrity, and (c) assuring confidentiality. I reviewed the

Belmont Report protocol and completed the Protecting Human Research Participants

training by the National Institutes of Health Office of Extramural Research. Statistical

analysis results were used to assess the significance of the correlation between the

dependent and independent variables (see McCusker & Gunyadin, 2015). Analysis of the

relationships was based on the degree of covariance found between the correlated

independent variables.

Researcher bias can occur in any stage of the data collection process and may

cause a misrepresentation of the study findings (Tasic & Feruh, 2012). My role as a

researcher aligned with standardized academic protocols established by Walden

University and its Institutional Review Board (IRB). The method, analysis, and

conclusions of a study are ensured with the alignment of established IRB protocols.

Implementing measures to ensure the validity and reliability in this study helped ensure

that data analysis was robust and rigorous. The intent of the study was to examine the

degree and extent of any positive, negative, or lack of correlation between SRI, NRI, and

profitability of tax credit developments.

Data were entered from the census population into SPSS. The research question

and hypotheses were addressed based on an analysis of the data from the correlation

testing. To avoid bias, I maintained the confidentiality of participating tax credit

companies and avoided subjectivity throughout the data analysis process (see Tasic &

Feruh, 2012).

Participants

This study did not involve the recruitment of individuals as study participants

because the aim was to collect and analyze secondary data from a sample of tax credit

developments that had a minimum of 200 units and operated in the state of Florida for the

year 2015. The eligibility criteria for the tax credit developments to merit inclusion in the

study were the following: (a) active status within the low-income housing tax credit

program in 2015 and (b) financial documents containing income statements, balance

sheets, and 1099 Misc. forms for 2015.

The strategy to access the data included requests via email to the Florida Housing

Finance Corporation and officials of public housing agencies. The clerk for the Florida

Housing Finance Corporation was contacted for a listing of low-income housing tax

credit companies. After selecting the sample firms, I requested financial statements from

the Florida Housing Finance Corporation. Public housing authority officials of the sample

firms were contacted for 1099 Misc. documents. The requested documentation from these

agencies was received either by email or regular mail. As noted by Johnson (2014),

Miller (2016), and Temtime (2016), archival financial reports can be used to test

hypotheses and answer research questions.

Research Method and Design

I chose a quantitative correlational design to examine the relationship between

SRI, NRI, and profitability of tax credit developments. Researchers may choose from

quantitative, qualitative, and mixed methodologies to conduct research (McKim, 2015).

Quantitative and qualitative methodologies are the two primary research methods, and

mixed-method is a combination of the two (Fetters & Molina-Azorin, 2017).

Research Method

I used a positivist philosophy and a quantitative method instead of qualitative and

mixed methods to conduct the study. Researchers who have a positivist worldview are

often inclined to use quantitative methodology in the social or natural sciences (Yin,

2017). Quantitative methodology is favored when researchers seek to examine

relationships between variables, which may include making predictions (Babones, 2016;

Onen, 2016).

The use of the quantitative methodology was justified in support of the need to

test a relationship between SRI, NRI, and profitability within the tax credit development

industry. Statistical testing may provide answers regarding the strength and degree of

positive or negative relationships, or a lack thereof, between the independent and

dependent variables. The objective for researchers using qualitative methods is to probe

for answers to why and how questions pertaining to the phenomenon, rather than

providing conclusive results from statistical or mathematical testing (Ott & Longnecker,

2015; Rahn, 2016; Yin, 2017).

The need for conclusive results based on experimental testing rendered the

qualitative method inappropriate for the study. Within quantitative methodology, a

hypothesis or theory is tested to explain relationships between dependent and independent

variables (Babones, 2016; Simpson & Lord, 2015). The selection of a research method is

based on the research question as well as external and internal conditions of validity

(Fetters & Molina-Azorin, 2017).

Researchers may choose to use the mixed-methods approach when ambiguity

exists within the statistical data (Fetters & Molina-Azorin, 2017). The intent of

researchers using a mixed-methods approach is to gain a better understanding of the

phenomenon by including both qualitative and quantitative methods of research

(Humphreys & Jacobs, 2015; Schoonenboom, 2017; Stockman, 2015; Venkatesh, Brown

& Sullivan, 2016). Requirements in terms of additional time and integration of skills for

the different methods rendered the mixed-methods approach inappropriate for this study

(see Mckim, 2015). Because only numerical analysis was used for this study, a

mixedmethods approach was not appropriate.

Experimental and nonexperimental are two types of quantitative designs used to

test the validity of a hypothesis (McCusker & Gunyadin, 2015). Correlational research is

included in nonexperimental designs and often involves an examination of relationships

between two or more nonmanipulated variables (Fetters & Molina-Azorin, 2017).

The research question for this study addressed the possible correlational

relationships between SRI, NRO, and profitability. A numerical interpretation of a

specified relationship is established in quantitative studies rather than an attempt to

provide an explanation of the data (McCusker & Gunyadin, 2015). Quantitative research

provides results from statistical or mathematical testing (Yin, 2017). The quantitative

method with a correlational design was appropriate for this study. Researchers use

quantitative methods with a correlational design to measure variables and to test

hypotheses while addressing research questions (Fetters & Molina-Azorin, 2017).

Research Design

Experimental and nonexperimental quantitative research designs were considered

for this study. The selection was guided by the purpose of the study. Experimental designs

are used to establish a cause-and-effect relationship and include random sampling and

manipulated data (McCusker & Gunyadin, 2015). The study was nonexperimental with

no (a) manipulation of variables, (b) random sampling, or (c) data to determine a cause-

and-effect relationship. The nonexperimental design was the appropriate choice for the

study because it aligned with the purpose of the study, which entailed hypothesis testing

to examine the correlation between variables.

A correlational design for testing hypotheses was useful to determine the degree

and extent of any positive or negative, or a lack thereof, the correlation between the

independent variables of SRI and NRI and the dependent variable of profitability of tax

credit developments. A correlational design is categorized as a nonexperimental design,

which also includes causal-comparative and descriptive (McCusker & Gunyadin, 2015;

Park & Park, 2016; Reio, 2016). Based on the need to examine relationships between

variables, a correlational design was appropriate for this study (see Wagner, 2017). With

the application of an appropriate research design, suitable research methods can be

applied to hypotheses in a study (Fetters & Molina-Azorin, 2017). The current focus was

on the research question addressing the correlation between SRI, NRI, and profitability of

tax credit developments.

Population and Sampling

The population for this study was LIHTC developments in the state of Florida for

the year 2015. Listings of LIHTC developments in the state of Florida are available

through the Florida Housing Finance Corporation. Records of financial statements and

1099 Misc. documents from Florida Housing Finance Corporation and public housing

agencies for the year 2015 were used to examine the relationship between SRI, NRI, and

profitability. Archival data were mined from the financial records of the sample

population to answer the research question. The population was appropriate for this study

because LIHTC companies use SRI and NRI as components of profitability (Johnson,

2014: Hsu, 2015). The data needed to answer the research question and test the

hypotheses were publicly available.

The sample for the study consisted of financial data for 31 tax credit housing

developments in the state of Florida for 2015. Samples must be large enough for

correlations to be established between variables, must be aligned with techniques for data

analysis, and must be representative of the population (Wagner, 2017). Random

probability sampling was used because selection bias and skewed results were minimized,

and because of the potential for high internal and external validity (see McCusker &

Gunyadin, 2015). I examined financial records drawn from the population of 1,050

records, and I used a sample of 31 tax credit developments with 200 or more units within

the state of Florida.

The data for SRI, NRI, and profitability were mined from income statements,

balance sheets, and 1099 Misc. forms. Researchers use effect size and value for small,

medium, and large to determine the sample size for a population (Tabachnick & Fidell,

2013). I used a power test analysis to calculate the minimum sample size required for the

study, as suggested by Greenland et al. (2016). The sample size generated by G*Power

3.10 software for conducting a one-tailed test (see Faul et al., 2009) in which α = .05,

power = .80, and effect size = .42 (large) was 31 (see Figure 1).

Figure 1. A priori sample size (N =31) generated by using the free G*Power 3.1 software

by Faul et al. (2009).

Ethical Research

Respect, beneficence, and justice are terms described in the Belmont

Report

(USDHH, 2015) as the essential guidelines for ethical research. Wallace and Sheldon

(2015) noted that some elements for ethical research include (a) voluntary participation,

(b) confidentiality, (c) informed consent, (d) protection of vulnerable populations, and (e)

harm protection. Voluntary participation, informed consent, and freedom to withdraw

from participation align with the imperative of ensuring respect to participants (Wallace

& Sheldon, 2015). Protection of vulnerable populations, avoidance of harm, and

confidentiality fall under the category of beneficence; informed consent and voluntary

participation refer to Justice (Wallace & Sheldon, 2015). The elements did not apply to

the study because the data were mined from secondary sources. Protection and

confidentiality of mined data was maintained through the coding of participating tax

credit companies during and after the stages of data analysis

There was no need for an agreement with representatives of tax credit

developments because all the information needed for the study consisted of publicly

available secondary data. Johnston (2017) noted that ethical considerations are minimal

with research using secondary data. Because no human subjects were directly involved in

the research, there should be minimal potential for ethical threats. However, ethical

concerns can be raised by biases in collecting and analyzing data (Tasic & Feruh, 2012).

The data were sanitized by the removal of all identifiers as all names of companies on the

tables were replaced with numerical codes. Access to all data was in the sole control of

the researcher. I saved the data on a USB drive, and it will be secured in a locked safe for

five years from the official completion date of the study. After this date, all text data will

be shredded, and electronic files destroyed by program deletion using the Active@

KillDisk. The IRB must approve research studies as mandated by the governing board of

Walden University to ensure compliance in accordance with the requirements of the

rubric (Walden University, 2016). Walden University’s approval number for this study

was 10-30-19-0544500. I have reviewed the Belmont Report protocol, and as required,

completed Protecting Human Research Participants training by the National Institutes of

Health Office of Extramural Research.

Data Collection Instruments

No formal instrument was used in the study as data were extracted, mined, and

analyzed from existing financial records, audited financial statements, and 1099-Misc

forms of tax credit developments from the state of Florida. Income statements and

balance sheets were used to determine ROA and GOP, proxies for the dependent variable

profitability. SRI and NRI are the two independent variables. SRI is reflected in the 1099

Misc. Forms as payments on behalf of residents from public housing agencies to tax

credit developments. NRI includes receipts for laundry, vending, and other activities

reported on the income statement. The mining of secondary data sources could uncover

new insights that may have been passed over and not examined by other researchers and

analysts. Raw data were available from the researcher upon request.

Instrumentation

Data for this study were collected from two secondary sources used in the operations of

tax credit developments: audited financial statements and 1099 forms of the sample 31

tax credit developments. Clear theoretical specifications of the constructs and variables

should be the basis for selecting secondary data proxies (Boyd et al., 2012). SRI, an

independent variable, is embedded within the income of audited financial statements.

Because it is not a separate income line item, the data must be mined from the 1099-

Misc forms. The form reflects the aggregate dollar value of rental assistance paid to the

tax credit developments by public housing agencies on behalf of low-income tenants.

Audited financial statements and 1099-Misc forms are accessible by the public,

and available upon request. The IRS requires each tax credit development company to

prepare and file annual financial reports that follow the Generally Accepted Accounting

Principles (GAAP) and laws governing financial and accounting reports. An independent

auditing firm verifies and attests to the credibility of the financial statements, as was true

of the records from the 31 sample tax credit housing companies.

The financial reports were appropriate sources of information to measure the

constructs of SRI, NRI, and profitability for several reasons. First, audited financial

statements and the 1099-Misc forms contain all the relevant information to operationalize

the constructs to be studied, contain answers to the research question, and can be used to

test the hypotheses. Second, from a historical standpoint, researchers are provided the

capability to examine the study within the context of events that have already occurred

(Johnston, 2017). Third, when the organization is used as a unit for analytical purposes,

financial reports provide reliable measures. Fourth, financial metrics are necessary tools

for performance evaluation. Last, creditors, investors, and financial analysts make

decisions based on the reliability of audited financial reports (Johnston, 2017).

Subsidized Rent Measure

SRI represents federally provided funds to households whose incomes range

between 80% and below area median income (HUD, 2015b). Tenants are provided a

payment standard based on household size from HUD with which to rent housing from

tax credit developers and private landlords. For this study, the payment to tax credit

developers on behalf of voucher holders was termed SRI. SRI is an independent variable

and was calculated as a percentage of total revenue earned by tax credit companies. The

percentage was converted to a value to test the hypotheses. Some tax credit developments

may reflect SRI, while others may not. A higher ratio would likely indicate a greater

reliance on SRI to support the operations of the tax credit development, and conversely, a

lower ratio would indicate a lesser dependence on the variable. The following ratio was

used in computing SRI:

Nonrental Income Measures

NRI, an independent variable, represents income that is not derived from rents

generated by the tax credit development entity such as laundry and vending. The amount

was calculated as a percentage of total revenue, and that percentage was converted to a

value. The NRI variable was mined from the income section of the independently audited

financial statement. The existence of NRI data was revealed through the examination of

income statements from the 31-sample tax credit housing companies. A higher ratio

would indicate a greater reliance on NRI to support the tax credit development, and

conversely, a lower ratio would indicate a lesser dependence on the variable. The

following ratio was used in computing Non-income rental value:

−

− =

Profitability Measures

The interests of stakeholders are reflected by profitability (Margeretha

Supartika, 2016). Multiple measures of profitability have been advocated by Falavi and

Abdoli (2015) and Feng et al. (2015) to convey the diversity of stakeholder interests. A

more comprehensive perspective of firm performance is gained through the application of

a multi-dimensional measurement approach versus the use of a single measurement

(Wang et al., 2016). Multiple calculations of measures of profitability were used in this

study to reflect the interests of stakeholders of tax credit development companies. The

ROA and GOP were used as independent variables to measure the construct of

profitability. ROA is a ratio that reflects net income to total assets (Faello, 2015), as

denoted by the following formula, to be used in this study.

( ) =

The ratio of GOP is derived from the application of sales minus costs of goods

sold to total assets minus financial assets (Enqvist et al., 2014). Financial assets are

characterized as non-physical in nature. A higher GOP reflects a better return (Ukaegbu,

2014). Because tax credit development companies fall within the service industry, gross

income or total revenue was used as the numerator, which is equivalent to sales minus

cost of goods sold within the industry, as reflected in the following formula:

− #

! , ! =

Table 2

Research Constructs, Variables, and Measures

Income

Non-rental Income Non-rental Income (NRI) ℎ

Profitability Return on Asset (ROA)

Gross Operating Profits #

Financial statements are invariably independently audited, verified, and attested as

reliable sources (Boyd et al., 2013). Research quality is indicated by the reliability and

validity of measurement instruments (Singh, 2017). Researchers are admonished to

provide controls for potential sources of error and can convey the reliability and validity

properties of the measurements (Tasic & Feruh, 2012). Reliability is characterized by

dependability and consistency in the measurement of variables (Du & Zhou, 2012).

(

−

)

Although audited financial reports are reliable sources, their reliability can be

affected by computational errors, sample errors, and missing data (Du & Zhou, 2012).

The occurrence of sampling error means that an equal chance of selection is not afforded

to each element of the population (Tasic & Feruh, 2012). To address possible sampling

errors in this study, I (a) used random sampling, (b) ensured that no changes had occurred

from the original financial statements to the date and time of retrieval and access, and (c)

personally checked for incorrect and missing entries. Missing and incomplete data were

replaced. The inclusion of all the data for a given period ensures there is no chance of

selection bias.

Measurement validity refers to the functionality of an instrument based on its

design (Tasic & Feruh, 2012). Du and Zhou (2012) postulated that there are usually no

objective statistical tests to evaluate the reliability and validity of archival data sources.

However, I (a) used prior studies to guide and follow a rigorous process to select the

secondary data proxies, (b) ensured that the theoretical specifications of the study

variables were precise, and (c) ensured that the measurement variables aligned with the

theoretical constructs.

Data Collection Technique

The aim of this correlation study was to examine the potential relationships

between the independent variables of SRI, NRI, and the dependent variable of

profitability within the tax credit housing development industry. Upon obtaining IRB

approval, I collected data through email requests for audited financial statements and

1099 forms of the 31 sample tax credit developments from the Florida Housing Finance

Corporation and from public housing authorities. The population from which the sample

was drawn consists of 1,050 tax credit developments within the state of Florida. A power

test analysis should be used to calculate sample size in quantitative research (Greenland

et al., 2016). The sample size generated by G*Power 3.10 software for conducting a

1tailed test in this research (Faul et al., 2009) where α = .05, power = .80, and effect size

= .42 (large) was 31 (see Figure 1, page 49).

The examination of financial documents to evaluate company performance is akin

to the data collection technique that financial analysts and investors use to analyze the

performance of publicly traded firms (Johnston, 2017). The use of secondary data

sources, such as financial statements, has been questioned by some scholars. However,

financial reports were shown as reliable and dependable sources through extensive

endorsements found in reviewing pertinent literature. Financial reports were considered

reliable data sources by Miller (2016) and Temtime (2016). Archival financial data were

used by Brown (2018) and Tannous (2018) to conduct their doctoral studies. These

researchers have considered archival data sources as reliable and dependable.

The sample of 31 tax credit companies containing 200 or more units was drawn

from a population of 1,050 developments within the state of Florida. The sample size

generated by G*Power 3.10 software for conducting a 1-tailed test in this research (Faul

et al., 2009) where α = .05, power = .80, and effect size = .42 (large) was 31 (see Figure

1). The Florida Housing Finance Corporation, a public entity charged with administering

the low-income housing tax credit program, is a repository of financial data for tax credit

developments. The initial step in collecting secondary data must be to ensure that the data

is relevant and can be used to answer the research question (Tasic & Feruh, 2012). The

second step in secondary data collection included (a) assembling and populating mined

data from financial documents into a Microsoft Excel Spreadsheet, (b) calculating the

measures for SRI, NRI, ROA, and GOP variables for the calendar year 2015, and (c)

importing the values of the independent and dependent variables to SPSS for analysis.

Tasic and Feruh (2012) posited that rigorous analysis is achieved when

researchers have comparable and consistent financial data across companies. Johnston

(2017) provided several arguments supporting the use of secondary data sources: (a)

secondary data are economical as researchers could bypass data collection and instrument

creation by mining data from existing sources, (b) secondary sources are reliable because

the data can be collected in a less obtrusive manner, (c) secondary data proxies are more

advantageous in minimizing biases than informant-sampling approaches, and (d)

secondary data sources are used by researchers to overcome the barriers of accessing the

research setting. Johnston (2017) argued that the use of secondary data in the absence of

human subjects minimizes biases. Identifiers were removed from documents. No

information was presented in the study that would compromise the confidentiality and

identity of the entities analyzed.

Table 3 shows an example of raw data from financial documents.

Table 3

Example of Raw Data Imported From Audited Financial Statements and 1099-Misc

Forms for Company 010

Company Code 010

2013

Variables

Values

Units

Subsidized Rental Income

$6,528

SRI

0.00

320

Non-rental Income

$306,814

NRI

0.08

Total Assets

$38,879,041

ROA

-0.03

Total Revenue

$3,684,939

GOP

0.10

Total Financial Assets

$2,642,728

Net Income / Loss

($1,250,331)

Data usage from archival documents will likely be beneficial in reducing the cost

of data collection, reducing the time required for data collection, and improving data

reliability (Johnston, 2017). The authors further argued that conceptual-substantive

reasons are used to justify secondary data as the information may be the only data

available for the study of a problem. For this study, the preferable method for collecting

data was to use existing data sources. The study data are secured in a locked safe for a

period of 5 years from the date of publishing this doctoral study. Table 4 shows an

example of the relevant variables.

Table 4

Example of Measures of Research Variables

Code

SRI

NRI

ROA

GOP

# of units

0.02

0.04

0.01

0.13

Units 336

0.01

0.06

(0.06)

0.15

Units 246

0.01

0.06

(0.05)

0.13

Units 212

0.01

0.06

0.02

0.18

Units 292

0.00

0.08

(0.03)

0.1

Units 320

0.01

0.08

(0.02)

0.08

Units 264

0.03

0.07

(0.01)

0.19

Units 300

0.12

0.06

(0.02)

0.09

Units 234

0.14

0.07

(0.40)

0.24

Units 360

0.35

0.06

0.00

0.04

Units 340

0.02

0.00

(0.03)

0.25

Units 376

Note. SRI = Subsidized Rental Income, NRI = Non-rental Income, ROA = Return on Asset, GOP

= Gross Operating Profit.

Table 5

Example of Calculation of Research Variables

Mined Raw Data

Values

Variables

Calculated Data

SRI

$27,905

SRI

$0.01

NRI

$237,059

NRI

$0.07

Total Revenue

$3,195,654

ROA

-$0.00

Total Assets

$21,384,996

GOP

$0.16

Total Financial

Assets

$1,572,425

Total Assets Less

Financial

$19,812,571

Net Income / Loss

-$92,351

Note. SRI = Subsidized Rental Income, NRI = Non-rental Income, ROA = Return on

Asset, GOP = Gross Operating Profit.

Data Analysis

This study was designed to answer the research question: What is the relationship

between SRI, NRI, and profitability? The central hypothesis of the study was: There is no

significant relationship between SRI, NRI, and profitability in the low-income housing

tax credit development industry. The independent variables were SRI and NRI; the

dependent variable is profitability.

Data quality of financial statements was confirmed through the process of

independent auditing, attesting, and verification. Independent auditors are required to

certify the reliability of financial statements (Du & Zhou, 2012). In addition to the

importance of descriptive, correlation, and regression analysis to answer the research

question and test the null hypothesis, Xu et al. (2015) noted that data editing and cleaning

are also necessary steps to confirm the quality of data.

Tabachnick and Fidell (2013) emphasized that the selection of a statistical analysis

technique is based on the type of research question, the number of variables in the

research, and the measurement scale. Data editing and cleaning are quality measures used

to verify the completeness, consistency, and accuracy of data (Xu et al., 2015).

Different approaches to data collection require different techniques for data screening

(DeSimone, Harms, & DeSimone, 2015; Lin, Shen, Chen, & Sehldon, 2017; Sleeper et

al., 2017).

The process of data cleaning included checking for inaccurate or missing

information within the dataset, such as duplicate entries, and applying corrective actions.

Any encounters with missing data resulted in the removal of the entire data record from

consideration within the study and replacing the missing record with a new complete data

record, an approach suggested by (Xu et al., 2015). Replacement or removal of outliers is

another aspect of data cleaning (Tabachnick & Fidell, 2013).

Descriptive analyses were calculated to examine raw data in a form that is easier

to understand and interpret. Descriptive statistics were used to show the mean and

standard deviation for SRI, NRI, and profitability of the sample 31 tax credit housing

developments. Data interpretation is generally facilitated using descriptive analyses

(Tabachnick & Fidell, 2013). Detection of data abnormalities and understanding of data

characteristics are achieved using descriptive analyses. Graphical illustrations were used

in the study. Tabachnick and Fidell (2013) advocated the use of graphical displays such as

charts, frequency tables, and scatter plots as necessary tools for visual checking of

normality. The use of the Shapiro-Wilk test is recommended to evaluate the normality

assumption (Tabachnick & Fidell, 2013).

Correlation analysis is important to determine the strength and direction of the

relationship between SRI and NRI to profitability. The ratio Return on Assets was used as

a proxy for profitability to examine the correlations between SRI and NRI. Gross

operating profit was used as the other proxy for profitability to examine the correlations

between SRI and NRI. A correlation coefficient is used to measure and establish a linear

relationship between SRI, NRI, and profitability. The correlation coefficient is presented

as values from -1 to +1 (Greenland et al., 2016). A correlation coefficient close to -1

indicates a strong negative relationship, a correlation coefficient close to +1 indicates a

strong positive relationship, and a correlation coefficient of zero indicates that no

relationship exists between the variables (Greenland et al., 2016).

Correlation analysis was also used to help test the assumptions of linearity,

normality, multicollinearity, autocorrelation, and homoscedasticity. Multicollinearity was

evaluated by examining the correlation coefficients collinearity coefficients. The bivariate

correlation between SRI and NRI was examined. An acceptable level (1,000), which

would indicate no violation of the assumption of multicollinearity. An evaluation was

completed of the normality, linearity, homoscedasticity, and independence of residuals by

examining the normal probability plot (P-P) of the regression standardized residuals and

the scatterplot of standardized residuals for both proxies of the independent variable. A

visual inspection of the normal probability plot was undertaken to determine whether or

not the normality assumption for ROA and GOP are violated. When the residuals are in a

somewhat straight-line pattern, the indication is assumed of the absence of serious

violations to the assumptions of normality. The scatterplot of the dependent residuals was

examined to determine the presence of a dispersed data set with limited or no visible

pattern, which would indicate no gross assumptions violation.

Features in SPSS, such as the normal probability plot (P-P) and the scatterplot, are

useful tools to examine linearity, homoscedasticity, and normality. Violations of statistical

assumptions can be addressed with the application of bootstrapping and a series of other

measures. Multicollinearity is described as a condition in which the independent variables

are highly correlated with values of .80 or higher, and singularity is described as

conditions in which the predictor variables reflect perfect correlation (Vatcheva, Lee,

McCormick, & Rahbar, 2016). SPSS output on the correlation matrix is used by

researchers to observe the correlation coefficients among the independent variables in

their examination of multicollinearity and singularity (Boyd et al., 2013). The assumption

of multicollinearity is also tested by researchers in the use of variance inflation factors

(VIF) and tolerance indices (Yu et al., 2015), and were likewise in this study as well.

The results of the study contained computations and reporting of all Variance

Inflation Factors and tolerances of variables, which measures violation of the assumption

of multicollinearity. SRI and NRI may affect profitability simultaneously, and therefore,

independently measuring the effect of each variable on profitability through only

correlation analysis would yield an incorrect result. Multiple regression analysis is a

statistical procedure used to test the hypothesized simultaneous relationship between the

study variables (Harrell, 2015; Montgomery, Peck, & Vining, 2015). Analysis of variance

(ANOVA) and Chi-square tests are inappropriate for the research. ANOVA and Chisquare

tests are geared toward evaluating the effects of different interventions and group

differences on the dependent variable (Wagner, 2017). The predictor variables in a study

are continuous and not categorical (Greenland et al., 2016). Lastly, the purpose of the

research was not to analyze variances among different groups, but rather to determine

whether a relationship existed between the independent and dependent variables. The

regression coefficient, R2, indicates the power of the independent variables in explaining

the variances in the dependent variable (Tabachnick & Fidell, 2013). The percentage of

variance in the data is demonstrated by the coefficient of determination within the

regression model (Harrell, 2015).

The size of the beta coefficients for the independent variables and their R2 values

were examined and statistically analyzed by using F tests to see whether they contribute

to improving the predictive efficiency of the equation. If the test shows a statistical

significance level greater than 0.05, it would indicate a lack of significant relationships

(Greenland et al., 2016). The coefficient of determination can have values ranging from 1,

which indicates a perfect negative correlation between variables to +1, which indicates a

perfect positive correlation between variables and a correlation coefficient of zero which

reflects that the variables have no relationship (Greenland et al., 2016). In this study, I

used IBM SPSS version 21 to complete the statistical analysis. The analysis and

interpretation were reflected in the study. SPSS is a commonly used statistical software in

academic research (Boyd, 2013; Wagner, 2017). The data were secured on a flash drive,

and a summary of findings is provided in the manuscript of this research

Study Validity

Validity related to quantitative research includes three general types:

measurement validity, design validity, and inferential validity (Singh, 2017).

Measurement validity refers to the quality of the instrument and how well the instrument

performs its intended function. There are no instruments in the current study; therefore,

measurement validity was not applicable. Data were mined from the income statements,

balance sheets, and 1099 Misc. documents of tax credit housing companies for the year

2015. Total revenue, net income, and NRI were mined from the income statements; total

assets and nonfinancial assets were derived from the balance sheet, and SRI were drawn

from the 1099 Misc. documents. The data were used to calculate and examine the

independent variables, SRI and non-rental income, and the ratios ROA and GOP, proxies

for the dependent variable profitability. Internal and external validity are included in

design validity; both are applicable to the study, as well as statistical validity.

External validity refers to how well the results of the research can be generalized

to similar groups and environments outside of the study (Mckibben & Silvia (2016).

Mckibben and Silvia (2016) described external validity as the accurate interpretation of

outcomes to reflect the truth in the objective world. The application of a random

probabilistic sampling strategy would indicate a strong external validity. The focus of

external validity is a broader application of the results of the study. Bolarinwa (2015)

posited that researchers obtain a representative sample of the target population as an

effective strategy to mitigate threats to external validity. As proposed by Mckibben and

Silvia (2016), potential threats to external validity should be addressed, and the results of

research should be validated. I employed a sample (N = 31) of tax credit housing

companies, which is adequate to generalize the results to a broader population.

The internal validity is applicable for quantitative and quasi-experimental designs

because it refers to approximate truth about causal relationship interferences. Internal

validity is only relevant in experimental and quasi-experimental studies in which a causal

relationship is sought (McCusker & Gunyadin, 2015). Threats to internal validity were

not applicable because the research was a nonexperimental correlation study. However,

threats to statistical conclusion validity were identified.

Inferential validity or statistical conclusion validity refers to how legitimate the

correlation is between the dependent and independent variables (Gözü, Anandarajan, &

Simmers, 2015). Threats to statistical conclusion validity are described as low reliability

of the measure, sample size, and assumptions derived from the data (Call-Cummings,

2017). Descriptive, correlation, and multiple regression are analysis tools frequently used

to mitigate the threats to validity (McCusker & Gunyadin, 2015). Type I error is defined

as the probability of incorrectly rejecting the null hypothesis, and Type II error is defined

as falsely accepting the null hypothesis (Johnston et al., 2017). Threats to statistical

conclusion validity are minimized by quantitative researchers selecting the appropriate

value of significance (p-value) for the study (Gözü et al., 2015).

Prior to conducting the statistical tests, relevant assumptions of the study were

addressed. Assumptions relevant to multiple regression tests were identified by Lopez,

Valenzuela, Nussbaum, and Tsa (2015) as (a) variables are normally distributed, (b) a

linear relationship between the dependent variable and the independent variables, (c) the

residual is homoscedastic, indicating the independent variables have equal variance of

errors and (d) reliability of the measurement. With the violation of any of the

assumptions, the threat to a statistical conclusion would increase. An appropriate level of

significance is helpful in minimizing the risk of a Type I error. Typically, business

researchers apply an α-value of .05 and represent what I used in the study (Greenland et

al., 2016; Sing, 2017).

Transition and Summary

Section 2 includes the purpose of the study, the role of the researcher, and the

research method and design selection. Section 2 also includes details of the population

and sampling, ethical research, instrumentation, data collection and analysis techniques,

and the validity of the study. The rationale for selecting a quantitative research method,

correlation design, and archival data to examine the relationship between SRI, NRI, and

profitability was also addressed in Section 2. Section 3 includes a discussion of the results

and findings of the research and the application to professional practice. The implications

for positive social change and recommendations for action are also included in Section 3.

Section 3: Application to Professional Practice and Implications for Change

Introduction

The purpose of this quantitative correlational study was to examine the

relationship between SRI, NRI, and profitability among LIHTC companies in the state of

Florida for the year 2015. I collected financial data from the Florida Housing Finance

Corporation and from public housing authorities regarding the relationship between SRI,

NRI, and profitability. The research question for this study was the following: What is the

relationship between SRI, NRI, and profitability? The hypotheses for the study were as

follows:

H0: There is no relationship between SRI, NRI, and profitability.

H1: There is a relationship between SRI, NRI, and profitability.

The independent variables were SRI and NRI. The dependent variable of profitability was

measured by ROA and GOP.

Two measures of profitability were used within the regression model

computations. Based on the regression results, I failed to reject the null hypothesis, which

indicated that a significant relationship did not exist between SRI, NRI, and profitability.

Tax credit developers may gain knowledge in the identification of appropriate income

streams that maximize profitability from the results of this study. Included in this section

are (a) the presentation of findings, (b) application to professional practice, (c) positive

social change implications, (d) recommendations for action and additional study, (e)

reflection of my research experience, and (f) conclusion of the study.

Tests of Assumptions

The assumptions necessary to run multiple linear regression include

nonmulticollinearity, normality, linearity, homoscedasticity, and independence of

residuals. The results of the tests of assumptions are presented in this section.

Multicollinearity

Vatcheva et al. (2016) described multicollinearity as a condition in which the

independent variables are highly correlated. The independent variables of SRI and NRI

were examined with collinearity statistics to determine the correlation coefficients and the

variance inflation factor. Researchers use correlation coefficients among independent

variables to examine multicollinearity (Boyd et al., 2013). The assumption of

nonmulticollinearity is also tested using variance inflation factors and tolerance indices

(Yu et al., 2015). Multicollinearity was evaluated by examining the correlation coefficient

collinearity statistics. The bivariate correlation between SRI and NRI was acceptable

(1.000), indicating no violation of the assumption of multicollinearity. The variables also

reflected a tolerance value of 1.000 and a variance inflation factor value of

1.000.

Normality, Linearity, Homoscedasticity, and Independence of Residuals

An evaluation was completed for the normality, linearity, homoscedasticity, and

independence of residuals by examining the normal probability plot (P-P) of the

regression standardized residuals and the scatterplot of the standardized residuals for both

proxies for the dependent variable, as shown in Figure 2.

Figure 2. Normality P-P plot for variables predicting ROA and GOP.

A visual inspection of the normal probability plot in Figure 2 indicated the

normality assumptions for ROA and GOP were not violated, which meant the multiple

linear regression fit test was appropriate for the study. The figures show that the residuals

followed a somewhat straight line, indicating no serious violations of the assumptions of

normality. The scatterplot of the standardized residuals was also evaluated. A review of

the scatterplot of the dependent residuals revealed a dispersed data set with limited or no

visible pattern, which meant the linear regression model was appropriate for the data. No

gross assumption violations were detected because there was a lack of clear patterns in

the scatterplot of standardized residuals (see Figures 3, 4, and 5).

Figure 3. Scatterplot of regression residuals for ROA (homoscedasticity test).

Figure 4. Scatterplot of regression residuals for GOP (homoscedasticity test).

Figure 5. Histogram (test of normally distributed residuals).

Presentation of the Findings

Pearson correlation and linear regression were the statistical tests applied for data

analysis. The independent variables were SRI and NRI. The dependent variable was

profitability, as measured by ROA and GOP. The first correlation test addressed whether

there was a significant relationship between SRI and ROA. The second correlation test

addressed whether there was a significant relationship between NRI and ROA. The third

correlation test addressed whether there was a significant relationship between SRI and

GOP. The fourth correlation test addressed whether there was a significant relationship

between NRI and GOP.

The alternative hypothesis stated that there was a relationship between SRI, NRI,

and profitability. The null hypothesis stated that there was no relationship between SRI,

NRI, and profitability. Descriptive statistics and correlation analysis were applied to

assess the data before testing the hypothesis with multiple regression analysis. The

descriptive statistics for the independent variables (SRI and NRI) and the proxies for the

dependent variable profitability (ROA & GOP) are presented in Table 6. Figure 3 and

Figure 4 scatterplots depict the linearity between each independent variable and the

dependent variable.

Descriptive Data of Independent and Dependent Variables

Descriptive analyses are used to facilitate data interpretation (Rovai et al., 2013).

Descriptive statistics in Table 6 shows the mean (M) and the standard deviation (SD) for

all 31 tax credit housing development companies.

Table 6

Descriptive Statistics for the Independent and Dependent Variables

Variable

Variance

SRI

.06

.07

.004

NRI

.06

.03

.001

ROA

-.02

.04

.002

GOP

.18

.10

.011

The ROA and the GOP models, proxies for profitability, were used to test the

hypotheses. Two correlations were tested to examine the ROA model. There was a weak

negative correlation between SRI and ROA, r(30) = -.368, that was not statistically

significant, p = .042. The results indicated that the null hypothesis could not be rejected,

and that support was not found for the alternative hypothesis; therefore, results indicated

no significant relationship between SRI and ROA.

The correlation between NRI and ROA was -0.119, indicating a weak negative

correlation that was not statistically significant, p = .525. The results indicated that the

null hypothesis could not be rejected, and grounds were not found for the alternative

hypothesis; therefore, results indicated no significant relationship between NRI and ROA.

The multiple linear regression model was performed to test whether ROA (dependent

variable) could be predicted from SRI and NRI (independent variables). NRI and SRI

account for approximately 14.8% of the variability in ROA (see Table 7), but the

regression model was not significant, F(2,28) = 2.426, p = .107 (see Table 8). Therefore,

support for the alternative hypothesis was not found, and the null hypothesis could not be

rejected. NRI and SRI were not significant predictors of profitability as measured by

ROA. SRI (independent variable) was not significant in the estimated model coefficients

.045 > .0125; NRI (independent variable) was not significant .525 > .0125 (see Table 9),

indicating no need to conduct a separate regression to determine whether one or both

could better predict ROA.

Table 7

Regression Model for Return on Assets

Model R R2 Adj R2 Std Err

ROA/NRI,SRI .384 .148 .087 .037

Table 8

ANOVA Analysis of Regression Model for Return on Assets

Model

df MS

ROA/NRI,SRI Regression

.007

2 .003

2.426

..107

Residual

.040

28 .001

Total

.047

Note. Dependent variable: ROA, Predictors: NRI, SRI.

Table 9

Regression Test Analysis of Return on Assets (N = 31)

Variable

SErr

Beta

Constant

.002

.016

0.151

.881

SRI

-.219

.104

-0.366

-2.095

.045

NRI

-.134

.210

-0.111

-0.638

.529

Note. Dependent Variable ROA.

Two correlations were tested to examine the GOP model. The results indicated

that the correlation was neutral to negative between SRI and GOP, r(30) = -.061, p =

.738). The correlation between NRI and GOP was moderately positive, r(30) = .401, and

not statistically significant, .025 > .0125. The results indicated that the null hypothesis

could not be rejected, and support was not found for the alternate hypothesis; therefore,

results indicated no significant relationship between NRI and GOP. The multiple linear

regression model was performed to test whether GOP (dependent variable) could be

predicted from SRI and NRI (independent variables). NRI and SRI account for

approximately 16.4% of the variability in GOP (see Table 10), but the regression model

was not significant, F(2,28) = 2.747, p = .081 (see Table 11). Therefore, support to accept

the alternative hypothesis was not found, and the null hypothesis could not be rejected.

NRI and SRI were not significant predictors of profitability as measured by GOP, as

shown in Table 12.

Table 10

Regression Model for Gross Operating Profits

Model R R2 Adj R2 Std Err

GOP/NRI,SRI .405 .164 .104 .100

Table 11

ANOVA Analysis of Regression Model for Gross Operating Profits

Model

df MS

GOP/NRI,SRI Regression

.055

2 .028

2.747

.081

Residual

.282

28 .010

Total

.338

Note. Dependent variable: ROA, Predictors: NRI, SRI.

Table 12

Regression Test Analysis for Gross Operating Profits (N = 31)

Variable

SErr Beta

Constant

0.093

.044

2.140

.041

SRI

0.088

.277

.055

.317

.754

NRI

1.290

.557

.400

2.316

.028

Note. Dependent variable GOP.

The results of the correlation tests and regression models are statistically

presented to answer the research question pertaining to supply-side and demand-side

economic theories. However, the results verified that there was no relationship between

SRI, NRI, and profitability within the tax credit housing industry. Therefore, SRI, NRI

(independent variables) cannot predict profitability (dependent variable), and the null

hypotheses failed to be rejected.

The purpose of this study was to examine the influence of demand-side and

supply-side practices on profitability within tax credit housing developments in the State

of Florida. The supply-side and demand-side programs, as the theoretical framework of

this study, are used to explain how SRI and NRI affect profitability. The central concept

in the framework is that tax credit housing developers achieve profitability through

effective management of SRI and NRI. The application of the supply-side and

demandside economic theories to this study yielded a deeper understanding of the

interrelationships between SRI, NRI, and profitability.

The concept held within the supply-side theory, in which economic activity is

spurred by tax reduction to the supplier, is that profitable operations will be derived from

the governmental infusion of cash in the form of tax credits to affordable housing

developers. The subsidy, which finances a significant portion of building costs, reduces

debt service, and effectively increases income and profitability. However, reduced income

because of rent restrictions, which make the developments affordable, may negatively

affect profitability. The NRI from vending, laundry, and other activities have the effect of

increasing net income, and ultimately, profitability. The demand-side theory proponents

have held that profitability is gained through a governmental infusion of cash in the form

of vouchers to renters of affordable housing. The increase in rental income increases net

income, and effectively increases profitability.

The regression results showed that the SRI, independent variable, is not a

significant predictor of ROA, a proxy of profitability, the dependent variable. The lack of

a significant relationship between the SRI and ROA means that the HCV program is not a

major determinant of profitable operations within the tax credit housing program. Instead,

tax credit subsidy and LIHTC program rents are used to sustain profitable operations that

are based on the extant literature (Johnson, 2014; Shaw, 2015; Sullivan & Anderson,

2017).

NRI is also not a significant predictor of ROA. The lack of a significant

relationship between NRI and ROA means that ancillary income from vending, laundry,

and other activities associated with development operations is not a major determinant of

profitable operations within tax credit housing operations. This means that the tax credit

subsidy and program rents are used to sustain profitable operations, which aligns with the

extant literature (Johnson, 2014; Shaw, 2015; Sullivan & Anderson, 2017). The literature

review reflects that IHTC subsidy and rents support profitable operations of tax credit

developments.

The regression also indicated that the SRI and NRI combined are not significant

predictors of GOP. The lack of a significant relationship between SRI and GOP means

that the HCV program is not a major contributor to profitability as measured by the GOP.

The tax credit subsidy and program rents sustain tax credit operations, which aligns with

the extant literature (Johnson, 2014; Shaw, 2015; Sullivan & Anderson, 2017).

The ROA is represented as a percentage of net income divided by total assets. A

significant portion of buildings was paid from supply-side tax credits, net income, and

profitability increase because of reduced debt service costs (Prizio, 2015). The GOP ratio,

as a proxy for profitability, is represented as a percentage of total revenue divided by total

physical assets of the tax credit development. The GOP aligns with supply-side theory

because it reflects the measure of a firm’s operating efficiency to physical assets (Enqvist et

al., 2014). SRI and NRI are components of total revenue, yet the results of findings

indicate that the independent variables are not significant predictors of profitability

measured by GOP.

Applications to Professional Practice

Based on the findings of this study, it appeared that SRI has no effect on

profitability as no significant relationship between SRI and profitability were observed

from the analysis. Tax credit developers use the supply-side program of the tax credit

subsidy and program rents to sustain profitable operations (Johnson, 2014; Shaw, 2015;

Sullivan & Anderson, 2017). Various layers of income streams are used to support the

supply-side program of LIHTC, including the demand-side program of HCVs (HSU,

2015; Johnson, 2014; Novogradac, 2014). SRI is known to be present in many tax credit

developments, providing additional income for operations (Novogradac, 2014).

Further, NRI was found to have no effect on profitability because there was no

significant relationship between NRI and profitability. In examining the regression

analysis, there were no significant relationships between NRI and the two proxies for

profitability, ROA and GOP. NRI could not be used as a predictor of profitability. As a

component of the supply-side program, which includes ancillary services such as laundry

and vending, NRI contributed more to the profitability of tax credit operations than SRI,

based on the findings of the study. SRI derived from the HCV program was found to be a

dwindling income stream because of Congressional budget cuts (Affordablehousing,

2017).

As a result, tax credit developers could improve the stability of their income

streams by targeting program eligible tenants because low-income housing tax credits are

not subject to annual Congressional budget cuts (Hammett, 2015). NRI, as part of

supplyside operations, is ancillary to program rental income. Tenants who live in the

LIHTC units who are not recipients of SRI are likely to continue using laundry and

vending services because they are not affected by subsidy cuts. The identification of

alternative streams of income for tax credit development operations should serve to

encourage researchers in their work.

Implications for Social Change

The implications for positive social change include the potential for tax credit

developers to provide jobs, benefits, and training to workers. Communities may benefit

from increased economic activity and capital improvements through the construction of

new and rehabilitated housing developments. Society may benefit from this study as

lowincome renters move from sub-standard living conditions to better affordable housing

opportunities

Another implication for positive social change from this study is the potential for

tax credit developers to use incremental income in ways that benefit the poor. The

incremental income component is found within the hybrid relationship between LIHTC

and the HCV program (HSU, 2015; Novogradac, 2014). The rate for rental income

derived from eligible LIHTC tenants is less than the HCV program (Novogradac, 2014).

Tax credit developers and public housing officials may be informed of ways to decrease

rent burdens, in which tenants pay more than 30% of monthly income in rent and utilities

(Bratt, 2016; HUD, 2015b; JCHS 2015).

Recommendations for Action

Tax credit developers may benefit from this study by identifying sustainable

income streams that support profitability. Many tax credit developers have considered

income from Section 8 vouchers as a preferred source of income and have targeted

renters from this demand-side program to fill their complexes (Hsu, 2015; Novogradac,

2014). However, rental income from eligible supply-side tenants was designed to sustain

profitable operations (Johnson, 2014; Shaw, 2015; Sullivan & Anderson, 2017).

Tax credit developers can benefit by identifying which income streams have the

greatest sustainable effect on profitability. I recommend that steps are taken by tax credit

housing developers to identify sources of income that offer sustainable profitability. It is

also important to identify which streams of income may pose the greatest risk to

profitability in the long run. Based on the findings of the study, SRI and NRI are not

significant predictors of profitability as measured by ROA and GOP.

I further recommend that tax credit developers focus their marketing efforts on

attracting traditional tax credit eligible tenants. Section 8, tenant income, as represented

by SRI, was present in 87% of the tax credit developments within the study. As indicated

by the regression analysis, however, no significant relationship existed between the

income generated from the HCV program and profitability, nor could the independent

variable be used as a predictor of profitability. From the regression analysis, NRI, which

includes income from vending, laundry, and other ancillary activities, held no significant

relationship between ROA and GOP, nor could it be used as a predictor of profitability.

Recommendations for Further Research

Future researchers who examine affordable housing data and practices may

benefit from the findings of this study by gaining an understanding of the relationship

between SRI, NRI, and profitability. Examination of potential income sources may

initially appear beneficial for profitable operations but could pose a risk depending on the

stability of the funding sources. Funding sources that are subject to annual reductions or

termination should be viewed as a risk to sustainable operations.

I also recommend further research from the perspective of a qualitative study. A

qualitative study would be required to explore how and why developers use incremental

income in their operations. Exploring income streams may lead to further insights into the

operational interplay between demand-side and supply-side programs.

Reflections

After learning the difference between research methods, my decision to use a

quantitative approach rather than a qualitative approach became quite clear. The purpose

of examining the effect of different income streams on profitability within tax credit

housing required a quantitative method. The process of acquiring data from various

public housing agencies posed the greatest challenge. A surprising discovery within my

research was that the ROA ratio, one of the proxies for profitability, yielded an

overwhelming majority of negative results because net income was negative. The lesson

learned is that data are not always what is expected by the researcher. Although the GOP

ratio, the other proxy for profitability, was positive, I was surprised that the results were

different from what I had anticipated. Profitability was not significantly affected by the

two independent variables of SRI and NRI. From a business standpoint, tax credit

developers use various layers of income, including SRI and NRI, to sustain operations.

The tax credit housing industry is comprised of public-private partnerships in which a

variety of funding sources is often required.

I found that gaining a working knowledge of the SPSS statistical software was

both interesting and challenging. The application of a quantitative methodology requires

the use of various statistical tests that include correlation and regression. A degree of

intense study was personally needed for me to understand the tests and then to properly

write the interpretation. Confidence was gained from using the SPSS software and

generating reports. In the beginning, my journey seemed endless and appeared enormous,

but through persistence and perseverance, milestones were reached, and the goal was

accomplished.

Conclusion

The purpose of this study was to examine relationships between SRI, NRI, and

profitability. The effect of SRI and NRI, independent variables, to predict ROA and GOP,

proxies for profitability, were examined within the research. This study was comprised of

a sample of 31 tax credit housing developments within the state of Florida for the year

2015. Within Section 3, the results of descriptive correlation and regression analysis on

the relationship between SRI, NRI, and profitability were provided. The findings

confirmed that SRI and NRI are not significant predictors of firm profitability. Tax credit

housing developers should consider the results of this test in assessing the viability of

different income streams.

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Appendix A: Certificate of Completion of the Student Researcher’s Course

Subsidized Rental Income, Nonrental Income, and Profitability in Tax Credit Development Companies

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