INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025

Socio-Economic Landscape and Housing Aspirations: The Role of

Staff Cooperative Societies in Nigerian Tertiary Institutions

Omotosho, Babatunde Olumakinde., Akinlotan, Peter Adetunji., Olatunji, Daniel Abiodun

Marine and Coastal Environmental Science, Texas A&M University, United States of America (USA)

DOI: https://dx.doi.org/10.47772/IJRISS.2025.910000800

Received: 07 November 2025; Accepted: 14 November 2025; Published: 24 November 2025

ABSTRACT

In Nigeria, where access to affordable housing remains a persistent challenge, public servants in tertiary

institutions are increasingly turning to community-driven solutions. This study quantitatively examines the role

of staff cooperative societies as crucial enablers of housing aspirations, using data from a large-scale cross-

sectional survey of 2,178 members across nine public tertiary institutions in Oyo State, Nigeria. The findings

reveal high homeownership rates (>70%), confirming cooperatives as effective, self-reliant systems in meeting

housing aspirations of members. However, a multivariate interaction model demonstrates that this success is

highly conditional and unevenly distributed across different groups. The analysis reveals a significant academic

advantage at lower-middle income thresholds (specifically, N100,000–N200,000), as the probability of

homeownership for academic staff increases substantially, whereas it declines for their non-academic peers in

the same income bracket. This conditional disparity based on professional status exists alongside a significant

and persistent gender gap that disadvantages female members. We conclude that staff cooperative societies

function as a potent but imperfect engine of housing aspiration, reflecting how broader institutional and social

hierarchies shape outcomes. The study highlights the need for policies that support these cooperatives and

address the intersecting inequalities within their ranks and operations.

Keywords: Housing aspirations, housing delivery, socio-economic characteristics, staff cooperatives societies,

tertiary institutions, Nigeria

INTRODUCTION

The housing sector, with its inherent complexity and dynamism, is an important sector of any nation's socio-

economic stability and development (Tulumello & Dagkouli-Kyriakoglou, 2024). An imbalance between

housing demand and supply, particularly when coupled with poor government regulation, inevitably leads to

price hikes that push the urban poor and middle class into precarious living conditions (Azam-Khan, 2023). This

reality is particularly evident in Nigeria, where access to adequate and affordable housing remains elusive for

many, especially for public servants in tertiary institutions. Similar to many urban and peri-urban areas in the

country, the study region of Oyo State is facing significant housing stress caused by rapid and often chaotic

urbanization (Okedare and Fawole, 2023). This expansion, however, has not been matched by infrastructural

development, leading to a host of urban pressures including congested housing, high land values, and social

exclusion (Akanmu et al., 2020; Yusuf and Ojewale, 2023). With government struggling to keep pace with the

infrastructural demands of this growth (Oladehinde et al., 2024), a significant gap in housing provision has

emerged. This situation stands in direct opposition to global development agendas, such as the UN's Sustainable

Development Goal 11, which aims to ensure access for all to safe, affordable, and sustainable housing

(Ebekozien et al., 2024). Caught between modest incomes and an ineffective formal mortgage system, these

employees navigate a landscape where the dream of homeownership is often distant (Adedeji, 2017). In this

context of state and market failure, institution-based cooperative societies have emerged as a grassroots

mechanism for self-reliance, offering members a viable pathway to achieving their housing goals through mutual

savings and collective action (Effioms et al., 2014).

While the role of cooperatives in economic empowerment is acknowledged, the specific dynamics of their

housing interventions remain underexplored. Previous and early research has established their general

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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025

effectiveness (Olagunju, 2023), but a gap persists in understanding how the diverse characteristics of their

members shape their success. The literature has not yet moved from a descriptive evaluation of cooperative

strategies to an explanatory analysis of member outcomes.

Therefore, this study presents a member-centric and outcome-oriented analysis that moves from asking what

cooperatives do to investigating who succeeds within them and why. Rather than treating cooperative members

as a homogeneous group, this study disaggregates the membership by key socio-economic characteristics to

provide a quantitative analysis of how factors such as professional hierarchy and gender mediate the success of

this vital community-driven housing model. We contend that the accurate measure of a cooperative's success

lies in its responsiveness to the lived realities of its members. By leveraging a large-scale survey of 2,178

members and multivariate analysis, this study seeks to answer the core question: How do members' socio-

economic characteristics, such as gender, professional identity, and income level, interact with the cooperative

system to produce specific and often unequal housing outcomes? By empirically modeling these relationships,

our study provides an insight into the internal aspects of this self-reliant model, offering a nuanced answer to

whether cooperatives work, and also how they work, for whom they work best, and where systemic challenges

remain.

The paper is structured as follows. The next section reviews the literature on three interconnected themes: the

socio-economic determinants of housing demand, the global and local context of the cooperative model as an

alternative to market-driven provision, and the specific analytical gaps in understanding its responsiveness to

member diversity. The third section outlines the study's methodology, including the sampling strategy, data

collection, and the multi-stage quantitative analysis employed. The fourth section presents the core empirical

findings of the paper, moving from descriptive statistics to a series of bivariate and multivariate regression

models that reveal the complex and often unequal housing outcomes. The fifth section discusses the broader

implications of these findings, interpreting them through the lens of self-reliance, institutional responsiveness,

and structural inequality. Finally, the conclusion summarizes the core arguments and offers specific policy

implications for strengthening this vital community-driven housing model.

LITERATURE REVIEW

For a significant majority of the Nigerian population, the aspiration of homeownership remains a deeply held

ambition, yet it is an objective that proves exceedingly difficult to achieve in practice. Defined as housing that

can be secured without financial distress, affordable housing has become unattainable for many, largely due to

a confluence of low incomes, the high cost of building materials, and an ineffective formal mortgage system

(Obi & Ubani, 2014). This challenge is particularly acute for public servants in tertiary institutions, who navigate

a precarious space between stable employment and modest salaries. In this context, where formal state and

market systems have proven inadequate, institution-based cooperative societies have emerged as a grassroots

mechanism for self-reliance. Understanding their role requires an engagement with the literature on the socio-

economic forces that shape housing demand, the nature of the cooperative model as a global and local alternative,

and the gaps in understanding its true impact on a diverse membership.

The literature universally acknowledges that socio-economic status acts as the primary gatekeeper to housing

access and quality (Ayodele & Eniola, 2021). While income is consistently cited as the most powerful variable

shaping a household's purchasing power (Yang & Chen, 2014), a singular focus on this metric is insufficient in

the Nigerian context. The narrative here is fundamentally different from that of developed economies; it is

characterized by income volatility, hyperinflation in building material costs that can derail projects mid-

construction, and a near-total reliance on informal finance due to the moribund state of the formal mortgage

sector (Adedeji, 2017). The existing literature often fails to provide an in-depth analysis of these variables as

they relate specifically to personnel in Nigerian tertiary institutions, a distinct group that contends with both rigid

salary frameworks and challenges in the widespread informal housing sector. This necessitates a shift from

examining broad determinants to investigating how particular institutional mechanisms, such as cooperatives,

can mediate or influence these structural limitations.

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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

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The cooperative society has emerged as one such mechanism, filling the institutional vacuum left by the state

and the formal private market. This localized response in Nigeria is not an isolated phenomenon but mirrors a

global reconsideration of cooperative models in the face of a worldwide housing crisis. As noted by Barenstein

et al., (2022), with over a billion people lacking adequate housing due to a convergence of failed state policies

and the private sector's inability to cater to low-income populations, cooperatives are being reconsidered as

relevant actors. This reframes the cooperative as a housing provider and an ideological alternative that champions

“de-commodified” housing, valuing it as a human right rather than a speculative commodity. The dual character

of housing thus situates cooperatives at the intersection of practical necessity and normative critique. This

ideological dimension and perspective is deepened by the work of Díaz-Parra et al., (2024), who conceptualize

cooperative housing as a form of "self-managed habitat production," arguing that this represents a potentially

universal model for anti-capitalist struggles in urban settings. This compelling theoretical lens allows the

Nigerian cooperative to be viewed as a pragmatic financial tool born of necessity, and equally as a local

manifestation of a global struggle against market-driven housing dispossession.

While this global perspective provides an ideological framework, the translation of the universalist aspiration

models into on-the-ground success is fraught with complexity and shaped by local context. The comparative

work of Encinales et al., (2024) in Latin America demonstrates that the success of such models is deeply

connected to the national political and institutional factors. Their research highlights a contrast between state-

supported models like Uruguay's and the challenging, often hostile, environments of other neo-liberal regimes.

Applying this insight to Nigeria, research on Nigerian housing cooperatives has evolved from institutional

descriptions to evaluations of their operational effectiveness. A "top-down" perspective has identified key

strategies and constraints from the viewpoint of executives (Azeez and Mogaji-Allison, 2017), while "bottom-

up" studies (Abdulkareem et al., 2020) have confirmed high levels of general member satisfaction (Olagunju,

2023). While this body of research establishes that cooperatives work, it often treats the membership as a

relatively homogenous group, thereby overlooking the critical question of responsiveness to diversity. What

remains underexplored is how member diversity in terms of gender, professional identity, life-cycle stage, and

income affects who ultimately achieves homeownership. Bridging this gap requires moving from describing

institutional mechanisms to explaining member-level outcomes. This raises a central question for the Nigerian

context: is the staff cooperative society a form of radical self-management, or is it a pragmatic coping mechanism

that ultimately props up a failing system? While Nigerian scholars rightly point to their effectiveness (Azih,

2021; Yakub et al., 2012), this success is achieved in a context of policy disconnect, forcing a degree of self-

reliance that places immense pressure on internal governance and financial discipline (Olotuah, 2015).

This brings into focus the central gap in the existing literature. Research on Nigerian housing cooperatives has

evolved from institutional descriptions to evaluations of their operational effectiveness. The literature has not

yet moved from a descriptive evaluation of strategies to an explanatory analysis of member outcomes. There is

a lack of research that statistically models how the diverse socio-economic characteristics of members, such as

gender, professional identity, life-cycle stage, and income level, independently and collectively predict the

ultimate outcome of homeownership. This study, therefore, intervenes in this debate by shifting the focus from

the cooperative's product line to the members' lived realities. We aim to provide a deeper, more analytical

understanding of who succeeds in this self-reliant ecosystem and why, thereby contributing a more nuanced and

equitable perspective to the global discourse on self-managed housing solutions.

DATA AND METHODS

Study Area

The study was conducted in Oyo State, located in the Southwest geopolitical zone of Nigeria. It is home to

Ibadan (the capital city), one of the largest metropolitan areas in West Africa, alongside numerous other major

towns such as Oyo, Ogbomoso, and Saki. The state's significant population and diverse urban landscape create

intense pressure on its housing market, making it a strategic locus for housing policy research.

Public tertiary institutions in Oyo State, totaling eighteen (18), serve as major centers of public sector

employment. Nine (9) institutions were purposefully selected for this study to ensure representation across

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universities, polytechnics, and colleges of education as well as the state’s three senatorial districts. The sampled

institutions include the University of Ibadan (UI), Ladoke Akintola University of Technology (LAUTECH), The

Polytechnic, Ibadan (TPI), and the Federal College of Education (Special), Oyo. Other sampled institutions

are The Oke-Ogun Polytechnic, Saki (TOPS), the Federal College of Animal Health and Production Technology

(FCAHPT), Ibadan, the College of Hygiene and Health Technology, Ibadan, the Oyo State College of Education,

Lanlate (CEL), and the Oyo State University of Education (OSUE). This sample, constituting 50% of the state’s

public tertiary institutions, provides a robust cross-section enabling broad generalization within this workforce.

As recommended by Creswell (2014), such a proportion provides sufficient statistical power while remaining

practical in terms of logistics, time, and resources.

Data

The primary data for this study consists of cross-sectional survey data collected in 2024 via a structured

questionnaire developed by adapting established instruments from previous cooperative housing studies (e.g.,

Azeez & Mogaji-Allison, 2017) and tailored to the context of tertiary institutions in Oyo State. To ensure clarity,

relevance, and content validity, the instrument was pre-tested, and feedback led to rewording unclear items and

improving response options. A proportional systematic sampling from stratified lists was utilized in this study.

Membership lists from each institution were used to establish sampling intervals (k), and every k-th member was

selected for participation, ensuring proportional representation across the sample. Of the 2,400 questionnaires

distributed, 2,178 valid responses were retrieved (response rate: 90.8%), minimizing non-response bias. No post-

stratification weighting was necessary. To ensure analytical transparency, the key variables utilized in this study

were carefully defined. Table I summarizes the descriptions, response options, and coding schemes for each

variable used in the analysis.

Table I: Variable Coding and Operationalization

Variable

Response Options

Code/Type

Analytical Role

Home

Ownership

Owner / Non-owner

1 = Owner, 0 = Non- Dependent variable (binary) in

owner logistic regression

Age

18–28, 29–39, 40–50, 1–5

Control

variable

(ordered

51–60, 61+

categorical)

Gender

Male, Female

1 = Male, 2 = Female

Key predictor variable (binary)

Emp_cat

Academic,

Academic

Non- 1 = Academic, 2 = Non- Key predictor & moderator variable

Academic

(binary)

Income

<₦100,000,

1–5

Key predictor variable (ordered

categorical)

100,000–200,000,

200,000–300,000,

300,000–400,000,

>₦400,000

Coop_years

Count

<5, 5–10, 11–15, >15

1–4

Control

categorical)

variable

(ordered

1–4

Integer

Integer; used as a measure of

member diversification

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Rooms

Cost

3–5

Numeric

Scale variable; outcome for group

comparisons (t-test)

Ordinal brackets by ₦ 1–5

Control variable (ordinal)

value

Received

Assistance

Yes / No

1 = Yes, 0 = No

Independent variable (binary) for

secondary analysis

Note: The table summarizes the coding and operational definitions of key analytical variables for the structured

questionnaire administered.

For each respondent, the dataset contains a comprehensive set of socio-economic attributes, including categorical

measures such as age, employment category (Academic/Non-Academic) and Monthly Income bracket. These

variables form the basis of our analysis of the demographic and economic landscape of the cooperative

membership. The choice of tertiary institutions was motivated by their relatively stable staff structures, the

established presence of cooperatives, and the significant housing needs of their employees.

Housing-specific attributes were gathered to detail the dependent and outcome variables of the study. These

attributes include the respondent's current housing ownership status (tenure), the type of housing design (e.g.,

bungalow, duplex), and the physical scale of the property, measured by the number of habitable rooms. Financial

aspects of housing projects were evaluated using categorical variables for the cost of land and the cost of

construction. Additionally, process-related variables such as the source of land procurement and the duration to

build the house were collected to provide a comprehensive understanding of the housing journey.

A set of variables was developed to assess the role and perception of the cooperative society. We measured the

depth of member engagement using variables such as Years of Cooperative membership and the number of

Cooperatives patronized. The main independent variables for evaluating the Cooperative's impact include a

binary indicator indicating whether the member received housing assistance from the Cooperative, the specific

type of assistance received, and an ordinal measure of the perceived benefits for housing projects. Table II

presents the description and summary statistics for some key variables used in the analysis.

Table II: Summary Statistics

Variable

Description

Obs

Mean

.75

Std. Dev.

.43

Min Max

Dependent Variable

Home Ownership

Private Homeownership Status

2178

0

1

Socio-Economic Predictors

Gender

Respondent's Gender

2178

1.41

3.54

1.55

3.01

.49

1

2

5

2

5

Age

Respondent's Age Group

Employment Category

1.22

.49

Emp_category

Income

Monthly Income Bracket (in ₦)

1.39

Housing & Cooperative Variables

Rooms

Number of Habitable Rooms

2178

3.88

.57

3

5

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Coop_years

Cost

Years of Coop. Membership

Cost of land

2178

2.71

2.58

2.17

2.21

.92

1.32

.66

.77

1

4

5

4

5

Count

Number of Cooperatives

Cost of construction

Construction

Note: This table presents the primary variables used in the regression and key bivariate analyses.

The data shows that the housing projects undertaken by members are relatively uniform in scale, with an average

of 3.88 habitable rooms and a low standard deviation (SD=0.57). The financial scope of these projects is also

moderate; the central tendency for construction costs falls within the ₦15-25 million bracket (mean category =

2.21), and the typical cost of land is centered around the ₦1-2 million bracket (mean category = 2.58). The

findings also highlight the long-term nature of this housing strategy. An average member has belonged to a

cooperative for over ten years (mean category = 2.71). A significant strategy employed by members is

diversification, with the typical member belonging to at least two cooperative societies (mean category = 2.17),

likely to pool sufficient capital for their housing projects.

METHODS

In this study, we adopted a quantitative survey research design to assess cooperative societies' roles in housing

delivery, measure the socio-economic characteristics of a large population of cooperative members, and model

the factors that predict housing outcomes. Our analytical strategy progresses from broad descriptive profiling to

bivariate and multivariate modeling, aiming to identify the independent predictors of homeownership and

explore how key socio-economic factors interact to shape housing outcomes.

Our analyses involve three stages. First, we use descriptive statistics (frequencies and percentages) to profile the

demographic and economic landscape of the sample. Second, we use bivariate statistical tests (Pearson's Chi-

Square test and independent-samples t-tests) to examine the relationships between member characteristics and

housing outcomes. For ordinal variables treated as pseudo-interval scales, robustness checks using non-

parametric equivalents (Mann-Whitney U tests) yielded substantively identical inferences. Third, to move

beyond simple associations, we estimate a series of multivariate logistic regression models. The dependent

variable, home ownership, is a binary indicator for private homeownership. Our initial model estimates the main

effects of key predictors, and we then build on this by estimating models that include interaction terms to test

whether the effects of specific characteristics are conditional upon others. Specifically, we test for interactions

between Gender × Employment Category and Income × Employment Category to determine if disparities

concentrate within specific strata. The model is conceptually robust, and its results are presented as odds ratios

(OR) and predicted probabilities for easier interpretation.

After estimating the model, we conducted post-regression diagnostics to assess its validity and robustness. We

conducted a Variance Inflation Factor (VIF) test, which showed a mean VIF of 4.09, with no individual predictor

exceeding the standard risk threshold of 10, indicating that collinearity did not distort the model's estimates. We

assessed model fit and calibration using the Hosmer-Lemeshow goodness-of-fit test and pseudo-R², with

classification accuracy metrics. For cases with missing data (less than 2% of observations), we employed listwise

deletion after confirming that it resulted in minimal demographic bias. Additionally, we performed a residual

analysis and examined influential observations to ensure the stability of the results and confirm that they were

not unduly affected by outliers.

RESULTS

The analysis of the survey data provides a comprehensive picture of the cooperative housing ecosystem,

revealing a model that is broadly successful yet influenced by the complex socio-economic realities of its

members. The findings are presented in four parts: the socio-economic profile of the membership, key bivariate

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relationships shaping housing outcomes, the universal perception of the cooperative’s value, and a multivariate

model identifying the independent predictors of homeownership.

The Socio-Economic Profile of Cooperative Members

The foundation of the cooperative system is its members. As detailed in Table III, the membership is primarily

composed of mid-to-late career staff. The majority of respondents are aged between 40 and 60 (63.2%) and are

non-academic staff (55.2%). Economically, the data reveals a demographic largely excluded from formal

mortgage finance, a significant majority (60.9%) earn less than ₦300,000 monthly. Despite these modest

incomes, the cooperative model demonstrates remarkable success as 75% of the members reported owning their

own private homes.

Table III: Socio-Economic Profile of Cooperative Members (N = 2,178)

Characteristic

Age Group

Category

Frequency (N)

143

Percentage (%)

6.6

18–28

29–39

352

16.2

40–50

550

25.3

51–60

438

20.1

61 and above

Male

695

31.9

1,200

978

55.1

Gender

Female

44.9

Academic

976

44.8

Employment Category

Monthly Income

Non-Academic

N30,000 – N100,000

N100,000 – N200,000

N200,000 – N300,000

N300,000 – N400,000

Above N400,000

1,202

416

55.2

19.1

433

19.9

434

19.9

486

22.3

409

18.8

Note: The table profiled the socio-economic characteristics of the Cooperative members for the study.

Bivariate Relationships Shaping Housing Outcomes

To understand the factors influencing housing success, bivariate analyses were conducted. These tests reveal

that outcomes are significantly associated with members' income, professional roles, and life-cycle stage.

A Pearson Chi-Square test revealed a significant non-linear relationship between income brackets and housing

tenure, as shown in Table IV. Counterintuitively, the highest income group had the lowest rate of homeownership

(66.8%), while the highest rate (85.7%) was found in the middle-income N200,000–N300,000 bracket. This

association was statistically significant (χ²(12) = 101.73, p < 0.001).

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Table IV: Housing Tenure Status by Monthly Income Bracket

Monthly Income Bracket

N30,000 – N100,000

N100,000 – N200,000

N200,000 – N300,000

N300,000 – N400,000

Above N400,000

Private Owner Institutional Rental

Family Home Total (N)

323 (77.6%)

320 (73.9%)

372 (85.7%)

349 (71.8%)

273 (66.8%)

1,637 (75.2%)

6 (1.4%)

68 (16.4%)

52 (12.0%)

54 (12.4%)

82 (16.9%)

88 (21.5%)

19 (4.6%)

17 (3.9%)

6 (1.4%)

8 (1.6%)

14 (3.4%)

416

433

434

486

409

2,178

44 (10.2%)

2 (0.5%)

47 (9.7%)

34 (8.3%)

133 (6.1%)

Total

344 (15.8%) 64 (2.9%)

Note: Row percentages are shown in parentheses. Pearson χ²(12) = 101.73, p < 0.001.

Professional identity also emerged as a key differentiator. As shown in Table V, a significant association was

found between employment category and housing design (χ²(2) = 21.98, p < 0.001). While bungalows are the

predominant housing type (91.3%), academic staff are nearly three times as likely to own a duplex (5.3%) as

their non-academic counterparts (1.8%). This difference in housing scale and design is a tangible manifestation

of institutional status, suggesting that the benefits derived from the cooperative extend beyond mere

homeownership to include the quality and prestige of the housing acquired.

Table V: Type of Housing by Employment Category

Employment Category

Academic

Bungalow

Duplex

Others

Total (N)

976

867 (88.8%)

1,122 (93.3%)

1,989 (91.3%)

52 (5.3%)

22 (1.8%)

74 (3.4%)

57 (5.8%)

58 (4.8%)

115 (5.3%)

Non-Academic

Total

1,202

2,178

Note: Row percentages are shown in parentheses. Pearson χ²(2) = 21.98, p < 0.001. The "Others" category

includes architectural styles such as Brazilian-style bungalows and other less common designs.

This difference in housing scale was further confirmed by an independent t-test, which showed that academic

staff reside in homes with a statistically significant higher average number of rooms (M = 3.92, SD = 0.57)

compared to non-academic staff (M = 3.85, SD = 0.58), t(2176) = 2.99, p = 0.003. Finally, members' strategies

evolve with their life-cycle stage, with a significant association found between age group and the source of land

procurement (χ²(16) = 54.09, p < 0.001). Younger members tend to rely on personal savings, while mid-career

members are more likely to purchase land, often with cooperative credit.

Crucially, despite the clear differences in housing outcomes, there was no statistically significant difference in

the perceived benefit of the cooperative between academic staff (M = 1.12, SD = 0.33) and non-academic staff

(M = 1.11, SD = 0.32), t(2176) = 0.49, p = 0.622. This indicates that members across the professional spectrum

feel equally and highly supported by the system, suggesting a universally high level of satisfaction with the

cooperative's role.

Multivariate Predictors of Homeownership

To build a comprehensive model of homeownership, we tested for interaction effects between our key predictors.

The final logistic regression model included interaction terms for both Gender × Employment Category and

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Income × Employment Category. The overall model was statistically significant (LR χ²(14) = 154.26, p < 0.001)

and showed an improved fit over a main-effects-only model (Pseudo R² = 0.0632). The analysis revealed highly

significant interaction effects, indicating that the impact of gender and income on homeownership is

fundamentally different for academic versus non-academic staff.

The full model results are presented in Table VII. The analysis revealed a highly significant interaction effect

between gender and employment category. To interpret this complex but central interaction, we calculated the

predicted probabilities of homeownership for each subgroup, holding all other variables at their means. Figure

1 provides a clear visualization of this relationship.

Figure 1: Marginal Effects Plot of the Interaction between Gender and Employment Category on Predicted

Homeownership Probability (with 95% CIs)

Note: The plot displays the predicted probability of owning a home, calculated from the logistic regression model

in Table VII, holding all other variables at their means.

The plot reveals that the gender penalty for homeownership is paradoxically most severe among the higher-

status academic staff. While the predicted probability of homeownership for an academic male is approximately

92%, it drops to 71% for an academic female, a gap of 21 percentage points. In contrast, the gender gap among

non-academic staff is substantially smaller, with predicted probabilities of 73% for males and 67% for females,

a gap of only 6 percentage points.

Figure 2: Marginal Effects Plot of the Interaction between Income and Employment Category on Predicted

Homeownership Probability (with 95% CIs)

Note: The plot displays the predicted probability of owning a home, calculated from the logistic regression model

in Table VII, holding all other variables at their means.

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Figure 2 illustrates the second significant interaction, revealing that the academic advantage is not constant but

is activated at a key income threshold. The plot traces the predicted homeownership probabilities for both

academic and non-academic staff across income levels. Initially, at the lowest income bracket (₦30,000–

₦100,000), there is no significant difference between the two groups. However, an obvious divergence occurs

in the ₦100,000–₦200,000 bracket. At this juncture, the probability of homeownership among academics surges

to 85.2%, while it drops to just 58.3% among their non-academic peers, opening a significant 27-percentage-

point gap. This academic advantage is maintained as both groups reach their peak probability in the ₦200,000–

₦300,000 bracket, where academics (92.4%) hold a substantial and statistically significant lead over non-

academics (78.5%). This visual evidence suggests that the benefits of the cooperative system are mediated by

professional status, particularly in the mid-income ranges vital for housing investment.

Table VI: Timely Project Completion by Receipt of Cooperative Housing Assistance

Received Direct Housing Assistance

Project Not Timely (<10 Project Timely (≤10 Total (N)

yrs)

Yes

0 (0.0%)

47 (3.6%)

11 (4.2%)

58 (3.5%)

58 (100.0%)

1,270 (96.4%)

251 (95.8%)

1,579 (96.5%)

58

No

1,317

262

Others

Total

1,637

Note: N refers to the subset of homeowners who provided data on project duration. Row percentages are in

parentheses. Pearson χ²(3) = 3.47, p = 0.325.

Table VII: Logistic Regression Predicting Likelihood of Homeownership

Predictor

Odds Ratio (OR) Std. Err. p-value

[95% Conf. Interval]

Main Effect

Age Group (Ref: 18–28)

29–39

2.12

1.46

1.54

1.62

0.51

0.32

0.35

1.33 – 3.39

0.95 – 2.24

0.99 – 2.40

1.06 – 2.48

0.002

0.086

0.054

0.026

40–50

51–60

61 and above

Gender (Ref: Male)

Female

0.46

0.05

0.37 – 0.57

0.001

Income and Employment Interaction

Income for Academic Staff (Ref:

N30,000–N100,000)

N100,000–N200,000

2.15

4.59

0.59

1.53

1.25 – 3.68

0.006

0.001

N200,000–N300,000

2.39 – 8.81

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N300,000–N400,000

1.24

1.10

0.31

0.32

0.399

0.746

0.76 – 2.02

Above N400,000

0.62 – 1.93

Interaction Term (Income x Non-

Academic)

N100,000–N200,000 x Non-Acad.

N200,000–N300,000 x Non-Acad.

N300,000–N400,000 x Non-Acad.

Above N400,000 x Non-Acad.

Model Fit Statistics

0.20

0.26

0.56

0.51

0.07

0.10

0.19

0.18

0.10 – 0.40

0.12 – 0.56

0.30 – 1.07

0.26 – 1.01

0.001

0.081

0.054

2178

Number of Obs

154.26

0.063

LR χ²(14)

Pseudo R²

Note: Table presents Odds Ratios (OR). p-values < 0.05 are in bold. Reference categories are in parentheses.

The main effect for Non-Academic staff is interpreted through the significant interaction terms.

Among the main effects, gender emerges as a strong and consistent predictor. Holding all other factors constant,

the odds of a female member owning a home are 54% lower than the odds for a male member (OR = 0.46, p <

0.001). Age also plays a significant role, with members in the 29-39 age group (OR = 2.12) and the 61 and above

group (OR = 1.62) having significantly higher odds of homeownership compared to the youngest members.

The core of the model lies in the significant interaction between income and employment category. For academic

staff, income has a powerful, positive effect at lower-middle levels; the odds of homeownership for an academic

earning N200,000–N300,000 are 359% higher (OR = 4.59) than for an academic in the lowest income bracket.

However, the significant and negative interaction terms indicate that this pattern is dramatically different for

non-academic staff.

Figure 3: Coefficient Plot of Odds Ratios for Key Predictors of Homeownership

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Note: The figure displays the odds ratios (dots) and their corresponding 95% confidence intervals from the

logistic regression model in Table VII. The dashed vertical line at 1.0 represents the null effect (no association).

Predictors with confidence intervals that do not cross this line are statistically significant at the p < 0.05 level.

To provide an intuitive visual summary of the logistic regression results from Table VII, Figure 3 presents a

coefficient plot. The plot displays the odds ratios and their 95% confidence intervals for the main effects of age

and gender, making the direction, magnitude, and statistical significance of the findings immediately accessible.

The plot clearly shows a strong, statistically significant negative association between being female and

homeownership; its confidence interval is entirely to the left of the null-effect line at 1.0. In contrast, the

significant positive effects of the 29–39 and 61-and-above age groups are also readily apparent, with their

confidence intervals lying entirely to the right of the null line. This graphical representation confirms the key

findings from the regression table in a highly accessible format, thereby enhancing clarity.

DISCUSSION

The findings of this study provide a multi-faceted evaluation of the staff cooperative society as a housing delivery

mechanism. The analysis reveals a self-contained and highly effective ecosystem, thriving due to its internal

strengths and responsiveness, yet operating largely in isolation from the formal policy environment and not

entirely immune to broader societal inequalities.

The cooperative’s success is rooted in its deep alignment with the socio-economic profile and life-cycle needs

of its members. This institutional intelligence, where the cooperative functions first as a disciplined savings

vehicle for younger members and later as a credit provider for those in their prime career years, demonstrates a

level of responsiveness that rigid top-down programs rarely achieve. The specific mechanism of this success is

the ecosystem as a whole. We found no statistically significant relationship between receiving a specific housing

assistance package and timely project completion, not because the assistance is ineffective, but because the

system as a whole is overwhelmingly effective. With over 96% of members completing their projects in a

relatively short period, the marginal effect of any single intervention is difficult to detect statistically.

This ceiling effect finding reinforces a robust real-world validation of cooperative governance theory. This

framework contrasts cooperative models with for-profit enterprises, highlighting how member-driven structures

reinforce solidarity and ensure responsiveness to lived needs (Crabtree-Hayes, 2024; Lang & Novy, 2014). The

cooperative's primary value lies not in any single product it offers, but in its existence as a continuous, reliable

system built on democratic principles and mutual trust (International Cooperative Alliance, 1995).

However, the cooperative's effectiveness is not absolute, the intersecting identities of its members mediate it.

The multivariate analysis, particularly the significant interaction between income and employment, reveals the

mechanics of inequality within the system. While the cooperative model provides an equitable foundation at the

lowest income levels, a dramatic divergence occurs at the ₦100,000–₦200,000 income bracket. This income

level likely represents a threshold for capital investment, and it is at this precise juncture that the structural

advantages of being an academic appear to be unlocked.

This empirical pattern can be understood through the theoretical lens of institutional hierarchy. The mechanism

for this activation can be explained by models of hierarchy formation, which show how collective beliefs and

individual actions create self-reinforcing status rankings (Gould, 2002). Within the cooperative, this might

manifest as members or loan committees making subtle status-conferring gestures, such as attributing greater

credibility or lower risk to academic staff based on a shared perception of their institutional standing. According

to Gould's (2002) model, such gestures reinforce the very hierarchy they are based on, providing a powerful

micro-level explanation for the macro-level divergence we observe in homeownership outcomes.

This intersectional reality is further complicated by the main effect of gender, which persist across all models.

The significant gender disparity directly reflects theories of gender stratification, which identify gender as a

foundational axis of inequality in asset accumulation (World Bank, 2018). The finding is consistent with

literature showing that men are significantly more likely to claim sole property ownership, a gap rooted in

patriarchal norms and structural biases in property markets (Deere & Doss, 2006). The paradoxical finding that

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this gap is widest among higher-status academic staff strengthens the argument that gendered disadvantage is

amplified as financial stakes rise, a form of double jeopardy for professional women. This powerfully highlights

that while the cooperative provides an essential mechanism, members' starting positions are profoundly shaped

by external societal structures.

Furthermore, this self-reliant system has largely evolved despite the formal policy environment, suggesting a

disconnect between national housing policies and their implementation at the grassroots level. This self-reliance

is both the cooperative's greatest strength, making it resilient, and a potential weakness, as it limits its ability to

scale and tackle systemic issues like infrastructure provision, which requires state collaboration.

Finally, it is important to situate the model's findings within its statistical performance. Our diagnostic tests

confirm that the model is well-specified and free of multicollinearity. However, its primary value is explanatory

rather than predictive, identifying the direction and significance of key relationships. The low Pseudo R² value

(0.063) indicates that homeownership is a complex outcome influenced by many factors beyond the scope of

our model. However, the powerful, independent effects of gender and the income-employment interaction

emerge as statistically significant signals of structural forces, even within a model that acknowledges substantial

unobserved heterogeneity.

CONCLUSION AND POLICY IMPLICATIONS

Conclusion

Staff cooperative societies in Nigerian tertiary institutions represent an effective model of community-driven,

self-reliant housing provision. They are flexible, trusted, and highly responsive to the needs of their core low-

to-middle-income membership. By functioning as a holistic support system, they have enabled thousands of

public servants, who are systematically excluded from formal mortgage markets, to achieve homeownership.

However, this study concludes that their success is a complex narrative. The cooperative model is a potent

amplifier of its members' capacities, but it is not a perfect equalizer in the sense that it is constrained by broader

societal and institutional structures related to gender and professional hierarchy. As a self-reliant ecosystem born

out of a policy vacuum, it is a testament to grassroots ingenuity but also a symptom of systemic state failure.

Policy Implications

The findings of this study generate several policy implications aimed at strengthening the cooperative model and

addressing its limitations. First, government policy must move beyond passive recognition to the active

integration of staff cooperative societies into national housing strategy. This implies creating dedicated credit

lines from development banks (e.g., the Federal Mortgage Bank of Nigeria) that cooperatives can access on

behalf of their members, leveraging their superior last-mile delivery capabilities and high repayment rates.

Then, the significant gender and employment disparities have direct implications for cooperative governance.

Cooperatives should be encouraged and supported to move beyond gender-neutral policies to proactive, gender-

transformative initiatives. This includes developing targeted financial literacy programs for female members and

creating loan products designed to accommodate women's unique economic life cycles. Similarly, institutions

could partner with their cooperatives to create dedicated support funds or provide institutional guarantees to

mitigate the disadvantages faced by non-academic staff.

Also, the self-reliance of cooperatives reaches its limit when faced with large-scale infrastructure needs. A key

policy implication is the need for local and state governments to form Public-Cooperative Partnerships (PCPs).

In this model, the government's role would be to provide affordable land and basic site-and-service infrastructure

(roads, water, electricity), while the cooperative manages the construction finance and project delivery for its

members. This leverages the strengths of both sectors to overcome a barrier to affordable housing development.

Ultimately, acknowledging and integrating the role of these self-reliant cooperative societies into formal urban

planning and governance is necessary for building more inclusive and sustainable cities in Nigeria. By providing

a proven pathway to affordable housing, these grassroots initiatives directly complement major development

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frameworks, including the UN's Sustainable Development Goal 11, which calls for ensuring access to adequate

and safe housing for all by 2030, and Africa’s Agenda 2063, which envisions resilient and inclusive urban

centers. Supporting and scaling these existing, effective community-driven models is not just a pragmatic

housing strategy; it is a direct investment in achieving these vital global and continental goals.

While this study offers valuable insights into cooperative housing, it is essential to recognize its limitations,

which highlight opportunities for future research. First, the analysis is conditional on being a cooperative

member, which introduces a potential for selection bias. The findings illuminate the factors that predict success

among members but cannot be generalized to all tertiary institution staff. It is plausible that individuals who join

cooperatives are already predisposed to be more organized, better at saving, or more motivated to achieve

homeownership than their non-member colleagues. Therefore, the high rate of homeownership observed, while

a testament to the cooperative model's effectiveness for its participants, cannot be interpreted as the effect of the

cooperative relative to non-membership without a proper control group.

Second, the study's cross-sectional design precludes strong causal claims. While we have identified powerful

statistical associations, for example, between gender and homeownership, we can only infer correlation, not

causation. A longitudinal study that tracks members and non-members over time would be required to establish

the causal impact of cooperative membership and its specific interventions on housing trajectories.

Finally, while our multivariate model controls for key socio-economic variables, the modest Pseudo R² value

indicates the presence of unobserved confounders. There are likely other important factors influencing

homeownership that were not measured in this survey. These could include individual-level variables such as

prior family assets, spousal income, and household debt levels, as well as institution-level factors like the quality

of each cooperative's governance and the specific risk policies of their loan committees. Future research

incorporating these variables could build a more comprehensive explanatory model. Despite these limitations,

this study provides one of the most detailed quantitative analyses to date of the internal dynamics of staff

cooperative societies in Nigeria, laying a foundation for future causal and comparative work.

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Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could

have appeared to influence the work reported in this paper.

CRediT Authorship Contribution Statement

All authors contributed to the study conception and design. OBO: Conceptualization, Resources, Data Curation,

Writing – Original Draft; APA: Methodology, Software, Writing – Review & Editing; ODA: Data Curation,

Writing – Review & Editing.

Data Availability

The data that support the finding of this study are available upon request.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or non-profit

sectors.

Ethical Approval

There are no ethical considerations for this research

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