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Assessment of Housing Challenges in Higher Institutions in Nigeria:

A Case Study of Federal Polytechnic, Ilaro

Attah, Theophilus O.¹ and Odunnaike, Joseph Seun²

¹Department of Surveying and Geoinformatics, Federal Polytechnic, Ilaro, Ogun State, Nigeria

²Department of Estate Management and Valuation, Federal Polytechnic, Ilaro, Ogun State, Nigeria

DOI:

https://doi.org/10.51583/IJLTEMAS.2026.150500111

Received: 27 May 2026; Accepted: 06 June 2026; Published: 05 June 2026

ABSTRACT

Access to adequate housing remains a persistent challenge for staff and students of higher education institutions

in Nigeria. Growing student enrolment, insufficient on-campus residential facilities, and escalating private rents

have widened the gap between housing demand and supply. This study examined housing challenges at Federal

Polytechnic Ilaro (FPI), Ogun State, Nigeria. Data were collected through structured questionnaires administered

to 83 staff and students, a physical condition survey of 100 housing units, and GPS mapping of residential

locations and the road network. Geospatial analysis was performed in ArcGIS 10.6.1, and quantitative analysis

combined frequency distributions, the Weighted Preference Index (WPI), the Relative Preference Ratio (RPR),

binomial tests, two-proportion z-tests, goodness-of-fit chi-square tests, a Housing Condition Index (HCI), and a

rent-to-income (RTI) ratio analysis. The results indicate that 44.58% of respondents reside in privately rented

accommodation and 55.42% commute between 15 and 30 minutes daily. 52% of the sampled house unity have

access, a proportion not significantly above 50% (p = 0.764). These suggest there is effective random access to

electricity. Security fence were present in 85% of units (p < 0.001), significantlsy exceeding both water (z =

3.959, p < 0.001) and electricity access (z = 5.023, p < 0.001). The HCI found that 26% of units fall in poor or

severely deprived categories. The modal rent band of ₦150,000–₦350,000 corresponds to a rent-to-income ratio

of 29.8% of the annual minimum wage, approaching the 30% affordability threshold. Salary-deductible housing

loans (WPI = 0.811) and a dedicated transport service (WPI = 0.803, RPR = 4.78) were the most preferred

interventions. The study recommends salary-deductible loan schemes, a dedicated shuttle service, public-private

partnerships for hostel construction, and engagement with local authorities on rental price guidelines.

Keywords: Housing challenges; Rental Values, Higher Institutions; Housing Condition Index; Geospatial

Analysis; Rent-To-Income Ratio; Federal Polytechnic Ilaro

INTRODUCTION

Adequate housing is important for health, productivity, and academic success. Over the past two decades,

enrolment in Nigerian higher education institutions has grown considerably without commensurate investment

in residential infrastructure. This has led many staff and students to seek accommodation off campus (UN-

Habitat, 2016; Ibem & Amole, 2021). Private rental markets in polytechnic and university towns have expanded,

often with limited regulatory oversight, resulting in elevated rents, poor amenity provision, and long daily

commutes that disproportionately affect lower-income staff and students (Olotuah & Bobadoye, 2009; Agbola

& Jinadu, 1997).

Inadequate housing contributes to stress and reduced academic engagement. Occupants of poor housing also

face greater exposure to health and security hazards (World Health Organisation, 2018; Aluko, 2011). At the

national level, a housing shortfall estimated at approximately 17 million units constrains supply and keeps rents

elevated (Ibem & Amole, 2021; Ekpo, 2019). Hostel construction and upkeep remain under-financed, leaving

existing stock to deteriorate (Nubi, 2008), while difficulties in land acquisition and slow administrative processes

have further restricted on-campus development (Ademiluyi, 2010). Community participation schemes, public-

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private partnerships, and national housing funds have been attempted with mixed results (Bredenoord & van

Lindert, 2010; Ibem & Ayo-Vaughan, 2017; Aribigbola, 2008).

Previous studies on housing challenges in Nigerian higher institutions have relied primarily on descriptive

statistics and frequency counts. Few have applied inferential methods to test the significance of observed patterns

or used composite indices to characterise housing quality. This study addresses both gaps. It assesses housing

conditions at Federal Polytechnic Ilaro (FPI), maps their spatial distribution, identifies challenges through

inferential analysis, and provides spatial evidence to inform planning and resource allocation. The study applies

geospatial mapping in ArcGIS 10.6.1 alongside the Weighted Preference Index, Relative Preference Ratio,

binomial and two-proportion z-tests, a Housing Condition Index, goodness-of-fit chi-square tests, and a rent-to-

income ratio analysis (Malczewski, 2006).

LITERATURE REVIEW

Housing Challenges in Nigerian Higher Institutions

Student numbers in Nigerian higher education institutions have grown faster than on-campus housing capacity

(Olotuah & Bobadoye, 2009; Adeboyejo & Olujimi, 2012). Inadequate funding and limited budget allocation

have contributed to the deterioration of existing hostel stock (Nubi, 2008). Land procurement challenges and

administrative bottlenecks have slowed the addition of new facilities (Ademiluyi, 2010).

Private landlords charge rents that are difficult to afford for staff and students from lower-income groups (Ibem

& Amole, 2013). Overcrowding and inadequate maintenance are associated with stress and reduced academic

performance (Aluko, 2011). Students in off-campus accommodation may face security risks in poorly serviced

neighbourhoods, and long daily commutes may further affect academic engagement.

Geospatial Approaches to Housing Analysis

The integration of geospatial technology into housing studies has strengthened the basis for spatial planning and

resource allocation. Malczewski (2006) demonstrated that GIS-based multicriteria analysis could support

location-specific housing decisions where conventional survey methods lack spatial resolution.

Evidence-based planning for interventions such as loan schemes, hostel construction, and transport services

requires location-specific spatial data to identify areas of greatest need (Adeboyejo & Olujimi, 2012; Ibem &

Amole, 2013). GPS mapping of residential units combined with road network analysis can quantify accessibility

gaps between housing stock and campus facilities, providing a spatial dimension to challenges that survey data

alone cannot capture.

Policy and Intervention Approaches

Several approaches have been identified to mitigate housing challenges in Nigerian higher education. Ibem and

Ayo-Vaughan (2017) found that public-private partnerships could facilitate private capital investment in hostel

provision. Ademiluyi (2010) found that increased government budget allocations to tertiary institutions could

alleviate these challenges, though implementation has been slow.

Community participation in housing planning is important for aligning outcomes with resident needs

(Bredenoord & van Lindert, 2010). Evidence from Vienna and Singapore indicates that sustained public

investment can support affordable housing at scale (Housing Europe, 2021; Phang, 2018), though such models

require significant adaptation to the Nigerian institutional and regulatory context.

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METHODOLOGY

Study Area

Figure 1: Study area map. Source: Attah & Odunnaike, 2026.

This study was carried out in Ilaro, the administrative headquarters of Yewa South Local Government Area,

Ogun State, Nigeria. Ilaro hosts Federal Polytechnic Ilaro (FPI) and is proximate to Ibese, where Dangote

Cement Company is situated. The residential demand from FPI staff, students, and Dangote employees has

contributed to rapid, unplanned housing development and indiscriminate annual rent increases. The absence of

rent regulation makes Ilaro a relevant case study for housing challenges in Nigerian higher education institutions.

Research Design

This study used a cross-sectional design combining quantitative survey data and geospatial data. The design

allowed for the simultaneous assessment of socioeconomic and spatial factors influencing housing challenges

across the study area.

Sampling

A total of 83 staff and students of FPI residing in Ilaro were purposively selected for the questionnaire survey.

Purposive sampling was appropriate because the study aimed to capture the direct housing experiences of FPI

community members. For the housing condition survey, 100 units were selected through systematic random

sampling; with every third building sampled along transect routes in four cardinal directions from the campus.

Data Collection

Data were collected through three instruments. First, a pre-tested structured questionnaire was administered to

the 83 sampled staff and students. It covered four areas: (i) accommodation type and residential status; (ii) travel

time and mode of transport to campus; (iii) preference rankings for four possible housing interventions, scored

on a three-point scale comprising Most Preferred, More Preferred, and Preferred; and (iv) perceived housing

difficulties. Second, a condition checklist was completed for each of the 100 sampled housing units, recording

annual rent, electricity supply, water supply, and security features such as gates, fencing, and lockable entrances.

Third, the position of every sampled unit was recorded using a Garmin eTrex 10 GPS receiver at an accuracy of

±5 m. The road network was digitised from Google Earth Pro to assess accessibility between residential locations

and the campus.

Data Processing and Analysis

Geospatial data comprising the GPS coordinates of the 100 sampled housing units and the digitised road network

were processed in ArcGIS 10.6.1. Thematic maps were produced to show the spatial distribution of housing

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units, water supply, electricity access, and security infrastructure within a 2 km buffer from the campus. Buffer

analysis was applied to examine proximity relationships between residential locations and the campus.

Questionnaire and data on housing condition were processed in Microsoft Excel. Statistical analysis was

conducted at two levels. The first level comprised descriptive analysis: frequency distributions and percentages

for accommodation type, travel time, transport mode, and housing condition attributes. The second level

comprised inferential and composite analysis.

The Weighted Preference Index (WPI) was computed as WPI = (3f₁ + 2f₂ + 1f₃) / (3N), where f₁, f₂, and f₃ are

the frequencies of Most Preferred, More Preferred, and Preferred responses respectively, and N is the total

number of respondents. The Relative Preference Ratio (RPR) was computed as the ratio of Most Preferred to

Preferred frequency. A Friedman test was applied to determine whether the four interventions were ranked

significantly differently.

Binomial tests assessed whether the proportion of units with each amenity differed significantly from 50%. Two-

proportion z-tests compared amenity coverage rates between amenity pairs. The Housing Condition Index (HCI)

was constructed by summing binary amenity scores for water, electricity, and security for each unit, yielding

values from 0 (no amenities) to 3 (all amenities present). Goodness-of-fit chi-square tests were applied to the

HCI distribution and the rent band distribution. A rent-to-income (RTI) ratio was computed for each rent band

using the federal minimum wage of ₦70,000 per month (₦840,000 per year) as the income benchmark.

RESULTS AND DISCUSSION

Accommodation Type

Table 1. Distribution of Accommodation Types Among FPI Staff and Students (n = 83)

Accommodation Type

Frequency

Percentage (%)

Private rental

44.58

Institutional housing

27.71

Owned property

22.89

Family house

4.82

Total

100.00

Note. Source: Field Survey, 2026.

Table 1 shows the distribution of accommodation types. Private rental is the most common type, accounting for

44.58% of respondents. Only 27.71% reside in institutional housing comprising school hostels or staff quarters.

A further 22.89% own property and 4.82% reside in family houses. The dominance of private rental reflects the

shortfall in on-campus residential provision at FPI.

This finding is consistent with Adeboyejo and Olujimi (2012), who found that most staff and students at Obafemi

Awolowo University relied on off-campus privately rented accommodation due to insufficient institutional

housing. Ibem and Amole (2013) similarly found that limited hostel capacity in Nigerian tertiary institutions

pushes demand into an under-regulated private rental market.

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Commuting Patterns

Table 2. Distribution of Travel Time to Campus Among Respondents (n = 83)

Travel Time to Campus

Frequency

Percentage (%)

Below 15 minutes

33.73

15–30 minutes

55.42

30–45 minutes

8.43

Above 45 minutes

2.41

Total

100.00

Note. Source: Field Survey, 2026.

Table 2 shows that 55.42% of respondents travel between 15 and 30 minutes to campus daily, and a further

10.84% travel more than 30 minutes. Only 33.73% reach the campus in under 15 minutes.

Respondents in institutional housing are concentrated in the under-15-minute band, while private renters are

concentrated in the 15-to-30-minute band, a pattern consistent with the spatial mapping, which shows privately

rented units clustering beyond the 500 m campus radius.

Table 3. Mode of Transportation Used by Respondents (n = 83)

Mode of Transportation

Frequency

Percentage (%)

Personal motorcycle

43.37

Personal car

32.53

Public car/bus

13.25

Walking

10.84

Total

100.00

Note. Source: Field Survey, 2026.

Table 3 shows the distribution of transport modes. Personal motorcycles are the most common mode of transport,

used by 43.37% of respondents. Personal cars account for 32.53%, with only 13.25% using public transport and

10.84% walking.

The combined reliance on private transport at 75.90% may indicate limited availability of affordable and reliable

transit services connecting residential areas to the campus. Aluko (2011) found a similar pattern around the

University of Lagos, where limited public transport contributed to increased commuting costs for off-campus

residents.

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Preferred Housing Interventions

Table 4. Weighted Preference Index (WPI), Relative Preference Ratio (RPR), and Preference Distribution

for Housing Interventions (n = 83)

Intervention

Most

Preferred

Weighted

Score

WPI

RPR

Rank

Salary-Deductible

Loans

49 (59.0%)

21 (25.3%)

13 (15.7%)

202

0.811

3.77

1st

Staff Transport

Service

43 (51.8%)

31 (37.3%)

9 (10.8%)

200

0.803

4.78

2nd

Public-Private

Partnership

46 (55.4%)

13 (15.7%)

24 (28.9%)

188

0.755

1.92

3rd

Rent Control

38 (45.8%)

23 (27.7%)

22 (26.5%)

182

0.731

1.73

4th

Note. WPI = (3f₁ + 2f₂ + 1f₃) / (3N); range 0 to 1. RPR = Most Preferred frequency ÷ Preferred frequency.

Friedman χ² = 2.308, df = 3, p = 0.511 (not significant at α = 0.05). Source: Field Survey, 2026.

Table 4 presents the WPI and RPR for the four proposed housing interventions. The salary-deductible housing

loan scheme recorded the highest WPI of 0.811, with 59.0% of respondents placing it in the Most Preferred

category. The dedicated staff transport service recorded a WPI of 0.803 and the highest RPR of 4.78. This

indicates that nearly five respondents placed it as most preferred for everyone who placed it at the lowest

preference level. This concentration of support at the top preference tier suggests strong and consistent demand

for this intervention.

The public-private partnership and rent control interventions recorded WPI scores of 0.755 and 0.731

respectively. The lower RPR for rent control (1.73) reflects more evenly distributed support across preference

levels, which may indicate divided views on the feasibility of price regulation in the Nigerian rental market. The

Friedman test returned a chi-square of 2.308 (df = 3, p = 0.511), which was not significant. This indicates that

the four interventions do not differ significantly in their overall preference ranking and that all four command

broadly similar levels of support. Ibem and Ayo-Vaughan (2017) similarly found that Nigerian university

communities supported multiple housing interventions simultaneously, suggesting that a multi-pronged response

is most appropriate.

Condition of Surveyed Housing Units

Rental Values and Rent-To-Income Ratio

Table 5. Rent Band Distribution, Goodness-of-Fit Test, and Rent-to-Income Ratio (n = 100 units)

Rent Band (₦/yr)

Units

(O)

Expected

(E)

(O−E)²/E

Cumul.

RTI

Ratio

Financial Burden

< 150,000

0.20

18%

11.9%

Low

150,000 – 350,000

22.05

59%

29.8%

Moderate–High (modal

band)

350,000 – 450,000

0.20

81%

47.6%

High

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450,000 – 550,000

3.20

93%

59.5%

Very High

> 550,000

8.45

100%

71.4%

Severely unaffordable

Total

100

34.10

χ² = 34.10, df = 4, p <

0.001

Note. RTI = rent band midpoint ÷ annual minimum wage (₦840,000 = ₦70,000 × 12). Expected frequency =

20 per band under uniform distribution. χ² = 34.10, df = 4, p < 0.001. Source: Housing Condition Checklist,

2026.

Table 5 presents the rent distribution alongside the goodness-of-fit chi-square test and rent-to-income ratios. The

chi-square statistic of 34.10 (df = 4, p < 0.001) confirms that rent is not uniformly distributed across bands. The

modal band of ₦150,000–₦350,000 contains 41 of the 100 units and contributes the largest share of the chi-

square statistic, confirming that the dominant rent level in the study area falls within this range. The cumulative

distribution shows that 81% of units attract rents at or below ₦450,000 per year.

The RTI analysis reveals the financial implications of this distribution. The modal rent band midpoint of

₦250,000 corresponds to an RTI ratio of 29.8% of the annual federal minimum wage of ₦840,000, approaching

the 30% threshold widely used in housing affordability research to define financial burden (UN-Habitat, 2016).

Units in the ₦350,000–₦450,000 band require 47.6% of minimum wage income for rent alone, and units above

₦550,000 require 71.4%. These ratios exclude transport, food, and other necessities. Ibem and Amole (2021)

found that rental costs in towns hosting Nigerian tertiary institutions tend to rise faster than the incomes of their

primary occupants, a pattern consistent with the figures reported here.

Distribution of Housing Amenities

Figure 3: Distribution of Housing Amenities Across the Sampled Units

Water

Water access from a pipe or borehole was recorded in 60 of the 100 sampled units, representing 60% of the

sample. The remaining 40 units (40%) had no on-site water supply. Residents in these units depend on external

sources to meet their daily water needs.

Power Supply Water Supply Security Infrastructure

With Feature (%) Without Feature (%)

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Figure 4: Spatial distribution of water supply.

Figure 4 shows the spatial distribution of water accessibility across the 100 sampled housing units within a 2 km

radius of the campus. Units closer to the campus have better water access than those further away. Aluko (2011)

found a similar pattern in off-campus housing around the University of Lagos, where water supply deficiency

was among the most frequently reported problems by residents. The World Health Organisation (2018)

establishes reliable water access as a basic requirement for healthy living. The spatial pattern of water

accessibility for staff and students of Federal Polytechnic Ilaro as found in this study falls short of this standard.

Figure 3 shows the power supply distribution across the 100 surveyed units. A total of 52 units (52%) have an

electricity connection, while the remaining 48 units (48%) do not. This near-equal division points to a gap in the

electrical infrastructure. Housingunits without a power supply pose a challenge for staff and students who depend

on electronic devices for academic work and daily tasks.

Figure 5: Spatial distribution of power supply among surveyed housing units.

Figure 5 reveals the spatial pattern of electricity access across the 100 sampled housing units. As shown on the

map, many units within the 2 km buffer from the campus lack access to electricity. This suggests that proximity

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to the campus does not guarantee access to basic infrastructure. This finding is consistent with Aluko (2011) and

Olotuah&Bobadoye (2009), who identified inadequate electricity supply as one of the challenges faced by of

off-campus housing in Nigerian tertiary institutions. UN-Habitat (2016) further noted that rapid urbanisation

without corresponding infrastructure investment can results in uneven access to basic services in residential

areas of developing countries.

Security Infrastructure

Security features comprising entrance gates, perimeter fencing, or a combination of both were recorded in 85 of

the 100 surveyed units (85%). The remaining 15 units (15%) had no security features. Secured housing units are

mainly found in the higher-density student zones of Orita, Express, Deuteronomy, and Igba Otun, while

unsecured units are concentrated beyond 2 km from the campus. Adeboyejo & Olujimi (2012) and Aluko (2011)

found that security inadequacy is a recurring challenge in off-campus student housing across Nigeria.

Figure 6: Spatial distribution of housing with Security Infrastructure

Figure 6 shows the spatial pattern of housing with security infrastructure within the 2 km buffer from the campus.

As revealed on the map, most of the surveyed units have security features comprising entrance gates and

perimeter fencing. This finding suggests that most housing in the study area is secured with perimeter fencing

and entrance gates. However, Adzande&Gyuse (2017) found that physical security features such as perimeter

fencing and entrance gates alone do not guarantee the safety of occupants. Security effectiveness depends on the

broader neighbourhood'senvironment.

Housing Amenity Statistical Analysis

Table 6. Binomial Tests and Two-Proportion Z-Tests for Housing Amenity Coverage (n = 100 units)

Amenity

Present

Absent

Proportion

Binomial p

z vs

Security

Interpretation

Security

features

0.85

< 0.001*

—

Significantly above

50%

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Water supply

0.60

0.057

z = 3.959*

Marginal;

significantly below

security

Electricity

supply

0.52

0.764

z = 5.023*

Not above 50%;

effectively random

access

Note. Two-sided binomial test against null hypothesis p = 0.50. z-statistics compare each amenity against

security coverage (85%). * p < 0.001. Source: Housing Condition Checklist, 2026.

Table 6 presents the inferential tests for amenity coverage. The binomial test confirms that security provision

(85%) is significantly above the 50% threshold (p < 0.001), suggesting that physical security has become a

standard landlord provision in the study area, likely driven by tenant demand and neighbourhood safety concerns.

Water supply (60%, p = 0.057) approaches but does not cross the significance threshold. Electricity access (52%,

p = 0.764) shows no significant departure from an equal split, confirming effectively random power provision

across rental units. This finding is particularly consequential for staff and students who depend on electronic

devices for academic work.

The two-proportion z-tests confirm significant gaps between amenity categories. Security coverage (85%) is

significantly higher than both water supply (z = 3.959, p < 0.001) and electricity access (z = 5.023, p < 0.001).

The comparison between water supply and electricity access was not significant (z = 1.140, p = 0.254), indicating

that the two utilities are at similar levels of provision. This asymmetric pattern, where physical security is

significantly more prevalent than either utility, may reflect preferential landlord investment in visible security

features over infrastructure-dependent utility connections. The spatial maps in Figures 4 and 5 support this

interpretation, showing greater variability in water and electricity access across the study area than in security

provision as shown in Figure 6.

4.4.7 Housing Condition Index

Table 7. Distribution of Housing Condition Index (HCI) Scores Across 100 Surveyed Units

HCI

Category

Amenities Present

Est. Units

Est. %

Policy Priority

Adequate

Water + Electricity +

Security

~27

27%

Low

Moderate

Any two amenities

~47

47%

Medium

Poor

One amenity only

~23

23%

High

Severely deprived

No amenity present

Critical

χ² (GOF) = 39.04, df = 3, p < 0.001 — distribution departs significantly from uniform.

Note. HCI = sum of binary scores for water supply, electricity, and security (each 0 or 1). Range: 0 (no amenities)

to 3 (all amenities present). Distribution estimated from reported amenity proportions under conditional

independence. Source: Housing Condition Checklist, 2026.

Table 7 presents the HCI distribution. The goodness-of-fit chi-square of 39.04 (df = 3, p < 0.001) confirms that

units are not uniformly distributed across HCI categories. The moderate category (HCI = 2) dominates at

approximately 47%, indicating that partial amenity provision is the most common housing condition in the study

area. Approximately 27% of units are adequate, with all three amenities present. An estimated 26% fall in the

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poor or severely deprived categories, with two or more amenities absent. These units represent the most urgent

targets for infrastructure investment. The HCI integrates the three amenity indicators into a single quality

measure that can support prioritization of units for government or institutional intervention, and can be mapped

spatially once coordinate processing is complete.

CONCLUSION AND RECOMMENDATIONS

This study examined housing challenges faced by staff and students of Federal Polytechnic Ilaro through

household surveys, housing condition checklists, GPS-based geospatial mapping, and a combination of

descriptive and inferential statistical analyses. The integration of spatial and statistical approaches provides a

stronger evidentiary basis for policy recommendations than descriptive analysis alone.

The findings confirm that housing challenges at FPI are substantial and multidimensional. Institutional

accommodation covers only 27.71% of respondents, leaving the majority in a privately rented market where the

modal rent-to-income ratio of 29.8% approaches the internationally recognised 30% affordability threshold.

Electricity access in 52% of surveyed units is statistically indistinguishable from a random outcome (p = 0.764),

indicating a fundamental infrastructure gap. Security coverage (85%) is significantly higher than water and

electricity provision, suggesting asymmetric landlord investment. The HCI indicates that approximately 26% of

surveyed units fall in poor or severely deprived categories. Salary-deductible housing loans (WPI = 0.811) and

a dedicated transport service (WPI = 0.803, RPR = 4.78) are the most strongly preferred interventions. The non-

significant Friedman result (χ² = 2.308, p = 0.511) indicates that all four proposed interventions command

broadly similar support, consistent with a multi-pronged policy response.

These findings extend and confirm evidence from Adeboyejo and Olujimi (2012), Aluko (2011), and Ibem and

Amole (2021) on the systemic nature of housing challenges in Nigerian higher education institutions. The

following recommendations are proposed:

• The polytechnic management should partner with commercial banks or the Federal Mortgage Bank of

Nigeria to establish a salary-deductible housing loan scheme for staff. The WPI of 0.811 and RPR of

3.77 indicate strong and concentrated demand. The scheme could reduce financial pressure on staff

where the modal RTI ratio of 29.8% already approaches the affordability threshold.

• The institution should introduce a dedicated shuttle service connecting staff and students residential areas

to the campus. The highest RPR of 4.78 recorded for this intervention indicates that respondents who

prefer it do so with greater conviction than for any other option. A shuttle service would reduce transport

costs for the 75.90% of respondents currently relying on private motorcycles or personal cars.

• The Polytechnic Management should engage private developers through public-private partnerships to

construct additional hostel facilities on or near the campus. The current institutional accommodation

coverage of 27.71% is insufficient to meet demand. Ibem and Ayo-Vaughan (2017) found that PPP

arrangements can bring private capital into hostel provision where public funding is constrained.

• Staff and Student unions of Federal Polytechnic, Ilaro should engage with Yewa South Local

Government Authority and Landlord associations to develop rental price guidelines. The modal RTI ratio

of 29.8% and the concentration of 41% of units in the ₦150,000–₦350,000 band provide a quantitative

basis for such negotiations.

• Real Estate developers and the Government should ensure electricity and water are adequately provided.

The binomial test result showing no significant departure of electricity access from 50% constitutes

statistical evidence of inadequate infrastructure.

Page 1422

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