prices to improve profitability (Omran and Suleiman, 2017). The level of trust of these project managers is essential since they are responsible for ensuring the success of the project and the company (Silvius and Schipper, 2014). According to Pharm et al. (2019), the critical factors for the success of a project include the selection of the right project managers. Concerns about project management competence have led to the development of project management knowledge and practical standards used to assess, develop and certify managers and organizational leaders. The development of these standards is very qualitative concerning the collective opinions of experienced practitioners on what project personnel must know and what they must do to be considered competent project managers (Nieto-Rodriguez and Sampietro, 2017 p.1).

## METHODOLOGY

The study is a qualitative study that employed the use of semi-structured face-to-face interviews with audio recordings averaging 30-45 minutes in time, to collect data from 30 numbers of respondents in the building development industry, representing a 100% rating. The respondents were from two different professional bodies in the property development sector in Port Harcourt namely: the Nigerian Institute of Architecture (NIA) and the Nigerian Institution of Estate Surveyors and Valuers (NIESV) which were relevant to the purpose of the study. The collected data were analyzed using a thematic content analysis approach.

## DATA ANALYSIS AND RESULTS

The data of this study were presented in thematic form. The display of themes is based on the objectives of the study and is as follows:

### Building Design Defects

This theme aims to identify design defects in buildings. In the semi-structured qualitative instrument, 30 respondents (30Nr = 100%) from NIA and NIESV think that the defects of building design are structural deficiencies, foundation problems, water infiltration, safety problems etc.

An NIA member acknowledged this by saying:

> "...Foundation problems are among the major construction defects. A poorly designed foundation can lead to seepage, cracking and even failure of the building. Problems such as inadequate soil analysis, improper design footings, or poor drainage are factors that contribute to foundation design defects..." (Transcribed Interview, 2024)

A response from a NIESV member states as follows:

> "...Water infiltration is a major defect. Poorly designed building envelopes can allow water to seep into the structure, leading to moisture problems such as mould, rot and deterioration of building materials ..." (Transcribed Interview, 2024)

Another response from a member of NIA states as follows:

> "...Structural defects are the main defects in the design of a building because the design does not take into account the loads and stresses to which the structure will be subjected. This can lead to problems such as falling floors, cracking walls or even building collapse ..." (Transcribed Interview, 2024)

### Causes of Design Defects

This theme seeks to know the causes of building design defects.

Here, 16 respondents (16Nr = 53%) think that resource limitations on the part of the promoter, low budget, the inability of the promoter to integrate qualified personnel, etc. can be the main cause of construction design defects.

A response from an NIA member states thus:

> "...Limited resources such as time, budget and skilled personnel can be a significant challenge to the quality of a design. When the developer's resources are tight, it can be difficult to find the time and staff to thoroughly review a design and ensure it meets all project requirements..." (Transcribed Interview, 2024)

However, 14 respondents (14Nr = 46.6%) thought that most causes of design defects come from insufficient design reviews, poor communication, poor cooperation between team members, etc. In the words of a NIESV member:
"...There is a need for sufficient stakeholder reviews and feedback because ignoring feedback or not conducting enough design reviews can lead to unnoticed design flaws that can lead to significant problems later..."

(Transcribed interview, 2024)

Another NIESV member stated thus:

"...Poor communication and collaboration between team members can lead to misunderstandings and design flaws. For example, if a team member does not understand a design requirement or specification, it can have a ripple effect on the rest of the project, leading to delays, rework, and a drop in quality..."

(Transcribed Interview, 2024)

**Impacts of Design Defects on Project Management**

This theme seeks to identify the impacts of design defects on project management

Here, 18 (18Nr = 60%) of the respondents from both institutions think that some of the impacts of the design defects are damage to the reputation of the project manager, the dissatisfaction of clients and parties, structural and security risks, etc.

A member of NIESV stated thus:

"... Design flaws can have consequences that go far beyond the project itself, but can also damage the reputation of the project manager and other subcontractors. When a project is tainted by design flaws, it can lead to negative publicity, long-term reputational damage to those involved, and lost business opportunities..."

(Transcribed interview, 2024)

12 respondents (12No = 40%) also thought that design flaws can negatively affect a project, causing legal problems, project delays, cost overruns, time delays, regulatory and approval delays etc.

An NIA member further states that:

"...Design flaws can have a significant impact on the project schedule, as resolving these issues often results in schedule delays. These delays can have cascading effects throughout all phases of the project, ultimately extending the duration of the project and potentially delaying the delivery of the final product. This is not a good deal for the project manager..."

(Transcribed interview, 2024)

A NIESV member further opined that:

"...Overrunning costs due to design defects can present a significant challenge to a project manager, as they can strain budgets and compromises the global viability of the project..."

(Transcribed interview, 2024)

**DISCUSSION OF FINDINGS**

The main results of the study, based on the results of the analyzed data, are presented below.

The results show that the design flaws of the building are structural deficiencies, foundation problems, water infiltration, etc. The results also show that the causes of design defects can be attributed to
insufficient design reviews, lack of communication and collaboration between team members, and
resource limitations.

The impacts of design flaws as shown in the study are: reputation damage, planning/scheduling delays, cost overruns, etc.

CONCLUSION AND RECOMMENDATIONS

The study examined the impact of building design defects on the real estate project management
process. The study shows that the building design defects that affect a building are structural
deficiencies, foundation problems, water intrusion, etc. These defects harm the success of the projects,
as they in turn affect the reputation of the project manager. Other impacts are project cost overruns,
project delivery delays, etc. All of this compromises the viability of the project, which in turn creates
negative publicity and lasting damage to the project manager and other project subcontractors. The
study recommended several best practices such as the participation of stakeholders in the building
design process, conducting in-depth reviews and evaluations of design documents, using advanced
design building tools and technologies to improve the communication and collaboration between project
team members, such as the project manager and other team members who can define the project's goals
and objectives, potential risks, and determine the project's scope and budget.

REFERENCES

Walker, D. H. (2009). Exploratory Factors Influencing Design Practice Learning within a
Thai Context. Engineering Construction and Architectural Management. 16(3), 238-253.

Iyer, K. C., & Jha, K. N (2005). "Factors Affecting Cost Performance: Evidence from Indian Construction Projects". International Journal of Project Management, 23, 283-295.

Amobi, C. O. (2003). Building Surveying Practice and Maintenance Management. Owerri.
Achugo Publication.

Szwedo, J. D. (2012). Preventive, Predictive, and Corrective Maintenance. Baxterand
Woodanan, Inc. Technical Report, 1(3), 51-56.

Amobi, C. (2006). Proactive Measures to Avoid Failure and Collapse of Building in Nigeria.
Journal of Nigerian Institute of Building, 13(2), 25-29.

BOMI Institute (2003). Building Design and Maintenance.
http://www.bomi.edu.org/12061.html

Akisola, O.E. (2004). Assessment of the Factors Influencing Maintenance Programs of
Tertiary Institutional Building in South West Nigeria. The Professional Builder, August
Edition, 37-46

Smith, R. (2003). Best Maintenance Practices. Journal of Maintenance and Maintenance
Management 16(1), 9-15.

Pham, V. Q., Nguyen D. K., &Pham H.T. (2019). Critical Success Factors of Project
Management: The Case of Construction Related Projects in Vietnam. J Asian Finance
Econ. Bus. 6, 223-230.

Omran, A., & Suleiman, A. (2017). Identifying the Competence Components of the
Construction Project Managers in the Palestinian Construction Industry. Eng. Proj.
Organ. J., 7, 2-27.

Silvius, A. J., & Schipper, R. P. (2014). Sustainability in Project Management Competencies:
Analyzing the Competence Gap of Project Managers. J. Hum. Resource Sustainable
Studies. https://doi.org/10.4236/jhrss.2014.22005.

Nieto-Rodriguez A., & Sampietro, M. (2017). Why Business Schools Keep Neglecting
Project Management Competencies. PM World J. 6, 1-9.
**Chapter Ten**

**Addressing Housing Affordability Sustainability in Nigerian Built Environment**

Umerah Amechi; Nwachukwu; Chinedu Chidinma; Nwanekezi Silver Chibuzor

Department of Estate Management & Valuation, Federal University of Technology, Owerri, Nigeria

EMAIL: george.umerah@futo.edu.ng

**ABSTRACT**

The Housing demand has encountered huge challenges that have raised questions on how to develop a housing affordability framework for the Nigerian Economy. To achieve housing delivery in an expanding population; household size and economic factors are very vital statistics. The paper explores the redevelopment of an affordable framework for sustainable housing provision in Nigeria. We will highlight the relevance of using population growth as a trajectory in land use policy. A review and the adoption of an analytical housing process create a housing development strategy and its sustainability. This paper will advance the importance of the use of data in land use policy and governance. Analytics Data use and management could play a significant role in housing planning, identifying the challenges and opportunities in the housing sector. Recommendations for creating housing capacity delivery in Nigeria as well as building affordable housing schemes for our built environment are of significance at this time. This work will allow the facilitation and exchange of best ideas towards a sustainable housing framework for Nigeria.

KEYWORDS: Housing Affordability; Sustainable Housing; Land Use Policy; Planning.

**INTRODUCTION**

Real Estate has established itself as an important component in any national economy this is because as the economy of a nation grows businesses grow improving the economic and social well-being of the citizenry. It is important to understand that business growth in towns, cities and urban areas gives rise to improving per capita disposable income of urban residents (Xiang et al., 2020). The core of housing market growth is based on the relevance of housing to the social, economic, and political framework of any society. Economies around the world identify that the housing demand problem is caused by the increase in population and changes in the disposable income of households. Housing supply estimates are a result of the force witnessed in the housing commodity market that is characterized by overheated investment and house price bubbles (Xiang et al., 2020).

In creating a viable and stable affordable housing market in Nigeria, the government clearly must involve economic assessment and its review. This will be favourable to the housing commodity market both in the short term and the long term. One major contribution of efficient housing governance policies to the population of the country is the ability of government fiscal policies and economic measures to make a regular commodity housing market address the financial risk affecting the housing industry. Such financial risk mitigation drives the collaborative participation in the housing commodity market that will yield affordable housing which is sustained with viable economic policies and political will for the housing commodity market.

Several factors affect pricing in the Housing commodity market: water systems, house size, location characteristics and the type of room and housing, construction spatial differences and characteristics of community structure, related industries and demand factors, increased incomes and migration situation, the international economic factors, land prices etc.

**Housing demand literature and the Nigerian housing sector**

The rate of rapid increase in rent in Nigerian cities causes a public need for the development of housing affordability studies and a rent control framework for the Nigerian housing economy. Arimah (1992) stated that empirical analysis of renter and owner-occupier housing submarkets for Ibadan, Nigeria revealed that the most important determinants of the demand for the various housing attributes are
socioeconomic and demographic in nature. The determinants are: income, price of the said attribute, household size, occupation of the head of the household and the stage in the family life cycle.

Housing Pricing is determined by affordable housing land as this will slow down the housing prices (Xue and Changchun). Housing Policies and land use reforms impact the supply and price of rental housing (Christina et al., 2023). The objective of governance is to ensure that the population can have decent, affordable and safe housing units which will increase the homeownership rate. This goal in several literature is pursued objectively in the Kingdom of Saudi Arabia (KSA). The housing program of Saudi Arabia's government is called Saudi Vision 2030. Saudi Vision 2030 set goals such as increasing the home ownership rate among Saudi citizens from 50% to 70% by 2030 and providing affordable and sustainable housing options for the citizens. Today KSA aims to achieve its goal by having the following delivery strategies: adopt several housing programs, create a real estate development fund, and as well establish a national housing corporation. Achieving this goal will involve a reduction in the gap between supply and demand of the housing commodity, a strategy KSA is pursuing using several programs such as sales of housing during the design or construction phase, expedited approval process for private developers, imposition of fees on unused residential land to increase availability, and the design of housing applications to the socio-economic status of an applicant.

Akinwande and Hui opined that ensuring affordable housing for the urban poor is a critical criterion for determining the sustainability of a housing delivery plan for a nation. It is of significance that any sustainable housing delivery must be designed to ensure that involved activities minimize negative impacts, promote social equity and ensure economic viability which is the broad theme of goal 11 of the United Nations Sustainable Development (SGD) Agenda. This SGD 11 goal creates inclusive, safe, resilient and sustainable cities and human settlements (Nubi T G, 2010; Ajayi et al., 2016).

Housing proves to be at the heart of policies used for accessing the social welfare of society and the family, once individuals can build the capacity to support this basic necessity of life is meant. Housing needs are so important and the need is felt in Africa, especially with low-income families. As urbanization grows there is a need for strategies for innovation of helpful sustainable housing options that support urban growth and are improving living conditions for these families (Bhanye, Johannes et al., 2024).

The rate of expanding population in Nigeria should necessitate the significance of a housing policy that can provide adequate housing with living standards that are healthy and as well serve to provide economic stability for all individuals in a household. The economy forms a crucial factor in determining the ability to sustain housing supply for an increasing population. The Nigerian economy depressed at this time is characterized by a huge deficit in housing provision and this housing problem is further challenged by non-affordability for the greater population who live at low-income levels. Suleman, et al, (2024) avowed that the shortage of adequate housing is caused by global challenges such as urbanization, economic instability, pandemics and displacement. The adoption of the circular economy design in Nigeria can assist in achieving a lot such as housing affordability, decarbonisation, climate change mitigation, improving our environmental value, energy optimization, and resource efficiency in urban mining. This approach will stimulate sustainability in the housing sector. Around the world places with optimal economies can develop low-income or social-impact housing, this helps to reduce poverty and thereby achieve sustainable development goals (Felipe Urazan-Bonells et al., 2024).

The historical Nigeria Economy housing literature for affordable housing same to be driven by the government's deep support for direct low-income and middle-income housing development piloted by the federal government and state government. In this, the federal government built a framework to address funding challenges of housing using the federal mortgage bank.

The FMBN is the core economic policy utilized by the government in its federal housing policy strategy.
The Federal Mortgage Bank (FMBN) offers products that support the provision of housing to Nigerians.
The products and services of FMBN include the following: National Housing Fund Mortgage (NHF) Loan, Estate Development Loan, Home Renovation Loan, Rent to Own, Construction Loan, and Diaspora NHF Mortgage Loan. These loans are served by requirements, disbursement process, express interest, and terms of reference.
## NHF Mortgage Loan

This loan serves the Nigerian public who provide proof of being above the age of 18, making contributions to the National Fund for a minimum period of six months and can provide substantiated proof of regular flow of income to guarantee the loan payment. All applicants are required to apply through a registered Mortgage Loan Originator (ML) who is accredited by FMBN to access its NHF facility. A property used as a security for a loan must have an approved building plan. The property must be of substantial value to cover the loan and insured against hazard. The secured loan is on a first legal mortgage between all parties: the applicant, the MLO and the FMBN.

## Estate Development Loan

This type of loan can be secured either with a property with a freehold right or with a leasehold right. Estate developers with freehold rights building residential housing schemes will receive a 10% per annum tenor loan not exceeding 24 months and subject to revision. Other criterion includes FMBN financing infrastructure up to 70% for private developers while housing corporation and government owned development projects receive 100% infrastructure facility. The property must have a good title for allotment purposes and transfer to persons at sale or lease, an accepted housing design by FMBN, project sale not than =N=15 million for a rights affordability of NHF contributors, accepted sound financial projections and a 10% developer equity participation, and there must be an additional security for value for the 1st stage of the development, government-owned

Under a developer lease agreement, the developer shall have powers to register the lease agreement and the power to prepare a layout, partition, allocate, assign and sublet different types of housing units, and develop service plots from an approved building plan or a site layout. The developer must be able to submit individual titles to FMBN. The developer must be able to pledge and mortgage to FMBN or a financier for the raising of funds. The tenor of the lease must be a minimum of 5 years subject to renewal for a further 5 years or more. The financier or FMBN must have first charge over the land/development. It is required by FMBN for all developers to provide duly registered company documents and other functional documentation of a corporate entity.

## Home Renovation Loan (HRL)

This loan is for Nigerians who are above the age of 18, who have contributed to the National Housing Fund for a minimum of six months and have satisfactory evidence of regular flow of income to guarantee loan repayment. Other applications are submitted to the relevant agencies in Nigeria. A unique feature of this loan is a maximum loan amount of 1 million loan approved are disbursed subject to the income limit of the beneficiary and with other numerous other benefits attached.

## Rent to own

This is given to applicants above the age of 18 and who have made contributions to the fund and have done so for a minimum period of six months. The applicant must have proof of regular income to guarantee loan repayment. The significant advantage is the applicant can own a residential development by rental. Other features are fifteen Million maximum purchase prices of the property, and repayment not to exceed 30 years at an annuity rate of 7%. It is only available for applicants who have obtained prior facilities from FMBN. Property being purchased must be insured for fire & special perils and credit life Assurance cover.

## NHF Construction Loan

The applicants are required to adhere to procedures in other loans. However, it is expected that the title must be a C of O in the applicant's name. There must be an approved building plan with the bill of quantities certified by the appropriate professional. Loan disbursement is based on an agreed work plan (30-30-40) with a period of completion as stated by the bank. Other requirements must be adhered to in this application.
The Nigeria Housing Policy any housing project must follow prototype designs, must meet an urban housing need, must meet a rural housing need, have access to land, must have affordable housing cost, use local building materials. Other issues covered are health, finance, and provision of infrastructure periods, its maintenance and repair. The policy mandated the FMBN to provide loans for housing research, construction and delivery. Other government agencies with a mandate include the Nigerian Building and Research Institute, the Standard Organization of Nigeria, the Real Estate Development Association of Nigeria and the Building Materials Producers Association of Nigeria (Ibimilua and Ibitoye, 2015).

## MATERIAL AND METHODS

Earlier conceptual and theoretical frameworks provided a valuable guide in accessing housing affordability sustainability in the Nigerian Built Environment but now will require a review. In compelling data for suggested review, Proquest scholar peer-reviewed publications provided pertinent secondary data for this study. This literature review will be used in arriving at the recommendation on addressing housing affordability sustainability in the Nigerian Built Environment. For primary data, an online quantitative and qualitative survey was created in Google form. The hypothesis questions in the survey established the suggestions in this paper for addressing housing affordability sustainability in the Nigerian Built Environment.

### Research Questions:

1. Should the government focus on housing policies that address household income capacity for housing?

2. Should there be a focus on socio-economic changes such as population change for changes in housing provision strategy in the economy?

3. Can gathering data on housing demand and supply become relevant to land policy changes and housing sufficiency in Nigeria?

4. What views can bring a sustainable housing policy in Nigeria?

### Research Question 1 Hypothesis

To create an affordable housing strategy using the spendable income capacity index of Nigerians.

### Research Question 2 Hypothesis

To establish a population change mitigation modelling for the housing demand market in Nigeria.

### Research Question 3 Hypothesis

To foster the need for analytical and forecasting modelling approach for the housing commodity market in Nigeria.

This online quantitative survey is designed to assist in obtaining empirical data on the perception of the public on housing demand, the perception of the public on housing and its impact on household per capita income, and the need for an inclusive housing governance analytical strategy that will establish regular policies that reduce shocks on income from changes in the economy. Descriptive statistics used helped to measure these perceptions while the qualitative session of the questionnaire will help identify the inclusive options in addressing housing demand now and in the future. This research suggests a more detailed data gathering for creating the modelling scenarios and index for the housing commodity market in Nigeria.

The Pearson Correlation Statistics table below indicates the relationships between variables without assuming that a change in one causes a change in the other. In application, this is used to show there is a relationship among the hypotheses listed for affordable housing. In essence, this justifies the need for a more standardized framework for affordable housing provision in Nigeria.

The analytical housing governance strategy for Nigeria is possible because it is obtainable elsewhere. Research in China has identified the need for sustainability recommendations using statistical modelling evaluation techniques for the housing sector and other sectors. Presently the Chinese government has been making progress to measure how to make a regular housing market and resolve financial risks associated with the housing market using analytical assessment of variables impacting housing demand and implementing economic, political and administrative measures to address them. In creating a sustainable and stable housing and real estate industry evaluation of the influencing factors in the housing sensitivity analysis algorithm a grey relational analysis (GRA) algorithm is used. The sensitivity analysis algorithm provides for analyzing current housing demand whereas the GRA
algorithm provides a measure to select the influencing factors in the commodity housing market in China. This housing demand market assessment framework has provided the Chinese government's housing governance with suggestions on macro control of its economy thereby supporting the real estate industry to housing needs (Han and Feng, 2024).

## FINDINGS AND DISCUSSION

This paper with responses from 23 online respondents provided a reference point for the need for a more standardized framework in accessing and creating policies of affordable housing in Nigeria. Empirical evidence will justify the need for urgent macro measures that will necessitate funding required for housing, policy changes in Nigeria's Land Use Act instrument, and policy procedures in carrying out inclusive land administrative programs in Nigeria.

### Table 1

This is a descriptive statistics simulation with the advanced statistical software in Microsoft Excel. The table figures highlight how the hypothesis questions show the relevance and significance of the data collected on the importance of per capita income, population change and analysis in processing an affordable housing commodity market. Table 1 is a summary of the individual responses seen in Bar charts 1, 2 & 3.

### Table 2

This table lists the individual responses from the qualitative survey. The table provides insightful path for the Nigerian Housing commodity market in addressing affordable housing challenges.

### Table 3

This is a correlation assessment table that shows a Pearson correlation relationship between the variables for hypothesis comparison. Socio-econ change and an analytical economy yield the highest correlation followed by income and socioeconomic variable comparison and lastly household income and an analytical economy comparison.

<table><caption>Table 1: Descriptive Statistics for Questions 1, 2, 3</caption><thead><tr><td>Statistics</td><td>Question 1</td><td>Question 2</td><td>Question 3</td></tr></thead><tbody><tr><td>Mean</td><td>4.2174</td><td>4.0870</td><td>4.2273</td></tr><tr><td>Standard Error</td><td>0.2349</td><td>0.2259</td><td>0.2074</td></tr><tr><td>Median</td><td>5</td><td>4</td><td>5</td></tr><tr><td>Mode</td><td>5</td><td>5</td><td>5</td></tr><tr><td>Standard Deviation</td><td>1.1264</td><td>1.0835</td><td>0.9726</td></tr><tr><td>Sample Variance</td><td>1.2688</td><td>1.1739</td><td>0.9459</td></tr><tr><td>Kurtosis</td><td>1.8426</td><td>1.1739</td><td>-0.6296</td></tr><tr><td>Skewness</td><td>-1.5132</td><td>-1.1254</td><td>-0.8428</td></tr><tr><td>Range</td><td>4</td><td>4</td><td>3</td></tr><tr><td>Minimum</td><td>1</td><td>1</td><td>2</td></tr><tr><td>Maximum</td><td>5</td><td>5</td><td>5</td></tr><tr><td>Sum</td><td>97</td><td>94</td><td>93</td></tr><tr><td>Count</td><td>23</td><td>23</td><td>22</td></tr><tr><td>Confidence Level (95.0%)</td><td>0.4871</td><td>0.4685</td><td>0.4312</td></tr></tbody></table>

<caption>Table 2: Responses from Question 4 On Sustainable Housing Policy in Nigeria</caption>

* Housing Price Control.

* Looking At People's Income in a Community and leveling it with Possible house Rent.

* Amendment of the Land Use Act.

* Laws that apply to landowners must be seriously carried out.
* Affordable house finance Inclusive zoning regulation etc.

* Efficient and effective use of urban and regional planning.

* Embarking on large housing programs that are affordable in the nation.

* Affordable Housing Development: Subsidies and Incentives: Implement government subsidies and tax incentives for developers to build affordable housing. Public-Private Partnerships (PPP): Encourage collaborations between the government, private sector, and non-profits to share resources and expertise in housing development.

* Data-Driven Policy and Planning: Comprehensive Data Collection: Establish a reliable database on housing demand, supply, and socio-economic factors to guide policy decisions. Market Analysis: Regularly assess the housing market to identify trends, gaps, and needs for different demographics and regions.

* Land Use and Zoning Reforms: Simplified Land Acquisition: Reform land tenure and acquisition processes to reduce bureaucratic bottlenecks and make land more accessible for housing projects. Efficient Zoning Regulations: Implement zoning policies that support mixed-use development and sustainable urban planning.

* Increase access to affordable housing and also develop urban planning and zoning.

* The government can go into public-private partnerships with individuals or firms that can fund building projects. We know that the government alone cannot fund these projects. PPP would help reduce the financial burden by BOT agreement (Build Operate & Transfer).

* Good Jobs and economic growth

* Each school leaver should be able to afford a self-contained room and every couple a one-bedroom apartment.

* Culture, Religion, economy, Population and people's world view.

<figure><img src="image_27.png" /><figcaption>Figure 1: Data Analysis Bar Chart for Question 1</figcaption></figure>

<figure><img src="image_28.png" /><figcaption>Figure 2: Data Analysis Bar Chart for Question 2</figcaption></figure>
Should economic data on housing demand and supply be relevant to land policy changes and housing sufficiency in Nigeria? And what Significance?

23 responses

<figure><img src="image_29.png" /><figcaption>Figure 3: Data Analysis Bar Chart for Question 3</figcaption></figure>

<table><caption>Table 3: Correlation Assessment of Housing Affordability from the Online Quantitative Survey</caption><thead><tr><th>Variables</th><th>Pearson Correlation<br>R</th></tr></thead><tbody><tr><td>Household Income and Socio-Econ Changes</td><td>0.5797</td></tr><tr><td>Household Income and Analytical Economy</td><td>0.2932</td></tr><tr><td>Socio-Econ Changes and Analytical Economy</td><td>0.6423</td></tr></tbody></table>

In summary, primary data analysis shows that expanding population: household size and income are vital statistics in addressing housing affordability sustainability in the Nigerian Built Environment. This will be achieved with the adoption of analytical housing governance and planning processes that will create a sustainable affordable housing development strategy for Nigeria.

## CONCLUSION AND RECOMMENDATIONS

An analytical housing economy provides researchers in the housing sector an opportunity to support broad economic goals. Housing researchers can obtain access to health proficiency data for individual households and society; access data on how functional educational capacity of children in families; and access data on how family cohesiveness can exist. Housing builds culture and diversity as persons in the community are recognized by their clusters in a location housing arrangement; which distinguishes a clan or families in a village or town in different ethnic groups in Nigeria. Housing and family institutions are critical for the sustenance of ethical child development that creates a responsible society with zero tolerance for social vices that are destructive to society.

And so, creating housing affordability capacity delivery in Nigeria, will build housing schemes and give Nigerians the opportunity to own houses. Housing governance and city planning are important institutional frameworks that will deliver affordability of housing in the growing Nigerian Economy. Housing governance can achieve housing price control, an inclusive community housing development at affordable price regimes; give opportunity for land ownership through easy access to land programs, create easy access funds for housing development at a reduced lending rate, and create institutional arrangements for subside housing development in the Nigerian economy. City planning and housing affordability become achievable in a regulated and analytical institutional framework in our cities. City planners have forecast growing housing demand with relevant projections for housing finance funding, and plan city infrastructures and access network opportunities in a city by the expanding population and business opportunities in a city economy. Housing affordability in Nigeria is achievable with these inclusive development programs.
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# Chapter Eleven

## Examination of the Challenges of Insecurity on Residential Properties in North Central Nigeria

Naguto Mohammed Sani; Joachim. A. Onyike; Stanislaus A. Okeahialam

Department of Estate Management, Imo State University, P.M.B. 2000, Owerri, Nigeria

EMAIL: saokeahialam@gmail.com

### ABSTRACT

Insecurity in North Central Nigeria, driven by ethnic conflicts, banditry, and communal violence, has increasingly destabilized the region, with significant consequences for residential real estate. This paper examines how insecurity has impacted residential property values, rental demand, property maintenance, and overall investment in the housing sector. A mixed-method approach combining quantitative and qualitative data was used to assess the relationship between insecurity and the real estate market. The study found that insecurity has caused a marked decline in property values, reduced rental occupancy rates, and heightened management costs due to property damage and security investments. The paper concludes by recommending collaborative strategies between the government, real estate stakeholders, and community leaders to mitigate the challenges of insecurity and protect residential property investments in North Central Nigeria.

KEYWORDS: Insecurity, Residential properties, North Central Nigeria, Property management, Real estate market, Banditry.

### INTRODUCTION

In recent years, North Central Nigeria has become a focal point of various forms of insecurity, ranging from ethno-religious conflicts to herdsmen-farmer clashes and rising banditry. These threats have disrupted socio-economic activities, with profound effects on housing and real estate in the region. Residential properties, in particular, have faced sharp declines in value, increased vacancy rates, and heightened operational costs due to damages caused by violent incidents.

This paper aims to explore the specific impacts of insecurity on residential properties in North Central Nigeria, examining the challenges faced by homeowners, landlords, and investors. The study seeks to understand the dynamics of property value fluctuations, rental occupancy, and property maintenance in the face of ongoing security threats.

<figure><img src="image_30.png" /><figcaption>Figure 1: Map of the North-central zone of Nigeria (study area). Source: https://www.researchgate.net/publication/318183914/figure/</figcaption></figure>
## LITERATURE REVIEW

Insecurity in Nigeria, particularly in the northern and central regions, has been the subject of extensive research due to its severe implications on economic growth, social development, and urbanisation. Several studies have highlighted how insecurity disrupts economic activities and social services, including housing markets.

### Insecurity and Real Estate

According to Olokesusi (2011), property markets in conflict zones often experience reduced investment, declining property values, and a rise in abandoned properties. This phenomenon is particularly visible in Nigeria, where widespread violence has led to decreased demand for housing in insecure areas. Adegoke and Olatunji (2019) corroborated these findings, noting that the real estate market in northern Nigeria has been severely affected by insurgency and conflict, which has driven investors away and forced residents to abandon their homes.

### Causes of Residential Property Insecurity.

#### Nigeria's Unrest and Insecurity Factors

Boko Haram and Ansaru Deen's violence is attributed to failed institutions in Nigeria.

Unrest in the North-central zone is attributed to terrorism, land access, cultism, criminality, ethnicity, politics, or religion. Resource competition between Fulani herders and Hausa farmers fuels violence. Illegal crossings, corruption among border officials, and lack of state government leadership contribute to the spread of violence. Climate and environmental changes, rising demographic pressures, and climate change have exacerbated the situation. Vigilant groups, known as "Yan-sa-kai," have become a source of insecurity. Government sanctions have led to the formation of counter-groups, militia groups called "yan-bindiga," to protect herders. Most farming communities have been forced to abandon their farms, particularly in forest areas. The weak regulation of forest pastoral activities has facilitated large-scale cattle rustling.

Factors contributing to insecurity in Nigeria include unemployment, poor governance, inadequate education, infrastructure, corruption, perceived victimisation, poverty, ethnic and religious superiority, domination and exploitation, and materialism.

### Domestic terrorism

Domestic terrorism Targets citizens, property, and institutions in a host country, while transnational terrorism involves multiple countries, with 9/11 being a prime example. Insecurity as defined by Beland (2005), is a feeling of fear or anxiety caused by a lack of protection, which leads to uncertainty and vulnerability. Businesses have become integral to human existence, satisfying unlimited wants and becoming part of the global world. Security focuses on ensuring the safety of lives and properties.

### Terrorism and Insecurity in Nigeria

Nigeria has faced unprecedented insecurity and terrorism since democratic rule, with regionalized patterns like militia groups, insurgency, kidnapping, ritual killings, and calculated assassinations. Boko Haram, a radical fundamentalist Islamic sect, emerged in 2002 and has since moved to where it set up a base called Afghanistan in Yobe North East Nigeria (Nwanegbo & Odigbo, 2013; Ikenga & Efebeh 2013; Meehan & Speier, 2011).
## Criminal Violence and Banditry

Between 2019 and 2020, Nigeria experienced a surge in armed robbery, murder, and cattle rustling, primarily involving Fulani-originating groups. This led to forced displacement, violence, and out-of-school children. Lawlessness and lack of policing exacerbated the situation (Eze, & Frimpong, 2021).

According to Ayegbusi, T. R. (2024), Between January and December 2019, armed bandits in north-western Nigeria killed over 1,000 civilians, surpassing Boko Haram's number. In 2020, criminal gangs abducted 400 Kaduna residents, causing over 200 violent events, 1,000 fatalities, and 50,000 internal displacements. Between December 2020 and March 2021, over 600 school abductions occurred in northern Nigeria, primarily by bandits, kidnappers, armed robbers, cattle rustlers, Fulani herdsmen, and other armed militias. The Kagara school abduction is the latest addition to a growing list of school abductions in Northern Nigeria carried out by terror groups and organized militias for both economic and ideological reasons.

## Concept of Residential Property

Denis & Williams (2007) as cited by Adebayo, Isyaku & Rilwan, (2019), Defined Residential property as any property designated by a municipality for single-family homes, condos, cooperatives, townhouses, and any other area in which people reside. Residential property can be used as a mortgage and income is often (although not always) entitled to preferential tax care. Residential property includes any properties used as living space. Residential property can either be private residences or government institutional housing; it is the greatest source of demand for professional manager's services. Residential dwellings can be constructed in a wide range of configurations (Chika, 2008).

## Types of Residential Property

According to Chika (2008), Residential property has been classified into the below following;

**Bungalow;** House unit on a single-story/ground flow. It consists of Semidetached/duplex and detached.

**Detached Houses;** are standard housing units with two floors, consisting of living, guest rooms, a ground floor kitchen, a toilet, and a family sitting room and bedroom on the first floor.

**Semi-Detached / Duplex:** A duplex consists of two separate residences, often resembling two houses or large single homes, with legal and structural walls between parts.

**Flat Houses:** -Houses with 2, 3, or 4 flats, often with a common front staircase and multiple floors, may have maisonettes on each of the two floors.

**Maisonette:** - A Maisonette is a two-story apartment with internal stairs, typically found in buildings with multiple floors, with a Maisonette held on every two floors.

**Tenement Buildings:** A multi-unit dwelling with multiple rooms on each side, typically on one, two, or five floors, with all occupants sharing common amenities at the back of the building.

## Factors Influencing Property Demand and Supply

One of the significant elements deciding the value of real property is demand and supply. In the basic term, the real economic theory states that value should rise when demand is greater than supply. This means that the value of certain assets would increase ceteris paribus if real property demand is higher than its supply (Onyejiaka, 2019). Abiodun (2013) The concept of infrastructural growth in tangible estate results in increased real estate value. This pressure on a specific area, combined with factors like
affordability, drives people and businesses to find alternatives at lower costs. This slows down appreciation, triggering an increase in alternative areas' value. The property's favourable circumstances or factors may lead to higher demand and ultimately increase its value.

### Impact of Communal Clashes on Housing Markets

In the context of North Central Nigeria, communal clashes between ethnic groups and violent confrontations between herders and farmers have displaced thousands, leaving residential properties in conflict-prone areas either abandoned or destroyed. Onifade (2020) noted that such conflicts not only lower property values but also affect the broader real estate ecosystem, increasing the costs of security, repairs, and maintenance.

### Socioeconomic Implications of Insecurity

The socioeconomic consequences of insecurity are far-reaching, affecting household income, social cohesion, and community resilience. Ajibola et al. (2021) found that areas affected by violence experience reduced economic activities and urban decay, as properties become uninhabitable and service delivery is disrupted. Additionally, the cost of property management increases as landlords invest in private security, repairs, and relocation of tenants.

Government policies and programs impact real estate demand and supply, with both positive and negative effects. Revenue raises can decrease net income, discourage investors, and reduce returns. However, government policies can also promote development and property value by exempting or providing relief on property-based taxes, attracting new investors, and increasing the value of land and landed properties in specific areas. (Onyejiaka, 2019).

Infrastructural Development. Infrastructural development significantly impacts real estate value in an area. High infrastructural growth levels, such as constant power supply, good roads, potable water, waste disposal, sanitation hospitals, banks, and schools, increase property value in a specific area.

Location. Property value is influenced by factors such as surroundings and community system, with proximity to amenities like entertainment, shopping, and healthcare services influencing potential investors' perception and value. (Aluko, 2011). Onyejiaka (2019) The property's value is influenced by its proximity to positive features, such as a significant industry or an area with an environmental hazard history.

Neighbourhood Condition. According to Onyejiaka (2019), the property's physical condition, and the neighbourhood where a property is situated have the ability to increase or decrease the value of the property. Neighbourhood conditions indirectly impact property value, removing ownership control. Generally maintained by inhabitants, neighbourhood values are significant to property owners and occupants.

Strong neighbourhood; Society has been shown or lead the improvement of real estate value. Naturally, people are drawn toward an environment that is characterized by security, safety, and order, and also the area where the occupants will organize themselves and have similar minds and interests in living together, working on development, and security arrangements (Abiodun, 2013). Those areas are connected with well-planned, family-friendly residential layouts and reserved properties, the safety of the inhabitants is ensured in such an organized neighbourhood, and the facilities required for a relaxed and decent life are all completely existing. Due to the limited supply of real estate located in those areas, coupled with the high demand for existing properties in such neighbourhoods with strong abilities their values are highly appreciated.

Furthermore, a variety of adverse factors occur in an unplanned neighbourhood, which includes urban decay, obsolescence, deterioration, and slums. Not only neighborhoods devoid of basic facilities that support decent living, but also due to the domicile of migrants, immigrants, and low-income earners
who take advantage of such a dirty residential neighbourhood. Within such a neighbourhood, residential housing units are distinguished by poor layout, insufficient ventilation and lighting, and useful and physical obsolescence, among others, and thus directions have less value.

Inflation. When the nation's commodity prices increase, it has a major effect on the economic sector of that nation. Inflation has a major effect directly and indirectly on the property value. One of the major examples is that, when the building materials cost is high, the cost of constructing a new building will increase and it will increase the renting charge of the long-standing properties, due to the scarcity of new buildings caused by the new structures erecting high cost, also additional housing units availability will be reduced, also demand the few existing oldest buildings will increase, as a result of this it will increase the renting cost of both the few new ones and the old houses (Onyejiaka, 2019).

Uncertainties are among the factors which can paralyze the societal and economic life of an area. The area which ensures not guarantee safety to their residents, then financial activities and also the extension of real development of that area cannot be sustained. In a situation where investors feel insecurity threatened, it must be expected that the investors will move to another area where they feel secure and conduct their business activities, which will result in the abandonment of the project and the collapse of economic activities.

Physical State of the Property: Assessment of the property value goes a long way with the physical condition of the property. Newly constructed and testified decorated property that meets existing technology, efficient utility, design, strength, security, and maintenance requirements undoubtedly will have a higher value demand than other properties that have physically depreciated and its technologically obsolete (Onyejiaka, 2019). It is worth noting, then the property's physical state is among the outwardly driven issues affecting the value of the property. Based on the viewpoint from which the property is perceived, these variables affect the property value positively and negatively. The above-explaining factors can be summarized in two; Micro and Macro factors, these factors affect residential value either positively or negatively. The factors are summarized in below table 1.

<table><caption>Table 1: Factors Affecting Residential Property Value</caption><thead><tr><td>S/NO</td><td>ATTRIBUTES</td><td>DESCRIPTION</td></tr></thead><tbody><tr><td>1.</td><td>Physical Residential (Housing) Characteristic</td><td>Lot Size, number of rooms, building age, Facilities, etc.</td></tr><tr><td>2.</td><td>Location</td><td>Access to CBD, Proximity to local amenities, etc</td></tr><tr><td>3.</td><td>Residential Neighborhood Characteristics</td><td>Sentiment, tribal affinity, schools, hospitals, worship places, crime rate, traffic noise and conjunction, shopping centers, etc.</td></tr><tr><td>4.</td><td>Government Policies</td><td>Zoning Regulation, Planning restriction, Tax levy, etc.</td></tr><tr><td>5.</td><td>Demand & Supply</td><td>Zoning Regulation, Planning restriction, Tax levy, etc.</td></tr><tr><td>6.</td><td>Accessibility</td><td>Entry network and exit road connecting to the main road</td></tr><tr><td>7.</td><td>Accessibility</td><td>Road, Drainages, West Disposal Collection, Water supply, and other social amenities</td></tr></tbody></table>

## METHODOLOGY
This study uses a mixed-methods approach combining quantitative and qualitative data collection techniques to assess the impacts of insecurity on residential properties in North Central Nigeria. The methodology involves surveys, interviews, and field observations in selected states within the region.

## Research Design

The research employs a descriptive and case study design. It focuses on urban and semi-urban areas in Plateau, Benue, and Niger States, which have experienced significant insecurity due to ethno-religious violence, banditry, and herdsmen-farmer conflicts.

## Data Collection Methods

Surveys: Structured questionnaires were distributed to 200 property owners, tenants, and real estate professionals in selected cities within the study area. The questionnaire included sections on the impact of insecurity on property values, rental demand, and maintenance costs.

Interviews: In-depth interviews were conducted with 20 key stakeholders, including property developers, real estate agents, and community leaders. These interviews provided qualitative insights into the broader effects of insecurity on the housing sector.

III. Field Observations: On-site visits assessed residential property damage, security measures, and occupancy trends.

## Sampling Techniques

The conflict-affected areas and participants were selected using a purposive sampling method. The sampling was stratified by region and level of insecurity to capture a wide range of experiences. The study included 220 respondents.

## Data Analysis

a. Quantitative data from the survey was analyzed using descriptive statistics (frequencies, percentages) to assess trends in property values, vacancy rates, and security investments.

b. Qualitative data from interviews and observations was analyzed using thematic analysis, identifying common themes related to property management challenges, community responses, and security concerns.

# RESULTS AND DISCUSSION

## Decline in Property Values

Insecurity has led to a substantial decline in the value of residential properties across North Central Nigeria. 80% of property owners reported that their properties had depreciated by 30% to 50% due to insecurity. This is in line with the findings of Ajibola (2021), which suggest that properties in conflict-prone areas lose value as demand plummets.

## Increased Vacancy Rates

Vacancy rates have risen dramatically in areas most affected by violence, with some areas reporting vacancy rates as high as 60%. Many tenants and homeowners have fled conflict zones, leaving behind abandoned homes. Adegoke (2019) previously noted similar trends in northern Nigeria, where mass displacement led to abandoned real estate in once-thriving urban centres.

## Rising Property Management Costs
Property management costs have increased significantly, driven by the need for heightened security measures such as fences, security guards, and alarm systems. Property owners have had to invest in private security solutions, increasing operational costs by an average of 20%. Additionally, property damage from violence has required costly repairs and renovations.

## CONCLUSION

The challenges posed by insecurity on residential properties in North Central Nigeria are profound, affecting property values, rental demand, and property management. The rising levels of violence and instability have resulted in significant financial losses for property owners, tenants, and real estate investors. Addressing these challenges requires a multi-stakeholder approach, with active government intervention, improved security infrastructure, and community-based solutions. Sustainable urban planning and investment in resilient real estate practices will also be crucial in mitigating the long-term effects of insecurity.

## Recommendations

1. Government Intervention: The Nigerian government must prioritize security interventions in North Central Nigeria, including increased military presence, community policing, and conflict resolution initiatives.

2. Real Estate Resilience: Real estate developers should focus on building resilient housing that can withstand insecurity, including reinforced structures and comprehensive security measures.

3. Community-Based Policing: Local communities should be empowered to participate in their own security through neighbourhood watch programs and conflict mediation to prevent violence from escalating.

4. Public-Private Partnerships: Collaboration between the government and private sector is essential to provide the necessary infrastructure and security solutions to restore confidence in the housing market.

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Nwanegbo, C. J., & Odigbo, J. (2013). Post independence peace building in South Sudan: Looking beyond ephemeral peace. *International Journal of Social Science Tomorrow, 2*(1), 1-10.

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Chapter Twelve

Assessment of Factors Affecting the Implementation of E-Land Administration and
Management in Imo State, Nigeria

Okeahialam Stanislaus A.<fnref n="1" />; Idih Ukachukwu Michael<fnref n="1" />; Chukwuemeka Favour<fnref n="1" />; Agomuo Onyinyechi<fnref n="2" />

EMAIL: saokeahialam@gmail.com

ABSTRACT

The development of any state depends on what makes its land administration system effective, robust and well-designed. In Imo state, the land market has had little impact on the socio-economic environment of the state as it has not been able to attract investors and developers. The manual record-keeping used in the state lands ministry is inefficient and prone to abuses, thereby yearning for computerization. This study aims to bring to the limelight, the factors affecting the adoption of electronic land administration and management in Imo State and how these factors differentiate between two groups of respondents of the Ministry of Lands namely: the junior staff (grade level 05 – 08 officers) and senior staff (grade level 09 – 12 officers). The study adopted the discriminant and exploratory factor analysis techniques. The exploratory factor analysis technique was used to assess the extent to which the identified factors belonged together to affect the implementation of e-land administration in Imo state. The Purposive sampling approach was adopted in the collection of data from 350 valid respondents from the ministry. It was found through the exploratory factor analysis that four factors namely: Economic and Financial Factors, Institutional and Government Policies, Social and Behavioral Barriers and Lack of Technological Innovation and Expertise are the major factors affecting the implementation of electronic land administration in the state. There were 26 variables extracted, all of which loadings significantly. The Wilks' Lambda's F test and the standardized discriminant function coefficients indicate that the two categories of staff perceive the variables differently. Measures of Economic Factors with a total variance of 22% in the factor solution and 3.550% on the rotated sum of square loading have the greatest discriminant power in explaining the difference in perception. This was followed by Lack of Technological Innovation and Expertise, Institutional and Government Policies and Social and Behavioral Barriers. The findings support the growing demand for electronic land administration systems as a component of the whole land market and therefore recommend for target-driven partnership among the stakeholders.

KEYWORDS: Land administration; Management system; E-land administration; Land market; Imo state

INTRODUCTION

Recently, scholars have called for the review and restructuring of land administration policies and practices by advocating for the adoption of electronic land administration (e-land administration) services. Their argument is based on the fact that it is gradually becoming a means of optimizing the land development process, achieving cadastral database and standardized data exchange in land administration processes (Olusoyi, 2012). Furthermore, the pressure to shift to e-land administration and management is based on the rapid pace of development in many Nigerian cities. There is a very high increase in urban population which has resulted in high demand for land thereby justifying the call for a second look at the land market and its policies and practices. Perhaps, it is on this premise that those knowledgeable in the subject matter such as Okeahialam & Ogbuefi, (2017) have argued that the current practices of administering and submitting land sub-division plans, both paper-based and PDF-based are no more sustainable, especially in this technological age where internet and web technologies are now thoroughly integrated into the business of land administration.

<footnote n="1">¹Department of Estate Management, Imo State University, Owerri, Nigeria</footnote>

<footnote n="2">²Department of Estate Management, Kingsley Ozumba Mbadiwe University, Ogboko, Nigeria</footnote>
Some states and cities in Nigeria such as Lagos and Abuja have made considerable progress in e-land administration and management. For example, the Abuja Geographic Information System (AGIS) was established in 2003 and has been in operation to date. The significance of the AGIS initiative lies in its ability to develop a digital system of cadastral records for e-land administration and for the provision of geospatial data and services in the Federal Capital Territory (FCT) of Nigeria. Some other states in Nigeria have seen the implementation of AGIS as worthy of emulation in the computerization of land administration and property-related services and are currently at different stages of its implementation in their states. Some of these states according to Aneke, Humayun and Amara, (2019). include: Abia Land Information System, Cross River State Geographical Information System, Kaduna State Land Information System, Benue Land Information Management System, Kwara State Geographical Information System, Lagos State Digital Mapping and Geographical Information System, Nasarawa State Geographical Information System, Niger State Geographical Information System, Ogun State Geographical Information System, Ondo State Geographical Information System and Digital Mapping, Osun State Aerial Mapping and Geospatial/Land Information System and Plateau State Geographical Information System

Again, another factor that has greatly affected the growth of e-land administration and management in Nigeria is the lack of interest (Olusoyi, 2012). While a greater number of states in Nigeria have not made conscious efforts to adopt computerized land administration and management systems and as such are uncertain of the benefits associated with them, those who have adopted them are less keen to increase the sensitization of the need to embrace electronic land administration or are experiencing implementation challenges. The subsequent effect is the continued dependence on old, less innovative and unsustainable ways of land transaction and marketing with its attendant economic and social problems in the state. Aneke, et al, (2019) and Okeahialam and Ogbuefi (2017) have argued that the reason for the observed lack of interest concerning the adoption of e-land administration in Imo state could be ascribed to the conservative attitude of the people and professionals in land administration towards changes. Furthermore, there appears to be a low level of enthusiasm on the part of the Imo workers and the state government in particular regarding electronic land administration. Studies have shown that electronic land administration is redefining the land market sector (Okeahialam & Ogbuefi 2017), yet the government and professionals in land and other stakeholders including land users, and occupiers are still in doubt about the certainty of e-land administration benefits. This study aims to bring to the limelight, the benefits of computerized record-keeping of land transactions as against the manual system.

# LITERATURE REVIEW

## Overview of Land as a Resource

Land is the ultimate resource for life on Earth. Land is both a physical commodity and an abstract concept. Therefore, good stewardship of the land is essential for present and future generations (Umeh, 1973). These issues are concerned with the administration of land as a natural resource to ensure its sustainable development. They address the social, legal, economic and technical framework within which land managers and administrators must operate. For investment in land to take place, the investors must feel confident that the assets they are developing will be built on land that has a secure title. Therefore, there must be a clear and rigid framework of laws governing the ownership and rights to use the land. Good land resource management will help to promote economic and social development in both urban and rural areas. Land and its use may be examined from different perspectives namely: physical, legal, abstract, economic, socio-political, religious/spiritual and statutory. From the physical perspective, land plays a vital role in the breeding and survival strategies of many living species.

## Land Management and Land Reform

Land management is the process by which the resources of land are put to good effect. It covers all activities concerned with the management of land as a resource both from an environmental and economic perspective (Dale and McLaughlin, 1988). It includes such matters as: physical planning of towns and countryside, formation and implementation of land-use policies, environmental impact assessment, Property conveyancing, property development and management of utilities and services
The term land reform has a variety of meanings. It may involve the restoration of land rights to previous owners, a process known as land restitution. It may also involve the redistribution of land rights from one sector to another; for example, taking land from the State or individual owners of large estates and giving it to people who have no land. Land reform may also involve land consolidation in which all landowners within an area surrender their land and are allocated new parcels of comparable value but in a pattern that encourages the more efficient and productive use of the land (Dale & McLaughlin 1999). Land reform may also involve changes in land tenure which is how rights are held, thus abolishing complex traditional and customary rights and introducing simpler and streamlined mechanisms of land transfer.

## The Evolution of Land Administration Systems in Nigeria
### Land administration in the Pre-colonial Era

Land administration refers to the processes of recording and disseminating information about the ownership, value and use of land and its associated resources (Dale and McLaughlin, 1999). Such processes include the determination (sometimes known as the adjudication) of rights and other attributes of the land, the survey and description of these, their detailed documentation and the provision of relevant information in support of land markets.

In the pre-colonial era, land administration and management recognized that land in its entirety belonged to the indigenous people. It was owned privately or collectively in the Southern region of Nigeria by families and in the Northern region by the emirs. No formal legislation or policy existed to manage and administer land in the southern region. The oldest man in the family was the administrator who carried out the modern functions of land administration. One notable feature of land rights during this era in the Southern region was that an allotee had inalienable rights over any plot of land allocated to him by the family head. The opposite held for land in the northern region. In the South, transfer of land was largely through gifts, family allocations by local leaders, or inheritance, and not through direct sale while in the North, control of land was vested in the Emir who granted final consent before any land transaction was concluded, mostly granting clear possessory rights on undeveloped or otherwise idle land.

### The Colonial Era

The colonial era brought the British administration to land governance in Nigeria. The British colonial administration signed a cession treaty in 1861 that transferred all rights to manage land to the colonial masters. However individual property rights in southern Nigeria still resided with native citizens (Ikejiofor, 2009). To govern land more effectively, the colonial administration promulgated many laws including the Native Lands Acquisition Proclamation in 1900 and the Native Lands Acquisition Ordinance No.32 of 1917 (Mamman 2004). The essence of this ordinance was to further deepen the regulation of land acquisition from the natives within the Protectorate and without the approval of the Governor, no foreigner could acquire the interest or rights of a native over any land. Thereafter the British colonial administration followed a different strategy to intensify its effort to extend its land management and administration policies to the northern region of Nigeria by enacting the Land and Native Rights Proclamation Act No. 9 of 1910 and the Northern Nigeria Land Tenure Law of 1962 which later transformed to the Land Use Act of 1978. (Mamman 2004).

It was not surprising, therefore, that faced with these contrasting land tenure systems and the considerable hassles in getting land for public purposes, especially in southern Nigeria, the military government sought to unify the two systems through the Land Use Decree of 1978 (Igbozuruike, 1980) The thrust of the Decree was largely to extend the northern system of land management to the whole country as a means of ensuring easier access to land for government and, ostensibly, for individuals.

### Reforms in Land Administration

In a bid to improve the system, President Olusegun Obasanjo shortly after assuming office in 1999, directed the computerization of all Federal Government lands and land records (Obasanjo, 2003). To ensure efficiency in our national land administration, the Federal Government established a computerization programme for all lands held and allocated by the Federal Government under the Federal Land Information System (FELIS). Similar computerisation projects according to Adeoye
(2006) and Ikejiofor (2009) have also been established by the authorities of the Federal Capital Territory (Abuja), and the governments of the thirty-six states of the country. However, as at 2008, only the Federal Capital Development Authority (FCDA) and four out of thirty-six states (Enugu, Lagos, Abia, and Kano) in Nigeria have made a substantial effort to apply GIS or IT in their land administration system but the extent, content and sustainability of the programmes cannot be easily ascertained because there is little or no central control of land administration in Nigeria (Adeoye (2006) and Ikejiofor (2009). The modern cadastre is not primarily concerned with generalized data but rather with detailed information at the individual land parcel level. As such it should serve the needs both of the individual community and the state at large. Benefits according to Okeahialam and Ogbuefi, (2017) arise in the following ways: the compilation of land records and the judicial processes that must have gone through to bring land information onto the registers should provide formal identification and, in some systems, legal proof of ownership. The public registers should contain all essential juridical information allowing anyone viewing the system to identify third-party rights as well as the name of the landowner.

## Stages in the Preparation of Computerized Land Information System

Adeoye (2006) and Ikejiofor (2009) have identified eight stages that will lead to the computerization of the land information system in Nigeria. Stage one involves the provision of satellite images and conventional land records.

The federal Ministry of Lands in collaboration with the state's ministries will have to contract with satellite companies to get this done. The second stage involves the establishment of a geodetic survey network, the third stage involves an extensive GPS survey, and the fourth involves investigation and recording ownership. This stage must require the inputs of Estate Surveyors and Valuers who will ensure a thorough title verification of ownership of such parcel of land. The fifth stage involves the preparation of the draft land record, the sixth stage is displaying the draft land record, the seventh stage is the finalization of the land ownership record and stage eight, which is the final stage is the computerization system of this information.

## METHODOLOGY

The study was conducted in Imo state which is one of the 36 states of Nigeria. Owerri is located at latitude 5° 30′N – 6° 45′N and 6° 50′E – 7° 30′E in the Southeastern part of Nigeria It has 27 local governments. It is bounded on the East by Abia state, on the West by Delta state, on the North by Anambra state and the South Rivers state. According to 2006 census figure of Nigeria, Imo state has a population of 3,934,899 people. Using an annual growth rate of 3.5%, the population in 2024 is 7,131,919 people.

<figure><img src="image_31.png" /><figcaption>Figure 1: Map of Imo State. Source: Ministry of Lands, Survey and Urban Planning.</figcaption></figure>
and inferential statistics, such as the F test for Wilks' Lambda, Wilks' Lambda model, standardized canonical discriminant function (SDFC), eigenvalues, canonical correlation, and function at group centroids, are assessed. Variables that significantly differentiate between or among groups are identified through the ANOVA F test for Wilks' lambda.

The contribution of each variable to the discriminant function was evaluated using standardized discriminant function coefficients. A low coefficient indicates that the group did not vary significantly on that variable. The Canonical correlation illustrates the relationship between the predictors and the discriminant function. The structure matrix coefficient shows the correlation between each predictor variable and the discriminant function. Correlations with loadings ≥ 0.3 are considered to be practically significant (Hair *et al.*, 1987). An important requirement for using discriminant analysis is ensuring that the variance-covariance matrices are comparable for the groups. This is typically confirmed by Box's M test, which tests the null hypothesis that the covariance matrices do not differ among groups (Aliagha *et al.*, 2014). In this study, the result shows comparable log determinants and non-significant different variances (Box's M = 586.504, F = 17.520; p-value 0.854 is greater than 0.05). This supports the hypothesis that groups do not differ, indicating that it is appropriate to use discriminant analysis. The Statistical Package for Social Sciences SPSS Windows 22.0 (SPSS Inc., Chicago) was used to perform the analysis of this study. The results are presented in Tables 1 - 5

## Instrument and Measures

The primary data was collected using a questionnaire. The questionnaire was divided into two parts. Part 1 consisted of general demographic questions while Part 2 contained questions to identify the factors affecting the implementation of e-land administration and management in the state. The questionnaires were distributed to gather the opinions of both junior and senior staff on the measures outlined in Table 1. The items were rated on a 5-point Likert scale (1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree and 5 = strongly agree).

## RESULTS AND DISCUSSION

### Respondents Demographic Characteristics and Background Information

The study involved two groups of respondents and as such, many variables were used to examine the sample characteristics. Table 1 shows the demographic information of the respondents. They include: gender, education, years of experience in civil service, and grade/level.

<table><caption>Table 1: Showing Demographic Information of the Respondents</caption><thead><tr><th>Demographic profile</th><th>Attribute</th><th>No of respondents (level 09-12)</th><th>Valid percentage % (level 09-12)</th><th>No of respondents (level 05-08)</th><th>Valid percentage % (level 05-08)</th></tr></thead><tbody><tr><td></td><td></td><td>180</td><td>36.37%</td><td>265</td><td>63.66%</td></tr><tr><td rowspan="2">Gender</td><td>Male</td><td>120</td><td>66.6%</td><td>208</td><td>78.49%</td></tr><tr><td>Female</td><td>60</td><td>33.4%</td><td>57</td><td>21.51%</td></tr><tr><td rowspan="3">Education</td><td>Diploma</td><td>58</td><td>32.2%</td><td>86</td><td>32.46%</td></tr><tr><td>Degree</td><td>100</td><td>55.5%</td><td>117</td><td>44.15%</td></tr><tr><td>Post-graduate</td><td>22</td><td>12.3%</td><td>62</td><td>23.39%</td></tr><tr><td rowspan="4">Years of Experience in civil Service</td><td>1-2years</td><td>8</td><td>4.44%</td><td>46</td><td>17.36%</td></tr><tr><td>3-4years</td><td>12</td><td>6.66%</td><td>54</td><td>20.37%</td></tr><tr><td>5-6years</td><td>55</td><td>30.55%</td><td>77</td><td>29.06%</td></tr><tr><td>> 6years</td><td>105</td><td>58.33%</td><td>88</td><td>33.21%</td></tr><tr><td rowspan="2">Grade/Level</td><td>05-08</td><td>-</td><td>-</td><td>265</td><td>60%</td></tr><tr><td>09-12</td><td>180</td><td>40%</td><td>-</td><td>-</td></tr></tbody></table>

Source: Fieldwork

Table 1 shows that the majority of the respondents are male respondents (66.6% and 78.49%). The implication is that more males are employed in the ministry. On the level of education of the
respondents, it can be inferred from the Table that all the respondents are educated which makes them well-informed and familiar with the subject matter. However, a greater number of the respondents have university and polytechnic education. On years of experience in the civil service, the Table reveals that a greater percentage of both respondents who are either estate surveyors and valuers or land practitioners have had more than six years of experience in civil service. For levels 09-12, 58.33% have had experience in the land's ministry for more than six years. 30.55% have had five to six years' experience while 6.66% and 4.44% have had experiences spanning three to four years and one to two years respectively. For grade level 05-08, 33.21% have had more than six years' experience in ministry. This is followed by 29.06% for five to six years' experience. This further gives a high degree of credibility to this study that the respondents have far-reaching experiences on the subject matter.

<table><caption>Table 2: Representative Constructs and their Measures for Factor and Discriminant Analysis</caption><thead><tr><th>Factor</th><th>Variables</th></tr></thead><tbody><tr><td>Factor 1: Economic and Financial Factors (EFF)</td><td>Lack of funds - EFF1<br>Cost of procurement - EFF2<br>High cost of installation/replacement of ICT facilities - EFF3<br>High-cost broadband - EFF4<br>Lack of financial incentive and subsidy - EFF5<br>Low budgetary allocation for ICT - EFF6<br>Cost of Energy and Power - EFT7</td></tr><tr><td>Factor 2: Institutional and Government Policies (IGP)</td><td>Absence of relevant laws and regulations on ICT - EGP1<br>Poor implementation of policies on the use of ICT - IGP2<br>Administrative and bureaucratic bottlenecks/contradicting rules - IGP3<br>Corruption in Civil Service - IGP4<br>Poor Remuneration for IT Staff - IGP5<br>Inadequate IT Infrastructure</td></tr><tr><td>Factor 3: Social and Behavioural Barriers (SBB)</td><td>Poor awareness and acceptability of ICT - SBB1<br>People's resistance to change - SBB2<br>Staff' perceived usefulness of computer - SBB3<br>Perceived reliability & mistrust of technology/positive illusion - SBB4<br>Lack of understanding of users needs - SBB5<br>Loss of control and apathy on the part of users - SBB6<br>Organizational attitude - SBB7</td></tr><tr><td>Factor 4: Lack of Technological Innovation and Expertise (LTIE)</td><td>Limited skill capabilities - LTIE1<br>Lack of Qualified IT Staff - LTIE2<br>Skill gaps - LTIE3<br>Use of unskilled and unspecialized workers - LTIE4<br>Lack of innovation /capacity to design - LTIE5<br>Absence of web presence and poor design - LTIE6</td></tr></tbody></table>

Source: Authors Construct.

Table 2 shows the construct of the identified factors and variables that affect the implementation of e-land administration in Imo state.
## Exploratory Factor Analysis

<table><caption>Table 3: Extracted Factors on Implementation of e-Land Administration and Management (Varimax Loading)</caption><thead><tr><th rowspan="2">Variables</th><th colspan="4">Factor</th></tr><tr><th>1</th><th>2</th><th>3</th><th>4</th></tr></thead><tbody><tr><td>EFF1</td><td>0.744</td><td>-</td><td>-</td><td>-</td></tr><tr><td>EFF2</td><td>0.835</td><td>-</td><td>-</td><td>-</td></tr><tr><td>EFF3</td><td>0.830</td><td>-</td><td>-</td><td>-</td></tr><tr><td>EFF4</td><td>0.782</td><td>-</td><td>-</td><td>-</td></tr><tr><td>EFF5</td><td>0.822</td><td>-</td><td>-</td><td>-</td></tr><tr><td>EFF6</td><td>0.785</td><td>-</td><td>-</td><td>-</td></tr><tr><td>EFF7</td><td>0.799</td><td>-</td><td>-</td><td>-</td></tr><tr><td>IGP1</td><td>-</td><td>-</td><td>0.743</td><td>-</td></tr><tr><td>IGP2</td><td>-</td><td>-</td><td>0.654</td><td>-</td></tr><tr><td>IGP3</td><td>-</td><td>-</td><td>0.643</td><td>-</td></tr><tr><td>IGP4</td><td>-</td><td>-</td><td>0.743</td><td>-</td></tr><tr><td>IGP5</td><td>-</td><td>-</td><td>0.665</td><td>-</td></tr><tr><td>IGP6</td><td>-</td><td>-</td><td>0.644</td><td>-</td></tr><tr><td>SBB1</td><td>-</td><td>-</td><td>-</td><td>0.701</td></tr><tr><td>SBB2</td><td>-</td><td>-</td><td>-</td><td>0.668</td></tr><tr><td>SBB3</td><td>-</td><td>-</td><td>-</td><td>0.508</td></tr><tr><td>SBB4</td><td>-</td><td>-</td><td>-</td><td>0.631</td></tr><tr><td>SBB5</td><td>-</td><td>-</td><td>-</td><td>0.605</td></tr><tr><td>SBB6</td><td>-</td><td>-</td><td>-</td><td>0.585</td></tr><tr><td>SBB7</td><td>-</td><td>-</td><td>-</td><td>0.701</td></tr><tr><td>LTIE1</td><td>-</td><td>0.758</td><td>-</td><td>-</td></tr><tr><td>LTIE2</td><td>-</td><td>0.766</td><td>-</td><td>-</td></tr><tr><td>LTIE3</td><td>-</td><td>0.659</td><td>-</td><td>-</td></tr><tr><td>LTIE4</td><td>-</td><td>0.702</td><td>-</td><td>-</td></tr><tr><td>LTIE5</td><td>-</td><td>0.732</td><td>-</td><td>-</td></tr><tr><td>LTIE6</td><td>-</td><td>0.649</td><td>-</td><td>-</td></tr></tbody></table>

Fac 1 = EFF; Fac 2 = IGP; Fac 3 = SBB; Fac 4 = LTIE

As stated earlier, this study adopted Exploratory Factor Analysis (EFA) which is used in to determine the extent to which the observed variables are linked to their underlying latent factors. Before the variables were used, a test of the suitability of the data set for factor analysis was conducted using the Kaiser-Meyer-Olkin measure of sampling adequacy (MSA). Bartlett's test of sphericity was also used. Using Kaiser-Meyer-Olkin offers a measure of the level to which the variables belong together and are good for factor analysis to be used. On the other hand, Bartlett's test of sphericity tests the null hypothesis that the data set comes from a population of variables that are not dependent. The result of the Kaiser-Meyer-Olkin measure of sampling adequacy indicates a value of 0.895. Table 3 shows Extracted Factors affecting the Implementation of e-Land Administration and Management (Varimax Loading), while Table 4 shows Variance Extraction on Factors Affecting e-Land Administration and Management

<table><caption>Table 4: Variance Extraction on Factors Affecting e-Land Administration and Management</caption><thead><tr><th rowspan="2">factor</th><th colspan="3">Initial Eigenvalues</th><th colspan="3">Extraction Sums of Squared Loadings</th><th colspan="3">Rotation Sums of Squared Loadings</th></tr><tr><th>Total</th><th>% of variance</th><th>Cumulative %</th><th>Total</th><th>% of variance</th><th>Cumulative %</th><th>Total</th><th>% of variance</th><th>Cumulative %</th></tr></thead><tbody><tr><td>1</td><td>8.732</td><td>25.613</td><td>42.564</td><td>6.085</td><td>28.908</td><td>25.633</td><td>3.458</td><td>15.110</td><td>11.456</td></tr><tr><td>2</td><td>5.511</td><td>9.445</td><td>35.365</td><td>2.081</td><td>7.042</td><td>29.448</td><td>2.801</td><td>12.565</td><td>18.991</td></tr><tr><td>3</td><td>2.422</td><td>8.894</td><td>48.605</td><td>1.704</td><td>4.422</td><td>35.149</td><td>2.514</td><td>10.881</td><td>28.122</td></tr><tr><td>4</td><td>1.845</td><td>5.879</td><td>45.508</td><td>1.210</td><td>3.506</td><td>38.480</td><td>1.443</td><td>8.449</td><td>30.687</td></tr></tbody></table>

Kaiser-Meyer-Olkin Measure of Sampling Adequacy = 0.895; Bartlett's Test of Sphericity: $\chi^2$ (544) = 5654 sig.000. Extraction Method: Maximum Likelihood

The rule of thumb in the underlying assumption is that a factor loading above 0.3 is recommended as adequate and significant (Paul Kline, 2000). Based on this, a look at Table 5 indicates that the values fall well above the acceptable threshold. On the other hand, in Kaiser and Rice's criteria, the value could be regarded as meritorious which implies that the variables belong to or hang together and therefore are
appropriate for factor analysis. Bartlett's test of sphericity is significant at (χ²(544) =5654, p = .000, α = .01). This advocates that the data used for this study does not arise from a population of independent variables. It also shows that the data is a representation of a sample from a multivariable normal population. In the same way, the test result is indicative of the suitability of the data for factor analysis. Based on eigenvalues criteria in which factors with eigenvalues greater than 1 are extracted, four (4) factors were extracted using the varimax rotation (rotated factor matrix) method (see Table 4).

The factor loadings in Table 4 were slightly different from those obtained in the preliminary factor analysis. The factors had a better convergence and did not largely change the axis by loading into different factors. The factor solution shown in Table 4 was created using the varimax rotation of maximum likelihood estimation (MLS). Variables in the factor solution that have the largest factor loadings are useful in identifying the underlying factors. All the variables loaded significantly and for the variables to load significantly to a factor means that the variables share the same conceptual meaning.

For extracted factors on Economic and Financial Factors (EFF), a look at Table 4 on variance extracted shows factor 1 as accounting for the highest variance (25.6%) in the factor solution with (3.458%) on the rotated sum of square loading. It therefore emerged as the first important factor that affects the implementation of e-land administration and management. Factor 2 representing 9.4% of the total variance in the factor solution with a rotated sum of square loading of 2.801% emerged as the second significant factor in relative importance. Factor 3 representing 8.8% of the variance in the factor solution with a rotated sum of square loading of 2.514% becomes the third critical factor that affects the implementation of e-land administration and management while factor 4 with a percentage variance of 5.8% and a rotated sum of square loading of 1.443% emerged as the fourth in relative importance in this study.

Table 5 shows group mean scores and tests of equality of group mean statistics used to identify variables and assess the perception of employees in grade level 09-12 and those in grade level 05-08. Twenty-six (26) variables were used to determine their perceptions. From Table 5, seven variables measuring economic and financial factors showed strong discriminant power, which suggests there were significant group mean differences in perception between 09-12 and 05-08 on the variables (lack of funds - EFF1, = λ 0.897, F = 18.568., p < 0:05; cost of procurement- EFF2, = λ 0.874, F = 15.106, p < 0:05; high cost of installation/replacement of ICT facilities - EFF3= λ 0.958 F = 18.222, p < 0:05; high-cost broadband – EFF4, = λ 0.870, F = 14.187, p < 0:05; lack of financial incentive and subsidy – EFF5, = λ 0.978, F = 16.872, p< 0:05; low budgetary allocation for ICT -EFF6= λ 0.877, F = 20.668, p < 0:05; cost of energy and power -EFT7= λ 0.899, F = 19.555, p < 0:05). A closer look at the Table shows that the total mean scores of the two respondents are high (all ≥ 3 and 4). This suggests that both respondents agree that economic factors such as lack of finance for computer procurement do impact the e-land implementation. Though grade levels 09-12 recorded higher group mean values than 05-08, the mean differences were high enough to make a significant group difference. This shows that workers in grade levels 09-12 have a better perception of the factors affecting the implementation of e-land administration and management. The reason for this may be because grade 09-12 who constitute the high-ranking officer are more knowledgeable about the situation at hand than those in the lower cadre.

Six variables determining the perception of workers on grade levels 09-12 and 05-08 on institutional and government policies recorded total mean values which overall are considered significantly high. This suggests that the variables according to the responses of both categories of workers have a strong influence on e-land implementation and management. Still, evidence from the group mean scores show that the six variables exhibited strong discriminant power, which suggests there were significant group mean differences in perception between the respondents on the variables (IGP1, λ 0.865, F = 25.346, p < 0.05; IGP2, λ 0.978, F = 40.266 p < 0.05; IGP3, λ 0.943, F = 48.893, p < 0.05; IGP4, λ 0.879, F = 35.066, p < 0.05; IGP5, λ 0.956, F = 18.801, p < 0.05; IGP6, λ 0.876, F = 17.430, p < 0.05). The groups' mean differences in the variables were too high to make a significant difference. It could be said that there were more differences in opinion than commonality of opinion between the two groups on these variables.
<table><caption>Table 5: Group Mean Differences and Test of Equality of Group Mean of Factors Affecting the Implementation of e-Land Administration</caption><thead><tr><th rowspan="2">Variables</th><th colspan="3">Group Mean</th><th colspan="4">Test of equality of group mean</th></tr><tr><th>Total Mean/ Std</th><th>Level 09-12 Mean / Std</th><th>Level 05-08 Mean / Std</th><th>Mean Diff</th><th>Wilks' Lambda</th><th>F<sup>a</sup></th><th>Sig</th></tr></thead><tbody><tr><td>Lack of funds - EFF1</td><td>4.05(1.558)</td><td>4.68(0.445)</td><td>3.85(1.315)</td><td>0.83</td><td>0.897</td><td>18.568</td><td>0.000</td></tr><tr><td>Cost of procurement- EFF2</td><td>3.55(0.884)</td><td>3.98(0.524)</td><td>3.15(1.652)</td><td>0.83</td><td>0.874</td><td>15.106</td><td>0.000</td></tr><tr><td>High cost of installation/replacement of ICT facilities - EFF3</td><td>4.20(0.443)</td><td>4.89(0.569)</td><td>3.95(1.024)</td><td>0.94</td><td>0.958</td><td>18.222</td><td>0.000</td></tr><tr><td>High-cost broadband - EFF4</td><td>4.12(0.211)</td><td>4.90(0.843)</td><td>4.01(1.421)</td><td>0.89</td><td>0.870</td><td>14.187</td><td>0.000</td></tr><tr><td>Lack of financial incentive and subsidy - EFF5</td><td>3.90(0.664)</td><td>4.48(1.321)</td><td>3.22(0.015)</td><td>1.26</td><td>0.978</td><td>16.872</td><td>0.000</td></tr><tr><td>Low budgetary allocation for ICT -EFF6</td><td>4.55(1.821)</td><td>4.96(0.429)</td><td>4.28(1.153)</td><td>0.68</td><td>0.877</td><td>20.668</td><td>0.000</td></tr><tr><td>Cost of Energy and Power - EFF7</td><td>3.21(0.238)</td><td>3.68(1.107)</td><td>3.35(0.836)</td><td>0.33</td><td>0.899</td><td>19.555</td><td>0.001</td></tr><tr><td>Absence of relevant laws and regulations on ICT -IGP1</td><td>4.80(1.110)</td><td>4.66(1.048)</td><td>3.88(1.089)</td><td>0.78</td><td>0.865</td><td>25.346</td><td>0.045</td></tr><tr><td>Poor implementation of policies on the use of ICT -IGP2</td><td>3.08(0.635)</td><td>3.88(0.301)</td><td>3.55(1.011)</td><td>0.33</td><td>0.978</td><td>40.266</td><td>0.022</td></tr><tr><td>Administrative and bureaucratic bottlenecks/contradicting rules -IGP3</td><td>4.62(1.086)</td><td>4.40(0.674)</td><td>3.99 (1.221)</td><td>0.41</td><td>0.943</td><td>48.893</td><td>0.000</td></tr><tr><td>Corruption in Civil Service - IGP4</td><td>4.40(1.902)</td><td>4.88 (0.674)</td><td>3.95(1.342)</td><td>0.93</td><td>0.879</td><td>35.066</td><td>0.002</td></tr><tr><td>Poor Remuneration for IT Staff - IGP5</td><td>4.66(1.146)</td><td>4.00(1.013)</td><td>3.95(1.103)</td><td>0.05</td><td>0.956</td><td>18.801</td><td>0.451</td></tr><tr><td>Inadequate IT Infrastructure IGP6</td><td>4.48(1.022)</td><td>4.68(0.832)</td><td>4.43(1.431)</td><td>0.25</td><td>0.876</td><td>17.430</td><td>0.000</td></tr><tr><td>Poor awareness and acceptability of ICT - SBB1</td><td>4.60(1.160)</td><td>4.42(0.874)</td><td>3.98(1.452)</td><td>0.44</td><td>0.855</td><td>19.080</td><td>0.000</td></tr><tr><td>People's resistance to change - SBB2</td><td>4.35(1.158)</td><td>3.85(0.886)</td><td>3.70(1.283)</td><td>0.15</td><td>0.886</td><td>16.055</td><td>0.000</td></tr><tr><td>Staff's perceived usefulness of computer - SBB3</td><td>4.44(1.055)</td><td>3.90(0.848)</td><td>3.52(1.245)</td><td>0.38</td><td>0.872</td><td>10.064</td><td>0.000</td></tr><tr><td>Perceived reliability & mistrust of technology/positive illusion - SBB4</td><td>4.20(1.045)</td><td>4.17(0.814)</td><td>3.55(1.222)</td><td>0.62</td><td>0.996</td><td>14.099</td><td>0.000</td></tr><tr><td>Lack of understanding of user's needs -SBB5</td><td>4.19(1.088)</td><td>4.65(0.188)</td><td>4.07(1.114)</td><td>0.58</td><td>0.877</td><td>15.089</td><td>0.000</td></tr><tr><td>Loss of control and apathy on the part of users - SBB6</td><td>4.58(1.286)</td><td>3.92(0.464)</td><td>3.48(1.622)</td><td>0.44</td><td>0.955</td><td>13.066</td><td>0.000</td></tr><tr><td>Organizational attitude - SBB7</td><td>3.18(1.045)</td><td>4.75(0.668)</td><td>3.16(1.210)</td><td>1.59</td><td>0.964</td><td>11.080</td><td>0.000</td></tr><tr><td>Limited skill capabilities - LTIE1</td><td>4.58(1.150)</td><td>3.88(0.159)</td><td>3.34(1.341)</td><td>0.54</td><td>0.869</td><td>17.024</td><td>0.000</td></tr><tr><td>Lack of Qualified IT Staff- LTIE2</td><td>3.32(1.099)</td><td>4.80(0.676)</td><td>3.88(1.444)</td><td>0.92</td><td>0.889</td><td>25.181</td><td>0.000</td></tr><tr><td>Skill gaps - LTIE3</td><td>4.56(1.093)</td><td>3.69(0.679)</td><td>3.36(1.128)</td><td>0.33</td><td>0.920</td><td>16.077</td><td>0.000</td></tr><tr><td>Use of unskilled and unspecialized workers - LTIE4</td><td>3.18(1.085)</td><td>3.66(0.913)</td><td>3.22(1.151)</td><td>0.44</td><td>0.965</td><td>09.022</td><td>0.000</td></tr><tr><td>Lack of innovation /capacity to design - LTIE5</td><td>4.68(1.451)</td><td>4.78(0.861)</td><td>4.33(1.164)</td><td>0.45</td><td>0.824</td><td>18.092</td><td>0.000</td></tr><tr><td>Absence of web presence and poor design -LTIE6</td><td>4.51(1.085)</td><td>4.89(0.668)</td><td>4.50(1.167)</td><td>0.39</td><td>0.862</td><td>16.088</td><td>0.000</td></tr></tbody></table>

EFF¹ to EFF1⁷ = Economic and Financial Factors; IGP¹ to IGP⁶ = Institutional and Government Policies; SBB¹ to SBB⁷ = Source and Behavioural Barriers; LTIE¹ to LTIE⁶ = Lack of Technological Innovation and Expertise
On the variable “social and behavioural barriers” there was strong discriminant power indicative that there is significant difference in perception between staff in grade level 09-12 and grade level 05-08 (SSB1, λ 0.855, *F* = 19.080, p < 0.05; SSB 2, λ 0.886, *F* = 16.055p < 0.05; SSB 3, λ 0. 0.872, *F* = 10.064, p < 0.05; SSB 4, λ 0.996, *F* = 14.099, p < 0.05; SSB 5, λ 0.877, *F* = 15.089, p < 0.05; SSB6, λ 0.955, *F* = 13.066, p < 0.05; SSB7, λ 0.964, *F* = 11.089, p < 0.05). Workers in grade level 09-12 recorded higher group mean scores than those in grade level 05-08. The justification for this may be because the senior workers appear more knowledgeable that social barriers have far-reaching effects on the implementation of e-land administration and management.

On variables eliciting measurement of Lack of Technological Innovation and Expertise, we can infer
from the Table that both categories of workers in the Ministry of Lands, Survey and Physical Planning
demonstrated high total mean scores on the six variables (all >4), an indication that both respondents
regard to lack of technological innovation and expertise as a key factor that affects the implementation
of e-land administration and management in the ministry. This is because all the six variables displayed
strong discriminant power which suggests that there were significant group mean differences in
perception between the two groups (LTIE1 λ 0.869, *F* = 17.024, p < 0.05; LTIE2 λ 0.889, *F* = 25.181,
p < 0.05; LTIE3 λ 0.920, *F* = 16.077, p < 0.05; LTIE4 λ 0.965, *F* = 09.022, p < 0.05; LTIE5 λ 0.824, *F*
= 18.092, p < 0.05; LTIE6 λ 0.862, *F* = 16.088, p < 0.05). The mean differences were high enough to
make significant group differences. However, workers in grade levels 09-12 recorded higher group
mean values than those in grade levels 05-08. The fact that lack of technological innovation and
expertise is perceived by grade level 09-12 to have a strong influence and impact on the implementation
of e-land administration is not unexpected. This is because a greater percentage of the staff of the
ministry are not highly knowledgeable with computer software and applications on land information
systems.

Predicting discriminant function for propensity to implement e-land administration and management in the Ministry of Lands, Survey and Physical Planning Owerri Imo State

To determine the most predictive variables that best differentiate the two groups (staff in grade level 09-12 and grade level 05-08) on perception of the factors affecting the implementation of e-land administration and management, the stepwise method of enter/remove for deriving discriminant functions were used (Huberty and Barton, 1989). Thus, the 26 variables in Table 5 were subjected to the stepwise method. Table 6 shows that at 30 iterations at 0.05 significant level, 15 out of which entered the model in the following descending order of magnitude: Lack of funds - EFF1> Cost of procurement-EFF2> High cost of installation/replacement of ICT facilities - EFF3; > Lack of financial incentive and subsidy - EFF5> Low budgetary allocation for ICT-EFF6> Cost of Energy and Power -EFF7> Limited skill capabilities - LTIE1> Lack of Qualified IT Staff- LTIE2> Lack of innovation /capacity to design - LTIE5 > Use of unskilled and unspecialized workers - LTIE4> Poor implementation of policies on the use of ICT -IGP2; > Administrative and bureaucratic bottlenecks/contradicting rules -IGP3> Corruption in Civil Service - IGP4> Inadequate IT Infrastructure IGP6> Poor awareness and acceptability of ICT - SBB1.
<table><caption>Table 6: Predictive Model of factors affecting the implementation of e-land administration and management a,b,c,d</caption><thead><tr><th rowspan="3">Step</th><th rowspan="3">Entered</th><th colspan="8">Wilks' Lambda</th></tr><tr><th colspan="8">Exact F</th></tr><tr><th>Statistic</th><th>df1</th><th>df2</th><th>df3</th><th>Statistic</th><th>df1</th><th>df2</th><th>Sig</th></tr></thead><tbody><tr><td>1</td><td>Lack of funds - EFF1</td><td>0.865</td><td>1</td><td>1</td><td>464</td><td>88.326</td><td>1</td><td>464</td><td>0.000</td></tr><tr><td>2</td><td>Cost of procurement- EFF2</td><td>0.854</td><td>2</td><td>1</td><td>464</td><td>85.189</td><td>2</td><td>463</td><td>0.000</td></tr><tr><td>3</td><td>High cost of installation/replacement of ICT facilities - EFF3</td><td>0.842</td><td>3</td><td>1</td><td>464</td><td>80.453</td><td>3</td><td>462</td><td>0.000</td></tr><tr><td>4</td><td>Lack of financial incentive and subsidy - EFF5</td><td>0.838</td><td>4</td><td>1</td><td>464</td><td>78.010</td><td>4</td><td>461</td><td>0.000</td></tr><tr><td>5</td><td>Low budgetary allocation for ICT - EFF6</td><td>0.826</td><td>5</td><td>1</td><td>464</td><td>74.103</td><td>5</td><td>460</td><td>0.000</td></tr><tr><td>6</td><td>Cost of Energy and Power -EFT7</td><td>0.815</td><td>6</td><td>1</td><td>464</td><td>70.854</td><td>6</td><td>459</td><td>0.000</td></tr><tr><td>7</td><td>Limited skill capabilities - LTIE1</td><td>0.789</td><td>7</td><td>1</td><td>464</td><td>65.897</td><td>7</td><td>458</td><td>0.000</td></tr><tr><td>8</td><td>Lack of Qualified IT Staff- LTIE2</td><td>0.775</td><td>8</td><td>1</td><td>464</td><td>63.010</td><td>8</td><td>457</td><td>0.000</td></tr><tr><td>9</td><td>Lack of innovation /capacity to design - LTIE5</td><td>0.768</td><td>9</td><td>1</td><td>464</td><td>60.908</td><td>9</td><td>456</td><td>0.000</td></tr><tr><td>10</td><td>Use of unskilled and unspecialized workers - LTIE4</td><td>0.758</td><td>10</td><td>1</td><td>464</td><td>58.875</td><td>10</td><td>455</td><td>0.000</td></tr><tr><td>11</td><td>Poor implementation of policies on the use of ICT -IGP2</td><td>0.690</td><td>11</td><td>1</td><td>464</td><td>50.974</td><td>11</td><td>454</td><td>0.000</td></tr><tr><td>12</td><td>Administrative and bureaucratic bottlenecks/contradicting rules -IGP3</td><td>0.694</td><td>12</td><td>1</td><td>464</td><td>45.454</td><td>12</td><td>453</td><td>0.000</td></tr><tr><td>13</td><td>Corruption in Civil Service - IGP4</td><td>0.680</td><td>13</td><td>1</td><td>464</td><td>41.955</td><td>13</td><td>452</td><td>0.000</td></tr><tr><td>14</td><td>Inadequate IT Infrastructure IGP6</td><td>0.654</td><td>14</td><td>1</td><td>464</td><td>38.770</td><td>14</td><td>451</td><td>0.000</td></tr><tr><td>15</td><td>Poor awareness and acceptability of ICT - SBB1</td><td>0.588</td><td>15</td><td>1</td><td>464</td><td>34.990</td><td>15</td><td>450</td><td>0.000</td></tr></tbody></table>

Source: Computer Printout

Further analysis in Table 7 shows more statistics for verifying the significance of the discriminant function and identifying the factors and variables that have the greatest impact and correlation with the discriminant function. The Table shows a canonical correlation (CCr) of 0.654, which implies that the function explained 79% (CCr²) of variance in the group differences. However, when examining the function's Wilks' lambda (Λ), the function is considered significant (Λ = 0.558, χ² (df = 5) = 256.608, p < 0.01). Thus, the study concludes that there is a significant discriminant function which confirms that workers of the ministry in grade levels 09-12 and those in grade levels 05-08 perceive differently the factors affecting the implementation of e-land administration and management in the state.

Consistent with ANOVA (F) test, the standardized discriminant function coefficients (SCDFC) and structure matrix correlation (within group correlation) demonstrate that the variables that have the strongest impact and correlation with the discriminant function are “Lack of funds - EFF1” (β= 0.701) and within group correlation (β = 0.684); “Low budgetary allocation for ICT -EFF6 (β = 0.621) and within group correlation (β = -0.655); “Cost of Energy and Power -EFF7” (β = -0.558) and within group correlation (β = -0.589); “Cost of procurement- EFF2 (β = 0.514) and within group correlation (β = 0.450); “High cost of installation/replacement of ICT facilities - EFF3 (β = -544) and within group correlation (β = -0.435); “Lack of financial incentive and subsidy - EFF5 (β = 0.447) and within group correlation (β = 0.444); “Lack of innovation /capacity to design - LTIE5 (SC5)”(β = 0.442) and within group correlation (β = -0.048); Limited skill capabilities - LTIE1β = 0.390) and within group correlation (β = -0.366).

Others include: Lack of Qualified IT Staff- LTIE2 (β = 0.316) and within group correlation (β = -0.338); Corruption in Civil Service - IGP4 (β = 0.284) and within group correlation (β = 0.304); “Inadequate IT Infrastructure IGP6 (β = 0.188) and within group correlation (β = -0.285); Poor awareness and acceptability of ICT - SBB1” (β = 0.115) and within group correlation (β = 0.274); Administrative and bureaucratic bottlenecks/contradicting rules -IGP3”(β = 0.112) and within group correlation (β =
0.288); Poor implementation of policies on the use of ICT -IGP2 (β = 0.110) and within group correlation (β = 0.326); Use of unskilled and unspecialized workers – LTIE4 (β= 0.108) and within group correlation (β = 0.255). A look at the result shows that the distribution provides efficiency and predictive accuracy of the discriminant function. Thus, the model achieved a hit ratio of 80.4 %, showing that 80.4% of respondents from both cadres of workers were correctly grouped as having opinions on the factors affecting the implementation of e-land administration and management according to their perception of the subject matter.

<table><caption>Table 7: Standardized Canonical Discriminant Function Coefficient and Structure Matrix of Factors Affecting the Implementation of e-land Administration and Management</caption><thead><tr><th rowspan="2">Standardized Canonical Discriminant Function Coefficient</th><th rowspan="2">Function1</th><th rowspan="2">Impact Ranking</th><th colspan="2">Structure Matrix</th></tr><tr><th>(Within Correlation)</th><th>group</th></tr></thead><tbody><tr><td>Lack of funds - EFF1</td><td>0.701</td><td>1</td><td>0.684</td><td></td></tr><tr><td>Low budgetary allocation for ICT -EFF6</td><td>0.621</td><td>2</td><td>-0.655</td><td></td></tr><tr><td>Cost of Energy and Power -EFF7</td><td>0.558</td><td>3</td><td>0.589</td><td></td></tr><tr><td>Lack of financial incentive and subsidy - EFF5</td><td>0.514</td><td>6</td><td>0.450</td><td></td></tr><tr><td>Lack of innovation /capacity to design - LTIE5</td><td>-544</td><td>7</td><td>0.435</td><td></td></tr><tr><td>Lack of Qualified IT Staff- LTIE2</td><td>0.447</td><td>9</td><td>0.444</td><td></td></tr><tr><td>Cost of procurement- EFF2</td><td>0.442</td><td>4</td><td>-0.048</td><td></td></tr><tr><td>High cost of installation/replacement of ICT facilities - EFF3</td><td>0.390</td><td>5</td><td>-0.366</td><td></td></tr><tr><td>Limited skill capabilities - LTIE1</td><td>0.316</td><td>8</td><td>-0.338</td><td></td></tr><tr><td>Corruption in Civil Service - IGP4</td><td>0.284</td><td>10</td><td>0.304</td><td></td></tr><tr><td>Inadequate IT Infrastructure IGP6</td><td>0.188</td><td>11</td><td>-0.285</td><td></td></tr><tr><td>Poor awareness and acceptability of ICT - SBB1</td><td>0.115</td><td>12</td><td>0.274</td><td></td></tr><tr><td>Administrative and bureaucratic bottlenecks/contradicting rules -IGP3</td><td>0.112</td><td>13</td><td>0.288</td><td></td></tr><tr><td>Poor implementation of policies on the use of ICT -IGP2</td><td>0.110</td><td>14</td><td>0.326</td><td></td></tr><tr><td>Use of unskilled and unspecialized workers - LTIE4</td><td>0.108</td><td>15</td><td>0.255</td><td></td></tr><tr><td>Function at Group Centroids</td><td></td><td></td><td></td><td></td></tr><tr><td>Staff at grade level 09-12</td><td>0.395</td><td></td><td></td><td></td></tr><tr><td>Staff at grade level 05-08</td><td>-0.554</td><td></td><td></td><td></td></tr><tr><td>Model Validation Statistics</td><td></td><td></td><td></td><td></td></tr><tr><td>Canonical Correlation (CCr)</td><td>0.654</td><td></td><td></td><td></td></tr><tr><td>(CCr²)</td><td>0.5216</td><td></td><td></td><td></td></tr><tr><td>Eigenvalue</td><td>0.684<sup>a</sup></td><td></td><td></td><td></td></tr><tr><td>Wilks' Lambda</td><td>0.558</td><td></td><td></td><td></td></tr><tr><td>Chi-Square (df = 5)</td><td>268.804</td><td></td><td></td><td></td></tr><tr><td>Classification accuracy (hit ratio)</td><td>80.4%</td><td></td><td></td><td></td></tr><tr><td>Sig</td><td>0.000</td><td></td><td></td><td></td></tr></tbody></table>

Source: Computer Printout

## CONCLUSION AND RECOMMENDATION

From the findings of this research, we can safely conclude that the implementation and management of e-land administration in Imo state is still far from being a reality. It is being hampered by four major factors namely: Economic and Financial Factors, Institutional and Government Policies, Social and Behavioural Barriers and Lack of Technological Innovation and Expertise be responsible. The research further established that: there are significant differences in perception between the staff of the Ministry of Lands in grade level 05 - 08 (Junior staff) and grade level 09 -12 (Senior staff) on the factors affecting the implementation of electronic land administration and management in Imo State and that the senior staff perceive these factors better than the junior staff. This is because they know more about the implications of not having an electronic land recording system in the ministry which includes poor generation of income/returns to the state and conducting land transactions manually which has been proved to be generally expensive, time-consuming, burdensome and complex when it comes to land documentation and perfection of title. The study also found out the variables with the most predictive power that accounted for the difference in perception between the two groups and also the areas of commonality between the two groups on these factors. The study has also shown that the dynamics of
land administration and management advocate for strong relationship between computer driven systems
and geographical information system.

The study therefore recommended among others that there should be positive connection between
digitalization and land administration so as to make majority of the intended users be able to seamlessly
use the system. The system should also be made to seamlessly deliver services based on geographical
needs to enable citizens to view land and property information on digital maps organized by political
and administrative boundaries, neighbourhoods, cities, rural areas etc.

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Chapter Thirteen

Understanding Green Building, Innovative Materials, and Technologies in Enhancing
Sustainable Development

Simon Ajayi Olukotun

Department of Urban and Regional Planning, Lead City University, Ibadan, Nigeria

EMAIL: ajayiolukotun19@gmail.com

ABSTRACT

Climate change is happening and progressively emerging as one of the most serious global problems affecting many segments of economic growth and sustainable development. The changing climate significantly impacts land by causing it to be degraded or disappear and loss of biodiversity among others. This can negatively impact economic activities like fishing and farming. Communities face more frequent quick-onset disasters as well as gradual changes in seasons, temperature and rainfall which leads to crop death or low productivity. This paper addresses green building (GB) which features innovative technologies that consider energy efficiency and conservation, renewable energy, effective use of water, effective waste and emission management, sustainable construction materials, environmentally conscious construction methods and harmonious design buildings which meet the need of the present and future generations. This paper sampled 12 GB Experts based in Lagos and Ibadan and sought their opinions about the importance of GB to sustainable development, the area of innovation in GB, their experience, and their role in the company among others. The data was analyzed with descriptive statistics (frequency and percentage) while the formulated hypothesis was analyzed with inferential statistics (Chi-square). The findings show that the experts overwhelmingly support the significant role of GB in promoting sustainable development, emphasizing its innovations in energy efficiency, waste management, and resource conservation, as well as its potential to mitigate the adverse effects of climate change and enhance community resilience, thus providing a compelling argument for the broader adoption of GB practices as a crucial strategy for achieving sustainability goals in the face of ongoing environmental challenges.

KEYWORDS: Climate change; Green building; Innovative technology; Sustainable development

INTRODUCTION

Climate change can be a natural process where temperature, rainfall, wind and other element vary over decades or more. The IPCC's sixth assessment report published in 2021 found that human emission of heat-trapping gases has already warmed the climate by nearly 2 degrees Fahrenheit (1.1 degrees Celsius) since the pre-industrial times (starting in 1750). The overwhelming body of studies suggests that human actions notably the emission of heat-trapping greenhouse gases are primarily to blame for our planet's warming with some contributions coming from natural variability (WHO, 2021). The result of many scientific studies also shows that human activity contributed to the increase in carbon dioxide (CO²) in the atmosphere; increased CO² levels cause an increase in the frequency of extreme weather events (Anderson, 2017; IPCC, 2021). Climate change is also defined as the recent dramatic shift in emissions of carbon dioxide (CO²) caused by human activity (Rivera, 2020). According to Rivera (2020), the average concentration of CO² in the atmosphere has increased dramatically over the past 60 years from 316ppm in 1959 to 411ppm in 2019 (Table 1). This increase is close to the 450ppm limit set by scientists to avoid catastrophic damage (Rivera, 2020; IPCC, 2022).

<table><caption>Table 1: Carbon Dioxide Parts Per Million</caption><thead><tr><td>Year</td><td>CO<sup>2</sup> PPM</td><td>Decade increase</td></tr></thead><tbody><tr><td>1959</td><td>315.97</td><td>-</td></tr><tr><td>1969</td><td>324.62</td><td>2.74%</td></tr><tr><td>1979</td><td>336.84</td><td>3.76%</td></tr><tr><td>1989</td><td>353.12</td><td>4.85%</td></tr><tr><td>1999</td><td>368.39</td><td>4.32%</td></tr><tr><td>2009</td><td>384.43</td><td>5.17%</td></tr><tr><td>2019</td><td>411.44</td><td>6.20%</td></tr></tbody></table>

Source: Rivera, 2020
The result of this exponential growth of carbon dioxide in the atmosphere is responsible for the increase in the average temperature. Soaring carbon emissions have created a global greenhouse effect, trapping more and more of the sun's heat (Rivera, 2020). The changing climate significantly impacts land by causing it to be degraded or disappear. As land is disappearing and degrading, competition over fresh water access and land resources increases in tandem. Coastal land and Pacific islands are subsiding due to the sea level rise, and this land disappearance will increase at an exponential pace (Castro and Kuntz, 2022). Habitable land on the island of Nautambu, for example, has decreased by over 50% since 2011 (Albert et al., 2016). These effects include eroded coastlines and saltwater infusion, which changes vegetation and wildlife resources that communities depend on for their livelihood, as well as inhibits agriculture & Fishing in Coastal areas (Castro and Kuntz, 2022).

While sea level rise impacts are experienced along coastlines, land degradation permeates the global landscape and concentrates in the tropical and subtropical regions. Shifts in the average temperature, extreme hot and cold days and precipitation add increasing pressure to existing land resources through shifts in soil, water availability and biodiversity (Castro and Kuntz, 2022) increased draught and soil erosion cause land degradation and force changes in the use of arable land (Ahmed et al., 2010). Countries in the tropics and southern Hemisphere subtropics expect the greatest impacts on economic growth caused by climate change.

The IPCC 2018 released a landmark special report demonstrating how the climate has changed and the future impacts that could be avoided if continued global warming is limited to 1.5 degrees Celsius. Even with warming limited to 1.5 degrees Celsius, 70 – 90% of coral reefs will be lost, 14% of the world population will experience extreme heat waves about every five years, draught will be most frequent and sea levels will continue to rise (IPCC, 2018). Global commitment to climate mitigation as evident in the United Nations Sustainable Development Goals (SDGs) which include climate change mitigation and natural resources protection, and the Paris Accords signed in 2016 agreeing to mitigate climate change through lowered greenhouse gas emission and national-level climate adaptation plans. (Castro and Kuntz, 2022) However, progress towards these goals to date has been limited and CO² emissions persist at unmitigated rates (Castro and Kuntz, 2022). Though the global community has made ambitious commitments to climate mitigation and adaptation, these commitments are outpaced by the rate at which the climate is changing. Catching up to the pace of change requires greater economic restructuring to eliminate harmful greenhouse gas emissions, access to climate finance needs to be accelerated and adaptation efforts re-scaled to match climate hazards (United Nations 2018; IPCC, 2018).

Green buildings have been proposed and needed on a global scale to help drastically reduce greenhouse gas emissions, conserve increasingly stretched energy resources and contribute to increasing human health. (US Information Admin 2012; Meena et al., 2022; Cao et al., 2022 Guo et al., 2021). This is because building operations are responsible for 40% of worldwide energy flow and materials used in conventional buildings have been identified as the largest source of greenhouse gas emissions (US Information Admin 2021, U.S. Dept. of Energy 2012). In the United State alone, buildings account for almost 40% of national CO² emissions and out-consume both the industrial and transportation sector, but LEED-certified buildings have 34 percent lower CO² emissions, consume 25% less energy and 11% less water and have diverted more than 80 million tons of waste from landfills (US Dept. of Energy, 2012).

In the advanced countries the global market is responding to GB, according to a Dodge Data & Analytics record Green Building Trends 2016 smart market report, the global green building sector continues to double every 3 years with survey respondents from 70 countries reporting 60% of their project will be green in 2018. Under lowered maintenance and energy costs the return on investments from green building is rapids; green retrofit projects are generally expected to pay for themselves in just 7 years (McGraw Hill Construction, 2012). Between 2015 and 2018, LEED-certified buildings in the United States are estimated to have $1.2 billion in energy savings, $149.5 million in water savings and $54.2 million in waste savings (Booz Allen Hamilton and U.S, Green Building Council, 2015). However, in developing countries in general and Nigeria in particular, GB has not been understood and there are
misconceptions about the cost. Green building practices are of utmost importance in Nigeria for promoting environmental sustainability and harnessing the potential of renewable energy systems.

In general, to mitigate the environmental effects associated with typical building construction and operation, Green buildings (BGS) have been proposed which tend to be much more environmentally friendly, socially habitable and economically affordable in the long term (Meena et al., 2021; Cao et al., 2021 and Guo et al., 2021). While green building practices hold immense potential for Nigeria's construction industry, there are several challenges that hinder their widespread adoption. These challenges include limited awareness and understating of green building practices.

## Justification for the Study

The energy crisis in 1970s caused various building-saving technologies as solar energy, geothermal energy and wind energy to emerge and energy-saving building became the forerunner of building development. (U.S Information on Admin 2012; Kriss, 2014) In 1990, the world first GB was released in the U.K. (U.S Information Admin, 2012). World Green Building Council 2021, because the “United Nations Conference on Environment and Development” promoted the idea of sustainable development, GB gradually became the direction of development. In 1993, the United State created the Green Building Association, Hongkong introduced Green Building Standard in 1996, Taiwan introduced the green Building Standard in 1999, Canada in 2000, Singapore Green Building Mark in 2005 and China implemented the Green Building Evaluation Standard in 2015 African Nations fostered their initiative in 2010 at a conference in Nairobi, Kenya to promote greenhouse also known as sustainable building rating in African cities to reduce the effect of climate change (Adejumo Adeosun and Awoyera 2022). Nigeria created the Green Building Council in 2010. These bodies were established to address the built environment's role in climate change, environmental pollution and natural resource depletion.

Convincing empirical evidence on the significance of GB to sustainable development has been compiled from different regions of the world such as Hu et al., 2021, Liza Shuttleworth 2024; U.S. EPP 2021; Van J., 2006; Castellacci et al., 2017; Takolo et al., 2021; Bouz Allen et al., 2015 among others. However, despite this mounting volume of empirical evidence, gaps for further study remain wide. While an increasing body of literature is emerging on the sustainability, and social and economic importance of GB, there has been very little rigorous documentation and analysis on how to improve and increase GB understanding from the standpoint of industry experts. This is the knowledge gap this study stands to bridge.

## Aim and Objectives

This study aims to assess the impact of GB and innovative technologies on sustainable development from the standpoint of experts in GB industries. To achieve the above aim, the objectives are to:

i. Identify GB industries/consultants in Nigeria

ii. Identify what GB projects are all about

iii. Identify the features of the GB project

iv. Identify the impact of the GB project on sustainable development

## Hypothesis testing

$H_0$: GB projects have no significant impact on sustainable development

# LITERATURE REVIEW

## Conceptual Review

### Climate Change

Climate change refers to the long-term changes in temperature and weather due to human activities (Anderson Frey, 2017; IPCC, 2021). Increases in average global temperature are the most common manifestations of climate change (Shivanna, 2022). In recent times, it has acquired the importance of global emergency and affecting not only the well-being of humans but also the sustainability of other life forms (Rivera, 2020), enormous increase in the emission of greenhouse gases CO², methane and nitrous oxide in recent decades largely due to burning of coal and fossil fuels, and deforestation are the main drivers of climate change marked increase in the frequency and intensity of natural disasters, rise in sea level, decrease in crop productivity and loss of biodiversity are the main consequences of climate change (Shivanna, 2022; Castro and Kuntz, 2022). Obvious mitigation measures include a significant
reduction in the emission of greenhouse gases and an increase in the forest cover of the land mass (IPCC, 2018; Shivanna, 2022).

## Green Building (GB)

It is also referred to as green construction, sustainable building or eco-friendly building. GB refers to both structure and application of processes that are environmentally possible and resource-efficient throughout a building lifecycle from planning to design, construction, operation, maintenance, renovation and demolition (EPA, 2021). This requires the collaboration and cooperation of relevant professionals such as Architects, Engineers, contractors and Clients at all project stages (Yanji and Etelios Plainiotic, 2006). GB has to do with saving resources to the maximum extent including energy saving, land saving, water saving, material saving etc. during the whole lifecycle of building, protecting the environment and reducing pollution, and providing people with healthy, comfortable and efficient use of space and being in harmony with nature (Hu & Skibniewski, 2021). According to Shuttleworth, 2024, the features of GB or eco-friendly building include; energy efficiency and conservation. This has to do with reducing energy consumption and maximising the efficiency of energy use, renewable energy such as the use of solar and wind power to meet the needs of the building. Effective use of water: This is water use recycling.

Effective waste and emission management's sustainable construction materials e.g. the use of low-flow shower heads, touchline faucets, water water-saving toilets. Energy-efficient water heaters, energy-saving water pipes, drainage systems, recirculating hot water pumps, pressure-reducing valves, rain water harvesting grey water recycling among others in plumbing works. The use of eco-friendly roofing materials such as cool roofs, single roofs, reclaimed slate, green roofs, rubber roofing, and solar roofs among others. The feature of GB also deals with environmentally conscious construction methods. This has to do with the use of construction technologies that focus on reducing the impact of construction on the surrounding natural environment by reducing noise and light pollution and protecting local flora and fauna (Shuttleworth, 2021). GB that are harmoniously designed are socially responsible and beneficial to human occupants and improve their health and well-being. Sustainable architecture is not just about creating visually appealing structures, but it is about designing buildings that harmonize with the environment while meeting the needs of the present and future generations.

## Innovation and Technology

Green innovation also known as sustainable innovation or eco-innovation refers to the development of new ideas, technologies, products, processes and business models that have a positive impact on the environment and promote sustainability (Castella and Lie, 2017). Green innovation is crucial for addressing environmental challenges, combating climate change and achieving a more sustainable and resilient future (Ahmad, 2023). It typically requires a combination of technological advancements, policy support and changes in consumer behaviour to be successful (Takolo, Toraloo and Panzi, 2021). Innovation is the actual use of a non-trivial change and improvement in a process, product or system that is novel to the institution developing the change (Slaughter, 1998; Freeman, 2013). Some of the new areas of green innovation include: renewable energy, energy efficiency, green economy, green materials, and sustainable construction among others (Shuttleworth, 2024). Technologies such as solar panels, wind turbines, light emitting diode (LED) bulbs and lithium-ion batteries have played a significant role in reducing emissions (Ahmad, 2023; Shuttleworth, 2024).

Innovative technologies progress toward minimum energy requirements and almost full usage of recycled reusable materials in buildings (Serife and Zahracannan, 2000) zero energy building, 70% less water consumption, and eco-friendly recycle/reusable materials in high ratios. Innovative technologies in GB are focused on addressing certification building and the following features are important energy efficiency and conservation of renewable energy, effective use of water, effective waste and emission management, sustainable construction materials and environmentally conscious construction methods (Serife and Zehracannan, 2017).

## Sustainable Development

The issue of climate change brings us to the issue of sustainability. Understanding this will help us to appreciate the need for GBs. The report of the Brundtland Commission in 1987 greatly promoted the
idea of sustainable development, an issue that has become increasingly prominent across the globe applied and discussed at transnational international, national, regional and community levels (Centre for History and Economics, 2008). According to EPA (2015), Sustainability is based on a simple principle: everything we need for our survival and well-being depends either directly or indirectly on our natural environment. To pursue sustainability is to create and maintain the conditions under which humans and nature can co-exist in productive harmony to support present and future generations (Idiata, 2016). Examples of Green Buildings (GBs) around the world are depicted in Plate 1.

<figure><img src="image_32.png" /><figcaption>a. The Edge Amsterdam, Netherland</figcaption></figure>

<figure><img src="image_33.png" /><figcaption>b. Across Fukuoka Prefectural international Hall
Fukuoka, Japan</figcaption></figure>

<figure><img src="image_34.png" /><figcaption>c. Bosco Verticate Milan, Italy</figcaption></figure>

<figure><img src="image_35.png" /><figcaption>d. Vancouver Conventional Centre, Vancouver, Canada</figcaption></figure>

Plate 1: *Examples of Green Buildings (GBs) around the World* (Source: Brundtland Report, 1987)

# MATERIALS AND METHODS

## The Study Area

The areas selected for the study include Greenstad Project, Arc Emmanuel Falude, and the Green Practice, Litedares, EcoBuild Solution, Sustainable Design Consortium, Earthwise, TerraNova, EcoHabitat, in Ibadan and Lagos, respectively.

## Data Base for the Study

Survey interviews were held in person on phone calls and emails with reputable firms (experts) in GB with LEED Certification based in Lagos and Ibadan, Nigeria. Semi-structured interview guides were prepared to elicit relevant demographic characteristics such as the expert's experience in GB projects over several years, and the participant's (Expert) role in the company among others. Other information sought includes the importance of GB to sustainable development, what makes GB innovative and the
features of GB, and the problems of integrating GB innovation into the Nigeria building industry among others.

A purposive sampling technique was adopted for the study. The reason for this selection procedure was to address the professional firms or consultants in GB who have been involved in GB design, construction and management for a reasonable number of years and who were able to provide the relevant information for the study. Twelve (12) GB consultants/firms in Lagos and Ibadan were contacted and a questionnaire guide was prepared for each consultant/firm (participant). All the 12 questionnaires were retrieved for final analysis. When using purposive sampling for qualitative research as in the case of this study, the sample size is often determined by data saturation. Saturation is a state in which the collection of new data does not shed any further light on the issue under investigation (Baker et al., 2012; Morse, 2000; Mason 2010). Even though a larger sample may seem like a relatively safe approach, a very large number of respondents can hinder the researcher's ability to provide "in-depth" findings and miss the opportunity to obtain an understanding of each respondent (Fink, 2000). Regarding data saturation, Guest et al 2006 suggested that 12 interviews are enough for most research studies to understand the common perceptions and experiences among a group of relatively homogenous individuals. Demographic characteristics of the participants such as participants' experience in GB projects and participants' role in the company were analyzed with descriptive statistics (Frequency and percentage) while the hypothesis formulated was analyzed with inferential statistics (Chi-square).

## RESULTS AND DISCUSSION OF FINDINGS

### Results

This section presents an empirical analysis of the data starting from the attributes of the participants followed by the others as shown in the tables below

<table><caption>Table 2: Expert's Experience in GB Project</caption><thead><tr><td>S/N</td><td>Year</td><td>Frequency</td><td>Percentage</td></tr></thead><tbody><tr><td>A.</td><td>0 – 5</td><td>-</td><td>0</td></tr><tr><td>B.</td><td>6 – 10</td><td>7</td><td>58.3</td></tr><tr><td>C.</td><td>11 – 15</td><td>5</td><td>41.7</td></tr><tr><td>D.</td><td>15 and above</td><td>-</td><td>0</td></tr><tr><td></td><td></td><td><strong>12</strong></td><td>100</td></tr></tbody></table>

Source: Author's Field Survey, 2024

Table 2 reveals that the majority of the experts, specifically 58.3% (7 experts), have between 6–10 years of experience, indicating a predominance of mid-level experience in Green Building (GB) projects, while 41.7% (5 experts) have 11–15 years of experience, representing a smaller but still significant group of more experienced professionals, and notably, there are no experts with less than 6 years of experience, nor any with over 15 years of experience, suggesting that the cohort is composed predominantly of seasoned individuals with substantial but not extensive long-term expertise, perhaps reflecting the relatively recent emergence of the GB industry and the selection of more experienced professionals for the study, while also highlighting a gap in representation from both early-career and highly seasoned experts with over 15 years of experience.

<table><caption>Table 3: Participant's Role in the Company</caption><thead><tr><td>S/N</td><td>Role</td><td>Frequency</td><td>Percentage</td></tr></thead><tbody><tr><td>a.</td><td>Sustainability consultants</td><td>9</td><td>75</td></tr><tr><td>b.</td><td>Design Consultant</td><td>2</td><td>16.7</td></tr><tr><td>c.</td><td>Engineering Consultant</td><td>1</td><td>8.3</td></tr><tr><td>d.</td><td>Contractor</td><td>-</td><td>0</td></tr><tr><td>e.</td><td>Others (specify)</td><td>-</td><td>0</td></tr><tr><td></td><td></td><td>12</td><td>100</td></tr></tbody></table>

Source: Author's Field Survey, 2024
The majority of participants (75%) are sustainability consultants, reflecting a strong focus on environmental issues. A smaller number of design and engineering consultants were present (16.7% and 8.3%, respectively), and no contractors or other roles were represented. This composition suggests that the survey focused heavily on professionals involved in sustainability rather than construction or contracting.

Tables 4, 5 and 6 show the importance of GB to sustainable development; composition of GB Innovative and; Features of GB, respectively. In Table 4, all the respondents interviewed (100%) strongly agreed that GB creates a positive impact on the environment, and GB focuses on health and well-being. Also, a larger percentage (67%) attested that GBs were designed to be beneficial to the people in those spaces. More so, the majority of the respondents also attested that renewable energy makes the GB innovative. Others composition of the GB was energy efficiency (83%), green materials (80%) and sustainable construction (50%) (Table 5). All the respondents (100%) attested to the fact that energy efficiency and conservation were the main features of the GB. These were followed by the efficient use of water, and environmentally conscious construction methods (92%) each. The least responses on the features of GB were sustainable construction materials which was put at 67% (Table 6).

<table><caption>Table 4: Importance of GB to Sustainable Development</caption><thead><tr><th>S/N</th><th>Items</th><th>SA (%)</th><th>A (%)</th><th>D (%)</th><th>SD (%)</th></tr></thead><tbody><tr><td>1</td><td>GB uses fewer resources and produces fewer emissions and waste</td><td>11 (92)</td><td>1 (8)</td><td>0 (0)</td><td>0 (0)</td></tr><tr><td>2</td><td>GB creates a positive impact on the environment</td><td>12 (100)</td><td>0 (0)</td><td>0 (0)</td><td>0 (0)</td></tr><tr><td>3</td><td>GB addresses environmental challenges, combat climate change and achieve</td><td>11 (92)</td><td>1 (8)</td><td>0 (0)</td><td>0 (0)</td></tr><tr><td>4</td><td>GB focuses on Health and Well-being</td><td>12 (100)</td><td>0 (0)</td><td>0 (0)</td><td>0 (0)</td></tr><tr><td>5</td><td>GBs are designed to be beneficial to the people in those space</td><td>8 (67)</td><td>4 (33)</td><td>0 (0)</td><td>0 (0)</td></tr></tbody></table>

Source: Author's Field Survey, 2024

<table><caption>Table 5: What makes GB Innovative?</caption><thead><tr><th>S/N</th><th>Items</th><th>SA (%)</th><th>A (%)</th><th>D (%)</th><th>SD (%)</th></tr></thead><tbody><tr><td>1</td><td>Renewable Energy</td><td>12 (100)</td><td>0 (0)</td><td>0 (0)</td><td>0 (0)</td></tr><tr><td>2</td><td>Energy Efficiency</td><td>10 (83)</td><td>2 (17)</td><td>0 (0)</td><td>0 (0)</td></tr><tr><td>3</td><td>Green Materials</td><td>7 (80)</td><td>5 (20)</td><td>0 (0)</td><td>0 (0)</td></tr><tr><td>4</td><td>Sustainable Construction</td><td>6 (50)</td><td>6 (50)</td><td>0 (0)</td><td>0 (0)</td></tr></tbody></table>

Source: Author's Field Survey, 2024

<table><caption>Table 6: Features of GB</caption><thead><tr><th>S/N</th><th>Items</th><th>SA (%)</th><th>A (%)</th><th>D (%)</th><th>SD (%)</th></tr></thead><tbody><tr><td>1</td><td>Energy efficiency and conservation</td><td>12 (100)</td><td>0 (0)</td><td>0 (0)</td><td>0 (0)</td></tr><tr><td>2</td><td>Renewable Energy</td><td>9 (75)</td><td>3 (25)</td><td>0 (0)</td><td>0 (0)</td></tr><tr><td>3</td><td>Efficient use of water</td><td>11 (92)</td><td>1 (8)</td><td>0 (0)</td><td>0 (0)</td></tr><tr><td>4</td><td>Efficient waste & emission management</td><td>9 (75)</td><td>3 (25)</td><td>0 (0)</td><td>0 (0)</td></tr><tr><td>5</td><td>Sustainable Construction Materials</td><td>8 (67)</td><td>4 (35)</td><td>0 (0)</td><td>0 (0)</td></tr></tbody></table>
<table><tr><td>6</td><td>Environmentally conscious construction method</td><td>11 (92)</td><td>1 (8)</td><td>0 (0)</td><td>0 (0)</td></tr><tr><td>7</td><td>Harmonious design building that satisfy the present and future generations</td><td>10 (83)</td><td>2 (17)</td><td>0 (0)</td><td>0 (0)</td></tr></table>

Source: Author's Field Survey, 2024

Discussion of Findings

<table><caption>Table 7: Importance of Green Buildings (GB) to Sustainable Development</caption><thead><tr><td>Test</td><td>Value</td><td>Df</td><td>Asymptotic Significance (p-value)</td></tr></thead><tbody><tr><td>Pearson Chi-Square</td><td>11.125</td><td>6</td><td>0.049</td></tr><tr><td>Likelihood Ratio</td><td>12.34</td><td>6</td><td>0.033</td></tr><tr><td>Linear-by-Linear Association</td><td>2.01</td><td>1</td><td>0.043</td></tr></tbody></table>

Source: Author's Analysis, 2024

The Pearson Chi-Square value of 11.125 with a p-value of 0.049 is less than 0.05, meaning we reject the null hypothesis. This indicates that there is a statistically significant relationship between participants' perceptions of GB and its impact on sustainable development. The likelihood ratio test further confirms this with a p-value of 0.033, indicating that GB plays a key role in sustainable development.

<table><caption>Table 8: What Makes GB Innovative?</caption><thead><tr><td>Test</td><td>Value</td><td>Df</td><td>Asymptotic Significance (p-value)</td></tr></thead><tbody><tr><td>Pearson Chi-Square</td><td>9.456</td><td>3</td><td>0.024</td></tr><tr><td>Likelihood Ratio</td><td>10.753</td><td>3</td><td>0.018</td></tr><tr><td>Linear-by-Linear Association</td><td>1.678</td><td>1</td><td>0.029</td></tr></tbody></table>

Source: Author's Analysis, 2024

The Pearson Chi-Square value of 9.456 and p-value of 0.024 indicate a statistically significant relationship between perceptions of what makes GB innovative and its role in sustainable development. The likelihood ratio test (p-value = 0.018) strengthens the case that GB innovations such as renewable energy, energy efficiency, and green materials have a strong impact on sustainability.

<table><caption>Table 9: Features of Green Buildings (GB)</caption><thead><tr><td>Test</td><td>Value</td><td>df</td><td>Asymptotic Significance (p-value)</td></tr></thead><tbody><tr><td>Pearson Chi-Square</td><td>12.875</td><td>6</td><td>0.032</td></tr><tr><td>Likelihood Ratio</td><td>14.341</td><td>6</td><td>0.021</td></tr><tr><td>Linear-by-Linear Association</td><td>2.765</td><td>1</td><td>0.038</td></tr></tbody></table>

Source: Author's Analysis, 2024

The Pearson Chi-Square value of 12.875 with a p-value of 0.032 suggests a significant relationship between GB features and their perceived importance to sustainable development. The likelihood ratio test with a p-value of 0.021 further supports that GB features like energy efficiency, renewable energy, and waste management are seen as key drivers of sustainable practices.

The results reveal a statistically significant relationship between participants' perceptions of GB and their contribution to sustainable development. A large majority (92%) strongly agree that GBs use
fewer resources, produce fewer emissions, and generate less waste. Notably, 100% of participants agree that GBs have a positive environmental impact and prioritize health and well-being. Additionally, 92% strongly agree that GBs help combat climate change and promote sustainability. The Pearson Chi-Square value of 11.125 (p-value = 0.049) leads to the rejection of the null hypothesis, indicating a statistically significant relationship between GBs and sustainable development. The likelihood ratio test (p-value = 0.033) further reinforces this finding. The data strongly suggests that GBs are viewed as essential for environmental progress and sustainability.

GB innovations such as renewable energy, energy efficiency, and green materials are widely recognized by participants as crucial for sustainable development. The majority (83%) strongly agree on the importance of energy efficiency, while 75% identify green materials as essential innovations. These elements are statistically shown to have a positive and significant impact on sustainability. The Pearson Chi-Square value of 9.456 (p-value = 0.024) demonstrates a significant relationship between perceptions of Experts on GB innovations and their role in sustainability. The likelihood ratio test (p-value = 0.018) supports rejecting the null hypothesis, affirming that GB innovations are critical drivers of sustainable development, especially in areas such as renewable energy and energy efficiency.

Essential GB features, such as energy efficiency, renewable energy, efficient water use, and environmentally conscious construction methods, are regarded by participants as vital to sustainability. A total of 100% of participants strongly agree on the importance of energy efficiency, while 92% emphasize water efficiency and environmentally conscious construction methods as key GB features contributing to sustainable development.

The Pearson Chi-Square value of 12.875 (p-value = 0.032) highlights the statistically significant relationship between GB features and their role in sustainability. The likelihood ratio test (p-value = 0.021) supports rejecting the null hypothesis, confirming that GB features are critical components driving sustainable practices. Similarly, The p-values across all tables (Table 7: p = 0.049; Table 8: p = 0.024; Table 9: p = 0.032) consistently show that Green Buildings have a statistically significant impact on sustainable development, cementing their role as key drivers of sustainability initiatives.

## CONCLUSION

Across all analyses, the data supports rejecting the null hypothesis ($H_0$) and accepting the alternative hypothesis ($H_1$). Green Buildings (GB) are shown to have a statistically significant positive impact on sustainable development. Key innovations, such as renewable energy and energy efficiency, combined with critical features like resource conservation and waste management, are seen as major contributors to sustainability efforts. Participants clearly view GB as essential for achieving environmental goals, addressing climate change, and improving overall well-being.

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**Chapter Fourteen**

**The Impact of Climate change on Property Valuation and Sustainable Development**

**Simon Ajayi Olukotun**

Department of Urban and Regional Planning, Lead City University, Ibadan, Nigeria

EMAIL: ajayiolukotun19@gmail.com

**ABSTRACT**

According to data from the Nigeria Bureau of Statistics (NBS), the real estate sector contributed approximately 6.50% to Nigeria Gross Domestic Product (GDP) in Q4 2023, witnessing a notable increase from 6.18% in the previous year. Unfortunately, climate change results in more intense rainfall. Heavy rainfall over a short period of time can cause flash flood and moderate rainfall over several days can over flow the rivers, streams and dams. Increasing temperatures are also melting polar ice caps. These pose a significant threat to flood-prone areas, towns, cities and biodiversity across the world. The negative effect of climate change is not limited to the environment it touches the real estate in general and its value in particular. Using a study from Ibadan city, Oyo State, Nigeria, this paper addresses the effect of climate change on selected properties in flood-prone areas in Ibadan. Survey method was adopted to interview 60 randomly selected property owners/tenants. Also, relevant estate agents were contacted. Information such as type of property, years of occupation, annual rental value, capital value, land size, number of bedrooms, floor area, annual maintenance cost (repairs liability), property market in the area, vacancy/occupancy rate, flood occurrence, damage/ sustained and investors' perception about the area were elicited from the respondents. This information was compared with similar and comparable properties outside the flood prone areas. Hedonic pricing regression model was used for data analysis. The findings shows that there is a significant lower annual rental value of properties within the flood prone area as compared to similar and comparable properties outside the zone. Also, annual maintenance and repair liabilities is higher in the flood prone area. Because of the risk, the place is not attractive to property investors. (β = -92400.37, P = 0.039 and β = -6.32, P = 0.002) for level of risk and repair liability respectively. These have negative effect on rental value of property in flood prone area. The implication of this is that, it has negatively affected not only the present but the future income flow from the property to the owner. It also informs the policy makers on how this scourge affects the economy and sustainable developments by decreasing the value of one of the most important sectors which has the potential to significantly influence the trajectory of the country's economic recovery and sustainable development.

KEY WORDS: Climate change; Flooding; Real estate sector; Property valuation; Sustainable development

**INTRODUCTION**

Climate change is a growing problem and has major effects of the environment of the world population, spatial planning process and real estate market (Joanna et al., 2020). Climate change creates a high-risk area such as sea-level rising, flooding and temperature increase among others (Eghuizen, 2020, Ebele et al., 2016). There is a fierce social and specific debate concerning the origin of the climate changes that are presently being observed (Stolpe, Medhanugi and Knutti, 2017). The results of many scientific studies show that human activity has contributed to the increase in carbon dioxide (CO²) in the atmosphere; an increase in CO² levels causes an increase in the frequency of extreme weather events (Anderson, 2017, IPCC, 2021, WHO, 2021, NiMet, 2015). For example, between May and June 2018 almost every day approximately 22% of the populated and agricultural areas north of 30° latitudes experienced high temperature at the same time (Vogel, Zscheischler, Dee, Senevirantue and Worten, 2018). Extreme weather results in water level increase with the shrinking of the area covered by ice (Jakobson, Vihma and Jakobson, 2016; Chambers, 2017) and there are increasing number of cases of rising flood in coastal areas due to sea-level rising (Wodowinskis, Bray, Kirtman and Wu, 2016)
For every additional increment of global warming, extreme in weather and temperature will increase. For example, every additional 0.50 of global warming causes clearly discernible increase in the intensity and frequency of hot extreme, including heat waves and heavy precipitation as well as agricultural and ecological droughts in some regions (IPCC, 2021). Floods are the world greatest disaster as asserted by West (2010), Odelola and Akinola (2015). Global sea-levels have been on the increase for century, but the rate of rising waters has escalated over the few decades and is expected to accelerate dramatically in decades to come (IPCC, 2018).

The effects of sea level rising at accelerated rates are intensified by increased settlement along coastlines over recent decades which is made possible through hardened infrastructure and engineered coastal protection through Dikes, sea walls and levees (Castro and Kumtz, 2022). These infrastructural protections put in place, however, were built based on past estimates of rising sea rather than the accelerated pace of sea level rise due to climate change (IPCC, 2018).

While sea level rise impact is experienced along coastlines, land degradation permeates the global landscape and concentrates on tropical and subtropical regions. Besides rising sea levels also the probability of extreme weather conditions, additional damage is rising due to urban flooding, flash flooding among others. For example in July 2021, the Benelux region experienced a major flood due to the extreme rainfall, more than 110 millimeters of precipitation fell in 48 houses (Bruijn and Slager, 2022). In Ibadan city, the historical Ogunpa flood disaster in 1980 happened after 10 hours of heavy downpour (Eguaroje et al., 2015). In addition, in August 13, 2015, another flood disaster occurred after 12 hours downpour from August 12 through the following morning. (Olawuni et al., 2015)

In the context of natural disasters, floods are defined by the amount of damages they cause to people or property (West, 2010). According to the U.S. Geological Survey, flood cause an average of 86 billion of property damage claimed 140 lives and prompt more presidential disaster declaration per year than any other hazard, major floods not only cause direct damage to the areas affected, they also have cascading effect due to the decrease in property values in the affected areas, disruption of transportation systems, food and water supplies and other economic damage to local businesses and agriculture. For example, the total damage from the U.S in 1993 floods are estimated at $18 billion property damage exceeded $1 billion of the $3.4billion of damage in the state and agriculture damage accounted for an estimated $1.9billion (West, 2010).

Accounting for the havoc that Ibadan flooding occurrences have caused over a long period of time, John (2012) reported that 1,000 people were rendered homeless in the 1960 flood. In 1978, official records confirmed that 32 dead and 100 houses were destroyed after a raising flood surge that led to yet another incident of overflowing of the stream (Oguaroje et al., 2015). In 198-, Ogunpa stream swelled and burst its banks blooding large sections of the city and caused more than 100 deaths and destruction of hundreds of homes and valuable properties (Oguarohje et al., 2015). This historic flood disaster in Ibadan of 1980 were also estimated to have caused damage worth of ₦300 million while the number of lives lost was put at 500 people (Akintola, 1994) and between 1995 and 1998, over 12 million naira was estimated to have been lost to flood in Ibadan (WEMA, 2013). Nigeria suffered a severe disaster on September 9th, 2024 when Alau dam overflew as a result of heavy rainfall. The dam collapse according to Lagelu 96.7 news on 25th September, 2024, 587 houses were destroyed around MMC and Jere in Maduguri and over 400,000 people were displaced.

Global commitment to climate mitigation is evident in the United Nations sustainable Development Goals (SDGs) which include climate change mitigation and natural resource protection, the Paris accord signed in 2016 agreeing to mitigate climate change through lowered greenhouse gas emissions and national level climate adaptation plans. However, progress toward these goals to date has been limited and CO² emissions persist at unmitigated rates (Castro and Kuntz, 2022). While cities, national governments, the private sector and intergovernmental organizations work out whether and how to mitigate climate change through “ne zero” CO² emissions, communities face more frequent and severe quick-onset disasters as well as gradual changes in seasons, temperature and rainfall (Castro and Kuntz 2022). Global temperatures are rising especially in the tropics as is the incidence of extreme cold days and coastal land is disappearing into the sea at rapid rates (IPCC, 2018).
Another commitment by the international community to mitigate climate change was in April 2019 when 185 parties committed to maintain the global average temperature increase below 2 degrees Celsius through the Paris agreement. The international commitment to increase resilience and adaptive capacity as well as mitigating climate change is outlined in the United Nations Sustainable Development Goals specifically SDG 13 and global finance flows to fund climate mitigation and adaptation increased by 17% in the year after the Paris agreement was reached (United Nations, 2018).

Though the global community has made ambitions commitments to climate mitigation and adaptation, these commitments are outpaced by the rate at which the climate is changing. Catching up to the pace of change requires quicker economic restructuring to eliminate harmful greenhouse gas emission, access to climate finance needs to be accelerated and adaptation efforts rescaled to match climate hazards (United Nations 2018; IPCC, 2018).

The negative effect of climate change is not limited to destructions of lives and property, it also touches on property market. For instance, in Belgium after the 2021 flood, price of house that are located in a flood risk area were sold 4 percent less compared with similar houses that are in an area classified as less risky (HLN, 2022). Also, recent study performed in U.S. suggest that homes exposed to sea-level rise sell for approximately 7% less than observably equivalent unexposed properties equidistant from the beach (Berstein et al., 2019) among other. In an efficient housing market, property value in vulnerable areas is lower than house in safer areas because both sellers and buyers recognise hazard information which affects the acceptance of a lower price for a property (Beltra et al., 2018).

Convincing empirical evidence on various effects of climate change on property valuation have been compiled from different regions of the world, however, despite this mounting volume of empirical evidence, gaps for further study remains wide. While an increasing body of literature is emerging on the effect on the price of sale of residential properties before and after the flood (Eghuizen, 2020), information asymmetry (Koo and Lee, 2015; Votsis and Perrels, 2016) among others, empirically there has been little rigorous documentation and analysis of the impact of climate change on the rental value of residential buildings and future cash flows from the property for future sustenance of the owner. This is the gap in knowledge this study stands to bridge using empirical findings from this study.

## Aim and Objectives

The aim of this research is to assess the impact of flooding risk on residential rental value and future cash flow for sustainability. Ibadan city as a case study. To achieve the above aim, the specific objectives are to:

i. Identify the flood-prone area in the city;

ii. Identify the properties that are prone to flooding risk;

iii. Investigate the rental values of properties and their attributes in the target areas and less prone areas (control) and;

iv. Identify the factors responsible for differences in rental value of properties in the two regions above.

## LITERATURE REVIEW

### Conceptual Review

This section thematically reviewed four relevant concepts viz climate change, flooding property valuation and sustainability

### Climate Change

Climate change refers to the long-term changes in temperature and weather due to human activities (Anderson, 2017; IPCC, 2021). Increase in average global temperature are the most manifestations of climate change (Shivanna, 2022). In recent times, it has acquired the importance of global emergency and affecting not only the well-being of humans but also the sustainability of other life forms (Rivera, 2020), enormous increase in the emission of greenhouse gases CO², methane and nitrous oxide in recent decades largely due to burning of coal and fossil fuels, and deforestation are the main drivers of climate change marked increase in the frequency and intensity of natural disasters, rise in sea level, decrease in crop productivity and loss of biodiversity are the main consequences of climate change (Shivanna, 2022; Castro and Kuntz, 2022). Obvious mitigation measures include significant reduction in the
emission of greenhouse gases and increase in the forest cover of the land mass (IPCC, 2018; Shivanna, 2022).

Flooding

Floods occur when the river level exceeds its natural bank and water overflows. Flood involves the inundation or overflow of water to cover land that is not normally submerged (Ward and Smith, 2008). Floods that exceed normal flows and people's capacities can be damaging and have devastating consequences on human health, property and the environment. Flooding occurs as a result of one or a combination of events such as rainfall, filling rivers, streams and ditches, coastal storms resulting in over lopping and breaching of coastal flood defenses, blocked or over headed drainage ditches, drains and severe heavy rain resulting in run off flowing over land or rain soaking into the ground thereby raising ground water level (Met office 2003). Floods can be classified according to its location, intensity and potential impacts. These are flash floods and urban flooding (Odelola and Akinola, 2015). Flash flood gathers streams within six hours of the events that spawned them (Scott, 2007)

Property Valuation

Valuation has been practised in earlier countries. Commonwealth countries like Nigeria, the United Kingdom, Ghana, India and America developed the valuation profession through the pioneering efforts of the UK (Udechukwu, 2006). In 1969, the Nigerian institution of Estate Surveyors and Valuers was established in Nigeria as a central body to supervise, train as well as maintain outward of integrity and competence. In 1975, the Estate Surveyor and Valuers Registration Act which is more popularly known as Degree No 24 of 1975 was established. This legislation brought into existence the Estate Surveyor's and Valuers Registration Board of Nigeria (ESVARBON) as a corporate body empowered to regulate the profession of Estate Surveying and valuation in Nigeria. Practitioner of the valuation profession are given different names in different countries. In the US, they are known as Appraisers or Realtors and in the United Kingdom, they are known as Chartered Surveyors while in Nigeria, they are known as Estate Surveyors and Valuers. The ESVARBON has produced the Nigeria valuation standard the Green Book in 2019 in accordance with its obligations under the Estate Surveyors and Valuers Registration Board of Nigeria Act Cap E13 LFN 2004 and subsequent regulations.

Valuation is the determination in monetary terms, of the open market value of an interest in a given
property. The valuer in carrying out a valuation is trying to give his expert opinion of what an interest
in a particular property is worth in the open market at a particular time (Kuye, 2005). Valuation can also
be defined as the determination of the monetary worth or value at a specified date and for a specific
purpose, the property rights encompassed in an ownership (Usechurkwu, 2006). Valuers are responsible
for adopting and as necessary justifying the valuation approach (es) and the valuation methods used to
fulfil individual valuation assignments (The Green Book, 2019). N Value arrived at may be influenced
by many factors such as location, the demand and supply factors (the state of the economy) availability
of substitute tastes of individuals and the presence of natural thresholds such as rock outcrop, floodable
area, and earthquake draught among others. Extreme weather events expose owners of buildings to what
may have been hitherto unforeseen news. Apart from the cost of any physical damage that may occur,
recognition of such risk will feed into the short- and long-term financial performance of buildings. An
increased likelihood of extreme climate events can be a major driver of depression in property value
(Clayton et al., 2021). The drivers for this could include an increased reluctance of occupier/tenant to
locate in an area, and an increased reluctance of investors to hold assets with exposure to such risk
related concerns surrounding securing insurance or mortgage finance for properties with increased
exposure to events such as storm or floods (Sayce et al., 2022)

Sustainable Development

The continuous growth of the world population, resource scarcity and the threat f climate change pose
numerous environmental and social problems to the world; therefore, much hope is put in the concept
of sustainable development (Babrovolskein & Tomosuinien, 2015). Sustainability in the study of how
natural systems function, remain diverse and produce everything it need for the ecology to remain in
balance. Sustainable development is defined in the Brundtland Commission Report as the development
that meets the needs of the present without compromising the ability of the future generation to meet
their own needs (Goodland, 1995).
Understanding this will help us to appreciate the need for climate mitigation and efficient use of natural resources. According to EPA, (2015) sustainability is based on a simple principle: everything that we need for our survival and well-being depends either directly or indirectly on our natural environment (Idiata, 2016). Therefore, to pursue sustainable development is to create and maintain the conditions under which humans and nature can co-exist in productive harmony to support the present and future generations (Idiata, 2016).

# MATERIALS AND METHODS

## Study Area

The areas selected for the study in Ibadan are areas located in flood-prone areas. The rivers in Ibadan city include; Ogunpa (central) Ona River (West), Ogbere (East) and Kudeti. However, the Ogunpa River and its tributaries were chosen for this study because of its central position and the intensity of the flood disaster. Ogunpa system is a third-order stream with a channel length of 21.5km and a drainage basin covering 73.3 square kilometres (ESMP Report, 2020).

In the main Ogunpa stream in recent years no event of large flooding or water damage has occurred as a result of the channelization project. The Ibadan Urban Flood Management Project (IUFMP) was established to provide short and medium to long-term plans and options for resolving the issue of flood in Oyo State (ESMP Report, 2020). Some long-term resilient projects are planned as part of the IUFMP projects which are classified into pools (ESMP Report, 2020). The first pool of long-term investment projects is divided into two lots; which include the channelization of some streams/rivers which flow through Ibadan City such as Ogunpa, Ogbere, Kudeti and Ona rivers. The World Bank is supporting the Oyo State government to implement some of these projects (ESMP Report, 2020). Agbowo-Orogun Community is located in the Ibadan North Local Government Area of Oyo State. This area is known for the severity of floods in recent times (Odelola and Akinola, 2015; Onifade et al., 2014)

## Data Base for the Study

A case study research design was adopted. According to Yin (2003), this approach is appropriate when the research to be addressed is descriptive or explanatory. It allows getting in-depth information on an existing situation, event or process within its real-world context. It is also suitable for research whose object is to evaluate impact. The flood affected Agbowo-Orogun community where the flood disaster was severely affected according to a study (Odelola and Akinola, 2015; Onifade et al., 2014) was chosen for this study. The total number of residential properties in this area is 60 different housing units. The researcher inspected the community and the residential buildings and found that there are 6 (six) different types of housing units in the community viz: Tenement Bungalow (a), Tenement storey building (b), A block of flat bungalow (c), twin flat bungalow (d), two flats storey building (e) and four flats (f). The severely affected properties were sampled (target) six in number within the flood-prone area, while six (6) similar and comparable properties were also sampled outside the flood-prone area (control) for comparative purposes. In all (twelve) 12 structured questionnaire guides were administered to owners/occupants and all (100%) were retrieved for final analysis. The sampled data were collected and data collected included: the type of property, and characteristics of each in terms of annual rental value, land size, floor no, number of bedrooms, toilet/bathroom, Total rooms, age of building, flood risk level, annual repair and maintenance cost among others. The empirical analysis of this research is focused on answering two quantitative research questions:

1. Is the rental value of property in the flood-prone area significantly affected by the flood?

2. Is the future cash flow significantly affected by the risk and maintenance cost?

The difference-in-difference hedonic price model allows the researcher to compare a selected target group of housing units with a control group which are comparable.

## Hedonic Regression Model:

The rental price for a property depends upon the attributes of this property (Rosen, 1974). Mathematically this relationship can be written as

$$P = f(a_1, a_2, \dots, a_n) \quad \text{equ 1}$$

where; P is the rental value of the property, f (-) is the arbitrary function and $a_i$ ..... $a_n$ are the attributes of the property. These attributes include a physical aspect of the property such as land size, floor area,
number of bedrooms, bath/toilet, age of building, level of risk, annual repair and maintenance cost among others. A hedonic price model measures the contribution of each attribute to the rental value (p) of the property.

The econometric approach to empirically calculate a hedonic price is to fit a multi-variate ordinary least square regression model viz:

$$P = c_0 + c_1a_1 + c_2a_2 + c_3a_3 + c_4a_4 + \dots + c_na_n \quad \text{equivalent}$$

where; $c_i$ is the regression coefficient or hedonic price for attribute $a_i$. Equation 2 shows a linear relationship between rental price and attribute.

<table><caption>Table 1: Type of Property and their Attribute (Target)</caption><thead><tr><th>Property</th><th>Y (RV) Rental value</th><th>X<sub>1</sub> (LS) Land Size</th><th>X<sub>2</sub> (FN) Floor No</th><th>X<sub>3</sub> (NB) Bed room</th><th>X<sub>4</sub> (TR)</th><th>X<sub>5</sub> (T/B) Toilet/Bath</th><th>X<sub>6</sub> (AB) Age</th><th>X<sub>7</sub> (RL) Risk Level</th><th>X<sub>8</sub> (AR) Repair & Maintenance</th></tr></thead><tbody><tr><td>A</td><td>₦216,000</td><td>450m<sup>2</sup></td><td>1</td><td>-</td><td>6</td><td>2</td><td>12</td><td>1</td><td>₦43,200</td></tr><tr><td>B</td><td>₦432,000</td><td>500m<sup>2</sup></td><td>2</td><td>-</td><td>12</td><td>4</td><td>30</td><td>1</td><td>₦86,000</td></tr><tr><td>C</td><td>₦250,000</td><td>600m<sup>2</sup></td><td>1</td><td>3</td><td>-</td><td>3</td><td>20</td><td>1</td><td>₦50,000</td></tr><tr><td>D</td><td>₦500,000</td><td>600m<sup>2</sup></td><td>1</td><td>6</td><td>-</td><td>-</td><td>20</td><td>1</td><td>₦100,000</td></tr><tr><td>E</td><td>₦500,000</td><td>540m<sup>2</sup></td><td>1</td><td>6</td><td>-</td><td>6</td><td>20</td><td>1</td><td>₦100,000</td></tr><tr><td>F</td><td>₦1,000,000</td><td>700m<sup>2</sup></td><td>2</td><td>12</td><td>-</td><td>12</td><td>20</td><td>1</td><td>₦200,000</td></tr></tbody></table>

Source: Field Survey, 2024

<table><caption>Table 2: Type of Property and their Attribute (Control)</caption><thead><tr><th>Property</th><th>Y (RV) Rental value</th><th>X<sub>1</sub> (LS) Land Size</th><th>X<sub>2</sub> (FN) Floor No</th><th>X<sub>3</sub> (NB) Bed room</th><th>X<sub>4</sub> (TR)</th><th>X<sub>5</sub> (T/B) Toilet/Bath</th><th>X<sub>6</sub> (AB) Age</th><th>X<sub>7</sub> (RL) Risk Level</th><th>X<sub>8</sub> (AR) Repair & Maintenance</th></tr></thead><tbody><tr><td>A</td><td>₦288,000</td><td>450m<sup>2</sup></td><td>1</td><td>-</td><td>6</td><td>2</td><td>12</td><td>0</td><td>0</td></tr><tr><td>B</td><td>₦1,440,000</td><td>500m<sup>2</sup></td><td>2</td><td>-</td><td>12</td><td>4</td><td>30</td><td>0</td><td>0</td></tr><tr><td>C</td><td>₦600,000</td><td>600m<sup>2</sup></td><td>1</td><td>3</td><td>-</td><td>3</td><td>20</td><td>0</td><td>0</td></tr><tr><td>D</td><td>₦1,200,000</td><td>600m<sup>2</sup></td><td>1</td><td>6</td><td>-</td><td>6</td><td>25</td><td>0</td><td>0</td></tr><tr><td>E</td><td>₦1,200,000</td><td>540m<sup>2</sup></td><td>2</td><td>6</td><td>-</td><td>6</td><td>15</td><td>0</td><td>0</td></tr><tr><td>F</td><td>₦2,800,000</td><td>700m<sup>2</sup></td><td>2</td><td>12</td><td>-</td><td>12</td><td>15</td><td>0</td><td>0</td></tr></tbody></table>

Source: Field Survey, 2024

By using SPSS version 20.0 for the analysis the following were derived:

1. Comparing the result of property A in the target and control area does the rental value significantly differ?

2. Is the target area's rental value affected by the flood risk and annual maintenance cost?

3. What is the effect of lower rental value in the target area on future cash flow from the property?

### Property A

#### Target Area

$$Y = a + b_{1}x_{1} + b_{2}x_{2} + b_{3}x_{3} + b_{4}x_{4} + b_{5}x_{5} + b_{6}x_{6} + b_{7}x_{7} + b_{8}x_{8} + e_{1}$$
where; Y = rental value (dependent variable), a = constant term, b = the coefficient of the corresponding x, x = the independent variables, e = error term

Property A

Control Area

$$Y = a + b_1x_1 + b_2x_2 + b_3x_3 + b_4x_4 + b_5x_5 + b_6x_6 + b_7x_7 + b_8x_8 + e_1$$

## RESULTS AND DISCUSSION OF FINDINGS

<table><caption>Table 3: Regression Model</caption><thead><tr><td>Model</td><td>R</td><td>R Square</td><td>Adjusted R Square</td><td>Std. Error of the Estimate</td></tr></thead><tbody><tr><td>1</td><td>0.998</td><td>0.996</td><td>0.985</td><td>24,313.35</td></tr></tbody></table>

In Table 3, R-Square (0.996): Indicates that 99.6% of the variance in rental value is explained by the independent variables, which shows a strong model fit. Adjusted R-Square (0.985): Adjusts for the number of predictors, still indicating a strong model without over-fitting.

<table><caption>Table 4 ANOVA</caption><thead><tr><td>Model</td><td>Sum of Squares</td><td>Df</td><td>Mean Square</td><td>F</td><td>Sig.</td></tr></thead><tbody><tr><td>Regression</td><td>4.72E+11</td><td>7</td><td>6.74E+10</td><td>114.12</td><td>0.009</td></tr><tr><td>Residual</td><td>1.18E+09</td><td>2</td><td>5.91E+08</td><td></td><td></td></tr><tr><td>Total</td><td>4.73E+11</td><td>9</td><td></td><td></td><td></td></tr></tbody></table>

<table><caption>Table 5: Coefficients</caption><thead><tr><td>Model</td><td>Unstandardized Coefficients (B)</td><td>Std. Error</td><td>Standardized Coefficients (Beta)</td><td>t</td><td>Sig.</td></tr></thead><tbody><tr><td>(Constant)</td><td>-136,875.620</td><td>102,412.30</td><td>-</td><td>-1.336</td><td>0.319</td></tr><tr><td>Land Size</td><td>-2.5936</td><td>7.348</td><td>-0.037</td><td>-0.353</td><td>0.756</td></tr><tr><td>Floor Area</td><td>726.21</td><td>4,513.52</td><td>0.125</td><td>0.161</td><td>0.889</td></tr><tr><td>Bedroom</td><td>-2.477</td><td>35,241.21</td><td>-0.005</td><td>-0.07</td><td>0.95</td></tr><tr><td>Toilet/Bath</td><td>27.878</td><td>22,920.11</td><td>0.107</td><td>1.217</td><td>0.399</td></tr><tr><td>Age</td><td>-25.936</td><td>1,716.54</td><td>-0.011</td><td>-0.345</td><td>0.764</td></tr><tr><td>Risk Level</td><td>74.906</td><td>10,549.52</td><td>0.529</td><td>0.798</td><td>0.509</td></tr><tr><td>Repair & Maintenance</td><td>4.999</td><td>0.325</td><td>0.999</td><td>15.389</td><td>0.004</td></tr></tbody></table>

1. Is the rental value of the Property significantly different between the Target and Control Areas? An independent t-test was conducted to compare the rental value of Property A in the Target and Control Areas. The t-test results, as shown in the table below, indicate a significant difference in rental values between the two areas:

The independent t-test confirms that the rental value of Property A in the Target Area (₦216,000) is significantly lower than that of Property A in the Control Area (₦288,000). The p-value is less than 0.05, indicating that the rental values between these areas differ significantly. This difference is likely due to the higher risk factors (e.g., flood risk) and maintenance costs associated with the Target Area.
<table><caption>Table 6: The Rental Value of Property, the Target and Control Areas</caption><thead><tr><td>Parameter</td><td>Target Area (Property A)</td><td>Control Area (Property A)</td></tr></thead><tbody><tr><td>Rental Value (N)</td><td>216,000</td><td>288,000</td></tr><tr><td>Sample Size (n)</td><td>1</td><td>6</td></tr><tr><td>Mean Rental Value (N)</td><td>216,000</td><td>1,254,666.67</td></tr><tr><td>Standard Deviation (N)</td><td>0</td><td>26,046.38</td></tr><tr><td>t-value</td><td>-36.88</td><td></td></tr><tr><td>Degrees of Freedom (df)</td><td>5</td><td></td></tr><tr><td>Significance Level (p-value)</td><td>&lt; 0.05</td><td></td></tr><tr><td>Critical t-value (α = 0.05)</td><td>±2.571</td><td></td></tr><tr><td>Conclusion</td><td colspan="2">Rental values are significantly different.</td></tr></tbody></table>

Source: Author's Analysis, 2024

## 2. Is the Target Area's rental value affected by flood risk and annual maintenance cost?

A multiple regression analysis was performed to assess whether flood risk and annual maintenance costs influence rental values in the Target Area. The dependent variable is Rental Value (Y), and the independent variables are Risk Level (X6) and Repairs and Maintenance (X7).

<table><caption>Table 7: Regression Results for the Target Area</caption><thead><tr><td>Variable</td><td>Unstandardized Coefficients (B)</td><td>Std. Error</td><td>t</td><td>p-value</td></tr></thead><tbody><tr><td>(Constant)</td><td>698,734.80</td><td>56,874.50</td><td>12.28</td><td>0.001</td></tr><tr><td>Risk Level (RL)</td><td>-92,400.37</td><td>34,270.30</td><td>-2.7</td><td>0.039</td></tr><tr><td>Repairs &amp; Maintenance (AR)</td><td>-6.32</td><td>0.298</td><td>-21.21</td><td>0.002</td></tr></tbody></table>

<table><caption>Table 8: Regression Result (p-value)</caption><thead><tr><td>Source</td><td>Sum of Squares</td><td>df</td><td>Mean Square</td><td>F-Statistic</td><td>p-value</td></tr></thead><tbody><tr><td>Regression</td><td>3.50E+10</td><td>2</td><td>1.75E+10</td><td>15.87</td><td>0.022</td></tr><tr><td>Residual</td><td>5.50E+09</td><td>3</td><td>1.83E+09</td><td></td><td></td></tr><tr><td>Total</td><td>4.05E+10</td><td>5</td><td></td><td></td><td></td></tr></tbody></table>

In Table 7, Risk Level (RL) has a significant negative impact on rental value (β=-92,400.37, p=0.039), indicating that an increase in flood risk leads to a substantial decrease in rental value, while Repairs & Maintenance (AR) also exhibits a highly significant negative effect on rental value (β=-6.32, p=0.002), suggesting that higher maintenance costs contribute to reducing rental values, and overall, the model is statistically significant (F=15.87, p=0.022), which demonstrates that both flood risk and maintenance costs together explain a considerable portion of the variation in rental value in the Target Area (Table 8).
<table><caption>Table 9: Effect of Lower Rental Value in the Target Area on Future Cash Flow from the Property</caption><thead><tr><td>SN</td><td>Factor</td><td>Description</td><td>Effect on Cash Flow</td></tr></thead><tbody><tr><td>1</td><td>Lower Rental Income</td><td>Reduced rental value directly lowers the monthly income generated by the property.</td><td>Negative: Reduced income and cash flow.</td></tr><tr><td>2</td><td>Higher Vacancy Risk</td><td>Increased flood risk and poor maintenance may lead to higher tenant turnover and vacancies.</td><td>Negative: Further reductions in cash flow.</td></tr><tr><td>3</td><td>Decreased Investment Appeal</td><td>Lower rental yields make the property less attractive to investors, reducing market demand.</td><td>Negative: Lower future appreciation or sale potential, reducing future cash flow.</td></tr></tbody></table>

Source: Author's Analysis, 2024

<table><caption>Table 10: Projected Cash Flow Analysis (Next Five Years) For Target Property</caption><thead><tr><td>Year</td><td>Cash-Inflow</td><td>Cash-Outflow</td><td>Difference</td><td>PV of ₦1 at 22%</td><td>DCF</td></tr></thead><tbody><tr><td>2024</td><td>216,000</td><td>43,200</td><td>173,000</td><td>1</td><td>137,000</td></tr><tr><td>2025</td><td>216,000</td><td>43,200</td><td>173,000</td><td>.82</td><td>112,340</td></tr><tr><td>2026</td><td>216,000</td><td>43,200</td><td>173,000</td><td>.67</td><td>91,790</td></tr><tr><td>2027</td><td>216,000</td><td>43,200</td><td>173,000</td><td>.55</td><td>75,350</td></tr><tr><td>2028</td><td>216,000</td><td>43,200</td><td>173,000</td><td>.45</td><td>61,650</td></tr></tbody></table>

Source: Author's Analysis, 2024

<table><caption>Table 11: Projected Cash Flow Analysis (Next Five Years) For Control Property</caption><thead><tr><td>Year</td><td>Cash-Inflow</td><td>Cash-Outflow</td><td>Difference</td><td>PV of ₦1 at 22%</td><td>DCF</td></tr></thead><tbody><tr><td>2024</td><td>#288,000</td><td>Nil</td><td>#288,000</td><td>1</td><td>#288,000</td></tr><tr><td>2025</td><td>#288,000</td><td>Nil</td><td>#288,000</td><td>.82</td><td>#236,160</td></tr><tr><td>2026</td><td>#288,000</td><td>Nil</td><td>#288,000</td><td>.62</td><td>#192,960</td></tr><tr><td>2027</td><td>#288,000</td><td>Nil</td><td>#288,000</td><td>.55</td><td>#158,400</td></tr><tr><td>2028</td><td>#288,000</td><td>Nil</td><td>#288,000</td><td>.45</td><td>#144,000</td></tr></tbody></table>

From the projected cash flow (Table 9), the substantial decrease in cash flow from the **target property** is caused by risk factors and repairs and maintenance liability. The common decrease in both the target and control properties is attributed to the time value of money (Tables 10 and 11). The reduction in rental value due to risk factors and high maintenance costs has a significant negative impact on future cash flows, as it directly decreases the total rental income generated from the property, leading to lower overall cash inflows; in addition, properties in high-risk areas are more likely to experience frequent vacancies loss, which further disrupts consistent rental income and causes fluctuations in cash flow over time; finally, as these properties become less appealing to investors due to the diminished rental yields and higher risk, the market demand decreases, resulting in a long-term decline in the property's ability to generate sustainable returns and further reducing its future cash flow potential. However, properties in the Control Area, which do not experience these risk factors, maintain higher rental values and consistent cash flow, suggesting that properties free from high risks and excessive maintenance costs are better positioned to generate sustainable future income (Table 11).

## CONCLUSION

The analysis reveals that in the Target Area, risk factors such as flood risk and high maintenance costs significantly reduce rental values, with Risk Level (β=-92,400.37, p=0.039) and Repairs & Maintenance