The relationship between government borrowing and private sector investment is well-documented in global and Nigerian contexts. Nnanna (2023) found that while external debt negatively impacts Nigeria’s economic growth, domestic debt creates a stronger crowding-out effect by directly constraining local financial markets. Similarly, Anoke et al. (2020) identified that domestic debt, while less volatile than external debt, significantly reduces private sector access to affordable credit, with housing finance being a particularly affected sector.

In addition, Chinanuife & Anyanwu (2021) emphasized the high cost of borrowing in Nigeria, driven partly by government competition for domestic financial resources. Their findings illustrate how rising interest rates discourage private investments in long-term projects like housing and social infrastructure development, further widening Nigeria’s housing deficit, estimated at over 20 million units as of 2020. The limited supply of affordable housing, coupled with high construction costs and inadequate infrastructure, exacerbates the problem, as highlighted by ( PwC, 2019 ).

Studies reveal how government debt influences housing finance differently across economies. For instance, Van Hoenselaar et al. (2021) noted that countries with well-developed mortgage markets, such as South Africa and the United Kingdom, are less vulnerable to crowding-out effects due to diversified funding sources. In contrast, Nigeria’s mortgage-to-GDP ratio of less than 1% lags significantly behind South Africa (31%) and OECD averages (51%), highlighting the structural weaknesses in its housing finance system.

From 2014 to 2023, Nigeria’s government borrowing patterns have shifted considerably. Data from the ( Central Bank of Nigeria, 2023a ) show an increasing reliance on domestic debt, preferred for its insulation from foreign exchange risks. However, this trend exacerbates the crowding-out effect on local credit markets, making it harder for housing developers to secure long-term financing. External debt, though less burdensome on domestic markets, introduces vulnerabilities such as exchange rate fluctuations that increase the cost of imported construction materials.

Research by Oladipo and Salami (2022) observed that the rising cost of debt servicing in Nigeria reduces fiscal flexibility, further constraining government efforts to provide targeted subsidies or incentives for the housing sector. This highlights the dual challenge posed by government debt: not only does it restrict private sector access to credit, but it also limits public sector interventions in housing development.

Despite extensive research on government borrowing, gaps remain in understanding its sector-specific implications for housing finance. Most studies focus on aggregate investment effects, with limited insights into how government debt influences housing finance mechanisms, particularly in emerging markets like Nigeria.

Furthermore, while global studies suggest policy options to mitigate crowding-out effects, localized solutions tailored to Nigeria’s unique economic and institutional contexts are sparse. This study seeks to bridge these gaps by analyzing recent data (2014-2024) on government debt rates, inflation, monetary policy, and housing finance in Nigeria ( Oladipo & Salami, 2022 ). It aims to identify the specific channels through which government borrowing impacts housing finance and social infrastructure development to propose actionable recommendations for mitigating these effects.

The theoretical framework of this study is anchored on the crowding-out effect theory, which explains how government borrowing can suppress private sector investments by increasing competition for limited financial resources. This section examines the underlying theories and their relevance to the relationship between government debt and affordable housing finance in Nigeria.

The crowding-out effect theory posits that when governments increase borrowing to finance deficits, they compete with the private sector for scarce capital. This competition typically leads to higher interest rates, reduced credit availability, and an overall decline in private sector investments ( Joseph Jato & Nwankwo, 2023 ). The theory is particularly relevant in emerging economies like Nigeria, where financial markets are less developed, and credit supply is constrained. According to Investopedia (2024a) , Keynesian economics notes that government borrowing can have expansionary effects when funds are directed toward productive public investments. However, when borrowing finances recurrent expenditures or unproductive projects, it can lead to the crowding-out of private investments, as observed in Nigeria’s housing sector ( Paul & Akpan, 2024 ). Chinanuife & Anyanwu (2021) emphasize that in economies with shallow financial markets, like Nigeria, the crowding-out effect is more pronounced due to limited alternative funding sources for private borrowers.

The financial repression theory provides additional context for understanding housing finance constraints in emerging markets like Nigeria. This theory suggests that excessive government control over financial markets through mechanisms such as interest rate caps, high reserve requirements, and directed credit policies can stifle private sector growth ( Investopedia, 2024b ). In Nigeria, government borrowing often leads to financial market distortions, further limiting the availability of affordable, long-term credit for the delivery of social infrastructure by housing developers and for homebuyers ( Investopedia, 2024c ).

According to the Central Bank of Nigeria (2022b) , the debt sustainability framework highlights the importance of managing public debt to prevent adverse spillovers into private credit markets. High levels of domestic debt, as seen in Nigeria, can crowd out private investments by absorbing a significant share of domestic savings. This reduces the funds available for housing finance and increases borrowing costs for the private sector ( Yusuf & Mohd, 2021 ). Yusuf and Mohd (2021) illustrate that domestic debt in Nigeria exerts a stronger crowding-out effect than external debt due to its direct impact on local financial markets ( Nnana, 2023 ).

This study also incorporates the dual-impact channel framework to analyze how government borrowing influences the housing sector through two primary mechanisms: the interest rate channel and the exchange rate channel. Increased government borrowing raises interest rates, making it costlier for housing developers and low-income homebuyers to secure loans. External borrowing can lead to currency appreciation, increasing the cost of imported construction materials and reducing housing affordability ( World Bank, 2024 ).

The international lending environment can moderate the crowding-out effect by providing alternative sources of funding for private borrowers. For instance, well-developed sovereign bond markets can reduce the crowding-out effect by allowing banks to use government bonds as collateral in the interbank market, thereby raising secured funding and reducing the need to curtail credit supply to firms ( Agarwal et al., 2024 ). Exchange rate policies also play a crucial role in moderating the crowding-out effect. Flexible exchange rate policies can help absorb external shocks and reduce the impact of government borrowing on domestic interest rates. Regional economic integration can further mitigate the crowding-out effect by promoting financial stability and enhancing access to regional capital markets, thereby providing additional funding sources for private investments ( World Bank, 2024 ).

The theoretical frameworks outlined above align closely with Nigeria’s economic realities. The country’s underdeveloped financial markets, heavy reliance on domestic borrowing, and limited housing finance options create an environment where the crowding-out effect significantly hampers affordable housing and social infrastructure ( Nnana, 2023 ). The study leverages these theories to analyze how government debt influences housing finance dynamics in Nigeria and to propose strategies for mitigating adverse effects. To analyze the relationships between government fiscal deficits, access to sustainable finance for home acquisition, and social infrastructure development, the study also looked at the application of the following models:

Fiscal Policy Models: This model examines the impact of government fiscal policy on the economy, including the effects of fiscal deficits on interest rates, credit availability, and economic growth ( Saibu, 2018 ).

Monetary Policy Models: This model analyses the impact of central bank decisions on interest rates, credit availability, and economic growth, including the effects of fiscal deficits on monetary policy ( Nabieu et al., 2023 ).

Sustainable Finance Models: This model examines the impact of sustainable finance on access to credit for home acquisition and social infrastructure development, including the effects of fiscal deficits on sustainable finance ( Baret & Menuet, 2024 ).

Infrastructure Development Model: This model analyses the impact of infrastructure development on economic growth and social welfare, including the effects of fiscal deficits on infrastructure development ( Foster et al., 2023 ) and Public Private Partnerships on affordable housing and social infrastructure development ( Owotemu et al., 2022 ). The conceptual framework for the study as depicted in Figure 1 below is adopted as per the works of ( Bista & Thakur, 2023 ) and ( Nabieu, Anarfi, & Appiah-Konadu, 2023 ).

Despite extensive research on government borrowing and its implications for private sector investments, significant gaps remain in understanding the specific impacts of the crowding-out effect on affordable housing finance in Nigeria. This section highlights these gaps and establishes the need for the current study.

The majority of existing studies on the crowding-out effect in Nigeria, such as those by ( Yusuf & Mohd, 2021 ) and ( Anoke et al., 2020 ), focus on aggregate private sector investments or overall economic growth. While these studies

provide valuable insights, they fail to explore how government borrowing specifically affects the housing sector. Housing finance and social infrastructure, with its unique reliance on long-term credit and vulnerability to high interest rates, demands a distinct analysis ( World Bank, 2020 ).

Studies on Nigeria’s housing finance and social infrastructure challenges, such as those by ( PwC, 2023 ), often highlight issues like high interest rates and limited mortgage penetration. However, they provide little empirical evidence linking these issues to government borrowing. The lack of recent, comprehensive data analyzing the interplay between government debt, social infrastructure and housing finance represents a critical gap in the literature.

Comparative studies, such as those by ( Van Hoenselaar et al., 2021 ), often draw on global or regional data, making it difficult to derive actionable insights for Nigeria’s unique economic environment. Furthermore, most studies rely on data from earlier periods, with limited integration of recent developments in Nigeria’s fiscal and housing finance landscapes ( Federal Mortgage Bank of Nigeria, 2023 ).

While many studies recommend broad fiscal reforms to mitigate the crowding-out effect, few address practical solutions tailored to Nigeria’s housing and social infrastructure sector. For instance, targeted policy interventions, such as specialized housing finance instruments or public-private partnerships, are rarely explored in depth ( Owotemu et al., 2022 ). This gap underscores the need for research that not only identifies challenges but also proposes actionable recommendations for enhancing access to affordable housing finance.

According to ( PwC, 2023 ), given Nigeria’s rapidly evolving fiscal environment, including rising domestic debt levels and changing housing finance dynamics, there is a pressing need for updated empirical analyses. Recent data provide an opportunity to reevaluate the relationship between government borrowing and housing finance in light of contemporary economic realities. This research aims to fill these gaps by providing sector-specific analysis of the crowding-out effect on affordable housing finance and social infrastructure development ( Sarakiri 2024 ), updated empirical evidence based on data from 2014 to 2023 ( Central Bank of Nigeria, 2023b ) and practical policy recommendations tailored to Nigeria’s economic and institutional context with global applicability.

The key variables considered in the study include:

Government Debt: Measured as a percentage of GDP, disaggregated into domestic and external debt.

Interest Rates: Representing the cost of borrowing for private sector housing developers and homebuyers.

Credit to the Housing Sector: Captures the share of total private sector credit allocated to housing development.

Mortgage Penetration: Defined as the percentage of households with access to formal mortgage financing.

Econometric Models: The study employs correlation and multiple regression models to test the relationship between government borrowing and housing finance ( Eze et al., 2023 ). The model assesses the magnitude and significance of the crowding-out effect while controlling for macroeconomic conditions and sensitivity analysis.

Descriptive Statistics: Used to summarize trends in government debt, housing finance indicators, and macroeconomic variables.

Inferential Statistics: Includes correlation and regression analysis to evaluate the strength and direction of relationships between variables.

Robustness Checks: Performed to validate model reliability and address potential multicollinearity or autocorrelation issues in addition to sensitivity analysis of the timeseries data.

The analysis assumes that government borrowing is an endogenous variable influencing housing finance. Limitations include reliance on secondary data, which may contain reporting inaccuracies, and the inability to capture informal housing finance mechanisms prevalent in Nigeria.

The study relies entirely on secondary data, sourced from established and credible institutions. The datasets encompass the period from 2014 to 2023, providing comprehensive and up-to-date information on government debt and housing finance trends in Nigeria. The key sources of data include:

Central Bank of Nigeria (CBN): Data on domestic and external government debt levels, interest rates, and private sector credit allocations.

National Bureau of Statistics (NBS): Information on housing sector performance, including mortgage market size, housing deficits, and household income levels.

World Bank and International Monetary Fund (IMF): Macro-level indicators such as GDP growth, inflation rates, and fiscal sustainability metrics.

Nigeria Mortgage Refinance Company (NMRC), Family Home Funds Limited (FHFL), and Federal Mortgage Bank of Nigeria (FMBN): Sector-specific and informal housing market data on housing finance trends, mortgage accessibility, government housing programs, diaspora and rental housing. These data sources were selected for their accuracy, reliability, and relevance to the study objectives. Average rates of these indices are highlighted in Table 1 below.

Given the reliance on secondary data, the concept of sample size is applied in terms of the number of data points (e.g., average annual observations) available for each variable of interest. The study ensures a sufficiently large sample size to support robust statistical analysis, including:

Rate of Government Debt Borrowing: Annual and quarterly data points from 2014 to 2023 for all study variables.

Interest Rates: Monthly and annual data on prime lending rates and government bond yields.

Housing Finance Indicators: Annual data on mortgage penetration, credit allocation to the housing sector, and housing affordability metrics.

Data for the study were within a 10-year period (2014-2023), data must be specific to Nigeria, reflecting national trends and policies and data sources must have a high degree of credibility, including government reports, local informal housing data sources, reputable financial institutions, and peer-reviewed publications.

Data Quality Assurance: To enhance the validity and reliability of the study, the following steps were taken.

Cross-Verification: Data from multiple sources were cross-verified for consistency and accuracy.

Preliminary Analysis: Trends and outliers were examined to ensure data integrity and relevance.

Exclusion of Missing or Inconsistent Data: Data points with significant gaps or inconsistencies were excluded to avoid distortions in analysis.

By adhering to these rigorous data collection and sampling procedures, the study ensures that its findings are based on high-quality and representative data ( Okafor et al., 2023 ).

This section addresses the validity and reliability of the study, ensuring that the research findings are robust, credible, and can be generalized to other contexts, particularly within Nigeria’s housing finance and government borrowing landscape. Validity refers to the extent to which the study accurately measures what it intends to measure. In this research, the key concepts—government borrowing, crowding-out effect, and affordable housing finance—are central to the analysis.

Construct Validity: Ensured through the careful selection of relevant variables. Government debt (domestic and external) and housing finance indicators (e.g., mortgage penetration, credit to the housing sector) were chosen based on their established roles in the literature and their relevance to the research objectives ( Adebayo et al., 2022 ). The relationships between these variables are well-documented, and the econometric models used reflect these theoretical frameworks.

Internal Validity: The study uses a multiple regression model, controlling for other variables (e.g., inflation, debt rates, GDP growth), to isolate the impact of government debt on housing finance. This approach strengthens internal validity by ensuring that observed relationships are not confounded by other factors.

External Validity: Refers to the generalizability of the study’s findings beyond the specific sample or context. While this study focuses on Nigeria, the theoretical framework and empirical findings may be relevant to other emerging economies facing similar fiscal challenges and housing finance constraints. However, care is taken to note that the conclusions are primarily applicable to Nigeria’s unique economic and institutional environment.

Reliability concerns the consistency and dependability of the study’s measurements and results. The study relies on local informal housing data and high-quality secondary data from credible institutions (CBN, NBS, World Bank, FMBN, NMRC, and FHFL). By using consistent data sources over the period from 2014 to 2023, the study ensures that the findings are not influenced by data inconsistencies.

The methods and data sources used in this study are transparent and publicly available, which allows other researchers to replicate the analysis. The correlation and regression models, along with the statistical techniques, is clearly outlined, ensuring that the study’s findings can be reproduced with similar data.

To ensure the reliability of the results, robustness checks were performed. These included testing for multicollinearity, autocorrelation, and heteroscedasticity in the applied regression models of the study. The results of these tests affirmed the reliability of the model and its ability to produce valid inferences ( Bello et al., 2021 ).

Sensitivity analysis was conducted to assess how changes in key assumptions (e.g., debt rates, interest rates) might affect the findings. This analysis helped confirm the stability of the results under varying conditions.

While every effort was made to ensure validity and reliability, there are some limitations:

Secondary Data: The study is limited by the availability and accuracy of secondary data. Discrepancies in reporting or missing data may impact the precision of the findings.

External Factors: While the study controls macroeconomic variables, external shocks (such as the COVID-19 pandemic) or policy changes during the study period could introduce unforeseen influences on the housing sector.

Despite these limitations, the study provides a rigorous and reliable analysis of the crowding-out effect of government debt on affordable housing finance in Nigeria ( Nwachukwu et al., 2023 ).

Table 2 below highlight the results of the study variables correlation coefficient measures subsequently.

The correlation matrix shows the relationships between the variables with the following key observations:

NMRC Mortgage Refinance Rate and Market Mortgage Rates have a strong positive correlation (0.973), indicating that they tend to move together. This suggests that changes in the NMRC Refinance Rate are closely associated with changes in the Market Mortgage Rate.

CBN Monetary Policy Rate and NMRC Mortgage Refinance Rate have a strong positive correlation (0.954), suggesting that changes in monetary policy rates are associated with changes in mortgage refinance rates.

NBS Inflation Rate and FGN Bonds Rate (10 Years) have a strong positive correlation (0.925), indicating that inflation rates and bond yields are closely related.

There is a strong positive correlation between the CBN Monetary Policy Rate and the FGN Bonds Rate (0.896). This indicates that changes in the CBN Monetary Policy Rate are closely tied to changes in the FGN Bonds Rate.

There is a moderate positive correlation between the NMRC Refinance Rate and the CBN Monetary Policy Rate (0.859). This suggests that changes in the NMRC Refinance Rate are somewhat associated with changes in the CBN Monetary Policy Rate.

There is a moderate positive correlation between the Market Mortgage Rate and the CBN Monetary Policy Rate (0.895).

There is a moderate negative correlation between the NBS Inflation Rate and the GDP Growth Rate (−0.449). This indicates that higher inflation rates are associated with lower GDP growth rates.

The study also involved various levels of regression analysis aimed at deepening the findings by using the GDP Growth Rate as the dependent variable and the NMRC Refinance Rate, FMBN Subsidized Rate, Market Mortgage Rate, CBN Monetary Policy Rate, NBS Inflation Rate, and FGN Bonds Rate as independent variables ( Climate Finance Lab, 2022 ) and ( Nabieu, Anarfi, & Appiah-Konadu, 2023 ).

The regression equation is stated below:

GDP Growth Rate = β₀ + β₁(NMRC Refinance Rate) + β₂(FMBN Subsidized Rate) + β₃(Market Mortgage Rate) + β₄(CBN Monetary Policy Rate) + β₅(NBS Inflation Rate) + β₆(FGN Bonds Rate) + ε

The regression analysis models the relationships between the independent variables and GDP growth rates with the following key observations (see Table 3 ).

The NBS Inflation Rate has a significant negative impact on the GDP Growth Rate (p-value = 0.009).

The CBN Monetary Policy Rate has a positive impact on the GDP Growth Rate, but it’s not significant at the 5% level (p-value = 0.063).

NMRC Mortgage Refinance Rate: A 1% increase in the mortgage refinance rate is associated with a 0.035% decrease in GDP growth rates. This suggests that higher mortgage refinance rates may have a negative impact on economic growth.

Market Mortgage Rates: A 1% increase in market mortgage rates is associated with a 0.041% decrease in GDP growth rates. This indicates that higher market mortgage rates may also have a negative impact on economic growth.

CBN Monetary Policy Rate: A 1% increase in the monetary policy rate is associated with a 0.051% decrease in GDP growth rates. This suggests that tighter monetary policy may have a negative impact on economic growth.

NBS Inflation Rate: A 1% increase in the inflation rate is associated with a 0.084% decrease in GDP growth rates. This indicates that higher inflation rates may have a negative impact on economic growth.

FGN Bonds Rate (10 Years): A 1% increase in the bond yield is associated with a 0.078% decrease in GDP growth rates. This suggests that higher bond yields may also have a negative impact on economic growth.

The regression analysis was applied to further examine the impact of government borrowing (via function of the FGN Bonds rate), inflation, and CBN monetary policy rates on access to sustainable finance for affordable housing and social infrastructure development with following key findings:

NBS Inflation Rate has a significant negative impact on GDP Growth Rate: This suggests that higher inflation rates lead to lower GDP growth rates. This finding supports the alternative hypothesis (H1).

CBN Monetary Policy Rate has a positive impact on GDP Growth Rate, but it’s not significant at the 5% level: This indicates that changes in the CBN Monetary Policy Rate may have a positive effect on GDP growth rates, but the relationship is not statistically significant.

The results support the alternative hypothesis (H1), which states that there is a significant impact of government borrowing, inflation, and monetary policy rates on access to sustainable finance for affordable housing and social infrastructure development. The interplay of the modelled variables and its linear implications from 2014-2023 are graphically represented in Figure 2 below.

To further analyze the relationship between the variables and access to sustainable credit for home acquisition and social infrastructure development, additional models and techniques were applied. The additional analysis also considered the relationship between the variables and access to sustainable credit for home acquisition and social infrastructure development ( Climate Finance Lab, 2022 ).

Vector Autoregression (VAR) Model: A VAR model can help us understand the relationships between the variables and how they respond to shocks. The VAR model was computed using the following variables: GDP Growth Rates, NMRC Mortgage Refinance Rate, Market Mortgage Rates, CBN Monetary Policy Rate, NBS Inflation Rate, and FGN Bonds Rate (10 Years) ( Mignamissi et al., 2023 ). The results of the VAR model output is detailed in Table 4 below.

The VAR model output shows the relationships between the variables and how they respond to shocks. For example, a shock to the NMRC Mortgage Refinance Rate leads to a decrease in GDP Growth Rates and an increase in Market Mortgage Rates. According to ( Forster & Sun, 2022 ), “the response of housing returns to a mortgage rate shock is larger in magnitude when the federal funds rate is at its zero lower bound”.

Impulse Response Functions: The impulse response function shows how the variables respond to a shock to the NMRC Mortgage Refinance Rate. For example, a shock to the NMRC Mortgage Refinance Rate leads to a decrease in GDP Growth Rates that persists for several periods. ( Chowdhury, 2023 ) found that “macroeconomic variables such as interest rate spreads and housing prices have a different effect compared to the normal situation”.

Forecast Error Variance Decomposition: The forecast error variance decomposition shows that the NMRC Mortgage Refinance Rate and Market Mortgage Rates are important in explaining the forecast errors of GDP Growth Rates. This suggests that changes in mortgage refinance rates and market mortgage rates can have a significant impact on economic growth. ( Yiu, 2023 ) states, “VAR models have become the main econometric tool to test if there may exist a relationship between variables and to assess the effects of policy”. The analysis also has several policy implications such as:

Monetary Policy: The analysis suggests that monetary policy can play a crucial role in influencing access to sustainable credit for home acquisition and social infrastructure development. Policymakers should carefully consider the impact of monetary policy decisions on mortgage refinance rates and market mortgage rates.

Mortgage Refinance Rates: The analysis highlights the importance of mortgage refinance rates in influencing access to sustainable credit. Policymakers should consider implementing policies to reduce mortgage refinance rates, such as providing subsidies or guarantees for mortgage lenders.

Financial Inclusion: The analysis suggests that financial inclusion is critical for access to sustainable credit. Policymakers should consider implementing policies to increase financial inclusion, such as expanding access to banking services or providing financial education programs.

Housing Finance: The analysis highlights the importance of housing finance in influencing access to sustainable credit. Policymakers should consider implementing policies to increase access to housing finance, such as providing housing impact funds or guarantees for housing loans.

The study also estimated regression analysis to examine the impact of government borrowing, inflation, and monetary policy rates on access to sustainable finance for affordable housing and social infrastructure development.

Access to Sustainable Finance for Home Acquisition: To examine the impact of the economic variables (independent variables) on access to sustainable finance for home acquisition, the study also applied a logistic regression model. The outcome of the logistic regression model application for the study is detailed in Table 5 below.

The logistic regression model results suggest that:

A higher NMRC Refinance Rate reduces the likelihood of access to sustainable finance for home acquisition.

A higher FMBN Subsidized Rate increases the likelihood of access to sustainable finance for home acquisition.

A higher CBN Monetary Policy Rate increases the likelihood of access to sustainable finance for home acquisition.

A higher NBS Inflation Rate reduces the likelihood of access to sustainable finance for home acquisition.

A higher GDP Growth Rate increases the likelihood of access to sustainable finance for home acquisition.

Access to Sustainable Finance for Social Infrastructure Development: To examine the impact of the independent variables on access to sustainable finance for social infrastructure development, the study also applied a linear regression model. The results of the Linear Regression Model are stated in Table 6 below.

The additional regression model results yielded the following observations:

1) Government deficits (as function of debt rates to GDP growth) have a negative impact on access to sustainable finance for home acquisition and social infrastructure development.

2) Higher interest rates due to fiscal deficits reduce access to credit for homebuyers and social infrastructure projects.

3) Reduced credit availability due to fiscal deficits reduces access to sustainable finance for home acquisition and social infrastructure development.

Based on the analysis, the following policy recommendations can be made:

A higher NMRC Refinance Rate reduces access to sustainable finance for social infrastructure development.

A lower FMBN Subsidized Rate increases access to sustainable finance for affordable housing and social infrastructure development.

A higher CBN Monetary Policy Rate reduces access to sustainable finance for social infrastructure development.

A higher NBS Inflation Rate reduces access to sustainable finance for social infrastructure development.

A higher GDP Growth Rate increases access to sustainable finance for social infrastructure development.

To account for the volatility of the time series data and possible external shocks, we conducted a sensitivity analysis using the Probit Model to examine the impact of external shocks on access to sustainable credit for home acquisition, social infrastructure development and economic growth. The probit model estimation was evaluated using the following study variables: NMRC Refinance Rate, FMBN Subsidized Rate, Market Mortgage Rate, CBN Monetary Policy Rate, NBS Inflation Rate, FGN Bonds Rate and GDP Growth Rate. The external shocks were then simulated using the following scenarios:

Base Case Scenario: In this instance all variables treated based on the current live measures as at the time of the study.

Pandemic Scenario: A 10% decrease in GDP growth rate and a 5% increase in inflation rate.

Economic Recession Scenario: A 5% decrease in GDP growth rate and a 10% increase in unemployment rate.

The probit model sensitivity analysis results are presented in Tables 7-9 below:

The base case scenario assumes no external shocks. The results show that:

The pandemic scenario assumes a 10% decrease in GDP growth rate and a 5% increase in inflation rate. The results show that:

Economic Recession Scenario: The economic recession scenario assumes a 5% decrease in GDP growth rate and a 10% increase in unemployment rate. The results show that:

The probit model sensitivity analysis results show how changes in the independent variables affect the probability of access to sustainable credit for home acquisition and social infrastructure development under different scenarios. The sensitivity analysis results show that external shocks such as pandemics and economic recessions can significantly impact access to sustainable credit, social infrastructure development, and economic growth. The results highlight the importance of policymakers and stakeholders considering these external shocks when making decisions about sustainable credit, social infrastructure development, and economic growth.

Incorporating exogenous variables or external factors like global economic trends, rule of law, political stability, and credit default rates (level of non-performing loans) could provide a more nuanced understanding of the relationships between the variables. To further deepen the analysis, future research could examine the relationships between levels of government fiscal deficits, access to sustainable finance for home acquisition, and the impact of remittances on housing and social infrastructure development ( Owotemu et al., 2024 ). These variables are further explained below for future consideration:

Government Fiscal Deficits: Government fiscal deficits occur when a government spends more than it receives in revenue. This can lead to increased borrowing, higher interest rates, and reduced access to credit for the private sector.

Access to Sustainable Finance for Home Acquisition: Access to sustainable finance for home acquisition is crucial for promoting affordable housing and economic growth. However, government fiscal deficits can impact this access in several ways:

Increased borrowing costs: Higher interest rates due to fiscal deficits can make borrowing more expensive, reducing access to credit for homebuyers.

Reduced credit availability: Fiscal deficits can lead to reduced credit availability, making it harder for homebuyers to secure mortgages.

Increased risk: Fiscal deficits can increase the risk of lending, leading to stricter lending standards and reduced access to credit.

Social Infrastructure Development: Social infrastructure development, such as schools, hospitals, and transportation systems, is critical for promoting economic growth and social welfare. However, government fiscal deficits can impact this development in several ways:

Reduced funding: Fiscal deficits can lead to reduced funding for social infrastructure projects, delaying or canceling essential development.

Increased costs: Higher interest rates due to fiscal deficits can increase the costs of borrowing for social infrastructure projects.

Reduced private sector investment: Fiscal deficits can reduce private sector investment in social infrastructure, as investors seek more stable and secure opportunities.