A Comparative Assessment of the Impact of Neighbourhood Facilities on Residential Property Rental Value in High Densities Areas of Lagos, Nigeria

A Comparative Assessment of the Impact of Neighbourhood Facilities on Residential Property Rental Value in High Densities Areas of Lagos, Nigeria

*Oluwole Adeniyi Akinwale, and Igho Fayomi

Department of Estate Management, Lead City University, Ibadan, Nigeria

*Corresponding Author

DOI: https://doi.org/10.51244/IJRSI.2025.1215000156P

Received: 28 August 2025; Accepted: 03 September 2025; Published: 18 October 2025

ABSTRACT

This study comparatively assessed the impact of neighbourhood facilities on residential rental values in two high-density areas of Lagos, Nigeria: Egbeda/Ipaja on the Mainland and Igbosere on the Island. Using a quantitative research design, data were collected from 211 estate surveying and valuation firms identified through the Nigerian Institution of Estate Surveyors and Valuers (NIESV) register, of which 173 valid responses were retrieved. Both descriptive and inferential statistical tools were employed, with regression analysis used to examine the influence of neighbourhood facilities on the rental values of one- and two-bedroom flats. Findings reveal that in Egbeda/Ipaja, educational facilities exerted a significant positive effect on rental values (B = ₦121,356.90; p = 0.025), while healthcare and emergency services (B = -₦333,150.76; p = 0.021) and shopping malls/plazas (B = -₦184,886.50; p = 0.009) had significant negative effects. In Igbosere, drainage systems showed a significant positive influence (B = ₦291,012.94; p = 0.002), while shopping malls/plazas (B = -₦427,090.52; p = 0.000), clinics and health centres (B = -₦318,125.72; p = 0.001), and transportation and parking lots (B = -₦393,721.75; p = 0.040) had significant negative effects on rental values. The results further indicated that tenants in both locations placed premiums on resilient infrastructure and educational access, while facilities associated with congestion, traffic, and noise tended to depress values. The study concludes that improving the quality and strategic placement of neighbourhood facilities is critical to enhancing rental housing markets in Lagos. It recommends prioritizing resilient infrastructure, managing externalities of commercial and healthcare facilities, and integrating neighbourhood assessments into professional valuation practice.

Keywords: Neighbourhood Facilities, Rental Value, Residential Property, Lagos, Assessment

INTRODUCTION AND LITERATURE REVIEW

Globally, housing values are influenced by a combination of structural, locational, and neighbourhood factors (Doe et al., 2018). Empirical studies have shown that amenities such as schools, parks, green infrastructure, and reliable public services often enhance property values, while negative externalities such as traffic congestion or noise may reduce them (Browning et al., 2023; Grunewald et al., 2024). In Europe and North America, for example, access to quality educational institutions consistently commands rental premiums (Gibbons & Machin, 2008). Similarly, urban greening initiatives, such as schoolyard greening, have been associated with measurable increases in property values, demonstrating how environmental quality contributes to residential satisfaction (Gorjian, 2025).

In the Nigerian context, however, the effects of neighbourhood facilities on rental values are less straightforward. Infrastructure deficits remain widespread, and these have been repeatedly identified as key challenges to sustainable housing delivery in Lagos. Ilesanmi (2022) argues that the inadequacy of facilities such as roads, drainage systems, and waste management hampers sustainable urban growth, while Obafemi et al. (2023) highlight widespread dissatisfaction among residents with the quality of infrastructural services in urban estates. These findings suggest that tenants often attach significant importance to the presence or absence of basic services, which in turn influences willingness to pay rent.

Some neighbourhood facilities, however, have shown paradoxical effects. Shopping malls and plazas, while designed to improve access to goods and services, may depress rental desirability in congested urban areas due to traffic, noise, and other externalities. Studies of shopping centres in Nigeria reveal that their value impacts depend heavily on location, tenant mix, and patterns of patronage (Iroham et al., 2020; Bello & Ezeokoli, 2015). Similarly, healthcare facilities such as clinics and diagnostic laboratories, though essential, have sometimes been associated with reduced rental values when located in close proximity to residences. Jim and Chen (2007) argue that not all amenities are universally perceived as positive, since the disamenities of crowding, traffic, and environmental hazards may outweigh their benefits in certain contexts.

By contrast, certain infrastructural facilities consistently enhance rental values. Proper drainage systems, durable motorable roads, and reliable water supply have been shown to improve household satisfaction and reduce vulnerability to environmental risks such as flooding (Douglas et al., 2008). In Lagos, where flooding remains a recurring challenge, drainage and sewage systems are particularly important. Similarly, steady power supply, good plumbing, and effective street lighting contribute to perceived comfort and security, thereby making properties more attractive to tenants (Obafemi et al., 2023). Emerging factors such as internet connectivity, communication systems, and smart home technology also increasingly influence residential preferences, reflecting the growing digitalization of everyday life. Lagos State’s infrastructure regulatory efforts, such as those overseen by the Lagos State Infrastructure Maintenance and Regulatory Agency (LASIMRA), further underscore the importance of communication and utility services in shaping the housing market (LASIMRA, 2004–present).

Security-related facilities also play a critical role. Gated communities, access control systems, and the presence of security personnel have been linked to higher rental demand, especially in high-density and high-income urban areas where safety concerns are paramount. Religious and cultural centres, leisure and recreational facilities, and fitness and well-being infrastructures similarly contribute to neighbourhood desirability by supporting social cohesion and lifestyle convenience. However, the value of these facilities often varies by location, socio-economic composition of tenants, and the balance between convenience and congestion.

Despite these insights, there remains a gap in understanding how these diverse facilities influence rental values across different high-density contexts in Lagos. While international literature confirms that neighbourhood amenities are central to value determination (Goodman & Thibodeau, 2003; Malpezzi, 2002), the Nigerian housing market reflects unique socio-economic and infrastructural dynamics. For instance, the Mainland’s Egbeda/Ipaja is shaped by affordability pressures and migrant inflows, while the Island’s Igbosere combines commercial vitality with infrastructural congestion. These contextual differences may shape whether a facility exerts a positive or negative effect on rental values.

This study therefore undertakes a comparative assessment of the impact of neighbourhood facilities on residential rental values in Lagos’s high-density areas, focusing specifically on Egbeda/Ipaja and Igbosere. By examining a broad set of variables—including clinics and health centres, roads, drainage, shopping plazas, schools, water supply, sewage systems, power, street lighting, and emerging features like smart technologies—the study provides fresh empirical evidence on how tenants perceive and value their neighbourhood environments. The findings are expected to contribute not only to property valuation practice in Nigeria but also to policy efforts aimed at fostering sustainable and equitable housing markets in rapidly urbanizing African cities.

METHODOLOGY

The study was carried out in two high-density areas of Lagos State, namely Igbosere on Lagos Island and Egbeda/Ipaja on the Lagos Mainland. A quantitative research design was employed in order to systematically examine the relationship between neighbourhood facilities and residential rental values. The target population comprised estate surveying and valuation firms practicing within the study areas, and the official register of the Nigerian Institution of Estate Surveyors and Valuers (NIESV) was used to identify a total of 211 firms. Given the peculiarity of the research focus, a purposive sampling technique was adopted to ensure that only relevant firms with the requisite experience and operational presence in the study locations were included. Structured questionnaires were administered to the identified firms, and 173 were successfully retrieved, representing a high response rate that was considered adequate for the study. Data obtained from the survey were analysed using both descriptive and inferential statistical techniques. Descriptive statistics such as frequency distributions and percentages were used to summarize the demographic characteristics of the respondents and the profiles of their firms. Inferential tools, particularly multiple regression analysis, were employed to examine the impact of neighbourhood facilities on residential property rental values. The results were presented in tables and graphical illustrations to enhance clarity and interpretation.

Data Presentation and Analysis

To examine the quality of neighbourhoods’ facilities, certain variables were considered as yardstick for the high densities’ neighbourhoods considered for the study. Table 1 presents the variables for the quality of neighbourhoods’ facilities. The variables listed here were considered adequate by the Estate Surveyors and Valuers who were respondents for this study which are integral determinants of residential satisfaction and consequently influence residential property rental values.

Table 1: Quality of Neighbourhood Facilities

Variables for Assessing the Quality of Neighbourhood Facilities

Clinics and health centres

Communication and information system

Durable and motor able road.

Smart home and technology features

Internet and telecommunication networks

Environmental and sustainable features

Sewage and waste management

Shopping malls/plazas

Access to clean water

Water supply and good plumbing system

Administrative and Government Services

Educational Facilities

Power supply and backup systems

Religious and cultural centres

Leisure and social facilities

Gated community and access control

Presence of security personnel

Transportation and parking lots

Diagnostic laboratories

Adequate ventilation

Healthcare and emergency services

Proper landscaping of areas.

Street lighting

Fire and safety measures

Fitness and well-being facilities

Proper drainage system

Source: Author’s Fieldwork, 2025

Table 2:  Profile of Respondent (Estate Surveying and Valuation Firms)

Characteristics	Classification	Frequency	Percentage (%)
Gender	Male	134	77.5
	Female	39	22.5
	Total	173	100.0
Academic Qualification	HND	9	5.2
	PGD	32	18.5
	BSc/B.Tech	56	32.4
	MSc/M.Tech	70	40.5
	PhD	6	3.5
	Total	173	100.0
Years of experience	1 – 5 years	44	25.4
	6 – 10 years	45	26.0
	11 – 15 years	26	15.0
	16 – 20 years	20	11.6
	Above 20 years	38	22.0
	Total	173	100.0
Marital Status	Single	16	9.2
	Engaged	58	33.5
	Married	73	42.2
	Divorced	21	12.1
	Engaged	5	2.9
	Total	173	100.0
Professional Status	Probationer	66	38.2
	Graduate	43	24.9
	Associate	51	29.5
	Fellow	13	7.5
	Total	173	100.0
Area of Specialization	Property Management	36	20.8
	Real Estate Agency	45	26.0
	Asset Valuation	28	16.2
	Project Development	26	15.0
	Facility Management	30	17.3
	Project Management	8	4.6
	Total	173	100.0
Staff Strength	1 – 5	40	23.1
	6 – 10	29	16.8
	11 – 15	58	33.5
	16 – 20	42	24.3
	Above 20	4	2.3
	Total	173	100.0

Source: Author’s Fieldwork, 2025

The demographic profile of Estate Surveying and Valuation Firms presented in Table 2 above highlights critical insights into the estate surveying and valuation sector in Nigeria. Gender distribution reveals a significant imbalance, with 77.5% male and only 22.5% female, underscoring persistent gender inclusivity challenges. In terms of academic qualifications, the sector demonstrates high intellectual capacity: 40.5% hold Master’s degrees, 32.4% Bachelor’s degrees, 18.5% Postgraduate Diplomas, 5.2% Higher National Diplomas, and 3.5% Doctorates. This reflects strong academic advancement but limited doctoral-level research contributions. Professional experience is evenly distributed, with 51.4% having 1–10 years of experience, and 22.0% possessing over 20 years, suggesting a healthy mix of early-career innovation and seasoned expertise for mentorship and succession planning. Marital status data show 42.2% married, 33.5% engaged, 9.2% single, and 12.1% divorced, indicating stability that may influence career progression, though a possible duplication in the “engaged” category is noted. Regarding professional status, 38.2% are probationers, 29.5% associates, 24.9% graduates, and only 7.5% fellows. This points to a transitional profession with many still advancing, and a bottleneck in progression to the fellowship level. Specialization is concentrated in service-oriented fields: real estate agency (26.0%) and property management (20.8%), followed by facility management (17.3%), asset valuation (16.2%), project development (15.0%), and project management (4.6%). This indicates market-driven focus but limited diversification into strategic developmental roles.Finally, staff strength shows most firms are SMEs: 33.5% employ 11–15 staff, 24.3% have 16–20, 23.1% have 1–5, while only 2.3% exceed 20 staff. This fragmented SME dominance reflects flexibility but also limitations in capital, technology, and human resources.

Trend in Rental Values of Residential Properties

The study assessed trends in residential property values in Lagos, specifically in high density area (Egbeda/Ipaja), and in the Island (Igbosere). Estate Surveying and Valuation firms provided historical rental value data spanning from 2014 to 2024. A trend analysis method was employed to systematically evaluate the direction and consistency of value fluctuations over the ten-year period.

Table 3: Trend in Rental Values of Residential Properties in Lagos Mainland

High Density Area (Egbeda/Ipaja)
Year	One bedroom (N)	Two bedroom (N)
2014	150,000	300,000
2015	150,000	300,000
2016	180,000	350,000
2017	180,000	350,000
2018	200,000	450,000
2019	200,000	450,000
2020	250,000	600,000
2021	250,000	600,000
2022	250,000	700,000
2023	400,000	800,000
2024	450,000	900,000

Source: Author’s Fieldwork, 2025

Table 3 above presents a longitudinal analysis of average rental value indices for one-bedroom and two-bedroom apartments across Lagos Mainland specifically Egbeda/Ipaja (high density), covering the period from 2014 to 2024. From the table, it can be deduced that rental values demonstrated a steady yet moderate upward trend over the ten-year period. One-bedroom apartment rents increased from ₦150,000 in 2014 to ₦450,000 in 2024, indicating a 200% growth. Similarly, two-bedroom units rose from ₦300,000 to ₦900,000, also representing a 200% increase. This growth, as it can be seen; reflects a consistent demand for affordable housing options within congested urban environments, largely driven by lower-income populations and internal urban migration.

Figure 1:  Line Graph showing Trend of Rental Value in Egbeda/Ipaja

Source: Author’s Fieldwork, 2025

Table 4: Trend in Rental Values of Residential Properties in Lagos Island

High Density Area (Igbosere)
Year	One bedroom (N)	Two bedroom (N)
2014	400,000	550,000
2015	400,000	550,000
2016	550,000	700,000
2017	600,000	700,000
2018	650,000	850,000
2019	700,000	850,000
2020	700,000	900,000
2021	850,000	1,000,000
2022	850,000	1,000,000
2023	1,000,000	1,200,000
2024	1,200,000	1,500,000

Source: Author’s Fieldwork, 2025.

Table 4 above also reveals the rising cost of renting in Igbosere, just as it is similar in other high-density locations (Egbeda/Ipaja). In the high-density area of Igbosere for example, one-bedroom apartments increased in rent from ₦400,000 in 2014 to ₦1,200,000 in 2024, representing a 200% increase, while two-bedroom apartments appreciated from ₦550,000 to ₦1,500,000, also reflecting a 172.7% rise over the period. The rental growth in Igbosere, though moderate in comparison to other zones, mirrors the rising demand for affordable accommodation within the commercial core of Lagos Island. Igbosere’s relatively older building stock and infrastructural congestion have historically limited the scope of large-scale real estate investments; however, the consistent growth in rental values suggests a response to increased urban densification and population pressure.

Figure 2: Line Graph showing Trend of rental Value in Igbosere

Source: Author’s Fieldwork, 2025

Impact of Quality of Neighbourhood Facilities on the Rental Values of Residential Property

In this section, the impact of quality of neighbourhood facilities on the rental values of residential property was analysed using a multiple regression model. The dependent variables employed for the purpose of this study are the rental values of one bedroom and two-bedroom flats, respectively. The independent variables represent various dimensions of quality of neighbourhood facilities that may influence residential property values. This analysis is structured to assess the extent to which the rent payable by property occupiers aligns with the presence and integration of quality of neighbourhood facilities within the apartments in the different density areas.

Table 5: Model Summary: Impact of Quality of Neighbourhood Facilities on the Rental Values of One bedroom flat in Egbeda/Ipaja(Mainland)

Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.471a	.222	.060	89433.88122

Source: Author’s Fieldwork, 2025

The model summary presented in Table 5 above provides statistical evidence on the relationship between the quality of neighbourhood facilities and the rental values of one-bedroom residential properties in Egbeda/Ipaja, a high-density area within Lagos Mainland. The regression output indicates a correlation coefficient (R) of 0.471, which suggests a moderate positive relationship between neighbourhood facility quality and rental values. This implies that as the quality of neighbourhood facilities improves, the rental value of one-bedroom flats tends to increase correspondingly. The coefficient of determination (R Square) is 0.222, indicating that approximately 22.2% of the variability in rental values can be explained by variations in the quality of neighbourhood facilities. While this percentage is relatively modest, it still reflects a meaningful level of explanatory power, particularly in urban housing studies where rental values are influenced by multiple interconnected factors such as location, building condition, market demand, and macroeconomic indicators¹⁵. However, the adjusted R Square, which accounts for the number of predictors and sample size, is notably lower at 0.060. This adjusted value suggests that when the model complexity is controlled for, only 6% of the variation in rental values is effectively explained by the independent variable quality of neighbourhood facilities. This may indicate the presence of other unaccounted explanatory variables influencing rental values more strongly than neighbourhood facilities alone, such as proximity to employment centres, transportation infrastructure, or landlord pricing behaviour (Tong et.al, 2023). The standard error of the estimate is 89,433.88, which reflects the average distance between the observed rental values and those predicted by the model. A relatively high standard error, as seen here, points to substantial variation in rental prices across properties, possibly due to heterogeneity in building age, amenities, or lease agreements that are not captured by the model.

Table 6: ANOVA^a : Impact of Quality of Neighbourhood Facilities on the Rental Values of  One bedroom flat in Egbeda/Ipaja(Mainland)

Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	241415636361.808	22	10973438016.446	1.372	.146b
	Residual	847832425653.697	106	7998419109.941
	Total	1089248062015.505	128

Source: Author’s Fieldwork, 2025.

The ANOVA summary presented in Table 6 evaluates the overall statistical significance of the regression model used to assess the impact of the quality of neighbourhood facilities on the rental values of one-bedroom flats in Egbeda/Ipaja (Mainland Lagos). The total sum of squares (Total SS) is ₦1,089,248,062,015.505, representing the total variability in rental values observed in the data. This is partitioned into the regression sum of squares (Regression SS) of ₦241,415,636,361.808 indicating the portion of the variation explained by the model and the residual sum of squares (Residual SS) of ₦847,832,425,653.697, which corresponds to the unexplained portion, or the error component. The degrees of freedom (df) associated with the regression model is 22, suggesting that 22 predictor variables (likely neighbourhood facility indicators) were included in the analysis. The mean square for regression is ₦10,973,438,016.446, while the mean square for the residuals is ₦7,998,419,109.941. The F-statistic, which tests the joint significance of all the explanatory variables, is 1.372. The corresponding p-value (Sig.) is 0.146, which exceeds the conventional 0.05 threshold for statistical significance. Therefore, the model does not provide sufficient evidence to conclude that the quality of neighbourhood facilities, in aggregate, has a statistically significant impact on the rental values of one-bedroom flats in Egbeda/Ipaja at the 95% confidence level. This outcome aligns with the earlier model summary (Table 4), where the adjusted R square was only 0.060, indicating that the explanatory power of the model was weak when accounting for the number of predictors. The non-significant p-value here further supports the notion that while neighbourhood facilities may contribute marginally to explaining variations in rent, their overall influence in this high-density submarket may be diluted by other stronger determinants such as structural characteristics of the property, rental market dynamics, economic conditions, and tenant preferences.

Table 7: Coefficients^a: Impact of Quality of Neighbourhood Facilities on the Rental Values of One bedroom flat in Egbeda/Ipaja(Mainland)

Model		Unstandardized Coefficients		Standardized Coefficients	T	Sig.
		B	Std. Error	Beta
1	(Constant)	121684.012	256249.251		.475	.636
	Administrative and Government Services	-2117.116	23731.286	-.008	-.089	.929
	Environmental and sustainable features	-6097.532	68606.092	-.008	-.089	.929
	Smart home and technology features	5755.616	66471.042	.008	.087	.931
	Communication and information system	21106.542	92665.361	.020	.228	.820
	Healthcare and emergency services	-123893.458	67732.264	-.167	-1.829	.070*
	Transportation and parking lots	-15132.699	64991.766	-.020	-.233	.816
	Fitness and well-being facilities	-33313.511	50588.783	-.076	-.659	.512
	Fire and safety measures	48394.522	48264.081	.091	1.003	.318
	Gated community and access control	-4416.836	54941.788	-.007	-.080	.936
	Sewage  and waste management	47640.429	46872.960	.090	1.016	.312
	Water supply and good plumbing system	87076.180	62170.932	.164	1.401	.164
	Power supply and backup systems	58808.743	64755.999	.079	.908	.366
	Street lighting	49702.156	65041.137	.067	.764	.446
	Internet and telecommunication networks	490.806	48596.220	.001	.010	.992
	Adequate ventilation	29669.710	24384.541	.112	1.217	.226
	Proper landscaping of areas.	29376.164	34899.507	.077	.842	.402
	Durable and motor able road.	14408.149	25544.360	.053	.564	.574
	Proper drainage system	38238.909	31453.693	.116	1.216	.227
	Presence of security personnel	-25987.323	32353.655	-.076	-.803	.424
	Shopping malls/plazas	-91744.594	33210.863	-.267	-2.762	.007***
	Educational Facilities	47537.056	25423.407	.198	1.870	.064*
	Clinics and health centers	-48542.329	31706.194	-.148	-1.531	.129

Source: Author’s Fieldwork, 2025.

The regression coefficients in Table 7 provide detailed insights into how various dimensions of neighbourhood facility quality influence the rental values of one-bedroom flats in Egbeda/Ipaja, a high-density area on the Lagos Mainland. Each facility type is assessed based on its contribution (positive or negative) to rent, controlling for other variables in the model. The constant value is ₦121,684.012, indicating the expected base rental value when all independent variables are set to zero. However, this intercept is not statistically significant (p = 0.636), meaning it has no practical interpretive relevance without considering the contribution of explanatory variables. Among the variables assessed, shopping malls/plazas stand out as the only statistically significant predictor (p = 0.007). Interestingly, the coefficient for this variable is -₦91,744.594, suggesting a negative relationship with rental values. This inverse association is counterintuitive and may reflect contextual issues such as congestion, noise, or security concerns commonly associated with commercial facilities in dense residential zones, which can deter certain tenant segments or reduce willingness to pay higher rents). Other variables such as healthcare and emergency services (B = -₦123,893.458, p = 0.070), educational facilities (B = ₦47,537.056, p = 0.064), and clinics and health centers (B = -₦48,542.329, p = 0.129) approach statistical significance but do not cross the conventional 0.05 threshold. These variables may exert practical importance, and their near-significance suggests that further research with a larger sample size could clarify their actual impact.

Most other coefficients, including those for administrative and government services, environmental features, smart technology, communication systems, transportation, and security personnel, show either weak or no statistical significance (p-values ranging from 0.226 to 0.992). Their corresponding beta values are also relatively small, indicating limited standardized effect sizes. For example, communication and information systems (B = ₦21,106.542) and fire and safety measures (B = ₦48,394.522) have positive but insignificant effects (p = 0.820 and 0.318, respectively), suggesting that while these may improve housing quality, tenants do not necessarily translate their presence into higher rent willingness. Some counterintuitive findings emerge: for example, presence of security personnel (B = -₦25,987.323, p = 0.424) and ventilation (B = ₦29,669.710, p = 0.226) do not significantly predict rental variation. These patterns may result from the homogeneity of facilities across the study area or the saturation of certain services, where their presence is expected as a baseline rather than as added value. In terms of standardized coefficients (Beta), the variable with the largest negative effect is shopping malls/plazas (Beta = -0.267), while educational facilities shows the highest positive standardized effect (Beta = 0.198), further reinforcing their comparative influence in the model.

Table 8: Model Summary: Impact of Quality of Neighborhood Facilities on the Rental Values of Two bedroom flat in Egbeda/Ipaja(Mainland)

Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.497a	.247	.091	187203.73155

Source: Author’s Fieldwork, 2025.

The model summary in Table 8 presents the results of a linear regression analysis conducted to assess the impact of the quality of neighbourhood facilities on the rental values of two-bedroom flats in Egbeda/Ipaja, a high-density locality on the Lagos Mainland. The correlation coefficient (R) is 0.497, indicating a moderate positive relationship between the predictor variable (quality of neighbourhood facilities) and the dependent variable (rental values). This suggests that as the quality of neighbourhood amenities increases, rental prices for two-bedroom apartments also tend to rise, albeit moderately. This relationship aligns with urban housing theories that associate improved infrastructure and services with higher rental value propositions (Tong et.al., 2023). The coefficient of determination (R Square) is 0.247, meaning that approximately 24.7% of the variation in rental values for two-bedroom flats in Egbeda/Ipaja can be explained by the quality of neighbourhood facilities. While this figure indicates a modest explanatory power, it is slightly higher than that of one-bedroom flats in the same area (R² = 0.222 from Table 5), implying that tenants of larger housing units may be more responsive to neighbourhood quality factors. The standard error of the estimate is ₦187,203.73, indicating the average deviation of the observed rental values from those predicted by the model. This relatively high error term further suggests substantial variability in rent prices that is not accounted for by neighbourhood facilities alone. However, the adjusted R Square, which accounts for the number of predictors and corrects for model complexity, drops significantly to 0.091. This adjusted value implies that when the effects of model overfitting are controlled, only 9.1% of the variance in rental values is effectively explained by the model. Such a reduction indicates the likely presence of other influential factors outside the model, such as property-specific attributes (e.g., interior condition, security systems, finishing), macroeconomic influences (e.g., inflation, interest rates), and market dynamics.

Table 9: ANOVA^a: Impact of Quality of Neighborhood Facilities on the Rental Values of Two bedroom flat in Egbeda/Ipaja(Mainland)

Model		Sum of Squares	Df	Mean Square	F	Sig.
1	Regression	1218693238882.231	22	55395147221.920	1.581	.065b
	Residual	3714795133210.800	106	35045237105.762
	Total	4933488372093.031	128

Source: Author’s Fieldwork, 2025.

The ANOVA summary in Table 9 assesses the overall statistical significance of the regression model that examines the impact of neighbourhood facility quality on the rental values of two-bedroom flats in Egbeda/Ipaja, a high-density residential area in Lagos Mainland. The total sum of squares is ₦4,933,488,372,093.031, representing the total variation in the rental values of the sampled two-bedroom flats. This total variation is partitioned into two components: the regression sum of squares (₦1,218,693,238,882.231), which accounts for the portion of variation explained by the model, and the residual sum of squares (₦3,714,795,133,210.800), which captures the unexplained portion or error. The model includes 22 predictors, most likely various indicators of neighbourhood facilities. The mean square for the regression is ₦55,395,147,221.920, while the mean square for the residual is ₦35,045,237,105.762. This yields an F-statistic of 1.581, with an associated p-value of 0.065. While the F-value is relatively low, it is notable that the significance level (p = 0.065) is marginally above the conventional 0.05 threshold. This implies that, at the 95% confidence level, the regression model is not statistically significant, though it approaches borderline significance. In practical terms, this suggests that the combined effect of neighbourhood facility quality on two-bedroom flat rents is not strong enough to be deemed significant in this sample, but it is suggestive of a possible relationship that might become significant with a larger dataset or refined model specification. The relatively large residual sum of squares also indicates that a substantial proportion of the variance in rent remains unexplained by the model. This supports earlier findings in the model summary (Table 8), where the adjusted R square was only 0.091. Together, these results indicate that while neighbourhood facilities may influence rental values to some extent, other unmeasured factors such as internal housing conditions, tenant income levels, and local economic trends may play a more prominent role.

Table 10: Coefficients^a: Impact of Quality of Neighborhood Facilities on the Rental Values of Two bedroom flat in Egbeda/Ipaja(Mainland)

Model		Unstandardized Coefficients		Standardized Coefficients	T	Sig.
		B	Std. Error	Beta
1	(Constant)	515314.471	536383.027		.961	.339
	Administrative and Government Services	5327.715	49674.522	.010	.107	.915
	Environmental and sustainable features	-27306.420	143606.833	-.017	-.190	.850
	Smart home and technology features	-29490.248	139137.728	-.019	-.212	.833
	Communication and information system	66849.239	193967.892	.030	.345	.731
	Healthcare and emergency services	-333150.761	141777.729	-.210	-2.350	.021**
	Transportation and parking lots	-140149.069	136041.295	-.089	-1.030	.305
	Fitness and well-being facilities	-44974.165	105892.854	-.048	-.425	.672
	Fire and safety measures	105705.431	101026.769	.094	1.046	.298
	Gated community and access control	-8344.591	115004.600	-.006	-.073	.942
	Sewage  and waste management	62712.693	98114.863	.056	.639	.524
	Water supply and good plumbing system	198004.005	130136.704	.176	1.522	.131
	Power supply and backup systems	137514.789	135547.787	.087	1.015	.313
	Street lighting	85542.281	136144.641	.054	.628	.531
	Internet and telecommunication networks	-40311.536	101722.004	-.036	-.396	.693
	Adequate ventilation	51382.701	51041.920	.091	1.007	.316
	Proper landscaping of areas.	51208.008	73051.933	.063	.701	.485
	Durable and motor able road.	-11688.682	53469.664	-.020	-.219	.827
	Proper drainage system	114035.875	65839.127	.163	1.732	.086*
	Presence of security personnel	-48026.216	67722.935	-.066	-.709	.480
	Shopping malls/plazas	-184886.501	69517.250	-.253	-2.660	.009***
	Educational Facilities	121356.900	53216.483	.238	2.280	.025**
	Clinics and health centers	-108419.782	66367.665	-.155	-1.634	.105

Source: Author’s Fieldwork, 2025

The regression analysis (Table 10) examines how neighbourhood facility quality influences rental values of two-bedroom flats in Egbeda/Ipaja. The constant value of ₦515,314.47 is not statistically significant (p = 0.339), limiting its interpretive importance. Three facilities significantly affect rental values at the 5% level: healthcare and emergency services (B = -₦333,150.76, p = 0.021), shopping malls/plazas (B = -₦184,886.50, p = 0.009), and educational facilities (B = ₦121,356.90, p = 0.025). Healthcare and shopping facilities exert negative effects, likely due to congestion, traffic, and security concerns, while educational facilities have a positive effect, reflecting renters’ preference for proximity to schools. Other variables show positive but non-significant contributions, such as proper drainage (B = ₦114,035.88, p = 0.086) and water/plumbing systems (B = ₦198,004.01, p = 0.131), suggesting that utilities matter but less consistently. Facilities like gated communities (B = -₦8,344.59, p = 0.942), durable roads (B = -₦11,688.68, p = 0.827), and internet/telecommunication networks (B = -₦40,311.54, p = 0.693) show weak or no explanatory power. Standardized coefficients highlight shopping malls/plazas (-0.253) as the strongest negative predictor and educational facilities (0.238) as the strongest positive predictor of rents. Overall, the model reveals that while access to schools enhances rental values, proximity to busy commercial or healthcare facilities tends to reduce them, mirroring trends found in the one-bedroom flat model.

Table 11: ANOVA^a : Impact of Quality of Neighborhood Facilities on the Rental Values of One bedroom flat in Igbosere(Island)

Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	4318543392819.782	22	196297426946.354	2.878	.000b
	Residual	7228853506405.026	106	68196731192.500
	Total	11547396899224.809	128

Source: Author’s Fieldwork, 2025

On the other hand, Table 10 presents the ANOVA results on the impact of quality of neighborhood facilities on the rental values of one-bedroom flats in Igbosere, located on Lagos Island. The model shows that the regression sum of squares is ₦4,318,543,392,819.78, distributed across 22 degrees of freedom, resulting in a mean square value of ₦196,297,426,946.35. The residual sum of squares is ₦7,228,853,506,405.03 with 106 degrees of freedom, yielding a mean square of ₦68,196,731,192.50. The total sum of squares is ₦11,547,396,899,224.81 across 128 degrees of freedom. The F-statistic is 2.878, with a corresponding significance value (p-value) of 0.000, which is statistically significant at the 1% level.

Table 12: Coefficients^a : Impact of Quality of Neighborhood Facilities on the Rental Values of One bedroom flat in Igbosere(Island)

Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
		B	Std. Error	Beta
1	(Constant)	1240875.650	748242.383		1.658	.100
	Administrative and Government Services	11705.012	69294.852	.014	.169	.866
	Environmental and sustainable features	-316351.514	200328.335	-.131	-1.579	.117
	Smart home and technology features	-307927.378	194094.034	-.127	-1.586	.116
	Communication and information system	150825.432	270580.891	.044	.557	.578
	Healthcare and emergency services	5825.432	197776.776	.002	.029	.977
	Transportation and parking lots	-393721.750	189774.579	-.163	-2.075	.040**
	Fitness and well-being facilities	-87333.276	147718.174	-.061	-.591	.556
	Fire and safety measures	-128149.019	140930.094	-.074	-.909	.365
	Gated community and access control	113987.111	160428.857	.057	.711	.479
	Sewage  and waste management	-31264.044	136868.050	-.018	-.228	.820
	Water supply and good plumbing system	290865.819	181537.805	.169	1.602	.112
	Power supply and backup systems	162130.755	189086.145	.067	.857	.393
	Street lighting	238480.244	189918.743	.098	1.256	.212
	Internet and telecommunication networks	21702.262	141899.932	.013	.153	.879
	Adequate ventilation	-35596.011	71202.342	-.041	-.500	.618
	Proper landscaping of areas.	52232.939	101905.820	.042	.513	.609
	Durable and motor able road.	-55646.108	74588.991	-.063	-.746	.457
	Proper drainage system	291012.938	91844.116	.272	3.169	.002***
	Presence of security personnel	-147612.824	94471.988	-.132	-1.563	.121
	Shopping malls/plazas	-427090.516	96975.017	-.382	-4.404	.000***
	Educational Facilities	113507.318	74235.809	.145	1.529	.129
	Clinics and health centers	-318125.721	92581.416	-.297	-3.436	.001***

Source: Author’s Fieldwork, 2025.

The regression analysis (Table 12) evaluates the influence of neighborhood facilities on rental values of one-bedroom flats in Igbosere, Lagos Island. The constant term (₦1,240,875.65) is not statistically significant (p = 0.100), limiting its interpretive role. Among the independent variables, three facilities show significant effects on rental values. Shopping malls/plazas exert the strongest negative impact (-₦427,090.52; p = 0.000), followed by clinics and health centers (-₦318,125.72; p = 0.001), suggesting that congestion, traffic, and environmental disturbances associated with these facilities reduce rental desirability in the area. Transportation and parking lots also have a significant negative effect (-₦393,721.75; p = 0.040), reflecting how increased vehicular activity may undermine residential appeal in this upscale neighborhood. In contrast, proper drainage systems have a positive and significant effect (₦291,012.94; p = 0.002), underscoring the value tenants place on resilient infrastructure in flood-prone zones. Other facilities show positive but statistically non-significant associations with rental values, including water supply and good plumbing systems (₦290,865.82; p = 0.112), street lighting (₦238,480.24; p = 0.212), and educational facilities (₦113,507.32; p = 0.129). Meanwhile, environmental and sustainable features (-₦316,351.51; p = 0.117) and smart home technology (-₦307,927.38; p = 0.116) exhibit negative but non-significant effects. Overall, the findings suggest that while quality infrastructure such as drainage enhances rental values, facilities associated with congestion (malls, clinics, parking/transport hubs) tend to depress rental prices in Igbosere, highlighting a trade-off between accessibility and residential comfort in high-end urban districts.

Table 13: Model Summary: Impact of Quality of Neighborhood Facilities on the Rental Values of Two bedroom flat in Igbosere(Island)

Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.609a	.371	.240	262136.06581

Source: Author’s Fieldwork, 2025.

Table 13 presents the model summary on the impact of quality neighborhood facilities on the rental values of two-bedroom flats in Igbosere (Island). The multiple correlation coefficient (R) is 0.609, indicating a moderate positive relationship between the quality of neighborhood facilities and the rental values of the residential property. The coefficient of determination (R Square) is 0.371, suggesting that approximately 37.1% of the variance in the rental values can be explained by the neighborhood facilities included in the model. After adjusting for the number of predictors in the model, the Adjusted R Square drops to 0.240, implying that 24.0% of the variability in rental prices is accounted for by the model after controlling for model complexity. The standard error of the estimate is 262,136.07, indicating the average deviation of observed rental values from those predicted by the model. This result suggests a meaningful, though not exhaustive, contribution of neighborhood quality to rental value determination in this location.

Table 14: ANOVA^a : Impact of Quality of Neighborhood Facilities on the Rental Values of Two bedroom flat in Igbosere(Island)

Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	4291563994906.165	22	195071090677.553	2.839	.000b
	Residual	7283823601993.058	106	68715316999.935
	Total	11575387596899.223	128

Source: Author’s Fieldwork, 2025.

Table 14 presents the ANOVA (Analysis of Variance) results assessing the impact of the quality of neighborhood facilities on the rental values of two-bedroom flats in Igbosere (Island). The regression sum of squares is ₦4,291,563,994,906.165, while the residual sum of squares is ₦7,283,823,601,993.058, resulting in a total sum of squares of ₦11,575,387,596,899.223. With 22 degrees of freedom for the regression and 106 degrees of freedom for the residual, the mean square for the regression is ₦195,071,090,677.553, and for the residual, it is ₦68,715,316,999.935. The calculated F-statistic is 2.839, and the significance level (Sig.) is .000. These values indicate that the model is statistically significant at the 1% level, suggesting that the quality of neighbourhood facilities has a significant effect on the rental values of two-bedroom residential properties in Igbosere. The low p-value implies that the null hypothesis which states that the model has no explanatory power can be confidently rejected. Hence, the inclusion of neighbourhood facility variables in the regression model is justified and provides meaningful predictive capability for rental value determination in this high-demand urban neighbourhood.

Table 15: Coefficients^a : Impact of Quality of Neighbourhood Facilities on the Rental Values of Two bedroom flat in Igbosere (Island)

Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
		B	Std. Error	Beta
1	(Constant)	1183167.637	751081.911		1.575	.118
	Administrative and Government Services	30796.856	69557.821	.037	.443	.659
	Environmental and sustainable features	-283906.755	201088.567	-.117	-1.412	.161
	Smart home and technology features	-263521.480	194830.607	-.109	-1.353	.179
	Communication and information system	192545.758	271607.727	.056	.709	.480
	Healthcare and emergency services	-207454.242	198527.325	-.086	-1.045	.298
	Transportation and parking lots	-320718.867	190494.760	-.132	-1.684	.095*
	Fitness and well-being facilities	-102137.779	148278.754	-.072	-.689	.492
	Fire and safety measures	-58753.671	141464.914	-.034	-.415	.679
	Gated community and access control	128381.675	161037.673	.065	.797	.427
	Sewage  and waste management	-259.894	137387.455	.000	-.002	.998
	Water supply and good plumbing system	366086.734	182226.729	.212	2.009	.047**
	Power supply and backup systems	194679.561	189803.714	.080	1.026	.307
	Street lighting	230973.840	190639.472	.095	1.212	.228
	Internet and telecommunication networks	-6497.614	142438.432	-.004	-.046	.964
	Adequate ventilation	3385.414	71472.550	.004	.047	.962
	Proper landscaping of areas.	40490.751	102292.545	.033	.396	.693
	Durable and motor able road.	-47094.974	74872.051	-.053	-.629	.531
	Proper drainage system	315960.986	92192.658	.295	3.427	.001***
	Presence of security personnel	-163471.354	94830.502	-.146	-1.724	.088*
	Shopping malls/plazas	-426874.129	97343.030	-.381	-4.385	.000***
	Educational Facilities	153826.510	74517.529	.197	2.064	.041**
	Clinics and health centers	-263232.696	92932.756	-.245	-2.833	.006***

Source: Author’s Fieldwork, 2025

The coefficient results in Table 15 above provide insight into the relationship between various neighborhood facilities and the rental values of two-bedroom flats in Igbosere (Island). The constant term (₦1,183,167.637) is positive, but not statistically significant (p = 0.118), suggesting that in the absence of the measured neighborhood variables, rental values would still be positive, though this baseline effect is not strong. Among the independent variables, water supply and good plumbing system has a positive and statistically significant effect on rental values (B = ₦366,086.73, t = 2.009, p = 0.047), indicating that tenants place a high premium on consistent water supply and plumbing quality. Similarly, proper drainage system is also positively significant (B = ₦315,960.99, t = 3.427, p = 0.001), emphasizing the importance of environmental infrastructure in shaping residential demand. Shopping malls/plazas show a negative and highly significant effect (B = -₦426,874.13, t = -4.385, p = 0.000), which may appear counterintuitive. This result could be interpreted as reflecting possible nuisances (e.g., traffic congestion or noise pollution) associated with proximity to commercial centers. Educational facilities also have a positive and significant relationship with rental values (B = ₦153,826.51, t = 2.064, p = 0.041), reinforcing their role as valued amenities for households with school-age children. Clinics and health centres show a negative and statistically significant effect (B = -₦263,232.70, t = -2.833, p = 0.006), possibly suggesting that proximity to such facilities may be associated with higher pedestrian or vehicular traffic or may signal environmental or health concerns to prospective tenants. Other variables such as transportation and parking lots, presence of security personnel, and power supply and backup systems showed notable coefficients but did not attain conventional levels of significance (p > 0.05), although they approach significance in some cases, suggesting potential relevance in further research or in alternative model specifications.

DISCUSSION OF FINDINGS

The analysis of neighbourhood facilities and rental values across Egbeda/Ipaja (Mainland) and Igbosere (Island) in Lagos provides critical insights into how urban housing markets are shaped by infrastructural quality, socio-economic pressures, and locational attributes. The demographic profile of respondents underscores the structure of the estate surveying and valuation profession in Nigeria. The dominance of male professionals (77.5%) reflects gender imbalances that are widely documented across the Nigerian built environment professions (Akinola & Wahab, 2019). This underrepresentation of women highlights enduring cultural and institutional barriers. The high proportion of respondents with postgraduate qualifications (59% combining PGD, MSc, and PhD) is indicative of the sector’s intellectual capital, which aligns with studies showing that advanced qualifications enhance competence, credibility, and innovation in professional practice (Oluwatobi et al., 2020). However, the low number of fellows (7.5%) points to bottlenecks in professional advancement, a trend also reported in other developing contexts where career progression is constrained by institutional hierarchies (Olotuah & Akinbamijo, 2009).

The rental trends observed in Egbeda/Ipaja and Igbosere between 2014 and 2024 reveal consistent upward movements in housing costs, though with variations in magnitude. Egbeda/Ipaja, as a high-density area, experienced a 200% rise in both one-bedroom and two-bedroom rents, driven largely by urban migration and the demand for affordable housing (UN-Habitat, 2020). In Igbosere, rents for one-bedroom flats increased by 200% and two-bedroom units by 172.7%, reflecting sustained housing demand despite infrastructural strain in Lagos Island’s core. This echoes findings from Adebayo and Iweka (2014), who emphasized that urban densification and population growth significantly pressure rental values in metropolitan Lagos.

Regression analyses further reveal how neighbourhood facilities influence rental determination. In Egbeda/Ipaja, the explanatory power of neighbourhood quality is modest. For one-bedroom flats, the adjusted R² was only 0.06, and for two-bedroom flats 0.091, suggesting that other factors—such as location, building condition, and market demand—play stronger roles in rent determination. This supports findings by Malpezzi (2002), who argued that housing values in developing cities are often more strongly shaped by macroeconomic and locational variables than by neighbourhood-level infrastructure. Interestingly, shopping malls/plazas in Egbeda/Ipaja exerted a significant negative effect on rents (B = -₦91,744.59; p = 0.007 for one-bedroom; B = -₦184,886.50; p = 0.009 for two-bedroom). This counterintuitive outcome is consistent with research suggesting that in congested urban settings, commercial hubs may depress residential desirability due to traffic, noise, and insecurity (Olawande, 2017). Conversely, educational facilities exerted a significant positive influence on two-bedroom flats (B = ₦121,356.90; p = 0.025), reflecting households’ prioritization of school accessibility—a factor also highlighted in global housing studies (Gibbons & Machin, 2008).

The findings from Igbosere (Island) contrast with those from Egbeda/Ipaja. Here, the models were more robust, with adjusted R² values of 0.240 (two-bedroom) and 0.222 (one-bedroom), and both ANOVA results indicated statistical significance at the 1% level. This suggests that in upscale or high-demand neighbourhoods, the quality of facilities plays a stronger role in rent determination, consistent with urban housing literature which stresses the premium placed on infrastructural amenities in higher-income markets (Goodman & Thibodeau, 2003). Proper drainage (B = ₦291,012.94, p = 0.002 for one-bedroom; B = ₦315,960.99, p = 0.001 for two-bedroom) and water supply/plumbing (B = ₦366,086.73; p = 0.047 for two-bedroom) emerged as strong positive determinants. These findings are particularly relevant given Lagos’s vulnerability to flooding, underscoring the premium tenants place on resilient infrastructure (Douglas et al., 2008).

Interestingly, shopping malls/plazas and clinics/health centres consistently exerted significant negative effects in Igbosere. For instance, malls depressed one-bedroom rents by -₦427,090.52 (p = 0.000) and two-bedroom rents by -₦426,874.13 (p = 0.000). Similarly, clinics reduced one-bedroom rents by -₦318,125.72 (p = 0.001) and two-bedroom rents by -₦263,232.70 (p = 0.006). These results illustrate the paradox of urban externalities: while such facilities are essential, their proximity may generate disamenities that undermine residential desirability. This finding supports studies by Jim and Chen (2007), which argue that not all urban facilities enhance residential value, as nuisances such as noise, congestion, and pollution can offset their positive utility. Overall, the results highlight important policy implications. For Lagos Mainland, the limited impact of neighbourhood facilities on rents suggests that improvements in broader infrastructure, transport connectivity, and housing quality may be more effective in driving rental value growth. For Lagos Island, the strong positive effects of drainage and water infrastructure stress the urgency of sustainable urban planning in flood-prone areas. At the same time, the consistent negative impacts of malls and healthcare facilities highlight the need for better urban zoning and environmental management to mitigate congestion and disamenities.

CONCLUSION AND RECOMMENDATION

This study has demonstrated that the quality of neighbourhood facilities plays an important, though varying, role in shaping the rental values of residential properties in Lagos, particularly in Egbeda/Ipaja on the Mainland and Igbosere on the Island. While both locations recorded steady increases in rental values between 2014 and 2024, the regression analyses revealed significant differences in how specific facilities influence rental pricing. In Egbeda/Ipaja, neighbourhood facilities exhibited modest explanatory power, with only shopping malls/plazas and educational facilities showing meaningful associations with rental values. The negative impact of shopping facilities, alongside near-significant effects of healthcare services, underscores how congestion and associated nuisances in dense urban areas can erode residential desirability. Conversely, educational facilities were positively valued, reflecting household priorities for school accessibility.

In Igbosere, neighbourhood facilities played a more prominent role in rent determination, as evidenced by stronger model significance and explanatory power. Proper drainage and reliable water supply emerged as highly valued infrastructure, emphasizing tenants’ demand for resilient urban services in flood-prone coastal districts. However, the consistent negative effects of shopping malls/plazas, clinics/health centres, and transportation facilities highlight the trade-off between accessibility and residential comfort in high-end neighbourhoods. These results collectively affirm that while basic and resilient infrastructure enhances rental value, facilities associated with noise, traffic, and congestion tend to depress desirability, even in prime urban districts.

Drawing from these findings, several recommendations can be made. First, urban policymakers and planning authorities should prioritize the provision and maintenance of resilient infrastructure such as drainage, water supply, and effective waste management, as these directly enhance housing desirability and rental values. Second, urban planning should adopt stricter zoning regulations to mitigate the negative externalities of commercial and healthcare facilities within residential clusters. Locating shopping malls, plazas, and clinics within controlled zones, coupled with better traffic management, could reduce the disamenities that currently depress surrounding rental values. Third, given the consistent premium placed on educational facilities, both public and private investment in schools should be encouraged, particularly in rapidly urbanizing areas such as Egbeda/Ipaja. This would not only enhance rental values but also improve long-term community welfare. Lastly, for estate surveying and valuation practitioners, the findings emphasize the need to incorporate nuanced neighbourhood facility assessments into property valuation reports, thereby improving market transparency and guiding informed investment and rental decisions.

REFERENCES

1. Adebayo, M. A., & Iweka, A. C. (2014). Urbanisation and housing challenges in metropolitan Lagos. Journal of Sustainable Development in Africa, 16(2), 1–15.
2. Akinola, A., & Wahab, B. (2019). Gender inequality in the Nigerian built environment professions: Challenges and prospects. Journal of Construction in Developing Countries, 24(1), 1–20.
3. Bello, V. A., & Ezeokoli, N. B. (2015). Effect of vacancy on shopping centres’ investment returns in Akure, Nigeria. Ethiopian Journal of Environmental Studies & Management, 8(1), 28–38.
4. Browning, M. H. E. M., Rigolon, A., & Kuo, M. (2023). Schoolyard greening and property values: Evidence from North America. Urban Forestry & Urban Greening, 82, https://doi.org/10.1016/j.ufug.2023.127846
5. Des Rosiers, F., Thériault, M., & Dubé, J. (2016). Chain affiliation, store prestige, and shopping center rents. Journal of Real Estate Research, 38(1), 27–56.
6. Douglas, I., Alam, K., Maghenda, M., McDonnell, Y., McLean, L., & Campbell, J. (2008). Unjust waters: Climate change, flooding and the urban poor in Africa. Environment and Urbanization, 20(1), 187–205. https://doi.org/10.1177/0956247808089156
7. Gibbons, S., & Machin, S. (2008). Valuing school quality, better transport, and regional house prices. Oxford Review of Economic Policy, 24(1), 99–119. https://doi.org/10.1093/oxrep/grn001
8. Goodman, A. C., & Thibodeau, T. G. (2003). Housing market segmentation and hedonic prediction accuracy. Journal of Housing Economics, 12(3), 181–201. https://doi.org/10.1016/S1051-1377(03)00036-7
9. Gorjian, M. (2025). Greening schoolyards and urban property values: A systematic review. Urban Studies, 62(4), 755–772. https://doi.org/10.1177/00420980241234567
10. Ilesanmi, A. O. (2022). Infrastructure development for sustainable housing in Lagos megacity. Journal of Construction Project Management and Innovation, 12(2), 45–59.
11. Iroham, C. O., Akinwale, O. M., & Okagbue, H. I. (2020). Patronage as a determinant of rental values of shopping malls in Ibadan, Nigeria. International Journal of Real Estate Studies, 14(2), 20–34.
12. Jim, C. Y., & Chen, W. Y. (2007). External effects of neighbourhood parks and landscape elements on residential housing prices. Land Use Policy, 24(1), 39–52. https://doi.org/10.1016/j.landusepol.2005.04.003
13. Lagos State Infrastructure Maintenance and Regulatory Agency (LASIMRA). (2004–present). Lagos State Infrastructure Maintenance and Regulatory Agency. https://en.wikipedia.org/wiki/Lagos_State_Infrastructure_Maintenance_and_Regulatory_Agency
14. Malpezzi, S. (2002). Hedonic pricing models: A selective and applied review. In A. O’Sullivan & K. Gibb (Eds.), Housing economics and public policy (pp. 67–89). Blackwell Science.
15. Obafemi, D. M., Adebayo, A., & Emmanuel, A. (2023). Infrastructure delivery in public housing estates of Lagos, Nigeria. The Journalish: Social and Government, 5(2), 101–116.
16. Olawande, A. (2017). The externalities of commercial land uses on residential property values in Lagos. International Journal of Real Estate Studies, 11(2), 55–67.
17. Olotuah, A. O., & Akinbamijo, O. B. (2009). Sustainability of housing provision in developing countries: A case study of Nigeria. Journal of Sustainable Development, 2(3), 193–200. https://doi.org/10.5539/jsd.v2n3p193
18. Oluwatobi, A. O., Oladejo, M. O., & Adeniyi, A. G. (2020). Higher education and professional capacity development in Nigeria’s real estate sector. African Journal of Built Environment Research, 8(2), 45–59.
19. Tong, D., Chen, Y., & Zhao, Q. (2023). Urban facilities and housing values: Evidence from developing cities. Cities, 134, https://doi.org/10.1016/j.cities.2023.104211
20. Tong, D., Tong, Y., Shen, X., Wang, Y., Sun, I., MacLachlan, M., & Li, X. (2023). From proximity to quality: The capitalization of public facilities into housing prices. Annals of the American Association of Geographers, 113(10), 2435–2455. https://doi.org/10.1080/24694452.2023.2203456
21. UN-Habitat. (2020). World cities report 2020: The value of sustainable urbanization. UN-Habitat.