Housing Rental Differentials and Neighbourhood Characteristics: A Hedonic Price Approach in Two Nigerian Communities
DOI:
https://doi.org/10.5281/zenodo.22817895Keywords:
Affordable housing, hedonic price model, housing rental values, neighbourhood characteristics, Sango, BasinAbstract
This study examines housing rental differentials and the relationship between neighbourhood characteristics and residential rental values in Sango and Basin, two contrasting residential communities in Ilorin, Nigeria, using the Hedonic Price Model as its analytical framework. Data were obtained through structured questionnaires administered to residential property occupiers and Estate Surveyors and Valuers, producing 255 valid responses comprising 168 property occupiers and 87 property professionals (a 78.4% response rate among the professional population). Descriptive statistics and multiple regression analysis were used to examine the data. Basin recorded higher average rental values than Sango across all five property types examined — tenement buildings, self-contained apartments, and one-, two- and three-bedroom apartments. Regression results showed stronger relationships between neighbourhood characteristics and rental values in Basin for tenement, self-contained and one-bedroom properties, with R² values of 62.5%, 59.3% and 60.1% respectively, compared with generally lower explanatory power in Sango. Environmental and waste management, affordable housing and recreational spaces were significant predictors of rental value in Basin, while education, community life and road accessibility were more relevant in Sango. The study concludes that neighbourhood characteristics contribute differently to rental values across the two communities and recommends improved infrastructure, environmental management, security and affordable housing provision to enhance neighbourhood quality and support more sustainable residential property markets. Some methodological limitations, including small sub-sample sizes for individual property types and the need to re-verify one regression result, are discussed and should be addressed before the underlying dataset is relied upon for policy purposes.
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