A Categorical Modelling Approach for determining Rental House Prices in Low- Income Neighbourhoods of North-Central Nigeria.

Abstract

Over the years, researchers in developing countries have applied varied methodologies to study housing attributes and house prices. This study utilised the categorical modelling approach to determine the contributory effect of housing attributes on rental house prices in a major town in North-Central Nigeria. A total of 827 housing units were sampled by stratified and random selection. The data used were generated through a questionnaire survey. Nine housing attributes were found to sustain residential buildings in the study area and these accounted for 45% and 61% variance in the rental prices of two major low-income house types. Results revealed that the identified housing attributes significantly predicted rental values for the sampled house types. The mean of predicted rental values was further computed for each house type and compared to the means of the actual rental values collated in the course of data collection and presented with line graphs. Results showed predicted values that are reasonably similar to the actual rental values of the dwelling units. This suggested a reasonably accurate prediction of rental house prices using the categorical regression approach. The study recommended the model to house managers to ensure accurate house pricing.