Residential Housing Demand in Nairobi; A Hedonic Pricing Approach

Abstract

Purpose: The purpose of this study was to asses the residential housing demand in Nairobi using a hedonic pricing approach. Methodology: The study used an OLS regression model to link House rent to various determinants. For the purpose of analysis the population o be sampled was based in Nairobi. The researcher focused on the Nairobi up market residential areas and the Nairobi lower market residential areas. The sampling frame consisted of residential housing facilities in both the up market and lower market Nairobi area. The sample size was specifically fifty five up market and lower market residents in Nairobi. Short and simple questionnaires were the main data collection method used to obtain the primary data of the information. Results: Results revealed that that the HSESIZE (number of bedrooms) were positively and significantly correlated to the VALUE (house rent). This implies that the higher the pollution number of bedrooms, the higher the rent. The results also indicate that EXPLEVEL (exposure level to pollution) was negatively and significantly correlated to VALUE (House rent). This implies that the higher the pollution exposure, the lower the rent. The results also indicate that ESLVRS (Level of Ease to Recreational Facilities) was negatively and significantly correlated to VALUE (House rent). This implies that the higher the difficulty of accessing recreational facilities, the lower the rent. The results also indicate that ESLGDF (Level of Ease to Garbage Disposal) was negatively and significantly correlated to VALUE (House rent). This implies that the higher the difficulty of accessing garbage collection facilities, the lower the rent. An R squared of 0.639 indicated that the goodness of fit of the model was satisfactory. An F statistics of 6.917 and a pvalue of 0.000 indicate that the overall model was significant. Unique contribution to theory, practice and policy: Based on the findings, the study recommended that more effort should be employed to construct a housing price index which can be studied in its own right or be used as an explanatory variable in housing demand equations. Large scale data should also be employed in order to achieve a more detailed analysis.