Does proximity to tourist site affect the rental value of residential properties? Empirical evidence from Nigeria

Abstract

The property market plays a vital role in the economy of any nation. The industry provides jobs, investment opportunities and constructed space for productive activities, among others. Several authors have developed predictive models for the rental value of residential properties. However, little is known about the impact of tourist site on the rental value of residential properties. This study seeks to examine the effect of tourist sites on the rental value of residential properties using an artificial intelligence technique. The predictive modelling approach was utilised in this study. It was found that proximity to tourist site and security are the most important factors influencing rental prices of residential properties. In addition, the developed Neural Network (NN) model could adequately predict the rental value of residential properties (93.75% were correctly predicted). The results of this study demonstrate that the NN model is a useful tool for forecasting of the rental value of properties. The findings of this study provide valuable information for policy makers, professionals in the built environment and property investors.

Keywords

• forecasting
• modelling
• neural network
• rental value
• residential property