| Issue |
MATEC Web Conf.
Volume 312, 2020
9th International Conference on Engineering, Project, and Production Management (EPPM2018)
|
|
|---|---|---|
| Article Number | 04003 | |
| Number of page(s) | 9 | |
| Section | Integration of Engineering Management and Project Management | |
| DOI | https://doi.org/10.1051/matecconf/202031204003 | |
| Published online | 03 April 2020 | |
Does proximity to tourist site affect the rental value of residential properties? Empirical evidence from Nigeria
Sustainable Human Settlement and Construction Research Centre, Faculty of Engineering and the Built Environment, University of Johannesburg, South Africa
* Corresponding author: os.oshodi@gmail.com
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.
Key words: Forecasting / modelling / neural network / rental value / residential property
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.