Using neural network model to estimate the rental price of residential properties

Olalekan Shamsideen Oshodi (Department of Construction Management and Quantity Surveying, University of Johannesburg, Johannesburg, South Africa)
Wellington Didibhuku Thwala (SARChi in Sustainable Construction Management and Leadership in the Built Environment, University of Johannesburg, Johannesburg, South Africa)
Tawakalitu Bisola Odubiyi (SARChi in Sustainable Construction Management and Leadership in the Built Environment, University of Johannesburg, Johannesburg, South Africa)
Rotimi Boluwatife Abidoye (Faculty of Built Environment, University of New South Wales, Sydney, Australia)
Clinton Ohis Aigbavboa (SARChi in Sustainable Construction Management and Leadership in the Built Environment, University of Johannesburg, Johannesburg, South Africa)

Journal of Financial Management of Property and Construction

ISSN: 1366-4387

Publication date: 5 August 2019

Abstract

Purpose

Estimation of the rental price of a residential property is important to real estate investors, financial institutions, buyers and the government. These estimates provide information for assessing the economic viability and the tax accruable, respectively. The purpose of this study is to develop a neural network model for estimating the rental prices of residential properties in Cape Town, South Africa.

Design/methodology/approach

Data were collected on 14 property attributes and the rental prices were collected from relevant sources. The neural network algorithm was used for model estimation and validation. The data relating to 286 residential properties were collected in 2018.

Findings

The results show that the predictive accuracy of the developed neural network model is 78.95 per cent. Based on the sensitivity analysis of the model, it was revealed that balcony and floor area have the most significant impact on the rental price of residential properties. However, parking type and swimming pool had the least impact on rental price. Also, the availability of garden and proximity of police station had a low impact on rental price when compared to balcony.

Practical implications

In the light of these results, the developed neural network model could be used to estimate rental price for taxation. Also, the significant variables identified need to be included in the designs of new residential homes and this would ensure optimal returns to the investors.

Originality/value

A number of studies have shown that crime influences the value of residential properties. However, to the best of the authors’ knowledge, there is limited research investigating this relationship within the South African context.

Keywords

Citation

Oshodi, O.S., Thwala, W.D., Odubiyi, T.B., Abidoye, R.B. and Aigbavboa, C.O. (2019), "Using neural network model to estimate the rental price of residential properties", Journal of Financial Management of Property and Construction, Vol. 24 No. 2, pp. 217-230. https://doi.org/10.1108/JFMPC-06-2019-0047

Publisher

:

Emerald Publishing Limited

Copyright © 2019, Emerald Publishing Limited

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