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A Model Validation and Predicting the Rental Values of Residential Properties Using Logistic Regression Model

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Collaboration and Integration in Construction, Engineering, Management and Technology

Part of the book series: Advances in Science, Technology & Innovation ((ASTI))

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Abstract

The property market is a key contributor to the economic growth of many countries. This makes information from property valuation reports vital for decisions on real estate investments and property tax. Unfortunately, the literature reveals that inaccurate property valuation arising from a reliance on traditional methods of valuation remains a major problem facing real estate practice. To improve the prediction accuracy of property valuation estimates, modelling techniques such as neural networks have previously been applied to this problem. This present study uses a logistic regression model to predict the rental values of residential properties in Cape Town, South Africa. Field survey data was divided into two groups: training and test sets. The training set was used for model development while the test set was used for model validation. The results of the study revealed that parking, garden, number of bedrooms and floor area have the most significant impact on the rental values of residential properties. Surprisingly, proximity to a police station has one of the least effects on the rental values of residential properties. With a prediction performance of over 70% accuracy, findings indicate that the logistic regression model is suitable for predicting the rental values of residential properties. This study evaluates the factors that influence the rental values of residential properties located within the study area. The developed model can serve as a decision support tool for estimating the tax payable by property owners.

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Authors and Affiliations

  1. SARChl in Sustainable Construction Management and Leadership in the Built Environment Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa

    Tawakalitu Odubiyi, Anthony Ugulu, Olalekan Oshodi, Clinton Aigbavboa & Wellington Thwala

Authors
  1. Tawakalitu Odubiyi
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  2. Anthony Ugulu
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  3. Olalekan Oshodi
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  4. Clinton Aigbavboa
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  5. Wellington Thwala
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Corresponding author

Correspondence to Tawakalitu Odubiyi .

Editor information

Editors and Affiliations

  1. Department of Construction Management, East Carolina University, Greenville, NC, USA

    Syed M. Ahmed

  2. School of Architecture & Built Environment, University of Wolverhampton, Wolverhampton, UK

    Paul Hampton

  3. McWhorter School of Building Science, Auburn University, Auburn, AL, USA

    Salman Azhar

  4. Department of Construction Management, East Carolina University, Greenville, NC, USA

    Amelia D. Saul

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Odubiyi, T., Ugulu, A., Oshodi, O., Aigbavboa, C., Thwala, W. (2021). A Model Validation and Predicting the Rental Values of Residential Properties Using Logistic Regression Model. In: Ahmed, S.M., Hampton, P., Azhar, S., D. Saul, A. (eds) Collaboration and Integration in Construction, Engineering, Management and Technology. Advances in Science, Technology & Innovation. Springer, Cham. https://doi.org/10.1007/978-3-030-48465-1_56

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