Impact of Security on Rental Price of Residential Properties: Evidence from South Africa

Abstract

The property market plays a vital role in the economy through the provision of constructed space for productive activities and employment opportunities. Evidence gleaned from the literature suggests that the level of security within an area affects property prices. In the current study, the distance between a property and the police station was used as a proxy variable for measuring the perceived level of security. The data on rental prices of residential properties and its attributes were retrieved from a reliable property source (www.property24.com). A Neural network model was used for evaluating the impact of the presence of a police station on rental prices of residential properties within Cape Town, South Africa. Experimental results showed that the developed model is 77.27% accurate when used to predict the rental prices of residential properties. Floor area, number of bathroom, number of bedroom and proximity of a police station have the most significant impact on the rental price of residential properties. Greater efforts are needed to provide insights into the effect of sustainability on rental prices of residential properties. This information would serve a justification for embedding sustainability into residential construction projects.

Value Pertinence for Residential Property Investments in Christian Mission Estates: An Investigation of Canaan City in Ogun State, Nigeria

“…1) Physical attributes Within this sphere of literature, several studies have found that the availability of multiple toilets, burglar alarms, and the condition of a building are considered key factors determining the rental value as well as capital values of residential properties (Adegoke et al, 2016;Andreasen et al, 2020). According to Odubiyi et al (2019), total floor area posed the most important attribute of a building influencing rental price. In order of priority, floor space, number of bathrooms, number of bedrooms and the proximity of police stations have the greatest effect on the rental prices of residential properties.…”

Rent index forecasting through neural networks

“…As compared to house price forecasting, research on rental price forecasting through machine learning (e.g. Clark and Lomax, 2018; Embaye et al , 2021; Hu et al , 2019; Li, 2018; Li and Li, 1996; Ma et al , 2018; Ma and Liu, 2019; Ming et al , 2020; Odubiyi et al , 2019; Oshodi et al , 2020, 2021; Oyedeji Joseph et al , 2018; Oyedeji and Oyewale, 2018; Rafatirad, 2017; Tsai and Pan, 2014; Wang and Cao, 2019; Zhang et al , 2019) seems relatively scare. Hu et al.…”

“…Clark and Lomax (2018) investigate the generalized linear regression, machine learning and pseudo practitioner-based approaches for the mass appraisal purpose in English rental housing markets and determine that two tree-based models beat the regression-based model. Odubiyi et al. (2019) use the neural network model to assess the impact of the presence of a police station on rental prices of residential properties in Cape Town, South Africa, and achieve accuracy of 77.27%.…”

“…As compared to house price forecasting, research on rental price forecasting through machine learning (e.g. Clark and Lomax, 2018;Embaye et al, 2021;Hu et al, 2019;Li, 2018;Li and Li, 1996;Ma et al, 2018;Ma and Liu, 2019;Ming et al, 2020;Odubiyi et al, 2019;Oshodi et al, 2020Oshodi et al, , 2021Oyedeji Joseph et al, 2018;Oyedeji and Oyewale, 2018;Rafatirad, 2017;Tsai and Pan, 2014;Wang and Cao, 2019;Zhang et al, 2019) seems relatively scare. Hu et al (2019) explore the random forest, extra-trees, gradient-boosting, support vector, multi-layer perceptron neural network and k-nearest neighbor when building housing rent prediction models for Shenzhen in China in October 2017 and February 2018 and find that all of these algorithms, except for the support vector, generally present good performance with the random forest and extra-trees being the leaders.…”

“…Clark and Lomax (2018) investigate the generalized linear regression, machine learning and pseudo practitioner-based approaches for the mass appraisal purpose in English rental housing markets and determine that two tree-based models beat the regression-based model. Odubiyi et al (2019) use the neural network model to assess the impact of the presence of a police station on rental prices of residential properties in Cape Town, South Africa, and achieve accuracy of 77.27%. Zhang et al (2019) employ a least square-based linear weighting learner to combine price forecasts based on the XGBoost, light gradient boosting and CatBoost algorithms to improve model performance for the Chinese house rental market.…”