Modelling property values in Nigeria using artificial neural network

Abstract

Unreliable and inaccurate property valuation has been associated with techniques currently used in property valuation. A possible explanation for these findings may be due to the utilization of traditional valuation methods. In the current study, an artificial neural network (ANN) is applied to property valuation using the Lagos metropolis real estate market as a representative case. Property transactions data (11 property attributes and property value) were collected from registered real estate firms operating in Lagos, Nigeria. The result shows that the ANN model possesses a good predictive ability, implying that it is suitable and reliable for property valuation. The relative importance analysis conducted on the property attributes revealed that the number of servants' quarters is the most important attribute affecting property values. The findings suggest that the ANN model could be used as a tool by real estate stakeholders, especially, valuers and researchers for property valuation.

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

“…Research into residential property value forecasting has a long history. Some of the studies found in the literature include Abidoye and Chan [8] and Lam et al [6], among others. Bond and Hopkins [7] found that proximity to high voltage overhead transmission lines does not affect the value of residential properties in New Zealand.…”

“…Lam et al [6] reported that the presence of a garden has a significant impact on the value of residential properties in Hong Kong. Other attributes affecting the value of residential properties include property location; a number of bedrooms; a number of toilets; a number of bathrooms; property type; a number of boy's quarters; number of parking space; age of property, number of floors and availability of sectors [6,8,10]. Information gleaned from the literature shows that a multiplicity of factors affect the value of residential properties.…”

“…The theory upon which the model is developed is revised or discarded if the predictive accuracy of the developed model is low. Based on a review of the literature, the techniques used for modelling and forecasting of the value of residential properties include analytical, regression (also referred to as hedonic), neural network and support vector machine, among others [4,6,8]. These techniques can be classified into two broad groups: linear and non-linear approaches.…”

“…To this end, transaction data were collected from real estate firms and property owners. This is because there is no centralised database for property transactions in Nigeria [8]. The collected data contained information about transaction details of properties located in Oshogbo, Osun State.…”

“…These shreds of evidence found in the literature show that the residential property market is an important segment of any economy. In the existing literature, the predictive models for this class of properties focused on countries such as Australia [4]; Dubai [5]; Hong Kong [6]; New Zealand [7]; Nigeria [8]; United Kingdom [9] and United States of America [10], among others.…”