Modelling property values in Nigeria using artificial neural network

Rotimi Boluwatife Abidoye, Ping Chuen Chan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

29 Citations (Scopus)


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 utilisation of traditional valuation methods. In the current study, an artificial neural network (ANN) is applied in property valuation using the Lagos metropolis property market as a representative case. Property sales 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.
Original languageEnglish
Pages (from-to)36-53
Number of pages18
JournalJournal of Property Research
Issue number1
Publication statusPublished - 2 Jan 2017


  • Artificial neural network
  • Lagos metropolis
  • property attributes
  • property valuation
  • valuation accuracy

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Urban Studies


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