Integration of GIS and Data Mining for Residential Property Valuation: Case study District ۵ of Tehran

Residential property taxation provides a large contribution to the sustainable income of urban governance. Therefore, a fair and uniform price estimation system is essential, e.g, to safeguard independence. In this research, the integration of geospatial information systems (GIS) and data mining has been used for property valuation in District ۵ of Tehran. Naïve Bayes (NB) and K-Nearest Neighbors (KNN) data mining methods have been used to classify residential properties' prices. The NB method has been employed as well to model uncertainties existing in the implemented data. The results showed that the NB method performed better than the KNN method in classifying residential property values.