Abstract
Previous studies have revealed that historical sites have impact on property value. However, none of the previous studies have forecasted the impact of historical site on residential property value using a classification model. Data for the study were gathered from the record of recent letting in the study area. For the purpose of precision, this study adopted artificial neural network, logistic regression and support vector machine as model of classifying the rental value of residential property in Osogbo, Nigeria. The study considered relevant variables which include distance to cultural site, age of building, state of exterior and state of interior as input variables. Findings from the study revealed that the three adopted forecasting models have over 80% percent of the forecasted properties correctly classified which make the property forecasting reliable.
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