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Şahin O. , Gökaşar I., Arısoy A. A.

6th International Scientific Research Congress, Şanlıurfa, Turkey, 1 - 03 November 2019, vol.1, no.1, pp.107-119

  • Publication Type: Conference Paper / Full Text
  • Volume: 1
  • City: Şanlıurfa
  • Country: Turkey
  • Page Numbers: pp.107-119
  • Yıldız Technical University Affiliated: Yes


The effects of transportation systems on its vicinity has been a popular investigation topic. The main reason behind that popularity is that the population of cities have been increased rapidly and caused a need for either new living spaces or new transportation systems. The analysis of the effects of new transportation projects in the vicinity is important for guiding to the policy makers at implementation of proper transportation systems. In this study a model which predict the real estate prices is established by using the direct distances of real estate properties to the closest BRT stations and the neighborhood characteristics. The performances of the established models are compared by using linear regression and random forest algorithm (RFA). The closer real estate properties to the BRT stations are expected to have higher prices. However, the correlation test performed before the analysis have shown a directly opposite result from the expectations. This output supports the existence of other external factor that might have an effect on the real estate prices rather than the direct distances to the BRT stations. Due to the prices of the real estate properties and their direct distances to the BRT stations have a weak and nonlinear relationship, as the direct distance between the real estate property and the closest BRT station increases the performance of the linear regression model decreases. On the other hand, the model that is created by RFA shows a more irregular performance. When the dataset is limited to a real estates that are at most 2000 meters away from the closest BRT line, RFA shows its best performance at predictions, however when the dataset is limited to real estates that are at most 2900 meters away from the closest BRT station, the RFA model shows its worst performance at predictions. In future studies, different types of data mining methods (Support Vector Regression, SVR; Gradient Boosting, GB; etc.) and more detailed datasets will be used in order to establish the predictive models