prices are an important reflection of the economy, and housing price ranges are
of great interest for both buyers and sellers. The goal of this paper is to
empirically conduct the best machine learning regression model for Turkish
Housing Market by comparing accuracy scores and absolute deviations of test
results by using Python programming language and Keras library for the
five-year period from January 2015 to December 2019. This study consists of 15
explanatory variables describing (almost) every aspect of houses in Istanbul,
Izmir and Ankara. These fifteen explanatory building and dwelling variables are
used for each prediction model. In this study, three different data models are
created by using support vector machine, feedforward neural networks and
generalized regression neural networks algorithms. The experiments demonstrate that
the Feedforward Neural Network model, based on accuracy, consistently
outperforms the other models in the performance of housing price prediction.
According to another result of the study, the most important variables in the
model are the location of the house and the size of the house, while the size
of the terrace is determined as the least important variable.
JEL classification numbers: R10, R15, R19
Keywords: Housing market, Zingat.com, Machine
learning, House price prediction, Python programming language, Keras library.