  This study contributes a house price prediction model selection in Tehran
City based on the area between Lorenz curve (LC) and concentration curve (CC)
of the predicted price by using 206,556 observed transaction data over the
period from March 21, 2018, to February 19, 2021. Several different methods
such as generalized linear models (GLM) and recursive partitioning and
regression trees (RPART), random forests (RF) regression models, and neural
network (NN) models were examined house price prediction. We used 90% of all
data samples which were chosen randomly to estimate the parameters of pricing
models and 10% of remaining datasets to test the accuracy of prediction.
Results showed that the area between the LC and CC curves (which are known as
ABC criterion) of real and predicted prices in the test data sample of the
random forest regression model was less than by other models under study. The
comparison of the calculated ABC criteria leads us to conclude that the
nonlinear regression models such as RF regression models give an accurate
prediction of house prices in Tehran City.
