Housing Price Prediction Model Selection Based on Lorenz and Concentration Curves: Empirical Evidence from Tehran Housing Market

TL;DR

This paper evaluates various house price prediction models in Tehran using Lorenz and concentration curves, finding that random forest regression provides the most accurate predictions among tested methods.

Contribution

It introduces a novel model selection approach based on Lorenz and concentration curves for house price prediction accuracy assessment.

Findings

Random forest regression outperformed other models in prediction accuracy.

The ABC criterion effectively measured the accuracy of predicted house prices.

Nonlinear models like RF regression are more suitable for Tehran's housing market.

Abstract

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.