Evolutionary Approach to Feature Elimination in House Price Estimation

Abstract

One of the most basic human needs is the need for shelter. Since ancient times, people have been looking for a house that is both safe and affordable. However, in modern times, although safety is no longer an issue, the definition of an affordable house has changed. In the past, affordability did not depend on many parameters as it does today. However, today, this definition depends on different features, such as the location of the house, the year of construction, the number of rooms, etc. These features affect the level of affordability, and consequently the price of the house. Since houses are also used as an investment option, correct estimation of house prices is an important issue. The determination of features that have a significant impact on the price of a house is a subjective notion and, therefore, requires an objective approach. Thanks to technological developments, Artificial Intelligence algorithms remove the human factor in most of the decision-making processes. In this study, a naive approach was proposed to estimate house prices by selecting the most effective features of a house (https://github.com/OmerMintemur/Feature-Elimination-Using-GA.). To select the most effective features, Genetic Algorithm approach was utilized. For estimation, LightGBM was used. The AmesHouse data set was used for the experiments. The results suggested that the proposed method both reduced the features and produced lower estimation errors than other proposed methods that used the same dataset.