An Optimal House Price Prediction Algorithm: XGBoost

Hemlata Sharma, Hitesh Harsora, Bayode Ogunleye

Research output: Contribution to journalArticlepeer-review


An accurate prediction of house prices is a fundamental requirement for various sectors, including real estate and mortgage lending. It is widely recognized that a property’s value is not solely determined by its physical attributes but is significantly influenced by its surrounding neighborhood. Meeting the diverse housing needs of individuals while balancing budget constraints is a primary concern for real estate developers. To this end, we addressed the house price prediction problem as a regression task and thus employed various machine learning (ML) techniques capable of expressing the significance of independent variables. We made use of the housing dataset of Ames City in Iowa, USA to compare XGBoost, support vector regressor, random forest regressor, multilayer perceptron, and multiple linear regression algorithms for house price prediction. Afterwards, we identified the key factors that influence housing costs. Our results show that XGBoost is the best performing model for house price prediction. Our findings present valuable insights and tools for stakeholders, facilitating more accurate property price estimates and, in turn, enabling more informed decision making to meet the housing needs of diverse populations while considering budget constraints.
Original languageEnglish
Pages (from-to)30-45
Number of pages16
Issue number1
Publication statusPublished - 2 Jan 2024


  • feature engineering
  • feature importance
  • house price prediction
  • hyperparameter tuning
  • machine learning
  • regression modeling
  • XGBoost


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