# An Optimal House Price Prediction Algorithm: XGBoost

arXiv: https://arxiv.org/abs/2402.04082 · HTML: https://mlsanity.com/paper/2402.04082/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 46 references that are in the map.
- First posted on arXiv 6 Feb 2024.

## Builds on (most cited first, 4 of 4)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice](https://mlsanity.com/paper/2007.15745/) (2020)
- [Predicting the direction of stock market prices using random forest](https://mlsanity.com/paper/1605.00003/) (2016)
- [Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges](https://mlsanity.com/paper/1709.06599/) (2017)

## Built on by (most cited first, 1 of 1)

- [Modern approaches to building interpretable models of the property market using machine learning on the base of mass cadastral valuation](https://mlsanity.com/paper/2506.15723/) (2025)

## Abstract (arXiv)

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 value is not solely determined by its physical attributes but is significantly influenced by its surrounding neighbourhood. 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 techniques capable of expressing the significance of independent variables. We made use of the housing dataset of Ames City in Iowa, USA to compare support vector regressor, random forest regressor, XGBoost, 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.