House Price Prediction in Beijing

Gundu, Vinay Babu (2024) House Price Prediction in Beijing. Masters thesis, Dublin, National College of Ireland.

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Abstract

Real estate price in particular, and house price specifically, has been an important area of research as it aims at predicting forecast that assists the side stakeholders in the decision-making process of the property. Such old models as Linear Regression, Decision Trees, and Random Forests were applied to predict house prices a long time ago; however, they do not handle well second-order effects and nonlinear interactions of features that are always present in real-life data. Even these models have also come with the problem of causing overfitting and do not generalize properly to new data sets. To overcome these challenges, this study will incorporate ordinary learners and complex algorithms of XGBoost and ANN learners. The strategy of this work involves comparing six models which include Linear Regression, Decision Tree Regressor, Random Forest Regressor, Gradient Boosting, XGBoost Regressor, and Artificial Neural Network. Among these models, the performance of XGBoost was the highest since it attained an MSE of 2,873.27 and R² of 0.947 indicating that this model can easily identify intricate patterns as well as interaction. The ANN also demonstrated good results achieving MAE = 33.53 as well as R² = 0.9344 while stressing its flexibility. As this research shows, with enhanced artificial learning methods, the prediction of house prices can be made more accurate and reliable than with the regular approaches.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Sahni, Anu UNSPECIFIED
Uncontrolled Keywords:	House Price Prediction; Real Estate; Machine Learning; Artificial Neural Network (ANN); XGBoost
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > HD Industries. Land use. Labor > Housing Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	02 Sep 2025 12:08
Last Modified:	02 Sep 2025 12:08
URI:	https://norma.ncirl.ie/id/eprint/8704

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