| Issue |
E3S Web Conf.
Volume 418, 2023
African Cities Conference (ACC 2023): A part of African Cities Lab 2023 Summit
|
|
|---|---|---|
| Article Number | 03001 | |
| Number of page(s) | 6 | |
| Section | Emerging Technologies and Applications to African Cities Issues | |
| DOI | https://doi.org/10.1051/e3sconf/202341803001 | |
| Published online | 18 August 2023 | |
A Comparative Study of Urban House Price Prediction using Machine Learning Algorithms
1 Laroseri Laboratory, Chouaib Doukkali University, Morocco
2 The National School of Business and Management of Dakhla, Ibn Zohr University, Morocco
3 Scientific Computing, Computer Science and Data Science Research Unit (CSIDS), University of Nouakchott, Mauritania
4 Center of Urban Systems (CUS), Mohammed VI Polytechnic University (UM6P), Hay Moulay Rachid, 43150 Benguérir, Morocco
5 Department of Computer and Educational Technology, Higher Teacher Training College (HTTC), University of Yaoundé I, Yaoundé, Cameroon
Accurate housing price forecasts are essential for several reasons. First, it allows individuals to make informed decisions about buying or selling real estate and to determine appropriate prices. Secondly, it helps real estate agents and investors make better investment decisions and negotiate contracts more effectively. In addition, housing prices are often an indication of the general state of the economy. A price decrease may indicate an economic recession, while an increase in prices may signal economic growth. In this study, we proposed to address this subject by predicting house prices using machine learning by choosing three types of machine learning: Linear Regression (LN), Random Forest (RF) and GradientBoosting (GB). We tested our models on the Melbourne real estate dataset, which includes 34,857 property sales and 21 features.
Key words: urban real estate / house price / machine learning / house price prediction.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.