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House Price Prediction using Random Forest Machine Learning Technique
Open accessAbstract
Predicting a price variance rather than a specific value is more realistic and attractive in many real-world applications. Price prediction can be thought of as a classification issue in this situation. However, the House Price Index (HPI) is a common tool for estimating the inconsistencies of house prices. Since housing prices are closely correlated with other factors such as location, city, and population, predicting individual housing prices needs information other than HPI. The HPI is a repeat-sale index that tracks average price shifts in repeat transactions or refinancing of the same assets. Therefore, HPI is ineffective at predicting the price of a single house because it is a rough predictor based on all transactions. This study explores the use of Random Forest machine learning technique for house price prediction. UCI Machine learning repository Boston housing dataset with 506 entries and 14 features were used to evaluate the performance of the proposed prediction model. A comparison of the predicted and actual prices predicted revealed that the model had an acceptable predicted value when compared to the actual values with an error margin of ±5.
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“The evaluation con- ducted on the UCI Machine Learning Repository Boston housing dataset, consisting of 14 features and 506 entries, demonstrates that the proposed model achieves an acceptable level of accuracy with a prediction error margin of ± 5 when compared to actual prices.”
From Intelligent Decision Making for Commodities Price Prediction: Opportunities, Challenges and Future Avenues (2025) · cites this paper as Adetunji et al., 2022
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Research works printed but not linked: 11
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Cited by
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Saeed, N., Shafi, I., Pervez, S., Thompson, E. B., Castilla, A. K., Samad, M. A., & Ashraf, I. (2025) · Computational Economics · doi:10.1007/s10614-024-10837-5 · Cited by 8
Metadata
- Authors
- Adetunji, A. B., Akande, O. N., Ajala, F. A., Oyewo, O., Akande, Y. F., & Oluwadara, G.
- Affiliations
- Ladoke Akintola University of Technology (NG)
- Landmark University (NG)
- Venue
- Procedia Computer Science
- Publisher
- Elsevier BV
- Published
- 2022
- Volume
- 199
- Pages
- 806-813
- ISSN
- 1877-0509 (ISSN-L)
- OpenAlex
- W4210341208
- Cited by
- 189
- References
- 0
- Citations by year
- 2026: 31 · 2025: 65 · 2024: 42 · 2023: 44 · 2022: 7
- FWCI
- 144.97
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Procedia Computer Science (publishedVersion, cc-by-nc-nd)
- UiTM Institutional Repositories (Universiti Teknologi MARA) (acceptedVersion)
- Funders
- Landmark University Centre for Research and Development
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Housing Market and Economics
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Keywords
- house price prediction, random forest, Boston housing dataset, housing prices, housing price index, feature importance
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- Adetunji, A. B., Akande, O. N., Ajala, F. A., Oyewo, O., Akande, Y. F., & Oluwadara, G. (2022). House Price Prediction using Random Forest Machine Learning Technique. Procedia Computer Science. https://doi.org/10.1016/j.procs.2022.01.100
