Using machine learning algorithms for housing price prediction: The case of Fairfax County, Virginia housing data
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Park and Bae (2015) RIPPER algorithm routinely beats other models in accurately forecasting house prices.
“The research shows that when it comes to accurately forecasting house prices, the RIPPER algorithm routinely beats other models.”
From Intelligent Decision Making for Commodities Price Prediction: Opportunities, Challenges and Future Avenues (2025) · cites this paper as Park and Bae, 2015
Hedonic pricing can help identify how much different consumers may be willing to pay for an environmental good or service, which is a noisy measure for the monetary value of that good or service; these values are typically inferred using regression or ML techniques on historical market data.
“Hedonic pricing can also help identify how much different consumers may be willing to pay for a environmental good or a service, which is a noisy measure for the monetary value of that good or service; these values are typically inferred using regression or ML techniques on historical market data [175,594,613,629].”
From Tackling climate change with machine learning (2023) · cites this paper as [613]
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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
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 · see citing works
- Authors
- Park, B., & Bae, J. K.
- Affiliations
- Keimyung University (KR)
- Corresponding author
- Bae, Jae Kwon
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2015-04
- Volume
- 42
- Issue
- 6
- Pages
- 2928-2934
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2251575556
- Cited by
- 401
- References
- 18
- Citations by year
- 2026: 21 · 2025: 47 · 2024: 53 · 2023: 48 · 2022: 63 · 2021: 52 · 2020: 53 · 2019: 32 · 2018: 10 · 2017: 13 · 2016: 6 · 2015: 2 · 2014: 1
- FWCI
- 18.24
- Type
- journal article
- Language
- en
- Update notices
- erratum (2015-11-01)
- Open access
- closed
- Locations
- Expert Systems with Applications (publishedVersion)
- 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, residential property valuation, Fairfax County, Virginia, townhouses, RIPPER, C4.5 decision tree, naive Bayes, AdaBoost, housing market
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