Hedonic Housing Theory — A Machine Learning Investigation
Abstract
The hedonic pricing theory suggests that house is a differ-entiated commodity, whose value depends on its hetero-geneous characteristics. Application of the theory has been well implemented using OLS Regression. Our study investigates this econometric concept using machine learning algorithms. An improved pricing will benefit buyers, sellers, investors, banks and real estate professionals. Normality test for the experiment was done using Chi-Square Quantile-Quantile plot and Henze-Zirkler's Multivariate Normality Test. Statistical relationship was based on correlation matrix, Kaiser-Meyer-Olkin and Bartlett tests. Support Vector Regression (SVR), K-Nearest Neighbor (K-NN) and Principal Component Re-gression (PCR) were used as learning algorithms. Per-formance comparison of the learning algorithms was done using spearman's rho correlation coefficient. The performance of the model showed that PCR has a slight edge over SVR and K-NN. Also, the study validated the suitability and substitutability of PCR, SVR and K-NN in the implementation of the hedonic pricing theory.
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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 [594]
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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
- Oladunni, T., & Sharma, S.
- Affiliations
- Bowie State University (US)
- Venue
- 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)
- Publisher
- IEEE
- Published
- 2016-12
- Pages
- 522-527
- OpenAlex
- W2585575883
- Cited by
- 46
- References
- 12
- Citations by year
- 2025: 5 · 2024: 9 · 2023: 1 · 2022: 8 · 2021: 8 · 2020: 5 · 2019: 5 · 2018: 2 · 2017: 3
- FWCI
- 17.52
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Machine Learning and Applications (publishedVersion)
- Funders
- National Science Foundation
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Housing Market and Economics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- hedonic pricing, housing prices, housing characteristics, support vector regression, k-nearest neighbors, principal component regression, algorithm comparison, property valuation
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