Integrating Machine Learning and Hedonic Regression for Housing Price Prediction - A Systematic International Review of Model Performance and Interpretability

Aug 3, 2026

Home price prediction is increasingly critical for reducing investment risk, informing policy, and supporting market stability. A systematic comparative assessment is used to evaluate how predictive accuracy and interpretability differ across hedonic regression and advanced machine learning models across international settings. Searches in Scopus, Google Scholar, and Web of Science identify empirical studies published after 2000 with reliable transaction data, model comparisons, and PRISMA 2020 reporting compliance. Across 23 eligible investigations, Random Forest is most frequently used and performs consistently well, while hedonic models remain valuable for explaining key drivers such as location and property attributes.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages23

Reading Time58 min

AccessFree to download

Introduction

Importance of Housing Price Prediction

Traditional Approaches and Hedonic Price Models

Methodological Challenges in Hedonic Modeling

Systematic International Review Methodology

Search Strategy and Databases

Inclusion and Exclusion Criteria

PRISMA 2020-Based Evaluation

Results and Comparative Insights

Model Performance Patterns

Interpretability and Explanatory Value

Role of Hedonic and Hybrid Approaches

Conclusions and Implications

Practical Guidance for Researchers and Policymakers

Munich Personal RePEc Archive Integrating Machine Learning and Hedonic Regression for Housing Price Prediction: A Systematic International Review of Model Performance and Interpretability Gorjian, Mahshid 2025 Online at [https://mpra. ub. uni-muenchen. de/125676/](https://mpra. ub. uni-muenchen. de/125676/) [MPRA Paper No. 125676](MPRA Paper No. 125676) , [posted 27 Aug 2025 08:29 UTC](posted 27 Aug 2025 08:29 UTC) Title Integrating Machine Learning and Hedonic Regression for Housing Price Prediction: A Systematic International Review of Model Performance and Interpretability Author: Mahshid Gorjian Affiliation: University of Colorado Denver [Emai:](Emai: Mahshid.gorjian@ucdenver.edu)[ ](Emai: Mahshid.gorjian@ucdenver.edu)[Mahshid.gorjian@ucdenver.edu](Emai: Mahshid.gorjian@ucdenver.edu) ORCID: [https://orcid.org/0009-0000-9135-0687](https://orcid.org/0009-0000-9135-0687) [Correspondent Author:](Correspondent Author: Mahshid Gorjian)[ Mahshid Gorjian](Correspondent Author: Mahshid Gorjian) Abstract It is becoming increasingly important to predict property prices to mitigate investment risk, establish policies, and preserve market stability. To determine the practical utility and anticipated efficacy of the sophisticated statistical and machine learning models that have emerged, a comparative analysis is required. The purpose of this systematic study is to assess the predictive effectiveness and interpretability of hedonic regression and complex machine learning models in the estimation of housing prices in a wide range of foreign scenarios. In May 2024, a thorough search was conducted in Scopus, Google Scholar, and Web of Science. The search terms included "hedonic pricing models," "machine learning," and"housing price prediction," in addition to others. The inclusion criteria required the utilization of empirical research published after 2000, a comparison of at least two predictive models, and reliable transaction data. Research that utilized non-empirical methodologies or webscraped prices was excluded. Twenty-three investigations met the eligibility criteria. The evaluation was conducted in accordance with the reporting criteria of PRISMA 2020. Random Forest was the most frequently employed and consistently high-performing model, being selected in 14 of 23 studies and regarded as exceptional in five. Despite their lack of precision, hedonic regression models provided critical explanatory insights into critical variables, such as proximity to urban centers, property characteristics, and location. The integration of hedonic and machine learning models improved the interpretability and accuracy of the predicted results. Many of the studies included in this review were longitudinal, covered a diverse range of international contexts (specifically, Asia, Europe, America, and Australia), and demonstrated a rise in research output beyond 2020. Even though hedonic models retain a significant amount of explanatory power, the precision of home price predictions is improved by machine learning, particularly Random Forest and neural networks. The optimal results for researchers, real estate professionals, and policymakers who aim to improve market transparency and enlighten effective policy decisions are achieved through the seamless integration of these techniques. Keywords housing price prediction; machine learning; hedonic price model; Random Forest; real estate valuation; artificial neural networks; systematic review; property market analysis Introduction Importance of Housing Price Prediction It is essential to predict home prices, as residential property is the primary asset formost individuals and influences the overall macroeconomic trends (Mark & Kim, 2007) . The distribution of wealth, investment strategies, housing affordability, and market stability are all directly influenced by price differences. The combination of numerous property-specific and environmental factors results i

FAQs

What is the main goal of the systematic review?

To assess the predictive effectiveness and interpretability of hedonic regression versus complex machine learning models for housing price estimation across varied international contexts.

How were studies selected for inclusion in the review?

A search in Scopus, Google Scholar, and Web of Science used terms related to hedonic pricing models, machine learning, and housing price prediction, and required empirical research after 2000 with reliable transaction data and comparisons of at least two predictive models.

Which model performed best overall and why does it matter?

Random Forest was the most frequently used and consistently high-performing model across the included studies; the review also notes that hedonic regression provides important explanatory insights into key variables even when precision is lower.

Aug 3, 2026

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