In recent years, artificial intelligence (AI) has revolutionized numerous sectors, with machine learning algorithms becoming increasingly prevalent in complex decision-making processes. Among these transformative applications is the use of AI for housing price appraisal—a domain traditionally dominated by human expertise and conventional econometric models. A groundbreaking study conducted in South Korea now reveals an unsettling disparity in how AI algorithms impact housing valuations across neighborhoods differentiated by educational attainment. This research highlights a growing concern about algorithmic equity, underscoring significant social consequences stemming from the deployment of AI in real estate markets.
The study meticulously compares machine learning (ML) algorithms with traditional hedonic pricing models (HPMs) across four metropolitan cities in South Korea, focusing on apartment buildings due to their market prevalence. Housing price appraisals, fundamental to urban planning, financing, and investment decisions, have evolved with AI, enabling nuanced analyses that surpass classical models in precision and complexity. However, this increased sophistication is not without pitfalls—AI models, exhibiting greater flexibility, can inadvertently exacerbate existing societal inequalities, as evidenced by the divergent effect on neighborhoods stratified by educational levels.
In less-educated neighborhoods, the research finds that AI algorithms systematically depreciate housing prices relative to valuations derived from conventional models. This depreciation may be linked to algorithmic sensitivity to variables correlated with lower educational attainment, which disproportionally penalize these communities in housing market appraisals. Conversely, in well-educated neighborhoods, AI valuations exhibit a pronounced appreciation trend, favoring these areas and leading to more favorable price assessments than those from HPMs. This polarization intensifies the valuation gap, consequentially reinforcing socioeconomic divides through housing affordability challenges.
The disparity arises primarily from the inherent adaptability of machine learning models. Unlike rigid econometric models, ML algorithms uncover complex, nonlinear relationships among fluctuating variables and generate valuations that better fit training data. Yet this flexibility, while enhancing predictive accuracy, imparts susceptibility to embedding and amplifying social biases present in input features, such as education levels, neighborhood amenities, or socioeconomic indicators. Thus, AI valuation frameworks may inadvertently codify existing inequities into seemingly objective price estimates.
Such findings carry profound repercussions for various stakeholders—homebuyers, policymakers, financial institutions, and urban planners. The differential impact of AI appraisal systems risks deepening residential segregation and social exclusion by systematically undervaluing properties in less-educated areas, potentially discouraging investment and perpetuating disinvestment cycles. For decision-makers, acknowledging that AI tools can perpetuate or even escalate inequality is essential to instituting checks and balances within appraisal methodologies.
The authors advocate for careful incorporation of variables that might induce inequality into AI model design, underscoring the necessity for transparency and fairness in algorithmic decision-making. This involves rigorous scrutiny of training data, feature selection, and model validation to detect and rectify asymmetries in valuation outputs. Regulatory frameworks are also called for, specifically tailored to address negative externalities arising from AI algorithm deployment in real estate markets. Targeted policy intervention can provide guardrails to prevent marginalization of vulnerable groups via skewed pricing mechanisms.
In particular, the FinTech sector, which increasingly relies on AI-based valuation systems, must prioritize monitoring models for asymmetric effects during both development and implementation stages. Validation protocols should incorporate metrics beyond mere prediction accuracy, evaluating fairness and equity implications to mitigate biased outcomes. This is imperative as AI-driven valuations increasingly influence mortgage underwriting, pricing negotiations, and investment decisions, making equitable outcomes vital for social sustainability.
Despite the study’s current focus on apartment buildings in Seoul, Busan, Daegu, and Incheon, its conceptual framework possesses wider applicability. Researchers can extend this analytical lens to different types of housing, such as single-family homes or commercial real estate, and to other geopolitical contexts where educational segregation shapes urban landscapes. By broadening scope, the interplay between AI valuation effects and social stratification can be better understood across diverse housing markets globally.
Moreover, while education proved a pivotal factor in driving valuation asymmetries, the research underscores the need to incorporate additional social and environmental variables. Factors such as air quality, urban greenery, noise pollution, and access to amenities have strong bearing on housing desirability and affordability, yet remain underexplored in the context of AI appraisal fairness. Future scholarship should thus integrate environmental justice concerns alongside socioeconomic indicators to holistically evaluate AI’s impact.
The longitudinal tracking of individuals’ educational trajectories also promises to enrich understanding by linking micro-level educational achievements to housing affordability and market dynamics. This approach could unearth causal mechanisms by which educational attainment influences residential segregation patterns mediated by AI valuations. Such micro-level insights would further empower developers of AI models with the nuanced data necessary for reducing systemic bias.
Addressing the tradeoffs between enhanced prediction precision and the emergent unbalanced pricing remains a formidable challenge intrinsic to AI valuation. Fine-tuning machine learning algorithms to retain accuracy while dampening disproportionate effects demands innovative training strategies, including fairness-aware learning and bias mitigation techniques. Improving model interpretability can also facilitate stakeholder engagement, promoting accountability and trust in AI-powered appraisal processes.
The implications of this study resonate beyond academic circles. They signal a pressing need for interdisciplinary collaboration among data scientists, urban economists, social scientists, and regulatory bodies to create equitable AI applications. Housing markets, intimately tied to social welfare, reflect and reinforce patterns of equality and exclusion. As such, wielding AI responsibly in this sphere is imperative to fostering inclusive urban development and mitigating systemic inequities entrenched over decades.
In sum, this research elucidates the paradoxical nature of AI advances in housing price valuation: while technology accelerates analytical capabilities, it simultaneously risks entrenching social disparities if left unchecked. The insights emphasize that adopting AI in real estate demands vigilance, holistic modeling, and targeted governance to ensure these powerful tools uplift rather than undermine social equity. As digitalization reshapes urban realities, embracing such balanced approaches will be critical for building resilient, just cities of the future.
The study’s findings compel policymakers, developers, and researchers alike to reevaluate the promises and perils of AI in property markets. Integrating fairness assessments, continuous model audits, and responsive regulatory measures can transform AI from a tool of accentuated inequality into one of inclusive progress. Ultimately, deploying AI responsibly presents an opportunity to craft more accurate, transparent, and just valuation systems—strengthening foundations for equitable access to housing and sustainable urban growth.
Subject of Research: Asymmetric impacts of artificial intelligence on housing price valuation across education levels in South Korea, focusing on the comparative performance of ML algorithms versus traditional hedonic pricing models in metropolitan apartment markets.
Article Title: Asymmetric impacts of artificial intelligence on housing price valuation across education levels.
Article References:
An, S., Song, Y., Jang, H. et al. Asymmetric impacts of artificial intelligence on housing price valuation across education levels. Humanit Soc Sci Commun 12, 1884 (2025). https://doi.org/10.1057/s41599-025-06153-4
Image Credits: AI Generated
