Inconsistent regulations across regions (Abdul-Rahman et al., 2021)

Ambiguity around AI accountability (Zulkifley et al., 2020)

Policy resistance to AI-driven decision-making (Abidoye et al., 2019)

Data privacy and protection (Y. Yang et al., 2023)

Government bodies can standardise AI regulation to ensure fairness (Abut et al., 2023)

Valuers and property firms can push for clear legal guidelines to reduce liability risks (Almaslukh, 2020)

Industry leaders can advocate for policy framework supporting AI adoption (Chandu and Bharatha Devi, 2023)

Technology developers can design privacy centric AI models to meet compliance standards (Borodulin et al., 2024)

High implementation and maintenance costs (Forys, 2022).

Uncertainty in AVMs’ long-term ROI (Genc et al., 2025)

AVMs’ inability to fully capture market dynamics (Gude, 2024)

Impact of economic downturns on AI innovation (Habbab et al., 2025)

Property firms can invest in AI to enhance cost efficiency and valuation accuracy (Hanuma Reddy and Sriramya, 2022)

Investors can mitigate risks by piloting AI projects before full-scale adoption, testing models such as SVM and DT for reliable turns in property investments (Cekic et al., 2022; Chou et al., 2022)

Analysts can combine AI insights with traditional valuation methods, using models such as Regressions and RF to enhance their understanding of market behaviour (Gnat, 2024)

Financial institutions can use AI to identify emerging real estate opportunities during downturns, using SVM and ANN (Habbab et al., 2025)

Public distrust in AVMs (Hong et al., 2020)

Fear of job displacement in valuation roles

(Sun and Peng, 2022)

Perceived bias in AI algorithms (Tran et al., 2025)

Limited public understanding of AI processes (Y. Yang et al., 2023)

Real estate agencies can build trust by promoting AI transparency and fairness, especially with explainable AI models such as DT and Regression (Tran et al., 2025)

Valuers can utilise AI for routine tasks while focusing on complex appraisals, using AI models such as ANN and CNN to assist in AVM processes (Vargas-Calderón and Camargo, 2022)

Educational bodies can offer training programs on AI’s role in real estate valuation, helping agents understand how models such as regressions and RF work in practice (Lee et al., 2024; Li et al., 2021)

Limited interpretability of complex AI models (Phan, 2019)

Data quality and integration issues (Vestly et al., 2024)

Rapid technological changes outpacing policy (Yakub et al., 2021)

Difficulty adapting AI to local market (Vargas-Calderón and Camargo, 2022)

Developers can prioritise explainable AI to ensure stakeholder trust and transparency (Przekop, 2022), focusing on models such as DT and Regression for better clarity (Rodriguez-Serrano, 2025)

Data providers can enhance data sharing frameworks to improve AI model performance, enabling better integration of models such as RF and SVM with real estate data (Sun and Peng, 2022)

Policymakers can work closely with AI experts to stay ahead of emerging technologies, ensuring that models such as ANN and CNN are continuously adapted to the real estate sector (Yakub et al., 2021)

Undefined legal liability for AI decisions

(Kmen et al., 2024)

Compliance with multiple jurisdictional laws (Lahmiri et al., 2023)

Data ownership and intellectual property issues (Cekic et al., 2022)

Legal advisors can ensure AI models such as SVM and Regression comply with standards (Danona et al., 2023)

Global firms can ensure AI models such as RF and CNN meet multi-region compliance standards (Cekic et al., 2022; Gampala et al., 2022)

Data owners can set clear agreements to ensure responsible use of SVM and RF models, protecting IP rights (Gnat, 2024; Hjort et al., 2022)

Lack of environmental factor integration in AVMs (Borodulin et al., 2024)

Limited AI ability to predict long-term impacts

(Abut et al., 2023)

Environmental agencies can push for AI models that account for climate risks (Gampala et al., 2022), incorporating environmental data into models, such as ANN and CNN, to improve long-term property valuations (Gnat, 2024)

Sustainability advocates can promote AI for long-term environmental analysis (Habbab et al., 2025), using models such as RF to predict how properties will be impacted by environmental factors (Hoxha, 2024)