Outdated law and professional conservatism, rather than faulty algorithms, are stalling adoption of valuation technology in New Zealand; modernising regulation, building secure national data infrastructure and targeted training are needed to unlock more accurate valuations and greater market confidence.

Main Finding

Adoption of valuation technology (VTech) in property valuation is constrained mainly by institutional, regulatory and cultural barriers rather than technical limitations. The study reframes uptake as a legitimacy-seeking process: valuers and firms resist VTech until legal frameworks, professional norms and data governance provide trusted pathways for use. Left unaddressed, this institutional inertia poses risks for asset pricing, collateral risk modelling and investor confidence in property and financial markets.

Key Points

• Primary barriers
  • Institutional conservatism and professional logics that prioritise human judgement over automation.
  • Outdated regulation (e.g., Valuers Act 1948) that limits modern practice and experimentation.
  • Fragmented data infrastructure and sovereignty/privacy concerns that inhibit secure data sharing.
  • Cultural factors: generational divides, protectionist attitudes within the profession, and fear of automation.
• Perception of technology
  • Practitioners view VTech as an aid to, not a replacement for, professional judgement.
  • Adoption is driven more by needs for legitimacy, trust and compliance than by pure efficiency gains.
• Framework contribution
  • An institutionally mediated diffusion of innovations (IDOI) framework synthesises Rogers’ diffusion of innovations with institutional theory to explain how professional logics mediate perceptions and uptake of innovation.
• Market-level consequences
  • Broader VTech adoption can improve valuation accuracy, portfolio transparency and collateral risk assessment.
  • Institutional inertia can exacerbate information frictions, mispricing and systemic vulnerabilities in property-backed lending.

Data & Methods

• Design: Qualitative, semi-structured interview study.
• Respondents: Valuers, firm leaders and regulators in New Zealand (participants drawn from the professional and regulatory ecosystem; exact sample size not reported in the summary).
• Analysis: Thematic analysis guided by Rogers’ diffusion of innovations and institutional theory, culminating in the IDOI conceptual framework.
• Scope/limitations: Context-specific (New Zealand) and qualitative—findings emphasise mechanisms and meanings rather than quantitative effect sizes.

Implications for AI Economics

• For asset pricing and information economics
  • Reduced adoption of VTech maintains higher information asymmetries in property markets; wider adoption could compress pricing dispersion and improve market efficiency.
  • Institutional barriers create non-technical frictions that must be modelled when forecasting the pace and productivity gains from AI-driven valuation tools.
• For credit risk and systemic stability
  • Improved, more transparent valuations through VTech can strengthen collateral risk models and lender monitoring, reducing tail risk in mortgage and commercial property exposures.
  • Slow diffusion increases the chance of heterogeneous valuation quality across lenders, elevating systemic risk from correlated misvaluation.
• For policy and regulation
  • Priorities: modernise valuation law/regulation, create secure national data infrastructure (interoperability and sovereign control), and implement targeted digital training to build legitimacy and competence.
  • Regulatory sandboxes, certified-data standards and third-party validation of VTech outputs can accelerate trustworthy adoption.
• For research and implementation
  • Quantify macroeconomic impacts: estimate how VTech adoption alters pricing volatility, credit spreads and default correlations.
  • Cross-country comparative work to test the transferability of the IDOI framework in different regulatory regimes.
  • Design pilot interventions (data platforms, certifications, training) and evaluate causal effects on valuation quality and lending outcomes.

Overall, addressing institutional and governance barriers is crucial for realising the economic benefits of AI and automation in property valuation and for mitigating market-level risks associated with uneven diffusion.