Automated Valuation Models (AVMs) are typically trained by learning to replicate observed housing transaction prices. This paper argues that such benchmarking is theoretically debatable. Market transaction prices are not direct measures of underlying property value but are realised outcomes of exchange processes that involve buyer-specific attributes that are unobservable prior to sale. Using residential housing transactions from Auckland, New Zealand, and buyers’ gender inferred from unstructured purchaser name data via artificial intelligence-based natural language processing, we provide empirical evidence that buyer attributes systematically affect transaction prices. Specifically, gender composition is shown to influence the discrepancy between AVM estimates and transaction prices, while no corresponding effect is found when AVMs are compared with capital values, which are the Council’s appraisals for rating purposes. This asymmetry reflects the shared information set of AVMs and professional appraisals, as both are based only on property and market information available prior to sale and do not incorporate buyer identity. The findings provide initial evidence for valuers to address the latest professional requirements of using AVMs.
Yiu et al. (Sat,) studied this question.