Strategic underpricing and time-on-market: evidence from South Australia’s housing market

Clayton Pilat and Chris Leishman

International Journal of Housing Markets and Analysis, 2025, vol. 18, issue 7, 66-86

Abstract: PurposeThis study aims to examine how real estate agents in South Australia adapt pricing strategies to market conditions. Moving beyond behavioural explanations, it frames underpricing as a rational response to liquidity. Using a machine learning benchmark for fair value and a lagged time-on-market (TOM) indicator as a proxy for liquidity at listing, the analysis shows how strategies vary across hot and cold markets. The findings provide context-specific evidence that underpricing is most aggressive in liquid markets, highlighting its role as a market-driven practice with implications for housing economics, policy and regulation.Design/methodology/approachUsing a data set of approximately 55,000 private treaty property transactions from 2018 to 2024, the study applies a machine learning-based price prediction model (Random Forest) to estimate expected market values. Listing strategies are categorised by quantiles of underpricing relative to predicted prices. A multinomial logistic regression model is used to assess how agent strategies correlate with lagged TOM, location and property attributes.FindingsThe results show that agents are significantly more likely to adopt aggressive underpricing strategies when market liquidity is high. As TOM increases, this indicates cooler market conditions where agents reduce the intensity of underpricing or adopt at-market pricing. Descriptive statistics also confirm a clear relationship between underpricing and shorter TOM.Research limitations/implicationsThe analysis is limited to South Australian private treaty sales and may not generalise to other markets or auction contexts. While the predictive price model incorporates neighbourhood-level affordability via Socio-Economic Indexes for Areas Index of Relative Socio-Economic Advantage and Disadvantage, unobserved agent- and seller-specific characteristics (such as experience, expectations or urgency) remain outside the scope of the data. The lagged TOM indicator provides a robust proxy for liquidity but cannot fully capture real-time buyer sentiment or pandemic-era structural shocks. Finally, the categorisation of underpricing simplifies inherently continuous pricing decisions, and the results should be interpreted as relative rather than absolute effects.Practical implicationsThe results provide actionable insights for regulators and practitioners in South Australia. They suggest that underpricing is most aggressive in hot markets and should not always be conflated with deceptive underquoting. This highlights the value of adaptive policy benchmarks and the need for agent training that balances strategic flexibility with transparency.Social implicationsThe findings underline that pricing strategies directly shape affordability, buyer experiences and perceptions of fairness in the housing market. In highly liquid markets, aggressive underpricing can fuel competition and disadvantage marginal buyers, reinforcing inequality. Improving transparency and distinguishing between legitimate strategy and misleading practice can help protect consumer trust and market integrity.Originality/valueThis paper advances the housing economics literature by reframing underpricing as a rational, market-driven strategy rather than a behavioural anomaly. It contributes methodological novelty through the integration of a machine learning-based pricing benchmark and a lagged TOM indicator as a liquidity proxy, enabling a more precise characterisation of market conditions at the time of listing. By providing context-specific evidence from South Australia, the study extends international research and clarifies how agents adapt strategies dynamically in response to liquidity, offering both theoretical and policy-relevant insights.

Keywords: Australia; Housing; Housing markets; Housing prices; Housing market analysis; Buying (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eme:ijhmap:ijhma-05-2025-0126

DOI: 10.1108/IJHMA-05-2025-0126

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