The effect of nearby listings on house sale prices in Sydney: A spatio‐temporal regularization approach

Abstract We estimate the price impact of very nearby concurrently listed properties in the Sydney housing market and assess their competition effects. We apply a hedonic model with spatiotemporal effects regularized via a graph Laplacian prior at the month‐by‐SA2 regional level to seven SA4 subregions of metropolitan Sydney. The model structure enables localized identification of the effect of nearby active listings, while controlling for local trends in a data sparse environment. We find that an additional active listing within 250 m reduces the sale price by 0.5%–1.3%, of which 0.3%–0.9% reflects the estimated competition effect when compared to the impact of listings further away. In contrast, nearby listings that have been settled show no consistent effect, highlighting that the price discount arises from buyer perceptions of competing alternatives during the active sale window, rather than supply conditions. The effect is strongest in middle‐tier regions such as Parramatta and Blacktown, and weaker in the inner core and outer fringe. These findings are relevant to sellers and agents, as very nearby competing listings that are active at the time of sale arise in 10%–25% of cases in Sydney.

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Publication Details

Journal
Real Estate Economics
Published
2026-03-29
DOI
https://doi.org/10.1111/1540-6229.70046
Primary Topic
Housing Market and Economics
Type
article
Field-Weighted Citation Impact
0.00
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article

The effect of nearby listings on house sale prices in Sydney: A spatio‐temporal regularization approach

Mengheng Li, Willem P. Sijp
Real Estate Economics
Housing Market and Economics
article

The effect of nearby listings on house sale prices in Sydney: A spatio‐temporal regularization approach

Mengheng Li, Willem P. Sijp
article en

Abstract

Abstract We estimate the price impact of very nearby concurrently listed properties in the Sydney housing market and assess their competition effects. We apply a hedonic model with spatiotemporal effects regularized via a graph Laplacian prior at the month‐by‐SA2 regional level to seven SA4 subregions of metropolitan Sydney. The model structure enables localized identification of the effect of nearby active listings, while controlling for local trends in a data sparse environment. We find that an additional active listing within 250 m reduces the sale price by 0.5%–1.3%, of which 0.3%–0.9% reflects the estimated competition effect when compared to the impact of listings further away. In contrast, nearby listings that have been settled show no consistent effect, highlighting that the price discount arises from buyer perceptions of competing alternatives during the active sale window, rather than supply conditions. The effect is strongest in middle‐tier regions such as Parramatta and Blacktown, and weaker in the inner core and outer fringe. These findings are relevant to sellers and agents, as very nearby competing listings that are active at the time of sale arise in 10%–25% of cases in Sydney.

Real Estate Economics
University of Technology Sydney (AU), Neovacs (France) (FR)
Sustainable cities and communities
Openalex Percentile: Top 2%
Housing Market and Economics
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