Demystifying planning application uncertainty: Using machine learning to predict and explain planning application assessment timeframes

William Thackway, Balamurugan Soundararaj and Christopher Pettit

No prm25_v1, SocArXiv from Center for Open Science

Abstract: Despite housing supply shortages in financialised housing markets and acknowledgement of planning application (PA) assessment times as a supply side constraint, reliable and accessible information on PA assessment timeframes is limited. It is in this context that we built a model to predict and explain PA assessment timeframes in New South Wales, Australia. We constructed a dataset of 17,000 PAs (submitted over 3 years) comprising PA attributes, environmental and zoning restrictions, and features derived from PA descriptions using natural language processing techniques. Quantile regression was applied using machine learning modelling to predict probabilistic intervals for assessment timeframes. We then employed an advanced model explanation tool to analyse feature contributions on an overall and individual PA basis. The best performing model, an extreme gradient boosted machine (XGB), achieved an R2 of 0.431, predicting 60.9% of assessment times within one month of actual values. While performance is moderate, the model significantly improves upon previous studies and the current best practice in NSW, which is simply average estimates, by council area, for PA assessment timeframes. The paper concludes by outlining suggestions for further improving model performance and on the benefits of a predictive tool for planners.

Date: 2025-09-18
New Economics Papers: this item is included in nep-cmp
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:prm25_v1

DOI: 10.31219/osf.io/prm25_v1

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