Solving burglary offences: building a model to predict clearance of burglary following initial investigation

Research output: Contribution to journalArticlepeer-review

Colleges, School and Institutes

External organisations

  • University of Cambridge


This research identifies solvability factors for burglary offences, and develops previous research by building, and testing, an algorithmic prediction model for solvability of burglary offences. It is based on a full population of 9,655 burglary offences reported to a UK Police force between 1st April 2012 and 30th April 2015. The dataset was split in half, with half being used to build the model and half to test it. Thirty-one solvability factors were identified, along with nine case-limiting factors, allowing a logit model to be built to predict solvability of burglary offences. When compared to a model of investigating all burglary offences, use of this model would reduce investigative workload by up to 42.2%, or 1,321 cases per year.


Original languageEnglish
Number of pages16
JournalPolicing: A journal of Policy and Practice
Early online date21 Feb 2019
Publication statusE-pub ahead of print - 21 Feb 2019


  • clearance, solvability, burglary, prediction, algorithmic prediction, predictive accuracy