Bidding Behaviour and Outcomes in Choice-Based Lettings

Bidding Behaviour and Outcomes in Choice-Based Lettings

Principal Investigator: Dr Jerry Chen, University of Cambridge
Council Partner: London Borough of Camden

Social housing is a vital public resource, and in England it is allocated through the choice based lettings (CBL) system. Instead of the council deciding who gets which house, CBL lets eligible residents browse available properties and bid on the ones they want. Although intended to promote transparency and choice, CBL often leaves residents making repeated decisions with limited information. Outcomes depend on complex interactions between types of property, household needs, and the distribution of points, so it’s not always easy for tenants to understand how the system works. Housing officers must manage fairness, expectations, and the workload created by unsuccessful bids and uneven demand.

This project proposes a platform to support transparent, fair and efficient decision-making in CBL. It will apply machine learning to nearly a decade of de-identified administrative data from Camden, to analyse demand patterns, bidding behaviour and how the points system works within the allocations system. The resulting platform aims to deepen understanding of how the CBL system works in practice. It aims to help officers understand and predict demand and behaviour, enabling more effective use of scarce housing resources. The platform also aims to improve the resident experience by making decision-making more transparent and reducing repeated unsuccessful bids.