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Digital tenancy application technologies (DTATs) are becoming the dominant means through which renters in the private rental sector (PRS) apply for housing. These PropTech tools, which claim to streamline application processes to save renters and lessors time and effort, necessitate the collection of data. Though the collection of certain data – such as income and rental history – has long been a standard part of rental application processes, DTATs now facilitate the collection of additional data including social media activity, behavioural data, and more. Increasingly, DTATs offer the ability to 'make sense' of this data, evaluating applicants through the use of algorithms. Drawing on lessons from banking and insurance sectors, this article outlines how DTAT algorithms can reshape individuals' access to essential services delivered through competitive markets. It explains how algorithmic processes can introduce and exacerbate the unfair and unlawful treatment of renters, which can result in significant harms. To identify, redress, and prevent these harms, I argue that it is crucially important to use shared terminology to describe how DTATs are collecting and using data. This article introduces a framework for understanding how algorithms 'screen' and 'sort' applicants based on the data that is collected through DTATs. The process of 'sorting' is further broken down into three categories – 'scoring', 'rating', and 'ranking'. The article concludes by explaining how this framework can assist researchers and policymakers to identify, analyse and prevent harms that are catalysed, or exacerbated by DTATs.
Linda Przhedetsky (Mon,) studied this question.