The term “matching” refers to building suitable pairs. In the Re:Match context, a match is made between people seeking protection and welcoming cities by considering the needs and preferences of the former and the capacities and individual profiles of the latter. The matches created by our specifically developed algorithm ensure that the participating protection seekers and cities are compatible to the greatest extent possible. The goal is to improve relocation and reception processes and pave the way into a future co-determined by those affected.

There are many innovative ideas and approaches to matching protection seekers with geographical locations – but the focus rarely is on the individual preferences of the newcomers or the capacities of municipalities and cities. Re:Match closes this gap by centring its matching around directly asking cities and the affected protection seekers about their individual profiles and preferences and bringing them together via a bespoke preference-based algorithm.

Such a preference-based algorithmic matching serves several important functions: it centres individual priorities, capacities, and needs, prevents bias in matching, allows for analysis of large datasets, and makes the best possible matches for all participants given accommodation availability in participating cities. Verified data also serves the important function of baseline metrics for analysing relationships between quality of matches, programme satisfaction, and beneficiary integration outcomes.

You want to learn more about the Re:Match matching procedure? Check out chapter 4 of our interim evaluation report!

Algorithm-based and needs-oriented matching as a tool for migration management is gaining global traction.

Re:Match builds on and complements international experience in the field of matching. Interdisciplinary teams in several countries are currently exploring various matching-based migration strategies, including in the fields of resettlement and community sponsorship. Government-funded community sponsorship programmes in Canada, for example, have demonstrated success with integrating refugees into local communities and private households for several years now. Stanford University and ETH and their GeoMatch project  currently test their matching tool in a Swiss resettlement programme, focusing on predicting probabilities for certain integration outcomes. Meanwhile, the Annie™ MOORE machine learning software is being employed by a big resettlement agency in the United States to find placements for individuals who lack family ties in the US. The primary objective in these matching initiatives is to place individuals in locations that align with their labour market profiles, based on historic data and predictive analytics. The universities of Hildesheim and Erlangen-Nuremberg are jointly working on the Match’In project, developing a tool for the 4 participating German federal states to match asylum seekers/refugees in their reception centers more effectively with their cities.

Also, research publications consistently underscore the potential of matching as an effective approach.  With the development of a relocation tool for the European level that is based on human rights and strengthens the role of cities, Re:Match is making a significant contribution to this global movement. As a novel concept and practical approach, Relocation via Matching offers EU member states the opportunity to explore innovative distribution mechanisms alongside their national distribution systems. It also provides them the opportunity to integrate such mechanisms into their long-term strategies.