Imagine you run a city and thousands of new residents show up each year, but you have no idea where they'll come from or when. FUME built computer models that look at what makes people move — jobs, climate, conflict, economics — and simulates where migrants are likely to go across Europe, all the way down to the city level. Think of it like a weather forecast, but for population movement. The team tested this across 8 European countries to help planners stop guessing and start preparing.
European cities are growing unpredictably due to migration, and businesses that depend on demographic forecasting — real estate, insurance, infrastructure, retail — are working with outdated or national-level data that misses where people actually settle. Poor population predictions lead to overbuilt housing in the wrong places, mispriced insurance products, and under-resourced public services.
FUME built a spatio-temporal simulation model for predicting migration flows across Europe at international, regional, and city levels. The project delivered 22 outputs including model implementation with technical documentation, covering initial model development, training, and optimization.
If you are a real estate developer trying to anticipate housing demand in European cities — this project developed spatio-temporal migration models that predict population inflows at city level under different future scenarios. Instead of relying on outdated census data, you could use migration forecasts to decide where to build next. The model covers patterns across 8 European countries.
If you are an insurance company that needs to model demographic shifts for pricing life, health, or property products — this project built simulation tools that map how regional socio-economic, environmental, and political changes drive population movement. With 9 research partners validating the models across 8 countries, these scenarios can feed directly into your actuarial risk projections.
If you are a consulting firm helping cities plan schools, hospitals, and transport for growing populations — this project created future migration scenarios showing how different demographic and economic challenges reshape migrant flows. The models work at regional and local scales, giving you data-driven inputs instead of rough national estimates when advising municipal clients.
Based on available project data, no commercial pricing or licensing model is indicated. FUME was a publicly funded research project coordinated by Aalborg Universitet. Access to models and outputs would likely require a research collaboration or licensing agreement with the consortium.
The project produced a demo deliverable described as 'Model implementation and technical documentation,' indicating a working model exists. However, with zero industry partners in the consortium, there is no evidence of commercial-scale deployment or real-time operational use. Significant engineering work would be needed to productize the models.
As an EU-funded Research and Innovation Action, IP typically remains with the consortium partners who created it. With 9 partners across 8 countries — 3 universities and 5 research organizations — licensing negotiations would involve multiple academic institutions. No patents or commercial IP are mentioned in the deliverables.
The project explicitly aimed to model migration at multiple geographical scales: international, regional, and local (city-level). The objective states that nearly all international migrants move to the largest cities, and the models were designed to capture these local patterns rather than relying on national-level data alone.
Based on the project objective, the models incorporate demographic, socio-economic, political, and environmental data to identify push and pull factors for migration. The keyword 'spatio-temporal modelling' and the demo deliverable referencing 'model training and optimization' suggest machine learning approaches applied to multi-source datasets.
The project was funded under the EU's MIGRATION-01-2019 topic, indicating alignment with EU policy priorities. The objective explicitly mentions supporting 'appropriate planning and policy-making.' However, no specific government adoption or regulatory endorsement is documented in the available data.
The project closed in May 2023, meaning the models reflect data and patterns up to that period. Post-2023 events like the Ukraine displacement crisis may not be fully captured. The models would need retraining with recent data to remain accurate for current forecasting.
The FUME consortium is purely academic — 9 partners across 8 countries (Austria, Denmark, Germany, Italy, Netherlands, Poland, Sweden, UK) with 3 universities and 5 research organizations but zero industry partners and zero SMEs. For a business buyer, this means the research is scientifically robust but has had no commercial validation. There is no built-in path to market, no industry feedback loop, and any commercialization would require finding a technology partner to productize the models. The geographic spread is a strength for pan-European coverage, but the absence of any private-sector involvement is a clear gap if you are looking for deployment-ready tools.
Aalborg Universitet, Denmark — reach the project coordinator through their research department or the project website contact page
Want to explore how FUME's migration forecasting models could inform your business planning? SciTransfer can arrange a direct introduction to the research team and help scope a licensing or collaboration agreement.
