How well can the migration component of regional population change be predicted? A machine learning approach applied to German municipalities

For several decades, demographic forecasts had predicted that the majority of Germany's cities and rural areas would experience population decline in the early 21st century. Instead, recent trends show a growing population size in three out of every four German districts. As a result, there are currently severe shortages of housing and childcare in regions that were projected to decline but have instead grown in recent years. Other regions, by contrast, continue to lose young people in particular. Most of these differences between regions stem from within-country as well as international migration. An important question for both regional demographic research as well as local policy-makers is thus how well net migration rates in cities and rural districts can be predicted several years into the future. In this study, we develop models that predict migration (both within-country as well as international migration) at the level of municipalities for two demographic groups, namely young people aged 18 to 24 years, and families (people aged 30 to 49 years and underage children). We collect data on economic, demographic and other characteristics such as distances to large cities or universities for around 3,000 German municipalities (Gemeinden). The model is trained on a subset of these data from the period 2005-2009 and predicts net migration rates among young people on an unseen test dataset in the future (i.e. for the period 2011-2015). The results show that the model can predict future net migration by young people aged 18 to 24 years reasonably well (R² > 0.5), although there were quite significant changes during the period under study, for example refugee immigration to Germany. Family migration, on the other hand, cannot be predicted equally well (R² = 0.25). Some important lessons emerge concerning the predictability of regional and international migration and the usefulness of demographic forecasts for local policy-makers.