Introduction

Which are the booming cities in North Germany? We will get to the bottom of this question in the following study! The underlying criteria for a city to be a „boom town“, in our opinion, is growth. This can be reflected in a growing overall population, increase in purchasing power, and also in increasing rents.

Hence, we will consider different factors to calculate a general city ranking of 15 chosen cities of North Germany for our evaluation. We will examine the main boom factors for the highest-ranked cities and evaluate why some cities might not be considered a „boom town“ in this study.

Data and methods

To calculate a ranking, we have first defined different criteria as follows:

The first one is the population. In this case, we will only consider population changes since these are more meaningful in this context. Therefore, we will use the values of 2017 and compare them to the values of 2020, showing the changes over this three-year-period. Since the influx of younger people (< 30 years) is a strong sign of a boom town, in our opinion, will consider this change as well. This leads to our first two categories:

Total Population change (2017 - 2020): \[ \begin{equation*} \text{Pop_change} = (\frac{\text{Pop}_{2020}}{\text{Pop}_{2017}} \cdot100)-100 \end{equation*} \]

Change of the percentage of people under 30 years old (2017 – 2020): \[ \begin{align*} \text{Pop30_percent}_{2017} &= \frac{\text{Pop30}_{2017}}{\text{Pop}_{2017}} \cdot100 \\ \text{Pop30_percent}_{2020} &= \frac{\text{Pop30}_{2020}}{\text{Pop}_{2020}} \cdot100 \\ \text{Pop30_change} &= \text{Pop30_percent}_{2020} - \text{Pop30_percent}_{2017} \end{align*} \]

The second criterion considers wealth and economy. This includes the purchasing power index and the average gross income. We will only consider their changes again, because a city can be considered booming if the economic values are increasing rather than staying at the same level over the years. This might be a factor for steadiness and prosperity, but not necessarily for a boom town.

Change of gross salary (2017 – 2020): \[ \begin{equation*} \text{Salary_change} = (\frac{\text{Salary}_{2020}}{\text{Salary}_{2017}} \cdot100)-100 \end{equation*} \]

Change of purchasing power index (2017 – 2020): \[ \begin{equation*} \text{PP_change} = (\frac{\text{PP}_{2020}}{\text{PP}_{2017}} \cdot100)-100 \end{equation*} \]

The third criterion considers the net rents. One could argue that consistent or even dropping rents seem attractive for people to move to a city. However, increasing rents in general show an upturn in the economy, attract investors, and represent the appeal of this location. hence, for this study, we will consider the increase in rent prices and let this influence the rating in a positive way.

Change of net rents (2017 - 2020): \[ \begin{equation*} \text{Rent_change} = (\frac{\text{Rent}_{2020}}{\text{Rent}_{2017}} \cdot100)-100 \end{equation*} \]

All of these categories will then be rated individually with 1 being the lowest and 5 being the highest rating. The rating will be carried out by a grouping based on the values. This means that the lowest 20% of all values will get a rating of 1 […] and all values in the upper 80% and higher will get a rating of 5. This is carried out separately for all 5 categories. Afterwards, we will calculate the mean value of all 5 categories to build the final rating of the city. The following example should illustrate this strategy:

Example of the ranking system
Categories Rating
Total Population change 2.0
Change in people <30 3.0
Change of Gross Salary 5.0
Change of Purchasing Power Index 3.0
Change of Net Rent 4.0
Final rating 3.4

Mapping the results

The interactive map below shows the final ranking of the fifteen cities based on all the aforementioned categories which can also be individually selected. These categories are: total population change (2017 - 2020), change of population under 30 years old (2017 - 2020), change of purchasing power index (2017 - 2020), net rent change (2017 - 2020), and change of the average gross salary (2017 - 2020).