Data on the rentals in the Netherlands
By: Manabu Togawa
Tags: Netherlands, Property, Data Science

It is becoming increasingly difficult to find a rental property in the Netherlands. For my data science exercise, we’ll use Kaggle’s dataset to analyse an overview of the rental market in the Netherlands.

Questions.

Which area is most expensive to live? Compare price and space, what type of property is good value? Which feature have the greatest impact on price?

Prepare Dataset

I searched in Kaggle and found Juan Gesino uploaded 245MB of RentalProperties Dataset in the Netherlands.

There’re 46,000s of data from 2019–07–14 to 2020–03–03

data describe

histgram of modified dataset

Overview of dataset

Which area is most expensive to live?

10 most popular cites overview.

If you want to live in an apartment, Amsterdam is the most expensive city with an average of 1600€/month, followed by Den Haag at 1250€/m and Enschede, Rotterdam and utrecht at around 1100€/m.

For a room, 700€/m in Amsterdam, but the price per square meter will be the highest at 50€/m.

2. Compare price and space, what type of property is good value?

Obviously, the wider the size, the higher the price from this plot.

Rooms are small but the cheapest, while apartments range in size and price.

It is also clear that apartments can have a larger range of price and size compared to rooms.

We will find that renting an Anti-squat is a constant price regardless of the size of property. But Anti-squat is a temporary residence and not linked to any other property, so we will delete this data to create a model.

City is also big factor in the rent price

This is the top 10 number of properties listed per city.

This plot is the rent and distance from the Amsterdam central station.

There appears to be a large city in the area that is divided into several groups.

This plot is from the closest distance and price from the 10 cities above.

Prices for properties within 10km of the center are particularly expensive, and within 25km appears to be within the city area.

The further from the city, the cheaper it gets, but room prices do not drop below a 500euro level.

To find the answer of the second question, let’s look at price per area and area chart.

It is obvious that the price per unit area of the room is higher. The larger the area, the exponentially lower the price per unit area.

If you are a landlord, renting out a smaller room at a higher price seems to be more profitable, but if you are a tenant, renting a larger apartment seems to be a better choice!

Create Model

3. Which feature have the greatest impact on price?

Created LinearRegression model from this dataset.

From this dataset, I created two LinearRegression models with targets Rent and Rent/sq2

Model1 : Target Rent

This model’s r2 score is 0.797.

From this table, we can see that single-person rooms are a -200 euro factor in rent price.

Table blow is only the data related to the city, this is distance(km) from city, so to increase rent need to have distance from Delft and close Amsterdam / Rotterdam

Model2 : Target Rent / sq2

From this model, it turns out that to maximize the rental price per area, it is better to provide a room that is fully furnished and has a shared living room. And far from Delft, close to Rotterdam.

Conclusion

Using 46,000 data about rental properties in the Netherlands, we were able to find the answers, city with the highest rent, the property with the best value for the price per area, and the factors that determine the price.

If you are a lender, you can use this model to set the optimal price.

If you are a tenant, you should be able to find cost-effective properties by looking for properties that outliner from this model.

You can find python code from https://github.com/togawamanabu/DataSciencenND_prj1_netherlands_rental_price