Tourism for low income

There is some kind of magic in traveling far away and then coming back all changed, it leaves marks on your memory, on your consciousness, on your heart and on your body.

“Investment in travel is an investment in yourself”

That feeling of being anonymous in a city you’ve never been before, or to awaken quite alone in a strange town is one of the most pleasant sensations in the world.

Once that bug bites you there is no known antidote, and you will be happily infected until the end of your days.

“At first it leaves you speechless, and then turns you into a storyteller”

That said, we know that traveling is expansive and not everyone can afford to travel as much as they would like to.

My objective in this analysis is to find the cheapest locations to travel so people with low income can enjoy the pleasure of adventuring into new places.

I will try to find the disparity between the accommodation prices thought for tourists and the local prices among some of the most visited cities in the world.

To do that I am going to compare the average price of the accommodation listings in Airbnb and the GDP (gross domestic product) per capita.

Why Airbnb?

I’ve chosen Airbnb listings simply because usually they are cheaper than traditional hotel rooms and that fits the purpose of this analysis.

To get the Airbnb average price I had to download a dataset with the total listings for each city and then change the price value to USD so all cities follow the same pattern, I could get all those from a website called http://insideairbnb.com/ , a web with many datasets of the different cities gathered using airbnb API.

Why GDP per capita?

GDP is the primary measure of a country’s economic productivity, it shows the market value of goods and services it produces, that is, those that are bought by the final user, so with this measure we can get some hints about the local prices for each city.

For the GDP per capita for each city I scrapped tables from different websites with the most known cities and it’s GDP statistics.

Now let’s take a look at the graph we’ve got after plotting the table with both airbnb average and GDP per capita :

I used a scatter plotting visualization in order to show one data point for each city.

Down at the X axis we have the GDP per capita and to the left at the Y axis we have the average AirBnB price.

For visualization purposes I added different colors and symbols for each continent, and also I related the size of each data point with their Airbnb average price, so the bigger it is, the more expansive is the accommodation in that city.

Now let’s add a tendency line to see more insights about the relationship between accommodation price and GDP per capita:

The more distance a datapoint has from the tendency line will mean that the prices have less relation to it’s GDP Per capita.

So in Austin for example, we can see a huge distance from the trendline, that tells us that their arbnb pricing is way higher than it should be in relation to their local prices, in other words, their accomodation prices are fattened.

Now from this plot we can easily spot the cheapest deals overall the cities analized.

The cheapest cities for tourism would be Buenos Aires city, with an average Airbnb price of 52 USD/night and a GDP per capita of 23–24k and Mexico City, with an average Airbnb price of 76 USD/night and a GDP per capita of 19–20k.

In addition to this, there is another important factor you should consider, that is the traveling prices , that depend on the place you live in so i will leave that one to you .