The housing market boom in Hungary, mapped

Category: Best visualization (small and large newsrooms)

Country/area: Hungary

Organisation: index.hu

Organisation size: Big

Publication date: 12 Nov 2019

Credit: Tamas Szemann

Project description:

The Hungarian housing market recently went through a price boom mostly affecting larger cities and the capital, partly as a result of the stabilisation following the economic world crisis and low interests. Budapest prices increased extraordinarily even in worldwide comparison, as over the past decade, prices quadrupled in numerous streets. We mapped this process, highlighting neighbourhoods and construction methods that were considered cheaper and less popular earlier, but were greatly affected by the changes. We attempted to show how housing became a severe problem for some in Budapest where others might have seen a profitable investment opportunity.

Impact reached:

The article achieved its goal, our readers received an easy-to-grasp overview of the processes on the housing market that made home purchases significantly more difficult for some while creating a lucrative investment opportunity for others. A fifth of Index’s daily visitors have read the original Hungarian article, a tenth of whom went on to share it on social media.

Techniques/technologies used:

We used the database of Open Street Map in order to show the streets of different cities, and we have filtered the data to show only residential areas. We used the cartography software Qgis to pair streets to zip codes and filter streets with duplicate names. From Qgis, we exported the map files into Tableau. We used Tableau to categorize and express values. After that, we transferred these files to Adobe Illustrator in an SVG format, where we finalised the design and created the different formats to suit ideal viewing on various devices (desktop, mobile, tablet). To display the final code and graphics, we used the Ai2html script, developed by the team at the New York Times.

What was the hardest part of this project?

The most difficult task was to marry the incomplete database with the similarly incomplete cartographic data. The most complicated question during the visualization process was how to express the increasing prices in a way that is obvious, simple, and easy to consume. Designing how to display the graphics conveniently on different devices also required considerable effort.

What can others learn from this project?

That a seemingly simple, but unique data visualization requires many technological layers and work processes.

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