There has recently been a flurry of activity by self-made mappers on the net that major media have noticed. It seems that proliferation of tools such as the excellent TileMill does help to make custom maps a relatively painless, yet still laborious process.
In my experience, a major hurdle in this process is getting good data. Governments and corporations around the globe have made acquiring the goods easier, but the quality frequently leaves one wanting. More about this particular dataset later.
This map is my attempt to visualize real estate prices in Slovenia. Buildings are colored according to the most expensive unit they contain, except in some cases where data is bad. More below.
See the map!
- A map of real estate prices in Slovenia.
About the dataset
This dataset is provided by GURS, a government institution. I used it before, to make the map of structure ages in Ljubljana. It comes in a variety of formats, such as SHP (geometry) and text (building properties) files, which were clearly dumped from database tables.
It has some severe problems. For example, some bigger and more expensive buildings contain many units, but these units all hold the same value regardless of their useful area. To make matters more complicated, other multiunit buildings don’t hold the same value for the units they contain. They are, in other words, evidenced correctly. Then, there are building compounds, like the nuclear power plant in Krško, in which every building clearly holds the exorbitant value of entire compound. Some other buildings have price value as zero, and so on.
All of this doesn’t even start to address the quality of valuation the government inspectors performed. In the opinion of many property owners, the values are too low. There’s a new round of valuation coming, in which the values are reportedly bound to drop by further five to twenty percent, if I remember correctly. It will be interesting to make another map with the valuation differences some day.
Massaging the data
This means that the above map is my interpretation of the dataset beyond the visualization itself. In calculating values for visualization, there were several decisions I made:
- For multiunit buildings, I calculated the cost of square meter for every unit, then colored the building with color value of the most expensive unit. This was necessary, because some buildings contain many communal areas, garages and parking lots, which are all independently valued. I first tried with a simple average value, but the apartment buildings with many parking boxes and garages were then valued deceivingly low. I tried to make the map more apartment-oriented, so this was a necessary decision to make it more accurately reflect the market.
- For incorrectly evidenced buildings with same value (high) unit value, I took the price of one unit, divided by sum of unit areas. I could do this on one unit only, but which one? There’s no easy answer. The average seemed the way to go.
I also made a list of the most expensive buildings by their total Euro value. Individual unit values were summed, except in cases described in the second bullet point above. there I simply took the price of one unit. It’s accessible as a separate vector layer under “Most expensive buildings” menu item.
Turns out the most expensive buildings are mostly power plants, which is not surprising. In Ljubljana, two of the most expensive buildings were completed recently. Well, the Stožice stadium was not really completed. I don’t know whether it was paid for or not – this is a discourse best suited for political tabloids. See the gallery:
It’s also hardly surprising that the capital and the coast are areas with the most expensive real estate available. The state of city of Maribor is sad to see, though, at least in comparison to Ljubljana.
I suggest taking the tour in the map itself, where I go into a little more depth for some towns and cities. Also, be sure to click the “Most expensive buildings”, then hovering the mouse pointer over highlighted buildings to get an idea of their total cost and price per square meter, which in many cases diverges dramatically.
Here are two charts showing price/m2 distribution at different intervals in time.
This one is an all-time chart. Most buildings are valued low, since all ages were taken into account.
This one shows the period between year 2008 and now, in other words, since the crisis struck. Nevertheless, more expensive buildings seem to prevail. No wonder, since they are new. But that probably also means that there’s more apartment building construction relative to countryside development. I’m not really a real estate expert, so if anyone has a suggestion, comment away.
Inspiration for the tour was this excellent visualization by the Pulitzer center.
I also have to thank the kind people at GURS for providing me with data. They know it’s flawed somewhat, but all in all it’s not so bad.
As I’ve noted before, this map is a result of my interpretation of government data. I’m in no way I responsible for any misunderstandings arising from this map. If you want to see the actual valuation of your building or building unit, please consult GURS or use their web application to find out.
Structure ages map in Ljubljana.