Tutorial: Using Google Refine to clean mortgage data

The Swedish newspaper Svenska Dagbladet got a lot of attention last week for its succesful call to homeowners to share their interest rate of their mortgage loans. About 20 000 people from around the country has answered the call giving the paper – and us! (thanks for sharing the data) – some great insight into how the housing market works.

For a data nerd the dataset is also interesting from another perspective. It offers an excellent playground for aspiring datajournos that want to discover the power of Google Refine.

The data

SvD uses a Google Docs form to collect the data. The data is gathered in a public spreadsheet. As you will quickly notice the data is pretty messy. The size of the loan is written in different forms, bank names are spelled differently and so on.

Some good cleaning needs to be done. Enter Google Refine.

Open Refine, click Create Project and chose to import data from Google Docs. The url of the file can be found if you inspect the html code of this page and locate the iframe containing the spreadsheet:


The import might take a couple of minutes as we are working with almost 20.000 rows of data (maybe even more by now).

Step 1: Correct misspelled bank names

Lets start with the names of the banks. If you browse the list you will soon see how the same bank could be spelled in a lot of different ways. Take Danske Bank for example:

  • Dansk Bank: 1
  • Danska Bank: 2
  • Danska bank: 1
  • danske: 8
  • Danske: 23
  • danske bank: 95
  • Danske bank: 142
  • Danske BAnk: 1
  • Danske Bank: 439
  • Danske bank: 1
  • Danske Bank (ÖEB): 1
  • Danske Banke: 1

We want to merge all these spellings into one single category. Doing this is simple in Google Refine. Click the arrow in the header of the column Bank? and choose Facet > Text facet.

You’ll see a list of 257 banks. Click Cluster to start merging. I’m not going to be able to give you a detailed account of what the different methods technically do, but if you play around with the settings you should be able to reduce the number of banks to about 100. Go through the list manually to do additional merges by changing the names yourself (move the mouse over the bank you want to rename and click edit).

Step 2: Turn strings into values

The interest rates (Ränta) has typically been entered with a comma as the decimal separator. Therefore Refine thinks it’s a string and not a number. Fixing this is pretty easy though. Click Ränta? > Edit Cells > Transform.

We are going to write a simple replace() function to do this:

replace(value, “,” , “.” )

That is: take the value in the cell and replace the comma with a dot. And to make it a number we put it all inside a toNumber() function:


However, not everyone has entered the values in this format. If you click Ränta? > Facet > Numeric Facet you’ll see that there are almost 1500 non-numeric values. Most of these cells contain dots and have therefore wrongly been formated as dates.

Fortunately we can rescue these entries. Select the cells by clicking the non-numeric checkbox in the left-side panel. Then click Ränta? > Edit cells > Transform. First we are going to use the split() function separate the values:


I value like 03.55.00 is transformed into an array: [ “03”, “55”, “00” ]. Secondly we want to take the first and the second value in this array and make a value out of them:


But the cell is still a string, so we need to use the toNumber() function once again to make it a number that we can use in calculations:


And bam…

…we are down to 72 non-numeric values. And that will be good enough for now.

Step 3: Unify “1,9 mkr”, “3000000” and “2,270 mkr”

The real mess in this dataset is the price column (Hur stort är ditt lån?). Some state the size of the loan in millions, others thousands. Some use “kr”, others don’t. We want to try to unify these values.

Our first objective is to remove all letters. But before we do that we will change all the “t” to “000” to get rid of that category. You should know how by now:

replace(value, “t”, “000 “)

Now let’s get rid of the letters. Go to the transform window (Hur stort är ditt lån? > Edit cells > Transform). Once again we are going to let the replace() function does the work, but this time using a regular expression:

replace(value,/[A-Ö a-ö]/,””)

That is: take any character from A-Ö and repalce it with nothing. The following step should be a piece of cake by now. Replace the commas with dots and make numbers out of the strings:

toNumber(replace(value, “,”, “.”))

Lets examine what the dataset looks like by now. Click Hur stort är ditt lån? > Facet > Numeric faset. There are still a few hundred non-numeric values, some of which could be turned into numbers in the same way as we did with the interest rate, but lets leave those for now and instead focus on the 13.500 values that we got.

These values come in two formats: 1.1 (million) and 1100000. We want to use the latter in order to have the size of the loan in actual kronor. And we do this with an if() statement and some simple algebra:

  • If the value is less than 100, multiply by 1.000.000 (assuming that no one took a loan of more than 100 million kronor).
  • Else, do nothing.

Which translates into this in GREL:


Step 4: Export and conclude

Export data as csv or any other format and open it in Excel or Google Docs. You’ll find my output here, uploaded on Google Docs.

The dataset is still not perfect. You will see a couple of odd outliers. But it is in much better conditions than when we started and we are able to draw some pretty solid conclusions. A pivot table report is a good way to sum up the results:

Bank Interest rate Number of replies
SEB 3.44 2701
Danske Bank 3.67 582
Sparbanken Nord 3.82 10
Skandiabanken 3.82 359
Nordea 3.87 1764
Handelsbanken 3.88 2884
Bättre Bolån 3.91 15
Swedbank 3.91 3132
ICA 3.93 10
Landshypotek 3.95 24
Icabanken 3.96 24
Sparbanken 1826 3.98 13
Länsförsäkringar 4 803
SBAB 4.03 673
Ålandsbanken 4.05 10
Sparbanken Öresund 4.07 113

SEB seems to be your bank of choice if your just looking for a good interest rate, in other words. But this is just the beginning. With the clean dataset in hand we can start to ask questions like: how does the size of the mortgage and the interest rate correlate? What is the relationship between interest rate and area of residence? And so on.

Good luck.