Tuesday, 8 April 2014

Mortgage lending data in Great Britain - a step in the right direction

Since late in 2013 data on bank lending at the postcode sector level for Great Britain has been available via the Council of Mortgage Lenders (mortgages) and the British Bankers' Association (personal loans). This followed an announcement in July 2013 that such data would be made available in order to - among other things - "boost competition" and "identify where action is required to help boost access to finance". It was also said at the time that the data would be "a major step forward in transparency on bank lending". My assessment is that this is only partly true. The new data do represent a major step forward and organisations such as the CML are to be commended for their work on this, but in relation to mortgage lending at least things are more opaque than transparent, as I attempt to explain below.

Location quotient lending map for Liverpool - HSBC lending

I should begin by saying that I think this newly available data is a fantastic resource and that it does allow us to ask important questions and - to an extent - hold lenders to account. However, since we have no data on local demand - as they do in the United States under the terms of the Home Mortgage Disclosure Act of 1975 - then the extent to which we can identify which lenders are rationing finance, excluding areas or 'redlining' is extremely limited. In fact, I would concur with George Benston, who said in 1979 (p. 147) that:

  • “If the focus is on the supply of mortgages, either in terms of numbers or dollars, a demand as well as a supply function must be constructed and specified. When demand is not accounted for, there is no way to determine the reason for any given level of supply.”
So, in the image above, which shows lending location quotients for HSBC in Merseyside, there is no way of knowing whether areas of lower lending receive less finance because fewer people there apply for mortgages or whether some other supply-side mechanism is in force (e.g. bank lending policy). All we can really do is compare the lending practices of different institutions and note the differences. The same type of map is shown below for Lloyds banking group (the UK's biggest lender). This would appear to suggest some significant differences in relation to where these two banks lend the most. People with a knowledge of Merseyside will recognise that Lloyds has many higher lending location quotients in poorer areas. Is this 'evidence' of financial exclusion, redlining, sub-prime lending? No, it definitely is not. It does show, however, that banks lend differently at the local level. This is not news, but the new data releases allow us to identify very local patterns and ask questions about it. 

Location quotient lending map for Liverpool - HSBC lending

Interpreting change
Following the first release of data in December 2013 - which included all outstanding mortgage debt up to the end of June 2013 - an updated dataset was released in April 2014, covering the period up to the end of September 2013. Once again, this is a fantastic development in many respects, but it poses new questions of interpretation. We don't have any idea at all - and please let me know if I'm wrong - on how much of the change is down to people paying off mortgages, how much is down to new loans being taken out (none?) and how much is down to data disclosure mechanisms put in place by the banks. The CML's chief economist, Bob Pannell, said this upon the release of the second iteration of the dataset:

  • "Unsurprisingly, with data covering outstanding lending rather than new flows, there are only small changes since the last quarter. It is likely to take some time before any discernable changes or trends emerge from this quarterly data series."

My calculations indicate that most areas experienced only modest change (c. +/- 2%) but as far as I can tell it's not possible to say exactly what causes the change in each area. There are some big changes at the local level in relation to individual postcode sectors, but it's only really possible to speculate at the causes. Nonetheless, here are the top five:

  • London NW9 1 - £907,640 (end Q2, 2013) to £1,396,795 (end Q3, 2013) - 53.9% increase
  • Exeter EX5 7 - £16,894,775 to £23,497,142 - 45.0% increase
  • London EC3A 5 - £2,729,724 to £3,677,645 - 34.7% increase
  • Cambridge CB2 9 - £56,992,750 to £75,546,239 - 32.6% increase
  • London EC1V 8 - £23,254,820 to £30,342,713 - 30.5% increase

Without some additional contextual information - as is available in the United States under the provisions of the HMDA, we can only really guess at the causes of such change. That's why various organisations have been campaigning for greater financial transparency for some time - most notably Friends Provident in their report from 2012.

The data
I've done a reasonable amount of analysis with the new mortgage lending data, including writing and submitting an academic paper, a series of maps and various other bits and bobs via twitter. My assessment closely mirrors that of Owen Boswarva, who notes the 'open-ish' nature of the data releases. The data do not, as far as I can see, come with any kind of licence (such as the Open Government Licence) but I and many others have just taken for granted that the data are 'open'.

The way the data are released is also interesting. The press releases and aggregate lending figures for mortgages are released via the CML, and cover about 73% of the mortgage market. This is obviously a significant advance on what was previously in the public domain, but in terms of getting your hands on individual bank data in one file, you have to scrape and mash the data together, like I did when creating my mortgage lending map site. Since I've worked with the data quite a bit, I thought I'd give a little overview of how I think individual lenders have done in relation to making the data available:

  • Barclays - if you go to the postcode sector data page on the CML website, there are links to data for all banks. When you click on 'Barclays' it will take you to their 'Citizenship' pages (as of 8 April 2014). From there you can link to the new Q3 2013 data release within a news article. It's not the easiest of journeys and could be made much more obvious. The file itself is, rather strangely, called 'Satellite.xlsx'. I think they could do better.
  • Clydesdale & Yorkshire Banks - these institutions are part of the same banking group and so report together as one. The data are pretty easy to find.
  • HSBC - for me, this is the most troublesome data release since I can only find it in PDF. It's not a massive task to convert it into a usable format, but it seems really odd in this day and age that a major financial institution would choose to release 10,000 rows of data in PDFs. If anyone has spotted another format please get in touch. The HSBC approach is at odds with the spirit of the exercise, surely.
  • Lloyds - the UK's biggest lender (following a series of acquisitions) also have a nice data page, which is easy to navigate. They provide some useful information, such as the fact that most buy-to-let mortgages are included in the data, and a direct link to the Excel spreadsheet.
  • Nationwide - my analysis suggests that Nationwide (the only building society to release data) truly live up to their name in terms of the the geography of their lending. Their data page is basic but it does the job. 
  • Santander - this institution also provides clear and simple access to their lending data. This is now different from the link provided on the CML website. 
  • RBS - as far as I can tell, RBS are the only bank to have produced their own maps of lending patterns across Great Britain and their data page is really quite good. The new data are currently provided via a news page link.

Despite the fact that some of the data are a little hard to find, it's mostly quite a good situation - apart from the HSBC PDFs of course. It would be much better if all the data were put together in a single spreadsheet by the CML but perhaps this is something the individual lenders are not too keen on (!) so it's up to people like me to stitch it all together. In which case, it would be great if HSBC started publishing in a more convenient format.

What's the point of all this?
I can sum up quite simply. If this data were released as part of a drive to increase transparency in the banking sector, then I think a few more things need to happen next:

  1. We need some indication of demand - e.g. number of applications, number of refusals, and so on.
  2. We can't do research into subjects like 'redlining' because we don't have the above information. We can make comparisons between banks in poorer areas but that's about it for now. If we really want to look at transparency, we need more information.
  3. We need more of a breakdown of the data in each new release to say how much is new lending and how much of the change is down to other factors - particularly so at the postcode sector level.
  4. Some additional clarity on the 'open' nature of the data would be very welcome.
  5. We need more banks to follow the example of the first seven and make their data available. With more than 100 lenders in the market it's probably not possible to get all to comply, but more work here would be useful.

All of the above represents really positive progress, but I think more is needed. I do realise of course that the CML are "considering additional features and functionality for future reporting waves", so I look forward to seeing what happens next.

Friday, 4 April 2014

Some thoughts on mapping spatial patterns of deprivation

In my research into the geography of deprivation across the UK, I frequently use maps to illustrate the spatial patterns associated with the areas identified as 'least' or 'most' deprived according to official indices such as the Scottish Index of Multiple Deprivation or the English Indices of Deprivation. Lots of other people do similar kinds of things, including mapping gurus such as Oliver O'Brien from UCL (his work is much nicer). A recent example is shown below, which I also tweeted this week. It's difficult to know exactly how people will interpret such maps, particularly when they are only seeing them on twitter without much in the way of context being provided, so this short blog fills some of the gaps and discusses some wider issues.

In previous academic papers (e.g. Urban Studies, 2009; Regional Studies, 2012; Local Economy, 2012) I've written about deprivation quite a bit, and on the need for the debate to centre not just on 'deprived' areas but more widely upon the wider dynamic of socio-spatial inequality. It's a shame that the focus is still very much on 'poor' or 'deprived' areas so in an attempt to draw attention to the urban inequalities which exist across the UK I attempt to illustrate the socio-spatial disparities within different cities. I also did this in a report on Sheffield from 2011 where I tried to draw attention to the socio-spatial inequalities within English cities, as shown below. It's not that concentrated deprivation isn't a problem (far from it) but rather that it's part of something much bigger.

These kinds of maps do draw attention to the general issue but of course they can lead to all sorts of other conclusions and claims because as we know, maps are an abstraction from reality and they do not represent an absolute 'truth'. These maps simply colour small areas within cities according to how they are classified by a government metric which attempts to say how 'deprived' places are. This may be a dubious practice in many respects, but it is woven into the fabric of how places are understood in a policy context and how problems are defined. It's important that we understand what this kind of mapping allows us to say and what it does not. Some of this is covered on the 'What does it all mean' tab of my Scottish Index of Multiple Deprivation 2012 map site, but I want to make a few more points here...

1. Colours. They are not intended to match up to any political party but some people inevitably make such inferences. The maps say nothing about the causes of the patterns, or who is responsible for them. But it doesn't stop people from talking about it and that's no bad thing. There is a lot more that could be said about colour choice but I'm going to leave it there for now.

2. The trouble with choropleth maps. Maps shaded according to some value (such as deprivation rank) present a misleading picture in a number of ways but two important ones stand out here: a) not all people in the area are 'deprived' or 'non-deprived'. This is the classic 'ecological fallacy' issue at work - the third paragraph here says more about that; and b) the shaded zoned themselves cover much wider areas than people actually live in so a big blue or red area gives the impression of a lot of something, when in fact the population of larger spatial units is similar to the smaller ones (as it often is with LSOAs or Data Zones in the examples above). 

3. The sometimes arbitrary nature of local authority boundaries. Places like Leeds are often said to be 'overbounded', whereas Manchester is 'underbounded'. This means that the local authority boundary either extends beyond the core urban area or it doesn't include much of it at all. So, in the cases of Manchester and Liverpool above if you were to extend the boundary of the map you would see more areas that are not so deprived. However, the point here is that local authorities have to deal with the financial, social and spatial implications of these patterns. What happens beyond the boundary is not part of their remit - even if it does impact upon them. The boundaries may be arbitrary in some respects but they have very real implications.

4. Why not take a different approach? A good idea, and one that Oliver O'Brien in particular has been very successful with. If we only look at where people actually live then we get a more realistic (but still not 100% accurate) picture of the spatial patterns associated with deprivation. This can be done using dasymetric mapping, where we assign the attributes of areas to individual features. This isn't a perfect definition, and the technique itself can lead people to assume a higher level of accuracy than can actually be obtained from the underlying data but it has advantages over standard choropleth maps in relation to depicting the places where people actually live or work. See also Neal Hudson's London tenure map in this style. The new OS Open Data VectorMap District buildings layer for Great Britain allows us to do this, so I've produced an example map for Glasgow based on the one in the first image above. This time you can only see the areas where there are buildings (though many are not residential properties).

Is the map above more 'truthful' than the normal choropleth? Probably not. However, this is all irrelevant if we aren't concentrating on the underlying patterns we're trying to draw attention to in the first place. The point is that in cities like Liverpool, Manchester and Glasgow the high levels of deprivation/poverty/disadvantage sit in stark contrast to areas at the other end of the scale. Also, places that we think of as 'deprived' are often far from it - as Peter Matthews might also argue. It's this kind of inequality which I'm attempting to highlight with my mapping - though I do of course like a nice looking map (I've also produced more than a few stinkers in my time). The point of all this? I hope that these maps can start a conversation about the underlying issue. I'll end with an extract from my 2012 Regional Studies paper on the issue...