Can you assess the prosperity and performance of a global city when there is so much going on at once? How do you reflect this complexity and balance a range of indicators, capturing aspects of the city that might be missed in more traditional, one-dimensional analyses?

We are trying to do this at Centre for London with our new quarterly publication, The London Intelligence, which provides a regular picture of London’s performance across a range of indicators.

We are still reviewing the lessons learned from our first edition, which came out in July. We found no shortage of data, but bringing it together with the careful analysis that creates intelligence was a more complex matter – as shown in the examples below.

Immigration to London: National Insurance Number Registrations

One frequent measure of immigration is the number of overseas nationals registering for National Insurance numbers (NINos), which they need to work or claim benefits in the UK. As a full administrative data set, these tell us information about people coming to London, including their nationality and place of application.

There is some quarterly variation, but data shows a 15 per cent reduction in the number of registrations from January to March this year, compared to 2016. Most of this fall came from EU citizens, perhaps signalling a worrying fallout from Brexit.

Click to expand.

Some nuances in interpretation exist, however. The quarter does not necessarily reflect when an individual arrives in the UK – the figures above probably demonstrate a lag effect.

While the numbers reflect international migration, they do not paint a complete picture: not accounting for people coming for other purposes (such as students), and not saying anything about length of stay, or if they have left. For example, we showed a drop-off in Europeans arriving; Europeans may be leaving in even greater numbers, but these data would not show that.

So while this decline is worrying, it is not necessarily significant in isolation – using future releases combined with other datasets, it will give more insight.

Young people not in education, employment or training (NEETs)

There are many economic indicators available – the publication includes eight – but here we discuss NEET rates, because it provides an indication of inclusiveness of London’s labour market.

According to the data, outcomes of young people in London have been improving recently, reaching a record low of 8.6 per cent. The observed seasonality results from school/university leavers, giving spikes in each third quarter.

Click to expand.

However, data on young people at the margin of London’s labour market is relatively poor. NEET estimates come mainly from surveys compiled by the Department for Education – meaning there are margins of error to each estimate. Therefore, while London’s rate may appear lower than the England rate in the most recent quarter, we cannot say for certain this is the case.

Further, evidence suggests the survey misses up to a quarter of youngsters in the capital, whose status is declared ‘unknown’, and so London’s young people may not be doing as well as suggested.

House Prices

National house price changes are often quoted in the media, but this belies layers of complexity. In the publication, we use mean house prices using Land Registry data on housing transactions, which acts as a headline indicator for London’s housing market (and a wider acid-test of the economy).

The data can be tracked over time and space in London. Borough-level analysis reveals distinct patterns over the year to April: outer boroughs are largely experiencing strong growth, while inner east boroughs in particular are cooling off.

Click to expand.

There are some limitations to be aware of. At borough level, prices are not mix or seasonally adjusted, and with small volumes of transactions, comparisons can be difficult – average (mean rather than median) prices tend to jump around a little each month dependent on the properties sold. Further, not all transactions are registered immediately, so readjustments (mostly small) happen in the medium term.

The borough data is useful for a spatial interpretation of trends, but does not give significant insight into the performance of different housing sub-markets, which often have divergent trends. Even within boroughs, different areas may be seeing dramatic differences in property market performance.

Looking at datasets in conjunction can be a powerful tool, especially when limitations are acknowledged, and numbers are explained and given meaning. It can provide a holistic and accessible, rather than parochial and specialist, view into our rapidly changing city, as the uncertainties of Brexit start to bite.

Tom Colthorpe is a researcher at Centre for London. You can learn more about the London Intelligence, and get your copy, here.

Want more of this stuff? Follow CityMetric on Twitter or Facebook.