The need for better data to get to zero carbon.

Original Article
August 13th, 2021


There’s a consensus that if the UK is to meet our overall carbon reduction target – to net zero by 2050 – we must tackle the housing stock. The UK’s building stock of 29 million homes needs to be nearly completely decarbonised by 2050.

Compared to the owner occupied and private rented sectors, the UK’s social housing performs significantly better. But what’s the strategy for getting to net zero carbon?

Like any strategy, you need to ask three questions. Where are you now, where do you want to be and how will you get there. This article provides some answers to the first question – how to establish the current performance of your stock.

It’s a cliché, but if you can’t measure it, you can’t manage it. A housing provider has a lot of data. Data that has come from all sorts of sources. Some high quality, some poor quality. If you take a housing provider with 10,000 assets, each asset could have up to 150 pieces of energy data associated with it. That’s 1.5 million pieces of data. It can therefore be overwhelming to make sense of it. But understanding your data and establishing where you are at this moment, has to be the first stage of your net zero strategy.

A piece of research Sava undertook a few years ago revealed that over a third of housing providers did not have confidence in the energy data they hold. And only 14% said that they had any confidence in spotting inconsistencies in their data.

Here are six pieces of advice:

  1. Gain access to your EPC input data. Since 2008 you’ve been commissioning EPCs for every home upon change of tenancy. There are between 100 and 150 valuable pieces of data that go into creating an EPC and yet it’s surprising how many social landlords don’t have access to this rich data set. If you lodge your EPCs with the Elmhurst EPC accreditation scheme it’s possible to retrieve the underlying RdSAP data through an automatic routine that inputs the data straight into your asset management system.
  2. Use high quality energy assessors. You need to be able to trust the data that your assessors are collecting. Ideally you would train someone internally to do your EPCs. But if you do outsource it, find a small group of assessors who you can work with as if they were part of your organisation. Educate them about your housing stock. Allow them access to information and experts so that they can feed this knowledge into their assessments. They’re not just providing you with a certificate that no one reads. They are providing detailed quality information about your assets. It’s worthwhile helping them to be as accurate as possible.
  3. Pool all your energy data into a single version of the truth. Put all your energy data in a single place – ideally your asset management system. This means that your energy data is a subset of your full asset dataset and avoids your energy data becoming stranded and subsequently inaccurate which is often the case when energy data lives outside of the main asset management system.
  4. Consider your data hierarchy. What data do you trust and what do you have less confidence in? Data sources such as gas maintenance records, condition surveys, void checks – will add to the energy story of your stock. But there will be conflicts. For instance, you are likely to have more confidence in your boiler data from your latest CP17 gas safety certificate than you would the boiler data that was on an 8 year old EPC.
  5. Create routines to continually improve your energy data. For instance, ensure that those CP17 certificates, or schedule of works are continually cross referenced against the energy data stored in your asset management system.
  6. Look at your whole stock. Most social landlords only have full RdSAP data – the data that is used to produce an EPC – on no more than 50% of their housing stock. Only analysing 50% of your stock is not very helpful and will misguide your investment strategy. With energy analysis software it’s possible to generate a SAP rating with just 12 to 15 pieces of essential data. This essential data is put through a series of intelligent inference engines that infer the missing data and this allows you to generate meaningful outputs such as the SAP rating, the carbon emissions and even the possible improvement options.

We talk more about these data improvement strategies in our regular free to access technical webinars. For more information and to book up, visit our technical webinars page.