Lacking energy?

During my years with Keystone, I often discussed with colleagues, customers and consultants the potential for achieving an improvement in a landlords average SAP by carrying out a purely desktop exercise. There tended to be general agreement that this was a worthwhile exercise, and that once the typical plethora of errors and data gaps had been addressed, we would start to see rating increases commensurate with the investment being made in energy efficiency.

This seems to therefore be an obvious piece of work to embark upon – but due to lack of appropriately skilled resource, or because of the age-old issue of making sense of the data requirements and errors, many customers put it off.

However, with the improvements in AutoAssessor’s error reporting in recent years at least one aspect of the task has been made easier – so when a customer recently approached me to enquire as to whether I would be interested in such a project, I was keen to finally have a chance to take on the challenge myself.

We started by taking the results of the latest energy assessment, and reviewing the common errors being reported that were preventing the calculation of a rating. Having identified these errors, and manually tested the appropriate data fix, we were then able to use the KGI to apply bulk fixes to all properties with the same issue. Already we were quickly reducing the number of properties reported as having No Rating.

Clearly substantial improvements in the SAP rating can be gleaned by getting the Heating data right – and to this end, live use of KSI for Gas Servicing is obviously a major boon. Entering the actual Boiler ID, instead of just completing the generic fields relating to the boiler brings an improvement in the majority of cases – especially, of course, if the boilers concerned are highly efficient models. All that has to be done is to cross reference the Boiler information held in KSI with the ID’s from the rdSAP boiler database, and the ID’s can be populated en masse. If you have a wide variety of boilers in use, this cross-referencing can take some time – but it is certainly time well spent.

Even stock condition data can be used to make assumptions about some of the fields in the energy dataset. Information about installation dates for Windows and Doors can inform assumptions about insulation in parts of the dataset – and, you might even find information there about the provision of Renewables that can make significant rating improvements.

While applying all of these data fixes you’ll start to wonder why your data isn’t reflecting (either fully or partially) all of the energy efficiency investments you’ve made over the past few years. And, of course, as is frequently the case, it’s down to your Completed Works Rules. Many organisations’ CWR’s just don’t include actions to update the energy dataset – so they’re carrying out all of this work without seeing the benefits in terms of SAP rating.

So – a review of, and extension to your Completed Works Rules is an essential part of the process.

After just a few days’ work with the customer above, we managed to push their average SAP from 69, to nearly 71 – just by using the data they already had sitting in their Keystone database – with virtually all properties returning a rating, and zero errors!

It was worth the energy we put into the exercise!