Social Housing providers are constantly looking at ways to invest and expand their stock to meet the ever-increasing demand for efficient homes. The list of activities and works for housing providers is never-ending, but the monetary capital available is a limited and valuable commodity. They are pulled simultaneously in a number of different directions:
- Retrofit Existing Stock to increase their performance
- Reduce carbon outputs to meet their organisation’s Carbon Targets
- Improve the energy efficiency of their existing homes
- Increase the use of renewable energy where possible
- Conduct additional newbuild development
The good news is that there are some great software tools available that help organisations to look at what investment model is the best, and how to hone in on what should be a priority and what is unlikely to provide a significant return on investment. Most of these models are based on a set of sample data from a point in the past and they use this data in order to predict the best outcomes.
Utilising Data To Visualise Performance
With the IoT (Internet of things) revolution now in full swing, we are seeing more and more ‘real-time’ data that can be fed into investment modelling. Over time this data will allow organisations to more accurately model the value of future investment programs as well as repairs and maintenance work. For example, through OpenTherm, it is possible for organisations to receive remote notification of heating system faults and the diagnostics alongside them. Through sustained data collection on this information, it is possible to identify a certain make and model of boiler as having more faults than expected. Further to that, identifying which are the most efficient boilers that you currently have deployed. Looking deeper into this data, it is also then possible to identify if a boiler model is ‘firing’ more frequently than the norm. With remote data collection and analysis - affordable housing organisations are now more capable than ever at seeing the wider picture. This same type of data can be deployed in a similar way for broader property performance. An organisation might find that the overall average spend on a property lies within bounds that they are comfortable with. After a flurry of voids, floods or fire damage, however, individual ‘problem’ properties can be identified as bringing the entire maintenance spend up. This leads to the skewing of the overall average. Being able to look at your property performance on both a macro and micro level ensures that your organisation is able to detect rogue or on-off cases in your portfolio and account for them when making stock-wide decisions.
Legal Requirements Are Increasingly Reliant on Data
PAS2035 may seem a long way off, but it’s already upon us for some renewable components in affordable housing projects that are reaching the end of their life-cycle and are due for replacement. Whilst the standards of replacements are identifiable, the validation of those requirements aren’t always as easy. For example, is there a performance difference on external wall insulation for differing archetypes? What true impact is there on solar gain/loss from properties facing North/South for a PAS2035-specified dwelling? This applies with Domestic Energy Performance Certificates as well. EPC’s for new dwellings are grouped by floor area, size, energy use, carbon dioxide emissions and fuel costs. When it was first implemented, the surveys conducted provided information on the overall performance of a property in terms of energy performance. It was a great source of information for providing initial insight into energy consumption for housing providers. Unfortunately, the data has flaws. Properties were not grouped by archetype - traditional brick or modular etc. EPC’s also do not account for changes in condition over the lifetime of the certificate and they do not judge the efficiency of the measures installed. With the widespread installation of smart meters, the government has been signalling for some time its intention to move towards more of a ‘live-data’ regulatory environment.
Only through further IoT data and analysis can we truly start to understand the ‘real-time’ performance of individual properties and how those components compliment and work effectively. Through the device’s live sensor data, Switchee already provides detailed data analysis on temperature and humidity which empowers landlords in their management of properties.
How This Looks in The Real World
The practical effects of a data-based approach to property performance are becoming increasingly apparent. We have been working with some of our customers to use the property performance data that Switchee collects to benefit housing providers as a whole.
One example of this is a joint effort with two of our customers to better understand the impact of different housing archetypes on the energy efficiency of properties. The study is of the actual performance of a traditional build when compared to that of a modular build of the same property type and size. Ultimately, the conclusions of our initial study will provide real-world data insights that can inform future business cases and the wider execution of strategies for new home development. This combined with additional emerging data, such as the differing results for North and South facing locations can ensure better-informed decisions around future property investment. Such conclusions being enabled by the analysis of data that a product like Switchee collects can ultimately be a catalyst to more efficient and better homes for everyone.