As part of our Total Asset Management approach, we analysed 1.6 million Schedule of Rates (SOR) codes, 836,000 repairs and 149,000 components across 35,000 properties. Our Asset Intelligence Model combines stock condition and live repairs data to identify where housing providers can target capital investment for the greatest impact.
Overview
We analysed asset and repairs data across 35,000 properties, to combine recorded stock condition with live repairs data to create a richer view of asset condition, performance and potential investment need.
The challenge
Most capital investment decisions are driven by stock condition data. Meanwhile, thousands of repairs transactions are generated every day but rarely influence strategic investment decisions. While stock condition data provides the foundation for investment planning, much of it originates from point in time surveys which can continue to inform investment decisions for years. During that time, assets continue to deteriorate, fail, be repaired and change without those changes always being recorded in the condition record.
Repairs data provides a continuous source of evidence about how those same assets are actually performing. Our Asset Intelligence Model brings these two views together, allowing recorded condition to be validated and challenged against real world performance.
What our Asset Intelligence Model has uncovered
- Hidden investment need: Assets appearing but generate excessive repairs, highlighting potential deterioration or investment need that may not be visible from condition data alone.
- Investment priorities: Components where both condition and repairs data indicate urgent need, providing stronger evidence to prioritisation.
- Deferrable investment: Assets approaching renewal but performing well in practice, providing evidence to challenge assumptions around remaining life and planned investment.
- Data quality improvements: Discrepancies between repairs activity and recorded asset information, helping identify where condition records warrant validation and enabling stock condition survey activity to be targeted more effectively.
- Confidence in existing data and plans: Where recorded condition and repairs performance align, the model provides additional confidence in the underlying data and the investment decisions being made from it.
- Future cost reduction: Opportunities to replace recurring repair demand with targeted capital interventions, focusing investment on assets generating disproportional operational demand.
What this means for housing providers
By combining stock condition data with repairs intelligence, our Asset Intelligence Model allows housing providers to prioritise investment where evidence is strongest and in return, improve return on capital expenditure. Our model not only identifies
opportunities to reduce recurring responsive repairs demand; it also provides evidence to validate asset data, challenge assumptions around replacement cycles and target further stock condition activity where uncertainty is greatest.
Key findings
- Repairs demand is highly concentrated round 5% of assets typically accounted for more than 25% of repairs demand at a component level.
- Existing condition data is often supported by real world performance for the majority of components analysed, repairs behaviour broadly aligned with recorded condition, providing additional confidence in the data and existing investment plans.
- Some components are performing better than their records suggest identifying opportunities to validate condition and potentially review or defer planned investment.
- Others show signs of hidden investment need unusually high repairs demand can identify assets warranting investigation even where recorded condition suggests otherwise.
- The model provides a clear focus for further investigation rather than surveying or reviewing assets indiscriminately, our Asset Intelligence Model can be used to target validation and stock condition activity where the evidence indicates it will add most value.
