Here's a question that should keep housing executives awake at night: What does a repair actually cost?

Not what you pay for it. Not what the schedule of rates says it should cost. But what it genuinely costs, in real terms, to restore a specific component in a specific property to a satisfactory condition.

Most organisations can't answer that question with any confidence. And that's not because they lack data — it's because the economic frameworks we use to understand repair costs were designed for a world that no longer exists.

The Illusion of Certainty

Walk into any repairs meeting, and you'll see the same ritual: someone pulls up a spreadsheet showing actual spend against budget, variances are discussed, adjustments are proposed. The numbers feel precise, authoritative, real.

But scratch beneath the surface, and those numbers are built on assumptions that would make an economist wince.

Schedule-of-rates models — the backbone of repair procurement across the sector, were designed for an era of predictable labour costs, stable materials pricing, and steady inflation. They assume that a "kitchen tap replacement" costs roughly the same whether you're doing it in a 1960s tower block or a Victorian terrace, in Cornwall or Newcastle, in January or July.

Anyone who's actually managed repairs knows this is fiction. But it's convenient fiction, so we've built an entire economic infrastructure around it.

The result? Trusted by 600+ social housing providers and contractors, these standardised rates have become the language of repair economics, even as they diverge further from reality with each passing year.

Why This Matters Now

Three things are making the limitations of traditional repair economics impossible to ignore:

Material volatility: In the past five years, we've seen unprecedented fluctuations in construction materials. Timber prices have doubled and halved. Insulation costs have spiked due to supply chain disruption. Energy-intensive products like glass and metals swing wildly with fuel costs. The idea that you can publish an annual rate book that accurately prices repairs for 12 months is increasingly absurd.

Labour market fragmentation: The days of predictable local labour rates are gone. Skilled trades are scarce in some regions, abundant in others. Contractor availability varies by season, by property type, by the complexity of compliance requirements. A "standard rate" masks massive variation in what it actually takes to get the work done.

Regulatory complexity: Every repair now carries additional costs that schedule-of-rates struggles to capture: fire safety checks, building safety documentation, Awaab's Law compliance timelines, resident vulnerability assessments. These aren't peripheral concerns they're core elements of the service, but they often exist outside traditional costing models.

The result is a growing disconnect between what repairs are supposed to cost and what they actually cost. And in that gap, value bleeds away.

What Other Sectors Already Know

Here's where it gets interesting. Housing isn't the first sector to face this problem. Healthcare, manufacturing, and utilities all went through similar reckonings and developed more sophisticated approaches.

The NHS and Patient-Level Costing

Micro-costing is a cost estimation method that allows for precise assessment of the economic costs of health interventions. It has been demonstrated to be particularly useful for estimating the costs of new interventions, for interventions with large variability across providers.

The NHS spent the last two decades moving from crude averages to patient-level information and costing systems (PLICS). Instead of saying "a hip replacement costs X," they now understand the resource requirement for each patient's specific pathway: their comorbidities, the surgeon's experience level, the hospital's overheads, the length of stay, the complications that arose.

This isn't just academic rigour. It's enabled the NHS to identify where money is wasted, which providers deliver best value, and how to price services more accurately. The costing process will transform raw and unorganised facts (data) into useful information. Increased data accuracy improves confidence in the resulting patient-level costs and enables managers to improve patient care.

Manufacturing and Activity-Based Costing

Manufacturing firms abandoned simple unit costing decades ago in favour of activity-based costing (ABC). Instead of spreading overheads evenly across products, ABC traces costs to specific activities and then to the products that consume those activities.

With predictive maintenance, you avoid emergency repairs, which means you also avoid related costs. When a critical machine in a facility breaks down unexpectedly, manufacturers understand the cascade of costs: the direct repair, the production loss, the rushed parts procurement, the overtime labour. PdM can lower maintenance costs by up to 25% by understanding these true costs and acting before failure occurs.

The parallel to housing repairs is obvious, yet we're still pricing jobs as if they exist in isolation.

Introducing Micro-Data Costing: A Framework for Housing Repairs

What if social housing adopted a similar approach? Not copying the NHS or manufacturing directly, but adapting their principles to the specific context of housing repairs?

I'm calling this concept micro-data costing and while it's not yet a defined methodology, the principle is straightforward: understand cost at the smallest possible unit, using the data you already hold.

The Building Blocks

Instead of pricing by job type, micro-data costing would link:

Component-level data: Not just "boiler repair" but the specific make, model, age, and maintenance history of that particular boiler. Different boilers have radically different failure modes and repair requirements.

Property archetype data: A tap washer replacement in a high-rise flat with complex water pressure issues costs more than the same component change in a ground-floor house. The archetype matters.

Local market intelligence: Real-time data on local labour availability, material prices, and contractor capacity. Not annual averages, but dynamic understanding of what resources actually cost right now.

Environmental and occupancy factors: Properties with damp problems cost more to maintain. Homes with vulnerable residents require different service approaches. These factors affect true cost but rarely appear in rate books.

Regulatory compliance requirements: The cost of meeting Awaab's Law timelines, building safety documentation, or gas safety checks isn't constant across all jobs. It varies by property type, contractor familiarity, and risk profile.

What It Would Enable

If you could link these datapoints effectively, you'd move from reactive cost management to predictive cost intelligence:

True lifecycle costing: Understanding the actual 30-year cost of maintaining different dwelling archetypes, not based on theoretical component lifespans but on verified performance data from your own stock.

Dynamic pricing: Adjusting repair costs based on real-time data rather than annual negotiations. If local labour markets tighten, prices reflect that immediately. When material costs spike, you see it in your forecasts before it hits your budgets.

Smarter procurement: Instead of negotiating fixed rates that incentivise contractors to game the system (cherry-picking profitable jobs, cutting corners on unprofitable ones), you could price contracts on verified data streams. The contractor gets paid fairly for the actual work required; you get transparency on where costs arise.

Investment insight: Identifying where recurring small repairs exceed the cost of component renewal. Many organisations know this intuitively but can't prove it systematically. Micro-data costing would make these patterns visible and actionable.

The Economic Shift We're Not Discussing

There's a deeper point here about how we think about repair economics.

Traditional schedule-of-rates models assume repairs are discrete, comparable events. Job A is like Job B if they involve the same component. This assumption allows for standardisation, which allows for bulk procurement, which (theoretically) delivers economies of scale.

But what if repairs aren't actually comparable in that way? What if each repair is a unique intervention shaped by dozens of contextual factors that dramatically affect its cost?

In healthcare, they learned this lesson: treating diabetes isn't a standard unit of service. It's a complex intervention shaped by the patient's age, diet, mobility, social support, complications, and dozens of other factors. Patient-level costing acknowledges this complexity rather than flattening it.

Housing repairs might need the same conceptual shift. A boiler repair isn't a standardised service , it's an intervention in a complex system (the property, the resident's needs, the contractor's capabilities, the local market conditions) that produces different costs depending on how those factors combine.

The Practical Barriers

I'm not pretending micro-data costing is easy to implement. The barriers are real and substantial:

Data integration challenges: Most organisations hold repair data, asset data, resident data, and financial data in separate systems that don't talk to each other effectively. Building the technical infrastructure to link these data streams is expensive and time-consuming.

Skills gaps: Understanding and implementing this kind of costing requires capabilities that most housing associations don't currently have: data science, health economics, advanced analytics. These skills are scarce and expensive.

Cultural resistance: Schedule-of-rates, for all its limitations, provides psychological certainty. Moving to a more complex, nuanced approach feels risky. What if the data shows your costs are higher than you thought? What if it reveals patterns you'd rather not acknowledge?

Procurement friction: The entire repairs supply chain is built around schedule-of-rates. Contractors bid on the assumption of fixed prices. Changing that model would require fundamental shifts in how contracts are structured, risks are shared, and value is defined.

What Success Would Look Like

Imagine if a housing provider could tell you, with confidence:

• "Maintaining our pre-1919 terraced stock costs 40% more per property than our 1980s estates, primarily due to damp intervention frequency and component incompatibility with modern materials."

• "Emergency repairs in properties with residents over 75 cost an average of £180 more than the same repairs elsewhere, due to extended appointment times, additional safety checks, and follow-up visits."

• "Our boiler repairs in tower blocks cost 60% more than schedule-of-rates suggests because access restrictions, parts procurement delays, and safety compliance requirements add significant hidden costs."

These aren't hypothetical insights — they're the kinds of patterns buried in every housing provider's data right now. Micro-data costing would make them visible and actionable.

The Path Forward

I'm not suggesting every housing provider needs to build a sophisticated micro-costing system tomorrow. But the sector needs to start moving in this direction, and there are practical first steps:

Start linking data: Even basic integration of repairs, assets, and complaints data would reveal patterns invisible in siloed systems.

Pilot on high-cost archetypes: Test micro-data costing approaches on your most expensive property types first. The return on investment will be clearest where costs are highest.

Learn from other sectors: Healthcare costing guidance, manufacturing ABC literature, and utilities asset management frameworks all offer transferable lessons. We don't need to reinvent this from scratch.

Build the business case: Calculate what better cost intelligence could save through improved procurement, targeted investment, and reduced waste. The numbers will justify the investment in better systems.

Create a shared language: Work with contractors to develop costing approaches that reflect reality rather than convenient fiction. The best contractors already know schedule-of-rates is broken — they'd welcome a more honest conversation about cost.

The Bigger Question

Repair economics matters because repairs aren't just a spending category , they're the primary way most residents experience their landlord's service.

When we can't accurately cost repairs, we can't price services fairly, forecast budgets reliably, or invest strategically. We end up making decisions based on incomplete information and then wondering why outcomes don't match expectations.

The Activity-Based Costing approach will be utilized to separate and define the costs of maintenance in ways that reveal where value is created and where it's destroyed.

The future of repair economics isn't about more sophisticated rate books. It's about moving beyond rates entirely, towards dynamic, data-driven understanding of what maintenance actually costs and why.

The information is already there, buried in your work orders, asset registers, and invoice records. We just haven't learned how to read it yet.

The question is whether the sector will make that leap or keep operating on increasingly obsolete assumptions until the gap between reality and our models becomes impossible to ignore.

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