What a managing agent already holds
A property management system accumulates years of useful data: repair jobs, contractor invoices, rent payments, tenancy events, compliance certificates and communication logs.
It is rarely used analytically because it was built for administration. Repair descriptions are free text, categories are inconsistent, and the same fault is recorded five ways. That is the work to do first.
Predicting maintenance before the call
Reactive repairs cost more than planned ones - emergency callout rates, greater consequential damage, and a worse experience for the resident.
- Component age and last replacement date, where records exist
- Repair history for that property and that component type
- Property characteristics - construction, age, exposure
- Seasonality, since heating failures concentrate in the first cold snap
- Preceding minor faults, which frequently precede a major one
The heating example is worth planning around. Demand spikes when the weather turns and everyone discovers the problem simultaneously. Predicting which systems are most at risk and servicing them in autumn shifts work into a cheaper period and prevents failures in the worst week.
Arrears risk, handled properly
Predicting which accounts will fall into arrears allows earlier, gentler intervention. Done well this benefits the resident too, since early contact usually resolves a problem that would otherwise escalate.
Done badly it becomes a tool for penalising people, which brings regulatory and reputational risk and in some jurisdictions is directly restricted.
| Use | Assessment |
|---|---|
| Prioritise supportive early contact | Defensible and usually helpful |
| Flag for payment plan discussion | Reasonable with human involvement |
| Refuse a tenancy application | High risk - legal advice required |
| Set differential terms by predicted risk | High risk - likely restricted |
We would keep this to prioritising support and route anything affecting a tenancy decision to a person with the reasoning visible.
Contractor performance
Most agents hold years of job data across contractors: time to attend, time to complete, repeat visits for the same fault, cost against estimate, and resident feedback.
Analysing that properly usually changes which contractors get which work. The important adjustment is for job difficulty - a contractor handling the hardest jobs will look worse on raw completion times, and penalising them for that is both unfair and counterproductive.
Start with the repair categories
Almost every analysis here depends on knowing what the repair actually was. Standardising historical repair descriptions into consistent categories is the unglamorous first project, and it unlocks everything else.
It also has immediate value on its own - consistent categories mean you can finally answer which faults cost most across the portfolio, which is a question most agents cannot currently answer.
Until repairs are categorised consistently, nobody knows what the portfolio actually costs to maintain.