Property Management Analytics: What to Track and Use

What property management analytics actually means

MRI Software describes real estate data analytics as collecting, analyzing and interpreting property-related information to guide decisions. Raw inputs such as occupancy rates or maintenance costs become insights that can improve performance and reduce risk. ThoughtSpot frames it similarly, as a way to move from gut feelings to data-backed choices across properties, markets and tenants.

In practice, for a property manager, that boils down to three questions:

1. What is happening across the portfolio right now (requests open, spend this month, units with repeat issues)?
2. Why is it happening (a failing boiler model, a slow vendor, a building with aging plumbing)?
3. What should change (reserve budgets, vendor assignments, preventive schedules, staffing)?

Analytics is useful only when it ends in a changed decision. A dashboard that nobody acts on is a reporting cost, not an analytics program.

The metrics worth tracking

There is no shortage of possible measures. Re-leased lists common property KPIs including NOI, occupancy rate, tenant retention rate, arrears as a percentage of collectible rent, maintenance cost per square foot, and average response time for service requests. Its argument is that tracking them consistently gives managers the data to optimize income and reduce risk.

BOMA Canada makes the same point about discipline: establishing KPIs and regular reporting helps surface the insights in your data and lets you track and adjust strategy.

A practical starting set for a multifamily or residential portfolio:

One caution on benchmarks. A guide from ManageCasa, written for the US market, suggests a vacancy rate below 5% in most markets and an operating expense ratio of 35 to 45% for well-run properties. Treat figures like these as rough reference points, not Canadian targets. Rent controls, heating costs, building age and regional vacancy vary widely between Halifax, Moncton and Charlottetown, so your own trend over time is a better yardstick than a generic number.

Maintenance data: the richest source in your portfolio

Occupancy and rent figures change monthly. Maintenance data changes daily, and it records what is physically happening in your buildings. It is also the data most often captured badly: a phone call, a voicemail, a note on a spreadsheet, a vendor invoice that arrives three weeks later.

A maintenance-history guide from Oxmaint says complete work-order records should include asset IDs, cost, vendor, warranty information and documentation, and that repair history by asset can reveal failure patterns. It also argues that an incomplete history is almost as useless as none, because you cannot trust the patterns it shows.

A workable minimum record for every request:

• Unit, building and the specific asset involved (water heater, fridge, a particular window)
• Date and time the tenant reported it
• Tenant’s description and any photos
• Category and urgency, including whether it was an emergency
• What troubleshooting was tried and whether it resolved the issue
• Vendor assigned, dispatch time, visit date and completion date
• Cost, invoice and warranty status
• Follow-up outcome: did the tenant confirm the fix?

For renovation-related jobs, the dates and permit steps matter as much as the cost, and a tenant improvement timeline guide shows the kind of milestones worth logging on larger work orders.

What a long-run Canadian example looks like

A 2019 CSCE conference paper describes a Montreal case study covering 30 residential rental properties. Its maintenance database, entered since the 2000s, records for each intervention the date, building element, code, specifications and, where applicable, cost, quantity and quality. The data feeds a dashboard that summarizes each building’s actual condition and helps managers set annual budgets and schedule short- and long-term work. Interventions are grouped by building component, such as envelope, ventilation, electricity, plumbing, fire protection and apartment interiors.

You do not need a research program to borrow the idea. Consistent categories and consistent fields are what make a decade of repairs comparable.

How to analyze maintenance data: a step-by-step approach

1. Standardize categories first. Agree on a short list (plumbing, heating, electrical, appliances, building envelope, pests, general) and apply it to every request. Ten clean categories beat forty overlapping ones.
2. Pull twelve months of history. Include cost, vendor and timestamps. Gaps are information too: note which fields are routinely empty.
3. Rank by cost and by frequency. The costliest category and the most frequent category are often different. A leaking tap is frequent and cheap; a roof or boiler repair is rare and expensive.
4. Look for repeats. The same unit, same asset or same building generating repeat requests points to a root cause that a single repair did not fix.
5. Separate avoidable from unavoidable calls. Count how many service visits ended with a problem a tenant could have resolved with basic guidance, such as a tripped breaker, a reset button or a clogged aerator.
6. Measure the coordination lag. Time from report to dispatch, dispatch to visit, visit to closure. The gaps between steps are usually where tenants get frustrated and staff hours are lost.
7. Decide and schedule. Turn each finding into an action with an owner: replace an asset, change a vendor, add a preventive inspection, adjust a reserve.

Reading the results

Two examples show how findings turn into decisions. If three units in one building report slow drains within a season, a plumber’s snake on each visit is the symptom response; a line inspection is the decision. If after-hours calls cluster around heating failures in the same building every cold snap, the data supports a pre-winter service of that system rather than more on-call coverage.

These are illustrations of method, not benchmarks. Your own numbers will tell you which patterns are real.

Portfolio analytics: prioritizing preventive work and judging vendors

Once each building produces clean records, you can compare across the portfolio. A portfolio dashboard concept from Oxmaint for facility directors argues that comparing sites on backlog, preventive maintenance compliance, asset risk, cost and SLA performance shows where the next problem is forming. The same source supports comparing contractor response times and costs across sites.

For a residential portfolio, that translates into a monthly review of:

• Backlog: open requests by age. Anything older than your internal target gets named and assigned.
• Cost per unit by building: outliers deserve a look at asset age, not just vendor pricing.
• Vendor scorecards: response time, first-visit completion, repeat visits for the same issue, invoice accuracy and tenant feedback. If you rely on several trades across multiple towns, our guide to managing maintenance vendors at scale goes deeper on this.
• Preventive versus reactive mix: the share of work you scheduled versus the share that arrived as an emergency.

BOMA Canada’s Digital Transformation Guide also notes how connected systems can support early issue spotting, with sensors and remote diagnosis anticipating problems before tenants notice. Sensors are one route. For many portfolios, the earliest warning still comes from the tenant, which is why capturing their report cleanly and quickly matters so much. We cover the full measurement set in maintenance performance metrics for rental portfolios.

Data quality comes before dashboards

Analytics fails quietly when the inputs are weak. A 2021 audit of contracted property management services at Toronto Community Housing found that KPI data was not always reliable because the corporation and its contracted property managers did not consistently and completely track relevant information. It also found that the measures did not effectively support monitoring work quality or the factors affecting tenant satisfaction.

The lesson applies at any scale. If five coordinators log requests five different ways, the dashboard reflects the logging habits, not the buildings.

EY Canada adds a governance angle for teams adopting AI tools: poor data management can lead to poor-quality outcomes, and it recommends data audits, data cleansing and structuring, data governance and regular compliance checks, with privacy by design. BOMA Canada’s AI report likewise points to data privacy and cyber security policies and an annual gap analysis to confirm data is accurate and integrated between systems and software.

A short data-quality checklist:

• One intake channel per tenant, so no request is lost between phone, email and portal
• Required fields on every work order, filled in at the source rather than reconstructed later
• A single naming convention for buildings, units and assets
• A clear owner for each KPI
• A documented approach to tenant personal information, aligned with Canadian privacy law and your own policies

Adding analytics without replacing your software

Most portfolios already have a property management platform handling leases, accounting and reporting. Replacing it to get better maintenance analytics is rarely necessary. BOMA Canada describes turning data into useful analytics as work that takes skill and concerted effort, including deciding which metrics could improve operations and setting regular touchpoints across the organization to collect ideas. Its guidance on KPIs and ongoing reporting points to a phased approach.

A sensible rollout:

1. Phase one, one building or one region. Pick three KPIs, such as response time, cost per unit and repeat requests.
2. Phase two, fix capture. Make sure every request enters the system the same way and carries the same fields.
3. Phase three, review monthly. Name an owner, hold a short meeting, record decisions.
4. Phase four, expand. Add buildings, add vendors, add preventive scheduling.

If you use Buildium or a similar platform, look at how maintenance coordination can sit beside it. Our notes on connecting maintenance to property software explain how that works in practice.

Our perspective

Most analytics projects in property management start at the report and work backward to the data. We think it should go the other way. If you capture every maintenance request the same way, from the first text to the final follow-up, the reporting largely takes care of itself.

That is the idea behind GoLando. Tenants text us about an issue, with no app to download and no portal to learn. Our AI handles the conversation, troubleshoots where it can, identifies emergencies, and coordinates approved service providers when a professional is needed. Every step leaves a structured record: what was reported, what was tried, who was dispatched, when, and what it cost. That record feeds a central dashboard with maintenance history, vendor activity, expenses and performance metrics.

We built it to complement your existing property management software, not replace it. Your leasing, accounting and established workflows stay where they are. We automate the maintenance communication and coordination that consumes your team’s time, so a growing portfolio does not require a proportionally growing maintenance staff.

GoLando is Canadian-owned and operated, and Canada is where we are launching first.

Ryan

Put your maintenance data to work with GoLando

If your maintenance records are scattered across inboxes, voicemails and spreadsheets, analytics will always be a manual project. GoLando coordinates maintenance requests by text, so tenant reports, troubleshooting, vendor dispatch and follow-ups are captured in one place and visible in one dashboard.

Published plans are tiered by portfolio size and listed in Canadian dollars on the pricing page:

Jobs routed through the GoLando Vendor Network carry a network fee of 5 to 10%, described on the vendor page. You can also estimate your current spend with the rental maintenance cost calculator, and see how automated vendor dispatch works for teams using their own or network trades.