Local Government KPIs: 143 Examples

The 5 KPI categories every local government should track, all 143 measures defined, and what 165 US cities actually track: a median of 394 KPIs — none under 10.

Key Takeaways

Name an owner for each objective and KPI first. Owned city and county objectives are on track about twice as often (29% vs 15%).
Attach one or two measures to each objective. Governments with 1,000+ KPIs attach 6.3 per objective for the same goals, then cannot keep them current.
Give each KPI an update cadence and a next-due date. 56% of local-government KPIs went 12 months without a fresh data point.
Measure outcomes residents feel, not activity counts. DARE completions rose for years while youth drug use did not fall.
Pick measures from the free Local Government KPI Library. It defines all 143 measures; use it to staff objectives you already have.

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A KPI in government is a quantified measure of whether a public agency — a city, county, or state — is delivering on its mission across the services it provides, the money it manages, and the outcomes residents actually feel. The five categories that matter most for a local government contain 143 individual measures in total: service delivery, financial stewardship, operational efficiency, community outcomes, and workforce capacity. The complete, defined list of all 143 is in this article.

But before the list, one number that should change how you read every other "top 10 KPIs for local government" article on the internet. We looked at the live scorecards of 165 US cities, counties, and municipal agencies running on ClearPoint as of August 2026. Not one of them tracks fewer than 10 KPIs. The median tracks 394. Two-thirds track more measures than this entire 143-item library contains.

So the "pick your top 10" advice is not a simplification. It describes a city that does not exist. Real local governments track far more than ten — which makes ownership the real work. The hard part was never picking the metrics. It is keeping the ones you picked alive.

This article does both jobs. First, the five categories that tell you whether a city is actually working — the lens. Then the full bench of 143 defined measures underneath them, so you can staff the lens without inventing metrics from scratch.

The 5 KPIs every government should track

Service delivery — Is the service reaching residents, on time? (e.g., emergency response time, permit turnaround)
Financial stewardship — Is the money sound and sustainable? (e.g., general fund balance as a share of spending)
Operational efficiency — What does one unit of service cost? (e.g., cost per ton of waste collected)
Community outcomes — Are residents' lives measurably better? (e.g., the share of residents who feel safe)
Workforce capacity — Can the organization deliver at all? (e.g., vacancy rate, time-to-hire)

Each one maps to an established public-sector framework. That is the floor, not the ceiling — the real work starts with who owns each number. Here is the whole set at a glance.

KPI category	The question it answers	Example metric	Framework
1. Service delivery	Is the service reaching residents, on time?	Emergency response time; permit turnaround	ICMA output & outcome measures
2. Financial stewardship	Is the money sound and sustainable?	General fund balance ≥ ~2 months of spending	GFOA financial benchmarks
3. Operational efficiency	What does one unit of service cost?	Cost per ton of waste collected	ICMA efficiency measures
4. Community outcomes	Are residents' lives measurably better?	Share of residents who feel safe	The National Community Survey
5. Workforce capacity	Can the organization deliver at all?	Vacancy rate; time-to-hire	ICMA HR benchmarks

We have spent more than 15 years watching these measures live or die inside real city and county scorecards. Here is what each category measures, a real city that used it, and the one mistake that quietly kills most of them.

1. Service delivery: are residents getting what they pay for, on time?

Service-delivery KPIs measure whether a government actually delivers its core services — and how fast. These are the metrics residents notice first. The pothole. The permit. The 911 call that has to be answered in minutes, not hopes.

Real examples governments publish:

Emergency response time

LaggingBetter when lowerAverage police and fire/EMS response time is a core benchmark in ICMA's measurement program.

Formula: Sum of all response times ÷ number of incidents.
Example: 5,400 minutes ÷ 720 incidents = 7.5 min average.
What good looks like: NFPA 1710 sets a 4-minute first-engine response standard for career fire departments and ~8 minutes for EMS; response time is one of the most-tracked KPIs across the governments on the ClearPoint platform
Weak → strong: "We respond to emergencies quickly" → "Median fire/EMS response under 7 minutes for 90% of priority-1 calls, owned by the Fire Chief, reviewed monthly".

If it drifts up, residents are waiting longer in the worst moments of their lives — no other metric on the scorecard matters more viscerally.

Pothole repair turnaround

LaggingBetter when lowerSan Francisco sets a public 72-hour target on its city performance scorecard; Washington, D.C.'s DDOT commits to three business days.

Formula: Sum of repair times ÷ number of potholes filled.
Example: 4,800 hours ÷ 1,600 potholes = 3 hours average.
What good looks like: there is no universal number — publish a repair target, report against it by season, and drive the trend down (lower is better)
Weak → strong: "Fill potholes faster" → "Cut average pothole repair time from 3 hours to 2.5 hours by spring, owned by the Street Maintenance Superintendent".

Permit and plan-review processing time

LaggingBetter when lowerDays from application to decision — the number every contractor and resident actually cares about.

Formula: Sum of processing days ÷ number of permits decided (issued or denied).
Example: 4,200 days ÷ 600 permits = 7 days average.
What good looks like: there is no universal number — publish a target by permit type, report against it, and drive the average down (lower is better)
Weak → strong: "Approve permits faster" → "Cut average permit processing time from 7 to 5 business days by Q4, owned by the Permit Center Manager".

When it doubles, construction stalls and the city's tax base stalls with it.

Pavement Condition Index (PCI)

LaggingBetter when higherA 0–100 score for road quality, published openly by cities like San Francisco.

Formula: Weighted average of individual segment condition scores across the network.
Example: Network-wide weighted average = 72 PCI (good).
What good looks like: there is no universal target — keep the network out of the poor range, since roads cost far more to fix once they fall into poor condition, and drive the average up over time
Weak → strong: "Fix the roads" → "Raise the network PCI from 72 to 75 within three years, owned by the Public Works Director via preventive maintenance".

In ICMA's taxonomy these are output and outcome measures. In a public-sector Balanced Scorecard, they live in the internal-process and citizen perspectives.

The City of Bartlett, Tennessee did not pick its KPIs from a framework binder. Chief Administration Officer Steve Sones and his team chose five focus areas residents actually cared about, split them across all 11 city departments, and took the whole plan from zero to a public dashboard in 13 months. “We're not working in silos,” Sones said once it was live. “What ClearPoint and the strategic plan helped us do is bring all that together.”

2. Financial stewardship: is the money sound and sustainable?

Financial-stewardship KPIs answer two questions at once. Can the government pay its bills today? And will it still be standing in ten years? This is where a single, well-chosen number does enormous work.

General fund balance as a share of expenditures

LaggingThe GFOA recommends keeping unrestricted general-fund balance of no less than two months — roughly 16.7% — of regular operating revenues or expenditures.

Formula: Unassigned fund balance ÷ average monthly operating expenditure.
Example: $30M ÷ $5M per month = 6 months of reserves.
What good looks like: GFOA recommends unrestricted general fund balance of no less than two months of regular operating revenues or expenditures; governments with volatile revenue or disaster risk often hold more
Weak → strong: "Keep healthy reserves" → "Hold unassigned fund balance at or above 6 months of operating expenditure each year, owned by the Finance Director".

It is the most-cited number in government finance for a reason: it is the cushion that absorbs a bad year. Drop below it, and one recession or one lawsuit forces service cuts.

Cost of government per capita

LaggingTotal spending divided by population — the simplest measure of what government costs each resident.

Formula: Total spending ÷ population
Example: $90,000,000 ÷ 50,000 residents = $1,800 per resident.
What good looks like: there is no universal number — compare against similar-sized governments providing the same services, and adjust for inflation when tracking the trend
Weak → strong: "Run a lean government" → "Hold inflation-adjusted cost of government at or below $1,800 per resident over the next three years, owned by the City Manager".

Debt service ratio

LaggingDebt payments as a share of revenue, so today's borrowing does not strangle tomorrow's services.

Formula: Debt payments ÷ revenue
Example: $6,000,000 ÷ $60,000,000 = 10%.
What good looks like: there is no universal number — set a cap in your debt policy, compare against similarly rated peers, and watch the trend, since a rising share crowds out operating spending
Weak → strong: "Keep debt affordable" → "Hold debt service at or below 10% of revenue across the five-year capital plan, owned by the Finance Director".

Show all 5 KPIs

Budget execution rate

LaggingHow much of the adopted budget was actually spent as planned — a quiet tell for whether the plan was real.

Formula: (Actual spending ÷ adopted budget) × 100.
Example: $47,500,000 ÷ $50,000,000 = 95%.
What good looks like: there is no universal number — close to 100% signals realistic budgeting, while large, persistent underspending or overspending signals weak forecasting
Weak → strong: "Spend the budget as planned" → "Keep departmental budget execution between 95% and 100% each fiscal year, owned by the Budget Director, reviewed quarterly".

Pension funded ratio

LaggingBetter when higherPlan assets as a share of liabilities.

Formula: Plan assets ÷ liabilities
Example: $800M ÷ $1,000M = 80%.
What good looks like: U.S. state and local plans averaged ~80% funded on a market-value basis in 2024; 100% means promised benefits are fully covered
Weak → strong: "Shore up the pension" → "Raise the pension funded ratio from 80% to 85% within five years, owned by the Finance Director via full actuarial contributions".

Track the trend; resist the myth of a single "healthy" line — even the 80% benchmark is one that actuaries caution against.

In the government Balanced Scorecard, finance is reframed as stewardship — not the bottom line, but the enabler of everything else. Start with the fund-balance ratio. If you publish only one financial KPI, publish that one.

3. Operational efficiency: what does one unit of service cost?

Efficiency KPIs connect the money to the work. They answer a single, uncomfortable question: what does it cost to deliver one unit of service? Two cities can spend the same and get wildly different results. Efficiency measures show you which one you are.

Cost per ton of solid waste collected

LaggingBetter when lowerA staple of ICMA's solid-waste benchmarking — and a clean way to compare your operation to peers.

Formula: Total collection cost ÷ tons collected.
Example: $3,000,000 ÷ 25,000 tons = $120 per ton.
What good looks like: there is no universal number — compare against peers with similar density and service type, and separate collection cost from disposal fees (lower is better)
Weak → strong: "Collect trash more cheaply" → "Cut solid waste collection cost from $120 to $110 per ton within two years, owned by the Sanitation Manager via route optimization".

A city paying $95 a ton next to a peer paying $60 has found either a contract to renegotiate or a route map to redraw.

Cost per capita, by service

LaggingWhat parks, or policing, or road maintenance costs each resident.

Formula: Total cost of the service ÷ population served.
Example: $4,500,000 ÷ 50,000 residents = $90 per resident.
What good looks like: there is no universal number — compare against similar-sized peers delivering the same service level, since a low cost can mean a thin service rather than an efficient one
Weak → strong: "Deliver services efficiently" → "Cut road maintenance cost per capita from $90 to $85 within two years without lowering pavement condition, owned by the Public Works Director".

Fleet availability and work-order backlog

How much of the fleet is actually on the road, and how deep the maintenance queue runs.

Share of IT problems resolved within 24 hours

LaggingBetter when higherThe back-office metric that quietly decides whether every other department can do its job.

ICMA calls these efficiency measures — ratios that relate efforts (the inputs) to accomplishments (the outputs). They are the bridge between your finance KPIs and your service KPIs. And they are the ones that survive a budget hearing.

4. Community outcomes: are residents' lives actually better?

Outcome KPIs are the hardest to measure and the most important to track. They answer the only question that ultimately matters: did conditions in the community actually improve? Not whether the government was busy. Whether life got better.

Resident satisfaction

LaggingBetter when higherThe National Community Survey measures resident sentiment across ten livability domains — safety, mobility, economy, environment, and more — with national benchmarks to compare against.

Formula: (Satisfied respondents ÷ total respondents) × 100.
Example: 1,560 ÷ 2,000 = 78% satisfaction.
What good looks like: there is no universal number — use the same survey each cycle, compare with peer communities on the same questions, and focus on the services residents rate lowest
Weak → strong: "Make residents happier" → "Raise resident satisfaction from 78% to 82% on the next community survey, owned by the City Manager".

Crime rate per 1,000 residents

LaggingBetter when lowerThe classic public-safety outcome.

Formula: (Reported crimes ÷ population) × 1,000.
Example: (1,050 ÷ 50,000) × 1,000 = 21 per 1,000.
What good looks like: lower is better — the FBI's 2025 national estimates were about 3.3 violent and 15.3 property crimes per 1,000 residents, so report the two separately and compare with similar-sized communities
Weak → strong: "Reduce crime" → "Cut the crime rate from 21 to 18 per 1,000 residents within two years, owned by the Police Chief via hot-spot policing".

Environmental and quality-of-life measures

Air and water quality, park acres per capita, traffic fatalities per 100,000.

This is the perspective a government Balanced Scorecard moves to the top. For a business, the customer serves the bottom line. For a city, the resident is the bottom line. Which is exactly why the next distinction matters so much.

5. Workforce capacity: can the organization deliver at all?

The first four KPIs are impossible without this one. Workforce KPIs measure whether the government has the people to do the work at all — and right now, many do not.

Vacancy rate

LaggingBetter when lowerThe share of budgeted positions sitting empty.

Formula: (Number of unfilled budgeted positions ÷ total budgeted positions) × 100.
Example: 18 unfilled ÷ 400 budgeted = 4.5%.
What good looks like: U.S. job openings averaged ~4.3% of filled jobs plus openings in 2025 — ~3.2% in state and local government and ~5.7% in health care and social assistance — so compare against your own sector (lower is better)
Weak → strong: "Fill our open positions" → "Bring the vacancy rate from 4.5% to 3% of budgeted positions by year-end, owned by the HR Director via a weekly requisition review".

The National League of Cities has documented a deepening talent crunch across city government. A 15% vacancy rate in public works is why the pothole KPI misses — the two numbers are the same story.

Employee turnover

LaggingBetter when lowerHow fast experience walks out the door.

Formula: (Separations ÷ average headcount) × 100.
Example: 18 leavers ÷ 200 avg headcount = 9%.
What good looks like: ~10% annual voluntary turnover is often considered healthy; sustained 20%+ usually signals a retention problem, though it varies sharply by sector
Weak → strong: "Keep turnover low" → "Hold voluntary turnover below 10% annually, owned by the People team, reviewed each quarter".

Time-to-hire

LaggingBetter when lowerDays from posting to filled — the metric that decides whether a vacancy is a blip or a hole.

Formula: Total days from job posting to accepted offer ÷ number of hires.
Example: 2,100 days ÷ 50 hires = 42 days.
What good looks like: SHRM's 2026 benchmarking reports a median of 39 calendar days to fill nonexecutive roles; lower is better, and a long tail of slow requisitions usually matters more than the average
Weak → strong: "Fill jobs quicker" → "Cut time-to-hire from 42 to 35 days within two quarters, owned by the recruiting lead, reviewed monthly by role family".

Training hours per employee

LeadingWhether the organization is building capacity or just burning it.

Formula: Total training hours ÷ total headcount.
Example: 4,000 hours ÷ 200 employees = 20 hours.
What good looks like: there is no universal number — set a target by role family and count formal training separately, since on-the-job learning comes on top
Weak → strong: "Invest in training" → "Deliver 24 hours of formal training per employee this year, up from 20, owned by the Learning and Development Manager".

In Balanced Scorecard terms, this is the learning-and-growth foundation — the people, skills, and systems everything else stands on. Skip it, and your service KPIs will quietly miss for reasons no service report can explain.

What these KPIs look like on a real dashboard

A KPI list is a plan. A dashboard is a promise — the place where the five categories stop being theory and start being a Tuesday-morning meeting. Here is what a city's public-facing view typically looks like, one tile per category:

Anytown Community DashboardFY2026 · Q2

Service delivery

6:42 avg response

● On track · target < 7:00

Financial stewardship

18.2% fund balance

● On track · GFOA floor 16.7%

Community outcomes

71% feel safe

● Watch · target 75%

Workforce capacity

9.8% vacancy rate

● Off track · target < 7%

Operational efficiency · permits closed within target, last 6 months

JanFebMarAprMayJun

Illustrative demo data — modeled on the live public dashboards cities publish with ClearPoint, linked below.

The demo above is fake. These are not:

City of Bartlett, TN — five focus areas across 11 departments, live for every resident to inspect, built in 13 months.
City of Germantown, TN — the dashboard behind Germantown Forward 2030 and its eight key performance areas, updated on a quarterly review cadence.
Sugar Land TX and Fort Worth also published ClearPoint-powered community dashboards — status icons, trend charts, and the underlying measures one click deep.

Notice what a public dashboard changes: the KPI stops being an internal spreadsheet cell and becomes a public commitment. Skipping an update is no longer invisible. That social pressure — more than any software feature — is what keeps the numbers fresh.

Watch: how local governments automate strategy reporting, link budget to strategy, and collect updates without spreadsheets (30 min)

What is the difference between an output and an outcome?

An output measures activity — how many. An outcome measures result — whether anything got better. It is the single most important distinction in government measurement, and the one most KPI lists get wrong.

Most metric lists lean hard on outputs — permits issued, inspections completed, programs delivered — because they are easy to count. That is the trap. A rising activity number feels like progress, even when nothing in the community actually improved.

The classic cautionary tale is the DARE program. As the National League of Cities tells it, cities spent years counting how many students completed DARE — a clean output number that always went up. The actual goal — lowering youth drug use — was an outcome. Research later found the program did not move it. The counter looked like accountability. It was activity wearing accountability's coat.

We have watched a version of this inside our own customer base. Before ClearPoint, the City of Germantown, Tennessee tracked performance in disconnected spreadsheets and argued over whose numbers were right. After it built its plan around nine performance areas and a public dashboard, the argument changed shape: fewer fights over data accuracy, more questions about whether outcomes were actually moving. That shift in the room — not a new framework — is a large part of why Germantown became one of only four U.S. municipalities ever to win the Malcolm Baldrige National Quality Award.

This is why the standard-setters push hard toward outcomes. GASB's guidance on performance reporting says it should focus primarily on measures of service accomplishments — outputs and outcomes. A useful test for any KPI: if you hit the number, does a resident's life actually improve? If not, you are tracking effort, not effect.

How many KPIs should a city actually track?

The honest answer from the data: far fewer than you do, and nobody manages to. We pulled the live scorecards of 165 US cities, counties, and municipal agencies on ClearPoint in August 2026 and counted every active measure. The distribution is not what the listicles describe.

ClearPoint platform data

How many KPIs a local government actually tracks

Fewer than 25 KPIs1.2%

25–9924.3%

100–24917.6%

250–49916.4%

500–99918.8%

1,000 or more21.8%

ClearPoint platform data · 165 US city, county and municipal scorecards with at least one active measure · August 2026. Median 394 KPIs · 25th percentile 99 · 75th percentile 883 · 90th percentile 1,955.

Read the top row again: 1.2% of local governments track fewer than 25 KPIs, and none at all track fewer than 10. The 25th percentile is 99 measures. The 90th percentile is 1,955. A "top 10 KPIs for cities" article is not describing a lean, disciplined municipality — it is describing nobody.

Size explains part of it, but not in a straight line. Small governments with fewer than 25 licensed users hold around 105 measures. Mid-size governments — 25 to 49 users, the classic city-manager-plus-department-heads setup — jump to 540. That is the moment a performance program goes from one person's spreadsheet to every department reporting, and the measure count multiplies with it.

Government size (licensed users)	Orgs	Median KPIs	Median objectives	Median scorecards
Small — under 10 users	10	108	34	6
Mid-small — 10 to 24 users	50	105	48	8
Mid — 25 to 49 users	53	540	106	33
Large — 50 to 99 users	12	929	212	44
Very large — 100+ users	40	650	84	25

The more useful question is not how many but how many per goal. Here the pattern is clean and unflattering.

ClearPoint platform data

KPI sprawl dilutes objectives: measures attached to each strategic goal

Focused portfolios — under 100 KPIs (n=42)2.0 KPIs per objective

Broad — 100 to 399 KPIs (n=42)2.9

Very broad — 400 to 999 KPIs (n=45)4.2

Sprawling — 1,000+ KPIs (n=36)6.3

ClearPoint platform data · 165 US local government scorecards · August 2026. Median KPIs per strategic objective, by size of the measure portfolio.

Governments with focused portfolios attach 2.0 measures to each strategic objective. Governments with 1,000 or more KPIs attach 6.3 — over three times as many. The number of strategic objectives barely moves between the two groups; only the measuring does. Sprawl is not a sign of a more ambitious plan. It is the same plan, measured six ways, by people who then have six times as much to keep current — which is precisely how KPIs end up going a full year without a data point.

The working rule we would defend: one to two measures per objective, every one of them with a named owner, before you add a 143rd. If you cannot name the person who updates a KPI and the date they last did, that KPI is decoration.

Why most government KPIs fail — and the one fix that works

Most governments do not have a KPI problem. They have an ownership problem. The metrics get chosen. The dashboard gets built. And then nobody is on the hook to keep it true.

We have watched the fix work across the ClearPoint platform, where more than 120 cities and counties run their strategy. Look at the city-and-county data on its own, and the pattern is remarkably consistent: ownership is what keeps a KPI true.

ClearPoint platform data

Ownership keeps the plan on track

Objectives on track with a named owner23.6%

Objectives on track with no owner10.6%

Source: ClearPoint platform data · 100+ U.S. cities and counties · strategic-planning research dataset.

Read those two bars together and the story is clear: initiatives with a named owner complete at 2.5× the rate (21.5% vs. 8.7%). ClearPoint shows which objectives still need a name on them. ClearPoint also shows which KPIs are due for a fresh number.

The payoff shows up downstream. In ClearPoint data, initiatives with a named owner are completed 2.5x as often (21.5% vs 8.7%). KPIs that nobody owns do not just go stale — they quietly stop steering the work they were built to guide.

Ownership by city size

We cut the same research dataset by scorecard size — a rough proxy for city size, since bigger governments simply track more. The pattern is not subtle.

Data freshness looks about the same no matter how big the government is. Ownership does not. Mid-size governments in our dataset name KPI owners more consistently than the smallest ones.

Our read: it is not that small cities care less. It is that mid-size and large governments are more likely to have someone whose actual job is performance management. Germantown, Tennessee is the case that makes the point — a Senior Budget & Performance Analyst owns the numbers full-time, and ownership follows the org chart, not the org's good intentions.

“When I pulled the ownership numbers by scorecard size, I expected them to flatten out somewhere past the small cities. They didn't. Mid-size governments in our data name KPI owners more consistently than the smallest ones — not because they care more, but because someone on staff owns performance management as their actual job. That's the fix hiding in the data: it's not a software problem, it's a staffing one.”
— Alexandre Teulade, Growth Marketing Lead, ClearPoint Strategy

Source: segmented cut of ClearPoint's research dataset of over 20,000 analyzed strategic plans, local-government scorecards only, demo and training records excluded. Small tier = 35 organizations / 1,886 measures; medium and large tiers = 37 organizations each. The small-tier figure is directional given the sample size.

Here is the part that should change how you build your scorecard. The fix is not a better framework. It is a name.

ClearPoint platform data

Give a KPI an owner, and it is twice as likely to be on track

Share of city & county objectives currently rated on-track

With a named owner29%

With no owner15%

Source: ClearPoint platform data · city & county strategic objectives.

One assignment roughly doubles the odds that an objective is actually moving. Not a new dashboard. Not a new strategy offsite. A person, by name, who reports the number on a schedule. That is the whole difference between a KPI that runs a city and a KPI that decorates a PDF.

So here is the rule we would put on the wall — and it runs against most performance advice, which tells you to track more. Five KPIs someone actually owns will tell you more than eighty that nobody does. If a metric has no owner, it is not a KPI yet. It is a wish with a number attached. Before you add the next one, give the last one a name.

This is also where the tooling earns its keep. Choosing five strong KPIs is a planning exercise. Keeping them owned, fresh, and visible quarter after quarter is an operating one — and it is exactly what performance management for government is built to do. A purpose-built strategy and KPI platform for local government updates each metric once. From that single source, the council report, the public dashboard, and the internal review all refresh together. The owner manages the work, not the spreadsheet — you can see how that works in a short demo.

How to choose, structure, and review a local government KPI

Before the full library, the mechanics — because the list below is a menu, not a plan. Choosing badly from a good menu is still a bad plan.

How do you choose measures that reflect performance?

Start from the objective, never from the metric. For each strategic objective in your plan, ask what would have to be true for a resident to say it worked — then measure that. Choose one or two measures per objective, not the six the data shows sprawling governments end up with. Prefer the measure you can actually collect on a repeating cadence over the theoretically better one you will collect once and abandon.

Balance leading and lagging indicators. Lagging indicators (crime rate, graduation rate, fund balance) tell you whether you got there; they move slowly and confirm the past. Leading indicators (foot patrol hours, permit review turnaround, vacancy rate) tell you whether you are on the way; they move fast enough to act on. A scorecard of pure lagging indicators is a history book. A scorecard of pure leading indicators never proves anything mattered.

How should a KPI be structured?

Use SMART as a formatting test, not a philosophy: Specific (it names one thing), Measurable (a number, with a defined calculation), Attainable (a target a department could actually hit), Realistic (given the budget and headcount you actually have), and Timely (a reporting frequency and a deadline).

Then add the two fields SMART leaves out and our data says decide everything: a named owner — a person, not a department — and an update cadence with a next-due date. A KPI without those two fields is the 75% case.

How should you report on and review KPIs?

Set a fixed review rhythm and hold it: monthly for operational measures at department level, quarterly for the strategic scorecard with the leadership team, annually for the plan itself. The review meeting should discuss the measures that are off track and the ones that are stale — not walk the entire list. Report the same numbers publicly that you report internally; the discipline of a public dashboard is what keeps the internal one honest.

What is government performance management?

Government performance management is the discipline of using KPIs to hold a public organization accountable for delivering its mission. It turns a strategic plan into measured, owned, and regularly reviewed results — the connective tissue between what a government promised and what residents actually get. The five categories above are what you measure. Performance management is how you keep measuring it after the launch meeting ends.

If you want a deeper bench of metrics to draw from, the full list of 143 measures below breaks them down by department, and our city KPI benchmarks show how your numbers compare to peers.

The full list: 143 local government KPIs by category

The five categories above are the lens. Underneath them sits the full bench: 143 individual measures spanning 14 municipal functions — arts & culture, building, citizen engagement, economic development, education, environment, finance, housing, human resources, information technology, parks & recreation, public works, safety, and transportation. Every measure below is defined, so you can lift one straight into a scorecard without rewriting it.

One warning before you scroll. Given what the data above shows about sprawl, this list is deliberately a menu. Two-thirds of local governments already track more measures than it contains. Use it to find the right measure for an objective you already have — not to find more objectives.

Service delivery — 26 measures

Drawn from the Building, Public Works, and Transportation functions. These answer whether the service reached the resident, on time.

Building

Value of Commercial Projects Constructed

LaggingThe value of new commercial projects constructed is a key measure that allows you to trace the amount of new commercial investment in a municipality.

Formula: Sum of estimated construction values on building permits in the time frame.
Example: $40M + $25M + $15M = $80M.
What good looks like: there is no universal number — it swings with a few large projects, so trend it over several years and compare with your economic development goals
Weak → strong: "Attract more commercial investment" → "Reach $90M in permitted commercial construction value this year, up from $80M, owned by the Economic Development Director".

It is calculated by adding all of the estimated costs of construction values on building permits in a given time frame.

Total Number of Permits Issued

LaggingThe total number of building permits issued indicates the amount of construction occurring in any given year in a municipality.

Formula: Count of building permits issued in the period.
Example: 1,800 residential + 450 commercial = 2,250 permits.
What good looks like: there is no universal number — it follows the local construction cycle, so trend it against your own baseline and pair it with processing time
Weak → strong: "Issue more permits" → "Issue 2,400 building permits this year, up from 2,250, while holding processing time steady, owned by the Building Official".

Total Number of Inspections Performed

This metric looks at the number of construction projects completed in any given year in a municipality.

Formula: Count of inspections completed in the period.
Example: 12,000 building + 6,000 trade + 2,000 code = 20,000 inspections.
What good looks like: there is no universal number — read it alongside permit volume and inspections per inspector, and track the share completed on the requested day
Weak → strong: "Do more inspections" → "Complete 22,000 inspections this year with 90% on the requested day, owned by the Chief Building Inspector".

Show all 6 Service delivery — 26 measures

Percentage of Historic Preservation Cost Funded by Municipality Funds

LaggingCalculating this percentage helps take account of how much of the funding for historical preservation is coming from outside sources as compared to what’s coming from municipal resources.

Formula: (Municipal funds spent on preservation ÷ total preservation costs) × 100.
Example: $300,000 ÷ $1,200,000 = 25%.
What good looks like: there is no universal number — a lower share usually means outside funding is stretching local dollars further, so track it against your own baseline
Weak → strong: "Find more preservation funding" → "Cut the municipal share of preservation costs from 25% to 20% within two years, owned by the Historic Preservation Officer via state grant applications".

Total Permit Revenue

LaggingSumming permit revenue aggregates the dollar amount a municipality brings in from building permits, contractor registration, violation search, and lien settlement requests.

Formula: Sum of fees from building permits, contractor registration, violation searches, and lien settlement requests in the period.
Example: $2,400,000 + $300,000 + $200,000 + $100,000 = $3,000,000.
What good looks like: there is no universal number — compare it with what the permitting program costs to run, and read it alongside permit volume, since revenue tracks the construction cycle
Weak → strong: "Grow permit revenue" → "Recover the full cost of the permitting program, with fee revenue at $3,200,000 next fiscal year, owned by the Building Official".

Percentage of Plans Approved After First Review

LaggingThis measure looks at the rate at which plans are approved on the first attempt, in order to better understand the speed of the review process.

Formula: (Plans approved on first review ÷ total plans reviewed) × 100.
Example: 270 ÷ 450 = 60%.
What good looks like: there is no universal number — raise it against your own baseline with clear checklists and pre-application meetings, and track the most common reasons plans are sent back
Weak → strong: "Speed up plan review" → "Raise first-review approvals from 60% to 70% within a year, owned by the Plan Review Manager via published submittal checklists".

Public Works

Percentage of Capital Projects Completed on Time

LaggingBetter when higherObserving the percentage of capital projects completed on time is a way to see how successful the municipality is at opening new facilities in a timely fashion.

Formula: (Projects delivered on time ÷ total projects completed) × 100.
Example: 34 ÷ 40 = 85% on-time.
What good looks like: there is no universal number — set it against your own baseline, measure against the original schedule rather than a revised one, and report the reasons for each delay
Weak → strong: "Deliver projects on time" → "Complete 90% of capital projects by their baseline date this year, up from 85%, owned by the Capital Projects Director".

Percentage of Capital Projects Completed Within Project Budget

LaggingBetter when higherThis is a critical measurement that relates directly to maintaining the municipal budget.

Formula: (Projects completed within budget ÷ total projects completed) × 100.
Example: 27 ÷ 30 = 90%.
What good looks like: there is no universal number — track it against your own baseline, report the size of overruns alongside the count, and use the approved budget as the fixed baseline
Weak → strong: "Stay on budget" → "Complete 95% of capital projects within approved budget this fiscal year, up from 90%, owned by the Capital Projects Manager".

Percentage of Projects Initiated Within 12 Months of Funding Authorization

LaggingBetter when higherThe percentage of projects initiated within one year shows the speed and efficiency with which capital projects move through the appropriate pipelines and go into construction phase.

Formula: (Projects started within 12 months of funding ÷ total projects funded) × 100.
Example: 34 ÷ 40 = 85%.
What good looks like: there is no universal number — track it against your own baseline and push it up, since slow starts tie up authorized funds and expose projects to rising costs
Weak → strong: "Start projects faster" → "Initiate 90% of funded capital projects within 12 months of authorization, up from 85%, owned by the Capital Projects Manager".

Show all 10 Service delivery — 26 measures

Total Miles of Municipal Streets Paved

LaggingThis performance indicator is measured in part by the miles of municipal streets paved annually.

Formula: Sum of street miles paved across all projects.
Example: 18 miles resurfaced + 7 miles reconstructed = 25 miles.
What good looks like: there is no universal number — set it against your total lane miles and pavement condition so streets are repaved before they fall into poor condition
Weak → strong: "Pave more roads" → "Pave 30 miles of streets this year, up from 25, owned by the Streets Superintendent via the pavement management plan".

Total Miles of Municipal Sidewalk Repaired

LaggingSidewalk maintenance productivity can be reflected by the miles of sidewalks maintained per year.

Formula: Sum of sidewalk miles repaired across all projects.
Example: 8 miles by contract + 4 miles by staff crews = 12 miles.
What good looks like: there is no universal number — set it against your total sidewalk network and repair backlog so repairs keep pace with deterioration
Weak → strong: "Fix sidewalks" → "Repair 15 miles of sidewalk this year, up from 12, prioritizing school routes, owned by the Public Works Director".

Number of Water Line Breaks

LaggingBetter when lowerTallying the number of water line breaks is a way to assess the proactive water line maintenance efforts of the municipality.

Formula: Count of water line breaks in the period; for comparison, (breaks ÷ miles of main) × 100.
Example: (66 breaks ÷ 600 miles) × 100 = 11 breaks per 100 miles.
What good looks like: a large study of North American utilities found an average of 11.1 breaks per 100 miles of pipe per year, so beating that rate and trending down is good (lower is better)
Weak → strong: "Fix water main breaks" → "Cut water main breaks from 11 to 9 per 100 miles this year, owned by the Utilities Director via targeted pipe replacement".

Percentage of Bridges with a Sufficiency Rating of 50%

LaggingBetter when higherUsing the Federal Highway Administration (FHA) bridge sufficiency formula, municipalities can assess the percentage of bridges that meet sufficiency standards.

Formula: (Bridges with a sufficiency rating of 50 or higher ÷ total bridges) × 100.
Example: 114 ÷ 120 = 95%.
What good looks like: a rating below 50 has traditionally flagged a bridge for replacement; FHWA now grades bridges good, fair, or poor, and ~6.8% of U.S. bridges were rated poor in 2024
Weak → strong: "Keep bridges safe" → "Raise the share of bridges rated 50 or higher from 95% to 98% within three years, owned by the City Engineer via the bridge rehabilitation program".

Average Number of Customer Complaints per Week

LaggingBetter when lowerThis measure offers a method for tracking the resident satisfaction of the public works department.

Formula: Total complaints received ÷ number of weeks.
Example: 520 ÷ 52 weeks = 10 complaints per week.
What good looks like: there is no universal number — normalize by population or service volume, track by complaint type, and drive the trend down (lower is better)
Weak → strong: "Get fewer complaints" → "Cut public works complaints from 10 to 7 per week by year-end, owned by the Public Works Director via missed-pickup fixes".

Percentage of Resident Complaints Addressed Within 24 Hours

LaggingBetter when higherThe percentage of resident complaints addressed in 24 hours measures the speed at which resident concerns are acknowledged and logged into the system.

Formula: (Complaints acknowledged within 24 hours ÷ total complaints received) × 100.
Example: 4,600 ÷ 5,000 = 92%.
What good looks like: there is no universal number — publish the 24-hour standard, report the share by complaint type, and pair it with time to resolution so fast logging does not hide slow fixes
Weak → strong: "Respond to residents faster" → "Acknowledge 95% of resident complaints within 24 hours by Q4, up from 92%, owned by the Customer Service Manager".

Percentage of Street Lights in Service

LaggingBetter when higherObserving the percentage of street lamps in service is a way to shine light on the effectiveness of inspections and work order response times.

Formula: (Street lights working ÷ total street lights) × 100.
Example: 9,850 ÷ 10,000 = 98.5%.
What good looks like: there is no universal number — aim for nearly every light working and track days from outage report to repair, since outages left for weeks erode safety and trust
Weak → strong: "Keep the lights on" → "Keep 99% of street lights in service each month, up from 98.5%, owned by the Street Lighting Supervisor".

Transportation

Driver Courtesy Satisfaction Rating

LaggingBetter when higherDriver courtesy satisfaction rating can be gathered in a survey, and can help evaluate the happiness of residents with work commutes.

Formula: (Riders rating drivers as courteous ÷ total respondents) × 100.
Example: 1,700 ÷ 2,000 = 85%.
What good looks like: there is no universal number — survey the same way each cycle, break results out by route and shift, and follow up on courtesy complaints alongside the score
Weak → strong: "Improve driver courtesy" → "Raise driver courtesy satisfaction from 85% to 90% on the next rider survey, owned by the Transit Operations Manager".

Passenger Trips on Buses per Gallon of Fuel

LaggingCalculating total passenger trips per gallon of fuel provides valuable information in regard to the costs and value of providing municipal bus services.

Formula: Total passenger trips ÷ gallons of fuel.
Example: 1,500,000 trips ÷ 300,000 gallons = 5 trips per gallon.
What good looks like: there is no universal number — trend it against your own baseline and by route, since it rises with ridership and fleet fuel efficiency
Weak → strong: "Run buses more efficiently" → "Raise passenger trips per gallon from 5.0 to 5.5 within two years, owned by the Transit Operations Manager via route redesign".

Public Transportation Satisfaction Rating

LaggingBetter when higherOverall public transportation satisfaction can be measured with a survey, and can show how happy residents are with public transportation options (and how likely they are to use those options).

Formula: (Satisfied respondents ÷ total respondents) × 100.
Example: 1,400 ÷ 2,000 = 70%.
What good looks like: there is no universal number — survey riders and non-riders the same way each cycle, and break results out by route and reliability, since frequency and on-time service drive satisfaction
Weak → strong: "Make transit better" → "Raise public transportation satisfaction from 70% to 75% on the next rider survey, owned by the Transit Director".

Show all 10 Service delivery — 26 measures

Average Speed on Neighborhood Streets

LaggingTracking average speed on neighborhood streets reveals long-term variation in traffic on municipal small streets.

Formula: Sum of measured vehicle speeds ÷ number of vehicles measured.
Example: 6,250 mph ÷ 250 vehicles = 25 mph.
What good looks like: there is no universal number — compare against the posted limit; on neighborhood streets slower is generally safer, so also watch the share of drivers exceeding it
Weak → strong: "Slow down traffic" → "Cut average neighborhood street speed from 25 to 22 mph within a year, owned by the Traffic Engineer via traffic-calming projects".

Average Speed on Arterial Streets

LaggingFollowing the trends of the average speed on arterial streets gives a good indication of the increases and decreases of traffic on important road sections.

Formula: Segment length ÷ average travel time.
Example: 3 miles ÷ 0.1 hours = 30 mph.
What good looks like: there is no universal number — compare against the posted limit and your own baseline; speeds well below it signal congestion, and speeds well above it signal safety risk
Weak → strong: "Keep traffic moving" → "Hold average peak-hour speed at 30 mph or better on the five busiest arterial corridors, owned by the Traffic Engineer, reviewed quarterly".

Average Hours to Remove Snow from Streets

LaggingBetter when lowerFor municipalities that receive significant snowfall, marking the hours it takes to get roads plowed after a snowfall is a good way to benchmark and monitor the snow removal processes.

Formula: Total hours to clear streets across storms ÷ number of storms.
Example: 180 hours ÷ 12 storms = 15 hours.
What good looks like: there is no universal number — set targets by street priority with main routes first, measure from the end of snowfall, and drive the average down (lower is better)
Weak → strong: "Clear snow faster" → "Clear all priority-1 streets within 12 hours of snowfall ending, down from a 15-hour average, owned by the Streets Superintendent".

Percentage of Commuters Using Public Transportation

LaggingThis metric indicates how effective the department is at providing alternatives to car and pedestrian traffic.

Formula: (Workers commuting by public transportation ÷ total workers) × 100.
Example: 3,000 ÷ 60,000 = 5%.
What good looks like: nationally 3.1% of workers commuted by public transportation in 2022, down from 5.0% in 2019, per the Census Bureau; compare with peers that have similar transit service
Weak → strong: "Get more people on transit" → "Raise the transit commute share from 5% to 6% within three years, owned by the Transit Director".

Walkability Score

LaggingBetter when higherWalkability score is a benchmark for comparison with other cities, measuring the “pedestrian friendliness” of a municipality.

Formula: Population-weighted average of neighborhood walkability scores (0–100).
Example: ((60 × 40,000) + (75 × 20,000)) ÷ 60,000 = 65.
What good looks like: higher is better; compare neighborhoods against each other and track your own trend rather than one citywide number
Weak → strong: "Make the city more walkable" → "Raise the citywide walkability score from 65 to 70 within five years, owned by the Planning Director".

Resident Satisfaction with Municipal Traffic

LaggingBetter when higherA survey of resident satisfaction can evaluate happiness with the traffic in a municipality, and help align municipal goals with the traffic concerns of the residents.

Formula: (Satisfied respondents ÷ total respondents) × 100.
Example: 900 ÷ 2,000 = 45%.
What good looks like: there is no universal number — ask the same survey question each cycle, compare with peer communities using the same survey, and pair it with travel-time data
Weak → strong: "Fix traffic" → "Raise traffic satisfaction from 45% to 52% on the next resident survey, owned by the Traffic Engineer".

Percentage of Commuters Biking or Walking

LaggingNoting the percentage of residents who walk or bike to work is a way to estimate the non-public transportation alternatives to car commuting.

Formula: (Workers who bike or walk to work ÷ total workers) × 100.
Example: 2,400 ÷ 60,000 = 4%.
What good looks like: nationally only 2.9% of workers biked or walked to work in 2022, per the Census Bureau, so beating that share and growing it each year is a strong result
Weak → strong: "Get more people biking and walking" → "Raise the walk-and-bike commute share from 4% to 5% within three years, owned by the Transportation Planning Manager".

Financial stewardship — 10 measures

Drawn from the Finance function. These answer whether the money is sound and sustainable.

Budgeting Ratio

LaggingThe budgeting ratio is calculated by dividing the ongoing operating expenditure by the total ongoing operating revenues.

Formula: Ongoing operating expenditure ÷ total ongoing operating revenues.
Example: $48M ÷ $50M = 0.96.
What good looks like: a ratio at or below 1.0 means ongoing revenues cover ongoing spending, the structural balance GFOA recommends; above 1.0 means reserves or one-time money are filling the gap
Weak → strong: "Balance the budget" → "Hold the budgeting ratio at or below 0.96 in each adopted budget, owned by the Budget Director".

Real Estate Assessment to Sales Ratio (ASR)

LaggingBetter when in target rangeThe ASR compares the assessed values of local real estate to the selling prices of normal, single-family sales of real property.

Formula: Assessed value ÷ sale price, taken as the median across qualified sales.
Example: $285,000 ÷ $300,000 = 0.95.
What good looks like: the IAAO ratio study standard sets an acceptable band for the median assessment-to-sales ratio around full market value; hold it consistently across neighborhoods, price ranges, and property types
Weak → strong: "Assess at market value" → "Bring the median assessment-to-sales ratio from 0.95 to 0.98 by the next reassessment, owned by the Chief Assessor".

A ratio of 100% is ideal, because it shows that property was appropriately assessed.

Real Estate Coefficient of Dispersion (COD)

LaggingBetter when lowerThe COD provides a metric of the variation for individual assessment ratios around the median level of assessment.

Formula: (Average absolute deviation from the median ratio ÷ median ratio) × 100.
Example: (0.09 ÷ 0.90) × 100 = 10.0 COD.
What good looks like: the IAAO ratio study standard calls for a COD of 15.0 or less for single-family homes, and 10.0 or less in newer, fairly similar neighborhoods (lower is better)
Weak → strong: "Assess fairly" → "Bring the residential COD from 10.0 to under 8.0 by the next reassessment, owned by the Chief Assessor".

In other words, the lower the rate, the more fair the spread of the tax burden. (15% is the industry benchmark.)

Show all 10 Financial stewardship — 10 measures

Debt per Capita

LaggingDebt per capita measures the condition of municipal debt in relation to the total population.

Formula: Municipal debt ÷ total population.
Example: $180M ÷ 120,000 residents = $1,500 per resident.
What good looks like: there is no universal number — compare with peers of similar size and read it alongside resident income and tax base, since the same debt load is lighter in a wealthier community
Weak → strong: "Keep debt manageable" → "Hold debt per capita at or below $1,500 through the five-year capital plan, owned by the Finance Director".

Full-Time Employees per Capita

LeadingLooking at the number of municipal staff in relation to total municipal population provides a ratio that can be compared to other similar municipalities.

Formula: (Full-time employees ÷ population) × 1,000.
Example: (1,200 ÷ 150,000) × 1,000 = 8 employees per 1,000 residents.
What good looks like: there is no universal number — staffing depends on which services the municipality delivers itself, so compare only with peers of similar size and service mix
Weak → strong: "Right-size the workforce" → "Hold staffing at or below 8 full-time employees per 1,000 residents through the next budget cycle, owned by the City Manager".

This helps determine whether the staff size is too small or large for a given population.

Number of Grant Compliance Violations

LaggingBetter when lowerExposing the number of grant compliance violations allows for tracking compliance improvement over time.

Formula: Count of grant compliance violations identified in the period.
Example: 4 reporting + 2 procurement + 1 unallowable cost = 7 violations.
What good looks like: the goal is a steady decline toward none, with no repeat violations; lower is better, and each one should have a documented corrective action
Weak → strong: "Stay compliant on grants" → "Cut grant compliance violations from 7 to 3 this fiscal year, owned by the Grants Manager via quarterly compliance reviews".

Number of Employees Trained in Grant Writing

LeadingKeeping tabs on the number of employees trained in grant writing allows municipalities to maximize grant-seeking activities.

Formula: Count of employees completing grant-writing training in the period.
Example: 3 sessions × 8 employees = 24 employees trained.
What good looks like: there is no universal number — aim for trained staff in every department that pursues grants, and check that training leads to more and stronger applications
Weak → strong: "Train more grant writers" → "Train 30 employees in grant writing by year-end, up from 24, owned by the Grants Manager".

Total Number of Audit Findings

LaggingBetter when lowerThis measure evaluates the findings in the financial auditing process in a municipality.

Formula: Count of findings reported in the annual financial audit.
Example: 3 material weaknesses + 5 significant deficiencies = 8 findings.
What good looks like: the goal is no material weaknesses and no repeat findings from the prior year; track how many findings are closed before the next audit (lower is better)
Weak → strong: "Clean up our audits" → "Cut audit findings from 8 to 3, with no repeat findings, by the next annual audit, owned by the Finance Director".

Bond Rating

LaggingBetter when higherMunicipal bond rating, as determined by agencies such as Moody's, Standard & Poor's, and the National Bond Rating Agency, are indicators of the financial stability of local governments.

Formula: The letter grade assigned by a rating agency, tracked by notch (for example, AA−, AA, AA+).
Example: AA− to AA+ = 2-notch upgrade.
What good looks like: AAA (Aaa at Moody's) is the top grade, and anything BBB− (Baa3) or better is investment grade; the goal is to protect or step up your current rating
Weak → strong: "Keep finances strong" → "Earn a one-notch upgrade from AA+ to AAA within three years, owned by the Finance Director via reserve policy and multiyear forecasting".

Total Revenue Generated

LaggingBetter when higherThis measure is an aggregation of all sources of municipal revenue, and the basis for building the municipal budget.

Formula: Price of goods × number of goods sold.
Example: $50 × 20,000 = $1,000,000.
What good looks like: there is no universal number — judge it against plan, prior periods, and market growth, and read it alongside margin so growth stays profitable
Weak → strong: "Grow the business" → "Grow annual revenue from $1,000,000 to $1,200,000 by fiscal year-end, owned by the Chief Revenue Officer".

Operational efficiency — 6 measures

Drawn from the Information Technology function. These answer what one unit of service costs to deliver.

Number of Major Network Outages

LaggingBetter when lowerThis metric tracks the incidents where a certain percentage of users (i.e. 30%) were negatively affected for a certain period of time (i.e. 3 hours).

Formula: Count of outages in the period that meet the major-outage threshold for users affected and duration.
Example: 3 major outages in a year = 3.
What good looks like: lower is better and the goal is zero — there is no universal number, so fix the threshold for 'major' in writing and trend the count against your own history
Weak → strong: "Have fewer network outages" → "Cut major network outages from 3 to 1 this fiscal year, owned by the network operations lead via redundancy upgrades".

Satisfaction with Municipal Website

LaggingBetter when higherSatisfaction with the municipal website, as measured in a survey, reflects the overall quality and user-friendliness of this particularly important means of communication with residents.

Formula: (Satisfied respondents ÷ total respondents) × 100.
Example: 1,300 ÷ 2,000 = 65%.
What good looks like: there is no universal number — ask the same survey question each cycle, compare with peer communities using the same survey, and pair it with task-completion data
Weak → strong: "Improve the website" → "Raise website satisfaction from 65% to 72% on the next resident survey, owned by the Communications Director".

Percent of Repair Calls Resolved Within 24 Hours

LaggingBetter when higherThis measure looks at the efficiency of the IT department in supporting vital communication networks and solving problems for citizens as they arise.

Formula: (Repair calls resolved within 24 hours ÷ total repair calls) × 100.
Example: 850 ÷ 1,000 = 85%.
What good looks like: there is no universal number — set it against your service-level agreement, track it by call type, and look hard at the calls that miss the window
Weak → strong: "Fix problems faster" → "Resolve 92% of repair calls within 24 hours by Q4, owned by the IT support supervisor, reviewed weekly".

Show all 6 Operational efficiency — 6 measures

Average Number of Hours to Complete Service Desk Request

LaggingBetter when lowerThe average number of hours it takes to complete a service desk request is a metric that tracks the improvements (or lack thereof) in service desk efficiency over time.

Formula: Total hours to complete requests ÷ number of requests completed.
Example: 4,800 hours ÷ 600 requests = 8 hours.
What good looks like: there is no universal number — set targets by request priority, measure against your service-level agreement, and drive the trend down (lower is better)
Weak → strong: "Close tickets faster" → "Cut average service desk completion time from 8 to 6 hours by Q4, owned by the IT Service Desk Manager via self-service password resets".

Backup Success Rate

LaggingBetter when higherThe backup success rate indicates the percentage of total backup jobs for protected servers that ran successfully.

Formula: (Successful backups ÷ total scheduled backups) × 100.
Example: 990 ÷ 1,000 = 99%.
What good looks like: there is no universal number — every failed job should be fixed and rerun promptly, and regular restore tests matter as much as the success rate
Weak → strong: "Make backups reliable" → "Raise backup success from 99% to 99.8% by Q3, with a monthly restore test, owned by the infrastructure lead".

IT Customer Satisfaction Rate

LaggingBetter when higherIT customer satisfaction rate measures the percentage of customers happy with their interaction with the IT service desk.

Formula: (Satisfied survey responses ÷ total survey responses) × 100.
Example: 410 ÷ 500 = 82%.
What good looks like: service desk satisfaction has averaged ~81% in MetricNet's benchmarks for more than a decade, so clearing that mark is solid — and the reasons behind unhappy responses matter more than the score
Weak → strong: "Keep users happy with IT" → "Lift IT service desk satisfaction from 82% to 88% by year-end, owned by the service desk manager, reviewed monthly".

Community outcomes — 91 measures

Drawn from Arts & Culture, Citizen engagement, Economic Development, Education, Environment, Housing, Parks & Recreation, and Safety. This is the largest bucket by far — and, per the sprawl data above, the one local governments most consistently over-track. Take a few, not the set.

Arts & Culture

Total Attendance at Festivals and Cultural Events

LaggingBetter when higherAttendance for festivals and events in a municipality demonstrates the engagement of residents with the broader cultural community.

Formula: Sum of attendance at each festival and cultural event.
Example: 45,000 + 30,000 + 25,000 = 100,000 attendees.
What good looks like: there is no universal number — grow it against your own baseline, count the same way every year, and pair it with visitor spending or resident satisfaction
Weak → strong: "Draw bigger crowds" → "Grow total festival and event attendance from 100,000 to 115,000 next year, owned by the Special Events Manager".

Number of Performances at Performing Arts Center

LeadingRecording the number of events at a community performing arts center illustrates the long-term use and viability of the facility.

Formula: Count of performances held at the venue in the period.
Example: (4 weekly shows × 40 weeks) + 20 special events = 180 performances.
What good looks like: there is no universal number — trend it against your own baseline and track attendance per performance, since steady use with healthy audiences signals a viable facility
Weak → strong: "Use the arts center more" → "Grow performances from 180 to 200 next season, owned by the Performing Arts Center Manager, with attendance tracked per show".

Average Percentage of Capacity Filled at Performing Arts Center

LeadingBetter when higherObserving the average percentage of capacity filled during events at a public performing arts center is a good way to gauge community interest and engagement with that particular institution.

Formula: (Seats filled ÷ seats available) × 100, across all events.
Example: 33,600 ÷ 48,000 = 70%.
What good looks like: there is no universal number — set it against your own baseline by event type, and watch it alongside ticket revenue since discounting can fill seats at a loss
Weak → strong: "Fill the theater" → "Raise average capacity filled from 70% to 78% next season, owned by the Performing Arts Center Director via subscription packages".

Show all 5 Community outcomes — 91 measures

Percentage of Dates Booked in Arena

LeadingWatching the number of available dates booked in a municipal arena helps to assess the effectiveness of utilizing that piece of infrastructure to benefit the quality of life in a community.

Formula: (Dates booked ÷ dates available) × 100.
Example: 219 ÷ 300 = 73%.
What good looks like: there is no universal number — set it against your own baseline and peer venues, and watch revenue per booked date so a fuller calendar is not filled with low-value bookings
Weak → strong: "Book the arena more often" → "Raise arena bookings from 73% to 80% of available dates by year-end, owned by the Venue Manager".

Number of Festivals and Cultural Events

LeadingThe number of festivals and cultural events held in a given time frame is a good metric for evaluating the cultural, artistic, and entertainment options that might attract residents and tourists to a municipality.

Formula: Count of festivals and cultural events held in the period.
Example: 18 festivals + 30 cultural events = 48 events.
What good looks like: there is no universal number — grow it against your own baseline and pair it with attendance, so more events also means more residents taking part
Weak → strong: "Host more community events" → "Grow festivals and cultural events from 48 to 55 this year, owned by the Parks & Recreation Director, with attendance tracked per event".

Citizen

Number of Town Hall Participants

LeadingThis measure demonstrates how engaged residents are with the functions and policy of a city hall.

Formula: Count of residents attending town hall meetings in person or online in the period.
Example: 4 meetings × 150 participants = 600 participants.
What good looks like: there is no universal number — track participation against your own trend and check whether attendees reflect the community's neighborhoods and demographics
Weak → strong: "Engage more residents" → "Grow town hall participation from 600 to 750 this year, owned by the Community Engagement Manager via online attendance".

Resident Satisfaction with Appearance of Municipality

LaggingBetter when higherA survey that evaluates how satisfied residents are with the overall appearance of the community can provide insight into the importance of an investment in “municipal beautification.”

Formula: Residents rating the overall appearance excellent or good ÷ total survey respondents.
Example: 1,110 ÷ 1,500 = 74%.
What good looks like: there is no universal number — track the excellent-or-good share against your own prior surveys and national resident-survey benchmarks, and break results out by neighborhood and demographic group
Weak → strong: "Make the community look better" → "Raise satisfaction with community appearance from 74% to 80% by the next resident survey, owned by the Public Works Director via a litter and blight program".

Show all 11 Community outcomes — 91 measures

Resident Satisfaction with Municipality as Place to Live

LaggingBetter when higherA survey that evaluates how satisfied residents are with the municipality as a place to live can help dictate the direction and investment(s) of the municipal government.

Formula: Residents rating the municipality excellent or good as a place to live ÷ total survey respondents.
Example: 1,320 ÷ 1,500 = 88%.
What good looks like: there is no universal number — track the excellent-or-good share against your own prior surveys and national resident-survey benchmarks, and break results out by neighborhood and demographic group; it is a headline measure that most other services feed into
Weak → strong: "Be a great place to live" → "Raise the share rating the municipality excellent or good as a place to live from 88% to 91% by the next resident survey, owned by the City Manager".

Poverty Rate

LaggingBetter when lowerPoverty rates offer insight into the status of a municipality as a place that fosters upward mobility.

Formula: Residents below the federal poverty threshold ÷ residents whose poverty status is determined.
Example: 10,600 ÷ 100,000 = 10.6%.
What good looks like: the official U.S. poverty rate was 10.6% in 2024, so compare your local rate with that and your state figure (lower is better)
Weak → strong: "Reduce poverty" → "Cut the poverty rate from 10.6% to 9.5% within five years, owned by the Human Services Director".

This measure also acts as a benchmark against state and national poverty rates.

Resident Satisfaction of Municipality as a Workplace

Better when higherThis measure looks at the feelings of residents toward the municipality's economic outlook; it can be captured in a survey that measures citizen happiness.

Formula: Residents rating the municipality excellent or good as a place to work ÷ total survey respondents.
Example: 975 ÷ 1,500 = 65%.
What good looks like: there is no universal number — track the excellent-or-good share against your own prior surveys and national resident-survey benchmarks, and break results out by neighborhood and demographic group, and read it with local job and wage data
Weak → strong: "Be a good place to work" → "Raise resident ratings of the municipality as a place to work from 65% to 70% by the next resident survey, owned by the Economic Development Director".

Number of Active Municipal Mobile App Users

LeadingMobile applications have become an increasingly popular tool for communication.

Formula: Count of unique users who opened the app at least once in the period.
Example: 8,000 iOS users + 4,000 Android users = 12,000 active users.
What good looks like: there is no universal number — track active users as a share of residents and against total downloads, since installs that go unused add no reach
Weak → strong: "Get people using the app" → "Grow monthly active app users from 12,000 to 15,000 within a year, owned by the Digital Services Manager".

Taking account of active municipal app downloads and users is crucial to understanding effectiveness of this new communication method.

Resident Satisfaction with Municipal Communication

LaggingBetter when higherSatisfaction with the overall communication of the municipality reflects general feelings that cannot be captured by other quantitative measures, and is important to understanding the true success of information distribution.

Formula: Residents rating municipal communication excellent or good ÷ total survey respondents.
Example: 1,050 ÷ 1,500 = 70%.
What good looks like: there is no universal number — track the excellent-or-good share against your own prior surveys and national resident-survey benchmarks, and break results out by neighborhood and demographic group
Weak → strong: "Communicate better" → "Raise resident satisfaction with municipal communication from 70% to 76% by the next resident survey, owned by the Communications Director".

Voter Turnout

LaggingBetter when higherVoter turnout for local elections is an important indicator of community engagement and local government participation.

Formula: (Ballots cast ÷ registered voters) × 100.
Example: 9,000 ÷ 45,000 = 20%.
What good looks like: local turnout is often very low, so steady gains matter more than a fixed bar
Weak → strong: "Get more people voting" → "Raise municipal election turnout from 20% to 25% by the next election, owned by the City Clerk via mail-ballot outreach".

Number of Municipal Website Visitors

LeadingA municipality’s website is one of the main tools of communication and collaboration.

Formula: Count of website visits in the period.
Example: 45,000 visits a month × 12 = 540,000 visits.
What good looks like: there is no universal number — track the trend against your own baseline and pair it with tasks completed online, such as payments and service requests
Weak → strong: "Get more people online" → "Grow website visits from 540,000 to 600,000 this year, owned by the Communications Director via online service requests".

Observing the number of website visits in a given time period helps to understand its effectiveness.

Median Household Income

LaggingBetter when higherMedian household income is a vital indicator of general economic conditions in a given area.

Formula: The middle value when all household incomes are ranked from lowest to highest.
Example: Middle two of 10,000 ranked households: ($79,000 + $81,000) ÷ 2 = $80,000.
What good looks like: U.S. median household income was $83,730 in 2024; compare your area against the state and national figures and watch the inflation-adjusted trend
Weak → strong: "Raise incomes" → "Lift median household income from $80,000 to $86,000 within five years, owned by the Economic Development Director via higher-wage employer recruitment".

Economic Development

Change in Value of Commercial Property

LaggingThe change in the value of commercial property indicates the current state of the commercial real estate market in a municipality.

Formula: (Current commercial property value − prior value) ÷ prior value.
Example: ($2.1B − $2.0B) ÷ $2.0B = 5%.
What good looks like: there is no universal number — compare the change with inflation and the regional market, and separate new construction from growth in existing property values
Weak → strong: "Grow the commercial tax base" → "Grow assessed commercial property value from $2.1B to $2.3B within two years, owned by the Economic Development Director".

This measure is also correlated with the condition of the municipal economy.

Number of Employers at Municipal Job Fairs

LeadingThe total number of employers at municipal job fairs indicates the relative number of job openings in a municipality.

Formula: Count of participating employers across municipal job fairs in the period.
Example: 2 fairs × 60 employers = 120 employers.
What good looks like: there is no universal number — track it against your own trend and pair it with the openings employers bring, so more booths mean more real jobs
Weak → strong: "Bring in more employers" → "Recruit 140 employers across municipal job fairs this year, up from 120, owned by the Business Engagement Coordinator".

Number of Attendees at Municipal Job Fair

LeadingThe number of attendees at a municipal job fair is a reflection on the number of residents actively looking for jobs.

Formula: Count of checked-in attendees across municipal job fairs in the period.
Example: 2 fairs × 450 attendees = 900 attendees.
What good looks like: there is no universal number — track attendance against your own trend and pair it with the interviews and hires that follow, since turnout alone does not show results
Weak → strong: "Hold bigger job fairs" → "Draw 1,100 attendees across municipal job fairs this year, up from 900, owned by the Workforce Development Coordinator".

Show all 11 Community outcomes — 91 measures

Total Tourism Tax Dollars

LaggingBetter when higherTotal tourism tax income is a metric that reflects the economic impact of tourism on a municipality.

Formula: Taxable lodging revenue × tourism tax rate.
Example: $40,000,000 × 6% = $2,400,000.
What good looks like: there is no universal number — grow it against your own baseline and track it alongside hotel occupancy so a rate change is not mistaken for more visitors
Weak → strong: "Bring in more visitors" → "Grow tourism tax revenue from $2.4M to $2.6M by next fiscal year, owned by the Tourism Director via off-season event marketing".

Unemployment Rate

LaggingBetter when lowerThe percentage of individuals who are of working age, who are (a) not in school, and (b) actively seeking work, provides a snapshot of the economic health of the people in the community.

Formula: Unemployed residents ÷ civilian labor force.
Example: 2,100 ÷ 50,000 = 4.2%.
What good looks like: the U.S. unemployment rate was 4.2% in June 2026, so compare your local rate with the national and state figures each month (lower is better)
Weak → strong: "Reduce unemployment" → "Bring the local unemployment rate from 4.2% to 3.8% within two years, owned by the Workforce Development Director".

Number of Jobs in Municipality

LaggingTallying the number of jobs in the municipality reveals growth or decline in employment opportunities.

Formula: Total jobs located in the municipality at period end.
Example: 48,000 private-sector + 7,000 public-sector = 55,000 jobs.
What good looks like: there is no universal number — track growth against your own trend and the regional rate, using a consistent source such as federal employment statistics
Weak → strong: "Grow jobs" → "Grow jobs located in the municipality from 55,000 to 57,000 within two years, owned by the Economic Development Director".

Real Gross Domestic Product (GDP) of Municipality

LaggingBetter when higherReal GDP measures the economic production of a municipality over time, adjusting for inflation by setting a standard year's prices to compare levels in different years.

Formula: Nominal GDP ÷ (price index for the period ÷ 100).
Example: $5.5B ÷ (110 ÷ 100) = $5.0B.
What good looks like: there is no universal number — compare your real growth rate against the state and national rate over the same years rather than a fixed target
Weak → strong: "Grow the economy" → "Grow real GDP from $5.0B to $5.3B within three years, owned by the Economic Development Director via business retention and recruitment".

Municipality-Wide Commercial Vacancy Rate

LaggingThe municipality-wide commercial vacancy rate reports on the availability of commercial property, and reflects on the state of the municipal economy.

Formula: Vacant commercial square feet ÷ total commercial square feet.
Example: 1,200,000 ÷ 10,000,000 = 12%.
What good looks like: national office vacancy was ~18.3% in Q2 2026 in one broker's count, and other trackers report different levels, so compare by property type and against your own trend (lower is better)
Weak → strong: "Fill empty storefronts" → "Cut commercial vacancy from 12% to 10% within two years, owned by the Economic Development Director via a vacant-property reuse program".

Number of New Businesses

LaggingThe number of new businesses is a measure that sizes up economic development.

Formula: Count of new business licenses or registrations issued in the period.
Example: 380 new licenses + 70 home-based registrations = 450 new businesses.
What good looks like: there is no universal number — compare it with your own trend and nearby communities, and follow how many new businesses are still open after a few years
Weak → strong: "Attract new businesses" → "Grow new business registrations from 450 to 500 this year, owned by the Economic Development Director via a small-business permit desk".

This is directly related to increases in municipal revenue.

Number of Business License Renewals

LaggingKeeping tabs on returning businesses is a way to evaluate the business climate and commercial retention rate.

Formula: Count of business licenses renewed in the period.
Example: 3,900 on-time + 200 late renewals = 4,100 renewals.
What good looks like: there is no universal number — divide renewals by licenses up for renewal to get a retention rate, and track that against your own trend
Weak → strong: "Keep businesses here" → "Renew 4,300 business licenses this year, up from 4,100, owned by the Business License Manager via online renewal reminders".

Number of New Jobs Created

LaggingThe number of new jobs created is an indicator that shows growth in the economy of a municipality; it’s directly linked to the unemployment rate.

Formula: Count of new jobs added by new and expanding employers in the period.
Example: 1,200 from expansions + 600 from new businesses = 1,800 jobs.
What good looks like: there is no universal number — compare it with your own trend and similar communities, and track wages and job quality alongside the count
Weak → strong: "Grow the economy" → "Add 2,000 new jobs this year, up from 1,800, owned by the Economic Development Director via business-expansion incentives".

Education

Percentage of Residents with a GED or High School Diploma

LaggingBetter when higherFollowing the percentage of residents who have obtained high school diplomas or GEDs is a good way to review overall secondary education effectiveness.

Formula: (Residents 25+ with a high school diploma, GED, or higher ÷ total residents 25+) × 100.
Example: 43,200 ÷ 48,000 = 90%.
What good looks like: nationally ~91% of adults 25 and older had completed high school in 2021, so a share below that signals a gap worth targeting with adult education
Weak → strong: "Raise education levels" → "Lift the share of adults 25+ with a diploma or GED from 90% to 92% within five years, owned by the Workforce Development Manager via adult GED programs".

Percentage of On-Time Bus Arrivals at School

LaggingBetter when higherIdentifying this percentage helps school districts better understand school transportation logistics, and what they need to make improvements to.

Formula: Bus arrivals at school by the scheduled time ÷ total bus arrivals.
Example: 5,700 ÷ 6,000 = 95%.
What good looks like: there is no universal number — set the on-time window in advance, track it by route, and fix the routes that are late most often
Weak → strong: "Get buses there on time" → "Raise on-time bus arrivals from 95% to 98% this school year, owned by the Transportation Director via route redesign".

Total Library Use per Capita

LeadingBetter when higherTracking the amount of library use per resident can help reveal how beneficial and accessible its resources are.

Formula: Total library visits ÷ service-area population.
Example: 350,000 ÷ 100,000 = 3.5 visits per resident.
What good looks like: U.S. public libraries averaged ~3.9 visits and ~6.9 items borrowed per resident in FY2019, so reaching those pre-pandemic levels signals strong use
Weak → strong: "Get more people to the library" → "Raise library visits from 3.5 to 3.9 per resident by FY2028, owned by the Library Director via expanded evening hours".

Show all 7 Community outcomes — 91 measures

Private High School Graduation Rate

LaggingBetter when higherThe graduation rate at private schools allows municipalities to understand the schools’ academic performances (and offers a helpful point of comparison to public school success).

Formula: Private school students graduating within four years ÷ students in the entering cohort.
Example: 475 ÷ 500 = 95%.
What good looks like: there is no common national standard for private schools, so compare it with the public four-year rate and track the trend, noting that the two sectors may count their cohorts differently
Weak → strong: "Track private school success" → "Hold the private high school graduation rate at 95% or above each year, reported alongside the public rate, owned by the Education Liaison".

Percentage of Residents Rating Public Schools Excellent or Good

LaggingBetter when higherResident satisfaction with public schools can be measured in a survey.

Formula: (Respondents rating schools excellent or good ÷ respondents giving a rating) × 100.
Example: 1,050 ÷ 1,500 = 70%.
What good looks like: in a recent national PDK poll, 43% of Americans gave their local public schools an A or B, so track your own trend and compare against peer community surveys
Weak → strong: "Improve the schools' reputation" → "Raise the share of residents rating public schools excellent or good from 70% to 75% by the next biennial survey, owned by the Community Engagement Manager".

The results can assist in quantifying aspects that increase or decrease positive perception with public schools. It’s important to note that this is directly tied the attraction and retention of young families to the area.

Percentage of Residents with a Bachelor's Degree or Higher

LaggingBetter when higherHigher educational attainment levels are an important metric to assess the socio-economic condition of the area.

Formula: (Residents 25+ with a bachelor's degree or higher ÷ total residents 25+) × 100.
Example: 18,000 ÷ 48,000 = 37.5%.
What good looks like: nationally ~38% of adults 25 and older held a bachelor's degree or higher in 2022, so compare against that and your peer communities rather than a fixed bar
Weak → strong: "Attract educated residents" → "Raise the share of residents 25+ with a bachelor's degree from 37.5% to 39% within five years, owned by the Economic Development Director".

Public High School Graduation Rate

LaggingBetter when higherThis is an important measure for gauging the condition of academic success and effectiveness of the education system as a whole.

Formula: Students graduating with a regular diploma within four years ÷ adjusted 9th-grade cohort.
Example: 1,740 ÷ 2,000 = 87%.
What good looks like: the national public high school four-year graduation rate is ~87% (2021–22), so beating it is strong; break results out by student group to find gaps
Weak → strong: "Improve graduation" → "Raise the four-year graduation rate from 87% to 90% by the class of 2029, owned by the Superintendent".

Environment

Percentage of Hybrid Buses

LeadingDividing the number of hybrid buses over the total number of city buses gives you this measure.

Formula: Hybrid buses ÷ total city buses.
Example: 36 ÷ 120 = 30%.
What good looks like: there is no universal number — about 18.8% of U.S. transit buses were hybrid electric in 2020, so track your hybrid and zero-emission share against that and plan replacements to keep it rising
Weak → strong: "Green the bus fleet" → "Raise hybrid buses from 30% to 40% of the fleet by FY2028 through scheduled replacements, owned by the Transit Fleet Manager".

It helps illustrate the presence (or lack) of environmentally-friendly municipal investments.

Landfill Diversion Rate

LaggingBetter when higherLandfill diversion rate measures the proportion of total municipal waste diverted to landfill alternatives (like recycling centers and waste-to-energy plants).

Formula: Tons diverted from landfill (recycled, composted, or recovered) ÷ total tons of waste generated.
Example: 32,000 ÷ 80,000 = 40%.
What good looks like: the U.S. recycled and composted ~32% of municipal solid waste in 2018, so a diversion rate above that national figure puts a program ahead of the average
Weak → strong: "Recycle more" → "Raise landfill diversion from 40% to 50% within three years, owned by the Solid Waste Manager via curbside organics collection".

Miles of Roadside Litter Collected

LeadingEstimating miles of roadside litter collected determines the extent of cleanup and area-wide beautification efforts in a given time period.

Formula: Sum of roadside miles cleared of litter across all pickups.
Example: 120 miles by staff crews + 60 miles by volunteers = 180 miles.
What good looks like: there is no universal number — set it against the roadside miles you are responsible for, aim to cover every mile on a regular cycle, and pair it with litter complaints
Weak → strong: "Clean up roadsides" → "Clear litter from 220 roadside miles this year, up from 180, owned by the Streets Superintendent via an adopt-a-road program".

Show all 10 Community outcomes — 91 measures

Percentage of Good Air Quality Days

LaggingBetter when higherThe overall air quality can be calculated by taking the total of days in which the air quality meets the standard of "good" (as determined by the Air Quality Index) and dividing it by the total number of days in the given time period (usually one year).

Formula: Days with good air quality ÷ total days in the period.
Example: 292 ÷ 365 = 80%.
What good looks like: a day counts as good when the AQI is 0–50; aim for a rising share of good days and few or none above 100, the level that generally matches the national standard
Weak → strong: "Clean up the air" → "Raise good air quality days from 80% to 85% of the year within three years, owned by the Environmental Services Director".

Tons of Community-Recycled Composted Materials

LaggingBetter when higherDetermining how many tons of recycled materials a community discards in a given time frame is an indicator of growth in waste diversion.

Formula: Tons recycled + tons composted.
Example: 6,800 tons recycled + 2,400 tons composted = 9,200 tons.
What good looks like: track tonnage alongside your diversion rate; nationally ~32% of municipal solid waste was recycled or composted in 2018, so compare your share of total waste against that mark
Weak → strong: "Recycle more" → "Raise recycled and composted tonnage from 9,200 to 10,000 tons this year, owned by the Solid Waste Manager via curbside organics pickup".

Municipality-Wide Greenhouse Gas Emissions per Capita

LaggingBetter when lowerAs a municipality becomes more sustainable through a variety of initiatives, one way to track progress is by monitoring the decrease (or increase, if there is one) of greenhouse gas emissions per capita.

Formula: Total community greenhouse gas emissions (metric tons CO2e) ÷ population.
Example: 1,500,000 ÷ 100,000 = 15 metric tons CO2e per person.
What good looks like: there is no universal number — measure it with a consistent community inventory method, compare it with your baseline year and adopted reduction target, and look for a steady decline (lower is better)
Weak → strong: "Be more sustainable" → "Cut emissions from 15 to 12 metric tons CO2e per person by 2030, owned by the Sustainability Director".

Number of New Trees Planted in Public Places

LaggingTallying the number of new trees planted each year is a good way to measure an activity that helps create a more beautiful and sustainable area.

Formula: Count of new trees planted in public places during the period.
Example: 650 street trees + 350 park trees = 1,000 trees.
What good looks like: there is no universal number — tie the target to a tree-canopy goal and track survival after planting, since a tree lost in its first years adds nothing
Weak → strong: "Plant more trees" → "Plant 1,200 public trees this year, up from 1,000, with 90% surviving two years, owned by the City Forester".

Percentage of Municipal Facilities Incorporating Energy Efficient Upgrades

LeadingBetter when higherThis measure is a method used for looking at the increase of cost-cutting, sustainable initiatives in area-wide infrastructure.

Formula: (Facilities with completed energy-efficiency upgrades ÷ total municipal facilities) × 100.
Example: 33 ÷ 60 = 55%.
What good looks like: there is no universal number — prioritize the largest energy users first and pair this rate with measured energy savings so upgrades show real results
Weak → strong: "Make buildings greener" → "Raise upgraded facilities from 55% to 70% within three years, owned by the Facilities Manager".

Average Days to Close a Code Violation Case

LaggingBetter when lowerAverage days to close a code violation is a metric that represents the progression toward a more efficient code enforcement process.

Formula: Total days open across closed cases ÷ number of cases closed.
Example: 27,000 ÷ 600 = 45 days.
What good looks like: there is no universal number — set targets by violation type, count calendar days the same way every year, and aim for a falling average (lower is better)
Weak → strong: "Resolve code cases faster" → "Cut average days to close a code case from 45 to 35 within one year, owned by the Code Enforcement Manager".

Water Usage per Capita

LaggingBetter when lowerThis measure indicates the effectiveness of long-term water conservation strategies.

Formula: Total water use (gallons) ÷ population served ÷ days in period.
Example: 2,920,000,000 ÷ 100,000 ÷ 365 = 80 gallons per day.
What good looks like: Americans use ~88 gallons of water a day each at home on average, so residential use well below that signals conservation is working (lower is better)
Weak → strong: "Conserve water" → "Cut residential water use from 80 to 72 gallons per person per day within five years, owned by the Utilities Director via rebates and tiered rates".

Housing

Number of Supported Affordable Housing Units Created per Year

LaggingBetter when higherThe number of affordable houses created with support from the local government tracks efforts to maintain the affordability of living in an area.

Formula: Count of affordable units completed in the year with local funding, land, or incentives.
Example: 120 new + 30 preserved = 150 units.
What good looks like: there is no universal number — set the target from your housing needs assessment and track units against it each year
Weak → strong: "Build more affordable housing" → "Create 180 supported affordable units next year, up from 150, owned by the Housing Development Manager".

Amount of Housing Relief Distributed

LaggingThe dollar amount of housing relief provided by the municipality is also an effective illustration of municipal involvement for promoting affordable living.

Formula: Sum of housing relief dollars paid out in the period.
Example: $1,800,000 rent + $700,000 utilities = $2,500,000.
What good looks like: there is no universal number — pair dollars distributed with households served and evictions prevented, and track how quickly approved aid reaches households
Weak → strong: "Help residents stay housed" → "Distribute $2.8 million in housing relief next fiscal year, up from $2.5 million, owned by the Housing Programs Manager".

Number of Residents Assisted in Housing Relief Programs

LaggingLooking at the number of actual recipients of housing relief can provide insight into the number of residents impacted by housing relief programs.

Formula: Count of unique residents receiving housing relief in the period.
Example: 320 rental assistance + 80 mortgage assistance = 400 residents.
What good looks like: there is no universal number — compare residents assisted against eligible applicants and the waitlist, and pair the count with how quickly aid reaches people
Weak → strong: "Help more residents with housing" → "Assist 480 residents through housing relief programs this fiscal year, up from 400, owned by the Housing Assistance Manager".

Show all 10 Community outcomes — 91 measures

Percentage of Residents Satisfied with Affordable Housing

LaggingBetter when higherEvaluating the overall satisfaction of affordable housing helps gauge performance in providing high-quality, affordable housing to residents.

Formula: Residents rating affordable housing excellent or good ÷ total survey respondents.
Example: 540 ÷ 1,500 = 36%.
What good looks like: there is no universal number — track the excellent-or-good share against your own prior surveys and national resident-survey benchmarks, and break results out by neighborhood and demographic group; read it alongside hard measures such as cost burden
Weak → strong: "Improve affordable housing" → "Raise satisfaction with affordable housing from 36% to 42% by the next resident survey, owned by the Housing Director".

Housing Opportunity Index Percentage

LaggingBetter when higherThis measure requires observing the number of new and existing homes which were affordable to families earning the area median income and dividing that by the total number of homes using income and sales prices.

Formula: Homes affordable at area median income ÷ total homes.
Example: 1,900 ÷ 5,000 = 38%.
What good looks like: nationally, just 37.7% of new and existing homes sold were affordable to median-income families in the last national Housing Opportunity Index (Q4 2023), near a historic low, so beating that is a strong result
Weak → strong: "Make housing affordable" → "Raise the housing opportunity index from 38% to 42% within three years, owned by the Housing Director via inclusionary zoning".

Number of Municipality-Wide Chronically Homeless Individuals

LaggingBetter when lowerExposing the number of chronically-homeless individuals in a given area illustrates the progress (or lack thereof) achieved in helping these individuals into better situations and accommodations.

Formula: Count of chronically homeless individuals in the annual point-in-time count.
Example: 140 sheltered + 180 unsheltered = 320 individuals.
What good looks like: nationally, chronic homelessness among individuals reached a record ~155,750 in 2025, about double its 2016 low, so a steady local decline is strong (lower is better)
Weak → strong: "Reduce homelessness" → "Cut chronically homeless individuals from 320 to 250 by the 2028 point-in-time count, owned by the Homeless Services Manager".

Percentage of Households That Pay 30% or More of Income on Housing

LaggingBetter when lowerThe 30% of income toward rent looks at the percentage of municipal households who pay at or above HUD's standard for burdensome housing cost in relation to total household income.

Formula: (Households paying 30% or more of income on housing ÷ total households) × 100.
Example: 10,200 ÷ 30,000 = 34%.
What good looks like: about 24% of U.S. owner households and roughly half of renters were cost-burdened in 2023, so a rate below the national level and falling is strong (lower is better)
Weak → strong: "Make housing affordable" → "Cut cost-burdened households from 34% to 31% within five years, owned by the Housing Director".

Number of HUD Eligible First-Time Homebuyers

LaggingThe number of HUD eligible first-time home buyers ensures adequate, safe, and affordable housing options for residents at or below 80% of area median income (based on data from the Consolidated Annual Performance and Evaluation Report by HUD).

Formula: Count of first-time homebuyers assisted with household income at or below 80% of area median income.
Example: 45 down-payment assistance + 15 homebuyer-education closings = 60 homebuyers.
What good looks like: there is no universal number — set the target in your Consolidated Plan, report progress in the annual CAPER, and judge results against that goal and the funding available
Weak → strong: "Help more people buy homes" → "Assist 70 HUD-eligible first-time homebuyers this program year, up from 60, owned by the Housing Programs Manager".

Total Attendance at Neighborhood Meetings

LeadingBetter when higherThis measure helps disclose the level of community engagement in the municipality.

Formula: Sum of attendees across all neighborhood meetings in the period.
Example: 48 meetings × 25 attendees = 1,200 attendees.
What good looks like: there is no universal number — grow it against your own baseline and track how many different neighborhoods and first-time attendees take part, not just the total
Weak → strong: "Engage more residents" → "Raise neighborhood meeting attendance from 1,200 to 1,400 next year, owned by the Neighborhood Services Coordinator".

Resident Satisfaction with Neighborhood Meetings

LaggingBetter when higherResident satisfaction with neighborhood meetings, taken from a satisfaction survey, can aid in determining whether or not residents find neighborhood meetings informative.

Formula: Attendees rating meetings informative ÷ total attendees surveyed.
Example: 312 ÷ 400 = 78%.
What good looks like: there is no universal number — survey attendees after each meeting, track the trend against your own baseline, and read the comments for what made a meeting useful
Weak → strong: "Run better meetings" → "Lift attendee satisfaction with neighborhood meetings from 78% to 85% within a year, owned by the Neighborhood Services Manager".

Parks & Recreation

Recreation Center Attendance

LaggingBetter when higherRecreation center attendance tracks the number of people utilizing public recreation centers and reflects their popularity.

Formula: Sum of visits across all public recreation centers in the period.
Example: 96,000 + 74,000 = 170,000 visits.
What good looks like: there is no universal number — grow it against your own baseline, count visits the same way at every center, and compare visits per square foot or per operating hour
Weak → strong: "Get more people using rec centers" → "Grow recreation center visits from 170,000 to 185,000 next fiscal year, owned by the Recreation Center Manager".

Acres of Park Land

LaggingWith park land growing or decreasing in size over time, marking the yearly acreage of space can be helpful in tracking long-term availability of park space availability.

Formula: (Total park acres ÷ population) × 1,000.
Example: 720 ÷ 80,000 × 1,000 = 9 acres per 1,000 residents.
What good looks like: the typical park and recreation agency has ~10 acres of parkland per 1,000 residents and one park for every 2,437 residents
Weak → strong: "Add parkland" → "Grow parkland from 9 to 10 acres per 1,000 residents within five years, owned by the Parks Planning Manager".

Percentage of Residents Satisfied with Parks

LaggingBetter when higherThis metric helps determine whether or not the department is doing its job to provide high-quality recreation programs and spaces for its citizens.

Formula: Residents rating parks excellent or good ÷ total survey respondents.
Example: 1,230 ÷ 1,500 = 82%.
What good looks like: there is no universal number — track the excellent-or-good share against your own prior surveys and national resident-survey benchmarks, and break results out by neighborhood and demographic group; parks are often among the best-rated services, so a drop deserves attention
Weak → strong: "Have great parks" → "Raise park satisfaction from 82% to 86% by the next resident survey, owned by the Parks Director via a maintenance-standards program".

Show all 13 Community outcomes — 91 measures

Number of Rounds of Golf Played at Public Courses

LaggingIn order to create a municipality with accessible and affordable public golf, it is important to keep track of the use of and costs associated with these public golf courses.

Formula: Sum of rounds played across all public courses in the year.
Example: 38,000 + 31,000 = 69,000 rounds.
What good looks like: there is no universal number — compare rounds per course against your own trend and regional market reports, and watch revenue per round alongside volume
Weak → strong: "Get more golfers" → "Grow rounds played from 69,000 to 74,000 across public courses next season, owned by the Golf Operations Manager".

Total Park Attendance

LaggingBetter when higherTracking the total number of park visitors can be used to determine the cost per visit for municipal parks, and whether it needs to increase or decrease.

Formula: Sum of counted or estimated visits across all parks in the period.
Example: 410,000 + 260,000 + 130,000 = 800,000 visits.
What good looks like: there is no universal number — use the same counting method each year (counters, surveys, or phone-location data) and compare visits per resident over time
Weak → strong: "Get more people to the parks" → "Grow annual park visits from 800,000 to 860,000 within two years, owned by the Parks Director".

Miles of Trails

LaggingBetter when higherTotal miles of trails in parks is an important metric to estimate because paths are beneficial to promoting healthy, active lifestyles.

Formula: Sum of trail miles across all parks.
Example: 7.5 + 5.0 + 3.5 = 16 miles.
What good looks like: the typical park and recreation agency manages ~16 miles of trails for walking, hiking, running, and biking; scale your target to population and land area
Weak → strong: "Add trails" → "Expand park trails from 16 to 20 miles within three years, owned by the Parks Superintendent".

Number of Community Gardens

LeadingThis metric is helpful to understand the impact of such programs on the community-building, health, and attractiveness of the municipality.

Formula: Count of active community gardens at period end.
Example: 18 existing + 4 new − 1 closed = 21 gardens.
What good looks like: there is no universal number — compare gardens per 10,000 residents with similar communities, and track waitlists and plot occupancy to see whether supply meets demand
Weak → strong: "Support community gardening" → "Grow active community gardens from 21 to 25 by next growing season, owned by the Community Programs Coordinator".

Percentage of Residents Satisfied with Recreational Options

LaggingBetter when higherSurveying resident satisfaction with recreational options ensures that the parks and recreation department is providing programs that align with the interest of community members.

Formula: Residents rating recreational options excellent or good ÷ total survey respondents.
Example: 1,020 ÷ 1,500 = 68%.
What good looks like: there is no universal number — track the excellent-or-good share against your own prior surveys and national resident-survey benchmarks, and break results out by neighborhood and demographic group, since program interests differ by age
Weak → strong: "Offer better programs" → "Raise resident satisfaction with recreation options from 68% to 75% by the next resident survey, owned by the Recreation Director".

Percentage of Playgrounds Inspected

LeadingBetter when higherThis metric helps ensure that equipment is safe for children to use.

Formula: Playgrounds inspected on schedule ÷ total playgrounds.
Example: 38 ÷ 40 = 95%.
What good looks like: every playground should be inspected on its scheduled cycle, with routine staff checks between periodic in-depth inspections by a certified inspector; any playground missed is a safety gap
Weak → strong: "Keep playgrounds safe" → "Inspect 100% of playgrounds on their scheduled cycle, up from 95%, starting next quarter, owned by the Parks Maintenance Supervisor".

Number of Parks and Recreation Volunteer Hours

LeadingBenchmarking the number of parks and recreation volunteer hours serves as a means for observing growth with engaging residents around community parks and recreation projects and initiatives.

Formula: Sum of hours logged by parks and recreation volunteers in the period.
Example: 1,200 volunteers × 10 hours = 12,000 hours.
What good looks like: there is no universal target — grow it against your own baseline; valuing each hour at the national estimate of $36.14 (2025) shows what volunteers contribute in budget terms
Weak → strong: "Get more volunteers" → "Grow parks volunteer hours from 12,000 to 14,000 this fiscal year, owned by the Volunteer Coordinator via adopt-a-park programs".

Number of Youth Participating in Summer and Recreational Programs

LeadingBetter when higherCounting the number of youth who participate in recreational programs year-round tracks the popularity of public recreational programs.

Formula: Count of unique youth enrolled in summer and year-round recreation programs.
Example: 1,900 summer + 1,300 year-round = 3,200 youth.
What good looks like: demand far outstrips supply nationally: about 3 in 4 children whose parents want an afterschool program do not have one (2025), so growth in unique youth served is strong
Weak → strong: "Serve more young people" → "Grow unique youth in summer and recreation programs from 3,200 to 3,600 within two years, owned by the Youth Services Manager".

Percentage of Residents Who Live Within 10-Minute Walk of Park

LaggingBetter when higherThe percentage of residents who live within a 10-minute walk to a park evaluates the accessibility of community parks.

Formula: (Residents within a 10-minute walk of a park ÷ total residents) × 100.
Example: 46,200 ÷ 70,000 = 66%.
What good looks like: nationally ~76% of residents of the 100 largest U.S. cities live within a 10-minute walk of a park (2025), so steady gains toward full coverage are strong
Weak → strong: "Make parks easier to reach" → "Raise 10-minute-walk park access from 66% to 72% of residents within three years, owned by the Parks Planning Manager".

Percentage of Active Space in Use

LeadingThe percentage of active space in use is an indicator of how utilized the facilities in the parks and recreation departments are.

Formula: Hours facility space is in use ÷ hours available.
Example: 3,600 ÷ 4,800 = 75%.
What good looks like: there is no universal number — compare it against your own baseline by facility type and season, and treat space that sits idle for long stretches as a candidate for new programs or reuse
Weak → strong: "Use our facilities more" → "Raise active space in use from 75% to 82% across recreation centers by year-end, owned by the Recreation Facilities Manager".

Safety

Number of Roadway Accidents

LaggingBetter when lowerThis measure offers a way to identify the success of traffic safety programs and initiatives over time.

Formula: Count of police-reported crashes in the period.
Example: 620 + 580 + 610 + 590 = 2,400 crashes.
What good looks like: there is no universal number — counts rise with traffic, so also track crashes per vehicle mile and focus on the locations with the most severe injuries (lower is better)
Weak → strong: "Reduce crashes" → "Cut reported crashes from 2,400 to 2,100 within two years, owned by the Traffic Engineer via high-injury corridor fixes".

Number of Fires Occurring per 10,000 Residents

LaggingBetter when lowerThe fire incident rate can be measured against national standards to determine the total effectiveness of fire safety.

Formula: (Fires ÷ population) × 10,000.
Example: (400 ÷ 100,000) × 10,000 = 40 per 10,000.
What good looks like: there is no universal number — the smallest communities tend to have the highest fire incident rates, so compare with communities of similar size and trend your own rate (lower is better)
Weak → strong: "Have fewer fires" → "Cut fires from 40 to 35 per 10,000 residents within two years, owned by the Fire Marshal via prevention education".

Municipal Fire Cost per Capita

LaggingBetter when lowerFire loss per capita reveals the amount of damage caused by fires per person in a particular area during a year.

Formula: Total fire property loss ÷ population.
Example: $5,000,000 ÷ 100,000 = $50 per resident.
What good looks like: U.S. fires caused an estimated $19 billion in property damage in 2024; one large loss can swing a year, so track a multiyear average (lower is better)
Weak → strong: "Reduce fire losses" → "Cut the three-year average fire loss from $50 to $40 per resident, owned by the Fire Marshal via sprinkler and inspection programs".

Show all 24 Community outcomes — 91 measures

Injury Rate of Firefighters per Fire

LaggingBetter when lowerInjury rate of firefighters is closely tied to the severity of fires, and the number of firefighters available at a fire.

Formula: (Firefighter injuries ÷ fires) × 1,000.
Example: (12 ÷ 2,000) × 1,000 = 6 per 1,000 fires.
What good looks like: U.S. data show ~4.5 fire-related firefighter injuries per 1,000 fires overall and ~10 per 1,000 structure fires (2018–2020), so compare like fire types (lower is better)
Weak → strong: "Keep firefighters safe" → "Cut firefighter injuries from 6 to 4.5 per 1,000 fires within two years, owned by the Safety Officer via fireground training".

Therefore, this measure can provide insight into fire prevention success and fire department staffing needs.

Number of Community Relation Initiatives by Police

LeadingCommunity relation initiatives (like neighborhood meetings) are important for building the trust and respect of citizens and a safe municipality.

Formula: Count of community relation initiatives held in the period.
Example: 24 meetings + 12 youth events + 12 listening sessions = 48 initiatives.
What good looks like: there is no universal number — set a regular cadence in every neighborhood, and pair the count with resident trust survey results so activity links to outcomes
Weak → strong: "Build community trust" → "Hold 60 community initiatives this year with at least one per neighborhood each quarter, owned by the Community Relations Sergeant".

Number of Foot and Bike Patrol Hours

LeadingBetter when higherMaximizing the number of hours spent by police on foot and bike patrol helps develop positive and beneficial relationships between law enforcement and community members.

Formula: Officers on foot or bike patrol × hours each officer logs.
Example: 12 officers × 520 hours = 6,240 hours.
What good looks like: there is no universal number — set a target by district, schedule hours where residents and businesses gather, and pair the count with community trust survey results
Weak → strong: "Do more community policing" → "Raise foot and bike patrol from 6,240 to 7,500 hours this year, owned by the Community Policing Lieutenant".

Number of Hours Spent Interacting with Students in Public School by Police

LeadingTracking time spent by police and school resource officers interacting with students in the schools is an important metric, as it teaches young members of the community about law enforcement and the justice system.

Formula: Sum of logged hours of direct student engagement by police and school resource officers.
Example: 6 officers × 900 hours = 5,400 hours.
What good looks like: there is no universal number — focus on how the hours are spent (mentoring, classes, and prevention rather than enforcement) and pair them with student and staff feedback
Weak → strong: "Build trust with students" → "Raise logged mentoring and classroom hours from 5,400 to 6,000 this school year, owned by the School Resource Officer Supervisor".

Civilian Fire Injuries per 100,000 Residents

LaggingBetter when lowerCivilian fire injuries per 100,000 residents is a measure that keeps tabs on the rate of non-firefighters physically harmed during a fire.

Formula: (Civilian fire injuries ÷ population) × 100,000.
Example: (8 ÷ 200,000) × 100,000 = 4 per 100,000.
What good looks like: there is no widely published rate benchmark — trend your rate against your own baseline, review where each injury happened, and aim down (lower is better)
Weak → strong: "Prevent fire injuries" → "Cut civilian fire injuries from 4 to 3 per 100,000 residents within two years, owned by the Fire Marshal via smoke alarm installation".

Number of Fire Prevention Code Violations

LaggingCounting the number of fire prevention code violations during routine fire inspections can help identify the effectiveness of fire training and education.

Formula: Sum of violations found across routine inspections in the period.
Example: 260 + 240 + 210 + 190 = 900 violations.
What good looks like: there is no universal number — divide by inspections completed to get violations per inspection, and expect that rate to fall as education and re-inspections work
Weak → strong: "Improve fire code compliance" → "Cut violations from 900 to 750 across this year's routine inspections, owned by the Fire Marshal via business outreach".

Total Attendance at Safety Education Programs

LeadingBetter when higherThis metric counts the number of residents who show up to events like citizen fire safety training, school fire safety programs, and Citizens Emergency Response Team training.

Formula: Sum of attendance across all fire safety, school safety, and CERT training sessions.
Example: 2,600 + 3,800 + 400 = 6,800 attendees.
What good looks like: there is no universal number — grow it against your own baseline and track reach by neighborhood and school, aiming every student and high-risk household to be reached on a regular cycle
Weak → strong: "Teach more safety" → "Grow safety education attendance from 6,800 to 7,500 next fiscal year, owned by the Fire Prevention Captain".

Immunization Compliance Rate by Age 2

LeadingBetter when higherThe immunization compliance rate discloses the number of children who have received the recommended shots (4 DTaP, 3 Polio, 1 MMR, 3 HIB, 3 HepB, and 1 VAR) within their first 24 months.

Formula: (Children fully vaccinated by 24 months ÷ children reaching 24 months) × 100.
Example: 1,380 ÷ 2,000 = 69%.
What good looks like: U.S. coverage for the closely related combined 7-vaccine series sits near ~68% by age 2 and ranges from ~53% to ~83% by state
Weak → strong: "Vaccinate more kids" → "Raise on-time immunization by age 2 from 69% to 75% within two years, owned by the Public Health Nursing Manager via reminder-recall outreach".

This is vital to prevent the spread of disease in the wider community.

Percentage of Overweight and Obese Children Enrolled in Public School Kindergarten

LaggingBetter when lowerRecording weight statistics on entry physicals of kindergarteners gauges the health of the community (primarily because these problems in young children have been proven to increase risk of chronic disease later in life).

Formula: (Kindergartners with overweight or obesity at entry physical ÷ kindergartners screened) × 100.
Example: 660 ÷ 2,400 = 27.5%.
What good looks like: nationally ~13% of children ages 2–5 have obesity, before counting overweight (2017–2018), so a falling local rate is the goal (lower is better)
Weak → strong: "Help kids be healthier" → "Cut kindergarten overweight and obesity from 27.5% to 25% within three years, owned by the Public Health Director".

Emergency Medical Services Incidents per 1,000 Residents

LaggingThe relationship between emergency dispatches that meet a certain criteria and the overall population shows the rate in which emergencies occur.

Formula: (EMS incidents ÷ population) × 1,000.
Example: (15,000 ÷ 100,000) × 1,000 = 150 per 1,000.
What good looks like: there is no universal number — rates swing with resident age and visitor traffic, so trend it against your own baseline and use it to plan staffing
Weak → strong: "Plan for EMS demand" → "Hold EMS incidents at or below 150 per 1,000 residents next year, owned by the EMS Chief via a frequent-caller outreach program".

Number of Violent Crimes

LaggingBetter when lowerCounting the number of violent crimes is one of the most standard means for determining the overall safety of the municipality and seriousness of crime in the area.

Formula: Sum of reported violent offenses in the period.
Example: 410 assaults + 150 robberies + 35 rapes + 5 homicides = 600 crimes.
What good looks like: the U.S. violent crime rate was ~328 per 100,000 residents in 2025, so convert your count to a rate before comparing with peers (lower is better)
Weak → strong: "Reduce violent crime" → "Cut reported violent crimes from 600 to 540 within two years, owned by the Police Chief via a focused violence-reduction strategy".

Number of Non-Violent Crimes

LaggingBetter when lowerMeasuring the total number of non-violent crimes is a way to scope the pervasiveness of white collar crime in a municipality.

Formula: Sum of reported non-violent offenses in the period.
Example: 2,100 thefts + 600 burglaries + 900 other = 3,600 crimes.
What good looks like: there is no universal number — raw counts rise with population, so also convert to a rate per 1,000 residents and compare the trend year over year (lower is better)
Weak → strong: "Reduce crime" → "Cut reported non-violent crimes from 3,600 to 3,200 within two years, owned by the Police Chief via a theft prevention program".

Percentage of Residents Who Feel Safe in Parks

LaggingBetter when higherThe percentage of residents who indicate that they feel safe in parks is a reflection of the perception of comfort in municipal parks.

Formula: (Residents who feel safe in parks ÷ total survey respondents) × 100.
Example: 1,440 ÷ 1,800 = 80%.
What good looks like: there is no universal number — ask the same question each survey year, break results out by neighborhood and time of day, and focus on the parks scoring lowest
Weak → strong: "Make parks feel safe" → "Raise the share of residents who feel safe in parks from 80% to 85% by the next survey, owned by the Parks Director with police park patrols".

FEMA Community Rating (CRS) Score

LeadingThe FEMA CRS score can serve as a benchmark of community preparedness for natural disasters.

Formula: Flood insurance discount = (10 − CRS class) × 5%.
Example: (10 − 6) × 5% = 20%.
What good looks like: every community starts at Class 10 with no discount; each class gained adds 5%, up to a 45% flood insurance discount at Class 1, so a lower class is better
Weak → strong: "Be flood-ready" → "Move from CRS Class 6 to Class 5 at the next verification, lifting resident flood insurance discounts from 20% to 25%, owned by the Floodplain Manager".

Number of Juvenile Arrests

LaggingTracing the number of juvenile arrests illustrates the prevalence of youth criminal activity in a municipality.

Formula: Count of arrests of people under 18 in the period.
Example: 45 + 60 + 70 + 45 = 220 arrests.
What good looks like: there is no universal number — compare against your own trend per 1,000 youth, and pair it with diversion program participation and repeat-offense rates
Weak → strong: "Reduce youth crime" → "Cut juvenile arrests from 220 to 190 within two years, owned by the Youth Services Lieutenant via a diversion program".

Number of Criminal Investigations

LeadingCounting the number of criminal investigations is a method for determining police department activity over time.

Formula: Count of criminal investigations opened in the period.
Example: 310 + 290 + 330 + 270 = 1,200 investigations.
What good looks like: there is no universal number — the count reflects workload, not success, so pair it with case clearance rates and caseload per detective
Weak → strong: "Investigate more crimes" → "Work 1,200 investigations this year while keeping caseloads under 20 per detective, owned by the Investigations Division Commander".

Number of Emergency Response Calls

LaggingThis metric offers a way to track the long-term trends of fire, rescue, and miscellaneous emergencies.

Formula: Fire calls + medical and rescue calls + other emergency calls.
Example: 600 + 9,800 + 2,600 = 13,000 calls.
What good looks like: medical and rescue calls made up ~64% of reported U.S. fire department runs in 2020 and fires only ~4%, so track the mix by call type rather than judging the total as good or bad
Weak → strong: "Know our call volume" → "Report emergency calls by type each month and forecast next year's 13,000+ calls for staffing, owned by the Deputy Fire Chief".

Number of Residents Who Feel Safe in Their Neighborhood

LaggingBetter when higherBased on a survey, the number of residents who feel safe in their neighborhood helps to determine the overall perception of public safety.

Formula: Survey respondents who feel safe ÷ total respondents × adult population.
Example: 1,050 ÷ 1,500 × 60,000 = 42,000 residents.
What good looks like: nationally ~71% of U.S. adults say they feel safe walking alone at night where they live (2024); report the share alongside the count so population growth does not flatter the trend
Weak → strong: "Help residents feel safe" → "Raise residents who feel safe from 42,000 (70%) to 45,000 (75%) by the next resident survey, owned by the Police Chief".

Percentage of Fire Response Time Within Goal (8 Minutes)

LaggingBetter when higherThe percentage of fire response times that meets the goal time tracks the percentage of fires responded to within 8 minutes of the phone call, the time set by the National Standards for Fire.

Formula: (Fire calls with response within 8 minutes ÷ total fire calls) × 100.
Example: 1,558 ÷ 1,900 = 82%.
What good looks like: NFPA 1710 sets 240 seconds of travel time for the first engine and 480 seconds for the full first alarm on 90% of fire calls, so 90% within goal is the usual target
Weak → strong: "Respond to fires faster" → "Respond to 90% of fire calls within 8 minutes by year-end, up from 82%, owned by the Fire Chief".

Emergency Medical Service Response Time Within Goal (8 Minutes)

LaggingBetter when higherThis metric tracks the percentage of emergencies successfully responded to within 8 minutes of the phone call, the national standard time.

Formula: (EMS calls with response within 8 minutes ÷ total EMS calls) × 100.
Example: 3,690 ÷ 4,500 = 82%.
What good looks like: NFPA 1710 sets 480 seconds (8 minutes) of travel time for an advanced life support unit on at least 90% of EMS calls, so 90% is the usual target
Weak → strong: "Get to patients faster" → "Respond to 90% of EMS calls within 8 minutes by year-end, up from 82%, owned by the EMS Division Chief".

Percentage of Public & Private Schools with Fire Drills Conducted

LeadingBetter when higherTo create an atmosphere of safety for children in school, this measure indicates the percentage of schools where fire drills were successfully conducted (with a goal of 100%).

Formula: (Schools that completed all required fire drills ÷ total schools) × 100.
Example: 57 ÷ 60 = 95%.
What good looks like: NFPA 101, the Life Safety Code, calls for an emergency egress drill every month school is in session, so the target is 100% of schools completing every required drill
Weak → strong: "Make sure schools run drills" → "Reach 100% of public and private schools completing all required fire drills this school year, up from 95%, owned by the Fire Marshal".

Workforce capacity — 10 measures

Drawn from the Human Resources function. These answer whether the organization can deliver at all.

Diversity of New Hires

LaggingUsing application questions, municipalities can measure the diversity of new hires to make sure that they are promoting an accepting and healthy environment.

Formula: (New hires from underrepresented groups ÷ total new hires) × 100.
Example: 54 ÷ 180 = 30%.
What good looks like: there is no universal number — compare new-hire demographics with the applicant pool and the local labor force; a hiring rate above current workforce representation moves the whole workforce
Weak → strong: "Hire more diverse candidates" → "Raise the share of new hires from underrepresented groups from 30% to 35% next fiscal year, owned by the Talent Acquisition Manager".

(Of course, gender, race, and ethnicity are not and should not be used as a factor in hiring decision-making.)

Diversity of Workforce

LaggingDiversity of the overall workforce evaluates the success in retaining a diverse representation in municipal staff.

Formula: (Employees from underrepresented groups ÷ total employees) × 100.
Example: 420 ÷ 1,200 = 35%.
What good looks like: there is no universal number — compare workforce demographics with the community and local labor force, and track them by job level so gaps in senior roles are visible
Weak → strong: "Build a more diverse workforce" → "Raise representation from 35% to 38% to better mirror the community within three years, owned by the HR Director".

It also reflects the openness and acceptance of the work environment to all types of employees.

Number of Applications Received

LeadingNumber of applications received for new positions for local government positions helps weigh the level of interest and success that new job postings are generating.

Formula: Sum of applications received across all job postings in the period.
Example: 450 + 380 + 370 = 1,200 applications.
What good looks like: there is no universal number — track applications per posting by job family, and watch for hard-to-fill roles where the count falls too low to hire well
Weak → strong: "Attract more applicants" → "Raise applications from 1,200 to 1,500 a year, owned by the Recruitment Manager via new outreach channels".

Show all 10 Workforce capacity — 10 measures

Percentage of Positions Filled Internally

LeadingDividing the number of promotions by the number of positions filled reveals the availability of advancement opportunities and the success of internal talent development.

Formula: (Internal moves ÷ total roles filled) × 100.
Example: 12 internal moves ÷ 40 roles = 30%.
What good looks like: employees who made an internal move had a ~75% chance of staying two years versus ~56% for those who did not, so a rising internal-fill share usually pays off in retention
Weak → strong: "Promote from within" → "Fill 35% of open roles internally, up from 30%, by year-end, owned by the Talent Acquisition Manager via posting every role internally first".

Employee Satisfaction Index

LeadingBetter when higherAn employee satisfaction index can be determined by conducting an employee survey and taking into account the number of positive versus negative responses.

Formula: (Positive responses ÷ (positive + negative responses)) × 100.
Example: (1,800 ÷ (1,800 + 700)) × 100 = 72%.
What good looks like: the Conference Board's 2025 survey found U.S. job satisfaction at its highest level since the series began in 1987, with ~72% of workers 55 and older satisfied versus ~57% under 25, so benchmark by group, not one number
Weak → strong: "Make employees happier" → "Raise the satisfaction index from 72% to 78% on the next annual survey, owned by the CHRO via action plans for the lowest-scoring teams".

Total Hours of Training Attended

LeadingBetter when higherAggregating combined training hours can indicate how much employee development took place in a given period of time.

Formula: Number of employees × average training hours per employee.
Example: 500 × 16 = 8,000 hours.
What good looks like: report hours per employee alongside the total so headcount changes do not mask the trend; ATD's 2025 State of the Industry benchmark found an average of about 13.7 hours of formal learning per employee
Weak → strong: "Invest in our people" → "Raise training from 8,000 to 9,000 total hours next fiscal year, owned by the HR Training Manager".

Absenteeism Rate

LaggingBetter when lowerThis rate reveals the percentage of lost work hours due to worker absence.

Formula: (Absent days ÷ total scheduled workdays) × 100.
Example: 400 absent days ÷ 50,000 scheduled = 0.8%.
What good looks like: U.S. full-time workers lost ~1.7% of usual hours to absence in 2025 and ~3.2% were absent in a given week; use that as an outside reference and watch for teams trending up (lower is better)
Weak → strong: "Reduce absences" → "Hold the absenteeism rate at or below 0.8% in every department this year, owned by department managers via return-to-work check-ins".

Percentage of Performance Evaluations Completed on Time

LeadingBetter when higherPercentage of performance evaluations completed on time is a critical component to a successful performance management process.

Formula: (Evaluations completed on time ÷ evaluations due) × 100.
Example: 765 ÷ 900 = 85%.
What good looks like: there is no universal number — aim for nearly every evaluation on time, report completion by department, and follow up on late reviews before they pile up
Weak → strong: "Get reviews done" → "Complete 95% of performance evaluations by their due date this cycle, owned by the HR Director, reported monthly by department".

Employee Retention Rate

LaggingBetter when higherEmployee retention rate can be calculated by dividing the number of avoidable separations by the number of regular full-time employees on the staff.

Formula: (Employees retained ÷ headcount at start) × 100.
Example: 184 retained ÷ 200 at start = 92%.
What good looks like: there is no universal number — read it alongside voluntary turnover (U.S. quits ran ~2.0% of employment per month in 2025) and break it out by team and tenure to find where people leave
Weak → strong: "Keep good people" → "Raise annual employee retention from 92% to 94%, owned by the HR Director, reviewed quarterly by department".

Number of Incidents per 100,000 Miles Driven

LaggingBetter when lowerMarking the number of incidents per 100,000 miles driven in a municipal vehicle is important in evaluating safety standards and costs of vehicles.

Formula: (Incidents ÷ miles driven) × 100,000.
Example: (18 ÷ 3,000,000) × 100,000 = 0.6 per 100,000 miles.
What good looks like: there is no universal number — compare against your own fleet history by vehicle class, count preventable incidents separately, and drive the rate down (lower is better)
Weak → strong: "Drive more safely" → "Cut fleet incidents from 0.6 to 0.45 per 100,000 miles this year, owned by the Fleet Manager via driver training".

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The takeaway

Five numbers will not run a city. But five numbers — one for service, one for money, one for efficiency, one for outcomes, one for the people behind it all — will tell you the truth, every week, while there is still time to act. The categories tell you what to watch. An owner makes it real. Start with five. Give each one a name.

Common questions

How many KPIs should a local government track?

Fewer than most do. Across 165 US local governments on ClearPoint in August 2026, the median tracks 394 active measures, the 25th percentile 99, and the 90th percentile 1,955 — and none track fewer than 10, so the common "top 10 KPIs" framing describes no real city. The defensible rule from the same data is one to two measures per strategic objective: focused governments average 2.0 KPIs per objective while those with 1,000+ measures average 6.3, without having more goals to show for it.

Are KPIs and metrics the same thing?

No. Every KPI is a metric, but very few metrics are KPIs. A metric is anything you can count. A KPI is the small subset of metrics tied directly to a strategic objective — the ones where a change in the number should change what someone does. The 143 measures in this article are a library of candidate metrics; the ten or twenty you attach to your objectives, with owners, are your KPIs.

What is the difference between an output and an outcome measure?

An output measures activity — how many units of service were produced (for example, the number of students who completed a program). An outcome measures the result — whether conditions actually improved (for example, whether youth drug use fell). Government performance standards, including GASB's, direct agencies to focus primarily on outcomes, because activity counts can rise while the real goal goes unmet.

Does city size affect KPI ownership rates?

Yes. In a size-segmented cut of ClearPoint's research dataset, mid-size local governments had the most KPIs with a named owner, ahead of large ones and then the smallest governments. Measure freshness was similar across sizes — smaller governments likely lack a dedicated performance-management role, rather than caring less about their KPIs.

Which cities publish their KPI dashboards publicly?

Cities including Bartlett TN, Germantown TN, Durham NC, and Fort Collins CO publish live, public-facing KPI dashboards powered by ClearPoint. Residents can see each focus area's status, drill into individual measures, and check when the data was last updated — which is itself a powerful accountability mechanism.

Alexandre Teulade

Head of Growth Marketing & Coffee enthusiast

Head of Growth Marketing at ClearPoint Strategy. Columbia-trained in sustainable development — strategy, sustainability, and applied AI.

Alexandre Teulade is Head of Growth Marketing at ClearPoint Strategy, working at the intersection of strategy, sustainability, and AI engineering. A Columbia University graduate in sustainable development, he pairs a deep grounding in environmental and public-sector strategy with hands-on AI engineering — designing the multi-agent systems that power ClearPoint's go-to-market and its work with government, utility, and nonprofit clients.He is a contributor to JustTech (Pragmatic Panic), a research initiative on regulatory frameworks for emerging technologies and AI governance. He writes about strategy execution, sustainability, and applied AI from the operator's side — where the frameworks meet the ground.