Students, facilities, and the budget
Do our schools have the space they need — and are we facing this alone?
Livingston is growing, our schools are feeling capacity pressure, and the facilities and budget questions are connected.
The important question is what students and educators need now, what it will take to support them, and whether school districts that actually resemble Livingston are wrestling with the same kinds of choices. From there, the focus should be on what Livingston needs next and what happens if we wait.
The 30-second answer
No, Livingston is not looking at these questions in isolation.
Several districts that repeatedly emerge as useful structural comparisons have also confronted facilities, financing, budget pressure, or long-term planning decisions. That does not tell Livingston what to do. It gives us better context for understanding the choices in front of our students, educators, families, and taxpayers.
Who actually looks like Livingston?
Familiar towns are useful guesses, but the comparison set should come from the school-system data rather than from reputation or instinct alone.
Some comparisons hold up better than others.
A few districts keep looking structurally close when the analysis is changed in reasonable ways. Others look close only under certain assumptions.
The facilities question is not happening in a vacuum.
Comparable districts give us context for facilities, financing, timing, State support, and tradeoffs — without implying that Livingston should copy another community’s choice.
It does not show that another district made the “right” decision, that Livingston should approve or reject any future referendum, or that one financial measure causes another. It is a way to identify useful comparisons and then study them more carefully.
So who actually looks like Livingston?
The towns that feel similar are not always the ones whose school systems operate under similar financial conditions. So the comparison starts with the data, not the town name.
The eye test is still useful. It tells us which towns people naturally think of. But those names should be treated as guesses to test, not conclusions to protect.
That leads directly to the next question: which characteristics actually matter for this comparison?
What makes a school district a fair comparison?
Why this matters: two communities can look alike from the outside while their school systems operate under very different financial conditions.
The goal was not to find towns with similar houses, reputations, or test scores. The question was simpler: does this school system operate under conditions that look like Livingston's? Six practical measures were chosen before looking at which districts came out closest.
The method also needed to avoid giving any one underlying pattern too much influence. When two measures tend to move together, they can contain overlapping information. That is a weighting issue, not a claim that one thing causes another.
How big is the district?
Scale affects staffing, transportation, facilities, administration, and how fixed costs are spread across students.
What does it cost to operate per student?
A per-student measure gives us a common operating baseline across districts of different sizes. It is not an efficiency score.
What local property-value capacity supports each student?
In New Jersey, local property wealth matters because districts rely heavily on the local tax base. Similar household incomes do not necessarily mean similar tax capacity.
How heavily is that tax base already used for schools?
Having tax capacity and already relying heavily on it are different things. This helps describe how much of the local base is already committed to school funding.
How much State aid supports each student?
Two districts can otherwise look alike but have very different funding mixes if one receives much more State support.
How broad is the residential tax base supporting the district?
The number of residential properties sharing the local burden helps describe how that burden is spread across the community.
What did I intentionally leave out?
School rankings, test scores, referendum outcomes, political choices, and whether a town simply “feels like Livingston” were not used to choose the comparison districts. Those may be useful later, but using them here could bias the answer before the structural comparison is complete.
Now the eye test could be challenged with data instead of reinforced by instinct.
Which comparisons kept holding up?
Why this matters: any one formula can give a neat-looking answer. A more useful comparison is one that still looks similar when reasonable parts of the analysis change.
Think of it this way
If two students only look alike from one camera angle, you would not call them twins. I treated the district comparison the same way. A town had to keep looking like Livingston from several reasonable angles before I treated it as a strong comparison.
So the analysis changed the angle: how the six measures were combined, how closeness was measured, and even whether one measure was removed at a time. A district that kept coming back meant more than one that looked close only once.
The model I used
The model is called Principal Component Analysis, or PCA. The name sounds technical, but the idea is fairly simple: if several measurements are partly telling you the same thing, PCA helps combine that overlap so you do not accidentally count the same signal more than once.
Think of six people describing the same event. If three of them repeat the same detail, you would not want to treat that repeated detail as three completely separate pieces of evidence. PCA helps separate the repeated information from the genuinely different information before the districts are compared.
Technical detail: why use PCA instead of something more complicated?
This was a small, structured dataset and the question was about similarity, not prediction. I wanted a method that was transparent enough to explain and audit. A neural network would add complexity without making the underlying comparison easier to understand.
PCA is a long-established dimensionality-reduction technique that is still used in modern machine learning. It is also used by researchers to inspect and simplify high-dimensional representations from transformer-based AI systems. It is not what powers a generative AI model; here, it is simply a practical way to organize overlapping information.
Background: scikit-learn PCA documentation and ACL research using PCA on transformer embeddings.
What were the 13 checks?
I did not build 13 unrelated models. I checked the same answer 13 different ways. For example, I removed one measure at a time, changed how the measures were combined, and changed how closeness between districts was calculated.
The point was not to prove causation. It was to see whether the same comparison districts kept showing up when I made reasonable changes to the method. If a district only looked close once, I treated that as fragile. If it kept coming back, I took the comparison more seriously.
This is where the eye test started to fail. Some districts looked close under one view of the data and then moved far away when the assumptions changed. Others kept coming back.
Kept coming back
Westfield
Westfield kept showing up as a close comparison when the analysis was tested in different reasonable ways.
What that means: the comparison does not depend on one formula.Kept coming back
Ridgewood
Ridgewood also kept returning as a close match across most of the reasonable ways the data was tested.
What that means: Ridgewood keeps looking similar from several different angles.Looked close, then moved
Toms River
Toms River looked very close under one view of the data, but that closeness weakened when the assumptions changed.
What that means: it is a reason to be cautious about using Toms River as a core peer.Tested separately
Smaller K–12 districts
I also reran the work after adding 53 smaller K–12 districts that were outside the original comparison group. None of them displaced the strongest recurring matches.
What that means: the starting pool was tested to make sure it was not hiding an obvious answer.Have a town in mind?
Test it yourself.
Search Millburn, Chatham, Westfield, Montclair, or any other New Jersey K–12 district and see which parts of the comparison hold up — and which do not.
Explore a districtThat gave me a defensible place to look next: what have those communities actually been dealing with?
What are comparable districts actually dealing with?
Why this matters: structural similarity does not prove another district made the right choice. It tells us where to look for useful context about planning, financing, facilities, and voter decisions.
Once the comparison set was established independently, I looked at what those communities had actually put before voters. The outcomes are not uniform, and that is useful: approvals and rejections both help show the kinds of questions communities have had to work through.
Westfield
Voters approved an additional-spending proposal and two bond questions covering school-building improvements and full-day kindergarten expansion. District communications also identified up to $57.5 million in State debt-service aid.
Source: Westfield Public Schools →Moorestown
Voters approved two bond questions totaling about $108 million for facilities work that included additions, renovations, infrastructure, safety work and full-day-kindergarten-related space. The district reported about $18.7 million in State aid eligibility.
Source: Moorestown Township Public Schools →South Brunswick
This was not a construction bond. Voters rejected a $350,000 additional-spending proposal for community use of certain school facilities and related operating costs.
Source: NJ School Boards Association →West Windsor-Plainsboro
Voters approved a $114.875 million facilities referendum. NJSBA reported $25.77 million in State funding associated with the projects.
Source: NJ School Boards Association →Ridgewood
Voters approved a $48.27 million construction referendum covering high-school work and additions or renovations at eight other schools. NJSBA reported $9.84 million eligible for State funding.
Source: NJ School Boards Association →Montgomery
A subsequent Board bond resolution records voter authorization of an $18.502 million school facilities project.
Source: Montgomery Township Board of Education →The useful question is what they understood about need, scope, timing, financing, and tradeoffs before they decided.
What does this help Livingston understand?
Why this matters: a referendum question arrives on one day. The planning choices behind it develop over years.
These are the questions I want answered clearly.
And if you think I picked the wrong comparison district, you should be able to test that too.
Challenge the work
Why this district — and not another one?
Search the expanded 221-district K–12 lookup. The primary comparison started with 168 regular K–12 districts; the larger lookup also includes the smaller-district sensitivity test. The answer comes from the frozen analysis, not from an open-ended chatbot.
Where the evidence leaves us
What we know — and what still needs an answer.
What the analysis supports
Livingston has a smaller set of structurally comparable districts that recur across multiple reasonable ways of running the comparison. Those districts are useful places to look for context.
What comparison alone cannot answer
A peer district’s referendum, capital plan, or financing choice does not determine what Livingston should do. Local building conditions, enrollment, project scope, timing, funding, and taxpayer impact still matter.
What remains open
The important next questions are the condition and capacity needs Livingston is solving, the cost and timing of available options, what State support may be available, and the consequences of acting sooner, acting later, or changing the scope.
The point of the comparison is not to end the conversation. It is to make the next questions more informed.
For anyone who wants the math
The simple story should still lead all the way to the source.
The full methodology documents the universe, six variables, transformations, missing-data rules, standardization, PCA retention, distance methods, sensitivity checks, bootstrap stability, expanded small-district universe, and separate household-affordability lens.