Session two
Session Two — Local Environment Analysis
A free companion artifact from The Enrollment Director Field Guide to AI. The guide explains when and how to use it; this copy is yours to adapt for your school. More at haitani.org/field-guides/enrollment-director
Q3 2026 edition · last updated August 13, 2026
What this is. The second of five working sessions. It builds a data-grounded picture of your catchment area: who lives there, what they earn, where the economy is heading, and how far families will realistically drive.
This is the session that most resembles the demographic study schools pay consultants for. It will not replace one, and it does not need to. What it produces is the data spine, assembled from public sources, with the sources named so you can check them.
Open a new conversation in your project. Turn web search on. Have Session 1 Summary in the project folder.
Paste this to begin
This is Session 2 of 5: Local Environment Analysis. Read my Session 1 Summary in the project files before you begin. Your goal: build a data-grounded picture of my school's catchment area and what it means for enrollment. The territory: - Demographics: number of families with children at the right ages for our focus grades, demographic shifts over the past 5 years, projected kindergarten or entry-grade population over the next 5 years. - Income and affordability: the distribution of family incomes in the catchment area, not just the median. Ask what share of families in the area could afford our full tuition for one child, and at roughly what income level that becomes true. Then identify which commutable zip codes hold the highest concentration of families above that line. Do not compute tuition as a percentage of median income: the median local family is not the family that buys private school, so a ratio against the midpoint answers a question nobody is asking. - Economy: current and projected unemployment, significant employers moving in or out whose employees live in the catchment area, layoffs, new housing developments. - Commute: realistic drive-time radius for the school, traffic patterns that affect school choice, and availability of age-appropriate transportation. How to work: confirm the geography with me first, then ask what I already know, then offer research tasks one at a time. Run one research task, present the findings with sources, and discuss the enrollment implications with me before offering the next. Do not dump everything at once. Use census and American Community Survey data, local news, public school district enrollment data, public school district board meeting coverage and minutes, and economic development sources. Always state the vintage of the data and be honest about uncertainty in projections. Start by confirming the geography.
What to expect, and where to push
Confirm the geography carefully. If the AI assumes your catchment is a county when it is really three towns and a bridge, every number that follows is answering the wrong question. Give it the zip codes your families actually come from.
The entry-grade projection is the most valuable number in this session and the one most worth scrutinizing. Births in your county five years ago are a matter of record; what share of those families will consider private school is a judgment. Make the model separate the two and say which is which.
Data vintage matters more here than anywhere else in the kit. American Community Survey estimates lag, district enrollment counts lag differently, and a confident sentence that blends a 2021 figure with a 2025 one will read as current. Ask for the year on every number.
Close the session
Summarize this session using the Session Two template. Capture the specific figures, the geography we settled on, and every source with its URL and data vintage. Where a projection rests on an assumption, name the assumption.
Correct it against what you know about your own market — you have watched these neighborhoods for years and you will spot what the data misses. Save it in your Sessions folder as Session 2 Summary.
From The Enrollment Director Field Guide to AI (Q3 2026 edition) · haitani.org/field-guides/enrollment-director · Linda Haitani LLC
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