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← MILBASE COLLEGEMAP About / Thesis

COLLEGEMAP — College Market Investment Screen

COLLEGEMAP applies the same demand-floor logic as MILBASE to university towns: screen ~6,000 colleges and their surrounding housing markets by enrollment durability, endowment stability, and local real estate economics. The goal is a ranked, sourced acquisition pipeline for residential investors near colleges — enrollment as the rent-floor analog.

What's Live

All figures sourced from public government data or explicit NULL — no fabrication.

Live · IPEDS 2022

Institutions

6,253 Title IV-eligible colleges: name, state, city, lat/lon, control (public / private / for-profit), level (4yr / 2yr / <2yr).

Source: IPEDS via Urban Institute Education Data API (free, no key)

Live

Coordinates

All 6,253 institutions have real lat/lon from IPEDS HD. Map renders every college — dot size fixed until enrollment lands.

Source: IPEDS HD LATITUDE/LONGITUD fields

Pending · Y-COLLEGE.enrich

Enrollment (time series)

Total enrollment per year 2013–present; YoY trend; FTE headcount. Feeds dot sizing on the map and the demand score.

Source planned: IPEDS EF tables by UNITID

Pending · Y-COLLEGE.enrich

Endowment & Scorecard

Total endowment, per-student endowment, tuition, admit rate, median earnings. The stability and selectivity signals.

Sources planned: NACUBO / College Scorecard (DoE, free bulk CSV)

Pending · P-COLLEGE.surround-towns

Real Estate (feeder towns)

Median home value + rent for towns within 15 miles of each campus. Same ACS / Zillow / HUD FMR pipeline as MILBASE.

Sources planned: Census ACS B25077/B25064 · Zillow ZHVI/ZORI

Pending · P-COLLEGE.surround-towns

Town Economics

Population trend, employment, college-dependence ratio (enrollment ÷ town pop), housing permits, crime index, vacancy.

Sources planned: BLS LAUS/QCEW · Census PEP · FBI Crime Data Explorer

FieldCoverageStatus
name / state / city6,253 / 6,253 (100%)Live
lat / lon6,253 / 6,253 (100%)Live · IPEDS
control (public/private/for-profit)6,223 / 6,253 (99%)Live · IPEDS
level (4yr / 2yr / <2yr)6,223 / 6,253 (99%)Live · IPEDS
enrollment (total, time series)0 / 6,253Pending · Y-COLLEGE.enrich
endowment0 / 6,253Pending · Y-COLLEGE.enrich
tuition / admit rate0 / 6,253Pending · Y-COLLEGE.enrich
median rent (feeder towns)0 / 6,253Pending · surround-towns
median home value (feeder towns)0 / 6,253Pending · surround-towns

What COLLEGEMAP Is

COLLEGEMAP is a companion to MILBASE. Where MILBASE uses BAH — a DoD-published housing allowance — as a mechanical rent-floor signal near military bases, COLLEGEMAP uses enrollment as the demand-durability signal near universities. Large, stable enrollment drives persistent rental demand in college towns independent of the broader housing cycle.

The enrollment thesis

A college with large, stable enrollment creates a structural rental demand floor analogous (not identical) to BAH near a base:

Key difference from MILBASE

BAH is a published dollar rate that directly sets a housing budget. Enrollment is a headcount, not a dollar amount — the rent-floor analogy is structural (demand durability), not mechanical (a set payment per occupant). The yield calculation differs: COLLEGEMAP compares market rent vs. local home values, with enrollment and endowment as stability weights, not as a direct yield input. No enrollment-to-rent conversion is fabricated.

Institution breakdown (live data)

6,253 Title IV-eligible institutions as of IPEDS 2022:

How the Screen Will Work

When enrollment + endowment data is available, COLLEGEMAP will rank institutions using logic that mirrors MILBASE's scoring model:

Views: College Explorer (sortable by rank / enrollment / endowment) · Map (all 6,253 plotted now; dot size = enrollment when available) · All-Colleges scatter (enrollment vs. price-to-rent; endowment vs. yield, etc.).

Honest Caveats

Enrollment ≠ BAH

The rent-floor analogy between enrollment and BAH is structural, not mechanical. BAH is a published dollar rate that directly sets a housing budget. Enrollment is a headcount that creates demand — but the translation to rental pricing depends on campus housing policies, off-campus inventory, and local economics in ways BAH does not. The screen uses enrollment as a demand signal, not a rent guarantee.

For-profit risk

For-profit institutions have materially different risk profiles than public or non-profit colleges. Several large for-profits have closed rapidly (Corinthian Colleges, ITT Technical Institute). The screen flags control type and applies a higher risk multiplier to for-profits — 2,352 of the 6,253 institutions in the current data are for-profit. Do not treat enrollment at a for-profit as the same signal as at a public flagship.

Endowment data lag

NACUBO endowment data is published annually with a 6–12 month lag. Endowment values can move rapidly (market-driven). The screen uses endowment as a slow-moving stability indicator, not a current-value precision metric. Source date stamped with every value.

No backtest (same as MILBASE)

The thesis that high-enrollment, low-price-to-rent college markets outperform is designed but unvalidated. No return claims are made until a pre-registered backtest runs. The screen is a discovery tool, not a return forecast. Any score is a transparent model on partial public data — not a prediction.

Research & Spec Docs

Pink-lane specifications driving the build. Pre-rendered from source docs — no CDN, no fetching.

Source: projects/collegemap/SOURCES.md Pink branch · shipped 2026-05-17

All sources join on UNITID (NCES institution ID) — the canonical primary key for every COLLEGEMAP table. Same join-key discipline as MILBASE's mha_code: verify match rate, NULL unmatched, never invent.

IPEDS / NCES (spine)

  • What: every Title IV-eligible US institution. HD (Directory) file: name, city, state, ZIP, control, sector, lat/lon, ICLEVEL. Enrollment from EF/12-month headcount file. Graduation rates, financial aid from GR/SFA tables.
  • Access: NCES IPEDS data-center bulk CSV — no API key, public domain.
  • PK: UNITID — canonical join key for everything.
  • Notes: Academic year lags ~18 months; FY2022 data released ~Dec 2023.

College Scorecard (US DoE)

  • What: cost (COSTT4_A), median earnings, completion rate, admit rate, debt load, Pell grant share.
  • Access: api.data.gov/ed/collegescorecard/v1/schools — free api.data.gov key required (free ≠ keyless; plan the key). Full bulk CSV fallback requires no key.
  • Join: Scorecard id = IPEDS UNITID (documented by DoE). This is the UNITID-join — the MHA-join-class risk: verify match rate, NULL unmatched.

Carnegie Classification

  • What: R1/R2/D3 doctoral, master's, baccalaureate — research intensity + undergraduate profile (residential vs commuter, full-time vs part-time mix).
  • Access: XLSX download from Carnegie website — no API, no key, CC-BY.
  • Join: UNITID (column name varies by edition — check header).
  • Notes: Updated ~every 3 years; 2021 edition covers 3,982 institutions.

Join Strategy

IPEDS HD{year} ←UNITID→ College Scorecard ←UNITID→ Carnegie Classification IPEDS = canonical institution list. Scorecard + Carnegie enrich it. Not all IPEDS institutions appear in Scorecard (Title IV + sufficient data only). Not all appear in Carnegie (degree-granting only). Unmatched rows → NULL fields, never dropped or fabricated.
Source: projects/collegemap/SURROUND-TOWNS.md Pink branch · shipped 2026-05-17

The MILBASE surrounding-towns idea applied to college towns. Free sources only; no fabrication; verification labeled. Gated on Y-COLLEGE.recon data — this is the spec, ready when data lands.

Gate: all surround-towns metrics require IPEDS lat/lon (live) + Census Gazetteer place FIPS (queued). No surround data is fabricated in the interim — fields show "pending" not invented values.

Geography

Each college's town = IPEDS HD CITY/STABBR + lat/lon → place FIPS via Census Gazetteer. Optional ≤15mi adjacent places (college commute radius is tighter than MILBASE's 25mi base feeder radius).

Metrics → Free Sources

DomainMetricSource (free)Key/Geo
DemandTown population + growthCensus ACS / PEPfree key · place FIPS
EconomyUnemployment, employmentBLS LAUSoptional key · county
EconomyIndustry mix / employersBLS QCEWnone · county
CostMedian rent / home valueCensus ACS B25064/B25077free key · place/ZCTA
SupplyPermits, student-housing pressureCensus BPSnone · place/county
SafetyCrime indexFBI Crime Data Explorernone · agency→place
Town-vs-gownEnrollment ÷ town populationIPEDS enrollment ÷ ACS popderived

College-Dependence Ratio (key signal)

The college-dependence ratio (enrollment / town population) is the analog of MILBASE's base-monoculture risk. A town where students are most of the population is exposed to enrollment shocks exactly as a base-town is to BRAC. Ranges from near-zero (urban university in a major city) to >1.0 (tiny college town where students outnumber residents).

Derived Profile

  • Diversified college town (enrollment a modest share, broad economy) = resilient; lower enrollment-shock risk.
  • Pure college town (high dependence ratio) = enrollment-shock exposed — flagged with the ratio + sources; never hidden.
  • Supply/affordability (permits vs pop; rent burden) = livability read.

Each component: source + year + coverage%. NULL where unsourced — never imputed.

Output Schema (yellow writes)

college_surround(unitid, metric, value, year, source) Mirrors MILBASE surround shape — tooling and QA shared across both efforts. Coverage% per college; partial real > fake.
Source: projects/collegemap/TICKETS.md Pink branch · shipped 2026-05-17 · ~60 tickets

Full ~60-ticket build plan authored by pink. Modeled on MILBASE's actual build path and its hard-won lessons. Pink does not implement — architect queues from this.

Critical Path (11 tickets)

C1 → C2 → C10 → C11 → C12 → C20 → C30 → C31 → C40 → C41 → C55 spec → schema → Scorecard pull → IPEDS join → geocode → DB → bridge → fixture → map → explorer → QA-ship Everything else parallelizes off these.

Phase Summary

PhaseLaneKey workMILBASE lesson applied
0 — Spec & contractsPCONTRACT.md · SOURCES.md · join-key spec · metric dict · heuristicsFront-load contracts to prevent schema churn
1 — Data acquisitionYScorecard pull · IPEDS join on UNITID · geocode · coverage reportUNITID-join = MHA-join-class risk; verify match rate
2 — DatabaseYcollegemap.db SQLite · load + constraints · join-integrity test · commit immediatelyUncommitted DB was lost once in MILBASE
3 — Bridge db→jsonG/Ycollege_export.py · fixture data.json · _meta coverage% · NULLs as nullFixture-parallelism: UI starts while Y works
4 — MapBmap.html dep-free · √area enrollment dots · postMessage contract · pan/zoom · state paletteApply all MILBASE map lessons from day 1
5 — ExplorerGsearchable/sortable list · bidirectional map sync · NULLs last · rank N-of-MSame ratio-rank / NULL-sort rules
6 — ScatterGX/Y metric pickers · global-scale axes · select-linked · mode-switch wiringGlobal-scale invariant; axis-compat from HEURISTICS
7 — QARschema conformance · 0 fabricated rows · served not file:// · merge re-verify guardsPublic deploy = HARD GATE; recurring re-verify budgeted

Key Lessons Baked In

  • Spec phase first: MILBASE's worst time sinks (MHA-join, schema churn, 1-year redo, uncommitted DB loss) were all preventable by contracts + early coverage measurement.
  • Fixture-parallelism: UI lanes start against a fixture immediately; live-data swap waits on Y. Proven in MILBASE.
  • Guard tickets: merge-revert re-verify, NULL-sort-last, join-integrity — budgeted as recurring, not one-and-done.
  • UNITID join = MHA-join-class risk: verify match rate after every data pull; NULL unmatched, never drop silently.
  • DB committed immediately: MILBASE lost yellow's DB once to the uncommitted trap. C23 enforces commit-on-write.

Data & Coverage

Current live data as of 2026-05-17. All sourced or explicit NULL — no fabricated values.

Institutions

6,253 total · all with lat/lon · 2,016 public · 1,855 private NP · 2,352 private FP · 2,866 four-year · 1,753 two-year · 1,604 less-than-2yr

Source: IPEDS 2022 via Urban Institute Education Data API

Enrollment / Endowment

0 / 6,253 — pending Y-COLLEGE.enrich. Map dot sizes are fixed until enrollment lands; no size is fabricated.

Real Estate (feeder towns)

0 / 6,253 — pending surround-towns pipeline. Price-to-rent, yield, and composite score are uncomputable until this lands.

Coverage gaps are honest, not bugs. Any NULL field means "not yet sourced" — never assumed zero or interpolated. Data expands as yellow lane ships enrollment pulls, Scorecard joins, and Census ACS feeder-town data.
Disclaimer: COLLEGEMAP is a data aggregation and research tool. Nothing on this site constitutes investment advice, financial advice, or a solicitation. All data is sourced from public government sources; coverage is partial; enrollment and endowment trends are not a guarantee of future demand or returns. Any investment decision requires independent due diligence.
Methodology limits: Scores and derived ratios are a transparent, weighted model on partial public data. Weights are stated assumptions; coverage is shown per result. A score is not a prediction of returns. Institutions with sparse data return "insufficient data," not a guessed score.
No fabrication: This project never fabricates an enrollment figure, endowment value, price, or coordinate. Partial real data is preferred over invented completeness. Unknown = null.
Enrollment risk: Rapid enrollment decline, campus closure, or for-profit collapse are real risks that are flagged by the college-dependence ratio and control-type signals, not hidden. These are reasoned indicators from cited public data, not probabilities.

See Also: MILBASE

MILBASE is the military-installation analog — same architecture, BAH as the rent-floor signal, 459 active US military installations, 2013–2026 data, live scoring. The pattern COLLEGEMAP is built on.

Live tool
MILBASE — Military Installation Investment Screen
BAH × local RE economics × closure risk · 459 US bases · 2013–2026 data
Research & thesis (27 tabs)
MILBASE — Thesis & Research Library
ETF thesis · opportunity analysis · risk framework · scoring model · DSCR · BAH growth windows · dataset product · underwriting tool