Forecasting Love and Weather Requirements: Best Practices for Success

Before anyone can forecast the weather, they need instruments: thermometers, barometers, satellites, and a long historical record to compare against. Before anyone could seriously claim to be "forecasting love," they would need data on behavior, context, and prior patterns. The lesson for education planners is that a forecast is only as good as the requirements you satisfy before you build it. Too many institutions rush to a projection without first assembling the inputs that make prediction possible. This article lays out the requirements — the prerequisites — for trustworthy enrollment, retention, and budget forecasts.

Want expert help putting this into practice? EdForecast can guide you through it.

Requirement one: a clean, sufficiently long historical record

Weather models lean on decades of observations. Human-behavior prediction depends on repeated interactions over time. Education forecasting is no different: you need history to detect pattern from accident. A rough working rule is that you want at least five years of consistent data for annual cycles, because fewer than three points cannot distinguish a trend from a coincidence.

The word "consistent" is load-bearing. If your student information system changed definitions two years ago — reclassifying part-time thresholds, or how you count readmits — your history is not comparable, and a naive model will read the definitional break as a real change. The first requirement, then, is a documented, reconciled time series where you know exactly what each number counts.

Requirement two: the right granularity

Related: edforecast - Expert Advice for Informed Decision-Making.

You cannot forecast a thunderstorm's path with only monthly average temperatures; the resolution is too coarse. Likewise, an institution-wide headcount total is often too blunt for planning. If you need to staff sections, you need forecasts by program and level. If you need to project net tuition, you need forecasts by residency and aid tier.

The requirement is to define the smallest unit at which a decision is actually made, then forecast at that level and aggregate up. A checklist for choosing granularity:

• Identify the decision the forecast will drive (hiring, budgeting, space).
• Determine the unit that decision operates on (course, program, campus).
• Confirm you have enough historical observations at that unit to be stable.
• Where cells are too small, group similar programs to preserve signal.

Requirement three: leading indicators captured in real time

Predicting rain requires present conditions, not just climatology. Predicting whether a relationship deepens requires observing current behavior. For enrollment, the equivalent requirements are pipeline data captured as it happens: applications, admits, deposits, orientation registrations, and course registrations, each time-stamped so you can compare "where we are today" against "where we were on this date last year."

A frequent failure is having this data trapped in a CRM that nobody reconciles against the official system of record until it is too late. The requirement is a governed feed of leading indicators, updated at least weekly during active cycles, with someone accountable for its accuracy.

Requirement four: defined assumptions and their owners

See also: edforecast - expert advice for education planning.

Every forecast rests on assumptions — that a tuition increase holds, that a new program launches on schedule, that a competitor does not open a rival campus. Weather models make explicit assumptions about physics; behavioral predictions assume stability of circumstances. Education forecasts too often bury their assumptions in an analyst's head.

The requirement is a written assumption register: each assumption stated plainly, assigned to the person who owns that decision, with a date to revisit it. When reality diverges, you can trace it to the failed assumption rather than concluding the whole model is broken. This single practice separates forecasts that improve from forecasts that get quietly abandoned.

Requirement five: a measure of acceptable error

Meteorologists know how far off a five-day forecast usually is; that error budget shapes how the forecast is used. Before you forecast enrollment, you should decide how much error your decisions can tolerate. A residence-life plan that can flex a dozen beds tolerates more error than a faculty-hiring plan locked in months ahead.

The requirement is to state, in advance, the accuracy target for each forecast and the consequence of missing it. This turns the abstract goal of "be accurate" into a concrete specification: for example, "first-year headcount within plus or minus 3 percent by the May commitment date." Without this requirement, no one can tell whether a forecast succeeded.

Requirement six: governance and a single source of truth

When two forecasters use different numbers, the resulting confusion is worse than having no forecast at all. Weather agencies publish an official forecast even as many models run underneath. Institutions need the same discipline: one official forecast of record, produced through a known process, that the cabinet plans against.

The requirement here is organizational, not technical. Decide who produces the official forecast, who reviews it, how often it updates, and how disagreements are resolved. A forecast without an owner drifts into a dozen competing spreadsheets, each defended by its author, none trusted by the people who must act. The most damaging version of this failure is subtle: two well-meaning offices publish slightly different numbers, leadership quietly picks whichever is more convenient for the decision at hand, and the discipline of forecasting collapses into the politics of number-shopping.

Before your next forecasting cycle, run a short readiness audit against these requirements. Do you have five clean years of comparable history? Is your granularity matched to the decisions the forecast will drive? Are leading indicators feeding in reliably and on a known cadence? Is there an assumption register with named owners and revisit dates? Have you set an explicit accuracy target and named the consequence of missing it? Is there a single source of truth with clear governance? An institution that answers yes to all six is ready to forecast; one that answers no to several is not forecasting so much as guessing with decimals, and it should invest in the missing inputs before it invests in more elaborate models.

The love-and-weather framing endures because it insists on humility about inputs: you cannot predict a complex human system without instruments, history, and honest assumptions. Platforms like EdForecast can hold the historical series, the assumption register, and the accuracy log in one place, but the requirements themselves are what make prediction credible. Satisfy them first, and the forecast that follows will be worth the trust your institution places in it.