Footfall near universities in term time and out of it

A shop or cafe next to a university lives on two calendars: term and holidays. Footfall shows the difference directly. Measure the same address over a window inside term and a window inside the break, on weekdays, and compare footfall per day, the busiest hour and how the day splits. The gap between the two is the part of your trade that depends on students, and it is the number to plan rent, stock and staff around.

This piece is for cafes, copy and print shops, student housing operators, food delivery and quick-service brands, gyms and banks that open near campuses, and the property people who lease to them. It covers why the campus calendar matters, how to read term and break windows, a made-up example, the usual mistakes, and an AI employee that tracks it.

Why a campus street behaves differently

Most streets have a weekly rhythm. A campus street has a yearly one on top: lectures pull people in from early morning on weekdays, then exams, then weeks when the buildings are nearly empty. A site that looks busy on a Tuesday in October may be quiet on a Tuesday in July. If you see it only in term, you overrate it; only in the holidays, you underrate it.

Three things make the gap bigger or smaller:

• Who else uses the street. A campus in the middle of a city shares its streets with offices and shoppers, who stay through the holidays. A campus on the edge of town has little else.
• Where students live. Halls on campus keep some people around in the short breaks; a commuter campus empties.
• Summer schools, conferences and resits. Some campuses are half busy in the long break.

How to measure term and break

Mobility Footfall works on a window of days that ends about 10 days ago, because each day arrives about 10 days later. Choose the window and the day type, and read these fields for each place:

• footfall_daily: the average day's people-hours of presence around the place in the window.
• footfall_peak_hour: the busiest hour. On campus streets it often sits around lecture changes in term and drifts later in the holidays.
• footfall_by_part: the share in the morning, midday, evening and late night.
• footfall_trend: the last 7 days against the 7 before, useful in the weeks when term starts or ends.

1. Set Day type to Weekdays only. Weekends on campus streets behave like the rest of the city and blur the comparison.
2. Read a 14-day window in the middle of term, away from the first and last week.
3. Read the same 14-day window in the middle of the break, from the same place with the same distance.
4. Divide the break figure by the term figure. That is the share of your term-time crowd that stays.

A made-up example in Montreal

Invented numbers for illustration. A bubble tea brand is offered two units in Montreal: one on the main street beside a large downtown university, one opposite a smaller campus near the edge of the city. Rent is similar. The brand reads weekday footfall 200 m around each unit during term and during the winter break.

Invented people-hours for illustration; not real figures for Montreal.

The edge-of-city unit looks fine in term but loses most of its weekday crowd when students leave. The downtown unit keeps three quarters, because offices and shoppers share the street. The brand takes the downtown unit and, if it ever opens by the smaller campus, asks for lower rent in the summer months or a shorter lease.

Mistakes to avoid

• Reading only in term. An October survey of a campus street is the best case. Wait for, or ask about, the break.
• Windows that straddle the start of term. A 30-day window that includes two weeks of holiday and two of term gives a number that describes neither.
• Weekends in the mix. Use Weekdays only for the comparison.
• Taking residents for students. Demographics counts the people who live in each small block, in the countries it covers (Canada and France, for example, but not the United Kingdom or the United States). It does not change with the term and does not tell you who studies where.
• Treating presence as spend. Students often stay long and spend little. Footfall is a measure of how busy, not of sales.

Track it with an AI employee

1. Schedule: every other Monday at 09:00, all year.
2. Area of interest on your city, then Nearby places with the types University and College.
3. Mobility Footfall: 200 m, 14 days, Weekdays only, with Footfall, Busy hours and Trend.
4. A Google Sheets step that adds a row per campus: footfall_daily, footfall_peak_hour, footfall_from and footfall_to.
5. xMap LLM on the whole list, asked to name the campuses whose footfall_trend moved most, up or down.
6. Email the short note to the expansion team.

Over a year the sheet holds each campus's term and break levels without anyone remembering to look. Every other Monday the inbox gets the campuses ranked by their latest weekday footfall, the dates covered, and which ones are starting to empty or fill. For the people side of a campus, the templates campuses with the most students nearby and student housing around each university count who lives around each one.