Site selection for a coworking space: near where members live or where they work?

A coworking space does best when it sits within a short, easy commute of the people it is built for. For freelancers and remote employees that usually means near where they live, in a neighbourhood with cafes and a station; for small companies and project teams it means near the business district they visit. Decide which members you are building for, count them within that commute, check how many coworking spaces already serve them, and look for growing companies nearby as a sign of demand.

This article is for coworking operators, landlords turning a floor into flexible space, and companies opening a satellite office for staff who live far from headquarters. It covers the two kinds of coworking site, the signals worth reading, a worked example in Mumbai, the mistakes, and an AI employee that checks candidate buildings.

Two kinds of coworking site

• The neighbourhood space serves people who would otherwise work from home: freelancers, remote employees, founders. They want a 10 to 15-minute walk, ride or drive, good coffee nearby and a quiet desk. Mornings are busy and Fridays quiet.
• The district space serves small companies and visiting teams who need an address near clients and partners. They will travel further if the building is easy to reach by train, has meeting rooms and sits among other offices.

The same building can rarely be both. A neighbourhood space in a business district stands empty in the evening; a district space in a residential street struggles to sell meeting rooms.

The signals to read around a site

1. People of working age nearby. Demographics gives residents aged 20 to 59 within a travel area, where the country has figures (among them India, France, Canada, Australia and Thailand; not the United Kingdom or the United States).
2. Spaces already open. Nearby places finds places of the type Coworking space and Business center around the site, with their ratings and the number of reviews.
3. Companies that are growing. Job postings shows public job postings near the site. Many small companies hiring nearby is a sign that people will need desks and meeting rooms.
4. What is around the door. Cafes, lunch places, gyms and a station within a few minutes' walk. Members choose a space for the day around it, not only the desk.
5. How busy the area is in the day. Mobility Footfall shows footfall per day as people-hours of presence and how it splits across the day, about 10 days behind. A neighbourhood that empties at 09:00 is full of people who commute away, which can be exactly the people you want to keep.

A worked example in Mumbai

A made-up case. An operator in Mumbai has run one district space near the business hub for three years and wants a second site for members who now work mostly from home. It compares two buildings. The figures below are invented, to show the reading.

Made-up figures, for illustration only.

The hub building has more hiring and more cafes, but 19 spaces already compete for the same companies, and the operator already has a site there. The suburban building has more than twice the working-age residents and only four spaces serving them. For members who want to stop commuting, the suburb is the right choice. The operator prices it for monthly desks rather than meeting rooms, and opens earlier than the hub site.

Mistakes to avoid

• Counting every office worker as a member. Most office workers already have a desk. The people you want are those without one nearby.
• Copying the city's busiest district. Business districts are where competition is fiercest. A residential area with few spaces can fill faster.
• Ignoring the evening. A neighbourhood space can rent rooms for classes and events after 18:00; a district space usually cannot.
• Reading job postings as desks. A company hiring 50 people may move them all into its own office. Hiring is a signal, not a booking.
• Trusting a single count. A company that calls itself a business center may be a virtual office address with no desks. Look at the list, not just the number.

Doing it with an AI employee

An AI employee can check each building your broker sends and, once you open, keep an eye on new spaces near it. The blocks, in order:

1. My data: the candidate buildings.
2. Travel area: 15 minutes by car, or walking for a dense centre, around each.
3. Demographics: working-age residents in each area, where there are figures.
4. Nearby places: coworking spaces, business centers and cafes in each area, counted.
5. Job postings: postings near each building in the last 30 days.
6. xMap LLM: score each building for the kind of space you are opening, with one line on why, from these figures only.
7. Email: the ranked buildings to you.

What arrives: a ranked list of buildings with the residents, rival spaces, hiring and cafes beside each. After opening, New offices that need business internet shows the same kind of new small offices as prospects, and Coworking spaces and universities for developer meetups lists the spaces and venues that host the community events members come for.