Restaurant Location Analysis: A Practical Method

Most restaurant location decisions are made by visiting neighbourhoods, noticing that one feels alive, and looking for a unit there. That instinct is picking up something real, but it is also biased towards the times you visited and towards areas that are busy precisely because they are already well served.

Here is a method that keeps the instinct and adds the parts it cannot see.

Step 1: Define your comparable set honestly

Your competition is not every food venue in the area. It is the venues that compete for the same occasion at a similar price point. A ramen bar and a fine-dining room on the same street rarely compete at all; two mid-priced Italian restaurants two streets apart compete constantly.

• Cuisine or format, using a standardised scheme rather than whatever labels the sources happen to use.
• Price level, which usually matters more than cuisine for occasion competition.
• Service model: counter, casual, full service, delivery-only.
• Time of day: a breakfast and lunch venue barely competes with a dinner venue in the same unit type.

Step 2: Use walk time, not radius

For most urban food and drink, the catchment is a walking one. Five, ten and fifteen minutes on the pedestrian network are the useful bands, and they look nothing like circles in any city with a river, a rail line or a major road.

Compute the resident population inside each band, and separately the workplace population. The ratio between them tells you what kind of trading pattern to expect. A catchment that is 80% workplace will be busy at lunch on weekdays and empty on Sunday, and no amount of marketing changes that.

Step 3: Measure density per unit of demand

A raw count of competitors is nearly meaningless because it does not account for how much demand exists. The measure that matters is comparable venues per thousand reachable residents and workers.

Market Square has the most venues and feels the most alive. It also has three times the competitive density of Riverside North. Both facts are true, and only one of them shows up on a Saturday walk.

Step 4: Read the incumbents

Ratings and review counts are imperfect but informative. High average ratings across incumbents means a demanding market where a mediocre offer will struggle. A wide spread means inconsistent quality and room for someone competent. Very low review counts across the board usually means low footfall rather than unpopular venues.

Opening hours are underrated as a signal. If almost no venue in an area opens in the evening, the operators there have collectively learned something about evening demand that is worth knowing before you sign a lease.

Step 5: Look for gaps, then check they are gaps

Compare the local cuisine and price mix against the city-wide baseline. Categories that are strongly under-represented locally are candidate gaps. Then apply the obvious test: is it absent because nobody has tried, or because it has been tried and failed? Historical collection answers this, which is one of the clearest arguments for tracking a market over time rather than photographing it once.

What this method cannot tell you

• Whether your food is good enough. No dataset addresses this.
• Whether a specific unit has the right frontage, extraction or licence.
• What the rent should be, though it does tell you what the location is worth relative to alternatives.
• How a new development two years out will change the catchment, unless planning data is available.

It narrows a city to a defensible shortlist and tells you what you would be walking into. The remaining risk is the part that belongs to the operator.