The rent already knows Commercial rent · 59 Seoul submarkets × 424 neighbourhoods of footfall × 72 industries · public-data analysis

The rent already knows what we know. Find the conditions that make sales high in a neighbourhood, move there, and the rent is higher by exactly that much — what you keep is the same. And if you avoid neighbourhoods with high closure rates, the quiet ones turn out not to be safe but frozen: nobody is opening there either. This piece measures those two dead ends, then says what choice is left.

01Rent rose while customers left

Only 10 of 59 submarkets cut rent, and the deepest cut was −4.2%. Over 18 quarters Seoul small-retail rent is up 7.8% without a single reversal. There is arithmetic behind this. Three quarters of a landlord's return comes not from rent but from the building's value (income yield 1.6%/yr vs capital yield 4.4%/yr), and that value is back-solved from rent at a capitalisation rate of 7.04% — ₩10,000 of monthly rent is ₩1.7M of building value. Cutting rent does not reduce income so much as write down an asset, so landlords hold out through vacancy instead. The "for lease" sign that stays up for months is not stupidity. It is arithmetic.

02Which neighbourhoods are expensive — daytime decides

What does rent actually track? I split Seoul's 424 neighbourhoods by hour and age and joined them to shop rent. Three variables attach.

This means the rental market is rational. The conditions that make trade work — daytime footfall, spending-age population — are already priced in. And that rationality is exactly the trap for the tenant.

03I picked the high-sales neighbourhoods. The burden ratio did not move

The conditions that raise a given industry's per-shop sales are genuinely findable. Join 23 industries to the footfall variables, apply multiple-testing correction, and each industry has one variable that attaches hardest.

It looks like an answer: coffee where the crowds are, Korean food in daytime districts, schools where the children are. But those are the same variables that raised rent in section 02. Divide rent by per-shop sales — the burden ratio — and this happens.

Picking a "good neighbourhood" on its own buys you nothing. That information is public, the landlord and the agent read the same tables, and it is already in the price. Two things remain that a tenant can take without paying for them: change the rent has not yet caught (the previous piece's "demand arrived before supply" districts — rent statistics are contract averages and lag), and your own execution, which is not the average.

04Low-closure neighbourhoods were not the safe ones

What about risk? People open the closure-rate map and avoid the red. I measured that intuition too — 3,581 industry × neighbourhood pairs, four quarters cumulative.

That neither expensive rent nor weak sales lifts the closure rate sounds wrong until you see turnover as the mechanism: where sites circulate, openings and closures are both high; where they are frozen, both are low. The closure map is not a map of where businesses die. It is a map of where sites change hands. For someone looking for a site, circulation is not purely bad news — it also means sites become available.

05Industry explains 37%. Neighbourhood explains 4%

So in the outcome we call closure, how much weight sits on choosing the industry versus choosing the neighbourhood? Variance decomposition on the same 3,581 pairs.

This retroactively validates the order used in the previous piece — industry, then place, then form. And the remaining 59% is this essay's ceiling. Public data explains less than half. The rest lives in who runs the shop and how, and no map holds that.

06What that 37% looks like — an 8.4× spread between industries

If industry explains 37% of closure, it is worth seeing which industries. I ranked 72 industries — those with at least 30 cells of 20+ stores — by four-quarter cumulative closure rate.

But closure rate alone hides half the story, because industries that close a lot often open a lot (section 04). Put the two side by side and four groups appear.

Read it this way. Coffee (4.0 closing / 3.5 opening) is risky but circulating — a lot closes and nearly as much opens. Sites appear; so does competition. E-commerce (4.3 / 0.1) is different — it closes and does not reopen. That is not turnover, it is contraction. Same closure rate, opposite character.

At the other end sits sports clubs (2.6 / 6.5): a closure rate above the median and still +3.9pp net, because the market is growing faster than it sheds. "High closure rate" and "do not enter" are different statements.

07Use the neighbourhood to choose the industry — run the table backwards

Section 03 said choosing a "good neighbourhood" is wasted effort, because rent already took that value. But read the same table backwards and it costs nothing. Instead of picking a neighbourhood to suit an industry, pick the industry that suits the neighbourhood you already have. Sites are usually decided first by budget and connections — if so, choosing what to open there is the whole of the remaining decision.

The method is to overlay the two tables. Take the industries the neighbourhood profile points to, then apply the four-way split from section 06, and what remains is "suits this neighbourhood and is not a shrinking market." In a neighbourhood full of under-teens the candidates are cram schools and sports coaching, and both sit on the growing side (language schools +2.6pp, sports clubs +3.9pp). In a young neighbourhood with few over-60s, one candidate is pubs — at −1.7pp, pointing the other way. A neighbourhood fitting does not mean the market fits.

One caution: this table says "per-shop sales run high in neighbourhoods like this," not "go to this neighbourhood." It is correlation, and as section 03 showed, much of that premium is already in the rent. Its use is narrowing the industry once the site is settled — not choosing the site.

08The affordability arithmetic — still calculate your ceiling

``` Rent burden = (rent per ㎡ × floor area) ÷ per-shop monthly sales for that neighbourhood and industry ```

All three inputs are public: rent from the appraisal board by submarket, sales from card-based estimates, floor area from the median of that neighbourhood's ground-floor lease transactions. The Seoul arithmetic here uses the median submarket rent of ₩51,500 per ㎡ × 10 pyeong (33㎡) = ₩1.7M a month. Whether that is heavy depends entirely on the denominator.

And a lease sits on top of time. Run the measured two-year slopes — denominator (coffee transactions) −18.6%, numerator (rent) +5.3% — across the lease term and today's 26% becomes 32%. Sign on the renewal-day arithmetic, not today's.

Negotiation has to follow the arithmetic too. On the landlord's books, rent is not income so much as the size of an asset.

That asymmetry is the whole of the negotiation. What the tenant asks for and what the landlord loses differ by a factor of fourteen, so nominal cuts barely move. What moves is rent-free months, fit-out contributions, and caps on increases — forms that do not touch the building's book value. Three rent-free months is economically the same as a 12.5% rent cut over a two-year lease, while the landlord's headline rent stays where it was.

09Four things to know before using this data

First, treat a vacancy rate of 0 as half-believed. 876 of 4,748 cells are zero (18.4%), and 21 of them are sandwiched between positive quarters (Gangnam-daero runs 0 → 15.0 → 0). True zero vacancy and no-sample are recorded identically.

Second, some industries do not show up on cards. Wholesale and B2B-flavoured industries book far less card revenue than they actually earn, and dividing by that denominator makes the burden ratio explode — one measured row had per-shop monthly sales of ₩120,000 and a burden ratio of 2,016% (computer and peripheral sales). Below a floor (₩3M a month) the ratio should not be computed at all. You cannot have a trustworthy ratio on an untrustworthy denominator.

Third, do not treat a fallback as the neighbourhood's own number. Query a neighbourhood the submarket statistics do not cover and the system will hand back a higher aggregate. If it is the region your neighbourhood actually belongs to, you can use it while saying so; the catch-all "other regions" subtotal and the Seoul average do not represent it. Better to leave the cell empty than to show someone else's average as yours.

Fourth, this rent is not an asking price. It is an average of contracted rents, so renewals are mixed in and it moves slowly. New-tenant asking prices usually start above it. Read it as "the level and direction of this submarket," not "what I would pay to move in today."

10So what should you actually do

Eight rules side by side tell you nothing about what to look at first. There is an order — choose the industry, then look at the site, then negotiate the price. If you fail at an earlier step there is no point reading the later ones.

The figure gets you as far as what order to look in. What to measure at each step, and what to do when it trips, is in the table below. The figure orients; the table executes.

11What this data cannot see

1. Correlation is not causation. Not "move to a daytime-busy neighbourhood and sales will rise" but "in neighbourhoods like that, this industry's per-shop sales run high." 2. 59% is unexplained. Operations, capital and the specific site sit outside public data. No table here substitutes for that half. 3. Submarket and neighbourhood boundaries differ. Rent is by submarket; sales and footfall are by administrative neighbourhood. They are joined by mapping, and unmapped neighbourhoods were left empty. 4. Deposits and key money are missing. A deposit can be converted to monthly terms at the cap rate; key money has barely any public statistics at all. 5. Yields and cap rates are Seoul averages. A landlord holding without debt does entirely different arithmetic. 6. Industry closure rates use similar-industry stores as the denominator, four quarters cumulative. Dividing by own-industry stores inflates them fourfold (the trap confirmed in the previous piece). Only 72 industries with at least 30 cells of 20+ stores are included. 7. The variance decomposition uses four quarters. With a single quarter, 52% of cells show zero closures and industry differences vanish — cumulating brings that to 33%.

12So this is what it comes down to

I opened the rent data expecting to calculate an affordable rent. The calculation is there — the same ₩1.7M is 9% of sales in one neighbourhood and 79% in another, and today's 26% is 32% by renewal day. But the larger finding sat outside the calculation. The conditions that make a neighbourhood good are already in the rent, so following them leaves the burden ratio unchanged (+0.01). A low closure rate marks a frozen neighbourhood, not a safe one (it tracks only the opening rate, +0.45). And 37% of the closure outcome is set by the industry, 4% by the neighbourhood.

The 37% has a shape, too. Closure rates run 8.4× apart across industries (e-commerce 4.3% ↔ judicial scriveners 0.5%), and the rate alone cannot separate a circulating market from a disappearing one — coffee (4.0/3.5) and e-commerce (4.3/0.1) share a closure rate while one churns and the other contracts. The opening rate is what splits them.

So the order is the conclusion. Spend nine times the hours on the industry, reading closure and opening rates together; if the site is already settled, use its night/day ratio and age profile to choose the industry backwards; sign on the renewal-day burden; and negotiate in forms that leave the landlord's book value alone. Public data takes you this far. The remaining 59% is how you run it.

Where to look first, wherever you sit

One last thing — the calculations in this piece are what hicce, the property and district diagnostic we build, runs at the neighbourhood level. Pick a neighbourhood and its rent comes back (including whether the rent you have in mind is reasonable against the local median of actual transactions), with the industry rent-burden layered on top — the same formula as here, using that neighbourhood's ground-floor lease areas, submarket rent and per-shop sales. The guards above (denominator floor, refusing fallbacks, leaving thin samples empty) are that screen's rules too. It currently runs as a beta for estate agents — hicce.co.kr