How to Score Secondary Cities for Rental Demand
A weighted scoring model for comparing rental markets, with the weights argued rather than asserted — and an honest account of what scoring cannot tell you.
A scoring model does one useful thing: it forces you to compare markets on the same criteria, in the same order, with your weights fixed in advance. That is not because the score is accurate. It is because without it you will unconsciously reweight the criteria to favour the market you already liked.
The five categories, and why they are weighted as they are
The weights below are a defensible starting point for a buy-and-hold rental thesis. Adjust them for your strategy — but adjust them before you score, and write down why.
| Category | Weight | Rationale |
|---|---|---|
| Demand growth | 30% | The engine. Without it nothing else matters. |
| Supply pressure | 25% | The most common cause of a good thesis producing a bad outcome. |
| Affordability headroom | 20% | Determines whether rent growth can continue. |
| Operating feasibility | 15% | Kills more strategies than bad markets do. |
| Liquidity | 10% | Matters at exit, which is why it is easy to ignore and expensive to. |
Two of these weights are deliberately higher than convention. Supply at 25% reflects that the 2021–2022 underwriting failures were overwhelmingly supply failures, not demand failures. Operating feasibility at 15% reflects that an investor who cannot find a competent manager or a second lender does not get to enjoy the market's fundamentals.
Scoring each category
Score 1 to 5 per metric, average within the category, then apply the weight. Assemble the inputs using the population, jobs and supply data framework.
Demand growth — 30%
- Net domestic migration, 3-year trend (positive and accelerating = 5)
- Household formation rate versus national
- Employment growth versus national
- Sector diversity — top sector's share of employment (lower = higher score)
- Wage growth versus national
Supply pressure — 25%
Note that this category scores inversely: less supply scores higher.
- Permits per 1,000 households versus that market's own 10-year average
- Multifamily units permitted in the last 24 months as a share of existing stock
- Units under construction relative to annual absorption
- Land and entitlement constraint (constrained = higher score, because it protects you later)
Affordability headroom — 20%
- Median rent ÷ median household income (below 30% = 5, above 35% = 1)
- Rent growth versus wage growth over 3 years
- Rent-versus-own cost gap — a large gap supports rental demand
- Absolute rent level relative to peer markets
Operating feasibility — 15%
The category that requires phone calls rather than downloads:
- Number of credible third-party property managers (three or more = 5, one = 1)
- Contractor availability and typical turn times
- Number of lenders actively financing your asset type locally
- Landlord-tenant legal environment and eviction timelines
- Insurance availability and recent premium trajectory
Liquidity — 10%
- Comparable transactions closed in the last 24 months
- Buyer mix — a market with only local buyers has a narrower bid
- Typical days on market for comparable assets
- Bid-ask spread; see the bid-ask spread tracker
Using the output
Compare, do not threshold. The score is meaningful relative to the other markets you scored the same way. A 3.8 means nothing on its own.
Look at the category scores, not the total. Two markets can reach 3.8 by opposite routes. One with strong demand and terrible supply is a different bet from one with modest demand and no new construction, and the second is usually the safer entry.
Apply veto conditions. Some failures should disqualify regardless of total score:
- Operating feasibility below 2 — you cannot execute.
- Supply pressure below 2 — you are buying into a delivery wave.
- One employer above roughly 25% of employment with no second sector — a single-decision market.
A market with a strong total score and a vetoed category is the classic trap: it looks best on the spreadsheet precisely because the disqualifying factor is only 15% of the weight.
What scoring cannot do
Worth stating plainly, because scoring models invite more confidence than they earn.
It cannot time a market. All the inputs are backward-looking. A market that scored well for three years may be scoring well because it already grew.
It cannot see a submarket. Metro-level data averages over neighbourhoods that behave nothing alike. A high-scoring metro contains submarkets you should not buy in.
It cannot price a deal. A good market at the wrong price loses money. The score tells you where to look, not what to pay.
It is only as honest as the weights. If you find yourself adjusting weights after seeing results, stop — you are no longer scoring, you are rationalising.
Cadence
Re-score quarterly. Track the change, not just the level: a market moving from 3.2 to 3.6 over a year is more interesting than one sitting at 3.8 and drifting down. The direction is the signal; the level is the context.
Once you have scores for several markets, the next question is position sizing rather than selection — how much capital each market should carry. The market selection and allocation playbook covers that step.
What to do next
- Confirm the definition you are scoring against in what defines an emerging market for CRE investors.
- Cross-check with the qualitative list in 12 signals a secondary city is entering a growth window.
- Present the result with the emerging market scorecard template for investment committees.
- Watch for deterioration with 10 signals to start de-risking an emerging market position.
Sources
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