Multifamily Affordability Gap Dataset for Non-Core Markets
Build a rent-to-income dataset from free federal sources to find the ceiling on rent growth before you underwrite past it. Which series, and how to read them.
The most common way a multifamily pro forma fails is not a demand collapse. It is rent growth that was never affordable in the first place — an assumption of 4% annual increases in a market where incomes are growing at 2%.
The gap between what rents are doing and what incomes can support is measurable from free federal data, and it tells you where the ceiling is before you underwrite through it.
The core ratio
Rent-to-income = median gross rent ÷ median household income.
The conventional threshold is 30%; a household paying above it is "cost-burdened" in federal terminology, and above 50% is "severely cost-burdened."
What matters for underwriting is not the label but the direction and the headroom:
- Below 25% — real headroom. Rent growth above wage growth is sustainable for a while.
- 25–30% — normal. Rent growth should roughly track wage growth.
- 30–35% — tight. Expect resistance, longer lease-up, more concessions, and rising delinquency when anything else goes wrong.
- Above 35% — the ceiling. Rent increases produce turnover and bad debt rather than income.
A market at 33% and rising is a market where your 4% rent growth assumption is a claim that households will absorb an increasing share of income indefinitely. They will not.
The series to pull
All free, all reproducible.
| What | Source | Geography | Cadence |
|---|---|---|---|
| Median gross rent | ACS table B25064 | County, tract | Annual |
| Median household income | ACS table B19013 | County, tract | Annual |
| Rent burden distribution | ACS table B25070 | County | Annual |
| Renter-occupied units | ACS table B25003 | County, tract | Annual |
| Average weekly wages | BLS QCEW | County | Quarterly |
| Employment by sector | BLS QCEW | County | Quarterly |
| Building permits | Census BPS | County | Monthly |
Use the ACS 5-year estimates for county and tract geography, and read the published margins of error — at tract level they are large enough to matter. The 5-year file is a five-year average, so it lags a fast-moving market; QCEW wages update quarterly and are the better read on current direction.
Beyond the median: the distribution
The median hides the thing you actually need. Two markets with identical 29% medians can be completely different:
- One where most renters sit between 25% and 32%
- One where a third are below 20% and a third are above 40%
The second has a large cost-burdened population that will show up as delinquency and turnover the moment anything else moves — a rate reset, a utility increase, a slow quarter at the dominant employer.
ACS table B25070 gives the distribution of rent as a share of income in bands. The share above 35% is a better predictor of collections risk than the median is.
The gap that matters for a specific deal
The market-level ratio sets context. For an underwriting, compute the ratio at your rents:
Your asking rent ÷ the median income of the households you are targeting.
If the submarket median income is $52,000 and you plan to push rents to $1,600, that is 37% — and the honest question is whether the households who can afford that live in your submarket, or whether you are underwriting a tenant base that is not there.
This is the check that catches value-add plans premised on a renter profile the market does not contain.
Cross-checks that change the reading
Supply. Affordability headroom plus heavy permitting means the headroom is about to be competed away. Check Census BPS permits per 1,000 households against the market's own average, per population, jobs and supply.
Wage growth versus rent growth over three years. If rents grew 15% while wages grew 6%, the market consumed its cushion — and the current ratio understates how stretched it now is.
Employer concentration. A market whose income depends on one employer has affordability that is contingent rather than structural. Check the sector share in QCEW.
Owner-occupancy cost. Where buying is far cheaper than renting, your rent ceiling is set by the mortgage payment on a comparable house, not by the income ratio. The housing affordability index for investors tracks that relationship across markets and is a faster way to spot a fragile rent market than rebuilding the ratio yourself.
Building the dataset
One row per county, refreshed annually with ACS and quarterly with QCEW:
| County | Median rent | Median income | Ratio | % above 35% | 3-yr rent Δ | 3-yr wage Δ | Permits/1k HH | Top sector share |
|---|
Two years of this gives you something no purchased dataset will: your own baseline, computed consistently, that lets you compare a new market against the ones you already know.
Reading it into an underwriting
Cap your rent growth assumption at wage growth where the ratio is above 30. That is the defensible position, and it is usually more conservative than what the model was carrying.
Model a downside where rent growth is zero in markets above 33%. Not as a tail case — as the base case for the second half of the hold.
Expect higher bad debt where the share above 35% is large, and raise the delinquency assumption rather than leaving it at a national default.
Watch the interaction with insurance. In a stretched market you cannot pass an insurance increase through to rents, so the expense drift lands entirely on NOI — see expense drift benchmarks by market maturity tier.
What to do next
- Assemble the wider dataset: population, jobs and supply: data framework for market entry.
- Score the market: how to score secondary cities for rental demand.
- Check the source list: top 10 data sources for emerging market underwriting.
- Test the deal: DSCR sensitivity design for smaller lending pools.
Sources
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