The Area Statistics section is the demographic and economic profile of the property's ZIP code. It feeds the AI Summary and informs the rent-growth assumption in the pro-forma.
What's on this card
- Population — the ZIP code's population, with 2010 and 2020 census counts, a 5-year forecast, and a population trend.
- Median household income — a critical input for rent affordability — alongside average household income.
- Education — attainment from high school through graduate degree.
- Employment — labor force size and unemployment.
- Housing and households — owner-occupied vs renter-occupied share, median home value, household size, and households with and without children.
- Demographics — median age, sex, marital status, and race distribution.
- Commute — average commute time and how people get to work, including the work-from-home share.
- Crime risk — an overall crime index and a breakdown by type, where 100 is the national average.
Where the data comes from
The base layer is ZIP-code-level demographic data from property-data providers and US Census demographic data, supplemented by city-level statistics and public crime data.
For non-U.S. properties, this section is currently sparse — we're working on Canadian census coverage as a follow-up.
Why these stats matter for underwriting
A few quick rules of thumb the AI Summary uses:
- Median income to rent ratio. Affordability of the in-place rent given local median income; a number above ~30% rent-to-income flags a market-risk note.
- Household formation rate. Strong population + household growth supports rent growth assumptions; flat or shrinking suggests caution.
- Employer diversity. A submarket with one dominant employer carries concentration risk; the summary calls this out when present.
Editing
In Edit Mode you can override the four Key Statistics at the top of the Area Statistics page: Population (2020), Median Income, Average Income, and Crime Risk Score. Census data is the standard reference and we'd recommend leaving it as the source of truth unless you have more recent local figures.
Caveats
- US Census demographic data lags by 1–2 years, so very recent shifts won't show up. The pro-forma's Year 1 assumptions can compensate.
- ZIP-code data can smooth over micro-neighborhoods — one ZIP code can mix very different blocks. Pair Area Statistics with Comps to triangulate.
- Top-line population growth can mask demographic shifts (aging in place, Hispanic/Latino growth, family vs single-household). The full distribution is in the deep-dive page if you need it.