Submarket report

The submarket report describes how the area around each place is doing: whether jobs are growing, who works there, what people earn and what they pay for housing, compared with the county, the state and the US. Use it to size up a real estate submarket, such as the area around an office building or an apartment site.

What's in it

Compared with: each number for its county, its state and the US, and for rates and medians, where the place ranks among census tracts (neighborhood-sized areas of about 4,000 people).

Usual area: a 3-mile radius around each place (everywhere within 3 miles in a straight line). You can choose another.

Cost: 7 credits a place for its 7 groups.

Job growth

Job growth is the change in the number of jobs located in the area over the last 5 and 10 years, as a number and as a percent. The years count back from the newest year of jobs data in the release, so today they're 2018 to 2023 and 2013 to 2023; each answer's vintage names both years. Jobs come from the U.S. Census Bureau's LEHD Origin-Destination Employment Statistics (LODES), which count jobs where people work.

If an area had no jobs at the start, there's no percent change: the warning no_base says so. If a state's jobs data is missing for one of the years, the change has no value and the warning partial_coverage says why.

Run it in Kadrin Batch

Choose New request, pick your file of places, and choose Submarket report. Batch suggests a 3-mile radius around each place. The Batch quickstart goes through it step by step.

Run it with the API

Send "preset": "submarket_report" to /enrich, with the area around each place. Here is a 3-mile radius around Kendall Square in Cambridge, Massachusetts:

curl https://api.kadrindata.com/v1/enrich \
  -H "Authorization: Bearer $KADRIN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "areas": [{"id": "kendall-square",
               "geometry": {"type": "buffer", "center": [-71.0862, 42.3625], "radius": 3, "unit": "mi"}}],
    "preset": "submarket_report"
  }'

import os

import requests

r = requests.post(
    "https://api.kadrindata.com/v1/enrich",
    headers={"Authorization": f"Bearer {os.environ['KADRIN_API_KEY']}"},
    json={
        "areas": [{"id": "kendall-square",
                   "geometry": {"type": "buffer", "center": [-71.0862, 42.3625], "radius": 3, "unit": "mi"}}],
        "preset": "submarket_report",
    },
)
r.raise_for_status()
values = r.json()["results"][0]["values"]
for measure in ("labor.growth.jobs_change_5yr", "labor.growth.jobs_change_5yr_pct"):
    v = values[measure]
    print(f"{measure} ({v['vintage']}): {v['value']}")

const r = await fetch("https://api.kadrindata.com/v1/enrich", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.KADRIN_API_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    areas: [{ id: "kendall-square",
              geometry: { type: "buffer", center: [-71.0862, 42.3625], radius: 3, unit: "mi" } }],
    preset: "submarket_report",
  }),
});
if (!r.ok) throw new Error(await r.text());
const { values } = (await r.json()).results[0];
for (const measure of ["labor.growth.jobs_change_5yr", "labor.growth.jobs_change_5yr_pct"]) {
  console.log(`${measure} (${values[measure].vintage}): ${values[measure].value}`);
}

With a live key this costs 7 credits. For a whole spreadsheet of places, send it as a file: see Send a file of places.

Reading it

• Each number is for the area around the place, with its margin of error (how far the true number could reasonably be from our estimate) where it's a survey estimate. Job counts and job growth have none, since they count every job rather than surveying some.
• benchmarks gives the same number for the county and state holding most of the area's people, and for the US.
• percentiles, for rates, medians and job growth as a percent, says where the area ranks among census tracts (neighborhood-sized areas of about 4,000 people) in the county, the state and the US: 80 means higher than 80% of them.