⚙️ How-To Building permits & new housing Census & demographics Notebook By

How to Compare Housing Permits With Population Growth by County

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Problem statement

Is a county building enough homes for the people moving in? Two Census Bureau products answer half each: the population estimates say how many people a county gained, and the permit files how many homes it authorised. Put side by side, the ratio of people added to units permitted shows where building ran ahead of growth and where it fell behind. The years have to line up, Connecticut's geography has to match, and a county losing people while building is a real case, not an error. This page compares July 2020 to July 2025 with the permits of 2021–2025.

Quick answer

Sum the 2021–2025 permits by county, join them to the population change from July 2020 to July 2025, and divide:

built = pd.concat([permits(f"co{y}a.txt") for y in range(2021, 2026)]).groupby("GEOID").units.sum()
g = pop.set_index("GEOID")[["STNAME", "CTYNAME", "POPESTIMATE2020", "POPESTIMATE2025"]].join(built)
g = g[g.STNAME != "Connecticut"]
g["added"] = g.POPESTIMATE2025 - g.POPESTIMATE2020
print(f"July 2020 to July 2025: {g.added.sum():,} more people, {g.units.sum():,.0f} units permitted 2021-2025; {g.added.sum() / g.units.sum():.2f} people per unit")
July 2020 to July 2025: 10,097,900 more people, 7,794,045 units permitted 2021-2025; 1.30 people per unit

The US gained 10,097,900 people and permitted 7,794,045 units over those five years: 1.30 people per unit. The ratio is not a verdict by itself, because new units also replace demolished ones and house households that split; it is a yardstick for comparing counties with each other.

Scatter plot of counties with population change from July 2020 to July 2025 against housing units permitted 2021-2025, both on log-like scales, with a dashed line at 1.30 people per unit; Los Angeles County sits far below zero and Canadian County well above the line. Scatter plot of counties with population change from July 2020 to July 2025 against housing units permitted 2021-2025, both on log-like scales, with a dashed line at 1.30 people per unit; Los Angeles County sits far below zero and Canadian County well above the line.
People added against homes permitted, by county.

Matching the years and the counties

1. Which years go with which

print(pop[["ESTIMATESBASE2020", "POPESTIMATE2020", "POPESTIMATE2025"]].sum().to_string())
print(f"Connecticut rows: {(pop.STNAME == 'Connecticut').sum()} in the estimates, "
      f"{built.index.str.startswith('09').sum()} GEOIDs in the 2021-2025 permits")
ESTIMATESBASE2020    331516113
POPESTIMATE2020      331578104
POPESTIMATE2025      341784857
Connecticut rows: 9 in the estimates, 17 GEOIDs in the 2021-2025 permits

The estimates start at the April 2020 census base, then give 1 July of each year. July 2020 to July 2025 covers five years of change, so it pairs with five calendar years of permits; a permit in 2021 is a home finished in 2022 or later, which this lines up roughly, not exactly. Connecticut has 9 planning regions in the estimates but 17 different codes across its 2021–2025 permits, eight old counties and nine regions, so it is left out here.

2. People added per unit permitted

h = g[g.units >= 1000].copy()
h["per_unit"] = h.added / h.units
print(h.per_unit.describe()[["count", "25%", "50%", "75%"]].round(2).to_string())
print(h.nlargest(6, "per_unit")[["CTYNAME", "STNAME", "added", "units", "per_unit"]].round(2).to_string(index=False))
count    861.00
25%        0.69
50%        1.48
75%        2.18
         CTYNAME        STNAME  added  units  per_unit
 Canadian County      Oklahoma  31754 1813.0     17.51
   Parker County         Texas  35208 2877.0     12.24
 Rockland County      New York  19968 2177.0      9.17
Hampshire County Massachusetts  15468 1700.0      9.10
  Kaufman County         Texas  62070 6876.0      9.03
     Wise County         Texas  14847 1667.0      8.91

Among the 861 counties that permitted 1,000 or more units, the median county added 1.48 people per unit, and a quarter added under 0.69. At the other end, Canadian County, Oklahoma, outside Oklahoma City, added 31,754 people on 1,813 units, 17.51 per unit, and Parker and Kaufman counties near Dallas–Fort Worth around 9 to 12.

Histogram of people added per unit permitted for 861 counties with 1,000 or more units, centred between 1 and 2 with a median of 1.48 and tails below zero. Histogram of people added per unit permitted for 861 counties with 1,000 or more units, centred between 1 and 2 with a median of 1.48 and tails below zero.
People added per unit permitted: a median of 1.48.

3. Building while shrinking

shrink = g[(g.added < 0) & (g.units >= 1000)].sort_values("units", ascending=False)
print(f"counties that lost people but permitted 1,000+ units: {len(shrink)}")
print(shrink.head(6)[["CTYNAME", "STNAME", "added", "units"]].to_string(index=False))
counties that lost people but permitted 1,000+ units: 119
            CTYNAME       STNAME   added    units
 Los Angeles County   California -301576 116564.0
   San Diego County   California  -18862  54142.0
       Kings County     New York  -68296  52476.0
Philadelphia County Pennsylvania  -26456  38671.0
      Orange County   California  -36660  32250.0
        Cook County     Illinois  -69026  31444.0

119 counties lost people while permitting 1,000 or more units. Los Angeles County lost 301,576 residents and permitted 116,564 units; Brooklyn, Philadelphia and Cook County did the same on a smaller scale. Building in a shrinking county is not a mistake in either file: households get smaller and older units are demolished, so a county can need new homes while its population falls.

States and a map of the gap

The data used on this page

The US Census Bureau's Building Permits Survey county files (annual 1990–2025 and year-to-date 2025–2026), its Northeast place files for 2020–2025, the Vintage 2025 county population estimates and the 2024 cartographic boundary counties, all public domain. They were downloaded on 16 October 2026 and saved on this site in one archive, because Census revises recent months as late reports arrive.

from pathlib import Path
import zipfile

import geopandas as gpd
import numpy as np
import pandas as pd
import requests

Path("data").mkdir(exist_ok=True)
# US Census Bureau Building Permits Survey: annual county files co1990a.txt-co2025a.txt, year-to-date county files
# co2501y.txt-co2512y.txt and co2601y.txt-co2608y.txt, Northeast place files ne2020a.txt-ne2025a.txt; the Vintage 2025
# county population estimates; 2024 cartographic boundary counties. Downloaded 16 October 2026, saved here unchanged.
# Live: https://www2.census.gov/econ/bps/County/co2025a.txt (and the same folder for the others),
# https://www2.census.gov/econ/bps/Place/Northeast%20Region/ne2025a.txt,
# https://www2.census.gov/programs-surveys/popest/datasets/2020-2025/counties/totals/co-est2025-alldata.csv,
# https://www2.census.gov/geo/tiger/GENZ2024/shp/cb_2024_us_county_20m.zip
ARCHIVE = "census-building-permits-2026-10-16.zip"
if not Path(f"data/{ARCHIVE}").exists():
    Path(f"data/{ARCHIVE}").write_bytes(requests.get(f"https://data.spatialworkflow.io/{ARCHIVE}", timeout=600).content)
Z = zipfile.ZipFile(f"data/{ARCHIVE}")

SIZES = ["1", "2", "34", "5"]  # buildings with 1 unit, 2 units, 3-4 units, 5 or more units
COLS = (["survey", "state", "county", "region", "division", "name"]
        + [f"{k}{s}_{m}" for k in ("est", "rep") for s in SIZES for m in ("bldgs", "units", "value")])


def permits(name):
    """One county file: two header rows and a blank line, then a row per county. est = estimate with
    imputation for places that did not report, rep = reported only."""
    d = pd.read_csv(Z.open(name), skiprows=3, header=None, names=COLS, skipinitialspace=True,
                    dtype={"survey": str, "state": str, "county": str, "name": str})
    d["survey"], d["name"] = d.survey.str.strip(), d.name.str.strip()
    d["state"], d["county"] = d.state.str.strip().str.zfill(2), d.county.str.strip().str.zfill(3)
    return d.assign(GEOID=d.state + d.county,
                    units=d[[f"est{s}_units" for s in SIZES]].sum(axis=1),
                    reported=d[[f"rep{s}_units" for s in SIZES]].sum(axis=1),
                    value=d[[f"est{s}_value" for s in SIZES]].sum(axis=1))


pop = pd.read_csv(Z.open("co-est2025-alldata.csv"), encoding="latin-1", dtype={"STATE": str, "COUNTY": str})
pop = pop[pop.SUMLEV == 50].assign(GEOID=lambda p: p.STATE + p.COUNTY)
Path("data/cb_2024_us_county_20m.zip").write_bytes(Z.read("cb_2024_us_county_20m.zip"))
counties = gpd.read_file("data/cb_2024_us_county_20m.zip")
counties = counties[~counties.STUSPS.isin(["AK", "HI", "PR"])].to_crs(5070)
y25 = permits("co2025a.txt")
print(f"2025 county file: {len(y25):,} rows, {y25.units.sum():,} units permitted; {len(pop):,} counties in the estimates")
2025 county file: 3,029 rows, 1,431,616 units permitted; 3,144 counties in the estimates

Example 1: by state

s = g.groupby("STNAME")[["added", "units"]].sum()
s["per_unit"] = s.added / s.units
s = s[s.added > 0].sort_values("per_unit")
print("fewest people per unit:", ", ".join(f"{k} {v:.2f}" for k, v in s.per_unit.head(4).items()))
print("most people per unit:", ", ".join(f"{k} {v:.2f}" for k, v in s.per_unit.tail(4).items()))
fewest people per unit: Vermont 0.14, New Mexico 0.25, Oregon 0.34, Pennsylvania 0.42
most people per unit: Texas 2.06, Massachusetts 2.20, Oklahoma 2.22, Rhode Island 2.43

Rhode Island added 2.43 people per unit permitted, Oklahoma 2.22, Massachusetts 2.20 and Texas 2.06. Vermont added 0.14, New Mexico 0.25 and Oregon 0.34. States that lost population are left off the ranking.

Example 2: map

g["per_unit"] = g.added / g.units
m = counties.merge(g[g.units >= 500][["per_unit"]].reset_index(), on="GEOID", how="left")
m["cls"] = pd.cut(m.per_unit, [-1e9, 0, 1, 2, 3, 5, 1e9], right=False)
print(f"counties with 500+ units: {m.per_unit.notna().sum():,}")
print(m.cls.value_counts(sort=False).to_string())
ax = m.plot(column="cls", cmap="RdYlBu_r", legend=True, missing_kwds={"color": "lightgrey"}, figsize=(10, 6))
ax.set_axis_off()
counties with 500+ units: 1,250
cls
[-1000000000.0, 0.0)    203
[0.0, 1.0)              279
[1.0, 2.0)              397
[2.0, 3.0)              262
[3.0, 5.0)               75
[5.0, 1000000000.0)      34
Map of United States counties shaded by people added per unit permitted, red where population fell, mostly in California, the Northeast and around the Great Lakes, and blue where growth outran building; grey counties permitted fewer than 500 units or are in Connecticut. Map of United States counties shaded by people added per unit permitted, red where population fell, mostly in California, the Northeast and around the Great Lakes, and blue where growth outran building; grey counties permitted fewer than 500 units or are in Connecticut.
People added per housing unit permitted, July 2020 to July 2025.

203 of the counties with 500 or more units lost people, and 34 added five or more people per unit. Most of the counties that lost people are in California, the Northeast and around the Great Lakes; the highest ratios include the outer counties of Dallas–Fort Worth and Oklahoma City listed above.