⚙️ How-To Aircraft registrations Data cleaning Notebook By · Updated

How to Measure the Age of the US General Aviation Fleet

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Problem statement

How old are America's private aircraft? The FAA registry records the year each aircraft was built, so a median age is one subtraction away, but the column has blanks, zeros and, since drones started being registered, a large group of brand-new aircraft with no year at all. Which aircraft you count changes the answer by decades. This page measures the age of the registered fleet with the 16 October 2026 file, by kind of aircraft, by owner and by state, and shows what the missing years are.

Quick answer

Treat the year as a number, drop anything before 1903, and subtract from 2026:

v = reg[reg["STATUS CODE"] == "V"].copy()
v["year"] = pd.to_numeric(v["YEAR MFR"], errors="coerce").where(lambda y: y >= 1903)
v["age"] = 2026 - v.year
print(f"valid registrations: {len(v):,}; with a usable year: {v.year.notna().sum():,}; median year {v.year.median():.0f}, median age {v.age.median():.0f}")
valid registrations: 310,844; with a usable year: 243,254; median year 1979, median age 47

Of 310,844 valid registrations, 243,254 have a usable year. The median aircraft was built in 1979 and is 47 years old.

Bar chart of registered aircraft by year built from about 1925 to 2026, with a spike of 8,449 in 1946, a hump in the 1960s and 1970s, a trough around 1990 and a second hump in the 2000s. Bar chart of registered aircraft by year built from about 1925 to 2026, with a spike of 8,449 in 1946, a hump in the 1960s and 1970s, a trough around 1990 and a second hump in the 2000s.
Registered aircraft by year built: 1946 left the most.

Years that are not years, and ages by kind

1. Blank and zero years

y = pd.to_numeric(v["YEAR MFR"], errors="coerce")
print(f"blank: {(v['YEAR MFR'] == '').sum():,}; zero: {(y == 0).sum():,}; earliest real year: {y[y > 0].min():.0f}")
blank = v[v["YEAR MFR"] == ""]
print((blank["TYPE AIRCRAFT"].map(TYPE_AIRCRAFT) + ", engine " + blank["TYPE ENGINE"]).value_counts().head(4).to_string())
blank: 67,442; zero: 148; earliest real year: 1909
fixed wing single engine, engine 1    32242
rotorcraft, engine 10                 12439
hybrid lift, engine 10                 3263
fixed wing single engine, engine 8     3133

67,442 valid registrations have a blank year and 148 a year of 0; the earliest real year is 1909. Nearly half the blanks are piston single-engine aeroplanes (engine code 1), and 12,439 are electric rotorcraft, the registered drones, which almost never carry a year, with 3,263 hybrid-lift drones besides. A median that ignores the blanks therefore leaves out most of the newest aircraft in the file.

2. Median age by kind of aircraft

v["kind"] = v["TYPE AIRCRAFT"].map(TYPE_AIRCRAFT).mask((v["TYPE AIRCRAFT"] == "6") & (v["TYPE ENGINE"] == "10"), "electric rotorcraft")
k = v.groupby("kind").agg(aircraft=("age", "size"), median_age=("age", "median"), dated=("age", "count"))
print(k[k.aircraft >= 1000].sort_values("median_age", ascending=False).to_string())
                          aircraft  median_age   dated
kind                                                  
fixed wing single engine    217878        50.0  179743
glider                        4822        45.0    3734
fixed wing multi engine      48682        25.0   41543
powered parachute             1896        23.0    1505
balloon                       5206        22.0    3863
rotorcraft                   15027        21.0   11605
weight-shift                  1042        20.0     833
electric rotorcraft          12510         2.0      71
hybrid lift                   3300         2.0      19

Single-engine aeroplanes have a median age of 50, gliders 45; multi-engine aeroplanes and helicopters are about 25 and 21. The electric rotorcraft and hybrid-lift aircraft show a median of 2 years, but on 71 and 19 dated aircraft out of thousands.

Horizontal bars of median age by kind of aircraft: single-engine aeroplanes 50 years, gliders 45, multi-engine 25, then powered parachutes, balloons, fuel-engine rotorcraft and weight-shift around 20, and hybrid lift and electric rotorcraft at 2 shown in grey because few have a year. Horizontal bars of median age by kind of aircraft: single-engine aeroplanes 50 years, gliders 45, multi-engine 25, then powered parachutes, balloons, fuel-engine rotorcraft and weight-shift around 20, and hybrid lift and electric rotorcraft at 2 shown in grey because few have a year.
Median age by kind of aircraft; grey bars rest on few dated aircraft.

3. Built per year, still registered

per_year = v.year.value_counts().sort_index()
print(per_year.loc[[1946, 1966, 1978, 1979, 1990, 2007, 2015, 2025]].to_string())
year
1946.0    8449
1966.0    5614
1978.0    6749
1979.0    6373
1990.0    1170
2007.0    4702
2015.0    2880
2025.0    3691

1946 alone left 8,449 aircraft still registered, more than any year since: the post-war boom in light aircraft. 1978 and 1979 left 6,749 and 6,373, 1990 only 1,170, after product-liability costs had cut light-aircraft production, and 2025 added 3,691.

Owners and states

The data used on this page

The FAA's Releasable Aircraft Database, public domain, downloaded on 16 October 2026 and saved on this site, because the FAA replaces it every day. The saved copy leaves out the owners' names and street addresses and keeps five-digit ZIP codes; every other column is as published. With it come the Census Bureau's Vintage 2025 county population estimates, 2024 county boundaries and 2024 ZCTA gazetteer.

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)
# FAA Releasable Aircraft Database, 16 October 2026: MASTER.txt without the NAME, STREET, STREET2 and OTHER NAMES
# columns and with five-digit ZIP codes (MASTER-no-names.txt), ACFTREF.txt and ENGINE.txt as published; Census
# Vintage 2025 county estimates, 2024 cartographic boundary counties and 2024 ZCTA gazetteer. Saved here unchanged
# otherwise, because the FAA replaces the file daily.
# Live: https://registry.faa.gov/database/ReleasableAircraft.zip (needs a browser User-Agent),
# https://www2.census.gov/geo/docs/maps-data/data/gazetteer/2024_Gazetteer/2024_Gaz_zcta_national.zip
ARCHIVE = "faa-aircraft-registry-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}")


def faa(name):
    """One registry file: comma-separated, every field padded with spaces, a byte-order mark and a trailing comma."""
    d = pd.read_csv(Z.open(name), dtype=str, encoding="utf-8-sig", keep_default_na=False)
    d.columns = d.columns.str.strip()
    d = d.loc[:, [c for c in d.columns if c and not c.startswith("Unnamed")]]
    return d.apply(lambda c: c.str.strip())


reg = faa("MASTER-no-names.txt")
ref = faa("ACFTREF.txt")
TYPE_AIRCRAFT = {"1": "glider", "2": "balloon", "3": "blimp", "4": "fixed wing single engine", "5": "fixed wing multi engine",
                 "6": "rotorcraft", "7": "weight-shift", "8": "powered parachute", "9": "gyroplane", "H": "hybrid lift", "O": "other"}
REGISTRANT = {"1": "individual", "2": "partnership", "3": "corporation", "4": "co-owned", "5": "government", "7": "LLC",
              "8": "non-citizen corporation", "9": "non-citizen co-owned", "": "blank"}
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")
STATEFP = counties.drop_duplicates("STUSPS").set_index("STUSPS").STATEFP
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)
print(f"FAA registry, 16 October 2026: {len(reg):,} aircraft, {len(ref):,} make-and-model codes")
FAA registry, 16 October 2026: 317,604 aircraft, 94,115 make-and-model codes

Example 1: by type of owner

v["owner"] = v["TYPE REGISTRANT"].map(REGISTRANT)
print(v.groupby("owner").age.agg(["count", "median"]).sort_values("median").to_string())
                         count  median
owner                                 
non-citizen co-owned        14     5.0
government                3668    20.0
non-citizen corporation    980    20.0
corporation              45788    29.0
LLC                      68921    30.0
co-owned                 22015    53.0
individual               99457    53.0
partnership               2411    58.0

Individuals' and co-owners' aircraft have a median age of 53, LLCs' 30 and corporations' 29: newer aircraft are more often owned through a company.

Example 2: by state

s = v[v.COUNTRY == "US"].groupby("STATE").age.agg(["count", "median"])
s = s[s["count"] >= 1000].sort_values("median")
print("youngest:", ", ".join(f"{k} {m:.0f}" for k, m in s["median"].head(4).items()))
print("oldest:", ", ".join(f"{k} {m:.0f}" for k, m in s["median"].tail(4).items()))
youngest: UT 18, DE 26, LA 36, IL 36
oldest: ME 51, MN 51, WA 51, AK 59
Map of lower-48 states shaded by median age of registered aircraft, youngest in Utah and Delaware, oldest across the northern states. Map of lower-48 states shaded by median age of registered aircraft, youngest in Utah and Delaware, oldest across the northern states.
Median age of registered aircraft by owner's state.

Utah's registered aircraft have the youngest median age, 18 years, then Delaware's 26; both states hold many company-registered aircraft at trust and agent addresses. Alaska's are the oldest, 59 years, followed by Maine, Minnesota and Washington at 51.