saturn·

← all readings

rent burden

saturn notebook · generated 2026-05-01 Report Notebook

Overview

Source: /home/coolhand/html/datavis/data_trove/cache/rent_burden.parquet

Saturn profiled 3,222 rows across 5 columns. The stats below are deterministic and machine-readable; the prose is a language-model interpretation of those stats (opt-in, added after the fact, never sees raw rows).

[2]:
!pip install saturn-dissect
import subprocess
subprocess.run([
    "saturn", "analyze", "/home/coolhand/html/datavis/data_trove/cache/rent_burden.parquet",
    "--findings", "rent_burden.json",
    "--llm", "anthropic:claude-opus-4-7",
])

Summary confidence: high

This dataset contains 3,222 rows of U.S. county-level rent burden statistics, with each row identified by a county name and FIPS code and described by total renters and the share of renters paying 30%+ or 50%+ of income on rent. Total renters is extremely skewed (skew 15.8, max 1,810,929 vs. median 2,579.5), so a handful of large urban counties dominate the distribution and warrant separate treatment. Rent-burden percentages are more well-behaved: about 36.4% of renters per county are cost-burdened at the 30%+ threshold and 17.4% at the 50%+ threshold on average, both fairly symmetric. The most useful first look is comparing the two rent-burden distributions and isolating the outlier counties on total_renters.

citing: row_count · column_count · columns.total_renters.stats · columns.pct_rent_burdened_30plus.stats · columns.pct_rent_burdened_50plus.stats · columns.county_name.stats

Fig 1.
total_renters · Highly skewed: most counties are small but a few exceed a million renters — consider a log scale.
Show data table
bincount
28 – 4.53e+043019
4.53e+04 – 9.057e+04109
9.057e+04 – 1.358e+0538
1.358e+05 – 1.811e+0517
1.811e+05 – 2.264e+0511
2.264e+05 – 2.717e+059
2.717e+05 – 3.169e+055
3.169e+05 – 3.622e+050
3.622e+05 – 4.075e+052
4.075e+05 – 4.528e+052
4.528e+05 – 4.98e+053
4.98e+05 – 5.433e+051
5.433e+05 – 5.886e+051
5.886e+05 – 6.338e+051
6.338e+05 – 6.791e+050
6.791e+05 – 7.244e+051
7.244e+05 – 7.697e+051
7.697e+05 – 8.149e+050
8.149e+05 – 8.602e+050
8.602e+05 – 9.055e+051
9.055e+05 – 9.508e+050
9.508e+05 – 9.96e+050
9.96e+05 – 1.041e+060
1.041e+06 – 1.087e+060
1.087e+06 – 1.132e+060
1.132e+06 – 1.177e+060
1.177e+06 – 1.222e+060
1.222e+06 – 1.268e+060
1.268e+06 – 1.313e+060
1.313e+06 – 1.358e+060
1.358e+06 – 1.403e+060
1.403e+06 – 1.449e+060
1.449e+06 – 1.494e+060
1.494e+06 – 1.539e+060
1.539e+06 – 1.585e+060
1.585e+06 – 1.63e+060
1.63e+06 – 1.675e+060
1.675e+06 – 1.72e+060
1.72e+06 – 1.766e+060
1.766e+06 – 1.811e+061
Fig 2.
pct_rent_burdened_30plus · Roughly symmetric around a median of 37%, showing how widespread moderate rent burden is.
Show data table
bincount
0 – 1.6249
1.624 – 3.2485
3.248 – 4.8723
4.872 – 6.4965
6.496 – 8.129
8.12 – 9.74413
9.744 – 11.3711
11.37 – 12.9916
12.99 – 14.6226
14.62 – 16.2419
16.24 – 17.8635
17.86 – 19.4943
19.49 – 21.1152
21.11 – 22.7452
22.74 – 24.3673
24.36 – 25.9899
25.98 – 27.61109
27.61 – 29.23116
29.23 – 30.86132
30.86 – 32.48159
32.48 – 34.1189
34.1 – 35.73209
35.73 – 37.35227
37.35 – 38.98239
38.98 – 40.6205
40.6 – 42.22209
42.22 – 43.85210
43.85 – 45.47190
45.47 – 47.1131
47.1 – 48.72114
48.72 – 50.34118
50.34 – 51.9769
51.97 – 53.5951
53.59 – 55.2234
55.22 – 56.8424
56.84 – 58.466
58.46 – 60.093
60.09 – 61.712
61.71 – 63.343
63.34 – 64.963
Fig 3.
pct_rent_burdened_50plus · Severe rent burden centers near 17%, with a thinner right tail of hardest-hit counties.
Show data table
bincount
0 – 1.62442
1.624 – 3.24827
3.248 – 4.87234
4.872 – 6.49663
6.496 – 8.12102
8.12 – 9.744148
9.744 – 11.37163
11.37 – 12.99214
12.99 – 14.62242
14.62 – 16.24310
16.24 – 17.86315
17.86 – 19.49332
19.49 – 21.11335
21.11 – 22.74264
22.74 – 24.36219
24.36 – 25.98150
25.98 – 27.6199
27.61 – 29.2364
29.23 – 30.8639
30.86 – 32.4820
32.48 – 34.121
34.1 – 35.739
35.73 – 37.352
37.35 – 38.983
38.98 – 40.61
40.6 – 42.221
42.22 – 43.851
43.85 – 45.470
45.47 – 47.11
47.1 – 48.720
48.72 – 50.340
50.34 – 51.970
51.97 – 53.590
53.59 – 55.220
55.22 – 56.840
56.84 – 58.460
58.46 – 60.090
60.09 – 61.710
61.71 – 63.340
63.34 – 64.961
Fig 4.
fips · Even spread across FIPS ranges confirms broad national coverage with no obvious gaps.
Show data table
bincount
1001 – 278097
2780 – 455915
4559 – 6337133
6337 – 811659
8116 – 989514
9895 – 1.167e+044
1.167e+04 – 1.345e+04226
1.345e+04 – 1.523e+045
1.523e+04 – 1.701e+0449
1.701e+04 – 1.879e+04189
1.879e+04 – 2.057e+04204
2.057e+04 – 2.235e+04184
2.235e+04 – 2.413e+0439
2.413e+04 – 2.59e+0415
2.59e+04 – 2.768e+04170
2.768e+04 – 2.946e+04196
2.946e+04 – 3.124e+04150
3.124e+04 – 3.302e+0427
3.302e+04 – 3.48e+0421
3.48e+04 – 3.658e+0495
3.658e+04 – 3.836e+04153
3.836e+04 – 4.013e+04155
4.013e+04 – 4.191e+0446
4.191e+04 – 4.369e+0467
4.369e+04 – 4.547e+0451
4.547e+04 – 4.725e+04161
4.725e+04 – 4.903e+04268
4.903e+04 – 5.081e+0429
5.081e+04 – 5.259e+04133
5.259e+04 – 5.436e+0494
5.436e+04 – 5.614e+0495
5.614e+04 – 5.792e+040
5.792e+04 – 5.97e+040
5.97e+04 – 6.148e+040
6.148e+04 – 6.326e+040
6.326e+04 – 6.504e+040
6.504e+04 – 6.682e+040
6.682e+04 – 6.86e+040
6.86e+04 – 7.037e+040
7.037e+04 – 7.215e+0478
Fig 5.
Per-column null rate across the corpus. Columns are ordered by input position.
Show data table
columnkindnull %
fipsnumeric0.0%
county_nametext0.0%
total_rentersnumeric0.0%
pct_rent_burdened_30plusnumeric0.0%
pct_rent_burdened_50plusnumeric0.0%
Fig 6.
Pearson correlation across numeric columns (sampled, bounded).
Show data table
fipstotal_renterspct_rent_burdened_30pluspct_rent_burdened_50plus
fips+1.00-0.06-0.16-0.10
total_renters-0.06+1.00+0.23+0.20
pct_rent_burdened_30plus-0.16+0.23+1.00+0.82
pct_rent_burdened_50plus-0.10+0.20+0.82+1.00

fips numeric identifier

This is the FIPS county/state code, used as a unique geographic identifier — every one of the 3,222 rows has a distinct value with no nulls. The range from 1001 to 72153 and the low skew (0.157) reflect the standard FIPS numbering across U.S. states and territories rather than a meaningful numeric distribution. Treating these as numbers (mean 31,377, std 16,299) is misleading; they are categorical codes.

Treatment: Cast to string and use as a join key on geographic reference tables; do not model as numeric.

anthropic:claude-opus-4-7 · confidence high
Out[12]:

saturn.columns["fips"].stats

statvalue
n3,222
nulls0 (0.0%)
unique3,222
min 1,001
max 72,153
mean 3.138e+04
median 30,022
std 1.63e+04
q1 1.903e+04
q3 4.61e+04
iqr 27,075
skew 0.1574
kurtosis -0.6314
n_outliers 0
outlier_rate 0
zero_rate 0
Fig 7.
Distribution of fips. Vertical dash marks the median.
Show data table
bincount
1001 – 278097
2780 – 455915
4559 – 6337133
6337 – 811659
8116 – 989514
9895 – 1.167e+044
1.167e+04 – 1.345e+04226
1.345e+04 – 1.523e+045
1.523e+04 – 1.701e+0449
1.701e+04 – 1.879e+04189
1.879e+04 – 2.057e+04204
2.057e+04 – 2.235e+04184
2.235e+04 – 2.413e+0439
2.413e+04 – 2.59e+0415
2.59e+04 – 2.768e+04170
2.768e+04 – 2.946e+04196
2.946e+04 – 3.124e+04150
3.124e+04 – 3.302e+0427
3.302e+04 – 3.48e+0421
3.48e+04 – 3.658e+0495
3.658e+04 – 3.836e+04153
3.836e+04 – 4.013e+04155
4.013e+04 – 4.191e+0446
4.191e+04 – 4.369e+0467
4.369e+04 – 4.547e+0451
4.547e+04 – 4.725e+04161
4.725e+04 – 4.903e+04268
4.903e+04 – 5.081e+0429
5.081e+04 – 5.259e+04133
5.259e+04 – 5.436e+0494
5.436e+04 – 5.614e+0495
5.614e+04 – 5.792e+040
5.792e+04 – 5.97e+040
5.97e+04 – 6.148e+040
6.148e+04 – 6.326e+040
6.326e+04 – 6.504e+040
6.504e+04 – 6.682e+040
6.682e+04 – 6.86e+040
6.86e+04 – 7.037e+040
7.037e+04 – 7.215e+0478

county_name text identifier

This column holds fully-qualified US county names (e.g., 'X County, State'), with 3222 rows all unique and zero nulls. The token 'county,' appears 2999 times, suggesting ~223 entries don't follow that exact pattern — likely Louisiana parishes, Alaska boroughs, or independent cities worth checking. State frequencies match expectations, with Texas (256) leading.

Treatment: Split into county and state fields and left-join on FIPS rather than this string.

anthropic:claude-opus-4-7 · confidence high
Out[15]:

saturn.columns["county_name"].stats

statvalue
n3,222
nulls0 (0.0%)
unique3,222
len_min 16
len_max 59
len_mean 24.32
len_median 24
len_p95 31
word_mean 3.248
word_median 3
n_empty 0
n_duplicates 0
duplicate_rate 0
vocab_size 1,990
readability_flesch_mean 10.28
emoji_rate 0
url_rate 0
one_word_rate 0
allcaps_rate 0
boilerplate_rate 0
alert: near_unique100.0% of rows are unique strings
Fig 8.
Character-length distribution for county_name.
Show data table
charscount
16 – 1726
17 – 1872
18 – 19121
19 – 20190
20 – 21264
21 – 22407
22 – 24420
24 – 25363
25 – 26320
26 – 27240
27 – 28231
28 – 29152
29 – 30139
30 – 31165
31 – 3241
32 – 3328
33 – 3416
34 – 3510
35 – 365
36 – 380
38 – 391
39 – 401
40 – 410
41 – 421
42 – 431
43 – 440
44 – 452
45 – 460
46 – 471
47 – 481
48 – 490
49 – 500
50 – 510
51 – 530
53 – 542
54 – 551
55 – 560
56 – 570
57 – 580
58 – 591

total_renters numeric feature

This is a numeric count of renters per record, ranging from 28 to 1,810,929 with a median of 2,579.5 — likely an aggregate count at some geographic or entity level. The distribution is extremely right-skewed (skew 15.82, kurtosis 398.15) with the mean (13,851) over five times the median, and 449 outliers (13.9%) inflate the std to 55,351. No nulls or zeros, and 2,709 unique values across 3,222 rows suggest minor repetition but largely distinct totals.

Treatment: log-transform before modelling to tame the heavy right tail.

anthropic:claude-opus-4-7 · confidence high
Out[18]:

saturn.columns["total_renters"].stats

statvalue
n3,222
nulls0 (0.0%)
unique2,709
min 28
max 1.811e+06
mean 1.385e+04
median 2580
std 5.535e+04
q1 1004
q3 7396
iqr 6,392
skew 15.82
kurtosis 398.2
n_outliers 449
outlier_rate 0.1394
zero_rate 0
alert: high_skewskew=+15.82
alert: outliers13.9% rows beyond 1.5 IQR
Fig 9.
Distribution of total_renters. Vertical dash marks the median.
Show data table
bincount
28 – 4.53e+043019
4.53e+04 – 9.057e+04109
9.057e+04 – 1.358e+0538
1.358e+05 – 1.811e+0517
1.811e+05 – 2.264e+0511
2.264e+05 – 2.717e+059
2.717e+05 – 3.169e+055
3.169e+05 – 3.622e+050
3.622e+05 – 4.075e+052
4.075e+05 – 4.528e+052
4.528e+05 – 4.98e+053
4.98e+05 – 5.433e+051
5.433e+05 – 5.886e+051
5.886e+05 – 6.338e+051
6.338e+05 – 6.791e+050
6.791e+05 – 7.244e+051
7.244e+05 – 7.697e+051
7.697e+05 – 8.149e+050
8.149e+05 – 8.602e+050
8.602e+05 – 9.055e+051
9.055e+05 – 9.508e+050
9.508e+05 – 9.96e+050
9.96e+05 – 1.041e+060
1.041e+06 – 1.087e+060
1.087e+06 – 1.132e+060
1.132e+06 – 1.177e+060
1.177e+06 – 1.222e+060
1.222e+06 – 1.268e+060
1.268e+06 – 1.313e+060
1.313e+06 – 1.358e+060
1.358e+06 – 1.403e+060
1.403e+06 – 1.449e+060
1.449e+06 – 1.494e+060
1.494e+06 – 1.539e+060
1.539e+06 – 1.585e+060
1.585e+06 – 1.63e+060
1.63e+06 – 1.675e+060
1.675e+06 – 1.72e+060
1.72e+06 – 1.766e+060
1.766e+06 – 1.811e+061

pct_rent_burdened_30plus numeric feature

This column captures the percentage of households spending 30%+ of income on rent, reported per row across 3,222 records with no nulls. Values span 0 to 64.96 with a median of 37.36 and IQR of 30.67-43.48, mildly left-skewed (-0.57) and tightly clustered (std 10.0). About 0.25% are exact zeros and 58 rows (1.8%) flag as outliers, but the distribution is otherwise well-behaved and ready to use as-is.

Treatment: Use directly as a continuous feature; no transform needed given the near-symmetric, bounded distribution.

anthropic:claude-opus-4-7 · confidence high
Out[21]:

saturn.columns["pct_rent_burdened_30plus"].stats

statvalue
n3,222
nulls0 (0.0%)
unique2,146
min 0
max 64.96
mean 36.44
median 37.36
std 10.01
q1 30.67
q3 43.48
iqr 12.81
skew -0.5673
kurtosis 0.5032
n_outliers 58
outlier_rate 0.018
zero_rate 0.002483
Fig 10.
Distribution of pct_rent_burdened_30plus. Vertical dash marks the median.
Show data table
bincount
0 – 1.6249
1.624 – 3.2485
3.248 – 4.8723
4.872 – 6.4965
6.496 – 8.129
8.12 – 9.74413
9.744 – 11.3711
11.37 – 12.9916
12.99 – 14.6226
14.62 – 16.2419
16.24 – 17.8635
17.86 – 19.4943
19.49 – 21.1152
21.11 – 22.7452
22.74 – 24.3673
24.36 – 25.9899
25.98 – 27.61109
27.61 – 29.23116
29.23 – 30.86132
30.86 – 32.48159
32.48 – 34.1189
34.1 – 35.73209
35.73 – 37.35227
37.35 – 38.98239
38.98 – 40.6205
40.6 – 42.22209
42.22 – 43.85210
43.85 – 45.47190
45.47 – 47.1131
47.1 – 48.72114
48.72 – 50.34118
50.34 – 51.9769
51.97 – 53.5951
53.59 – 55.2234
55.22 – 56.8424
56.84 – 58.466
58.46 – 60.093
60.09 – 61.712
61.71 – 63.343
63.34 – 64.963

pct_rent_burdened_50plus numeric feature

Likely the percentage of households spending 50%+ of income on rent at some geographic unit (e.g., county). Values span 0 to 64.96 with mean 17.35 and median 17.62, a near-symmetric distribution (skew 0.05) and modest tails (kurtosis 0.98). Only 0.93% are zero and 1.46% flagged as outliers, so the signal is clean and ready to use.

Treatment: Use as-is as a continuous feature; no transform needed given symmetry.

anthropic:claude-opus-4-7 · confidence high
Out[24]:

saturn.columns["pct_rent_burdened_50plus"].stats

statvalue
n3,222
nulls0 (0.0%)
unique1,769
min 0
max 64.96
mean 17.35
median 17.62
std 6.577
q1 13.07
q3 21.63
iqr 8.557
skew 0.05436
kurtosis 0.9823
n_outliers 47
outlier_rate 0.01459
zero_rate 0.009311
Fig 11.
Distribution of pct_rent_burdened_50plus. Vertical dash marks the median.
Show data table
bincount
0 – 1.62442
1.624 – 3.24827
3.248 – 4.87234
4.872 – 6.49663
6.496 – 8.12102
8.12 – 9.744148
9.744 – 11.37163
11.37 – 12.99214
12.99 – 14.62242
14.62 – 16.24310
16.24 – 17.86315
17.86 – 19.49332
19.49 – 21.11335
21.11 – 22.74264
22.74 – 24.36219
24.36 – 25.98150
25.98 – 27.6199
27.61 – 29.2364
29.23 – 30.8639
30.86 – 32.4820
32.48 – 34.121
34.1 – 35.739
35.73 – 37.352
37.35 – 38.983
38.98 – 40.61
40.6 – 42.221
42.22 – 43.851
43.85 – 45.470
45.47 – 47.11
47.1 – 48.720
48.72 – 50.340
50.34 – 51.970
51.97 – 53.590
53.59 – 55.220
55.22 – 56.840
56.84 – 58.460
58.46 – 60.090
60.09 – 61.710
61.71 – 63.340
63.34 – 64.961

How to cite

click to copy

BibTeX
@misc{saturn-rent-burden-2026,
  author       = {Steuber, Luke},
  title        = {Saturn reading: rent burden},
  year         ={2026},
  howpublished = {\url{https://dr.eamer.dev/saturn/view/rent_burden}},
  note         = {Profiled with saturn-dissect v0.2.0, prompt saturn-insight-v2, model anthropic:claude-opus-4-7},
}
APA
Steuber, L. (2026). Saturn reading: rent burden. Source: /home/coolhand/html/datavis/data_trove/cache/rent_burden.parquet. Profiled with saturn-dissect v0.2.0 (saturn-insight-v2, anthropic:claude-opus-4-7). Retrieved from https://dr.eamer.dev/saturn/view/rent_burden