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rent burden

source /home/coolhand/html/datavis/data_trove/cache/rent_burden.parquet 3,222 rows 5 columns profiled 2026-05-01 raw JSON static .html .ipynb Report Notebook

Reading

dataset summary · high confidence anthropic:claude-opus-4-7

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

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
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
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
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

Schema

5 columns
Alerts
fips numeric 0.0% 3,222
county_name text 0.0% 3,222
near_unique
total_renters numeric 0.0% 2,709
high_skew outliers
pct_rent_burdened_30plus numeric 0.0% 2,146
pct_rent_burdened_50plus numeric 0.0% 1,769

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. high · anthropic:claude-opus-4-7
n
3,222
nulls
0 (0.0%)
unique
3,222
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

county_name

text identifier near_unique
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. high · anthropic:claude-opus-4-7
n
3,222
nulls
0 (0.0%)
unique
3,222
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

total_renters

numeric feature high_skew outliers
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. high · anthropic:claude-opus-4-7
n
3,222
nulls
0 (0.0%)
unique
2,709
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

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. high · anthropic:claude-opus-4-7
n
3,222
nulls
0 (0.0%)
unique
2,146
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

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. high · anthropic:claude-opus-4-7
n
3,222
nulls
0 (0.0%)
unique
1,769
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