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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 1 new columns ({'code'}) and 2 missing columns ({'city', 'population'}).
This happened while the csv dataset builder was generating data using
hf://datasets/crawlora-net/housing-affordability/data/by-state.csv (at revision d77f4f4087d13893faf2795cf5f81357872bcfa8), ['hf://datasets/crawlora-net/housing-affordability@d77f4f4087d13893faf2795cf5f81357872bcfa8/data/by-city.csv', 'hf://datasets/crawlora-net/housing-affordability@d77f4f4087d13893faf2795cf5f81357872bcfa8/data/by-state.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1837, in _prepare_split_single
writer.write_table(table)
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
pa_table = table_cast(pa_table, self._schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
state: string
code: string
medianListPrice: int64
medianIncome: int64
ratio: double
salaryNeeded: int64
gap: int64
yoySalePerSqft: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1207
to
{'city': Value('string'), 'state': Value('string'), 'population': Value('int64'), 'medianListPrice': Value('int64'), 'medianIncome': Value('int64'), 'ratio': Value('float64'), 'salaryNeeded': Value('int64'), 'gap': Value('int64'), 'yoySalePerSqft': Value('string')}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1839, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
...<4 lines>...
)
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 1 new columns ({'code'}) and 2 missing columns ({'city', 'population'}).
This happened while the csv dataset builder was generating data using
hf://datasets/crawlora-net/housing-affordability/data/by-state.csv (at revision d77f4f4087d13893faf2795cf5f81357872bcfa8), ['hf://datasets/crawlora-net/housing-affordability@d77f4f4087d13893faf2795cf5f81357872bcfa8/data/by-city.csv', 'hf://datasets/crawlora-net/housing-affordability@d77f4f4087d13893faf2795cf5f81357872bcfa8/data/by-state.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
city string | state string | population int64 | medianListPrice int64 | medianIncome int64 | ratio float64 | salaryNeeded int64 | gap int64 | yoySalePerSqft string |
|---|---|---|---|---|---|---|---|---|
Los Angeles | CA | 3,878,718 | 1,150,000 | 82,263 | 13.98 | 310,563 | 228,300 | -2.7% |
New York | NY | 8,478,072 | 938,000 | 81,228 | 11.55 | 253,312 | 172,084 | -4.7% |
Irvine | CA | 318,693 | 1,650,000 | 145,731 | 11.32 | 445,591 | 299,860 | -5.2% |
Miami | FL | 487,006 | 690,000 | 66,337 | 10.4 | 186,338 | 120,001 | -9.41% |
Newark | NJ | 317,303 | 600,000 | 58,490 | 10.26 | 162,033 | 103,543 | -0.19% |
Long Beach | CA | 450,917 | 867,000 | 91,318 | 9.49 | 234,138 | 142,820 | -6.5% |
Anaheim | CA | 344,579 | 956,000 | 101,145 | 9.45 | 258,173 | 157,028 | -1.0% |
Boston | MA | 673,822 | 899,000 | 97,791 | 9.19 | 242,779 | 144,988 | +1.3% |
Scottsdale | AZ | 246,183 | 943,000 | 104,893 | 8.99 | 254,662 | 149,769 | +3.8% |
San Francisco | CA | 827,526 | 1,250,000 | 139,801 | 8.94 | 337,569 | 197,768 | +16.8% |
Santa Ana | CA | 316,188 | 850,000 | 95,118 | 8.94 | 229,547 | 134,429 | +1.9% |
Hialeah | FL | 235,384 | 499,000 | 57,151 | 8.73 | 134,757 | 77,606 | +2.3% |
San Jose | CA | 997,395 | 1,290,000 | 148,226 | 8.7 | 348,371 | 200,145 | +0.7% |
San Diego | CA | 1,404,461 | 934,000 | 111,032 | 8.41 | 252,231 | 141,199 | -1.1% |
Chula Vista | CA | 278,548 | 815,000 | 105,101 | 7.75 | 220,095 | 114,994 | -5.2% |
Riverside | CA | 323,792 | 675,000 | 90,004 | 7.5 | 182,287 | 92,283 | -1.5% |
Jersey City | NJ | 302,822 | 752,000 | 100,751 | 7.46 | 203,081 | 102,330 | -0.17% |
Reno | NV | 281,684 | 620,000 | 85,605 | 7.24 | 167,434 | 81,829 | +0.9% |
Boise | ID | 237,959 | 575,000 | 81,102 | 7.09 | 155,282 | 74,180 | +3.1% |
Oakland | CA | 443,575 | 697,000 | 102,235 | 6.82 | 188,228 | 85,993 | +2.0% |
Honolulu | HI | 344,977 | 588,000 | 86,169 | 6.82 | 158,792 | 72,623 | +13.4% |
Seattle | WA | 780,992 | 790,000 | 118,745 | 6.65 | 213,343 | 94,598 | -5.0% |
Nashville | TN | 704,965 | 525,000 | 80,090 | 6.56 | 141,779 | 61,689 | +1.5% |
St Petersburg | FL | 267,073 | 470,000 | 73,048 | 6.43 | 126,926 | 53,878 | +1.1% |
Richmond | VA | 233,655 | 400,000 | 63,390 | 6.31 | 108,022 | 44,632 | +6.1% |
Las Vegas | NV | 678,924 | 477,000 | 78,556 | 6.07 | 128,816 | 50,260 | -0.38% |
Glendale | AZ | 258,121 | 459,000 | 75,711 | 6.06 | 123,955 | 48,244 | -0.42% |
Dallas | TX | 1,326,093 | 445,000 | 74,323 | 5.99 | 120,174 | 45,851 | -3.0% |
Tampa | FL | 414,575 | 500,000 | 84,114 | 5.94 | 135,028 | 50,914 | +8.2% |
Denver | CO | 729,019 | 549,000 | 92,504 | 5.93 | 148,260 | 55,756 | -1.6% |
Austin | TX | 993,771 | 535,000 | 90,430 | 5.92 | 144,479 | 54,049 | -1.5% |
Stockton | CA | 324,980 | 465,000 | 78,627 | 5.91 | 125,576 | 46,949 | -1.3% |
Sacramento | CA | 535,787 | 525,000 | 91,387 | 5.74 | 141,779 | 50,392 | +0.0% |
Fresno | CA | 550,091 | 425,000 | 74,491 | 5.71 | 114,773 | 40,282 | +1.6% |
Madison | WI | 285,318 | 450,000 | 79,254 | 5.68 | 121,525 | 42,271 | +2.5% |
Phoenix | AZ | 1,673,122 | 482,000 | 85,246 | 5.65 | 130,167 | 44,921 | -0.89% |
Mesa | AZ | 517,142 | 479,000 | 85,580 | 5.6 | 129,356 | 43,776 | -0.76% |
Lexington | KY | 329,437 | 390,000 | 69,989 | 5.57 | 105,321 | 35,332 | +1.0% |
Colorado Springs | CO | 493,540 | 465,000 | 83,672 | 5.56 | 125,576 | 41,904 | +1.4% |
Portland | OR | 636,958 | 508,000 | 91,478 | 5.55 | 137,188 | 45,710 | +0.6% |
Henderson | NV | 350,020 | 530,000 | 95,415 | 5.55 | 143,129 | 47,714 | -1.9% |
New Orleans | LA | 362,701 | 325,000 | 58,821 | 5.53 | 87,768 | 28,947 | +0.0% |
North Las Vegas | NV | 294,042 | 429,000 | 78,969 | 5.43 | 115,854 | 36,885 | -4.0% |
Arlington | VA | 239,807 | 725,000 | 133,582 | 5.43 | 195,790 | 62,208 | +7.1% |
Washington DC | DC | 702,250 | 593,000 | 109,707 | 5.41 | 160,143 | 50,436 | +0.8% |
Tucson | AZ | 554,011 | 325,000 | 60,483 | 5.37 | 87,768 | 27,285 | -3.7% |
Albuquerque | NM | 560,333 | 380,000 | 71,494 | 5.32 | 102,621 | 31,127 | +0.9% |
Chesapeake | VA | 254,997 | 475,000 | 89,265 | 5.32 | 128,276 | 39,011 | +3.7% |
Houston | TX | 2,387,910 | 340,000 | 64,361 | 5.28 | 91,819 | 27,458 | -3.2% |
Bakersfield | CA | 417,461 | 432,000 | 82,093 | 5.26 | 116,664 | 34,571 | +2.6% |
Durham | NC | 301,877 | 430,000 | 82,916 | 5.19 | 116,124 | 33,208 | +0.9% |
Winston Salem | NC | 255,782 | 300,000 | 57,758 | 5.19 | 81,017 | 23,259 | -4.0% |
Cincinnati | OH | 314,914 | 295,000 | 56,910 | 5.18 | 79,666 | 22,756 | -1.1% |
Charlotte | NC | 943,474 | 435,000 | 86,416 | 5.03 | 117,474 | 31,058 | -0.4% |
Chandler | AZ | 281,243 | 555,000 | 110,284 | 5.03 | 149,881 | 39,597 | -2.4% |
Port St Lucie | FL | 258,578 | 434,000 | 86,241 | 5.03 | 117,204 | 30,963 | -2.7% |
Irving | TX | 258,052 | 425,000 | 84,849 | 5.01 | 114,773 | 29,924 | -3.7% |
Raleigh | NC | 499,637 | 425,000 | 85,060 | 5 | 114,773 | 29,713 | -2.8% |
Virginia Beach | VA | 454,808 | 473,000 | 94,579 | 5 | 127,736 | 33,157 | +3.4% |
Lincoln | NE | 300,626 | 360,000 | 72,008 | 5 | 97,220 | 25,212 | +2.4% |
Frisco | TX | 235,221 | 719,000 | 145,444 | 4.94 | 194,170 | 48,726 | -4.8% |
Chicago | IL | 2,721,326 | 395,000 | 80,613 | 4.9 | 106,672 | 26,059 | +9.6% |
Gilbert | AZ | 288,797 | 610,000 | 124,968 | 4.88 | 164,734 | 39,766 | +1.1% |
El Paso | TX | 681,724 | 286,000 | 59,932 | 4.77 | 77,236 | 17,304 | +2.6% |
Plano | TX | 292,615 | 550,000 | 115,901 | 4.75 | 148,530 | 32,629 | +1.6% |
Greensboro | NC | 307,372 | 300,000 | 63,516 | 4.72 | 81,017 | 17,501 | -2.8% |
Arlington | TX | 403,657 | 350,000 | 74,388 | 4.71 | 94,519 | 20,131 | -3.2% |
Enterprise | NV | 250,461 | 520,000 | 111,128 | 4.68 | 140,429 | 29,301 | -6.2% |
Aurora | CO | 402,452 | 435,000 | 93,837 | 4.64 | 117,474 | 23,637 | +2.4% |
Philadelphia | PA | 1,573,916 | 279,000 | 60,521 | 4.61 | 75,345 | 14,824 | +4.1% |
Orlando | FL | 334,871 | 356,000 | 77,597 | 4.59 | 96,140 | 18,543 | -2.0% |
Tulsa | OK | 413,652 | 278,000 | 60,930 | 4.56 | 75,075 | 14,145 | +0.7% |
Anchorage | AK | 289,600 | 475,000 | 105,356 | 4.51 | 128,276 | 22,920 | +7.4% |
Minneapolis | MN | 428,572 | 345,000 | 77,732 | 4.44 | 93,169 | 15,437 | +2.1% |
Columbus | OH | 931,551 | 295,000 | 67,084 | 4.4 | 79,666 | 12,582 | +2.1% |
Oklahoma City | OK | 713,014 | 305,000 | 70,040 | 4.35 | 82,367 | 12,327 | +0.0% |
Garland | TX | 250,571 | 325,000 | 75,797 | 4.29 | 87,768 | 11,971 | -4.3% |
Atlanta | GA | 520,066 | 375,000 | 88,165 | 4.25 | 101,271 | 13,106 | +2.1% |
Corpus Christi | TX | 317,314 | 284,000 | 66,967 | 4.24 | 76,696 | 9,729 | -1.2% |
Fort Worth | TX | 1,014,376 | 349,000 | 82,503 | 4.23 | 94,249 | 11,746 | -1.1% |
St Paul | MN | 307,484 | 295,000 | 70,182 | 4.2 | 79,666 | 9,484 | +2.6% |
Louisville | KY | 640,793 | 280,000 | 67,251 | 4.16 | 75,615 | 8,364 | +1.8% |
Laredo | TX | 261,408 | 255,000 | 61,519 | 4.15 | 68,864 | 7,345 | null |
Jacksonville | FL | 1,009,831 | 300,000 | 72,389 | 4.14 | 81,017 | 8,628 | +2.7% |
Kansas City | MO | 516,045 | 289,000 | 69,958 | 4.13 | 78,046 | 8,088 | +2.0% |
Pittsburgh | PA | 307,670 | 275,000 | 66,954 | 4.11 | 74,265 | 7,311 | +4.1% |
San Antonio | TX | 1,526,621 | 268,000 | 66,176 | 4.05 | 72,375 | 6,199 | -0.94% |
Milwaukee | WI | 563,512 | 230,000 | 56,792 | 4.05 | 62,113 | 5,321 | +5.5% |
St Louis | MO | 279,695 | 215,000 | 53,374 | 4.03 | 58,062 | 4,688 | +6.8% |
Wichita | KS | 400,993 | 265,000 | 65,855 | 4.02 | 71,565 | 5,710 | +4.8% |
Omaha | NE | 489,263 | 285,000 | 71,640 | 3.98 | 76,966 | 5,326 | +4.5% |
Lubbock | TX | 272,085 | 245,000 | 62,360 | 3.93 | 66,163 | 3,803 | +32.6% |
Indianapolis | IN | 890,315 | 260,000 | 66,900 | 3.89 | 70,214 | 3,314 | +1.4% |
Fort Wayne | IN | 273,425 | 230,000 | 61,436 | 3.74 | 62,113 | 677 | +4.4% |
Buffalo | NY | 276,618 | 195,000 | 52,211 | 3.73 | 52,661 | 450 | +1.6% |
Cleveland | OH | 365,391 | 149,000 | 43,383 | 3.43 | 40,238 | -3,145 | +22.8% |
Memphis | TN | 610,936 | 176,000 | 52,679 | 3.34 | 47,530 | -5,149 | -6.7% |
Baltimore | MD | 568,271 | 215,000 | 64,778 | 3.32 | 58,062 | -6,716 | +8.9% |
Detroit | MI | 645,702 | 104,000 | 39,209 | 2.65 | 28,086 | -11,123 | +3.8% |
Toledo | OH | 265,651 | 125,000 | 50,562 | 2.47 | 33,757 | -16,805 | +1.8% |
End of preview.
US Housing Affordability Index 2026 — open data
Ownership affordability for all 50 US states + DC and the 100 most-populous US cities, July 2026 snapshot: what the median home lists for, what the median household earns, how many years of income a home costs, and the salary you would need to buy it.
Published by Crawlora. Full write-up, maps and charts: Unaffordable America: The Salary You Need to Buy a Home, Mapped (2026)
Headline findings (July 2026)
- The median US home ($414,900, NAR closed-sale median) costs 5.08× the median household income ($81,604, ACS 2024) — against the classic 3× rule of thumb.
- No US state is at or below 3× anymore. The closest is Iowa (3.64×); 28 of 51 are at 5× or worse.
- Buying the median US home at 6.49% (30-yr fixed, 20% down, 28% DTI, taxes + insurance) takes about $112,046 a year — ~$30,000 more than the median household earns.
- Montana is the least affordable state at 7.96× ($600K median listing vs $75,340 income), ahead of California (7.49×), New York (7.47×) and Hawaii (7.44×).
- Among the 100 largest cities, the median household can afford the median home in exactly five: Detroit, Toledo, Cleveland, Memphis, and Baltimore. Los Angeles is the least affordable at 13.98×; Irvine, CA demands the highest salary ($445,591).
Files
| File | What it is |
|---|---|
data/by-state.csv |
One row per state + DC (51 rows) |
data/by-city.csv |
One row per city — the 100 most-populous US census places |
data/summary.json |
National benchmarks, full assumptions, coverage, and headline extremes |
CSV schema (both files)
| Column | Meaning |
|---|---|
state / city + state |
Geography (city rows carry the state code) |
population |
City rows only — ACS 2024 total population (B01003) |
medianListPrice |
Median listing price, USD (Redfin region median, July 11 2026) |
medianIncome |
Median household income, USD (Census ACS 2024 1-year, B19013) |
ratio |
medianListPrice / medianIncome |
salaryNeeded |
Gross annual income at which the monthly cost of the median home = 28% of pay |
gap |
salaryNeeded − medianIncome (negative = the median household clears the bar) |
yoySalePerSqft |
Redfin year-over-year change in median sale price per sq ft (as reported) |
Salary-needed assumptions: 20% down, 30-year fixed at 6.49% (Freddie Mac PMMS, week of 2026-07-09), property tax 1.0%/yr of home value, homeowners insurance 0.5%/yr, 28% front-end debt-to-income. Uniform national simplifications — see caveats.
Method
- Prices: median listing price per region from Redfin's public market data — one region-trends request per region (51 states, 100 cities) via Crawlora's structured web-data API, July 11, 2026.
- Incomes: US Census ACS 2024 1-year, table B19013 (median household income), matched by state and by census place; city population ranks from B01003. Census data is public domain.
- Join + metrics: ratio, salary-needed (assumptions above), and gap computed per geography; national anchor uses NAR's closed-sale median ($414,900).
Caveats (read before citing)
medianListPriceis an asking-price proxy, not a closed-sale median. List medians run above sale medians in cool markets and below in hot ones.- City limits ≠ metro areas. "San Jose 8.70×" is the city proper; metro-based trackers put the San Jose MSA near 11.7× because metro prices pair with a different income mix. Redfin's public region data has no metro level, so this dataset ranks cities and says so.
- ACS 2024 income vs July 2026 prices is a ~1.5-year vintage mismatch; ratios are slightly overstated everywhere.
- One-day snapshot (2026-07-11); thin markets move month to month.
- Property tax and insurance are uniform national assumptions; they understate NJ/TX/IL taxes, overstate HI, and ignore FL's insurance crisis.
License & citation
CC BY 4.0 — free to use with attribution. Cite as:
Crawlora, "US Housing Affordability Index 2026 (51 states + 100 cities)", July 2026. https://github.com/Crawlora-org/housing-affordability-data
Sources to credit alongside: Redfin (market data), US Census Bureau ACS 2024 (income, public domain), Freddie Mac PMMS (mortgage rate), NAR (national sale median).
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