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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 7 new columns ({'type', 'living_space_sqm', 'price_pln', 'rooms', 'marketing_type', 'is_private_owner', 'price_per_sqm'}) and 5 missing columns ({'median_rooms', 'listings', 'median_living_space_sqm', 'median_price_per_sqm', 'median_price_pln'}).
This happened while the csv dataset builder was generating data using
hf://datasets/Anatolii2026/poland-flat-prices-by-city-otodom-2026/otodom-pl-schema-sample.csv (at revision 72ff81817f8ca7bfbe6b8972ef98f1b35f26898b), ['hf://datasets/Anatolii2026/poland-flat-prices-by-city-otodom-2026@72ff81817f8ca7bfbe6b8972ef98f1b35f26898b/otodom-pl-market-summary-2026-09-01.csv', 'hf://datasets/Anatolii2026/poland-flat-prices-by-city-otodom-2026@72ff81817f8ca7bfbe6b8972ef98f1b35f26898b/otodom-pl-schema-sample.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 1848, 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 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
city: string
province: string
type: string
marketing_type: string
price_pln: double
living_space_sqm: double
price_per_sqm: int64
rooms: int64
is_private_owner: bool
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1347
to
{'city': Value('string'), 'province': Value('string'), 'listings': Value('int64'), 'median_price_pln': Value('int64'), 'median_price_per_sqm': Value('int64'), 'median_living_space_sqm': Value('int64'), 'median_rooms': Value('int64')}
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 1694, 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 1850, 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 7 new columns ({'type', 'living_space_sqm', 'price_pln', 'rooms', 'marketing_type', 'is_private_owner', 'price_per_sqm'}) and 5 missing columns ({'median_rooms', 'listings', 'median_living_space_sqm', 'median_price_per_sqm', 'median_price_pln'}).
This happened while the csv dataset builder was generating data using
hf://datasets/Anatolii2026/poland-flat-prices-by-city-otodom-2026/otodom-pl-schema-sample.csv (at revision 72ff81817f8ca7bfbe6b8972ef98f1b35f26898b), ['hf://datasets/Anatolii2026/poland-flat-prices-by-city-otodom-2026@72ff81817f8ca7bfbe6b8972ef98f1b35f26898b/otodom-pl-market-summary-2026-09-01.csv', 'hf://datasets/Anatolii2026/poland-flat-prices-by-city-otodom-2026@72ff81817f8ca7bfbe6b8972ef98f1b35f26898b/otodom-pl-schema-sample.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 | province string | listings int64 | median_price_pln int64 | median_price_per_sqm int64 | median_living_space_sqm int64 | median_rooms int64 |
|---|---|---|---|---|---|---|
Warszawa | mazowieckie | 221 | 949,000 | 18,374 | 53 | 3 |
Kraków | małopolskie | 135 | 839,000 | 16,250 | 57 | 3 |
Wrocław | dolnośląskie | 98 | 696,468 | 13,733 | 51 | 3 |
Gdańsk | pomorskie | 79 | 769,000 | 14,907 | 48 | 2 |
Poznań | wielkopolskie | 55 | 629,000 | 13,556 | 48 | 2 |
Rzeszów | podkarpackie | 43 | 539,000 | 10,980 | 48 | 2 |
Katowice | śląskie | 42 | 571,623 | 10,766 | 51 | 2 |
Łódź | łódzkie | 39 | 419,000 | 10,000 | 42 | 2 |
Szczecin | zachodniopomorskie | 28 | 525,104 | 10,157 | 54 | 3 |
Lublin | lubelskie | 28 | 580,750 | 10,513 | 58 | 3 |
Toruń | kujawsko-pomorskie | 20 | 495,000 | 10,317 | 49 | 3 |
Bielsko-Biała | śląskie | 16 | 482,500 | 9,594 | 55 | 3 |
Bydgoszcz | kujawsko-pomorskie | 16 | 520,319 | 8,590 | 52 | 2 |
Gliwice | śląskie | 15 | 467,847 | 8,379 | 56 | 3 |
Sosnowiec | śląskie | 14 | 346,079 | 8,117 | 46 | 2 |
Gdynia | pomorskie | 14 | 658,875 | 13,701 | 54 | 3 |
Rumia | pomorskie | 10 | 612,544 | 10,534 | 51 | 3 |
Częstochowa | śląskie | 10 | 281,500 | 6,522 | 44 | 2 |
Radom | mazowieckie | 9 | 499,000 | 8,999 | 62 | 3 |
Łomża | podlaskie | 9 | 481,791 | 9,024 | 53 | 3 |
Bytom | śląskie | 9 | 240,000 | 5,549 | 48 | 2 |
Jelenia Góra | dolnośląskie | 8 | 364,500 | 6,731 | 50 | 2 |
Kołobrzeg | zachodniopomorskie | 8 | 528,175 | 13,287 | 43 | 2 |
Marki | mazowieckie | 8 | 609,256 | 11,938 | 46 | 3 |
Olsztyn | warmińsko-mazurskie | 8 | 629,500 | 13,462 | 48 | 3 |
Białystok | podlaskie | 8 | 484,000 | 10,354 | 48 | 3 |
Opole | opolskie | 8 | 708,500 | 11,444 | 60 | 3 |
Piaseczno | mazowieckie | 7 | 765,000 | 13,077 | 57 | 3 |
Kielce | świętokrzyskie | 7 | 379,999 | 9,905 | 38 | 2 |
Koszalin | zachodniopomorskie | 7 | 295,000 | 7,723 | 47 | 2 |
Słupsk | pomorskie | 7 | 399,000 | 9,765 | 43 | 2 |
Ruda Śląska | śląskie | 7 | 290,000 | 6,897 | 40 | 2 |
Chorzów | śląskie | 7 | 466,833 | 9,000 | 51 | 3 |
Ostrów Mazowiecka | mazowieckie | 7 | 440,200 | 7,689 | 55 | 3 |
Stargard | zachodniopomorskie | 6 | 437,025 | 7,473 | 63 | 3 |
Zabrze | śląskie | 6 | 259,250 | 5,612 | 55 | 2 |
Zielona Góra | lubuskie | 6 | 593,500 | 10,951 | 56 | 3 |
Polkowice | dolnośląskie | 6 | 555,000 | 8,352 | 66 | 3 |
Siewierz | śląskie | 5 | 383,525 | 6,897 | 54 | 4 |
Elbląg | warmińsko-mazurskie | 5 | 285,000 | 7,766 | 44 | 2 |
Kalisz | wielkopolskie | 5 | 428,900 | 7,969 | 48 | 3 |
Lubin | dolnośląskie | 5 | 369,000 | 6,845 | 73 | 3 |
Dąbrowa Górnicza | śląskie | 5 | 709,999 | 9,373 | 60 | 3 |
Siemianowice Śląskie | śląskie | 5 | 329,148 | 7,918 | 43 | 2 |
Warszawa | mazowieckie | null | null | null | null | null |
Gdańsk | pomorskie | null | null | null | null | null |
Poznań | wielkopolskie | null | null | null | null | null |
Gdańsk | pomorskie | null | null | null | null | null |
Warszawa | mazowieckie | null | null | null | null | null |
Gdańsk | pomorskie | null | null | null | null | null |
Kraków | małopolskie | null | null | null | null | null |
Kraków | małopolskie | null | null | null | null | null |
Warszawa | mazowieckie | null | null | null | null | null |
Tarnowskie Góry | śląskie | null | null | null | null | null |
Gdańsk | pomorskie | null | null | null | null | null |
Toruń | kujawsko-pomorskie | null | null | null | null | null |
Łódź | łódzkie | null | null | null | null | null |
Rzeszów | podkarpackie | null | null | null | null | null |
Warszawa | mazowieckie | null | null | null | null | null |
Wrocław | dolnośląskie | null | null | null | null | null |
Kraków | małopolskie | null | null | null | null | null |
Katowice | śląskie | null | null | null | null | null |
Wieliczka | małopolskie | null | null | null | null | null |
Opole | opolskie | null | null | null | null | null |
Gdańsk | pomorskie | null | null | null | null | null |
Katowice | śląskie | null | null | null | null | null |
Kalisz | wielkopolskie | null | null | null | null | null |
Łomża | podlaskie | null | null | null | null | null |
Warszawa | mazowieckie | null | null | null | null | null |
Oława | dolnośląskie | null | null | null | null | null |
Rzeszów | podkarpackie | null | null | null | null | null |
Rybnik | śląskie | null | null | null | null | null |
Dąbrowa Górnicza | śląskie | null | null | null | null | null |
Warszawa | mazowieckie | null | null | null | null | null |
Polkowice | dolnośląskie | null | null | null | null | null |
Mierzyn | zachodniopomorskie | null | null | null | null | null |
Lublin | lubelskie | null | null | null | null | null |
Kamienna Góra | dolnośląskie | null | null | null | null | null |
Białystok | podlaskie | null | null | null | null | null |
Warszawa | mazowieckie | null | null | null | null | null |
Siemianowice Śląskie | śląskie | null | null | null | null | null |
Wrocław | dolnośląskie | null | null | null | null | null |
Kraków | małopolskie | null | null | null | null | null |
Szczecin | zachodniopomorskie | null | null | null | null | null |
Warszawa | mazowieckie | null | null | null | null | null |
Wrocław | dolnośląskie | null | null | null | null | null |
Gdańsk | pomorskie | null | null | null | null | null |
Kraków | małopolskie | null | null | null | null | null |
Łódź | łódzkie | null | null | null | null | null |
Gdańsk | pomorskie | null | null | null | null | null |
Wrocław | dolnośląskie | null | null | null | null | null |
Warszawa | mazowieckie | null | null | null | null | null |
Bydgoszcz | kujawsko-pomorskie | null | null | null | null | null |
Radom | mazowieckie | null | null | null | null | null |
Poznań | wielkopolskie | null | null | null | null | null |
Ruda Śląska | śląskie | null | null | null | null | null |
Zalasewo | wielkopolskie | null | null | null | null | null |
Poznań | wielkopolskie | null | null | null | null | null |
Katowice | śląskie | null | null | null | null | null |
Poland Flat Prices by City — Otodom.pl, September 2026
Median asking prices for flats on sale in Poland, by city, collected on 1 September 2026.
What is inside
| File | Rows | What it is |
|---|---|---|
otodom-pl-market-summary-2026-09-01.csv |
44 | One row per city with at least 5 priced listings: median price, median price per m2, median living space, median rooms |
otodom-pl-schema-sample.csv |
55 | A small sample of individual listings, to show the field structure |
field-dictionary.md |
— | Every field a full export contains |
A few numbers from the summary
- Warszawa is the most expensive city in this sample: median 18,374 PLN per m2, median asking price 949,000 PLN for a median 53 m2 flat.
- Krakow: 16,250 PLN per m2, median 839,000 PLN for 57 m2.
- Wroclaw: 13,733 PLN per m2, Gdansk: 14,907 PLN per m2.
- Bytom (Silesia) is the cheapest city in the sample: 5,549 PLN per m2.
- The spread between the most and least expensive city is about 3.3x per m2.
How it was collected — and what that means for you
I took the first 1,500 listings from the nationwide flats-for-sale search on Otodom.pl on 1 September 2026. 1,339 of them had a price; the summary counts only those.
Two things worth knowing before you use this.
First, the search is sorted newest-first, so these listings were nearly all published in the days around 1 September 2026. The sample is good for asking prices of flats coming on the market now, and not for "days on market" analysis — I left that column out on purpose.
Second, about 6% of Otodom listings have type: investment — a developer
project rather than one specific flat. They carry no price, area or room count.
They are excluded from the medians.
The per-city medians are computed from cities with at least 5 priced listings, so small towns with one or two listings are not in the table.
What is deliberately not here
No listing descriptions, no photo URLs, no agency names, no seller details. Those belong to the portal and to the people who wrote them. This dataset is my own aggregation plus a small structural sample.
Licence
CC BY 4.0 — this covers my aggregation, my sample selection and my field dictionary. It is not a copy of anyone's database.
If you need more
The full export has 30 fields per listing — including coordinates, agency, publication date and photo URLs — and you can run it yourself for any Polish city or filter:
👉 https://apify.com/skubadev/otodom-scraper
A run of 500 listings costs about $1.00 at the current price of $2.00 per 1,000.
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