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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
meta: struct<currency: string, period: string, as_of: string, source: string, disclaimer: string, updated_ (... 83 chars omitted)
  child 0, currency: string
  child 1, period: string
  child 2, as_of: string
  child 3, source: string
  child 4, disclaimer: string
  child 5, updated_at: timestamp[s]
  child 6, snapshot_date: timestamp[s]
  child 7, license: string
  child 8, source_url: string
rents: list<item: struct<city: string, district: string, rooms: int64, median: int64, sigma: double, n: int (... 4 chars omitted)
  child 0, item: struct<city: string, district: string, rooms: int64, median: int64, sigma: double, n: int64>
      child 0, city: string
      child 1, district: string
      child 2, rooms: int64
      child 3, median: int64
      child 4, sigma: double
      child 5, n: int64
title: string
keywords: list<item: string>
  child 0, item: string
id: string
contributors: list<item: struct<title: string, path: string, role: string>>
  child 0, item: struct<title: string, path: string, role: string>
      child 0, title: string
      child 1, path: string
      child 2, role: string
resources: list<item: struct<name: string, path: string, format: string, mediatype: string, schema: struct<fiel (... 74 chars omitted)
  child 0, item: struct<name: string, path: string, format: string, mediatype: string, schema: struct<fields: list<it (... 62 chars omitted)
      child 0, name: string
      child 1, path: string
      child 2, format: string
      child 3, mediatype: string
      child 4, schema: struct<fields: list<item: struct<name: string, type: string, description: string>>>
          child 0, fields: list<item: struct<name: string, type: string, description: string>>
              child 0, item: struct<name: string, type: string, description: string>
                  child 0, name: string
                  child 1, type: string
                  child 2, description: string
description: string
name: string
version: timestamp[s]
homepage: string
created: timestamp[s]
sources: list<item: struct<title: string, path: string>>
  child 0, item: struct<title: string, path: string>
      child 0, title: string
      child 1, path: string
licenses: list<item: struct<name: string, title: string, path: string>>
  child 0, item: struct<name: string, title: string, path: string>
      child 0, name: string
      child 1, title: string
      child 2, path: string
to
{'name': Value('string'), 'id': Value('string'), 'title': Value('string'), 'description': Value('string'), 'version': Value('timestamp[s]'), 'created': Value('timestamp[s]'), 'homepage': Value('string'), 'licenses': List({'name': Value('string'), 'title': Value('string'), 'path': Value('string')}), 'keywords': List(Value('string')), 'sources': List({'title': Value('string'), 'path': Value('string')}), 'contributors': List({'title': Value('string'), 'path': Value('string'), 'role': Value('string')}), 'resources': List({'name': Value('string'), 'path': Value('string'), 'format': Value('string'), 'mediatype': Value('string'), 'schema': {'fields': List({'name': Value('string'), 'type': Value('string'), 'description': Value('string')})}})}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_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
              meta: struct<currency: string, period: string, as_of: string, source: string, disclaimer: string, updated_ (... 83 chars omitted)
                child 0, currency: string
                child 1, period: string
                child 2, as_of: string
                child 3, source: string
                child 4, disclaimer: string
                child 5, updated_at: timestamp[s]
                child 6, snapshot_date: timestamp[s]
                child 7, license: string
                child 8, source_url: string
              rents: list<item: struct<city: string, district: string, rooms: int64, median: int64, sigma: double, n: int (... 4 chars omitted)
                child 0, item: struct<city: string, district: string, rooms: int64, median: int64, sigma: double, n: int64>
                    child 0, city: string
                    child 1, district: string
                    child 2, rooms: int64
                    child 3, median: int64
                    child 4, sigma: double
                    child 5, n: int64
              title: string
              keywords: list<item: string>
                child 0, item: string
              id: string
              contributors: list<item: struct<title: string, path: string, role: string>>
                child 0, item: struct<title: string, path: string, role: string>
                    child 0, title: string
                    child 1, path: string
                    child 2, role: string
              resources: list<item: struct<name: string, path: string, format: string, mediatype: string, schema: struct<fiel (... 74 chars omitted)
                child 0, item: struct<name: string, path: string, format: string, mediatype: string, schema: struct<fields: list<it (... 62 chars omitted)
                    child 0, name: string
                    child 1, path: string
                    child 2, format: string
                    child 3, mediatype: string
                    child 4, schema: struct<fields: list<item: struct<name: string, type: string, description: string>>>
                        child 0, fields: list<item: struct<name: string, type: string, description: string>>
                            child 0, item: struct<name: string, type: string, description: string>
                                child 0, name: string
                                child 1, type: string
                                child 2, description: string
              description: string
              name: string
              version: timestamp[s]
              homepage: string
              created: timestamp[s]
              sources: list<item: struct<title: string, path: string>>
                child 0, item: struct<title: string, path: string>
                    child 0, title: string
                    child 1, path: string
              licenses: list<item: struct<name: string, title: string, path: string>>
                child 0, item: struct<name: string, title: string, path: string>
                    child 0, name: string
                    child 1, title: string
                    child 2, path: string
              to
              {'name': Value('string'), 'id': Value('string'), 'title': Value('string'), 'description': Value('string'), 'version': Value('timestamp[s]'), 'created': Value('timestamp[s]'), 'homepage': Value('string'), 'licenses': List({'name': Value('string'), 'title': Value('string'), 'path': Value('string')}), 'keywords': List(Value('string')), 'sources': List({'title': Value('string'), 'path': Value('string')}), 'contributors': List({'title': Value('string'), 'path': Value('string'), 'role': Value('string')}), 'resources': List({'name': Value('string'), 'path': Value('string'), 'format': Value('string'), 'mediatype': Value('string'), 'schema': {'fields': List({'name': Value('string'), 'type': Value('string'), 'description': Value('string')})}})}
              because column names don't match

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Poland Rental Market Medians

Median asking rents for residential rentals across Poland, by city, district and flat size, measured from live listings. The collection pipeline runs nightly; these downloadable editions are periodic, not automatically refreshed nightly. Use the dated live API for the current serving snapshot.

  • Snapshot: 2026-09-13
  • Coverage: 49 cities, 96 city districts, 1–4 room flats
  • Sum of city-level sample sizes (district rows overlap these): 22,036
  • History: monthly series from 2026-06 to 2026-09
  • Licence: CC-BY-4.0 — free to use commercially, attribution required
  • DOI: 10.5281/zenodo.21892638 — permanent, resolves to the latest archived edition (which may lag the API)
  • Source: https://polandrent.com

Where else this lives

Canonical repository GitHub
Archived with a DOI Zenodo
ML ecosystem mirror Hugging Face
Data-science mirror Kaggle
Entity record Wikidata

Files

File Rows What it is
poland-rent-medians.csv 470 Current snapshot. One row per city × district × room-count.
poland-rent-medians.json — Same data plus metadata, as served by the public API.
poland-rent-history.csv 621 Monthly city-level series. Forward-only.

Columns

Column Meaning
city City slug (ASCII, e.g. warszawa, lodz).
district District slug, empty for city-wide rows.
level city or district. Lets you filter without knowing the sentinel.
rooms Number of rooms (1–4). Polish listings count rooms, not bedrooms.
median_pln Median monthly asking rent in PLN.
sigma Fitted lognormal dispersion. Higher means a wider local spread.
n_listings Listings behind that median. Treat small n with caution.
snapshot_date Date the snapshot was computed (UTC).

Method

Listings are scraped nightly from three Polish rental portals (Otodom, OLX, Morizon) across all 16 voivodeships, deduplicated across sources by a two-stage entity resolution (exact source-id, then cross-source fuzzy block-and-score), and aggregated over a 14-day active window. Medians are computed with a lognormal fit and outlier trimming. Districts are used where the sample supports it and otherwise fold into the city aggregate.

These are asking rents, not signed rents. They measure what landlords list, and signed rents may differ. Fees and flat quality vary across listings. n_listings is published on every row precisely so you can judge how much weight a given cell carries.

The history is forward-only. Listings disappear when they let, so a month that was not captured cannot be reconstructed afterwards. The monthly series starts when collection started and grows from there.

Correction and provenance

The August 11, 2026 edition contained 528 cells, including 60 district cells whose median, sigma and n exactly matched the old modelled seed. Those values were not verified listing observations. The site emitter removed the seed overlay on September 4; the mirrors missed that correction until this edition. Use editions dated September 13, 2026 or later for measured-only current cells. Older Git/HF/Kaggle versions and archived DOI editions remain historical records; their files are not rewritten. Their current-grid sample counts must not be interpreted as entirely measured. The monthly city-history file is separate.

The current month in history is partial and uses the latest captured snapshot, not a final month-end value. City coverage can change. Sum only city-level samples when counting observations across the current grid: district samples overlap. A provenance manifest records the exact source commit and file SHA-256 digests.

Citation

If you use this downloadable edition, cite its snapshot date and the repository, and retain provenance.json with the source commit and file hashes. See CITATION.cff, or:

PolandRent (2026). Poland Rental Market Medians, snapshot 2026-09-13 [Data set]. https://github.com/thomasthinks/poland-rent-data

The Zenodo concept DOI identifies archived editions, which may lag this download. Only cite a Zenodo version DOI for these files after verifying that its snapshot and file hashes match; an export date is not proof of archival.

BibTeX:

@dataset{polandrent_2026_rentmedians,
  author    = {{PolandRent}},
  title     = {Poland Rental Market Medians},
  year      = {2026},
  version   = {2026-09-13},
  publisher = {PolandRent},
  url       = {https://github.com/thomasthinks/poland-rent-data}
}

Updates

Published periodically from the nightly collection pipeline. Check the snapshot date in every row and the provenance manifest; mirrors can serve different editions. Use the live API when you need the current serving snapshot.

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