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Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
id: int64
title: string
display_address: string
bedrooms: int64
bathrooms: double
property_type: string
transaction_type: string
price_display: string
price_amount: int64
price_qualifier: string
currency: string
latitude: double
longitude: double
agent_branch: string
property_url: string
thumbnail_url: string
image_count: int64
has_floorplan: bool
added_or_reduced: string
first_visible_date: string
listing_update_date: string
listing_update_reason: string
online_viewings: bool
tenure: string
key_features: string
summary: string
scraped_at: string
city: string
source_file: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 3809
to
{'id': Value('string'), 'display_address': Value('string'), 'bedrooms': Value('string'), 'bathrooms': Value('string'), 'property_type': Value('string'), 'transaction_type': Value('string'), 'price_display': Value('string'), 'price_amount': Value('int64'), 'price_qualifier': Value('string'), 'currency': Value('string'), 'latitude': Value('string'), 'longitude': Value('string'), 'agent_branch': Value('string'), 'property_url': Value('string'), 'thumbnail_url': Value('string'), 'image_count': Value('int64'), 'has_floorplan': Value('bool'), 'added_or_reduced': Value('string'), 'first_visible_date': Value('string'), 'listing_update_date': Value('string'), 'listing_update_reason': Value('string'), 'online_viewings': Value('bool'), 'tenure': Value('string'), 'key_features': Value('string'), 'summary': Value('string'), 'scraped_at': Value('string'), 'city': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                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 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2260, in _iter_arrow
                  pa_table = cast_table_to_features(pa_table, self.features)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2223, in cast_table_to_features
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              id: int64
              title: string
              display_address: string
              bedrooms: int64
              bathrooms: double
              property_type: string
              transaction_type: string
              price_display: string
              price_amount: int64
              price_qualifier: string
              currency: string
              latitude: double
              longitude: double
              agent_branch: string
              property_url: string
              thumbnail_url: string
              image_count: int64
              has_floorplan: bool
              added_or_reduced: string
              first_visible_date: string
              listing_update_date: string
              listing_update_reason: string
              online_viewings: bool
              tenure: string
              key_features: string
              summary: string
              scraped_at: string
              city: string
              source_file: string
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 3809
              to
              {'id': Value('string'), 'display_address': Value('string'), 'bedrooms': Value('string'), 'bathrooms': Value('string'), 'property_type': Value('string'), 'transaction_type': Value('string'), 'price_display': Value('string'), 'price_amount': Value('int64'), 'price_qualifier': Value('string'), 'currency': Value('string'), 'latitude': Value('string'), 'longitude': Value('string'), 'agent_branch': Value('string'), 'property_url': Value('string'), 'thumbnail_url': Value('string'), 'image_count': Value('int64'), 'has_floorplan': Value('bool'), 'added_or_reduced': Value('string'), 'first_visible_date': Value('string'), 'listing_update_date': Value('string'), 'listing_update_reason': Value('string'), 'online_viewings': Value('bool'), 'tenure': Value('string'), 'key_features': Value('string'), 'summary': Value('string'), 'scraped_at': Value('string'), 'city': Value('string')}
              because column names don't match

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UK Residential Property Listings — Rightmove, 8 Cities

Weekly-refreshed UK residential property listing data sourced from Rightmove — the UK's largest property portal with 90%+ market coverage. Covers London, Manchester, Birmingham, Leeds, Bristol, Cambridge, and Edinburgh.

2,360 records | Updated every Monday | CSV + JSON


What's Included

City Records Types
London 747 Buy + Rent
Manchester 374 Buy
Birmingham 250 Buy
Leeds 250 Buy
Bristol 250 Buy
Cambridge 240 Buy
Edinburgh 249 Buy

Price Range

  • Min: £624 / Max: £60,000,000
  • Average: £3,219,666
  • Currency: GBP

Property Types

Detached, Semi-Detached, Terraced, Apartment, Flat, House, Bungalow, Land, and more.


Schema

Column Type Description Example
id string Unique Rightmove property ID 168376049
display_address string Human-readable address "Otto Building, Hackney, E5"
bedrooms integer Number of bedrooms 2
bathrooms integer Number of bathrooms 2
property_type string Property classification "Apartment"
transaction_type string buy or rent "buy"
price_display string Formatted price "£850,000"
price_amount integer Raw price in GBP 850000
price_qualifier string Price qualifier "Guide Price"
currency string Always GBP "GBP"
latitude float GPS latitude (WGS84) 51.555892
longitude float GPS longitude (WGS84) -0.059926
agent_branch string Estate agency branch "Easthaus, London"
property_url string Direct listing URL "https://www.rightmove.co.uk/..."
thumbnail_url string Lead photo URL "https://media.rightmove.co.uk/..."
image_count integer Number of photos 18
has_floorplan boolean Floorplan available true
added_or_reduced string Listing status note "Reduced on 14/05/2026"
first_visible_date datetime When first listed "2025-10-20T10:56:31Z"
listing_update_date datetime Last price/status change "2026-05-14T16:53:12Z"
listing_update_reason string Reason for update "price_reduced"
online_viewings boolean Virtual viewing available false
tenure string Ownership type "FREEHOLD"
key_features string Pipe-separated features "Garden | Parking | New kitchen"
summary string Listing description snippet "An exceptional duplex..."
scraped_at datetime Collection timestamp (UTC) "2026-05-26T14:30:00Z"
city string City grouping "London"

Usage

import pandas as pd

df = pd.read_csv("uk_property_listings_20260526.csv")

# Filter London flats under £500k
london_flats = df[
    (df['city'] == 'London') &
    (df['property_type'] == 'Apartment') &
    (df['price_amount'] < 500000) &
    (df['transaction_type'] == 'buy')
]

print(f"{len(london_flats)} listings found")
print(london_flats[['display_address','bedrooms','price_display']].head(10))
# Plot listings on a map
import folium

m = folium.Map(location=[51.5, -0.1], zoom_start=10)
for _, row in df[df['city'] == 'London'].iterrows():
    if row['latitude']:
        folium.CircleMarker(
            location=[float(row['latitude']), float(row['longitude'])],
            radius=4,
            popup=f"{row['display_address']}{row['price_display']}",
            color='blue', fill=True
        ).add_to(m)
m.save("london_listings_map.html")
# Price per bedroom analysis by city
df['price_per_bed'] = df['price_amount'] / df['bedrooms'].replace(0, 1).astype(float)
print(df.groupby('city')['price_per_bed'].median().sort_values(ascending=False))

Use Cases

  • Automated Valuation Models (AVM) — price prediction and comparable analysis
  • Investment screening — yield estimation, price-per-bedroom by area
  • Geospatial visualisation — price heat maps, listing density analysis
  • AI/ML training — property description NLP, price regression models
  • Proptech development — search tools, recommendation engines, market alerts
  • Academic research — urban housing studies, affordability analysis

Data Quality

Metric Coverage
GPS coordinates 100%
Price data ~99%
Key features 76.5%
Duplicate records 0

Methodology

Data collected from publicly accessible Rightmove listings via structured HTML parsing of the embedded __NEXT_DATA__ JSON object. No authentication required. Deduplicated on Rightmove property ID. Collected weekly every Monday at 06:00 UTC.


Updates

This dataset is refreshed every Monday. Each new version is a full snapshot — not a delta. To track price changes over time, join on the id field across weekly versions.

License

Free to use for research and evaluation. For commercial licensing contact the provider via dataset discussions.

Provider

Grayling Data — UK property and AI training data, updated weekly. For custom cities, enrichment (postcodes, EPC ratings, yield estimates), or API delivery — open a discussion.

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