Datasets:
The dataset viewer is not available for this split.
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 matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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.
- Downloads last month
- 50