Real Estate
See the market listing by listing.
Collect public property listings from portals and agency websites. Normalize locations and property attributes, then follow new listings, price changes and withdrawals over time.
The detail behind every insight.
Matched records, source context and the fields your team needs to investigate a change. Explore an example of the underlying real estate dataset.
| Listing ID | Area | Property | Beds | Floor area | Asking price | Previous price | Days listed | Status |
|---|---|---|---|---|---|---|---|---|
| RE-1842 | Riverside | Riverside Court | 2 | 72 m² | £425,000 | £445,000 | 24 | Price reduced |
| RE-1843 | Riverside | Quay House | 2 | 81 m² | £460,000 | — | 1 | New listing |
| RE-1844 | Riverside | Bridge Apartments | 2 | 68 m² | £410,000 | £410,000 | 12 | Active |
| RE-1845 | North Quarter | Terraced house | 3 | 104 m² | £510,000 | — | 2 | New listing |
| RE-1846 | Old Town | First-floor apartment | 1 | 48 m² | £285,000 | £295,000 | 38 | Price reduced |
| RE-1847 | City Centre | Studio apartment | 0 | 31 m² | £210,000 | £210,000 | 45 | Listing removed |
The dataset, and one way to look at it.
Every pipeline publishes a checked dataset. Want a view without a BI team? Jsonify renders one from the schema — or send the rows to Sheets, Snowflake or your agent.
Property market insights
Property market insightsMedian asking price
Share of listings with parking
Latest observations
| Area | Property | Latest change |
|---|---|---|
| Riverside | 2-bed apartment | £445k → £425k |
| North Quarter | 3-bed house | New listing · £510k |
| Old Town | 1-bed apartment | Listing removed |
Follow asking-price changes
Keep a dated record of reductions and updates instead of overwriting the last price.
Avoid double-counting
Match listings for the same property across portals and agencies.
Understand local inventory
Compare new listings, withdrawals and available stock by area and property type.
Sources change.
Your data keeps flowing.
A retailer moves its price field. Jason diagnoses the failure, repairs the extraction code, and verifies the change before activating a new pipeline revision.
The next scheduled run uses the repaired code. Your dataset stays dependable, even when its sources don’t.
From your sources to your dataset.
Choose the scope. Jsonify builds the pipeline and keeps it running.
Your next dataset starts here.
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