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.

What the dataset gives you.

An example of the real estate dataset. Each note points to the columns that answer it.

  1. Follow asking-price changes

    Keep a dated record of reductions and updates instead of overwriting the last price.

    Previous price
  2. Avoid double-counting

    Match listings for the same property across portals and agencies.

    Listing ID
  3. Understand local inventory

    Compare new listings, withdrawals and available stock by area and property type.

    Status
Datasets · Real Estate
property_listings Synced
Illustrative Real Estate dataset
Listing IDAreaPropertyBedsFloor areaAsking pricePrevious priceDays listedStatus
RE-1842RiversideRiverside Court272 m²£425,000£445,00024Price reduced
RE-1843RiversideQuay House281 m²£460,000—1New listing
RE-1844RiversideBridge Apartments268 m²£410,000£410,00012Active
RE-1845North QuarterTerraced house3104 m²£510,000—2New listing
RE-1846Old TownFirst-floor apartment148 m²£285,000£295,00038Price reduced
RE-1847City CentreStudio apartment031 m²£210,000£210,00045Listing removed
Checked rows, ready to useExample data

Provenance

One listing, traced back to the portal.

Every row links to the listing it was read from, when it was read and which pipeline version read it. Each run publishes a new dataset version, so a reduction is kept as history instead of overwriting the last price.

Jason builds the pipeline and repairs it when a site changes. The scheduled runs repeat the same steps every time, so today’s row compares directly with the last one. There are no scrapers for your team to build or keep alive.

Datasets · property_listings · row RE-1842
P
Riverside Court · 2 bedPortal A · Riverside · 72 m² · 24 days listed
£425,000
Source page
portal-a › riverside › re-1842
Read at
07:10:05 UTC
Pipeline
v4.1

Checks

  • Matched across Portal A and Portal B
  • Floor area in m²
  • Previous price kept

Dataset versions

  1. v57Today 07:10£425,000 Price reduced
  2. v56Yesterday 07:09£445,000 No change
  3. v5529 Sep 07:10£445,000 No change
  4. 25 SepPipeline v4.1 Portal B changed its search results page. Jason repaired the extraction and re-ran the day.

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.

Dashboards · Property market insights

Property market insights

property_listings · 3,842 active listings · updated 06:05

30 days
Active listings3,842+64 this week
New this week216+12%
Price reductions88+9 this week
Median asking price£425k−£13k

Median asking price

Median asking prices are down £13k across the period. Compare like-for-like property types before drawing conclusions.

£441.2k£431.5k£421.8kDay 1Day 15Day 30 Riverside reductions begin

Share of listings with parking

Riverside82%
North Quarter68%
Old Town43%
City Centre24%

Latest changes

AreaPropertyChange
RiversideRiverside Court · 2 bed£445k → £425k
North QuarterTerraced house · 3 bedNew listing
Old TownFirst-floor apartment · 1 bed£295k → £285k
City CentreStudio apartmentListing removed

Illustrative dashboard with example data. Views are built around your dataset and use case.

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Philipp HodlPlug and Play Ventures

From your sources to your dataset.

Choose the scope. Jsonify builds the pipeline, runs it on your schedule and repairs it when a source changes. See how it works →

00 / BRIEF

Track public property listings across portals and agency websites in Lisbon. Collect asking price, area, bedrooms, floor space and availability. Deduplicate properties and record new listings, reductions and withdrawals daily.

01 / SOURCES Areas & property types Your market boundaries 02 / COLLECT IN PARALLEL Read property portals Prices and listing details Read agency websites Local inventory and updates 03 / PROCESS & VERIFY Deduplicate listings Match properties across sources 04 / PUBLISH Email report Property listings A history of market activity Snowflake XLSX
  1. 01 / Sources

    Areas & property typesYour market boundaries

  2. 02 / Collect in parallel

    Read property portalsPrices and listing details

    Read agency websitesLocal inventory and updates

  3. 03 / Process & verify

    Deduplicate listingsMatch properties across sources

  4. 04 / Publish

    Property listingsA history of market activity

    Email reportSnowflakeXLSX

Illustrative pipeline. Customize the sources, fields, schedule and delivery.

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