Real Estate Listing & Price Intelligence Across Property Portals

 


Residential Listing Intelligence

Case Study | PropTech / Real Estate Data

At a Glance

  • IndustryPropTech / real-estate platform

  • Sources MonitoredMajor residential listing portals

  • FocusListing prices, inventory, days-on-market, price cuts

  • DeliveryDaily structured feed + trend rollups

  • EngagementOngoing managed data pipeline

Ideal Customer Profile (ICP)

Who they are: A PropTech platform, portal, or investment/analytics team that needs reliable, structured housing-market data to power a product, report, or investment decision.

Core pain points:

  • Listing data is scattered across portals with inconsistent formats.

  • No clean view of inventory, price cuts, or days-on-market over time.

  • Building and maintaining scrapers in-house pulls engineers off the core product.

What success looks like: A single, clean, daily feed of listings and price movements they can build on — without running the scraping themselves.

The Client (anonymized)

The client is a real-estate technology company that surfaces market insights to its users. Identifying details are withheld; all figures below are illustrative.

The Challenge

The client's product depended on fresh listing data, but sourcing it was a constant drain. Portals changed layouts, listings appeared and disappeared, and price cuts — a key signal for their users — were easy to miss without day-over-day comparison. Their small engineering team was spending more time maintaining scrapers than improving the product.

Three gaps stood out: inconsistent data across portals, no reliable days-on-market or price-cut tracking, and no continuity when a listing briefly vanished and returned.

The Solution

Actowiz built a managed listing-intelligence pipeline:

  • Structured daily listings — price, location, size, type, and status, normalized across portals into one schema.

  • Price-cut & days-on-market tracking — day-over-day comparison flags reductions and measures how long listings sit.

  • Continuity handling — a listing that briefly disappears is retained and flagged, not treated as new when it returns.

  • Trend rollups — median price, inventory, and price-cut share by area, ready for the client's dashboards.

Sample Output

Illustrative sample data — not real listings.

Daily listing feed

  • Listing ID: RE-10245

    • Area: Area A

    • Price: ₹82,00,000

    • Beds: 3

    • Days on Market: 12

    • Flag: —

  • Listing ID: RE-10388

    • Area: Area A

    • Price: ₹74,50,000

    • Beds: 2

    • Days on Market: 41

    • Flag: Price cut −5%

  • Listing ID: RE-10512

    • Area: Area B

    • Price: ₹1,20,00,000

    • Beds: 4

    • Days on Market: 3

    • Flag: New listing

  • Listing ID: RE-10233

    • Area: Area B

    • Price: ₹68,00,000

    • Beds: 2

    • Days on Market: 58

    • Flag: Stale (relisted)

Area rollup

  • Area A

    • Active Listings: 1,240

    • Median Price: ₹79,00,000

    • Listings with Price Cut: 14%

  • Area B

    • Active Listings: 980

    • Median Price: ₹1,05,00,000

    • Listings with Price Cut: 9%

Results


  • Data Sourcing

    • Before: In-house scrapers with high maintenance

    • After: Fully managed feed

  • Price-Cut Visibility

    • Before: Often missed

    • After: Flagged daily

  • Days-on-Market

    • Before: Not tracked

    • After: Tracked per listing

  • Engineering Time

    • Before: Spent on scraping

    • After: Focused back on the core product

Key outcomes: a single clean daily feed, reliable price-cut and days-on-market signals, and an engineering team refocused on the product rather than pipeline maintenance.

FAQ

What real-estate data can be collected?

Listing price, location, size, type, status, price changes, and days-on-market across major portals.

How is listing continuity handled?

Listings that briefly disappear are retained and flagged, so returning listings aren't mistaken for new ones.

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