Real Estate Listing & Price Intelligence Across Property Portals

Author : Actowiz Solution | Published On : 14 Aug 2026

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.