Real Estate Listing & Price Intelligence Across Property Portals

By Author: Actowiz Solution
Total Articles: 73
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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.