Real Estate Listing & Price Intelligence Across Portals

Monitor real estate listings, property prices, availability, and market trends across leading property portals. Gain real-time intelligence to benchmark competitors, optimize pricing, and identify investment opportunities.

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	Area	Price	Beds	Days on market	Flag
RE-10245	Area A	82,00,000	3	12	—
RE-10388	Area A	74,50,000	2	41	⚠ Price cut −5%
RE-10512	Area B	1,20,00,000	4	3	🆕 New listing
RE-10233	Area B	68,00,000	2	58	ⓘ Stale (relisted)

Area rollup

Area	Active listings	Median price	% with price cut
Area A	1,240	79,00,000	14%
Area B	980	1,05,00,000	9%

Results

Metric	Before	After
Data sourcing	In-house scrapers, high maintenance	Fully managed feed
Price-cut visibility	Missed	Flagged daily
Days-on-market	Not tracked	Tracked per listing
Engineering time	Spent on scraping	Back on 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.

All client details anonymized. Figures and sample data are illustrative.

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