The Client

A US proptech company building an analytics and deal-sourcing product for real estate investors who work across asset classes — commercial (offices, retail, industrial via LoopNet), residential for-sale (Redfin), and multifamily rentals (Apartments.com). Their product's promise was a single pane of glass over a market that publishes itself across a dozen incompatible surfaces. Their problem was that no such single pane existed to build on: each source spoke its own dialect of "property," and stitching them into one queryable API was the hard part standing between them and a shippable product.

They came to Actowiz Solutions not for "scraping" in the raw sense, but for a unified real estate data API — one schema, one feed, three very different sources normalized into something an application could actually query.

The Challenge

Real estate data is a normalization problem wearing a collection problem's clothes:

Three sources, three ontologies. LoopNet thinks in cap rates, price-per-square-foot, NNN leases, and building class. Redfin thinks in beds, baths, lot size, HOA fees, and days-on-market. Apartments.com thinks in floor plans, unit availability, rent ranges, and lease terms. Forcing these into one schema without flattening away what makes each meaningful is the core design challenge — a naive merge produces a table where most fields are null for most rows.
"A listing" means different things. A LoopNet listing might be an entire office building or a single suite; a Redfin listing is one home; an Apartments.com listing is a community containing dozens of floor plans, each with variable unit availability. The unit of a record has to be defined deliberately per source and reconciled at the API layer.
Geography must be the join key. The one thing all three share is location — and it's the connective tissue that makes the unified product valuable (show me everything at this address, in this submarket, in this radius). That demands rigorous address normalization and geocoding across sources that format locations inconsistently.
Freshness expectations differ by asset class. Rental availability changes daily; for-sale status (active, pending, sold) changes fast and matters enormously; commercial listings move slower but their price and status changes are high-value events. One refresh cadence doesn't fit all three.
And these are defended, structured, frequently-changing surfaces — the standing case for self-healing extraction feeding an API that downstream applications depend on not breaking.

The Actowiz Solution

1. Source-specific extraction, unified target schema.

We built per-source extraction tuned to each platform's ontology, feeding a single normalized property schema with a typed asset_class and source-specific attribute blocks — so commercial-specific fields (cap rate, lease type, building class) and residential-specific fields (beds, baths, HOA) coexist without forcing every record into a lowest-common-denominator shape. Common fields (location, price/rent, status, size, media, listing metadata) are normalized across all three; specialized fields live in typed extensions.

2. Address normalization and geocoding as the backbone.

Every listing's location parsed, standardized, and geocoded to coordinates plus normalized address components and submarket tags — the join key that lets the client's users query across asset classes by geography, the feature that made the unified API worth more than three separate feeds.

3. Record-unit reconciliation.

Defined deliberately per source: building-or-suite for LoopNet (with parent-child linking where a building contains listed suites), property for Redfin, and community-with-floor-plans for Apartments.com (floor plans as child records under a parent community, with unit availability tracked). The API exposes both levels so the client could query at whichever grain their feature needed.

4. Status and price-change event tracking.

Beyond snapshots: status transitions (active → pending → sold; available → leased), price and rent changes, and days-on-market computed from history — the event stream that deal-sourcing products actually run on, delivered alongside the current state.

5. Volatility-tiered refresh.

Rental availability and for-sale status on fast cycles, commercial listings on a cadence matched to their slower movement but with status/price changes prioritized — the delta-based economics from our pipeline work, applied per asset class.

6. API-shaped delivery.

Not a file drop — a queryable feed in the client's schema, with filtering (by geography, asset class, price band, status), pagination, and change-since semantics so their application could sync efficiently. Freshness metadata on every record so their product could reason about recency, the agent-and-app-ready standard from our data-for-agents work.

7. Compliance.

Public listing data only; the personal-data edges specific to real estate (agent contact details are professional-public; any incidental personal data masked at the edge) handled per our compliance framework, with GDPR/CCPA-mapped controls and per-record lineage.

Sample Structure (Illustrative)

Unified property record (sample, abbreviated):

{
  "property_id": "unified-re-2026-771204",
  "asset_class": "residential_sale",
  "source": "redfin",
  "location": {"lat": 30.27, "lng": -97.74, "address_norm": "…", "submarket": "sample_district"},
  "price": 549000, "status": "active", "days_on_market": 12,
  "size": {"sqft": 1840, "lot_sqft": 6200},
  "residential": {"beds": 3, "baths": 2, "hoa_monthly": 0},
  "media_count": 34,
  "collected_at": "2026-08-11T05:20:00Z",
  "lineage_id": "lin-8890-r"
}

Cross-source coverage snapshot (sample market):

Asset Class	Source	Active Listings*	Median Price/Rent*	Refresh
Commercial	LoopNet	1,240	$312/sqft	Daily
Residential sale	Redfin	4,880	$549K	Intraday status
Multifamily rent	Apartments.com	2,110 communities	$1,840/mo	Daily availability

Sample data — illustrative of deliverable format. Actual API is queryable at listing and unit level with change-since semantics.

Engagement Metrics (Representative)

Metric	Value*
Sources unified	3 (LoopNet, Redfin, Apartments.com)
Asset classes	Commercial, residential-sale, multifamily-rent
Common normalized fields	30+
Address geocoding accuracy (audited)	98%+
Delivery	Queryable API, client schema, change-since sync
Source layout changes absorbed, first quarter	12 (11 auto-repaired)
Time to first API endpoint live	7 weeks

Representative engagement figures — illustrative of project structure.

The Outcome

The client shipped their single-pane product on the unified API, and the effect their founder highlighted was the one the architecture was built for: users could finally ask geographic questions across asset classes — everything for sale, for lease, and commercially available within a radius, in one query — which was the product's entire reason to exist and had been impossible against three separate data silos. Address normalization was the unglamorous feature that made the glamorous one work.

The status-change event stream became the deal-sourcing engine's heartbeat: new-listing and price-drop events, delivered fresh, drove the alerts their investor users came for. And the per-asset-class refresh tiering kept the feed both fresh where it mattered (for-sale status, rental availability) and economical where it didn't — a balance a one-size cadence would have gotten expensively wrong in both directions.

The engagement continues as the client expands source coverage (additional CRE and rental platforms onto the same schema) and enriches the geographic layer with submarket analytics — the unified schema absorbing new sources without breaking the application built on it, which was the durability the API design existed to provide.

Why This Pattern Repeats

Any product unifying a fragmented market faces the same truth: the value is in the normalization, not the collection. Real estate, travel, automotive, jobs, financial products — wherever a market publishes itself across incompatible surfaces, the winning product is the one that makes them queryable as one, and geography or another shared key is usually the join that unlocks it. The transferable design: source-specific extraction into a unified-but-extensible schema, a rigorously normalized join key, deliberate record-unit reconciliation, event-stream deltas over snapshots, tiered freshness, and API-shaped delivery an application can actually build on.

Frequently Asked Questions

Can data from LoopNet, Redfin, and Apartments.com really be unified?

Yes — into a schema with common normalized fields plus typed, asset-class-specific extensions, joined on rigorously geocoded location, so cross-asset-class geographic queries become possible without flattening away what makes each source meaningful.

Why is address normalization so important in real estate data?

Because location is the one field all sources share, and it's the join key for the queries that make a unified product valuable — everything at an address, in a submarket, or in a radius, across asset classes.

Is this delivered as files or as an API?

As a queryable API in the client's schema, with geographic and attribute filtering, pagination, and change-since sync semantics — plus status and price-change event streams, not just static snapshots.

How is different data freshness handled across asset classes?

Volatility-tiered refresh: fast cycles for rental availability and for-sale status, cadence matched to slower commercial movement with price/status changes prioritized. Contact Actowiz Solutions to scope a unified real estate feed.

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