Market Velocity Tracker — Pending & Contingent Properties

"The MLS shows you what's for sale. This actor shows you what's already sold."

The single most powerful signal in real estate market analysis is not the list price of a home — it is the speed at which that home disappears from inventory. The moment a property transitions from Active → Pending or Active → Contingent, it tells a sophisticated analyst something far more valuable than any asking price ever could: the true demand pressure in that zip code, right now.

This Apify Actor is not for casual homebuyers. It is purpose-built for institutional operators, quant analysts, and data-driven real estate firms who need structured, machine-readable pipeline data on properties that have just gone under contract — before they close, before they hit the sold comps, and before any competing analyst has even noticed.

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What Does "Market Velocity" Actually Mean?

Market Velocity is the rate at which active inventory is absorbed by buyers. A high-velocity metro (like South Florida or Austin, TX) sees properties go pending within days — sometimes hours — of listing. A low-velocity market sees inventory sit for weeks or months.

By tracking the ratio of pending + contingent to active listings across specific geographies, you can calculate:

• Absorption Rate: How many months of inventory remain at current demand.
• Demand Curves: Which price bands are evaporating the fastest — $200K–$400K homes or $800K+ luxury?
• Hyper-Local Momentum: Which specific zip codes are heating up before any public headline breaks?
• Deal Competitiveness Scores: How many offers are typically being made before a listing goes pending?

This is data that takes institutional research teams weeks to compile manually. This Actor compiles it in under 3 minutes.

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Who Uses This Data?

🏦 Hedge Funds & Private Equity Real Estate Portfolio construction teams use velocity data to identify which metros are transitioning from a buyer's market to a seller's market. Acquiring before the inflection point is the entire game.

📊 Real Estate Analytics Platforms Build dashboards showing daily/weekly pending volume indexes by metro. Layer it with economic data (interest rate movements, employment reports) to build proprietary market prediction models.

🏗️ Homebuilders & Developers If existing-home velocity in a specific suburb is extreme, it is a leading indicator that land acquisition there will become intensely competitive. Get ahead of entitlement costs.

🏡 iBuyers & Algorithmic Offer Platforms Velocity data feeds directly into offer calibration models. High pending-to-active ratios mean offers need to exceed list price. Low ratios allow more conservative pricing strategies.

📰 Real Estate Journalists & Researchers Track breaking shifts in market temperature with actual structured data instead of depending on lagging monthly aggregates from NAR or Zillow.

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How it works

This Actor utilizes proprietary ingestion filters confirmed to return exclusively pending and contingent inventory. This is a direct backend constraint that ensures the entire search pipeline is locked to active-offer properties only, resulting in maximum data accuracy and minimum overhead.

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Input Parameters

Parameter	Type	Default	Description
postal_code	string	—	Pinpoint a single ZIP code. Best for hyper-local velocity snapshots.
state_code	string	CA	2-letter US state code. Run statewide velocity sweeps.
city	string	Los Angeles	City-level velocity analysis. Combine with state_code.
propertyType	array	—	Segment velocity by asset class: single_family, condo_townhome, multi_family.
list_price_min / max	integer	—	Isolate velocity by price band to find where the real compression is happening.
beds_min / beds_max	integer	—	Narrow by unit size to understand family vs. investor demand pressure.
maxItems	integer	5000	Total property cap per run. For statewide runs, set to 5000.
proxyConfiguration	object	US Datacenter	Proxy routing is essential for high-volume concurrent scraping.

Example: Metro Velocity Snapshot

{
    "state_code": "CA",
    "city": "Los Angeles",
    "propertyType": ["single_family"],
    "list_price_min": 400000,
    "list_price_max": 1200000,
    "maxItems": 2000,
    "proxyConfiguration": {
        "useApifyProxy": true,
        "apifyProxyCountry": "US"
    }
}

Run this query on a Monday morning, export the JSON, compare it against last Monday's output. The delta in total pending count is your weekly velocity index.

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Output Schema

Every property record that went under contract is delivered as a flat JSON object, ready to pipe directly into BigQuery, Snowflake, or your internal analytics warehouse.

Field	Type	Analytical Significance
property_id	string	De-duplication key for longitudinal trend modeling
list_price	integer	Contract price bracket for absorption rate segmentation
is_pending	boolean	Hard confirmation: accepted offer, all contingencies waived
is_contingent	boolean	Soft signal: offer accepted but financing/inspection clauses active
list_date	string	Days-on-market calculation anchor point
type / sub_type	string	Asset class segmentation for velocity by property type
beds / baths_consolidated	integer	Unit size demand profiling
sqft / lot_sqft	integer	Per-square-foot velocity analysis
address_line / city / postal_code	string	Hyper-local geographic clustering
latitude / longitude	number	Spatial heatmap generation (MapBox / Google Maps compatible)
county_name / county_fips	string	County-level aggregation for regulatory and tax jurisdiction mapping
advertiser_1_name / email / phones	string	Listing agent attribution for commission flow analysis
scrapedAt	ISO 8601	Run timestamp for time-series versioning

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Scheduling for Time-Series Analysis

The real power of this tool unlocks under a recurring schedule. When you run this Actor weekly or daily on the same geographic target, you generate a sequential dataset that lets you plot inventory absorption curves over time.

Suggested Setup:

1. Configure the Actor with your target metro and price band.
2. Set an Apify schedule to run every Monday at 06:00 UTC.
3. Pipe results via webhook to a Google Sheet or BigQuery sink.
4. Plot total pending count as a time series — any acceleration above a 3-week rolling average signals an incoming seller's market inflection.

Start tracking market velocity today. Stop reacting to the market. Start predicting it.