# Market Velocity Tracker (`kawsar/market-velocity-tracker`) Actor Market velocity tracker for analysts and institutional investors. Measure real-time demand by extracting properties that just went under contract or are pending across any US market. - **URL**: https://apify.com/kawsar/market-velocity-tracker.md - **Developed by:** [Kawsar](https://apify.com/kawsar) (community) - **Categories:** Real estate, Agents, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $3.00 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## 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. --- ### 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. --- ### 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. --- ### 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. --- ### 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 ```json { "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. *** ### 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 | *** ### 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. # Actor input Schema ## `postal_code` (type: `string`): Filter properties exactly by a 5-digit US ZIP or postal code. This is the most reliable way to target a specific neighborhood. Example: '90210' for Beverly Hills, CA. ## `state_code` (type: `string`): Filter properties broadly by a 2-letter US state code. Best used in combination with other filters. Example: 'CA' for California, 'NY' for New York. ## `city` (type: `string`): Filter properties by the exact city name. Usually best to combine with state\_code. Example: 'Los Angeles' or 'Austin'. ## `street_name` (type: `string`): Target properties located on a specific street name. Example: 'Sunset Blvd' or 'Main St'. ## `address` (type: `string`): Filter for a very specific property address line. Example: '123 Fake Street'. ## `propertyType` (type: `array`): Filter the results by specific property types. Supported options include: apartment, condo\_townhome, condo\_townhome\_rowhome\_coop, condo, condos, single\_family, multi\_family, mobile, land, farm, other. ## `keywords` (type: `array`): Filter properties by specific amenities or descriptive features. Options include: basement, carport, central\_air, den, hardwood\_floors, pool, waterfront, ocean\_view, fireplace. ## `beds_min` (type: `integer`): Minimum threshold for the number of bedrooms. ## `beds_max` (type: `integer`): Maximum threshold for the number of bedrooms. ## `baths_min` (type: `integer`): Minimum threshold for the number of bathrooms (including half-baths). ## `baths_max` (type: `integer`): Maximum threshold for the number of bathrooms. ## `list_price_min` (type: `integer`): Minimum active asking list price in USD. ## `list_price_max` (type: `integer`): Maximum active asking list price in USD. ## `sold_price_min` (type: `integer`): Minimum sold price in USD. ## `sold_price_max` (type: `integer`): Maximum sold price in USD. ## `year_built_min` (type: `integer`): Filter by the year the construction was completed. ## `year_built_max` (type: `integer`): Set a maximum year of completion to target historic or older builds. ## `hoa_fee_max` (type: `integer`): Maximum Homeowners Association monthly fee allowed. ## `limit` (type: `integer`): The number of listings fetched in a single API call. ## `offset` (type: `integer`): The exact positional index the scraper starts at. ## `maxItems` (type: `integer`): The hard cap on total listings the scraper will collect across all paginated API calls. ## `timeoutSecs` (type: `integer`): If the scraper surpasses this runtime duration, it will safely halt and exit. ## `requestTimeoutSecs` (type: `integer`): How long the scraper waits for the server to respond per API query slice. ## `proxyConfiguration` (type: `object`): Select proxies to obscure IP and evade blocking. Note: USA datacenter proxy is set as the default. ## Actor input object example ```json { "postal_code": "91601", "state_code": "CA", "city": "Los Angeles", "propertyType": [ "condo_townhome", "single_family" ], "keywords": [ "basement", "pool" ], "beds_min": 1, "beds_max": 5, "baths_min": 1, "list_price_min": 200000, "list_price_max": 900000, "year_built_min": 2017, "year_built_max": 2023, "limit": 200, "offset": 0, "maxItems": 5000, "timeoutSecs": 300, "requestTimeoutSecs": 30, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyCountry": "US" } } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "postal_code": "91601", "proxyConfiguration": { "useApifyProxy": true, "apifyProxyCountry": "US" } }; // Run the Actor and wait for it to finish const run = await client.actor("kawsar/market-velocity-tracker").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "postal_code": "91601", "proxyConfiguration": { "useApifyProxy": True, "apifyProxyCountry": "US", }, } # Run the Actor and wait for it to finish run = client.actor("kawsar/market-velocity-tracker").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "postal_code": "91601", "proxyConfiguration": { "useApifyProxy": true, "apifyProxyCountry": "US" } }' | apify call kawsar/market-velocity-tracker --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=kawsar/market-velocity-tracker", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Market Velocity Tracker", "description": "Market velocity tracker for analysts and institutional investors. Measure real-time demand by extracting properties that just went under contract or are pending across any US market.", "version": "0.0", "x-build-id": "tMvn1pEQ93lWuf11u" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/kawsar~market-velocity-tracker/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-kawsar-market-velocity-tracker", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/kawsar~market-velocity-tracker/runs": { "post": { "operationId": "runs-sync-kawsar-market-velocity-tracker", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/kawsar~market-velocity-tracker/run-sync": { "post": { "operationId": "run-sync-kawsar-market-velocity-tracker", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "postal_code": { "title": "Postal code", "type": "string", "description": "Filter properties exactly by a 5-digit US ZIP or postal code. This is the most reliable way to target a specific neighborhood. Example: '90210' for Beverly Hills, CA." }, "state_code": { "title": "State code", "type": "string", "description": "Filter properties broadly by a 2-letter US state code. Best used in combination with other filters. Example: 'CA' for California, 'NY' for New York." }, "city": { "title": "City", "type": "string", "description": "Filter properties by the exact city name. Usually best to combine with state_code. Example: 'Los Angeles' or 'Austin'." }, "street_name": { "title": "Street name", "type": "string", "description": "Target properties located on a specific street name. Example: 'Sunset Blvd' or 'Main St'." }, "address": { "title": "Address", "type": "string", "description": "Filter for a very specific property address line. Example: '123 Fake Street'." }, "propertyType": { "title": "Property type", "type": "array", "description": "Filter the results by specific property types. Supported options include: apartment, condo_townhome, condo_townhome_rowhome_coop, condo, condos, single_family, multi_family, mobile, land, farm, other.", "items": { "type": "string" } }, "keywords": { "title": "Feature keywords", "type": "array", "description": "Filter properties by specific amenities or descriptive features. Options include: basement, carport, central_air, den, hardwood_floors, pool, waterfront, ocean_view, fireplace.", "items": { "type": "string" } }, "beds_min": { "title": "Min bedrooms", "type": "integer", "description": "Minimum threshold for the number of bedrooms." }, "beds_max": { "title": "Max bedrooms", "type": "integer", "description": "Maximum threshold for the number of bedrooms." }, "baths_min": { "title": "Min bathrooms", "type": "integer", "description": "Minimum threshold for the number of bathrooms (including half-baths)." }, "baths_max": { "title": "Max bathrooms", "type": "integer", "description": "Maximum threshold for the number of bathrooms." }, "list_price_min": { "title": "Min list price", "type": "integer", "description": "Minimum active asking list price in USD." }, "list_price_max": { "title": "Max list price", "type": "integer", "description": "Maximum active asking list price in USD." }, "sold_price_min": { "title": "Min sold price", "type": "integer", "description": "Minimum sold price in USD." }, "sold_price_max": { "title": "Max sold price", "type": "integer", "description": "Maximum sold price in USD." }, "year_built_min": { "title": "Year built from", "type": "integer", "description": "Filter by the year the construction was completed." }, "year_built_max": { "title": "Year built to", "type": "integer", "description": "Set a maximum year of completion to target historic or older builds." }, "hoa_fee_max": { "title": "Max HOA fee", "type": "integer", "description": "Maximum Homeowners Association monthly fee allowed." }, "limit": { "title": "Results per pagination page", "minimum": 1, "maximum": 200, "type": "integer", "description": "The number of listings fetched in a single API call.", "default": 200 }, "offset": { "title": "Initial page offset", "minimum": 0, "type": "integer", "description": "The exact positional index the scraper starts at.", "default": 0 }, "maxItems": { "title": "Maximum total results to scrape", "minimum": 1, "maximum": 5000, "type": "integer", "description": "The hard cap on total listings the scraper will collect across all paginated API calls.", "default": 5000 }, "timeoutSecs": { "title": "Overall actor timeout (seconds)", "minimum": 30, "maximum": 3600, "type": "integer", "description": "If the scraper surpasses this runtime duration, it will safely halt and exit.", "default": 300 }, "requestTimeoutSecs": { "title": "Per-request timeout (seconds)", "minimum": 5, "maximum": 120, "type": "integer", "description": "How long the scraper waits for the server to respond per API query slice.", "default": 30 }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Select proxies to obscure IP and evade blocking. Note: USA datacenter proxy is set as the default." } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```