# Parcel Owner Lookup — Address to Owner & Assessor Record (`malonestar/parcel-owner-lookup`) Actor Turn street addresses into parcel IDs, owner names, mailing addresses, assessed values & coordinates from official assessor rolls (Chicago/Cook County, Philadelphia, NYC) with Census-geocoder fallback. Address-grounding primitive for AI agents, proptech & skip tracing. $0.02/lookup. - **URL**: https://apify.com/malonestar/parcel-owner-lookup.md - **Developed by:** [Kyle Maloney](https://apify.com/malonestar) (community) - **Categories:** Real estate, Developer tools, Agents - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $11.00 / 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## Parcel Owner Lookup — Address to Owner & Assessor Record API Turn any US street address into a **parcel ID, owner name, owner mailing address, assessed value, property class, and coordinates** — straight from official government assessment rolls, with a US Census geocoder fallback. One address in, exactly one structured row out. **$0.02 per lookup** (pay per result). Built as an **address-grounding primitive**: the boring-but-critical first step of property research, lead generation, skip tracing, and AI-agent real-estate workflows. ### Who it's for - **AI agents & LLM apps** — ground a free-text address to a canonical parcel + owner before doing anything else (valuation, permits, liens, outreach). Clean, described output fields make this a chainable MCP tool. - **Proptech developers** — add "who owns this property?" to your product without licensing a parcel-data vendor. - **Skip tracing / real-estate lead gen** — the `owner_mailing_address` field is the pre-step for direct-mail and owner-outreach lists. - **Title, lending & due-diligence analysts** — confirm the recorded owner and parcel ID behind an address in seconds. ### Coverage (v1 — more counties compound) | Area | Roll | What you get | |---|---|---| | Chicago / Cook County, IL | Cook County Assessor Parcel Universe (Socrata) | PIN, owner/taxpayer + mailing address, class, lat/lon | | Philadelphia, PA | OPA property roll (Carto SQL) | OPA parcel number, owner(s), mailing address, market value, category, lat/lon | | New York City (5 boroughs) | MapPLUTO (Socrata) | BBL, owner name, total assessed value, building class, lat/lon | | Everywhere else | US Census geocoder | Normalized address + lat/lon (`match_confidence: "geocoded"`, no parcel/owner) | These three rolls cover ~13M people and three of the largest US property markets. More counties are added over time — each new roll is one config block. ### Example input ```json { "addresses": [ "1060 W Addison St, Chicago, IL", "1234 Market St, Philadelphia, PA", "350 5th Ave, New York, NY" ], "maxResults": 10 } ```` ### Example output row ```json { "input_address": "1234 Market St, Philadelphia, PA", "matched_source": "philadelphia-opa", "match_confidence": "exact", "parcel_id": "453337700", "owner_name": "SEPTA", "owner_mailing_address": "1234 MARKET ST, PHILADELPHIA PA, 19107", "assessed_value": 38400000, "property_class": "4", "property_class_desc": "COMMERCIAL", "situs_address": "1234 MARKET ST, PHILADELPHIA, PA, 19107", "lat": 39.9522, "lon": -75.1605, "source_url": "https://phl.carto.com/api/v2/sql?q=..." } ``` ### Output fields - `input_address` — your address string, echoed for joining. - `matched_source` — `cook-county-parcel-universe` | `philadelphia-opa` | `nyc-pluto` | `census-geocoder` | null. - `match_confidence` — `exact` (house + street matched the roll verbatim), `fuzzy` (same street with a drifted suffix — e.g. Wrigley Field is "W ADDISON **AVE**" on the Cook roll — or nearest house number on the right street), `geocoded` (coordinates only), `none` (no match; the row is still emitted — this actor **never fails on a bad address**). - `parcel_id` — Cook 14-digit PIN / Philadelphia OPA number / NYC 10-digit BBL. - `owner_name`, `owner_mailing_address` — owner or taxpayer of record + mailing address (mailing is null for NYC; PLUTO doesn't publish it). - `assessed_value` — NYC total assessed value; Philadelphia OPA certified market value; **null for Cook County v1** (its parcel-universe roll carries no value column). - `property_class`, `property_class_desc` — jurisdiction class code + readable description. - `situs_address`, `lat`, `lon` — the roll's own address for the parcel and WGS84 coordinates. - `source_url` — direct API link to the underlying official record for citation/re-fetch. ### How matching works Addresses are normalized (case, punctuation, St↔Street, Ave↔Avenue, W↔West, 5th↔5↔Fifth) and routed by city/ZIP to the right roll. The matcher then tries, in order: exact house + street; nearest house number on the same street (within 100); same house with a drifted street suffix. Anything still unmatched falls back to Census geocoding, then to an honest `none` row. ### Use as an MCP tool Every Apify actor is callable by AI agents via [mcp.apify.com](https://mcp.apify.com). This actor is designed for it: pass `addresses`, read back fully-described fields, and chain the `parcel_id` / `lat` / `lon` into other tools (permit lookups, flood/wildfire screeners, comparable sales). Ideal first hop of any property-research agent. ### FAQ **How do I find out who owns a property by address?** Run this actor with the address; it returns the owner of record and their mailing address from the official assessor roll (Chicago, Philadelphia, NYC in v1). **Is this legal / where does the data come from?** All sources are public, official government open-data APIs (Cook County Socrata, Philadelphia OPA via Carto, NYC Open Data, US Census). No scraping of private sites. **What happens with an address outside Chicago/Philly/NYC?** You still get a row: the Census geocoder supplies normalized address + coordinates with `match_confidence: "geocoded"` and no parcel/owner. **Why did my lookup come back `fuzzy`?** Assessor rolls often disagree with common usage (suffix drift, canonical lot addresses). `fuzzy` means the right street was found but not a verbatim match — check `situs_address` to verify. **Can I look up thousands of addresses?** Yes — pass up to 5,000 per run (`maxResults`). Each address = one result = $0.02. **How current is the data?** Live API calls at run time. Philadelphia and NYC rolls are actively maintained; the Cook County parcel-universe vintage trails the assessor's cycle. # Actor input Schema ## `addresses` (type: `array`): US street addresses to resolve to a parcel + owner record, one per line (e.g. '1060 W Addison St, Chicago, IL'). v1 matches against the Cook County IL (Chicago), Philadelphia PA, and New York City assessment rolls; addresses outside those areas fall back to Census geocoding (lat/lon only). Every input address always yields exactly one output row — unmatched addresses come back with match\_confidence 'none'. ## `maxResults` (type: `integer`): Maximum number of addresses to process (one output row per address). Extra addresses beyond this cap are skipped. ## Actor input object example ```json { "addresses": [ "1060 W Addison St, Chicago, IL", "1234 Market St, Philadelphia, PA", "350 5th Ave, New York, NY" ], "maxResults": 10 } ``` # Actor output Schema ## `results` (type: `string`): The default dataset (one row per input address). # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "addresses": [ "1060 W Addison St, Chicago, IL", "1234 Market St, Philadelphia, PA", "350 5th Ave, New York, NY" ], "maxResults": 10 }; // Run the Actor and wait for it to finish const run = await client.actor("malonestar/parcel-owner-lookup").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = { "addresses": [ "1060 W Addison St, Chicago, IL", "1234 Market St, Philadelphia, PA", "350 5th Ave, New York, NY", ], "maxResults": 10, } # Run the Actor and wait for it to finish run = client.actor("malonestar/parcel-owner-lookup").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 '{ "addresses": [ "1060 W Addison St, Chicago, IL", "1234 Market St, Philadelphia, PA", "350 5th Ave, New York, NY" ], "maxResults": 10 }' | apify call malonestar/parcel-owner-lookup --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=malonestar/parcel-owner-lookup", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Parcel Owner Lookup — Address to Owner & Assessor Record", "description": "Turn street addresses into parcel IDs, owner names, mailing addresses, assessed values & coordinates from official assessor rolls (Chicago/Cook County, Philadelphia, NYC) with Census-geocoder fallback. Address-grounding primitive for AI agents, proptech & skip tracing. $0.02/lookup.", "version": "1.0", "x-build-id": "iWyRPbBzBTFEFUWq1" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/malonestar~parcel-owner-lookup/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-malonestar-parcel-owner-lookup", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/malonestar~parcel-owner-lookup/runs": { "post": { "operationId": "runs-sync-malonestar-parcel-owner-lookup", "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/malonestar~parcel-owner-lookup/run-sync": { "post": { "operationId": "run-sync-malonestar-parcel-owner-lookup", "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": { "addresses": { "title": "Addresses", "type": "array", "description": "US street addresses to resolve to a parcel + owner record, one per line (e.g. '1060 W Addison St, Chicago, IL'). v1 matches against the Cook County IL (Chicago), Philadelphia PA, and New York City assessment rolls; addresses outside those areas fall back to Census geocoding (lat/lon only). Every input address always yields exactly one output row — unmatched addresses come back with match_confidence 'none'.", "items": { "type": "string" } }, "maxResults": { "title": "Max results", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Maximum number of addresses to process (one output row per address). Extra addresses beyond this cap are skipped.", "default": 100 } } }, "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 } } } } } } } } } } ```