# HUD QCT LIHTC Boost Screener — 130% Basis by Coordinate (`malonestar/hud-qct-lihtc-boost-screener`) Actor Qualified Census Tract (QCT) lookup for any coordinate — instantly flag the LIHTC 130% eligible-basis boost per site. Batch lat/lon points against HUD's official QCT layer; each returns in\_qct, the 11-digit tract GEOID, state, county FIPS, tract, QCT vintage, and boost-eligible flag. MCP-ready. - **URL**: https://apify.com/malonestar/hud-qct-lihtc-boost-screener.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 $3.30 / 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 ## HUD QCT LIHTC Boost Screener — 130% Basis by Coordinate Batch-check any list of coordinates against **HUD Qualified Census Tracts (QCT)** and get a clean, per-site answer to the question every affordable-housing underwriter asks first: **is this site in a QCT, so my LIHTC project qualifies for the 130% eligible-basis boost?** Give it a list of `{lat, lon, label}` points; it returns **one row per site** with `in_qct`, the containing **11-digit tract GEOID** (state FIPS + county FIPS + tract), the state, and a ready-to-use `lihtc_130_basis_boost_eligible` flag. Points are screened server-side (point-in-polygon) against HUD's official QCT feature layer — no API key, no shapefiles, no GIS desktop. ### Who it's for - **LIHTC / affordable-housing developers** running site selection and pre-development screens across a pipeline of parcels. - **Affordable-housing finance & underwriting** teams sizing eligible basis and the 30% basis boost before an application. - **Tax-credit syndicators and investors** verifying boost eligibility in a deal's due diligence. - **CRE analysts and acquisition teams** flagging basis-boost upside across a portfolio of candidate sites. - **AI agents** that need a chainable "is this coordinate in a QCT?" tool (see MCP note below). ### What a QCT is (and why the boost matters) A **Qualified Census Tract** is a census tract where at least **50% of households earn below 60% of Area Median Gross Income**, or the tract has a **poverty rate of at least 25%**. Under IRC **§42(d)(5)(B)**, a Low-Income Housing Tax Credit project placed in a QCT gets its eligible basis increased to **130%** — a materially larger credit. This actor tells you, per coordinate, whether that boost applies. ### Example input ```json { "assets": [ { "lat": 40.815, "lon": -73.904, "label": "South Bronx, NY" }, { "lat": 34.073, "lon": -118.400, "label": "Beverly Hills, CA" } ], "maxAssets": 500 } ```` ### Output fields (one row per asset) | Field | Description | |-------|-------------| | `asset_label` | Your label, echoed back. | | `asset_lat` / `asset_lon` | The screened coordinate (WGS84). | | `in_qct` | `true` if the point is inside a Qualified Census Tract, else `false`; `null` on error rows. | | `tract_geoid` | 11-digit tract GEOID of the containing QCT (`null` if not in a QCT). | | `state` | USPS state abbreviation (from the 2-digit state FIPS). | | `county_fips` | 3-digit county FIPS. | | `tract` | 6-digit census tract code. | | `qct_year` | HUD QCT designation vintage screened against (currently **FY2025**). | | `lihtc_130_basis_boost_eligible` | `true` when the site qualifies for the LIHTC 130% eligible-basis boost (equals `in_qct`). | | `error` | Populated only on invalid/failed rows; one bad point never fails the run. | | `source_url` | HUD QCT FeatureServer layer queried. | | `checked_at` | ISO 8601 UTC timestamp of the run. | ### Use as an MCP tool This actor is a clean, chainable tool for AI agents via `mcp.apify.com`. Each output field is described in the dataset schema, so an agent can call it with a coordinate and read back `in_qct` / `lihtc_130_basis_boost_eligible` to gate a downstream LIHTC feasibility step. ### Notes on vintage and DDA - **QCTs are re-designated annually.** This actor screens against HUD's currently-published QCT layer (**FY2025** as served). The `qct_year` field records the vintage used; always confirm the applicable program year for your application. - **DDA is a separate designation.** The 130% basis boost also applies inside a HUD **Difficult Development Area (DDA)**, which HUD publishes as ZIP-code / county lists rather than a point-queryable feature layer. This actor (v1) covers **QCT only**; DDA screening is a planned **v1.1** add. Because a site can qualify via **either** QCT **or** DDA, an `in_qct: false` result does **not** by itself rule out the boost. - This is an **informational intersection screen**, not legal or tax advice. Confirm against HUD's official QCT dataset (huduser.gov/portal/datasets/qct.html) and your tax-credit counsel before relying on boost eligibility. ### FAQ **Is this address in a QCT?** Geocode it to a lat/lon, pass it in `assets`, and read `in_qct`. **Does a QCT mean I get the 130% basis boost?** Yes — a LIHTC project placed in a QCT qualifies for the 130% eligible-basis boost under IRC §42(d)(5)(B). This actor sets `lihtc_130_basis_boost_eligible` accordingly. **What if my site isn't in a QCT?** It may still get the boost via a Difficult Development Area (DDA). This actor screens QCT only (DDA is coming in v1.1), so verify DDA status separately. **Which year's QCTs are used?** The currently-published HUD QCT layer (FY2025 as served); see the `qct_year` field on every row. **Do you need an API key?** No. The source is HUD's public ArcGIS feature service. **How is it billed?** One result row per screened asset (Pay Per Event). Empty input costs nothing beyond the tiny actor start. ### Source U.S. Department of Housing and Urban Development — Qualified Census Tracts (LIHTC), ArcGIS FeatureServer, `services.arcgis.com/VTyQ9soqVukalItT/.../Qualified_Census_Tracts/FeatureServer/0`. # Actor input Schema ## `assets` (type: `array`): List of points to check against HUD Qualified Census Tracts (QCT). Each item is an object with numeric lat and lon (WGS84 decimal degrees) and an optional label echoed into the output. One billable result row is produced per asset, telling you whether the coordinate falls in a QCT (and therefore qualifies a LIHTC project for the 130% eligible-basis boost). ## `maxAssets` (type: `integer`): Safety cap on how many assets to screen in one run. Extra assets beyond this limit are skipped. ## Actor input object example ```json { "assets": [ { "lat": 40.815, "lon": -73.904, "label": "South Bronx, NY" } ], "maxAssets": 500 } ``` # Actor output Schema ## `results` (type: `string`): The default dataset with one QCT screening row per asset. # 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 = { "assets": [ { "lat": 40.815, "lon": -73.904, "label": "South Bronx, NY (low-income QCT)" }, { "lat": 34.073, "lon": -118.4, "label": "Beverly Hills, CA (affluent control)" } ] }; // Run the Actor and wait for it to finish const run = await client.actor("malonestar/hud-qct-lihtc-boost-screener").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 = { "assets": [ { "lat": 40.815, "lon": -73.904, "label": "South Bronx, NY (low-income QCT)", }, { "lat": 34.073, "lon": -118.4, "label": "Beverly Hills, CA (affluent control)", }, ] } # Run the Actor and wait for it to finish run = client.actor("malonestar/hud-qct-lihtc-boost-screener").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 '{ "assets": [ { "lat": 40.815, "lon": -73.904, "label": "South Bronx, NY (low-income QCT)" }, { "lat": 34.073, "lon": -118.4, "label": "Beverly Hills, CA (affluent control)" } ] }' | apify call malonestar/hud-qct-lihtc-boost-screener --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=malonestar/hud-qct-lihtc-boost-screener", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "HUD QCT LIHTC Boost Screener — 130% Basis by Coordinate", "description": "Qualified Census Tract (QCT) lookup for any coordinate — instantly flag the LIHTC 130% eligible-basis boost per site. Batch lat/lon points against HUD's official QCT layer; each returns in_qct, the 11-digit tract GEOID, state, county FIPS, tract, QCT vintage, and boost-eligible flag. MCP-ready.", "version": "1.0", "x-build-id": "hIZGRL0g1KV2SlaSI" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/malonestar~hud-qct-lihtc-boost-screener/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-malonestar-hud-qct-lihtc-boost-screener", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/malonestar~hud-qct-lihtc-boost-screener/runs": { "post": { "operationId": "runs-sync-malonestar-hud-qct-lihtc-boost-screener", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/malonestar~hud-qct-lihtc-boost-screener/run-sync": { "post": { "operationId": "run-sync-malonestar-hud-qct-lihtc-boost-screener", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "assets" ], "properties": { "assets": { "title": "Assets (coordinates to screen)", "type": "array", "description": "List of points to check against HUD Qualified Census Tracts (QCT). Each item is an object with numeric lat and lon (WGS84 decimal degrees) and an optional label echoed into the output. One billable result row is produced per asset, telling you whether the coordinate falls in a QCT (and therefore qualifies a LIHTC project for the 130% eligible-basis boost)." }, "maxAssets": { "title": "Max assets", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Safety cap on how many assets to screen in one run. Extra assets beyond this limit are skipped.", "default": 500 } } }, "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 } } } } } } } } } } ```