# CFPB FFIEC HMDA Mortgage Lending Disclosure Scraper (`jungle_synthesizer/cfpb-ffiec-hmda-mortgage-lending-disclosure-scraper`) Actor Pulls loan-level HMDA mortgage disclosure records from the CFPB/FFIEC Data Browser -- pricing, underwriting, and tract-demographic detail for every originated, denied, or withdrawn application, filterable by year, state, loan outcome, and filer LEI, with filer names joined in. - **URL**: https://apify.com/jungle_synthesizer/cfpb-ffiec-hmda-mortgage-lending-disclosure-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** Business - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.80 / 1,000 record scrapeds 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/actors/running/actors-in-store.md#pay-per-event ## What's an Apify Actor? An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes. 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. Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md. ## 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. Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run. For examples already wired to this Actor's own input schema, see the [API](#api) section below. Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`). # README ## CFPB HMDA Mortgage Disclosure Scraper Pulls loan-level mortgage disclosure records from the [CFPB/FFIEC HMDA Data Browser](https://ffiec.cfpb.gov/data-browser/). Returns pricing (rate, points, fees), underwriting (DTI, LTV, credit score type, automated underwriting results), applicant demographics, and census-tract detail for every originated, denied, or withdrawn mortgage application reported under the Home Mortgage Disclosure Act — filterable by year, state, loan outcome, and filer LEI, covering 2018 through 2025. *** ### CFPB HMDA Scraper Features - Returns all 78 loan-level fields per record — pricing, underwriting, applicant, and tract demographics, not just a summary count - Joins each record's filer LEI to its institution name, so output is usable without a second lookup - Filters stack in one run: select multiple years, multiple states, and multiple loan outcomes together - Covers every published HMDA year, 2018-2025, and all 50 states, DC, and US territories - No account or API key required — the data is public *** ### Who Uses HMDA Mortgage Data? - **Mortgage lenders** — benchmark competitor pull-through, pricing, and denial rates by state or MSA - **Fair-lending and CRA consultants** — run disparity analysis across race, ethnicity, and sex fields - **Bank M\&A teams** — size a target institution's origination book before a deal - **Proptech and mortgage-fintech teams** — pull market-sizing data for a new product or region - **Community development and housing researchers** — track lending patterns by census tract over time *** ### How CFPB HMDA Scraper Works 1. Choose the years, states, and loan outcomes you want — every filter accepts more than one value. 2. The actor queries the Data Browser for that selection and returns the full loan tape that matches. 3. Each record's filer LEI is resolved to the reporting institution's name automatically. 4. Records stream into your dataset as structured JSON, capped at your `maxItems`. *** ### Input ```json { "years": ["2024"], "states": ["DC"], "actionsTaken": ["1"], "leis": [], "maxItems": 500 } ``` | Field | Type | Default | Description | |----------------|------------------|------------------|---| | `years` | array (required) | `["2024"]` | HMDA activity year(s) to query. 2018 through 2025 — select more than one to pull multiple years in one run. | | `states` | array | *(nationwide)* | US state/territory code(s) to filter by. Leave empty to query nationwide — a much larger result set. | | `actionsTaken` | array | *(all outcomes)* | Loan outcome code(s): 1 Originated, 2 Approved not accepted, 3 Denied, 4 Withdrawn, 5 Closed for incompleteness, 6 Purchased loan, 7 Preapproval denied, 8 Preapproval approved not accepted. Leave empty for every outcome. | | `leis` | array | *(all filers)* | Legal Entity Identifier(s) of specific reporting institutions. Leave empty to include every filer matching the other filters. | | `maxItems` | integer | `500` | Maximum number of loan records to return. | Filtering to a single state's originated loans for one institution: ```json { "years": ["2023", "2024"], "states": ["CA"], "actionsTaken": ["1"], "leis": ["549300AG64NHILB7ZP05"], "maxItems": 1000 } ``` *** ### CFPB HMDA Scraper Output Fields ```json { "lei": "549300AG64NHILB7ZP05", "institution_name": "LOANDEPOT.COM, LLC", "activity_year": 2023, "state_code": "DC", "county_code": "11001", "census_tract": "11001001901", "derived_msa_md": "47894", "action_taken": 1, "loan_type": 2, "loan_purpose": 1, "loan_amount": 465000, "interest_rate": 6.625, "rate_spread": -0.1, "loan_to_value_ratio": 80, "income": 185, "debt_to_income_ratio": "36%-<40%", "derived_race": "Black or African American", "derived_sex": "Joint", "applicant_race_1_5": [3], "aus_1_5": [1], "tract_minority_population_percent": 48.2, "scrapedAt": "2026-10-15T07:33:31.931Z" } ``` | Field | Type | Description | |--------------------------------------------------------------------------------------------------------|----------------|---| | `lei` | string | Legal Entity Identifier of the reporting institution | | `institution_name` | string | Reporting institution's name | | `activity_year` | integer | HMDA reporting year | | `state_code` / `county_code` / `census_tract` | string | Property location (state, county FIPS, tract FIPS) | | `derived_msa_md` | string | Metropolitan statistical area / metro division code | | `action_taken` | integer | Loan outcome code (see the Input table) | | `loan_type`, `loan_purpose`, `lien_status` | integer | Conventional/FHA/VA/RHS, purchase/refi/other, first/subordinate lien | | `loan_amount`, `property_value`, `income` | number | Dollar figures (income in thousands) | | `interest_rate`, `rate_spread`, `loan_to_value_ratio` | number | Pricing figures | | `total_loan_costs`, `origination_charges`, `discount_points`, `lender_credits` | number | Closing-cost breakdown | | `debt_to_income_ratio` | string | DTI bucket or exact value | | `applicant_credit_score_type`, `co_applicant_credit_score_type` | integer | Credit scoring model used | | `derived_race`, `derived_ethnicity`, `derived_sex` | string | Single-value derived demographic categories | | `applicant_race_1_5`, `applicant_ethnicity_1_5`, `co_applicant_race_1_5`, `co_applicant_ethnicity_1_5` | array | Up to 5 reported race/ethnicity codes per applicant | | `aus_1_5` | array | Automated underwriting system(s) used and result code(s) | | `denial_reason_1_4` | array | Denial reason code(s), populated when `action_taken` is 3 | | `tract_population`, `tract_minority_population_percent`, `ffiec_msa_md_median_family_income` | number/integer | Census-tract demographics joined in by the source | | `scrapedAt` | string | ISO-8601 timestamp when the record was emitted | 78 fields are returned per record in total — the table above covers the ones most buyers filter or join on. *** ### FAQ #### How do I scrape HMDA mortgage data? Give the CFPB HMDA Mortgage Disclosure Scraper a year (or several), optionally a state and a loan outcome, and run it. It returns every matching loan-level record as structured JSON — no manual CSV export from the Data Browser required. #### What data can I get from the HMDA Data Browser? Every field the CFPB publishes under Regulation C: loan pricing and terms, underwriting detail (DTI, LTV, AUS results, denial reasons), applicant demographics, and census-tract statistics, for any combination of year, state, and loan outcome from 2018 through 2025. #### Can I filter by lender? Yes. Pass one or more Legal Entity Identifiers in `leis` to narrow the result to specific reporting institutions. The actor also resolves each LEI to the filer's name automatically. #### Do I need an account or API key for the HMDA Data Browser? No. HMDA data is published by the CFPB as public record, and this actor needs no credentials to query it. #### How much does the CFPB HMDA Scraper cost to run? Cost scales with the number of records returned — narrow your year/state/outcome selection to control it, or set `maxItems` to cap a run outright. *** ### Need More Features? Need a different filter, a county/MSA-level breakout, or a cross-year rollup? [File an issue](https://console.apify.com/actors/issues) or get in touch. ### Why Use This Scraper? - **Loan-level, not just counts** — returns the full 78-field loan tape, not a state/year aggregate. - **LEI-keyed output** — every record carries both the filer's LEI and its resolved institution name, so it joins straight into any other LEI-keyed dataset without a manual lookup step. - **One run, many filters** — years, states, and outcomes all stack in a single request instead of forcing one run per combination. # Actor input Schema ## `sp_intended_usage` (type: `string`): What will this data feed? E.g. lead lists, KYB checks, price tracking. ## `sp_improvement_suggestions` (type: `string`): Provide any feedback or suggestions for improvements. ## `sp_contact` (type: `string`): We'll personally help with your use case. No spam. ## `years` (type: `array`): HMDA activity year(s) to query. Comma-filters stack in one request -- select as many as you need. ## `states` (type: `array`): U.S. state/territory code(s) to filter by. Leave empty to query nationwide (a much larger result set). ## `actionsTaken` (type: `array`): Loan outcome code(s) to filter by. Leave empty to include every outcome. ## `leis` (type: `array`): Legal Entity Identifier(s) of specific reporting institutions to filter by. Leave empty to include every filer matching the other filters. ## `maxItems` (type: `integer`): Maximum number of loan records to return. ## Actor input object example ```json { "sp_contact": "Share your email here...", "years": [ "2024" ], "states": [ "DC" ], "actionsTaken": [ "1" ], "maxItems": 500 } ``` # Actor output Schema ## `results` (type: `string`): No description # 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 = { "sp_contact": "Share your email here...", "years": [ "2024" ], "states": [ "DC" ], "actionsTaken": [ "1" ], "maxItems": 500 }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/cfpb-ffiec-hmda-mortgage-lending-disclosure-scraper").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 = { "sp_contact": "Share your email here...", "years": ["2024"], "states": ["DC"], "actionsTaken": ["1"], "maxItems": 500, } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/cfpb-ffiec-hmda-mortgage-lending-disclosure-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}") for item in client.dataset(run.default_dataset_id).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "sp_contact": "Share your email here...", "years": [ "2024" ], "states": [ "DC" ], "actionsTaken": [ "1" ], "maxItems": 500 }' | apify call jungle_synthesizer/cfpb-ffiec-hmda-mortgage-lending-disclosure-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "type": "http", "url": "https://mcp.apify.com/?tools=fetch-actor-details,jungle_synthesizer/cfpb-ffiec-hmda-mortgage-lending-disclosure-scraper" } } } ``` The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer ` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations). ## OpenAPI specification Download the OpenAPI definition: https://api.apify.com/v2/actors/1oxvn7dNSKhcUfQtq/builds/uwhaTjX5d4T8VkapC/openapi.json