HMDA Mortgage Loan Data Scraper

HMDA Mortgage Loan Data Scraper

Scrape HMDA mortgage data from the CFPB for any year, state, or loan type, up to a million records per run. Each record includes loan amount, action taken, race, and loan type. No API key or login required. Export to CSV, JSON, Excel, or XML.

The CFPB's HMDA platform publishes millions of mortgage loan records, but downloading them manually means navigating a clunky web interface and stitching together files. This Actor reads the public data directly, filtered by year, state, action taken, race, and loan type, and returns each match in one flat schema.

Who uses it	What they scrape HMDA for
Fair lending analysts	Compare loan outcomes across demographic groups for a given state and year.
Mortgage market researchers	Track origination volumes and loan type mix by geography over time.
Real estate investors	Identify neighborhoods with rising mortgage activity to spot demand trends.
Compliance officers	Audit lending patterns against regulatory benchmarks for specific loan types.
Data journalists	Pull loan-level records to investigate lending disparities or market shifts.

What it does

This Actor collects HMDA mortgage loan records by year, state, action taken, race, and loan type, and returns each one as a flat row.

• 📅 Year filtering: comma-separated years, e.g. 2023,2024, to pull only the periods you need.
• 🗺️ State filtering: comma-separated state codes, e.g. CA,NY, to focus on specific markets.
• 🏦 Loan type filtering: codes 1-4 to isolate conventional, FHA, VA, or USDA loans.
• 👥 Race filtering: comma-separated race codes to analyze demographic lending patterns.
• ✅ Action taken filtering: codes 1-8 to include originated, denied, or withdrawn applications.
• 📊 Flat output: every record is a single row with consistent fields, ready for analysis.

Results export to CSV, JSON, Excel, or XML, or straight from the API.

What you can do with HMDA data

📊 Analyze lending disparities.

A fair lending analyst pulls 2023 records for California, filters by race and action taken, and compares denial rates across demographic groups to flag potential redlining.

🏠 Track mortgage market trends.

A market researcher collects quarterly HMDA data for Texas and Florida, groups by loan type, and reports on shifts from conventional to FHA lending.

📈 Spot real estate demand.

An investor scrapes recent originations in a metro area, maps them by ZIP code, and identifies neighborhoods with rising purchase activity before prices move.

🔍 Audit compliance.

A compliance officer pulls all denied applications for a lender's state, filters by loan type, and checks for patterns that could indicate discriminatory practices.

📰 Investigate lending stories.

A journalist scrapes HMDA records for a specific county, filters by race and action, and builds a dataset to support a story on mortgage access.

Why choose this scraper

	What you get
No API key	Access public HMDA data without registration or rate limits.
Flexible filters	Combine year, state, action, race, and loan type to narrow your query.
Scalable	Collect up to 1,000,000 records per run for large-scale analysis.
Clean schema	Every record is a flat row with consistent fields, no nested JSON.

How it compares

This Actor focuses on core HMDA filters like year, state, action, race, and loan type, while competitors add lender LEI, county, and property details.

Feature	ParseForge	HMDA Mortgage Loan Data Scraper - Lender API	HMDA Mortgage Loan Applications & Lending Data Scraper	HMDA Mortgage Data
Filter by year	Yes	Yes	Yes	Yes
Filter by state	Yes	Yes	Yes	Yes
Filter by loan type	Yes	Yes	Yes	Not listed
Filter by action taken	Yes	Yes	Yes	Not listed
Filter by race	Yes	Not listed	Not listed	Not listed
Filter by lender LEI	Not listed	Yes	Yes	Not listed
Filter by county	Not listed	Yes	Yes	Not listed

Configure the run

Drive the Actor from years, states, action codes, race codes, and loan type codes, alone or together, and filters run as each record is read so only matches reach your dataset. The Input tab lists every parameter.

A first run with the defaults:

{
 "years": "2023",
 "states": "CA",
 "maxItems": 10
}

A larger pull:

{
 "years": "2023",
 "states": "CA",
 "maxItems": 200
}

Pricing

Pay-per-result: $0.0085 per result collected. You pay only for the results written to your dataset.

Results collected	Approximate cost
100 results	$0.85
1,000 results	$8.50
10,000 results	$85.00

New Apify accounts start with $5 in free credit.

Free users

Free-plan runs return up to 10 results as a preview. Upgrade your Apify plan to collect up to 1,000,000 results per run.

Run it

1. Create a free Apify account with $5 in credit.
2. Open the HMDA Mortgage Loan Data Scraper.
3. Set your inputs and any filters, then click Start.
4. Export the results as CSV, Excel, JSON, or XML from the Dataset tab.

Run it programmatically through the Apify API (run-sync-get-dataset-items) or the ApifyClient for JavaScript and Python.

Use with AI agents (MCP)

Give an AI agent live access to HMDA through the Model Context Protocol. Add the Actor to Claude, Cursor, or any MCP client:

claude mcp add --transport http apify "https://mcp.apify.com?tools=parseforge/hmda-mortgage-loan-data-scraper"

Then prompt it in plain language to run the scraper and read back the results.

Troubleshooting

Why am I getting no results?

Check your filter inputs. If you set a state code that has no records for the selected year, or combine filters that are too narrow, the Actor may return nothing. Try broadening your filters or removing some.

The run is taking too long.

Reduce the maximum records limit or narrow your filters. Large queries with many years and states can take time to process.

I got an error about invalid input.

Ensure your comma-separated values are formatted correctly, with no spaces after commas. For example, use CA,NY not CA, NY.

The output has fewer records than expected.

The Actor stops when it reaches the maximum records limit. If you need more, increase the maxItems value up to 1,000,000.

Can I get data for a specific lender?

This Actor does not filter by lender. Use the state, year, and other filters to narrow your query, or consider a competitor that supports lender LEI filtering.

FAQ

Question	Answer
What is HMDA mortgage data?	HMDA stands for Home Mortgage Disclosure Act. It requires many US lenders to report loan-level details like amount, race, loan type, and action taken to the CFPB, which publishes the data publicly.
Do I need an API key or login?	No. This Actor reads the public HMDA data directly from the CFPB platform, so no registration or key is required.
Can I filter by state and year?	Yes. Use the years and states inputs with comma-separated values, like 2023,2024 and CA,NY, to narrow your query.
What loan types can I filter?	You can filter by loan type codes 1 through 4, which correspond to conventional, FHA, VA, and USDA loans.
How many records can I collect per run?	Set the maximum records input up to 1,000,000. The Actor will stop once it reaches that limit.
What format is the output?	Each record is a flat row with consistent fields. You can export the dataset as CSV, JSON, Excel, or XML from Apify.
Can I filter by action taken, like denied loans?	Yes. Use the actionsTaken input with comma-separated codes 1-8 to include originated, denied, withdrawn, and other outcomes.
Is this data updated regularly?	The CFPB updates HMDA data annually. This Actor pulls the latest available records for the years you specify.
Can I filter by race?	Yes. Use the races input with comma-separated race codes to analyze lending patterns by demographic group.
What if I need data for all states?	Leave the states input empty to collect records for all states, or list specific codes to focus on certain markets.

Related actors

Browse the full ParseForge collection for more scrapers.

🆘 Need help? Email parseforge@protonmail.com with your run ID, your input, and what you expected.

⚠️ Disclaimer. This Actor is unofficial and is not affiliated with, endorsed by, or sponsored by Consumer Financial Protection Bureau. It collects only publicly available data. You are responsible for using the collected data in compliance with the source's terms of service and applicable data-protection laws, including GDPR, CCPA, and PIPL. Do not use it to collect personal data unlawfully.