FHFA

Overview

The Federal Housing Finance Agency (FHFA) was established in 2008 by the US Housing & Economic Recovery Act and is responsible for oversight and regulation of Fannie Mae, Freddie Mac, and the Federal Home Loan Bank System. FHFA publishes monthly and quarterly reports of housing finance markets.

Example topics covered:

• Singe family home appraisals for new purchases and refinances
• Average housing price changes in repeat sales or refinances on the same properties
• Mortgage originations, terms, performance, and borrower demographics
• Rates of delayed payments, foreclosures, bankruptcies, and forbearance of mortgages

Key Attributes

Geographic Coverage	United States
Entity Level	House Prices and Mortgage Performance: Country, State, Census Metropolitan Division, Census Core Based Statistical Area Uniform Appraisals: Country, State, County, Census Core Based Statistical Area, Census Metropolitan Division, Census Tract
Time Granularity	House Prices: Monthly, Quarterly Mortgage Performance: Quarterly Uniform Appraisals: Quarterly, Annually
Release Frequency	House Prices: Monthly, last Tuesday Mortgage Performance and Uniform Appraisals: Quarterly, last day of the quarter
History	Uniform Appraisals: Since March 2013 Mortgage Performance: Since March 2002 House Prices: Since March 1975

info

As with all Public Domain datasets, Cybersyn aims to release data on Snowflake Marketplace as soon as the underlying source releases new data. We check periodically for changes to the underlying source and, upon detecting a change, propagate the data to Snowflake Marketplace immediately. See our release process for more details.

Notes

All Cybersyn products follow the EAV (entity, attributes, value) model with a unified schema. Entities are tangible objects (e.g. geography, company) that Cybersyn provides data on. All timeseries' dates and values that refer to the entity are included in a timeseries table. Descriptors of the timeseries are included in an attributes table. Data is joinable across all Cybersyn products that have a GEO_ID. Refer to Cybersyn Concepts for more details.

Uniform Appraisal Dataset (UAD): Details trends found in single family appraisals collected by Fannie Mae and Freddie Mac for both new purchases and refinances since 2013. It is based on a 5% nationally representative random sample of appraisals for single-family mortgages acquired by the Enterprises. The appraisal statistics may be grouped by neighborhood characteristics, building details, and geographic levels (e.g. national, state, Metropolitan Statistical Areas (MSAs), county, and census tract) and are collected for both new purchases and refinances.

National Mortgage Database (NMDB): In partnership with the CFPB, the FHFA developed the National Mortgage Database (NMDB), a nationally representative 5% sample of residential mortgages in the US. It details loan-level data covering mortgage originations, terms, and performance, along with borrower demographics.; and indicates rates of delayed payments, foreclosures and bankruptcies, and forbearance. The data is grouped by geographic levels (national, state, CBSA/MSD level) and includes both Enterprise and non-Enterprise individual mortgages from 2002 onwards.

House Price Index (HPI): A collection of publicly available price indexes that measure movement of single-family house prices in the US since 1975. The FHFA compiles this data by examining repeat mortgage transactions on single-family properties whose mortgages have been purchased or securitized by Fannie Mae or Freddie Mac, across various geographic levels. The data is useful for estimating changes in house price fluctuations and affordability in specific geographic areas (national, state, CBSA/MSD level).

Cybersyn Products

Tables above are available in the following Cybersyn data products:

• US Real Estate
• Cybersyn Foundations - PAID

Sample Queries

Determine the percent of conventional mortgages at or nearing default in California

Create a timeseries chart with percent of conventional mortgages either: 90-180 days past due, in forbearance, or in bankruptcy in California

SELECT
	ts.date,
	geo.geo_name,
	SUM(ts.value) AS pct_near_default
FROM
	cybersyn.fhfa_mortgage_performance_timeseries AS ts
	JOIN
cybersyn.fhfa_mortgage_performance_attributes AS att ON (ts.variable = att.variable)
	JOIN cybersyn.geography_index AS geo ON (geo.geo_id = ts.geo_id)
WHERE
	att.variable_group IN (
    	'Percent 90 to 180 Days Past Due Date',
    	'Percent in Forbearance',
    	'Percent in the Process of Foreclosure, Bankruptcy, or Deed in Lieu'
	)
	AND att.market = 'All Mortgages'
	AND geo.geo_name = 'California'
GROUP BY
	ts.date,
	geo.geo_name
ORDER BY
	ts.date,
	geo.geo_name;

Find addresses in metros that experienced the top house price growth over 5 years to target for a marketing campaign to drive home equity lines of credit

WITH base_price AS (
    	SELECT
        	ts.geo_id,
        	ts.value
    	FROM
        	cybersyn.fhfa_house_price_timeseries ts
    	WHERE
        	ts.date = '2018-03-31'
        	AND ts.variable = 'FHFA_HPI_traditional_purchase-only_quarterly_SA'
	),
	current_price AS (
    	SELECT
        	ts.geo_id,
        	ts.value
    	FROM
        	cybersyn.fhfa_house_price_timeseries ts
    	WHERE
        	ts.date = '2023-03-31'
        	AND ts.variable = 'FHFA_HPI_traditional_purchase-only_quarterly_SA'
	),
	metros AS (
    	SELECT
        	geo.geo_name AS metro_name,
        	geo.geo_id,
        	100 * (c.value - b.value) / NULLIFZERO(b.value) AS growth
    	FROM
        	base_price b
        	JOIN current_price c ON c.geo_id = b.geo_id
        	JOIN cybersyn.geography_index geo ON geo.geo_id = b.geo_id
        	AND geo.level IN (
            	'CensusCoreBasedStatisticalArea',
            	'CensusMetropolitanDivision'
        	)
    	ORDER BY
        	growth DESC
    	LIMIT
        	3
	)
SELECT
	metros.metro_name,
	addresses.number,
	addresses.street,
	addresses.street_type,
	addresses.city,
	addresses.state,
	addresses.zip
FROM
	metros
	JOIN cybersyn.geography_relationships cities ON metros.geo_id = cities.geo_id
	and cities.related_level = 'City'
	JOIN cybersyn.geography_relationships zips ON metros.geo_id = zips.geo_id
	AND zips.related_level = 'CensusZipCodeTabulationArea'
	JOIN cybersyn.us_addresses addresses ON addresses.id_zip = zips.related_geo_id
	AND addresses.id_city = cities.related_geo_id
LIMIT
	12;

Create a map to visualize average home prices across the country

Join the house prices timeseries data with the geospatial boundaries

SELECT
ts.*,
char.value AS coordinates_geojson
FROM cybersyn.fhfa_house_price_timeseries AS ts
JOIN cybersyn.geography_characteristics AS char
    ON (
ts.geo_id = char.geo_id
AND char.relationship_type = 'coordinates_geojson'
  )
LIMIT 10;

Disclaimers

The data in this product is sourced from the Federal Housing Finance Agency (FHFA).

This product uses FHFA data but is neither endorsed nor certified by FHFA.

Cybersyn is not endorsed by or affiliated with any of these providers. Contact snowflake-public-data@snowflake.com for questions.