Federal Reserve Economic Data

Mortgage loans allow households to buy a home or to borrow money against the value of a home they already own. With mortgage loans, the real estate is offered as collateral, a payback guarantee for the lender.

Our FRED graph above shows the billions of dollars lent by large US banks to consumers in newly issued mortgage loans. Data are available between the third quarter of 2013 and the second quarter of 2025. Each of the three lines represents a separate purpose for borrowing these funds:

• Purchasing a home (solid blue line)
• Refinancing the mortgage to secure more affordable repayment terms, including the interest rate on the loan (dashed green line)
• Refinancing the mortgage to receive a cashout for the difference between the new mortgage loan and the original loan (dashed orange line)

The relative importance of each purpose has changed over time.

Between 2013 and 2016, for every new dollar borrowed to purchase a home, another dollar was borrowed to refinance a mortgage loan. Between mid-2017 and mid-2019, new borrowing for home purchases outpaced refinancing. Between 2019 and  2021, during the COVID-19 pandemic, refinancings rose above new mortgages as homeowners took advantage of lower interest rates, despite many homeowners changing locations and moving into new homes.

Starting in 2022, when interest rates began to rise, mortgage refinancing quickly declined. Soon after, borrowing to purchase a home also declined, but not as much. As of mid-2025, the latest data available at the time of this writing, new borrowing to purchase a home was 2.7 times larger than borrowing to refinance a mortgage loan.

How this graph was created: Search FRED for and select “Large Bank Consumer Mortgage Originations: Purpose Type: Purchase.” Click on the “Edit Graph” button and select the “Add Line” tab to search for and select “Large Bank Consumer Mortgage Originations: Purpose Type: Refinance – Rate / Term & Other Refinance.” Don’t forget to click on “Add data series.” Repeat the last two steps to search for and add the third series: “Large Bank Consumer Mortgage Originations: Purpose Type: Refinance – Cashout.”

Suggested by Diego Mendez-Carbajo.

When the US Census conducts the monthly Current Population Survey on behalf of the Bureau of Labor Statistics (BLS), it asks households more than 200 questions about their employment status. One of those questions is “Do you currently want a job, either full or part time?”

As the BLS explains it: “People who want a job now are a subset of those not in the labor force.” The labor force is made up of (1) persons who are employed and (2) persons who are not employed but who had actively looked for employment in the four weeks prior to being asked about it.

Let’s put these concepts in the context of data available in FRED.

Our FRED graph above shows the percentage of persons who want a job out of the total pool of those who aren’t in the labor force. The blue line represents men, and the green line represents women.

The data, available since 1994, show very similar proportions for men and women. The rates ebb and flow with the business cycle, noticeably increasing during recessions (shaded areas in the graph). Over the past decade, an average of 6.6% of men and 5.1% of women who weren’t in the labor force would have liked to have gotten a job. Aside from the large spikes during the COVID-19 pandemic, these values have been fairly constant, suggesting the size of the pool of people interested in joining the labor market is relatively stable.

How this graph was created: Search FRED for and select “Not in Labor Force – Want a Job Now, Men.” Click on “Edit Graph” and use the “Edit Line” tab to customize the data by searching for “Not in Labor Force, Men.” Don’t forget to click on “Add.” Next, type in the formula (a/b)*100 and click on “Apply Formula.” Next, use the “Add Line” tab to search for and add “Not in Labor Force – Want a Job Now, Women.” Lastly, customize those data by adding “Not in Labor Force, Women” and applying the formula described earlier.

Suggested by Diego Mendez-Carbajo.

The government shutdown affected economic data collection

The 43-day lapse in congressional appropriations, which began on October 1 and lasted until November 12, 2025, has been the longest on record. Without funds to pay for employee salaries and business operations, multiple federal agencies stopped collecting and reporting data.

After funding was restored and normal operations resumed, plans were announced to backfill many of the data gaps created by the shutdown. But not all gaps will be filled. Some will likely remain blank spaces.

An example of missing data

Our FRED graph above offers a historical example of a missing observation in a time series of data. The solid blue line shows the average price of field-grown tomatoes in US cities between January 2012 and December 2015. The source of the data, the US Bureau of Labor Statistics, did not report a value for October 2013. So, there’s a gap in the plotted line on that date. If you download the data file for this time series shown in the graph, there’ll be a blank value next to the 2013-10-01 date.

How do statistical agencies deal with missing data?

Statistical agencies use a process known as imputation to handle missing data points in surveys and to estimate the value of certain economic activities that are not directly measurable. However, when the surveys themselves are not completed, the imputation techniques cannot be applied and there will be missing data across multiple data releases. You can see a complete list of canceled releases from the BLS here.

Note: The FRED team updates the FRED database as soon as new data are made available. When individual data are not reported by the source, they’re shown as blank spaces in a FRED graph and as empty values in a data file download. The FRED Team does not impute values for missing observations.

How this graph was created: Search FRED for and select “Average Price: Tomatoes, Field Grown (Cost per Pound/453.6 Grams) in U.S. City Average.”

Suggested by Diego Mendez-Carbajo.