FRED has data on an array of consumer expectations, confidence, and sentiment that can potentially yield information about future economic conditions.

FRED recently added the home purchase sentiment index (HPSI) to that collection of data. Reported by Fannie Mae, the HPSI is a composite index designed to track consumers’ housing-related attitudes based on six questions from the National Housing Survey:

• Is it a good time to buy or sell a house?
• Will home prices and mortgage interest rates rise, fall, or stay put?
• How concerned are you about losing your job?
• How does your current income compare with last year’s?

The index has a value of 60 in March 2011, its reference period. Its March 2025 value (latest available at the time of this writing) is 68.1, with a high of 93.8 in August 2019 and a low of 56.7 in October 2022.

The FRED graph above shows the HPSI (solid blue line) along with the University of Michigan’s overall consumer sentiment index (dashed green line). Between March 2011 and March 2025, these indexes have broadly moved hand in hand. That may not be surprising because overall consumer sentiment encompasses home purchase sentiment.

But, since 2020, changes to housing market conditions affecting the financial burden of households may have driven these sentiments apart. For example, some estrangement is visible from mid 2021 to mid 2022, with a wider gap between the two lines.

To learn even more about the HPSI, see this Economic Letter published by the San Francisco Fed.

How this graph was created: Search FRED for and select “Fannie Mae’s National Housing Survey: Home Purchase Sentiment Index (HPSI).” From the “Edit Graph” panel, use the “Add Line” tab to search for and select “University of Michigan: Consumer Sentiment.” Make sure to click “Add data series.” Change its units to be 100 on 2011-03-01 and apply the formula (a/100)*60 so that it has the same reference period and base value as the HPSI.

Suggested by Diego Mendez-Carbajo.

On April 18, 2025, the Bureau of Labor Statistics released the first quarter data for total nonfarm employees at the state and metro levels. At the state level, Texas led all states, adding 74,100 jobs in the first quarter. California had the largest decline, losing 54,800 jobs.

The FRED map above shows the change in employment in each state during the first quarter. If you sum up the individual states, you’ll see a net gain of 301,600 jobs. This is different from the reported number for the nation, which was 456,000 as of April 29. This difference is because the state level has different sampling and tends to have a larger margin of error than the national number.

At the metro level, the Philadelphia-Camden-Wilmington MSA led the nation with 18,700 jobs added in the first quarter. The Los Angeles-Long Beach-Anaheim MSA had the largest decline, losing 25,200 jobs in the first quarter. These numbers tend to vary greatly from quarter to quarter, with even greater sampling errors than the errors at the state and national levels. So, take these numbers with a grain of salt.

How these maps were created: Search FRED for “total nonfarm employees in Missouri” (or any other state). Click “View Map” and then “Edit Map.” Change the units to “Change, Thousands of Persons” and the frequency to quarterly with aggregation method “End of Period.” Under “Format,” select “User Defined Method” for how to group the data: Switch the number of color groups to 3 and change the colors to red for states that shed jobs (or a value less than or equal to 0), light green for states with modest job growth (or less than 10), and dark green for states with strong growth (or a value large enough to incorporate the rest of the states). For the second map, repeat the process with an MSA—St. Louis, for example.

Suggested by Jack Fuller and Charles Gascon.

FRED has data for various segments of the US labor force, including employment of native-born and foreign-born workers. Today’s post taps into unemployment data for these groups.

The FRED graph above shows US Bureau of Labor Statistics data on the fraction of the native-born labor force (solid blue line) and foreign-born labor force (dashed green line) who are out of a job and actively seeking one.

These data don’t show major differences between the groups, but recent research from Alexander Bick at the St. Louis Fed uncovered nuances in the data. He used the BLS survey that collects household labor market information to examine unemployment rates of immigrants according to how long they had resided in the US.

Between 2014 and 2024, immigrants in the US for more than 3 years often had slightly lower unemployment than native-born workers. More-recent immigrants often had higher unemployment than both those groups.

Average unemployment rates since 2022

• Non-recent immigrants 3.3%
• U.S. natives 3.8%
• Recent immigrants 7.6%

Bick’s analysis also considers the potential effects of undercounting immigrants. If unemployed immigrants are undercounted to a large-enough degree, actual demand for labor may be weaker than what official data show. But he finds the impact to be small: In October 2024, an estimate of unreported recent immigrants would have increased the overall unemployment rate by 0.1 percentage points.

For more about this and other research, visit the publications page of the St. Louis Fed’s website, which offers an array of economic analysis and expertise provided by our staff.

How this graph was created: Search FRED for and select “Unemployment Rate – Native Born.” Click on the “Edit Graph” button, select the “Add Line” tab, and search for “Unemployment Rate – Foreign Born.” Don’t forget to click “Add data series.”

Suggested by Diego Mendez-Carbajo.