The FRED^® Blog

The takeaway

The most recent measure of the cost of owning a home (vs. renting a home) remains near historical peaks, suggesting renting today is a relatively better financial choice for housing.

To own or not to own?

Which is the better financial choice—owning or renting your home? The FRED Blog has discussed how the cost of owning a home and the cost of renting a home tend to move together. An indexed ratio of house prices to rents helps show their relative values more clearly and, by extension, can help examine the value of one choice over the other.

A high ratio means house prices are growing faster than rents, making homeownership a relatively more expensive proposition. A lower ratio means buying a home is a comparatively better deal.

The data

Researchers at the Dallas Fed used price index data for homeownership and renting to create an index that shows the rapid rise of homeownership costs in 2020 and the high values since then.

Our FRED graph above allows us to approximate their ratio and track the relative cost of owning a home from first quarter 1975 to first quarter 2026. Here we use two different price indexes to create our ratio of house prices to rents: the all-transactions house price index from the U.S. Federal Housing Finance Agency and the CPI for rents from the Bureau of Labor Statistics. Both indexes were “re-based” to a value of 100 in January 2015. Our index shows a rapid rise and continued high values of homeownership cost, similar to what the Dallas Fed research showed.

Deeper analysis

The report from the Dallas Fed uses both U.S. and international data to identify patterns and the likely future evolution of an elevated house price-to-rent ratio. It concludes that “Rents rise along with inflation, while nominal house-price growth trails inflation. On an inflation-adjusted basis, rents change very little, but house prices fall sharply.”

In this homeownership costs ratio, the numerator is house prices and the denominator is rent prices. The ratio will become smaller if the numerator falls or the denominator rises. Because inflation increases rent prices relatively more than house prices, higher inflation would drive the ratio down, closer to its historical average.

How this graph was created: Search FRED for and select the series “All-Transactions House Price Index for the United States.” Click “Edit Graph,” use “Customize data” to search for “Consumer Price Index for All Urban Consumers: Rent of primary residence,” and click “Add.” Input the formula a/b and click “Apply.”

Suggested by Houston Myer and Diego Mendez-Carbajo.

The takeaway

More workers are using generative AI tools, using them more often, and saving more time because of them, according to new survey data in FRED.

The survey

Economists Alexander Bick, Adam Blandin, and David Deming built the Generative Artificial Intelligence Adoption Tracker using data from the Real-Time Population Survey (RPS). The RPS is a nationally representative online survey of working-age adults aged 18 to 64. Each quarter, employed respondents report how fast and how intensely they’re adopting AI technology both at work and outside of work.

The data

Our FRED graph above shows three data series from the generative AI adoption tracker: “Use Last Week for Work” (solid blue line), “Work Hours Assisted” (dashed green line), and “Time Savings” (dashed orange line)—all measured as a percent of employed adults (left axis) or percent of work hours (right axis).

All three lines trend steadily upward. The share of employed adults who used generative AI for work in the past week grew from 28.2% in Q3 2024 to 39.2% by Q2 2026, indicating adoption is broadening. The share of work hours assisted by generative AI grew from 4.1% to 6.3% over roughly the same stretch. Perhaps most importantly, the share of work hours saved thanks to AI assistance grew from 1.6% to 2.2%.

The summary

The information is based on how respondents self-report their “time saved,” so the measurements are inherently approximate. But these three data series together do tell a coherent story of AI adoption: More workers are trying generative AI, and the ones already using it are leaning on it more heavily. Rising time savings suggest AI is not just a novelty; it’s starting to free up real work hours. If this trend holds, generative AI could show up in broader productivity statistics in the years ahead.

How this graph was created: Search FRED for “Generative Artificial Intelligence, Use Last Week for Work: Employed Adults.” Click “Edit Graph” and select the “Add Line” tab. Search for “Generative Artificial Intelligence, Work Hours Assisted: Employed Adults” and click “Add Data Series.” Repeat the last step to search for and add “Generative Artificial Intelligence, Time Savings: Employed Adults.” Select the “Format” tab to customize line 2 and line 3 by selecting “Y-Axis position: Right.”

Suggested by Diego Mendez-Carbajo.

National employment growth in the second quarter of 2026 accelerated slightly to 0.3% compared with one year ago. However, this aggregate figure conceals wide variation in job growth across U.S. states: 38 states experienced job growth, while 12 states plus the District of Columbia experienced job losses relative to one year ago. The median state experienced job growth of roughly 0.4%. Moreover, 25 states experienced growth above the median, highlighting growing resilience.

Our FRED map above shows the percent change from a year ago in employment in each state during the second quarter. Minnesota and Nevada had the strongest growth, with net job gains of 1.4% and 2%, respectively. The District of Columbia and Montana had the largest net job losses, at -4.8% and -1%, respectively.

At the metro level, job growth on average displayed weaker trends than the state-level data, with the median MSA experiencing a slight job gain of 0.2% in the second quarter of 2026 relative to one year ago. The Waterloo-Cedar Falls, Iowa, MSA had the largest net job loss at -3.2%. In contrast, the Sandusky, Ohio, MSA had the strongest job growth, at 5%. 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, be careful not to read too much into these data.

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 “Percent Change from Year Ago” 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 yellow for states with modest job growth (or less than 0.4), 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 Violeta Gutkowski and Rehann Silvanus.