Federal Reserve Economic Data

The housing market has been a hot topic of conversation over the past two years, and the FRED Blog has discussed its cycles of sales and new construction, how fast houses sell, and state-level differences in prices and inventories. Today, we revisit the topic by exploring an evocatively named dataset: the hotness index.

Our first FRED map shows the July 2022 values of the market hotness index reported by Realtor.com. This index aims to reflect “fast moving supply and rising demand” conditions and does not necessarily represent high or rising housing prices. (See the source’s site for a description of the index.)

The data, available for selected counties, are color-coded in the map using a scale of cool blue-greens: darker equals hotter. At the time of this writing, two of the three hottest counties are less than an hour’s drive from each other in central Ohio. (Hovering over the map lets you see county names and hotness index scores.)

However, the housing market conditions measured with this index seem to be as fickle as Fall weather. The second FRED map shows the percent change in the index value between July 2021 and July 2022. The county experiencing the largest annual change in market hotness is in the northwest corner of New Mexico. The quadruple-digit change recorded there highlights another trait of the data: Even after excluding this New Mexico measure, which could arguably be labeled as an outlier, the average (mean) percent increase in market hotness was much higher than the typical (median) percent increase. That certainly signals a warming housing market.

Finally, all these county-level changes in housing market conditions are taking place while, at the national level, the number of home sales steadily declined between January 2022 and the time of this writing. You can count on the FRED Blog to continue taking the temperature of this topic for months to come.

How these maps were created: Search FRED for “Market Hotness: Hotness Score in Knox County, OH.” Select the series and click “View Map.” To change the data units to annual growth rates, use “Edit Map” and select “Units: Percent change from year ago.”

Suggested by Latham Fisher and Diego Mendez-Carbajo.

This summer marked the 50th anniversary of the passage of the Education Amendments of 1972, which protect people from discrimination based on sex in education programs or activities that receive federal financial assistance. The well-known Title IX in this legislation made equal access to athletic programs mandatory, which resulted in more women playing sports while enrolled in school. But does a more-even playing field in high school and college athletics result  in comparable employment in the sports industry?

The FRED graph above shows the number of men and women working as athletes, coaches, umpires, and related occupations. Since 2000, when the data from the U.S. Bureau of Labor Statistics became available, for every two women employed in sports, there are on average slightly more than seven men. At the time of this writing, gender parity in sports employment is no closer than it was 20 years ago.

Research has shown that expanding females’ participation in sports increases their labor force participation, and some data available in FRED reflect that trend in particular industries. Because we don’t have data in FRED about women in the sports industry before the passage of the Education Amendments of 1972, we can’t tell if the gender gap we described above is smaller than, equal to, or perhaps even larger than it used to be.

How this graph was created: In FRED, search for and select “Employed full time: Wage and salary workers: Athletes, coaches, umpires, and related workers occupations: 16 years and over: Women.” From the “Edit Graph” panel at the top right corner, use the “Add Line” tab to search for and select “Employed full time: Wage and salary workers: Athletes, coaches, umpires, and related workers occupations: 16 years and over: Men.” Game on!

Suggested by Cameron Tucker and Diego Mendez-Carbajo.

The FRED Blog uses a variety of graph types (line, area, bar, scatter, and pie) to help tell the story behind the numbers. But, sometimes, data plots reveal features in the data we can’t completely understand.

The FRED graph above shows the unemployment rates for men and women who are at least 25 years old and hold a master’s degree. The data, available since the year 2000, are provided by the U.S. Bureau of Labor Statistics’ Current Population Survey (Household Survey). The metadata about these series tells us the data are not seasonally adjusted, so they’re reported without taking into consideration the recurring ups and downs associated with the seasons. The seesaw shape of the data lines is a telltale sign of those seasonal patterns and that’s what piques our curiosity.

Why does the unemployment rate of women holding master’s degrees regularly spike during the third quarter of the year? We don’t know.

The same pattern is also noticeable among men and women holding a bachelor’s degree, but it isn’t immediately visible among men and women holding associate, professional, or doctoral degrees.

We can’t provide an explanation based on solid evidence because there is no additional data that can help us dive deeper on the topic. For example, although there are data on the percent of employees who are women in multiple industries, such as the education sector, those data are seasonally adjusted and can’t help us illuminate the reason behind the seasonal pattern shown in the graph. As is often the case, more research is needed. Stay tuned to the FRED Blog and we’ll share what we find to help tell the story behind the numbers.

How this graph was created: In FRED, search for “Unemployment Rate – College Graduates – Master’s Degree, 25 years and over, Men.” From the “Edit Graph” panel, use the “Add Line” tab to search for and select “Unemployment Rate – College Graduates – Master’s Degree, 25 years and over, Women.”

Suggested by Lily Levin and Diego Mendez-Carbajo.