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.

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.

In the early stages of the pandemic, stimulus checks and other factors made vehicles a popular large purchase for households. But disruptions in supply chains resulted in a shortage of new vehicles, which are strongly reliant on global supply chains. The shortage in semiconductors played a big role here, given their importance in the production of cars and their components. New vehicle production slowed down as a result, decreasing inventories and increasing prices.

The reopening of the economy and the shortage of new vehicles increased demand for used vehicles, which led to substantial inflation. Indeed, over the course of the pandemic, used car inflation was considered a major driving force of headline inflation.

In January 2022, however, the trend reversed: Used car inflation has started to decline. And, as supply chains are returning to normal, new cars have become more readily available and the excess demand previously directed toward  used cars has shifted back to new cars.

Used cars dealers had been setting their prices or selling their cars at auction for a profit, but they’re now forced to negotiate with buyers, thus pushing prices down. Whether or not these trends continue in the medium term will partially depend on how supply chain disruptions will affect the manufacturing of new vehicles.

How this graph was created: Search FRED for “CPI Used Vehicles” and select “Consumer Price Index for All Urban Consumers: Used Vehicles and Trucks in U.S. City Average.” From the “Edit Graph” panel, change the units for both lines to “Percent change from a year ago.”

Suggested by Ana Maria Santacreu and Jesse LaBelle.