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How housing prices have impacted PCE inflation

Two new measures of PCE inflation from the BEA

FRED recently added two new personal consumption expenditures (PCE) price index data series from the US Bureau of Economic Analysis: one excluding the energy and housing categories from the all-items PCE price index and a second one excluding the food, energy, and housing categories. These series are timely additions to FRED’s substantial repository of measures of trend inflation.

The FRED graph above shows these two new PCE price index series from the BEA (blue and red lines), along with the all-items price index (green line). The data are plotted as inflation rates, or percent changes from a year ago.

Between April 2020 (the end of the COVID-19-induced recession) and roughly the last quarter of 2021, the three measures of PCE inflation moved broadly in sync. However, during the better part of 2022, food, energy, and housing prices changed at a different pace from the remaining PCE price categories. Russia’s invasion of Ukraine was a large shock to international energy and food markets, but housing markets are local. So, what happened to those prices?

In short, and paraphrasing Jerome Powell, because rental leases are renewed annually or even less frequently, housing price inflation tends to lag other prices after speedups or slowdowns in overall inflation. This apparent lack of co-movement between the all-items PCE inflation and the other two measures of personal consumption expenditure prices was due to the timing of new housing data, particularly rental prices. This phenomenon has also been visible during other time periods when inflation changed its direction of growth, particularly during the 2007-2009 recession: See this FRED graph with the three PCE series plotted since 1960.

How this graph was created: In FRED, search for and select “Personal Consumption Expenditures: Services Excluding Energy and Housing (Chain-Type Price Index).” From the “Edit Graph” panel, use the “Add Line” tab to search and select “Personal Consumption Expenditures Excluding Food, Energy, and Housing (Chain-Type Price Index).” Repeat the last step to add “Personal Consumption Expenditures: Chain-type Price Index.” Lastly, use the “Edit Lines” tab to change the units into “Percent Change from Year Ago” and click on the “Copy to all” button to apply the change to the other two series in the graph.

Suggested by Diego Mendez-Carbajo.

Trends in the construction of multifamily housing

The missing middle

The FRED Blog has discussed the relationship between single-family housing starts and completions and also how changes in overall housing market prices are measured. Today we build on the topic of housing by comparing trends in the type of residential constructions erected.

The FRED graph above shows data from the US Census and the US Department of Housing and Urban Development (HUD) on the number of new, privately owned, completed housing units since 1968. There are three size types: single-family buildings (the blue area), buildings with 2 to 4 separate dwellings (the red area), and buildings with 5 or more dwellings (the green area).

The data are shown in a stacked area graph to highlight the relative amounts of each type of housing structure. We also changed the data frequency from monthly to annual to observe the trends more easily. So, what does the graph show?

The number of single-family structures, as a proportion of all types of housing structures, is clearly larger than the number of multifamily structures. This might reflect a preference for single-family housing, but we can’t say for sure because the data do not capture the exact number of individual dwellings in large multi-unit housing.

However, the data do show a trend in the construction of multifamily buildings with 2 to 4 units. That housing trend even has a name: the “missing middle.”

The term was coined to reflect the fact that construction of small-scale and affordable multifamily dwellings has decreased over time. “Middle” housing surged in the early 1970s during a boom in the overall construction of multifamily housing. During this period, nearly half of all new homes were multifamily. This type of housing became relatively less and less popular—as revealed by the shrinking red area in the graph.

Recent research coauthored by Raphael Bostic from the Atlanta Fed notes that small and medium multifamily properties, defined as buildings with 2 to 49 units, comprise over 20% of the US housing stock. This housing segment contains the largest percentage of the lowest-income households and the majority of rental units across the country. You can learn more about this topic here.

How this graph was created: Search the alphabetical list of FRED releases for “New Residential Construction” and select “Table 5. New Privately-Owned Housing Units Completed.” Select the three series naming the number of units per structure and click “Add to Graph.” Use the “Format” tab to change the graph type to “Area” and the stacking option to “Percent.”

Suggested by Zach Wallace-Wright and Diego Mendez-Carbajo.

The housing market hotness index

Can Realtor.com data help Goldilocks find a house?

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.

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