Federal Reserve Economic Data

The FRED® Blog

Houses, up and down

An international comparison of house price movement

We recently highlighted state-by-state comparisons of house price appreciation. Today, we’re going international. Thanks to the Bank for International Settlements, we have residential property prices for a selection of countries, in both nominal and real terms. Here we focus on the latter, which show how house prices evolve compared with other prices. We also focus on countries with relatively long sample periods so we can document long-term trends.

The graph above shows data for a set of countries where houses have significantly appreciated over the long haul. It’s not a steady trend (e.g., Hong Kong) and doesn’t last through the whole period (e.g., the U.K.’s “weak” property market over the past 10 years); these patterns highlight the adage that past behavior isn’t necessarily a good predictor of future behavior. The graph below shows a different set of countries where the long-term trend is more mixed, even downward facing. The U.S. is part of this group with its distinct “bubble” that the housing market is still recovering from. Switzerland is surprisingly stagnant despite strong population growth, and Korea is even trending down.

How these graphs were created: Search for “BIS house price,” then click the “real” tag in the side bar. Check the series you want shown, and click “Add to Graph.”

Suggested by Christian Zimmermann.

View on FRED, series used in this post: QCAR628BIS, QCHR628BIS, QGBR628BIS, QHKR628BIS, QKRR628BIS, QNZR628BIS, QUSR628BIS, QZAR628BIS

To every thing, there is a season…

Playing with retail data

FRED recently added a lot of new data from the U.S. retail sector—just in time for the holidays. So let’s take this opportunity to play a little game. The release table for monthly retail sales shows plenty of subsectors involved in retail trade. Because these series are not seasonally adjusted, they may show some large seasonal factors at work. The game is to try to predict what the seasonal factors for each sector will look like before displaying the graph for that sector. The graph above reveals the seasonality for three sectors: Sales of office supplies peak in August with the return to school. Sales of gifts and novelties peak in December as people scramble to fill Christmas stockings. And sales of used merchandise bottom out at the start of the year for reasons that escape us. Hint: To identify the months more easily on the graph, reduce the sample period to a few years and hover over the lines to identify the months.

How this graph was created: Go to the release table for monthly retail sales (not seasonally adjusted), check the series you want, and click on “Add to Graph.”

Suggested by Christian Zimmermann.

View on FRED, series used in this post: MRTSSM45321USN, MRTSSM45322USN, MRTSSM45330USN

What makes an economy grow?

The contributions of production factors

What makes an economy grow? At its most basic level, the production of goods and services requires people, machinery, tools, buildings, and know-how. To provide a simple context, we’ll use the growth accounting framework to track the contributions of those factors to the growth of GDP. The graph above does this for the United States, although the picture would be similar for almost any country.

  • The full area shown in the graph is the growth rate of GDP.
  • The blue area is the contribution of increased capital to the growth of output. Capital here means machinery, tools, computers, and structures in which production of goods and services takes place. It is the growth rate of capital multiplied by 0.38, because about 38% of production occurs because of capital.
  • The red area is the contribution of labor in the production process. Here, we add the growth rates of the number of people working and the average hours they work—in other words, the growth rate of the total hours worked in the economy—and multiply that by 0.62, the complement to the 0.38 from capital.
  • The green area is the “magic sauce” that’s not strictly labor or capital: It’s the know-how, the technical innovation, organization, externalities (pollution, for example), and complementarities (public goods, for example, that reinforce each other).

We can see some fluctuations, most notably with labor, but overall all three factors contribute roughly equally to the growth of GDP. It’s no secret that growing an economy requires more investment, people, and innovation and some solid means of organization.

How this graph was created: All the data in the graph are from the Penn World tables. Search for “capital” and click on the U.S. series. From the “Edit Graph” tab, choose “Percent change from previous year” as the units and apply formula a*.38. Then from the “Add Line” option, search for “persons engaged United States.” Select the numbers series. In the “Customize Data” section, search again and take the hours series, then apply formula (a+b)*.62. For the last line, search for “real GDP at constant national prices United States” (this should ensure you find the PWT series) and add the series to the graph. Then, in the “Customize Data” section, add successively the capital, number, and hours series from above. Apply equation a-b*.38-(c+d)*.62. Open the “Format” tab, select graph type “Area” with stacking. Reorder the series to make sure GDP is on top. Change the sample to start in 1952 to avoid the odd data point for capital.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: AVHWPEUSA065NRUG, EMPENGUSA148NRUG, RGDPNAUSA666NRUG, RKNANPUSA666NRUG


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