Introduction HOUSE PRICES HAVE NOW RECOVERED BACK TO THEIR PRE-RECESSION PEAK, at least according to some indices. The Freddie Mac House Price Index, for example, surpassed its pre-2008 peak in the latest release for data through September 2016. In this post I’ll be exploring trends in house prices and exploring different ways of showing how far house prices have come, and in some cases, how far they still have to go.
IN THIS POST I WANT TO LOOK AT THE HISTORY OF HOMEOWNERSHIP in the United States. I’m going to go relatively far back in time, using Census data to compare the evolution of the homeownership rate by state from 1900 through 2015. Along the way we’ll make several different visualizations. Data For data we’re going to rely on the U.S. Census Bureau. This page has a tabulation of historical homeownership rates from 1900 through 2000.
A couple of views of mortgage rates: Mortgage Rates in 2016 This chart shows the weekly average for the 30-year fixed rate morrtgage. Comparing mortgage rates by week This viz compares weekly mortgage rates (30-year fixed rate mortgage) by year. Each line represents a different year. The x-axis display the week of the year (from 1 to 52). Homeownership gif A homeownership rate viz. This shows the U.S. homeownership rate and breaks it out by the age of householder:
IN THIS POST I WANT TO CREATE some data visualizations with R using the recently released Home Mortgage Disclosure Act (HMDA) data. For this post I’m going to return to the 2015 HMDA that you can get from the Consumer Financial Protection Bureau (CFPB) webpage and I discussed earlier. Check out my prior post for more discussion of how we build these data visualizations. R code for graphs posted below
IT WAS A BUSY WEEK FOR ECONOMIC AND HOUSING DATA this week. Below are some data visaulizations I made tracking key trends in economic and housing market data. Homeowner equity increases to $12.7 Trillion in the second quarter of 2016 With house prices rising by nearly 6 percent on a year-over-year basis, homeowners are building back equity. According to the Federal Reserve’s Flow of Funds, owners' equity in real estate was $12.
IN MY PREVIOUS POST we looked at the Job Openings and Labor Turnover Survey (JOLTS) data and plotted a Beveridge Curve. In this post I want to add some more code that allows us to plot Beveridge Curves by industry. For more on the analysis of industry-specific Beveridge Curves, see this paper published in the June 2012 Monthly Labor Review that decomposes shifts in the Beveridge Curve and looks at it by industry.
IN THIS POST WE’LL LOOK AT recent job openings and hires data from the Bureau of Labor Statistics Job Openings and Labor Turnover Survey (JOLTS). R code for selected graphs posted below Job openings and labor turnover Total nonfarm trends Let’s start by looking at aggregate national trends for total nonfarm sector. The plot below compares hires, job openings and separations (the sum of quits, layoffs and discharges, and other separations) over time.
EARLIER THIS WEEK THE U.S. BUREAU OF LABOR STATISTICS released data on consumer expenditures in 2015. In this post I want to examine these data and make a few visualizations. R code for graphs posted below One area I pay close attention to is housing. Housing is the largest single category of expenditure, averaging about 1/3 of total consumer expenditures. The BLS breaks the data out by tenure, so we can see how expenditures vary by owners versus renters.
IN THIS POST WE’LL REVIEW some recent economic and housing market trends. R code for graphs posted below Low mortgage rates Mortgage rates remain low, with the 30-year fixed mortgage averaging 3.43 percent for the week of 8/25. That’s nine consecutive weeks with rates under 3.5 percent. Ever since Brexit. One way I like to look at rates is to compare the weekly rates by week of year (e.g. first week of 2016 compared to first week of 2015).
WE ARE GOING TO EXAMINE THE DISTRIBUTION OF US POPULATION and make an animated gif combining a map and a kernel density estimate of the distribution of county population densities. Density of densities, or density squared. We are going to use the same US County Population Estimates 1790-2010 we used in my previous post. We’ll end up with this: How do we do it? Code First, we’ll load the data and do some manipulations.