25th, Median (50th) and 75th Percentile Asking Prices for US Homes

US Home Inventory

Sources: Manufacturers' Annual Shipments comes from the Census' M3 report, manufacturing employment is from the BLS. Inflation adjustments made using the GDP implicit price deflator.

The first chart shows the fraction of US government outlays (i.e. spending) that went to defense and non-defense research and development. The 1960s peak coincides with the Cold War Space Race that culminated with the Apollo program moon landing, a feat not repeated by humanity for 39 years now (not that China isn't working toward this goal as we speak). Both defense and non-defense spending have flattened out since then as a fraction of outlays.

The next chart breaks out funding for three Federal research agencies that operate in the non-defense category. The value is again expressed as a fraction of total Federal outlays. Anyone who has spent time in the academic world knows how critical grants from institutions like the NSF, NASA and NIH are to funding day-to-day research operations.

But perhaps looking at R&D spending as a fraction of total government spending is a bit misleading. As the country has developed over the last 60 years, we've instituted new programs and created new agencies which have increased our total expenditures. In reality R&D spending has been growing; it just hasn't been growing as fast as many other components of government spending. The next chart shows the R&D spending in 2005 constant (i.e. real, inflation adjusted) dollars.

Indeed, non-defense R&D spending was 40% higher in 2011 than 2001 on a real basis, but dipped slightly over the same period when expressed in terms of total government spending. So we continue to spend more and more money on research, it's just that we spend even more on other parts of government.

My real interest here isn't to critique the amounts we've been spending but instead goes back to that idea that Tyler Cowen put forth in the Great Stagnation, that is, to "raise the social status of scientists." Admittedly, status goes beyond financial considerations and includes a significant cultural component, but these (and other) research agencies help confer status to US scientists to the extent we define status as funding and employment within the scientist's field of expertise. We haven't done a bad job of things so far, but what could we accomplish if we were to increase funding to these agencies? What might we have already accomplished had that increase started 10, 15 or 20 years back? Can we reasonably expect that spending more in this category would yield positive results over the long term? Would it help foster a cultural appreciation of science and scientists?

With the sluggish economy and political deadlock in Washington, I doubt that we'll see significant increases in funding going to research and development organizations in the coming years — at least not from the Federal government. But I wonder if there isn't a way to leverage the institutions that we've already created. Can we build public/private partnerships that channel additional financial resources into the agencies we've already built? If so, would this be a wise thing to do?

The chart is also a good reminder that not all unemployed persons can claim unemployment benefits. Of the nearly 14 million unemployed in August of 2011, a rough estimate suggests that around 5.6 million were not receiving benefits (it's rough because I'm estimating from a moving average which is inherently lagged).

The unemployment insurance program is complex and evolving, but the Center on Budget and Policy Priorities has an excellent overview of unemployment insurance if you're interested in learning more about the mechanics of the program.

Correlator Results for the Period from 8/2007 to 9/2011 for Select ETFs

The chart above shows the correlation coefficients calculated from monthly returns of 9 ETFs representing the US equity market (VTI), foreign developed markets (VEA), emerging markets (VWO), US REITS (VNQ), TIPs (TIP), Gold (GLD), Cash (SHV), Intermediate-term Treasuries (IEF) and Long-term Treasuries (TLT). Right now the tool only has 13 ETFs available for correlation analysis, but I intend to add a good deal more after testing things out and hearing your suggestions (my contact info is at the bottom of the page!). Something like Mebane Faber's "expanded list" of asset classes would seem like a good next step.

So what is this correlation coefficient business? In layman's terms, the correlation coefficient describes how the returns for different assets move with respect to one another. A correlation coefficient of 1.0 implies that two assets tend to move in the same direction (though not necessarily in steps of equal magnitude). A -1.0 correlation value suggests that they move in opposite directions. Correlation values of 1.0 or -1.0 are unheard of however. What you see in reality are evolving correlations that range between -1.0 and 1.0 suggesting imperfect interrelationships where one asset tends to move with another (correlation > 0) or against another asset (correlation < 0) to a moderate degree.

I should also point out that I built this tool with R which enables this neat clustering option (enabled above) that groups like assets. For instance, VTI, VEA and VWO are all pretty similar qualitatively (they're relatively highly correlated because they're all larger cap equities). The clustering recognizes this in a quantitative manner and groups these three tickers together when creating the chart. For similar reasons IEF and TLT are placed next to one another as well as TIP and GLD. For much more on the clustering method see the R documentation for hierarchical clustering.

Go try out the correlator yourself here!

Update 1: I added a bunch more tickers.

Update 2: I changed the correlator's methodology (sorry, it's still a beta project folks). I had previously been including dividends in the correlation calculations. Other correlation tools on the web do not do this, so I've decided to remove dividends from the calculation for the time being for consistency sake. I'll add them back in as an option later. The chart above has been updated.

Update 3: More changes! Sorry folks... While the correlation tool previously agreed with the S&P sector tool I referenced in the last update, it turns out that's not really a metric that I want to agree with! The S&P site seems to be using correlation of prices rather than correlation of returns — at least that's what I infer from the results. I think investors are more interested in the correlation of returns (including dividends). I'll lay this all out in a new post eventually, and I'll try to stop flip-flopping so much.

While the immediate motivation for these explorations (for me at least) is the looming deadline for raising the debt ceiling so that the US will avoid defaulting on its debt, it should be clear that the issue of debt and deficits (and thus taxes and spending) is not going away anytime soon. This is an issue we'll be working through for many years, and I want to be able to keep track of our progress. So then here are the current charts for US debt, receipts, expenditures, and budget deficits or surpluses all as a fraction of GDP. The latest data (including updated versions of these charts) lives here.

Total Federal Debt as a Fraction of GDP

Government Receipts and Expenditures as a Fraction of GDP

Government Budget Surplus or Deficit as a Fraction of GDP: ( Receipts - Expenditures )

That chart of extrapolated income derives from the chart below produced by Lane Kenworthy showing median family income and per capita GDP series as indices.

I've long wondered what median income would look like after taxes were taken into account and if the structure of Lane's chart might change given the dynamic nature of tax policy. Bruce Bartlett's recent post on average tax rates for four-person families pointed out the data I needed to make such a comparison. The Tax Policy Center produces annual average tax rates for four-person families at the median income level. Using their historical data I can back out the growth of after-tax median family income since 1980 (just after GDP per capita and median family income start to diverge) and add that data to the chart that Lane produced.

GDP per Capita, Real Median Family Income and Real Median Family Income After Taxes

The results, though not earth shattering, are interesting. Prior to 2000, both real (i.e. inflation adjusted) median family income and real median family income after taxes grew at about the same rate. Real median family income has actually declined since 2000, but when you look at after-tax dollars received by households it's been relatively flat. In other words, the median family has been able to avoid a more substantial decline in income by paying somewhat less in taxes. It's not a huge difference, but treading water certainly feels better than sinking. And part of me wonders if this phenomenon doesn't play some small role in the toxic policy debate surrounding taxes. If tax rates on the median family rise now, it's likely that the after-tax income of those families will fall from the plateau it has been riding along for the last decade. If real median income were growing, however, I suspect there would be at least somewhat less resistance for increasing tax rates as it would only slow growth in after-tax income as opposed to causing its outright decline.

David Leonhardt, Calculated Risk and myself have updated our respective home affordability metrics in the last day or so. We're all using slightly different numbers (the housing world is awash in data attempting to measure the same thing), but I'd say we all have about the same conclusions to draw. Housing is definitely becoming more affordable. CR notes that the national price-to-rent index from the CoreLogic home price series has now returned to 1999 levels. As the 90s were generally a non-boomy time for housing, this is real progress.

Looking at the data from a metro perspective, David Leonhardt sees more price-to-rent ratios under 15 (his threshold for affordability). He mentions Atlanta, Los Angeles, Miami, Minneapolis, St. Louis, Las Vegas, Cleveland, Detroit, Phoenix, Pittsburgh and Tampa as places where the rent-ratios on homes are looking good these days. I agree with the assessment for all of those cities with the exception of Los Angeles. I'm not sure how Moody's Analytics (Leonhardt's source) arrived at the low rent-ratio they did, but I'm not finding comparable figures. The home affordability numbers I've calculated show considerably higher price-to-rent and price-to-income ratios for LA.

So let's look at the LA data a little closer as an example of how we arrive at these ratios. According to Realtor metro price statistics, the median home in Los Angeles sold for $292,700 in the first quarter of 2011. The Census reports that the median contract rent for the city in 2009 (the latest data available) was $1,111. This yields a price-to-annual rent ratio of 21.95 $292,700/(12*$1,111). If you adjust the median contract rent by the household size (as described here), the adjusted median contract rent for LA increases to $1,206 per month and this reduces the rent ratio to 20.22. This still seems fairly high to me; it's certainly not in the realm of Atlanta, Las Vegas or Tampa affordability at least.

More importantly though, I think David answers the buy vs. rent question exactly right. In many places it's not a bad time to buy a home assuming relative affordability, access to credit, a steady income and (perhaps most significantly) no need to sell for at least five years.

I've appended the metro affordability ratio table below so you can compare cities yourself. In addition to the price-to-rent and price-to-income ratios there is also the rent-to-mortgage ratio. It behaves just like the price-to-rent ratio when sorted, but the value of this ratio actually has a bit more intuitive meaning. A value of 1.0 means that the rent is equal to the payment on a 100% loan-to-value 30 year fixed-rate mortgage at prevailing rates. Values greater than 1.0 mean that renting costs more and values less than 1.0 mean the mortgage payment costs more (under these particular mortgage terms). As a potential buyer or investor, you're looking for rent-to-mortgage ratios of greater than 1.0 because it means you could conceivably pay less to own than to rent. Click on the heading name to sort the cities. It's fun — unless you're thinking about buying in Honolulu.

Note: The latest metro affordability data is always available here.

Home Income and Rent Ratios Using Realtor Prices by City - Q1 2011