Category Archives: Useful Data

2nd Quarter Home Price Indices

Someone just paid $1.1 million for a tear-down/fixer-upper in Mountain View, California. That’s not really news, as prices in Silicon Valley have been increasingly outrageous. What’s news is that they bought the house with the provisos that the existing owner will get to live there for seven years; the buyer didn’t get to see the interior of the home; and the buyer is required to make improvements before closing on the home. As the San Francisco Chronicle says, the new owner probably figures it will take seven years to get the permits to rebuild the house anyway.

The problem for the buyer is that the same forces that have made housing prices rise in Silicon Valley–namely the urban-growth boundaries adopted by San Mateo and Santa Clara counties–have also made prices more volatile. In other words, what goes up will come down. As shown in the chart above, San Jose prices today are already higher than they were at the peak of the 2006 housing bubble, indicating that another bubble is likely to deflate fairly soon. Continue reading

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FTA Historic Times Series Through 2016

Since 1992, taxpayers have spent $364 billion (in 2016 dollars) on transit capital improvements. More than $257 billion of this went to rail transit, while $94 billion went to bus transit. The Antiplanner calculated this information on the Federal Transit Administration’s historic time series capital costs spreadsheet.

The official data show that transit ridership peaked in 2014 at 10.5 billion trips and by 2016 had declined 2.5 percent to 10.2 billion trips. This ridership includes urban, rural, and tribal transit agencies, but rural and tribal together add up to only about a million trips per year. The Antiplanner calculated this information on the Federal Transit Administration’s operations spreadsheet.

Tuesday’s post about the 2016 National Transit Database mentioned that the Federal Transit Administration has also posted the 2016 update to its historic time series, which has operating and ridership data back to 1991, capital costs back to 1992, and fares back to 2002 broken down by transit agency and mode. Except for the capital costs, which are in a separate file, all of the information is on worksheets that can be sorted in the same order, allowing users to make such calculations as operating cost per trip or fare per passenger mile. Continue reading

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FTA’s 2016 National Transit Database

The Federal Transit Administration has posted its 2016 National Transit Database in the form of some two dozen Excel files. As in each of the past ten years, the Antiplanner has summarized some of the most important data in a single spreadsheet. This spreadsheet includes trips, passenger miles, fares, costs, vehicle data, rail miles, energy consumption, and greenhouse gas emissions (in grams) for every transit agency and mode of travel (rows 2 through 3798), totals for each mode (rows 3802 to 3820), and totals by urbanized area (rows 3851 through 4339). Because some of the smaller agencies were not required to report energy consumption, there are also totals for those systems for which energy consumption can be calculated (rows 3826 through 3844), making it possible to calculate average BTUs and greenhouse gas emissions per passenger mile.

In making this spreadsheet, I noticed some minor errors in my 2015 spreadsheet, mainly in some of the mode totals. So I’ve posted a revised version. It includes all of the calculations I’ve happened to make in the past year, including (in cells BH3644 through BK4150) a comparison of passenger miles by automobile vs. transit for each urban area. (Transit carried 11 percent of passenger miles in the New York urban area, 7 percent in San Francisco-Oakland, 4 percent in Honolulu, 3 to 4 percent in Chicago, Seattle, and Washington, 2 to 3 percent in Baltimore, Los Angeles, Philadelphia, and Portland, and under 2 percent just about everywhere else.) I won’t be able to make this calculation for the 2016 database until the Federal Highway Administration posts 2016 Highway Statistics.

In addition to the National Transit Database, the FTA has posted transit data tables in about a dozen different spreadsheets. The tables contain much of the same information but are a bit easier to read than the database, though a bit harder to use for mass calculations (especially since the spreadsheets have been “locked”). This year, some of the data tables come with interactive graphics, though they don’t seem to work on my Mac. Continue reading

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August 2017 Ridership Down 4.0% from ’16

Last week, the Antiplanner reported that July 2017 transit ridership was 3.6 percent below the same month of 2016. Now the Federal Transit Administration has posted data for August 2017 showing that ridership for that month was 4.0 percent less than in August 2016.

Naturally, the Antiplanner has posted an enhanced version of this data file showing totals by year from 2002 through 2017, as well as totals by transit agency and for the 200 largest urban areas. The file also shows the change in transit riders in August 2017 vs. August 2016, January-August 2017 vs. same in 2016 as well as 2014 and 2010, and 2016’s total vs. the peak for each mode, transit agency, or urban area from 2008 through 2015.

These numbers have to be frightening transit industry leaders. Update: They are. Just comparing the first eight months of 2017 against 2016, ridership has fallen by more than 10 percent in Philadelphia, Milwaukee, Charlotte, El Paso, and Albuquerque, and nearly 10 percent in Miami, Cleveland, San Jose, and Raleigh, among other urban areas. Since this decline is, in most cases, on top of declines in 2016, we’re seeing 25 to 40 percent declines in some urban areas over the past few years.

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July 2017 Transit Riders Drop 3.6% from 2016

Nationwide transit ridership continues to decline, and that decline, if anything, is accelerating. Ridership in July 2017 was 3.6 percent lower than the same month in 2016, while ridership in the first seven months of 2017 was 3.0 percent lower than the same months in 2016. These numbers are from the National Transit Database monthly data reports.

The monthly reports have every month from January 2002 through July 2017. The Antiplanner has summed the data by year, and also summed the first seven months of 2016 and 2010 for comparison with 2017. At the bottom of the original spreadsheet, the Antiplanner has also summed the data by transit agency (rows 2100-3098) and for the 200 largest urbanized areas (rows 3102-3301). Finally, columns HH through HJ calculate the percentage change from July 2017 vs. July 2016; January-July change from 2016 to 2017; and the January-July change from 2010 to 2017. Data junkies are welcome to download this 7.7-MB Excel file.

As shown in the table below, of the nation’s 100 largest urbanized areas, only a handful enjoyed ridership gains for all three time periods considered: Houston, Minneapolis-St. Paul, New Orleans, McAllen (TX), Albany, Columbia (SC), and Colorado Springs. Houston’s ridership may have grown since 2010, but its 2010 ridership had fallen by more than 20 percent since 2006, and 2017 numbers were still well short of 2006. Previous reports had shown Seattle ridership growing, but that region’s ridership declined by 1.8 percent in July 2017 vs. July 2016. Update: I am reliably informed that the Seattle decline is solely due to an error in the data. It should be corrected by FTA’s August update. Continue reading

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Black Population Trends

Between 2015 and 2016, the total population of the San Francisco-Oakland urban area grew by 13,773 people, but the black population shrank by 5,839, suggesting that Bay Area land-use policies continue to push low-income people out of the region by making housing unaffordable. The Austin urban area, to its shame, saw a decline of 4,439 blacks despite a total population growth of 25,316.

Race is a complicated issue, made more complicated by the increasing (and healthy) mixture of races. According to the 2016 American Community Survey, the number of Americans who are “white alone” declined by 296,061 in 2016, while the number who identify themselves as “two or more races” grew by 445,000; some of the decline of the former and growth of the latter is probably because people are more willing to self-identify as being of mixed races.

In the past, I’ve used blacks as a bellwether of housing affordability problems because black per capita incomes have consistently been about 60 percent of whites’. I’ve previously used “black alone,” but this year that produced some odd results: both white alone and black alone populations declined in sixteen different states. For example, California’s total population grew by 105,000, but its white-alone population shrank by 404,000 while its black-alone population shrank by nearly 12,000. It seems likely that most of the changes in white-alone and black-alone numbers are due to redefinitions, not migrations. Continue reading

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More 2016 Commuting Data

People who earn more than $75,000 a year are more likely to ride transit than people in any other income bracket. Most of those high-income transit riders live not in big cities like New York or Chicago but in suburbs of those cities.

That information is from table B08119 from the 2016 American Community Survey. I’ve downloaded the table for the nation, states, counties, cities, and urbanized areas and posted it with calculations showing what percentage of people in each income bracket use each form of transportation. The calculations don’t show this, but you can calculate it for yourself, but about 18.5 percent of people earn more than $75,000 a year, but a full 24 percent of people riding transit earn more than that amount.

I was surprised to discover that New York City was not one of the places where people earning more than $75,000 were the most likely to take transit, so I added a column, EB, that flags those areas where the $75,000 bracket is the most likely to take transit. On a state level, this included Idaho, Illinois, Massachusetts, New Jersey, Virginia, and Wyoming. Continue reading

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Housing Affordability in 2015

Today the Antiplanner continues reviewing 2016 American Community Survey data by looking at housing affordability, a common measure of which is median house prices divided by median family incomes, or value-to-income ratio. Median family incomes are in ACS table B19113, while median home prices are in table B25077.

To save you time, I’ve downloaded these tables, pasted the value and income data into one table, and calculated the ratio for the nation, states, counties, cities, and urban areas. For comparison, I have the same data for 2015, 2010, and 2006. As noted yesterday, only some counties, cities, and urban areas are used each year and the list varies from year to year so the rows are not identical each year. The states don’t vary from year to year, so I’ve also provided a spreadsheet comparing value-to-income ratios for the nation and each state for all four years.

All of the numbers, by the way, are actually for the previous year, as the surveys asked people how much they earned and how much their homes were worth the year before the survey. So the number shown as the 2016 value-to-income ratio is actually the ratio in 2015, etc. That means the data are a couple of years behind the current state of housing affordability. Zillow shows that prices in some areas have dramatically increased in the last couple of years to the point where many Silicon Valley homes are selling for 50 percent above their asking prices. Continue reading

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Commuting Data for 2016

Last week, the Census Bureau posted 2016 data from the American Community Survey, including population, income, housing, employment, and commuting data among many other categories. The survey is based on data from more than 3.5 million households. Today, the Antiplanner will look at commuting data: how people got to work in 2016 compared with previous years.

To save you time, I’ve downloaded and posted 2016’s table B08301, “Means of Transportation to Work,” for the nation, states, counties, cities, and urbanized areas. I’ve also posted similar tables for 2006, 2010, and 2015.

In columns Z through AE, I’ve calculated the shares of commuters (excluding people who work at home) who traveled to work by driving alone, carpooling, transit, rail transit, bicycling, and walking. (These won’t quite add up to 100 percent as are other categories such as taxi and motorcycle.) Only some cities, counties, and urban areas are included because others were too small for the sample size to be valid. Since the places that are included may vary from year to year, the rows of the various spreadsheets do not line up below the state level.

The data show that, nationwide, transit’s share of travel grew from 5.03 percent in 2006 to 5.49 percent in 2015. This growth was at the expense of carpooling, as driving alone’s share also grew. In 2016, however, transit’s share fell to 5.36 percent while both driving alone and carpooling grew. Continue reading

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New Driving Data

The Federal Highway Administration has just released urban highway statistics for 2015, including the miles of roads and daily vehicle miles of driving by road type and “selected characteristics” for each urban area, including population, land area, freeway lane miles, and similar information. These data are quite useful as they allow interregional comparisons as well as, when combined with past data, a look at trends over time.

For example, the Los Angeles urban area is more than twice as dense as the Houston urban area, yet both report the same number of miles of driving per capita (see population note below). Though there is a weak correlation between density and driving, it isn’t as strong or as certain as urban planners would like you to believe.

As published by the FHwA, each table of more than 400 urban areas is divided into nine worksheets of 50 urban areas. Since this is clumsy, I’ve copied-and-pasted them into one worksheet each, which you can download for the miles of roads and selected characteristics. Continue reading

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