I was working on a custom data request for a new business owner who was considering a particular location for a new venture. This owner wanted California traffic count data for the highway near the potential location. Here’s how I got traffic count data for this California state highway.

What’s a Postmile(& Why Should You Care)?
Wikipedia says “California uses a postmile highway location marker system on all of its state highways, including U.S. Routes and Interstate Highways. The postmile markers indicate the distance a route travels through individual counties, as opposed to mile markers that indicate the distance traveled through a state.” You should care about Postmiles, because you need to know the postmile to look up the traffic count data.

A. How to Lookup the Postmile

1. Go to Caltrans Earth – which is a GIS provided by the California Department of Transportation.
   1. You will probably have to download the Earth for Web plugin. Clicking on the download plugin link provided by Caltrans didn’t work for me. So I searched for “GoogleEarth-Mac-Plugin-Latest.dmg” and found this site with the Mac version of the Google Earth Plugin. If you are running a PC, you’ll need to find the PC plug-in.
2. Once you get the Caltrans Earth working, enter your area of interest to zoom to location.
   1. Note: since the data are state highways, your area of interest will likely be a state highway AND an intersecting street. The intersecting street can be a smaller road or it can also be another state highway. I’m going to use Santa Monica (Route 2) and North Crescent Heights in Los Angeles County as an example.
3. Click on Postmile look up.
4. WithClick selected in the Postmile look up tool, click on the map where your area of interest is. So in my example, I’m clicking on the intersection of Santa Monica (2) & North Crescent Heights. (You can turn on the Roads in the left navigation to see the street names).
5. The map will then show you the closest County Postmile.

B. How to Use the Postmile to look up Traffic Counts

1. Download the most current traffic volume data from the Caltrans Traffic Data Branch. The most current data that I could find was 2012. The department will release 2013 and 2014 traffic data as soon as it’s available, but for my purposes, 2012 is the best data available & will work.
2. Here’s the 2012 traffic counts that we can use as an example. Looking at the data look first at the Route, then the County, and then the nearest post mile.

For our example, I’d want to double check that Highland Avenue is close to North Crescent Heights. And according to Google maps, Highland Avenue is 4 minutes or 1.6 miles away from North Crescent Heights.

C. How to Read the Traffic Count Data

1. This handy Caltrans webpage provides us with some important definitions.
   1. Back AADT, Peak Month, and Peak Hour usually represents traffic South or West of the count location.
   2. Ahead AADT, Peak Month, and Peak Hour usually represents traffic North or East of the count location.
   3. Annual Average Daily Traffic(Annual ADT) is the total volume for the year divided by 365 days.
   4. Peak Hour is an estimate of the “peak hour” traffic at all points on the state highway system.
   5. Peak Month ADT is the average daily traffic for the month of heaviest traffic flow.

   So with the above resources, you are now able to get traffic counts for California state highways. While Caltrans does not collect traffic data on locally maintained roads, oftentimes the city or county has a traffic engineer who has this data. If you need traffic counts for local roads, you should call the City and ask to speak with either the traffic engineer, Public works department or the Planning department.

How to pull Building Construction Permit data for Counties from the US Census Bureau

For a recent custom data request, I pulled building construction permit data for US counties and places (aka cities). Many people don’t realize that the US Census Bureau collects data on building permits by county and place, and makes it publicly available.

Below are step by step instructions for how to pull this building construction permit data from the US Census Bureau’s website. We’ll use Orange County, Florida as an example — but the same  steps apply if you want to pull data for a city.

Step-by-Step Guide to Finding Building Permits by County

The building permits page of the US Census allows you to search for building permits either monthly or yearly, going back to 1996. Not all areas report to the Census Bureau monthly – some only report yearly.

If when you are searching for building permit data you cannot find the county or place you are searching for, go back and change your search to “Yearly.” And there may be cases where building permit data are just NOT available for smaller counties (in terms of population) and smaller cities.

Now, here are the steps to follow:

Step 1: On the building permits page, choose the month and year you want building permit data for.

Step 2: Pick county or place. Place reports are usually from individual municipalities, but some townships or unincorporated towns also report to the Census Bureau.

Step 3: Pick the state you are interested in finding data from. For our example here, we’ll be looking at Counties in Florida.

Step 4: Click “Submit”

Step 5: On the next page, pick the county you want data from. Let’s pick Orange County. If the county we were looking for wasn’t listed, that would mean some of the municipalities in the county only report yearly, so we’d have to go back to the prior page and change our time to annual.

Once you click “Submit” on the second page, you will see the data building permits in Orange County for the month you selected. The information is broken down into permits for Single Family, Two Family, Three and Four Family, Five and More Family, and the total for all building types. In March of 2013, we can see that 353 Single Family building permits were reported, that there were 353 units in those buildings and that the construction cost was $76,359,451. We can also see the US Census Bureau’s estimate of building permits including any that may not have been reported (353 for March, so the same number reported.) The chart also includes totals for the year so far.

In the very left column of the chart there is a “Browse” button. Clicking on this allows us to compare building permits by county throughout Florida. We can choose to compare building permits by county for a specific type of building, or the total building permits in each county. If we “Browse” Five and More Family buildings, we can see that while Orange county only reported 3 building permits for this building category in March, Miami-Dade reported 13 building permits for the county. Clicking on the “Profile” button for Orange County brings us back to the building permits by county page for Orange that we came from.

At the top of this page, you will see drop down boxes for month and year. If you want to compare building permits for Orange County for March in different years, you can pick 2012 from the drop down menu and see that Orange County reports only 267 building permits issued in March of 2012 – meaning nearly 100 more building permits were reported in 2013.

I hope the above steps and screenshots save you time when pulling building construction permit data for counties. If you need to pull lots of building permit data, like all monthly permit data since 1996, or if you need to pull building permit data AND other types of Census data (like year structure built, median value, median rent, etc.), you should check out Cubit’s custom data pull option.

Photo by Pepi Stojanovski on Unsplash

Updated: August 2020.

Recently for a custom data request project, I needed to find historic home sales data by zip code for the entire United States. While the US Census Bureau has median home value data for ZCTAs, you can’t get count of sales or sale prices for individual home sales data from the Census datasets.

In addition to the Census Bureau, the county appraisal districts are a fantastic source of real estate data. But in Texas, you can’t even get home sales data from the county appraisal districts, because Texas law makes reporting home sales optional. And even for states unlike Texas that do require the reporting of home sales, it would be painful, time consuming, and expensive to contact every county appraisal district/office in the United States & request this data.

Below are all of the options that I explored to get historic home sales data by zip code for the entire US.

2 Viable Options to get Historic Home Sales Data by Zip Code

1. BEST OPTION – Purchase the data from DataQuick. DataQuick has been selling real estate data for over 30 years. They have reasonable prices. For example, I could get 2 data points (like number of sales and median sale price) for each MONTH for zip codes in the US for the past 10 years for $10,000. If I needed historic home sales data by zip code for the previous 10 years, that price was $7,000. And if I wanted additional data points in addition to the 2 mentioned above, each data point was $1,000 for 10 years. This pricing structure was easy for me to understand and to communicate. Also, DataQuick returned my phone calls quickly and were pleasant to work with. Their turnaround time for the data was 4 days. But there’s one small catch – they only have 70% coverage of US zip codes – which I understand to be based on population. Basically, you can get home sales data for the 70% of US zip codes with the largest populations. For the custom data request research that I was doing, 70% of zip codes was good enough. One could assume that the smaller the population, the fewer home sales are likely. But if you HAVE to have a higher coverage level than 70% of US zip codes, I did find a second option for you.
2. Buy the data for Real Quest. Real Quest is a division of CoreLogic – which is also a company that sells real estate data. The benefit of Real Quest is that they have data for 98 to 99% of residential properties in the US. That’s pretty impressive. What wasn’t impressive was their customer service or their pricing. I never could get a price quote from the sales person who was “helping” me. The best I could get is that “we’ll work with you if you have a $50K budget.” The other specific issue that bugged me was that they kept asking me to identify my client who I was doing research for – which I wasn’t comfortable doing without permission from my client. Since I had such a poor experience with their sales person, I hesitate to even list these guys as a viable option. But I can’t overlook the 98-99% data coverage.

Not Viable Options For My Purposes But These Options Might Work For You 

1. Policy Map. Policy Map looks to be a pretty sweet web app for pulling demographic data. I keep meaning to sign up for a free trial & check ‘em out, but it never gets to the top of my to-do list. Policy Map wasn’t a good fit for this particular request, because their sales data only went back to 2006. It appears they have number of sales, median sale price, aggregate sales amounts & loan-to-value ratios on a quarterly basis for 2007 to 2012 and on an annual basis for 2006 to 2007. 1 note: you can’t access this data as part of the Free Account.
2. Regional Multiple Listing Services (MLS). A real estate multiple listing service is basically a shared database of that allows real estate brokers & realtors to see what homes are for sale & have currently sold in the past. There are 900+ regional multiple listing services in the United State. During my research, someone told me that there are 935 MLSs over the phone & I haven’t been able to verify it. I did find this link with data sources indicating that the number is between 900 and 1000. And then I found to buy access to 1 MLS would be $350. A rough estimate of 1,000 MLS x $350 for access = $350K. And if that price point is no problem, I think [emphasis on think – I stopped digging into this option at this point] you have to be a realtor/have a real estate license to get access to a MLS.
3. Real Estate data APIs like Trulia & Zillow. Using an API like Trulia or Zillow to get sales data was my idea for where to go to get historic home sales data by zip code. But when Anthony actually read the terms of service for the APIs, it was against the terms & service of the APIs for us to use them in such a way that we could pull all current & historic sales data for the US. Both Trulia & Zillow implement throttling limits, which I presume, prevents someone from downloading their entire database.
4. National Association of Realtors. While they have home sales data, they don’t have it at the zip code level.
5. Realtor.com. These guys never emailed or called me back.
6. Update August 2020: Since I wrote this blog post, I’ve pulled home data from Attom Data before they were Attom Data (I can’t remember their old business name). And a prospect mentioned to me that that they would be sourcing vacant land data from Data Tree. So you might try these 2 companies as well.

I hope this information saves you some serious time, because it took me awhile to piece it all together. If you know of another way to get historic home sales data for US zip codes, please contact me. I’m interested!

Recently, I did a little research for a custom data request about zip code income data for the entire US. I was looking for both historic & current income data. I found 2 different data sources that both provide current & historic US zip code income data: the US Census Bureau and the Statistics of Income program data on the IRS website.

The Least You Need to Know

The most current zip code income dataset that’s available right now for the entire US is the US Census Bureau’s 2011 ACS 5 year estimates. An updated ACS dataset will be released every year (i.e. by the end of 2013, the 2012 income data will be released.)

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Data Source 1: US Census Bureau

Year	Dataset
2011	ACS 5 year estimates
2000	Decennial Census 2000 SF3 Data
1990?	Census FTP site
1980?	Census FTP site

First, the US Census Bureau provides income data for zip code tabulations areas (ZCTAs) — which are very similar to zip codes. I’ve got another blog post that explains the difference between ZCTAs and zip codes.

You can pull income data by ZCTA for all US ZCTAs for the years 2000 and 2011 on the Census Bureau’s website. For the 2000 income data, you’ll want to use the Decennial Census 2000 SF3 dataset. For the 2011 income data, you’ll want to use the ACS 5 year estimates dataset. Learn more about ACS data vs Decennial Census data. Each year, the Census Bureau will release updated ACS income data (i.e. in 2013, they will release the 2012 income data).

While there are zip code level data for the 1990 & 1980 decennial censuses AND there are income data for the 1990 & 1980 decennial censuses, I haven’t done the necessary digging to figure out if there are 1990 & 1980 zip code income data available. You can do this research by reading through the documentation on the Census’ FTP site (links are in the table above).

Your next question is probably “what income related data points can I get from the US Census Bureau”?

Popular Zip Code Income Data for 2011

I’m defining popular as my personal favorite tables as well as the tables that I pull most often for custom data requests. Most of the data below are in 2011 inflation-adjusted dollars.

General

• Mean Income in the Past 12 months
• Median Income in the Past 12 months
• Median Family Income in the Past 12 months
• Per Capita Income in the Past 12 months

Household

• Median Household Income in the Past 12 Months
• Median Household Income in the Past 12 Months for White Alone Householder; Black or African American Alone Householder; American Indian and Alaska Native Alone Householder; Asian Alone Householder; Native Hawaiian and Other Pacific Islander Alone Householder; Some Other Race Alone Householder; Two or More Races Householder; Hispanic or Latino Householder

Type of Income

• Wage or Salary Income in the Past 12 Months for Households
• Self-Employment Income in the Past 12 Months for Households
• Interest, Dividends, or Net Rental Income in the Past 12 Months for Households
• Social Security Income in the Past 12 Months for Households
• Supplemental Security Income (SSI) in the Past 12 Months for Households
• Public Assistance Income or Food Stamps/Snap in the Past 12 Months for Households
• Retirement Income in the Past 12 Months for Households

Poverty

• Poverty Status in the Past 12 Months
• Poverty Status in the Past 12 Months of Families by Family Type by Social Security Income by Supplemental Security Income (SSI) and Cash Public Assistance Income

Other

• Gini Index of Income Inequality
• Median Income in the Past 12 Months by Sex by Work Experience in the Past 12 Months for the Population 15 Years and over With Income
• Receipt of Supplemental Security Income (SSI), Cash Public Assistance Income, or Food Stamps/Snap in the Past 12 Months by Household Type for Children Under 18 Years in Households

Popular Zip Code Income Data for 2000

I was going to put together a list of tables for 2000 income data like the above list of tables for 2011 income data, but when I was working on the list for 2000 income data tables, there was a lot of overlap. Basically, you can get 2000 income data tables that are similar to the 2011 income data tables with a couple of exceptions. For example, there’s no Gini Index of Income Inequality table for 2000. The 2000 income data are in 1999 dollars.

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Data Source 2 – SOI Tax Stats on the IRS website

Year	Dataset
2008	Free
2007	$500 for US/$25 per state
2006	$500 for US/$25 per state
2005	$500 for US/$25 per state
2004	$500 for US/$25 per state
2002	$500 for US/$25 per state
2001	Free
1998	Free

“SOI” stands for Statistics of Income. SOI is a program that publishes income data and is run by the Statistical Information Services, a US government agency.

The SOI is currently reviewing its methodology to safeguard individual taxpayer confidentiality. So the 2008 data set is a preliminary dataset that contains data for zip codes in which 250 or more returns were filed — which means that you won’t be able to get income data about rural zip codes from the preliminary data set. The data are based on individual income tax returns from the IRS’ Individual Master File system, which includes a record for every Form 1040, 1040, and 1040EZ. The records included in the 2008 dataset were returns that were filed between January 1, 2009 and December 31, 2009.

The 2008 dataset include the following data:

Number of returns [1]
Number of joint returns	Filing Status is Married filing jointly
Number of returns with paid preparer’s signature
Number of exemptions	1040:6d
Number of dependents	1040:6c
Adjust gross income (AGI) [2]	1040:37 / 1040A:21 / 1040EZ:4
Salaries and wages	1040:7 / 1040A:7 / 1040EZ:1
Taxable interest	1040:8a / 1040A:8a / 1040EZ:2
Ordinary dividends	1040:9a / 1040A:9a
Business or professional net income (less loss)	1040:12
Net capital gain (less loss)	1040:13  1040A:10
Taxable individual retirement arrangements distributions	1040:15b / 1040:11b
Taxable pensions and annuities	1040:16b / 1040A:12b
Unemployment compensation [3]	1040:19 / 1040A:13 / 1040EZ:3
Taxable Social Security benefits	1040:20b / 1040A:14b
Self-employment retirement plans	1040:28
Total itemized deductions [4]	Schedule A:29
State and local income taxes	Schedule A:5a
State and local general sales tax	Schedule A:5b
Real estate taxes	Schedule A:6
Taxes paid	Schedule A:9
Mortgage interest paid	Schedule A:10
Contributions	Schedule A:19
Taxable income	1040:43 / 1040A:27 / 1040EZ:6
Total tax credits [5]	1040:56 / 1040A:34
Residential energy tax credit	Form 5695:27
Child tax credit	1040:52 / 1040A:32
Child and dependent care credit	1040:47 / 1040A:29
Earned income credit [6]	1040:66a / 1040A:40a / 1040EZ:8a
Excess earned income credit (refundable) [7]	1040:66a / 1040A:40a / 1040EZ:8a
Alternative minimum tax	6251:35
Income tax [8]	1040:56 / 1040A:28 / 1040EZ:10
Total tax liability [9]	1040:61 / 1040A:37 / 1040EZ: 10
Tax due at time of filing [10]	1040:76 / 1040A:46 / 1040EZ:12
Overpayments refunded [11]	1040:73 / 1040A:44a / 1040EZ:11a

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Zip Code Income Data Maps

Now that you know where to get income data for zip codes, you can use data to do some pretty cool analysis — like building zip code maps.

Here are some quick zip code income maps for the US and a couple of the largest states using the US Census Bureau’s 2011 data and my favorite free GIS – QGIS.

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Comparing Historic Income Data

If you are interested in looking at changes in income over time, you’ll probably want to adjust for inflation. Check out this other blog post about a cool & free tool that helps you calculate buying power.

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Don’t Have Time to do Any of the Above Work but Still Need Zip Code Income Data?

I pull zip code income data for businesses for a fee. If you’d like to get a quote for me to pull this data for you, please complete the Custom Data Request form.

Photo by Brandon Griggs on Unsplash

So you need apartment statistics.

I often get requests for apartment data from folks who are:
1. researching where to locate a business that provides services to apartment dwellers like a washateria or car wash; or
2. researching where to buy or build a new apartment facility (and the bank has asked them to include the number of apartment units in an area in their business plan).

What you might not know is that the US Census Bureau reports apartment statistics — which means you don’t HAVE to pay a ton of money to a real estate data company to get an estimate of the number of apartment units in a particular area.

How to get Apartment Statistics from the US Census

If you’d rather spend your time doing more interesting things than learning how to use the American Fact Finder 2 — you can always pay me to pull this data for you using the custom data request form. But just in case you are curious, a glutton for punishment or a college student doing research, here’s how:

1. Head over to the American Fact Finder 2 – Advanced Search.

2. In the Geographies tab on the left, select your geographic area of interest (i.e. New York city, NY or Cook County, Illinois).

3. Then type in these magic words into the search box: “Units in Structure”

4. Then look for the Units in Structure table in the list.

5. Depending on the size of your geographic area & the margin of error that you are comfortable with, you’ll need to choose between the American Community Survey (ACS) 1 year, 3 year or 5 year data. As of today, the ACS 1 year 2011 data are the most current data available. Take a look at the margins of error in the different ACS products to see what table will be the best fit for your purposes.

Fun fact! Now that you know how to pull data via the American Fact Finder 2, there are other statistics that you can get about apartments — not just Units in Structure data.

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Understanding Your Apartment Statistics

Now you have your apartment data from the Census — but what do the row headings “1, attached”, “1, detached”, “2” actually mean? Here are the Census’ definitions per the American Community Survey definitions.

• 1-Unit, Detached – This is a 1-unit structure detached from any other house, that is, with open space on all four sides. Such structures are considered detached even if they have an adjoining shed or garage. A one-family house that contains a business is considered detached as long as the building has open space on all four sides. Mobile homes to which one or more permanent rooms have been added or built also are included.
• 1-Unit, Attached – This is a 1-unit structure that has one or more walls extending from ground to roof separating it from adjoining structures. In row houses (sometimes called 7 townhouses), double houses, or houses attached to nonresidential structures, each house is a separate, attached structure if the dividing or common wall goes from ground to roof.
• 2 or More Apartments – These are units in structures containing 2 or more housing units, further categorized as units in structures with 2, 3 or 4, 5 to 9, 10 to 19, 20 to 49, and 50 or more apartments.
• Boat, RV, Van, Etc. – This category is for any living quarters occupied as a housing unit that does not fit the previous categories. Examples that fit this category are houseboats, railroad cars, campers, and vans. Recreational vehicles, boats, vans, tents, railroad cars, and the like are included only if they are occupied as someone’s current place of residence.
• Mobile Home – Both occupied and vacant mobile homes to which no permanent rooms have been added are counted in this category. Mobile homes used only for business purposes or for extra sleeping space and mobile homes for sale on a dealer’s lot, at the factory, or in storage are not counted in the housing inventory.

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US Cities with the Most Apartment Units

Just for fun, here are the 40 US cities with the largest number of apartment units according to the American Community Survey 2011 data. No real surprises here. The cities with the largest populations have the most apartment units as do tourist and college towns.

	Geography	Apartment Units
1	New York, NY	2,829,021
2	Chicago, IL	849,096
3	Los Angeles, CA	781,045
4	Houston, TX	442,551
5	Dallas, TX	256,739
6	San Francisco, CA	256,289
7	San Diego, CA	228,212
8	Boston, MA	224,289
9	Philadelphia, PA	219,932
10	Phoenix, AZ	190,495
11	Washington, DC	183,906
12	Austin, TX	175,766
13	San Antonio, TX	173,339
14	Columbus, OH	160,684
15	Seattle, WA	157,533
16	Milwaukee, WI	143,193
17	Denver, CO	135,336
18	Miami, FL	121,343
19	Atlanta, GA	121,086
20	Indianapolis, IN	120,819
21	Charlotte, NC	111,772
22	Jacksonville, FL	110,341
23	Portland, OR	105,165
24	San Jose, CA	104,905
25	Nashville-Davidson, TN	103,981
26	Memphis, TN	100,851
27	Cleveland, OH	98,037
28	Baltimore, MD	96,381
29	Cincinnati, OH	96,051
30	Minneapolis, MN	94,733
31	Detroit, MI	93,888
32	San Juan, PR	93,132
33	St. Louis, MO	93,101
34	Urban Honolulu CDP, HI	92,137
35	Jersey, NJ	92,098
36	Las Vegas, NV	90,887
37	Long Beach, CA	90,168
38	Oakland, CA	90,024
39	Fort Worth, TX	86,814
40	Buffalo, NY	85,595

U.S. Census Bureau. 2011 American Community Survey: B25024 UNITS IN STRUCTURE. Retrieved February 26, 2013 from http://factfinder2.census.gov 

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If you need help pulling apartment statistics for your area, let me know what data you need by filling out the custom data request form, and I’ll email you back with a quote.