Free Samples Excel (xls) Format CSV Format License Terms Commercial application Yes Yes No Max Company Size Unlimited 5 people No commercial usage Redistribution Internal only Internal only No Lookup tool on commercial website Yes Yes No Personal / Educational Use Yes Yes Yes Download available 90 days 90 days N/A Classification Data Included All ZIPs with cities used by USPS plus the primary timezone, area code, and county Yes Yes Yes Area and population overlap with all city limits and counties Yes No No Coordinate Data Included Approx. Latitude / Longitude Yes Yes Yes Precise Latitude / Longitude Yes Yes No Population Latitude / Longitude Yes No No Statistical Data Included Estimated Population Yes Yes Yes Census Population and Housing Units Yes Yes No Historic Population and Households (2005-2016) Yes No No Age, Gender, and Race Stats Yes Yes No Income and Education Levels Yes No No Home/Rent Costs Yes No No. We've been providing data to our clients for years and over 225,000 different individuals, organizations, and corporations of all sizes have trusted us to provide them with the high quality data that they need. We don't just tell you about the quality, we show you the results of our verification and research (see below) and make sure you can quickly understand the most accurate ways to use the data we provide.

• Dma Zip Code List

All data is consistently labeled, there are no duplicates, quality data sources minimize any inaccuracies, and multiple data sources verify accuracy. We've done the work to combine data from multiple sources to make sure you have all of the fields you need. Plus, we haven't included dozens of fields that you will never use and only slow you down. Is your data up-to-date?

Why don't you offer monthly/quarterly updates?. No recurring fee lock-in. Update when you want. We constantly monitor USPS for changes. We only issue updates for significant changes We constantly monitor USPS data for major changes and release new data when there are significant changes. If you can find any major changes before we make data updates, we'll issue a full refund.

Finding Zip Codes in Nielsen DMAs. Maybe we just want to know demographics. Or maybe we want to know what restaurants are nearby. Those TV/radio areas are called.

Instead of locking our users into a recurring fee, we let you decide when updates are significant enough to warrant the time and cost of updating. Quite frankly, we don't offer weekly/monthly updates because new ZIP codes aren't frequently introduced. We also don't issue data updates for insignificant changes like updating a ZIP to show it has 3869 deliverable addresses instead of 3866. Because it can take up to 5 years for people to fully utilize a ZIP, ZIP changes are minimized and a 10 year old data set would still contain 99% of currently active 5 digit ZIP codes. Even so, when evaluating how often you want to update your data, keep in mind that many pieces of information about a ZIP code can be updated over time.

We recommend you update your list between once every year and once every 5 years depending on your data needs. We've included the number of new ZIP codes added each year to assist you in making your decision.

All ZIP Codes zip ZIP Code type Military, PO Box, Standard, or Unique decommissioned Whether this zip has been decommissioned Matching to Other Regions primarycity Primary city according to USPS acceptablecities Acceptable cities according to USPS unacceptablecities Unacceptable cities according to USPS state U.S. Samples Excel (xls) Format CSV Format All ZIP Codes zip ZIP Code Overlapping City Info statename State name (ex. Mississippi) stateabbr State abbreviation (ex.

MS) statefips FIPS identifier for the state (ex. 28) stateansi ANSI identifier for the state (ex. 01779790) placegeoid Concatenated FIPS codes (ex.

2876720 or 2899999) placename Name of the city (ex. Vicksburg city or Outside city limits) placetype Type of place (Incorporated, Census Designated, or None) placefips FIPS identifier for the city (ex. 76720) placeansi ANSI identifier for the city (ex.

02405648) Overlap Statistics percentofzipareainplace 0.0 to 100.0 percentofplaceareainzip 0.0 to 100.0 percentofziplandareainplace 0.0 to 100.0 percentofplacelandareainzip 0.0 to 100.0 percentofzippopulationinplace 0.0 to 100.0 percentofplacepopulationinzip 0.0 to 100.0 percentofziphouseholdsinplace 0.0 to 100.0 percentofplacehouseholdsinzip 0.0 to 100.0. Samples Excel (xls) Format CSV Format All ZIP Codes zip ZIP Code Overlapping County Info statename State name (ex. Mississippi) stateabbr State abbreviation (ex. MS) statefips FIPS identifier for the state (ex. 28) stateansi ANSI identifier for the state (ex. 01779790) countygeoid Concatenated FIPS codes (ex. 28149) countyname Name of the county (ex.

Warren County) countyfips FIPS identifier for the county (ex. 149) countyansi ANSI identifier for the county (ex. 00695795) Overlap Statistics percentofzipareaincounty 0.0 to 100.0 percentofcountyareainzip 0.0 to 100.0 percentofziplandareaincounty 0.0 to 100.0 percentofcountylandareainzip 0.0 to 100.0 percentofzippopulationincounty 0.0 to 100.0 percentofcountypopulationinzip 0.0 to 100.0 percentofziphouseholdsincounty 0.0 to 100.0 percentofcountyhouseholdsinzip 0.0 to 100.0.

One of the most frequent questions we get is related to matching ZIPs to cities. Remember that the boundaries of a ZIP code generally have nothing to do with city limits.

ZIP boundaries are set to aid mail delivery. City limits are not. Generally, USPS determines a mail route that best suites their needs, they assign a ZIP to that area, and they name the 'city' of the ZIP after the post office(s) in that ZIP. As you can see from the example image, about the only time the city limits and ZIP boundaries match up are across state lines and that isn't even universally true. Many ZIPs cross state boundaries.

Think of it this way: if the postal carrier is driving down a road delivering mail and happens to cross the city limits, it makes little sense to have them stop delivering for the rest of the houses on the street simply because the city limits changed. Instead, they'll keep delivering along the street to the next intersection or some other boundary that makes more sense to allocating their available resources, i.e. Delivery personnel.

ZIP 'Cities' Often Aren't Incorporated Cities. Futher, USPS does not always use the name of the incorporated city in which the ZIP code is located.

The assignment of cities to ZIP codes is more general. The city is usually the name of the main post office.

The image example of New York, NY illustrates this point. The black outline shows the area of the official city limits. Every color coded region within is a different ZIP code. ZIPs with the same city according to USPS have the same color. To avoid overcrowding, only a few labels are shown.

As you can see, the area inside New York city is actually split into many ZIP codes that are each named after different places. Staten Island, Brooklyn, and Bronx are all famous parts of NYC that can easily be picked out in the example. None of them are actually an incorporated city. However, USPS uses those names to indicate the city for the ZIP codes located in those areas. Because the area is so densely populated, many different areas within NYC are given names that are not 'New York.' Only a small subset of ZIPs in Manhattan use the designation 'New York' even though many others exist within the city limits. ZIP Codes Include Rural Areas.

Explanation Remember that people in rural areas outside of city limits still need to receive mail. So, ZIP codes cover a much larger portion of the land area in the US than city limits. This coverage is easily seen in the image examples shown for the area around Memphis, TN. One map shows the color coded areas that make up the official city limits for the various cities in the region. The second map shows all ZIP codes with the same primary city shaded using the same color. Notice how many rural areas are grouped with nearby cities because they share delivery resources.

Correctly Matching ZIPs to Cities and Counties To solve these issues, we have compiled the overlap data between ZIP codes and cities as well as ZIP codes and counties. We analyzed every block that the Census Bureau assigned within the U.S.

This means that without complex analysis of tons of addresses, shapes, or areas that you can determine what percentage of the population of a ZIP code is located within various city limits. We list the overlap based on area, land area, population, and number of households to suite various use cases. The overlaps are given as percentages of the ZIP in an area and the area in the ZIP so that you can find the primary city/county for a ZIP or the primary ZIP for a city/county based on the metric of your choosing. To facility easier matching, various identifiers of cities and counties are included such as FIPS and ANSI codes. If you are comparing to other data sets with these identifiers, these identifiers avoid complications that arise from matching to cities/counties that are frequently spelled differently. For instance, DC can be represented as 'Washington, DC', 'Washington D.C.'

, 'District of Columbia', etc. However, it will always have an FIPS code of 11. As another example, St.

Louis, MO could be spelled as 'St Louis', 'St. Louis', or 'Saint Louis' in various data sets. However, it will always have a state FIPS of 29 and a place/city FIPS of 65000 which makes the geoid 2965000.

Keep in mind that there may be multiple rows in the data set for a single ZIP - for instance when a ZIP overlaps 2 counties, there will be two rows for that ZIP. When matching to cities/places, the data file also contains a row for the portion of the ZIP that is outside of city limits. These rows can be identified because the placename will be 'Outside city limits', the last 5 digits of the placegeoid will be 99999, and the placetype will be 'None'. Note that not all ZIP codes, cities, or counties will be included in this data file. Only the ZIPs that are studied by the Census Bureau that are geographically based (approximately 33,000) are included.

Approximately 98% of counties and 95% of cities overlap with a ZIP code and are included. Approximate Coordinates (Green) The approximate latitude and longitude coordinates from the National Weather Service that are rounded to 2 decimal places should be sufficient for many use cases.

Remember that a ZIP code often covers a large geographic area and that very precise latitude and longitude coordinates may be precise down to a very small radius - down to very small fractions of a mile which is much smaller than the geographic area covered by a ZIP code. Changing the latitude or longitude coordinate by a single hundredth of a decimal place results in a shift of approximately 1 mile in any direction. The radius of this shift is shown in the graphic in green. As you can see, the lack of precision due to rounding to two decimal places is insignificant when compared to the reduction in precision caused by reducing a complex shape to a single point. Many of these coordinates have been hand chosen.

The lack of additional digits after the decimal serves as a reminder to keep precision in mind when performing any calculations. Bounding Box Coordinates (Red) The most simple method is to use the formula for a rectangle that uses ( latitudemin + latitudemax) / 2 for the latitude coordinate.

This method is shown in the example graphic in red. As you can see, this can result in coordinates that do not actually lie within the region covered by the ZIP code. The point chosen using this method is actually located within a small area that is not covered by the ZIP code. Using this calculation method will result in a point outside of the ZIP code approximately 8% of the time. Internal Point Coordinates The internal points are calculated by the. They use a method similar to the bounding box method.

However, should a coordinate lie outside of the ZIP, as it does in the ZIP pictured, the coordinates will be shifted to the nearest internal point or internal point within land. Polygon Offset Coordinates (Black) Another method for calculating the ZIP coordinates involves complex shape analysis of the polygon that represents the ZIP code called polygon offsetting or polygon buffering. The polygon/shape that represents the ZIP code is repeatedly reduced in size until only a single point remains. The result is a coordinate that will be within the largest section of the ZIP code.

This method is quite well suited to label positioning and is actually how the labels positions are determined on maps throughout the site. As you can see from the image where this method is shown in black, this may be significantly different from the other two calculation methods. It can also result in a point that is a much more significant distance from some sections of the ZIP code. For instance, the example has a point that is very far from the south west portion of the ZIP.

Population Center Coordinates While the Census Bureau publishes centers of population for many different geographic areas of the country (ex. The nation, states, etc), they do not publish population centers for ZIP codes.

We use to calculate the population mean center. As discussed in their analysis, the population mean center is frequently preferred over the population median center because it responds to more slight changes in population. Realize that the population center of a ZIP code may actually lie outside the boundary of the ZIP code just as the bounding box coordinates do. However, this occurrence should be rare. Less than 3% of ZIP codes have a population center that lies outside of the boundary of the ZIP. The approximate coordinates that are a combination of hand-picked coordinates and those from the National Weather Service (NWS) cover over 98% of all active and decomissioned ZIP codes - more than most of our competitors that only provide coordinates calculated by the Census Bureau.

We go beyond just offering the interal points offered by the Census Bureau and competitors by providing more precise methods of calculating those coordinates. Because all of the coordinate calculation methods other than the National Weather Service data rely on Census data, those coordinates are only available for the ZIP codes included by the Census Bureau. The Census Bureau researched 33,120 of the approximately 42,000 currently active ZIP codes. While that may seem like a large discrepancy, you can see from the chart that over 93% of those ZIPs without Census data are for single buildings or military usage where an approximation is more than adequate. For these reasons, any of the methods for calculating coordinates should suffice for the majority of the population. However, we recommend you fall back to using the approximate coordinates from the National Weather Service in the case that you need coordinates for specific ZIPs other than the most commonly used ones. Mileage Differences For most ZIP codes, the difference in coordinate calculation methods equates to a few miles.

However, for some, the choice of coordinates can make as much as a 20 or 30 mile difference. Take ZIP code 71373 for instance. To see how different the population center is from the actual center of the ZIP. To see the population center of 81650 which is another good example of where the population center is far from the center of the area. A Comparison of ZIP Code Population Estimates A Brief Overview: Why not use the most recent data?

The census conducted every 10 years by the Census Bureau is the only population estimate that makes an attempt to count every member of the population by sending a survey to every household in the country. Completion is required. It sets the bar for precision/accuracy extremely high and the population size changes relatively little over time.

Over a 5 year period, the U.S. Population is only expected to grow by 3.9% and an individual ZIP code is likely to vary be less than 10%. That leaves an extremely small amount of room for error. Another way to approximate the population is to use sampling. Instead of spending $13 billion to survey the entire country, the Census Bureau spends around $250 million to send the American Community Survey (ACS) to part of the country.

The answers (such as the number of people in the household) help to estimate the population as a whole. To get those huge savings, only a small percentage of the population is surveyed which leaves us with a range the population likely falls within instead of a precise number. The margin of error that determines the size of the range is larger than the expected population growth. Another way to approximate the population is to use an estimator. As confirmed by the Internal Revenue Service (IRS), the number of tax returns filed for a ZIP code can be used to approximate the number of households and the number of exemptions can be used to approximate the population.

However, as an estimator, it isn't perfect. As discussed below, it is affected by economic changes as well as tax policy changes.

It also has strict privacy limits on data release such that it underestimates the population by more than the expected population growth. Coverage: 3-Way Tie The 2010 Census was done precisely to estimate population sizes and so provides estimates for the most ZIP codes. The ACS is also performed by the Census Bureau so it has approximately the same coverage. The IRS lacks data in 3 key areas:. The IRS data omits population estimates for almost all PO Box and unique ZIP codes (approximately 3,730 ZIPs with a total population of 2 million). The IRS does not provide estimates for Puerto Rico (approximately 125 ZIPs with a total population of 3.7 million). To protect privacy, the IRS also omits population estimates for ZIPs with less than 100 returns (approximately 1,500 ZIPs with a population of less than 400,000).

Of the remaining ZIPs not included in #1 and #2 above that are omitted by the IRS, nearly 95% have a population under 500. Other estimates for ZIPs with a very low population should be viewed with skepticism because the IRS data implements other privacy protection measures. Readers should notice that the ZIP codes omitted from the IRS data set only account for around 2% of the total population so this is by no means a major issue.

'There are significant changes to the 2010 Code Tabulation Areas delineation from that used in 2000. For 2010 only legitimate five-digit areas are defined so there is no longer full nation-wide coverage.

The 2010 ZCTAs will better represent the actual Zip Code service areas because the Census Bureau initiated a process before creation of 2010 blocks to add block boundaries that split polygons with large numbers of addresses using different ZIP Codes.' 'Data users should not use Code Tabulation Areas to identify the official USPS ZIP Code for mail delivery. The USPS makes periodic changes to ZIP Codes to support more efficient mail delivery. Graphic Designer. The Code Tabulation Areas process used primarily residential addresses and was biased towards ZIP Codes used for city-style mail delivery, thus there may be ZIP Codes that are primarily nonresidential or boxes only that may not have a corresponding ZCTA.'

Dma Zip Code List

Census Bureau Exemptions Aren't a Perfect Estimator. The first issue with the accuracy of the IRS estimates is that their are using exemptions as an estimator for populations as opposed to directly trying to calculate population size. Because it is only an estimator, it is still subject to variation due to other variables.

For instance, economic changes or changes in tax policy are likely to affect the population estimates. It is highly unlikely that the population shrank by 5% in 2008. It is much more likely that the economic downturn affected the estimates by changing how the population files their tax returns. While other competitors that offer a free download with IRS data have suggested using the formula of 'returns + joint returns + dependents' to estimate population size, the IRS suggests using the number of exemptions.

Our research backs up the suggestions put forth by the IRS. Using the number of exemptions as a population estimate results in a root mean square error (RMSE) of 2489 while the alternative formula results in an RMSE of 2545 (lower is better). In general, the IRS underestimates the population of a ZIP as compared to ZCTAs by 10% to 20% for two reason. Both of which are documented by the IRS. The full U.S. Population is not represented because many individuals are not required to file an income tax return.

The IRS documents only around 289 million exemptions compared to a population of 312 million estimated by the Census. Privacy protection measures prevent the IRS from disclosing ZIP level data for all income brackets. While 289 million exemptions are reported when examining state level data that is not subject to privacy protection, only 277 million are reported after privacy protection eliminates some data. Surveying a large portion of the population is expensive (especially with the large number of questions besides population included in the ACS). The Census Bureau publishes population estimates based on ACS surveys using data from the past 1 year, 3 years, or 5 years of data.

Including data from more years increases the sample size to improve the precision of the estimate at the cost of using less recent data. We include the estimates based on the past 5 years worth of surveys because they are based on the largest sample to provide the most precise estimates. Even so, the ACS only samples approximately 10% of the population over a 5 year period.

Recall that the population of the country is expected to change by less than 5% over a 5 year period and the population of any given ZIP is likely to change by less than 10%. The easiest way to think about this is that it is difficult to provide an estimate with only a 5%-10% margin of error based on only surveying 10% of the population. As you can see from the graph, the margin of error is 10% or more for over half of the ZIP codes - which is higher than the estimated population change. To further illustrate this point, the 2010 Census population is within the most recent ACS margin of error for nearly 75% of ZIPs.

So, by choosing the ACS estimates over the Census, the population estimate would improve for 25% of ZIPs while becoming less accurate for nearly 75%. We have included the margin of error with the ACS estimates so that those looking to create their own estimate can make their own judgement calls as to their formula for estimating the population for a given ZIP. For those diving deeper into population estimates, we have examined whether the IRS and ACS estimates show the same relative change in population over various periods of time.

In other words, we asked this question: if the IRS estimates that the population of a ZIP increased over a certain period of time, does the ACS data also indicate a population increase? We have found that there is a correlation between the two data sets. However, that correlation only becomes apparent on estimates for ZIPs that have a very low margin of error.