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An Investigation into Expanding the CAUS Universe1

Bryan Schar, James Lawrence, Star Ying, Jim Hartman

U.S. Census Bureau 

Washington, DC 20233 

Abstract

The Community Address Updating System (CAUS) conducts targeted block listings to improve the coverage of the 

American Community Survey’s sample frame. Blocks with certain address characteristics have traditionally been excluded 

from listing by the CAUS program because it was believed that data provided by other sources kept them current. However, 

results from the nationwide 2010 Census Address Canvassing operation suggested that some of these excluded blocks may 

benefit from being listed by the CAUS program. Therefore, a study was conducted to determine if the universe of CAUS 

blocks should be expanded. This paper will discuss the methods being used to determine if the CAUS universe should be 

expanded, some preliminary results of the investigation, and plans for future research. 

1. Introduction

1.1. Master Address File – Description and Updating

The Master Address File (MAF) is a U.S. Census Bureau file that contains an up to date inventory of all known living 

quarters in the United States. It is the sole source of housing unit records for the American Community Survey’s (ACS) 

sampling frame, and the main source of group quarters (GQ) information for the ACS’s GQ sampling frame (Bates, 2011). 

Between Decennial Censuses, the U.S. Postal Service’s (USPS) Delivery Sequence File (DSF) is the primary source of citystyle2 address updates for the MAF. The Demographic Area Address Listing operation, of which the Community Address 

Updating System (CAUS) targeted census block listing operation is a part, is the primary source of non-city-style3 address 

updates (GEO, 2011b). 

1.2. Delivery Sequence File

The DSF is the USPS’s list of delivery points in the country, with a delivery point being a single mailbox or other place at 

which mail is delivered. It is assumed that city-style delivery points on the DSF represent the location of the associated living 

quarters. The DSF also contains information about non-city-style delivery points; however, this information is not used by 

the Census Bureau to update the MAF for the following reasons: 

The non-standard format of certain types of non-city-style addresses on the MAF, such as physical description only 

addresses, makes it difficult to match them to non-city-style delivery points on the DSF. Thus, updating the MAF 

with non-city-style delivery points from the DSF increases the risk of adding duplicate living quarters to the MAF. 

There is a potential that non-city-style delivery points on the DSF may be duplicates of city-style delivery points in 

areas that have undergone a conversion from non-city-style to city-style. 

It cannot be assumed that non-city-style delivery points on the DSF represent the location of living quarters. For 

instance, a PO Box does not represent the location of the living quarters of the person renting the P.O. Box. 

The information provided by the DSF for many non-city-style delivery points cannot be used to geocode (i.e., assign 

it to a census block) the address represented by it. 

Due to these concerns, non-city-style delivery points on the DSF are not used to update the MAF. Instead, CAUS targeted 

block listing operations are a major source of address updates to the MAF in areas with a high proportion of non-city-style 

address (GEO, 2011b). 

1 This report is released to inform interested parties of ongoing research and to encourage discussion of work in progress. The views 

expressed are those of the author and not necessarily those of the U.S. Census Bureau.

2 Addresses that contain at least a house number and street name. 

3 Examples of non-city-style addresses are PO Boxes, rural routes, and description only addresses. 

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1.3. CAUS Listings 

A census block, or block for short, is the smallest geographic entity for which the Census Bureau collects and tabulates data. 

When a block is listed, a field representative canvasses that block in order to identify all living quarters present within the 

block. The CAUS branch is responsible for selecting the blocks that are listed through the CAUS program. 

When selecting blocks to list, the CAUS branch first considers the Address Characteristic Type (ACT) code assigned yearly 

by the U.S. Census Bureau’s Geography Division (GEO). This code indicates both the style of MAF addresses in a block and 

the percent of addresses that can be matched to delivery points on the DSF (GEO, 2011a). Note that a block’s ACT code may 

change from year to year based on changes in its MAF information. The CAUS branch classifies blocks into one of three 

groups based on their ACT code. 

Yes, or “Y”, blocks are considered to be in the CAUS universe and eligible for listing. 

Maybe, or “M”, blocks are considered to be in the margins of the CAUS universe. 

No, or “N”, blocks are considered to be outside the CAUS universe. 

Blocks in the “N” group contain either only city-style addresses, non-residential addresses, or no addresses. Blocks in the 

“M” group contain a mixture of city-style and non-city-style addresses where some of the addresses match to a DSF delivery 

point and at least 80 percent of the addresses are city-style. Blocks in the “Y” group contain either only non-city-style 

addresses, a mixture of city-style and non-city-style addresses where either none or all of the addresses match to a DSF 

delivery point, or a mixture of city-style and non-city-style addresses where some of the addresses match to a DSF delivery 

point and less than 80 percent of the addresses are city-style. Table 1 gives the ACT codes that belong to each group, and 

attachment A provides information about the types of addresses in each ACT code. 

Table 1: ACT Code Groups Used for CAUS Listings 

CAUS Group ACT Codes 

Y M1, ME, MF, MG, M3, N1, N2, N3, P1, P2, P3, R1, R2, R3 

M MA, MB, MC, MD 

N B1, B2, B3, C1, C2, C3, Z0 

1.4. Motivation for Research 

Traditionally, blocks within the “M” and “N” groups were not considered for listing because it was believed that delivery 

point data provided by the USPS were sufficient to keep those blocks current. Results from the 2010 Census Address 

Canvassing operation (AdCan) conducted in 2009 provided listing data for blocks in these groups. These data allowed the 

CAUS branch to examine if blocks that were traditionally excluded from the CAUS universe need to be listed through the 

CAUS program to pick up new units. In addition, research conducted by the U.S. Census Bureau’s Demographic Statistical 

Methods Division suggested there might be benefits to using an expanded definition of the CAUS universe based on the 

percent of MAF addresses that can be matched to a DSF delivery point (Kennel, 2010). 

2. Research 

2.1. Data Used 

2.1.1. 2010 Address Canvassing 

One source of data for this research was the 2010 AdCan results. For this operation, census workers around the nation looked 

for every place where people live, stay, or could live or stay. They compared what they saw on the ground to what was shown 

on the MAF. Based on their observations, they verified, updated, or deleted addresses already on the MAF, and added 

addresses that were missing from it (ALOIT, 2012). Only those 2010 AdCan unit records that met all the criteria below were 

considered for this study: 

The unit returned from the 2010 AdCan with an action code that indicated it was a new unit added by the operation. 

The unit was in the final 2010 Census universe, that is, it was classified as a housing unit in the 2010 Census. 

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• The unit was included in the 2012 ACS main4 housing unit frame universe. 

Information from the 2012 main version of the ACS unit frame universe was used to filter, that is include or exclude, these 

records since the version of the MAF used in its creation was the first to include information about whether or not a unit was 

in the final 2010 Census universe. 

2.1.2. CAUS Experimental Listings 

Given the unique nature of the 2010 AdCan, the CAUS branch conducted experimental block listings of its own. One goal of 

these listings was to obtain a second source of data that could be used to determine if and where the CAUS universe should 

be expanded. 

When a block is listed by CAUS, census workers look for every place where people live, stay, or could live or stay in that 

block. They compare what they see on the ground to what is shown on the MAF. Based on their observations, they verify, 

update, or delete addresses already on the MAF, and add addresses that are missing from it. 

Blocks were selected for this experiment in March of 2010, which was approximately a year after the 2010 AdCan was 

conducted. The first step in selecting blocks for this study was to assign each block to a category. The category a block was 

assigned to was based on which of the three CAUS ACT groups (“Y”, “M”, “N”) it belonged to and which of five possible 

housing unit based groups it belonged to. The definitions of the five possible housing unit based groups are detail in Table 2 

below. This resulted in each block being assigned to one of 15 categories as shown in Table 3. 

Table 2: Housing Unit Groups for CAUS Experimental Listings 

Housing Unit Group Number of Pre-Listing Housing Units 

1 3 or fewer 

2 4-8 

3 9-15 

4 16-29 

5 30 or more 

Groupings based on housing units were used because initial attempts to model the expected number of new housing units, or 

adds, that were found during 2010 AdCan listings showed that the number of pre-existing housing units in a block was a 

major predictor of the number of adds that would be found when listing. Since the distribution of pre-listing housing units per 

block has a strong positive skew, including this variable in the models resulted in approximately 90 percent of the blocks 

(those with less than 30 housing units) being clustered together with very low predicted adds and the remaining 10 percent 

(those with 30 or more housing units) of blocks having high predicted adds. However, the CAUS branch wanted to be able to 

better distinguish between blocks with lower predicted adds rather than have them treated as essentially equivalent. 

Therefore, it was hoped that transforming pre-existing units into a categorical variable would help to provide more gradation 

to predicted adds. 

The blocks within each of these groups with 100 or less pre-existing housing units were then sorted based on a selection 

score assigned to them. This score was derived from a generalized linear model that was developed using 2007 and 2008 

block characteristic data to predict 2010 AdCan adds, and it indicated the expected number of new housing units, or adds, 

that would be obtained by listing the blocks. Only blocks with 100 or less pre-existing housing units were considered for this 

study due to the cost associated with listing blocks with a large number of pre-existing housing units. At the time of this 

study, approximately eight million of the 8.2 million tabulation blocks in the country had 100 or less pre-existing housing 

units. 

A systematic sample of these sorted blocks was then selected from within each group. This was done to allow for a study of 

how well the selection score ranked blocks by adds, and how well it estimated the number of adds across a range of scores. 

The number of blocks selected from each group was driven by the various goals and constraints of the study. First, the CAUS 

branch estimated that it could afford to list around 7,500 blocks for this study. Based on previous experience, the CAUS 

branch only expected half as many blocks as it selects and sends out for listing to be completed. Therefore, it wanted to send 

4 Two versions of the ACS unit frame universe are created for each ACS survey year, the main and the supplemental. 

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out approximately 15,000 blocks in total for this study. Also, at the time that blocks were selected, the main interest was 

determining whether the “M” blocks should be added to the CAUS universe. The status of the “N” blocks was of secondary 

interest. Therefore, approximately four times as many “M” blocks were selected than “N”. In addition, the CAUS branch 

wanted to make sure that the listings resulted in a good number of new addresses being added to the MAF. Therefore, 

approximately half of the blocks it selected were “Y” blocks, and approximately three times as many blocks were selected 

from housing unit groups three, four, and five as compared to housing unit groups one and two. 

This resulted in 15,241 blocks being selected and sent out for listing during April 2010 through June 2010. Of the blocks sent 

out, 8,498 were actually listed in the field. Tables 3 and 4 provide more details of this study’s block distribution. 

Table 3: Design of 2010 CAUS Experimental Listings 

Pre-Listing 

CAUS 

Group 

Block 

Size 

Group 

Blocks in Group Blocks Sent for 

Listing Blocks Listed 

Count Percent 

of Total Count Percent of Group Count Percent of Sent 

Y 1 252,893 3.2 892 0.4 439 49.2 

2 158,925 2.0 892 0.6 440 49.3 

3 87,656 1.1 1,863 2.1 977 52.4 

4 62,347 0.8 1,862 3.0 951 51.1 

5 46,037 0.6 1,562 3.4 817 52.3 

M 1 9,659 0.1 787 8.2 438 55.7 

2 62,596 0.8 792 1.3 437 55.2 

3 79,397 1.0 1,766 2.2 994 56.3 

4 88,030 1.1 1,768 2.0 978 55.3 

5 102,720 1.3 1,465 1.4 783 53.5 

N 1 3,648,644 45.7 169 0.0 129 76.3 

2 871,375 10.9 169 0.0 128 75.7 

3 850,954 10.7 392 0.1 289 73.7 

4 934,728 11.7 392 0.0 299 76.3 

5 736,974 9.2 470 0.1 399 84.9 

Total 7,992,935 100.0 15,241 0.2 8,498 55.8 

Sources: U.S. Census Bureau, CAUS Targeting Database, February 2010; U.S. Census Bureau, 

CAUS Sample Control Output File, July 2010. 

Table 4: Distribution of 2010 CAUS Experimental Listings by ACT Code 

PreListing 

CAUS 

Group 

Pre-Listing 

ACT Code 

Blocks in ACT Blocks Sent for 

Listing Blocks Listed 

Count Percent 

of Total Count Percent of Group Count Percent of Sent 

Y M1 106,398 1.3 1,629 1.5 812 49.9 

ME 50,441 0.6 700 1.4 355 50.7 

MF 22,437 0.3 436 1.9 237 54.4 

MG 219,141 2.7 3,028 1.4 1,563 51.6 

M3 133 0.0 4 3.0 3 75.0 

N1 131,929 1.7 935 0.7 466 49.8 

N2 53 0.0 1 1.9 0 0.0 

P1 21,224 0.3 115 0.5 56 48.7 

R1 56,092 0.7 223 0.4 132 59.2 

R2 8 0.0 0 0.0 0 

R3 2 0.0 0 0.0 0 

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PreListing 

CAUS 

Group 

Pre-Listing 

ACT Code 

Blocks in ACT Blocks Sent for 

Listing Blocks Listed 

Count Percent 

of Total Count Percent of Group Count Percent of Sent 

M MA 87,325 1.1 1,587 1.8 851 53.6 

MB 109,513 1.4 2,096 1.9 1,180 56.3 

MC 74,685 0.9 1,376 1.8 769 55.9 

MD 70,879 0.9 1,519 2.1 830 54.6 

N B1 457 0.0 0 0.0 0 

B2 156 0.0 0 0.0 0 

B3 235,951 3.0 12 0.0 10 83.3 

C1 181,195 2.3 59 0.0 41 69.5 

C2 942,867 11.8 397 0.0 305 76.8 

C3 3,241,112 40.6 984 0.0 787 80.0 

Z0 2,440,937 30.5 140 0.0 101 72.1 

Total 7,992,935 100.0 15,241 0.2 8,498 55.8 

Sources: U.S. Census Bureau, Geography Reference File-Codes, July 2009; U.S. Census Bureau, CAUS 

Sample Control Output File, July 2010. 

Only those unit records from these listings that met the criteria below were considered for the preliminary part of this study: 

The unit was accepted by GEO for inclusion on the MAF as a new address record. 

The unit was included in the 2011 ACS supplemental housing unit frame universe. 

Information from the 2011 supplemental version of the ACS unit frame universe was used for these records since this is the 

first version of the ACS unit frame to include the addresses added by these listings. 

Additional data used in this research comes from the MAF extracts and geography files used by ACS to create its unit frame 

universes each year. These data sources contain such information as a unit’s DSF status, whether or not a unit is residential, 

and if the unit is considered to be within the ACS universe at the time of frame creation. 

2.2. Decision Criteria 

The primary function of CAUS listings is to serve as a complement to the updates provided by the DSF. This is why CAUS 

has traditionally only listed areas with a high proportion of pre-existing non-city-style addresses. However, if there was 

evidence that the DSF was not providing sufficient and/or timely updates to other areas, there could be a benefit from 

including them in the CAUS universe. 

With this in mind, the CAUS branch decided to base its decision on whether or not to include new areas in the CAUS 

universe on a measure of how well DSF updating captures the adds from the 2010 AdCan and the 2010 CAUS experimental 

listings. In particular, if an address that was added to the MAF by one of these operations could not be matched by the 

Census Bureau to a delivery point on the DSF within a reasonable amount of time, this would be considered a deficiency in 

the ability of DSF updating to capture that add. 

There are two aspects to this measure. First is the expected lack of DSF usability for capturing non-city-style adds. Though 

this deficiency is expected, since the Census Bureau does not use the DSF to add non-city-style addresses to the MAF, it still 

provides a measure of how much an area would benefit from being listed by the CAUS program. Second, is the unexpected 

lack of DSF usability for capturing city-style adds. Thus an area can be considered to have a poor DSF capture rate if most or 

all of it adds were non-city-style adds and/or were city-style adds that could not be matched to a delivery point on the DSF 

within a reasonable amount of time. Groups of blocks whose DSF match rates of adds are low enough, as measured by the 

percent of adds that can be matched to a delivery point on the DSF, may be considered for inclusion into the CAUS universe. 

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What constitutes a reasonable amount of time to wait for an added city-style address to match to a DSF delivery point is still 

an open question. It takes approximately a year, from beginning to end, for a block’s information to be updated on the MAF 

through the CAUS program. This includes creating the frame from which to select the blocks, selecting blocks from this 

frame to list, having the selected blocks listed in the field, receiving the block updates from field, processing and editing this 

information, and finally applying the vetted updates to the MAF. During this same time period, the MAF is updated twice by 

the DSF (GEO, 2011b). If all the new city-style addresses obtained by listing a block through CAUS were also added by 

these two DSF updates, then there was no benefit, in terms of adds, gained by listing that block. However, what if it takes one 

year after the listing updates are applied for a city-style address to be captured using a DSF update? What about two years? 

One important factor in determining this criterion is cost. The Census Bureau will continue to use DSF updates to keep a 

block’s city-style address information on the MAF current regardless of whether or not that block is listed by the CAUS 

program. This means any costs associated with acquiring DSF updates are independent of the decision to list a block. 

Therefore, it will always be cheaper, in terms of dollars, to wait for city-style addresses to be added by DSF updates (when 

possible). However, other criteria, such as frame coverage, should be considered when making this assessment. 

Since the traditional CAUS universe only includes areas with high concentrations of non-city-style addresses, and the DSF is 

not used to add these types of addresses to the MAF, the cost, in terms of frame coverage, of not listing these areas is clear. 

The properties of non-city-style addresses are different enough from those of city-style addresses that it would be difficult to 

justify imputing estimates for the former using information from the latter. However, if the CAUS universe is expanded to 

include areas that contain high concentrations of city-style addresses that, for reasons yet unknown, are not being added to 

the MAF by the DSF updates, then it is not as clear-cut that the coverage issues caused by this could not be accounted for 

through methods other than block listings. This suggests future research to investigate if city-style addresses being added to 

the MAF by the DSF are representative of those that are not. 

2.3. Limitations 

2.3.1. Possible Overestimation of the AdCan Adds 

When census field representatives were assigned to canvass blocks during AdCan, they were provided with a list of the MAF 

addresses that had been geocoded to the assigned blocks. They were instructed to add addresses they found in the block that 

did not appear on that list and to delete any addresses from the list that they could not find in the block. This means that some 

of the adds in AdCan may have represented an address that existed on the MAF before AdCan but was either not geocoded to 

a block or was geocoded to the wrong block. Every effort was made during data processing to identify these false adds, but 

some of them could have been missed (ALOIT 2012, 115). This could result in an overestimate of the adds in the AdCan 

data, because some of the adds in AdCan were corrections to the geocoding of existing units. 

In addition, some city-style addresses added by AdCan actually represented housing units that already existed as non-citystyle addresses on the MAF that had been converted to city-style as part of enhancements to an area’s emergency 911 system. 

Since census workers could have deleted the units’ associated non-city-style address, these would form another type of adddelete pair that could have also slipped through the add-delete matching done during data processing. This could result in an 

overestimate of the city-style adds in the AdCan data, because some of the adds in AdCan were just conversions of existing 

units’ addresses from non-city-style to city-style. 

A complication introduced by the possible existence of false adds in the AdCan data is that they may behave differently, in 

terms of matching to DSF delivery points, than true adds. If this is the case, then inferences made about the DSF match rate 

of adds based on AdCan data may not be reflective of what would occur in regular CAUS listings. 

2.3.2. ACT Code Representation in 2010 CAUS Experimental Listings Data 

As mentioned in Section 2.1., blocks were selected for the 2010 CAUS experimental listings based on their ACT group and 

housing unit group. This was done because it was felt that blocks that belonged to the same ACT and block size groups were 

similar in terms of adds. In addition, preference was given to selecting blocks in the “Y” and “M” ACT groups and blocks 

with more pre-existing residential housing units. A consequence of this is that some ACT codes were not represented in this 

study, while others only had a limited number of blocks selected. These ACT codes tended to occur rarely in the universe of 

blocks and/or be in the “N” ACT group. 

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2.3.3. Sparsity of Adds in Data 

Reliably estimating the expected DSF match rate of adds for a group of blocks requires that enough adds were found in 

enough of its blocks during AdCan and/or the 2010 CAUS experimental listings. This can present a challenge since only 16.0 

percent of the blocks listed during AdCan contained adds, and 31.5 percent of the blocks listed during the 2010 CAUS 

experimental listings contained adds. Also, those blocks with adds in AdCan only contained about 4.2 new units on average, 

while those blocks with adds in the 2010 CAUS experimental listings contained about 5.4 new units on average. 

2.4. Results to Date 

An initial examination of the change over time in the DSF match rate of adds from both AdCan and the 2010 CAUS 

experimental listings was conducted. Figures 1 and 2 give two measures of the percent of adds matched to the DSF for the 

AdCan and the 2010 CAUS experimental listings respectively. The square data points in each figure show the percent of citystyle adds that have been matched to a delivery point across different vintages of the DSF. The diamond data points in each 

figure show the percent of total adds, both city-style and non-city-style, that have been matched to a delivery point across 

different vintages of the DSF. Note that, since non-city-style addresses are not matched to DSF delivery points (with only a 

handful of exceptions), the match rate for all addresses will always be less than or equal to the match rate for city-style 

addresses . The first DSF vintage shown on these figures, Spring 2008 for AdCan and Fall 2009 for the 2010 CAUS listings, 

is the one used as an input when creating the initial address lists for these operations. 

As can be seen from Figures 1 and 2, the DSF match rate increases over time for both sets of data. However, the behavior of 

the percent adds matched to the DSF for AdCan adds suggests the rate of the increase may slow as time goes on. Further 

examination could be conducted to determine if there is a point where the marginal cost savings of waiting for an address to 

come in on the DSF is outweighed by the costs of excluding that unit from the sampling frame. 

It is important to note that the match rates shown in Figures 1 and 2 are not cumulative, but represent how many adds were 

matched to a delivery point on each vintage of the DSF independent of whether or not they match to a delivery point on a 

previous vintage. There are cases of delivery points that stop appearing on DSF updates. This occurs when the USPS no 

longer considers an address to be a valid delivery point for mail. For example, when a previously occupied housing unit has 

been demolished. This means that the overall DSF match rate of adds could decrease from one vintage of the DSF to the next, 

though this has not yet been observed in this research. 

 

DSF Match Rate of AdCan Adds 

30.0% 

25.0% 

20.0% 

15.0% City-Style 

Total 

10.0% 

5.0% 

0.0% 

Spring Fall 2008 Spring Fall 2009 Spring Fall 2010 Spring 

2008 DSF DSF 2009 DSF DSF 2010 DSF DSF 2011 DSF 

23.3% 24.1% 

19.5% 

21.9% 

13.6% 18.2% 18.8% 

7.7% 

15.2% 

17.1% 

10.6% 

0.0% 6.0% 

Figure 1: DSF Match Rate of AdCan Adds Over Time 

Source: U.S. Census Bureau, Master Address File, July 2011. 

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DSF Match Rate of 2010 CAUS Listing Adds 

1.4% 

1.2% 

1.0% 

0.8% 

1.2% 

0.8% 

0.4% 

0.0% 0.0% 0.3% 

City-Style 0.6% 

Total 

0.4% 

0.2% 

0.0% 

Fall 2009 DSF Spring 2010 Fall 2010 DSF Spring 2011 

DSF DSF 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 2: DSF Match Rate of 2010 CAUS Experimental Listing Adds Over Time 

Source: U.S. Census Bureau, Master Address File, July 2011. 

One thing that stands out from examining the DSF match rate of adds given in Figures 1 and 2, is how much higher the 

values are for the AdCan adds than the 2010 CAUS experimental listings adds. For example, the adds from AdCan had an 

overall DSF match rate of 15.2 percent from the Fall 2009 DSF, which represents three post listing DSF updates. The adds 

from the 2010 CAUS experimental listings have an overall DSF match rate of only 0.8 percent for an equivalent number of 

DSF updates. This could be due to the limitations of the AdCan data as discussed in Section 2.3. In addition, many of the 

blocks canvassed in AdCan had not been fully listed since the 2000 Census, and therefore, the adds from AdCan can 

represent up to ten years worth of changes. This means that, for some blocks, the DSF had up to twelve years to catch up to 

ground truth given by AdCan. By comparison, the 2010 CAUS experimental listing was conducted only a year after AdCan. 

This means the DSF had only one and a half years to catch up to the ground truth given by the CAUS experimental listing. 

This initial analysis suggests that a conservative method of estimating the DSF match rate of adds for the purposes of 

excluding and including blocks in the CAUS universe is to use the most recent version of the DSF available each time the 

CAUS universe is created. The underlying assumption of this method is that as long as an add eventually comes in on a DSF 

it is worth the wait. This is the rule that was used for the preliminary investigation. 

Since the traditional CAUS universe is defined in terms of the ACT code, this was the first type of block groupings that were 

examined. Tables 5 and 6 give the DSF match rate of adds grouped by their pre-AdCan and pre-2010 CAUS experimental

listing ACT codes respectively. Pre-listing ACT code, rather than post-listing ACT code, of blocks were examined because 

that is the ACT code that is known at the time of selection. The values for the DSF match rate of adds in these tables are 

based on the most recent version of the DSF, the Spring 2011 vintage, available to the Census Bureau. Attachments B and C 

give the DSF match rate of adds by pre-listing ACT code for all post-operation vintages of the DSF. 

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Table 5: DSF Match Rate of AdCan Adds by Pre-AdCan ACT Code 

PreAdCan 

CAUS 

Group 

PreAdCan 

ACT 

Blocks in 

ACT 

Blocks 

with 

Adds 

AdCan Adds Adds on Spring 2011 DSF 

CityStyle 

NonCityStyle 

Total 

City-Style 

Adds on 

DSF 

Percent 

of CityStyle 

Adds 

Percent 

of Total 

Adds 

Y M1 115,534 65,248 252,423 125,559 377,982 16,100 6.4 4.3 

ME 50,110 24,843 74,718 35,805 110,523 10,857 14.5 9.8 

MF 22,281 14,554 52,697 26,676 79,373 6,681 12.7 8.4 

MG 213,379 113,539 447,057 247,330 694,387 51,524 11.5 7.4 

M3 159 83 1,104 14 1,118 33 3.0 3.0 

N1 141,587 52,647 87,218 114,810 202,028 12,503 14.3 6.2 

N2 61 45 121 178 299 35 28.9 11.7 

N3 1 0 0 0 0 0 

P1 21,993 5,438 6,092 5,337 11,429 452 7.4 4.0 

R1 62,439 16,095 16,942 15,670 32,612 3,736 22.1 11.5 

R2 16 7 19 4 23 12 63.2 52.2 

R3 4 2 0 2 2 0 0.0 

Total 627,564 292,501 938,391 571,385 1,509,776 101,933 10.9 6.8 

M MA 106,272 67,150 359,935 83,086 443,021 99,673 27.7 22.5 

MB 111,730 61,792 188,519 80,167 268,686 38,385 20.4 14.3 

MC 75,585 40,048 119,237 57,516 176,753 20,908 17.5 11.8 

MD 70,604 34,826 93,493 48,014 141,507 15,312 16.4 10.8 

Total 364,191 203,816 761,184 268,783 1,029,967 174,278 22.9 16.9 

N B1 439 40 32 62 94 2 6.3 2.1 

B2 168 9 29 0 29 19 65.5 65.5 

B3 238,471 4,395 84,196 1,872 86,068 48,626 57.8 56.5 

C1 189,406 44,415 98,449 17,282 115,731 6,295 6.4 5.4 

C2 1,021,573 315,568 1,123,949 146,779 1,270,728 283,202 25.2 22.3 

C3 3,297,091 371,906 1,121,747 142,310 1,264,057 379,973 33.9 30.1 

Z0 2,466,679 80,816 215,683 60,453 276,136 51,277 23.8 18.6 

Total 7,213,827 817,149 2,644,085 368,758 3,012,843 769,394 29.1 25.5 

Total 8,205,582 1,313,466 4,343,660 1,208,926 5,552,586 1,045,605 24.1 18.8 

Sources: U.S. Census Bureau, Geography Reference File-Codes, July 2008; U.S. Census Bureau, Master Address File, July 2011. 

Looking at Table 5, many of the blocks currently outside the CAUS universe have a DSF match rate of total adds that make 

them promising candidates for inclusion in the CAUS universe. In particular, blocks that had ACT codes of C1, MB, MC, 

and MD (which are bolded in Table 5) going into AdCan have an above average proportion of blocks with adds and below 

average DSF match rates for total adds. Other ACT codes had much smaller DSF match rates of adds than would be expected 

given the DSF coverage of existing units. For example, blocks with an ACT code of C3 contain only city-style addresses that 

can be matched to delivery points on the DSF. However only 33.9 percent of the city-style AdCan adds and 30.1 percent of 

the total adds were matched to a delivery point on the Spring 2011 DSF. 

9 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 6: DSF Match Rate of 2010 CAUS Experimental Listings by Pre-Listing ACT Code 

PreListing 

CAUS 

Group 

PreListing 

ACT 

Blocks 

Listed 

Blocks 

with 

Adds 

Listing Adds Adds on Spring 2011 DSF 

CityStyle 

NonCityStyle 

Total 

Spring 

2011 

DSF 

Spring 

2011 

DSF 

Percent 

CS 

Adds 

Spring 

2011 

DSF 

Percent 

All Adds 

Y M1 812 325 740 608 1,348 2 0.3 0.1 

ME 355 148 253 216 469 8 3.2 1.7 

MF 237 101 158 175 333 2 1.3 0.6 

MG 1,563 682 1,431 1,773 3,204 10 0.7 0.3 

M3 3 0 0 0 0 0 

N1 466 166 108 501 609 1 0.9 0.2 

P1 56 10 4 23 27 0 0.0 0.0 

R1 132 24 8 38 46 0 0.0 0.0 

Total 3,624 1,456 2,702 3,334 6,036 23 0.9 0.4 

M MA 851 293 4,102 346 4,448 44 1.1 1.0 

MB 1,180 324 677 284 961 8 1.2 0.8 

MC 769 233 578 273 851 6 1.0 0.7 

MD 830 210 304 227 531 8 2.6 1.5 

Total 3,630 1,060 5,661 1,130 6,791 66 1.2 1.0 

N B3 10 2 171 0 171 0 0.0 0.0 

C1 41 1 14 0 14 0 0.0 0.0 

C2 305 79 994 43 1,037 27 2.7 2.6 

C3 787 74 448 15 463 5 1.1 1.1 

Z0 101 7 5 22 27 1 20.0 3.7 

Total 1,244 163 1,632 80 1,712 33 2.0 1.9 

Total 8,498 2,679 9,995 4,544 14,539 122 1.2 0.8 

Sources: U.S. Census Bureau, Geography Reference File-Codes, July 2009; U.S. Census Bureau, Master Address File 

Transaction File, December 2010; U.S. Census Bureau, Master Address File, January 2011 and July 2011. 

As mentioned previously, the overall DSF match rate of adds from the 2010 CAUS experimental listings is much lower than 

the overall DSF match rate of adds from AdCan. Table 6 shows that this is the case for each ACT code group as well as 

overall. This low level of DSF matching suggests there could be benefit from including all blocks in the CAUS universe. 

Examining the ACT groups that AdCan data suggested as candidates for inclusion into the CAUS universe, we see that only 

one of the 41 blocks in the C1 group that were listed had adds. This does not provide enough information to make a 

conclusion about the post AdCan DSF match rate of adds for this ACT group. All ACT code groups in the “M” CAUS group 

have a good number of blocks with adds and a low DSF match rates of adds. This, together with the AdCan data, provides 

evidence that MB, MC, MD, and possibly MA should be considered for inclusion in the CAUS universe. 

2.5. Future Work 

Preliminary investigations showed that blocks with certain ACT codes may benefit from being listed through the CAUS 

program. Future research will look into using additional block characteristics to refine what, if any, blocks are added to the 

CAUS universe. Some possible characteristics of interest are geographic location, number of pre-existing units, types of preexisting units, percent of pre-existing units that are city-style, and percent of pre-existing units that can be matched to a 

delivery point on the DSF. When conducting this examination, the sparseness of blocks with adds in the data will be taken 

into consideration. If these investigations suggest groups of blocks to be added to the CAUS universe, additional 

experimental listings will be conducted as a confirmation. Similar investigations will be conducted for other geographic types 

such as block groups and tracts. 

10 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

• 

• 

• 

The CAUS branch will also look into whether or not the criteria currently used to filter adds in this study should be changed. 

Some possible alternatives to the current criteria used to filter the data for this research are: 

Using inclusion in or exclusion from the most recent version of the ACS housing unit frame universe to filter the 

data. 

Using the same criteria that are used to filter housing unit for inclusion in or exclusion from the list of existing 

MAF addresses provided to Census field representatives when they conduct listings. 

Using the same criteria that are used to filter housing units for inclusion in or exclusion from the addresses used 

when computing a block’s ACT code. 

The CAUS branch will also work with MAF stakeholders to determine how long it should wait for an address added by a

listing to appear on the DSF when determining the DSF match rate of adds, and if non-listing techniques can be used to 

compensate for the issues caused by groups of blocks with poor DSF match rates for city-style address adds. 

3. References 

ALOIT (Address List Operations Implementation Team). (2012). “2010 Census Address Canvassing Operational Assessment 

Report.” http://2010.census.gov/2010census/pdf/2010%20Census%20Address%20Canvassing%20Operational%20 

Assessment.pdf, January 17, 2012. Washington D.C.: U.S. Census Bureau. 

Bates, Lawrence. (2010). “Editing the MAF Extracts and Creating the Unit Frame Universe for the American Community 

Survey (2011 Supplemental Phase).” Internally distributed software specification published as DSSD 2012 American

Community Survey Memorandum Series #ACS11-UC-4, December 29, 2011. Washington D.C.: U.S. Census Bureau. 

———. (2011). “Editing the MAF Extracts and Creating the Unit Frame Universe for the American Community Survey 

(2012 Main Phase).” Internally distributed software specification published as DSSD 2012 American Community Survey 

Memorandum Series #ACS12-UC-1, June 2, 2011. Washington D.C.: U.S. Census Bureau. 

GEO (Geography Division). (2011a). “Address Characteristic Type Software Requirement Specification.” Internally 

distributed software specification, version 1.5, last modified February 3, 2011. Washington D.C.: U.S. Census Bureau. 

———. (2011b). “Delivery Sequence File Refresh Software Requirements Specification.” Internally distributed software 

specification, version 1.8, last modified July 28, 2011. Washington D.C.: U.S. Census Bureau. 

Kennel, Timothy and Joel Martin. (2010). “Summary Report for Evaluating MAF Content Quality After the 2010 Decennial 

Address Canvassing (Doc. #2010-4.0-G-16).” Internally distributed research report, version 1.0, November 15, 2010. 

Washington D.C.: U.S. Census Bureau. 

11 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Attachment A 

Address Characteristic Type (ACT) Code Definitions 

Two-character variable that describes the type of Master Address File (MAF) addresses in the block, and indicates 

how many of the block’s MAF addresses can be matched to delivery points on the US Postal Service’s Delivery 

Sequence File (DSF). 

Table A-1: ACT Code Definitions with CAUS Groups 

CAUS 

Group 

ACT 

Code 

Definition 

Y D1 Description only, MAF description only addresses cannot be matched to DSF addresses 

M1 City-style and noncity-style, no addresses matched to DSF 

ME City-style and noncity-style, some addresses matched to DSF where the percent City-style is in [75, 80) 

MF City-style and noncity-style, some addresses matched to DSF where the percent City-style is in [70, 75) 

MG City-style and noncity-style, some addresses matched to DSF where the percent City-style is in (0, 70) 

M3 City-style and noncity-style, all addresses matched to DSF 

N1 Assorted noncity-style, no addresses matched to DSF 

N2 Assorted noncity-style, some addresses matched to DSF 

N3 Assorted noncity-style, all addresses matched to DSF 

P1 PO Box, no addresses matched to DSF 

P2 PO Box, some addresses matched to DSF 

P3 PO Box, all addresses matched to DSF 

R1 Rural Route, no addresses matched to DSF 

R2 Rural Route, some addresses matched to DSF 

R3 Rural Route, all addresses matched to DSF 

M MA City-style and noncity-style, some addresses matched to DSF where the percent City-style is in [95, 100) 

MB City-style and noncity-style, some addresses matched to DSF where the percent City-style is in [90, 95) 

MC City-style and noncity-style, some addresses matched to DSF where the percent City-style is in [85, 90) 

MD City-style and noncity-style, some addresses matched to DSF where the percent City-style is in [80, 85) 

N B1 Non-residential only, no addresses match to DSF 

B2 Non-residential only, some addresses matched to DSF 

B3 Non-residential only, all addresses matched to DSF 

C1 City-style, no addresses match to DSF 

C2 City-style, some addresses match to DSF 

C3 City-style, all addresses match to DSF 

Z0 No addresses 
 

 

 

 

 

 

 

 

 

 

 

 

 

Attachment B 

This attachment provides detailed information about the DSF match rate of 2010 AdCan adds. Note that only AdCan adds which were both in the final 2010 Census universe and 

residential housing units within the 2012 ACS main frame were included in the counts below. The Spring 2008 DSF was used as an input into creating the AdCan frame, and the 

Spring 2010 DSF is the first DSF update to the MAF after AdCan adds were added to the MAF. 

Table B-1: DSF Match Rate History of AdCan Adds by Pre-AdCan ACT Code 

PreAdCan 

CAUS 

Group 

PreAdCan 

ACT 

Blocks 

with 

AdCan 

Adds 

Total 

AdCan 

Adds 

Spring 

2008 

DSF 

Fall 2008 DSF Spring 2009 DSF Fall 2009 DSF Spring 2010 DSF Fall 2010 DSF Spring 2011 DSF Any DSF 

AdCan 

Adds 

on 

DSF 

AdCan 

Adds 

on DSF 

Percent 

of 

AdCan 

Adds 

AdCan 

Adds 

on DSF 

Percent 

of 

AdCan 

Adds 

AdCan 

Adds 

on DSF 

Percent 

of 

AdCan 

Adds 

AdCan 

Adds 

on DSF 

Percent 

of 

AdCan 

Adds 

AdCan 

Adds on 

DSF 

Percent 

of 

AdCan 

Adds 

AdCan 

Adds on 

DSF 

Percent 

of 

AdCan 

Adds 

AdCan 

Adds on 

DSF 

Percent 

of 

AdCan 

Adds 

Y M1 65,248 377,982 0 4,025 1.1 8,036 2.1 11,102 2.9 12,401 3.3 14,216 3.8 16,101 4.3 16,193 4.3 

ME 24,843 110,523 0 3,152 2.9 5,871 5.3 8,224 7.4 9,375 8.5 10,270 9.3 10,857 9.8 10,900 9.9 

MF 14,554 79,373 0 1,913 2.4 3,481 4.4 4,955 6.2 5,601 7.1 6,143 7.7 6,681 8.4 6,711 8.5 

MG 113,539 694,387 0 14,555 2.1 26,179 3.8 37,871 5.5 42,646 6.1 47,881 6.9 51,527 7.4 51,775 7.5 

M3 83 1,118 0 4 0.4 33 3.0 33 3.0 33 3.0 33 3.0 33 3.0 33 3.0 

N1 52,647 202,028 0 3,285 1.6 6,520 3.2 9,288 4.6 10,128 5.0 11,537 5.7 12,503 6.2 12,535 6.2 

N2 45 299 0 6 2.0 19 6.4 25 8.4 27 9.0 34 11.4 35 11.7 35 11.7 

P1 5,438 11,429 0 108 0.9 192 1.7 292 2.6 352 3.1 426 3.7 452 4.0 455 4.0 

R1 16,095 32,612 0 1,035 3.2 2,006 6.2 2,718 8.3 3,028 9.3 3,380 10.4 3,736 11.5 3,741 11.5 

R2 7 23 0 11 47.8 11 47.8 12 52.2 12 52.2 12 52.2 12 52.2 12 52.2 

R3 2 2 0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 

Total 292,501 1,509,776 0 28,094 1.9 52,348 3.5 74,520 4.9 83,603 5.5 93,932 6.2 101,937 6.8 102,390 6.8 

M MA 67,150 443,021 0 33,323 7.5 59,597 13.5 83,373 18.8 91,715 20.7 96,638 21.8 99,673 22.5 100,079 22.6 

MB 61,792 268,686 0 12,698 4.7 22,210 8.3 30,685 11.4 34,048 12.7 36,679 13.7 38,385 14.3 38,505 14.3 

MC 40,048 176,753 0 6,365 3.6 11,530 6.5 16,221 9.2 18,144 10.3 19,790 11.2 20,908 11.8 20,984 11.9 

MD 34,826 141,507 0 4,631 3.3 8,322 5.9 11,850 8.4 13,290 9.4 14,499 10.2 15,312 10.8 15,361 10.9 

Total 203,816 1,029,967 0 57,017 5.5 101,659 9.9 142,129 13.8 157,197 15.3 167,606 16.3 174,278 16.9 174,929 17.0 

N B1 40 94 0 1 1.1 1 1.1 1 1.1 2 2.1 2 2.1 2 2.1 2 2.1 

B2 9 29 0 0 0.0 0 0.0 19 65.5 19 65.5 19 65.5 19 65.5 19 65.5 

B3 4,395 86,068 0 12,159 14.1 27,654 32.1 39,799 46.2 46,228 53.7 48,174 56.0 48,626 56.5 48,709 56.6 

C1 44,415 115,731 0 1,085 0.9 2,580 2.2 3,983 3.4 4,740 4.1 5,439 4.7 6,295 5.4 6,533 5.6 

C2 315,568 1,270,728 0 91,104 7.2 158,096 12.4 225,620 17.8 254,302 20.0 273,247 21.5 283,202 22.3 283,996 22.3 

C3 371,906 1,264,057 0 127,460 10.1 220,121 17.4 317,509 25.1 355,199 28.1 372,513 29.5 379,973 30.1 380,791 30.1 

Z0 80,816 276,136 0 17,547 6.4 28,172 10.2 41,515 15.0 47,917 17.4 49,950 18.1 51,278 18.6 51,431 18.6 

Total 817,149 3,012,843 0 249,356 8.3 436,624 14.5 628,446 20.9 708,407 23.5 749,344 24.9 769,395 25.5 771,481 25.6 

Total 1,313,466 5,552,586 0 334,467 6.0 590,631 10.6 845,095 15.2 949,207 17.1 1,010,882 18.2 1,045,610 18.8 1,048,800 18.9 

Sources: U.S. Census Bureau, Geography Reference File-Codes, July 2008; U.S. Census Bureau, Master Address File, July 2011. 
 

 

 

 

 

 

 

 

 

 

 

 

Attachment C 

This attachment provides detailed information about the DSF match rate of adds from the 2010 CAUS experimental listings. Note that only adds which were residential housing 

units within the universe of the 2011 ACS supplemental frame were included in the counts below. The Fall 2009 DSF was used as an input into creating the frame for these 

listings, and the Spring 2011 DSF is the first DSF update to the MAF after the adds from these listings were added to the MAF. 

Table C-1: DSF Match Rate History of 2010 CAUS Experimental Listings by Pre-Listing ACT Code 

PreListing 

CAUS 

Group 

PreListing 

ACT 

Blocks 

with 

adds 

Total 

Adds 

Fall 2009 DSF Spring 2010 DSF Fall 2010 DSF Spring 2011 DSF Any DSF 

Fall 

2009 

DSF 

Fall 

2009 

DSF 

Percent 

All 

Adds 

Spring 

2010 

DSF 

Spring 

2010 

DSF 

Percent 

All 

Adds 

Fall 

2010 

DSF 

Fall 

2010 

DSF % 

All 

Adds 

Spring 

2011 

DSF 

Spring 

2011 

DSF 

Percent 

All 

Adds 

Any 

DSF 

Any 

DSF 

Percent 

All 

Adds 

Y M1 325 1,348 0 0.0 0 0.0 1 0.1 2 0.1 2 0.1 

ME 148 469 0 0.0 0 0.0 0 0.0 8 1.7 8 1.7 

MF 101 333 0 0.0 0 0.0 0 0.0 2 0.6 2 0.6 

MG 682 3,204 0 0.0 0 0.0 4 0.1 10 0.3 10 0.3 

N1 166 609 0 0.0 0 0.0 1 0.2 1 0.2 1 0.2 

P1 10 27 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 

R1 24 46 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 

Total 1,456 6,036 0 0.0 0 0.0 6 0.1 23 0.4 23 0.4 

M MA 293 4,448 0 0.0 0 0.0 4 0.1 44 1.0 44 1.0 

MB 324 961 0 0.0 0 0.0 2 0.2 8 0.8 8 0.8 

MC 233 851 0 0.0 0 0.0 3 0.4 6 0.7 6 0.7 

MD 210 531 0 0.0 0 0.0 4 0.8 8 1.5 8 1.5 

Total 1,060 6,791 0 0.0 0 0.0 13 0.2 66 1.0 66 1.0 

N B3 2 171 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 

C1 1 14 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 

C2 79 1,037 0 0.0 0 0.0 18 1.7 27 2.6 27 2.6 

C3 74 463 0 0.0 0 0.0 1 0.2 5 1.1 5 1.1 

Z0 7 27 0 0.0 0 0.0 0 0.0 1 3.7 1 3.7 

Total 163 1,712 0 0.0 0 0.0 19 1.1 33 1.9 33 1.9 

Total 2,679 14,539 0 0.0 0 0.0 38 0.3 122 0.8 122 0.8 

Sources: U.S. Census Bureau, Geography Reference File-Codes, July 2009; U.S. Census Bureau, Master Address File Transaction File, December 2010; U.S. Census Bureau, Master 

Address File, January 2011 and July 2011.