Evaluation of Gross Vacancy Rates From the 2010 Census Versus 

Current Surveys: Early Findings from Comparisons with the 2010 

Census and the 2010 ACS 1-Year Estimates

Arthur R Cresce, Ph. D. 

Assistant Division Chief for Housing Characteristics

Social, Economic and Housing Statistics Division 

U.S. Census Bureau 

The views expressed in this paper are solely attributable to the author and do not necessarily reflect the position of 

the United States Census Bureau. 

1 
Acknowledgements 

I would like to recognize the following persons who helped make this paper possible. First of all, I would like to 

recognize Chris Mazur who did an amazing job verifying the data and making sure all the statistical testing was 

done properly. I would also like to recognize Dale Garrett in the Decennial Statistical Studies Division who 

performed the statistical review. The following members of the ACS-Decennial Working Group provided extremely 

useful comments: Omolola Anderson, Fern Bradshaw, Sandra Clark, Deborah Griffin, Samantha Fish, Steve Hefter, 

Sarah Heimel, Geoffrey Jackson, David Raglin, Greg Robinson, Ellen Wilson, and Jeanne Woodward. I also 

received helpful comments from Jane Ingold, Dennis Stoudt, and Christa Jones. Of course, I am totally responsible 

for the accuracy of the data presented in this paper. 

 

2 
Evaluation of Gross Vacancy Rates From the 2010 Census Versus Current Surveys: Early 

Findings from Comparisons with the 2010 Census and the 2010 ACS 1-Year Estimates

Purpose of this paper

This paper is part of a larger effort to understand why there are differences in the level of occupied and vacant 

housing units among the 2010 Census, the 2010 American Community Survey (ACS), the Current Population 

Survey/Housing Vacancy Survey1

 (HVS), and the American Housing Survey2 (AHS). The specific focus of this 

paper is to provide a snapshot of research completed to date on factors that might explain differences in the level of 

vacant and occupied housing units between the 2010 Census and 2010 American Community Survey (ACS). Thus, 

this paper is not intended to answer all questions or issues concerning these differences. The 2010 ACS 1-year 

estimate for the gross vacancy rate (GVR) was 13.1 percent compared to 11.4 percent for the 2010 Census. We 

expect to produce a more comprehensive report on the 2010 Census – ACS differences in 2012 with additional 

reports to address the differences between the 2010 Census, the HVS and AHS. The goals of these reports are: 1) to 

understand better why these totals differ and 2) to address particular factors, where possible, that might lead to more 

consistent results across data collection efforts in the future. 

Introduction 

When different data collection efforts, whether they are a decennial census or a statistically representative sample 

survey of households, attempt to measure a similar phenomenon, there is the prima facie expectation that these 

efforts will produce similar results. However, when these data collection efforts differ in their purpose, design, 

operations, or procedures, differences can occur even when the phenomenon is being measured during a similar time 

period. In the case of the decennial census and the ACS, we expected to find some differences based on a number of 

factors discussed below. The Census Bureau has dealt with this issue by conducting detailed studies that attempt to 

compare the results from these efforts and provide an explanation for the differences based on the best information 

available. One example of this type of analysis is the effort to compare the results from the 2000 Census with those 

from the Census 2000 Supplemental Survey (C2SS), which was the immediate precursor to the ACS (see 

http://www.census.gov/acs/www/library/by_year/2004/ which includes a set of comparison studies and Love,

2001). In some cases, differences (beyond sampling error) may be due to different reference periods, different 

residence rules, or perhaps different data collection or processing procedures. 

 

 

1

 The Housing Vacancy Survey (HVS) is a supplement of the Current Population Survey (CPS) and provides current 

information on the rental and homeowner vacancy rates, and characteristics of units available for occupancy. There are 

about 72,000 housing units both occupied and vacant contained in the CPS sample. Approximately 10,800 are visited, but 

found to be vacant or otherwise not interviewed each month. These vacant units are included in the HVS.

2

 The American Housing Survey (AHS) is conducted by the Bureau of the Census for the Department of Housing and 

Urban Development (HUD). The AHS collects data on the Nation's housing, including apartments, single-family homes, 

mobile homes, vacant housing units, household characteristics, income, housing and neighborhood quality, housing costs, 

equipment and fuels, size of housing unit, and recent movers. National data are collected in odd numbered years, and data 

for each of 47 selected Metropolitan Areas are collected currently about every six years. The national sample covers an 

average 55,000 housing units. Each metropolitan area sample covers 4,100 or more housing units. The AHS returns to the 

same housing units year after year to gather data; therefore, this survey is ideal for analyzing the flow of households 

through housing. 

3 
Historical Differences in Estimates of Gross Vacancy Rates 

Figure 1 below shows the pattern of differences among the decennial census, the ACS, the AHS, and the HVS gross 

vacancy rates (GVRs) 3

 starting in 1990 through 2010. 

 

6.0

7.0

8.0

9.0

10.0

11.0

12.0

13.0

14.0

15.0

Gross Vacancy Rate 

Figure 1. Comparison of Gross Vacancy Rates: 1990-2010

Decennial Census

ACS

AHS

HVS

Source: HVS: http://www.census.gov/hhes/www/housing/hvs/historic/files/histtab7.xls; AHS: 

http://www.census.gov/housing/ahs/data/national.html for 1997 through 2009 and published paper reports for1991 

through 1995; ACS: 1-year tabulations from American FactFinder; Decennial Census: 

http://www.census.gov/hhes/www/housing/census/censushousing.html for 1990 and 2000 and American FactFiinder 

for 2010. 

Several observations should be noted at this point:

1. The Gross Vacancy Rate (GVRs) for the HVS are consistently higher than those for each of the decennial

censuses, with a difference of 1.2 percentage points 1990, 2.6 percentage points in 2000, and 2.9 

percentage points in 2010. 

2. The GVRs for the AHS (for which the national survey is conducted only in odd-numbered years) are also 

consistently higher than those for the decennial census. In 1991, 1993, 1997, 2003, 2005, and 2007 the 

AHS GVRs were not statistically different from the HVS GVRs. However, the AHS GVRs were lower 

than the HVS GVRs in 1995, 1999, 2001, and 2009. 

3. The GVRs for the ACS are also higher than those for the 2000 and 2010 censuses, however, the difference 

in 2000 was smaller (0.6 percentage points compared to 1.7 percentage points) 

4. All data sources point to an increase in the GVR between 2000 and 2010. 

As noted in the beginning, the purpose of this paper will be to focus on differences between the 2010 Census and the 

2010 ACS. More specifically, we will focus on differences in overall levels of vacant and occupied housing units, 

GVRs, and vacancy status4

. We will also briefly touch on differences in the average household size (also stated as 

3

 The gross vacancy rate (GVR) is the percentage of the total housing inventory that is vacant. The rate is computed 

with the formula: (All vacant units/All housing units (occupied + vacant)) * (100) 

4

 For the 2010 Census and the 2005 through the 2010 ACS, vacant units were subdivided according to their housing 

market classification as follows: “For Rent,” “Rented, Not Occupied,” “For Sale Only,” “Sold, Not Occupied,” “For 

Seasonal, Recreational, or Occasional Use,” “For Migrant Workers,” and “Other Vacant.” For the 2000 through 

4 
persons per household) that has been affected by the overall differences in occupied and vacant housing units. As 

will become clear in the discussion that ensues, one cannot talk about differences in levels of vacant units without 

discussing the impact those levels have on occupied housing units (households).

Comparison of Statistics at the National Level

Table 1 below shows a comparison of results from the 2010 Census and the 2010 ACS: 

Table 1. Comparison of 2010 ACS 1-Year and 2010 Census Housing Unit Status and Household Size: 

 U.S. Total 

 

2010 ACS 1-

Year Estimate 

2010 ACS 

Margin of 

Error 2010 Census 

Difference 

(ACS-Census) 

Total Housing Units 131,791,065 5,741 131,704,730 86,335 

Total Occupied Housing Units 114,567,419 163,249 116,716,292 -2,148,873 

Total Vacant Housing Units 17,223,646 167,247 14,988,438 2,235,208 

Gross vacancy rate 13.1 0.1 11.4 1.7 

Total Population in Housing Units 301,362,366 (NA) 300,758,215 604,151 

Average household size 2.63 0.01 2.58 0.05 

 (NA) Not applicable 

 Source: 2010 ACS 1-year tables and 2010 Census Summary File 1 in American FactFinder 

Observations include: 

1. At the national level the 2010 ACS 1-year GVR (13.1 percent) is estimated to be about 1.7 percentage points 

higher than that of the 2010 Census (11.4 percent). The margin of error around the ACS national-level estimate 

is relatively low and the difference is statistically significant. 

2. As a result of the difference in the estimate of vacant housing units, there is a corresponding difference of about 

2.1 million fewer occupied housing units5

 or households in the 2010 ACS. 

3. The combined effect of fewer total households in the 2010 ACS 1-year estimates and an estimated total 

population that was similar to that for the 2010 Census6

 means that the 2010 ACS 1-year estimate of household 

size (also expressed as persons per household) is higher than that in the 2010 Census. Although analysis of the 

impact of these differences on average household size will not be the focus of this analysis, it is an important 

demographic measure that affects a wide range of activities such as small-area population estimation, 

determination of housing needs, and assessment of the impact of income programs. 

2004 ACS, the two categories “Sold, Not Occupied” and “Rented, Not Occupied” were grouped under one category, 

“Rented or Sold, Not Occupied.” 

5

 It is important to note that the total housing unit counts for the 2010 ACS was controlled to independent estimates 

based on the 2010 that were estimated forward from our Population Estimates Program to July 1, 2010. However, 

there is no control for occupied housing units. 

6

 This follows from the fact that the total population was controlled to independent estimates based on the 2010 

Census and were estimated forward from our Population Estimates Program to July 1, 2010. 

5 
Delving a Little Deeper – Comparison of Vacancy Status at the National Level 

Both the decennial census and the ACS used the same categories to determine the reason why a housing unit was 

vacant, or “vacancy status.” Table 2 shows both some consistency and some striking differences between the 2010 

ACS and the 2010 Census. 

Table 2. Comparison of 2010 ACS and 2010 Census Information on Vacancy Status: United States 

 2010 ACS 

2010 

Census 

 (3) 

Difference 

(ACSCensus) 

 (4) 

2010 ACS 2010 

Census Difference 

(ACSCensus) 

 (5-7)

 

Estimate 

 (1) 

Margin 

of Error 

 (2) 

 

Percent 

 (5) 

Margin 

of Error 

 (6) 

Percent 

 (7) 

Total Vacant Units 17,223,646 167,247 14,988,438 2,235,208 100.0 (NA) 100.0 (NA) 

 For rent 3,587,148 44,663 4,137,567 -550,419 20.8 0.2 27.6 -6.8 

 Rented (not yet occupied) 610,827 17,510 206,825 404,002 3.5 0.1 1.4 2.2

 For sale 1,929,351 34,885 1,896,796 32,555 11.2 0.2 12.7 -1.5 

 Sold (not yet occupied) 610,798 15,083 421,032 189,766 3.5 0.1 2.8 0.7 

 For seasonal, recreational, or 

0.2 

 

 occasional use 5,153,003 56,603 4,649,298 503,705 29.9 31.0 -1.1 

 For migrant workers 31,607 3,768 24,161 7,446 0.2 0.0 0.2 0.0 

 Other vacant 5,300,912 67,127 3,652,759 1,648,153 30.8 0.3 24.4 6.4 

(NA) Not applicable 

Source: ACS 1-year tables and 2010 Census Summary File 1 in American FactFinder

The largest difference in absolute numbers, by far, between the decennial census and the 2010 ACS was in the 

“Other vacant” category, which is a “none of the above” category with no provision on the question to provide 

further information on why the housing unit was vacant. The 2010 ACS also had a larger number of vacant housing 

units in the “For seasonal, recreational, or occasional use” category than did the 2010 Census. We believe that 

focusing on the differences in both the “Other vacant” and “For seasonal, recreational, or other use” categories will 

help explain some of the reasons for the large overall difference. 

The only vacancy status category where the decennial census had a higher number was in the “For rent” category. 

This is not unexpected because in the ACS most vacant units from a given month’s sample are not identified as 

vacant until about three months later during the personal visit phase of the ACS. Given the amount of time between 

the original mail out and the personal interview and, together with the ACS ‘s current residence rule7

, a rental unit 

that may have originally been vacant at the time of the mail out may be legitimately occupied by the time of the 

personal visit interview over two months later. Reflecting this relationship, the rental vacancy rate8

 (which is a key 

measure of housing availability and is shown in Table 3 below) for the 2010 ACS (8.2 percent) is lower than that for 

the 2010 Census (9.2 percent). 

 

7

In the ACS, residence is determined as of the date of the interview. A person who is living or staying in a sample 

housing unit on interview day and whose actual or intended length of stay is more than 2 months is considered a 

current resident of the unit. The Census Bureau classifies as vacant a housing unit in which no one is determined to 

be a current resident. 

8

 The rental vacancy rate is the proportion of the rental inventory that is vacant “for rent.” It is computed by dividing 

the number of vacant units “for rent” by the sum of the renter-occupied units, vacant units that are “for rent,” and 

vacant units that have been rented but not yet occupied, and then multiplying by 100. 

6 
The only vacancy status category where there was no statistical difference in the estimates was for the “For sale” 

category. Reflecting this relationship, the 2010 ACS 1-year national level homeowner vacancy rate9 of 2.5 percent 

is not statistically different from the 2010 Census homeowner vacancy rate of 2.4 percent. This could reflect the 

fact that both ACS Field Representatives (FRs) and census enumerators had little difficulty identifying vacant for 

sale units with a “For Sale” sign or some other obvious indicator that the housing unit was for sale. 

Table 3. Comparison of 2010 ACS 1-Year and 2010 Census Housing Homeowner and Rental Vacancy Rates : 

U.S. Total 

 

 

2010 

Census 

 

 

2010 ACS 

1-Year 

Estimate 

2010 

ACS 

Margin 

of Error 

Difference 

(ACS- Census) 

Homeowner vacancy rate 2.5% 0.1 2.4% 0.1 

Rental vacancy rate 8.2% 0.1 9.2% -1.0 

 Source: ACS 1-year tables and 2010 Census Summary File 1 in American FactFinder 

Now, we will look at comparisons of occupancy status and vacancy status below the national level to see if there are 

any noticeable patterns. 

Analysis of Vacancy Status Below the National Level 

Attachment A provides a table showing the GVR in the 2010 census and the 2010 ACS by state. While the

phenomenon of higher GVRs for the ACS persists with most states, the following states had 2010 Census GVRs that 

were not statistically different from the ACS GVRs but appeared to be slightly above, below or equal to the ACS 

GVRs: Idaho, Iowa, Minnesota, Nebraska, New Hampshire, North Dakota, Utah, Vermont, and Wisconsin. There

were no states where the ACS GVRs were lower than the 2010 Census GVRs and statistically significant. 

When these differences in the GVR by State are sorted by size of percentage point difference, there is a clear 

regional character in these differences (See Attachment B). Of the 7 states and the District of Columbia for which 

the percentage point difference in the GVR is statistically higher than 2 percentage points (District of Columbia, 

Florida, Delaware, Alabama, New Mexico, Mississippi, and Georgia), 6 of them are from the South region and 1 is 

from the West region (New Mexico). By contrast, among the 7 states for which the percentage point difference in 

the GVR was statistically less than a 1 percentage-point (Washington, Nebraska, Wisconsin, Utah, Iowa, Vermont, 

and Minnesota), the Midwest region had the largest number (4 states), followed by the West region (2 states) and the 

Northeast region (1 state). 

It is important to note at this point of the analysis that a high GVR, in and of itself, is not necessarily an indicator of 

a troubled housing market. We note in the 2010 Census Brief Housing Characteristics : 

Of the 50 states, nine states had gross vacancy rates greater than 15.0 percent in 2010. Of these nine states, 

three were located in the Northeast (Maine, Vermont, New Hampshire), three in the South (Florida, South 

 

9

 The homeowner vacancy rate is the proportion of the homeowner inventory that is vacant “for sale.” It is computed 

by dividing the number of vacant units “for sale only” by the sum of the owner-occupied units, vacant units that are 

“for sale only,” and vacant units that have been sold but not yet occupied, and then multiplying by 100.

7 
Carolina, Delaware), and three in the West (Arizona, Alaska, Montana). Though these states had the 

highest gross vacancy rates, it is of note that all but South Carolina had a higher-than-average proportion of 

vacant units classified as “Vacant—for seasonal, recreational, and occasional use” in 2010 ...” (Census 

Bureau, 2011, p. 4). 

Thus, in analyzing differences in vacancy rates, it is very important (as noted in the national-level analysis) to look 

at differences in vacancy status. 

Shifting the focus to differences in reporting of vacancy status (expressed as a percent of all housing units) by State, 

the largest difference in the reporting of vacancy status occurs in 23 States and the District of Columbia (11 in the 

South)10 for the “Other vacant” category (See Attachments C and D). This is not surprising given the large 

difference at the national level. Florida was the exception with “For seasonal, recreational, or occasional use” 

having the largest difference. There was no single category in the other 26 states that was statistically the largest 

category. 

Looking at differences in the GVR by cities, 39 of the largest 50 cities in the United States had ACS GVRs that were 

larger than the census GVRs. Among those cities, the following cities had GVR differences that were statistically 

higher than 3 percentage points: Miami, Florida (8.5); Detroit, Michigan (6.4); Atlanta, Georgia (6.0); Washington, 

DC (4.9); and Jacksonville, Florida (4.5). Twenty-six of the top 50 cities experienced ACS GVRs that were 

statistically greater than 10 percent with some cities such as Detroit, Atlanta, Miami, and Cleveland experiencing 

ACS GVRs over 20 percent. 

Table 4 shows that for a number of these cities, the percent of vacant units designated as “Other vacant” was much 

higher than the national average (30.8 percent) for the ACS. These same cities also had census GVRs that were 

higher than the national average (24.4 percent) for the 2010 Census. 

Table 4. Percent of Vacant Units Designated as “Other Vacant” in the 2010 ACS and the 2010 Census 

for Selected Cities 

City Percent Other Vacant of 

Total Vacants 

2010 ACS 

Percent Other Vacant of Total 

Vacants 

2010 Census 

United States 30.8 24.4 

Chicago, IL 49.0 31.6 

Philadelphia, PA 52.3 41.1 

Indianapolis (balance), IN 56.5 36.3 

Baltimore, MD 63.1 48.7 

Milwaukee, WI 75.3 34.7 

Oklahoma City, OK 54.3 31.1 

Cleveland, OH 63.4 45.5 

Source: 2010 ACS 1-year estimates and 2010 Census from Summary File 1 on American FactFinder 

These are extraordinarily high percentages (both for the 2010 ACS and for the 2010 census) for what should be a 

“residual” category and provide evidence of the difficultly that both ACS field representatives and decennial census 

enumerators faced as they tried to identify and classify vacant units. 

 

10 Alabama, Arizona, California, Georgia, Illinois, Indiana, Louisiana, Maryland, Massachusetts, Michigan, 

Mississippi, Montana, New Jersey, New York, North Carolina, Ohio, Oklahoma, Pennsylvania, Rhode Island,

Tennessee, Texas, Virginia, and Wisconsin. 

8 
In response to concerns about the size and pattern of these differences, we organized an interdivisional working 

group to investigate in a systematic and comprehensive manner the possible causes for these differences. What 

follows is a summary of research findings to date. 

Findings of the Working Group

We have been conducting our analysis along a number of different but related topic areas: 

1. Field Procedures (2010 Census and ACS) 

2. Evaluation of 2010 Census Methods 

3. ACS Methods, Frame, Sampling, and Estimation 

4. Analysis of ACS-Census Match Cases 

5. "Demographic Analysis" 

Field Procedures (2010 Census and ACS) 

Nearly all of the housing units in the 2010 Census and the vast majority of housing units in the ACS that are 

assigned a status of “vacant” obtain that status during a personal interview. An ACS personal interview is 

conducted by a field representative (FR) and a census personal visit interview is conducted by an enumerator. While 

the title given to the personal interviewers is different between the ACS and the census, the job is very similar in 

nature. Both FRs and enumerators are required to complete training and are given field procedures and other 

materials to aid them in their work. 

One task of the working group was to review the field procedures and training materials to see if there were any 

differences that could lead us to suspect differences in the way the interviewers classified housing units. In addition, 

the working group conducted debriefing interviews with selected ACS FRs (some of whom also served as 2010 

Census enumerators) from 4 regional offices. After a review of the material and debriefing sessions, the working 

group concluded that the instructions were generally consistent about how to define a housing unit. Furthermore, 

the vacancy status categories used in the ACS and the decennial census were identical. 

However, there were differences in the way in the way each data collection effort determined occupancy status. As 

we will discuss below, there are some obvious differences in the residence rules (the 2010 Census uses a “usual 

residence” rule while the ACS uses a “current residence” rule) and reference date (the 2010 Census uses a reference 

date of April 1, 2010 while the ACS bases the reference date as of the date of the interview). There are some other 

differences we have noted. For example, the ACS FR is expected to determine occupancy status basically at the 

beginning of the interview, while the census enumerator must first ask a series of 4 questions that will help the 

enumerator determine the occupancy status. However, it does not appear at this point of the analysis that any of 

these differences have played a major role in explaining differences in the GVR. 

During the debriefings, the FRs identified a number of situations that made identification of occupancy status and 

vacancy status very difficult in both the ACS and the census: 

1. Seasonal housing units and trailers. FRs sometimes had difficulty finding a neighbor or person who knew 

the status of a unit during the summer in an area with temporary residents. 

2. Foreclosed homes – some did not have any signs posted and it was hard to determine whether they were 

either occupied or vacant properties that were abandoned (no sign posted and owners/residents absent). The 

situation was made worse when neighbors were reluctant to give information or were not knowledgeable. 

3. Accessibility problems, especially at gated communities, large properties with security, and townhouses 

with locked gates and no manager available on site11

. 

4. Communities with homeowner’s associations and no manager11

 

11

 2010 Census included special “blitz” operations to improve enumeration in these types of areas. 

9 
5. Situations with apartments which are described as vacant by office managers but observed as occupied by 

residents 

6. Townhouses with locked gates and no manager available on site 11

7. A home that does not look habitable, but is occupied 

8. Houses that are not primary residences but look inhabited. 

9. Housing units where people walk away leaving no lockbox or a “for sale” sign in areas where neighbors do 

not know anything or are reluctant to discuss the status of the unit 

Clearly, FRs and enumerators had a very difficult task, especially given the housing crisis, the surge of foreclosures, 

and the limited time frame within which they had to work. It is also clear that there was room for a great deal of 

personal interpretation when classifying housing units. 

Evaluation of 2010 Census Methods 

As part of every decennial census, the Census Bureau conducts a wide range of assessments to determine how well 

the operations were conducted, which serves as a “lessons learned” for the next census. The primary focus of the 

decennial census is to count every resident in the United States to fulfill its constitutional mandate for 

reapportionment and its legislative requirement to provide counts of the population down to the block level to enable 

states to conduct legislative redistricting. While the 2010 Census benefited from the publicity and perceived

importance of a decennial census, its design had to accommodate a tremendous workload under tight operational 

scheduling constraints. Thus, to accurately and fairly produce census counts, a single reference date must be used. 

The 2010 census results therefore must describe the population and housing as it was on April 1, 2010. 

The Census Bureau, in meeting its Constitutional mandate, is committed to achieving a complete enumeration, and 

does not rely on the use of statistical sampling to adjust the counts to correct for possible errors. Given this and the 

large number of temporary staff that must be hired to conduct the enumeration, the Census Bureau relies on special 

coverage improvement procedures. These procedures have been shown to improve the completeness of the housing 

unit inventory and the count of the population. For example, if the U.S. Postal Service identifies a particular maildelivered form as “undeliverable as addressed12,” the Census Bureau will include this address in its nonresponse 

followup (NRFU) operation to make sure that the address is either: 1) truly nonexistent or not a housing unit and 

should be deleted, 2) vacant, or 3) actually occupied. In addition, if during nonresponse follow up (NRFU) 

operations, an enumerator identified a housing unit as vacant or to be deleted and there was no designation of 

“Undeliverable as Addressed” (UAA) or no other source existed to confirm the housing status, the housing unit was 

revisited in a subsequent operation, called the “Vacant Delete Check” (VDC) operation, to make sure that the unit 

was truly vacant or should be deleted. 13

 VDC also included a first-time enumeration of a number of records from 

other sources.14

 NRFU operations were conducted from May through July and VDC operations were conducted 

 

12

 In the ACS, mail questionnaires returned as “Undeliverable as Addressed” were included in computer assisted 

telephone interview (CATI) and computer assisted personal interview (CAPI) operations. 

13

 In pretests conducted prior to the 1970 Census, the Census Bureau found that occupied housing units that were 

incorrectly classified as vacant were a significant factor in the population undercount (U.S. Census Bureau, 1974). 

The National Vacancy Check that was conducted for a sample of addresses during the 1970 Census detected a 

misclassification rate of 11.4 percent among units initially classified as non-seasonal vacant (U.S. Census Bureau, 

1974). Starting in the 1980 Census, a comprehensive review of all units classified as vacant (the predecessor of the 

VDC) was added as a coverage improvement mechanism. In 1980 this check converted about 10.1 percent of 

initially classified vacant units to occupied, but it also found that after all coverage improvement efforts, about 0.9 

percent of all occupied units were over-enumerated (U.S. Census Bureau, 1985). Similar methods were used in the 

1990 and 2000 censuses with similar findings – coverage of the population could be achieved with some added level 

of duplication or over-enumeration. 

14

 This operation also enumerated added housing units discovered in an earlier census operation such as those added 

or reinstated through the 2010 Local Update of Census Addresses (LUCA) appeals process; records added from the 

Housing Unit Address Review conducted as part of the Count Review operation; records added as a result of 

research into potentially missed addresses in Address Canvassing (as reported on internal documents known as 

INFO-COMMS); previously un-geocoded addresses in the Master Address File; new addresses from periodic postal 

10
July through August. The fact that the VDC operation is conducted three months or more after Census Day 

increases the chance that information obtained from respondents (inhabitants of the housing unit or “knowledgeable 

sources”) or poor execution of enumeration procedures could result in erroneous classification as occupied or 

vacant. 

One important finding from a review of the VDC operation was that the count of vacant units that were included in 

this operation declined from 4,379,192 before the VDC operation to 3,840,147 afterwards. Table 5 below shows the 

summary of this operation’s outcomes. 

Table 5. Status Outcome of NRFU Cases Included in the Vacant Delete 

Check Operation : 2010 Census 

Status 

NRFU Status Before 

VDC Status After VDC Difference 

Number Percent Number Percent (VDC-NRFU) 

 

 Total 5,625,001 100 5,625,001 100 (NA) 

Occupied 40,789 0.7 1,081,913 19.2 1,041,124 

Vacant 4,379,192 77.9 3,840,147 68.3 -539,045 

Deleted 1,155,708 20.5 702,466 12.5 -453,242 

Unresolved 49,312 0.9 475 0 -48,837 

 (NA) Not applicable 

 Source: Heimel, S., Jackson, G., Winder, S. and Walker, S. (2011). 2010 Census Nonresponse 

 Follow Up Operations Assessment 

The decrease in the vacant count was the net effect of the shift of 842,140 housing units from “vacant” in NRFU to 

“occupied” in VDC; a shift of 278,175 housing units from “deleted” in NRFU to “vacant” in VDC; and a shift of 

24,920 housing units from “unresolved” in NRFU to “vacant” in VDC. This can be seen quite clearly in Table 6 

below. 

Table 6. Source of Changes to Counts of Vacant Units in 

2010 Census VDC Operation 

Vacant units at the start of VDC 4,379,192 

 

Vacant units identified as occupied in VDC -842,140 

 

Deleted units identified as vacant in VDC 278,175 

 

Unresolved units identified as vacant in VDC 24,920 

 

Vacant units after VDC 3,840,147 

Source: Heimel, S., Jackson, G., Winder, S. and Walker, S. (2011). 2010 Census Nonresponse Follow 

 Up Operations Assessment 

 

updates; records added by the Update/Leave operation; and addresses provided in the New Construction operation 

by Tribal and local governments. 

11
 It is not clear at this point to what extent any of those 842,140 housing units re-classified as occupied might have 

been misclassified in that category. 

Although the processing phase of the census covered a wide range of activities, the Working Group focused on the 

results of analysis of the impact that the count imputation process might have had on the count of vacant units in 

2010. There were 3 types of “count imputation” that were conducted in the 2010 census: 1) status imputation15, 2) 

occupancy imputation16, and 3) household size imputation17. For the purposes of the Working Group’s 

investigation, status imputation and occupancy imputation (whether occupied or vacant) were the key operations of 

interest. Only 197,146 records were accounted for by status imputation and occupancy imputation (158,971 status 

imputation cases and 38,175 occupancy imputation cases) out of a total 521,947 count imputation cases (Census 

Bureau, 2011). Before count imputation, the GVR among all address records determined as occupied or vacant was 

11.35 percent. After count imputation, the gross vacancy rate increased, but only by 0.03 percentage points to 11.38 

percent. Even if all the status and occupancy cases had been imputed as occupied, the GVR would only have 

decreased only from 11.35 percent to 11.33 percent. On the other hand, if all these cases had been imputed as 

vacant, the GVR would have increased only from 11.35 percent to 11.48 percent (Heimel, 2011). Thus, the impact 

of the census count imputation on differences in GVRs between the 2010 Census and the 2010 ACS is negligible. 

ACS Methods Frame, Sampling, and Estimation 

Before we begin the discussion of the ACS frame, sampling and estimation, it is important to understand where the 

ACS differs from the census in purpose, scope, and procedures. 

The ACS is designed to efficiently collect detailed data to measure the characteristics of the Nation’s population and 

housing in a survey setting. The primary goal of the ACS is to provide survey estimates every year instead of once a 

decade – giving communities the current information they need between censuses to plan investments and services. 

The ACS estimates the characteristics of the Nation over the course of a year, requiring that responses by collected 

continuously with a floating reference date based on the date of the interview. Thus, 2010 ACS 1-year estimates 

show the average characteristics (in this case, occupancy status and vacancy status) during 2010, not the specific rate 

as of any specific day in 2010. The decennial census, by contrast, provides counts of the population and housing, 

including occupancy status and vacancy status, as of April 1, 2010. 

Another key difference with the decennial census is the fact that the ACS uses a concept of “current residence” as of 

as of the time of the interview given the monthly samples distributed throughout the year, rather than the census 

concept of “usual residence” as of April 1. Furthermore, as noted above, the ACS employs a “two-month” residency 

rule that, for example, would allow someone who is temporarily residing at a housing unit for more than two months 

to be interviewed as being an occupant of the housing unit. 

Although coverage of the entire population is critical in the decennial census, the special efforts undertaken in the 

decennial census to ensure complete coverage are impractical and cost-prohibitive in a sample survey like the ACS. 

The need for such coverage checks may also be less important in a survey setting such as the ACS with more fulltime staff. Finally, while statistical adjustments cannot be made to census counts, such adjustments are standard 

procedure in surveys to account for possible coverage shortcomings. 

 

15

 Required by records with conflicting or insufficient information on whether an address represented a valid, nonduplicated unit. Count imputation imputed these records to have a status of occupied, vacant, or non-existent. If 

imputed to be occupied, a household size from one to nine is also imputed. 

16 Required by records for which the unit is only known to exist as a housing unit. Count imputation imputed these 

records to have a status of occupied or vacant. If imputed to be occupied, a household size from one to nine is also 

imputed. 

17 Required by records for which the unit is known to be occupied but is missing a population count. Count

imputation imputed these records to have a household size from one to nine. 

12
As part of the analysis of the GVR differences, the following data from various operational phases of the ACS were 

reviewed. For the purposes of this paper, we will focus on the following three: 

1. Analysis of GVRs by panel month18 (to assess the impact of year-round data collection and to determine if 

GVRs varied, both during and after the period of the 2010 Census) 

2. Changes in the Master Address File19 (MAF) during the time period when the main and supplemental20

ACS samples were selected 

3. ACS GVRs at various stages of ACS weighting and estimation (to assess the possible effects of those 

weights on the estimate) 

Analysis of Vacancy Rates by Panel Month - Since the ACS is conducted continuously throughout the year, it 

seemed plausible that the ACS would differ from the census if vacancy rates during non-census months (months 

other than March through July) were different from those experienced during census months. However, analysis of 

ACS GVRs by panel month over several years, including 2010, revealed no distinct seasonal pattern in the ACS that 

would help explain differences with 2010 Census GVRs. 

 

Changes in the MAF during the time the main and supplemental samples were drawn - As noted above, the main 

sample for the 2010 ACS was drawn from the MAF in August and September of 2009 and the supplemental sample 

was drawn in January and February of 2010. Thus, because the 2010 Address Canvassing21 was still being 

conducted while 2010 ACS main sample selection was being implemented, the main sample selection, at least 

through the period of January through April 2010, was taken from an older sampling frame and could not take 

advantage of the massive updating of the MAF that the Address Canvassing operation and subsequent Census 2010 

operations represent. Although the supplemental sample was drawn from a MAF-based sampling frame updated 

by Address Canvassing, this sampling frame also was subject to the same process of deleted units and added units 

through 2010 Census operations that affected the frame from which the main sample was selected. We believe that 

this difference in frames between the 2010 ACS and the 2010 Census played a role in explaining some of the 

difference in the GVRs, but this hypothesis requires more investigation. 

ACS GVRs at various stages of ACS weighting and estimation – The weighting and estimation method used for the 

ACS is explained quite thoroughly in the following url: 

http://www.census.gov/acs/www/Downloads/survey_methodology/Chapter_11_RevisedDec2010.pdf . We 

evaluated the GVR at each stage of the estimation process and we found that after application of the weights to 

reflect sampling probabilities22, the estimation process had little or no effect on the level of the GVR23

. 

 

18

 There is a new panel (sample) for each month of the year consisting of approximately 250,000 addresses per 

month. 

19

 The Master Address File (MAF) is a Census Bureau file that contains an accurate, up to date inventory of all 

known living quarters in the United States, Puerto Rico and associated island areas. The MAF is used to support 

most of the census and surveys that the Census Bureau conducts including the decennial census, the American 

Community Survey and ongoing demographic surveys. The content of the MAF includes address information, 

Census geographic location codes, as well as source and history data. 

20

 The sample selected for the ACS in any given year is done in two phases. The “main” sample is selected from 

the Master Address File (MAF) in the August and September preceding the data collection year (e.g., 2009 for the 

2010 ACS, 2008 for the 2009 ACS, etc). A “supplemental” sample consisting of additions (mainly new 

construction) to the MAF is selected in January and February of the data collection year (e.g., 2010 for 2010 ACS, 

2009 for 2009 ACS, etc.) and in 2010 was included starting in May (U.S. Census Bureau, 2009). 

21 The Census Bureau needs the address and physical location of each living quarter in the United States to conduct 

the census. During the address canvassing operation, the Census Bureau verifies that its master address list and maps 

are accurate so that it can mail or hand-deliver questionnaires to housing units and potential group quarters. A 

complete and accurate address list is the cornerstone of a successful census. 

22

 Cases where neither a completed mail questionnaire has been received nor a computer assisted telephone 

interview (CATI) interview completed are eligible for computer assisted personal interview (CAPI) in the third 

month, as are the unmailable addresses. An address is considered unmailable if the address is incomplete or directs 

mail to only a post office box. The subsampling rate for CAPI cases vary from 1-in-3 to 2-in-3.

13
Analysis of ACS-Census Match Cases 

We succeeded in matching the 2010 ACS sample to a record in the 2010 Census. Furthermore, we were able to 

distinguish matched cases by whether they were in the main sample or the supplemental sample, the month the cases 

were in sample, and many other characteristics. Although analysis of the results from these cases is ongoing, the 

following observations can be made: 

1. For both the main sample and the supplemental sample, the proportion of cases where the 2010 ACS 

designated the unit as vacant and the 2010 census designated the same unit as occupied was larger than the 

proportion of cases where the 2010 ACS designated a unit as occupied and the 2010 Census the same unit 

as vacant. 

2. Units assigned for collection between January and April were much more likely to be designated as 

”deleted” in the census than units assigned to the rest of the year. This is because the supplemental sample 

not only provided additional units from a frame improved by Census operations, but also cleaned up sample 

units in the main sample that were assigned to the remainder of the year (after April). 

Clearly, more work is needed to determine how the results of this match study will help explain differences in the 

levels of occupied and vacant units. 

Demographic Analysis 

This line of analysis was comprised primarily by observing cross-tabulations of variables thought to be related to the 

differences in the GVRs with the expectation that certain patterns in the data would reveal potential factors that 

might explain these differences. Multivariate analysis will also play a role in this research; however, there are no 

results to report at this point. 

Probably, one of the more important findings to date, already been reported above, is that most of the largest 

differences in the GVR in 2010 are concentrated in the South region. Figures 2-5 below provides the same view 

from an historical perspective. 

 

 

23

 We should note that in 2006, we implemented a new estimation methodology that accomplished two important 

goals: 1) ensured equality of husbands and wives through the process of family equalization and 2) produced 

logically consistent estimates of occupied housing units, households, and householders. The new estimation 

methodology also had the side effect of increasing the estimate of vacant units, raising the GVR slightly from 10.8 

percent to 11.0 percent. 

14
 

2

4

6

8

10

12

14

16

199019911992199319941995199619971998199920002001200220032004200520062007200820092010

Percent 

Figure 3. Gross Vacancy Rates: Northeast 

Census ACS HVS 

 

 

2

4

6

8

10

12

14

16

199019911992199319941995199619971998199920002001200220032004200520062007200820092010

Percent 

Figure 2. Gross Vacancy Rates: Midwest 

Census ACS HVS

0

2

4

6

8

10

12

14

16

18

199019911992199319941995199619971998199920002001200220032004200520062007200820092010

Percent 

Figure 4. Gross Vacancy Rates: South 

Census ACS HVS

0

2

4

6

8

10

12

14

16

199019911992199319941995199619971998199920002001200220032004200520062007200820092010

Percent 

Figure 5. Gross Vacancy Rates: West 

Census ACS HVS 

 Source for Figures 2-5: ACS 1-Year, HVS, and 2010 Census data from 

 American FactFinder

15
Furthermore, there appears to be a positive relationship between the percent of Computer Assisted Personal 

Interviews (CAPI) interviews in the ACS and the level of differences in vacant housing units by state24. Figure 6 

below shows the greater differences in vacant housing units among states seem to be associated with higher CAPI 

rates. For example, for states in the highest quartile of CAPI rates, the average difference is 19.4 percent. By 

contrast, for states in the lowest quartile of CAPI rates, the average difference is 9.2 percent. 

 

9.2 

12.8 

15.9 

19.4 

0

5

10

15

20

25

1 (CAPI=

29-36%)

2 (CAPI=

37-42%)

3 (CAPI=

43-46%)

4 (CAPI=

47-62%)

Percent Difference in Vacant Units 

States by CPI Quartile 

Figure 6. Percent Difference in Vacant Units* Between the 2010 ACS 

and the 2010 Census by State Sorted by Quartile of ACS CAPI Rates 

 

 * Percent expressed as a percent of 2010 Census vacant units. 

 Source for Figure 7: 2010 ACS 1-Year, 2010 Census data from American FactFinder 

 and unpublished ACS tabulations from the American Community Survey 

However, CAPI rates not only provide an indication of the level of vacant units; they also provide an indicator of 

cooperation with the ACS, much like what mail response rates or “participation rates” represent for the decennial 

census. Evidence obtained thus far seems to indicate that the same areas that have relatively high CAPI rates (and 

correspondingly high differences in GVRs and in the percent of “Other Vacants” such as in Table 4 above) are also 

the same types of areas where census mail response rates are low, requiring extensive nonresponse followup. Many 

of these areas are also areas that were identified as having high “hard-to-count” (HTC) scores25 for the 2010 census. 

Recalling the section above concerning the debriefing of ACS FRs (many of whom also participated as census 

enumerators), these areas pose significant problems not only getting an accurate count of the population but also 

determining occupancy status and the vacancy status. For example, Figures 7 and 8 show the GVRs for New York 

City, and the rest of the State of New York. It is clear among these graphs that when one excludes New York City 

from the comparison of GVRs between the 2010 ACS and the 2010 census, the GVRs are almost identical in 2000 

and are much closer to each other than for New York City in 2010. 

24

 It is important to note that virtually all vacant units in the ACS are identified in the CAPI operation so that one 

would expect higher CAPI rates in part to be associated with areas having higher vacancy rates 

25

 “Hard-to-count” (HTC) scores summarize 12 attributes or variables of each tract in terms of reasons people are 

missed in the census. These 12 variables include housing indicators (percent renters, multi-units, crowded housing, 

lack of telephones, vacancy) and people indicators (poverty, not high school graduate, unemployed, complex 

households, mobility, language isolation). Other operational and demographic data are also included, such as 

race/ethnic distributions. The highest HTC scores (for example, more than 60) usually predict areas of high 

nonreturn rates and undercount rates while areas with the lowest scores are likely to be areas with low nonreturn 

rates. HTC scores can range from 0 to 132.

16
 

0

2

4

6

8

10

12

14

199019911992199319941995199619971998199920002001200220032004200520062007200820092010

Percent 

Figure 7. Gross Vacancy Rates: New York City 

Census ACS

2010 CAPI = 55% 

 

0

2

4

6

8

10

12

14

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010

Percent 

Figure 8. Gross Vacancy Rates: Rest of New York State 

Census ACS

2010 CAPI= 40% 

Source for Figures 7 and 8: 2010 ACS 1-Year and 2010 Census from American FactFinder 

 and unpublished tabulations from the ACS 

Figures 9 and 10 below show the contrast in the pattern of ACS GVRs relative to census GVRs for Baltimore City 

and Baltimore County (Baltimore County surrounds but does not include Baltimore City). It is very clear that 

Baltimore City, which had a much higher “hard-to-count” score than Baltimore County, had significant differences 

in the GVR between the ACS and the census in 2000 and 2010. On the other hand, the GVRs for Baltimore County 

were very close in both 2000 and 2010. 

 

17
 

0

4

8

12

16

20

24

199019911992199319941995199619971998199920002001200220032004200520062007200820092010

Percent Vacant 

Figure 9. Gross Vacancy Rate: Baltimore City 

Census ACS

2010 CAPI=52% 

 

0

4

8

12

16

20

24

199019911992199319941995199619971998199920002001200220032004200520062007200820092010

Percent Vacant 

Figure 10. Gross Vacancy Rate: Baltimore County 

Census ACS

2010 CAPI=32% 

Source for Figures 9 and 10: 2010 ACS 1-Year and 2010 Census from American FactFinder 

 and unpublished tabulations from the ACS 

Conclusions 

From the research conducted thus far, we draw the following tentative conclusions: 

1. Although the census and the ACS have different reference periods and different residence rules, we do not

believe differences in the reference period and residence rules were major contributors to the overall

difference in the gross vacancy rates. However, problems can arise when implementing reference periods 

18
combined with residence rules. In the 2010 census, vacant housing units were enumerated in either 

Nonresponse Followup (NRFU) or in Vacant Delete Check (VDC) which was at least two months after 

Census Day. This enumeration of the Census Day reference date can make the determination of occupancy 

status problematic. FRs in the ACS and census enumerators can also misunderstand or misapply a usual 

residence or current residence rule. 

2. Response categories for occupancy status and vacancy status are similar between the ACS and the 2010 

Census, but the way the questions are asked are different. It is not clear, though, if this played a role in 

explaining some of the differences in classification of housing units. 

3. The 2010 ACS sample was not drawn from the 2010 Census, which may help to explain at least a portion 

of the difference between the ACS and census GVRs. 

4. Large differences in the reporting of “Other” vacancy status and a possible connection between difficulty in 

obtaining a response (as measured by percent CAPI in the ACS and “hard-to-count” scores in the census) 

and differences in the GVR may provide some clues to understanding these differences. 

5. The census implemented coverage improvement procedures, such as special methods to review and 

confirm the status of housing, which are unique to the census and are not implemented in the ACS. The 

VDC operation in 2010 resulted in a net decrease of about 537 thousand vacant units. 

6. It was clear from debriefings with interviewers that they faced a very difficult task, Despite common 

procedures, differences in interpretation of what is an occupied unit can occur, especially in hard to count 

areas and, in general, in areas experiencing large numbers of foreclosures. Determining the occupancy

status of a unit is especially hard in some areas when no household members can be contacted and 

neighbors are unwilling to provide information. 

We plan to produce a series of reports in 2012 that will provide a more in depth analysis of potential factors that 

could explain the reasons for these differences, not only between the ACS and the census, but also among the ACS, 

the census and the Housing Vacancy Survey. From these reports, we hope to draw conclusions that will enable us, 

where possible, to take specific actions that could help provide more consistent results between the ACS and the 

census and, in general, among all our current surveys. 

Bibliography 

Heimel, S., Jackson, G., Winder, S. and Walker, S. 2011. 2010 Census Nonresponse Follow Up Operations 

Assessment 

Heimel, S. 2011. Statement on impact of count imputation on gross vacancy rates. 

Love, S. 2001. Analysis of the Census 2000 Vacancy Rates: Report No. 1. Internal Memorandum for D. Weinberg. 

U.S. Census Bureau. 2011. 2010 Census Briefs. “Housing Characteristics” by Christopher Mazur and Ellen Wilson. 

U.S. Census Bureau. 2009. Design and Methodology: American Community Survey. U.S. Government Printing 

Office, Washington, DC. 

U.S. Census Bureau. 1985. 1980 Census of Population and Housing. Evaluation and Research reports. PHC80-E. 

The Coverage of Housing in the 1980 Census. 

U.S. Census Bureau. 1974. Census of Population and Housing:1970. Evaluation and Research Program PHC(E) – 

6. Effect of Special Procedures to Improve Coverage in the 1970 Census. 

19
 Total Housing 

Units

 Vacant 

Housing Units

Total Housing 

Units

 Vacant Housing 

Units MOE

Gross Vacancy 

Rate MOE

United States 131,704,730 14,988,438 131,791,065 17,223,646 167,247 2,235,208 11.4 13.1 0.1 1.7

Alabama 2,171,853 288,062 2,174,428 359,276 10,906 71,214 13.3 16.5 0.5 3.3

Alaska 306,967 48,909 307,065 52,455 2,826 3,546 15.9 17.1 0.9 1.1

Arizona 2,844,526 463,536 2,846,738 512,688 12,983 49,152 16.3 18.0 0.5 1.7

Arkansas 1,316,299 169,215 1,317,818 202,916 8,121 33,701 12.9 15.4 0.6 2.5

California 13,680,081 1,102,583 13,682,976 1,276,501 16,972 173,918 8.1 9.3 0.1 1.3

Colorado 2,212,898 240,030 2,214,262 253,677 9,277 13,647 10.8 11.5 0.4 0.6

Connecticut 1,487,891 116,804 1,488,215 129,406 7,141 12,602 7.9 8.7 0.5 0.8

Delaware 405,885 63,588 406,489 77,724 4,193 14,136 15.7 19.1 1.0 3.5

District of Columbia 296,719 30,012 296,836 44,448 3,730 14,436 10.1 15.0 1.3 4.9

Florida 8,989,580 1,568,778 8,994,091 1,959,023 25,736 390,245 17.5 21.8 0.3 4.3

Georgia 4,088,801 503,217 4,091,482 609,062 15,594 105,845 12.3 14.9 0.4 2.6

Hawaii 519,508 64,170 519,992 74,180 4,221 10,010 12.4 14.3 0.8 1.9

Idaho 667,796 88,388 668,634 91,925 5,747 3,537 13.2 13.7 0.9 0.5

Illinois 5,296,715 459,743 5,297,077 544,220 13,706 84,477 8.7 10.3 0.3 1.6

Indiana 2,795,541 293,387 2,797,172 326,267 11,365 32,880 10.5 11.7 0.4 1.2

Iowa 1,336,417 114,841 1,337,563 114,124 6,761 -717 8.6 8.5 0.5 -0.1

Kansas 1,233,215 121,119 1,234,037 132,379 7,007 11,260 9.8 10.7 0.6 0.9

Kentucky 1,927,164 207,199 1,928,617 244,269 9,881 37,070 10.8 12.7 0.5 1.9

Louisiana 1,964,981 236,621 1,967,947 278,125 9,586 41,504 12.0 14.1 0.5 2.1

Maine 721,830 164,611 722,217 176,800 5,588 12,189 22.8 24.5 0.8 1.7

Maryland 2,378,814 222,403 2,380,605 253,166 8,692 30,763 9.3 10.6 0.4 1.3

Massachusetts 2,808,254 261,179 2,808,727 288,308 10,381 27,129 9.3 10.3 0.4 1.0

Michigan 4,532,233 659,725 4,531,231 724,610 12,485 64,885 14.6 16.0 0.3 1.4

Minnesota 2,347,201 259,974 2,348,242 256,694 8,244 -3,280 11.1 10.9 0.4 -0.1

Mississippi 1,274,719 158,951 1,276,441 196,442 8,351 37,491 12.5 15.4 0.7 2.9

Missouri 2,712,729 337,118 2,714,017 363,389 10,284 26,271 12.4 13.4 0.4 1.0

Montana 482,825 73,218 483,006 80,259 4,183 7,041 15.2 16.6 0.9 1.5

Nebraska 796,793 75,663 797,677 78,373 4,772 2,710 9.5 9.8 0.6 0.3

Nevada 1,173,814 167,564 1,175,070 185,259 6,974 17,695 14.3 15.8 0.6 1.5

New Hampshire 614,754 95,781 614,996 99,565 4,537 3,784 15.6 16.2 0.7 0.6

New Jersey 3,553,562 339,202 3,554,909 382,488 11,773 43,286 9.5 10.8 0.3 1.2

New Mexico 901,388 109,993 902,242 137,059 5,660 27,066 12.2 15.2 0.6 3.0

New York 8,108,103 790,348 8,108,211 911,784 16,313 121,436 9.7 11.2 0.2 1.5

North Carolina 4,327,528 582,373 4,333,479 662,620 14,806 80,247 13.5 15.3 0.3 1.8

North Dakota 317,498 36,306 318,099 37,687 3,292 1,381 11.4 11.8 1.0 0.4

Ohio 5,127,508 524,073 5,128,113 603,047 16,088 78,974 10.2 11.8 0.3 1.5

Oklahoma 1,664,378 203,928 1,666,205 233,246 7,555 29,318 12.3 14.0 0.5 1.7

Oregon 1,675,562 156,624 1,676,476 169,339 8,193 12,715 9.3 10.1 0.5 0.8

Pennsylvania 5,567,315 548,411 5,568,820 632,790 14,216 84,379 9.9 11.4 0.3 1.5

Rhode Island 463,388 49,788 463,416 61,121 4,276 11,333 10.7 13.2 0.9 2.4

South Carolina 2,137,683 336,502 2,140,337 378,944 10,727 42,442 15.7 17.7 0.5 2.0

South Dakota 363,438 41,156 364,031 45,076 3,527 3,920 11.3 12.4 1.0 1.1

Tennessee 2,812,133 318,581 2,815,087 374,424 13,014 55,843 11.3 13.3 0.5 2.0

Texas 9,977,436 1,054,503 9,996,209 1,257,545 20,394 203,042 10.6 12.6 0.2 2.0

Utah 979,709 102,017 981,821 101,796 5,016 -221 10.4 10.4 0.5 0.0

Vermont 322,539 66,097 322,698 65,776 2,734 -321 20.5 20.4 0.8 -0.1

Virginia 3,364,939 308,881 3,368,674 375,942 14,732 67,061 9.2 11.2 0.4 2.0

Washington 2,885,677 265,601 2,888,594 281,731 9,799 16,130 9.2 9.8 0.3 0.5

West Virginia 881,917 118,086 882,213 140,273 5,743 22,187 13.4 15.9 0.7 2.5

Wisconsin 2,624,358 344,590 2,625,477 345,945 9,456 1,355 13.1 13.2 0.4 0.0

Wyoming 261,868 34,989 262,286 39,483 3,602 4,494 13.4 15.1 1.4 1.7

Source: 2010 1-Year ACS and 2010 Census Summary File 1 from American FactFinder.

Attachment A. Gross Vacancy Rates for the United States by State: 2010 Census and 2010 American Community Survey

2010 Census 2010 ACS

ACS-Census

2010 Census 

Gross Vacancy 

Rate

2010 ACS

ACS-Census
Gross Vacancy 

Rate MOE

United States 11.4 13.1 0.1 1.7

District of Columbia South 10.1 15.0 1.3 4.9

Florida South 17.5 21.8 0.3 4.3

Delaware South 15.7 19.1 1.0 3.5

Alabama South 13.3 16.5 0.5 3.3

New Mexico West 12.2 15.2 0.6 3.0

Mississippi South 12.5 15.4 0.7 2.9

Georgia South 12.3 14.9 0.4 2.6

Arkansas South 12.9 15.4 0.6 2.5

West Virginia South 13.4 15.9 0.7 2.5

Rhode Island Northeast 10.7 13.2 0.9 2.4

Louisiana South 12.0 14.1 0.5 2.1

Texas South 10.6 12.6 0.2 2.0

Virginia South 9.2 11.2 0.4 2.0

Tennessee South 11.3 13.3 0.5 2.0

South Carolina South 15.7 17.7 0.5 2.0

Kentucky South 10.8 12.7 0.5 1.9

Hawaii West 12.4 14.3 0.8 1.9

North Carolina South 13.5 15.3 0.3 1.8

Oklahoma South 12.3 14.0 0.5 1.7

Arizona West 16.3 18.0 0.5 1.7

Wyoming West 13.4 15.1 1.4 1.7

Maine Northeast 22.8 24.5 0.8 1.7

Illinois Midwest 8.7 10.3 0.3 1.6

Ohio Midwest 10.2 11.8 0.3 1.5

Pennsylvania Northeast 9.9 11.4 0.3 1.5

New York Northeast 9.7 11.2 0.2 1.5

Nevada West 14.3 15.8 0.6 1.5

Montana West 15.2 16.6 0.9 1.5

Michigan Midwest 14.6 16.0 0.3 1.4

Maryland South 9.3 10.6 0.4 1.3

California West 8.1 9.3 0.1 1.3

New Jersey Northeast 9.5 10.8 0.3 1.2

Indiana Midwest 10.5 11.7 0.4 1.2

Alaska West 15.9 17.1 0.9 1.1

South Dakota Midwest 11.3 12.4 1.0 1.1

Massachusetts Northeast 9.3 10.3 0.4 1.0

Missouri Midwest 12.4 13.4 0.4 1.0

Kansas Midwest 9.8 10.7 0.6 0.9

Connecticut Northeast 7.9 8.7 0.5 0.8

Oregon West 9.3 10.1 0.5 0.8

Colorado West 10.8 11.5 0.4 0.6

New Hampshire Northeast 15.6 16.2 0.7 0.6

Washington West 9.2 9.8 0.3 0.5

Idaho West 13.2 13.7 0.9 0.5

North Dakota Midwest 11.4 11.8 1.0 0.4

Nebraska Midwest 9.5 9.8 0.6 0.3

Wisconsin Midwest 13.1 13.2 0.4 0.0

Utah West 10.4 10.4 0.5 0.0

Iowa Midwest 8.6 8.5 0.5 -0.1

Vermont Northeast 20.5 20.4 0.8 -0.1

Minnesota Midwest 11.1 10.9 0.4 -0.1

Source: 2010 1-Year ACS and 2010 Census Summary File 1 from American FactFinder.

Region

2010 Census 

Gross Vacancy 

Rate

2010 ACS

ACS-Census

Attachment B. Gross Vacancy Rates Sorted by Size of Difference in Gross Vacancy Rate by 

State: 2010 Census and 2010 American Community Survey
Estimate MOE Estimate MOE Estimate MOE

United States 14,988,438 17,223,646 4,137,567 3,587,148 44,663 -550,419 206,825 610,827 17,510 404,002 1,896,796 1,929,351 34,885 32,555

Alabama 288,062 359,276 79,265 59,569 4,881 -19,696 3,761 16,251 2,613 12,490 35,903 34,853 3,894 -1,050

Alaska 48,909 52,455 6,729 3,628 1,001 -3,101 667 1,858 903 1,191 2,876 2,615 871 -261

Arizona 463,536 512,688 120,490 102,806 7,084 -17,684 5,449 14,711 2,756 9,262 64,407 70,058 6,061 5,651

Arkansas 169,215 202,916 46,443 42,929 4,613 -3,514 2,139 4,090 1,457 1,951 18,500 21,342 2,896 2,842

California 1,102,583 1,276,501 374,610 345,695 10,315 -28,915 20,347 54,643 4,688 34,296 154,775 156,825 7,298 2,050

Colorado 240,030 253,677 57,644 48,991 4,734 -8,653 3,058 12,206 2,349 9,148 32,673 33,172 3,987 499

Connecticut 116,804 129,406 40,004 33,772 3,558 -6,232 1,960 7,122 1,906 5,162 15,564 16,043 2,470 479

Delaware 63,588 77,724 11,399 13,907 2,175 2,508 676 922 610 246 5,985 8,440 1,698 2,455

District of Columbia 30,012 44,448 13,393 15,572 2,428 2,179 926 2,341 880 1,415 3,930 3,636 1,020 -294

Florida 1,568,778 1,959,023 371,626 317,500 12,645 -54,126 15,438 50,978 4,870 35,540 198,232 209,037 10,048 10,805

Georgia 503,217 609,062 174,416 159,230 8,025 -15,186 6,792 21,032 2,871 14,240 83,852 91,305 6,462 7,453

Hawaii 64,170 74,180 16,441 18,395 2,382 1,954 954 2,737 942 1,783 4,277 5,944 1,365 1,667

Idaho 88,388 91,925 16,360 15,433 2,254 -927 997 4,014 1,367 3,017 12,814 12,909 2,327 95

Illinois 459,743 544,220 158,882 128,698 6,089 -30,184 7,998 18,439 2,802 10,441 82,739 78,208 6,028 -4,531

Indiana 293,387 326,267 93,029 72,083 5,571 -20,946 3,859 14,924 2,712 11,065 46,410 46,418 4,453 8

Iowa 114,841 114,124 31,812 23,284 2,931 -8,528 1,803 3,277 986 1,474 18,405 19,374 2,724 969

Kansas 121,119 132,379 40,445 28,678 3,335 -11,767 1,962 6,321 1,676 4,359 16,286 18,027 2,813 1,741

Kentucky 207,199 244,269 56,960 43,906 4,196 -13,054 3,059 11,069 2,161 8,010 27,286 30,189 3,348 2,903

Louisiana 236,621 278,125 66,857 55,186 3,781 -11,671 3,273 11,280 1,931 8,007 21,480 25,523 3,597 4,043

Maine 164,611 176,800 15,738 14,545 2,182 -1,193 1,021 2,735 1,092 1,714 9,711 11,933 1,805 2,222

Maryland 222,403 253,166 61,874 57,868 5,126 -4,006 3,742 9,107 1,839 5,365 32,883 33,153 2,751 270

Massachusetts 261,179 288,308 66,673 59,440 4,841 -7,233 3,822 13,531 2,519 9,709 25,038 24,570 3,009 -468

Michigan 659,725 724,610 141,687 111,891 6,070 -29,796 6,684 16,842 2,430 10,158 77,080 71,061 4,943 -6,019

Minnesota 259,974 256,694 48,091 36,319 3,901 -11,772 3,198 8,355 1,868 5,157 30,726 29,712 2,826 -1,014

Mississippi 158,951 196,442 44,735 47,780 4,099 3,045 1,920 4,706 1,321 2,786 16,886 19,377 2,634 2,491

Missouri 337,118 363,389 92,946 66,493 4,395 -26,453 4,290 9,850 1,898 5,560 44,200 45,061 4,198 861

Montana 73,218 80,259 10,082 9,001 1,900 -1,081 773 1,958 862 1,185 5,964 4,801 1,141 -1,163

Nebraska 75,663 78,373 24,404 16,626 2,776 -7,778 1,279 3,112 1,014 1,833 9,167 8,298 1,792 -869

Nevada 167,564 185,259 61,985 51,653 4,311 -10,332 1,838 6,881 1,705 5,043 32,949 25,786 2,946 -7,163

New Hampshire 95,781 99,565 13,293 8,294 2,160 -4,999 787 2,662 951 1,875 7,521 6,834 1,822 -687

New Jersey 339,202 382,488 92,118 87,470 5,014 -4,648 4,578 10,898 2,003 6,320 39,260 42,865 3,810 3,605

New Mexico 109,993 137,059 22,150 19,629 2,835 -2,521 1,303 3,562 1,017 2,259 11,050 12,046 2,326 996

New York 790,348 911,784 200,039 176,495 7,068 -23,544 12,786 41,538 4,160 28,752 77,225 72,179 4,691 -5,046

North Carolina 582,373 662,620 156,587 135,499 7,497 -21,088 6,671 18,867 3,066 12,196 71,693 77,975 4,952 6,282

North Dakota 36,306 37,687 7,422 6,424 1,566 -998 554 1,321 715 767 2,734 2,800 908 66

Ohio 524,073 603,047 184,143 151,266 7,487 -32,877 8,126 20,344 3,097 12,218 78,089 75,435 5,074 -2,654

Oklahoma 203,928 233,246 59,264 43,934 3,918 -15,330 2,717 9,978 2,332 7,261 22,671 23,944 3,002 1,273

Oregon 156,624 169,339 40,193 33,958 4,043 -6,235 2,608 7,487 1,831 4,879 24,191 25,048 3,125 857

Pennsylvania 548,411 632,790 135,262 103,459 6,238 -31,803 9,386 21,851 3,113 12,465 64,818 63,631 5,685 -1,187

Rhode Island 49,788 61,121 15,763 16,831 2,463 1,068 727 3,111 1,125 2,384 5,171 5,797 1,638 626

South Carolina 336,502 378,944 92,758 88,692 5,989 -4,066 3,957 7,774 1,709 3,817 36,523 38,338 3,988 1,815

South Dakota 41,156 45,076 10,366 8,357 1,852 -2,009 642 1,495 845 853 3,696 4,388 1,133 692

Tennessee 318,581 374,424 98,370 91,638 5,940 -6,732 3,980 17,918 3,078 13,938 47,274 44,516 4,405 -2,758

Texas 1,054,503 1,257,545 394,310 383,448 10,770 -10,862 16,509 52,512 4,621 36,003 121,430 126,253 8,339 4,823

Utah 102,017 101,796 20,176 18,859 2,809 -1,317 1,408 2,536 1,000 1,128 14,580 12,741 2,362 -1,839

Vermont 66,097 65,776 5,635 6,087 1,325 452 597 1,221 530 624 3,598 2,654 751 -944

Virginia 308,881 375,942 82,493 74,992 6,001 -7,501 5,408 25,210 3,024 19,802 44,881 44,539 4,074 -342

Washington 265,601 281,731 72,112 59,901 5,428 -12,211 4,877 10,683 2,463 5,806 41,417 43,467 4,649 2,050

West Virginia 118,086 140,273 19,521 18,933 2,293 -588 1,366 3,907 1,197 2,541 10,381 12,556 2,021 2,175

Wisconsin 344,590 345,945 63,268 47,366 4,150 -15,902 3,695 8,446 1,715 4,751 34,219 31,479 3,186 -2,740

Wyoming 34,989 39,483 7,304 4,738 1,301 -2,566 458 2,214 932 1,756 3,376 2,196 774 -1,180

Source: 2010 1-Year ACS and 2010 Census Summary File 1 from American FactFinder.

2010 

Census

2010 ACS

ACS-Census

For sale only

Attachment C. Vacancy Status by State: 2010 Census and 2010 American Community Survey

2010 Census 

Total Vacant

2010 ACS Total 

Vacant

For rent Rented, not yet occupied

ACS-Census

2010 

Census 

2010 ACS

ACS-Census

2010 

Census

2010 ACS
Estimate MOE Estimate MOE Estimate MOE Estimate MOE

United States 421,032 610,798 15,083 189,766 4,649,298 5,153,003 56,603 503,705 24,161 31,607 3,768 7,446 3,652,759 5,300,912 67,127 1,648,153 Other

Alabama 9,227 11,783 2,544 2,556 63,890 82,506 5,790 18,616 238 452 557 214 95,778 153,862 7,508 58,084 Other

Alaska 1,006 1,030 641 24 27,901 29,919 2,148 2,018 362 897 537 535 9,368 12,508 1,627 3,140

Arizona 10,550 18,663 2,616 8,113 184,327 192,310 8,104 7,983 538 1,159 722 621 77,775 112,981 6,136 35,206 Other

Arkansas 4,995 17,558 2,339 12,563 38,153 49,798 3,827 11,645 345 145 203 -200 58,640 67,054 4,606 8,414

California 34,288 50,059 3,816 15,771 302,815 338,825 8,021 36,010 2,100 1,867 821 -233 213,648 328,587 11,294 114,939 Other

Colorado 5,418 5,442 1,576 24 101,965 106,349 6,070 4,384 524 303 266 -221 38,748 47,214 3,895 8,466

Connecticut 3,729 4,765 1,331 1,036 29,618 30,074 3,411 456 55 41 68 -14 25,874 37,589 3,667 11,715

Delaware 1,011 1,137 616 126 35,939 42,046 2,824 6,107 43 251 263 208 8,535 11,021 2,101 2,486

District of Columbia 1,007 3,804 1,304 2,797 3,537 2,946 1,117 -591 8 0 292 -8 7,211 16,149 2,200 8,938 Other

Florida 31,911 61,194 4,558 29,283 657,070 877,251 18,485 220,181 1,541 4,124 1,338 2,583 292,960 438,939 13,841 145,979 Seasonal

Georgia 13,118 21,218 3,185 8,100 81,511 97,534 6,364 16,023 854 389 365 -465 142,674 218,354 9,985 75,680 Other

Hawaii 1,151 1,139 502 -12 30,079 32,295 3,238 2,216 117 147 181 30 11,151 13,523 2,266 2,372

Idaho 2,177 2,797 941 620 41,660 38,456 2,892 -3,204 632 809 535 177 13,748 17,507 2,335 3,759

Illinois 16,677 27,920 2,974 11,243 47,289 46,429 3,690 -860 315 41 70 -274 145,843 244,485 7,667 98,642 Other

Indiana 10,862 19,202 2,940 8,340 45,571 43,155 3,168 -2,416 200 306 284 106 93,456 130,179 7,239 36,723 Other

Iowa 5,555 5,190 1,209 -365 21,020 20,825 2,064 -195 87 135 207 48 36,159 42,039 3,920 5,880

Kansas 5,267 4,399 1,242 -868 12,763 14,128 1,875 1,365 129 926 611 797 44,267 59,900 4,641 15,633

Kentucky 8,687 24,938 2,878 16,251 38,616 42,980 3,153 4,364 627 716 496 89 71,964 90,471 6,082 18,507

Louisiana 7,294 8,315 1,686 1,021 42,253 54,455 4,321 12,202 999 1,293 629 294 94,465 122,073 6,531 27,608 Other

Maine 2,089 1,673 767 -416 118,310 120,881 4,186 2,571 160 206 169 46 17,582 24,827 2,759 7,245

Maryland 6,586 8,014 2,017 1,428 55,786 56,219 4,021 433 177 349 257 172 61,355 88,456 5,098 27,101 Other

Massachusetts 6,408 6,187 1,485 -221 115,630 118,991 4,979 3,361 161 0 285 -161 43,447 65,589 5,148 22,142 Other

Michigan 17,978 30,672 3,351 12,694 263,071 278,351 6,325 15,280 1,773 1,331 544 -442 151,452 214,462 7,757 63,010 Other

Minnesota 6,232 6,195 1,309 -37 130,471 119,544 3,970 -10,927 334 176 106 -158 40,922 56,393 4,362 15,471

Mississippi 4,915 3,931 1,327 -984 28,867 37,631 3,350 8,764 318 378 348 60 61,310 82,639 5,561 21,329 Other

Missouri 11,098 11,354 1,788 256 80,374 88,434 4,555 8,060 193 485 404 292 104,017 141,712 7,382 37,695

Montana 1,353 1,597 861 244 38,510 38,434 2,782 -76 283 429 269 146 16,253 24,039 2,334 7,786 Other

Nebraska 2,804 2,896 952 92 13,881 14,588 1,404 707 60 33 52 -27 24,068 32,820 2,912 8,752

Nevada 3,416 5,250 1,616 1,834 32,703 46,371 4,144 13,668 242 0 309 -242 34,431 49,318 4,044 14,887

New Hampshire 1,393 1,118 563 -275 63,910 64,281 3,289 371 27 24 41 -3 8,850 16,352 2,286 7,502

New Jersey 8,145 11,164 2,149 3,019 134,903 135,173 5,737 270 156 277 284 121 60,042 94,641 5,804 34,599 Other

New Mexico 2,143 5,756 1,193 3,613 36,612 50,450 3,745 13,838 229 1,338 736 1,109 36,506 44,278 3,181 7,772

New York 21,027 36,570 3,810 15,543 289,301 297,944 8,273 8,643 892 1,410 691 518 189,078 285,648 8,778 96,570 Other

North Carolina 14,510 14,662 2,753 152 191,508 201,893 6,746 10,385 1,620 696 517 -924 139,784 213,028 8,169 73,244 Other

North Dakota 1,043 1,850 557 807 11,483 13,082 1,368 1,599 319 965 555 646 12,751 11,245 1,450 -1,506

Ohio 19,263 23,564 2,702 4,301 58,591 58,776 4,048 185 346 270 304 -76 175,515 273,392 10,946 97,877 Other

Oklahoma 8,405 8,894 1,735 489 35,187 41,405 2,831 6,218 318 1,285 791 967 75,366 103,806 5,244 28,440 Other

Oregon 4,401 6,965 1,737 2,564 55,473 60,551 4,709 5,078 461 668 402 207 29,297 34,662 3,394 5,365

Pennsylvania 20,131 25,485 2,783 5,354 161,582 179,482 5,698 17,900 411 311 327 -100 156,821 238,571 9,319 81,750 Other

Rhode Island 1,219 785 547 -434 17,077 17,265 2,230 188 12 132 185 120 9,819 17,200 2,738 7,381 Other

South Carolina 8,519 9,532 1,922 1,013 112,531 125,994 6,742 13,463 370 604 540 234 81,844 108,010 6,754 26,166

South Dakota 1,314 1,102 535 -212 13,277 15,661 1,916 2,384 88 154 168 66 11,773 13,919 1,635 2,146

Tennessee 10,518 14,835 2,566 4,317 60,778 67,862 4,955 7,084 392 717 598 325 97,269 136,938 7,122 39,669 Other

Texas 30,437 37,644 4,198 7,207 208,733 241,746 8,119 33,013 2,209 1,949 676 -260 280,875 413,993 12,483 133,118 Other

Utah 2,828 5,003 1,319 2,175 47,978 45,822 3,141 -2,156 232 315 224 83 14,815 16,520 2,105 1,705

Vermont 615 432 371 -183 50,198 46,915 2,200 -3,283 39 273 361 234 5,415 8,194 1,395 2,779

Virginia 9,570 16,284 2,897 6,714 80,468 97,828 5,434 17,360 608 918 628 310 85,453 116,171 6,459 30,718 Other

Washington 7,623 9,013 1,791 1,390 89,907 87,564 5,823 -2,343 1,328 832 520 -496 48,337 70,271 5,650 21,934

West Virginia 4,597 6,346 1,459 1,749 38,283 44,809 3,163 6,526 118 246 293 128 43,820 53,476 4,450 9,656

Wisconsin 5,741 4,954 1,126 -787 193,046 179,435 5,304 -13,611 249 213 174 -36 44,372 74,052 4,883 29,680 Other

Wyoming 781 518 492 -263 14,892 17,340 2,200 2,448 322 622 477 300 7,856 11,855 1,910 3,999

Source: 2010 1-Year ACS and 2010 Census Summary File 1 from American FactFinder.

2010 

Census

2010 ACS

ACS-Census

2010 

Census

Attachment C. Vacancy Status by State: 2010 Census and 2010 American Community Survey (Cont.)

Sold, not yet occupied For seasonal, recreational, or occasional use For migrant workers Other Vacant Statistcally 

Largest Vacancy 

Status Category

2010 

Census

2010 ACS

ACS-Census

2010 

Census

2010 ACS

ACS-Census

2010 ACS

ACS-Census
Estimate MOE Estimate MOE Estimate MOE Estimate MOE

United States 3.1 2.7 0.0 -0.4 0.2 0.5 0.0 0.3 1.4 1.5 0.0 0.0 0.3 0.5 0.0 0.1

Alabama 3.6 2.7 0.2 -0.9 0.2 0.7 0.1 0.6 1.7 1.6 0.2 -0.1 0.4 0.5 0.1 0.1

Alaska 2.2 1.2 0.3 -1.0 0.2 0.6 0.3 0.4 0.9 0.9 0.3 -0.1 0.3 0.3 0.2 0.0

Arizona 4.2 3.6 0.2 -0.6 0.2 0.5 0.1 0.3 2.3 2.5 0.2 0.2 0.4 0.7 0.1 0.3

Arkansas 3.5 3.3 0.3 -0.3 0.2 0.3 0.1 0.1 1.4 1.6 0.2 0.2 0.4 1.3 0.2 1.0

California 2.7 2.5 0.1 -0.2 0.1 0.4 0.0 0.3 1.1 1.1 0.1 0.0 0.3 0.4 0.0 0.1

Colorado 2.6 2.2 0.2 -0.4 0.1 0.6 0.1 0.4 1.5 1.5 0.2 0.0 0.2 0.2 0.1 0.0

Connecticut 2.7 2.3 0.2 -0.4 0.1 0.5 0.1 0.3 1.0 1.1 0.2 0.0 0.3 0.3 0.1 0.1

Delaware 2.8 3.4 0.5 0.6 0.2 0.2 0.1 0.1 1.5 2.1 0.4 0.6 0.2 0.3 0.2 0.0

District of Columbia 4.5 5.2 0.7 0.7 0.3 0.8 0.3 0.5 1.3 1.2 0.3 -0.1 0.3 1.3 0.4 0.9

Florida 4.1 3.5 0.1 -0.6 0.2 0.6 0.1 0.4 2.2 2.3 0.1 0.1 0.4 0.7 0.0 0.3

Georgia 4.3 3.9 0.2 -0.4 0.2 0.5 0.1 0.3 2.1 2.2 0.1 0.2 0.3 0.5 0.1 0.2

Hawaii 3.2 3.5 0.4 0.4 0.2 0.5 0.2 0.3 0.8 1.1 0.3 0.3 0.2 0.2 0.1 0.0

Idaho 2.4 2.3 0.3 -0.1 0.1 0.6 0.2 0.5 1.9 1.9 0.3 0.0 0.3 0.4 0.1 0.1

Illinois 3.0 2.4 0.1 -0.6 0.2 0.3 0.1 0.2 1.6 1.5 0.1 -0.1 0.3 0.5 0.1 0.2

Indiana 3.3 2.6 0.2 -0.8 0.1 0.5 0.1 0.4 1.7 1.7 0.1 0.0 0.4 0.7 0.1 0.3

Iowa 2.4 1.7 0.2 -0.6 0.1 0.2 0.1 0.1 1.4 1.4 0.2 0.1 0.4 0.4 0.1 0.0

Kansas 3.3 2.3 0.2 -1.0 0.2 0.5 0.1 0.4 1.3 1.5 0.2 0.1 0.4 0.4 0.1 -0.1

Kentucky 3.0 2.3 0.2 -0.7 0.2 0.6 0.1 0.4 1.4 1.6 0.2 0.1 0.5 1.3 0.1 0.8

Louisiana 3.4 2.8 0.2 -0.6 0.2 0.6 0.1 0.4 1.1 1.3 0.2 0.2 0.4 0.4 0.1 0.1

Maine 2.2 2.0 0.3 -0.2 0.1 0.4 0.2 0.2 1.3 1.7 0.2 0.3 0.3 0.2 0.1 -0.1

Maryland 2.6 2.4 0.2 -0.2 0.2 0.4 0.1 0.2 1.4 1.4 0.1 0.0 0.3 0.3 0.1 0.1

Massachusetts 2.4 2.1 0.2 -0.3 0.1 0.5 0.1 0.3 0.9 0.9 0.1 0.0 0.2 0.2 0.1 0.0

Michigan 3.1 2.5 0.1 -0.7 0.1 0.4 0.1 0.2 1.7 1.6 0.1 -0.1 0.4 0.7 0.1 0.3

Minnesota 2.0 1.5 0.2 -0.5 0.1 0.4 0.1 0.2 1.3 1.3 0.1 0.0 0.3 0.3 0.1 0.0

Mississippi 3.5 3.7 0.3 0.2 0.2 0.4 0.1 0.2 1.3 1.5 0.2 0.2 0.4 0.3 0.1 -0.1

Missouri 3.4 2.4 0.1 -1.0 0.2 0.4 0.1 0.2 1.6 1.7 0.1 0.0 0.4 0.4 0.1 0.0

Montana 2.1 1.9 0.4 -0.2 0.2 0.4 0.2 0.2 1.2 1.0 0.2 -0.2 0.3 0.3 0.2 0.1

Nebraska 3.1 2.1 0.3 -1.0 0.2 0.4 0.1 0.2 1.2 1.0 0.2 -0.1 0.4 0.4 0.1 0.0

Nevada 5.3 4.4 0.3 -0.9 0.2 0.6 0.1 0.4 2.8 2.2 0.2 -0.6 0.3 0.4 0.1 0.2

New Hampshire 2.2 1.3 0.3 -0.8 0.1 0.4 0.2 0.3 1.2 1.1 0.3 -0.1 0.2 0.2 0.1 0.0

New Jersey 2.6 2.5 0.1 -0.1 0.1 0.3 0.1 0.2 1.1 1.2 0.1 0.1 0.2 0.3 0.1 0.1

New Mexico 2.5 2.2 0.3 -0.3 0.1 0.4 0.1 0.3 1.2 1.3 0.3 0.1 0.2 0.6 0.1 0.4

New York 2.5 2.2 0.1 -0.3 0.2 0.5 0.1 0.4 1.0 0.9 0.1 -0.1 0.3 0.5 0.0 0.2

North Carolina 3.6 3.1 0.2 -0.5 0.2 0.4 0.1 0.3 1.7 1.8 0.1 0.1 0.3 0.3 0.1 0.0

North Dakota 2.3 2.0 0.5 -0.3 0.2 0.4 0.2 0.2 0.9 0.9 0.3 0.0 0.3 0.6 0.2 0.3

Ohio 3.6 2.9 0.1 -0.6 0.2 0.4 0.1 0.2 1.5 1.5 0.1 -0.1 0.4 0.5 0.1 0.1

Oklahoma 3.6 2.6 0.2 -0.9 0.2 0.6 0.1 0.4 1.4 1.4 0.2 0.1 0.5 0.5 0.1 0.0

Oregon 2.4 2.0 0.2 -0.4 0.2 0.4 0.1 0.3 1.4 1.5 0.2 0.1 0.3 0.4 0.1 0.2

Pennsylvania 2.4 1.9 0.1 -0.6 0.2 0.4 0.1 0.2 1.2 1.1 0.1 0.0 0.4 0.5 0.0 0.1

Rhode Island 3.4 3.6 0.5 0.2 0.2 0.7 0.2 0.5 1.1 1.3 0.3 0.1 0.3 0.2 0.1 -0.1

South Carolina 4.3 4.1 0.3 -0.2 0.2 0.4 0.1 0.2 1.7 1.8 0.2 0.1 0.4 0.4 0.1 0.0

South Dakota 2.9 2.3 0.5 -0.6 0.2 0.4 0.2 0.2 1.0 1.2 0.3 0.2 0.4 0.3 0.1 -0.1

Tennessee 3.5 3.3 0.2 -0.2 0.1 0.6 0.1 0.5 1.7 1.6 0.1 -0.1 0.4 0.5 0.1 0.2

Texas 4.0 3.8 0.1 -0.1 0.2 0.5 0.0 0.4 1.2 1.3 0.1 0.0 0.3 0.4 0.0 0.1

Utah 2.1 1.9 0.3 -0.1 0.1 0.3 0.1 0.1 1.5 1.3 0.2 -0.2 0.3 0.5 0.1 0.2

Vermont 1.7 1.9 0.4 0.1 0.2 0.4 0.2 0.2 1.1 0.8 0.2 -0.3 0.2 0.1 0.1 -0.1

Virginia 2.5 2.2 0.2 -0.2 0.2 0.7 0.1 0.6 1.3 1.3 0.1 0.0 0.3 0.5 0.1 0.2

Washington 2.5 2.1 0.2 -0.4 0.2 0.4 0.1 0.2 1.4 1.5 0.2 0.1 0.3 0.3 0.1 0.0

West Virginia 2.2 2.1 0.2 -0.1 0.2 0.4 0.1 0.3 1.2 1.4 0.2 0.2 0.5 0.7 0.2 0.2

Wisconsin 2.4 1.8 0.2 -0.6 0.1 0.3 0.1 0.2 1.3 1.2 0.1 -0.1 0.2 0.2 0.0 0.0

Wyoming 2.8 1.8 0.5 -1.0 0.2 0.8 0.3 0.7 1.3 0.8 0.3 -0.5 0.3 0.2 0.2 -0.1

Source: 2010 1-Year ACS and 2010 Census Summary File 1 from American FactFinder.

2010 ACS

2010 Census ACS-Census

2010 ACS

ACS-Census 2010 Census

2010 ACS

ACS-Census 2010 Census

2010 ACS

ACS-Census 2010 Census

Attachment D. Percent Distribution of Vacancy Status by State: 2010 Census and 2010 American Community Survey

(Percent of total housing units)

For rent Rented, not yet occupied For sale only Sold, not yet occupied
Estimate MOE Estimate MOE Estimate MOE

United States 3.5 3.9 0.0 0.4 0.0 0.0 0.0 0.0 2.8 4.0 0.0 1.2

Alabama 2.9 3.8 0.2 0.9 0.0 0.0 0.0 0.0 4.4 7.1 0.3 2.7

Alaska 9.1 9.7 0.5 0.7 0.1 0.3 0.2 0.2 3.1 4.1 0.5 1.0

Arizona 6.5 6.8 0.2 0.3 0.0 0.0 0.0 0.0 2.7 4.0 0.2 1.2

Arkansas 2.9 3.8 0.2 0.9 0.0 0.0 0.0 0.0 4.5 5.1 0.3 0.6

California 2.2 2.5 0.0 0.3 0.0 0.0 0.0 0.0 1.6 2.4 0.1 0.8

Colorado 4.6 4.8 0.2 0.2 0.0 0.0 0.0 0.0 1.8 2.1 0.2 0.4

Connecticut 2.0 2.0 0.2 0.0 0.0 0.0 0.0 0.0 1.7 2.5 0.2 0.8

Delaware 8.9 10.3 0.4 1.5 0.0 0.1 0.1 0.1 2.1 2.7 0.5 0.6

District of Columbia 1.2 1.0 0.4 -0.2 0.0 0.0 0.0 0.0 2.4 5.4 0.6 3.0

Florida 7.3 9.8 0.2 2.4 0.0 0.0 0.0 0.0 3.3 4.9 0.1 1.6

Georgia 2.0 2.4 0.1 0.4 0.0 0.0 0.0 0.0 3.5 5.3 0.2 1.8

Hawaii 5.8 6.2 0.5 0.4 0.0 0.0 0.0 0.0 2.1 2.6 0.4 0.5

Idaho 6.2 5.8 0.2 -0.5 0.1 0.1 0.1 0.0 2.1 2.6 0.3 0.6

Illinois 0.9 0.9 0.1 0.0 0.0 0.0 0.0 0.0 2.8 4.6 0.1 1.9

Indiana 1.6 1.5 0.1 -0.1 0.0 0.0 0.0 0.0 3.3 4.7 0.2 1.3

Iowa 1.6 1.6 0.1 0.0 0.0 0.0 0.0 0.0 2.7 3.1 0.2 0.4

Kansas 1.0 1.1 0.1 0.1 0.0 0.1 0.0 0.1 3.6 4.9 0.3 1.3

Kentucky 2.0 2.2 0.1 0.2 0.0 0.0 0.0 0.0 3.7 4.7 0.3 1.0

Louisiana 2.2 2.8 0.2 0.6 0.1 0.1 0.0 0.0 4.8 6.2 0.3 1.4

Maine 16.4 16.7 0.2 0.3 0.0 0.0 0.0 0.0 2.4 3.4 0.4 1.0

Maryland 2.3 2.4 0.1 0.0 0.0 0.0 0.0 0.0 2.6 3.7 0.2 1.1

Massachusetts 4.1 4.2 0.1 0.1 0.0 0.0 0.0 0.0 1.5 2.3 0.2 0.8

Michigan 5.8 6.1 0.1 0.3 0.0 0.0 0.0 0.0 3.3 4.7 0.2 1.4

Minnesota 5.6 5.1 0.0 -0.5 0.0 0.0 0.0 0.0 1.7 2.4 0.2 0.7

Mississippi 2.3 2.9 0.2 0.7 0.0 0.0 0.0 0.0 4.8 6.5 0.3 1.7

Missouri 3.0 3.3 0.1 0.3 0.0 0.0 0.0 0.0 3.8 5.2 0.2 1.4

Montana 8.0 8.0 0.4 0.0 0.1 0.1 0.1 0.0 3.4 5.0 0.4 1.6

Nebraska 1.7 1.8 0.1 0.1 0.0 0.0 0.0 0.0 3.0 4.1 0.3 1.1

Nevada 2.8 3.9 0.3 1.2 0.0 0.0 0.0 0.0 2.9 4.2 0.3 1.3

New Hampshire 10.4 10.5 0.2 0.1 0.0 0.0 0.0 0.0 1.4 2.7 0.4 1.2

New Jersey 3.8 3.8 0.1 0.0 0.0 0.0 0.0 0.0 1.7 2.7 0.1 1.0

New Mexico 4.1 5.6 0.3 1.5 0.0 0.1 0.1 0.1 4.0 4.9 0.3 0.9

New York 3.6 3.7 0.1 0.1 0.0 0.0 0.0 0.0 2.3 3.5 0.1 1.2

North Carolina 4.4 4.7 0.1 0.2 0.0 0.0 0.0 0.0 3.2 4.9 0.2 1.7

North Dakota 3.6 4.1 0.2 0.5 0.1 0.3 0.2 0.2 4.0 3.5 0.3 -0.5

Ohio 1.1 1.1 0.1 0.0 0.0 0.0 0.0 0.0 3.4 5.3 0.2 1.9

Oklahoma 2.1 2.5 0.1 0.4 0.0 0.1 0.0 0.1 4.5 6.2 0.2 1.7

Oregon 3.3 3.6 0.2 0.3 0.0 0.0 0.0 0.0 1.7 2.1 0.2 0.3

Pennsylvania 2.9 3.2 0.1 0.3 0.0 0.0 0.0 0.0 2.8 4.3 0.1 1.5

Rhode Island 3.7 3.7 0.4 0.0 0.0 0.0 0.0 0.0 2.1 3.7 0.5 1.6

South Carolina 5.3 5.9 0.3 0.6 0.0 0.0 0.0 0.0 3.8 5.0 0.3 1.2

South Dakota 3.7 4.3 0.4 0.6 0.0 0.0 0.0 0.0 3.2 3.8 0.3 0.6

Tennessee 2.2 2.4 0.2 0.2 0.0 0.0 0.0 0.0 3.5 4.9 0.2 1.4

Texas 2.1 2.4 0.1 0.3 0.0 0.0 0.0 0.0 2.8 4.1 0.1 1.3

Utah 4.9 4.7 0.2 -0.2 0.0 0.0 0.0 0.0 1.5 1.7 0.2 0.2

Vermont 15.6 14.5 0.3 -1.0 0.0 0.1 0.1 0.1 1.7 2.5 0.4 0.9

Virginia 2.4 2.9 0.1 0.5 0.0 0.0 0.0 0.0 2.5 3.4 0.1 0.9

Washington 3.1 3.0 0.2 -0.1 0.0 0.0 0.0 0.0 1.7 2.4 0.2 0.8

West Virginia 4.3 5.1 0.3 0.7 0.0 0.0 0.0 0.0 5.0 6.1 0.4 1.1

Wisconsin 7.4 6.8 0.1 -0.5 0.0 0.0 0.0 0.0 1.7 2.8 0.2 1.1

Wyoming 5.7 6.6 0.6 0.9 0.1 0.2 0.2 0.1 3.0 4.5 0.6 1.5

Source: 2010 1-Year ACS and 2010 Census Summary File 1 from American FactFinder.

ACS-Census 2010 Census

2010 ACS

ACS-Census

Attachment D. Percent Distribution of Vacancy Status by State: 2010 Census and 2010 American Community Survey (Cont)

(Percent of total housing units)

For seasonal, recreational, or occasional use For migrant workers Other Vacant

2010 Census

2010 ACS

ACS-Census 2010 Census

2010 ACS