People Might Move but Housing Units Don’t: An Evaluation of the State and 

County Housing Unit Estimates 

by 

Jason Devine and Charles Coleman 

Population Division 

U.S. Census Bureau 

Washington, D.C. 20233 

This paper reports the results of research and analysis undertaken by U.S. Census 

Bureau staff. It has undergone a more limited review than official U.S. Census Bureau 

publications. This report is released to inform interested parties of research and to 

encourage discussion. Presented at the Annual meeting of the Southern Demographic 

Association, 2002, Austin, TX. 
CONTENTS 

Background

Measures of Accuracy

National and State Level Housing Unit Estimates 

County Level Housing Unit Estimates 

Errors by Size and Growth 

Geographic Differences 

Alternative Estimates 

Conclusion 

References 

Appendix: Estimates Methodology 

Figures 

1. MAPE of April 1, 2000 County Housing Unit Estimates by Size Class 

2. MAPE of April 1, 2000 County Housing Unit Estimates by Change in Housing Units 

3. MAPE of April 1, 2000 County Housing Unit Estimates by Size and Growth Class 

4. MALPE of April 1, 2000 County Housing Unit Estimates by Size and Growth Class 

5. Accuracy of April 1, 2000 County Estimates versus Alternative Estimates

Tables 

1. Measures of Error in the April 1, 2000 Housing Unit Estimates by State 

2. MAPE of April 1, 2000 County Housing Unit Estimates by size 

3. MAPE of April 1, 2000 County Housing Unit Estimates by Change in Housing Units: 1990 to 2000

4. County Measures of Error in the April 1, 2000 Housing Unit Estimates by State 

5. MAPE of April 1, 2000 County Housing Unit Estimates by size 

Maps 

1. Percent Error in the April 1, 2000 Housing Unit Estimates by State 

2. MAPE of April 1, 2000 County Housing Unit Estimates by State 

3. Percent Error in the April 1, 2000 Housing Unit Estimates by State 

2
ABSTRACT 

Throughout the 1990s the Population Division of the U.S. Census Bureau produced state 

and county level housing unit estimates. While the state level estimates were released to 

the public, the county level estimates were considered experimental. The county level 

estimates were produced using a component method whereby we begin with the 1990 

Census housing unit count and update the Census count using administrative records data 

on building permits, mobile home shipments, and housing unit loss. The county estimates 

were summed to obtain the state estimates. The availability of Census 2000 data provides

the opportunity to evaluate the accuracy of these estimates. 

This paper compares Census 2000 results with April 1, 2000 housing unit estimates using 

a variety of statistical measures including the Mean Absolute Percent Error and the Mean 

Algebraic Percent Error. The results of this analysis will be used to inform Census Bureau 

analysts on ways by which the current housing unit estimates can be improved. 

3
BACKGROUND

This report presents an evaluation of estimates of total housing units for the Nation, states, and counties 

produced by the Population Division of the Census Bureau. At the national and state level these estimates 

were released to the public every year in the 1990s except 1997 and 1999. While the county level 

estimates were not released to the public, they were used in an evaluation of Census 2000 housing unit 

coverage designed to improve the accuracy of census data (Barron, 2001). The comparison of the April 1, 

2000 estimates to the April 1, 2000 decennial census counts forms the basis for this report. 

The housing unit estimates were produced using an administrative records based method by adding data 

on building permits, mobile home shipments, and housing unit loss to the 1990 Census unadjusted housing 

unit count. These data were collected and processed at the subcounty level for areas such as cities, 

boroughs, villages, towns, and townships and then summed to the county, state, and national level. See 

the Appendix for a more detailed explanation of the methodology. 

MEASURES OF ACCURACY 

For the purposes of this evaluation, the differences between the estimates and the census counts are 

assumed to be due to errors in the estimates. This paper considers two aspects of quality: bias and 

accuracy, as measured by the Mean Algebraic Percent Error (MALPE), and Mean Absolute Percent Error 

(MAPE), respectively. The MALPE is simply the average of all the percent errors, and differs from the 

MAPE only in that the MAPE involves taking the absolute values of the percent errors. MALPE is a 

measure of mean bias and can be used as the basis for testing for the presence of significant mean bias 

(Coleman, 1999 and 2001). MAPE is a measure of accuracy. That is, it provides a measure of how “close” 

the estimates get to the truth, on average. 

NATIONAL AND STATE LEVEL HOUSING UNIT ESTIMATES 

The April 1, 2000 estimate for the Nation of 115,547,749 housing units was 0.3 percent lower than the 

Census 2000 housing unit count of 115,904,641. The MAPE for all states was 1.5 percent. A similar 

analysis of the housing unit estimates for the 1980’s showed a similar error of 0.3 percent for the nation and

a MAPE for all states of 1.8 percent (Prevost, 1994). Half of all states had absolute errors less than 1.0 

percent (Table 1, Col. 5). Montana had the highest absolute error of 5.9 percent while Minnesota had the 

lowest at 0.008 percent. The error varied by number of housing units in the state. Generally, the higher the 

1990 housing unit count, the more accurate the estimate. Of the ten states with the largest number of 

housing units in 1990, all but two, New York (-2.0) and California (1.9), had absolute percent errors lower 

than 1 percent. The MALPE for all states was -0.9 percent. Thirty-three states had estimates that were 

below the Census 2000 count, indicating negative bias. These results can also be compared with the 

4
Census Bureau’s independently derived 1990 based population estimates which had an error of -2.4 

percent for the Nation and a MAPE of 2.6 percent for all states (Davis, 2001). 

Table 1. Measures of Error in the April 1, 2000 Housing Unit Estimates by State 

State Name 1990 to 2000 Percent 

Change 

((4)-(2))/(2) 

(1) 

Census 1990 

Housing Unit 

Count 

(2) 

April 1, 2000 Housing 

Unit Estimate 

(3) 

Census 2000 

Housing Unit 

Count 

(4) 

Percent 

Difference 

 ((3)-(4))/(4) 

(5) 

United States 13.3 102262196 115547749 115904641 -0.3

Alabama 17.6 1670258 1915989 1963711 -2.4

Alaska 12.2 232608 251878 260978 -3.5

Arizona 31.9 1659439 2111282 2189189 -3.6

Arkansas 17.2 1000579 1121396 1173043 -4.4

California 9.2 11182503 12448944 12214549 1.9

Colorado 22.4 1477348 1804536 1808037 -0.2

Connecticut 4.9 1320851 1396296 1385975 0.7

Delaware 18.3 289919 335201 343072 -2.3

District of Columbia -1.3 278489 262386 274845 -4.5

Florida 19.7 6100248 7262782 7302947 -0.5

Georgia 24.4 2637906 3336316 3281737 1.7

Hawaii 18.1 389811 447488 460542 -2.8

Idaho 27.7 413322 523021 527824 -0.9

Illinois 8.4 4506275 4847481 4885615 -0.8

Indiana 12.7 2246044 2573171 2532319 1.6

Iowa 7.8 1143666 1226337 1232511 -0.5

Kansas 8.3 1044111 1154001 1131200 2.0

Kentucky 16.2 1506930 1707718 1750927 -2.5

Louisiana 7.6 1716229 1835845 1847181 -0.6

Maine 11.0 587045 635294 651901 -2.5

Maryland 13.4 1891665 2146667 2145283 0.1

Massachusetts 6.0 2472710 2594259 2621989 -1.1

Michigan 10.0 3847940 4260370 4234279 0.6

Minnesota 11.8 1848567 2066101 2065946 0.0

Mississippi 15.0 1010421 1136163 1161953 -2.2

Missouri 11.0 2199084 2439544 2442017 -0.1

Montana 14.3 361155 388380 412633 -5.9

Nebraska 9.4 660634 723446 722668 0.1

Nevada 59.5 518778 825925 827457 -0.2

New Hampshire 8.6 503904 549664 547024 0.5

New Jersey 7.6 3075310 3286619 3310275 -0.7

New Mexico 23.5 632058 773301 780579 -0.9

New York 6.3 7227059 7525613 7679307 -2.0

North Carolina 25.0 2818073 3525871 3523944 0.1

North Dakota 4.8 276340 297404 289677 2.7

Ohio 9.4 4371944 4765853 4783051 -0.4

Oklahoma 7.7 1406495 1481939 1514400 -2.1

Oregon 21.7 1193574 1446582 1452709 -0.4

Pennsylvania 6.3 4938225 5296608 5249750 0.9

Rhode Island 6.1 414572 434637 439837 -1.2

South Carolina 23.2 1423771 1756098 1753670 0.1

South Dakota 10.5 292436 327816 323208 1.4

Tennessee 20.4 2026066 2392755 2439443 -1.9

Texas 16.4 7008887 8109855 8157575 -0.6

Utah 28.4 598388 759971 768594 -1.1

Vermont 8.5 271214 292473 294382 -0.6

Virginia 16.3 2496519 2936497 2904192 1.1

Washington 20.6 2032344 2462487 2451075 0.5

West Virginia 8.1 781295 798643 844623 -5.4

Wisconsin 12.9 2055774 2331401 2321144 0.4

Wyoming 10.0 203413 217445 223854 -2.9

5
The relationship between growth and accuracy was less clear. Some states, such as Nevada, had high 

growth but low absolute percent errors while other states such as Arizona had high growth and high 

absolute percent errors. Table 1 shows the percent change in housing units between 1990 and 2000, the 

1990 Census count, the April 1, 2000 housing unit estimate and Census 2000 count, and the percent error 

for each state. Map 1 shows the percent error for each state.

Map 1. Percent Error in the April 1, 2000 Housing Unit Estimates by State

COUNTY LEVEL HOUSING UNIT ESTIMATES 

A comparison of the Census 2000 housing unit counts with the April 1, 2000 housing unit estimates shows 

that the overall MAPE for all 3,141 counties is 4.6 percent. The overall MALPE is -1.5 percent, which 

indicates that, on average, the estimates were below the Census 2000 housing unit counts. On average, 

the counties experienced a 12.0 percent change in housing units between the 1990 Census and Census 

2000. For comparison, the county level population estimates had a MAPE of 3.3 percent and a MALPE of 

-1.6 percent (Davis, 2001).

Of all the counties, 643 had estimates within one percent of the census count. Only 359 counties had 

estimates that were more than 10 percent different than the Census 2000 count. 1,813 counties had 

housing unit estimates below the Census 2000 count, 1,327 had estimates that were higher, and one 

county (Potter County, South Dakota) had an estimate exactly the same as the Census 2000 count. 420 

6
counties had housing unit loss between 1990 and 2000. For 374 (89.0 percent) of these counties, the 

estimates correctly indicated that the county had housing unit loss. 

ERRORS BY SIZE AND GROWTH

When considered by population size (Table 2 and Figure 1), the larger counties were found to have smaller 

MAPEs. This is a pattern found also in prior analyses of county (Davis, 1994) and subcounty population 

estimates (Galdi, 1985), (Harper, Devine and Coleman, 2001). 

Table 2. MAPE of April 1, 2000 County Housing Unit Estimates by Size 

1990 Housing Unit 

Count 

Number of Areas Mean Absolute 

Percent Error 

All 3141 4.6

0-2,499 336 7.3

2,500-4,999 518 5.7

5,000-9,999 749 5.0

10,000-19,999 653 4.4

20,000-49,999 507 3.1

50,000-99,999 178 2.2

>100,000 200 1.9

Figure 1. MAPE of April 1, 2000 County Housing Unit Estimates by Size Class 

0

1

2

3

4

5

6

7

8

0-2,499 2,500-4,999 5,000-9,999 10,000-19,999 20,000-49,999 50,000-99,999 >100,000

1990 Housing Units

Average Asolute Percent Error

7
When classified by change in the number of housing units between the 1990 Census and Census 2000 

(Table 3 and Figure 2), counties with little change have lower MAPEs than counties with large changes in 

housing unit counts. This is a common pattern found also in prior analyses of subcounty (Galdi, 1985), 

(Harper et al., 2001) and county population estimates (Davis, 1994) . 

Table 3. MAPE of County Housing Unit Estimates by Change in Housing Units: 1990 to 2000 

Percent Change Number of Areas Mean Absolute 

Percent Error 

All 3141 4.6

< -5 185 8.1

-4.9 to 0.1 231 3.1

0 to 4.9 456 2.7

5 to 9.9 565 2.9

10 to 14.9 500 3.4

15 to 24.9 712 5.5

> 25 492 7.0

Figure 2. MAPE of April 1, 2000 County Housing Unit Estimates by Change in Housing Units 

0

1

2

3

4

5

6

7

8

9

< -5 -5 to 0 0 to 4.9 5 to 9.9 10 to 14.9 15 to 24.9 > 25

1990 - 200 Change in Housing Units (percent)

Average absolute percent error

Figure 3 shows a 3-dimensional graph of MAPEs by size and growth categories, the same categories that

are used in Figures 1 and 2. Figure 3 shows that the U-shaped curves vary dramatically by size class, 

achieving troughs at differing growth classes. Moreover, the 100,000+ housing units size class has two 

troughs: at the 5-9.9 percent and 15-24.9 percent growth classes. The trend for increased accuracy as the

number of housing units increases remains generally true for growing counties, but not for declining 

counties. 

8
Figure 3: MAPE of April 1, 2000 County Housing Unit Estimates by Size and Growth Class 

<-5% -5% to

0%

0% to

4.9%

5% to

9.9%

10% to

14.9%

15% to

24.9%

25%+

100,000+

50,000-99,999

20,000-49,999

10,000-19,999

5,000-9,999

2,500-4,999

0-2,499

0

2

4

6

8

10

12

14

16

Percent

Growth

1990 Housing Units

Figure 4 shows MALPE by size and growth class. Again, these classes are the same as in the preceding 

Figures. Figure 4 shows the origin of the U-shaped curves: growing areas were systematically 

underestimated while declining areas were systematically overestimated. In the growth classes of 5 

percent and more, the magnitude of MALPE systematically declines in size as the 1990 housing unit count

increases, which leads to MAPE’s general declines in these classes. The growth classes under 5 percent 

do not show this systematic decline, leading one to suspect that they were generated by different 

processes. These differences account for the breakdown between size and accuracy in these classes. An 

important interaction effect shows up dramatically: small size interacts with large growth rates to decrease 

MALPE. 

9
Figure 4: MALPE of April 1, 2000 County Housing Unit Estimates by Size and Growth Class 

<-5% -5% to

0%

0% to

4.9%

5% to

9.9%

10% to

14.9%

15% to

24.9%

25%+

0-2,499

2,500-4,999

5,000-9,999

10,000-19,999

20,000-49,999

50,000-99,999

100,000+

-20

-15

-10

-5

0

5

10

Percent

Growth

1990 Housing Units

GEOGRAPHIC DIFFERENCES 

Map 2. MAPE of April 1, 2000 County Housing Unit Estimates by State

10
Table 4 and Map 2 show the MAPEs of the county housing unit estimates by state. These MAPEs range 

from a low of 0.7 percent for Connecticut to a high of 14.8 percent for Hawaii. Connecticut, Rhode Island, 

New Hampshire, New Jersey, Massachusetts, and Pennsylvania have county MAPEs lower than 2 percent. 

Montana, Nevada, Tennessee, Arkansas, Alaska, and Hawaii have county MAPEs higher than 7 percent. 

Table 4. County Measures of Error in the April 1, 2000 Housing Unit Estimates by State 

State Name April 1, 2000 

Housing Unit 

Estimate 

(2) 

Census 2000 

Housing Unit 

Count 

(3) 

County

MALPE 

(4) 

County

MAPE 

 (5) 

United States 115547749 115904641 -1.5 4.6

Alabama 1915989 1963711 -5.0 5.6

Alaska 251878 260978 -8.0 10.1

Arizona 2111282 2189189 -9.3 9.3

Arkansas 1121396 1173043 -5.6 7.6

California 12448944 12214549 0.5 2.4

Colorado 1804536 1808037 -0.8 5.7

Connecticut 1396296 1385975 0.5 0.7

Delaware 335201 343072 -1.9 2.6

District of Columbia 262386 274845 -4.5 4.5

Florida 7262782 7302947 -4.8 5.7

Georgia 3336316 3281737 -2.1 5.6

Hawaii 447488 460542 -14.4 14.8

Idaho 523021 527824 -0.7 4.1

Illinois 4847481 4885615 -0.4 2.8

Indiana 2573171 2532319 2.0 3.6

Iowa 1226337 1232511 -0.7 2.0

Kansas 1154001 1131200 2.7 3.7

Kentucky 1707718 1750927 -5.0 6.1

Louisiana 1835845 1847181 -2.2 3.9

Maine 635294 651901 -3.4 4.1

Maryland 2146667 2145283 0.0 2.5

Massachusetts 2594259 2621989 -1.5 1.8

Michigan 4260370 4234279 0.2 2.5

Minnesota 2066101 2065946 -0.2 2.7

Mississippi 1136163 1161953 -4.3 6.1

Missouri 2439544 2442017 -3.0 5.4

Montana 388380 412633 -3.4 7.3

Nebraska 723446 722668 0.0 2.9

Nevada 825925 827457 -2.0 7.4

New Hampshire 549664 547024 0.6 1.6

New Jersey 3286619 3310275 -0.2 1.6

New Mexico 773301 780579 2.2 8.5

New York 7525613 7679307 0.0 2.3

North Carolina 3525871 3523944 -0.9 3.4

North Dakota 297404 289677 4.9 5.2

Ohio 4765853 4783051 -1.6 2.9

Oklahoma 1481939 1514400 -2.5 5.6

Oregon 1446582 1452709 0.1 2.8

Pennsylvania 5296608 5249750 0.9 1.8

Rhode Island 434637 439837 -1.0 1.1

South Carolina 1756098 1753670 -1.3 3.2

South Dakota 327816 323208 2.0 4.4

Tennessee 2392755 2439443 -5.7 7.5

Texas 8109855 8157575 -0.8 6.3

Utah 759971 768594 -2.0 5.0

Vermont 292473 294382 -1.3 2.4

Virginia 2936497 2904192 -0.2 3.5

Washington 2462487 2451075 -0.9 3.1

West Virginia 798643 844623 -7.5 8.2

Wisconsin 2331401 2321144 0.3 3.0

Wyoming 217445 223854 -3.4 4.5

11
 

Map 3 shows the percent error for each county. Various geographic patterns may be observed. The South 

contains many counties with underestimates of housing units. The northeastern end of their range is in 

West Virginia. This range extends westward through Kentucky into Arkansas and eastern Oklahoma and 

southwestward to the southern tip of Texas, interrupted by a set of overestimates in the Mississippi River 

valley. The eastern boundary of this region is an arc through Kentucky, Tennessee and Alabama into the 

Florida Panhandle. Underestimates also predominate in eastern Mississippi and parts of southern 

Louisiana. Another region of underestimates occurs in the Mountain West, particularly in New Mexico and

western Montana. Other areas of this region contain clusters of under- and overestimates. Alaska contains

similar clusters of under- and overestimates. The western Great Plains generally contain overestimates. 

All of these areas reflect various problems with the housing unit estimation process, generally the lack or 

poor quality of building permit data. The overestimates in the Great Plains may reflect underestimation of 

demolitions. 

Map 3. Percent Error in the April 1, 2000 Housing Unit Estimates by State 

 

12
ALTERNATIVE ESTIMATES 

Because of the effort required to produce housing unit estimates based on administrative records, mainly 

building permits, it is worth asking whether these estimates offer any improvement over using an easier 

estimation method. This section compares the housing unit estimates developed from administrative 

records (building permit method) to two alternative sets of estimates. The first set was produced by 

applying the vacancy and persons per household rates from the 1990 Census to the April 1, 2000 county 

population estimates developed using the tax return method (population estimate method). The second set 

simply used the 1990 Census housing unit count as the estimate. The results of this comparison appear in

Table 5 and Figure 5. For each size class, the housing unit method is preferable to the other estimates. 

This demonstrates the value of using a building permit based approach to estimate housing units. 

Table 5. MAPE of April 1, 2000 County Housing Unit Estimates by size 

1990 Census Housing 

Unit Count 

Number of Areas MAPE – Building Permit 

Method 

MAPE - Population 

Estimate 

Method 

MAPE - 1990 

Census Housing Unit 

Count 

All 3141 4.6 5.8 12.0

0-2,499 336 7.3 8.1 9.8

2,500-4,999 518 5.7 5.9 10.2

5,000-9,999 749 5.0 6.0 11.7

10,000-19,999 653 4.4 5.5 13.1

20,000-49,999 507 3.1 5.4 14.0

50,000-99,999 178 2.2 4.6 14.0

>100,000 200 1.9 3.9 11.0

Figure 5. Accuracy of April 1, 2000 County Estimates versus Alternative Estimates 

0

2

4

6

8

1 0

1 2

1 4

1 6

A l l 0 - 2 , 499 2 , 500- 4 , 999 5 , 0 0 0 - 9 , 999 10, 0 00- 19, 999 20, 000- 49, 9 9 9 50, 0 0 0 - 9 9 , 999 > 100, 000

1990 H o u s in g U n it C o u n t

Mean Absolute Percent Error

M ean A b s o lu t e P e r c e n t E r r o r B u ild ing P e r m it M e thod

M ean A b s o lu t e P e r c e n t E r r o r C ount y P opula tio n B a s e d E s t im a t e

M ean A b s o lu t e P e r c e n t E r r o r 1990 C ens u s H o u s ing U n it C o u n t

13
CONCLUSION 

This evaluation has found that the 2000 state and county level housing unit estimates developed from 

building permit, mobile home shipment, and demolition data performed with a degree of accuracy similar to

the state and county April 1, 2000 population estimates produced by the Population Division of the Census

Bureau. The housing unit estimates follow a pattern similar to other estimates produced by Population 

Division in that they tended to be more accurate for larger states and counties. The county level housing 

unit estimates follow a pattern similar to other estimates in that they are more accurate for areas that

experienced the smallest amount of housing unit change throughout the decade. At the state level the 

relationship between housing unit change and accuracy is less clear. 

The housing unit estimates show clear geographic variations. The number of housing units in counties 

were generally underestimated in large parts of the South and Mountain West, and generally overestimated 

in the western Great Plains. Some areas, such as Alaska contain mixtures of under- and overestimates. 

These areas are the most difficult to estimate. We hypothesize that the problems are due to input data 

deficiencies. 

The comparison with estimates produced using alternative methods indicates that the building permit 

method performed better than the alternative methods for all size and growth categories. While the building

permit based estimates are more accurate than the county population based estimates, the accuracy of the 

county population based estimates relies heavily on the accuracy of the vacancy and persons per 

household rates. The county population based method used persons per household and vacancy rates 

from the 1990 Census. Improvements in our ability to estimate these rates would improve the county 

population based estimates. 

Through this analysis we have begun to look at the discrepancies between our housing unit estimates and 

Census 2000. Future research should focus on identifying the components of the estimates that 

contributed the most to these discrepancies. 

14
REFERENCES 

Barron, W. J., Jr. 2001. “Recommendation on Adjustment of Census Counts.” Memorandum to Donald L. 

Evans, Secretary of Commerce, March 1. 

Breiman, Leo, 1999. “Random Forests-Random Features,” Technical Report No. 567, Statistics 

Department, University of California-Berkeley. 

Coleman, Charles D. 1999, “Nonparametric Tests for Bias in Estimates and Forecasts,” in American 

Statistical Association: 1999 Proceedings of the Business and Economic Statistics Section, 251-256. 

Coleman, Charles D. 2001, “Non-i.i.d. Generalizations of the Matched-Pairs t-Test,” in American Statistical 

Association: 2001 Proceedings of the Business and Economic Statistics Section. 

Davis, ST 1994. “Evaluation of Postcensal County Estimates for the 1980s”, Technical Working Paper #5. 

Davis, Sam T., Josephine D. Baker, Marc J. Perry, Signe Wetrogan, and Carolette Norwood, 2001. “An 

Early Comparison of Postcensal County Population Estimates with Results from the 2000 Census.” Paper 

Presented at the Annual Meetings of the American Statistical Association, Atlanta, GA, August 2001. 

Friedman, Jerome, 2001. “A Statistical View of Boosting,” presentation made to the Joint Statistical 

Meetings 2001, Atlanta, GA. 

Galdi, David 1985. “Evaluation of 1980 Subcounty Population Estimates, U.S. Census Bureau.” Current 

Population Reports, Series P-25, No. 963, U.S. Government Printing Office. 

Harper, Greg, Jason Devine and Charles Coleman, 2001. “Evaluation of 2000 Subcounty Population 

Estimates.” Paper Presented at the Annual Meetings of the Southern Demographic Association, Miami, FL, 

September 2001. 

Prevost, Ron 1994. “State Housing Unit and Household Estimates: April 1, 1980, to July 1, 1993, U.S. 

Census Bureau.” Current Population Reports, Series P-25, No. 1123, U.S. Government Printing Office. 

15
16

APPENDIX: HOUSING UNIT ESTIMATES METHODOLOGY 

State and County Level Housing Unit Estimates Methodology 

The Population Estimates Branch produces the housing unit estimates in the following steps: 

1. Obtain unadjusted total housing units from the 1990 Census. 

Note: The April 1, 1990 census housing unit count used for these estimates is a count of the number of 

housing units in an area as reported in the 1990 census of population or recalculated to match legal 

boundary changes and geocoding enhancements in the Census Bureau's TIGER system. The closing date 

to include these revisions in this set of estimates was January 1, 1998. 

2. Estimate residential construction from building permits compiled from internal data files developed by the 

Manufacturing and Construction Statistics Division. 

Note: Estimates of nonpermitted residential construction are calculated using the following steps at the 

county level: 

a) Calculate the number of 1990 county housing units located in non-permit-issuing 

jurisdictions within the county by subtracting the number of units located in permit-issuing jurisdictions from 

the total units. 

b) Sum the housing units located in non-permit issuing jurisdictions to the national level. 

c) Calculate the county's share of units located in jurisdictions not issuing permits by 

dividing the county's units by the nation's units (a)/(b). 

d) Multiply the total number of units reported in the Survey of Construction as constructed 

without building permits by the county's share of units located in jurisdictions not issuing 

building permits. 

3. Estimate new mobile home placements from Series C-40 reports. Ninety-eight percent of all mobile 

homes shipped to states are used for residential purposes. State mobile home information is distributed to 

counties based upon a county's proportion of the state's mobile homes as of the 1990 census. 

4. Estimate housing loss from demolition permits from internal data files developed by the Manufacturing 

and Construction Statistics Division (MCSD). These files include imputed permits where a permit-issuing 

jurisdiction did not report permit issuance for the entire year. No lag time is assumed for demolition permits. 

Note: MCSD stopped collecting data on demolition permits in 1995. After 1995, all data on housing unit 

loss is estimated. Estimates of nonpermitted housing loss are calculated from the county’s share of 

structures in region at risk of loss. The risk is based on the county’s share of the following types of 

structures: (1) Mobile homes and other; (2) Older units (pre-1939 construction); (3) Vacant for Seasonal 

and Recreational Use; and (4) Boarded up units. 

5. Adjust estimates. After the housing unit estimates are produced, adjustments are made to ensure that 

the estimates are consistent with the county population estimates produced using the Tax Return method. 

County household estimates are produced using tax return data, which are controlled to state household 

estimates. The state household estimates are based on household formation rates from the Current 

Population Survey (CPS). Controlled county household estimates and county housing unit estimates are 

used to derive a vacancy rate. The county household population estimates from the Tax Return method 

and the controlled county household estimates are used to estimate persons per household (PPH). 

Adjustments are made to the housing estimates to ensure that these vacancy and PPH values fall within 

certain tolerances. 

6. State Estimates. Sum the county level estimates to get the state level estimates