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A Picture of Subsidized Households File Sizes and Technical Comments


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File Sizes
Introduction to the Data
Technical Comments


Files Sizes


Projects Census Tracts
Records Bytes Records Bytes
Total 55,349 13,283,760 124,992 29,998,080
US 52 12,480 0 0
AK 428 102,720 299 71,760
AL 1,448 347,520 2,089 501,360
AR 844 202,560 1,354 324,960
AZ 689 165,360 1,700 408,000
CA 3,293 790,320 12,192 2,926,080
CO 701 168,240 2,129 510,960
CT 788 189,120 1,881 451,440
DC 229 54,960 192 46,080
DE 144 34,560 313 75,120
FL 1,450 348,000 5,406 1,297,440
GA 1,787 428,880 2,420 580,800
GQ 21 5,040 0 0
HI 185 44,400 685 164,400
IA 652 156,4801,598 383,520
ID 287 68,880 540 129,600
IL 2,221 533,040 4,641 1,113,840
IN 999 239,760 2,542 610,080
KS 802 192,480 1,340 321,600
KY 1,228 294,720 2,099 503,760
LA 1,098 263,520 2,005 481,200
MA 1,434 344,160 3,583 859,920
MD 900 216,000 1,842 442,080
ME 537 128,880 960 230,400
MI 1,755 421,200 4,578 1,098,720
MN 1,468 352,320 2,614 627,360
MO 1,825 438,000 2,926 702,240
MS 792 190,080 1,012 242,880
MT 417 100,080 581 139,440
NC 2,096 503,040 3,006 721,440
ND 388 93,120 476 114,240
NE 700 168,000 852 204,480
NH 311 74,640 670 160,800
NJ 1,148 275,520 4,611 1,106,640
NM 648 155,520 826 198,240
NV 283 67,920 642 154,080
NY 2,446 587,040 10,594 2,542,560
OH 2,449 587,760 4,948 1,187,520
OK 1,173 281,520 2,221 533,040
OR 749 179,760 1,556 373,440
PA 2,535 608,400 6,226 1,494,240
RI 400 96,000 601 144,240
RQ 710 170,400 0 0
SC 791 189,840 1,768 424,320
SD 591 141,840 381 91,440
TN 1,578 378,720 2,596 623,040
TQ 14 3,360 0 0
TX 3,139 753,360 9,330 2,239,200
UT 285 68,400 854 204,960
VA 918 220,320 2,479 594,960
VQ 52 12,480 0 0
VT 241 57,840 415 99,600
WA 1,123 269,520 2,324 557,760
WI 1,529 366,960 2,772 665,280
WV 430 103,200 1,052 252,480
WY 148 35,520 271 65,040


Introduction to the Data

This set of reports sketches a picture of subsidized households across the United States. There are national and regional reports and an accompanying computer data file. Each line is identified by key numbers and letters which are explained in the Detailed Explanation section

Sources

Data on households were sent by local housing agencies to the U.S. Department of Housing and Urban Development (HUD), and were summarized by HUD. This report uses the latest report on each household, provided it was submitted in the last 30 months. Most w ere submitted in the last 12 months. Earlier data on Public and Indian Housing are available for 1993 (see Family Data for Public and Indian Housing). The completeness or incompleteness of the data is shown on every line and is discussed below. The data collection forms are printed at the end of this report.

Data on the size and location of each program and project are from HUD's own administrative records, plus a database on Tax Credit projects collected by a contractor. (This is the only source used for Tax Credits, since data on individual households are n ot collected in that program.)

Program Overview

Rents are subsidized in all programs covered in this report. Households generally pay rent equal to 30% of their incomes, after deductions, and the Federal government pays the rest. To enter the programs, people must generally be below an income limit, wh ich varies by household size and location. They apply, and wait until their name rises to the top of the waiting list for the limited number of subsidized units available. Tax Credits are a program of the Internal Revenue Service, where landlords obtain t ax benefits for renting to low income households. Some projects have a mix of subsidized and unsubsidized units; just the subsidized units are counted here.

Report Overview

The reports and the data file show summary records for: the whole United States, each state, each housing agency, neighborhood (Census tract) and individual housing project. Only the summary records are included; no records for individual households. A Ce nsus tract is a compact area averaging about 1,500 households. We lack addresses for about 19% of subsidized housing units, so they cannot be placed in tracts, and thus the tract totals are underestimates. The data file is complete; the reports include th e following types of data:

Summaries Included


National Report Regional Reports Data File
U.S. and State Totals All All All
Housing Agencies Agencies with 300+ units All All
Projects Projects with 300+ units All All
Census Tracts:


Certificates+Vouchers
Tracts with 150+ C+V Tracts with 40+ C+V All
Totals of All Programs
Tracts with 300+ subsidized units, if these are 30%+ of tract Tracts where subsidized are 20%+ of tract All
Maps Selected areasNone User-created

Each summary record shows the number of housing units being summarized, completeness of the household data, average household income, percent minority, elderly, female headed, Zip code, county, metropolitan area, etc. In the reports, the information is spread across two facing pages.

Summary records are shown even when data are incomplete. To protect reliability we do not show household information where fewer than 40% of households are reported. We still show project number, project size, location, etc. To protect privacy we also do not show household information where 10 or fewer households are reported. All these households are still included in larger summaries, such as for the agency, state and United States.

Order of the Book

The records are grouped by state. Within each state they are sorted by project number, so individual project numbers are easy to find. One can also look through the list to find all projects in a city. If we had grouped them by address, it would have been somewhat easier to find cities, but impossible to find specific project numbers. The numbers for different programs are sorted with Public & Indian numbers first in each state, then Section 8 project numbers, then Federal Housing Administration (FHA) pro ject numbers, then Tax Credit projects, and finally Census tract summaries that total across all programs.

Suggestions for Using the Data File

Groups of projects can be compared. In some areas it may be useful to use the file to prepare a referral list of all subsidized housing in the area, with size, turnover, % elderly, neighborhood characteristics, etc. You can use various software tools to e xplore the data base. If you read the file on Internet, your browser probably has a 'Find' button, so you can move quickly to any city name, project name, project number, etc. If you download the data to your own computer, you can use a spreadsheet or dat abase package. Such packages let you:

  1. Jump to any part of the file (often using the f5 key, or a command to 'Find' a specific name)
  2. Sort the records to see the highest incomes, or most concentrated tracts, or most integrated large projects, etc. (for example sort by record type and income)
  3. Print selected items (in spreadsheet set width of other items to zero; in database list items you want in a report)
  4. Map the data (by selecting the records you want to map, then asking for an X-Y graph, using longitude for the X axis, and latitude for the Y axis, with a small symbol at each point, and no lines to connect the points; some databases do not give such graphs).
The national data file is about 43 megabytes. State files are smaller. File names include 'hud2' to distinguish them from the first file, which had Public and Indian Housing data for 1993. Three extra files come with the data:

hud2.wk1 Empty spreadsheet file, ready to fill: labels and widths of variables are already defined
hud2.dbf Empty data base file, ready to fill: names and widths of variables are already defined
Readme2.txt Documentation

Database Software

After downloading you can open hud2.dbf, which has the names and widths of the variables. Then import the file as delimited ASCII. In dBase:

  1. Hit Escape key
  2. . Use C:\hud2.dbf
  3. . Append from C:\hud2AK.txt Delimited
  4. . Go top
  5. . Assist Spreadsheet Software

    After downloading you can open hud2.wk1, which has the names and widths of the variables. Then import the file as an ASCII file with numbers separated by commas (text is enclosed in quotes). In Lotus:

    1. /File Retrieve C:\hud2.wk1
    2. /File Import Numbers C:\hud2AK.txt
    If your spreadsheet cannot read large files, use another package to split the file into pieces.

    Fixed Format Software

    Names and widths of the variables are in the layout printed on pages 18–22. You can skip over the commas and quotes. The files are delimited ASCII, with double quotes around text, and commas separating all fields, so they can be read directly by most prog rams. At the same time the files are in fixed format, so they can be read by programs that need fixed formats. We have kept the record length under 240 characters, which is a limit in Lotus, since we expect some users will use Lotus.

    Scope of the Data File

    The following table shows record types that are present in the computer file. For example there is one national summary record in each program, and 30–55 state summaries, depending on the program. Project records are present in some programs, tract record s in others, and Moderate Rehabilitation has neither, but just agency summaries. Summaries for parts of agencies are provided where an agency uses multiple numeric codes for various reasons.

    Number of Records by Type

    Total National Records Size Class Records State Records Housing Agency Records Parts-of-Agency Records Project Records Tract Records
    Total 180,341 9 43 458 6,572 115 48,152 124,992
    Totals 61,328 1 9 55


    61,263
    Indian Housing 2,624 1 7 30 177 9 2,400
    Public Housing 16,808 1 9 54 3,192 15 13,537
    S.8 Certificates+Vouchers 66,404 1 9 55 2,554 56
    63,729
    Moderate Rehab. 746 1 9 52 649 35

    S.8 New+Substant.Rehab. 15,235 1
    55

    15,179
    S.236 4,277 1
    52

    4,224
    Other Subsidy 3,420 1
    52

    3,367
    Housing Tax Credit 9,499 1
    53

    9,445


    Technical Comments

    Incomplete Data are shown by the column headed "% Reported," the column in the middle of the even-numbered pages.

    Some agencies reported zero in some items, rather than their actual data. We have removed most of the bias, generally by treating zero as missing data. Virtually all zero rents and zero incomes are indeed missing data, since the only households with truly zero gross income and zero gross rent are formerly homeless households who for some reason do not expect any income assistance in the coming year. Zero bedrooms are meaningful (efficiency apartments), and we try to minimize bias by treating units with no bedrooms reported and more than 2 people as missing data. We did not process households with invalid project numbers, or with no people reported, since we would not have been able to categorize them properly.

    Number of Subsidized Units by Missing Data


    Housing Agency Records Project Records
    Total % Lack Geography % Lack Tenant Data Total % Lack Geography % Lack Tenant Data
    Total 2,846,000 17 19 3,363,000 19 27
    Indian Housing 68,000 79 44 68,000 90 56
    Public Housing 1,326,000 25 16 1,326,000 32 22
    S.8 Certificates+Vouchers 1,346,000 7 20


    Moderate Rehab. 106,000 7 33


    S.8 New+Substant.Rehab.


    897,000 4 23
    S.236 447,000 2 22


    Other Subsidy


    292,000 10 32
    Housing Tax Credit


    332,000 24 56

    This table shows the number of subsidized units where agency or project records have no tenant data or geographic data (because of non-reporting of either, or suppression of tenant data to protect confidentiality). Agencies have less suppression of tenant data than projects, since they are larger. Agencies also have less missing geographic data, since we can have geographic codes for an agency if any of its projects are geographically coded. Specifically this table is based on the absence of data on bedrooms and latitude from the records. Other items may be more or less complete.

    Weighting for incomplete data

    The summaries are weighted to adjust for partial reporting in any agency: households reported are assumed representative of other occupied units in the same program and agency (or in the same project, in Office of Housing Programs). This assumption is not always correct. Furthermore, if far too many or too few households were reported in a program, the weight was limited to the range from .5 to 5, to avoid great discrepancies in weights, and resulting variance in the final data. There is no weighting to represent the places that did not report at all.

    Other Errors

    As many errors as possible have been corrected, but there undoubtedly remain errors, coming from household errors and from agency and HUD processing. Please tell us of any errors you find.

    Processing Steps

    The report merges data from 48 different computer tapes: 24 tapes of data from the September 1996 Multifamily Tenant Characteristics System (MTCS), 9 tapes of data from the December 1995 Tenant Rental Assistance and Certification System (TRACS, with data primarily through October 1995), 10 tapes of Census tract data, 4 tapes of geographic codes for Certificate+Voucher households, a tape of geographic codes for projects, and control totals for the number of units in each project.

    The Office of Policy Development and Research has standards for data documentation, that require listing all the steps of processing the data, so readers can see how problems were dealt with and how far the data have been changed from the originals. The processing steps for this report included:

    1. Master Control File

      1. Extracted records of Public & Indian projects & sub-projects from MTCS, sorted by project numbers, summarized sub-projects to projects, sorted by controlling agency, summarized units by program, project, agency and controlling agency
      2. Created list of which Section 8 agency numbers have been converted to, or are controlled by others (primarily state agencies)
      3. Extracted records of Section 8 contracts by effective date, sorted & summarized by agency & program, looked up controlling agency in lists from A1 and A2, inserted empty records for agency numbers that have been converted and no longer have units (so any tenants reported under old numbers can be converted), sorted by agency and added to control file of Public & Indian units
      4. Converted agency names to all capitals, project names to Initial Capitals, and flagged Indian rental projects.

    2. Geographic Files on Projects

      1. Extracted Census tract summary statistics, converted to percent, & stored them in a small file.
      2. Extracted Public Housing project records with geographic identifiers, and sorted by state, county, tract, looked up tract statistics & added these to project records. Sorted by project number to create lookup file. Added to master file from A4.
      3. Extracted necessary information from a control file of valid FHA & Section 8 numbers with geocodes. Sorted by FHA number, classified each number by combination of subsidies present. Edited inconsistent subsidy information. Wrote summaries, sorted by s tate, county, tract, looked up tract statistics & added these to project records. Sorted by FHA & Section 8 numbers to create lookup file.

    3. MTCS - Characteristics of Households in Programs of the Office of Public and Indian Housing

      1. Extracted program identifiers & allowances for each household & sorted by SSN
      2. Summarized income & household composition for each household & sorted by SSN
      3. Extracted rent calculation data for households in different sub-programs into a common format & sorted by SSN
      4. Extracted geographic codes from four tapes (Section 8 addresses had been sent to a geocoding contractor in four separate groups at different times) & sorted by SSN
      5. Matched all the above data extracts by SSN, taking most recent data for each household, & sorted by project number (agency in Section 8), state, county, tract, mcd
      6. Counted the number of records in each sub-program in each agency, compared to control file, & wrote file of weights to adjust for non-response & over-response. (Over-response happens when agencies do not report move-outs, so more than one household is in the HUD file, for the same unit.
      7. Summarized the weighted data into the categories of this report, at level of project and part-of-agency. (In Section 8 also looked up tract statistics, & for Certificates+Vouchers also summarized to tract.)

    4. TRACS - Characteristics of Households in Office of Housing Programs

      1. Extracted a record for each household from a 9–tape composite file, while comparing several FHA and Section 8 numbers on each record to obtain the cleanest identifiers, & sorted by FHA number and Section 8 number
      2. Summarized the extracted data for each combination of FHA number & Section 8 number
      3. For each summary of tenants from D2, looked for FHA number in control file from B3. Appended program code, unit counts, name & geographic codes. Where FHA number was not found, looked for Section 8 number & appended FHA number if available, program code, unit counts, name & geographic codes. Deleted tenant data not matched by either way. Then extracted project numbers, program code, unit counts, name & geographic codes for valid projects that had no tenants matched to them. Resorted by FHA number & Section 8 number. Re-summarized & weighted by unit totals all records with the same valid FHA number, or with no FHA number & the same valid Section 8 number.

    5. Tax Credits

      1. Extracted needed information from original file (prepared by Abt Associates from state-provided data, with four records per Tax Credit allocation), creating one record each. Compressed state code and state-determined project ID into an 11 character number.
      2. Sorted by address and name. Combined multiple records with one building address, unless address was simply 'Scattered' or 'Various,' in which case records were only combined if they had the same name.
      3. Number of low income units was missing on 8% of projects. On half of these it could be estimated from total project units (using 98% average found elsewhere). On the others this report shows 35 units each (the national average).

    Geographic Codes Used


    HUD Form 951 Office of Housing Summaries of Certificates+Vouchers LIHTC Merge Program
    Raw data Binary extract Control file of Public & Indian Raw data (in control file) Binary extract Summaries of Tracs Raw data Extract
    columns width columns width columns width columns width columns width columns width columns width record/columns width columns width columns width
    state 231-232 2 20 1

    205-6 2 25 1

    7-8 2 3/43-44 2



    county 234-236 3 21-22 2 77-79 3 207-9 3 26-27 2 13-15 3 10-12 3 3/45-47 3 12-14 3 1-3 3
    tract 252-258 7.2 23-26 4 80-86 7.2 221-8 8.2 28-31 4 16-22 7.2 14-20 7.2 3/48-54 7.2 15-21 7.2 4-10 7.2
    msa 247-250 4 27-28 2 87-90 4 217-220 4 32-33 2 23-26 4 21-24 4 3/22-25 4 22-25 4 11-14 4
    place 242-245 4 29-30 2 91-94 4 213-6 4 34-35 2 27-30 4 25-28 4 3/26-30 5 26-29 4 15-18 4
    cd 306-7 2 31 1 95-96 2 265-6 2 36 1 31-32 2 29-30 2 na


    19-20 2
    scatter
    (s.d. 100ft)
    82-86
    100-104
    5.5
    5.5
    32 1 97-98 2 69-74
    87-92
    6.4
    6.4
    37 1 33-34 2







    units 143-147 5 33-34 2

    326-31 f6 38-39 2 35-38 4

    3/55-58 4 p1 4 a-4 4
    name 34-63 30 35-50 16

    24-53 30 40-55 16 39-54 16

    1/7-36 30 32-47 16 1-16 16
    address 184-200
    150-180
    17
    31
    51-66 16 99-114 16 164-180
    133-163
    17
    31
    56-71 16 55-70 16

    1/37-66 30 48-63 16 17-32 16
    city 204-218 15 67-74 8 115-122 8 181-195 15 72-79 8 71-78 8

    1/67-96 30 64-71 8 33-40 8
    zip 221-229 9 75-76 2 123-7 5 196-204 9 80-81 2 79-83 5 31-35 5 1/99-108 10 72-76 5 21-25 5
    mcd 238-240 3 77-78 2 128-30 3 210-212 3 82-83 2 84-86 3 36-38 3 na


    26-28 3
    lat? 0-1

    p1 4 131 1

    84-87 4 b62 4 a(62) 4*

    p7 4* a62 4*
    latitude 71-80 10.6 p2 4 132-37 6.3 58-68 11.6 88-91 4 b63 4 a(63) 4* 3/1-9 9.6 p8 4* a63 4*
    longitude 88-98 11.6 p3 4 138-45 8.375-86 12.6 92-95 4 b64 4 a(64) 4* 3/10-20 11.6 p9 4* a64 4*
    tract? 0-1

    p4 4 146 1

    96-99 4 b65 4 a(65) 4*

    p10 4* a65 4*
    poor

    p5 4 147-8 2

    100-103 4 b66 4 a(66) 4*

    p11 4* a66 4*
    minority

    p6 4 149-50 2

    104-107 4 b67 4 a(67) 4* 4/95-99 5.2 p12(10) 4* a67 4*
    sfd-own

    p7 4 151-2 2

    108-111 4 b68 4 a(68) 4*

    p13 4* a68 4*
    * Stored as multiple of number of units, for convenient averaging
    Notes:
    1. When numeric items are stored in fewer characters than the raw data have, they are stored in binary format, where each position holds 9 bits (0-511 decimal). However the final public file expands them back to normal ascii codes.
    2. 'Scatter' measures scattered site projects: it is the standard deviation of the distance of each address from the average, measured in hundreds of feet. It may be included in a future release.


    Total Number of Records by State

    Total Agency+Other Summary Records Project Records Tract Records
    Indian Housing Public Housing S.8 New + Substantial Rehab S.236 Other Subsidy Housing Tax Credit Certificates + Vouchers Totals of All Programs
    Total 180,341 7,197 2,400 13,537 15,179 4,224 3,367 9,445 63,729 61,263
    U.S. Summaries 52 52







    AK 727 35 302 41 21 9 9 11 109 190
    AL 3,537 244 7 601 267 48 24 257 1,027 1,062
    AR 2,198 207 0 263 219 35 50 70 761 593
    AZ 2,389 75 294 108 104 40 19 49 890 810
    CA 15,485 262 123 685 1,139 584 226 274 6,334 5,858
    CO 2,830 111 27 162 235 62 45 59 1,150 979
    CT 2,669 108 4 191 240 81 96 68 1,047 834
    DC 421 12 0 62 79 36 32 8 0 192
    DE 457 21 0 34 54 7 7 21 138 175
    FL 6,856 225 17 419 392 128 116 153 2,958 2,448
    GA 4,207 235 0 858 275 122 71 226 950 1,470
    GQ 21 11 0 9 1 0 0 0 0 0
    HI 870 18 0 61 67 20 6 13 420 265
    IA 2,250 120 1 110 276 36 21 88 814 784
    ID 827 31 29 19 107 20 11 70 271 269
    IL 6,862 202 0 763 595 105 132 424 1,800 2,841
    IN 3,541 123 0 197 318 140 116 105 1,159 1,383
    KS 2,142 146 14 169 257 49 40 127 656 684
    KY 3,327 188 0 332 349 62 85 212 1,102 997
    LA 3,103 225 3 348 151 52 67 252 900 1,105
    MA 5,017 214 0 284 463 189 107 177 2,252 1,331
    MD 2,742 71 0 181 288 101 56 203 691 1,151
    ME 1,497 71 21 74 247 17 9 98 576 384
    MI 6,333 223 31 314 461 237 212 277 2,028 2,550
    MN 4,082 214 77 289 529 94 54 211 1,384 1,230
    MO 4,751 211 0 276 394 85 86 773 1,678 1,248
    MS 1,804 80 16 259 231 32 50 124 431 581
    MT 998 38 174 46 70 29 19 41 295 286
    NC 5,102 212 19 427 723 71 63 581 1,524 1,482
    ND 864 68 78 36 144 11 12 39 244 232
    NE 1,552 143 22 158 182 19 18 158 406 446
    NH 981 52 0 63 140 19 7 30 404 266
    NJ 5,759 192 0 331 348 124 85 68 2,673 1,938
    NM 1,474 94 228 100 75 20 33 98 436 390
    NV 925 32 84 53 42 17 14 41 373 269
    NY 13,040 323 17 573 933 300 129 171 5,736 4,858
    OH 7,397 157 0 522 765 246 292 467 2,086 2,862
    OK 3,394 169 396 213 125 53 54 163 1,229 992
    OR 2,305 67 13 131 217 70 56 195 829 727
    PA 8,761 222 0 698 620 158 74 763 3,059 3,167
    RI 1,001 68 0 102 136 38 15 41 366 235
    RQ 710 106 0 332 180 21 24 47 0 0
    SC 2,559 96 0 251 237 50 63 94 914 854
    SD 972 77 151 40 152 23 56 92 184 197
    TN 4,174 146 0 490 371 66 84 421 1,375 1,221
    TQ 14 5 0 0 9 0 0 0 0 0
    TX 12,469 610 6 992 426 171 262 672 5,284 4,046
    UT 1,139 47 21 69 78 16 9 45 454 400
    VA 3,397 83 0 179 287 122 56 191 806 1,673
    VQ 52 9 0 32 4 0 0 7 0 0
    VT 656 36 0 33 132 6 3 31 234 181
    WA 3,447 100 112 240 263 80 115 213 1,172 1,152
    WI 4,301 200 95 206 536 80 56 356 1,432 1,340
    WV 1,482 81 0 88 168 13 13 67 579 473
    WY 419 29 18 23 57 10 8 3 109 162


    Number of Subsidized Units, by State and Program

    Total Indian Housing Public Housing Certificates + Vouchers Moderate Rehabilitation S.8 New + Substantial Rehabilitation S.236 Other Subsidy Housing Tax Credit
    Total 4,814,983 67,744 1,326,224 1,346,306 105,845 897,160 447,382 292,237 332,085
    AK 11,448 5,047 1,629 2,618 0 887 642 238 387
    AL 94,771 155 45,175 18,369 1,770 13,775 5,234 1,555 8,738
    AR 53,632 0 15,666 19,203 255 7,939 2,955 3,880 3,734
    AZ 49,631 13,523 6,623 14,283 790 6,188 3,726 1,641 2,857
    CA 403,914 2,339 46,252 196,872 7,466 61,149 53,525 21,122 15,189
    CO 53,057 547 8,695 16,825 2,269 12,011 7,068 2,631 3,011
    CT 77,162 45 19,211 19,960 1,780 16,044 10,229 7,632 2,261
    DC 32,428 0 11,788 3,508 1,346 6,190 4,508 3,931 1,157
    DE 13,005 0 3,360 3,095 149 3,830 709 563 1,299
    FL 186,606 378 44,631 63,419 7,840 25,785 18,535 11,874 14,144
    GA 134,882 0 57,692 27,534 1,361 17,234 13,300 7,236 10,525
    GQ 2,295 0 751 1,419 75 5 0 0 0
    HI 20,581 0 5,263 8,238 46 2,591 2,205 1,587 651
    IA 40,504 20 4,682 15,583 1,066 11,727 3,133 1,366 2,927
    ID 13,393 793 817 4,529 144 3,497 820 399 2,394
    IL 223,965 0 80,870 45,998 3,815 46,844 12,633 16,738 17,067
    IN 95,912 0 18,574 25,720 1,491 21,304 14,999 9,809 4,015
    KS 39,540 416 9,279 7,163 350 9,082 3,209 3,381 6,660
    KY 82,320 0 25,888 20,213 1,613 18,438 6,531 5,856 3,781
    LA 93,472 40 32,722 23,364 1,843 11,789 6,131 6,419 11,164
    MA 163,465 0 35,396 46,070 3,675 36,320 20,497 12,650 8,857
    MD 99,866 0 26,216 23,099 2,702 17,869 14,918 5,843 9,219
    ME 26,786 467 4,167 8,468 1,491 7,308 1,655 624 2,606
    MI 149,287 772 29,261 26,282 1,192 40,618 23,351 16,908 10,903
    MN 92,977 1,735 22,002 23,002 890 23,866 10,243 4,362 6,877
    MO 103,122 0 21,910 28,916 3,285 21,204 8,277 7,146 12,384
    MS 55,108 719 16,338 12,651 1,082 11,932 3,234 4,230 4,922
    MT 18,616 5,203 2,074 3,457 764 2,762 2,222 1,136 998
    NC 122,251 1,261 39,924 35,455 3,925 21,547 6,750 5,318 8,071
    ND 16,111 2,726 1,939 5,457 554 3,146 859 384 1,046
    NE 30,977 677 7,823 9,727 715 5,625 1,789 835 3,786
    NH 19,275 0 4,310 6,142 424 4,938 1,969 432 1,060
    NJ 153,250 0 48,269 41,792 3,149 36,788 11,698 7,597 3,957
    NM 28,546 3,126 4,857 9,761 504 3,173 1,968 2,449 2,708
    NV 20,096 1,772 4,500 5,334 750 2,249 1,836 1,354 2,301
    NY 483,048 514 194,359 137,483 9,905 81,453 37,104 15,732 6,498
    OH 220,779 0 57,037 50,474 5,090 46,453 26,042 18,778 16,905
    OK 70,486 12,343 13,391 18,544 2,711 7,070 4,778 3,894 7,755
    OR 48,473 436 6,157 20,567 1,448 7,364 3,375 1,889 7,237
    PA 225,424 0 81,195 54,333 4,060 45,719 20,196 6,631 13,290
    RI 35,074 0 9,999 6,161 658 12,095 3,595 890 1,676
    RQ 112,869 0 57,288 21,302 3,095 18,388 4,511 4,328 3,957
    SC 60,048 0 16,870 14,790 2,066 12,884 5,030 4,817 3,591
    SD 21,357 6,131 1,695 3,887 599 3,582 1,099 2,130 2,234
    TN 110,452 0 42,958 18,897 1,917 21,621 8,530 8,036 8,493
    TQ 253 0 0 61 0 192 0 0 0
    TX 269,949 210 66,420 84,603 5,816 27,712 21,924 27,361 35,903
    UT 16,146 337 2,233 6,385 831 3,339 859 402 1,760
    VA 107,044 0 22,604 25,370 3,791 23,299 14,720 6,869 10,391
    VQ 5,101 0 4,376 444 0 124 0 0 157
    VT 10,493 0 2,013 3,646 268 3,045 292 279 950
    WA 73,072 3,180 17,108 22,023 965 9,992 5,348 6,338 8,118
    WI 82,538 2,268 13,904 20,267 890 25,610 6,2783,509 9,812
    WV 33,954 0 7,368 12,054 983 9,030 1,891 1,004 1,624
    WY 6,172 564 695 1,489 181 2,489 452 224 78