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Building a Housing Plan/Study
  .. using Federal GeoStatistical data
  .. focus on sites and local/regional areas
  .. interpretative data analytics; tools, data & methods

March 2025 .. Federal geostatistical data provide a wide range of housing-related data such as size, age and type of homes; home values, rents and mortgages, construction, prices and more. These housing data are important to private and public sector stakeholders. Together, these data provide a comprehensive picture of housing, that facilitate data-driven decision-making.

This section provides insights as to what relevant data are available, how to access the data, how to organize the data, how to use the data for collaboration and decision-making, Organize these data by geography, time-frame and subject matter into a plan or study. We provide how-to guides and examples. This section is initially focused on Manhattan, KS as a case study. We initially show a few lay-of-the-land graphics. These graphics have been developed using a VDA (Visual Data Analytics) GIS (Geographic Information System) project which is reviewed following the introductory graphics.

This section is still under development. Join us in the weekly web meeting sessions to learn more about using the tools and data referenced in this section.

Topics
  • 01 Manhattan, KS Area Housing Study Lay of the Land
  • 02 Manhattan, KS Area Demographic-Economic Characteristics
  • 03 Using the Housing Study Project with VDAGIS -- interactive mapping and geospatial analysis
  • 04 About the Housing Study Project
  • 05 Using DEDE to Extract/Integrate other Data
  • 06 Adding Other Layers/Data to the Project
  • 07 About VDAGIS
  • 08 Terms of Use

Manhattan, KS Housing Study Lay-of-the-Land
The following graphic shows Manhattan with an orange fill pattern in context of the state. Counties are shown with a black boundary and congressional districts are shown with a dark brown boundary.


The next graphic shows a further zoom-in showing Manhattan in context of Riley County, KS and Congressional District (CD) KS-01. Manhattan is mostly contained within Riley County and entirely contained within CD KS-01.


The next graphic shows selected Metropolitian Statistical Areas (MSAs) part of Core-Based Statistical Areas. Manhattan is comprised of three counties.


This graphic shows patterns of how and why population change by county occurred between 2020 and 2024. Click graphic for larger view.


Manhattan GeoDemographic Overview .. scroll section .. goto top
The Manhattan city, KS Census-sourced model-based population estimate changed from 54,158 (7/1/20) to the most recent 2023 estimate of 53,682. The 2023 American Community Survey (ACS2023) 5-year total housing units was 24,334. These estimates are centric to 2021; there is no Census sourced 2023 estimate of total housing units as the city of Manhattan fell below the population threshold of 65,000, required for the ACS 2023 1-year estimates. The metro census-sourced model-based population estimates and components of change are shown below. 
Popest24 = Popest23 + Births24 - Deaths24 + IntlMig24 + DomMig24 + Residual24
  134892 =   133371 +     1916 -      831 +      1318 -      879 -          3    

CBSA    STCTY   NAME                    Popest20  Popest21  Popest22  Popest23  Popest24
31740           Manhattan, KS MSA         134198    133885    133474    133371    134892
31740   20061   Geary County, KS           36766     36131     35525	 35192	   35459
31740   20149   Pottawatomie County, KS    25421     25884     26331     26506     26876
31740	20161	Riley County, KS           72011     71870     71618     71673     72557

                Births21  Births22  Births23  Births24  Deaths21  Deaths22  Deaths23  Deaths24 
                    2109      2012      1951      1916       885       960       854       831
                    1039       922       868       856       264       287       273       279
                     371       384       386       380       216       216       188       192
                     699       706       697       680       405       457       393       360
                                                  
               IntlMig21 IntlMig22 IntlMig23 IntlMig24  DomMig21  DomMig22  DomMig23  DomMig24              
                     107       532      1154      1318     -1629     -2007     -2391      -879
                      24       102       346       388     -1378     -1377     -1284      -686
                       2        22        66        79       299       271       -88       104   
                      81       408       742       851      -550      -901     -1019      -297
Manhattan-Kansas Demographic Comparative Insights .. goto top
.. using the Demographic Analytics tool
  • Narrative Comparative Analysis Report
  • Tabular Comparative Analysis Report

Manhattan, ACS 2023 5-Year Housing Characteristics .. scroll section .. goto top
  Item Value Percent
   HOUSING OCCUPANCY
     Total housing units 24,334 100.0
       Occupied housing units 21,677 89.1
       Vacant housing units 2,657 10.9
                                                                                                      
       Homeowner vacancy rate 2.7 -8.0
       Rental vacancy rate 6.9 -8.0
                                                                                                      
   UNITS IN STRUCTURE
     Total housing units 24,334 100.0
       1-unit, detached 10,936 44.9
       1-unit, attached 2,007 8.2
       2 units 1,417 5.8
       3 or 4 units 1,363 5.6
       5 to 9 units 1,819 7.5
       10 to 19 units 3,554 14.6
       20 or more units 2,261 9.3
       Mobile home 977 4.0
       Boat, RV, van, etc. 0 0.0
                                                                                                      
   YEAR STRUCTURE BUILT
     Total housing units 24,334 100.0
       Built 2020 or later 133 0.5
       Built 2010 to 2019 3,890 16.0
       Built 2000 to 2009 3,592 14.8
       Built 1990 to 1999 2,707 11.1
       Built 1980 to 1989 2,706 11.1
       Built 1970 to 1979 4,082 16.8
       Built 1960 to 1969 2,025 8.3
       Built 1950 to 1959 2,501 10.3
       Built 1940 to 1949 956 3.9
       Built 1939 or earlier 1,742 7.2
                                                                                                      
   ROOMS
     Total housing units 24,334 100.0
       1 room 516 2.1
       2 rooms 1,073 4.4
       3 rooms 3,019 12.4
       4 rooms 4,694 19.3
       5 rooms 3,975 16.3
       6 rooms 3,468 14.3
       7 rooms 2,413 9.9
       8 rooms 2,181 9.0
       9 rooms or more 2,995 12.3
       Median rooms 5.2 -8.0
                                                                                                      
   BEDROOMS
     Total housing units 24,334 100.0
       No bedroom 583 2.4
       1 bedroom 3,532 14.5
       2 bedrooms 6,749 27.7
       3 bedrooms 7,052 29.0
       4 bedrooms 4,344 17.9
       5 or more bedrooms 2,074 8.5
                                                                                                      
   HOUSING TENURE
     Occupied housing units 21,677 100.0
       Owner-occupied 8,953 41.3
       Renter-occupied 12,724 58.7
                                                                                                      
       Average household size of owner-occupied unit 2.5 -8.0
       Average household size of renter-occupied unit 2 -8.0
                                                                                                      
   YEAR HOUSEHOLDER MOVED INTO UNIT
     Occupied housing units 21,677 100.0
       Moved in 2021 or later 4,540 20.9
       Moved in 2018 to 2020 8,242 38.0
       Moved in 2010 to 2017 5,132 23.7
       Moved in 2000 to 2009 1,741 8.0
       Moved in 1990 to 1999 880 4.1
       Moved in 1989 and earlier 1,142 5.3
                                                                                                      
   VEHICLES AVAILABLE
     Occupied housing units 21,677 100.0
       No vehicles available 1,240 5.7
       1 vehicle available 8,150 37.6
       2 vehicles available 8,318 38.4
       3 or more vehicles available 3,969 18.3
                                                                                                      
   HOUSE HEATING FUEL
     Occupied housing units 21,677 100.0
       Utility gas 12,095 55.8
       Bottled, tank, or LP gas 313 1.4
       Electricity 9,088 41.9
       Fuel oil, kerosene, etc. 0 0.0
       Coal or coke 0 0.0
       Wood 43 0.2
       Solar energy 0 0.0
       Other fuel 86 0.4
       No fuel used 52 0.2
                                                                                                      
   SELECTED CHARACTERISTICS
     Occupied housing units 21,677 100.0
       Lacking complete plumbing facilities 0 0.0
       Lacking complete kitchen facilities 50 0.2
       No telephone service available 161 0.7
                                                                                                      
   OCCUPANTS PER ROOM
     Occupied housing units 21,677 100.0
       1.00 or less 21,198 97.8
       1.01 to 1.50 363 1.7
       1.51 or more 116 0.5
                                                                                                      
   VALUE
     Owner-occupied units 8,953 100.0
       Less than $50,000 998 11.1
       $50,000 to $99,999 209 2.3
       $100,000 to $149,999 714 8.0
       $150,000 to $199,999 1,452 16.2
       $200,000 to $299,999 2,594 29.0
       $300,000 to $499,999 2,259 25.2
       $500,000 to $999,999 618 6.9
       $1,000,000 or more 109 1.2
       Median (dollars) 243,700 -8.0
                                                                                                      
   MORTGAGE STATUS
     Owner-occupied units 8,953 100.0
       Housing units with a mortgage 5,520 61.7
       Housing units without a mortgage 3,433 38.3
                                                                                                      
   SELECTED MONTHLY OWNER COSTS (SMOC)
     Housing units with a mortgage 5,520 100.0
       Less than $500 25 0.5
       $500 to $999 377 6.8
       $1,000 to $1,499 1,355 24.5
       $1,500 to $1,999 1,666 30.2
       $2,000 to $2,499 1,068 19.3
       $2,500 to $2,999 342 6.2
       $3,000 or more 687 12.4
       Median (dollars) 1,770 -8.0
                                                                                                      
     Housing units without a mortgage 3,433 100.0
       Less than $250 246 7.2
       $250 to $399 405 11.8
       $400 to $599 847 24.7
       $600 to $799 694 20.2
       $800 to $999 822 23.9
       $1,000 or more 419 12.2
       Median (dollars) 668 -8.0
                                                                                                      
   SELECTED MONTHLY OWNER COSTS AS A PERCENTAGE OF HOUSEHOLD INCOME (SMOCAPI)
     Housing units with a mortgage (excluding units where SMOCAPI cannot be computed) 5,520 100.0
       Less than 20.0 percent 2,958 53.6
       20.0 to 24.9 percent 751 13.6
       25.0 to 29.9 percent 509 9.2
       30.0 to 34.9 percent 458 8.3
       35.0 percent or more 844 15.3
                                                                                                      
       Not computed 0 -8.0
                                                                                                      
     Housing unit without a mortgage (excluding units where SMOCAPI cannot be computed) 3,433 100.0
       Less than 10.0 percent 1,561 45.5
       10.0 to 14.9 percent 753 21.9
       15.0 to 19.9 percent 520 15.1
       20.0 to 24.9 percent 198 5.8
       25.0 to 29.9 percent 120 3.5
       30.0 to 34.9 percent 99 2.9
       35.0 percent or more 182 5.3
                                                                                                      
       Not computed 0 -8.0
                                                                                                      
   GROSS RENT
     Occupied units paying rent 12,566 100.0
       Less than $500 473 3.8
       $500 to $999 5,604 44.6
       $1,000 to $1,499 4,399 35.0
       $1,500 to $1,999 1,195 9.5
       $2,000 to $2,499 694 5.5
       $2,500 to $2,999 54 0.4
       $3,000 or more 147 1.2
       Median (dollars) 1,019 -8.0
                                                                                                      
       No rent paid 158 -8.0
                                                                                                      
   GROSS RENT AS A PERCENTAGE OF HOUSEHOLD INCOME (GRAPI)
     Occupied units paying rent (excluding units where GRAPI cannot be computed) 12,332 100.0
       Less than 15.0 percent 1,309 10.6
       15.0 to 19.9 percent 1,499 12.2
       20.0 to 24.9 percent 1,799 14.6
       25.0 to 29.9 percent 1,178 9.6
       30.0 to 34.9 percent 1,041 8.4
       35.0 percent or more 5,506 44.6
                                                                                                      
       Not computed 392 -8.0
 
 -6 value indicates estimate could not be computed due to insufficient number of sample observations.
 -8 value indicates estimate is not applicable or not available.
 
 

2024 Q1-Q3 Establishments, Employment, Earnings by Type of Business For Riley County, KS .. goto top
  each row shows attributes for Riley County, KS iterated by owner type, year/quarter and type of business
  more about these data .. more about related products
  click ShowAll button between queries.
  click column header to sort; click again to sort other direction.


Interactive Table Usage Notes
Operations
  • Click ShowAll between queries/filters.

Items in Table
    GeoId
    own_code
    industry_code
    agglvl_code
    size_code
    Year
    Qtr
    Disc code
    area_title
    own_title
    Industry
    agglvl_title
    size_title
    Qtr Estabs
    M1 Emp
    M2 Emp
    M3 Emp
    Total Qtrly Wages
    Taxable Qtrly Wages
    Qtrly Contr
    Avg Wkly Wage

Location Quotient
    lq_disclosure_code
    lq_qtrly_estabs_count
    lq_month1_emplvl
    lq_month2_emplvl
    lq_month3_emplvl
    lq_total_qtrly_wages
    lq_taxable_qtrly_wages
    lq_qtrly_contributions
    lq_avg_wkly_wage

Over the Year
    oty_disclosure_code
    oty_qtrly_estabs_count_chg
    oty_qtrly_estabs_count_pct_chg
    oty_month1_emplvl_chg
    oty_month1_emplvl_pct_chg
    oty_month2_emplvl_chg
    oty_month2_emplvl_pct_chg
    oty_month3_emplvl_chg
    oty_month3_emplvl_pct_chg
    oty_total_qtrly_wages_chg
    oty_total_qtrly_wages_pct_chg
    oty_taxable_qtrly_wages_chg
    oty_taxable_qtrly_wages_pct_chg
    oty_qtrly_contributions_chg
    oty_qtrly_contributions_pct_chg
    oty_avg_wkly_wage_chg
    oty_avg_wkly_wage_pct_chg

Using the Housing Study Manhattan Project with VDAGIS .. goto top
.. the Manhattan housing study project interactive iVDA start-up view is shown in the iframe below.
.. Manhattan city is shown with the salmon color fill pattern.
.. see more about iVDA features and operations.
.. the first thing to do when using iVDA is to have an objective.
.. objective: view humber of housing units by block group.

.. show/examine number/characteristics of housing units
.. click the checkbox on the "BG HsgUnits A23" layer in legend panel at left of map.
    - the map window refreshes showing the number of housing units by block group.
    - these data are based on the 2023 American Community Survey 5-year estimates (ACS2023).
.. show/examine attributes of a block group (BG) .. click a BG in map; profile shows in lower left panel.
.. the section being viewed is located at https://proximityone.com/housing_study.htm#ivda


Optionally use this full screen view of the housing study project

About the Housing Study Project .. goto top
The Housing Study Manhattan Project is not a housing plan. It is a set of Federal GeoStatistical Data organized to illustrate sources and uses of these data that can facilitate development of a plan or study that might integrate non-Federal sourced data.

Design
The Housing Study resource does not have an executive summary, projected trends nor conclusions and recommendations. It is a set of integrated resources that might be used to develop that scope of information.

The design of the housing study project involves organizing a set of geostatistical layers focused on small area geography but conveyed on a state or national scale. A few point layers are included (K-12 schools, banks, hospitals). Boundary layers include states, metros, congressional districts, congressional communities, state legislative districts, counties, urban areas, school districts, townships, tracts, block groups and census blocks. The interstate highway layer is included as a line layer; other road features and transporation features could be included. A raster graphics topology layer is included from OpenStreets Maps.

Structure and Content

Layers/Data Resources Used in the Housing Study Project
Most of the project layer geographies listed below are based on Census Bureau TIGER-based geography or shapefiles. Without the TIGER data we would not be able to assemble this integrated subject matter - geography view. The TIGER geospatial data uniquely enables a standardized view of these characteristics for anywhere in the U.S. A similar housing study can be developed/examined for any area by relocating (zoom/pan) the map window and/or setting seacrhes/queries to those locations.

The following list of layers is also shown in the VDAGIS legend panel.
United States
States
Interstate
Hospitals
K-12 Schools
FDIC Banks
Congressional Districts
St Leg Dist House
St Leg Dist Senate
Congressional Communities
CBSA/MSA
Counties
Urban Areas
City/Place Name
City/Place Boundary
Cities/Places
School Districts
Townships
Tract Code
Tracts LMI Label
Tracts LMI MFI Tract/MSA%
Tracts Rental Hsg Cost Burdened .. not active this version
Tracts $MHI
Tracts $MHV
BG HU Blt 2020+ A23
BG HsgUnits A23
Affordable Housing Areas (BGs) .. not active this version
Blocks
Blocks, GrpQtrs
Blocks HsgUnits Chg 20-24
Roads 20161
BaseLayer1
States1

Layers/Federal GeoStatistical Data Resources that Could be Added to the Housing Study Project
County - BEA REIS
County - BLS QCEW
County - Census CBP
County/Place - Census Building Permits
Census Tracts - HUD Qualified Census Tracts
Census Tracts - Housing Price Index

Using DEDE to Extract/Integrate other Data .. goto top

Use the Demographic-Economic Data Explorer (DEDE) to extract other Federal GeoStatistical Data to add to a housing study. ACS 2023 api_items_acs23_base_emp.txt

        DEDE Subject Matter Items File
B01001_001E TotPopA23  Total Population
B01001_002E MPopA23    Male
B01001_026E FPopA23    Female
B02001_002E White1A23  White alone
B02001_003E Black1A23  Black or African American alone
B02001_004E AIAN1A23   American Indian and Alaska Native alone
B02001_005E Asian1A23  Asian alone
B02001_006E NHOPI1A23  Native Hawaiian and Other Pacific Islander alone
B02001_007E Other1A23  Some other race alone
B02001_008E MultiA23   Two or more races
B03002_012E HispA23    Hispanic (any race)
B01001_003E A0004MA23  Male: Under 5 years
B01001_004E A0509MA23  Male: 5 to 9 years
B01001_005E A1014MA23  Male: 10 to 14 years
B01001_006E A1517MA23  Male: 15 to 17 years
B01001_027E A0004FA23  Female: Under 5 years
B01001_028E A0509FA23  Female: 5 to 9 years
B01001_029E A1014FA23  Female: 10 to 14 years
B01001_030E A1517FA23  Female: 15 to 17 years
B09020_001E Pop65upA23 Population 65 years and over
B11002_001E PopHHA23   Population in Households
B11001_001E HHA23      Total Households
B11001_002E FamA23     Family Households
B15002_001E Pop25upA23 Population 25 years and over
B15002_011E EAHSMA23   Male: High school graduate (includes equivalency)
B15002_015E EABMA23    Male: Bachelor's degree
B15002_028E EAHSFA23   Female: High school graduate (includes equivalency)
B15002_032E EABFA23    Female: Bachelor's degree
B29001_001E VATOTA23   Total Citizen, Voting-Age Population
B25003_001E TotHsgA23  Total housing units
B25003_002E OwnOccA23  Owner occupied units 
B25003_003E RntOccA23  Renter occupied units
B25002_003E VacantA23  Vacant units
B25105_001E MDMTHHCA23 Median Monthly Housing Costs (Dollars)
B19013_001E MHIA23     Median Household Income
B19113_001E MFIA23     Median Family Income
B25077_001E MHVA23     Median housing value 
B25064_001E MdRentA23  Median gross rent
B19083_001E GiniA23    Gini Index of Income Inequality
B23025_001E POP16UPA23 Population 16 years and over                                                                        
B23025_002E LFA23      In labor force                                                                                      
B23025_003E CLFA23     Civilian labor force                                                                                
B23025_004E EMPA23     Employed                                                                                            
B23025_005E UNEMPA23   Unemployed                                                                                          
B23025_006E AFA23      Armed Forces                                                                                        
B23025_007E NILFA23    Not in labor force                                                                                  
NAME        NAME       NAME


Adding Other Layers/Data to the Project .. goto top

Use other Visual Data Analytics (VDA) Geographic Information System (GIS) tools with the housing project shown above as used with iVDA. Add other layers/data using:
  • VDA Desktop (VDAD - Windows) offering maximum speed, processing features, maximum security
  • VDA Web (VDAW4) - like iVDA with full screen viewer and ability to add layers

Adding Data (from any source) using VDAW4 -- requires only a web browser
Select the Housing Study .. the application opens

Adding Streets Layer for Riley County, KS

Add your own shapefile/data to an existing project to view/analyze your data with other existing layers/data. Analytical opportunities are unlimited! Use any existing project to upload layers. With project open, use File>Upload and proceed as described here.

Integrating Detailed Streets/Roads .. click to expand/collapse
This section shows how to add 2024 TIGER detailed roads ("edges") for a county.

Riley County, KS is used here as an example. These are detailed road segments covering Riley County, KS based on the 2024 TIGER edges shapefile. Typically these are intersection to intersection, but often comprised of shorter connected segments.

Step 1. Extract the Riley County, KS (20161) edges shapefile to c:\vdauploads
.. https://www2.census.gov/geo/tiger/TIGER2024/EDGES/tl_2024_20161_edges.zip
.. it is suggested that all VDA upload files be kept in one folder, such as C:\vdauploads, and not contain other files.
Note: get the edges shapefile for any county from this set of links.
.. the counties are organized by state+county FIPS code
.. to use another county, replace 20161 in the following the state+county edges file that is downloaded.

Step 2. Delete all unzipped files in c:\vdauploads except these:
tl_2024_20161_edges.shp
tl_2024_20161_edges.shx
tl_2024_20161_edges.dbf

Step 3. Develop the INI file

Copy the 11 lines below, paste into Notepad (any text editor) and save as c:\vdauploads\tl_2024_20161_edges.shp.ini
.. when uploading a file that has large, detailed objects displayed, set Active=NO
.. otherwise set Active=YES
.. if Active=No, the layer will show as unchecked and not display in the Map Window

Active=NO
Caption=20161 Roads
CodePage=932
CS.EPSG=4269
Ground=OnDem
Label.Alignment=FOLLOW
Label.Color=$10101
Label.Field=FULLNAME
Label.OutlineWidth=2
Query=roadFLG='Y'
Visible=YES

.. the spelling/characters used in file names must match exactly.
.. the Riley County roads (edges) shapefile has state county code 20161

Step 4. Zip (for example using Winzip) these four files to the new file c:\vdauploads\tl_2024_20161_edges.zip

Step 5. With VDAW4 running using the Housing Project (VDAW4 using the Housing Study), use File>Userfile Upload Layer from the Main Menu bar to start the upload.

Choose the file "c:\vdauploads\tl_2024_20161_edges.zip" and proceed to upload. Large zip files may require several minutes to complete; wait for a message and do not click upload again.

Close the upload form when done. The following view shows the "20161 Roads". To obtain a view similar, enter "Kansas State University" in searchbar and press enter.



End of section.

About VDAGIS .. goto top

The Visual Data Analytics (VDA) Geographic Information System (GIS) is a suite of tools and data that you can use to examine multi-sourced geographic, demographic and economic data. VDAGIS resources are developed and maintained by Warren Glimpse, ProximityOne (Alexandria, VA) and Takashi Hamilton, Tsukasa Consulting (Osaka, Japan). See more about VDAGIS. Add your own data.

Terms of Use .. goto top
There is no warranty regarding any aspect of any information presented in this section. The user is solely responsible for any use of this section.

Support Using these Resources .. goto top
Learn more about accessing and using demographic-economic data and related analytical tools. Join us in a web meeting session. There is no fee for these one-hour Web sessions. Each informal session is focused on a specific topic. The open structure also provides for Q&A and discussion of application issues of interest to participants.

ProximityOne User Group
Join the ProximityOne User Group to keep up-to-date with new developments relating to geographic-demographic-economic decision-making information resources. Receive updates and access to tools and resources available only to members. Use this form to join the User Group.

Additional Information
ProximityOne develops geographic-demographic-economic data and analytical tools and helps organizations knit together and use diverse data in a decision-making and analytical framework. We develop custom demographic/economic estimates and projections, develop geographic and geocoded address files, and assist with impact and geospatial analyses. Wide-ranging organizations use our tools (software, data, methodologies) to analyze their own data integrated with other data. Contact ProximityOne (888-364-7656) with questions about data covered in this section or to discuss custom estimates, projections or analyses for your areas of interest.

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