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School District Housing Data Analytics -- ACS 2017 Housing Units, Age, Tenure, Occupancy, Rates, Value, Rent, more
January 2019. Based on ACS 2017 estimates, the U.S. median housing value was $193,500 while school districts ranged from $18,700 (Shannon County School District, SD -- among districts having 1,000 or more population) to $2,000,000+ (Palo Alto Unified, CA -- 15 districts were at or above this top-coded value). See item/column H089 in the interactive table below to view, rank, compare, analyze states based on this measure in context of related housing characteristics. These data uniquely provide insights into many of the most important housing characteristics for all school districts in the U.S. See about how to view/rank/compare districts below in this section. Create comparative analysis profiles for districts of interest. Approximately 600 subject matter items from the American Community Survey ACS 2017 database (released December 2018) are included in these sections: • General Demographics • Social Characteristics • Economic Characteristics • Housing Characteristics -- this section Visual Analysis of Patterns of Median Housing Value by School District The following map shows patterns of median housing value ($MHV) by school district in the San Antonio, TX MSA (bold brown boundary) area based on the ACS 2017 5-year estimates. Create a similar map for any area in the U.S. and/or variations of this view using the associated GIS project with the CV XE GIS software. Add your own data.  - larger view showing San Antonio city (cross-hatch pattern) - view developed with CV XE GIS software .. click graphic for larger view. Additional views: (opens in new page) • Wichita, KS MSA area Analytzing the School & School District Community ProximityOne works with schools, school districts and K-12 education stakeholders to develop, integrate and analyze demographic-economic data for planning, evaluation and policy analysis. Use our custom estimates and projections to better understand trends, what might change when and where -- and how it might impact you. Using the Interactive Table Use the interactive table below to view, query, rank, compare housing characteristics of the population, households and families for these areas. The scroll box shown below lists each of the subject matter items available for each area via the ranking table. In the subject matter items scroll box, subject matter items are organized into to mini-tables with related items. The number at the left of the subject matter item is also used as the short name for the subject matter item in the column header in the ranking table. Housing Characteristics Items .. a few item names and numbers changed from previous year data/table.
HOUSING OCCUPANCY
H001 Total housing units H002 Occupied housing units H003 Vacant housing units H004 Homeowner vacancy rate H005 Rental vacancy rate UNITS IN STRUCTURE H006 Total housing units H007 1-unit, detached H008 1-unit, attached H009 2 units H010 3 or 4 units H011 5 to 9 units H012 10 to 19 units H013 20 or more units H014 Mobile home H015 Boat, RV, van, etc. YEAR STRUCTURE BUILT H016 Total housing units H017 Built 2014 or later H018 Built 2010 or 2013 H019 Built 2000 to 2009 H020 Built 1990 to 1999 H021 Built 1980 to 1989 H022 Built 1970 to 1979 H023 Built 1960 to 1969 H024 Built 1950 to 1959 H025 Built 1940 to 1949 H026 Built 1939 or earlier ROOMS H027 Total housing units H028 1 room H029 2 rooms H030 3 rooms H031 4 rooms H032 5 rooms H033 6 rooms H034 7 rooms H035 8 rooms H036 9 rooms or more H037 Median rooms BEDROOMS H038 Total housing units H039 No bedroom H040 1 bedroom H041 2 bedrooms H042 3 bedrooms H043 4 bedrooms H044 5 or more bedrooms HOUSING TENURE H045 Occupied housing units H046 Owner-occupied H047 Renter-occupied H048 Average household size of owner-occupied unit H049 Average household size of renter-occupied unit YEAR HOUSEHOLDER MOVED INTO UNIT H050 Occupied housing units H051 Moved in 2015 or later H052 Moved in 2010 to 2014 H053 Moved in 2000 to 2009 H054 Moved in 1990 to 1999 H055 Moved in 1980 to 1989 H056 Moved in 1979 or earlier VEHICLES AVAILABLE H057 Occupied housing units H058 No vehicles available H059 1 vehicle available H060 2 vehicles available H061 3 or more vehicles available HOUSE HEATING FUEL H062 Occupied housing units H063 Utility gas H064 Bottled, tank, or LP gas H065 Electricity H066 Fuel oil, kerosene, etc. H067 Coal or coke H068 Wood H069 Solar energy H070 Other fuel H071 No fuel used SELECTED CHARACTERISTICS H072 Occupied housing units H073 Lacking complete plumbing facilities H074 Lacking complete kitchen facilities H075 No telephone service available OCCUPANTS PER ROOM H076 Occupied housing units H077 1.00 or less H078 1.01 to 1.50 H079 1.51 or more VALUE H080 Owner-occupied units H081 Less than $50,000 H082 $50,000 to $99,999 H083 $100,000 to $149,999 H084 $150,000 to $199,999 H085 $200,000 to $299,999 H086 $300,000 to $499,999 H087 $500,000 to $999,999 H088 $1,000,000 or more H089 Median (dollars) MORTGAGE STATUS H090 Owner-occupied units H091 Housing units with a mortgage H092 Housing units without a mortgage SELECTED MONTHLY OWNER COSTS (SMOC) H093 Housing units with a mortgage H094 Less than $500 H095 $500 to $999 H096 $1,000 to $1,499 H097 $1,500 to $1,999 H098 $2,000 to $2,499 H099 $2,500 to $2,999 H100 $3,000 or more H101 Median (dollars) H102 Housing units without a mortgage H103 Less than $250 H104 $250 to $399 H105 $400 to $599 H106 $600 to $799 H107 $800 to $999 H108 $1,000 or more H109 Median (dollars) SELECTED MONTHLY OWNER COSTS AS A PERCENTAGE OF HOUSEHOLD INCOME (SMOCAPI) H110 Housing units with a mortgage (excluding units where SMOCAPI cannot be computed) H111 Less than 20.0 percent H112 20.0 to 24.9 percent H113 25.0 to 29.9 percent H114 30.0 to 34.9 percent H115 35.0 percent or more H116 Not computed H117 Housing unit without a mortgage (excluding units where SMOCAPI cannot be computed) H118 Less than 10.0 percent H119 10.0 to 14.9 percent H120 15.0 to 19.9 percent H121 20.0 to 24.9 percent H122 25.0 to 29.9 percent H123 30.0 to 34.9 percent H124 35.0 percent or more H125 Not computed GROSS RENT H126 Occupied units paying rent H127 Less than $500 H128 $500 to $999 H129 $1,000 to $1,499 H130 $1,500 to $1,999 H131 $2,000 to $2,499 H132 $2,500 to $2,999 H133 $3,000 or more H134 Median (dollars) H135 No rent paid GROSS RENT AS A PERCENTAGE OF HOUSEHOLD INCOME (GRAPI) H136 Occupied units paying rent (excluding units where GRAPI cannot be computed) H137 Less than 15.0 percent H138 15.0 to 19.9 percent H139 20.0 to 24.9 percent H140 25.0 to 29.9 percent H141 30.0 to 34.9 percent H142 35.0 percent or more H143 Not computed School District ACS2017 Housing Characteristics .. interactive table .. goto top Districts initially sorted on state by name; click any column header cell to sort on that column/attribute. Example: click H002 header cell twice to sort on "Total Occupied Housing Units (Households)" in descending order. Click ShowAll button between Find/Queries. Use mouseover on column header to view column description. See usage notes below table. See related interactive tables. Usage Notes • If table not showing any rows; click ShowAll button below table. • Use vertical and horizontal scroll bars to navigate up/down or left/right. • Adjust column widths using column divider between header cells. • Find CBSA> button -- use to select CBSA/Metro based on exact CBSA 5 character code in column 3. .. CBSA/metro codes may be found in the table at http://proximityone.com/metros.htm#table. .. after entering the code click the Find CBSA> button ... table refreshes with all districts in this CBSA. .. example .. clicking 1) the ShowAll button, then 2) the Find CBSA button with default 41700 value. shows the 39 districts of this size in the San Antonio metro that can now be further examined as this specific set of districts. • Find Name> button -- use to select district based on exact spelling of partial name in column 1. .. Key in exact up/low case spelling of partial name of district in the edit box to right of Find Name> button. .. click the Find Name> button ... table refreshes with all districts with matched spelling in column 1. • Selected columns -- click the "Units.Occ.Vacant.%MdValue.$MdRent" button to show only these columns; .. click "All columns" button to restore view to all columns. • All items are estimates for the 5 year period 2013-2017 (centric to 2015). • Click ShowAll button between specific area queries. • Cells with negative value could not be estimated (for this geography and this time frame). Use school district code/area name based on address. Create a School District Profile .. goto top The interactive table above shows attributes for each school district as a row. To transpose the items into a downloadable profile, follow these steps. • Open DP4 XLS -- Housing Characteristics -- requires Excel • Select a district from interactive table above. .. click a row; it turns blue. .. right click and click copy. • Paste (CTRL-v) in column D1 in spreadhseet. • Right click D1 and Copy (all cells pasted in above step) • Right click D4 and Paste Special The D column is now populated with with items in the districts selected from interactive table. • Optionally save to local XLS file on your computer. • Optionally select another district and repeat paste steps into column E1. Completed example: • Housing Characteristics - San Antonio ISD, TX Optionally create four types of profiles for any district. • General Demographics • Social Charactersitics • Economic Characteristics • Housing Characteristics About these Demographic-Economic Data Data used in this section are estimates developed from the American Community Survey (ACS2017). These data differ from administratively reported data (enrollment, etc.). Data are for the total resident population in the district. Data are not shown separately for charter schools as charter schools have no defined boundaries. If a charter school located in district X has students from district Y, attributes of those students and households are shown in data for district Y. The importance of understanding the demographic-economic make-up and trends can hardly be overstated. Community and educational challenges and opportunities are shaped by demographic-economic dynamics. Only by knowing "where we are" can we develop the most effective plans for improvement. The American Community Survey provides "richer" demographic-economic characteristics for national scope wide-ranging geographic areas. While Census 2010 provides data similar to those items in the General Demographics section, only ACS sourced data provide details on topics such as income and poverty, labor force and employment, housing value and costs, educational participation and attainment, language spoken at home, among many related items. The approximate 600 items accessible via the dataset are supplemented by a wide range of additional subject matter. These data are updated annually in December of each year for each/all political/statistical areas, including census tracts, ZIP code areas and block groups.. FAQS & Related Notes .. goto top How to view/rank/compare largest districts based on total population • Use the General Demographics section/table. • Click the D001 (total population) table header cell. .. the table refreshes sorted on D001 in one direction; click again to sort in other direction. How to view/rank/compare enrollment • Use the Social Characteristics section/table. • Click the button School Enrollment below the table. .. the table refreshes with the school enrollment columns. ProximityOne User Group Join the ProximityOne User Group to keep up-to-date with new developments relating to metros and component geography decision-making information resources. Receive updates and access to tools and resources available only to members. Use this form to join the User Group. Support Using these Resources Learn more about accessing and using demographic-economic data and related analytical tools. Join us in a Data Analytics Lab 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. Additional Information ProximityOne develops geodemographic-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. Follow ProximityOne on Twitter at www.twitter.com/proximityone. Contact us (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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