City of Alexandria — Time Series Data
Tufte (2001) and other ambassadors of the visual display of data have shown us how easily it is to understand complex data when it is graphically represented in ways that our minds are designed to understand. Tufte argues that “experience with the analysis of data…is essential for achieving precision and grace in the presence of statistics, .but even textbook of graphical design are silent about how to think about numbers” (Tufte, 2001, p. 104). Tufke remarks, that “Illustrators too often see their work as an exclusively artistic enterprise — the words “creative,” “concept,” and “style” combine regularly in all possible permutations — a Big Think jargon for the small task of constructing a time-series a few data points long” (Tufte, 2001, p. 204). Visual display of data has other uses than simply an elegant way to view, appreciate, and analyze data. The process of completing a graphic display of data forces the issue of data integrity and completion of data sets. When data is missing in a graphic display, it is glaringly apparent. And the process of figuring out how to arrange data for best display generates an awareness of the assumptions that undergird the data collection — and ultimately, the data analysis. When creating a visual display of data, the analyst has cause to “muse on the ineffable origins of…insights” (Gladwell, 2007, p. 40) . The analyst admits, if only privately, that “There are ten different things it can mean… — all of those are possibilities. You can’t just look at one behavior [or data point] in isolation” (Gladwell, 2007, p. 43). When the data just doesn’t come together, we might do well to recall Averch’s caveat, that “If we believe that the information to be gained by evaluation should be proportional to the decision makers’ needs, time, budget, and attention, then conventional quantitative evaluations may be infeasible or inappropriate” (n.d., p. 292).
Indicator # 20 Median gross rent
Indicator Response #4 – Representing Variance in Time (2000 — 2010)
By 2010, 18.8 million renters had at moderate housing cost burdens. The numbers climbed in 2008-2009 by 1.2 million, which is nearly twice the rise in 2007 — 8.
More than 69% of the increase was among households paying more than half their incomes for housing. The number of severely burdened renters in 2009 was the largest annual increase over decades.
Source: America’s Rental Housing: Meeting Challenges, Building on Opportunities
The Joint Center for Housing Studies at Harvard University.
Note: This report contains a plethora of visual data, however, none can be copied from the PDF and would require replication and permission to replicate http://www.jchs.harvard.edu/publications/rental/rh11_americas_rental_housing/AHR2011-5-Affordability.pdf
Visual Representation of Indicator #4
Indicator Reponse #5 – Representing Variance in Time (Cross-Sectional Data)
For minimum-wage workers, using the 1/3 of earned income standard, an affordable monthly rent is just $377. Many households that rent units have incomes well below that level, including the working poor and those living on fixed incomes. The full-time minimum-wage equivalent is $14,500. About 1/4 of all renters and about 1/2 of all renters who receive assistance have household incomes below that figure. In 2009, most assisted units rented for less than $600. Thirty-five percent rented for less than $400 and about 20% had rents in the range somewhere between those two figures. In rental units where tenets did not get assistance, in comparison, only 31% rented for less than $600 and just 8% rented for less than $400.
Source: America’s Rental Housing: Meeting Challenges, Building on Opportunities
The Joint Center for Housing Studies at Harvard University.
Note: This report contains a plethora of visual data, however, none can be copied from the PDF and would require replication and permission to replicate.
http://www.jchs.harvard.edu/publications/rental/rh11_americas_rental_housing/AHR2011-5-Affordability.pdf
Visual Representation of Indicator #5
Income Standard
Equivalent Annual Income
Affordable Monthly Rent
Supportable Per/Unit Development Cost
Income to Afford Unit
$51,800
$1,300
$110,000
Median Renter Income
$30,500
$760
$64,800
Full-Time Minimum Wage Equivalent
$14,500
$360
$30,800
Source: America’s Rental Housing: Meeting Challenges, Building on Opportunities
JCHS calculations using data from U.S. Census Bureau, 2009 and 2010 Surveys of Market Absorption and 2009 American Community Survey.
Note: This report contains a plethora of visual data, however, none can be copied from the PDF and would require replication and permission to replicate.
Indicator #22 Number of foreclosures
Indicator Response #4 – Representing Variance in Time (2000 — 2010)
The 2009 foreclosure data from RealtyTrac indicates an increase year over year of 47% of national foreclosure rates. Foreclosure rates are reported to have leveled off in Q2 and Q3 2009. But economists anticipate a surge in the coming months as the breakdowns in the foreclosure processes are addressed. Also, adjustable-rate mortgages are due to reset, a substantive proportion of which are loans that do not require borrowers’ income verification.
Visual Representation of Indicator #4
Figure 2. Ranking foreclosures Across the States
State
Total foreclosures
1 foreclosure per # of households
% change from Feb 2007
% change from March 2006
Nevada
4,738
29.42
Colorado
6,267
18.02
16.23
California
31,434
35.49
Georgia
6,769
-7.2
-11.59
Arizona
4,476
43.65
Michigan
8,610
-7.4
11.43
Florida
14,303
-25.29
54.08
Ohio
8,222
9.95
74.23
Indiana
4,332
26
-12.18
Illinois
7,819
18.08
70.27
Texas
12,755
2.98
6.73
New Jersey
4,780
-4.53
28.91
Massachusetts
3,497
39.99
Tennessee
3,178
-17.15
-6.53
Connecticut
1,517
2.99
83.21
Missouri
2,494
5.77
60.59
North Carolina
3,241
1,086
-3.71
56.34
Utah
1,208
-25
-55.74
Oklahoma
1,138
1,313
-18.25
-16.69
Washington
1,736
1,378
7.16
-5.86
Rhode Island
1,392
15700.00*
Idaho
1,481
21.16
66.67
Pennsylvania
2,921
1,797
47.82
-20.69
Oregon
1,949
3.03
28.13
Nebraska
2,013
21.43
New York
3,711
2,069
-18.48
-7.11
Alaska
98
2,094
20.99
-3.92
Iowa
2,125
-2.36
Maryland
2,220
Arkansas
2,323
-61.86
-60.14
New Mexico
2,394
-32.08
27.84
Delaware
2,399
Alabama
2,436
79.51
Kentucky
2,501
11.46
Kansas
2,523
5.67
Minnesota
2,559
-13.02
44.54
Virginia
1,054
2,738
46.8
Wisconsin
2,785
-15.09
67.75
Louisiana
3,252
-24.51
Montana
3,522
17
-16.43
New Hampshire
4,757
South Carolina
5,155
-1.16
-55.67
Hawaii
65
7,085
10.17
44.44
West Virginia
95
8,891
26.67
Maine
65
10,029
South Dakota
32
10,100
45.45
33.33
North Dakota
24
12,070
33.33
14.29
Mississippi
94
13,498
-34.72
28.77
Wyoming
10
22,385
-66.67
-67.74
District of Columbia
5
54,969
-61.54
Vermont
1
294,382
0
-80
U.S.
149,150
6.6
46.81
*Actual increase may not be as high due to expanded data coverage.
Indicator Response #5 — Representing Variance (Cross-Sectional Data)
The ongoing foreclosure crisis is expected to encourage growth in the number of renter households as former owners switch to renting. Single-family home foreclosures will also add a steady flow of units to the rental market. The ability of renter households to occupy these homes will be an important factor in maintaining the stability of distressed neighborhoods hard hit by the foreclosure crisis.
Visual Representation of Indicator #5
Figure 3. Trends All Foreclosure: Boston, MA; Alexandria, VA; San Francisco, CA
Source: RealtyTrac
http://www.realtytrac.com//propertydetails/popuptrendtool.aspx?TrendToolType=GEFP&ComparisonID=&DefaultZipCode=CA&referrer=TrendCenter
Indicator Reponse #5 – Representing Variance in Time (Cross-Sectional Data)
Figure 4. Geographical Comparison Alexandria City, State of Virginia, National
Source: RealtyTrac
http://www.realtytrac.com//propertydetails/popuptrendtool.aspx?TrendToolType=GEFP&ComparisonID=&DefaultZipCode=CA&referrer=TrendCenter
Indicator Reponse #5 – Representing Variance in Time (Cross-Sectional Data)
Indicator #39 Number of new, affordable rental units in conjunction with new developments
Indicator Response #4 – Representing Variance in Time (2000 — 2010)
The State of Virginia uses the HUD data, statistics or method for determining State programs. Parameters are based on Fair Market Rents, Public Housing units, rental assistance vouchers, or low income vouchers.
Housing Affordability Data System. The Housing Affordability Data System (HADS) is a set of files derived from the 1985 and later national American Housing Survey (AHS) and the 2002 and later Metro AHS. This system categorizes housing units by affordability and households by income, with respect to the Adjusted Median Income, Fair Market Rent (FMR), and poverty income. It also includes housing cost burden for owner and renter households. These files have been the basis for the worst case needs tables since 2001. The data files are available for public use, since they were derived from AHS public use files and the published income limits and FMRs. We are providing these files give the community of housing analysts the opportunity to use a consistent set of affordability measures.
HUD 2011 Fair Market Rent Documentation System. This system provides complete documentation of the development of the Final FY 2011 Fair Market Rents (FMRs) for any area of the country selected by the user. After selecting the desired geography, the user is provided a page containing a summary of how the FY 2011 FMRs were developed and updated starting with the formation of the FY 2011 FMR Areas from the metropolitan Core-Based Statistical Areas (CBSAs) as established by the Office of Management and Budget, the 2000 Census benchmark, the newly available 2008 American Community Survey (ACS) 1 year data and the newly available 2006-2008 3-year data, incorporating information from Final FY 2010 FMRs, and updating to FY 2011 including information from local Random Digit Dialing (RDD) survey data. The tables on the summary page include links to complete detail on how the data were developed.
13
Visual Representation of Indicator #4
FY 2011 Income Limits Summary
Income Limits
FY 2011 Income Limit Area
Median Income
FY 2011 Income Limit Category
1 Person
2 Person
3 Person
4 Person
5 Person
6 Person
Alexandria city
$106,100
Very Low (50%) Income Limits
$37,150
$42,450
$47,750
$53,050
$57,300
$61,550
Extremely Low (30%)
$22,300
$25,500
$28,700
$31,850
$34,400
$36,950
$39,500
$42,050
Low (80%)
$47,350
$54,100
$60,850
$67,600
$73,050
$78,450
$83,850
$89,250
FY 2012 Fair Market Rents Summary – Alexandria city, Virginia
Efficiency
1 Bedroom
2 Bedrooms
3 Bedrooms
4 Bedrooms
Proposed FY2012 FMRs
$1,171
$1,334
$1,513
$1,952
$2,554
Alexandria City, Virginia
Year
% Change
Efficiency
2-BR
FMR
Percentile
FMR
1 Bedroom
2 Bedrooms
3 Bedrooms
4 Bedrooms
http://www.huduser.org/datasets/fmr/fmrs/fy2011_code/acstypesumm.odn?fmrtype=Final&data=2011
http://www.huduser.org/portal/datasets/fmr/fmr_il_history/select_Geography.odn
Indicator Reponse #5 – Representing Variance in Time (Cross-Sectional Data)
Visual Representation of Indicator #5
Indicator # 40 Price to Income Ratio / Percentage of Income Paid Towards Housing
Indicator Response #4 – Representing Variance in Time (2000 — 2010)
There is an acute shortage of affordable, adequate rental units for low income households. The widening supply gap is due to the ongoing reduction in the availability of low-cost units and the difficulty of producing market rate housing at affordable rents. Affordability problems are moving up the income scale, more and more renters who have middle-range incomes must compete for a steadily shrinking inventory of affordable units.
Source: America’s Rental Housing: Meeting Challenges, Building on Opportunities
The Joint Center for Housing Studies at Harvard University.
http://www.jchs.harvard.edu/publications/rental/rh11_americas_rental_housing/AHR2011-5-Affordability.pdf
JCHS calculations using U.S. Census Bureau, Survey of Market Absorption and New Residential Construction; and U.S. Department of Housing and Urban Development, Low Income
Housing Tax Credit database
Visual Representation of Indicator #4
Indicator Reponse #5 – Representing Variance in Time (Cross-Sectional Data)
Visual Representation of Indicator #5
References
Averch, H.A. (XXX). Chapter 10 Using expert judgment. [In XXXX].
Gladwell, M. (2007, November 12). Dangerous minds: Criminal profiling made easy. The New Yorker.
Miller, J.E. (2005). The Chicago guide to writing about multivariate analysis. Chicago, Il.: University of Chicago Press.
Meier,, K.J., Brudney, J.L., and Bohte, J. (2005). Applied statistics for public and nonprofit Administration, 6th ed. Belmont, CA: Thompson Wadsworth.
Tufte, E.R. (2001). The visual display of quantitative information. Cheshire, CT: Graphic Press.
America’s Rental Housing: Meeting Challenges, Building on Opportunities
The Joint Center for Housing Studies at Harvard University.
http://www.jchs.harvard.edu/publications/rental/rh11_americas_rental_housing/AHR2011-5-Affordability.pdf
Source: RealtyTrac
http://www.realtytrac.com//propertydetails/popuptrendtool.aspx?TrendToolType=GEFP&ComparisonID=&DefaultZipCode=CA&referrer=TrendCenter
Indicator Reponse #5 – Representing Variance in Time (Cross-Sectional Data)
http://www.huduser.org/portal/datasets/fmr/fmr_il_history/data_summary.odb http://www.huduser.org/datasets/fmr/fmrs/fy2011_code/acstypesumm.odn?fmrtype=Final&data=2011
http://www.huduser.org/portal/datasets/fmr/fmr_il_history/select_Geography.odn
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