Frequently Asked Questions
I am an entitlement city under the CDBG program, should I use the
CDBG, Place, or MCD button to get my data?
You
should use the Place or MCD button (the data should be the same). The CDBG and
HOME buttons are intended for use to allow CDBG Urban Counties, HOME
Consortiums, and States look at the data for those unique geographies that
don’t match up with a standard Census definition. Data provided under the CDBG
and HOME buttons are created by aggregating the data from the Census Tract
level. As a result, these data are less accurate than the Place, County, MCD,
and State buttons because the “noise” in the data caused by rounding at the
Tract level is added up. The Place, County, MCD, and State data are much less
impacted by the Census rounding requirements.
I want to map these data at the Census Tract level, how can I do
that?
The
“base files” used to create these tables can be downloaded from the following
site.
http://www.huduser.gov/datasets/cp.html
Most
of these data are available at the census tract (part) level, summary level
080, so you can map within your jurisdiction boundaries. To download the shape
files that link to the CHAS summary level 080 data, go to this web site:
http://www.huduser.gov/geo/summarylevel.asp
How do the CHAS data relate to the CDBG low-mod data?
The
CDBG low-mod data estimate the number of low-mod persons using a complicated
algorithm based on Census regular tabulation data. As such, the CDBG low-mod
data and the CHAS data may differ to some degree. You can download the CDBG
low-mod data from this site:
http://www.hud.gov/offices/cpd/systems/census/lowmod/index.cfm
I want to download the data into Excel, how do I do that?
Use
the non-frames version and click the first button underneath the chart:
http://socds.huduser.gov/scripts/odbic.exe/CHAS/statetable.htm
I want to download the data into the CPMP tool, how do I do that?
Use
the non-frames version of the SOCDS CHAS data:
http://socds.huduser.gov/scripts/odbic.exe/CHAS/statetable.htm
The
second button below the chart downloads the data into an Excel format that
matches the CPMP tool. Simply copy the data out of the downloaded Excel file
into the CPMP tool.
The
CPMP tool is available from the following web site, which also has detailed instructions
on how to copy the CHAS data into the CPMP tool:
http://www.hud.gov/offices/cpd/about/conplan/index.cfm
How can I use these data to estimate my worst case housing needs?
These
data cannot exactly match worst case housing needs because Census data do not
capture housing inadequacy. However, a reasonable proxy for worst cases housing
needs are the percent of very low-income renter households (less than 50% of
median income) paying more than 50% of their income for housing (severe cost
burden).
A
note of caution for communities with large numbers of college students living
in off-campus housing. College students living off-campus that are supported by
their parents appear to the Census as being very poor. As a result, their
presence tends to inflate the number of households with severe cost burden. We
recommend adding the data on elderly households and family households
(excluding “all other” households) to get a better estimate of housing needs
for non-college students.
CPMP tool version 1.2 indicates “persons” in need while the data
here indicates “households”, which is correct?
“Households”
is correct. CPMP version 1.3 and later has been corrected to indicated
households.
How can I break overcrowding separate from other problems?
In
our Special Tabulation Data,
tables A3A (owner) and A3B (renter) have overcrowding and severe overcrowding by
the HUD income breaks. The lone caveat for these data is that they do not
include overcrowded households that are also without complete kitchen or
plumbing facilities. About 183,550 of the 6,057,890 overcrowded households in
the U.S. are also without complete kitchen and plumbing.
Alternatively,
you can use Table HCT-22 in the U.S. Census Bureau’s American FactFinder. Table HCT-22
hasTenure by Poverty Status by Plumbing Facilities by Occupants Per Room. (Data
Sets -> SF3 -> List All Tables -> Select Table -> Next -> Select
Geographic Type -> Select State -> Select Geographic Area -> Select
Add -> Show Result).
If
you don't need HUD's income breaks, use HCT-22. If you do need HUD's income
breaks, use the A3A and A3B data with the understanding that there is a very
small undercount.
How can I use the affordability mismatch data?
The
rental and owner numbers need to be interpreted differently.
For
rental, it is helpful to get a sense of demand for units at different rents and
unit size. For example, what is the vacancy rate for 0-1 bedroom units
affordable at less than 30%, units affordable at 30-50%, etc..? Low vacancy rates (less than 6%) suggest a
pretty high demand for units in certain affordability categories. This probably
suggests you need to add more affordable housing units to your inventory. This
is especially true if you are continuing to experience an increase in the
number of households in your target community. High vacancy rates (greater than
10%), especially among affordable units, suggest an oversupply of housing. In
this case you should be very careful not to add to supply that would aggravate
the problem and you should be looking to remove or upgrade existing substandard
housing stock. This is especially true if you are experiencing a decline in the
number of households.
Looking
at the need data independent of this market data may give the wrong impression
about your housing needs. It is quite possible, even likely, that you could
have a soft rental market but a high number of low-income households with cost
burden. This can be due either to (a) the extreme low-incomes of households
(tenant based assistance rather than adding supply is a better solution) or (b)
low-income households concentrated within a tight housing submarket when other
markets in the target area are more affordable (mobility counseling might be
considered).
For
owners, these data are a bit more difficult to interpret. The data used for the
table are calculated based on what households valued their home at in 2000 and
how much it would cost to purchase that house at the interest rates prevailing
in 2000. Why these data should be used with caution is because (1) these are
estimated values by owners, not appraisers or based on recent sales information;
(2) in many markets, values have appreciated substantially since 2000; and (3)
if interest rates go up, owner affordability will certainly go down. In any
case, these data give you a sense of how affordable your owner stock is in
general. If most of your stock falls in the value affordable to those making
less than 50 percent of median, you probably have a very affordable owner stock
and homeownership programs for low-income folks are likely to be quite
successful. In soft sales housing markets, however, it is likely that
substandard housing would be a concern and homeownership programs would likely
require subsidy for rehabilitation. The biggest obstacle is unlikely to be
household income, but rather household credit.
If, however, most of your stock falls within a value affordable to those
over 80 percent of median, homeownership programs are likely to be much less
successful. Homeownership programs in these markets will likely require
substantial subsidy to make the units affordable to lower-income homebuyers.