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.
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:
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:
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:
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:
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:
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.