Presentation give by M William Sermons, Director of the Homelessness Research Institute on March 28, 2011 during the 2011 National Low Income Housing Coalition Conference.
15. Many states have been multiply-impacted. California, Florida, and Nevada have rates of unemployment, foreclosure, cost burden, lack of insurance and doubling up above the corresponding national rates. Arizona and Georgia are also impacted by multiple economic and demographic indicators.
Thanks to the National Low Income Housing Coalition and specifically Danilo for inviting me to speak. Thanks also to Steve and Mary; I look forward to presenting with you. I’m Bill Sermons, the Director of the Homelessness Research Institute at the National Alliance to End Homelessness.
My presentation covers these four primary topics:I’ll start by presenting what is a pretty enduring portrait of homelessness in America.I’ll then draw upon our State of Homelessness in America report and look at changes in homelessness from 2008 to 2009.Also from SOH, I’ll look at four economic and demographic indicators that were included in the report.
The graph shows the point in time homeless counts from 2005 to 2009 for the overall homeless population and for the subpopulation of families and chronically homeless individuals.I title this slide an enduring portrait of homelessness because despite a general trend downward in the counts, most consistently for chronically homeless individuals, the size of the overall homeless population and the relative sizes of the subpopulations have remained pretty consistent.
This is the current national PIT count, as documented in the SOH released in January of this year and based on 2009 PIT counts. Taking population into account, this corresponds to 21 people experiencing homelessness per 10,000 people in the general population or 0.2 percent. If we were to use HUDs estimate of 2 million over the course of a year (1.6M of which use the shelter system), as articulated in the Opening Doors plan and in other publications, we’d estimate that 65 of every 10,000 people experiences homeless over the course of a year.
Fortunately, most people experiencing homelessness are in shelters or transitional housing programs, but almost 40 percent are unsheltered (on the streets or in other places where people aren’t intended to live or sleep.
At the Alliance, we generally track homelessness and interventions by subpopulation, the largest of which is individuals who don’t meet a definition of chronic homelessness (who make up just under half of the homeless population), followed by persons in families, followed by chronically homeless individuals.
As I mentioned, chronically homeless individuals are the smallest of the groups that the PIT data allows us to reliably track and chronically homeless individuals make up under 20% of the overall homeless population and are still a minority of the individuals (people not in families) that experience homelessness at any given point in time. I clarify “that we can reliable track” because the Alliance is very interested in other subpopulations that can’t be reliable tracked thus far using the PIT data. One is Veterans, that are currently estimated to make up about 12% of the homeless population and Youth for whom reliable estimates of incidence or prevalence are quite elusive.
Everything that I was reporting on the slides to this point are based on the 2009 PIT data. The SOH report tracks changes in homeless from 2008 to 2009. Those familiar with the data know that some caveats are in order in interpreting changes. Though increasing in consistency in recent years, methodological or circumstantial changes from year to year can affect counts. Also, this is the first time that we’ve looked at changes over a single year because we’ve determined that a sufficient fraction are now counting every year, not just in the odd years. Lastly, we’ve made some edits to either 2008 or 2009 counts to reflect things like special circumstances like N’awlins.That said, what we found was increases in all subpopulations, including chronically homeless people, from 2008 to 2009 and an overall increase of 3 percent.
When you get down to the state level (and I think that the handout is of this version of the map so I’m not as embarrassed by the resolution here), you see that the changes range from 31 percent decrease in one state to doubling in another. Community level variation is even greater.
The assistance system is also changing. From 2008 to 2009, we’re continuing to see that PSH beds are increasing (I think this increase is just over 10 percent) at emergency shelter and transitional housing beds are at a constant level.
Perhaps the biggest difference in State of Homelessness as compared to our other reports on the size of the homeless population (e.g. the Counts reports) is the inclusion of these four economic indicators. These are based on the American Community Survey, BLS and RealtyTrak.They show that the economic circumstances of vulnerable populations worsen dramatically from 2008 to 2009 with the largest change in the number unemployed people and the number of poor people 50% or more of their income on rent.
Of course, housing affordability varies dramatically by region, state, community and neighborhood. Here, we show the states where poor people are most frequently paying 50 percent or more of their income on rent. At the state level this is most frequent in Florida, Nevada, California, Delaware, and Connecticut. The five states where this is least likely are on the right.
Another new element for this report was the inclusion of the size and changes in the size of four demographic groups thought to be at risk of homelessness. This includes doubled-up people, which appear to have increased by over 10 percent from 2008 to 2009. We also document increase increases in the number of people discharged from prison, who often end home entering or retuning to homleessness and to uninsured people, hardly a surprise given the economic circumstances..
When combined with data from HUD’s most recent AHAR report, specifically the data on living arrangement prior to entry into shelter, we calculated odds of leaving one situation to enter shelter. We see that two of the populations for which I noted increases in size - doubled up individuals and individuals discharged from prison - have what I see as pretty large odds 1 in 10 of going directly into shelter. A group that decreased in size from 2008 to 2009, young adults aging out of foster care, have the highest of the calculated odds.
Because we were looking at four difference homelessness subpopulations, four different economic indicators and four difference demographic indicators, we used this as an opportunity to looks at associates between the variables with limited results.What we did observe is that there were a handful of states that appeared to have been multiply impacted by the economic circumstances and that those communities also had either high rates of homelessness or increasing homeless populations.