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Home My WebLink AboutSpeaker Forum Gregg ColburnHomelessness is a Housing Problem Jefferson County Intergovernmental Collaborative Port Townsend, WA Gregg Colburn | June 26, 2023 University of Washington (The book) 2 According to the 2019 Point-in-Time homelessness census in Seattle/King County, survey results suggest the following events or conditions lead to homelessness: 3 Causes of Homelessness 0%5%10%15%20%25%30% Job loss Alcohol or drug use Eviction Divorce/separation Inability to pay rent Argument with family/friend Are these conventional explanations of homelessness root causes or precipitating events? 4 Causes of Homelessness Ten friends decide to play a game of musical chairs and arrange ten chairs in a circle. A leader begins the game by turning on the music, and everyone begins to walk in a circle inside the chairs. The leader removes one chair, stops the music, and the ten friends scramble to find a spot to sit—leaving one person without a chair. The loser, Mike, was on crutches after spraining his ankle. Given his condition, he was unable to move quickly enough to find a chair during the scramble that ensued. Causes of Homelessness What caused Mike’s chairlessness? •Research demonstrates that drug use, mental illness, and poverty increase the risk of homelessness at the individual level. •But why do these conditions produce homelessness in some geographic contexts (Boston) and not others? 6 Causes of Homelessness •Why do rates of homelessness vary so widely throughout the United States? Why, for example, does Seattle have between four and five times the per capita homelessness of Chicago? •Does Los Angeles have a large homelessness problem because it has more people with these individual vulnerabilities? 7 Introduction •This is a book about cities, not about people. •Understanding who becomes homeless is an important question, but it doesn’t help us understand regional variation (i.e. large racial disparities). •Our thesis: Tight housing markets accentuate vulnerabilities. •Individual vulnerabilities serve as a sorting mechanism in tight housing markets. Introduction Rates of Homelessness 10 0 1 2 3 4 5 6 7 8 9 10 Homelessness per 1,000 people Cities New York, New York Washington, District of Columbia San Francisco, California Boston, Massachusetts Atlanta, Georgia Baltimore, Maryland Philadelphia, Pennsylvania St. Louis, Missouri Detroit, Michigan Chicago, Illinois Indianapolis, Indiana Counties Los Angeles County, California Santa Clara County, California King County, Washington Multnomah County, Oregon Sacramento County, California Hennepin County, Minnesota Clark County, Nevada San Diego County, California Travis County, Texas Dallas County, Texas Mecklenburg County, North Carolina Maricopa County, Arizona Franklin County, Ohio Bexar County, Texas Hamilton County, Ohio Cuyahoga County, Ohio Miami-Dade County, Florida Cook County, Illinois Hillsborough County, Florida Per capita rates of homelessness in select U.S. regions, 2019 Dashed lines indicate city and county averages of per capita PIT counts Potential explanations: The individual 12 Potential explanations: The individual Poverty rate Cities Counties 5%10%15%20%25%30%35%40%5%10%15%20%25%30%35%40%Rate of homelessness per 1,000 people0 2 4 6 8 10 12 Percent with income below poverty level versus PIT count (per capita) Dashed lines indicate a linear regression of per capita PIT counts onto poverty rate between 2007 and 2019 for a sample of U.S. regions. Bands indicate 95% confidence intervals for the slope of the regression line. R² = 0.14 R² = 0.17 13 Potential explanations: The individual Rate of serious mental illness 3%4%5%6%Rate of homelessness per 1,000 people0 1 2 3 4 5 Rate of serious mental illness versus PIT count (per capita) Dashed lines indicate a linear regression of per capita PIT counts onto rates of serious mental illness in U.S. states between 2007 and 2019. Bands indicate 95% confidence intervals for the slope of the regression line. R² = 0.05 14 Potential explanations: The individual Rate of substance use disorder 6%7%8%9%10%11%12%Rate of homelessness per 1,000 people0 1 2 3 4 5 Rate of substance use disorder versus PIT count (per capita) Dashed lines indicate a linear regression of per capita PIT counts onto rates of substance use disorder in U.S. states between 2007 and 2019. Bands indicate 95% confidence intervals for the slope of the regression line. R² = 0.06 15 Potential explanations: The individual Potential explanations: Local context 17 Potential explanations: Local context 18 Potential explanations: Local context 19 Potential explanations: Local context •Observers frequently blame left-leaning local politicians for conditions that encourage or tolerate homelessness. Our sample cities were governed by Democrats 85% of the time (Republicans 8%, Independents 7%). •If Democrats are to blame, why don’t Chicago and Cleveland (Democratic strongholds) have a big problem with homelessness? 20 Potential explanations: Local context Potential explanations: Housing market 22 Potential explanations: Housing market 23 Potential explanations: Housing market Does homelessness thrive in certain cities because more people are moving to those cities? 24 Potential explanations: Housing market 25 Potential explanations: Housing market Typology •Housing supply elasticity measures the change in the supply of housing to a change in price. Supply elasticity is driven by regulations and topography. •Price elasticity of supply: % △ $% &’(%)$)* +’,,-$./ % △ $% ,0$1. 27 Typology 28 Typology Conclusion Regions need two types of investments: 1)Operating investments to fund housing support, maintenance, and services, and 2)Capital investments to construct housing. And where housing is difficult to construct, changes to regulations and land use policy are needed 30 Conclusion 31 Three Tensions Three tensions complicate this response: •Short vs long-term •Public versus private •Local versus federal government •Continuing to diagnose homelessness as a problem of the individual will undermine efforts to prevent and end it. •The country requires a structural understanding of and structural responses to homelessness. •Bright spot: the dramatic fall in veteran homelessness in the United States over the last decade 32 Conclusion Thank you! https://homelessnesshousingproblem.com colburn3@uw.edu @ColburnGregg Gregg Colburn Runstad Department of Real Estate University of Washington