1
00:00:00,166 --> 00:00:01,434
Understanding the root causes

2
00:00:01,434 --> 00:00:03,136
of homelessness
is really important

3
00:00:03,136 --> 00:00:05,372
if we're going to address this
crisis.

4
00:00:05,372 --> 00:00:06,506
Meet Gregg Colburn.

5
00:00:06,506 --> 00:00:09,809
He's an assistant professor
at the University of Washington.

6
00:00:09,809 --> 00:00:11,544
And this is Q&A,

7
00:00:11,544 --> 00:00:13,246
the show
where we look for answers

8
00:00:13,246 --> 00:00:15,482
to some of the biggest questions
in our region.

9
00:00:16,049 --> 00:00:17,617
Having interviewed
a lot of people experiencing

10
00:00:17,617 --> 00:00:18,785
homelessness,
I can tell you each

11
00:00:18,785 --> 00:00:21,721
story is unique, it's tragic,
and different factors

12
00:00:21,721 --> 00:00:23,923
come together, they interact
to produce an outcome.

13
00:00:23,957 --> 00:00:25,258
The research on homelessness

14
00:00:25,258 --> 00:00:27,660
is very clear
that some individual factors

15
00:00:27,660 --> 00:00:31,931
like poverty, that drug use
or addiction, mental illness,

16
00:00:31,931 --> 00:00:33,466
increase
the risk of homelessness.

17
00:00:33,466 --> 00:00:34,667
There's no doubt about that.

18
00:00:34,667 --> 00:00:36,936
The question is,
are these factors root

19
00:00:36,936 --> 00:00:39,205
causes of homelessness
in our region?

20
00:00:39,205 --> 00:00:40,840
The reality is,
is the consequences

21
00:00:40,840 --> 00:00:43,276
of those conditions in Seattle
are different than elsewhere.

22
00:00:43,977 --> 00:00:45,478
And so the example
that I like to use

23
00:00:45,478 --> 00:00:46,312
is West Virginia,

24
00:00:46,312 --> 00:00:49,315
which is the home of the opioid
epidemic in the United States.

25
00:00:49,315 --> 00:00:51,151
And so if opioid addiction,
which is a

26
00:00:51,151 --> 00:00:53,820
which is a tragic circumstance,

27
00:00:53,820 --> 00:00:55,155
if that's a driver
of homelessness,

28
00:00:55,155 --> 00:00:57,090
we should see a lot of
homelessness in West Virginia.

29
00:00:57,090 --> 00:00:58,491
But in fact, we don't.

30
00:00:58,491 --> 00:01:01,027
So the consequences of addiction

31
00:01:01,027 --> 00:01:02,495
in West
Virginia are very different

32
00:01:02,495 --> 00:01:03,830
than they are in Seattle.

33
00:01:03,830 --> 00:01:05,532
Gregg co-wrote this book.

34
00:01:05,532 --> 00:01:07,367
Homelessness
is a housing problem.

35
00:01:07,367 --> 00:01:08,134
The book compares

36
00:01:08,134 --> 00:01:09,102
rates of homelessness

37
00:01:09,102 --> 00:01:10,770
in different places
around the country

38
00:01:10,770 --> 00:01:13,573
to better understand
the root causes of homelessness.

39
00:01:13,573 --> 00:01:15,775
So on this chart
and this visual, what you'll see

40
00:01:15,775 --> 00:01:17,877
is New York and Boston
and Washington, D.C.

41
00:01:17,877 --> 00:01:18,878
are on the high end.

42
00:01:18,878 --> 00:01:20,780
And then Chicago, for example,
is on the low end.

43
00:01:20,780 --> 00:01:22,048
And there's about a 5 to 1

44
00:01:22,048 --> 00:01:24,184
ratio in per capita
rates of homelessness.

45
00:01:24,184 --> 00:01:26,619
When we move to counties,
the numbers are lower

46
00:01:26,619 --> 00:01:28,855
because we're including
the suburban locations.

47
00:01:28,855 --> 00:01:30,557
But what's interesting
is that relationship

48
00:01:30,557 --> 00:01:33,293
between high and low
still holds at about 5 to 1.

49
00:01:33,860 --> 00:01:35,595
Why do some places like L.A.

50
00:01:35,595 --> 00:01:36,496
and Seattle

51
00:01:36,496 --> 00:01:39,499
have five times the per capita
rates of homelessness

52
00:01:39,499 --> 00:01:42,302
compared with places
like Chicago and Cleveland?

53
00:01:42,302 --> 00:01:44,037
What is it about Seattle

54
00:01:44,037 --> 00:01:46,539
that produces this result
when compared to other cities

55
00:01:46,539 --> 00:01:48,274
around the country
that don't have anywhere near

56
00:01:48,274 --> 00:01:50,343
the per capita rates
of homelessness that we do?

57
00:01:50,343 --> 00:01:51,511
To answer this question,

58
00:01:51,511 --> 00:01:55,215
Greg used a statistical measure
called R squared.

59
00:01:55,215 --> 00:01:58,751
R squared helps to analyze how
the differences in one variable

60
00:01:58,751 --> 00:02:01,454
can explain the differences
in another variable.

61
00:02:01,454 --> 00:02:02,188
For example,

62
00:02:02,188 --> 00:02:04,858
does the amount of poverty
in a community predict

63
00:02:04,858 --> 00:02:07,160
the amount of homelessness
in that community?

64
00:02:07,160 --> 00:02:09,562
Do high rates of drug
use in a community

65
00:02:09,562 --> 00:02:11,764
also indicate
high rates of homelessness?

66
00:02:12,398 --> 00:02:15,235
Let's take a look
at some of his findings.

67
00:02:15,235 --> 00:02:18,338
So what we will see in
this graph is a relationship

68
00:02:18,338 --> 00:02:21,174
between poverty rates
and rates of homelessness.

69
00:02:21,174 --> 00:02:22,876
Where poverty
rates are really high.

70
00:02:23,877 --> 00:02:26,980
Homelessness
tends to be quite low.

71
00:02:26,980 --> 00:02:28,081
And so the point here is,

72
00:02:28,081 --> 00:02:28,982
is that Seattle

73
00:02:28,982 --> 00:02:30,283
doesn't
have a homelessness problem

74
00:02:30,283 --> 00:02:32,252
because we have more people
experiencing poverty.

75
00:02:32,252 --> 00:02:34,854
In fact, we're a relatively low
poverty community.

76
00:02:34,854 --> 00:02:35,722
Detroit, for example,

77
00:02:35,722 --> 00:02:38,124
is the most impoverished city
in the country.

78
00:02:38,124 --> 00:02:40,393
There at the far
right end of this graphic,

79
00:02:40,393 --> 00:02:42,362
but they have some of the lowest
rates of homelessness.

80
00:02:42,362 --> 00:02:43,229
And if poverty really

81
00:02:43,229 --> 00:02:45,431
were the root cause of this
at a community level,

82
00:02:45,431 --> 00:02:47,200
Detroit should have a huge
homelessness problem.

83
00:02:47,200 --> 00:02:48,001
But in fact, they don't.

84
00:02:48,001 --> 00:02:50,670
So we need to keep looking
for a better explanation.

85
00:02:50,670 --> 00:02:52,238
Do places with more drug use

86
00:02:52,238 --> 00:02:54,607
or mental illness
have more homelessness?

87
00:02:54,607 --> 00:02:56,409
The next two categories
we look at

88
00:02:56,409 --> 00:02:58,745
are actually calculated
at the state level.

89
00:02:58,745 --> 00:02:59,712
So these are state

90
00:02:59,712 --> 00:03:03,116
per capita rates of homelessness
relative to illicit drug use

91
00:03:05,018 --> 00:03:07,053
and mental illness.

92
00:03:09,422 --> 00:03:10,456
So what we see is that

93
00:03:10,456 --> 00:03:13,159
there is some variation
in rates of mental illness

94
00:03:13,159 --> 00:03:15,295
and drug use
in states around the country,

95
00:03:15,295 --> 00:03:17,764
but it doesn't correlate at all
with rates of homelessness.

96
00:03:17,764 --> 00:03:22,235
And so the places with higher
rates of mental illness and drug

97
00:03:22,235 --> 00:03:24,237
use aren't places
where homelessness is higher.

98
00:03:24,237 --> 00:03:26,773
Do places with good weather
have more homelessness?

99
00:03:26,773 --> 00:03:27,440
People frequently

100
00:03:27,440 --> 00:03:28,474
tell me that
we have a problem here

101
00:03:28,474 --> 00:03:29,676
because the weather is moderate

102
00:03:29,676 --> 00:03:32,278
and in Chicago it's cold
and therefore

103
00:03:32,278 --> 00:03:33,479
we have homelessness here.

104
00:03:33,479 --> 00:03:34,981
When you plot weather
and homelessness,

105
00:03:34,981 --> 00:03:36,816
what you see is there's actually
no relationship.

106
00:03:36,816 --> 00:03:39,385
Do welfare benefits encourage
homelessness?

107
00:03:39,385 --> 00:03:43,323
A second local contextual factor
that people frequently

108
00:03:43,323 --> 00:03:45,725
blame for homelessness
is a generous services.

109
00:03:45,725 --> 00:03:48,194
They're stingy states.
They're generous states.

110
00:03:48,194 --> 00:03:50,263
But we don't see
disproportionate

111
00:03:50,263 --> 00:03:52,365
homelessness in places
that are generous.

112
00:03:52,832 --> 00:03:55,301
Are unhoused people
moving to King County?

113
00:03:55,301 --> 00:03:57,470
People say, well,
there's a mobility story here,

114
00:03:57,470 --> 00:03:59,906
that people are moving here
because of

115
00:03:59,906 --> 00:04:01,140
weather, benefits, etc.

116
00:04:01,140 --> 00:04:03,977
And so what we look at is low
income migration.

117
00:04:03,977 --> 00:04:05,178
There are people below the

118
00:04:05,178 --> 00:04:08,348
poverty line moving to Seattle,
just as there are in all cities

119
00:04:08,348 --> 00:04:09,349
in the United States.

120
00:04:09,349 --> 00:04:11,150
But there's not
disproportionate movement

121
00:04:11,150 --> 00:04:12,952
to places
with high rates of homelessness.

122
00:04:12,952 --> 00:04:16,589
What role does political party
leadership play in homelessness?

123
00:04:16,589 --> 00:04:17,724
Well,
I've heard a lot of people say

124
00:04:17,724 --> 00:04:20,326
that it's left
leaning or democratic policies

125
00:04:20,326 --> 00:04:21,361
that are to blame.

126
00:04:21,361 --> 00:04:22,895
But what's interesting is
we looked at all

127
00:04:22,895 --> 00:04:23,896
the cities in our sample,

128
00:04:23,896 --> 00:04:25,765
which is 30 cities
across the United States.

129
00:04:25,765 --> 00:04:29,902
And in all of those data
points, 85% of those years,

130
00:04:29,902 --> 00:04:31,804
a Democrat was in charge.

131
00:04:31,804 --> 00:04:33,906
And so if Democratic policies
are to blame,

132
00:04:33,906 --> 00:04:34,440
how do we then

133
00:04:34,440 --> 00:04:37,944
explain the huge variation
between Chicago and Cleveland,

134
00:04:37,944 --> 00:04:40,613
which are Democratic strongholds
but have very low rates of

135
00:04:40,613 --> 00:04:42,882
homelessness,
and New York, Boston,

136
00:04:42,882 --> 00:04:45,385
Seattle, San Francisco,
which are also blue.

137
00:04:45,451 --> 00:04:47,887
Do places with higher rents
have more homelessness?

138
00:04:48,187 --> 00:04:49,422
We then turn to housing

139
00:04:49,422 --> 00:04:52,492
market characteristics
and this is where we start

140
00:04:52,492 --> 00:04:55,561
to see some pretty compelling
statistical evidence

141
00:04:55,561 --> 00:04:59,132
of explanatory power
between one of these variables

142
00:04:59,132 --> 00:05:00,133
and rates of homelessness.

143
00:05:00,133 --> 00:05:03,870
And so what we see is
when you plot median rents

144
00:05:03,870 --> 00:05:05,972
in places that are expensive,
homelessness is high

145
00:05:05,972 --> 00:05:09,042
and in places that are cheap,
homelessness is low.

146
00:05:09,042 --> 00:05:11,911
And we look at vacancy
rates, again, a similar story.

147
00:05:11,911 --> 00:05:12,512
And again,

148
00:05:12,512 --> 00:05:16,382
it's more statistically powerful
than other explanations.

149
00:05:16,382 --> 00:05:18,751
When vacancy rates are low
or housing is not available,

150
00:05:18,751 --> 00:05:21,254
we see much higher rates
of homelessness and vice versa.

151
00:05:21,254 --> 00:05:23,956
Ultimately, Chicago has
relatively high vacancy rates

152
00:05:23,956 --> 00:05:27,727
and is far more affordable
than Seattle.

153
00:05:27,727 --> 00:05:28,628
And it also explains

154
00:05:28,628 --> 00:05:30,763
the very odd situation
of Detroit,

155
00:05:30,763 --> 00:05:33,166
which has the highest rate
of poverty in the country,

156
00:05:33,166 --> 00:05:35,201
but has very,
very low rates of homelessness

157
00:05:35,201 --> 00:05:36,269
because housing is abundant.

158
00:05:36,269 --> 00:05:37,870
In fact, they're tearing down
housing in Detroit

159
00:05:37,870 --> 00:05:40,673
because of the huge population
losses that they've experienced.

160
00:05:40,673 --> 00:05:43,142
And so that abundant
and relatively cheap housing

161
00:05:43,142 --> 00:05:44,777
really mitigates

162
00:05:44,777 --> 00:05:47,213
the problem of homelessness
in those communities.

163
00:05:47,213 --> 00:05:50,316
If a lack of affordable
housing is driving homelessness.

164
00:05:50,316 --> 00:05:51,818
What are the obstacles
to building

165
00:05:51,818 --> 00:05:53,519
more housing in our region?

166
00:05:53,519 --> 00:05:54,921
One is topography,

167
00:05:54,921 --> 00:05:58,358
and so places with mountains
and water, it tends to be less

168
00:05:58,358 --> 00:06:00,660
elastic, meaning it's harder
to build in response

169
00:06:00,660 --> 00:06:02,228
because you run into mountains
or water.

170
00:06:02,228 --> 00:06:03,029
The second issue

171
00:06:03,029 --> 00:06:05,998
is the regulatory environment,
which you're trying to

172
00:06:05,998 --> 00:06:06,699
construct housing.

173
00:06:06,699 --> 00:06:08,968
So some places it's
very easy to build.

174
00:06:08,968 --> 00:06:10,002
It's easy to get a permit.

175
00:06:10,002 --> 00:06:14,006
The zoning accommodates
housing of all types.

176
00:06:14,006 --> 00:06:16,442
And in other places, it's
very difficult.

177
00:06:16,442 --> 00:06:19,212
So imagine ten friends
in a circle with ten chairs.

178
00:06:19,212 --> 00:06:20,179
They all stand up.

179
00:06:20,179 --> 00:06:22,815
A leader starts playing music,
they walk around in a circle.

180
00:06:22,815 --> 00:06:24,417
The leader takes one chair out.

181
00:06:24,417 --> 00:06:26,586
And in this case,
Mike has an ankle injury

182
00:06:26,586 --> 00:06:27,787
and was on crutches

183
00:06:27,787 --> 00:06:31,124
and given his impairment,
was unable to move quickly

184
00:06:31,124 --> 00:06:32,492
and therefore lost
the game was left

185
00:06:32,492 --> 00:06:34,193
without a chair
when the music stopped.

186
00:06:34,193 --> 00:06:35,294
And so if we were to interview

187
00:06:35,294 --> 00:06:36,629
Mike at the end of the game
and say, Mike,

188
00:06:36,629 --> 00:06:38,331
why do you think you lost
the game? What would you say?

189
00:06:38,331 --> 00:06:40,032
He'd say, Well,
I had a bad ankle,

190
00:06:40,032 --> 00:06:42,435
but if we take a step back
and really think critically,

191
00:06:42,435 --> 00:06:44,237
what was the cause of Mike's
chairlessness,

192
00:06:44,237 --> 00:06:46,472
was the fact that we didn't
have enough chairs.

193
00:06:46,472 --> 00:06:49,342
And that's really an analogy
for what's going on in Seattle

194
00:06:49,342 --> 00:06:51,577
right now, in the sense
that ankle injuries

195
00:06:51,577 --> 00:06:55,014
or other vulnerabilities
don't cause homelessness.

196
00:06:55,014 --> 00:06:56,616
But they do identify the people

197
00:06:56,616 --> 00:06:58,518
who are most likely
to experience homelessness

198
00:06:58,518 --> 00:07:00,653
in a constrained housing market
like Seattle.

199
00:07:00,653 --> 00:07:01,254
I would say that

200
00:07:01,254 --> 00:07:02,455
there's one real silver

201
00:07:02,455 --> 00:07:04,123
lining
in the homelessness story,

202
00:07:04,123 --> 00:07:05,925
and that is the story
of veterans homelessness.

203
00:07:05,925 --> 00:07:08,928
Veterans
homelessness has fallen by 50%

204
00:07:08,928 --> 00:07:11,063
over the last decade,
which is a real success story.

205
00:07:11,063 --> 00:07:12,932
And so people say, well,
how did we do it?

206
00:07:12,932 --> 00:07:14,867
And the answer is
that we gave them housing.

207
00:07:14,867 --> 00:07:18,271
And the question is, will we
then provide similar responses

208
00:07:18,271 --> 00:07:21,707
to a broader community of people
who need access to housing?

209
00:07:21,707 --> 00:07:24,410
And so that's really the

210
00:07:24,410 --> 00:07:27,947
ray of positive light in an
otherwise somewhat dismal story.

211
00:07:27,947 --> 00:07:30,950
And I think if we can replicate
that policy response

212
00:07:30,950 --> 00:07:32,351
a little more broadly,

213
00:07:32,351 --> 00:07:34,887
hopefully we can make
a similar dent in this problem,

214
00:07:34,887 --> 00:07:37,824
both here in King County
and around the country.