This document summarizes a study conducted by Startup Policy Lab comparing two different types of workspaces used by startups. They interviewed 82 startups across two workspaces, one for-profit and one non-profit, collecting over 200,000 data points. They found that location was the primary factor for startups choosing a workspace, most were at the seed stage with under $1 million in funding, and there was typically high churn with startups only staying around 9 months on average. The study provided insights into how workspaces can better support startups and recommendations for expanding the research.

1. Startup policy lab
SPL develops tools to address public
policy driven by emergent technology
Workspace Case Study 2015
Charles Belle and Kathie Chuang
Startup Policy Lab is a fiscally sponsored project of Community Initiatives – a 501c3 nonprofit organization

2. Startups live, work, and play in
workspaces all over the world.

3. HackerSpaces, a community generated wiki, lists
over 350 workspaces in the united states
hackerspaces.com

4. WorkSpaces are increasingly centers
of startup activity, so we wondered:
WHY DO STARTUPS SELECT ONE WORKSPACE OVER ANOTHER?
WHAT ARE THE CAPITAL NEEDS STARTUPS IN WORKSPACES HAVE?
HOW LONG WILL THE STARTUPS USE A WORKSPACE?

5. 80
startups
>200,000
Data points
WE CONDUCTED ONE ON ONE INTERVIEWS:
SPL spoke with 82 startups using a 10 question survey.
LEVERARGED SECONDARY DATA SOURCES:
One workspace partner shared >200,000 data points
related to membership activity.
WE ENGAGED IN INDEPENDENT RESEARCH:
SPL collected third party data about startups from Angel
List and Crunchbase.
We started by comparing two workspaces

6. Learn the best ways to collect data about the dynamic startup community
Compare two different types of workspaces to surface initial patterns and differences
Better understand the characteristics of startups within workspaces
Determine if there is sufficient support to launch an annual and more robust study
our goal was to develop the foundation for
more in-depth research

7. W E F O U N D P A T T E R N S , T A K E A W A Y S , A N D I D E A S F O R
H O W T O E X P A N D T H I S P R O J E C T G O I N G F O R W A R D

8. Location was the primary factor for selection
Nearly 50% of startups surveyed the workspace chose it because of its location.
Funding needs were at the seed stage
Median funding was just $1MM.
Companies had traction (customers) and business models
Median age of startups was 30 months.
High expectation of churn
At one workspace, the median time in residence was 9 months; and the
total length of time startups expected to stay was 17 months.
Patterns we found in both workspaces

9. Build to location:
support existing
communities
Startups gravitate to each other
and spaces that have other
startups. Building a supportive
ecosystem means going to that
community, not building a
workspace to locate a startup
community in a particular
location to drive outside capital
investment.
Bring in the Angels:
Target seed funding for
early stage startups
Startups in workspaces are likely
to be smaller and at the seed
stage. As a result, these startups,
for the most part, are not ready
for venture capital. A robust
Angel Investor community will
provide great impact for seed
stage startups.
Think local & global:
satellite offices are
valuable additions
to workspaces
While it is important to support
local startups, companies seeking
to locate their satellite office in a
workspace offer great benefits to
local startups. Satellite offices of
established startups from outside
a region provide stability, bring in
outside capital, and ideas that
local startups can leverage.
Three insights emerged
1 2 3

10. Workspace resource guide
Create an online resource with
information about types of workspaces.
Analytic tools Enable workspaces
(startups, investors, policymakers &
workspaces) to compare performance
metrics of workspaces.
Partner with third party
data experts Collaborate with
Angel List and Mattermark to build
automated collection of granular data
about startups.
Next steps to increase the value of our research
Increase the number of data
points Another year with our partners
and add (1) more partner.
increase frequency of data
collection Collect data on a
quarterly basis.
Structure data Structure data to
make it more consistent across
workspaces.
Research funding Raise funding to
continue this initiative.

11. O U R A P P R O A C H : S P L C O L L A B O R A T E D W I T H T W O
W O R K S P A C E S ( S A N F R A N C I S C O & S I L I C O N V A L L E Y )

12. Partnerships Two workspaces (Runway &
Hacker Dojo) provided access to their community,
promoted the survey, and shared their data.
Time period Research and data collection
occurred between July 2015 - Sept 2015.
Research Methodology Conducted in
person surveys, analyzed member check-in data,
and pulled secondary research.
Data collected
• Total number of startups surveyed: 80 (Runway:
39, Hacker Dojo: 41)
• Total number of survey respondents: 114
What we did: surveys and research
(Runway: 49, Hacker Dojo: 65)
• Total number of members at Hacker Dojo: 479
• Total number of member check-in data points
collected: 215,253
• Type of check-in data collected: name, date of
check-in, & time of check-in
Third party data Collected information
about participating startups from Angel List &
Crunchbase, including funding, location, and
company type.
expertise Support provided by data science
partner Datable.

13. Our Awesome Workspace partners
For - Profit Non - Profit
Member, month to month,
pay for access to desks
479 members
hackerdojo.com
Company, month to month,
pay for individual desks
80 companies
runway.is
Business model
Payment model
2012 2009
Mountain Veiw, CASan Francisco, CA
Year founded
Location
“World's Largest Non-profit
Community Hackerspace”
“Runway is a community and co-
working space for entrepreneurs,
influencers, and hustlers”Description
number of
companies/members
Website

14. The survey was static, which reduced the depth of insight.
There is a high rate of churn of startups at workspaces. The survey only provides a
snapshot of a moment in time.
Limited financial resources prevent more robust data
collection and analysis.
Robust research requires additional funding to create and maintain infrastructure for
data collection and analysis.
Data accuracy Is heavily dependent on self-reported data.
Data collected was not standardized; proprietary tools might be able to provide more
structure and accuracy, e.g. Mattermark.
A few (massive) caveats about our approach

15. S U R V E Y F I N D I N G S P R O V I D E A C O M P A R I S O N O F F O R P R O F I T
W O R K S P A C E V E R S U S A N O N - P R O F I T W O R K S P A C E

16. Member/company time cycle in innovation space
How long are the startups in the
space and how old are they?
Runway
Average: 10.87 Months
Median: 9.00 months
Standard deviation: 11.41
Average: 10.03 Months
Median: 8.00 months
Standard deviation: 7.85
Hacker Dojo
Workspaces are perceived as temporary offices.
Most startups have been in the workspace for less than a year.

17. Funding AmountFunding Amount
NumberofCompanies
NumberofCompanies
$0 to $500,000
$500,000 to $1M
$1M to $1.5M
$1.5M to $3M
$3M +
what level of funding do these startups have?
Runway Hacker Dojo
The majority of the startups were, unsurprisingly, at the seed stage.
The vast majority of startups had raised less than $500,000.
23
4 5 4 3
43
5 1 1 1

18. events/programs 22%
mentors/networking 9%
funding/capital 8%
price point 4%
location 53%
collaborative space 4%
events/programs 22%
equipment135
location 53%
investor link 14%
mentors/networking 25%
Why do startups select a particular workspace
Runway Hacker Dojo
LOCATION
Location was
overwhelmingly
important in the
work space at
nearly 63%.
LOCATION
Important in the
makerspaces as well
as the workspace.
Location accounts for
28% of the appeal of
the space.
MENTORS AND
NETWORKING
Refers to the availibility
of mentors and
networking
oppertunities in the
given space.

19. A D E E P E R D I V E I N T O T H E I N D I V I D U A L W O R K S P A C E S

20. R U N W A Y I N C U B A T O R I S A F O R - P R O F I T W O R K S P A C E
L O C A T E D I N S A N F R A N C I S C O ( T W I T T E R B U I L D I N G )

21. B2C
B2B
HQ
Satelite
Software
Hardware
46%
54%
31%
69%
10%
90%
90% of the startups in a general purpose workspace are in software.
B2C and B2B are equally represented, but satellite offices are 30% of residents.
companies @runway have clear characteristics
b2c vs b2b
companies
hq vs
satelite
Hardware vs
Software

22. events/programs
mentors/networking
funding/capital
location
price point
collaborative space
Why did the company choose runway
5%
5%
47%
7%
16%
20%
Startups chose Runway primarily based on location
Twitter
Square
Uber

23. $2.84M
$1.OM
$4.75M
How much capital have companies at runway
raised?
MEDIAN FUNDING OBTAINED
AVERAGE FUNDING OBTAINED
STANDARD DEVIATION
A few companies skew the data higher.
The vast majority of companies have raised less than $1M — most, in fact, have raised less than $500k.

24. What are The average and median length of
time companies are located at runway?
Expect high levels of churn at workspaces.
Startups perceive workspaces as a temporary location; with plans to stay for less than two years.
48.23
30.00
52.25
10.87
9.00
11.41
18.44
17.00
14.07
Total Time Since Company
Founding (months)
Average
Median
Standard
deviation
Total Time in Runway
Currently (months)
Total Time Estimated to
Be in Runway (months)

25. H A C K E R D O J O I S a N O N - P R O F I T W O R K S P A C E
L O C A T E D I N M O U N T A I N V I E W

26. Why did the company choose hacker dojo?
22
17
17
43
99
17
22
17
24
80
22
29
25
57
133
13
24
14
32
83
18
26
24
46
114
Regardless of the length of time companies expected to spend at Hacker Dojo, Location was the number one reason
for being there.
Length of time
Member Planned to
stay at Hacker Dojo
Events &
programs
0 - 3 months
4 - 6 months
6 - 12 months
12+ months
Total
Equipment Links to
investors
mentors &
networking
Location

27. 1-2 members
2-3 members
3-5 members
5-7 members
>7 members
Where hacker dojo members live based on zip code

28. What is the funding stage and size
of companies at the hacker dojo?
$0 - $50,000 1 - 2
2 - 4
2 - 5
3 - 5
$50,000 - $550,000
$230,000 - $810,000
$260,000 - $610,000
Length of time at Hacker Dojo does not necessarily indicate more funding.
Length of time
member planned
to stay at
Hacker Dojo
average funding
0 - 3 months
3 - 6 months
6 - 12 months
12+ months
“low estimate to high estimate” “low estimate to high estimate”
average current company size

29. Thank you
GENERAL INQUIRIES
info@startuppolicylab.org
SUPPORT OUR WORK
charles@startuppolicylab.org
@StartupPolicy
StartupPolicyLab
SPL HEADQUArTERS
1355 Market Street, 4th floor,
San Francisco, CA, 94103

30. The people behind this project
CHARLES BELLE
KATHIE CHUANG
DATA PARTNER
CEO, Startup Policy Lab
charles@startuppolicylab.org
Intern, Startup Policy Lab
kathie@startuppolicylab.org
DatableApp team on the 1’s and 0’s.
We love startups and they are
awesome.
lull@datableapp.com