The effect of home owning on business development micro-level evidence bingbing wang-slides

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The Effect of Home-owning on Business
Development: Micro-level Evidence
Bingbing Wang
Lusk Center of Real Estate, University of Southern California
01/04/2018
Bingbing Wang The Effect of Home-owning on Business Development: Micro

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Introduction
NIMBYism
Protesting Cases
Spring Valley residents in Las Vegas vs. the expansion of
an existing asphalt mixing plant;
Local residents vs. shopping centers in Lake Brandt North
Carolina for natural beauty preservation;
New York upper east side residents vs. subway entrances.
A material impact from home-owning on
business?

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Introduction: background
Home-owning impact on unemployment
Blanchﬂower and Oswald (2013); Oswald (1996 and 1997);
the aggregate and individual level studies (Goss and
Phillips 1997; Green, 2001; Genesove and Mayer, 2001;
Chan, 2001; Engelhardt, 2001; Coulson and Fisher, 2001
and 2009; Munch, 2006 and 2007; Mumford, 2013;
Valletta, 2013);
Mechanism: transaction cost, lock-in effect, job mismatch,
crowding out effect on renters and NIMBY effect.

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Introduction: micro-level effect of home-owning
Home-owning negative spillovers on
neighboring business:
NIMBYism (Oswald, 2013)
Home-owning positive spillovers on
neighboring business:
1. Stable employees (Coulson and Fisher, 2002);
2. Better amenities (DiPasquale and Glaeser, 1998; Rohe,
Zandt and McCarthy, 2002; Dietz and Haurin, 2003);
3. YIMBYism: Yes, in my back yard (Stephens, 2017).

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Introduction
Three factors: distance, income, and
industry types
Identify and quantify the home-owning
spatial impact on neighboring business:
At what distance, for which neighborhoods, for what kind of
business, the net impact is positive or negative.

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Framework
Producer function:
Max
q
πe = pqe − TCe
s.t. qe = f(K, L, M) TCe = vK + wL + r
Incorporating business location:
qe,i = f(K, Zi) TCe,i = vK + ZiPZi
e: establishment e
i: location i
Zi: attributes in location i including labor, land resources,
neighborhood amenity (population and income), tax policy
transportation access, CBD distance, agglomeration
PZi :land, labor, friction cost in location i

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Framework
Max
z
πi = p(f(K, Zi)) − Kv − PZi Zi (1)
Max
z
πi = p(f(Zi)) − PZi Zi (2)
Zi(labor stability) = fdistance(Owni)
Zi(neighborhood amenity) = fdistance,income(Owni)
PZi(friction cost) = fdistance,income,industry(Owni)
Max
Own
πi = Fdistance,income,industry(Owni) (3)

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Framework: with respect to distance
Max
z
πi = p(f(Zi)) − PZi Zi (2)
∂π
∂D
= p
∂f
∂Z
∂Z
∂D
−
∂PZ
∂D
Z − PZ
∂Z
∂D
(4)
Preferred and nuisance creating:
S1 :
∂f
∂Z
> 0 and PZ > 0
Preferred and not nuisance creating:
S2 :
∂f
∂Z
> 0 and PZ = 0
Not preferred:
S3 :
∂f
∂Z
< 0

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Framework: with respect to distance
∂π
∂D
= p
∂f
∂Z
∂Z
∂D
−
∂PZ
∂D
Z − PZ
∂Z
∂D
(4)
NIMBY distance Da :
If D < Da,
∂Pz
∂D
< 0 and PZ > 0
If D >= Da, PZ = 0
Positive spillover distance D0 :
If Da < D < D0,
∂Z
∂D
= 0 and PZ = 0
If D >= D0,
∂Z
∂D
< 0 and PZ = 0

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Predictions: distance
∂π
∂D
= p
∂f
∂Z
∂Z
∂D
−
∂PZ
∂D
Z − PZ
∂Z
∂D
(4)
S1 :
If D = 0, PZ = +∞ and π < 0
If 0 < D < Da,
∂π
∂D
> 0
If Da <= D <= D0,
∂π
∂D
= 0 and π(Da) = π(D0) > 0
If D >= D0,
∂π
∂D
< 0 and π > 0

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Predictions: distance
Distance
Net impact
D0Da

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Predictions: income
Distance
Net impact
D0Da
12
I2
I0
I1

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Predictions: industry
∂π
∂D
= p
∂f
∂Z
∂Z
∂D
−
∂PZ
∂D
Z − PZ
∂Z
∂D
(4)
S2 :
If 0 <= D <= D0,
∂π
∂D
= 0 and π > 0
If D > D0,
∂π
∂D
< 0 and π >= 0
S3 :
π < 0

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Predictions
1: Home-owning increases the job counts of preferred
business for low income neighborhoods for all distances.
2: Home-owning reduces the job counts of the preferred
but nuisance creating business located at adjacent
distances for high income neighborhoods.
3: Home-owning increases the job counts of preferred
business located at close but not adjacent distances for
high income neighborhoods.
4: Home-owning decreases the job counts of not preferred
business at all distances for both income groups.

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Outline
Research Design
Methodology
Data
Results and Conclusion

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Research design: the optimal one
Owner Renter

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Research design: real position

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Research design: former research on distance effect
Project

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Research design

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Methodology: alternative mechanisms
Transportation access:
FE: cluster
Agglomeration:
Employee population density and education
Local policies (tax policy):
CBSA FE
Common economic and local factors:
Space and time FE

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Methodology
Endogeneity: reverse causation
Owners do not choose to be near the business
Existing owners move to other places

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Methodology: IV
Instrument: 1) the Federal Housing Administration (FHA)
loan limit of the county where the donut ring resides
divided by the median house price of the donut ring; 2) the
ratio of families with children under 18.
log(JobCountj,t) =α + βOwnj,i,t + Controlsj,i,t + j,t
j: cluster j
i: donut ring i (.3, .3-1, 1-2, 2-3, 3-5, 5-10)
Crosscheck with OLS, FE and FD
Lagged home-ownership rates with OLS, FE, FD and IV

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Data
Job counts and employee controls (education)
Longitudinal Employer-Household Dynamics (LEHD)
Origin-Destination Employment Statistics (LODES) Work
Area Characteristics (WAC): 2009-2014
Ownership rates and residential controls (population,
education, income)
American Community Survey (ACS): 2009-2014
Latitude, longitude and CBSA codes
2010 Decennial Census geocodes

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Data: K-means clustering method
arg min
k
i=1 X∈Si
||X − µi||2
Wiki: https://en.wikipedia.org/wiki/K-means_clustering

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Kmeans: USC

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Kmeans: Orange County

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Job clusters
217,778 block groups in 2011/50,138 are job block groups
10,000 job clusters in the U.S.
1) The number of block groups for each cluster varies from
1 to 38 and averages at 5.0138 and the median is 4.
2) The job cluster average land area is 31.79 square miles
with an average radius of 2.008 miles.

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Cluster and donut rings

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Data: summary statistics
Residential features: home-ownership rates
Blkgp .3 .3-1 1-2 2-3 3-5 5-10
Own .6536 .6323 .6529 .6666 .6735 .6845 .6916
(.26) (.20) (.18) (.17) (.16) (.15) (.13)
Obs 1,069,231 54,643 51,790 53,991 53,993 57,168 58,896
Table: Resident characteristics descriptive statistics. Standard errors of the means are
in the parenthesis. The unit is mile. The data is from ACS from 2009 to 2014.

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Residential features: home-ownership rate change
Blkgp .3 .3-1 1-2 2-3 3-5 5-10
∆ Own -.0059 -.0066 -.0067 -.0063 -.0058 -.0054 -.0048
(.07) (.05) (.05) (.04) (.04) (.03) (.02)
Obs 1,069,231 54,643 51,790 53,991 53,993 57,168 58,896
Table: Resident characteristics descriptive statistics. Standard errors of the means are
in the parenthesis. The unit is mile. The data is from ACS from 2009 to 2014.

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Job features: the job count
Blkgp Ratio Cluster Ratio
Pooled 661 8859
Downtown 2685 .0089 17255 .2550
Suburbs 616 .9110 7592 .7450
High 604 .3980 11394 .5940
Low 698 .6020 6656 .4060
Downtown high 3435 .0225 20353 .1582
Downtown low 2442 .0654 13814 .0976
Suburban high 575 .3753 9826 .4359
Suburban low 643 .5368 5721 .3083
Obs 1070442 59772
Table: Work characteristics descriptive statistics. The data is from LEHD LODES WAC
from 2009 to 2014.Downtown is deﬁned as the block groups adjacent to the CBD, the
total population of which are no more than 5% of the CBSA population. The high
income group is categorized as the block groups with higher median income than the
average median income of the block groups.

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Job features: job count change
Blkgp Growth Cluster Growth US
Pooled -35 -.0530 569 .0642 .0162
Downtown -253 -.0942 991 .0574
Suburbs -30 -.0487 505 .0665
High -28 -.0464 754 .0662
Low -40 -.0573 398 .0598
Downtown high -244 -.0710 1178 .0579
Downtown low -243 - .0995 773 .0560
Suburban high -26 -.0452 681 .0693
Suburban low -34 -.0529 348 .0608
Obs 1070442 59772
Table: Work characteristics descriptive statistics. The data is from LEHD LODES WAC
from 2009 to 2014 and the Bureau of Labor Statistics.

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Pooled sample result: cluster with distance
.3 OLS FE FD OLSL FEL FDL
Own -.0319 -.1291 -.1187 -.0281 -.0592 -.0570
Std (.0097) (.0521) (.0536) (.0097) (.0284) (.0270)
P .0011 .0132 .0267 .0036 .0375 .0353
Obs 46498 44370 35483 45303 43253 34366
Table: The dependent variable is the log of job counts. Controls include lagged job
counts, residential population, employee education, employee population density,
residential median income, educational levels, ownership rates of other rings, distance
to CBD, cbsa and year ﬁxed effects. The interpretation of the coefﬁcient (.1291) for
within .3 miles for Column (2) is that if ownership rate increase by 1%, then job counts
increase by 12.91%. So if ownership rate increases from 65% to 66%, then job counts
might increase from 1000 to 1012.91. Standard errors are clustered at the cluster level
and are in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

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.3-1 OLS FE FD OLSL FEL FDL
Own -.0253 .0775 .0820 -.0327 -.0170 -.0039
Std (.0113) (.0552) (.0657) (.0117) (.0230) (.0258)
P .0255 .1714 .2117 .0053 .4603 .8656
Obs 44013 44012 33749 42971 42970 34156
to CBD, cbsa and year ﬁxed effects. Standard errors are clustered at the cluster level

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1-2 OLS FE FD OLSL FEL FDL
Own -.0256 -.0266 -.0588 -.0253 .0102 .0190
Std (.0128) (.0641) (.0690) (.0132) (.0304) (.0302)
P .0454 .6780 .3941 .0547 .7373 .5282
Obs 45666 45665 34887 44835 44834 35690

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Own .0166 -.1722 -.2062 -.0086 .0001 .0586
Std (.0148) (.0674) (.0759) (.0146) (.0288) (.0295)
P .2640 .0106 .0066 .5563 .9984 .0475
Obs 45614 45613 34890 44841 44842 34179

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Own -.0053 -.2247 -.1891 .0035 .1409 .1107
Std (.0172) (.1002) (.0981) (.0174) (.0450) (.0451)
P .7588 .0249 .0540 .8387 .0018 .0142
Obs 48103 48102 36651 47505 45285 36106

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Own .0303 .0966 .0552 .0260 -.0374 -.0083
Std (.0215) (.1855) (.1702) (.0218) (.0709) (.0562)
P .1590 .6023 .7457 .2321 .5977 .8824
Obs 49275 49273 37519 48940 48939 39070

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IV for .3, 2-3 and 3-5
.3 FHA First Stage Exogenous IV IVlag
Own .0745 -.0060 -.1086 -.1297
Std (.0047) (.0040) (.0524) (.0566)
P .0000 .1390 .0380 .0220
Obs 35519 35519 37211 37215
Table: The dependent variable is the log of job counts. The instrument is the FHA loan
limit divided by the median house price. Standard errors are clustered at the cluster
level and are in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

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IV for .3, 2-3 and 3-5
2-3 FHA First Stage Exogenous
Own .0968 -.0096
Std (.0035) (.0044)
P .0000 .0301
Obs 34931 34931
3-5 FHA
Own -.1596 -.0156
Std (.0499) (.0050)
P .0014 .0019
Obs 36712 35519
Table: The dependent variable is the log of job counts. The instrument is the FHA loan
limit divided by the median house price. Standard errors are clustered at the cluster
level and are in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

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IV for .3, 2-3 and 3-5
2-3 Child First Stage Exogenous IV
Own .2621 .0189 .0495
Std (.0130) (.0131) (.0470)
P .0000 .1489 .2918
Obs 34944 34944 34944
3-5 Child First Stage Exogenous IV IVlag
Own .2618 .0253 .0832 .1462
Std (.0145) (.0187) (.0660) (.0780)
P .0000 .1773 .2078 .0608
Obs 36716 36716 36716 36716
Table: The dependent variable is the log of job counts. The instrument is the ratio of
families with children under 18. Standard errors are clustered at the cluster level and
are in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

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Result: pooled sample and different income groups
Pooled .3 .3-1 1-2 2-3 3-5 5-10
Own -
OwnLag - +
Income .3 .3-1 1-2 2-3 3-5 5-10
Own -high
OwnLag +low +low
Table: The results for different income groups are from the FE speciﬁcation and are
consistent across other speciﬁcations.

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Result: .3-1 from high income groups
.3-1 >55 >60 >65 >70 >75
Own -.1191 -.1244 -.1373 -.1045 -.0807
Std (.0737) (.0740) (.0823) (.0926) (.1011)
P .1062 .0930 .0952 .2590 .4248
Obs 17358 15578 13474 11165 9327
.3-1 >80 >85 >90 >95
Own -.1792 -.2125 -.5416 -.6165
Std (.1164) (.1430) (.1767) (.2725)
P .1239 .1376 .0022 .0242
Obs 7242 5018 3174 1300

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Result: .3 of lagged ownership rates from low income
groups
.3 <5 <10 <15 <20 <25
Own .2060 .1962 .1241 .0547 .0516
Std (.2045) (.0870) (.0725) (.0611) (.0561)
P .3143 .0244 .0870 .3707 .3580
Obs 1284 3162 5083 7345 9564
.3 <30 <35 <40 <45 <50
Own .0149 -.0171 -.0182 -.0131 -.0293
Std (.0517) (.0469) (.0416) (.0410) (.0371)
P .7736 .7157 .6616 .7485 4292
Obs 11414 13849 16043 17805 21212

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Result: 3-5 of lagged ownership rates from low income
groups
3-5 <5 <10 <15 <20 <25
Own .2852 .1821 .1576 .3042 .2443
Std (.3543) (.1539) (.1228) (.1134) (.0990)
P .4215 .2371 .1996 .0074 .0137
Obs 1244 3023 4860 6975 9048
3-5 <30 <35 <40 <45 <50
Own .2430 .2296 .2086 .1938 .1622
Std (.0992) (.0889) (.0832) (.0757) (.0719)
P .0143 .0099 .0122 .0105 .0241
Obs 10832 13153 15210 16974 20224

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Summary statistics: industry
NAICS Max Max Location
Total 467929 -
NAICS sector 11 (Agriculture, Forestry,
Fishing and Hunting)
11322 North CA
NAICS sector 21 (Mining, Quarrying,
and Oil and Gas Extraction)
17320 Houston downtown
NAICS sector 22 (Utilities) 11712 LA downtown
NAICS sector 23 (Construction) 12023 Vegas
NAICS sector 31-33 (Manufacturing) 33260 Seattle (Boeing)
NAICS sector 42 (Wholesale Trade) 26227 NYC Manhattan
NAICS sector 44-45 (Retail Trade) 18221 New York
NAICS sector 48-49 (Transportation and
Warehousing)
52392 NYC (JFK airport)
NAICS sector 51 (Information) 47206
Seattle (Microsoft
headquarters)
NAICS sector 52 (Finance and
Insurance)
75889 NYC Manhattan

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NAICS Max Max Location
Total 467929 -
NAICS sector 53 (Real Estate and Rental and
Leasing)
15293 NYC Manhattan
NAICS sector 54 (Professional, Scientific, and
Technical Services)
73292 Chicago
NAICS sector 55 (Management of Companies
and Enterprises)
13630
Arkansas (Walmart home
office)
NAICS sector 56 (Administrative and Support
and Waste Management and Remediation
Services)
26435 Chicago
NAICS sector 61 (Educational Services) 173587 NYC Manhattan (NYU)
NAICS sector 62 (Health Care and Social
Assistance)
54194
Houston (Texas Medical
Center)
NAICS sector 71 (Arts, Entertainment, and
Recreation)
41671 Orlando Disney
NAICS sector 72 (Accommodation and Food
Services)
91530 Vegas
NAICS sector 81 (Other Services [except Public
Administration])
23551 Seattle downtown
NAICS sector 92 (Public Administration) 90563 NYC Lower Manhattan

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Agricultural: North CA Oil: Houston Utility: LA Construction: Vegas
Manufacturing: Seattle Wholesale Trade: NYC Retail Trade: NYC Transportation: JFK
Information: Seattle Finance & Insurance: NYC Real Estate: NYC Professional service: Chicago

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Management of
companies: Arkansas
Admin and support:
Chicago
Educational service:
NYC
Health care: Houston
Arts and entertain:
Orlando
Accommodation and
food: Vegas
Other service: Seattle Public admin: NYC

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Result: industry
Positive
Lower Income Higher Income
Manufacturing (+:1-2) Retail Trade (-: .3/+: 3-5)
Real Estate and Car Rental and
Leasing (+:1-2) Arts, Entertainment (+:.3)
Information (+:.3;+: .3-1) Public Administration (+: .3; -: 5-10)
Accommodation and Food Services
(+:.3-1;-: 3-5)
Professional, Scientific, and Technical
Services (-: .3/+: 3-5)
Administrative and Support and Waste
(+: .3-1; -: 2-3)
Negative
Lower Income Higher Income
Management of Companies (3-5) Manufacturing (.3-1)
Wholesale Trade (.3)
Real Estate and Rental and Leasing
(.3; 2-3; 5-10)
Other Services [except Public
Administration] (.3-1; 5-10) Wholesale Trade (.3)
Retail Trade (2-3)
Management of Companies (.3; .3-1;
2-3)
Arts, Entertainment (3-5) Finance and Insurance (.3; 1-2)
Public Administration (.3; 1-2: 3-10) Educational Services (.3; 5-10)

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Conclusion
Comfortable distance: 3-5 miles
Friction distance: adjacent distance of .3 or
.3-1 miles from high income groups
The combination of neighborhoods and
industries
High income with service industries like Art and Retail
Low income with job generating industries like
Manufacturing and Real Estate and Car Rental and
Leasing

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Q and A
Thank you!
bingbinw@usc.edu;
bingbingwang123@gmail.com

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