Does Homeownership Influence Political Behavior?
Evidence from Administrative Data∗
Andrew B. Hall†
Department of Political Science
Stanford University
Jesse Yoder‡
Department of Political Science
Stanford University
March 26, 2019

Abstract
We combine deed-level data on homeownership with administrative data on voter
turnout in local and national elections for more than 18 million individuals in Ohio andNorth Carolina. Using a difference-in-differences design, we find that buying a homeleads individuals to participate substantially more in local elections, on average. We
also collect data on local ballot initiatives, and we find that the homeowner turnout
boost is almost twice as large in times and places where zoning issues are on the ballot.
Additionally, the effect of homeownership increases with the price of the home purchase,suggesting that asset investment may be an important mechanism for the participatory
effects. Overall, the results suggest that individual economic circumstances importantly
influence political beliefs and behavior, and suggest that homeowners have special
influence in American politics in part because their ownership motivates them to pay
attention and to participate.

∗
Authors are listed in alphabetical order and contributed equally. For comments and suggestions, the authors thank Chris
Berry, Adam Dynes, Justin Grimmer, Alisa Hall, Jonathan Hall, Mike Hankinson, Greg Huber, Connor Huff, Kyle Knutson,
Elise Kostial, Zhao Li, Will Marble, Clayton Nall, Dan Thompson, and Jessica Trounstine, as well as participants at a 2018
APSA panel. The protocol for this study has been approved by the Stanford IRB (#39468).
†
Andrew B. Hall is an Associate Professor in the Department of Political Science at Stanford University(andrewbenjaminhall@gmail.com, http://www.andrewbenjaminhall.com; 616 Serra St, Stanford, CA 94305).
‡
Jesse Yoder is a Ph.D. Candidate in the Department of Political Science at Stanford University (yoderj@stanford.edu;
616 Serra St, Stanford, CA 94305).

“Those who hold and those who are without property have ever formed distinct
interests in society.”–James Madison, Federalist 10

1

Introduction

An important literature in political science and political economy shows that higher-wealthand higher-status groups participate more in politics (e.g., Bechtel, Hangartner, and Schmid
2016; Braconnier, Dormagen, and Pons 2017; Enos, Fowler, and Vavreck 2013; Fowler 2013;
Kasara and Suryanarayan 2015; Lijphart 1997; Verba, Schlozman, and Brady 1995). Thispattern is puzzling because participating is costly and has virtually no effect on politicaloutcomes (e.g., Downs 1957; Riker and Ordeshook 1968).1 Do individuals form political beliefs and choose to participate in elections because of their personal economic circumstances?
More generally, do economic circumstances lead individuals to take political action?2 We
study these questions in the context of property ownership. In particular, we ask: do individuals who own property turn out to vote or change their political views because they areproperty owners?
In addition to being fundamental in the study of political economy, these questions also
speak to recent concerns about “NIMBYism” and the political influence of homeowners(e.g., Einstein, Palmer, and Glick n.d.; Hankinson 2018; Marble and Nall 2018). Many local
governments use zoning laws to restrict the supply of housing, raising the value of existinghomes and reducing geographic mobility as a result (e.g., Ganong and Shoag 2017). A
recent study estimates that this loss in mobility lowered aggregate US economic growth by
36% between 1964 and 2009 (Hsieh and Moretti n.d.). Evidence suggests that homeowners,
who gain from high home values, oppose the development of new housing (Hankinson 2018;
1

More generally, the discipline has made great progress identifying factors that encourage turnout. For an
overview, see Green and Gerber (2008). Some key factors that might influence the decision to participate
include: the likelihood of influencing the outcome (Bursztyn et al. 2018; Enos and Fowler 2014); campaign
effects (e.g., Enos and Fowler 2016); the ideology of the candidates running (e.g., Rogowski 2014); andracial threat (e.g., Enos 2016).
2
Some studies show that large changes in economic circumstances – winning the lottery, for example – affects
political participation and preferences (Doherty, Gerber, and Green 2006; Peterson 2016).

1

Marble and Nall 2018), pay more attention to local politics (Fischel 2001), and participate
in local elections at higher rates (DiPasquale and Glaeser 1999; Einstein, Palmer, and Glickn.d.; Oliver and Ha 2007).3
This paper builds on this work by testing whether becoming a homeowner leads a person
to participate more in local politics, perpetuating these inequalities, or whether these inequalities instead reflect pre-existing differences in the socioeconomic status and backgrounds ofhomeowners and non-homeowners. To do so, we combine administrative data on more than
18 million voters in Ohio and North Carolina with deed-level data on property ownership.
The resulting panel dataset allows us to track when individuals become homeowners and tosee how often they turn out to vote in local and national elections, what specific local issues
they are most likely to vote on, and to see several indicators of shifts in their preferences.
Using a series of individual-level difference-in-differences designs to account for preexisting differences between homeowners and non-homeowners, we find that becoming ahomeowner substantially increases an individual’s propensity to participate in local elections. A main version of our design compares individuals who buy homes of similar prices
at different times, in case non-homebuyers provide bad counterfactual trends for the typesof people with enough money to buy homes. We also implement a version of the differencein-differences design in which we exactly match individuals based on their turnout in four
pre-treatment electoral cycles—essentially, the strongest version of the synthetic matchingapproach from Abadie, Diamond, and Hainmueller (2010). We find similar estimates acrossthese design choices, suggesting that parallel trends is a valid assumption. We also find thatthe effect of becoming a homeowner on local election turnout is largest among younger homeowners and those who purchase single family residences, which suggests that “adult roles”
like marriage or planning for children could explain some, but not all, of the homeownershipeffect (Highton and Wolfinger 2001).

3

Other important work links these land-use restrictions supported by homeowners to racial segregation in
American cities (Rothstein 2017; Trounstine 2018).

2

We also find that this homeowner turnout boost may be linked to zoning policy. In
particular, we study local issues in Ohio, essentially ballot initiatives on local policy matterslike school funding, property taxation, zoning, and liquor sales (for another study thatuses data on local issues in Ohio, in particular education funding, see Kogan, Lavertu, andPeskowitz 2016).4 We collect data on what kinds of local issues appeared on which ballots,
allowing us to compare the turnout effect of becoming a homeowner across contexts. We
find that the homeowner boost in turnout is particularly pronounced—almost twice as large
as the overall effect—when zoning issues are on the ballot.
To investigate the motivations behind these participatory effects another way, we estimate
the effect of homeownership on local political participation across deciles of home purchaseprice. The effect is present even in the lowest home-price deciles, rises dramatically acrossdeciles, and is more than twice as large in the highest decile than in the lowest. Homebuyers
who have bought more expensive homes increase their participation in local elections more,
relative to their prior behavior, suggesting that their motivation to participate may have to
do with the size of the investment they are interested in protecting.
Though most theoretical work on homeowners focuses on local politics, we also find that
becoming a homeowner increases the propensity to participate in national elections, suggesting that homeownership causes a more general shift in individuals’ attention to politics. We
find that the increase in national election participation becomes larger after local elections
have taken place and among those who purchase their homes using Federal Housing Administration (FHA) mortgages, which suggests that habit formation and individual experience
with federal housing policy could explain some of the spillover of the homeownership effectbeyond local participation and into national election turnout.
In sum, becoming a homeowner leads individuals to participate more in politics, to pay
attention to issues that affect them as homeowners, and to participate in ways consistent withprotecting their investment in the value of their property, on average. In addition to helping
4

For studies of local issues in Texas, see Coate and Conlin (2004) and Coate, Conlin, and Moro (2008).

3

us understand the political advantage, and inequality in representation, of homeowners in
American politics (Einstein, Palmer, and Glick n.d.), the results suggest that instrumental
motivations are an important part of individual political behavior, and also suggest thatgovernment policies that encourage homeownership may have unanticipated downstream
political consequences as they change the behavior and preferences of those who purchasehomes.52Administrative Data on Homeownership and Voting

To study the connection between homeownership and political behavior, we link two large
datasets on individuals’ real-world behavior. The first dataset contains administrative voter
files for Ohio and North Carolina, collected from each state’s Secretary of State website.
We focus on these two states because they are among the states which offer voter files for
free, feature robust two-party competition in national elections, provide different measuresof party affiliation (voting in a partisan primary, in the case of Ohio, and registering witha party, in the case of North Carolina), and overlap well with our second dataset on homeownership.
Each voter file offers different strengths. In Ohio, turnout history dates back to the 2000
primary, so we can observe a registered voter’s full turnout history—including local election
turnout—from 2000-2017.6 In Ohio, voters register in person or by mail with their county
board of elections, or voters can register to vote online.7 Voters must register or update theirregistration no later than 30 days prior to an election; otherwise the registration will apply
for the next election. Ohio has an open primary system, where voters are not required toregister with a party in order to vote in a party’s primary. In fact, Ohio voters do not officiallyregister with a party at all. Instead, they affiliate with a political party by requesting the5

For a formal model related to this idea, see Prato (2018).
We use a copy of the Ohio voter file dated August 16, 2017. To add 2017 general election turnout from theNovember 2017 local elections, we merge in 2017 general election turnout from a newer copy of the Ohio
voter file dated June 16, 2018.
7
See https://olvr.sos.state.oh.us/
64ballot of a political party in a partisan primary election.8 Therefore, turnout in the Ohio
voter file includes information not only on whether a voter turned out in a primary election,
but also on which party ballot they requested.
While North Carolina also has information on which primary election ballot a voter requests, turnout in North Carolina only dates back to the 2008 primary election. Nonetheless,
North Carolina’s voter file provides some useful information that Ohio’s voter file does not.
North Carolina has a semi-closed primary system, meaning voters who register with a party
can only request the ballot of their political party in a partisan primary election but unaffiliated registrants can request any party’s primary ballot. Voter files often only containcurrent party registration rather than a full history of party registration. Fortunately, however, North Carolina has a series of voter file snapshots, and a common identifier allows
us to link individual voters to their party registration over time.9 Given this information,
we can understand how voter file purges – or removing individuals from the voter rolls –might be affecting our results.10 In North Carolina, voters register in person or by mailing acompleted voter registration form to their county board of elections, and they must registerno later than 25 days before an election.11
The second dataset contains information on property ownership, and was collected countyby-county from public records by CoreLogic, a private data vendor. The dataset provides
a variety of information about individual properties, including addresses, sales price, assessment values, and home characteristics, as well as the full name of the property’s owner ineach year from 2000-2017. We join the two datasets together by matching exactly on county,
zip code, street number, street name, last name, and first initial.12 In our dataset, 49.87%
8https://www.sos.state.oh.us/elections/voters/register/
The North Carolina voter file used for the analysis is dated January 1, 2017. To assign party registration
to each registrant, we merge registrants to their party registration in voter file snapshots from the following
dates: October 20, 2006; November 4, 2008; January 1, 2010; January 1, 2011; May 8, 2012; November 6,
2012; May 6, 2014; November 4, 2014; March 15, 2016; and November 8, 2016.
10
We can also use these snapshots to estimate the effect of becoming a homeowner not just on partisan
primary participation, but also on party registration.
11
See https://www.ncsbe.gov/Voters/Registering-to-Vote.
12
The homeownership data has two fields for property owner, so if there are two property owners we treat
each owner as their own row for the purposes of the merge to the voter file.
95

of individuals are in the voter file but not in the property ownership records, 21.99% are in
the property ownership records but not in the voter file, and 28.14% are in the voter file andmatch to property records.13
For each unique homeowner that does not merge to the voter file, we include them in our
analysis as not having voted. We also merge in yearly property history files to identify individuals in the voter file who were homeowners at one time during our panel, but are no longer
homeowners. Switching from owning to renting is exceedingly rare compared to switchingfrom renting to owning: Sinai (1997) shows that less than four percent of observations transition from owning to renting at some point over a 22-year panel. Our Ohio local elections
panel shows something similar. Among over 8.5 million unique individuals, we observe morethan 2.5 million instances of transitioning from from non-homeowner to homeowner, but
only about 85,000 instances of switching from homeowner to non-homeowner.
To construct the homeowner variable, we first define an individual to be a homeowner ifthey are listed as a property owner for any property designated as a single family residence,residential condominium, or duplex. Then, for analyses where general election participationis the outcome, homeownership is defined as whether an individual has been a homeowner at
any time in the period between the general election at time t and the general election at timet − 1.14 For analyses where primary election participation is the outcome, homeownership isdefined as whether an individual has been a homeowner at any time in the period between
the primary election at time t and the primary election at time t − 1.
There are three key issues with the way we construct our dataset. The first is thepossibility of false matches in the merge. If there are false positives (for example, if wematch a voter to the homeownership data when he or she is in fact not that homeowner),
we would falsely code that individual as treated when he or she should be in the control
13

The merge rates are similar across both states. 50.62% and 49.05% are in the voter file but not the property
records in Ohio and North Carolina, respectively. 20.13% and 23.99% are in the property data but not
the voter file, and 29.25% and 26.95% are in the voter file and matched to the ownership records.
14
For example, an individual is defined as a homeowner for the 2012 general election if they owned a homeat any time between the 2010 and 2012 general elections.

6

group. If there are false negatives (meaning we fail to match a voter to his or her homeownerrecord when that record actually exists), we would falsely code that individual as being
in the control group. Because both datasets are high quality, featuring full names withfew misspellings, these types of merge errors are likely to be rare. The merge is relatively
constrained because we use information on name, street number, and street name to link
records – so we can be confident that there are very few false positives, meaning individuals
who are identified as homeowners but are not actually that homeowner. Moreover, more than
96% of entries in the Ohio voter file are unique within county, zip code, last name, street
number, street name, and first initial, which helps to alleviate concerns about false matches
due to duplicate records within the variables we use for the merge. To the extent that false
matches are present, however, they will attenuate, or bias toward zero, the estimated effectof homeownership. In Section A.1 of the Appendix we provide some suggestive evidence tovalidate our record linkage procedure.
The second potential source of bias is that we use a version of the Ohio voter file from2017, which means that we do not not observe individuals in Ohio who have been purged
from the voter file prior to 2017. Voter file purges have become a contentious issue inmany states, and most recently in Ohio.15 Closely related, the third issue we face is due to“deadwood,” meaning obsolete records in the voter file, which could lead us to incorrectly
think homebuyers moving into Ohio from out-of-state have never voted before becoming ahomeowner, or that home-sellers who move out of Ohio never vote after selling their home.
For example, a voter who moves out of state but remains in the voter file should be coded
as missing, but the voter file does not distinguish between not voting and missingness. We
address these two issues below, after presenting the main results. In both cases, we find noevidence that they affect our conclusions.

15

https://www.nytimes.com/2018/06/11/us/politics/supreme-court-upholds-ohios-purge-ofvoting-rolls.html

7

3

Homeownership Increases Local Turnout

We begin by estimating the effect of buying a home on turnout in local elections. For thisanalysis, we focus on Ohio because its state voter file provides turnout information on local
primary and general elections. We include in this analysis all Ohio elections occurring in
odd-numbered years, which cover a mix of statewide and local elected offices, as well as anumber of local issues.

3.1

Difference-in-Differences Design

Without access to a randomized experiment in which some individuals become homeowners
while others do not, we have to use patterns of observed homebuying behavior. Specifically,
we estimate equations of the form

T urnoutit = βHomeownerit + γi + δt + it ,(1)

where T urnoutit is a simple indicator variable for whether individual i turns out to votein the local election held at time t. In our data for Ohio, various local elections are held
in all odd years from 2001 to 2017. The variable Homeownerit is an indicator for whether
individual i is a homeowner during the two-year period after the election at t − 1 and before
the election at time t. For example, for the year 2017, the homeowner variable would switchfrom 0 to 1 if the individual purchased a home in 2016 or prior to the election in 2017. Finally,
γi and δt stand in for individual and year fixed effects, respectively. In some estimates below,
we alter the year fixed effects in order to create different counterfactual trends (e.g., homevalue-by-year fixed effects.)

3.1.1

Plausibility of Parallel Trends

This difference-in-differences design relies on the assumption of parallel trends—namely, thatchanges in individuals’ turnout behavior after purchasing a home at time t would be the same8

as changes in turnout behavior for individuals who did not purchase a home at time t. In
the absence of a random shock that induces home buying, we have to lean heavily on thisassumption—as such, we pay close attention to several tests for its validity. In Table A.1,
we pursue two strategies to validate the parallel trends assumption. First, we add leadsof the homeowner variable, to see if we find evidence of pre-trending; reassuringly, we findsubstantively small coefficients on these leads, and the coefficient on the main effect for
homeowner remains similar in magnitude as our formal estimates in Table 1. Second, we redo the results using county-by-year fixed effects, so that homeowners’ counterfactual trends
are computed using only individuals who did not buy a home but live in the same county.16
We find extremely similar results in this setup. Taken together, these tests further add tothe plausibility of the parallel trends assumption.
In addition to these tests, we also estimate a version of the difference-in-differences thatmakes comparisons only among the set of people who purchase a home at some point, sothat everyone in the sample achieves the wealth necessary to be a homeowner. We also
include a separate set of time fixed effects within each decile of the home purchase price,so that counterfactual trends are computed among people with similar levels of wealth but
who purchased their homes at different times. Together, these strategies help address themain substantive concern around parallel trends, the possibility that individuals choose topurchase homes in times when they become wealthier, which may also be times when theirpolitical behavior is changing for other reasons as well. We continue to find similar results
across all of these approaches, bolstering our confidence in the estimates.

3.1.2

Exact Matching on Pre-Trends

Despite these strategies to make the parallel trends assumption more plausible, we still
face a situation where individuals might select into homeownership for many time-varying,16

The county-by-year fixed effects control for common shocks to turnout separately for each county, so theyalso control for the possibility of an important time-varying factor: voters in each county have different
candidates running in the local elections in their county.

9unobservable reasons. Given this type of setting, we turn to a strategy that, unlike the simple
difference-in-differences design, can accomodate the presence of time-varying unobserved
factors. To do this, we exactly match individuals on the basis of their turnout over fourelectoral cycles in order to find a control group likely to offer more accurate counterfactual
trends. This matching exercise is similar in spirit to Abadie, Diamond, and Hainmueller(2010). Because our dataset is large, and because our outcome variable is binary, we cando better than a synthetic match and can in fact find exact matches on pre-trends for every
treated individual. Specifically, we first remove from the data anyone who purchases a home
before 2008, and we subset to individuals who are old enough that they were eligible tovote by 2001, the first year in our sample, so that we have non-missing outcome values for
all four pre-treatment periods. We then divide the remaining sample into treatment andcontrol, where treatment means purchasing a home in 2008-2009 and control means notpurchasing a home in 2008-2009. Each treated or control individual thus has four periods ofpre-treatment turnout, 2001, 2003, 2005, and 2007, leading to 24 = 16 possible pre-treatment
outcome strata. For example, the most common stratum in the data is the set of people who
never turn out to vote in any election prior to 2008.

3.1.3

Issues of Bundled Treatment

Even if parallel trends is met, another obstacle to our approach is that individuals may
be making the decision to become a homeowner at a point in their life when they aremaking other changes as well—for example, accepting a new job, moving to a new place,having children, and so forth. Without being able to randomize homeownership directly, ourdesign estimates the overall effect of buying a home along with any correlated changes that
individuals make when they become homeowners that also affect political participation. In
a series of follow-up analyses presented below, we use variation in the size of the effect to getat the underlying mechanisms of the effect. Overall, the patterns of effects suggest a direct
link between being a homeowner and acting to protect one’s financial interest in one’s home.

10

3.2

Homeownership and Turnout: Graphical Evidence

In the spirit of the exact matching exercise we just described, Figure 1 plots turnout overtime for people who: (a) were eligible to vote in 2001; (b) did not turn out for any of thelocal general elections between 2001 and 2009; and (c) never owned a home before 2008. The
solid line represents the turnout behavior of individuals in this group who purchased homes
in 2008-2009, while the dashed line represents the average turnout behavior of individuals
in this group who did not buy a home in 2008-2009 (they may or may not have purchaseda home after 2009, we do not condition on this choice). Prior to 2008, no one in either thetreated or control group ever turns out by construction, which is why both lines start out flat
at 0 on the plot. After 2008, as the plot shows, 2008-9 home buyers turn out at noticeably
higher rates than those who don’t buy homes in 2008-9. The analyses below will show that
this pattern holds when we include all 16 matched strata.

3.3

Homeownership and Turnout: Formal Evidence

We now turn to formal estimates of the effect of becoming a homeowner on participation
in local politics. Table 1 presents the results for turning out in general elections (first fourcolumns) and primary elections (second four columns). As the table shows, we find consistentevidence that individuals participate more in politics after becoming homeowners.
In column 1, we perform the simple difference-in-differences, which includes individual
fixed effects and year fixed effects. In this specification, we are estimating the counterfactual
trends for homebuyers using all non-homeowners. Since the obvious concern is that wealthier
individuals are more likely to buy homes and may have different trends in political behavior
than people who can’t afford to buy homes, in columns 2 and 3 we focus only on homeowners.
In column 2, using only the set of all individuals who buy a home at some point in our sample,
we include a separate set of year fixed effects for each decile of home price. This means that
we are constructing counterfactual trends for each homebuyer using the turnout behavior ofother individuals who go on to buy a similarly priced home but have not yet done so. The
11

Figure 1 – Homeownership and Turnout in Local Elections, Ohio,
Exact Matching on Four Cycles of Pre-Treatment Turnout. Individuals become substantially more active in local politics after purchasing ahome.
2008-9 Home Buyers

Rate of Turnout in General Election

.2

.15

.1

Non-Homeowners

.05

0
2000

2005

2010

2015

2020

In this analysis, we focus only on individuals who bought a home in 2008-2009, have never before
owned a home, did not turn out in any of the elections from 2001-2007, and were eligible to voteas of the local election in 2001. As the plot shows, those who buy homes in 2008-2009 go on toturn out in subsequent local elections at a markedly higher rate than those who do not buy homes
in this period.

results are extremely similar using this alternative setup. In column 3, we go back to using
simple year fixed effects, but we continue to only include homeowners, so that counterfactual
trends are coming from other homebuyers who have not yet bought their homes. Again, theestimate is very similar.
In column 4, we implement the formal version of the exact matching strategy we described
before. For each of the 16 strata in terms of turnout behavior in 2001, 2003, 2005, and2007, we calculate the difference in average turnout rates for 2008-9 homebuyers and nonhomebuyers for all election years from 2009 onwards, and we then average these differences
over the 16 strata, weighting the average by the number of observations in each stratum. Here
we find a similar, though smaller, estimate. Looking across the columns, the estimates range
from 2.7 percentage points to 5.1 percentage points. These estimates are highly precise, and12Table 1 – Effect of Homeownership on Political Participation in
Local Ohio Elections.

(1)
Homeowner

Turnout in General = 1
(2)
(3)

(4)

(5)

Turnout in Primary = 1
(6)
(7)

(8)

0.049
(0.000)0.049(0.000)0.051(0.000)0.027(0.000)0.019(0.000)0.022(0.000)0.022(0.000)0.022(0.000)Observations
76319157
Outcome Mean
0.265
Individual FEs
Yes
Year FEs
Yes
Year-by-Value FEs
No
SampleFull37897139
0.290
Yes
No
Yes
Owners37897139 7546032
0.290
0.262
Yes
No
Yes
Yes
No
No
Owners Matched

71366039
0.065
Yes
Yes
No
Full

36224042
0.075
Yes
No
Yes
Owners36224042
0.075
Yes
Yes
No
Owners4767732
0.063
No
Yes
No
MatchedRobust standard errors clustered by individual in parentheses; standard errors in columns 4 and 8 are robust
without clustering, as the data is collapsed by stratum-year. Columns 2, 3 and 6, and 7 include only
individuals who become homeowners at some point during the study period. Columns 4 and 8 include only
individuals who are exactly matched on the basis of 4 pre-treatment periods of the outcome variable.

they reflect large changes from baseline. The baseline turnout rate in local general electionsin Ohio is roughly 27%—that is, 27% of individuals present in our sample turn out to votein any given local election—which means that these estimates reflect roughly a 10 to 19%
increase in the propensity of turning out, across specifications.
The final four columns of the table repeat this exercise for primary elections, finding
similar results. Although the difference in raw probabilities of turning out are lower for
primaries, because fewer people vote, the proportional increase induced by homeownershipis actually considerably large for primaries. Using the exact-matching results, for example,
homeownership is estimated to cause roughly a 35% increase (2.2 / 6.3) in turning out inlocal primaries.

3.4

Adult Roles and Other Life Events

As mentioned before, we are estimating the overall effect of buying a home along with any
correlated changes that individuals make when they become homeowners that also affect political participation. Besides changes in wealth, another explanation for the observed effects
could be changes in adult roles, such as employment, marriage, or planning for children (e.g.,
Highton and Wolfinger 2001). If these time-varying, unobserved attributes lead individuals
13

to be more likely to buy a home and become more invested in local politics, this would bias
our estimates upward. While we cannot directly rule out some of these social or psychological explanations using this type of administrative data, we can provide some suggestive
empirical evidence for how important these explanations might be. First, to understand how
the effect of homeownership on local election turnout varies for individuals at different life
stages, in Figure 2 we estimate the effect separately for each year of birth, which we canobserve in the Ohio voter file.17 In practice, we estimate this equation using our preferred
specification of separate time fixed effects for each decile of home purchase price, as in, forexample, column 2 of Table 1.18 We find that the effect varies across age, with the effects
being largest among the youngest group in our sample. For example, our estimated effectfor those born in 1975, who were 25 years old at the beginning of our study period, is nearly
4 percentage points larger than the effect among those born in 1955, who were 45 years oldat the beginning of our study period. This is consistent with, although does not prove, thatsome of the effect of homeownership that we estimate could be a function of changing roles
in early adulthood that correlate both with the decision to buy a home and to become moreinvested in local politics.
To test these adult roles explanations more formally, in the first four columns of Table
2 we replicate our main results from Table 1, but we subset to individuals who were born
before 1960, or who were over the age of 40 at the start of our panel. These individuals areless likely to be experiencing some of the life changes that Highton and Wolfinger (2001)
identify as correlated with large changes in political participation, and that we speculate
likely correlate with the decision to purchase a home, like marriage or planning for children.
When we subset to those born before 1960, the effect of becoming a homeowner on turnout inlocal general elections remains substantively large, which is consistent with the explanation17

In these analyses, we include only individuals who are registered to vote because we observe individuals’
year of birth in the voter file but not in the homeownership records.
18
We privilege this specification because it compares homeowners to individuals who would eventually purchase a similarly priced home, but had not yet done so. By doing this, we are able to make comparisonsamong individuals who likely have more similar wealth, making the parallel trends assumption more plausible.

14Figure 2 – Effect of Homeownership on Local Turnout Across Yearof Birth.

Effect on Pr(Vote in General)

.12

.1

.08

.06
1945

1950

1955

1960

1965

1970

1975

1980

Year of Birth

Note: Each point represents a point estimate as in equation 1, estimated separately
for each birth year, including individual fixed effects and home value decile-by-year
fixed effects. 95% confidence intervals are from robust standard errors clustered byindividual.

that while individuals taking on adult roles might drive some of the effect, it cannot explain
all of the effect of becoming a homeowner that we observe.19 In column 5 we evaluate this
explanation in another way by including separate time fixed effects for every birth year, sothat homeowners counterfactual turnout trends are only computed using non-homeowners
who share the same birth year. By comparing individuals who are the same age, we are making counterfactual comparisons among those who are more likely to be experiencing similar
events in the political life cycle. Again, we find that the effect of becoming a homeowner issimilar.
Lastly, in column 6, we rely on information about the type of property that individuals
purchase to evaluate the hypothesis that the effect is driven by individuals planning forchildren, albeit indirectly. We include an interaction of our homeowner variable with whether

19

Note that because Table 2 includes only registered voters, the magnitude of the coeffcients should not
be compared directly with those in Table 1, where property owners that are not registered to vote areincluded. When comparing the effects as a percent increase over the baseline turnout mean, the effects inTable 2 are slightly smaller.

15

Table 2 – Effect of Homeownership on Political Participation in
Ohio Local Elections.

Homeowner

(1)

(2)

0.077
(0.001)

0.056(0.001)

Turnout in General = 1
(3)
(4)

(5)

(6)

0.084
(0.000)0.022(0.001)11667015 2438514 59564247
0.582
0.448
0.340
Yes
No
Yes
Yes
Yes
No
No
No
No
No
No
Yes
Owners Matched
Full
Yes
Yes
No

0.034
(0.001)42597560
0.282
Yes
No
Yes
No
OwnersNo

0.058(0.001)

0.075(0.000)Homeowner * Single Family Residence
Observations
Outcome Mean
Individual FEs
Year FEs
Year-by-Value FEs
Year-by-Age FEs
SampleBorn before 1960

22735762
0.508
Yes
Yes
No
No
Full
Yes

11667015
0.582
Yes
No
Yes
No
OwnersYes

Robust standard errors clustered by individual in parentheses; standard errors in columns 4 are robust
without clustering, as the data is collapsed by stratum-year. Columns 1 through 4 include only individuals
born before 1960. Columns 2, 3 and and 6 include only individuals who become homeowners at some point
during the study period. Column 4 includes only individuals who are exactly matched on the basis of 4
pre-treatment periods of the outcome variable.

the property is designated in the property records as a single family residence, where we might
expect that homebuyers of single family residences are more likely to have done so with theintention of planning for children. In this specification, we find that individuals become
about 2.2 percentage points more likely to participate in local elections after purchasing a
condominium or duplex, and they are about 5.6 percentage points more likely to participate
after purchasing a single family residence (summing the coefficient on homeowner and theinteraction term). Admittedly, this is consistent with many different interpretations, but
it might signal that the effect of becoming a homeowner is more pronounced among those
we might expect are planning to have children. However, the effect is positive and remains
substantively large even among those who do not purchase single family residences. Overall,the evidence suggests that adult roles and other life events could explain some, but not allof the effect of becoming a homeowner.

16

3.5

Voter-File Purges and Selection Bias

As we mentioned before, Ohio “purges,” or removes, individuals from the voter file if they
have not voted in recent elections. This could bias our estimates of the effect of homeownership if, for some reason, homeowners are purged at a higher rate than non-homeowners,
so that the homeowner turnout rate we compute is inflated because it omits a set of homeowners who turn out so infrequently that they are purged from the voter file. On the other
hand, if the rates of purging are roughly equal for the homeowners and non-homeowners,
then purging should not affect our estimates. To test for this potential bias, we exploit a
unique feature of the North Carolina voter file, where the information of individuals purged
from the voter file is preserved along with a variable indicating that they have been removed. In Table A.2, we compare results for the effect of homeownership on turnout inNorth Carolina national general elections where we include and exclude purged voters fromthe analysis. The effect sizes are quite similar, although the effect of becoming a homeowneron participation is slightly larger in percentage terms when we include the removed voters.
This suggests that non-homeowners are more likely to be purged than homeowners, so wemight be slightly overestimating turnout for non-homeowners, and thus underestimating theturnout boost from homeownership.
There is another potential source of bias in our estimates, which comes from the factthat we impute turnout equal to zero for those whose age made them eligible to vote but
did not turn out. It could be that someone moves in from out of state, where they had beena long time voter, but they have no history of voting in Ohio.20 In these cases, we impute
zero for turnout prior to their move, which would bias our estimate of the homeownershipeffect upward. Similarly, it could be that a habitual voter sells their home, moves out ofstate, and continues voting in another state – but we would impute their turnout as zero

20

This is unlikely to be a large concern, given that the rate at which individuals move from state to stateis relatively low. About 2.1% of individuals in the United States moved out of state from 2016-2017,and about 1.7% in the Midwest moved out of state in that period (see https://www.census.gov/data/tables/2017/demo/geographic-mobility/cps-2017.html).

17

after they move. This would also lead us to overestimate the effect of homeownership. To
deal with these potential issues, in Table A.3 we include a robustness check that includes
the same specifications as Table 1, with a few adjustments. First, to address the concernthat we are mistakenly setting turnout to zero for habitual voters that move in from outof state, we drop individuals whose registration year is greater than or equal to the year of
their home purchase. Second, to address the concern that we are mistakenly setting turnout
to zero for habitual voters that sell their home and move out of state, we set turnout tomissing for individuals after they sell their home. The results in Table A.3 are positive andin fact larger than the results in Table 1. The most likely explanation for why the incorrect
imputation of voting outcomes for out-of-state movers does not upward bias our estimatesis that out-of-state movers are a very small fraction of the dataset.

4

Homeownership and Turning Out for Zoning Initiatives

Thus far, we have explored the effect of homeownership on overall turnout in local electionsin Ohio. While homeownership appears to increase individual political participation in ameaningful way, we have not yet seen, beyond suggestive evidence about adult roles, any
indication of why it does so. Here, we explore the specific policy issues that appear togalvanize homeowner turnout. To do so, we take advantage of the particular institution of“local issues” used in Ohio. In Ohio, many questions of local policy are voted on in what
are essentially public referendums or ballot initiatives, called local issues. These issues are
voted on many different levels of aggregation, such as the county, the city, the village, thetownship, the school district, and a number of other special districts. Within a particularcounty, different individuals will face different ballots due to their locations in different
villages, townships, school districts, and so forth. These many overlapping districts lead to18

considerable variation in the issues that different individuals have the opportunity to voteon in any given year.21
We collected data on local issues for 2013, 2015, and 2017 in Ohio. The dataset indicateswhat types of issues were voted on in which political units for each year. Using descriptionsof each local issue provided in the data, we categorized the local issues into eleven mutually
exclusive categories.22 These are shown in Table 3.23
Using these topic codings, we estimate interactive difference-in-differences using the equation

T urnoutit = βHomeownerit +P11

ηj Homeownerit × 1{Topic j on ballot it }+P11

ρj 1{Topic j on ballot it } + γi + δt + it ,

j=1
j=1

(2)

which is the same as equation 1 but with the addition of interactions of the homeownervariable with indicators for whether each of the eleven topics j is present on voter i’s ballot
in the election at time t. With the inclusion of individual and year fixed effects, the variationfor estimating these interaction terms comes from the fact that in different election cycles, thecounty, city, township, village, and/or school district may each have different types of local
issues on the ballot. The omitted category is a catch-all for elections with miscellaneous or
no local issues. We estimate this equation using our preferrerd specification of separate time
fixed effects for each decile of home purchase price, as discussed with column 2 of Table 1. To
be clear, the variation in the presence of certain issues on ballots is not randomly assigned;
nevertheless, examining variation in the effect of homeownership across these issues at least

21

For more on the complicated nature of overlapping special districts, see Berry (2009).
For a detailed description of how we coded each local issue, see section A.4 of the Appendix.
23
In Ohio, many liquor referendums are voted on at the precinct level. Given the structure of the local
issues data and the fact that the precinct names are not standardized across the voter file and local issues
data, we often merge liquor referendums to the voter file at the city level. To the extent that there ismeasurement error in whether a particular voter had a liquor referendum on his or her ballot, this would
attenuate the additional effect of homeownership for when issues related to liquor are on the ballot.
22

19

Table 3 – Types of Local Issues Voted on in General Elections,
Ohio, 2013-2017% Voters with Issue on Ballot in General Election
Issue TypeCountyCity

TownshipSchoolVillage

Total

Aggregation
CemeteryCharterCurrent Expenses
LiquorParks
Public Safety
Roads
SchoolSeniors and Children
ZoningMiscellaneous

0.010
0.000
0.093
0.040
0.000
0.051
0.014
0.015
0.000
0.145
0.000
0.501

0.010
0.000
0.068
0.004
0.018
0.009
0.020
0.012
0.000
0.001
0.007
0.019

0.010
0.010
0.000
0.008
0.038
0.005
0.079
0.035
0.000
0.002
0.005
0.003

0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.187
0.000
0.000
0.000

0.003
0.001
0.002
0.007
0.001
0.002
0.008
0.003
0.000
0.000
0.000
0.002

0.030
0.011
0.157
0.056
0.056
0.067
0.117
0.064
0.187
0.148
0.012
0.513

Any

0.609

0.135

0.158

0.187

0.023

1.000

Note: The table shows the percentage of registered voters that have each issue type appear on theirgeneral election ballot, separated by locality. The total column shows the percentage of registeredvoters that have that issue type appear on their general election ballot in any of their registeredlocalities. Aggregation refers to electric and gas aggregation. Cemetery refers to issues related tomaintaining and operating cemeteries. Charter refers to amendments or revisions to local charters.
Current expenses refer to adding, renewing, or replacing levies to maintain the locality’s current
operating expenses. Liquor refers to liquor referendums, often relating to Sunday sales or liquor sales
at a particular location. Parks refers to maintaining or improving local parks, recreation centers,
and recreational services. Public safety refers to adding, renewing, or replacing levies for local fire
protection, police protection, or ambulances and emergency medical services. Roads refers to adding,renewing, or replacing levies to maintain roads, streets, bridges, and other local infrastructure. Schoolrefers to building or improving local schools, as well as covering operating expenses. Seniors andchildren refers to providing and maintaining services and facilities for senior citizens and children.
Zoning refers to local zoning plans or amendments. Miscellaneous is any issue not sorted into any of
the above categories. Any is whether the voter had any issue type, including miscellaenous, appear
on their general election ballot.

permits us to see if patterns are consistent with the idea that homeowners pay attention toparticular issues related to their homeownership.
Figure 3 presents the results. Each point in the plot reflects the corresponding interaction
coefficient from equation 2. The largest interaction coefficient is for zoning. When a local
issue concerning zoning is on the ballot, the effect of homeownership on turnout is nearly 4
percentage-points larger—an increase in effect size of roughly 100% over the baseline estimate
from column 2 in Table 1. Since zoning is one of the most important mechanisms by which
homeowners can influence the quantity and types of housing that a locality allows to be built,

20

Figure 3 – Homeownership and Turnout Across Local Issues. Eachpoint represents an interaction coefficient from equation 2, estimating theadditional effect of homeownership on general-election turnout when a given
type of local issue is being voted on in the county, city, township, school district, or village. The regression employs value decile-by-year fixed effects,
as in column 2 of Table 1. Bars are 95% confidence intervals from robust
standard errors clustered by individual. As the plot shows, homeownershipparticularly appears to encourage turnout when issues regarding aggregation, public safety, schools, seniors and children, or zoning are being voted
on.
zoningseniors and children
aggregation
public safetyschools
cemetaryroads
liquorcurrent expenses
charter
parks
-.04

-.02

0

.02

.04

Additional Effect of Homeownership on Turnout

the fact that this interaction coefficient is so large is consistent with the idea that homeowners
form political preferences, and act on these preferences in elections, partially on the basis of
their individual circumstances. Rather than behaving only based on long-running, inherited
views, individuals become more politically active after becoming homeowners, and become
especially active on zoning votes that have the potential to impact the value of their homes
directly.
After seniors and children, the category that exhibits the next largest additional effect of
homeownership on turnout is aggregation. Aggregation votes are votes on whether localities
should negotiate rates for service—usually, electricity service—as a collective. As such,
each aggregation vote has a direct impact on each individual’s pocketbook, increasing or
decreasing their personal costs depending on the terms and the status quo. The fact that21

aggregation votes are associated with a substantial increase in the effect of homeownership on
turnout again suggests that economic incentives can directly lead to costly political behavior.
The remainder of the issue areas exhibit a range of interaction effects, some negativeand some positive. While some of the variation seems plausibly related to preserving andincreasing home values—public safety, schools, and seniors and children issues are the next
three largest interactions, and all may have implications for home values as well as thepsychological value of one’s home—we hesitate to draw overly strong conclusions. Issues
related to cemeteries, charters, liquor, and parks may all be lower salience because they relateless clearly to home values, but votes concerning current expenses seem highly relevant tohomeowners’ political interests yet are associated with smaller effects of homeownership on
turnout. Likewise, votes to reform charters could potentially have important implications
for homeowners. Understanding the details of these votes, and how much of an impact each
vote would have on home values, would be a logical step to follow up on these analyses in
the future.
To summarize, a major shift in an individual’s economic and social situation—becoming
a homeowner—has important effects on the propensity to participate, and this effect is concentrated in votes on particular issues of interest to homeowners. Homeowners are especiallymore likely to turn out when they have the opportunity to vote on local issues related tozoning policy. These patterns are entirely consistent with the so-called “homevoter” hypothesis (Fischel 2001), and they help us to understand the behavioral roots of the homevoter.
Homeownership changes individuals’ political behavior, making them pay more attention
to political issues related to their new asset and encouraging them to participate in local
elections at higher rates.

22

Figure 4 – Effect of Homeownership on Local Turnout AcrossHome Purchase Price.

Effect on Pr(Vote in General)

.07

.06

.05

.04

.03

.02
0

2

4

6

8

10

Home Value Decile

Note: Each point represents a point estimate as in equation 1, estimated separately
for each decile in terms of home purchase price, including individual fixed effects andyear fixed effects. 95% confidence intervals from robust standard errors clusteredby individual are not plotted because they are too small.

5

Investment vs. Long-Term Stability Mechanisms

So far, we have estimated the effect of buying a home on local election turnout, and we havepresented evidence that specific policy issues—such as zoning initiatives—seem particularly
related to the homeowner turnout boost. Here, we consider whether these effects seem torelate to an investment motivation, as in the homevoter literature, where homeowners arethought to participate out of a desire to preserve and increase the value of their property,
and whether they relate to a time-horizon motivation in which homeownership creates longterm residential stability, leading individuals to care more about local politics regardless of
personal economic incentives. We stress that these are not mutually exclusive mechanisms.
To examine the investment mechanism, in which homeowners act to preserve the valueof their home, in Figure 4 we estimate the effect of becoming a homeowner on local turnout
across home purchase price. The logic is that purchases of more expensive homes have23

Figure 5 – General-Election Turnout Among Long-Term Homeowners and Long-Term Renters Who Voted in 2008.

Rate of Turnout in General Election

1.9Long-term Homeowners.8Long-term Renters
.7

.6
2010

2012

2014

2016

Note: The squares represent the general election turnout rate among long-term
homeowners, defined as those who live at the same home address from 2008-2016.
The circles represent the general election turnout rate among long-term renters,
defined as those who live at the same rental address from 2008-2016. Long-term
homeowners turnout in general elections at higher rates than long-term renters.
For this figure, we condition on residents who voted in the 2008 general election.

bigger assets to protect and therefore more incentive to participate.24 Each point in the
figure represents a point estimate from equation 1, but we estimate it separately for eachdecile of home purchase price. The effect of homeownership for individuals who buy more
expensive homes is clearly larger than for individuals who buy less expensive homes. Basedon this evidence, it appears that individuals with higher-valued assets are encouraged toparticipate even more in local elections. If homeownership only encouraged turnout throughthe time horizon mechanism, we would not expect to see effects vary as a function of purchaseprice.25
24

Relatedly, existing evidence suggests a link between home price and political preferences (Ansell 2014),
though the focus in that work is on appreciation while we are focusing on the initial size of the home
investment.
25
Related to the investment mechanism, it could be that the effect of becoming a homeowner on participationis larger for individuals with home mortgages than for those who do not take out mortgages. The logic isthat leveraged individuals with home mortgages face greater financial risk from their homes depreciating,and so might be more motivated to protect the value of their home. In Table A.4 in the Appendix, weinteract homeownership with whether there is a mortgage associated with the home purchase, and we do
not find substantial differences in the effect size among homeowners with and without mortgages.

24Second, it could be that homeowners and renters who have lived in the same area for along time are similarly motivated to participate in politics, but that homeowners have higher
residential stability than non-homeowners.26 Survey evidence suggests that homeowners are
more likely to vote than renters, but also that residents who have lived in an area for along time are much more active in their communities than newer residents (McCabe 2016).27
The residential stability explanation would suggest that long-term homeowners and longterm renters should be similarly likely to participate in politics. In Figure 5, we present
some descriptive evidence comparing general-election turnout among long-term homeowners
and long-term renters in North Carolina. We use North Carolina rather than Ohio for thisanalysis because the North Carolina voter file has snapshots over time, so we can identify
long-term residents. We categorize individuals as long-term residents if their residential
address in the voter file is the same for every voter file snapshot from 2008 through 2016.
Individuals that merge to the deed records are long-term homeowners, while those that do
not merge to the deed records are long-term renters. To avoid issues related to the possibility
that renters could disproportionately be deadwood in the voter file, we condition on having
voted in the 2008 general election, and then we compare general election turnout among theselong-term homeowners and long-term renters from 2010 through 2016. We can see in Figure 5that long-term homeowners are about 10 percentage points more likely to vote in the generalelections than long-term renters (overall turnout rates are high for both groups because wehave conditioned on voting in 2008). This evidence is descriptive, so we would not want toascribe the difference in general-election turnout to the effect of purchasing a home, alone, but
the figure does provide some suggestive evidence that long-term homeowners are behaving
differently than long-term renters. Again, this seems consistent with the explanation thatthe incentives associated with owning a home encourage political participation.
26

Residential stability positively correlates with political participation (Ansolabehere, Hersh, and Shepsle2012).
27
Interestingly, McCabe (2016) shows that residential stability explains differences in many communityminded social behaviors, like volunteering or other neighborly activities. The evidence for differences
between homeowners and renters on these types of activities, meanwhile, is mixed.

25

6

Homeownership and Turnout in National Elections

So far, we have documented the link between homeownership and local elections, following
the homevoter literature. However, we can also use our data to investigate possible links between homeownership and national elections. There are cross-cutting theoretical predictions
that make it valuable to study the effect of homeownership on national political behavior. On
the one hand, by stimulating political attention at the local level, homeownership may also
have spillover effects, leading individuals to become more politically aware in general, boosting turnout in national elections. This would be in line with predictions from Prato (2018),
which suggests that homeownership leads to a permanent shift in information acquisition
among voters. Relatedly, national election turnout might increase as a result of habit formation, where individuals become homeowners and participate more in local elections, andthis habit affects their participation in national elections. Additionally, while the homevoter
literature often suggests that homeowners should be relatively indifferent to national politics, the federal government does play an important role in subsidizing housing and offering
preferential tax treatment to homeowners. On the other hand, local political participation
could be a substitute for national political participation, in which case homeownership, by
shifting attention to local politics, could detract from national political participation. We
stress that these explanations need not be mutually exclusive.
Table 4 shows turnout estimates using the same approach from Table 1, following equation
1. As the table shows, we find substantial effects of homeownership on turnout in national
elections, both for general elections (left four columns) and primary elections (right fourcolumns). The wide variation across specifications makes us less confident about the precise
magnitude of these effects—and bear in mind that the baseline rate of turnout is quite abit higher in national elections—but we see across-the-board evidence for positive turnout
effects.
In Table 5 we rely on variation in the size of this effect on national turnout to better
distinguish between possible explanations for this increase. First, to test for the possibility
26

Table 4 – Effect of Homeownership on Political Participation in
National Elections, North Carolina and Ohio.

(1)
Homeowner

0.082(0.000)Observations
117864385
Outcome Mean
0.316
Individual FEs
Yes
Year FEs
Yes
Year-by-Value FEs
No
SampleFull

Turnout in General = 1
(2)
(3)

(4)

(5)

Turnout in Primary = 1
(6)
(7)

(8)

0.082
(0.000)0.084(0.000)0.041(0.000)0.051(0.000)0.032(0.000)0.034(0.000)0.050(0.000)44410953
0.422
Yes
No
Yes
Owners44410953
0.422
Yes
Yes
No
Owners13703508
0.250
No
Yes
No
Matched126673490
0.135
Yes
Yes
No
Full

44981204
0.208
Yes
No
Yes
Owners44981204
0.208
Yes
Yes
No
Owners14618592
0.115
No
Yes
No
MatchedRobust standard errors clustered by individual in parentheses; standard errors in columns 4 and 8 are robust
without clustering, as the data is collapsed by stratum-year. Columns 2, 3 and 6, and 7 include only
individuals who become homeowners at some point during the study period. Columns 4 and 8 include only
individuals who are exactly matched on the basis of 4 pre-treatment periods of the outcome variable.

of habit formation driving the increase in turnout, we include an interaction of the homeowner variable with an indicator for whether, for a given homeowner, the national election
occurs after a local election has taken place. The intuition is that if habit formation fromparticipating in local elections is driving all of the effect on national turnout, we should only
observe the effect of becoming a homeowner after there has been a local election for that
homeowner. In column 1 of 5 the coefficient on the homeowner variable suggests that theeffect of becoming a homeowner on national election turnout is about 4 percentage points
before a local election has taken place. The fact that this coefficient is positive suggests that
habit formation cannot explain all of the effect – however, looking at the interaction term,
the effect increases substantially, to about 9.5 percentage points, after a local election has
taken place. This is consistent with, although does not prove, that homeowners increase
their participation in local elections, and that voting habit spills over to national election
turnout.
Next, we test whether the federal government’s role in housing might explain some of
the increase in national election turnout. To do this, in column 2 of Table 5 we interact the
homeowner variable with whether a homeowner acquired their home through a mortgage fromthe Federal Housing Administration (FHA). If voters experiencies with national level housing

27

Table 5 – Effect of Homeownership on Political Participation in
National Elections by Local Election Timing and FHA Mortgage
Status, Ohio.

Turnout in General = 1(1)(2)

Turnout in Primary = 1(3)(4)

Homeowner

0.040(0.000)0.007(0.000)Homeowner × After Local Election

0.055(0.000)Homeowner × FHA Mortgage
Observations
Outcome Mean
Individual FEs
Year-by-Value FEs
Sample37815321
0.457
Yes
Yes
Owners0.077(0.000)0.032(0.000)0.031(0.000)0.021(0.001)37815321
0.457
Yes
Yes
Owners37815321
0.230
Yes
Yes
Owners0.001(0.001)37815321
0.230
Yes
Yes
OwnersRobust standard errors clustered by individual in parentheses; all columns include only individuals
who become homeowners at some point during the study period.

policies drive increases in national election turnout, we would expect that the effect should
be larger among those with FHA mortgages. Indeed, we see that the effect of becoming
a homeowner on national general election turnout is more than 2 percentage points higher
among those with FHA mortgages than among those without. This is consistent with the
explanation that voters’ firsthand experiences with federal housing policy encourage turnout
in national elections. In columns 3 and 4, we repeat the same results using turnout innational primary elections as the outcome, and we see similar patterns.
Overall, becoming a homeowner leads to an increase in national election turnout, suggesting that homeowners pay more attention to politics in general, beyond only local politics.28
Follow up analyses suggest that some of this increase could be coming from information
spillovers, from habit formation, and from firsthand experience with federal housing policy.

28

In Section A.6 of the Appendix, we also estimate the effect of homeownership on partisan preferences,finding that homeowners become more likely to participate in both Democratic and Republican primaryelections, on average.

28

7Conclusion

Understanding whether and how individuals translate their personal circumstances into
costly political behavior is a fundamental question in political economy. Top-down accountsof politics often focus on the conflict between the haves and the have-nots, but bottom-up accounts of individual voters in democracies struggle to explain how, if at all, individuals map
their experiences and incentives to political action. For example, summarizing the politicalbehavior literature that is largely pessimistic about the role of self interest in political behavior, Marble and Nall (2018: 1) write: “low-income conservative Republicans and affluent
liberal Democrats alike are described as voting ‘against their own interests’.”
As Marble and Nall (2018) argues, these questions are particularly salient with regard
to housing policy in the United States. The success of local communities in restricting the
housing supply has deterred many individuals from moving to the areas that offer the mosteconomic opportunities. In the aggregate, these outcomes suggest the success of homeowners
in pursuing their self interest. Our analyses suggest one part of the explanation for thissuccess. Becoming a homeowner causes individuals to participate more in local and national
politics, on average, at least in North Carolina and Ohio. These participatory effects ofhomeownership help to explain apparent homeowner advantages in the policy process at
both the local and national level.
Documenting these effects does not explain why homeowners choose to participate more.
We have attempted four main follow-up analyses to try to shed light on this question.
First, we explored how the effect of homeownership on local election turnout varies by age
and property type, finding that the effect is largest among younger homeowners and those
who purchase single family residences. This suggests that formative life experiences, or“adult roles,” could be motivating increased attention to local politics. Nonetheless, we stillfind postive and large effects among older homeowners and among owners of condomiums
and other types of property, suggesting that preserving home values could still explain asubstantial portion increased participation in local politics.
29

Second, we explored local issues voted on in Ohio, finding that homeowners are particularly mobilized to vote on initiatives related to zoning policy. This suggests that homeowners
at the local level are able to solve the collective action problem and mobilize to influence
the policies that most directly regulate the supply of homes. Whether they solve this problem simply through an individual preference to participate, or through group-level social
sanctioning or encouragement, or through other means, is an important question for future
work on this subject, but the initial takeaway seems relatively clear: many individuals inour sample translate their personal circumstances into costly political behavior despite the
incentives to free-ride off of the political actions of other citizens.
Third, we examined how the effect of homeownership on local political participation varies
with the price paid for the home. Homeowners who own more valuable homes exhibit larger
increases in their propensity to participate in local elections. This suggests that individuals
may be motivated to participate in local politics in order to preserve the value of theirinvestment in their home, just as the homevoter hypothesis would predict.
Fourth, we explored differences in the effect of homeownership on political preferencesat the national level, finding that voters become more likely to vote in national elections.
These effects are largest after a local elections have already taken place and among those with
FHA mortgages, which suggests that habit formation and individual experiences with federal
housing policies explain some of the increase in national election turnout. Although existing
research on the voting behavior of homeowners focuses on local politics, where homeowners’
economic incentives are sharpest, our evidence suggests that these incentives have spillovers
into national politics as well.
In addition, these results suggest that policies that encourage or reward homeownershipmay have political consequences. While we stress that we have not carried out a credible
program evaluation of an intervention to stimulate homeownership, by getting people into
homes through mortgage assistance, subsidies, and favorable tax treatment, the federal government may produce a more active citizenry. Normatively, this may be a double-edged30

sword. On the one hand, encouraging active participation in politics is generally considered a positive thing; on the other, encouraging it via homeownership may increase politicaland economic inequality. Because homeownership appears to encourage participation, policies that encourage homeownership may prop up existing status quos by creating a larger
constituency in favor of restrictive zoning policies and other pro-homeowner policies that
disadvantage those without property.
Finally, and more broadly, our analyses may suggest the value of turning to administrative
data to answer questions about individual political behavior. Although survey analyses ofhomeowners have proven fruitful for many purposes, there is considerable value in being able
to scrutinize the costly, real-world behavior of homeowners and non-homeowners choosing
whether to engage in the political process. The increased availability of voter-file data, alongwith the growth of proprietary datasets on property ownership, present exciting opportunities
for future research in this area.

31

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34

Online Appendix
Intended for online publication only.

A.1

Validating the Record Linkage

To help validate our record linkage procedure, for each county we compare the rates ofhomeownership from our merge to the proportion of individuals who live in owner-occupied
housing according to the 2010 Census. The results for Ohio are shown in Figure A.1. Our
homeownership rate in a county is given by the number of unique homeowners in the property
records divided by the sum of all homeowners plus registered voters who did not merge toa homeownership record. Because we miss individuals who are neither property owners nor
registered voters, our homeownership rate will be lower on average than the proportion livingin owner-occupied housing from the Census. Nonetheless, we find that these quantities arestrongly positively correlated: in counties where a larger proportion live in owner-occupied
housing, we find a larger proportion of individuals in the voter file are homeowners. Thisevidence makes us more confident in the record linkage procedure.
Figure A.1 – Rates of Homeownership Versus Rates of Living in
Owner-Occupied Housing, Ohio Counties

Percent Living in Owner Occupied Housing

.9

.8

.7

.6

.5
.3

.4

.5

.6

.7

Percent Homeowners

Note: Each point represents a county in Ohio. The x-axis is the rate of homeownership, where the numerator is all homeowners and the denominator is all homeowners
plus registered voters that did not merge to a homeownership record. The y-axis is
the percent of the population that lives in owner-occupied housing according to the2010 Census. These are positively related with a correlation coefficient of 0.394,
which helps to validate the record linking.

35

A.2

Validating Parallel Trends Further

Here, we carry out three further tests to validate the assumption of parallel trends, focusing
on our main analysis on the effect of homeownership on local election turnout.
Table A.1 – Validating Parallel Trends for the Local Turnout Analysis.

(1)

Turnout in General(2)(3)

Homeowner

0.050(0.000)0.040(0.000)Homeowner, t + 1-0.009(0.000)-0.001(0.000)33391889
0.290
Yes
Yes
No

0.003
(0.000)28892520
0.276
Yes
Yes
No

Homeowner, t + 2
Observations
Outcome Mean
Individual FEs
Year FEsCounty-Year FEs

0.051(0.000)37897139
0.290
Yes
No
Yes

(4)

Turnout in Primary
(5)
(6)

0.017
(0.000)0.019(0.000)0.006(0.000)-0.000(0.000)32220591
0.075
Yes
Yes
No

0.005
(0.000)27858002
0.077
Yes
Yes
No

0.021
(0.000)36224042
0.075
Yes
No
Yes

Robust standard errors clustered by individual in parentheses.

In the first test, we include a lead of the homeowner variable—i.e., a variable which takes
the value 1 if an individual is going to become a homeowner in the next time period, after
the election at t. A large coefficient on this lead would suggest the presence of pre-trending.
As columns 1 and 4 of Table A.1 show for general and primary election turnout, respectively,
the main effect on homeowner remains large and similar in magnitude to the estimates inTable 1 with the inclusion of this lead, and the coefficients on the leads themselves are anorder of magnitude smaller than the coefficients on the main effects, and in the opposite
direction in the case of general-election turnout (they are “statistically significant” becauseof the extremely large sample sizes.) In the second test, shown in columns 2 and 5 for generaland primary election turnout, respectively, we include two leads of the homeowner variable;
again we find similar coefficients on the main effects and small effects on the leads. As such,
this is reassuring evidence that parallel trends might hold. Nonetheless, in the main text
we carry out a series of additional robustness checks to make the parallel trends assumption
more plausible.
The third test is simply to alter the counterfactual time trends in a way different fromin the body of the paper. In the paper, we focused on using counterfactual trends based on
individuals’ homebuyer status and the value of their home; here, we instead focus on using
individuals who live near each other. To do so, we include county-by-year fixed effects. As
noted in the body of the paper, we also include county-by-year fixed effects for a substantive
reason: by giving each county its own set of time fixed effects, we control for common, countylevel unobservable shocks that could affect trends in local election turnout. For example,
36

voters in different counties vote for different sets of candidates in local elections. As theestimates in columns 3 and 6 show, estimates are unchanged, relative to those in Table 1,
when we use these alternative fixed effects.

A.3

Voter File Purge and Selection Bias
Table A.2 – Effect of Homeownership on Political Participation in
National General Elections, North Carolina.

Including Purged Voters
(1)
(2)
(3)
Homeowner

Excluding Purged Voters
(4)
(5)
(6)

0.094
(0.000)0.094(0.000)0.087(0.000)0.102(0.000)0.115(0.000)0.108(0.000)Observations
73429987
Outcome Mean
0.242
Individual FEs
Yes
Year FEs
Yes
Year-by-Value FEs
No29482978
0.332
Yes
No
Yes

22218491
0.385
Yes
Yes
No

42207777 24157335
0.420
0.405
Yes
Yes
Yes
No
No
Yes

19033142
0.450
Yes
Yes
No

SampleOwnersOwnersFull

Full

OwnersOwnersRobust standard errors clustered by individual in parentheses. The first three columns include
voters in North Carolina who were removed from the voter file, while the last three columns exclude
these voters. Columns 2, 3, 5, and 6 include only individuals who become homeowners at somepoint during the study period.

In this section, we first examine how voter file purges might affect our estimates of theeffect of homeownership. In the body of the paper, we use evidence from voter files inNorth Carolina and Ohio. One potential issue that arises when using voter files is that theircomposition changes over time. For example, voters might be purged from the voter rolls,
and each state has its own rules and guidelines for when voters should be removed from thevoter file.29 We would like to avoid conditioning on post-treatment behavior. For example,
if our treatment (homeownership) affects political participation, and political participationin turn affects the likelihood that a voter is purged and does not show up in the current
version of the voter file, our estimates could be biased by the fact that we are selecting
on those who remain in the voter file. Ideally we would like to be able to include not
only individuals in the most recent copy of the voter file, but also everyone who has everbeen registered at some point during the course of our study. Ex ante, the direction of
the bias is ambiguous. If we fail to observe homeowners who are purged from the voter
file and therefore do not turn out to vote, we would overestimate the effect of becoming
29

Federal law does prohibit states from removing voters from the voter rolls unless certain criteria are met.
For example, the National Voter Registration Act of 1993 prohibits states from removing voters “by reasonof the person’s failure to vote” (see https://www.gpo.gov/fdsys/pkg/STATUTE-107/pdf/STATUTE-107Pg77.pdf)

37

Table A.3 – Effect of Homeownership on Political Participation in
Local Ohio Elections, Robustness Check

Turnout in General = 1
(1)
(2)
(3)
Homeowner

Turnout in Primary = 1
(4)
(5)
(6)

0.106
(0.000)0.091(0.000)0.093(0.000)0.025(0.000)0.043(0.000)0.043(0.000)Observations
51563083
Outcome Mean
0.327
Individual FEs
Yes
Year FEs
Yes
Year-by-Value FEs
No
SampleFull

13735435
0.562
Yes
No
Yes
Owners13735435
0.562
Yes
Yes
No
Owners47424873
0.083
Yes
Yes
No
Full

12826623
0.156
Yes
No
Yes
Owners12826623
0.156
Yes
Yes
No
OwnersRobust standard errors clustered by individual in parentheses. Columns 2, 3 and 5, and 6
include only individuals who become homeowners at some point during the study period.

a homeowner on participation. However, if we fail to observe non-homeowners who arepurged from the voter file, we would underestimate the effect of becoming a homeowner onparticipation. In order to test how voter file purges might be affecting our estimates, welimit the analysis just to North Carolina, where the information of individuals purged fromthe voter file is preserved along with a variable indicating that they have been removed.30 In
Table A.2, we report the main results for the effect of homeownership on turnout in North
Carolina national general elections where we both include and exclude purged voters fromthe analysis. In our most preferred specification (Columns 2 and 5), the point estimatesare quite similar, although the effect of becoming a homeowner on participation is slightly
larger when we include the removed voters. This suggests that non-homeowners are more
likely to be purged than homeowners, so we might be slightly overestimating turnout for
non-homeowners, and thus underestimating the turnout boost from homeownership.
Another potential source of bias in our estimates comes from the fact that we imputeturnout equal to zero for those whose age made them eligible to vote but did not turn out.
It could be that someone moves in from out of state, where they had been a long time voter,but they have no history of voting in Ohio. In these cases, we impute zero for turnout prior
to their move, which would bias our estimate of the homeownership effect upward. Similarly,
it could be that a habitual voter sells their home, moves out of state, and continues voting inanother state – but we would impute their turnout as zero after they move. This would also
lead us to overestimate the effect of homeownership. In Table A.3 we include a robustness
check that includes the same specifications as Table 1, with a few adjustments. First, toaddress the concern that we are mistakenly setting turnout to zero for habitual voters thatmove in from out of state, we drop individuals whose registration year is greater than or equal
to the year of their home purchase. Second, to address the concern that we are mistakenly
setting turnout to zero for habitual voters that sell their home and move out of state, we
30

Ohio’s voter file does not have this feature, so we exclude Ohio voters for this part of the analysis.

38

set turnout to missing for individuals after they sell their home. In essence, we are now
estimating the “switch-on” effect of registered voters purchasing a home on their subsequent
turnout as new homeowners. The results in Table A.3 are similar to those from Table 1—in
fact, for this subset we find a significantly larger effect of homeownership on local turnout—suggesting that our overall positive effect is not driven by the potential bias from imputing
turnout for out-of-state movers.

A.4

Coding Local Issues in Ohio

In Table 3, we show the different types of local issues on voters’ ballots, sorted into elevenmutually exclusive categories. To generate these codings, we downloaded data on local issues
in 2013, 2015, and 2017 from the Ohio Secretary of State website.31 Each row in the data
is a local issue on the election ballot in a locality. We use a few columns to categorize eachspreadsheet of local issues. First, we identify the type of locality using “Subdivision Type,”
which indicates whether the local issues applies to a county, city, township, village, or school
district. Next, we categorize the issues based on two other variables in the local issues data.
The first variable, “Question Type,” indicates whether the local issue is a bond, levy, liquor
option, etc. The second variable, “Purpose,” includes a short description of the local issue.32
We sort each local issue into category according to the following simple coding rules:• Aggregation: Purpose contains “aggregation”• Cemetery: Purpose contains “cemete”• Charter : Purpose contains “charter”• Current Expenses: Purpose contains “current” and “expenses”• Liquor : Question Type is “liquor”• Parks: Purpose contains “park” or “recrea”• Public Safety: Purpose contains “fire” or “police” or “safety” or “emergency medical
services” or “ems” or “ambulance”• Roads: Purpose contains “road” or “street” or “bridg”• Schools: Subdivision Type is “school”• Seniors and Children: Purpose is “senior” or “elderly” or “child”• Zoning: Purpose is “zoning”
Anything not coded under any of the categories above is sort under the Miscellaneous
category.
31
32

See https://www.sos.state.oh.us/elections/election-results-and-data/
Examples of local issue purposes include “police protection,” “sunday sales,” “maintain and operate cemeteries,” “zoning amendment,” and “current expenses.”

39

A.5

Effect of Homeownership by Presence of Home
Mortgage

To further explore the investment mechanism of the effect of homeownership on local turnout,in this section we examine how this effect varies by whether there is a mortgage associatedwith the home purchase. The logic is that leveraged individuals with home mortgages standto lose (gain) more from their homes depreciating (appreciating) in value, so they might bemore motivated to protect the value of their home. To estimate the additional local turnout
boost from having a home mortgage, we interact the homeowner indicator with an indicator
for whether there is a mortgage associated with the home purchase. Of course, we do not
randomize access to home mortgages, so the individuals who choose to use them will differ
systematically from other individuals. However, we are still estimating within-person effects
of homeownership. In Table A.4, we do not find evidence of an added boost to local turnout
for homeowners with mortgages relative to homeowners without mortgages.
Table A.4 – Effect of Homeownership on Political Participation in
Local Ohio Elections, by Home Mortgage

Turnout in General = 1
(1)
(2)
(3)

Turnout in Primary = 1
(4)
(5)
(6)

Homeowner

0.050(0.000)0.052(0.000)0.051(0.000)0.019(0.000)0.021(0.000)0.021(0.000)Homeowner × Mortgage-0.002(0.000)0.000(0.000)-0.002(0.000)0.001(0.000)0.003(0.000)0.001(0.000)Observations
Outcome Mean
Individual FEs
Year FEs
Year-by-Value FEs
Sample76319157
0.265
Yes
Yes
No
Full

42597560
0.282
Yes
No
Yes
Owners37897139
0.290
Yes
Yes
No
Owners71366039
0.065
Yes
Yes
No
Full

40713777
0.073
Yes
No
Yes
Owners36224042
0.075
Yes
Yes
No
OwnersRobust standard errors clustered by individual in parentheses. Columns 2, 3 and 5, and 6
include only individuals who become homeowners at some point during the study period.

A.6

Effect of Homeownership on Partisan Preferences

In addition to examining effects on turnout, at the national level we can also take advantage
of indicators of partisanship in order to study effects on political preferences. Table A.5
presents the results for two outcome variables: turning out to vote in a Republican primaryin the election at time t, and turning out to vote in a Democratic primary in the election
at time t. As the results show, we find evidence for polarization, rather than only a shift
in the conservative direction. While some voters become more likely to vote in Republican40

Table A.5 – Effect of Homeownership on Partisan Preferences in
National Elections, North Carolina and Ohio.

(1)
Homeowner

Turnout in Rep Primary = 1
(2)
(3)
(4)

0.031
(0.000)Observations
126673490
Outcome Mean
0.059
Individual FEs
Yes
Year FEs
Yes
Year-by-Value FEs
No
SampleFull(5)Turnout in Dem Primary = 1
(6)
(7)
(8)

0.007
(0.000)0.009(0.000)0.025(0.000)0.015(0.000)0.017(0.000)0.018(0.000)0.024(0.000)44981204
0.097
Yes
No
Yes
Owners44981204
0.097
Yes
Yes
No
Owners13885830
0.046
No
Yes
No
Matched126673490
0.067
Yes
Yes
No
Full

44981204
0.097
Yes
No
Yes
Owners44981204
0.097
Yes
Yes
No
Owners14618484
0.059
No
Yes
No
MatchedRobust standard errors clustered by individual in parentheses; standard errors in columns 4 and 8 are robust
without clustering, as the data is collapsed by stratum-year. Columns 2, 3 and 6, and 7 include only
individuals who become homeowners at some point during the study period. Columns 4 and 8 include only
individuals who are exactly matched on the basis of 4 pre-treatment periods of the outcome variable.

primaries, others become more likely to vote in Democratic primaries. These effects are
similar in magnitude for both parties. Again, this pattern is potentially consistent with
Prato (2018), which predicts that, by obtaining more information about politics, homeowners
should better sort into parties.

A.7

Effect of Homeownership with FHA Mortgages

How and why homeownership shapes individuals’ political beliefs, leading some to become
Democratic while others become Republican, is hard to know.
In Table A.6, we examine one dimension that may help shed light on the process: whether
or not an individual received help from the federal government, in the form of mortgage
insurance, in order to qualify for their home loan. Specifically, we estimate effects of homeownership on partisanship for individuals who do and do not receive FHA-backed loans. To
measure partisanship, we again examine turnout in partisan primaries in Ohio and North
Carolina. Because we are now interested in detecting net shifts in preferences towards either
the Democrats or Republicans, we now create a single measure of partisanship, Republicanism, defined as
if i votes in Republican primary at time t
1
Republicanismit = 0
if i votes in no primary at time t
−1 if i votes in Democratic primary at time t.
We then re-estimate the difference-in-differences, as in equation 1, with the addition of
an interaction of the homeownership treatment variable with an indicator for whether theindividual received an FHA-backed loan. We omit the main effect on this FHA indicator as
it is subsumed by the individual fixed effects.

41

Table A.6 – Effect of Homeownership on Partisan Political Participation, Across Mortgage Type. Home buyers receiving FHA assistanceare more likely to shift in a Democratic direction in future elections.

Primary Turnout (1=Rep, 0=None, -1=Dem)
(1)
(2)
(3)
Homeowner

0.019(0.000)-0.005(0.000)-0.002(0.000)Homeowner × FHA-0.030(0.000)-0.036(0.001)-0.028(0.001)

Observations
Outcome Mean
Individual FEs
Year FEs
Value-by-Year FEs
Sample125006087-0.008
Yes
Yes
No
Full

43463595
0.000
Yes
No
Yes
Owners53321147
0.000
Yes
Yes
No
OwnersRobust standard errors clustered by individual inparentheses.

Table A.6 presents the results across the three main specifications we have used throughout the paper. As the table shows, individuals who receive FHA loans are substantially more
likely to move in a Democratic direction after purchasing a home. This is true across specifications. As the coefficients on the main effects show—focusing particularly on columns 2
and 3, which feature the strongest version of the design—there is little detectable net shift
towards either party among individuals who do not receive FHA assistance.
The leftward shift we observe among FHA recipients could indicate a change in preferences, or it could indicate mobilization of individuals who already held Democratic politicalpreferences. To investigate these respective mechanisms, we examine outcome data from2006 on, and we re-estimate the effects above for three types of individuals: those who’ve
never participated in a primary election before 2006 (“previous non-voters”); those who
participated in at least one Republican primary but no Democratic primaries before 2006(“previous Republicans”); and those who participated in at least one Democratic primary
but no Republican primaries before 2006 (“previous Democrats”).
Figure A.2 plots the resulting estimates, which were estimated using the value decile
by year fixed effects. The first two point estimates reflect the effect of homeownership on
Republicanism for previous non-voters. The diamond is the point estimate for individuals
who do not receive FHA loans, while the circle is for those who do. As the points show,
previous non-voters who do not receive FHA loans shift slightly towards the Democrats; thisshift is much more pronounced for those who do receive FHA loans. Because we do not know
the pre-existing political views of these individuals, since they did not previously vote in apartisan primary, these effects could indicate mobilization, a shift in preferences, or somecombination of the two, but they seem to offer some suggestive evidence that homeownershipproduces a political shift that is different for those who receive federal government help.
42

The second two point estimates are for previous Republicans. While previous Republicans
who do not receive FHA loans become more Republican (diamond point), those who doreceive FHA do not appear to shift noticeably in either direction. Inference is more difficult
here as relatively few individuals who previously participated in a Republican primary andreceived FHA loans.
The final two point estimates are for previous Democrats. Here we see substantial mobilizing effects of homeownership, which become extremely large for those individuals who
receive FHA loans.
What these effects mean is not precisely clear. We have not randomized access to FHA
mortgages, so the individuals who are eligible for them and choose to use them will differ
systematically from other individuals. On the other hand, we are still estimating withinperson effects of homeownership, and the counterfactual trends we generate use only otherindividuals in the same home-price decile. While we are hesitant to ascribe these patterns
to a causal effect of receiving an FHA mortgage, they show that the type of individuals who
receive these loans go on to become more left-leaning. This is at least consistent with thenotion that individuals form their political preferences in part based on their experiences,with those who benefit from federal government assistance becoming more supportive of the
expansion of the federal government and of the party that typically espouses related policy
positions (e.g., Campbell 2011; Pierson 1995). Given these results, we suspect that future
work leveraging quasi-random variation in access to government housing programs would bevaluable.

43

Figure A.2 – Effects of Homeownership on Partisanship, Measured
by Primary Turnout, Across Previous Partisanship and FHA Status. Homeownership increases the probability that individuals participate
in subsequent Democratic primaries when individuals receive FHA assistance for their home mortgage. This is true among individuals who had
not participated in a primary before becoming a homeowner (left two point
estimates in the plot) and among individuals who participated in at least
one Democratic primary before becoming a homeowner (right two point
estimates in the plot).

Effect of Homeownership on Republicanism

Previous
Non-Voters(N=2.8M)

Previous
Republicans(N=432K)

Previous
Democrats(N=374K)

.02

0

-.02

-.04

-.06

-.08

No FHA
FHA

Note: Outcome variable takes the value 1 if individual votes in Republican primary,
0 if votes in no primary, and -1 if votes in Democratic primary. Previous Non-Voters
are individuals who have never voted in a primary for either party before. Previous
Republicans are individuals who voted in at least one previous Republican primaryand no previous Democratic primaries. Previous Democrats are individuals who
voted in at least one previous Democratic primary and no previous Republican
primaries. Sample sizes in plot refer to number of individuals, not number ofobservations (which are individual-years).

44

A.8

Homeownership and Party Registration

In this section, we study the effect of homeownership on registering with a party, as distinct
from turning out to vote in a party’s primary, which we studied in the body of the paper.
In Ohio, individuals register for a party by voting in its primary, so there is no further party
registration to study; however, in North Carolina, individuals register directly with a party,
and then can choose whether or not to vote in the primary. As a result, we can use North
Carolina to study effects on party registration, directly. This is a relevant measure because
it may reflect political preference as distinct from mobilization, whereas turning out in apartisan primary is a combination of both preference shifts and mobilization.
As Table A.7 shows, we find positive effects on both Democratic and Republican registration patterns like we did in primary turnout patterns. Homeowners become more likely
to register for both parties.
Table A.7 – Effect of Homeownership on Party Primary Participation and Registration: North Carolina.

Turnoutin
Rep
Primary(1)

TurnoutinDem
Primary(2)

Register
Rep(3)

RegisterDem(4)Homeowner

0.008(0.000)0.018(0.000)0.009(0.000)0.006(0.000)Observations
Outcome Mean
Individual FEs
Year FEs

29666360
0.075
Yes
Yes

29666360
0.083
Yes
Yes

27498537
0.337
Yes
Yes

27498537
0.338
Yes
Yes

Robust standard errors clustered by individual in parentheses.

45