Political Uncertainty and Housing Markets · Van Nguyen† Carles Vergara-Alert ... since Q1 2007 and stood 29% lower by Q3 2019.5 Investors have long attributed those moves in housing

1

Political Uncertainty and Housing Markets

Van Nguyen† Carles Vergara-Alert‡

Abstract

This paper examines the causal effects of political uncertainty on housing markets. We use the U.S.

gubernatorial elections from 1982 to 2015 as a source of exogenous variation in political uncertainty

and exploit the regional variations in the residential housing market. We use neighboring states without

elections as a control group. We find that higher political uncertainty causes: (1) a decrease in house

prices; (2) a decrease in the number of housing transactions; and (3) an increase in the number of

building permits. These effects are stronger during election years in which the election outcomes present

higher uncertainty. We further examine the impact of political uncertainty on the mortgage market and

we find that both mortgage demand and supply decrease in election years.

Key words: Political uncertainty; housing; mortgages; house prices.

JEL codes: D80, R30, G21, D72

1. Introduction

Political uncertainty has increased in recent times. The U.S. Economic Policy Uncertainty Index

–normalized to 100 as of year 2010– has been only three times above the value of 200 during the

period 1985-2007, but it has been nine times above 200 since the collapse of Lehman Brothers in

September 2008.1 Recent research has shown that there is a direct relationship between political

† IESE Business School. Av. Pearson 21, 08034 Barcelona, Spain. Email: [email protected]. ‡ IESE Business School. Av. Pearson 21, 08034 Barcelona, Spain. Email: [email protected]. Vergara-Alert acknowledges

financial support from the State Research Agency of the Spanish Ministry of Science, Innovation and Universities and from the

European Regional Development Fund (Ref. PGC2018-097335-A-I00, MCIU/AEI/FEDER, UE). 1 See the index of economic policy uncertainty developed in Baker, Bloom, and Davis (2016). During the period 1985-2007, this

index has been only above the value of 200 during Gulf War I, 9/11, and Gulf War II, and only once above 250 (i.e., during the

9/11 event). However, during the period 2008-2019, this index has been nine times above the value of 200 and four times above

2

uncertainty and economic outcomes. However, the impact of political uncertainty on real estate

markets is an understudied area, despite the fact that real estate is one of the largest asset classes

in the economy. Since the Brexit referendum in June 2016, in the U.K., house sales in the prime

area of central London plunged by 19% and house prices went down 14% (between June 2016 and

October 2019).2 Commercial real estate in the U.K. also suffered from the Brexit and the

uncertainty surrounding the U.K.-Europe trade deal. Commercial real estate investments

experienced a sharp drop of 33% in 2016.3 In the U.S., policy uncertainty negatively affects the

growth rates of house prices and housing transactions. Figure 1 shows these negatives

relationships. Nevertheless, house price growth tends to slow down in election years.4 During the

period from 1980 to 2016, house price growth in off-election years have been 0.22% higher than

in election years, on average. In Italy, where political crisis has been ongoing for several years

without a stable government, residential house prices have been falling consistently in real term

since Q1 2007 and stood 29% lower by Q3 2019.5 Investors have long attributed those moves in

housing markets to the uncertainty associated with political events. However, there is a lack of

causal evidence linking political uncertainty to changes in housing markets.

[Insert Figure 1 around here]

In this paper, we attempt to fill this void by empirically examining whether and to what

extent uncertainty associated with potential changes in government leadership affect housing

markets. The public authority has the power to regulate the economy through various tools

including enforcing laws and regulations, imposing taxes, and providing rights and protection.

250 (i.e., during the debt ceiling debate in 2011, the Trump Election in 2016, and twice during the rising tariffs and trade policy

tensions between the U.S. and China in 2018 and 2019). 2 Financial Times, October 24, 2019: “Brexit bargains: London’s luxury homes take big price cuts”

https://www.ft.com/content/b788a366-f4c1-11e9-b018-3ef8794b17c6. 3 CBRE report, February 14, 2020: “From user to guest: The user in the center of Real Estate” (unpublished). 4 Data source from Freddie Mac: http://www.freddiemac.com/research/indices/house-price-index.page. 5 Data source from Bank of International Settlements: https://www.bis.org/statistics/pp_selected.htm?m=6%7C288%7C596.

https://www.ft.com/content/b788a366-f4c1-11e9-b018-3ef8794b17c6http://www.freddiemac.com/research/indices/house-price-index.page

3

These tools play an important role in shaping housing markets. Therefore, any changes in the

structure of the government as well as its policies could result in instability and uncertainty that

influence decisions in the housing markets.

We focus specifically on the U.S. residential housing markets and we study the causal

impact of political uncertainty surrounding elections on both housing and mortgage markets. We

do so because the majority of residential housing purchases are financed with mortgages, which

suggests that there is a strong link between mortgage and housing markets. The key feature of

housing investments is that although housing can offer investors a greater control over the value

and use of their investment, direct investments in housing present sizeable transaction costs. As

argued by the real option theory, transaction costs in housing investments can increase the real

option value of delaying the investment under high-uncertainty circumstances. This might drive a

dampening effect in house prices and transaction activity as investors wait for more and clearer

signals before making investment decisions.

In addition, we argue that political uncertainty can also have an impact on the number of

building permits. In the U.S. market, a building permit is obtained at the city where the construction

will take place. The city is required by law to enforce various construction and development

policies at the local, state, and federal level. Since obtaining a building permit can be taken as

buying an option to build or reconstruct a housing unit, investors might want to avoid the

uncertainty generated by gubernatorial elections as well as the associated potential changes in

construction and development regulations to be implemented by newly-elected government.

Therefore, we argue that investors are more likely to apply for building permits before an election

takes place, which leads to a surge in the number of building permits in election years. As the

number of potential new constructions increases, there would be an expectation of a future increase

in the supply of housing units, which in turn might have further dampening impact on house prices.

4

We use state-level data for 50 states in the U.S from 1982 to 2015 to examine the impact

of political uncertainty on both housing and mortgage markets. Our empirical study can be

summarized in three sets of results. Firstly, we show that political uncertainty has a negative

relationship with house prices and the number of housing transactions, but they have a positive

relationship with the number of building permits that are issued. Our results show that house prices

and the number of housing transactions are reduced by 0.6% and 6.5%, respectively, while the

number of building permits are increased by 2.2% in years of gubernatorial elections. To address

potential endogeneity concerns, we employ the “neighboring state estimation methodology” in

which we compare house prices, number of housing transactions, and the number of building

permits between a state with a gubernatorial election and its neighboring state without an election.

This methodology is based on the assumption that neighboring states share similar unobservable

shocks, which will be explained in detail later in the paper.

Secondly, we demonstrate that the degree of uncertainty matters, that is, the impact of

political uncertainty on housing markets increases with the degree of political uncertainty. In

particular, we provide evidence that there is a greater decrease in house prices and number of

housing transactions, but greater increase in number of building permits when gubernatorial

election outcomes are more unclear.

Thirdly, we demonstrate that political uncertainty causes a decline in residential mortgage

activity on both the demand and the supply sides of the mortgage markets. We find evidence that

suggests that both mortgage supply and mortgage demand fall during gubernatorial election years.

At the bank level, we show that banks reduce the volume of mortgages originated in their

headquarter states when gubernatorial elections are scheduled, while the number of mortgage

applications also drops. However, we find that the magnitude and the significance level are greater

on the demand side at both state and bank level.

5

The use of US gubernatorial elections as a source of political uncertainty has several

advantages in the study. Firstly, the sample for investigation is larger for gubernatorial elections

than it is for presidential elections. Over the course of 34 years from 1982 to 2015, there were 458

gubernatorial election compared to only 8 presidential elections in the US, which cannot be a

reliable source for making meaningful statistical inferences. Secondly, it is important to note that

policy changes are not exogenous by nature as they might be triggered by various political and

economic shocks. Nevertheless, US gubernatorial elections can be taken as exogenous events as

they are prescheduled and not held due to any economic situations. Hence, the use of gubernatorial

elections in our empirical setting helps to mitigate the potential endogeneity problem arising

between political uncertainty and economic conditions. Lastly, the housing market in any state is

significantly affected by changes in the state government’s policies and regulations including but

not limited to changes in building code, tax code, housing subsidies, employee relocation expense,

environmental protection rules. Different governing parties may favor different sets of industries,

and with respect to residential housing, those industry-specific policies can influence the house

demand, supply and prices in the area where firms are under those industry-specific policies.

Our paper contributes to two main bodies of literature. First, we contribute to the growing

body of literature that studies the channel through which economic factors respond to political

uncertainty. Past literature has examined several components of political uncertainty including

political instability (Alesina et al., 1996; Jong-A-Pin, 2009; Aisen el al., 2011), corruption (Leff,

1964; Mauro, 1995; Mo, 2001; Lash, 2004), as well as changes in legal and regulatory

environments (Mauro, 1995; La Porta et al., 1997; Battalio et al., 2011). The majority of theoretical

papers, however, center on the impact of political uncertainty on corporate investment decisions.

The common argument is that uncertainty associated with changes in financial regulation and

macroeconomic policy would hinder the firm’s investment. Early theoretical works (Cukierman,

6

1980; Bernanke, 1983) suggest that firms are more likely to delay investments when they are in

state of uncertainty in order to collect more information. Others (Pindyck, 1988; Ingersoll et al.,

1992; Bloom, 2009) provide theoretical frameworks to identify the effects of political uncertainty

shocks on corporate investment incentives. Several empirical evidences have supported these

theories. Julio and Yook (2012) document across different countries that firms reduce investment

expenses during national election years in which political uncertainty is heightened. Jens (2017)

suggests that firms’ investment at state level in the US is reduced in the period of escalating

political uncertainty due to gubernatorial elections after controlling for the general state economic

conditions. Colak, Durnev and Qian (2017) provide evidence that there are fewer IPOs in a state

when it is holding a gubernatorial election.

Second, our paper contributes to the growing body of literature on the impact of policy and

political uncertainty on asset pricing. Theoretical works by Pastor and Veronesi (2012; 2013)

provide a general equilibrium model showing that political uncertainty can dampen asset prices

and command a risk premium whose magnitude is greater in times of weak economic conditions.

They argue that investors demand higher return on their investment, raising the cost of capital for

firms, which creates more constraints on firms’ investment decisions. Their theoretical prediction

that political uncertainty has negative impacts on asset prices is supported by analysis of within-

country and cross-country evidence. In stock markets, Brogaard and Detzel (2015) employ the

economic policy uncertainty index (EPU) constructed by Baker, Bloom and Davis (as in Baker,

Bloom and Davis, 2016) to identify the negative relation between economic policy uncertainty and

US stock market return. Liu, Shu and Wei (2017) document that political uncertainty associated

with Bo scandal in China caused a significant drop in prices of stocks traded in Chinese market.

In bond markets, evidence found by Gao and Qi (2012) suggest that bond investors are less likely

7

to invest and demand higher risk premium to invest in municipal government bonds when political

uncertainty is high due to gubernatorial elections. Handler and Jankowitsch (2018) study the

impact of political uncertainty on prices and trading activity of Italian sovereign bonds and find a

significant price fall together with lower liquidity in the market before a political event. Our

findings in the residential housing market lend support to their theoretical framework. Recently,

Cao, Li and Liu (2019) showed that political uncertainty affects the volume and outcome of cross-

border acquisitions because upcoming national elections encourage firms to perform cross-border

acquisitions to target countries with free-trade agreements, military allies, or countries with better

governance. They also show that stock returns related to the announcement of cross-border deals

incorporate political uncertainty.

The rest of the paper is organized as follows: Section 2 uses existing option pricing models

in the literature in order to develop our testable hypotheses. Section 3 describes our data. Section

4 defines the identification strategies and reports the empirical results. Section 5 examines the

reverse effect, that is, how housing variables behave in post-election years. Section 6 provides

cross-sectional analyses with two subsamples of high- and low- uncertainty in gubernatorial

election outcomes. Section 7 analyses the potential impact of gubernatorial elections on mortgage

lending activities. Section 8 provides the conclusions of the paper.

2. Hypothesis development

We rely on the real option argument to construct our hypotheses about the impact of

political uncertainty on housing market. Myers (1977) was the one who coined the term “real

options” with the rationale of applying option pricing theory to the valuation of real investments,

8

while taking into account investors’ learning and adaptive behavior. Past theoretical and empirical

studies document that uncertainty negatively affects corporate investment decisions because the

value of the real option of waiting and learning more about the markets increases. (Dixit and

Pindyck, 1994; Leahy and Whited, 1996; Abel and Eberly, 1999; Julio and Yook, 2012; Jens

2016). The intuition behind the real option theory is that uncertainty, in general, can result in bad

economic outcomes; therefore, the option value of delaying investments until uncertainty is

resolved increases. This delaying effect is even stronger with investments that entail high reversal

costs, which have been termed “irreversible investments” by scholars.

Pastor and Veronesi (2012) present a theoretical model to prove that political uncertainty

dampens stock prices and commands a risk premium. In their theoretical prediction, they argue

that there are two effects generated from a political uncertainty on stock prices including an

increase in firms’ expected profitability and an increase in discount rates. Both of the foregoing

result in a decrease in stock prices. Their model also follows the logic of real option argument: the

greater the uncertainty triggered by political events, the greater the real option value of delaying

investing and financing decisions. Pastor and Veronesi (2013), Brogaard and Detzel (2015), Liu,

Shu and Wei (2017) document evidence that stock prices, on average, are dampened in period of

heightened uncertainty surrounding a political event as investors demand higher risk premium to

compensate for taking extra risk generated by political uncertainty.

The real option theory can also be applied to housing investments because these

investments are highly irreversible, which further increases the value of the real option to postpose

investments under circumstances of uncertainty. In particular, political uncertainty is triggered by

potential changes in regulatory environment, political leadership and government policies, which,

in turn, can highly affect investing decisions. In our paper, we employ gubernatorial elections as a

9

source of political uncertainty, which comes in three main forms: (1) uncertainty about the winning

parties; (2) uncertainty about possible changes in policies to be implemented by the newly elected

governors; and (3) uncertainty about the impact of possible policy changes on housing market.

State governments are responsible for implementing state housing policies and programs

using various tools. Housing transaction activity, therefore, can be affected either through direct

channels by the state government’s choices of policies regarding to taxes, housing subsidies,

building codes, landlord-tenant rights and responsibilities; and through indirect channels such as

changes in state citizens’ consumption behavior and living standards. It is from this argument that

we expect a lower house price and a lower number of housing transactions in election years.

Following the real option argument and the expected impact of political uncertainty on the

housing market, we propose the first hypothesis as follows.

Hypothesis 1: The return on house prices and the number of housing transaction are lower in

election years than in non-election years, while the number of building permits is greater in

election years than in non-election years.

The real option argument provides that under risky and uncertain environment, investors

can update their knowledge and adjust their behavior to increase the potential upside and reduce

the potential downside of their investment outcomes. The majority of past literature has come to

support the real option argument to delay investments in period of uncertainty, which means

uncertainty has a negative impact on investment activity. However, there are also other theories

suggesting that the real option framework might not hold when applied to real world situations

(Hartman, 1972; Abel, 1983; Caballero, 1991). The main issue with the real option framework is

that it is based on a strong assumption of replicating portfolio. In essence, it assumes that we can

construct a portfolio of traded investments to replicate the return of the real option free of arbitrage.

10

Moreover, the real option theory shows that the higher the uncertainty, the higher the value

of option to delay investments subject to transaction costs. This equilibrium result leads to our

second hypothesis with regard to the degree of political uncertainty surrounding gubernatorial

elections and its impact on housing market.

Hypothesis 2: The higher the degree of political uncertainty, the larger its impact on the housing

market. Specifically, the decrease in the house prices and number of housing transactions in

election years and the increase in the number of building permits are greater when the election

outcome is more uncertain.

Finally, we argue that political uncertainty matters for mortgage markets. As the majority

of home purchases are performed using leverage, the housing and the mortgage markets are highly

connected. This argument leads to our third hypothesis.

Hypothesis 3: The residential mortgage lending activity is lower in election years than in non-

election years, with reduction in both mortgage volume and number of mortgage applications.

3. Data

The full sample in this study covers state-level housing market data, election data and

economic data for the period from 1982 to 2015 for all 50 states in the U.S. Housing market data

include house price index; number of transactions and number of building permits. House price

indices provides a general picture of the health of the housing market, and is closely watched by a

market participants. In the United States, the two main house price indices frequently cited are

Federal Housing Finance Agency (FHFA) House price index and S&P/Case-Shiller index. Both

attempts to track the repeat sales of single-family housing units in order to measure the housing

value appreciation or depreciation in a particular market. The S&P/Case-Shiller index only cover

11

selected markets including specific metropolitan areas, top-20, top-10 metro areas and nation-

wide, whereas the FHFA House price index covers in-depth more geographical areas including

states, counties and zip codes. The FHFA House price index provides two types of indices:

“purchases only” index and “all transactions” index, which covers the longest history, and offers

the most granular level of housing market data in the U.S. Within the scope of this study, we opt

for state-level “all transactions” FHDA House price index as a proxy for house prices for the period

from 1982-2015 covering 50 states in the U.S. Then, we create a variable RH which measures the

annual return in the house price index. Transaction is the state-level number of housing

transactions per year provided by CoreLogic. Permit is the number of building permits issued by

a state within a year.

The gubernatorial election data is from Congressional Quarterly (CQ) Electronic library.

The data collected includes all election years, candidate names, the winning parties, and voting

margins. Election is a binary variable equal to one if a gubernatorial election occurred in a state.

Gubernatorial elections are held on the first Tuesday of November, with different sates holding

their elections in different years (see Appendix for more detailed description of gubernatorial

elections in the US). There was only one special non-prescheduled recall election that happened

in California in 2003; however, neither the exclusion nor inclusion of this observation affects the

empirical results. Pres_election is a binary variable equal to one if there is a presidential election

during a year in the sample. Incumbent_absence is a hand-collected binary variable equal to one

if the incumbent governor does not take part in the election either due to term limit regulation or

other personal reasons. Winning_margin is the difference between the percentage votes won by

the first and second place candidates. We sort gubernatorial elections based on winning margin

into terciles, then define Election_closeness as a binary variable equal to one if the election

winning margin is in the lowest tercile (5.1%).

12

Other variables included to control for the state-level economic conditions are retrieved

from the Bureau of Economic Analysis (bea.gov), the Bureau of Labor Statistics (bls.gov), the U.S

Census Bureau (census.gov) and the Federal Emergency Management Agency (fema.gov)

respectively: GDP as the annual growth rate in state gross domestic product; Unemployment as the

annual state unemployment rate; Population as the annual relative change in the state population;

Emergency as a binary variable equal to one if a state declares emergency in a given year.

Recession as a binary variable equal to one if the year is identified as being in a recession state by

the National Bureau of Economic Research (NBER). Other control variables include S&P500 as

annual return for the S&P500 index, and Bond_rate as the interest rates on the U.S. government

bonds from FRED database.

Table 1 presents descriptive statistics for the whole sample of 1700 state-year observations

as well as the sub-samples of observations with gubernatorial elections only. The return on house

price index (RH) in the two subsamples appears to be of smaller magnitude than in the whole

sample (3% in the subsamples versus 4% in the whole sample). The mean of the number of housing

transactions (Transaction) is also lower in the two subsamples than in the whole sample (46,508

in the subsample with gubernatorial elections only and 45,715 in the subsample with close

elections only versus 49,690 in the whole sample). The number of building permits (Permit), on

the contrary, exhibits higher means in the two subsamples (27,394 and 27,662 in the two

subsamples versus 26,386 in the whole sample). All these measures in the housing market display

stronger volatility in two subsamples with gubernatorial elections than in the whole sample.

[Insert Table 1 around here]

13

4. Identification strategies and empirical results

4.1. Baseline OLS multivariate analysis

We perform the following OLS regressions to examine the impact of political uncertainty

resulting from gubernatorial elections on housing variables including the return on HPI, the

number of housing transactions and the number of building permits:

RH i,t = a0 + a1Electioni,t + a2 Zi,t + a3Xt + μt + εi,t (1a)

Transactioni,t = b0 + b1Electioni.t + b2 Zi,t + a3Xt + μt + εi,t (1b)

Permiti,t = c0 + c1Electioni,t + c2 Zi.t + c3Xi,t + μt + εi,t (1c)

where RHi,t , Transactioni,t and Permiti,t are respectively the return on HPI ; the number of housing

transactions; and the number of building permits in state i and year t ; Our main variable of interest

is Electioni,t that equals 1 if there is a gubernatorial election in state i and year t, and 0 otherwise;

Zi.t is the set of control variables at state level; Xt is the set of control variable at country-wide

level; εi,t is the error term and μt is the state fixed effects. We include state fixed effects to address

the unobserved heterogeneity across states in terms of economic and political environment, and

use the 2-dimensional standard error clustering by state and year to address the concerns of

heteroskedasticity as well as correlations in the error terms.

Table 2 (Panel A, B, C) present the regression results of our multivariate analyses. The

results show that the coefficient of the election binary Election is always negative and highly

significant (with p_value ranging from 1% to 4%) for OLS regressions of equations (1a) and (1b),

implying that, after controlling for the overall health of the economy and heterogeneity across

states, political uncertainty caused by gubernatorial elections tends to decrease HPI (-0.6%) and

the number of housing transactions (approximately -3219 transactions). The regression on the

14

number of building permits, however, has a positive and significant at 5% level coefficient of the

election binary variable, suggesting an increase in permits granted in the year leading to

gubernatorial election (approximately +583 permits).


4.2. Neighboring state analysis

One benefit of using data on gubernatorial elections is that these elections are considered

prescheduled events, so they can be taken as exogenous events. Hence, the problem of potential

endogeneity between political uncertainty and other economic movements is alleviated to some

extent. However, there could exist other unobservable shocks in socioeconomic environment that

are not adequately captured within the above model specifications. We address this concern by

employing the neighboring-state difference-in-difference methodology (Colak, Durnev and Qian,

2017) with the assumption that neighboring states share similar unobserved shocks. Then by taking

the differences in the dependent variables, the unobservable shocks denoted as αi,t can be canceled

out.

State i: RHi,t = a0 + a1Electioni,t + a2 Zi,t + a3Xt + a4αi,t + μt + εi,t (2)

State j: RHj,t = a0 + a1Electionj,t + a2 Zj,t + a3Xt + a4αj,t + μt + εj,t (3)

where RHi,t is the change in HPI in state i and year t ; Electioni,t is the main variable of interest

that equals to 1 if there is a gubernatorial election in state i and year t, and 0 otherwise; Zi.t is the

set of control variables at state level; Xt is the set of control variable at country-wide level; αi,t is

the unobserved state variables; εi,t is the error term and μt is the fixed effects.

Taking the difference of equations (2) and (3) to derive the difference-in-difference equation:

(RHi,t - RHj,t) = a0 + a1 (Electioni,t – Electionj,t) + a2 (Zi.t - Zj.t ) + a4(αi,t - αj,t) + (εi,t - εj,t ). (4)

15

The assumption made in this setting is that the housing market variables including the return on

HPI, the number of housing transactions, and the number of building permits are subject to similar

unobservable state shocks; therefore, the term (αi,t - αj,t) is canceled out, resulting in the following

regressions:

∆RHi,j,t = a0 +a1 ∆Electioni,j,t + a2 ∆Zi,j,t + ∆εi,j,t (5a)

∆Transactioni,j,t =b0 + b1 ∆Electioni,j,t + b2 ∆Zi,j,t + ∆εi,j,t (5b)

∆Permiti,j,t = c0 + c1 ∆Electioni,j,t + c2 ∆Zi,j,t + ∆εi,j,t. (5c)

We keep all observations with ∆Electioni,j,t =1 and 0 only; hence, the variable ∆Electioni,j,t becomes

a binary variable for all observations for use in our regressions. In total, there are 3575 state pair-

year observations out of which 903 observations with ∆Electioni,j,t =1 (a state with an election and

its neighbor without one), and 2672 observations with ∆Electioni,j,t = 0 (both states without

election).

Table 3 presents 50 states in the U.S and their neighboring states (denoted as NS). Two

states Alaska and Hawaii have no neighbors, and their data will be excluded from our tests using

this methodology. On average, each state has four neighboring states with Maine has only one

neighboring state, and Missouri and Tennessee share borders with eight other states.


Table 4 (Panel A, B, C) report the OLS regression results for equations (5a), (5b), and (5c)

respectively, with different sets of control variables. We also perform weighted least squared

regression to account for the fact that our state pair-year observations have different probabilities

of being sampled, with weight being number of neighboring states. States with more neighboring

states have greater probability of being selected in the sample. The main variable of interest is

16

∆Election, and the dependent variables are ∆RH (the difference in the return on HPI); ∆Transaction

(the difference in the number of housing transaction) and ∆Permit (the difference in the number

of building permits) between a state and its neighboring state. Compare with the coefficients of

Election in regression equations (1a), (1b), (1c), the coefficients of ∆Election in regression

equations (5a), (5b), and (5c) are smaller in magnitude but much larger in statistical significance.

In Table 4.A, the coefficient of ∆Election is -0.001, implying the HPI is lower by 0.1% in a state

that holds a gubernatorial election compared to its neighboring state without one. The negative

impact of political uncertainty on housing transaction activities is illustrated in Table 4.B with the

coefficient of ∆Election equals -1029.329 which implies that there are approximately 1029 fewer

transactions. In Table 4.C, it is shown that there are approximately 183 more building permits in

a state with a gubernatorial election compared to its neighbor that does not hold one. We

acknowledge that different states have different number of neighboring states, and we address this

issue by including also the weighted least squared regressions as shown in specification (4) in

Table 4 (Panel A, B, C); however, the sign and the level of significance of the coefficient of the

election binary ∆Election remain unchanged.


4.3. Falsification tests

In order to validate the neighboring-state estimation methodology employed in this study,

we perform two placebo tests (falsification testing) including:

(1) Keeping election years as in the sample but randomly mapping neighboring states (every state

with four other states (as the average number of neighboring states is four in the sample);

(2) Keeping neighboring states as in the sample, but randomly assigning election years within a

four-year election cycle.

17

We estimate regressions similar to equation (5a), (5b) and (5c). As expected, we do not

obtain any significant results for the coefficients (a1), (b1) and (c1).

5. Reverse effect: Post-election analysis

As explained earlier with the real option value theory, the combined effect of a reduced

investment in housings and an increased number of building permits issued would further dampen

house prices in election years. In this section, we investigate the reverse effect of housing market

variables in the first two years following a gubernatorial election. Specifically, we test whether the

return on HPI and the number of housing transactions would increase, while the number of building

permits would decrease in the two years post-election. We estimate regressions similar to

equations (1a), (1b) and (1c); however, we replace the Election variable by the binary variable for

year T=1 and T=2 (with the election year set at T=0). The full set of control variables at the state-

and country-level is still applied. We estimate the following regressions:

RH i,t = a0 + a1Post_election_1i,t + a2 Zi,t + a3Xt + μt + εi,t (6a)

RH i,t = a0 + a1Post_election_1i,t + a2Post_election_2i,t + a3 Zi,t + a4Xt + μt + εi,t (6b)

Transactioni,t = b0 + b1Post_election_1i.t + b2 Zi,t + a3Xt + μt + εi,t (6c)

Transaction i,t = b0 + b1Post_election_1i,t + b2Post_election_2i,t + b3 Zi,t + b4Xt + μt + εi,t (6d)

Permiti,t = c0 + c1Post_election_1i,t + c2 Zi.t + c3Xi,t + μt + εi,t (6e)

Permit i,t = c0 + c1Post_election_1i,t + c2Post_election_2i,t + c3 Zi,t + c4Xt + μt + εi,t (6f)

Figure 2 illustrates the average return on HPI, the average number of housing transactions,

and the average number of building permits over the 4-year election cycle. Table 6 presents the

18

results of six OLS regressions. In columns 1, 3, and 5, the main variable of interest is the 1-year

post election binary (Post_election_1), and the coefficient of the binary variable is highly

significant and positive for RH and Transaction, but negative for Permit as expected. In columns

2, 4 and 6, the two main variables of interest are 1-year and 2-year post election binary

(Post_election_1 and Post_election_2), and the coefficients of the binary variables are

significantly positive for RH and Transaction, but negative for Permit. Economically, these results

imply that the return on HPI one-year post-election increases by 0.5%; the number of transactions

increases by approximately 915, the number of building permits issued decreases by approximately

418.

[Insert Figure 2 around here]

The results are consistent with our argument that investors delay making housing

transactions while applying for more building permits in the environment of greater political

uncertainty due to elections; however, once the uncertainty is resolved, there is a substantial

reverse effect observed in the housing market at state-level.


6. Cross-sectional analysis: The uncertainty degree of election outcomes

There are 903 observations in the merged difference-in-difference neighboring state

sample in which gubernatorial election is held only in a state and not in its neighboring state. We

first classify elections into two subsamples: High_Uncertainty and Low_Uncertainty. These

subsamples are based on two measures: (1) Close as a binary variable equal to one if the election

winning margin is in the lowest tercile (5.1%), and zero otherwise; (2) Incumbent_absence is a

hand-collected binary variable equal to one if the incumbent governor did not take part in the

19

election either due to term limit regulation or other personal reasons. As most of the incumbent

governors are re-elected (82.8% in our sample spanning from 1982-2015) if they are not restricted

by term-limit regulation or voluntarily withdraw from re-election due to other personal reasons,

we argue that the absence of the incumbent might lead to higher uncertainty surrounding an

election.

To examine whether the impact of political uncertainty on housing markets differs

between two subsamples, we then re-estimate the equations (5a), (5b), and (5c) for the

High_Uncertainty and Low_Uncertainty election subsamples. We find that the estimated

coefficient of ∆Election is more negative in equation (5a) and equation (5b), but more positive in

equation (5c) for the High_Uncertainty subsample than for the Low_Uncertainty subsample. We

further perform Wald test of the difference in the coefficients between each pair of

High_Uncertainty and Low_Uncertainty regressions. As reported in Table 6 (Panel A, B, C), the

difference is significant for the classification of elections based on both Election_closeness and

Incumbent_absence.

Accordingly, these results show that higher- uncertainty gubernatorial elections lead to a

greater decline in house price index and the number of housing transactions, but a greater increase

in number of building permits compared to lower-uncertainty elections. In other words, the

negative impact on house prices and transaction activities is mainly due to elections with highly

uncertain outcomes.


20

7. Gubernatorial elections and mortgage market

Most housing investors need leverage to finance their investments with mortgage loans. As

a result, housing and mortgage markets are closely connected. Therefore, effects of political

uncertainty on housing markets could come from the supply of housing financing. In this section,

we explore the mortgage lending channel. We investigate whether high political uncertainty is also

associated with reduced mortgage lending activities at both state level and bank level.

7.1. Data

We use the annual mortgage loan data between 1990 and 2015 from the Home Mortgage

Disclosure Act (HMDA). The loan-level HMDA data provides details on a substantial proportion

of mortgage market as it has been a mandatory reporting for most regulated depository institutions

including banks, credit unions, thrifts and other finance companies in the United States. The

HMDA data includes loan application, approval status, loan amount, as well as details on

borrowers and lenders. We only include home-purchase mortgage loans from bank lenders, and

we drop mortgages with missing loan characteristics such as loan size, loan type, lender’s approval

decision. We winsorize the mortgage loans based on their sizes at 1% level. We also exclude

mortgages subsidized by the Veterans Administration and the Federal Housing Authority. Then,

we aggregate the data and derive three main variables at both state level and bank level for our

analysis: log(Volume) as the natural logarithm of the volume of mortgages originated (with volume

in thousands of dollars), Application as the annual relative change in the number of mortgage

applications and Approval_rate as the ratio of the number of mortgage applications approved over

the total number of mortgage applications.

For the bank-level analysis, we use the data on banks’ financial statements collected from

Call Reports. The Call Reports data also provide us with the location of a bank’s headquarter,

21

which allows us to identify the home state of each bank in the sample. To examine the within-bank

variations in mortgage lending over time, we merge the annual HMDA data with the Call Reports

data in the period 1990-2015. Our sample results in 104,171 observations at the bank-, state-, and

year-level. We derive from the Call Reports data various variables representing banks’ financial

characteristics that may affect their lending decisions: Size as the logarithm of total Assets;

Return_on_equity as the ratio of net income to total equity; Liquid_assets as the ratio of liquid

assets to total assets; Home_mortgages as the ratio of residential mortgage loans to total loans;

Core_deposit as the ratio of core deposit to total assets; and Deposit_cost as the ratio of interest

expense on core deposit to core deposit. Detail descriptions of the variables used in this section

are in the Appendix. Table 7 presents the summary statistics of the variables that we use in this

analysis.


7.2. Methodology

At the state level analysis, we still employ the baseline multivariate regression analysis as

well as the difference – in – difference neighboring state methodology as described in section 4.

We examine both the supply side and the demand side of the mortgage market in election years.

In the baseline analysis, we use two dependent variables to capture the mortgage supply over time.

First, we use log(Volume)i,t which measures the logarithm of total mortgage volume originated in

state i in year t . Second, we use Approval_ratei,t which measures the mortgage approval rate in

state i in year t. To capture the mortgage demand overtime, we use Applicationi,t which measures

the relative change in the total number of mortgage applications in state i in year t. We then do the

following regressions:

log(Volume)i,t = a0 + a1Electioni,t + a2 Zi,t + a3Xt + μt + εi,t (7a)

22

Approval_ratei,t = b0 + b1Electioni.t + b2 Zi,t + a3Xt + μt + εi,t (7b)

Applicationi,t = c0 + c1Electioni,t + c2 Zi.t + c3Xi,t + μt + εi,t (7c)

Our main variable of interest is Electioni,t that equals 1 if there is a gubernatorial election in state

i and year t, and 0 otherwise; Zi.t is the set of control variables at state level; Xt is the set of control

variable at country-wide level; εi,t is the error term and μt is the state fixed effects. Table 8 reports

estimation results for the baseline multivariate regressions (7a), (7b), and (7c).


For the difference-in-difference neighboring methodology, we consider two dependent variables

to capture mortgage supply over time. First, we use ∆log(Volume)i,j,t which measures the difference

in the logarithm of total mortgage volume originated between state i and state j in year t . Second,

we use ∆Approval_ratei,j,t which measures the difference in the mortgage approval rate between

state i and state j in year t. For the demand side, we use ∆Applicationi,j,t which measures the

difference in the relative change in the total number of mortgage applications between state i and

state j in year t. We then do the following regressions:

∆log(Volume)i,j,t = a0 +a1 ∆Electioni,j,t + a2 ∆Zi,j,t + ∆εi,j,t (8a)

∆Approval_ratei,j,t =b0 + b1 ∆Electioni,j,t + b2 ∆Zi,j,t + ∆εi,j,t (8b)

∆Applicationi,j,t = c0 + c1 ∆Electioni,j,t + c2 ∆Zi,j,t + ∆εi,j,t. (8c)

We keep all observations with ∆Electioni,j,t =1 and 0 only; hence, the variable ∆Electioni,j,t becomes

a binary variable for all observations for use in our regressions. In total, there are 2635 state pair-

year observations out of which 589 observations with ∆Electioni,j,t=1 (a state with an election and

its neighboring state without an election), and 2037 observations with ∆Electioni,j,t = 0 (both states

23

without election) for the period from 1990 to 2015. Table 9 reports estimation results for diff-in-

diff neighboring state specifications (8a), (8b), and (8c).


At the bank level analysis, we focus on bank lenders and drop all mortgages originated by

credit unions, thrifts, and other non-bank lenders. We also use the timing of gubernatorial elections

as a proxy for exogenous political shocks and employ difference-in-difference with fixed effects

methodology to study the dynamics of banks’ mortgage lending in election years. For the supply

side, we consider two dependent variables that capture banks’ mortgage lending activities:

log(Volume)b,i,t as the natural logarithm of mortgage volume originated by bank b in state i in year

t ; and Approval_rateb,i,t as the approval rate of bank b in state i in year t . For the demand side,

we use Applicationb,i,t which measures the annual relative change in the number of mortgage

applications received by bank b in state i in year t. We use difference-in-difference methodology

and estimate the following specifications:

log(Volume)b,i,t = a0 + a1Electioni,t + a2 Bb,i,t-1 + a3 Zi,t-1 + a4Xt-1 + μt + μb + εb,i,t (9a)

Approval_rateb,i,t = a0 + a1Electioni,t + a2 Bb,i,t-1 + a3 Zi,t-1 + a4Xt-1 + μt + μb + εb,i,t (9b)

Applicationb,i,t = a0 + a1Electioni,t + a2 Bb,i,t-1 + a3 Zi,t-1 + a4Xt-1 + μt + μb + εb,i,t (9c)

Our main variable of interest is Electioni,t , which is equal to 1 if there is a gubernatorial

election in state i and year t, and 0 otherwise. Let Bb,i,t-1 denote a set of control variables at the bank

level; Zi,t-1 is the set of control variables at the state level. Xt-1 denotes the set of control variables

at country-wide level. We also include time fixed effects and bank fixed effects in this estimation.

Table 10 exhibits the estimation results for the specifications (9a), (9b), and (9c).


24

8. Concluding remarks

This paper shows large and significant dampening effects in the performance of the

residential housing market in the U.S. in periods of high political uncertainty. We employ

gubernatorial elections in the U.S. and an identification based on neighboring states to study the

causal effect of political uncertainty on the housing markets. We find that the return on house price

index and the number of transactions are both lower in the election year, suggesting a lower level

of house prices as investors demand higher premium for taking political risk. Our results also

suggest that there is an increase in the number building permits granted in the period leading to

the gubernatorial elections. This is because applying for a building permit is equivalent to buying

an option to construct in the future when the uncertainty is resolved. Therefore, permit holders can

hedge against potential regulatory changes from the newly elected government. Consequently, we

find that these results are stronger during election years in which the election outcomes are more

uncertain.

This study contributes to the growing body of literature on the impact of political and policy

uncertainty on the economy by demonstrating two points. First, uncertainty from the political

sources can significantly affect investors’ and households’ decision-making in the residential

housing market. Second, political uncertainty has a large effect on the lending activity in the

financial sector in both the supply and the demand sides of the mortgage markets.

25

References

Abel, A., 1983. Optimal Investment under Uncertainty. American Economic Review 73, 228-233

Aisen, A., José-Veiga, F., 2013. How does political instability affect economic growth?. European

Journal of Political Economy 29, 151-167.

Alesina, A., Ozler, S., Roubini, N., Swagel, P., 1996. Political Instability and Economic

Growth. Journal of Economic Growth 1, 189-211.

Baker, S., Bloom, N., Davis, S., 2016. Measuring Economic Policy Uncertainty. The Quarterly

Journal of Economics 131, 1593-1636

Battalio, R., Schultz, P., 2011. Regulatory Uncertainty and Market Liquidity: The 2008 Short Sale

Ban’s Impact on Equity Option Markets. Journal of Finance 66, 2013-2053

Bernanke, B., 1980. Irreversibility, Uncertainty, and Cyclical Investment. NBER Working

Papers 0502

Bloom, N., 2009. The Impact of Uncertainty Shocks. Econometrica 77, 623-685.

Brogaard J., Detzel, A., 2015. The Asset-Pricing Implications of Government Economic Policy

Uncertainty. Management Science 61, 3-18

Caballero, R., 1991. Earnings Uncertainty and Aggregate Wealth Accumulation. American

Economic Review 81, 859-871

Cao, C., Li, X. and Liu, G., 2019. Political uncertainty and cross-border acquisitions. Review of

Finance, 23, 439-470.

Chandrashekaran, V., 1999. Time-Series Properties and Diversification Benefits of REIT Returns.

Journal of Housing Research 17, 91-112.

https://econpapers.repec.org/article/aeaaecrev/v_3a73_3ay_3a1983_3ai_3a1_3ap_3a228-33.htmhttps://ideas.repec.org/a/eee/poleco/v29y2013icp151-167.htmlhttps://ideas.repec.org/s/eee/poleco.htmlhttps://ideas.repec.org/s/eee/poleco.htmlhttps://www.nber.org/people/alberto_alesinahttps://www.nber.org/people/sule_ozlerhttps://www.nber.org/people/nouriel_roubinihttps://www.nber.org/people/phillip_swagelhttps://ideas.repec.org/a/kap/jecgro/v1y1996i2p189-211.htmlhttps://ideas.repec.org/a/kap/jecgro/v1y1996i2p189-211.htmlhttps://ideas.repec.org/s/kap/jecgro.htmlhttps://econpapers.repec.org/article/oupqjecon/v_3a131_3ay_3a2016_3ai_3a4_3ap_3a1593-1636..htmhttps://econpapers.repec.org/article/blajfinan/v_3a66_3ay_3a2011_3ai_3a6_3ap_3a2013-2053.htmhttps://econpapers.repec.org/article/blajfinan/v_3a66_3ay_3a2011_3ai_3a6_3ap_3a2013-2053.htmhttps://ideas.repec.org/p/nbr/nberwo/0502.htmlhttps://ideas.repec.org/s/nbr/nberwo.htmlhttps://ideas.repec.org/s/nbr/nberwo.htmlhttps://ideas.repec.org/a/ecm/emetrp/v77y2009i3p623-685.htmlhttps://ideas.repec.org/s/ecm/emetrp.htmlhttps://econpapers.repec.org/article/inmormnsc/v_3a61_3ay_3a2015_3ai_3a1_3ap_3a3-18.htmhttps://econpapers.repec.org/article/inmormnsc/v_3a61_3ay_3a2015_3ai_3a1_3ap_3a3-18.htmhttps://econpapers.repec.org/article/aeaaecrev/v_3a81_3ay_3a1991_3ai_3a4_3ap_3a859-71.htm

26

Çolak, G., Durnev, A., Qian, Y., 2017. Political Uncertainty and IPO Activity: Evidence from U.S.

Gubernatorial Elections. Journal of Financial and Quantitative Analysis 52, 2523-2564

Cukierman, A., 1980. The Effects of Uncertainty on Investment under Risk Neutrality with

Endogenous Information. Journal of Political Economy 88, 462-75

Gao, P., Qi, Y., 2012. Political Uncertainty and Public Financing Costs: Evidence from U.S.

Municipal Bond Markets. Available at SSRN: https://ssrn.com/abstract=2024294

Handler, L., Jankowitsch, R., 2018. Political Uncertainty and Sovereign Bond Markets. Available

at SSRN: https://ssrn.com/abstract=3227790

Hartman, R., 1972. The effects of price and cost uncertainty on investment. Journal of Economic

Theory 5, 258-266

Hoesli, M., Lekander, L., Witkiewicz, W., 2004. International Evidence on Housing as a Portfolio

Diversifier. Journal of Housing Research 26, 161-206.

Ingersoll, J., Ross, S., 1992. Waiting to Invest: Investment and Uncertainty. The Journal of

Business 65, 1-29

Jens, C., 2017. Political uncertainty and investment: Causal evidence from U.S. gubernatorial

elections. Journal of Financial Economics, 124, 563-579.

Jong-A-Pin, R., 2009. On the measurement of political instability and its impact on economic

growth. European Journal of Political Economy 25, 15-29.

Julio B., Yook, Y., 2012. Political Uncertainty and Corporate Investment Cycles. Journal of

Finance 67, 45-84

https://econpapers.repec.org/article/cupjfinqa/v_3a52_3ay_3a2017_3ai_3a06_3ap_3a2523-2564_5f00.htmhttps://econpapers.repec.org/article/cupjfinqa/v_3a52_3ay_3a2017_3ai_3a06_3ap_3a2523-2564_5f00.htmhttps://econpapers.repec.org/article/ucpjpolec/v_3a88_3ay_3a1980_3ai_3a3_3ap_3a462-75.htmhttps://econpapers.repec.org/article/ucpjpolec/v_3a88_3ay_3a1980_3ai_3a3_3ap_3a462-75.htmhttps://ssrn.com/abstract=2024294https://ssrn.com/abstract=3227790https://econpapers.repec.org/article/ucpjnlbus/v_3a65_3ay_3a1992_3ai_3a1_3ap_3a1-29.htmhttps://econpapers.repec.org/article/eeejfinec/v_3a124_3ay_3a2017_3ai_3a3_3ap_3a563-579.htmhttps://econpapers.repec.org/article/eeejfinec/v_3a124_3ay_3a2017_3ai_3a3_3ap_3a563-579.htmhttps://ideas.repec.org/a/eee/poleco/v25y2009i1p15-29.htmlhttps://ideas.repec.org/a/eee/poleco/v25y2009i1p15-29.htmlhttps://ideas.repec.org/s/eee/poleco.htmlhttps://econpapers.repec.org/article/blajfinan/v_3a67_3ay_3a2012_3ai_3a1_3ap_3a45-84.htm

27

Kallberg, J., Liu, C., Greig, D., 1996. The role of housing in the portfolio allocation

process. Housing Economics 24, 359-377.

La Porta, R., Lopez- de-Silane, R., Shleiferand, A., Vishny, R., 1997. Legal Determinants of

External Finance. NBER Working Papers 5879

Lash, N. A., 2004. Corruption and economic development. Journal of Economic Asymmetries 1,

85-105

Leff, N. H., 1964. Economic Development through Bureaucratic Corruption. American Behavioral

Scientist 8 , 8–14.

Liu, L., Shu, H., Wei, J., 2017. The impacts of political uncertainty on asset prices: Evidence from

the Bo scandal in China. Journal of Financial Economics 125, 286-310.

Mauro, P., 1995. Corruption and Growth. The Quarterly Journal of Economics 110, 681-712.

Mo, P., 2001. Corruption and Economic Growth. Journal of Comparative Economics 29, 66-79.

Pastor, L., Veronesi, P., 2012. Uncertainty about Government Policy and Stock Prices. Journal of

Finance 67 , 1219-1264.

Pastor, L., Veronesi, P., 2013. Political Uncertainty and Risk Premia. Journal of Financial

Economics 110, 520-545.

Pindyck, R., 1988. Irreversible Investment, Capacity Choice, and the Value of the Firm. American

Economic Review 78, 969-85.

https://econpapers.repec.org/paper/nbrnberwo/5879.htmhttps://econpapers.repec.org/paper/nbrnberwo/5879.htmhttps://econpapers.repec.org/article/eeejfinec/v_3a125_3ay_3a2017_3ai_3a2_3ap_3a286-310.htmhttps://econpapers.repec.org/article/eeejfinec/v_3a125_3ay_3a2017_3ai_3a2_3ap_3a286-310.htmhttps://econpapers.repec.org/article/oupqjecon/v_3a110_3ay_3a1995_3ai_3a3_3ap_3a681-712..htmhttps://econpapers.repec.org/article/eeejcecon/v_3a29_3ay_3a2001_3ai_3a1_3ap_3a66-79.htmhttps://econpapers.repec.org/article/blajfinan/v_3a67_3ay_3a2012_3ai_3a4_3ap_3a1219-1264.htmhttps://econpapers.repec.org/article/aeaaecrev/v_3a78_3ay_3a1988_3ai_3a5_3ap_3a969-85.htm

28

Appendix. Variable definition

A. Housing market data

Housing data includes annual data on house price index, number of transactions and number

of building permits at state-level. The variables related to housing are the following:

- House price index is published by the Federal Housing Finance Agency (FHFA). The HPIi,t

is a weighted, repeat-sales index that measures average price changes in repeat sales or

refinancing on the same properties in state i in year t.

- RHi,t is the annual return or change in the HPI in state i in year t, and is calculated as follows

𝑅𝑖,𝑡𝐻 =

𝐻𝑃𝐼𝑖,𝑡 − 𝐻𝑃𝐼𝑖,𝑡−1𝐻𝑃𝐼𝑖,𝑡−1

- Transactioni,t is the number of housing transactions in state i in year t. The data is from

CoreLogic.

- Permiti,t is the number of building permits in state i in year t. The data is published by the

U.S. Census Bureau (census.gov)

B. Gubernatorial election data

The gubernatorial election data in our sample cover all gubernatorial elections across 50 states

in the U.S. in the period from 1982 to 2015. We collect most of the data from CQ Electronic

Library. There are 47 states that have 4-year election cycle; 3 states with 2-year election cycle

(New Hampshire, Vermont, Rhode Island adopted to 4-year election cycle in 1994). In general,

gubernatorial elections are held on the first Tuesday in November, with the earliest date being

the 2nd of November and the latest date being the 8th of November. The variables related to

gubernatorial elections are the following:

29

- Electioni,t is a yearly binary variable that is equal to 1 if there is a gubernatorial election in

state i in year t, and 0 otherwise.

- Pres_electiont is a yearly binary variable that takes value of 1 if there is a presidential

election in the U.S. in year t.

- Winning_margini,t is the difference between the percentage votes won by the first and

second place candidates in a gubernatorial election in state i in year t.

- Election_closenessi,t is a binary variable equal to one if the winning_margini,t is in the

lowest tercile (5.1%). In section 6, we divide our sample into two subsamples:

High_Uncertainty and Low_Uncertainty based on Election_closenessi,t :

Election_closenessi,t is equal to 1 for the High_Uncertainty subsample, and 0 for the

Low_Uncertainty subsample.

- Incumbent_absencei,t is a hand-collected binary variable equal to one if the incumbent

governor did not take part in a gubernatorial election in state i in year t either due to term

limit regulation or other personal reasons.

C. Mortgage data

Mortgage data are from Federal Financial Institutions Examination Council (FFIEC). Data are

made available publicly pursuant to the Home Mortgage Disclosure Act of 1975 (HMDA).

The HMDA requires most mortgage lenders to disclose information at the application level

about their mortgage lending activities every year. We aggregate mortgage data at the state

level and create three variables: log(Volume); Approval_rate; and Application

- log(Volume)i,t is the logarithm of the volume of mortgages originated in state i in year t,

with volume reported in thousands of dollars ; log(Volume)b,i,t is the logarithm of the

30

volume of mortgages originated by bank b headquartered in state i in year t, with volume

reported in thousands of dollars.

- Approval_ratei,t is the number of mortgage applications approved divided by the total

number of mortgage applications in state i in year t; Approval_rateb,i,t is the number of

mortgage applications approved divided by the total number of mortgage applications in

bank b headquarter in state i in year t.

Approval_ratei, t =𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑎𝑝𝑝𝑟𝑜𝑣𝑒𝑑 𝑚𝑜𝑟𝑡𝑔𝑎𝑔𝑒𝑠𝑖,𝑡

𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑚𝑜𝑟𝑡𝑔𝑎𝑔𝑒 𝑎𝑝𝑝𝑙𝑖𝑐𝑎𝑡𝑖𝑜𝑛𝑠𝑖,𝑡

Approval_rateb,i,t =𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑎𝑝𝑝𝑟𝑜𝑣𝑒𝑑 𝑚𝑜𝑟𝑡𝑔𝑎𝑔𝑒𝑠𝑏,𝑖,𝑡

𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑚𝑜𝑟𝑡𝑔𝑎𝑔𝑒 𝑎𝑝𝑝𝑙𝑖𝑐𝑎𝑡𝑖𝑜𝑛𝑠𝑏,𝑖,𝑡

- Applicationi,t is the relative change of the number of mortgage applications in state i in

year t; Applicationb,i,t is the relative change of the number of mortgage applications in bank

b headquartered in state i in year t.

Applicationi,t = 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑚𝑜𝑟𝑡𝑔𝑎𝑔𝑒 𝑎𝑝𝑝𝑙𝑖𝑐𝑎𝑡𝑖𝑜𝑛𝑠 𝑖,𝑡− 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑚𝑜𝑟𝑡𝑔𝑎𝑔𝑒 𝑎𝑝𝑝𝑙𝑖𝑐𝑎𝑡𝑖𝑜𝑛𝑠𝑖,𝑡−1

𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑚𝑜𝑟𝑡𝑔𝑎𝑔𝑒 𝑎𝑝𝑝𝑙𝑖𝑐𝑎𝑡𝑖𝑜𝑛𝑠𝑖,𝑡−1

Applicationb,i,t = 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑚𝑜𝑟𝑡𝑔𝑎𝑔𝑒 𝑎𝑝𝑝𝑙𝑖𝑐𝑎𝑡𝑖𝑜𝑛𝑠 𝑏,𝑖,𝑡− 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑚𝑜𝑟𝑡𝑔𝑎𝑔𝑒 𝑎𝑝𝑝𝑙𝑖𝑐𝑎𝑡𝑖𝑜𝑛𝑠𝑏𝑖,𝑡−1

𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑚𝑜𝑟𝑡𝑔𝑎𝑔𝑒 𝑎𝑝𝑝𝑙𝑖𝑐𝑎𝑡𝑖𝑜𝑛𝑠𝑏,𝑖,𝑡−1

D. Call reports data

Commercial Banks’ financial statement data are extracted from Bank Regulatory data section

in Wharton Research Data Services. Commercial banks are required to report either

“Consolidated Reports of Condition and Income” FFIEC 031 for banks with Domestic and

Foreign Offices or “Consolidated Reports of Condition and Income” FFIEC 041 for banks with

Domestic Offices only. These reports cover detailed financial statements of commercial banks

in the U.S. as well as their headquarter states, which enables us perform analyses on the impact

31

of gubernatorial elections. All the variables described below are for bank b in state i in year t,

and we follow Acharya and Mora (2012) to derive these variables from Call reports.

- Sizeb,i,t is the logarithm of the total assets (item RCFD2170 in call reports), with total assets

reported in thousands of dollars.

- Core_depositb,i,t (implicit) is the sum of transaction deposits, savings and small time

deposits that form a stable source of funds divided by total assets. We take the quarterly

avergage of core deposits: RCON3485 + RCONB563 + RCON3469 (before 1997Q1)/

RCONA529 (after 1997Q1).

- Deposit_costb,i,t (implicit) is the interest expense on core deposits divided by the quarterly

average of core deposits. The interest expense on core deposits: RIAD4508 + RIAD0093

(RIAD4509 + RIAD4511 before 2001Q1) + RIADA518 (RIAD4512 before 1997Q1).

- Return_on_equityb,i,t is the net income divided by the total equity (RIAD4340 divided by

RCFD3210)

- Liquid_assetsb,i,t is the sum of liquid assets divided by total assets. Liquid assets are cash,

federal funds sold, reverse repos, and securities excluding mortgage-backed and asset-

backed securities. Cash: RCFD0010; federal funds sold: RCFD1350 (before 2002Q1) +

RCONB987 + RCFDB989 (from 2002Q1); securities excluding mortgage-backed and

asset-backed securities before 2009Q2: (RCFD1745 + RCFD1773) - (RCFD8500

+RCFD8504 + RCFDC026 + RCFD8503 + RCFD8507+ RCFDC027); after 2009Q2:

(RCFD1754+ RCFD1733) – (RCFDG300 + RCFDG303+ RCFDG304 + RCFDG307 +

RCFDG308 + RCFDG311 + RCFDG312 + RCFAG315+ RCFDG316 + RCFDG319+

RCFDG320 + RCFDG323+ RCFDG324 + RCFDG327 +RCFDG328 + RCFDG331+

RCFDG336 + RCFDG339 + RCFDG340 + RCFDG343 + RCFDG344 + RCFDG347 +

RCFDC026 + RCFDC027)

32

- Home_mortgageb,i,t is the volume of residential mortgage loans divided by total loans.

Residential mortgage loans: RCON1797 + RCON5367 + RCON5368. Total loans:

RCFD2122.

E. Other macroeconomics variables

Macroeconomic data and financial market data are retrieved from various sources including

the Bureau of Economic Analysis (bea.gov), the Bureau of Labor Statistics (bls.gov), the U.S

Census Bureau (census.gov), the Federal Emergency Management Agency (fema.gov), and

FRED (fred.stlouisfed.org). The list of control variables goes as follows:

- GDPi,t is the gross domestic product’s growth rate in state i in year t.

- Unemploymenti,t is the unemployment rate in state i in year t.

- Populationi,t is the annual relative change in the population in state i in year t.

- Emergencyi,t is a binary variable that takes value of 1 if state i in year t declares emergency

in a given year, and 0 other wise.

- Recessiont is a binary variable that takes value of 1 if year t is identified as being in a

recession state by the National Bureau of Economic Research (NBER).

- S&P500t is relative return of the S&P500 index in year t.

- Bond_ratet is the 10-year interest rate on the U.S. government bonds in year t.

33

Tables and Figures

Figure 1: Policy uncertainty and housing markets

Figure 1 exhibits the dynamics of the U.S. Economic Policy Uncertainty Index (left axis scale) and the annual growth in U.S. house

prices (right axis scale) in Panel A and the housing transactions per year (right axis scale) in Panel B from January 1991 to April

2020. The U.S. Economic Policy Uncertainty Index has been developed and constructed in Baker, Bloom and Davis (2016). The

annual growth in house prices is computed using the seasonally adjusted Federal Housing Finance Agency (FHFA) monthly house

price index for the U.S. The data on housing transactions comes from the U.S. National Association of Realtors.

Panel A. Policy uncertainty and growth in house prices

Panel B. Policy uncertainty and housing transactions

-10,0%

-5,0%

0,0%

5,0%

10,0%

0

50

100

150

200

250

300

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

20

18

20

19

20

20

Ann

ual

grow

th i

n ho

use

pri

ces

(%)

Polic

y un

cert

ainty

in

dex

(10

0 =

yea

r 20

10

)

Policy uncertainty index Growth in house prices

0

1.000

2.000

3.000

4.000

5.000

6.000

7.000

8.000

0

50

100

150

200

250

300

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

Hous

ing

tran

sact

ions

per

yea

r

Polic

y un

cert

ainty

in

dex

(100 =

yea

r 2010)

Policy uncertainty index Housing transactions

Stimulus

debate

2nd Gulf

War

9/11

Covid-19

Lehman and

TARP Eurozone

crisis

Debt ceiling

debate

Gov’t

shutdown Brexit

Trump

election

U.S.-China

trade war

Fiscal

cliff

Stimulus

debate

2nd Gulf

War

9/11

Covid-19

Lehman

and

TARP Eurozone

crisis

Debt ceiling

debate

Gov’t

shutdown

Brexit

Trump

election

U.S.-China

trade war Fiscal

cliff

34

Table 1: Summary statistics

Table 1 provide the summary statistics for the variables we use in our study. All variables are in annual term. RH is the return of

the FHFA house price index at state level; Transaction is the number of housing transactions at state level; Permit is the number

of building permits issued at state level; Election is a dummy variable that equals to 1 if a state holds a gubernatorial election and

0 otherwise; Pres_election is a dummy variable that equals to 1 if there is a presidential election and 0 otherwise;

Incumbent_absence is a dummy variable that equals to 1 if the incumbent is absent from the election due to term limit or other

personal reasons; Winning_margin is the difference between the percentage votes won by the first and second place candidates in

a gubernatorial election; Election_closeness is a dummy variable equal to 1 if the Winning_margin is in the lowest tercile (5.1%)

and 0 otherwise; GDP is the growth rate in gross domestic product at state level; Unemployment is the unemployment rate at state

level; Population is the growth rate in the number of residents at state level; Emergency is the dummy variable that equals 1 if a

state declares it is under an emergency situation and 0 otherwise; Recession is the dummy variable that equals 1 if a year is identified

as being in a recession; S&P500 is the relative return of the S&P500 index; and Bond_rate is the 10-year interest rate on the U.S.

government bonds. The Appendix shows a detailed description of the variables.

Whole sample

Housing variables Min Mean Max Std. deviation Num. of obs.

RH -0.30 0.04 0.53 0.06 1,700

Transaction 0 49,690 980,433 110,544 1,700

Permit 555 26,386 314,641 36,425 1,700

Political variables Min Mean Max Std. deviation Num. of obs.

Election 0.00 0.27 1.00 0.44 1,700

Pres_election 0.00 0.24 1.00 0.42 1,700

Incumbent_absence 0.00 0.40 1.00 0.49 458

Winning_margin 0.00 0.16 0.65 0.14 458

Election_closeness 1.00 0.34 0.00 0.47 458

Economic variables Min Mean Max Std. deviation Num. of obs.

GDP -0.26 0.05 0.25 0.037 1,700

Unemployment 0.02 0.06 0.18 0.02 1,700

Population -0.06 0.01 0.12 0.01 1,700

Emergency 0.00 0.30 1.00 0.46 1,700

Recession 0.00 0.06 1.00 0.24 1,700

S&P500 -0.37 0.13 0.37 0.16 1,700

Bond_rate -0.11 0.09 0.33 0.10 1,700

35

Table 1: Summary statistics (cont.)

Sub-sample with gubernatorial elections only


RH -0.15 0.03 0.53 0.07 458

Transaction 0 46,508 780,870 106,887 458

Permit 692 27,394 314,641 35,892 458

Sub-sample with close elections only (elections with winning margin is less than 5.1%)


RH -0.15 0.03 0.17 0.07 154

Transaction 0 45,715 780,870 113,913 154

Permit 789 27,662 203,238 33,849 154

36

Table 2: House price returns, transactions, and building permits in election years

Table 2 reports the results of the baseline OLS estimates of the specifications in equations (1a), (1b), and (1c) that examine the

impact of gubernatorial election on the return on house price index RH (Panel A), the number of housing transaction (Panel B), and

the number of building permit (Panel C). All the specifications include state and year fixed effects and the standard errors are

clustered at the state and year levels. *** significant at 1% level. ** significant at 5% level. * significant at 10% level. Sample

period: 1982-2015.

Panel A. Return on house price index (RH) and gubernatorial election: Baseline OLS

RH

(1) (2) (3) (4)

Election -0.005***

(-2.68)

-0.006***

(-3.40)

-0.006***

(-3.01)

-0.006***

(-2.80)

GDP (lagged) 0.625***

(10.10)

0.644***

(9.24)

0.324***

(3.67)

0.318***

(2.90)

SP500 (lagged) 0.369***

(5.70)

0.341***

(6.36)

0.337***

(4.86)

0.384***

(5.66)

Unemployment (lagged) -0.209**

(-2.21)

-0.216***

(3.23)

-0.327***

(3.48)

Bond_rate (lagged) -0.110***

(-7.67)

-0.085***

(-5.51)

-0.059***

(-3.73)

Emergency -0.001

(-0.36)

Recession -0.025***

(-4.30)

Population(lagged) 0.873***

(4.75)

Pres_election 0.002

(0.98)

R2 0.1487 0.1811 0.4539 0.4641

Num. of observations 1700 1700 1700 1700

37

Table 2: House price returns, transactions, and building permits in election years. OLS (cont.)

Panel B. Number of housing transactions (Transaction) and gubernatorial elections: Baseline OLS

Transaction

(1) (2) (3) (4)

Election -5547.575***

(-3.41)

-3726.202***

(-2.63)

-2909.319***

(-2.58)

-3218.847***

(-2.95)

GDP (lagged) 4371.155***

(-3.67)

3872.446***

(-3.40)

473.367***

(-2.90)

444.973***

(-2.69)

SP500 (lagged) 9844.930***

(-5.13)

10070.700***

(-5.21)

15872***

(-3.22)

6355.766*

(-1.81)

Unemployment (lagged) -1586.937*

(-1.69)

-1073.862***

(-3.68)

-1688.747***

(-4.18)

Bond_rate (lagged) -1056.700***

(-5.71)

-1591.724

(-0.52)

1997.908

(0.63)

Emergency 1162.844

(0.84)

Recession -14115.840***

(-4.81)

Population (lagged) -89939.100

(-0.27)

Pres_election 7536.735***

(4.71)

R2 0.040 0.047 0.931 0.933


38

Table 2: House price returns, transactions, and building permits in election years. OLS (cont.)

Panel C. Number of building permits (Permit) and gubernatorial elections: Baseline OLS

Permit

(1) (2) (3) (4)

Election 1058.299**

(2.91)

1201.012***

(3.21)

813.855***

(2.80)

583.425**

(2.15)

GDP (lagged) 1235.050**

(3.47)

958.596***

(2.94)

388.769***

(3.61)

328.513***

(3.24)

SP500 (lagged) -2191.324*

(-1.73)

2464.412*

(1.78)

2632.739***

(4.00)

3306.844***

(4.35)

Unemployment (lagged) -2054.970***

(-5.57)

-706.088***

(3.80)

-714.569***

(3.94)

Bond_rate (lagged) -4683.753

(-1.52)

-8699.603***

(-3.60)

-4939.527**

(-2.54)

Emergency 694.743**

(2.17)

Recession -4335.058***

(-4.01)

Population (lagged) 99508.320***

(3.58)

Pres_election 71.058

(0.4)

R2 0.081 0.132 0.780 0.787


39

Table 3: U.S. states and their neighboring state

State

Num. NS1 NS2 NS3 NS4 NS5 NS6 NS7 NS8

Alaska AK 0

Alabama AL 4 TN GA FL MS

Arkansas AR 6 MO TN MS LA TX OK

Arizona AZ 5 UT CO NM CA NV

California CA 3 OR NV AZ

Colorado CO 7 WY NE KS OK NM AZ UT

Connecticut CT 3 MA RI NY

Delaware DE 3 PA NJ MD

Florida FL 2 GA AL

Georgia GA 5 NC SC FL AL TN

Hawaii HI 0

Iowa IA 6 MN WI IL MO NE SD

Idaho ID 6 MT WY UT NV OR WA

Illinois IL 5 WI IN KY MO IA

Indiana IN 4 MI OH KY IL

Kansas KS 4 NE MO OK CO

Kentucky KY 7 OH WV VA TN MO IL IN

Louisiana LA 3 AR MS TX

Massachusetts MA 5 NH RI CT NY VT

Maryland MD 4 PA DE VA WV

Maine ME 1 NH

Michigan MI 3 OH IN WI

Minnesota MN 4 WI IA SD ND

Missouri MO 8 IA IL KY TN AR OK KS NE

Mississippi MS 4 TN AL LA AR

Montana MT 4 ND SD WY ID

North Carolina NC 4 VA SC GA TN

North Dakota ND 3 MN SD MT

40

Table 3: U.S. states and their neighboring state (cont.)

State Num. NS1 NS2 NS3 NS4 NS5 NS6 NS7 NS8

Nebraska NE 6 SD IA MO KS CO WY

New Hampshire NH 3 ME MA VT

New Jersey NJ 4 NY CT DE PA

New Mexico NM 5 CO OK TX AZ UT

Nevada NV 5 ID UT AZ CA OR

New York NY 5 VT MA CT NJ PA

Ohio OH 5 PA WV KY IN MI

Oklahoma OK 6 KS MO AR TX NM CO

Oregon OR 4 WA ID NV CA

Pennsylvania PA 6 NY NJ DE MD WV OH

Rhode Island RI 2 MA CT

South Carolina SC 2 NC GA

South Dakota SD 6 ND MN IA NE WY MT

Tennessee TN 8 KY VA NC GA AL MS AR MO

Texas TX 4 OK AR LA NM

Utah UT 6 ID WY CO NM AZ NV

Virginia VA 5 MD NC TN KY WV

Vermont VT 3 NH MA NY

Washington WA 2 ID OR

Wisconsin WI 4 MI IL IA MN

West Virginia WV 5 PA MD VA KY OH

Wyoming WY 6 MT SD NE CO UT ID

41


Difference-in-Differences neighboring state methodology

Table 3 reports the results from the difference-in-difference neighboring state methodology with the specifications in equations

(5a), (5b), and (5c) that examine the impact of gubernatorial election on the differences in return on house price index ∆RH (Panel

A), the number of housing transaction ∆Transaction (Panel B), and the number of building permit ∆Permit (Panel C) between a

state and its neighboring state. The standard errors are clustered by pairs of state-neighboring state and year in all the specifications.

*** significant at 1% level. ** significant at 5% level. * significant at 10% level. Sample period: 1982-2015.

Panel A. Return on house price index (RH) and gubernatorial election: Difference-in-differences

RH

(1) (2) (3) (4)

∆Election -0.002***

(-32.62)

-0.002***

(-19.58)

-0.001***

(-18.06)

-0.001***

(-17.08)

∆GDP (lagged) 0.124***

(2.72)

0.157

(1.19)

0.183

(1.65)

∆Unemployment (lagged) -0.166

(0.87)

-0.103

(0.69)

∆Populatiton (lagged) 1.160***

(4.7)

1.179***

(4.79)

∆Emergency 0.001

(0.53)

0.001

(0.70)

R2 0.324 0.340 0.337 0.335


Note: Column (4) reports the weighted least square regression results, with weight

being the number of neighboring states.

42


Difference-in-Differences neighboring state methodology (cont.)

Panel B. Number of housing transactions (Transaction) and gubernatorial election: Difference-in-differences

Transaction

(1) (2) (3) (4)

∆Election -995.528***

(-53.47)

-1010.03***

(54.18)

-1029.329***

(-54.77)

-486.988***

(-14.97)

∆GDP (lagged) 391.475**

(-1.99)

435.226*

(-1.79)

422.988*

(-1.97)


(1.22)

-690.718

(1.15)

∆Populatiton (lagged) 1583.364

(1.45)

1794.482*

(1.72)

∆Emergency 967.277

(0.94)

1704.148*

(1.85)

R2 0.881 0.882 0.883 0.884


Note: Column (4) reports the weighted least square regression results, with weight being the

number of neighboring states.

43


Difference-in-Differences neighboring state method (cont.)

Panel C. Number of building permits (Permit) and gubernatorial election: Difference-in-differences

Permit

(1) (2) (3) (4)

∆Election 190.954***

(3.26)

193.056***

(3.35)

183.044***

(3.10)

232.871***

(6.53)

∆GDP (lagged) 160.544**

(2.58)

72.521

(1.20)

98.485*

(1.68)


(1.56)

-402.084*

(1.69)

∆Populatiton (lagged) 1644.365***

(4.56)

1862.433***

(4.33)

∆Emergency 473.017

(1.2)

464.346

(1.35)

R2 0.749 0.751 0.758 0.758


Note: Column (4) reports the weighted least square regression results, with weight being the

number of neighboring states.

44

Figure 2: Housing markets over the gubernatorial election cycle

Figure 2 plots the averaged-across state return on house price index RH (Panel A), averaged-across state number of housing

transactions Transaction (Panel B), and averaged-across state number of building permit Permit (Panel C) over the gubernatorial

election cycle during the sample period from 1982-2015. There are 458 gubernatorial elections in 50 states in which 47 states with

4-year election cycle and 3 states (NH, RI, VT) with of 2-year election cycle. In these graphs, we only include states with 4-year

election cycle. Sample period: 1982-2015.

Panel A. Average RH and Gubernatorial election cycle (Election year T=0)

Panel B. Average number of transactions and Gubernatorial election cycle (Election year T=0)

Panel C. Average number of building permits and Gubernatorial election cycle (Election year T=0)

3.35%

3.40%

3.45%

3.50%

3.55%

3.60%

3.65%

3.70%

3.75%

T=-1 T=0 T=1 T=2

Av

erag

e R

H

Gubernatorial election cycle

0

10,000

20,000

30,000

40,000

50,000

60,000

T=-1 T=0 T=1 T=2

Av

erag

e T

ran

sact

ion

s


0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

T=-1 T=0 T=1 T=2

Av

erag

e P

erm

its


45

Table 5: Post-election analysis of house price returns, transactions, and building permits

Table 5 presents the results of six OLS regressions (6a), (6b), (6c), (6d), (6e), and (6f) to examine the post-election reversal

effect on the return on house price index RH (columns 1 and 2); the number of housing transactions Transaction (columns 3 and

4); and the number of building permits Permit (columns 5 and 6). All the scpeficications include state and year fixed effects

and the standard errors are clustered at the state and year level. *** significant at 1% level. ** significant at 5% level. *

significant at 10% level. Sample period: 1982-2015.

Variables RH Transaction Permit

(1) (2) (3) (4) (5) (6)

Post_election_1 0.004***

(-2.97)

0.005***

(-3.46)

734.263***

(-2.98)

914.872***

(-3.32)

-334.182***

(3.03)

-417.631**

(2.18)

Post_election_2 0.003*

(1.80)

1031.283**

(2.06)

-287.319**

(1.99)

GDP (Lagged) 0.302***

(3.90)

0.314***

(4.62)

423.617***

(-3.01)

444.973***

(-2.69)

334.471***

(3.73)

421.491***

(4.21)

SP500 (Lagged) 0.043***

(6.47)

0.055***

(5.89)

-5541.029*

(-2.18)

-6355.766*

(-1.81)

3218.438***

(3.32)

3834.591***

(4.99)

Unemployment (Lagged) -0.228***

(3.96)

-0.234***

(4.03)

-1594.387***

(-4.24)

-1688.747***

(-4.57)

-721.041***

(3.12)

-681.381***

(4.03)

Bond_rate (Lagged) -0.067***

Documents

Political Uncertainty and Housing Markets · Van Nguyen† Carles Vergara-Alert ... since Q1 2007 and stood 29% lower by Q3 2019.5 Investors have long attributed those moves in housing