Second Home Buyers and the Housing Boom and Bust

Finance and Economics Discussion Series

Divisions of Research & Statistics and Monetary Aﬀairs

Federal Reserve Board, Washington, D.C.

Daniel Garc´ıa

2019-029

Please cite this paper as:

Garc´ıa, Daniel (2019). “Second Home Buyers and the Housing Boom and Bust,” Finance

and Economics Discussion Series 2019-029. Washington: Board of Governors of the Federal

Reserve System, https://doi.org/10.17016/FEDS.2019.029.

NOTE: Staﬀ working papers in the Finance and Economics Discussion Series (FEDS) are preliminary

materials circulated to stimulate discussion and critical comment. The analysis and conclusions set forth

are those of the authors and do not indicate concurrence by other members of the research staﬀ or the

Board of Governors. References in publications to the Finance and Economics Discussion Series (other than

acknowledgement) should be cleared with the author(s) to protect the tentative character of these papers.

Second Home Buying and the Housing Boom and Bust

Daniel Garc´ıa

∗

March 25, 2019

Abstract

Record-high second home buying (homeowners acquiring nonprimary residences) was

a central feature of the 2000s boom, but the macroeconomic eﬀects remain an open

question partly because reliable geographic data is currently unavailable. This paper

constructs local data on second home buying by merging credit bureau data with mort-

gage servicing records. The identiﬁcation strategy exploits the fact that the vacation

share of housing from the 2000 Census is predictive of second home origination shares

during the boom years, while also uncorrelated with other boom-bust drivers including

proxies for local housing expectations, the use of alternative and PLS mortgages, and

supply constraints. Localities with plausibly exogenous higher second home origination

shares experienced a more pronounced boom and bust – stronger growth in construc-

tion and house prices during the boom, and steeper declines in activity during the

recession years. Overall, second home buying could explain about 30 and 15 percent of

the run-up in construction employment and house prices, respectively, over 2000-2006.

JEL codes: R12, R21, R31

∗

Board of Governors of the Federal Reserve System, email: [email protected]. Thanks to Chris

Carroll, Neil Bhutta, Raven Molloy, John Sabelhaus, Wayne Passmore, Shane Sherlund, Steve Laufer, Ale-

jandro Justiniano, Gadi Barlevy, Jonathan Wright, Jon Faust, and Marco Bassetto for their comments on

this and previous versions. The analysis and conclusions set forth here are those of the authors and do not

indicate concurrence by other members of the research staﬀ or the Board of Governors.

1 Introduction

The record-high level of second home buying (homeowners acquiring nonprimary residences)

was a central feature of the 2000s housing boom.

Bhutta (2015) shows that second home

buyers contributed more to aggregate mortgage debt during the boom years than did all

ﬁrst-time buyers. Second home buyers were typically over-leveraged, and despite having

middle to high income and credit scores, experienced higher default rates than average

during the recession (Haughwout et al. (2011); Albanesi et al. (2017); Albanesi (2018)). The

macroeconomic eﬀects could have been sizable – Chinco and Mayer (2016) ﬁnd that second

home buying signiﬁcantly contributed to mispricing in housing during the boom years. Their

data covers only 21 US cities, however, and more comprehensive studies have so far been

limited by lack of adequate data.

This paper is the ﬁrst to measure second home buying based on property location with

broad coverage of the US economy, by combining credit bureau data with mortgage servicing

records. To estimate the eﬀects of second home buying on economic activity during the

housing boom and bust, I use as an instrument the vacation share of housing from the

2000 Census, to isolate the variation in second home buying purely explained by diﬀerences

in physical local amenity values versus other factors such as variation in housing market

expectations. I ﬁnd that localities with higher second home buying experienced a more

pronounced boom and bust – stronger growth in house prices and construction employment

over 2000-2006, and sharper declines in activity over 2006-2010. Overall, a partial equilibrium

aggregation exercise suggests second home buying could explain about 30 and 15 percent of

the run-up in construction employment and house prices, respectively, over 2000-2006.

The main novelty of this paper from a data perspective is to use the Credit Risk In-

sight Servicing McDash (CRISM) dataset, which merges credit bureau data (Equifax) with

mortgage servicing records (Black Knight McDash). I identify buyers of second homes as

those having 2 or more ﬁrst lien mortgages (same as Haughwout et al. 2011; Bhutta 2015

and others) and merge second home identiﬁers with property location from Black Knight

In the literature, buyers of second homes (nonprimary residences) are often referred to as property

investors. Instead, I use the terms second home buyers or nonprimary residence buyers, because some

second homes may have a strong consumption motive in addition to an investment one.

McDash. I deﬁne the second home origination share as the ratio of new home purchase loans

for nonprimary residences to total new home purchase loans at the county level.

There is a strong and positive OLS association between the county level second home

origination share and house price changes during the housing boom years. Variation in the

second home origination share explains almost 55 percent of the variation in house price

changes from 2000-2006 at the county level. This association may reﬂect diﬀerent factors.

The possibility assessed in this paper is that second home buying may have pushed up activity

and prices during the boom years. On the other hand, local house price expectations could

have attracted second home buyers investing in real estate. For example, many booming

areas had high second home origination shares, including the home counties of Los Angeles,

Las Vegas, Miami, and Phoenix. These localities also had high shares of alternative (not

ﬁxed rate) and privately securitized mortgages, making it challenging to isolate the causal

eﬀects of any single determinant of the housing boom.

To disentangle causality, I use an instrument for second home origination shares – the

vacation share of housing from the 2000 Census – which is uncorrelated with proxies for local

housing expectations and other drivers of the housing boom such as the use of alternative and

PLS mortgages as well as supply constraints. The identiﬁcation strategy exploits the fact

that predetermined, physical diﬀerences in amenity values help explain signiﬁcant geographic

variation in second home buying. Areas with high vacation shares have appealing physical

qualities, such as warm winters and a waterfront. These areas include localities in sand states

such as in Florida and California, but also localities along the Eastern Seabord, close to the

Great Lakes, and in locations with appealing terrain such as near the Ozark Mountains. In

fact, there is enough variation in the vacation share of housing to allow for speciﬁcations

with state ﬁxed eﬀects, which yield coeﬃcient estimates that are very similar to speciﬁcations

without them.

The main concern with instrument validity is that the vacation share of housing may

be correlated with other drivers of the housing boom. Vacation localities do diﬀer along

some observables, for example, they tend to have older, whiter, and more rural populations.

While I can control for these observables, unobserved characteristics such as housing expec-

tations may partly explain why vacation localities had high second home origination shares

during the boom. However, judging by the debt behavior of locals, it does not appear that

house price expectations were signiﬁcantly stronger in vacation localities than elsewhere.

Had locals in vacation areas expected stronger appreciation, they may have taken out more

home equity loans, mortgages, or bought more nonprimary residences than local elsewhere.

Instead, the vacation share of housing is not signiﬁcantly associated with changes in mort-

gage or home equity loan debt balances during the boom, or with second home origination

shares when measured at borrower (rather than property) location. Moreover, the vacation

share of housing is also uncorrelated with various drivers of the boom, including the local

share of subprime borrowers, the use of alternative and PLS mortgages, and housing supply

elasiticities. I also verify that vacation localities activity is not generally cyclical, with yearly

changes in house prices not statistically diﬀerent in vacation localities during both recession

and non-recession years, using local house price data going back to the 1970s. In fact, trends

in house prices and construction employment are essentially identical prior to 2000, with

diﬀerential patterns emerging only after 2000, when second home buying began to increase.

The results show that second home buying (when instrumented using the 2000 share of

vacation housing) contributed signiﬁcantly to the boom and bust in housing activity over

2000-2010. Areas with high second home origination shares during the boom years had

faster growth in construction employment and house prices over 2000-2006. In localities

with 10 percentage point higher second home origination shares, growth in construction

employment and house prices over 2000-2006 was higher on average by 7 and 16 percentage

points, respectively.

However, over the next years, the eﬀects of second home activity turn contractionary.

Areas with high second home originations shares over 2000-2006 contracted more severely

over 2006-2010. On average, in localities with 10 percentage point higher second home origi-

nation shares in 2000-2006, changes in delinquency rates were on average 2 percentage points

higher, and declines in house price and construction employment were 7 and 9 percentage

points more pronounced on average, respectively, over 2006-2010. These results are new

evidence pointing to the damaging eﬀects during the housing bust of second home loans

issued during the boom, consistent with Haughwout et al. (2011) and Albanesi (2018) who

ﬁnd that second home buyers had signiﬁcantly higher default rates than average.

Overall, localities with higher second home origination shares in the boom years grew

faster over 2000-2006, but contracted more sharply over 2006-2010. The losses in the reces-

sion years tend to dominate the gains during the boom years. When looking at changes in

construction employment and house prices over 2000-2010, the estimated eﬀects on construc-

tion employment are negative. For house prices, the estimated eﬀects are positive though

not statistically diﬀerent from zero. Overall, second home buying contributed to volatility:

higher activity during the boom, and reversals of that activity during the bust, especially so

in construction employment. The losses in construction employment extend to the 2000-2014

period, therefore providing empirical support to the overhang hypothesis in Rognlie et al.

(2018) which predicts persistent declines in construction following overbuilding during the

boom.

The eﬀects of second home buying appear concentrated in the housing sector. The em-

ployment eﬀects are not signiﬁcant for total private employment excluding construction and

nontradable employment, for both the 2000-2006 and 2006-2010 periods. It is possible that

the overall employment eﬀects were larger but are not captured by the county level models,

e.g. loan losses likely aﬀected the overall health of the ﬁnancial system. However, the lack of

signiﬁcant results in the county level estimates for broader employment categories does ame-

liorate concerns about instrument validity, since local shocks aﬀecting overall employment

are uncorrelated with the instrument. Moreover, the 2SLS point estimates are on average

about 50 percent smaller than their OLS counterparts, suggesting the latter are biased up-

wards due to other factors such as reverse causality. Results are also very similar when using

state ﬁxed eﬀects speciﬁcations.

To understand the extent to which second home buying may have aﬀected the severity of

the housing boom, I combine the 2SLS estimates with the counterfactual assumptions that

the share of second home buying remained at its 2000-2001 level instead of rising. In the

baseline scenario, I ﬁnd that second home buying could explain about 30 and 15 percent of

the run-up in construction employment, respectively, over 2000-2006. However, this estimate

is subject to uncertainty about coeﬃcient estimates, in addition to assumptions about both

the extent to which the increase in second home origination shares during the boom was

an endogenous response to other changes in the economy, as well as the magnitude of the

general equilibrium eﬀects of second home buying not captured in the county level models.

Reﬂecting uncertainty in the model estimates, I ﬁnd that second home buying could have

explained between 6 to 57 percent of the runup in construction employment, and between 6

and 23 percent of the increase in house prices over 2000-2006.

This paper adds to the growing literature showing that second home buyers were an

important driver of the boom and bust. Bhutta (2015) documents that second home buyers

contributed signiﬁcantly to the rise in aggregate mortgage debt during the housing boom.

Second home buyers had higher than average default rates during the recession (Haughwout

et al. 2011) though they were typically higher income and prime prior to it (Albanesi et al.

2017; Albanesi 2018). Quantitative work highlights how second home buyers can inﬂuence

other buyers and drive boom-bust episodes such as Piazzesi and Schneider (2009); Burnside

et al. (2016); DeFusco et al. (2017); Nieuwerburgh and Favilukis (2017). Chinco and Mayer

(2016) ﬁnd that second home buying led to higher house prices (and mispricing) in a panel

of 21 cities using a high frequency panel VAR identiﬁcation approach. Gao et al. (2018) also

ﬁnd that second home buying contributed to the boom-bust in activity, though they use

data from the Home Mortgage Disclosure Act, which is known to underreport second home

buying (Elul and Tilson (2015)). Overall, the results in this paper are complementary to

this literature; the main contribution is using new data combining the strength of datasets

previously used in isolation (credit bureau data and mortgage servicing records), a novel

identiﬁcation strategy, and results that include a broad set of outcome variables including

employment.

More broadly, this paper ﬁts in the extensive body of work studying the determinants

of the housing boom. The housing boom had many, often interrelated causes, involving

households up and down the income and credit score distributions (Adelino et al. 2016; Foote

et al. 2016; Albanesi et al. 2017). One of the main contributions of this paper is isolating

the eﬀect of second home buying (as instrumented via the vacation share of housing) on

changes in construction employment and house prices during the 2000s. I do so by showing

that the vacation share of housing is uncorrelated with major determinants of the housing

boom identiﬁed in the literature, including: the interaction of changes in housing demand

with supply constraints (Saiz 2010; Aladangady 2017); the use of alternative mortgages

such as interest-only or balloon mortgages (Barlevy and Fisher 2012; Foote et al. 2008); the

expansion in subprime credit (Mian and Suﬁ 2009; Demyanyk and Hemert 2011; Gerardi

et al. 2008); and the boom-bust in private label securitization (Keys et al. 2010; Nadauld

and Sherlund 2009; Mian and Suﬁ 2018; Garcia 2018).

2 Data

The FRBNY Consumer Credit Panel/Equifax contains credit reporting data for a nationally

representative 5 percent sample of all adults with a social security number and credit report

beginning in 1999. The data contain information on the number of open ﬁrst lien mortgages

per borrower. Second home purchase originations are measured as new purchase loans for

borrowers with 2 or more properties. For each origination, I use the borrower’s number of ﬁrst

mortgage accounts four quarters ahead of the origination, to avoid counting false positives,

e.g. a reﬁnancing or change in residency that temporarily shows the borrower as having

two properties due to reporting lags. Figure 1 shows the aggregate second home origination

share, which rose from 21 percent in 2000 to its peak of 36 percent in 2006, subsequently

falling back to near 20 percent over 2009-2011. These patterns are similar to those reported

in Haughwout et al. (2011) (using the same dataset) and Albanesi (2018) (using Experian),

with both identifying second home buyers using a similar approach. While credit bureau

data is helpful in analyzing aggregate trends in second home buying, these data generally

do not contain the address of nonprimary residences acquired.

On the other had, Black Knight McDash (formerly known as LPS) contains additional

loan level characteristics, including property location. The Black Knight McDash dataset is

comprised of the servicing portfolios of the largest residential mortgage servicers in the US,

covering about 60 percent of the mortgage market. The main dataset I use in this paper, the

Equifax Credit Risk Insight Servicing McDash (CRISM), contains credit bureau data from

Equifax, matched to the mortgage-level McDash servicing data. CRISM covers about 60

percent of the mortgage market (from McDash). The merge is key since McDash does not

contain data on the number of ﬁrst lien mortgages by borrower.

As before, a second home

McDash and also HMDA do contain primary residence identiﬁers, though these are self-reported and

Figure 1: The Aggregate Second Home Origination Share

0 .1 .2 .3 .4

Second Home Share

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Home Purchase Originations

The ﬁgure plots the aggregate second home origination (new loans for nonprimary residences) share by year.

Source: FRBNY Consumer Credit Panel/Equifax and author’s calculations.

origination is identiﬁed as an origination for which the borrower has 2 or more properties a

year after the origination.

Using CRISM, I measure county level second home origination shares as the ratio of

second home originations to total originations. Figure 2 plots the county level percent

change in the CoreLogic house price index against the second home origination shares, both

measured over 2000-2006. There is a strong positive association: areas with higher second

home origination shares experienced stronger growth in house prices over 2000-2006. The

second home origination share explains almost 55 percent of the variation in house price

changes. This association could be driven by a number of factors. One possibility, the

hypothesis assessed in this paper, is that historically elevated second home buying during

the housing boom contributed to increases in house prices and residential activity. On the

other hand, high shares of second home buying could instead reﬂect other factors, such

as expectations about house price appreciation, or easy credit conditions due to high local

prevalence of alternative rate or privately securitized mortgages.

To isolate the eﬀect of second home buying on local activity, I use as an instrument the

evidence in Haughwout et al. (2011) and Elul and Tilson (2015) ﬁnds that these data severely underreport

second home buying.

For originations prior to 2005, second home origination status is derived based on the borrower’s number

of ﬁrst lien mortgages in 2005, when the Equifax portion of CRISM is ﬁrst available.

Figure 2: ∆ HPI and Second Home Origination Shares 2000-2006

R-sq=0.535

0 .4 .8 1.2 1.6 2 2.4

∆ HPI 2000-2006

0 .2 .4 .6

Second Home Origination Share 2000-2006

The ﬁgure plots county level changes in house prices (y-axis) against second home origination shares (x-

axis) over 2000-2006. Observations are weighted by housing units in 2000 Census. Source: CoreLogic HPI,

CRISM, and author’s calculations.

vacation share of housing from the 2000 Census. The identiﬁcation strategy exploits the

fact that diﬀerences in physical, predetermined local amenity values help explain variation

in the second home origination shares. In particular, the vacation share of housing from the

2000 Decennial Census is positively correlated with the second home origination shares. The

vacation share of housing is deﬁned as the ratio of the stock of vacation units to the total

stock of housing units in a locality. Vacation units are those classiﬁed by the Census as vacant

for seasonal, recreational, or occasional use. Figure 3 plots second home origination shares

over 2000-2006 against the vacation share of housing from the 2000 Census; the vacation

share explains about 19 percent of the variation in the second home origination shares.

2.1 Vacation Localities

Figure 4 maps the top quartile of vacation localities. Vacation areas have appealing physical

characteristics: many are located near a body of water, such as along the Eastern Seaboard

or near the Great Lakes. They tend to have warm winters or to be located along mountain

ranges such as the Ozarks. The vacation share of housing is nearly collinear when measured

in diﬀerent Decennial Census years, reﬂecting the persistent nature of the underlying physical

Figure 3: Second Home Origination Shares and Vacation Share of Housing

R-sq=0.187

0 .2 .4 .6

Second Home Origination Share 2000-2006

0 .2 .4 .6

Vacation Share 2000

The ﬁgure plots county level second home origination shares over 2000-2006 (y-axis) against the vacation

share of housing from the 2000 Census. Observations are weighted by housing units in 2000. Source: CRISM,

2000 Census, and author’s calculations.

Figure 4: The Geography of Vacation Localities

The map shows the geographic distribution of the top quartile of the vacation share of housing from the

2000 Census versus other locations. The top quartile of vacation localities is shaded in dark blue, while the

other localities are in lighter blue. Source: 2000 Census, and author’s calculations.

qualities of the localities. For example, the correlation coeﬃcient is 0.97 between the vacation

shares in the 2000 and 2010 Decennial Census years.

There is a strong positive association between second home origination shares during the

boom years and the vacation share of housing, but not all areas with high second home

origination shares were vacation localities, in particular, some populous urban areas with

booming real estate markets in the 2000s, such as Los Angeles, Phoenix, and Miami. While

important observations, identifying what drove the housing boom from these localities alone

is challenging because they experienced not only high second home buying rates, but also high

shares of alternative mortgages and private label securitization. Each of the home counties of

Las Vegas, Phoenix, and Miami is in the 90th percentile or higher for shares of second home

originations, alternative mortgages, and private label securitization. All these factors are

likely important and intertwined. Mian and Suﬁ (2018) show that areas with higher private

label securitization experienced larger increases in house prices and construction. Barlevy

and Fisher (2012) show that areas with higher use of alternative mortgages during the boom

years also experienced stronger house price appreciation.

The identiﬁcation strategy exploits the fact that while the vacation share of housing is

on average strongly informative of second home origination shares during the boom years,

the vacation share is also uncorrelated with other drivers of the housing boom and bust. I

focus in particular on: the housing supply elasticity of Saiz (2010); the fraction of subprime

borrowers measured in 2000; the share of alternative mortgages measured over 2000-2006;

and the share of privately securitized mortgages also measured over 2000-2006. The fraction

of subprime borrowers is deﬁned as the ratio of borrowers with Equifax Risk Score 3.0 below

620 and is obtained from the FRBNY Consumer Credit Panel/Equifax. The local shares

of alternative and privately securitized mortgages are obtained from Black Knight McDash,

which identiﬁes for each purchase loan both the interest type at origination, as well as

the investor type (the institution type owning the mortgage in the secondary market). I

deﬁne alternative mortgages as those without a ﬁxed principal or interest rate, and privately

securitized mortgages as those owned by private securitizers in December 2006.

Figure 5 plots these boom drivers – the subprime fraction, housing supply elasticity, and

shares of PLS and alternative mortgages – against the vacation share of housing. The main

conclusion from the plots in Figure 5 is that the vacation share is largely uncorrelated with

the diﬀerent measures. The associations are either not signiﬁcant or only weakly signiﬁcant,

with the R-squared below 0.015 in each case. The highest R-squared (0.014) is between the

subprime fraction and the vacation share, though in this case the correlation is negative:

Figure 5: Other Boom Drivers and the Vacation Share of Housing

R-sq=0.013

0 5 10 15

Housing Supply Elasticity

0 .2 .4 .6

Vacation Share 2000

R-sq=0.014

.1 .2 .3 .4 .5 .6

Subprime fraction 2000

0 .2 .4 .6

Vacation Share 2000

R-sq=0.006

0 .2 .4 .6

PLS share 2000-2006

0 .2 .4 .6

Vacation Share 2000

R-sq=0.001

0 .2 .4 .6 .8

Alt Mortgage Share 2000-2006

0 .2 .4 .6

Vacation Share 2000

The ﬁgure plots other drivers of the housing boom (y-axis) against the vacation share of housing in 2000

(x-axis). The y-axis variables are: the housing supply elasticity of Saiz (2010) (top left); the fraction of sub-

prime borrowers in 2000 (top right); the share of privately securitized mortgages over 2000-2006 (bottom

left); and the share of alternative mortgages over 2000-2006 (bottom right). See text for details. Source:

FRBNY Consumer Credit Panel / Equifax, Black Knight McDash, 2000 Census, and author’s calculations.

the higher the vacation share of housing, the lower the fraction of subprime borrowers.

Therefore, the explanatory power of the vacation share on the second home origination

share is largely independent from any of the other major drivers of the boom-bust identiﬁed

in the literature.

It is possible, though, that unobservables asssociated with the boom are correlated with

the vacation share of housing, e.g. expected house price appreciation may partly explain why

vacation shares have high second home origination shares. Local housing expectations are

generally not observed, but we can measure changes in household debt balances, which are

likely correlated with housing expectations. All else equal, stronger expected appreciation

in vacation localities would predict stronger increases in debt balances, through looser credit

constraints and spending wealth eﬀects (Carroll et al. 2011; Mian and Suﬁ 2011; Kaplan

et al. 2017; Aladangady 2017). I measure county level median debt balances for ﬁrst mort-

In the plots, observations are weighted by population, though the results are very similar without weights.

gages and home equity loans for the 2000-2006 period from the FRBNY Consumer Credit

Panel/Equifax. These data are based on the primary residence of the borrower, i.e. if New

York City residents buy properties in Phoenix, those purchases are registered in New York

City. Figure 6 plots changes in median household debt balances by debt category against the

vacation share of housing. Neither changes in mortgage nor home equity loan debt balances

is signiﬁcantly associated with the vacation share of housing, with the R-squared in each

case below 0.010.

Thus, judging by the debt behavior of locals, it does not appear that local expectations

of house price appreciation were stronger on average in vacation localities. Therefore, it is

unlikely that expectations of house price appreciation originating from vacation localities

explain why they had higher second home origination shares.

The second home origina-

tion share when measured at borrower (rather than property) location provides additional

evidence. If locals expected strong appreciation on their primary residence, they may have

purchased more nonprimary residences in the same locality or elsewhere. Using the FRBNY

Consumer Credit Panel / Equifax, I measure second home origination shares at borrower

location, i.e. if a New York City resident buys a second home in Phoenix, that purchase is

registered in Phoenix at the borrower location. Figure 7 plots the second home origination

share over the peak boom years of 2004-2006 against the vacation share of housing: the two

are uncorrelated, with the R-squared rounding out to 0.00.

In sum, the evidence does not suggest that vacation localities were particularly bubbly

during the housing boom years, for any reason other than having high second home shares

by virtue of their appealing physical localities. The lack of an association between the

various boom drivers considered and the vacation share of housing is not likely explained by

measurement issues; changes in median debt balances and the other housing characteristics

considered are strongly associated with house price changes during the boom years. Figure 8

plots county level changes in house prices over 2000-2006 against changes in mortgage debt

balances, housing supply elasticity, and the shares of alternative and PLS mortgages. All

While locals of vacation localities do not appear to have had stronger than average expectations of house

price growth than residents elsewhere, that does not imply that out-of-town buyers held consistent beliefs.

Chinco and Mayer (2016) ﬁnd that out-of-town buyers appear generally less informed and experienced worse

loan losses than locals.

Figure 6: ∆ Housing Debt Balances and the Vacation Share of Housing

R-sq=0.008

0 .5 1 1.5

∆ First Mortgage Balance

0 .2 .4 .6

Vacation Share 2000

R-sq=0.004

-1 0 1 2 3 4

∆ Home Equity Loans Balance

0 .2 .4 .6

Vacation Share 2000

The ﬁgure plots county level percent changes in median household debt balances over 2000-2006 (y-axis)

against the vacation share of housing (x-axis). The left panel plots changes in ﬁrst lien mortgage balances,

and the right panel plots changes in home equity loan balances. Source: NYFRB Consumer Credit Panel /

Equifax, 2000 Census, author’s calculations.

the series are highly correlated. For example, PLS shares during the boom years explain

about 45 percent of the variation in house price changes, while changes in mortgage balances

explain slightly over 60 percent of the variation in house prices.

Another concern is that economic activity in vacation localities may tend to be proycli-

cal, reﬂecting for example, diﬀerences in industry composition. If so, we may expect that

activity in vacation localities tends to rise more during expansions, and contract more dur-

ing recessions, for reasons unrelated to second home buying. To check for this, I aggregate

house prices for the top quartile of vacation localities as well as for the remaining counties.

Figure 9 plots house prices for the two groups of vacation localities, both indexed to equal

100 in the year 2000. Figure 9 shows that house prices trended nearly identically between

1975-2000 in vacation localities as in other locations. Signiﬁcant diﬀerences in patterns only

emerge after 2000. To delve deeper into the question of cyclicality, I regress yearly changes

in the house price index on the vacation share of housing. Figure 10 provides a time plot

of the coeﬃcient estimates along with 95% conﬁdence intervals. Positive and signiﬁcant

Figure 7: Second Home Origination Shares (borrower location) and Vacation Share of

Housing

R-sq=0.000

0 .2 .4 .6

Second Home Orig. Share (borrower location)

0 .2 .4 .6

Vacation Share 2000

The ﬁgure plots second home origination shares over 2004-2006 (y-axis) against the vacation share of housing

(x-axis). The second home origination shares are measured based on borrower (rather than property) loca-

tion, i.e. New York City residents buying out-of-town second homes are counted in New York City. Source:

NYFRB Consumer Credit Panel / Equifax, 2000 Census, author’s calculations.

yearly estimates indicate that house price growth was stronger on average that year in va-

cation localities, and viceversa. Figure 10 shows that changes in house prices in vacation

localities were not statistically diﬀerent for almost all years between 1977-2000. The only

exceptions are during the 1980-1982 recession when house price changes in vacation localities

were countercyclical rather than procyclical. After 2000, however, the coeﬃcient estimates

are signiﬁcantly larger and signiﬁcant. House price growth in vacation localities was faster

than elsehwere over 2000-2006, and slower than elsewhere from 2006-2010, coinciding with

the aggregate trends in second home buying.

Figure 8: ∆ HPI and Other Boom Drivers

R-sq=0.631

0 .5 1 1.5 2

∆ HPI 2000-2006

0 .5 1 1.5

∆ Mortgage Balance 2000-2006

R-sq=0.440

0 .5 1 1.5 2

∆ HPI 2000-2006

0 .2 .4 .6

PLS share 2000-2006

R-sq=0.395

0 .5 1 1.5 2

∆ HPI 2000-2006

0 .2 .4 .6 .8

Alt Mortgage Share 2000-2006

R-sq=0.334

0 .5 1 1.5 2

∆ HPI 2000-2006

0 2 4 6 8

Housing Supply Elasticity

The ﬁgure plots changes in house prices over 2000-2006 (y-axis) against various housing characteristics (x-

axis). On the x-axis the ﬁgure plots changes in median ﬁrst lien mortgage balances over 2000-2006 (top left

panel); the share of privately securitized mortgages over 2000-2006 (top right panel); the share of alternative

mortgages (bottom left panel); and the housing supply elasticity of Saiz (2010) (bottom right panel). Source:

NYFRB Consumer Credit Panel / Equifax, Black Knight McDash, Corelogic HPI, and author’s calculations.

Figure 9: HPI in Vacation and Other Localities

60 80 100 120 140 160 180 200

House Price Index

1980 1990 2000 2010 2020

Top quartile vacation share Other

The ﬁgure plots house prices against time for two groups: the top quartile of vacation shares in the 2000

Census, and the other counties. House prices are indexed to equal 100 in the year 2000. Source: CoreLogic

HPI, 2000 Census, and author’s calculations.

Figure 10: Yearly ∆HPI for Vacation Localities

-.4 -.2 0 .2 .4 .6

Coeff Estimate/CI95

1977

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

The ﬁgure plots yearly coeﬃcient estimates α

with associated 95 percent conﬁdence intervals from esti-

mating the following equation each year: ∆HP I

i,t

= α

V acation Share

+ υ

i,t

. Observations weighted by

housing units in 2000 and standard errors clustered by state. Source: CoreLogic HPI, 2000 Census, and

author’s calculations.

3 Empirical Framework and Results

To estimate the eﬀects of high second home origination shares on local outcomes during

the boom, I isolate the variation in the second home origination shares explained solely by

the instrument, the vacation share of housing, conditional on various other characteristics

of localities. Vacation localities do diﬀer along some observables, e.g. they tend to have

older, whiter, more rural populations, as well as a higher share of employment in services.

To account for these diﬀerences, I control for a detailed set of county covariates including

demographics such as education, income, and age proﬁles in 2000; household ﬁnancial charac-

teristics such as the fraction of subprime borrowers and median credit scores in 2000; industry

composition including manufacturing, construction, and services employment shares in 2000;

and pretrends such as changes in house prices and employment from 1997-2000. A full list

of county covariates and data sources is provided in Table 1. Table 2 provides summary

statistics.

I now discuss results based on the following 2SLS speciﬁcation:

∆Y

= θX

+ β

Second Home Origination Shares

i,2000−2006

+ 

(1)

Second Home Origination Shares

i,2000−2006

= δX

+ ρV acation Share

i,2000

+ v

(2)

where observations are at the county i level; changes are taken over 2000-2006, 2006-2010, and

2000-2010 for diﬀerent outcome variables Y

(e.g. house prices, construction employment,

total private employment) each estimated separately; and X

are other county characteristics,

described in Table 1 with summary statistics in Table 2.

I use data on counties with over 10,000 housing units in the 2000 Census, which yields

slightly over 1,200 counties with house price data, accounting for about 92% of aggregate

employment. Observations are weighted by the number of households in the 2000 Decennial

Census, though results are similar without weighting and are also reported in the Results

section. Extreme observations (1% from each tail) are dropped from each dependent vari-

able. Standard errors are clustered at the state level. I report results for state ﬁxed eﬀects

speciﬁcations in the Results below, though they are not included in the baseline speciﬁca-

tions.

Table 1: Data Deﬁnitions

Variable Deﬁnition Source

Dependent Variables

∆ House Prices Percent change in house prices from

2000-2006, 2006-2010, and 2000-2010

CoreLogic HPI

∆ Emp

Percent change in employment category j

from 2000-2006, 2006-2010, and 2000-2010

QCEW, CBP

∆ Delinquency Rates Percentage point change in fraction of 90+

delinquent properties from 2006 to 2010

CoreLogic MarketTrends

Prerecession Characteristics

∆ House Prices Percent change in house prices 1997-2000 CoreLogic HPI

∆ Employment Percent change in total private employment

1997-2000

QCEW

∆ Construction Percent change in construction private

employment 1997-2000

QCEW

House prices Log level median house price 2000 Census

Household income Log of median 2000 Census

White population Fraction of population identiﬁed as white 2000 Census

Poverty rate Fraction of families below poverty line 2000 Census

Age proﬁle Fraction of population 55 years or older 2000 Census

College population Fraction of population with a college degree

or more

2000 Census

Urban rate Fraction of population in urban areas 2000 Census

Mortgage use Fraction of housing stock that had been

mortgage-ﬁnanced

2000 Census

Risk Score 3.0 Median 2000 FRBNY

Consumer Credit

Panel/Equifax

Subprime Fraction of households in a county with Risk

Score less than 620

2000 FRBNY

Consumer Credit

Panel/Equifax

Nontradable share Nontradable share of employment, as deﬁned

in Mian and Suﬁ (2014)

2000 CBP

Construction share Share of employment 2000 QCEW

Manufacturing share Share of employment 2000 QCEW

Services share Share of employment 2000 QCEW

Health and education share Share of employment 2000 QCEW

This table provides deﬁnitions and sources for the data used throughout the paper. CBP: County Business

Patterns; QCEW: Quarterly Census of Employment and Wages.

Table 2: County Summary Statistics

Dependent Variables

Mean SD p10 Median p90 N

∆ House Prices 2000-2006 0.52 0.37 0.18 0.40 1.13 1220

∆ Construction Emp 2000-2006 0.18 0.38 -0.14 0.13 0.58 1220

∆ Other Emp 2000-2006 0.05 0.15 -0.09 0.04 0.22 1220

∆ Nontradable Emp 2000-2006 0.11 0.18 -0.07 0.09 0.31 1220

∆ House Prices 2006-2010 -0.12 0.15 -0.35 -0.10 0.03 1220

∆ Delinquency Rate 2006-2010 0.04 0.03 0.02 0.03 0.08 1220

∆ Construction Emp 2000-2006 -0.23 0.33 -0.46 -0.26 -0.01 1215

∆ Other Emp 2006-2010 -0.04 0.08 -0.13 -0.04 0.05 1220

∆ Nontradable Emp 2006-2010 -0.03 0.13 -0.14 -0.04 0.09 1220

∆ House Prices 2000-2010 0.30 0.24 0.03 0.28 0.61 1220

∆ Construction Emp 2000-2010 -0.12 0.32 -0.40 -0.15 0.20 1220

∆ Other Emp 2000-2010 0.02 0.20 -0.17 -0.01 0.22 1220

∆ Nontradable Emp 2000-2010 0.08 0.24 -0.13 0.04 0.31 1220

County Characteristics

# Housing units (thousands), 2000 79.12 164.77 11.46 32.96 182.81 1220

% Educ ≥ College, 2000 0.21 0.09 0.11 0.18 0.33 1220

Home Value ($thousands), 2000 104.13 46.86 63.40 92.50 155.65 1220

% Equifax Risk Score 3.0 ≤ 620, 2000 0.27 0.08 0.17 0.25 0.38 1220

Median Equifax Risk Score 3.0, 2000 703.90 29.78 661.00 711.00 738.00 1220

% White Pop, 2000 0.87 0.12 0.72 0.91 0.98 1220

% Families below poverty line, 2000 0.08 0.04 0.04 0.08 0.13 1220

∆ Emp 1997-2000 0.07 0.08 -0.01 0.06 0.15 1220

∆ Construction Emp 1997-2000 0.15 0.19 -0.05 0.13 0.37 1220

∆ House Prices 1997-2000 0.18 0.10 0.08 0.16 0.31 1220

∆ Other Emp 1997-2000 0.06 0.08 -0.01 0.06 0.15 1220

% Urban population 0.61 0.25 0.26 0.64 0.94 1220

HH Median Income ($thousands), 2000 40.73 9.32 30.95 38.83 53.27 1220

Construction Share of Emp, 2000 0.07 0.03 0.04 0.06 0.11 1220

Manufacturing Share of Emp, 2000 0.20 0.12 0.06 0.18 0.38 1220

Nontradable Share of Emp, 2000 0.21 0.05 0.16 0.21 0.28 1220

Services Share of Emp, 2000 0.70 0.12 0.54 0.72 0.84 1220

Health & Edu Share of Emp, 2000 0.13 0.05 0.07 0.13 0.19 1220

% Age ≥ 50, 2000 0.29 0.05 0.22 0.29 0.35 1220

The table provides summary statistics for localities with over 10, 000 households in the 2000 Decennial Cen-

sus and with house price data. Changes for delinquency rates are in percentage point, all other are percent

changes.

3.1 Results

Areas with higher second home origination shares (instrumented with the vacation share of

housing) experienced a more pronounced boom and bust in activity. Higher second home

shares led to higher construction employment and house prices over 2000-2006. However,

those gains during the boom years were largely reversed over the next years: declines in

house prices and construction employment, and increases in delinquency rates, were more

severe in areas that high second home origination shares during the boom years. Overall,

looking at diﬀerences in activity for the whole decade 2000-2010, the eﬀects balance out for

house prices, but are negative for construction employment. Consistent with the overhang

hypothesis in Rognlie et al. (2018), overbuilding in the boom led to persistent declines in

construction.

Table 3 shows 2SLS coeﬃcient estimates for the 2000-2006 changes in house prices

and employment (for construction, nontradable, and total private employment) models.

Columns 1 and 2 of Table 3 show that house price and construction employment growth

during the boom was on average 16 and 7 percentage points higher, respectively, in localities

with 10 percentage point higher second home origination shares over 2000-2006. Though

house prices and construction employment grew faster in localities with higher second home

origination shares, those gains in real estate do not appear to have led to gains in overall

employment. Columns 3 and 4 show results for nontradable employment and other employ-

ment (total private employment excluding construction).

The coeﬃcient estimates are not

signiﬁcant and small, especially in the nontradable employment model (Column 3).

However, the increase in activity associated with higher second home origination shares

during the boom is largely reversed during the recession years. Second homes borrowers were

more levered during the boom and had higher default rates during the recession (Haughwout

et al. 2011; Albanesi 2018). Table 4 shows 2SLS coeﬃcient estimates for the 2006-2010 period.

Areas with 10 percentage point higher second home origination shares during the boom

The instrument is strong, with the Kleibergen-Paap ﬁrst stage F statistic about 100, considerably higher

than the rule of thumb F statistic value of 10 commonly used in the literature to indicate weak instrument

problems.

Nontradable employment is a category of local employment accounting about 20 percent of total private

employment, comprised mostly of local retail and food; see Mian and Suﬁ (2014).

experienced steeper declines in activiy: house price and construction employment declines

were 7 and 9 percentage points stronger on average (Columns 1 and 2, respectively), while

changes in delinquency rates were on average about 2 percentage points higher (Column

3). The overall employment eﬀects are mostly restricted to construction, with changes in

nontradable employment and other employment not signiﬁcant.

Looking at changes in activity for the whole decade, the eﬀects are negative, especially

for construction employment. Table 5 shows results for the 2000-2010 period. For localities

with 10 percentage point higher second home origination shares over 2000-2006, changes over

2000-2010 in house prices were on average 4 percentage points higher though not statistically

diﬀerent from zero (Column 1), and construction employment changes were on average 6

percentage points lower and signiﬁcant. For broader employment categories, the eﬀects on

employment losses are not signiﬁcant. When looking at changes in activity over a longer

horizon, such as 2000-2014, the results are similar: the construction employment declines

are persistent, but the coeﬃcient estimates for the house price and broader employment

categories models are close to zero and not signiﬁcant.

Overall, these results are consistent

with Rognlie et al. (2018), which predicts that the recovery from housing boom-bust episodes

is asymmetric, with the overbuilt sector left behind.

In sum, localities with high second home origination shares during the boom experienced

a more pronounced boom and bust in activity in house prices and construction employment,

with the losses in construction employment during the recession outweighing the gains during

the boom years. A limitation of the county level empirical strategy is that spillover eﬀects

across localities may not be captured. For example, high default rates for second home

buyers likely contributed to the poor health of the ﬁnancial system during the recession,

and so likely aﬀected overall credit supply. In turn, lower credit supply during the recession

likely contributed to the job losses (Duygan-Bump et al. 2015; Chodorow-Reich 2014; Garcia

2018). Nonetheless, the lack of signiﬁcance in the nontradable and other employment models

does ameliorate concerns about instrument validity, since the instrument is not correlated

with local shocks aﬀecting overall employment, i.e. it is unlikely that vacation localities had

high shares of second home originations because those localities experienced a positive shock

Results not shown to economize on space, but available upon request.

during the boom that increased overall employment.

3.2 Delving Deeper

The 2SLS coeﬃcient estimates contrast with their OLS counterparts, which are on average

about twice as large across models. Tables 6 and 7 report OLS coeﬃcient estimates for the

2000-2006 and 2006-2010 periods, respectively. The OLS coeﬃcient estimates are larger,

suggesting that the OLS estimates are biased upwards due to other factors such as reverse

causality. For example, the coeﬃcient on second home origination shares in the 2000-2006

2SLS house price model (Table 3 Column 1) is 1.608, while the OLS analog is 2.965 in Table

6 Column 1, with the diﬀerence statistically signiﬁcant. The diﬀerence between 2SLS and

OLS estimates is particularly large for the nontradable and other employment models. For

example, the coeﬃcient on second home origination shares in the 2000-2006 2SLS nontradable

employment model (Table 3 Column 3) is 0.016 and not signiﬁcant, while the OLS analog

is 0.319 and is highly signiﬁcant (Table 6 Column 3).

The results are also robust to using state ﬁxed eﬀects. Because the results are similar,

I use speciﬁcations without state ﬁxed eﬀects in the baseline to exploit both within and

across state variation, rather than restricting the data to using only within state variation.

Table 8 provides results for the 2000-2006 models with state ﬁxed eﬀects. The second home

origination shares coeﬃcient is similar to the baseline estimates in Table 3 – the coeﬃcient on

the house price model is slightly larger (Column 1), while the coeﬃcient in the construction

employment models is slightly smaller (Column 2), with neither diﬀerence being statistically

signiﬁcant. Table 9 provides results for the 2006-2010 models with state ﬁxed eﬀects. Again,

the results are very similar with coeﬃcient estimates not statistically diﬀerent.

The results reported are weighted by the number of housing units in 2000, though they are

robust to alternatives. Table 10 reports unweighted results for the 2000-2006 2SLS models.

The results are qualitatively the same: areas with higer second home origination shares

during the boom experienced signiﬁcantly higher growth in house prices and construction

employment, though not in broader employment categories. The instrument is stronger (the

Kleibergen-Paap ﬁrst stage F statistic is larger) than in the baseline model, reﬂecting that

localities with high second home origination shares during the boom that are not vacation

localities tend to be larger, e.g. the home counties of Miami, Phoenix, and Los Angeles. The

coeﬃcient estimates are not statistically diﬀerent from their counterparts in the baseline

(Table 3), though they are slightly smaller.

The results are also robust to controlling for diﬀerences across localities in housing supply

elasticities. The baseline results do not control for diﬀerences in elasticities, because these

are available only for the smaller sample of counties located within metropolitan statistical

areas (MSAs). Table 11 report results for the counties for which the housing supply elasticity

of Saiz (2010) is available. Because coeﬃcient estimates may change because of the sample

change (counties in MSAs only), or the inclusion of the housing supply elasticity control, I

report results for the MSA sample with and without the housing supply elasticity on the right

hand side. Columns 1 and 2 report coeﬃcient estimates for the house price and construction

employment models without the elasticity control, while Columns 3 and 4 add the control.

The coeﬃcient estimates for the MSA subsample tend to be larger than the full sample,

though they are not statistically diﬀerent. Adding the housing supply elasticity as a control

(Columns 3 and 4) slightly lowers the second home origination share coeﬃcients, reﬂecting

the negative correlation between supply elasticities and the vacation share of housing, though

the correlation is only very weak (see the top left panel of Figure 5). However, the coeﬃcient

estimates are very similar and well within one standard error.

Overall, the qualitative conclusions are the same and quantitative results not statistically

diﬀerent, when using alternative speciﬁcations, such as the inclusion of state ﬁxed eﬀects,

alternative weighting schemes, and sample restrictions (e.g. counties in MSAs only) and

controlling for diﬀerences in supply contraints.

The comparison for the 2006-2010 models is not reported to economize on space, but the discussion is

very similar to that of the 2000-2006 period. Results available upon request.

Table 3: 2000-2006 2SLS Estimates of the Eﬀects of Second Home Buying

Dependent variables 2000-2006:

∆ HPI ∆ Construction Emp ∆ NonTradable Emp ∆ Other Emp

Coef./SE Coef./SE Coef./SE Coef./SE

Second Home Origination

Share 2000-2006 1.608*** 0.742** 0.016 0.085

(0.55) (0.37) (0.14) (0.11)

All other controls Yes Yes Yes Yes

R-squared 0.70 0.54 0.50 0.60

Kleibergen-Paap F stat 95.79 110.58 108.38 105.84

Observations 1109 1115 1112 1112

This table shows 2SLS results from regressing changes in local outcomes on second home origination shares

(using the vacation share of housing in the 2000 Decennial Census as an instrument). The sample of coun-

ties includes localities with over 10,000 housing units in the 2000 Census. All equations include the controls

listed in Table 1. Observations weighted by the number of housing units in the 2000 Decennial Census. De-

pendent variable outliers (1 percent of each tail) are dropped. Standard errors are clustered at the state

level. *, **, and *** indicate signiﬁcance at the 0.10, 0.05 and 0.01 levels, respectively.

Table 4: 2006-2010 2SLS Estimates of the Eﬀects of Second Home Buying

Dependent variables 2006-2010:

∆ HPI ∆ Delinq. Rate ∆ Constr. Emp ∆ NonTrd. Emp ∆ Other Emp

Coef./SE Coef./SE Coef./SE Coef./SE Coef./SE

Second Home Origination

Share 2000-2006 -0.738*** 0.161** -0.870*** 0.060 -0.083

(0.25) (0.06) (0.22) (0.10) (0.07)

All other controls Yes Yes Yes Yes Yes

R-squared 0.54 0.62 0.42 0.17 0.37

Kleibergen-Paap F stat 108.97 108.31 101.92 107.17 100.94

Observations 1108 1108 1116 1112 1118

This table shows 2SLS results from regressing changes in local outcomes on second home origination shares

(using the vacation share of housing in the 2000 Decennial Census as an instrument). The sample of coun-

ties includes localities with over 10,000 housing units in the 2000 Census. All equations include the controls

listed in Table 1. Observations weighted by the number of housing units in the 2000 Decennial Census. De-

pendent variable outliers (1 percent of each tail) are dropped. Standard errors are clustered at the state

level. *, **, and *** indicate signiﬁcance at the 0.10, 0.05 and 0.01 levels, respectively.

Table 5: 2000-2010 2SLS Estimates of the Eﬀects of Second Home Buying

Dependent variables 2000-2010:

∆ HPI ∆ Construction Emp ∆ NonTradable Emp ∆ Other Emp

Coef./SE Coef./SE Coef./SE Coef./SE

Second Home Origination

Share 2000-2006 0.393 -0.602** 0.127 -0.054

(0.38) (0.26) (0.15) (0.15)

All other controls Yes Yes Yes Yes

R-squared 0.38 0.22 0.44 0.57

Kleibergen-Paap F stat 106.87 110.32 108.17 105.82

Observations 1112 1119 1112 1114

This table shows 2SLS results from regressing changes in local outcomes on second home origination shares

(using the vacation share of housing in the 2000 Decennial Census as an instrument). The sample of coun-

ties includes localities with over 10,000 housing units in the 2000 Census. All equations include the controls

listed in Table 1. Observations weighted by the number of housing units in the 2000 Decennial Census. De-

pendent variable outliers (1 percent of each tail) are dropped. Standard errors are clustered at the state

level. *, **, and *** indicate signiﬁcance at the 0.10, 0.05 and 0.01 levels, respectively.

Table 6: 2000-2006 OLS Estimates of Second Home Buying

Dependent variables 2000-2006:

∆ HPI ∆ Construction Emp ∆ NonTradable Emp ∆ Other Emp

Coef./SE Coef./SE Coef./SE Coef./SE

Second Home Origination

Share 2000-2006 2.965*** 1.158*** 0.319*** 0.164***

(0.42) (0.15) (0.07) (0.04)

All other controls Yes Yes Yes Yes

R-squared 0.71 0.55 0.51 0.61

Observations 1109 1115 1112 1112

This table shows OLS results from regressing changes in local outcomes on second home origination shares

measured over 2000-2006. The sample of counties includes localities with over 10,000 housing units in the

2000 Census. All equations include the controls listed in Table 1. Observations weighted by the number of

housing units in the 2000 Decennial Census. Dependent variable outliers (1 percent of each tail) are dropped.

Standard errors are clustered at the state level. *, **, and *** indicate signiﬁcance at the 0.10, 0.05 and

0.01 levels, respectively.

Table 7: 2006-2010 OLS Estimates of Second Home Buying

Dependent variables 2006-2010:

∆ HPI ∆ Delinq. Rate ∆ Constr. Emp ∆ NonTrd. Emp ∆ Other Emp

Coef./SE Coef./SE Coef./SE Coef./SE Coef./SE

Second Home Origination

Share 2000-2006 -1.415*** 0.234*** -1.366*** -0.326*** -0.268***

(0.14) (0.04) (0.16) (0.07) (0.04)

All other controls Yes Yes Yes Yes Yes

R-squared 0.58 0.63 0.44 0.22 0.39

Observations 1108 1108 1116 1112 1118

This table shows OLS results from regressing changes in local outcomes on second home origination shares

measured over 2000-2006. The sample of counties includes localities with over 10,000 housing units in the

2000 Census. All equations include the controls listed in Table 1. Observations weighted by the number of

housing units in the 2000 Decennial Census. Dependent variable outliers (1 percent of each tail) are dropped.

Standard errors are clustered at the state level. *, **, and *** indicate signiﬁcance at the 0.10, 0.05 and

0.01 levels, respectively.

Table 8: 2000-2006 2SLS Estimates of the Eﬀects of Second Home Buying, with State

Fixed Eﬀects

Dependent variables 2000-2006:

∆ HPI ∆ Construction Emp ∆ NonTradable Emp ∆ Other Emp

Coef./SE Coef./SE Coef./SE Coef./SE

Second Home Origination

Share 2000-2006 1.790*** 0.717** -0.087 0.071

(0.32) (0.29) (0.14) (0.12)

All other controls Yes Yes Yes Yes

R-squared 0.90 0.67 0.59 0.67

Kleibergen-Paap F stat 156.56 158.92 157.94 162.05

Observations 1109 1115 1112 1112

This table shows 2SLS results from regressing changes in local outcomes on second home origination shares

(using the vacation share of housing in the 2000 Decennial Census as an instrument). The sample of counties

includes localities with over 10,000 housing units in the 2000 Census. All equations include the controls listed

in Table 1. State ﬁxed eﬀects are included. Observations weighted by the number of housing units in the

2000 Decennial Census. Dependent variable outliers (1 percent of each tail) are dropped. Standard errors are

clustered at the state level. *, **, and *** indicate signiﬁcance at the 0.10, 0.05 and 0.01 levels, respectively.

Table 9: 2006-2010 2SLS Estimates of the Eﬀects of Second Home Buying, with State

Fixed Eﬀects

Dependent variables 2006-2010:

∆ HPI ∆ Delinq. Rate ∆ Constr. Emp ∆ NonTrd. Emp ∆ Other Emp

Coef./SE Coef./SE Coef./SE Coef./SE Coef./SE

Second Home Origination

Share 2000-2006 -0.496*** 0.118*** -0.675*** 0.030 -0.077

(0.11) (0.04) (0.17) (0.07) (0.06)

All other controls Yes Yes Yes Yes Yes

R-squared 0.86 0.83 0.66 0.39 0.55

Kleibergen-Paap F stat 156.47 155.11 145.55 158.36 149.33

Observations 1108 1108 1116 1112 1118

This table shows 2SLS results from regressing changes in local outcomes on second home origination shares

(using the vacation share of housing in the 2000 Decennial Census as an instrument). The sample of counties

includes localities with over 10,000 housing units in the 2000 Census. All equations include the controls listed

in Table 1. State ﬁxed eﬀects are included. Observations weighted by the number of housing units in the

2000 Decennial Census. Dependent variable outliers (1 percent of each tail) are dropped. Standard errors are

clustered at the state level. *, **, and *** indicate signiﬁcance at the 0.10, 0.05 and 0.01 levels, respectively.

Table 10: 2000-2006 2SLS Estimates of the Eﬀects of Second Home Buying (Unweighted)

Dependent variables 2000-2006:

∆ HPI ∆ Construction Emp ∆ NonTradable Emp ∆ Other Emp

Coef./SE Coef./SE Coef./SE Coef./SE

Second Home Origination

Share 2000-2006 0.966** 0.500* 0.024 -0.058

(0.48) (0.30) (0.13) (0.11)

All other controls Yes Yes Yes Yes

R-squared 0.50 0.31 0.34 0.42

Kleibergen-Paap F stat 137.06 146.49 206.40 147.70

Observations 1109 1115 1112 1112

This table shows 2SLS results from regressing changes in local outcomes on second home origination shares

(using the vacation share of housing in the 2000 Decennial Census as an instrument). The sample of coun-

ties includes localities with over 10,000 housing units in the 2000 Census. All equations include the controls

listed in Table 1. State ﬁxed eﬀects are included. Observations not weighted. Dependent variable outliers

(1 percent of each tail) are dropped. Standard errors are clustered at the state level. *, **, and *** indicate

signiﬁcance at the 0.10, 0.05 and 0.01 levels, respectively.

Table 11: 2000-2006 2SLS Estimates of the Eﬀects of Second Home Buying in MSAs

Dependent variables 2000-2006:

∆ HPI ∆ Construction Emp ∆ HPI ∆ Construction Emp

Coef./SE Coef./SE Coef./SE Coef./SE

Second Home Origination

Share 2000-2006 2.503*** 1.387** 2.359*** 1.339**

(0.60) (0.54) (0.62) (0.54)

Elasticity -0.054* -0.015

(0.03) (0.01)

All other controls Yes Yes Yes Yes

R-squared 0.75 0.64 0.75 0.64

Kleibergen-Paap F stat 37.19 42.30 38.61 43.76

Observations 613 616 613 616

This table shows 2SLS results from regressing changes in local outcomes on second home origination shares

(using the vacation share of housing in the 2000 Decennial Census as an instrument). The sample of counties

includes those located in MSAs for which housing supply elasticity data is available. All equations include

the controls listed in Table 1. State ﬁxed eﬀects are included. Observations weighted by the number of hous-

ing units in the 2000 Decennial Census. Dependent variable outliers (1 percent of each tail) are dropped.

Standard errors are clustered at the state level. *, **, and *** indicate signiﬁcance at the 0.10, 0.05 and

0.01 levels, respectively.

3.3 Aggregate Implications

To gain a sense of the aggregate implications of second home buying, I perform a par-

tial equilibrium aggregation excercise which combines the estimated causal eﬀects of having

higher second home origination shares during the housing boom, together with a counter-

factual time path of second home origination shares in which the shares stay ﬁxed at their

2000-2001 levels.

To begin, deﬁne the counterfactual 2000-2006 change in construction employment in

county i, ∆Constr. Emp

, as the predicted construction employment change if county i

second home origination shares had stayed at their 2000-2001 level (SHOS

i,2000−2001

) instead

of rising to the 2005-2006 level (SHOS

i,2005−2006

), conditional on all other observables X

∆Constr Emp

= ∆

Constr. Emp

− β(SHOS

i,2005−2006

− SHOS

i,2000−2001

)

where ∆

Constr. Emp

denotes the ﬁtted value from the baseline construction employment

2SLS model including all covariates X

, and β is the estimated elasticity of construction em-

ployment with respect to second home origination shares. I then recover 2006 construction

employment levels corresponding to both the counterfactual and ﬁtted changes in employ-

ment, using the initial-period employment level: Constr. Emp

i,2006

= Constr. Emp

i,2000

(1 +

∆Constr. Emp

) and

Constr. Emp

i,2006

= Constr. Emp

i,2000

(1 + ∆

Constr. Emp

The fraction in construction employment changes explained by second home buying is

given by:

[Constr. Emp

i,2006

−

Constr. Emp

i,2006

]

[Constr.Emp

i,2006

− Constr.Emp

i,2000

]

(3)

I also perform the analogous exercise for house prices.

I ﬁnd that the increase in second

home buying could explain about 32 and 15 percent of the run-up in construction employment

and house prices over 2000-2006, respectively, using the 2SLS baseline estimates reported in

Table 3. In other words, construction employment would have increased by about 10 rather

Aggregate changes in house prices are computed as the average house price change weighted by housing

units in 2000.

than 14 percent, and house prices by 56 rather than 66 percent, respectively, over 2000-2006.

The accuracy of the aggregation exercise depends on a number of factors. The counterfac-

tual asks how diﬀerent house prices and construction employment evolved had second home

origination shares remained at their 2000-2001 level rather than rising, but it is possible that

at least some of that increase was an endogenous response to other changes in the economy,

such as rising wealth and an aging population. From this point of view, the partial equi-

librium aggregation exercise would lead to overestimates. However, the aggregation exercise

does not take into account general equilibrium eﬀects which may go in the opposite direction.

For example, higher second home origination shares led to higher house prices, which could

have contributed to perceptions of a robust ﬁnancial system, and therefore contributed to

strong credit supply during the housing boom. Moreover, the results from the aggregation

exercise also depend on the precision of the estimated elasticities of activity to second home

origination shares. When repeating the aggregation exercise using the 90 percent conﬁdence

intervals for the second home origination shares coeﬃcients in the construction employment

and house price models, the conclusion is second home buying could have explained between

6 to 57 percent of the runup in construction employment, and between 6 and 23 percent of

the increase in house prices over 2000-2006.

4 Conclusion

In the peak years of the housing boom 2004-2006, about 35 percent of new home purchase

mortgages were for second homes, compared with about only 20 percent in other periods.

Second home buyers were typically over-leveraged, and despite having middle to high in-

come and credit scores, experienced higher default rates than average during the recession

(Haughwout et al. (2011); Albanesi et al. (2017); Albanesi (2018)). Studying the eﬀects of

second home buying on activity is complicated for at least two reasons: owner-occupancy in

loan level datasets (such as HMDA and Black Knight McDash) is underreported (Elul and

Tilson (2015)), and localities with high second home origination shares (e.g. Las Vegas, Mi-

ami, Phoenix) may have boomed for other reasons, such as strong house price appreciation

expectations, high shares of alternative or privately securitized mortgages, or tighter supply

constraints.

The contribution of this paper is to construct a new measure of second home origination

shares at the county level, by combining the best sources of data available: credit bureau

data for the number of properties held by each borrower, and mortgage servicing records for

the address of each new property acquired. Second home origination shares explain about 55

percent of the variation in house prices across localities over 2000-2006. To isolate the eﬀects

of high second home origination shares on activity, I use the vacation share of housing in the

2000 Census as an instrument. Because vacation locales have predetermined and persistent

high amenity values (such as warm winters and a watefront), the vacation share of housing

is on average strongly informative of second home origination shares during the boom. The

vacation share of housing is also uncorrelated with proxies for local housing expectations

(such as changes in local housing debt balances) and other drivers of the boom, such as the

fraction of subprime borrowers, the use of PLS mortgages, and housing supply elasticities.

I ﬁnd that localities with high second home origination shares (explained by the vacation

share instrument) experienced a more pronounced housing boom and bust. In those localities,

house prices and construction employment grew faster over 2000-2006, and contracted more

sharply over 2006-2010, with the losses in the latter years in construction employment having

dominating and persistent eﬀects until at least 2014. A partial equilibrium aggregation

exercise suggests high second home buying could explain about 30 and 15 percent of the

run-up in construction employment and house prices over 2000-2006.

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