Second Home Buyers And The Housing Boom And Bust

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Finance and Economics Discussion SeriesDivisions of Research & Statistics and Monetary AffairsFederal Reserve Board, Washington, D.C.Second Home Buyers and the Housing Boom and BustDaniel Garcı́a2019-029Please cite this paper as:Garcı́a, Daniel (2019). “Second Home Buyers and the Housing Boom and Bust,” Financeand Economics Discussion Series 2019-029. Washington: Board of Governors of the FederalReserve System, https://doi.org/10.17016/FEDS.2019.029.NOTE: Staff working papers in the Finance and Economics Discussion Series (FEDS) are preliminarymaterials circulated to stimulate discussion and critical comment. The analysis and conclusions set forthare those of the authors and do not indicate concurrence by other members of the research staff or theBoard of Governors. References in publications to the Finance and Economics Discussion Series (other thanacknowledgement) should be cleared with the author(s) to protect the tentative character of these papers.

Second Home Buying and the Housing Boom and BustDaniel Garcı́a March 25, 2019AbstractRecord-high second home buying (homeowners acquiring nonprimary residences) wasa central feature of the 2000s boom, but the macroeconomic effects remain an openquestion partly because reliable geographic data is currently unavailable. This paperconstructs local data on second home buying by merging credit bureau data with mortgage servicing records. The identification strategy exploits the fact that the vacationshare of housing from the 2000 Census is predictive of second home origination sharesduring the boom years, while also uncorrelated with other boom-bust drivers includingproxies for local housing expectations, the use of alternative and PLS mortgages, andsupply constraints. Localities with plausibly exogenous higher second home originationshares experienced a more pronounced boom and bust – stronger growth in construction and house prices during the boom, and steeper declines in activity during therecession years. Overall, second home buying could explain about 30 and 15 percent ofthe 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: daniel.i.garcia@frb.gov. Thanks to ChrisCarroll, Neil Bhutta, Raven Molloy, John Sabelhaus, Wayne Passmore, Shane Sherlund, Steve Laufer, Alejandro Justiniano, Gadi Barlevy, Jonathan Wright, Jon Faust, and Marco Bassetto for their comments onthis and previous versions. The analysis and conclusions set forth here are those of the authors and do notindicate concurrence by other members of the research staff or the Board of Governors.1

1IntroductionThe record-high level of second home buying (homeowners acquiring nonprimary residences)was a central feature of the 2000s housing boom.1 Bhutta (2015) shows that second homebuyers contributed more to aggregate mortgage debt during the boom years than did allfirst-time buyers. Second home buyers were typically over-leveraged, and despite havingmiddle to high income and credit scores, experienced higher default rates than averageduring the recession (Haughwout et al. (2011); Albanesi et al. (2017); Albanesi (2018)). Themacroeconomic effects could have been sizable – Chinco and Mayer (2016) find that secondhome buying significantly contributed to mispricing in housing during the boom years. Theirdata covers only 21 US cities, however, and more comprehensive studies have so far beenlimited by lack of adequate data.This paper is the first to measure second home buying based on property location withbroad coverage of the US economy, by combining credit bureau data with mortgage servicingrecords. To estimate the effects of second home buying on economic activity during thehousing boom and bust, I use as an instrument the vacation share of housing from the2000 Census, to isolate the variation in second home buying purely explained by differencesin physical local amenity values versus other factors such as variation in housing marketexpectations. I find that localities with higher second home buying experienced a morepronounced boom and bust – stronger growth in house prices and construction employmentover 2000-2006, and sharper declines in activity over 2006-2010. Overall, a partial equilibriumaggregation exercise suggests second home buying could explain about 30 and 15 percent ofthe 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 Insight Servicing McDash (CRISM) dataset, which merges credit bureau data (Equifax) withmortgage servicing records (Black Knight McDash). I identify buyers of second homes asthose having 2 or more first lien mortgages (same as Haughwout et al. 2011; Bhutta 2015and others) and merge second home identifiers with property location from Black Knight1In the literature, buyers of second homes (nonprimary residences) are often referred to as propertyinvestors. Instead, I use the terms second home buyers or nonprimary residence buyers, because somesecond homes may have a strong consumption motive in addition to an investment one.2

McDash. I define the second home origination share as the ratio of new home purchase loansfor 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 homeorigination share and house price changes during the housing boom years. Variation in thesecond home origination share explains almost 55 percent of the variation in house pricechanges 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 activityand prices during the boom years. On the other hand, local house price expectations couldhave attracted second home buyers investing in real estate. For example, many boomingareas 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 (notfixed rate) and privately securitized mortgages, making it challenging to isolate the causaleffects of any single determinant of the housing boom.To disentangle causality, I use an instrument for second home origination shares – thevacation share of housing from the 2000 Census – which is uncorrelated with proxies for localhousing expectations and other drivers of the housing boom such as the use of alternative andPLS mortgages as well as supply constraints. The identification strategy exploits the factthat predetermined, physical differences in amenity values help explain significant geographicvariation in second home buying. Areas with high vacation shares have appealing physicalqualities, such as warm winters and a waterfront. These areas include localities in sand statessuch as in Florida and California, but also localities along the Eastern Seabord, close to theGreat Lakes, and in locations with appealing terrain such as near the Ozark Mountains. Infact, there is enough variation in the vacation share of housing to allow for specificationswith state fixed effects, which yield coefficient estimates that are very similar to specificationswithout them.The main concern with instrument validity is that the vacation share of housing maybe correlated with other drivers of the housing boom. Vacation localities do differ alongsome 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 expectations may partly explain why vacation localities had high second home origination shares3

during the boom. However, judging by the debt behavior of locals, it does not appear thathouse price expectations were significantly stronger in vacation localities than elsewhere.Had locals in vacation areas expected stronger appreciation, they may have taken out morehome equity loans, mortgages, or bought more nonprimary residences than local elsewhere.Instead, the vacation share of housing is not significantly associated with changes in mortgage or home equity loan debt balances during the boom, or with second home originationshares when measured at borrower (rather than property) location. Moreover, the vacationshare of housing is also uncorrelated with various drivers of the boom, including the localshare of subprime borrowers, the use of alternative and PLS mortgages, and housing supplyelasiticities. I also verify that vacation localities activity is not generally cyclical, with yearlychanges in house prices not statistically different in vacation localities during both recessionand non-recession years, using local house price data going back to the 1970s. In fact, trendsin house prices and construction employment are essentially identical prior to 2000, withdifferential 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 ofvacation housing) contributed significantly to the boom and bust in housing activity over2000-2010. Areas with high second home origination shares during the boom years hadfaster growth in construction employment and house prices over 2000-2006. In localitieswith 10 percentage point higher second home origination shares, growth in constructionemployment and house prices over 2000-2006 was higher on average by 7 and 16 percentagepoints, 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 severelyover 2006-2010. On average, in localities with 10 percentage point higher second home origination shares in 2000-2006, changes in delinquency rates were on average 2 percentage pointshigher, and declines in house price and construction employment were 7 and 9 percentagepoints more pronounced on average, respectively, over 2006-2010. These results are newevidence pointing to the damaging effects during the housing bust of second home loansissued during the boom, consistent with Haughwout et al. (2011) and Albanesi (2018) whofind that second home buyers had significantly higher default rates than average.4

Overall, localities with higher second home origination shares in the boom years grewfaster over 2000-2006, but contracted more sharply over 2006-2010. The losses in the recession years tend to dominate the gains during the boom years. When looking at changes inconstruction employment and house prices over 2000-2010, the estimated effects on construction employment are negative. For house prices, the estimated effects are positive thoughnot 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 soin construction employment. The losses in construction employment extend to the 2000-2014period, therefore providing empirical support to the overhang hypothesis in Rognlie et al.(2018) which predicts persistent declines in construction following overbuilding during theboom.The effects of second home buying appear concentrated in the housing sector. The employment effects are not significant for total private employment excluding construction andnontradable employment, for both the 2000-2006 and 2006-2010 periods. It is possible thatthe 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 ofsignificant results in the county level estimates for broader employment categories does ameliorate concerns about instrument validity, since local shocks affecting overall employmentare uncorrelated with the instrument. Moreover, the 2SLS point estimates are on averageabout 50 percent smaller than their OLS counterparts, suggesting the latter are biased upwards due to other factors such as reverse causality. Results are also very similar when usingstate fixed effects specifications.To understand the extent to which second home buying may have affected the severity ofthe housing boom, I combine the 2SLS estimates with the counterfactual assumptions thatthe share of second home buying remained at its 2000-2001 level instead of rising. In thebaseline scenario, I find that second home buying could explain about 30 and 15 percent ofthe run-up in construction employment, respectively, over 2000-2006. However, this estimateis subject to uncertainty about coefficient estimates, in addition to assumptions about boththe extent to which the increase in second home origination shares during the boom wasan endogenous response to other changes in the economy, as well as the magnitude of the5

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 haveexplained between 6 to 57 percent of the runup in construction employment, and between 6and 23 percent of the increase in house prices over 2000-2006.This paper adds to the growing literature showing that second home buyers were animportant driver of the boom and bust. Bhutta (2015) documents that second home buyerscontributed significantly to the rise in aggregate mortgage debt during the housing boom.Second home buyers had higher than average default rates during the recession (Haughwoutet 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 influenceother buyers and drive boom-bust episodes such as Piazzesi and Schneider (2009); Burnsideet 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 panelof 21 cities using a high frequency panel VAR identification approach. Gao et al. (2018) alsofind that second home buying contributed to the boom-bust in activity, though they usedata from the Home Mortgage Disclosure Act, which is known to underreport second homebuying (Elul and Tilson (2015)). Overall, the results in this paper are complementary tothis literature; the main contribution is using new data combining the strength of datasetspreviously used in isolation (credit bureau data and mortgage servicing records), a novelidentification strategy, and results that include a broad set of outcome variables includingemployment.More broadly, this paper fits in the extensive body of work studying the determinantsof the housing boom. The housing boom had many, often interrelated causes, involvinghouseholds up and down the income and credit score distributions (Adelino et al. 2016; Footeet al. 2016; Albanesi et al. 2017). One of the main contributions of this paper is isolatingthe effect of second home buying (as instrumented via the vacation share of housing) onchanges in construction employment and house prices during the 2000s. I do so by showingthat the vacation share of housing is uncorrelated with major determinants of the housingboom identified in the literature, including: the interaction of changes in housing demandwith supply constraints (Saiz 2010; Aladangady 2017); the use of alternative mortgages6

such as interest-only or balloon mortgages (Barlevy and Fisher 2012; Foote et al. 2008); theexpansion in subprime credit (Mian and Sufi 2009; Demyanyk and Hemert 2011; Gerardiet al. 2008); and the boom-bust in private label securitization (Keys et al. 2010; Nadauldand Sherlund 2009; Mian and Sufi 2018; Garcia 2018).2DataThe FRBNY Consumer Credit Panel/Equifax contains credit reporting data for a nationallyrepresentative 5 percent sample of all adults with a social security number and credit reportbeginning in 1999. The data contain information on the number of open first lien mortgagesper borrower. Second home purchase originations are measured as new purchase loans forborrowers with 2 or more properties. For each origination, I use the borrower’s number of firstmortgage 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 havingtwo properties due to reporting lags. Figure 1 shows the aggregate second home originationshare, which rose from 21 percent in 2000 to its peak of 36 percent in 2006, subsequentlyfalling back to near 20 percent over 2009-2011. These patterns are similar to those reportedin 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 bureaudata is helpful in analyzing aggregate trends in second home buying, these data generallydo not contain the address of nonprimary residences acquired.On the other had, Black Knight McDash (formerly known as LPS) contains additionalloan level characteristics, including property location. The Black Knight McDash dataset iscomprised 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, theEquifax Credit Risk Insight Servicing McDash (CRISM), contains credit bureau data fromEquifax, matched to the mortgage-level McDash servicing data. CRISM covers about 60percent of the mortgage market (from McDash). The merge is key since McDash does notcontain data on the number of first lien mortgages by borrower.2 As before, a second home2McDash and also HMDA do contain primary residence identifiers, though these are self-reported and7

Figure 1: The Aggregate Second Home Origination Share.3.2.10Second Home Share.4Home Purchase Originations1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011The 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 ayear after the origination.3Using CRISM, I measure county level second home origination shares as the ratio ofsecond home originations to total originations. Figure 2 plots the county level percentchange in the CoreLogic house price index against the second home origination shares, bothmeasured over 2000-2006. There is a strong positive association: areas with higher secondhome origination shares experienced stronger growth in house prices over 2000-2006. Thesecond home origination share explains almost 55 percent of the variation in house pricechanges. This association could be driven by a number of factors. One possibility, thehypothesis assessed in this paper, is that historically elevated second home buying duringthe housing boom contributed to increases in house prices and residential activity. On theother hand, high shares of second home buying could instead reflect other factors, suchas expectations about house price appreciation, or easy credit conditions due to high localprevalence of alternative rate or privately securitized mortgages.To isolate the effect of second home buying on local activity, I use as an instrument theevidence in Haughwout et al. (2011) and Elul and Tilson (2015) finds that these data severely underreportsecond home buying.3For originations prior to 2005, second home origination status is derived based on the borrower’s numberof first lien mortgages in 2005, when the Equifax portion of CRISM is first available.8

2.4Figure 2: HPI and Second Home Origination Shares 2000-20060.4.81.2 HPI 2000-20061.62R-sq 0.5350.2.4Second Home Origination Share 2000-2006.6The figure plots county level changes in house prices (y-axis) against second home origination shares (xaxis) 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 thefact that differences in physical, predetermined local amenity values help explain variationin the second home origination shares. In particular, the vacation share of housing from the2000 Decennial Census is positively correlated with the second home origination shares. Thevacation share of housing is defined as the ratio of the stock of vacation units to the totalstock of housing units in a locality. Vacation units are those classified by the Census as vacantfor seasonal, recreational, or occasional use. Figure 3 plots second home origination sharesover 2000-2006 against the vacation share of housing from the 2000 Census; the vacationshare explains about 19 percent of the variation in the second home origination shares.2.1Vacation LocalitiesFigure 4 maps the top quartile of vacation localities. Vacation areas have appealing physicalcharacteristics: many are located near a body of water, such as along the Eastern Seaboardor near the Great Lakes. They tend to have warm winters or to be located along mountainranges such as the Ozarks. The vacation share of housing is nearly collinear when measuredin different Decennial Census years, reflecting the persistent nature of the underlying physical9

3Empirical Framework and ResultsTo estimate the effects of high second home origination shares on local outcomes duringthe boom, I isolate the variation in the second home origination shares explained solely bythe instrument, the vacation share of housing, conditional on various other characteristicsof localities. Vacation localities do differ along some observables, e.g. they tend to haveolder, 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 includingdemographics such as education, income, and age profiles in 2000; household financial characteristics such as the fraction of subprime borrowers and median credit scores in 2000; industrycomposition including manufacturing, construction, and services employment shares in 2000;and pretrends such as changes in house prices and employment from 1997-2000. A full listof county covariates and data sources is provided in Table 1. Table 2 provides summarystatistics.I now discuss results based on the following 2SLS specification: Yij θXi β Second Home \Origination Sharesi,2000 2006 i(1)Second Home Origination Sharesi,2000 2006 δXi ρV acation Sharei,2000 vi(2)where observations are at the county i level; changes are taken over 2000-2006, 2006-2010, and2000-2010 for different outcome variables Y j (e.g. house prices, construction employment,total private employment) each estimated separately; and Xi 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 yieldsslightly over 1,200 counties with house price data, accounting for about 92% of aggregateemployment. Observations are weighted by the number of households in the 2000 DecennialCensus, though results are similar without weighting and are also reported in the Resultssection. Extreme observations (1% from each tail) are dropped from each dependent variable. Standard errors are clustered at the state level. I report results for state fixed effectsspecifications in the Results below, though they are not included in the baseline specifications.18

Table 1: Data DefinitionsVariableDefinitionSourceDependent Variables House Prices Empj Delinquency RatesPercent change in house prices from2000-2006, 2006-2010, and 2000-2010Percent change in employment category jfrom 2000-2006, 2006-2010, and 2000-2010Percentage point change in fraction of 90 delinquent properties from 2006 to 2010CoreLogic HPIQCEW, CBPCoreLogic MarketTrendsPrerecession Characteristics House Prices Employment ConstructionHouse pricesHousehold incomeWhite populationPoverty rateAge profileCollege populationUrban rateMortgage useRisk Score 3.0SubprimePercent change in house prices 1997-2000Percent change in total private employment1997-2000Percent change in construction privateemployment 1997-2000Log level median house priceLog of medianFraction of population identified as whiteFraction of families below poverty lineFraction of population 55 years or olderFraction of population with a college degreeor moreFraction of population in urban areasFraction of housing stock that had beenmortgage-financedMedianFraction of households in a county with RiskScore less than 620Nontradable shareNontradable share of employment, as definedin Mian and Sufi (2014)Construction shareShare of employmentManufacturing shareShare of employmentServices shareShare of employmentHealth and education share Share of employmentCoreLogic susCensusCensusCensus2000 Census2000 Census2000 FRBNYConsumer CreditPanel/Equifax2000 FRBNYConsumer CreditPanel/Equifax2000 CBP2000200020002000QCEWQCEWQCEWQCEWThis table provides definitions and sources for the data used throughout the paper. CBP: County BusinessPatterns; QCEW: Quarterly Census of Employment and Wages.19

Table 2: County Summary StatisticsDependent Variables House Prices 2000-2006Construction Emp 2000-2006Other Emp 2000-2006Nontradable Emp 2000-2006House Prices 2006-2010Delinquency Rate 2006-2010Construction Emp 2000-2006Other Emp 2006-2010Nontradable Emp 2006-2010House Prices 2000-2010Construction Emp 2000-2010Other Emp 2000-2010Nontradable Emp 1220122012201220County Characteristics# Housing units (thousands), 200079.12% Educ College, 20000.21Home Value ( thousands), 2000104.13% Equifax Risk Score 3.0 620, 20000.27Median Equifax Risk Score 3.0, 2000703.90% White Pop, 20000.87% Families below poverty line, 20000.08 Emp 1997-20000.07 Construction Emp 1997-20000.15 House Prices 1997-20000.18 Other Emp 1997-20000.06% Urban population0.61HH Median Income ( thousands), 2000 40.73Construction Share of Emp, 20000.07Manufacturing Share of Emp, 20000.20Nontradable Share of Emp, 20000.21Services Share of Emp, 20000.70Health & Edu Share of Emp, 20000.13% Age 50, 20000.29The table provides summary statistics for localities with over 10, 000 households in the 2000 Decennial Census and with house price data. Changes for delinquency rates are in percentage point, all other are percentchanges.20

3.1ResultsAreas with higher second home origination shares (instrumented with the vacation share ofhousing) experienced a more pronounced boom and bust in activity. Higher second homeshares 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 inhouse prices and construction employment, and increases in delinquency rates, were moresevere 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 forhouse prices, but are negative for construction employment. Consistent with the overhanghypothesis in Rognlie et al. (2018), overbuilding in the boom led to persistent declines inconstruction.Table 3 shows 2SLS coefficient estimates for the 2000-2006 changes in house pricesand employment (for construction, nontradable, and total private employment) models.6Columns 1 and 2 of Table 3 show that house price and construction employment growthduring the boom was on average 16 and 7 percentage points higher, respectively, in localitieswith 10 percentage point higher second home origination shares over 2000-2006. Thoughhouse prices and construction employment grew faster in localities with higher second homeorigination shares, those gains in real estate do not appear to have led to gains in overallemployment. Columns 3 and 4 show results for nontradable employment and other employment (total private employment excluding construction).7 The coefficient estimates are notsignificant and small, especially in the nontradable employment model (Column 3).However, the increase in activity associated with higher second home origination sharesduring the boom is largely reversed during the recession years. Second homes borrowers weremore levered during the boom and had higher default rates during the recession (Haughwoutet 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 boom6The instrument is strong, with the Kleibergen-Paap first stage F statistic about 100, considerably higherthan the rule of thumb F statistic value of 10 commonly used in the literature to indicate weak instrumentproblems.7Nontradable employment is a category of local employment accounting about 20 percent of total privateemployment, comprised mostly of local retail and food; see Mian and Sufi (2014).21

experienced steeper declines in activiy: house price and construction employment declineswere 7 and

The record-high level of second home buying (homeowners acquiring nonprimary residences) was a central feature of the 2000s housing boom.1 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

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