The Role of Contagion in the American Housing Boom

DeFusco, Anthony; Ding, Weihua; Ferreira, Fernando; Gyourko, Joseph

doi:10.2139/ssrn.2696695

Cited by 14 publications

(21 citation statements)

References 42 publications

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“…Even after controlling for own lagged house price appreciation, the distance-weighted lagged house price appreciation in other counties strongly predicts future county house price growth. This is consistent with the findings in DeFusco et al (2015).…”

Section: House Price Contagionsupporting

confidence: 93%

“…We discuss a number of implications of these findings. First, we show that social dynamics might be an important mechanism to explain the geographic contagion in house prices documented by DeFusco et al (2015). In addition, we argue that the across-county-population average house price experiences of geographically-distant friends can, in many settings, constitute a valid instrument for house price changes.…”

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confidence: 80%

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Social Networks and Housing Markets

Bailey

Cao²,

Kuchler

et al. 2016

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may Social Networks and Housing Markets AbstractWe document that the recent house price experiences within an individual's social network affect her perceptions of the attractiveness of property investments, and through this channel have large effects on her housing market activity. Our data combine anonymized social network information from Facebook with housing transaction data and a survey. We first show that in the survey, individuals whose geographically-distant friends experienced larger recent house price increases consider local property a more attractive investment, with bigger effects for individuals who regularly discuss such investments with their friends. Based on these findings, we introduce a new methodology to document large effects of housing market expectations on individual housing investment decisions and aggregate housing market outcomes. Our approach exploits plausibly-exogenous variation in the recent house price experiences of individuals' geographically-distant friends as shifters of those individuals' local housing market expectations. Individuals whose friends experienced a 5 percentage points larger house price increase over the previous 24 months (i) are 3.1 percentage points more likely to transition from renting to owning over a two-year period, (ii) buy a 1.7 percent larger house, (iii) pay 3.3 percent more for a given house, and (iv) make a 7% larger downpayment. Similarly, when homeowners' friends experience less positive house price changes, these homeowners are more likely to become renters, and more likely to sell their property at a lower price. We also find that when individuals observe a higher dispersion of house price experiences across their friends, this has a negative effect on their housing investments. Finally, we show that these individuallevel responses aggregate up to affect county-level house prices and trading volume. Our findings suggest that the house price experiences of geographically-distant friends might provide a valid instrument for local house price growth.JEL-Codes: G120, D120, D140, D840, R210.Keywords: social networks, expectation formation, disagreement, house price dynamics. The past decades have seen large swings in U.S. house prices. Due to the role of these price movements in precipitating the Great Recession, there have been significant efforts by policy makers and researchers to better understand the drivers of house price dynamics. A prominent clas...

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Section: House Price Contagionsupporting

confidence: 93%

mentioning

confidence: 80%

Social Networks and Housing Markets

Bailey

Cao²,

Kuchler

et al. 2016

SSRN Journal

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show abstract

“…In practice, hedonic and repeat-sales estimates are very similar when both are computationally feasible. We construct our hedonic indices by regressing log prices on the square footage of the home (and its square), the number of bedrooms, the number of bathrooms, the age of the home, and an indicator for condominiums.Ferreira and Gyourko (2011) andDeFusco et al (2017) show that this model closely approximates the Case-Shiller index when estimated at the MSA level.…”

mentioning

confidence: 69%

Housing Disease and Public School Finances

Davis¹,

Ferreira²

2017

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Median expenditure per student in U.S. public schools grew 41% in real terms from 1990 to 2009. We propose a new mechanism to explain part of this increase: housing disease, a fiscal externality from local housing markets in which unexpected booms generate extra revenues that schools administrators have incentives to spend, independent of local preferences for provision of public goods. We establish the importance of housing disease by: (i) assembling a novel microdata set containing the universe of housing transactions for a large sample of school districts; and (ii) using the timelines of school district housing booms to disentangle the effects of housing disease from reverse causality and changes in household composition. We estimate housing price elasticities of per-pupil expenditures of 0.16-0.20, which accounts for approximately half of the rise in public school spending. School districts did not boost administrative costs with those additional funds. Instead, they primarily increased spending on instruction and capital projects, suggesting that the cost increase was accompanied by improvements in the quality of school inputs.

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“…Spillovers can be observed also at the city level, as the recent research for the Warsaw market by Waszczuk (2013) and Widłak et al (2014) shows. DeFusco et al (2013) use micro-data for the US and find that the dynamics are stronger during the boom period than during the bust period and the price impulse is stronger from large markets than from smaller markets. Dittmann (2013) analysed this question for Poland and we perform a similar analysis using a longer data set.…”

Section: Research Hypothesismentioning

confidence: 99%

Panel Analysis of Home Prices in the Primary and Secondary Market in 17 Largest Cities in Poland

Leszczyński¹,

Olszewski

2014

SSRN Journal

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We analyse the determinants of house prices in the primary and secondary market of 17 largest cities in Poland during the 2002-2013 period. We find that prices are driven by economic fundamentals, such as income growth or rise in employment.Prices in the secondary market react to increases in the loan availability, that was driven by low interest rates resulting from FX denominated housing loans that were granted since 2006. This finding does not hold for the primary market, which is to a large extent financed with cash. We confirm empirically that the house appreciation in the past period has a strong effect on the current price, which confirms herding behaviour in the housing market. Another finding is that the secondary market has a stronger effect on the primary market than the other way around. This means that housing demand is satisfied in the first place from the secondary market, and if prices rise, potential buyers go to the primary market. Finally, we find that price increases in Warsaw spill over to the local markets of 16 regional cities. This finding is consistent with the contagion theory in the real estate market, according to which price increases in the centre lead to price increases in the periphery.

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The Role of Contagion in the American Housing Boom

Cited by 14 publications

References 42 publications

Social Networks and Housing Markets

Social Networks and Housing Markets

Housing Disease and Public School Finances

Panel Analysis of Home Prices in the Primary and Secondary Market in 17 Largest Cities in Poland

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