California association of realtors real estate voices symposium
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STUART A. GABRIEL Arden Realty Chair & Professor of Finance, UCLA Anderson School of Management Fear and Loathing in the Housing Market: Evidence from Search Query Data. California Association of Realtors Real Estate voices symposium .

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California association of realtors real estate voices symposium

STUART A. GABRIEL

Arden Realty Chair & Professor of Finance, UCLA Anderson School of Management

Fear and Loathing in the Housing Market: Evidence from Search Query Data

California Association of RealtorsReal Estate voices symposium

* http://www.anderson.ucla.edu/Documents/areas/fac/finance/foreclosure_fear_root.pdf


Introduction

Introduction

  • Mortgage distress was endemic to the recent crisis

  • Such distress can dampen house prices, induce episodes of pessimism among consumers and investors, and wreak havoc on the macroeconomy and financial markets

  • Standard measures of investor fear or sentiment either did not focus on housing and mortgage markets or were characterized by limited sampling, low frequency, lags in dissemination, or lack of predictive power

    • VIX Index

    • Consumer Sentiment

    • ABX Indices

  • Paucity of information was striking given leading role of housing in the global downturn


Research approach

Research approach

  • Use Google search data to construct and test of a new Housing Distress Index (HDI)

    • Google is most popular search engine in US

      • Substantial search frequency: large sample of millions of households

      • Allows us to specify/aggregate search frequency into an index

      • Data is freely available and easily accessed

      • Data is real-time

    • Focus on internet search queries that include a housing-related keyword and a signal of distress (e.g. “foreclosure help” or mortgage assistance”)

    • When a user enters a search term such as “mortgage foreclosure help”, she divulges her concern about mortgage failure or foreclosure


Scientific literature

Scientific literature

  • The literature using Google search data is large and growing rapidly Among applications of search query data:

    • Racial animus in voting and child abuse (Stephens-Davidowitz, (2011))

    • Spread of influenza in US (Ginsburg, 2009)

    • Stock market attention and consumer sentiment (Da, Engleberg, and Gao (2011 and 2012))

    • International home bias and attention allocation (Mondria, Zu, Zhang (2010))

    • Construction of leading indicators (Arola and Galan (2012)) and (McLaren and Shanbhoge (2011))

    • Query indices and a 2008 downturn: Israeli data (Bank of Israel (2009))


Hdi construction

Hdi construction

  • Combine housing or mortgage related keyword with a signal of distress

  • Start with the “mortgage help” and “foreclosure help” search terms

    • During 2012, well after the height of the crisis, these terms were queried 594,000 and 266,400 times, respectively

  • Google trends reports similar queries

  • Sum the Google search frequencies from these queries to build the HDI

  • Assess robustness of index to other search terms

  • Seasonally adjust the HDI using the X12 algorithm

  • Standardize the HDI to have zero mean and unit variance and use its first difference in predictive analysis


Google searches for mortgage assistance

Google searches for “mortgage assistance”


Hdi search terms

Hdi search terms


Hdi and the case shiller

Hdi and the case-shiller


Hdi and bad time to buy due to uncertain future

Hdi and “bad time to buy due to uncertain future”


The hdi and the vix

The hdi and the vix


The hdi foreclosures and delinquencies

The hdi, foreclosures and delinquencies


The abx indices

The abx indices


Predictive effects of the new housing distress index

Predictive effects of the new housing distress index

  • The HDI is highly correlated with negative housing market sentiment

    • Correlation between the HDI and “now is a bad time to buy owing to UncertainFuture” is 0.74

    • Yet the HDI behaves differently over the sample period and predicts negative housing market sentiment

  • The HDI statistically predicts key housing and behavioral indicators, including

    • Negative consumer sentiment, ABX Indices, foreclosures, delinquencies, the VIX Index, and national and MSA-specific house price returns

    • Results are well-conditioned

    • HDI provides new information and captures a dimension of household behavior not previously observed in the literature


Predictive effects of the new housing distress index1

Predictive Effects of the new Housing Distress Index

  • Increases in the HDI predict a decrease in housing returns

    • Like the VIX and stock returns [Whaley (2000) and Szado (2009)], the relationship between the HDI and housing returns is asymmetric and most pronounced during times of crisis

    • HDI also leads to larger drop in future housing returns in low price momentum and high volatility local housing markets

    • Like the VIX, the HDI is more a barometer of fear associated with housing market implosion than a measure of agent exuberance during a period of market upturn


Conclusions

Conclusions

  • Google search query data provides new, timely insights re housing distress

  • The HDI predicts national and local housing returns, negative housing market sentiment, the VIX index, the ABX indices, and foreclosures

  • Predictive effects are stronger during times of crisis, for volatile housing markets, and for housing markets in distress

  • The HDI captures a new dimension of household behavior

    • The HDI behaves differently over the sample period compared to other behavioral indicators

    • Housing and Macro Indicators account for little variation in the HDI

  • Housing Distress Index may be interpreted as a fear gauge for the housing market

    • HDI matches anecdotal accounts of housing fear over the sample period

    • HDI is highly correlated with and predicts negative consumer housing sentiment, the ABX Indices, and the VIX index


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