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Impact of Low Income Home Owners on Housing Market Volatility

This study examines the role of low income home owners in the volatility of housing markets, focusing on their income risk, debt burdens, and potential destabilizing effects. It provides a literature review, empirical results, and outlines the methodology used.

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Impact of Low Income Home Owners on Housing Market Volatility

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  1. The Impact of Low Income Home Owners on the Volatility of Housing Markets Peter Westerheide ZEW European Real Estate Society Conference 2009 Stockholm June 26, 2009 1

  2. Outline • Motivation • Literature Review • Data • Methodology • Empirical Results • Conclusions and Outlook 2

  3. Motivation • Default of low income home owners (subprime borrowers) has triggered the current crisis • Usual focus is on the behavior of lenders, not of borrowers • Lending without properly checking creditworthiness • „Originate and distribute“ • Our Focus: What is the role of low income housing demand? • Can we observe any destabilizing impact of low income home ownership in the long run? 3

  4. Motivation • Arguments in favor of destabilizing impact: Low income households • face a higher income risk of unemployment • have low liquid wealth and often no buffer stock to compensate income fluctuations • are usually highly leveraged and have high interest/debt burdens • cannot rely on bailout by relatives 4

  5. Motivation • Competing arguments: Low income households are • less mobile (relative income position and regional location) -> „trading up the ladder“ is less likely • Wages are less variable than other components of income therefore total income might be less variable (as long as employed) • Net effect depends on institutional framework, might be different in the housing cycle • Political target high home ownership rate: Is there a potential tradeoff to the stability of housing markets? 5

  6. Literature Review Income Mortgage market Housing demand Housing prices Volatility of income in different income classes Access to mortgage markets Structure of demand and volatility of markets Financial leverage and volatility Extent of low income homeownership depends on completeness of mortgage markets (Chiuri/Japelli 2003, Bicacova/Siermienska 2007) Sensitivity of prices to income shocks depends on leverage (Lamont/Stein 1999, Benito 2006) Low income homeowners increase price volatility (Ortalo-Magné/Rady 2002) Down payment constraints affect the stability of housing markets (Ortalo-Magné/Rady 2005) Volatility of house prices depends on tax wedges (van den Noord 2005) Income volatility and probability of mortgage default Level and volatility of income are correlated (Diaz-Serrano 2004, Dynan/Elmendorff/Sichel 2005, Jensen/Shore 2008) Income volatility is related to mortage default (Diaz-Serrano 2004) 6

  7. Our approach • Direct analysis of impact of low income home ownership on house price volatility in a cross country comparison • 13 OECD countries, 1970-2006 • Panel Regression with Fixed Effects • Pooled VAR 7

  8. Data • Most important problem: Data on distribution of home ownership and income • No micro data for long time horizons • Assumption of fixed distribution not realistic • But: home ownership rate might be used as a proxy for share of low income households • Empirical evidence supports this assumption 8

  9. Data Stylized fact: home ownership rate and income distribution Rate of Home Ownership HHO Country LHO Country Income 9

  10. Data Ratio of home ownership 2nd/9th income decile and average home ownership rate GE US FIN UK IT Own calculations, based on Bicacova /Siermienska (2007). Household head/spouse18-40 years old. 10

  11. Data Ratio of home ownership 3rd/9th income decile and average home ownership rate Own calculations, based on Bicacova /Siermienska (2007). Household head/spouse18-40 years old. 11

  12. Data • Proxy Homeownership Rate • No annual data (except UK and US) • Survey based, 4 – 10 years • Common definition: Share of owner occupied dwellings in all dwellings • Estimation of long term trends necessary • Assumption: high inertia • Short term fluctuations mainly reflect measurement error 12

  13. Data • House Prices • OECD Database, Girouard et al. 2006 • Heterogeneous data, focussing mostly on used family homes • Other data OECD and UN, gaps filled by interpolation based on national data: • GDP per capita, long term interest rate, CPI, unemployment rate, debt/GDP-ratio 13

  14. Descriptive Evidence 14

  15. Descriptive Evidence 15

  16. Methodology • Multivariate Regression: Volatility of house prices • Volatility of Real Income Growth (proxied by Real GDP per Capita) • Unemployment rate • Interest Rate • Debt-to-GDP ratio • House Price growth • Volatility of Inflation Rate (CPI) • Home ownership rate Income Credit Market Prices 16

  17. Methodology • Time Period 1970 – 2006 • Problem: Measure volatility in sub periods that are long enough to show substantial variation… • …but short enough to have sufficient data points in the time series dimension • Tests with several period lengths • Finally: 11 overlapping periods of 7 years 17

  18. Econometric Results Results of Regression for Nominal House Price Volatility Fixed effects regression, adjusted for serial correlation. 18

  19. Econometric Results Results of Regression for Real House Price Volatility Fixed effects regression, adjusted for serial correlation. 19

  20. Methodology • Specification of a VAR with annual values 1970-2006 • Are house prices more sensitive to shocks in HHO vs. LHO countries? • Variables: real house prices, real interest rates, real GPD per capita, ownership ratio • 2 lags 20

  21. Econometric Results Reaction to House Prices Reaction to GDP Reaction to Interest Rates 21

  22. Conclusion • Ownership rate is proxy for share of low income home owners • Some evidence for a positive correlation of home ownership rate and volatility of house prices • Confirms theoretical findings 22

  23. Outlook • Inclusion of better credit market indicator (mortgage market index) • Account for age structures/demographic structures • Test with US micro data for different regions? • Refine estimation of VAR • … 23

  24. Thank you for your attention!!! Contact: Peter Westerheide ++49 621 1235 146 westerheide@zew.de 24

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