1.  Housing Financial Stress in Australia: An initial analysis of households reporting payment difficulties Scott Baum Griffith University Jung Hoon Han University of New South Wales

2. Outline  Background to this study  Australian State of Play  Households reporting housing payment difficulties

3. Background  Part of a broader study  Looking at the impacts of the post-GFC economic and social structure on performance of local communities and households  Study in Employment vulnerability during the GFC  One of the impacts we hypothesised was a potential change or shift in the patterns/ makeup of households suffering housing financial stress

4. Background

5. Australian research

6. General media interest

7. Australians for affordable housing

8. Fujitsu consulting ‘stress-o-meter’

9. Households with low paid blue collar or service sector jobs, living in urban fringe localities, low education and non-Anglo ethic background

10. Fujitsu consulting ‘stress-o-meter’ New home purchasers on new estates with low value housing

11. Fujitsu consulting ‘stress-o-meter’ Younger households concentrated in lower ses, higher than average density suburbs employed in vulnerable jobs

12. Fujitsu consulting ‘stress-o-meter’ Account for 60% of households estimated to be in mortgage stress

13. Causes of stress

14. Change in stress

15. Changes in stress

16. Geography of stress

17. Our preliminary work  On the back of this existing data we want to know:  What are the patterns of housing financial stress (esp in the post-GFC world)  Are we seeing different / new patterns  What are the patterns of people transitioning into and out of stress

18. Our preliminary work  Data:  Household Income and Labour Dynamics Australia (HILDA) survey  Possible indicators  Housing payments : income ratios  Could not meet repayments  Since January 200x did any of the following happen to you because of a shortage of money? b) Could not pay the mortgage or rent on time

19. Our preliminary work  Treated the data sets  as cross sectional  Longitudinal (transitions)  Considered  Demographic and other patterns  Major life changes  Undertaken preliminary regressions

20.  Cross section analysis

21. The 2009 data Stressed6.3 Generation y19.8 Generation x48.3 Baby Boomers +31.9 Couples15.6 Couples with Children28.2 Single parents19.8 Mortgage37.7 Private renter53.8 Separate house76.0 Flat/ unit16.1

22. Life changes 2009 StressedNot StressedTotal Worsen finances20.23.84.8* Fired9.23.74.1* Separated13.23.74.3* Got Married3.72.02.1 Got back together2.11.2 Got pregnant9.55.35.5* Gave birth6.33.33.5* Got injured11.99.09.2* Family member injured22.714.515.0* Death of spouse1.10.9 Went to jail1.30.20.3* Retired2.42.6 Changed jobs20.810.811.5* Promoted4.25.4 Improved financial position 3.23.0 Moved house28.816.817.5

23. Proportion households recording payment problems

24. Proportion of households reporting payment difficulties who have recorded life change

25. Financial security/ problems

27. stressed =1 Worse finance5.03*4.75* Separated2.64*1.55* Lost job1.64*1.62* Pregnancy1.621.58 Birth1.371.09 Injury1.091.17 Family injury1.53*1.52* Changed jobs1.48*1.23 Moved1.49*0.92 Separate house1.271.28 Private renter1.94*1.83* Mortgage0.64*0.65* Single parent1.75*1.61* Couple with kids1.03 Couple0.870.89 Generation y2.57*2.49* Generation x3.49*3.38* Low income2.39*2.20*

28.  Transitions

29. Proportion of households by transition type

30. Percentage of households by number of events

31. Life changes by transition 2008-2009 StressedMoved into stressMoved out of stressTotal Worsen finances19.918.39.34.8* Fired7.19.26.74.0* Separated12.211.86.64.4* Got Married3.53.21.91.8 Got back together2.81.9 1.1 Got pregnant6.410.37.14.7* Gave birth5.06.43.92.9* Got injured13.512.216.27.8 Family member injured31.219.918.212.7* Death of spouse1.40.60.00.8 Went to jail0.70.00.60.2 Retired1.42.6 2.3 Changed jobs19.122.416.29.7* Promoted5.01.98.44.5* Improved financial position 5.71.90.62.5* Moved house29.825.627.914.9

32. Financial security/ problems

34. Stressed t 1 and t 2 =1 Worse finance3.89*3.41* Separated2.31*1.08 Lost job1.141.11 Pregnancy0.920.78 Birth1.551.03 Family injury2.27*2.31* Promoted0.830.76 Changed jobs1.52~0.99 Improve finance1.93~2.16~ Separate house0.830.82 Private renter4.7*3.92* Mortgage1.441.16 Single parent2.1*2.10* Couple with kids1.261.39 Couple0.850.86 Generation y2.75*3.13* Generation x5.45*5.06* Low income2.07*2.01*

35. Into stress Stressed t 2 =1 Worse finance3.79*3.29* Separated2.37*1.42 Lost job1.451.48 Pregnancy1.87~1.89~ Birth1.221.11 Family injury1.261.22 Promoted0.30*0.31* Changed jobs1.89*1.51~ Improve finance0.670.76 Separate house1.62*1.73* Private renter1.92*1.69 Mortgage0.780.75 Single parent1.28 Couple with kids0.880.79 Couple1.020.99 Generation y2.21*2.06* Generation x2.88*2.63* Low income2.11*1.94*

36. Out of Stress t 2 =1 Worse finance0.190.22 Separated1.350.88 Lost job1.391.38 Pregnancy1.381.14 Birth0.850.72 Family injury1.211.20 Promoted1.70*1.60 Changed jobs1.351.00 Improve finance1.89~1.69~ Separate house1.411.40 Private renter1.351.25 Mortgage0.49*0.43 Single parent1.511.74* Couple with kids1.341.46 Couple1.72*1.71* Generation y1.551.98* Generation x2.09*2.43* Low income1.83*1.93*

37. Some brief conclusions  Some potentially interesting patterns  Still (way) more analysis to do  Investigate in more detail transitions  Possibly use a pooled data set (pooled across waves)  Introduce some aggregate housing market variables

38.  The end……..