Modelling Housing using spatial microsimulation

1. Modelling Housing using spatial microsimulation Presenter: Robert Tanton Position: Research Director, Social Inclusion and Small Area Modelling team Date: 31 July 2009

2. Format • Introduction to the Social Inclusion and Small Area Modelling (SISAM) team at NATSEM • Evidence based policy • Modelling Housing policy

3. Introduction to SISAM • New team formed this year • Concentrate on social inclusion and small area modelling (spatial microsimulation) • Robert Tanton – Research Director • Justine McNamara –Children and Families team • Research on housing, housing stress and homelessness under social inclusion • WWW.NATSEM.CANBERRA.EDU.AU/SISAM • Links to all papers, online maps, etc

4. What does SISAM do?

5. Evidence based policy • The Government’s agenda for the APS • “Today I want to discuss seven elements of the Government’s vision for the future Australian public service: • 3) Developing evidence-based policy making processes as part of a robust culture of policy contestability” • (Kevin Rudd, Address to Heads of Agencies and Members of Senior Executive Service,30 April 2008)

6. Limits to evidence based policy • Part of a complex process • Also consider politics, public mood and opinions • … but policy modelling will give some idea as to cost of policies, who benefits, and can affect public mood and opinion • Evidence based policy? Or evidence informing policy? • Is policy based purely on evidence? Or should evidence inform policy?

7. Research informing policy • Sandra Nutley (Professor of Public Management, University of St Andrews): • “if research is used to inform policy, what works best is interactive and ongoing processes and relationships between policymakers and researchers” • At a practical level, round-tables or workshops are seen as a cost effective, safe haven way of sharing ideas, analyses or perspectives. • Meredith Edwards, “Research shouldn’t stop at a report”, The Public Sector Informant, p. 28

8. Models NATSEM has to study housing and the effects of policy change • HOUSEMOD • Developed with AHURI • Used for a number of analysis and policy modelling • Before and after housing poverty • Small area housing stress • Housing affordability and Commonwealth Rent Assistance • Small area projections of the demand for housing assistance • Developed further with ACT Land Development Agency • Projections module added – to 2020

9. What can HOUSEMOD do? • Small area: • Change in housing markets, eg, effect of changes in interest rates • Estimates of housing stress before and after policy change • Can model complex policies • Example: Trying to get people onto CRA rather than public housing • Increase amount received through CRA to encourage people to use CRA • increase public housing rent to encourage people off public housing • impact of policy change on other Centrelink benefits can be included

10. What can HOUSEMOD do? • Projections based on current policies or new policies • Annual out to 2017

11. Small area housing stress • Online maps – Housing Stress using the 30/40 rule • http://web.natsem.canberra.edu.au/maps/AUST_SMSM08/atlas.html • Analysis of a number of different measures of housing stress • 30 rule • 30/40 rule • 30/10-40 rule • Measuring housing stress at small area levels: How much do definitions matter?, Binod Nepal, Robert Tanton, Ann Harding and Justine McNamara

12. Small area housing stress – Online maps

13. Examples of work done using HOUSEMOD • FAHCSIA – Estimates of housing stress • HIA – Effect on housing stress of interest rate increases • AHURI – Before and after housing poverty, Intergenerational sustainability of housing • St Vincent De Paul - How much would be required to put all low income renters out of housing stress? • $74 per household per week on average to put everyone out of housing stress

14. What can’t HOUSEMOD do? • Morning after effects only • Static microsimulation model – no behavioural impact • Eg, can look at effect on housing stress after interest rates increase, but cannot incorporate change in demand for housing because prices higher

15. What sort of policy options could be modelled? • Change in CRA (see above example) • Effect of interest rate changes on housing stress

16. What could be done • Introducing some behavioural model • Tenure choice • Improving projections module • Using method developed from other grants • Introducing Supply/Demand analysis • Link to macro model

17. Funding • Consultancies (see above – HIA, St Vincent De Paul, FAHCSIA, AHURI) • Short term use of the model – don’t provide funding for methodological developments unless required for consultancy • Linkage grants • Provide long term funding for methodological advances • Provide linkage partners with analysis using new methodological advances • Provide us with experts in each State to help develop and validate model and provide comments - collaboration

18. Future • Collaborations with States • Work with States to model different housing scenarios • Provide results in focus groups to talk through results • Linkage Grant • To introduce behavioural elements and conduct some policy analysis • Approx $30,000 per year commitment over 5 years • Funding matched by Commonwealth • Policy analysis agreed on by partners

19. Cost/Benefit analysis • How much does a policy with unintended consequences cost? • Making things much worse for low income families compared to high income families – was this intended? • Making thing worse for different areas – will rural areas be affected worse? Or inner city? Was this intended?

20. Questions/Comments/Discussion www.natsem.canberra.edu.au