1. Names Date

2. Agenda (Do we need this?) Decide on this after we finish the presentation

3. Introduction / Hook Housing market crash headlines Recent news about banks having inadequate housing models Vikas’ executive summary intro Possibly chart showing rapid decline in US housing prices Possibly a timeline of significant events in time => Motivation / Idea: Need better housing models!

4. Solution The need for better/good housing models Tim’s video about what people think => lack of information => inefficient => opportunities for refinement and profit

5. Project objectives/purpose/uses Mortgage-Backed Securities (MBS) valuation Housing market size MBS market size => Pie chart?? Uses Develop efficient and robust forecasting models to understand the housing price process Predict the evolution of housing prices over medium to long term horizon, Devise profitable trading strategies

6. Data Sources and Breakdown(?) Model state-level housing prices OFHEO HPI 1975-2008 quarterly Single family units… Other useful/interesting descriptors Simple returns

7. Methodology Build housing price model Build inputs for our forecast

8. Modeling Approach 2-phase approach in modeling the housing price returns data Time frequency: Monthly Drift Model Explain the relationship between housing price and macroeconomic variables Supply and demand equilibrium model Volatility Model Model the residuals from the drift model to account for extra sources of volatility (refines the drift model?) Time series techniques => Combine the 2-stage models to forecast state-level housing prices for xx- months

9. Drift Model (format 1) Demand Variables (How to present variables and metric used – maybe table format??) Unemployment rate Population size Median income Cost of credit / interest rate Availability of credit => Mortgage originations Supply Variables Housing stock => Building permits issuance Foreclosures

10. Drift Model (Bullet format) Demand Variables Unemployment rate Population size Median income Cost of credit / interest rate Availability of credit => Mortgage originations Supply Variables Housing stock => Building permits issuance Foreclosures

11. Volatility Model Goals/purpose: to explain away period of high & low volatility (volatility clustering). To account for serial autocorrelations Univariate time series analysis AutoRegressive-Moving Average ARMA/GARCH

12. Overview of Economic Variables Charts Special circumstances? => Alternative: Do this after we present our states cause it’s like “Now, let’s look at the inputs to our model?” => See slides after the states

13. Building forecasts (or should we put this after show individual states’ analysis) Forecast each predictor variable of the drift model Curve fitting Or we put the general slide here and go into detail after the individual states’ analysis (i.e. assumptions, charts, etc)

14. Distribution of states Heat map (hotpads map or then animation online) [Trouble/High…] States Medium states States that are opposite the national trend => put these 3 categories of states in a table format? Geographic segmentation??

15. Florida Historical (non-simple returns) graph Background information / Current climate Our forecast Challenges / Difficulties to overcome (curvature hump) Reasons for unusual behavior/trends

16. California Historical (non-simple returns) graph Background information / Current climate Our forecast Challenges / Difficulties to overcome (curvature hump) Reasons for unusual behavior/trends

17. New York Historical (non-simple returns) graph Background information / Current climate Our forecast Challenges / Difficulties to overcome (curvature hump) Reasons for unusual behavior/trends

18. Middle state Historical (non-simple returns) graph Background information / Current climate Our forecast Challenges / Difficulties to overcome (curvature hump) Reasons for unusual behavior/trends

19. Low state Historical (non-simple returns) graph Background information / Current climate Our forecast Challenges / Difficulties to overcome (curvature hump) Reasons for unusual behavior/trends

20. Opposite state Historical (non-simple returns) graph Background information / Current climate Our forecast Challenges / Difficulties to overcome (curvature hump) Reasons for unusual behavior/trends

21. Inputs to our model – Macroeconomic Variables Overview Possibly explain supply and demand economics??? Charts Special events/circumstances => This placement is an alternative to discussing this before the states

22. Forecasting Building on forecast relies on assumptions

23. Demand Variables Forecasts Building on forecast relies on assumptions Assumptions and Reasoning for: Unemployment rate Population size Median income 30-year commitment rate Mortgage originations => Alternative: Each variable has own slide with “Assumptions and Reasoning” along with an actual curve fit/ forecast for a state graph

24. Supply Variables Forecasts Assumptions and Reasoning for: Building permits issuance Foreclosures => Alternative: Each variable has own slide with “Assumptions and Reasoning” along with an actual curve fit/ forecast for a state graph

25. Volatility Forecasts Should we talk about this? What to talk about? -----

26. Important Take-aways Conclusions Results

27. (Special) Thanks / Acknowledgements Sponsor Dr. Paul Thurston Agamas Capital Management (?) Tower Research Capital (?) Advisors Dr. David Matteson Dr. David Ruppert Faculty & Staff (???) Dr. Kathryn Caggiano Selene Cammer (=> look up last name) Victoria ____ Judy Francis ???

28. Questions Should we put this before or after the special thanks slide??

29. Appendix Maybe include here data treatment Interpolation to monthly Influential points treatment Any formulas, etc