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Introduction to Real Estate

Introduction to Real Estate. History and Concepts. The Dynamics of Real Estate Markets. Real Estate Finance Spring 2005. From Pro Forma to Stochastic Processes. Pro Forma risk analysis Cash flows depend upon: Scenarios Probability assessments Discount rates depend upon

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Introduction to Real Estate

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  1. Introduction to Real Estate History and Concepts

  2. The Dynamics of Real Estate Markets Real Estate Finance Spring 2005

  3. From Pro Forma to Stochastic Processes • Pro Forma risk analysis • Cash flows depend upon: • Scenarios • Probability assessments • Discount rates depend upon • Systematic vs. unsystematic risk drivers • Is there any way to incorporate all of this?

  4. Simulation Tools • Value drivers: • Rents • Vacancies • Expenses • Drivers of value drivers:

  5. Simulation Methods • Requires structure/model • Rent processes • Vacancy processes • Interest rates • Covariance estimates • Model: Sivitanides, Torto, Wheaton (2003) • MSA/aggregate structural relationships

  6. Forward Looking? • Rational Model: Efficient markets • Agents anticipate future conditions and trends • Myopic model • Agents react to immediate conditions • Rational explanation? • Muth model

  7. Are Cycles Rational?

  8. Cobweb Model 1

  9. Cobweb Model 2

  10. STW Analysis • Interest rates matter • Spreads and Cap Rates not forward-looking • No trend towards efficiency

  11. Rents and Vacancies

  12. Patterns: • Autocorrelation • Inter-dependence • Mean reversion

  13. Cap Rates and Interest Rates • C = NOI/P e.g. Before Tax Yield • Why Negative? • Why Positive?

  14. Hypotheses • Inflation hedge. • Lower future growth. • GDP changes. • Recent trends: dropping since 2000 • 9.5 to 8.5 RCA • 8.5 – 7.5 NREI • NCREIF no change • What about diversification? • Trends in the equity market? • Sentiment?

  15. Survey of Institutional Investors

  16. STW Analysis • Cap Rates moved by interest rates • Historical analysis remains reliable • Other factors that could explain structure? • Strategy? • Other investments?

  17. Back to Simulation • CF depends on: • Rents, vacancies • Prices depend upon • interest rates • Growth expectations • inflation

  18. Simple Simulation: • Rents follow a random walk • R(t) = R(t-1) + e(t) • E(t) is a random error • Spreadsheet simulation straightforward • Take last qtr rent, add a normal error term to it, then move forward one cell for ten cells. • Do this 100 different times and look at range of outcomes.

  19. Problems • Random walk assumption • Normal errors and positive rents • Don’t know std of error • Don’t know if random walk makes sense • What to do?

  20. More Complex Recipe • If rents autocorrelated • Estimate an autoregression: • R(t) = a + bR(t-1) + e(t) SAVE ERRORS • Take R(0) as today’s rents • R(1) = a+bR(0) + e* where * means random draw from saved errors. • Move to the next cell

  21. Ultimate Recipe • Include other variables in estimation stage • Vector auto-regression • Allows rents to depend on past vacancies • Allows vacancies to depend on past rents • Allows them to depend on past interest rates • Also allows simulation of extreme cases

  22. VAR Forecasts

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