Forecasting Of Real Estate Markets

1. Dr. Julian Roche
Real Estate Forecasting

2. Agenda
• Welcome
• Housekeeping notes
• Session starts
• Q&A
• End of webinar

3. House Keeping
• Slides will be available on our SlideShare page, link will be
emailed to you
• Recording of the webinar will be available to download, link
will be emailed
• Take the time to complete post-webinar survey that will pop
up at the end
• You can type your questions throughout the session
• Time will be allocated in the end for the speaker to address
your questions

4. About Your Speaker
Dr. Julian Roche
From an old real estate family in the UK, Dr. Julian Roche worked as an
economist for DRI McGraw-Hill and as a partner in a real estate
development company with US investments before launching his own real
estate consultancy specialising in global data provision. Later he spent five
years as a senior consultant to a venture capital company in the UK, where
he advised on corporate structure, flotations, trade sales and business
valuations. He has also published a number of books on real estate
derivatives and business valuations and has presented real estate courses
internationally for a number of years.

5. Why Forecast?
• Assumptions critical for feasibility
studies
• Small variations in forecasts huge
impact on profitability
• DCF core of property valuation and
investment decision-making
• Most variables will change over lifetime
of property

6. What needs forecasting?
• Land – and how its price will evolve
• Net Operating Income, therefore – rents
and yields over the Economic Life of the
property
• Comparable assets
• Main economic variables such as
interest rates, exchange rates
• Commodity prices e.g. steel and glass

7. Choice of Forecasting
Methodologies
• Qualitative - ask the experts, review and
combine results from different sources
• Quantitative – time series
• Causal – contributory elements to
outcomes
• Combining results – taking the best of
each

8. Qualitative Methods
• Asking experts – when and how?
• Delphi Technique
• Eliminating outliers
• How many iterations?
• Other qualitative methods
• Auditing and recording results

9. Time Series
• Measuring forecast error
• The multiplicative time series model
• Naïve extrapolation
• The mean forecast model
• Moving average models
• Weighted moving average models
• Constructing a seasonal index using a centered
moving average
• Exponential smoothing

10. The model ttttt ICSTY ×××=
Where:
•Yt is the value of the time series variable in period t
(month t, quarter t, etc.)
•Tt trend component of the series in period t
•St is the seasonal component of the series in period t
•Ct is the cylical component of the series at period t;
and
•It is the irregular component of the series in period t.

11. Causal Forecasting Models
• The goal of causal forecasting model is to
develop the best statistical relationship between
a dependent variable and one or more
independent variables.
• The most common model approach used in
practice is regression analysis. Only linear
regression models are examined in this course.
• In causal forecasting models, when one tries to
predict a dependent variable using a single
independent variable, it is called a simple
regression model.
• When one uses more than one independent
variable to forecast the dependent variable, it is
called a multiple regression model.

12. Software
• Do NOT use Excel
• Forecast Pro or Smart Forecast
• E-views, WinForecast, Micro-TSP,
Stata, SPSS
• Which to use and when?
• To whom to outsource?