REAL ESTATE OCCUPANCY FORECAST
Forecasting highly seasonal occupancy rates for a leased real estate investment
An analyst at a real-estate management firm wants to forecast occupancy rates for a certain building. He possesses quarterly information starting on the fourth quarter of 2002 and finishing on the second quarter of 2018. Based on these 63 data points of quarterly occupancy, he wants to forecast the next three years or twelve quarters.
We will take advantage of @RISK’s time series tool to incorporate, among other things, seasonal factors into our three year forecast.
TIME SERIES IN @RISK
In the fields of statistics, economics, and mathematical finance, a time series is a sequence of observations, typically measured at regularly spaced times, such as every week, every month, or every quarter. We will use here an example of commodity prices for metals. Other examples of time series are weekly currency exchange rates, the daily closing value of the NASDAQ Composite index, and monthly crude oil prices or any other type of commodity.
@RISK Time Series command provides two types of tools: (1) Fit and Batch Fit tools for fitting various time series processes to historical data, and then projecting these to the future, and (2) a Define tool for simulating data from a selected time series process for use in a @RISK model. The time series results from such a simulation can be viewed with the normal @RISK results options or by using the Time Series Results window.