Browsing by Author "De Villiers, Peter Charl"

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    A practical framework to forecast wood prices for timberland appraisals
    (Stellenbosch : Stellenbosch University, 2024-03) De Villiers, Peter Charl; Ham, Cori; Talbot, Bruce; Stellenbosch University. Faculty of AgriSciences. Dept. of Forest and Wood Science.
    ENGLISH ABSTRACT: The study investigates a practical framework for price forecasting when estimating the fair market value of a forestry asset. Understanding the environment in which appraisals are conducted is important as the practice is regulated by different financial reporting and valuation standards. This extends to forecasting revenues when determining the expectation value of an asset. While these standards do not prescribe price forecasting, it provides a useful framework to ensure that the price forecasting technique used by appraisers are consistent with the principles of fair value accounting. This includes consideration of how market participants price assets. Price forecasting from the perspective of appraisal practice can be seen as a more distinct genre of forecasting, focused more on longer term price trends than trying to capture short term periodicity. This also reflects the long-term nature of forestry investments. A useful source of information on price forecasting is from financial statements of listed companies and from surveys. Secondary survey data in particular was useful in determining the methods most favoured by appraisers. The results show that a surprisingly large number of appraisals are still being conducted using basic price forecast techniques. This includes fixing prices in real terms based on recent evidence or forecasting prices based on a return to average or trend. Only a limited number of appraisers appear to favour more dynamic forecasting techniques, mentioned as econometric modelling. The survey results note that appraisers and their clients are wary of more dynamic price forecast methods and prefer stable trends over short term or cyclical volatility. Five price forecast techniques favoured by appraisers were developed into forecast models using real world data. This includes four basic and one dynamic price forecast method. Data from the period 2000 to 2014 was used to develop these models while data from the period 2015 to 2022 was used to validate the models. Using past data allowed testing the accuracy of the different forecast models against the actual prices achieved since 2014. The period analysed includes the explosive price period observed due to the COVID Pandemic and allowed referencing model limitations when dealing with such unexpected events. An analysis of the different price forecast models determined that a dynamic price model is better at predicting future prices and value estimates compared with basic price models. Dynamic price models do have their limitations and should be used with care. For a more dynamic approach to work, it is important to use and test outcomes using sound economic reasoning, as a correlation between variables does not necessarily equal causation. Ultimately, the market environment and the way products are traded is the most important indicator of which price forecast method to use. A decision matrix is proposed to help appraisers select the appropriate method under different market conditions. https://scholar.sun.ac.za The recommendations for using price forecast modelling from this study includes. • Even though appraisers have a strong preference for stable trends, they should not limit themselves only to basic forecast techniques. While informed market participants acting in their own best interest are not likely to consider highly speculative pricing models, they just as likely won’t consider overly simplistic pricing models either. Appraisers should emulate the way market participants price assets when considering which price forecast method to use. • A dynamic price model is best at emulating the market. It requires an appraiser to not only consider price trend but requires a much deeper understanding of supply and demand fundamentals impacting the market products are sold into, including potential developments likely to impact future prices. • Even if a more dynamic approach leads to applying a basic price trend, the process followed is important as it helps improve confidence in the final price outlook. • Developing a dynamic price forecast model can be resource intensive and may make limited financial sense, especially if the outcome cannot be justified clearly. A dynamic price model therefore works best in open markets with access to good quality data. • Basic price forecast models, such as those fixing future prices based only on current or nearterm prices, is risky in times of price change and should be avoided. Such methods have a limited place, but only in times of market stability.