In the 1960s and 1970s, trend projections, mathematical modelling and market surveys were used extensively as long-term planning and decision-making tools (Ratcliffe 2008). Then, from the late 1970s and towards the 1980s the view of the future and
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disruptions. Change, uncertainty and complexity became recognised as important factors shaping businesses and influencing the future but even when the corporate world responded relatively quickly to this, professions of the commercial real estate industry were slower to react and this is also a case in current times (Ibid.). Commercial real estate companies are more likely to be directed towards hard outputs such as forecasts and models (Leishman 2003), then to use of the futures approach (Ratcliffe 2002). Real estate analysis and prognosis are primarily based on quantitative data like prices per square meter, or demand and supply levels. Forecasts are frequently used in the decision-making process by investors, developers, landowners and buyers (Leishman op. cit.).
Most analysis and forecasts concerning real estate are based on quantitative methods (McMahan 2006). In property valuation the procedures are based on law regulations and industry standards (Maczynska 2008), for instance, International Valuation Standards (IVSs) introduced by The International Valuation Standards Council (IVSC). IVSs describe compulsory procedures and techniques required while undertaking the task of assessing the value of property; this includes valuation principles, concepts and definitions. Globally, there are dozens of national valuation organisations, as well as corporate and institution members of IVSC committed to application of IVSs framework in practice (IVSC n.d.). It is also important to mention RICS professional standards of valuation, generally known as the 'Red Book', which comprises description of best practices, rules for undertaking asset valuations and mandatory for all RICS members. Standards of valuation described in the ‘Red Book’ are consistent with IVSs to ensure compliance with the highest professional standards (RICS 2013).
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Generally, methods used in this sector are derived from the quantitative field and are focused on market data like rent volume or transaction prices. Major approaches to property value estimation are of a quantitative nature, for instance, the cost approach (calculation of total cost required to develop property that provides the same utility as the valued property) or the income approach (present value of the calculated future income generated by the property becomes basis for the current worth of the property) (Larsen 2003).
Properties are tangible assets; while they exist in a physical sense they are less liquid than shares and more capital-intensive than other consumer good like clothing or food (Floyd and Allen 2002). Therefore, financial feasibility and the estimated profitability of the project are crucial pieces of information for decision-makers. Investors employ a number of financial methods which are derived from the quantitative field, to determine the feasibility of the project. These include, among others, payback period and internal rate of return (Larsen 2003, Maczynska 2008):
1. Payback period illustrates the years required for the recovery of initial investment.
The general rule of this methods can be described as - the shorter the payback period, the greater the possibility that investment will not fail.
Payback period = Equity investment/ Annual after tax cash flow
2. Net present value (NPV), method used to discount future cash flow to its present value. NPV is a difference between present value of cash inflows and the present value of cash outflows. It is used to evaluate whether an investment is able to generate enough cash flow to generate a return. Investment can be accepted if NPV≥
0, but if NPV < 0 investment should be rejected.
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Most of real estate models and forecasts are of a quantitative character and as most tools used in the statistical or economic field use simplification and data aggregation, which can lead to errors and misjudgements of trends and issues (Geltner et al. 2006; Brooks and Tsolacos 2010). Quantitative forecasts are appropriate when historical data is available and when the relationships among variables are expected to stay constant in the future (Anderson 2005). As historical relationships in the researched area become less stable, quantitative forecasts can become less accurate and produce false results (Ratcliffe 2008). In the case of real estate market forecasting and analysis reasons for inadequacy and errors can lie in the nature of the market alone. As described in section 2.2, the real estate market is imperfect, while it consists of a number of distinct yet interlinked sectors and information flows about the value of transactions are poor Leishman (2003).
Accurate forecasts can build a competitive advantage for organisation, significantly support strategic management processes and contribute to the success of companies (Passemard and Kleiner 2000; David 2001). Quantitative forecasting based on econometric and statistical models became accessible to a growing number of companies with the advancements in computer software (Leishman op. cit.). Presently available software packets like Statistica enable business analysts and researchers to almost automatically generate complex models based on the time-series data and elaborate calculations (Sweeney et al. 2013). Additionally, quantitative forecasting techniques are in many cases cheaper and faster than qualitative methods (David op.
cit.), but researchers like Brooks and Tsolacos (2010) note that quantitative methods are more often used without validity and adequacy. Ratcliffe (2007), tackling the issues of overuse of quantitative techniques in the commercial real estate industry, points out that
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this approach is rational, linear, trend based and not adequate for exploring and explaining multi-various factors, trends, issues and challenges facing and shaping contemporary real estate markets. Ratcliffe (2009) advocates more frequent use of a qualitative approach to the analysis of the contemporary real estate markets, which is already employed in various economic fields in analysing imperfect markets. This includes the problem with information scarcity, lack of historical data and complex system variables prone to change.