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Looks at the philosophy of valuation, explaining the distinction between “worth” and “price”. Attempts to explain traditional techniques of assessing price. Looks at the development of discounted cash flow and its applications in valuations and calculations of worth. Concludes that there must be more openness with clients about valuation risks and issues so that they can make informed judgements.

It is well recognised that the UK commercial property market has traditionally used nominal market benchmarks such as the all‐risk yield based on the assumption that rents are received annually in arrears. Obviously, the reality of the market is that rents are invariably received quarterly in advance and it has been suggested that valuers should move towards valuation techniques that reflect the actual timing of the cash flow. The Investment Property Forum issued a paper in September 1999 promulgating the use of quarterly in advance valuations. Parry’s Tables provides quarterly in advance formulae that reflect the reality of rental income and indicates that an annual effective yield should be used instead of a nominal yield to compensate for the subsequent compounding resulting from an income received quarterly. However, as will be shown, the effective yield formula provided by Parry’s does not reflect quarterly payments that are received in advance so compromising the accurate transition from annually in arrears to quarterly in advance formulae based valuations. Tables produced by the IPF have rectified this problem in part as they correctly work on the premise that capital values will not change as the profession changes to a quarterly approach. It is the yield which will be expressed differently. The use of an all risk yield technique for valuation is actually a comparative method. The way in which the yield is expressed is not the critical issue, it is the multiplier against the rent which will determine value. This paper provides the formula required to accurately transfer annually in arrears data into quarterly in advance data together with the formulae required for contemporary growth explicit discounted cash flows (DCF).

The aim of this paper is to examine the degree to which it is possible or practicable to make the analysis and valuation of property investments an explicit exercise. This is in direct contrast to the closed implicit nature of the practice which currently prevails, and which is epitomised by the use of the ‘all‐risks yield’ as a single gauge of the complex relationships of advantages and disadvantages within a particular investment. To what extent can the ‘all‐risks yield’ be purified, and to what extent will comparison with non‐property investments be facilitated as a result?

Starts from the basic principles of property investment and shows that the initial yield conceals estimates of a risk premium, expected income growth and expected depreciation. Suggests that an explicit valuation procedure which can be used at any level ranging from a single property to the aggregate market may be constructed. Concludes that the surveying profession is under threat from those able to meet the growing demand for such explicit analyses.

The object of the Investment Profiles is to expose the bidden assumptions which lie behind valuations of investment properties, and in doing so to provide a more objective platform for debate and argument about any particular valuation. The traditional approach to valuation is to apply an ‘all risks’ yield to the flow of income from any property, reflecting the advantages and disadvantages of that investment. However, there is no sound objective basis on which to justify the ‘all risks’ yield used. In reality, the valuer arrives at the yield by making a number of subconscious mental adjustments to the yield based on the particular features or disadvantages of the property under consideration. Investment Profiles arrive at the same ‘all risks’ yield by an explicit approach, the valuer making a series of adjustments to the yield. These adjustments can then be debated and argued. The need to resort to unscientific cliches like ‘prime’, ‘semi‐prime’ and ‘secondary’ can be much reduced or avoided. The paper considers a number of example valuations, and demonstrates that the technique is easy to understand and simple to use.

The first of these two articles gave an insight into the basis on which the suitability of valuation methods to perform their functions should be judged. In this article attention turns to specific principles in the Equated Yield (EY) and Inflation Risk Free Yield (IRFY) approaches.

The purpose of this paper is to assess the production risk for winter wheat producers in Beijing, China, particularly in its 13 districts.

A parametric approach is used to model wheat‐yield distribution for samples and the Kolmogorov‐Smirnov test is used to choose the most appropriate yield distribution. Parameters of the special yield distribution are estimated through the maximum likelihood estimation approach.

The Burr distribution is found to be the most appropriate parametric distribution to model winter wheat‐production risks for the districts of Beijing, except in the districts of Fengtai and Shunyi. Findings also show that the Johnson family distribution is the most appropriate model for these two districts (SB for the Fengtai District and SU for the Shunyi District). The wheat‐production loss ratios of the Beijing districts are between 6 and 15 percent, which is considered medium range in most regions. The highest production risks are located in the Western regions of Beijing (Mentougou and Fengtai) while the lowest production risk is located in the Southeastern region of Beijing (Daxing District).

To generate an objective yield trend and an accurate production risk assessment, linear moving average, instead of linear (or quadratic) regression, is used in this paper.

The purpose of this study is providing a possible methodological solution to the valuation of cyclical.assets. International Valuation Standards introduce a brand new definition of property: the cyclical asset (International Valuation Standards Council 2017, IVS 105, p. 39 and p. 41). Among different property valuation methods, normally this kind of properties is appraised using income approach. In this group of methodology, the opinion of value is based on a proportional relationship between property value and rent. In the past years, a group of methods called cyclical capitalization has been proposed (d’Amato, 2003; d’Amato, 2013;d’Amato, 2015; d’Amato, 2017a; d’Amato 2017 b; d’Amato, 2017c). This method proposes an integration between property valuation and property market cycle.

Cyclical capitalization method is applied using a time series of property market rent of offices in prime location in the South Bank area in London. It consists of the determination of more than one all-risk yield to reproduce the property market cycle.

A comparison between the cyclical capitalization and two traditional capitalization rate shows how the proposed model is able to provide a stable opinion of value.

The method may represent a contribution for the determination of the value of cyclical assets or for the mortgage lending value.

This paper provides the possibility to have a property valuation method less sensitive to upturn and downturn of the property market.

The valuation based on cyclical capitalization are less sensitive to the upturn and the downturn of the market.

It is one of the first scientific paper addressing the problem of the determination of the value of cyclical assets.

The purpose of this paper is to propose and discuss practical approaches on how to address risk and uncertainty within valuation reports, particularly when there is only insufficient comparable transaction evidence available.

A four stage approach to property valuation is proposed that can be particularly useful if there is insufficient comparable transaction evidence available: Identifying, measuring and expressing risk by making use of property rating approaches. Transforming risk into risk premia for calculating the yield on a risk free basis by partially making use of models of risk and return usually applied in finance. Simulating risk premia (since there is great deal of uncertainty involved in determining these premia) by making use of a statistical method commonly referred to as Monte Carlo Simulation. Using the derived yield's probability distribution in combination with further probability distributions for other valuation input variables (e.g. market rent) to calculate a range of possible outcomes of Market Value as well as a number of statistical measures that can be indicative of the valuer's perceived uncertainty regarding the valuation assignment.

The empirical part shows that due to data limitations determining idiosyncratic risk premia for property assets is not yet possible. This significantly hampers the development of robust yield pricing models and reinforces the need to create databases including information on both individual property returns and associated building characteristics.

The paper postulates that there are few (if any) rational reasons for valuers not to use rating and simulation approaches as an indispensable element of the valuation process.

A valuation approach that allows simultaneously addressing risk and uncertainty as well as sustainability issues within commercial property valuation practice is proposed.

Valuation is the process of estimating price. The methods used to determine value attempt to model the thought processes of the market and thus estimate price by reference to observed historic data. This information is utilised in the discounted cash flow (DCF) valuation model to determine the single point valuation figure. However, the valuation will be affected by uncertainties: uncertainty in the comparable data available; uncertainty in the current and future market conditions and uncertainty in the specific inputs for the subject property. These input uncertainties will translate into an uncertainty with the output figure, the estimate of price. This paper discusses ways in which uncertainty can be incorporated into the DCF model.

This paper looks at the way in which uncertainty can be incorporated into the explicit DCF model. This is done by recognising that the input variables are uncertain and will have a probability distribution pertaining to each of them. Thus by utilising a probability‐based valuation model (using Crystal Ball) it is possible to incorporate uncertainty into the analysis and address the shortcomings of the current model.

The outcome of introducing uncertainty in the inputs is to produce a range of different answers. The central tendency of this distribution is very close to the single point estimate of the static model, yet the user of the technique now benefits from an understanding of the upside and downside risk pertaining to this single point estimate.

This study contributes significantly to the practical application of probability‐based models to valuation. In particular, the findings from the study will be useful for clients to understand better the context in which a valuation figure is provided to them.