Prime, secondary and tertiary are inconsistently used terms to describe building and location quality. But asset allocators need an accepted, accurate industry-wide definition, writes Malcolm Frodsham
There are well-established and well-understood variations in performance across property groupings by type and region. Rental values for offices, for example, have been more volatile than for shopping centres. However, analysis at the asset level has revealed a wide spread of returns within each grouping. This limits the effectiveness of asset allocation and risk management based solely on these two attributes.
The suspicion is that there are missing factors driving property return variations, particularly characteristics such as location, building condition, tenant strength and lease structures. If there are missing factors in the performance measurement framework then quantitative risk estimates will be mis-specified and the full asset allocation opportunities concealed from investors.
These factors can collectively be described as ‘quality’ factors. One of the barriers of introducing these factors is the lack of useable measures for some of them. It can also be difficult to articulate clearly how asset financial and physical characteristics affect return generation.
‘Defining Investment Quality’ is the latest research commissioned by the IPF Research Programme. The IPF Short Papers series addresses current issues facing the property investment market in a timely but robust format.
The purpose of the paper is to define measures of property and income quality that can be used to stratify real estate returns and describe how these factors affect asset level and market performance. The application of the research will provide investors with a more complete picture of market trends and deeper insights into the risks within their portfolios.
Prime, secondary and tertiary are widely used terms within the industry to describe building and location quality. They are also sometimes applied to lease characteristics.
Such terms are often derived from a marketing context with no universally agreed definition and no consistency from one year to the next. To codify property quality, criteria are required that are analogous to these terms but that are objective and flexible enough to be applied consistently over time.
Coding every property as prime, secondary or tertiary according to its building and location attributes is a laborious process, necessitating regular re-specifying to reflect changes to occupier requirements and it inevitably involves the application of arbitrary weightings to be applied to each feature.
This research searches for a more practical, robust and quantitative definition for quality that is acceptable to practitioners for use within a performance measurement and risk management framework.
“Prime, secondary and tertiary are widely used terms within the industry to describe building and location quality”
Two quality factors are specified: the first factor is property quality and the second is income security (figure 1). The research also specifies a composite measure for overall investment quality. The measure of property quality is built on two pillars – the building’s condition and the quality of the location.
The rental value per metre squared is put forward as a practical means of discriminating between high and low-quality properties. To make consistent comparisons, the rental value must be on a net effective basis. To distinguish the influence of property quality from the importance of the region, quality should be nested within a location (international, national, and so on) rather than administrative regions (for example, south-east, north-west) and also by function (supermarkets versus unit shops).
This measure cannot, however, discriminate between building quality and location quality, which is a useful distinction for developing asset and portfolio strategies.
Quantitative datasets are available to rank locations, with retail consultancies, in particular, having developed comprehensive measures of ‘pitch’. Further rule-based assessments for the quality of locations for every type of building use are likely to expand rapidly in the future by using ‘big’ datasets.
A comprehensive scale for property quality can be based either on the Valuation Office Ratings (VOA) list or on property valuations undertaken by investment funds. The problem with the VOA list is that it is only updated periodically and the problem with basing a scale solely on the holdings of investment funds is that it fails to encompass the full spectrum of assets.
The second component, income security, is determined by the strength of the tenants and the remaining lease durations.
Combining tenant credit rating and unexpired lease term into one measure of income security is possible, but only with a firm quantitative estimate of tenant default risk and the impact of credit enhancing techniques. The creation of past data series of the actual incidence and impact of tenant default would improve estimates of default risk.
This paper proposes that income security and property quality are united, in one measure of investment quality (figure 1), with assets of the highest investment quality combining the highest levels of property quality and current income security. To rank assets or markets, the required return (determined by cash-flow volatility, liquidity and transparency) is put forward as the key determinant of investment quality: the lower the investment quality, the higher the required return (figure 2).
Actual past and future returns will depend on prevailing pricing. The slope, or actual additional return required for every additional unit of required return, depends on investor preference.
Defining performance generation
The framework put forward articulates how the financial and physical characteristics of assets affect return generation – building on previous IPF reports into risk, especially ‘What is Fair Value’1 , ‘Risk Web 2.0’2, and also the more recent work of Jackson & Orr3.
Returns are driven by growth minus depreciation and vary due to the influence of economic variables on individual markets. The actual cash flow is determined by the leasing process, which determines the rent that can be received, costs and vacancies. Future lease terms are themselves determined by property quality.
How attractive the asset cash flow is to an investor depends on current pricing relative to the risk-free rate, the volatility of the cash flow, the liquidity of the investment and the confidence the investor has in the transparency of the market.
The performance of individual assets within the performance generation framework outlined is determined by the intertwining influences of income security and property quality, which introduces a variation in returns between otherwise similar assets (figure 3).
This explains why the performance of high-quality real estate investments can often be closer to that of other high-quality investments of different property types, than to poor-quality investments of the same type (a second ‘spatial level’4).
It also explains why segments defined by property type and region account for only a small portion of the variation of asset returns5, while simultaneously also reinforcing the importance of a type-region typology as the underlying driver of market returns.
Results and implications
Evidence is found of systematic variations in rental growth between high and low-quality properties, suggesting that asset allocation and forecasting should differentiate by asset quality (rental growth for high-quality City offices, for example). Other return factors also show variation by property quality, specifically new lease terms, costs and vacancies.
Property quality also appears to be a determinant of liquidity, and valuation cap rates are found to vary by both property quality and income security
Existing MSCI/IPD analysis using equivalent yields as a proxy for investment quality suggests that investment quality is indeed a systematic driver of performance. Low-yield assets (a proxy for high investment quality) are found to have led the recent upswing in market returns, and high-yield assets (a proxy for low investment quality) to have generated the highest returns in the bull market between 2000-07 (figures 4-6).
Changes in cap rates are found to have been the principal driver of this pattern of performance, with investors rushing from one side of the ‘risk boat’ to the other in search primarily of growth and then safety.
These findings have implications for portfolio construction and risk management; a portfolio allocation to a range of assets of differing types, in different regions, of varying quality and with a range of lease terms, will generate a return close to that of the market average. A specialist portfolio, with a concentration of assets with similar characteristics, such as poor-quality assets with short leases or high-quality assets with long, unexpired leases, will generate a return that tracks the average performance of other assets with those particular characteristics (results reported by Paul Mitchell in the IPF Report into Individual Property Risk)5.
This has repercussions for the measurement of portfolio risk and the information investors require on funds. Total portfolio risk will include both systematic risks and also any undiversified idiosyncratic risk. The residual risk will be lower, the greater the number of assets in the portfolio, but any concentration risks in the portfolio, such as clusters of particular lease expiry dates or of assets occupied by one particular tenant, will increase the total risk of the portfolio. Those concentration risks associated with income security will emerge and dissipate over time as leases move towards expiry or as new units are let. Constant monitoring and reporting of such concentration risks is therefore required.
Stock selection maintains its position of prominence in the outlined model of return-generation. But the contribution of stock selection to portfolio performance, or alpha, is more defined – stock selection skill is the identification of assets that under-price the prospects for:
• Locational obsolescence: perhaps dominant in their catchment area, or even have the potential to improve;
• Functional obsolescence: perhaps they are better specified than other assets, more sustainable or more flexible than other assets, and thus allow refurbishment to prolong their economic life.
Asset management skill will be required to unlock higher performance. If adopted, the measures of quality as defined can be used to rigorously examine the relationship between asset quality characteristics and returns. This will reveal to investors the full spectrum of strategic opportunities.
The quality definitions can also be used to link formerly disparate and fragmented data sources, providing the inputs required to power up forward-looking approaches to asset and portfolio risk.
The key is an accepted industry-wide definition of quality and the overlay of a performance-generation framework that recognises the influence of changes to these quality factors on asset returns.
1 Burston, B. and Burrell, A. (2015). What is Fair Value? IPF
2 Blundell, G., Frodsham, M. and Martinez Diaz, R. (2011). Risk Web 2.0, An Investigation into the Causes of Portfolio Risk, IPF
3 Jackson, C. and Orr, A. (2011). Real estate stock selection and attribute preferences, Journal of Property Research, 28:4, 317-339
4 Crosby, N., Jackson, C. and Orr, A. (2014) Extending the real estate pricing model, RICS.
5 Mitchell, P. (2015). Individual Property Risk, IPF
Malcolm Frodsham is director of Real Estate Strategies
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