Forecasting UK real estate cycle phases with leading indicators: a probit approach

This paper examines the significance of widely used leading indicators of the UK economy for predicting the cyclical pattern of commercial real estate performance. The analysis uses monthly capital value data for UK industrials, offices and retail from the Investment Property Databank (IPD). Prospective economic indicators are drawn from three sources namely, the series used by the US Conference Board to construct their UK leading indicator and the series deployed by two private organisations, Lombard Street Research and NTC Research, to predict UK economic activity. We first identify turning points in the capital value series adopting techniques employed in the classical business cycle literature. We then estimate probit models using the leading economic indicators as independent variables and forecast the probability of different phases of capital values, that is, periods of declining and rising capital values. The forecast performance of the models is tested and found to be satisfactory. The predictability of lasting directional changes in property performance represents a useful tool for real estate investment decision-making.

Real estate in the mixed-asset portfolio: the question of consistency

The recent poor performance of the equity market in the UK has meant that real estate is increasingly been seen as an attractive addition to the mixed-asset portfolio. However, determining whether the good return enjoyed by real estate is a temporary or long-term phenomenon is a question that remains largely unanswered. In other words, there is little or no evidence to indicate whether real estate should play a consistent role in the mixed-asset portfolio over short- and long-term investment horizons. Consistency in this context refers to the ability of an asset to maintain a positive allocation in an efficient portfolio over different holding periods. Such consistency is a desirable trait for any investment, but takes on particular significance when real estate is considered, as the asset class is generally perceived to be a long-term investment due to illiquidity. From an institutional investor’s perspective, it is therefore crucial to determine whether real estate can be reasonably expected to maintain a consistent allocation in the mixed-asset portfolio in both the short and long run and at what percentage. To address the question of consistency the allocation of real estate in the mixed-asset portfolio was calculated over different holding periods varying from 5- to 25-years.

Is ESG commitment linked to investment performance in the real estate sector?

The linkage between corporate commitment to environmental, social and governance (ESG) issues and investment performance has generated a substantial body of research outside the real estate sector. Nevertheless, the relationship between the environmental performance and financial performance of companies is still not well understood as studies have found mixed and contradicting results. Drawing upon the KLD database which contains ratings on seven ESG dimensions from 2003-2009, this paper investigates the relationship between the ESG rating and the financial performance of a sample of US real estate firms. Since the primary transmission channel from ESG activities to financial performance may be better reflected by a firm's intangible assets, we model both Tobin's q and the total annual return in a panel framework with time and sector specific fixed effects. Our results are largely consistent with the existing literature finding a positive relationship between CFP and CSP. Further, the time scale of the lagged effects seems plausible.

Re-thinking commercial real estate market segmentation

Investments in direct real estate are inherently difficult to segment compared to other asset classes due to the complex and heterogeneous nature of the asset. The most common segmentation in real estate investment analysis relies on property sector and geographical region. In this paper, we compare the predictive power of existing industry classifications with a new type of segmentation using cluster analysis on a number of relevant property attributes including the equivalent yield and size of the property as well as information on lease terms, number of tenants and tenant concentration. The new segments are shown to be distinct and relatively stable over time. In a second stage of the analysis, we test whether the newly generated segments are able to better predict the resulting financial performance of the assets than the old dichotomous segments. Applying both discriminant and neural network analysis we find mixed evidence for this hypothesis. Overall, we conclude from our analysis that each of the two approaches to segmenting the market has its strengths and weaknesses so that both might be applied gainfully in real estate investment analysis and fund management.

The impact of minimum services laws on real estate brokerage competitive intensity

As many as fourteen US states have now mandated minimum service requirements for real estate brokerage relationships in residential transactions. This study attempts to determine whether these minimum service laws have any impact on brokerage competition. Federal government agencies allege such laws discourage competition because they limit the offering of nontraditional brokerage services. However, alternatively, a legislative “bright line” definition of the lowest level of acceptable service may reduce any perceived risk in offering non-traditional brokerage services and therefore encourage competition. Using several empirical strategies and state-level data over nine years (2000-08), we do not find any consistent and significant impact (positive/negative) of minimum services laws on number of licensees per 100 households, our proxy for competition. Interestingly, we also find that association strength, as measured by Realtor association membership penetration, has a strong deterring effect on competition.

Do responsible real estate companies outperform their peers?

This paper investigates the relationship between corporate social and environmental performance and financial performance for a sample of publicly traded US real estate companies. Using the MSCI ESG (formerly KLD) database on seven Environmental, Social & Governance dimensions in the 2003-2010 period, and weighting the dimensions according to prominence in the real estate sector, we model Tobin's Q and annual total return in a panel data framework. The results indicate a positive relationship between ESG rating and Tobin's Q but this effect is driven by ESG concerns rather than strengths. Consistently across all model specifications, overall ESG ratings are associated with lower returns. Negative scores appear to result in higher returns in the short run but positive scores have no significant impact on returns.

Analyzing the performance of non-listed real estate funds: an panel data analysis

The rapid growth of non-listed real estate funds over the last several years has contributed towards establishing this sector as a major investment vehicle for gaining exposure to commercial real estate. Academic research has not kept up with this development, however, as there are still only a few published studies on non-listed real estate funds. This paper aims to identify the factors driving the total return over a seven-year period. Influential factors tested in our analysis include the weighted underlying direct property returns in each country and sector as well as fund size, investment style gearing and the distribution yield. Furthermore, we analyze the interaction of non-listed real estate funds with the performance of the overall economy and that of competing asset classes and found that lagged GDP growth and stock market returns as well as contemporaneous government bond rates are significant and positive predictors of annual fund performance.

Sector, region or function? A MAD reassessment of real estate diversification in Great Britain

This paper re-examines whether it is more advantageous in terms of risk reduction to diversify by sector or region by comparing the performance of the ‘conventional’ regional classification of the UK with one based on modern socio-economic criteria using a much larger real estate data set than any previous study and the MAD portfolio approach. The general conclusion of this analysis is that property market sectors still dominate regions, however defined and so should be the first level of analysis when developing a portfolio diversification strategy. This is in line with previous research. When the performance of Functional groups is compared with the ‘conventional’ administrative regions the results here show that, when functionally based, groupings can in some cases provide greater risk reduction. In addition the underlying characteristics of these functional groups may be much more insightful and acceptable to real estate portfolio managers in considering the assets that a portfolio might contain.