Rationality and momentum in real estate investment forecasts

This study examines the rationality and momentum in forecasts for rental, capital value and total returns for the real estate investment market in the United Kingdom. In order to investigate if forecasters are affected by the general economic conditions present at the time of forecast we incorporate into the analysis Gross Domestic Product(GDP) and the Default Spread (DS). The empirical findings show high levels of momentum in the forecasts, with highly persistent forecast errors. The results also indicate that forecasters are affected by adverse conditions. This is consistent with the finding that they tend to exhibit greater forecast error when the property market is underperforming and vice-versa.

The impact of cross-border real estate investment on commercial real estate capitalisation rates

This study examines the impact of foreign real estate investment on the US office market capitalization rates. The geographic unit of analysis is MSA and the time period is 2001-2013. Drawing upon a database of commercial real estate transactions provided by Real Capital Analytics, we model the determinants of market capitalization rates with a particular focus on the significance of the proportion of market transactions involving foreign investors. We have employed several econometric techniques to explore the data, potential estimation biases, and test robustness of the results. The results suggest statistically significant effects of foreign investment across 38 US metro areas. It is estimated that, all else equal, a 100 basis points increase in foreign share of total investment in a US metropolitan office market causes about an 8 basis points decrease in the market cap rate.

Assessing the accuracy and dispersion of real estate investment forecasts

Existing empirical evidence has frequently observed that professional forecasters are conservative and display herding behaviour. Whilst a large number of papers have considered equities as well as macroeconomic series, few have considered the accuracy of forecasts in alternative asset classes such as real estate. We consider the accuracy of forecasts for the UK commercial real estate market over the period 1999-2011. The results illustrate that forecasters display a tendency to under-estimate growth rates during strong market conditions and over-estimate when the market is performing poorly. This conservatism not only results in smoothed estimates but also implies that forecasters display herding behaviour. There is also a marked difference in the relative accuracy of capital and total returns versus rental figures. Whilst rental growth forecasts are relatively accurate, considerable inaccuracy is observed with respect to capital value and total returns.

Gauging the investment potential of international real estate markets

Investing in real estate markets overseas means venturing into the unknown, where you meet unfamiliar political and economic environments, unstable currencies, strange cultures and languages, and so although the advantages of international diversification might appear attractive, the risks of international investment must not be overlooked. However, capital markets are becoming global markets, and commercial real estate markets are no exception, accordingly despite the difficulties posed by venturing overseas no investor can overlook the potential international investment holds out. Thus, what strategies are appropriate for capitalising on this potential? Three issues must be considered: (1) the potential of the countries real estate market in general; (2) the potential of the individual market sectors; and (3) the investment process itself. Although each step in foreign real estate investment is critical, the initial assessment of opportunities is especially important. Various methods can be used to achieve this but a formal and systematic analysis of aggregate market potential should prove particularly fruitful. The work reported here, therefore, develops and illustrates such a methodology for the over 50 international real estate markets.

Does foreign investment affect US office real estate prices?

his article examines the impact of foreign real estate investment on U.S. office market capitalization rates. The geographic unit of analysis is MSA and the time period is 2001–2013. Drawing upon a database of commercial real estate transactions, the authors model the determinants of market capitalization rates with a particular focus on the significance of the proportion of market transactions involving foreign investors. Employing several econometric techniques to analyze the data, the results suggest statistically significant effects of foreign investment across 38 U.S. metro areas. It is estimated that, all else equal, a 100 basis point increase in foreign share of total investment in a U.S. metropolitan office market causes about an 8 basis point decrease in the market cap rate.

Depreciation and its impact on the total return of UK commercial real estate, 1994-2003

Depreciation is a key element of understanding the returns from and price of commercial real estate. Understanding its impact is important for asset allocation models and asset management decisions. It is a key input into well-constructed pricing models and its impact on indices of commercial real estate prices needs to be recognised. There have been a number of previous studies of the impact of depreciation on real estate, particularly in the UK. Law (2004) analysed all of these studies and found that the seemingly consistent results were an illusion as they all used a variety of measurement methods and data. In addition, none of these studies examined impact on total returns; they examined either rental value depreciation alone or rental and capital value depreciation. This study seeks to rectify this omission, adopting the best practice measurement framework set out by Law (2004). Using individual property data from the UK Investment Property Databank for the 10-year period between 1994 and 2003, rental and capital depreciation, capital expenditure rates, and total return series for the data sample and for a benchmark are calculated for 10 market segments. The results are complicated by the period of analysis which started in the aftermath of the major UK real estate recession of the early 1990s, but they give important insights into the impact of depreciation in different segments of the UK real estate investment market.

Non-normal real estate return distributions by property type in the UK

Investment risk models with infinite variance provide a better description of distributions of individual property returns in the IPD UK database over the period 1981 to 2003 than normally distributed risk models. This finding mirrors results in the US and Australia using identical methodology. Real estate investment risk is heteroskedastic, but the characteristic exponent of the investment risk function is constant across time – yet it may vary by property type. Asset diversification is far less effective at reducing the impact of non‐systematic investment risk on real estate portfolios than in the case of assets with normally distributed investment risk. The results, therefore, indicate that multi‐risk factor portfolio allocation models based on measures of investment codependence from finite‐variance statistics are ineffective in the real estate context

Hedging private international real estate

The performance of an international real estate investment can be critically affected by currency fluctuations. While survey work suggests large international investors with multi-asset portfolios tend to hedge their overall currency exposure at portfolio level, smaller and specialist investors are more likely to hedge individual investments and face considerable specific risk. This presents particular problems in direct real estate investment due to the lengthy holding period. Prior research investigating the issue relies on ex post portfolio measure, understating the risk faced. This paper examines individual risk using a forward-looking simulation approach to model uncertain cashflow. The results suggest that a US investor can greatly reduce the downside currency risk inherent in UK real estate by using a swap structure – but at the expense of dampening upside potential.

Analysing UK real estate market forecast disagreement

Given the significance of forecasting in real estate investment decisions, this paper investigates forecast uncertainty and disagreement in real estate market forecasts. Using the Investment Property Forum (IPF) quarterly survey amongst UK independent real estate forecasters, these real estate forecasts are compared with actual real estate performance to assess a number of real estate forecasting issues in the UK over 1999-2004, including real estate forecast error, bias and consensus. The results suggest that real estate forecasts are biased, less volatile compared to market returns and inefficient in that forecast errors tend to persist. The strongest finding is that real estate forecasters display the characteristics associated with a consensus indicating herding.

An evaluation of the performance of UK real estate forecasters

Given the significance of forecasting in real estate investment decisions, this paper investigates forecast uncertainty and disagreement in real estate market forecasts. It compares the performance of real estate forecasters with non-real estate forecasters. Using the Investment Property Forum (IPF) quarterly survey amongst UK independent real estate forecasters and a similar survey of macro-economic and capital market forecasters, these forecasts are compared with actual performance to assess a number of forecasting issues in the UK over 1999-2004, including forecast error, bias and consensus. The results suggest that both groups are biased, less volatile compared to market returns and inefficient in that forecast errors tend to persist. The strongest finding is that forecasters display the characteristics associated with a consensus indicating herding.

Non-normal real estate return distributions by property type in the U.K.

Investment risk models with infinite variance provide a better description of distributions of individual property returns in the IPD database over the period 1981 to 2003 than Normally distributed risk models, which mirrors results in the U.S. and Australia using identical methodology. Real estate investment risk is heteroscedastic, but the Characteristic Exponent of the investment risk function is constant across time yet may vary by property type. Asset diversification is far less effective at reducing the impact of non-systematic investment risk on real estate portfolios than in the case of assets with Normally distributed investment risk. Multi-risk factor portfolio allocation models based on measures of investment codependence from finite-variance statistics are ineffectual in the real estate context.

Serial persistence in individual real estate returns in the UK

Persistence of property returns is a topic of perennial interest to fund managers as it suggests that choosing those properties that will perform well in the future is as simple as looking at those that performed well in the past. Consequently, much effort has been expended to determine if such a rule exists in the real estate market. This paper extends earlier studies in US, Australian, and UK markets in two ways. First, this study applies the same methodology originally used in Young and Graff (1996) making the results directly comparable with those in the US and Australian property markets. Second, this study uses a much longer and larger database covering all commercial property data available from the Investment Property Databank (IPD), for the years 1981 to 2002 for as many as 216,758 individual property returns. While the performance results of this study mimic the US and Australian results of greater persistence in the extreme first and fourth quartiles, they also evidence persistence in the moderate second and third quartiles, a notable departure from previous studies. Likewise patterns across property type, location, time, and holding period are remarkably similar leading to the conjecture that behaviors in the practice of commercial real estate investment management are themselves deeply rooted and persistent and perhaps influenced for good or ill by agency effects

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.

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.