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

International evidence on the predictability of returns to securitized real estate assets: econometric models versus neural networks

The performance of various statistical models and commonly used financial indicators for forecasting securitised real estate returns are examined for five European countries: the UK, Belgium, the Netherlands, France and Italy. Within a VAR framework, it is demonstrated that the gilt-equity yield ratio is in most cases a better predictor of securitized returns than the term structure or the dividend yield. In particular, investors should consider in their real estate return models the predictability of the gilt-equity yield ratio in Belgium, the Netherlands and France, and the term structure of interest rates in France. Predictions obtained from the VAR and univariate time-series models are compared with the predictions of an artificial neural network model. It is found that, whilst no single model is universally superior across all series, accuracy measures and horizons considered, the neural network model is generally able to offer the most accurate predictions for 1-month horizons. For quarterly and half-yearly forecasts, the random walk with a drift is the most successful for the UK, Belgian and Dutch returns and the neural network for French and Italian returns. Although this study underscores market context and forecast horizon as parameters relevant to the choice of the forecast model, it strongly indicates that analysts should exploit the potential of neural networks and assess more fully their forecast performance against more traditional models

How does liquidity in the financial market affect the real estate market yields?

Purpose After the Global Financial Crisis in 2008, the impact of expanded liquidity in the financial market has drawn attention. The purpose of this paper is to examine the relationship between liquidity in financial markets and office markets across Asian countries. In particular, the research not only examines the effect of normal liquidity on real estate markets, but also the effects of excess liquidity are specifically highlighted. Design/methodology/approach This paper uses panel estimation utilizing quarterly data from the first quarter of 2007 to the fourth quarter of 2015. Taking both time and location dimensions into account allows for a more precise estimate of the relationship between liquidity and office market’s yields. Findings Per the empirical outcome, an increasing excess liquidity tends to decelerate the value of office yields in six major Asian office market centers due to the positive effect on commercial real estate value. This effect is also identified by comparing the difference between the level of fitted yields and actual yields. Practical implications The results enhance the understanding of commercial real estate yield determinants. Furthermore, the results can be used to assess the impacts of liquidity on major office markets in Asia. Originality/value This paper attempts to uncover the impact of liquidity in financial markets on the office market yields. To better understand the relationship, the concept of excess liquidity is adopted and further exploration of each office market is conducted by comparing the fitted yields, which is computed considering the effects of excess liquidity on yield levels and actual yields

Loan Characteristics as Predictors of Default in Commercial Mortgage Portfolios

This paper examines the role of loan characteristics in mortgage default probability for different mortgage lenders in the UK. The accuracy of default prediction is tested with two statistical methods, a probit model and linear discriminant analysis, using a unique dataset of defaulted commercial loan portfolios provided by sixty-six financial institutions. Both models establish that the attributes of the underlying real estate asset and the lender are significant factors in determining default probability for commercial mortgages. In addition to traditional risk factors such as loan-to-value and debt servicing coverage ratio lenders and regulators should consider loan characteristics to assess more accurately probabilities of default

Modelling credit spreads on commercial mortgage loans

Purpose This paper offers empirical evidence on factors influencing credit spreads on commercial mortgage loans. It extends existing work on the pricing of commercial mortgage loans. The authors examine the relative significance of a range of factors on loan pricing that are lender, asset and loan specific. The research explores and quantifies the sources of spread differentials among commercial mortgage loans. The paper contributes to a limited literature on the subject and serves the purpose of price discovery in commercial property lending. It offers a framework to compare actual pricing with fundamental-based estimates of loan spreads. Design/methodology/approach Panel analysis is deployed to examine the cross-section and time-series determinants of commercial mortgage loan margins and credit spreads. Using an exclusive database of loan portfolios in the United Kingdom (UK), the panel analysis enables the authors to analyse and quantify the impact of a number of theory-consistent and plausible factors determining the cost of lending to commercial real estate (CRE), including type and origin of lender, loan size, loan to value (LTV) and characteristics of asset financed – type, location and grade. Findings Spreads on commercial mortgages and, therefore, loan pricing differ by the type of lender – bank, insurance company and debt fund. The property sector is another significant risk factor lenders price in. The LTV ratio has increased in importance since 2012. Prior to global financial crisis (GFC), lenders made little distinction in pricing different LTVs. Loans secured in secondary assets command a higher premium of 50–60bps. The analysis establishes an average premium of 35bps for loans advanced in regions compared to London. London is particularly seen a less risky region for loan advancements in the post-GFC era. Research limitations/implications The study considers the role of lender characteristics and the changing regulation in the pricing of commercial mortgage loans and provides a framework to study spreads or pricing in this market that can include additional fundamental influences, such as terms of individual loans. The ultimate aim of such research is to assess whether mortgage loans are correctly priced and spotting risks emanating from actual loan spreads being lower than fundamental-based spreads pointing to tight pricing and over-lending. Practical implications The analysis provides evidence on lender criteria that determine the cost of loans. The study confirms that differences in regulation affect loan pricing. The regulatory impact is most visible in the increased significance of LTV. In that sense, regulation has been effective in restricting lending at high LTV levels. Originality/value The paper exploits a database of a commercial mortgage loan portfolio to make loan pricing more transparent to the different types of lender and borrowers. Lenders can use the estimates to assess whether commercial loans are fairly priced. Borrowers better understand the relative significance of risk factors affecting margins and the price they are charged. The results of this paper are of value to regulators as they can assist to understand the determinants of loan margins and gauge conditions in the lending market

UK Housing Market: Time Series Processes with Independent and Identically Distributed Residuals

The paper examines whether a univariate data generating process can be identified which explains the data by having residuals that are independent and identically distributed, as verified by the BDS test. The stationary first differenced natural log quarterly house price index is regressed, initially with a constant variance and then with a conditional variance. The only regression function that produces independent and identically distributed standardised residuals is a mean process based on a pure random walk format with Exponential GARCH in mean for the conditional variance. There is an indication of an asymmetric volatility feedback effect but higher frequency data is required to confirm this. There could be scope for forecasting the index but this is tempered by the reduction in the power of the BDS test if there is a non-linear conditional variance process

A theoretical analysis and empirical investigation of industrial property development in the UK

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