1. Introduction Real estate yields are important concepts in real estate investment. They indicate the rate of return from investing in real estate. Yield movements signal investorsÌ attitudes towards property and provide timely information on investment demand for property. Yields are also fundamental to the valuation of real estate and hence to the determination of capital values and total returns. Short- and long-term projections of capital values and returns require an estimate of the future value of yields. Given that a larger number of funds are now valued more frequently (monthly and quarterly) yield variation is inevitably under constant scrutiny. Understanding the movements of yields is a complex exercise. This is because changes in yields are driven by several factors. Yields adjust to changing conditions in the occupier market and the risks posed to investors in terms of rent voids, adverse rent growth and other. Yields are also linked to the wider capital markets and therefore the variation in government bond yields and equity yields is expected to influence real estate yields. The changing perceptions of investors of the relative risks from investing in property (changing risk premia) will also impact on real estate yields. Empirical work on yields is based on models, which factor in occupier market conditions and influences from the wider investment environment. Yields encompass an expectation element about the occupier market and risk premia. It is therefore conceivable to employ sentiment indicators to capture more directly such expectations and assess the impact on yields. Mainly these indicators are survey data that are considered to convey useful information complementing official data and contributing to a better assessment of the ÎtrueÌ state of the economy or the industry. They are well publicised and monitored by the wider investment market to gauge trends in sectors and the overall economy (for example the CBI and CIPS surveys in the UK, the Ifo surveys in Germany and INSEE surveys in France). One of the reasons for looking at the readings of these indicators is to discount future movements in the property market through the appropriate adjustment in yields and receive signals about the likely direction of the next movement in yields. The present investigation focuses on the responsiveness of retail yields to survey series, which capture sentiment in retail business. More specifically retail yield outcomes are related to four survey based series: consumer confidence, confidence in the retail sector, the expectations of retail sales and the expectations of distributive trades. These series are expected to reflect the influences from the risk free asset market and provide timely information on the state of retailing. This study produces empirical evidence on the success of the indicator variables to predict the direction in retail yield changes. A parallel to this is the explanation put forward for the low correlation of retail sales and retail equity prices. It is argued that when retail sales volumes fall share prices in the retail sector are not going to fall. This is because valuations in the retail sector are already marked down in anticipation of a slowdown so that when the official statistics are released the bad news has already been discounted into share prices. In a similar way yields may shift to discount information about the business of retailing and the retail property market on the basis of sentiment indicators. Studying the predictive power of sentiment indicators for retail yields should not been seen an exercise that supersedes any structural modelling of yields. But providing empirical evidence on the usefulness of these indicators supplements the forecasts produced by other modelling approaches. Empirical evidence on the relationship between yields and expectations or sentiment indicators will inform investors as to the weight they should attach to their signals for the direction in retail yields. This information becomes also valuable given the production lags associated with official real economy data and the updates of economic forecasts. Moreover, there is a stronger market need for more frequent assessments of yield trends especially in periods of uncertainty. The remainder of the paper is structured in four sections. Section two presents findings on the determination of yields from a range of studies. The literature review is followed by a discussion of the methodology that is deployed in this study in section three. Section four presents the empirical estimates and the output of the forecast exercise. Finally section five discusses the implications of this study.