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Recent research has found significant relationships between internet search volume and real estate markets. This paper aims to examine whether Google search volume data can serve as a leading sentiment indicator and are able to predict turning points in the US housing market. One of the main objectives is to find a model based on internet search interest that generates reliable real-time forecasts.

Starting from seven individual real-estate-related Google search volume indices, a multivariate probit model is derived by following a selection procedure. The best model is then tested for its in- and out-of-sample forecasting ability.

The results show that the model predicts the direction of monthly price changes correctly, with over 89 per cent in-sample and just above 88 per cent in one to four-month out-of-sample forecasts. The out-of-sample tests demonstrate that although the Google model is not always accurate in terms of timing, the signals are always correct when it comes to foreseeing an upcoming turning point. Thus, as signals are generated up to six months early, it functions as a satisfactory and timely indicator of future house price changes.

The results suggest that Google data can serve as an early market indicator and that the application of this data set in binary forecasting models can produce useful predictions of changes in upward and downward movements of US house prices, as measured by the Case–Shiller 20-City House Price Index. This implies that real estate forecasters, economists and policymakers should consider incorporating this free and very current data set into their market forecasts or when performing plausibility checks for future investment decisions.

This is the first paper to apply Google search query data as a sentiment indicator in binary forecasting models to predict turning points in the housing market.

This paper aims to introduce to marketing a discourse analytical framework on which future qualitative marketing research can draw.

The methodology is to utilize Michel Foucault's works and the discourse theory of Ernesto Laclau and Chantal Mouffe.

A discourse analytical framework for qualitative marketing research consisting of six central concepts – turning points, problematizations, articulations, nodal points, hegemony and deconstruction – is outlined.

The discourse analytical framework outlined can be used in future qualitative marketing research. It is mainly of value to marketing researchers.

The International Air Transport Association (IATA) New Distribution Capability (NDC) standard aims at modernising the airline distribution landscape. It has supported the spread of Direct Connects by providing a common standard for linking airlines to travel agencies. The purpose of this paper is to analyse the historical development of airline distribution and to derive implications for the future.

This paper follows the approach of Yeoman and McMahon-Beattie (2017) in providing a chronological account based on published research.

Direct Connects are discussed to be a step back in the evolution of the distribution landscape because they foster disaggregation. An analysis of the history of distribution finds that a comparison of Direct Connects to the early stages of computer reservation system technology falls short to recognise the tremendous technological and market changes connected to the internet, cloud computing and the rise of low-cost carriers. Moreover, drawing on the seminal article by Anderson and Tushman (1990) on technical discontinuities and dominant designs, the current state of the distribution landscape is characterised to be an era of ferment that is driven by design competition and that might end up in a new dominant design.

The originality of this paper lies in a critical review of the turning points of distribution. By reviewing the past developments, the paper sheds light on the contribution that IATA NDC and Direct Connect technology might deliver to the field of airline distribution.

This paper aims to study how the trajectory of fundamental values affects price discovery in an experimental asset market.

An experiment is conducted with two treatments, in which the time path of fundamentals differs between treatments. In the peak treatment, fundamentals first rise and then fall, while in the valley treatment fundamentals first fall and then recover. The experiment allows market prices to be compared to fundamental values.

Both peak and valley treatments experience bubbles when traders are inexperienced. However, price discovery is more rapid and complete in the peak than in the valley treatment. In the peak treatment, prices track the value, the direction of the trend, and changes in trend, more closely than in the valley treatment.

This paper documents the first experimental results regarding pricing behavior in markets with non‐monotonic fundamentals. It creates an environment (the valley treatment) in which convergence to close to fundamentals does not occur even with repetition of the market under identical conditions. The results demonstrate that the likelihood that an asset market tracks fundamentals depends on the time path of fundamentals.

The purpose of this paper is to study synchronization in stock index cycles across 82 countries and the linkage between macroeconomic and financial integration and stock market synchronization.

The author document the synchronization structure of the world equity index cycles and its evolution over time. The author examine the explanatory power of various economic and financial variables on cycle comovements.

Trade openness, capital openness, and an EU membership contribute to higher stock index cycle synchronization. Additionally, the macroeconomic and financial variables have asymmetric impacts on countries of different development levels.

The author is the first to thoroughly chronicle the turning points, i.e., bear and bull regimes, of world equity indexes and empirically examine determinants of their cyclical comovement across nations.

This study investigates whether cyclical turning points in the U.S. and U.K. stock markets are unevenly distributed over the year, that is, whether they are more likely to occur during certain months of the year. In examining this form of periodic seasonality, a Markov switching‐model is applied to U.S. and U.K. stock market chronologies of monthly peak and trough dates for the periods May 1835 through March 2000 and May 1836 through September 2000, respectively. In order to provide some evidence on robustness with respect to the sample data, results are obtained for the entire sample periods as well as for various sub‐. For both markets, the evidence indicates that while the probability of moving from an expansion to a contraction does not depend on the month of the year, the probability of switching from a contraction is greater for some months. Additionally, the durations of contractions, but not expansions, are dependent on the month of the year in which they begin.

The purpose of this study is to provide a chaos theory-based framework, which can be used to model commercial property market dynamics.

The paper is presented in two parts. In the first, rigorous mathematical reasoning is entertained, so to derive an attractor describing a set of feedback formulae. In the second part, the attractor definition is used to model the Auckland commercial office market. The model is exposed through a set of seven scenarios allowing for analysis of the market behaviour under various exogenously imposed conditions.

The general behaviour of the model is in agreement with the commercial property market conduct observed in Auckland. The model provides information related to the market turning points and allows for an explanation of some intricate market dynamics. These include the anatomy of a market peak and its response to the liquidity oversupply.

The model may be used to expand our understanding of the market performance under various exogenically imposed conditions, which allows for planning of market interventions in a more refined manner.

The paper is original, in the way the chaos theory is applied to the property markets modelling and allows for expanding the understanding of the market behaviour.

This study examines the durations of US stock market cycle expansions and contractions for the presence of seasonality. Specifically, it is determined whether the distributional characteristics (i.e., location and dispersion) of the durations of market expansions and contractions are dependent on the time of the year the market phase begins or ends. The duration data are obtained from a stock market chronology of monthly peak and trough dates for the period May 1835 through July 1998 and nonparametric rank‐based tests are used to test for the presence of seasonality. In order to provide some evidence on robustness with respect to the sample data, results are obtained for the entire sample period as well as for various sub‐periods. When the data are aggregated on a quarterly basis, the evidence suggests that seasonal structures are present in stock market cycle durations. These seasonals are related primarily to shifts in location over the course of the year and to when a market expansion or contraction begins. However, when the duration data are aggregated on a bi‐annual basis, support for seasonality is much more limited.

Residential property price indices (RPPI) are widely used to monitor trends. This article aims to pose the question as to whether the choice of methodology is significant or whether there are material differences in average prices, price trends, and market turning points between RPPI.

The differences between RPPI are explored for the UK, which has RPPI that use different methodologies and capture data at different stages in the transaction process. The different approaches are compared and contrasted.

Comparisons between RPPI indicate that there is a relatively low degree of association between them, particularly during periods of recession. There is a trade off between the timeliness with which the RPPI is produced and the reliability of the price data on which it is based. The RPPI that make use of the most reliable price data do not have access to the richest data about individual properties, exposing them to the risk of divergent groups of properties being aggregated together.

There are certain types of RPPI that are not found in the UK, principally median indices and sale price appraisal ratios so that the study is confined to mix adjustment, hedonic and repeat sales approaches.

Those relying on RPPI should exercise caution over their use and recognise that in periods of recession in particular their results may differ materially.

Economic policy makers and financial services regulators should adopt a cautious approach to the use of RPPI and not to become unduly reliant on a particular index.

The literature on RPPI tends to focus on the statistical techniques used to compile them, but neglects how these are related to the problems of data capture issue. By looking at the relationship between the statistical methodology and where in the transactions process data are captured it is able to bring another perspective to the debate about which methods are most effective and which should be relied on for public policy.

Technical analysis lies on the premiss that short‐term market price at any time is revealed by pattern of prior price movements. Tests empirically the pattern of the real estate prices by employing the ARIMA analysis. Results strongly show that there exist cyclical trends in the office and industrial property prices in Hong Kong. The forecasting method can provide an indication of short‐term market direction, a sense of whether or not the movement will be small or large, and advance warning well ahead of any turning points supplementary to investment strategy. The investor may wish to incorporate forecasts from an ARIMA model into his investment strategy, for timing purposes.