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This paper aims to investigate whether developers’ ask lower prices on homes in redevelopment sites than they do on similar units in smaller developments completed over a shorter time span. It also investigates whether developers price units differently at different stages of the redevelopment process. The development of designated redevelopment areas often consists of multiple projects spread across several years, some in parallel, some sequential. New units are put on the market in a piecemeal fashion, and infrastructure, shared green spaces and shared facilities are installed successively.

A hedonic price model is used to analyse sales prices of 7,000 new apartments in Oslo sold between 2011 and 2015, all else being equal. The paper distinguishes between infill as one-stage projects, and multi-staged competitive and multi-staged monopolistic redevelopments.

Dwellings in redevelopment projects sell at a lower price than similar dwellings in infill projects. In competitive redevelopments, those in charge of the last projects put a slightly higher price on apartments. In redevelopments involving only one developer, the last stages ask the lowest prices.

This research expands our understanding of developers’ pricing behaviour. Developers supplying housing for the private market through redevelopments land are willing to take risks particularly in the initial stage.

The findings indicate that credit institutions financing developers’ projects need to take into account the structure of selling prices, including lower prices and higher risk of pursuing redevelopment projects.

Gaining a better understanding of developers’ pricing behaviour deepens our insights into the dynamics of market-led urban brownfield developments; this knowledge may moreover inform policies on sustainable urban growth.

An original investigation of housing transactions in urban brownfield sites in Oslo provides fresh insights into developers’ pricing behaviour.

This paper aims to forecast inbound and outbound container throughput for Bangkok Port to 2041 and uses the results to inform the future planning and management of the port’s container terminal.

The data used cover a period of 16 years (192 months of observations). Data sources include the Bank of Thailand and the Energy Policy and Planning Office. Cause-and-effect forecasting is adopted for predicting future container throughput by using a vector error correction model (VECM).

Forecasting future container throughput in Bangkok Port will benefit port planning. Various economic factors affect the volume of both inbound and outbound containers through the port. Three cases (scenarios) of container terminal expansion are analyzed and assessed, on the basis of which an optimal scenario is identified.

The economic characteristics of Thailand differ from those of other countries/jurisdictions, such as the USA, the EU, Japan, China, Malaysia and Indonesia, and optimal terminal expansion scenarios may therefore differ from that identified in this study. In addition, six particular countries/jurisdictions are the dominant trading partners of Thailand, but these main trading partners may change in the future.

There are only two major projects that have forecast container throughput volumes for Bangkok Port. The first project, by the Japan International Cooperation Agency, applied both the trend of cargo volumes and the relationship of volumes with economic indices such as population and gross domestic product. The second project, by the Port Authority of Thailand, applied a moving average method to forecast the number of containers. Other authors have used time-series forecasting. Here, the authors apply a VECM to forecast the future container throughput of Bangkok Port.

Port throughput analysis is a challenging task, as it consists of intertwined interactions between a variety of cargos and numerous influencing factors. This study aims to propose a quantitative method to facilitate port throughput analysis by identification of important cargos and key macroeconomic variables.

Mutual information is applied to measure the linear and nonlinear correlation among variables. The method gives a unique measure of dependence between two variables by quantifying the amount of information held in one variable through another variable.

This study uses the mutual information to the Port of Isafjordur in Iceland to underpin the port throughput analysis. The results show that marine products are the main export cargo, whereas most imports are fuel oil, industrial materials and marine product. The aggregation of these cargos, handled in the port, meaningfully determines the non-containerized port throughput. The relation between non-containerized export and the national gross domestic product (GDP) is relatively high. However, non-containerized import is mostly related to the world GDP. The non-containerized throughput shows a strong relation to the national GDP. Furthermore, the results reveal that the volume of national export trade is the key influencing macroeconomic variable to the containerized throughput.

Application of the mutual information in port throughput analysis effectively reduces epistemic uncertainty in the identification of important cargos and key influencing macroeconomic variables. Thus, it increases the reliability of the port throughput forecast.