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This study aimed to identify the link between the income levels of government workers and the prices of real estate houses in Ghana to identify the prevailing mortgage gaps and to stimulate both reactive and proactive government policies backed by continuous stakeholder engagements under the new normal.

The quantitative approach was used for this study. Two data collection methods were used to achieve the objectives of the study: the survey method, using a questionnaire to collect the primary data, and the use of documentary information as the source of secondary data. For the primary data, prices of two-bedroom and three-bedroom houses were collected. The secondary data collected were: (1) salary levels of government employees and (2) mortgage values prevailing. The three data sets were analysed and structured to identify the relationship between income levels and the prices of real estate houses within the prevailing mortgage system.

It will require a quadrupling of the salaries of only the highest income earners of government employees to afford the average price of a basic two-bedroom and three-bedroom housing in Ghana. Largely, government employees cannot afford these houses with the current price levels and the mortgage systems available. The real estate market in Ghana has not focused on lower-earning groups. The effects of the new normal resulting from the effects of Covid-19 require a paradigm change.

The paper established the relationship between salary levels of government employees and the process of basic accommodation types on offer in the Ghanaian market by the real estate industry: two- and three-bedroom houses. The findings will help real estate developers to consider their approach to housing designs and construction methods and the pricing to ensure that they meet the needs of the public sector workers who could form a large customer base.

This paper aims to focus on developing a baseline model for time overrun.

Information on 321 completed construction projects used to assess the predictive performance of two statistical techniques, namely, multiple regression and the Bayesian approach.

The eventual results from the Bayesian Markov chain Monte Carlo model were observed to improve the predictive ability of the model compared with multiple linear regression. Besides the unique nuances peculiar with projects executed, the scope factors initial duration, gross floor area and number of storeys have been observed to be stable predictors of time overrun.

This current model contributes to improving the reliability of predicting time overruns.

This study aims to develop a building renovation duration prediction model incorporating both scope and non-scope factors.

The study used a questionnaire to obtain basic information relating to identified project scope factors as well as information relating to the impact of the non-scope factors on the duration of building renovation projects. The study retrieved 121 completed questionnaires from construction firms on tertiary education trust fund (TETFund) building renovation projects. Artificial neural network was then used to develop the model using 90% of the data, while mean absolute percentage error was used to validate the model using the remaining 10% of the data.

Two artificial neural network models were developed – a multilayer perceptron (MLP) and a radial basis function (RBF) model. The accuracy of the models was 86% and 80%, respectively. The developed models’ predictions were not statistically different from those of actual duration estimates with less than 20% error margin. Also, the study found that MLP models are more accurate than RBF models.

The developed models are only applicable to projects that suit the characteristics and nature of the data used to develop the models. Hence, models can only predict the duration of building renovation projects.

The developed models are expected to serve as a tool for realistic estimation of the duration of building renovation projects and thus, help construction project managers to effectively plan and manage it.

The developed models are expected to serve as a tool for realistic estimation of the duration of building renovation projects and thus, help construction project managers to effectively plan and manage it; it also helps clients to effectively benchmark projects duration and contractors to accurately estimate duration at tendering stage.

The study presents models that combine both scope and non-scope factors in predicting the duration of building renovation projects so as to ensure more realistic predictions.

Service quality is a major determinant of business performance. Empirical evidence from the literature indicates that, to attain a high level of customer satisfaction, a high standard of service quality should be provided by the service provider. This study aims to examine the relationship between the perceived service quality and the indicators of client satisfaction with particular reference to engineering consultancy services in building projects.

A survey research approach was adopted using a semi-structured questionnaire as an instrument of data collection. The questionnaire survey formed the basis for the descriptive and inferential (Pearson correlation and multiple regression) statistics that were used to evaluate the relationship between engineering consultants’ service quality and clients’ satisfaction indicators.

The study identified 10 key technical indicators and 10 key managerial indicators for measuring client satisfaction. Statistical analysis shows a positive significant relationship between the perceived service quality and all the indicators of client satisfaction. The positive correlation values show that as perceived service quality increases, both technical and management indicators of client satisfaction equally increase.

The results offer opportunity for professional service providers to continuously develop the technical and management indicators, embrace personnel training and key into continuous professional development for better service quality.