Quantifying Critical Success Factors (CSFs) in Management of Investment-Construction Projects: Insights from Bayesian Model Averaging

Correlation of Construction Performance Indicators and Project Success in a Portfolio of Building Projects

Construction management is a highly competitive project-based field of complex specialized services, creating or altering the built environment for a client. For construction projects to be successful, and in turn, for construction firms to be successful, understanding the relationship of performance statistics as indicators of project outcomes, such as cost, time, and profitability, is essential. There have been a number of efforts made to identify key performance indicators related to construction project success. However, due to lack of available data, many questions remain. There lies an opportunity to analyze project statistics as indicators of project success, similar to the way analytics have been used to predict success in sports. Construction firm project data for a portfolio of building projects were analyzed, and this study identifies correlated factors for completed building construction projects. A highlight of this correlation analysis identified profit differential as demonstrating a strong relationship with the number of requests for information and architects supplemental instructions on a project.

Private Renting vs. Mortgage Home Buying: Case of British Housing Market—A Bayesian Network and Directed Acyclic Graphs Approach

The worsening of housing problems in many countries has become a topic of global interest. Researchers point to a variety of factors that influence individual housing tenure decisions. Our study is based on longitudinal English Housing Survey (EHS) data (2008–2009 to 2019–2020, with survey years matching financial years, i.e., running April-March) and identifies flows between different forms of housing tenure in the U.K. and analyses conditional dependencies of a range of EHS variables using a directed acyclic graph (DAG). More specifically, we take into account variables such as first-time buyers (FTB), mortgage payments, rent payments, share of mortgage/rent in household income, and receipt of housing benefit (HB), with some variables also reflecting a regional breakdown (captured separately for London and England excluding London) to illustrate the complex nature of regional differences in explaining changes in housing tenure. We address some of the problems and challenges of the housing market in the U.K. today, and, in particular, examine what influences private renters and those buying with a mortgage. A key conclusion from this study is that housing benefit does not necessarily ease the way for private renters into their own housing. The study is quantitative in nature and uses the English Housing Survey and Bayesian network (BN) analysis. Unlike traditional methods, such as multiple regression or panel regression, where the researcher somehow suggests the type of a relationship between certain variables, BN’s learning algorithm analyses different iterations between variables and finds the most appropriate relationships between them.

Project Risk in the Context of Construction Schedules—Combined Monte Carlo Simulation and Time at Risk (TaR) Approach: Insights from the Fort Bema Housing Estate Complex

In this article, we present our own construction process model consisting of 16 stages and eight phases, which is particularly applicable to large investment projects. In the context of each project phase, we examine how the appropriate way of scheduling construction processes affects the problem of the risk of prolonging individual phases and the whole project, as well as of not meeting deadlines (which is one of the main problems faced by management practitioners in the construction industry). There are many methods for assessing risk in this context, but they tend to be overly complex and rarely used by construction practitioners. On the other hand, the risks associated with potential schedule delays can be considered holistically. One tool that can serve this purpose is the combined Monte Carlo simulation and Time-at-Risk (TaR) approach, which originates from the world of finance. We show how the implementation of the process model (individual phases) and the whole project can be considered in the context of the covariance matrix between all its phases and how changes in the arrangement of these phases can affect the risk of time extension of the whole project. Our study is based on simulation data for a large development project (Fort Bema/Parkowo-Leśne housing estate complex) in Bemowo, a district of Warsaw, carried out between 1999 and 2012. The entire investment project involved the construction of almost 120,000 m2 of floor space.