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Research Theme: Data Science & Spatial Analysis

Data-driven methodologies like GIS and remote sensing, as well as “big data”, artificial intelligence, and other methods of scholarship rooted in analyzing large datasets

Reflections on hedonic price modeling

Bourassa, S.C., Hoesli, M., Mayer, M. and Stalder, N. (2025), “Reflections on hedonic price modeling”, Journal of European Real Estate Research, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JERER-11-2024-0087

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Abstract

Purpose
This paper provides a critical history of residential hedonic price modeling, highlighting key issues and advances. It is based on the keynote address presented by the first author at the European Real Estate Society Annual Conference in Sopot (Gdańsk), Poland, in June 2024.

Design/methodology/approach
The core of the paper is a high-level review of the methodological literature, focusing on three issues: model specification, multicollinearity and functional form. This review is framed by an early example of hedonic price modeling and a current application. These examples demonstrate key issues and advances in hedonic price modeling.

Findings
Hedonic price research has expanded dramatically with the advent of personal computing. Increased availability of data has enabled better model specification. At the same time, the development of interpretable machine learning techniques has allowed much more flexible modeling of functional form. However, multicollinearity continues to be, by definition, an intractable problem.

Originality/value
This paper presents a review of residential hedonic price modeling intended to provide researchers with a useful high-level perspective on the topic. A case study of Gdańsk illustrates an approach to producing interpretable results from machine learning estimations.

Keywords

Hedonic modeling; house prices; specification issues; multicollinearity; functional form; interpretable machine learning; R31

Professors Sturts Dossick and Wu present at 2024 NWCCC Annual Conference

Professor Carrie Sturts Dossick, Associate Dean for Research, and Assistant Professor Lingzi Wu both from the department of Construction Management, presented at the 2024 Northwest Construction Consumer Council (NWCCC) Conference, “AI and Digital Technology in Construction” and Distinguished Project Awards. Their presentations are linked below. Assistant Professor Wu gave a presentation entitled “AI-Powered Solutions for Next-Generation Construction Management.” Professor Sturts Dossick presented on Cybersecurity Planning.  

Big data analytics in the AEC industry: scientometric review and synthesis of research activities

Ohene, E., Nani, G., Antwi-Afari, M. F., Darko, A., Addai, L. A., & Horvey, E. (2024). Big data analytics in the AEC industry: scientometric review and synthesis of research activities. Engineering, Construction, and Architectural Management. https://doi.org/10.1108/ECAM-01-2024-0144

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Abstract

Unlocking the potential of Big Data Analytics (BDA) has proven to be a transformative factor for the Architecture, Engineering and Construction (AEC) industry. This has prompted researchers to focus attention on BDA in the AEC industry (BDA-in-AECI) in recent years, leading to a proliferation of relevant research. However, an in-depth exploration of the literature on BDA-in-AECI remains scarce. As a result, this study seeks to systematically explore the state-of-the-art review on BDA-in-AECI and identify research trends and gaps in knowledge to guide future research.
This state-of-the-art review was conducted using a mixed-method systematic review. Relevant publications were retrieved from Scopus and then subjected to inclusion and exclusion criteria. A quantitative bibliometric analysis was conducted using VOSviewer software and Gephi to reveal the status quo of research in the domain. A further qualitative analysis was performed on carefully screened articles. Based on this mixed-method systematic review, knowledge gaps were identified and future research agendas of BDA-in-AECI were proposed.
The results show that BDA has been adopted to support AEC decision-making, safety and risk assessment, structural health monitoring, damage detection, waste management, project management and facilities management. BDA also plays a major role in achieving construction 4.0 and Industry 4.0. The study further revealed that data mining, cloud computing, predictive analytics, machine learning and artificial intelligence methods, such as deep learning, natural language processing and computer vision, are the key methods used for BDA-in-AECI. Moreover, several data acquisition platforms and technologies were identified, including building information modeling, Internet of Things (IoT), social networking and blockchain. Further studies are needed to examine the synergies between BDA and AI, BDA and Digital twin and BDA and blockchain in the AEC industry.
The study contributes to the BDA-in-AECI body of knowledge by providing a comprehensive scope of understanding and revealing areas for future research directions beneficial to the stakeholders in the AEC industry.

Keywords

Big data; Big data analytics; AEC; Bibliometric analysis; Systematic analysis

Key performance indicators for hospital planning and construction: a systematic review and meta-analysis

Liu, W., Chan, A.P.C., Chan, M.W., Darko, A. and Oppong, G.D. (2024), “Key performance indicators for hospital planning and construction: a systematic review and meta-analysis”, Engineering, Construction and Architectural Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/ECAM-10-2023-1060

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Abstract

Purpose
The successful implementation of hospital projects (HPs) tends to confront sundry challenges in the planning and construction (P&C) phases due to their complexity and particularity. Employing key performance indicators (KPIs) facilitates the monitoring of HPs to advance their successful delivery. This study aims to comprehensively investigate the KPIs for hospital planning and construction (HPC).

Design/methodology/approach
The KPIs for HPC were identified through a systematic review. Then a comprehensive assessment of these KPIs was performed utilizing a meta-analysis method. In this process, basic statistical analysis, subgroup analysis, sensitive analysis and publication bias analysis were performed.

Findings
Results indicate that all 27 KPIs identified from the literature are significant for executing HPs in P&C phases. Also, some unconventional performance indicators are crucial for implementing HPs, such as “Project monitoring effectiveness” and “Industry innovation and synergy,” as their high significance is reflected in this study. Despite the fact that the findings of meta-analysis are more trustworthy than those of individual studies, a high heterogeneity still exists in the findings. It highlights the inherent uncertainty in the construction industry. Hence, this study applied subgroup analysis to explore the underlying factors causing the high level of heterogeneity and used sensitive analysis to assess the robustness of the findings.

Originality/value
There is no consensus among the prior studies on KPIs for HPC specifically and their degree of significance. Additionally, few reviews in this field have focused on the reliability of the results. This study comprehensively assesses the KPIs for HPC and explores the variability and robustness of the results, which provides a multi-dimensional perspective for practitioners and the research community to investigate the performance of HPs during the P&C stages.

Keywords

Key performance indicators; hospital projects; planning and construction; systematic review; meta-analysis; project monitoring effectiveness; industry innovation and synergy

Hackathon co-supported by Urban Design and Planning featured in GeekWire

The Urban Resilience Hackathon took place in May 2024, and was facilitated by DemocracyLab, with support from the National Science Foundation LEAP-HI project, and the CBE Urban Design and Planning department. Hackathons are typically based in tech, so this urban planning and policy hackathon was unique in its focus. Dr. Branden Born, chair of Urban Design and Planning, said the hackathon supported community engagement, and explored ways to “do planning” better. Dan Abramson from Urban Design and Planning, along with…

Artificial Intelligence in Performance-Driven Design: Theories, Methods, and Tools

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Abstract

Artificial Intelligence in Performance-Driven Design: Theories, Methods, and Tools explores the application of artificial intelligence (AI), specifically machine learning (ML), for performance modeling within the built environment. This work establishes the theoretical foundations and methodological frameworks for utilizing AI/ML, with an emphasis on multi-scale modeling encompassing energy flows, environmental quality, and human systems.

The book examines relevant practices, case studies, and computational tools that harness AI's capabilities in modeling frameworks, enhancing the efficiency, accuracy, and integration of physics-based simulation, optimization, and automation processes. Furthermore, it highlights the integration of intelligent systems and digital twins throughout the lifecycle of the built environment, to enhance our understanding and management of these complex environments.

This book also:
• Incorporates emerging technologies into practical ideas to improve performance analysis and sustainable design
• Presents data-driven methodologies and technologies that seamlessly integrate into modeling and design platforms
• Shares valuable insights for developing decarbonization pathways in urban buildings
• Includes contributions from expert researchers and educators across a range of related fields

Artificial Intelligence in Performance-Driven Design is ideal for architects, engineers, planners, and researchers involved in sustainable design and the built environment. It’s also of interest to students of architecture, building science and technology, urban design and planning, environmental engineering, and computer science and engineering.

An Ontological Analysis for Comparison of the Concepts of Sustainable Building and Intelligent Building

Borhani, A., Borhani, A., Dossick, C. S., & Jupp, J. (2024). An Ontological Analysis for Comparison of the Concepts of Sustainable Building and Intelligent Building. Journal of Construction Engineering and Management, 150(4). https://doi.org/10.1061/JCEMD4.COENG-13711

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Abstract

The concept of intelligent building is emerging in the contemporary built environment. Intelligent buildings aim to leverage digital technologies and information throughout the building’s life cycle (design, construction, and operation phases) to improve the building’s performance and value. In recent years, academic scholars and industry practitioners have made efforts to articulate the intelligent building concept and identify its components. However, there is still no commonly accepted definition for the term intelligent (or smart) building. Furthermore, the term is used interchangeably with similar terms such as sustainable building and high-performance building. The primary gaps in research are the lack of a holistic and clearly defined list of intelligent building components. This gap limits building stakeholders’ abilities to decide which technologies to implement in their buildings, prove its capabilities and advantages, and improve its performance. In response to the identified gaps, this research conceptualizes intelligent building in comparison with the concept of sustainable building. We identified the key components that each concept entails and conducted a comparative analysis of the identified components. The findings of this research include a categorization of intelligent building’s definitions which helps to conceptualize intelligent building and distinguish it from other similar concepts. In addition, the research team used the developed ontologies for intelligent and sustainable buildings to provide a fundamental overview of the structure of building evaluation systems and their different approaches for determining evaluation criteria. Overall, this study contributes to the body of knowledge by identifying and classifying components of intelligent buildings, which is a prerequisite for intelligent buildings’ evaluation. It also makes a distinction between the concepts of intelligent building and sustainable building in order to determine their context and applications.

 

Suitability of the height above nearest drainage (HAND) model for flood inundation mapping in data-scarce regions: a comparative analysis with hydrodynamic models

Thalakkottukara, N. T., Thomas, J., Watkins, M. K., Holland, B. C., Oommen, T., & Grover, H. (2024). Suitability of the height above nearest drainage (HAND) model for flood inundation mapping in data-scarce regions: a comparative analysis with hydrodynamic models. Earth Science Informatics. https://doi.org/10.1007/s12145-023-01218-x.

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Abstract

Unprecedented floods from extreme rainfall events worldwide emphasize the need for flood inundation mapping for floodplain management and risk reduction. Access to flood inundation maps and risk evaluation tools remains challenging in most parts of the world, particularly in rural regions, leading to decreased flood resilience. The use of hydraulic and hydrodynamic models in rural areas has been hindered by excessive data and computational requirements. In this study, we mapped the flood inundation in Huron Creek watershed, Michigan, USA for an extreme rainfall event (1000-year return period) that occurred in 2018 (Father's Day Flood) using the Height Above Nearest Drainage (HAND) model and a synthetic rating curve developed from LIDAR DEM. We compared the flood inundation extent and depth modeled by the HAND with flood inundation characteristics predicted by two hydrodynamic models, viz., HEC-RAS 2D and SMS-SRH 2D. The flood discharge of the event was simulated using the HEC-HMS hydrologic model. Results suggest that, in different channel segments, the HAND model produces different degrees of concurrence in both flood inundation extent and depth when compared to the hydrodynamic models. The differences in flood inundation characteristics produced by the HAND model are primarily due to the uncertainties associated with optimal parameter estimation of the synthetic rating curve. Analyzing the differences between the HAND and hydrodynamic models also highlights the significance of terrain characteristics in model predictions. Based on the comparable predictive capability of the HAND model to map flood inundation areas during extreme rainfall events, we demonstrate the suitability of the HAND-based approach for mitigating flood risk in data-scarce, rural regions.

Keywords

Flood inundation mapping; Father's Day Flood; Data-scarce regions; HAND; HEC-RAS 2D; SMS-SRH 2D

Industry-Faculty-Student collaboration through the Applied Research Consortium

Owner of RDF Consulting Services and consultant for Turner Construction, Renzo di Furia, is working with Associate Dean for Research Carrie Sturts Dossick in supporting student-industry collaboration. “Applied Research Consortium brings together an interdisciplinary group of built environment firms with faculty experts and graduate student researchers at the University of Washington’s College of Built Environments (CBE) to address the most vexing challenges that firms face today.” A case study in applied research is highlighted in the article. 3D modeling was…

Machine Learning–Based Bayesian Framework for Interval Estimate of Unsafe-Event Prediction in Construction

Wu, L., Mohamed, E., Jafari, P., & AbouRizk, S. (2023). Machine Learning–Based Bayesian Framework for Interval Estimate of Unsafe-Event Prediction in Construction. Journal of Construction Engineering and Management, 149(11). https://doi.org/10.1061/JCEMD4.COENG-13549

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Abstract

Construction safety is a critical concern for industry and academia, and numerous models and algorithms have been developed to predict incidents or accidents to facilitate proactive decision-making. However, previous studies have been limited due to the inability to account for uncertainties because predictions are given as a single value (i.e., Yes or No) and the failure to integrate subjective judgment. To address these limitations, this research proposes a machine learning–based Bayesian framework for predicting construction incidents using interval estimates. This framework combines a state-of-the-art machine-learning algorithm with a binary Bayesian inference model to develop an incident predictor that considers a range of project characteristics and conditions. Notably, this framework also is capable of incorporating historical or subjective judgment through prior selection and outputs the unsafe event prediction as an interval of possibilities, thus accounting for various uncertainties. The efficacy of our framework was demonstrated in a real-life case study, showcasing its practical implications for proactive decision-making and risk management in the construction industry and representing a valuable contribution to the field of construction safety.