Property cash flow studies : focusing on model consistency and data accuracy

The high degree of variability and inconsistency in cash flow study usage by property professionals demands improvement in knowledge and processes. Until recently limited research was being undertaken on the use of cash flow studies in property valuations but the growing acceptance of this approach for major investment valuations has resulted in renewed interest in this topic. Studies on valuation variations identify data accuracy, model consistency and bias as major concerns. In cash flow studies there are practical problems with the input data and the consistency of the models. This study will refer to the recent literature and identify the major factors in model inconsistency and data selection. A detailed case study will be used to examine the effects of changes in structure and inputs. The key variable inputs will be identified and proposals developed to improve the selection process for these key variables. The variables will be selected with the aid of sensitivity studies and alternative ways of quantifying the key variables explained. The paper recommends, with reservations, the use of probability profiles of the variables and the incorporation of this data in simulation exercises. The use of Monte Carlo simulation is demonstrated and the factors influencing the structure of the probability distributions of the key variables are outline. This study relates to ongoing research into functional performance of commercial property within an Australian Cooperative Research Centre.

Occupant health and productivity : an Australian perspective

The issue of whether improved building services such as air quality, provision of daylight, thermal comfort etc, have a positive impact on the health and productivity of building occupants is still an open question. There is significant anecdotal evidence supporting the notion that health and productivity of building occupants can be improved by improving the quality of the indoor environment, but there are actually few published quantitative studies to substantiate this contention. This paper reports on a comprehensive review of the worldwide literature which relates health of building occupants with the different aspects of the indoor environment which are believed to impact of these issues, with a particular focus on studies in Australia, The paper analyses the existing research and identifies the key deficiencies in our existing understanding of this problem. The key focus of this research is office and school buildings, but the scope of the literature surveyed includes all commercial buildings, including industrial buildings. There is a notable absence of detailed studies on this link in Australian buildings, although there are studies on thermal comfort, and a number of studies on indoor air quality in Australia, which do not make the connection to health and productivity. Many international studies have focused on improved lighting, and in particular the provision of daylight in buildings, but again there are few studies in Australia which focus in this area.

Subtropical subdivisions : toward a lot-rating methodology for subtropical climates

What role can climatically appropriate subdivision design play in decreasing the use of energy required to cool premises by maximising access to natural ventilation? How can this design be achieved? The subdivision design stage is critical to urban and suburban sustainability outcomes, as significant changes after development are constrained by the configuration of the subdivision, and then by the construction of the dwellings. Existing Australian lot rating methodologies for energy efficiency, such as that by the Sustainable Energy Development Authority (SEDA), focus on reducing heating needs by increasing solar access, a key need in Australia’s temperate zone. A recent CRC CI project, Sustainable Subdivisions: Energy (Miller and Ambrose 2005) examined these guidelines to see if they could be adapted for use in subtropical South East Queensland (SEQ). Correlating the lot ratings with dwelling ratings, the project found that the SEDA guidelines would need to be modified for use to make allowance for natural ventilation. In SEQ, solar access for heating is less important than access to natural ventilation, and there is a need to reduce energy used to cool dwellings. In Queensland, the incidence of residential air-conditioning was predicted to reach 50 per cent by the end of 2005 (Mickel 2004). The CRC-CI, Sustainable Subdivisions: Ventilation Project (CRC-CI, in progress), aims to verify and quantify the role natural ventilation has in cooling residences in subtropical climates and develop a lot rating methodology for SEQ. This paper reviews results from an industry workshop that explored the current attitudes and methodologies used by a range of professionals involved in subdivision design and development in SEQ. Analysis of the workshop reveals that a key challenge for sustainability is that land development in subtropical SEQ is commonly a separate process from house design and siting. Finally, the paper highlights some of the issues that regulators and industry face in adopting a lot rating methodology for subdivisions offering improved ventilation access, including continuing disagreement between professionals over the desirability of rating tools.

The future of office property

The following paper considers the question, where to office property? In doing so, it focuses, in the first instance, on identifying and describing a selection of key forces for change present within the contemporary operating environment in which office property functions. Given the increasingly complex, dynamic and multi-faceted character of this environment, the paper seeks to identify only the primary forces for change, within the context of the future of office property. These core drivers of change have, for the purposes of this discussion, been characterised as including a range of economic, demographic and socio-cultural factors, together with developments in information and communication technology. Having established this foundation, the paper proceeds to consider the manner in which these forces may, in the future, be manifested within the office property market. Comment is offered regarding the potential future implications of these forces for change together with their likely influence on the nature and management of the physical asset itself. Whilst no explicit time horizon has been envisioned in the preparation of this paper particular attention has been accorded short to medium term trends, that is, those likely to emerge in the office property marketplace over the coming two decades. Further, the paper considers the question posed, in respect of the future of office property, in the context of developed western nations. The degree of commonality seen in these mature markets is such that generalisations may more appropriately and robustly be applied. Whilst some of the comments offered with respect to the target market may find application in other arenas, it is beyond the scope of this paper to explicitly consider highly heterogeneous markets. Given also the wide scope of this paper key drivers for change and their likely implications for the commercial office property market are identified at a global level (within the above established parameters). Accordingly, the focus is necessarily such that it serves to reflect overarching directions at a universal level (with the effect being that direct applicability to individual markets - when viewed in isolation on a geographic or property type specific basis – may not be fitting in all instances)

Success factors in an alliancing contract : a case study in Australia

Many studies have been carried out in relation to construction procurement methods. Evidence shows that there needs to be a change of culture and attitude in the construction industry, moving away from traditional adversarial relationship into cooperative and collaborative relationship. At the same time there is also an increasing concern and discussion on alternative procurement methods, drifting away from traditional procurement systems. Relational contracting approaches have become more popular in recent years, and have appeared in common forms such as partnering, alliancing and relationship management contracts. This paper reports the findings of a survey undertaken with a private organisation based on an alliance project during its design stage, identifying the critical factors that influence the success of the alliance project. Legal aspects focusing on dispute resolution in alliancing are also highlighted.

Data mining for lifetime prediction of metallic components

The ability to accurately predict the lifetime of building components is crucial to optimizing building design, material selection and scheduling of required maintenance. This paper discusses a number of possible data mining methods that can be applied to do the lifetime prediction of metallic components and how different sources of service life information could be integrated to form the basis of the lifetime prediction model

Life cycle modelling and design knowledge development in 3D virtual environments : final report

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This final report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

Improving the management of building life cycle : a data mining approach

The construction industry has adapted information technology in its processes in terms of computer aided design and drafting, construction documentation and maintenance. The data generated within the construction industry has become increasingly overwhelming. Data mining is a sophisticated data search capability that uses classification algorithms to discover patterns and correlations within a large volume of data. This paper presents the selection and application of data mining techniques on maintenance data of buildings. The results of applying such techniques and potential benefits of utilising their results to identify useful patterns of knowledge and correlations to support decision making of improving the management of building life cycle are presented and discussed.

Using Agents for Mining Maintenance Data while interacting in 3D Objectoriented Virtual Environments

This report demonstrates the development of: (a) object-oriented representation to provide 3D interactive environment using data provided by Woods Bagot; (b) establishing basis of agent technology for mining building maintenance data, and (C) 3D interaction in virtual environments using object-oriented representation. Applying data mining over industry maintenance database has been demonstrated in the previous report.

Developing Software Agents for Data Mining : [AIMM] Agents for Improving Maintenance Management

This report demonstrates the development of: • Development of software agents for data mining • Link data mining to building model in virtual environments • Link knowledge development with building model in virtual environments • Demonstration of software agents for data mining • Populate with maintenance data

Demonstration of Software Agents Prototype System : [AIMM] Agents for Improving Maintenance Management

This report presents the demonstration of software agents prototype system for improving maintenance management [AIMM] including: • Developing and implementing a user focused approach for mining the maintenance data of buildings. This report presents the demonstration of software agents prototype system for improving maintenance management [AIMM] including: • Developing and implementing a user focused approach for mining the maintenance data of buildings. • Refining the development of a multi agent system for data mining in virtual environments (Active Worlds) by developing and implementing a filtering agent on the results obtained from applying data mining techniques on the maintenance data. • Integrating the filtering agent within the multi agents system in an interactive networked multi-user 3D virtual environment. • Populating maintenance data and discovering new rules of knowledge.

Life Cycle Modelling and Design Knowledge Development in 3D Virtual Environments

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This Industry focused report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.