Complexity and uncertainty have also increased in part because of the growth in the number and interaction of complex adaptive systems in every aspect of CRE. ...the notion of a CRE industry may be ...much too narrow for effective strategy development. Specifically, they begin the work of identifying the relevant elements of a research framework: intellectual antecedents, pre-theoretical ideas, subject matter, analogies, concepts and language and methodology relevant to the topics at hand (Lachman et al., 1979).
Previous work on agglomeration economies ignores the built environment. This paper shows that the built environment matters, especially for commercial sectors that dominate city centers. Buildings ...are specialized beyond random assignment, in part because externality-generating anchor tenants skew a building's other tenants towards the anchor's industry. An anchor elsewhere on the blockface has a much weaker effect, and one that is weaker still if across the street, suggesting rapidly attenuating agglomeration economies. Attenuation is pronounced for retail and information-oriented office industries but is absent for manufacturing. Building managers have incentives and capacities to partly internalize local externalities, contributing to urban productivity.
Concerns over rising office vacancy rates and falling office building property values in many urban areas have increased the pressure on cities and developers to consider converting underused office ...space to residential use. To aid in current and future conversations surrounding the feasibility of conversion, we look to the recent past. In doing so, we provide an account of conversion and redevelopment activity in New York City over the past decade to uncover associated structural and locational characteristics. We find that office-to-residential conversions contributed the greatest share of residential rental units of all non-residential conversions from 2010 to 2020, with nearly 5900 units created. However, there is suggestive evidence that more recent obsolete office buildings generate significantly fewer units as compared to office conversions of the 1990s. We additionally model the probability of conversion and redevelopment. We find that hotels have the highest conversion probability, followed by loft, retail, industrial, and office. In general, relatively taller, narrower, older buildings with diminished value are more likely to be converted.
•Conversion is an important source of housing supply often occurring in very high-demand neighborhoods•Office buildings generate the greatest number of residential units per converted building than any other building class•The number of residential units generated per office conversion has declined substantially from the 1990s•Older, taller (shorter), smaller (larger) low-valued properties tend to attract conversion (redevelopment) rather than redevelopment (conversion)
•Employed multidisciplinary approaches to occupant behavior research.•Developed quantitative description and simulation methods of occupant behavior.•Developed occupant behavior modeling tools ...enabling co-simulation with BPS programs.•Developed methods to evaluate occupant behavior models and a fit-for-purpose application guide.•Case studies demonstrated applications of occupant behavior insights in the building life cycle.
More than 30% of the total primary energy in the world is consumed in buildings. It is crucial to reduce building energy consumption in order to preserve energy resources and mitigate global climate change. Building performance simulations have been widely used for the estimation and optimization of building performance, providing reference values for the assessment of building energy consumption and the effects of energy-saving technologies. Among the various factors influencing building energy consumption, occupant behavior has drawn increasing attention. Occupant behavior includes occupant presence, movement, and interaction with building energy devices and systems. However, there are gaps in occupant behavior modeling as different energy modelers have employed varied data and tools to simulate occupant behavior, therefore producing different and incomparable results. Aiming to address these gaps, the International Energy Agency (IEA) Energy in Buildings and Community (EBC) Programme Annex 66 has established a scientific methodological framework for occupant behavior research, including data collection, behavior model representation, modeling and evaluation approaches, and the integration of behavior modeling tools with building performance simulation programs. Annex 66 also includes case studies and application guidelines to assist in building design, operation, and policymaking, using interdisciplinary approaches to reduce energy use in buildings and improve occupant comfort and productivity. This paper highlights the key research issues, methods, and outcomes pertaining to Annex 66, and offers perspectives on future research needs to integrate occupant behavior with the building life cycle.
•An novel method to quantify C&D waste in China was developed.•2.36 billion tonnes of C&D waste were generated in China annually 2003–2013.•Potential recycling value of C&D waste is up to 401 billion ...USD in 2013.•With the current dumping rate, C&D waste could occupy an area of 7.5billionm3.
Associated with the continuing increase of construction activities such as infrastructure projects, commercial buildings, and housing programs, China has been experiencing a rapid increase of construction and demolition (C&D) waste. Till now, the generation and flows of China’s C&D waste has not been well understood. This paper aims to provide an explicit analysis of this based on a weight-per-construction-area method. Our results show that approximately 2.36 billion tonnes of C&D waste were generated in China annually during the period of 2003–2013, of which demolition waste and construction waste contributed to 97% and 3%, respectively, in 2013. East China contributed over half of the total C&D waste in China due to their rapid economic development and expansion of cities, followed by Middle China (21%) and South China (11%). Potential economic values from the recycling of C&D waste were found to vary from 201 billion (the worst scenario, i.e., the current practice of C&D waste management) to 401 billion US dollars in 2013 (the most optimistic scenario, i.e., C&D waste is assumed to be well recycled); and the landfill space demands were estimated to range from 7504millionm3 (the worst scenario) to 706millionm3 (the most optimistic scenario) accordingly. Consequently, increasing the recycling rate and reducing landfill rate of C&D waste could not only improve the potential recycling economic values, but also dramatically reduce land use and potential environmental impacts.
•Introduced machine learning baseline model based on gradient boosting machine algorithm for commercial building energy consumption prediction.•The performance of the gradient boosting machine was ...assessed on a large dataset of 410 commercial buildings.•Introduced a modified version of the k-fold cross validation approach to improve the gradient boosting machine predictive accuracy performance.
Accurate savings estimations are important to promote energy efficiency projects and demonstrate their cost-effectiveness. The increasing presence of advanced metering infrastructure (AMI) in commercial buildings has resulted in a rising availability of high frequency interval data. These data can be used for a variety of energy efficiency applications such as demand response, fault detection and diagnosis, and heating, ventilation, and air conditioning (HVAC) optimization. This large amount of data has also opened the door to the use of advanced statistical learning models, which hold promise for providing accurate building baseline energy consumption predictions, and thus accurate saving estimations. The gradient boosting machine is a powerful machine learning algorithm that is gaining considerable traction in a wide range of data driven applications, such as ecology, computer vision, and biology. In the present work an energy consumption baseline modeling method based on a gradient boosting machine was proposed. To assess the performance of this method, a recently published testing procedure was used on a large dataset of 410 commercial buildings. The model training periods were varied and several prediction accuracy metrics were used to evaluate the model’s performance. The results show that using the gradient boosting machine model improved the R‐squared prediction accuracy and the CV(RMSE) in more than 80 percent of the cases, when compared to an industry best practice model that is based on piecewise linear regression, and to a random forest algorithm.
Editorial Worzala, Elaine; Wofford, Larry; Wyman, David
Journal of property investment & finance,
07/2020, Letnik:
38, Številka:
4
Journal Article
Recenzirano
Proptech and entrepreneurship – innovation in real estate Someone rolled a rock to the entrance of a cave and created an enclosed space for his/her family – a warmer, more defensible shelter, ...distinct from the surrounding environment. The traditional set of skills used to analyse a real estate transaction or a real estate market are still important but advances in technology will make the analysis easier and in many cases more efficient. ...venture capitalists are actively investing in fintech and PropTech specialists but they may not be ready for prime time.
In commercial buildings, lighting contributes to about 20% of the total energy consumption. Lighting controllers that integrate occupancy and luminosity sensors to improve energy efficiency have been ...proposed. However, they are often ineffective because they focus solely on energy consumption rather than providing comfort to the occupants. An ideal controller should adapt itself to the preferences of the occupant and the environmental conditions. In this article, we introduce LightLearn, an occupant centered controller (OCC) for lighting based on Reinforcement Learning (RL). We describe the theory and hardware implementation of LightLearn. Our experiment during eight weeks in five offices shows that LightLearn learns the individual occupant behaviors and indoor environmental conditions, and adapts its control parameters accordingly by determining personalized set-points. Participants reported that the overall lighting was slightly improved compared to prior lighting conditions. We compare LightLearn to schedule-based and occupancy-based control scenarios, and evaluate their performance with respect to total energy use, light-utilization-ratio, unmet comfort hours, as well as light-comfort-ratio, which we introduce in this paper. We show that only LightLearn balances successfully occupant comfort and energy consumption. The adaptive nature of LightLearn suggests that reinforcement learning based occupant centered control is a viable approach to mitigate the discrepancy between occupant comfort and the goals of building control.
Display omitted
•LightLearn adapts to occupant preferences and environmental conditions.•Experimental demonstration of LightLearn in five offices over eight weeks.•LightLearn successfully balances energy consumption with occupant comfort.
PDF
