Christopher W. Starr, Jesse Saginor and Elaine Worzala
Industry 4.0 recognizes a broad set of technologies that rapidly redefine industry, including real estate. These broad technologies include the Internet of things (IoT), cloud…
Abstract
Purpose
Industry 4.0 recognizes a broad set of technologies that rapidly redefine industry, including real estate. These broad technologies include the Internet of things (IoT), cloud computing, decision automation, machine learning and artificial intelligence. This paper explores applies Industry 4.0 to commercial real estate, resulting in a framework defined here as Real Estate 4.0, a concept that encompasses fintech and proptech.
Design/methodology/approach
This research paper examines Industry 4.0 technology to construct a framework for Real Estate 4.0. We also focus on how the COVID-19 pandemic is accelerating proptech, particularly as it relates to getting employees back into their traditional work environments.
Findings
As a research paper, this is not a traditional research project with empirical findings. It is a primer on how the rapidly changing technologies of Industry 4.0 are now disrupting and transforming real estate today into what we are calling Real Estate 4.0.
Practical implications
Practitioner insight and future research are informed by a framework for Real Estate 4.0 drawn from the technologies of Industry 4.0. Additional implications are outlined for practical, systemic change as a result of the COVID-19 pandemic within the scope of Real Estate 4.0 technology.
Originality/value
This is a combined effort by experts in three contributing disciplines: systems science, planning and real estate. Our intent is to provide a primer for those of us in the latter two fields so that we can embrace the rapidly changing built environment landscape as it adjusts and adapts to a post COVID-19 environment that will be critical to maintain real estate investment values and enhance the real estate user's experience.
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Jesse Saginor and Yue Ge
The purpose of this research is to analyze a county’s housing market over 23 years to determine what impact, if any, multiple hurricanes have had on the residential real estate…
Abstract
Purpose
The purpose of this research is to analyze a county’s housing market over 23 years to determine what impact, if any, multiple hurricanes have had on the residential real estate market.
Design/methodology/approach
This research uses a hedonic price model to determine the impacts that multiple hurricanes had on housing values.
Findings
There was a significant and negative countywide impact on housing sales values in the 1996, which can directly be attributed to three hurricanes impacting Brunswick County. Economic factors, rather than hurricanes and related storms, are more likely to impact sales values in all other years.
Research limitations/implications
This research is limited only to single-family home sales in Brunswick County, North Carolina, from 1984 to 2007. The model does not include multi-family residential uses.
Practical implications
Unlike many other areas that have been studied regarding natural disasters, Brunswick County has been hit multiple times by hurricanes and related storms, providing some insight into the long-term implications of the impact of storms on housing values over an extended period of time. The practical implication is that despite the likelihood of hurricanes and proximity to the ocean, people are willing to pay to live in coastal areas, even an area with a history of repeated direct and indirect strikes by hurricanes.
Originality/value
Unlike much of the peer-reviewed research that looks at a single occurrence of a natural disaster, this research looks at the impacts of multiple hurricanes on a single county over 23 years to determine what impact, if any, these storms have on the overall housing market.
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Jesse Saginor, Robert Simons and Ron Throupe
This paper seeks to reduce the lack of quantitative research by addressing diminution in value to non‐residential property resulting from environmental contamination.
Abstract
Purpose
This paper seeks to reduce the lack of quantitative research by addressing diminution in value to non‐residential property resulting from environmental contamination.
Design/methodology/approach
This meta‐analysis extracts data from approximately a dozen peer‐reviewed articles and 100 case studies from real estate appraisers in the USA. A dataset containing 106 contaminated non‐residential observations is examined using Regression (OLS). Forward (stepwise) and backward selection was performed. The dependent variable included percentage loss and dollar amount. The independent variables were contamination type, US region, land use type, distance from the source (mostly contaminated subjects), passage of time, year, urban or rural, market conditions, litigation, and indemnification.
Findings
The model adjusted R squares range from 37 percent to 66 percent. Approximately a third of cases had no loss. This research used petroleum case studies as the reference category for comparison with other types of contamination. The following variables were statistically significant in all four models: Creosote/PCB and Other contamination. The following were significant in two models: Other land use, 30‐year mortgage rate, Rural location, TPH, Multiple contamination, TCE, Under‐remediation, and Mineral extraction region. Finally, the following variables were significant in one model at least at a 90 percent level of confidence: Heavy metals, Industrial Midwest region, and pre‐1995 sale.
Practical implications
Properties in the remediation phase show less of a loss in value. Selective case studies within the same period of the clean‐up cycle make the best comparables. The US regional location was less important.
Originality/value
This is the first empirical research using a meta‐analysis to study damage effects for non‐residential property affected by contamination.