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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.

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.

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.

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.

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.

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.

This research uses a hedonic price model to determine the impacts that multiple hurricanes had on housing values.

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.

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.

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.

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.

This paper seeks to reduce the lack of quantitative research by addressing diminution in value to non‐residential property resulting from environmental contamination.

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.

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.

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.

This is the first empirical research using a meta‐analysis to study damage effects for non‐residential property affected by contamination.