Introductory Note

A special issue of International Real Estate Review (IRER) on
Living with Virus: COVID-19 and Real Estate

At the height of the pandemic, countries worldwide closed their borders to stem contagion, bringing economic activities to nearly a standstill. This left many firms with no choice but to redesign their business models and fast-track digitalization.

More than two years on, countries worldwide have accepted that the virus will not completely disappear. The pandemic is transiting into an endemic phase, with a baseline number of people constantly affected by the disease.

Countries worldwide have opened their borders; businesses have gradually moved back onto the growth trajectory. Traveling activities have resumed and increased significantly, though they have not reached the same level as Pre-COVID. It will become a new normal for people to learn to adapt and live with the virus.

The International Real Estate Review (IRER) called for submissions for a special issue of “Living with Virus: COVID-19 and Real Estate” and will publish its inaugural special issue in Volume 25 No 4 in 2022.

The special issue comprises 5 papers on diverse topics on the COVID-19 pandemic impact on workspace, practices, and services in real estate markets. It also includes papers on how vaccination programs and government support during the pandemic induce real estate price responses; and predictive modeling on spatial distributions and spreads of the COVID virus.

Stay-at-home restrictions imposed during the pandemic have encouraged many workers and companies to adopt telework. The first paper by Takuya et al. (2022) examines how the housing environment affects the relationships between teleworking and childcare participation. The study uses data from the Japan Household Panel Survey and its supplementary modules on COVID-19, which were conducted in 2020. The results show that teleworkers living in owner-occupied detached housing devote more time to childcare than workers living in other housing arrangements during the pandemic after controlling for individual and household attributes, region, and housing characteristics. Female teleworkers in owner-occupied detached housing significantly increased the childcare time-to-work time ratio when regular schooling resumed in September.

The second paper by Apergis (2022) empirically tests the impact of the COVID-19 vaccination program on housing prices. Based on the data on residential housing prices across the US states and confirmed cases and deaths related to COVID-19, the panel regressions show a negative effect of COVID-19 on housing prices. The effect disappeared when the vaccination program was underway.

Özer et al. (2022), in their third paper, examine the nature of causal relations between COVID-19-related economic supports and real estate shocks in 58 countries between January 1, 2020, and September 3, 2022. They first apply the wavelet transformation to real estate price index shocks and Oxford COVID-19 Government Response Economic Support Index to decompose positive and negative shocks of real estate prices for each country. They then employ the fractional frequency flexible Fourier form Toda-Yamamoto causality test to obtain the causal relations. The results show that in most countries, COVID-19 economic supports significantly affect real estate prices. The effects were time-varying. Most of the asymmetric responses of the market take place in the medium and long term.

In the fourth paper, Shaheen Ali and Song (2022) investigate factors affecting the use of digital real estate platforms: Trade Me and realestate.co.nz, which are the two most popularly used by homebuyers in New Zealand. This study used the reviews on the two platforms from 2018 to 2021 in the analyses and found that perceived ease of use, usefulness, information, and system quality are the four main factors that affect the willingness of customers to use real estate digital platforms. However, the impact of these factors has significantly reduced after the spread of the pandemic.

The last paper by Yang (2022) applies an artificial intelligence (AI) based model to predict the infection rate of coronavirus disease 2019 (COVID-19). The machine learning algorithm is developed with the Python program to analyze pathways and evolutions of infection. The machine learning algorithms predict the rate of spread of COVID -19 with an accuracy of nearly 90%. The algorithms simulate the virus spread distance and coverage and find that self-isolation for suspected cases effectively contains the pandemic. However, the COVID-19 virus could spread asymptotically (silent spreader); therefore, earlier doctor consultation and testing of the virus could reduce its spread in local communities.

The five papers cover a wide range of topics that will have significant policy implications for various stakeholders, including policymakers, firms, and homebuyers. The “living with virus” theme is timely and aptly selected for this special issue of IRER, which, hopefully, will induce more research into post-COVID changes to workspace and real estate markets.

Sing Tien Foo
Editor of IRER

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