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The COVID-19 outbreak posed a considerable risk to the health of people in the US and across the world. To reduce its spread, various companies in America adopted a range of preventive measures, such as telework, for the majority of their workforces. Considering that these measures have disproportionate effects on individuals, this study examined the potential relationship between socioeconomic characteristics and telework status in the United States through mixed logit models developed in this research. Results indicated that telework status was significantly associated with the majority of the variables used in the models, namely, age, gender, educational status, marital status, difficulty with expenses, household size, work type, and anxiety.

Purpose This study aims to analyze the impact of COVID-19 on housing price within four major metropolitan areas in Texas: Austin, Dallas, Houston and San Antonio. The analysis intends to understand economic and mobility drivers behind the housing market under the inclusion of fixed and random effects. Design/methodology/approach This study used a linear mixed effects model to assess the socioeconomic and housing and transport-related factors contributing to median home prices in four major cities in Texas and to capture unobserved factors operating at spatial and temporal level during the COVID-19 pandemic. Findings The regression results indicated that an increase in new COVID-19 cases resulted in an increase in housing price. Additionally, housing price had a significant and negative relationship with the following variables: business cycle index, mortgage rate, percent of single-family homes, population density and foot traffic. Interestingly, unemployment claims did not have a significant impact on housing price, contrary to previous COVID-19 housing market related literature. Originality/value Previous literature analyzed the housing market within the first phase of COVID-19, whereas this study analyzed the effects of the COVID-19 throughout the entirety of 2020. The mixed model includes spatial and temporal analyses as well as provides insight into how quantitative-based mobility behavior impacted housing price, rather than relying on qualitative indicators such as shutdown order implementation.

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