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Urban Findings
March 22, 2023 AEST

Post-Pandemic Relocation Preferences of Remote Tech Workers

Simon Tan, Kevin Fang, T. William Lester,
teleworkingtelecommutingremote workICTrelocationsuburbanizationtech industryCOVID-19
Copyright Logoccby-sa-4.0 • https://doi.org/10.32866/001c.73259
Photo by Vladimir Kudinov on Unsplash
Findings
Tan, Simon, Kevin Fang, and T. William Lester. 2023. “Post-Pandemic Relocation Preferences of Remote Tech Workers.” Findings, March. https:/​/​doi.org/​10.32866/​001c.73259.
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  • Figure 1. Heatmap of relocation destinations based on ZIP codes reported by respondents with relocation interest (N=143).
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  • Figure 2. Housing type preferences among respondents with relocation interest (N=183).
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  • Figure 3. Renting vs. owning shifts by the time of the survey (N=235).
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Abstract

Will mass adoption of remote work lead to a mass exodus from Silicon Valley? A survey of over 660 high-tech workers in the San Francisco Bay Area ran from November 2021 to March 2022, asking about relocation interest and intended destinations.

53% of respondents expressed interest in moving or already moved, with post-pandemic preferences shifting towards suburbs and stand-alone homes. However, most moves merely represented a dispersal into Bay Area suburbs with a median relocation distance of 33.68km. The number of expected days in the office, dictated by employer remote work policies, significantly impacted relocation interest.

1. Questions

About one-third of all jobs are estimated to be “remote-eligible” (Dingel and Neiman 2020) but the San Francisco Bay Area is an outlier in that nearly 50% of the 1.79 million jobs in the region can be categorized this way (Bay Area Council Economic Institute 2020). This is more than any other metropolitan area in the United States, mostly due to the high concentration of employees in the fast-growing technology sector (i.e., Silicon Valley).

During the COVID-19 pandemic, the rapid adoption of remote work by tech employees (Levy 2020) led many to wonder if there would be a “mass exodus” (sf.citi 2022) of talent towards other tech hubs with a lower cost of living, less taxation, or higher quality of life. This research seeks to understand the true level of relocation interest among Bay Area tech workers, their underlying motivations, and the nature of intended destinations. Their migration patterns also suggest potential impacts on other “superstar” cities with tech-heavy workforces.

2. Methods

An online survey ran from November 2021 to March 2022, targeted at employees working at Silicon Valley technology companies. Participants only needed to meet the criteria of having a full-time employer in the software/technology sector and an assigned office in the Bay Area as of February 2020, thus allowing for employees who had relocated out of the region during the COVID-19 pandemic (though remaining with the same firm throughout was required). Participant recruitment began with known contacts identified at the top 20 (in terms of employee count) technology firms headquartered in the region, with subsequent waves reaching the target audience via snowball sampling.

Demographic data was summarized and compared with statistics from the American Community Survey (Czepiel 2016) and the U.S. EEOC (U.S. Equal Employment Opportunity Commission 2014) to ensure survey respondents were representative of the high-tech sector population as a whole. For example, the median age of the sample was 39.5 and thus comparable to the general population of software developers (39.0). The sample was also 66% male, compared to 64% of the population from EEOC data.

The survey included questions about participants’ employers and their remote work policies, interest in relocation due to remote work, potential destinations, and basic demographic information. The questions were crafted based on similar research regarding telework conducted on a national scale (Menon, Keita, and Bertini 2020) and commissioned research on a similar audience but in a different region (Sea.citi 2021).

661 survey respondents fit the screening criteria, out of 1,100 survey participants in total. Sample sizes for each question varied between 300-500 due to item non-response (all questions were optional). Over 120 unique technology firms were represented, with offices throughout the region and a slight concentration in downtown San Francisco.

3. Findings

53% of respondents expressed interest in relocation (out of N=382), with 20% having already moved by the time of survey. Of those with relocation interest, 64% cited remote work options as a primary factor in their consideration. A binary logit model for whether a participant had relocation interest showed meaningful contributing factors included age (younger) and number of children (fewer), but the most significant driver was the number of days expected in the office after the pandemic (Table 1). In fact, controlling for other variables, each additional day expected in the office post-pandemic reduced odds of relocation interest by 3%.

Table 1.Binary logit model – Relocation Interest (N=312, McFadden’s R2 = 0.097, AICc = 401).
Variable Relative Importance Coefficient (B) Odds ratio (eB) P-Value
Days Expected in Office (After Pandemic) 32% -0.0320 0.9685 0.000451
Age 31% -0.0053 0.9947 0.00140
Number of Children 22% -0.0547 0.9468 0.0235
Company Employee Count (1000s) 15% -6.89x10-3 0.9931 0.0433

In turn, the number of employer-mandated days in the office as part of remote work policy accounted for 86% of days expected in the office in a separate linear regression model (R2 = 0.61, P-Value < 0.01, AIC = 348). Hence, as tech employers mandate more days in the office, employees feel the need to remain close by and reconsider their relocation plans.

The median potential relocation distance (based on reported ZIP codes) was 33.68 km (N=143), suggesting that most moves would be within the region as opposed to between states. In fact, most participants with relocation interest were planning to stay within California (Figure 1). Other studies found pandemic-related decentralization of the tech industry, but that effect seems to stem primarily from distributed growth in new tech jobs rather than relocation of existing workers (Muro and You 2022).

Figure 1
Figure 1.Heatmap of relocation destinations based on ZIP codes reported by respondents with relocation interest (N=143).

Instead of long-distance relocations, evidence pointed to tech workers simply dispersing into nearby suburbs. 43% of respondents with relocation interest expressed a preference to live in a suburb near a large city after the pandemic, as compared to 32% pre-pandemic (N=202). Additionally, 19% of those respondents cited “more space” as a reason for moving (N=203), echoed in preferences shifting towards stand-alone homes. (Figure 2).

Figure 2
Figure 2.Housing type preferences among respondents with relocation interest (N=183).

Analyzing ZIP codes that respondents with relocation interest reported potential moves from/to revealed shifts towards areas with more traditionally “suburban” qualities such as lower population density and more households with children (Table 2). The slightly lower median home values in the destination ZIP codes suggest movers may also be seeking housing affordability.

Table 2.Characteristics of origin/destination ZIP codes (ACS 2020, 5-Year Estimates) reported by respondents with relocation interest (N=160).
Statistic Mean (From ZIP) Mean (To ZIP) Paired Diff Mean
Pop. Density (people per sq. mile) 17639.78 13073.67 -4566.12***
Transportation characteristics
Drove Alone to Work (%) 42.43% 51.40% +8.97%***
Public Transit to Work (%) 22.19% 18.19% -4.00%***
Median Commute Time (minutes) 30.86 30.84 -0.02
Households with No Vehicles (%) 21.64% 15.51% -6.13%***
Housing characteristics
Owner-occupied (%) 42.08% 51.01% +8.93%***
Single-Family Detached Units (%) 29.45% 42.09% +12.64%***
Family Households (%) 51.67% 58.29% +6.62%***
Households with Children (%) 22.25% 26.26% +4.01%***
Crowded (%) 6.44% 4.99% -1.45%***
Median Rent $2,167.16 $2,079.74 -$87.42
Rent Burdened (%) 38.83% 40.46% +1.64%
Median Rent Burden (%) 25.97% 27.01% +1.05%***
Median Home Value $1,174,821 $1,029,763 -$145,058***
Homes >$1M (%) 56.50% 46.93% -9.58%***
Ownership Burdened (%) 41.59% 39.44% -2.14%***
Median Ownership Burden 19.94% 19.68% -0.26%
Social characteristics
Gini Index 0.48 0.46 -0.02***
Below Poverty Level (%) 9.48% 8.63% -0.85%
White (%) 51.06% 57.86% +6.80%***

*** Significant difference: P(T<=t) two-tail < 0.01

Those who rented prior to the pandemic were also more likely to have already moved, and a substantial number became homeowners (Figure 3). A chi-squared test showed a correlation between renter/owner status before COVID-19 and interest in relocation (N=199, Cramér’s V = 0.160, P-Value = 0.0366). This aligned with national trends showing homeownership rates spiking during the pandemic (U.S. Department of Commerce 2022).

Figure 3
Figure 3.Renting vs. owning shifts by the time of the survey (N=235).

These findings mostly reflect the “donut effect” found by (Ramani and Bloom 2021) and aligns with the historical growth of suburbs over the last decade (Muller 2017), as nearly all relocation interest remained within the immediate suburbs of the region. The desired characteristics of moving destinations (e.g., single-family homes, more space) also reflected mostly “lifestyle” based rationale, matching findings for high-income households in the study by (Haslag and Weagley 2022).

Together, this paints a narrative of tech workers, flush with cash and stock equity during the COVID-19 pandemic, deciding to finally leave their expensive apartments in San Francisco and purchase larger homes in the surrounding suburbs. Notably, the lower cost of living in other states was not enough to sway most tech workers from migrating out of California. Employer pressure to come back into the office at least some days of the week has tempered what fleeting interest there was in more far-flung relocation.

Submitted: January 24, 2023 AEST

Accepted: March 11, 2023 AEST

References

Bay Area Council Economic Institute. 2020. “Remote Work in the Bay Area.” http:/​/​www.bayareaeconomy.org/​report/​remote-work-in-the-bay-area/​.
Czepiel, S. 2016. “Age and Tech.” https:/​/​czep.net/​16/​age-and-tech.html.
Dingel, Jonathan, and Brent Neiman. 2020. “How Many Jobs Can Be Done at Home?” National Bureau of Economic Research. https:/​/​doi.org/​10.3386/​w26948.
Haslag, Peter H., and Daniel Weagley. 2022. “From L.A. to Boise: How Migration Has Changed During the COVID-19 Pandemic.” SSRN. https:/​/​doi.org/​10.2139/​ssrn.3808326.
Google Scholar
Levy, A. 2020. “The Tech Industry’s Early Work-from-Home Mandates Helped California and Washington Flatten the Curve,” April 16, 2020. https:/​/​www.cnbc.com/​2020/​04/​16/​california-and-washington-tech-work-from-home-edicts-flattened-curve.html.
Menon, Nikhil, Y. Keita, and R. L. Bertini. 2020. “Impact of COVID-19 on Travel Behavior and Shared Mobility Systems.” Tampa: University of South Florida. Center for Urban Transportation Research. https:/​/​doi.org/​10.5038/​cutr-nctr-rr-2020-30.
Muller, P.O. 2017. “Transportation and Urban Form.” In The Geography of Urban Transportation, edited by G. Giuliano and S. Hanson, 57–85. New York: The Guilford Press.
Google Scholar
Muro, M., and Y. You. 2022. “Superstars, Rising Stars, and the Rest: Pandemic Trends and Shifts in the Geography of Tech,” March 8, 2022. https:/​/​www.brookings.edu/​research/​superstars-rising-stars-and-the-rest-pandemic-trends-and-shifts-in-the-geography-of-tech/​.
Ramani, Arjun, and Nicholas Bloom. 2021. “The Donut Effect of Covid-19 on Cities.” Cambridge: National Bureau of Economic Research. https:/​/​doi.org/​10.3386/​w28876.
Sea.citi. 2021. “Tech Civic Survey 2021.” https:/​/​www.seaciti.org/​our-work/​techsurvey2021/​.
sf.citi. 2022. “Tracking the San Francisco Tech Exodus.” San Francisco, California, United States.
U.S. Department of Commerce. 2022. “QUARTERLY RESIDENTIAL VACANCIES AND HOMEOWNERSHIP, FIRST QUARTER 2022.” United States Census Bureau. https:/​/​www.census.gov/​housing/​hvs/​files/​currenthvspress.pdf.
U.S. Equal Employment Opportunity Commission. 2014. “DIVERSITY IN HIGH TECH.” https:/​/​www.eeoc.gov/​special-report/​diversity-high-tech.

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