Has the COVID-19 Pandemic Changed People’s Attitude about Where to Live? Some Preliminary Answers from a Study of the Atlanta Housing Market
Bibliographic record
Abstract
In March 2020, the national lockdowns and social distancing mandates to contain the COVID-19 pandemic in the US abruptly disrupted all aspects of urban life, requiring people to conduct daily activities including work, shopping, learning, schooling, and socializing, from home using online tools. These lockdowns and stay-at-home orders sharply increased unemployment and hindered active transactions in the housing market in the second quarter of 2020 (Liu & Su, 2021). While the high unemployment rate was a severe economic and social concern affecting housing demand, monetary easing and low interest rates increased liquidity and the flow of money into the housing market (Zhao, 2020).\n\nA growing body of work started to examine the overall vitality of the housing market in response to the disruptions caused by the pandemic (D’Lima et al., 2020; Liu & Su, 2021; Yoruk, 2020; Zhao, 2020). In addition, reports in popular media have highlighted trends in cities like New York and San Francisco, where many households were giving up expensive central city residences for low-density suburban houses with large yards. This finding implied that cities were losing their appeal given the reduction in the need for commuting in a work-from-home culture and the desire for security and open space in a low-density environment in the suburbs. Despite this type of anecdotal evidence, we know very little about how the preferences for housing in different locations are changing in response to the COVID-19 pandemic.\n\nThis study explores whether and how the pandemic affected the housing preferences in the Atlanta single-family housing market. The focus goes to locational characteristics such as the accessibility to the rail transit system, accessibility to freeway systems, and walkability. The housing market participants’ attitudes toward the different travel modes can be revealed with the price effects of the accessibility-related locational characteristics. The impact of whether a house is in the inner city, inner-ring suburb, or outer-ring suburb on housing prices is also examined. \n\nA few main findings are derived from comparing the descriptive statistics and hedonic price models for 2018, 2019, and 2020. First, a steep drop in the number of transactions in the second quarter of 2020 was followed by an increase in the number of transactions and housing prices. The observed boom in the Atlanta single-family housing market aligns with the arguments of Zhao (2020) and Liu and Su (2021) that the lowered mortgage rate caused the influx of money to the housing markets across the US. Second, the positive price effect of parcel size and a pool increased in 2020 while that of square footage decreased. Third, the recently increasing preference for the inner city over the suburban area was restrained in 2020, which might have resulted from the diminished advantage of staying near the city center for job accessibility. Fourth, the pandemic did not substantially change the capitalization effect of the accessibility to a MARTA rail station and freeway.\n\nA few suggestions are made for future studies. First, the endeavor to further clarify the underlying reasons for the observations from this study would be necessary, which hedonic price models alone cannot do. Conducting a customized survey is one way to reveal the existence of and reasons for the changes in the attitudes, lifestyle, and travel patterns of diverse market participants covering both the supply and demand sides. Second, investigating the parts of the housing market that are not examined in this study will bring a comprehensive and detailed understanding of the housing market and the changes the market went through. The houses for rent and the houses other than detached single-family houses are not included in this study. Moreover, the transactions of the newly constructed houses are not usually in the FMLS data even though they take up a significant proportion of the transactions in the Atlanta region. Third, the analyses with some submarket segmentation using such criteria as the housing price, number of rooms, and location are expected to bring useful policy implications enabling detailed and customized solutions to the issues that planners are tackling.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".