The Current State of the Commercial Real Estate Office Sector
Bibliographic record
Abstract
In January 2023, approximately 50% of Manhattan office workers were in the office on an average weekday; roughly 10% of the local workforce was fully remote; and only 9% of employees were in the office five days a week. These city-level trends are also reflected at the submarket, market, and national levels. As of 2023, 13% of full-time U.S employees work entirely from home, while 28% work a hybrid model. The Covid-19 pandemic’s impact on commercial real estate, particularly the office sector, is still being felt three years later. Due to the 2020 outbreak of the coronavirus, regulators worldwide implemented lockdowns, forcing employees to work remotely indefinitely. And to the surprise of many, this trend has continued unabated. The adaptation of the work-from-home model by a myriad of office real estate tenants caused a significant decline in office space demand. According to commercial real estate services firm, CBRE, the U.S. national office market reported 16.5 million sq. ft. of negative net absorption in Q1 2023 (the weakest quarter for office demand in two years), bringing overall vacancy up to 17.8%. The concept of remote working has long been criticized and rejected. The prevailing belief was that employees are simply not as motivated nor productive working from home as opposed to the office. Additionally, critics further argue that it is impossible to build and maintain a company office culture if your employees are not physically present in the office. Simply put: the remote work model was widely regarded and portrayed as a productivity and culture “killer”. The temporary lockdowns in 2020 however presented a unique (and forced) opportunity for those theories to be tested. Three years later, it’s safe to say that the paradigm of traditional workspaces has undergone a seismic shift thanks to the Covid-19 pandemic. The remote-work model's benefits and limitations have largely come to light, prompting employers and employees to respond accordingly. With an increasing number of companies cutting down their real estate footprints, rising vacancy rates, and plummeting valuations, what exactly does the future hold for the office sector? How are investors, landlords, and tenants affected? These are some of the questions that I look to address throughout this paper, for which I’ve interviewed three highly regarded and respected industry experts.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.062 | 0.020 |
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".