From Cafes to Competitiveness: The Influence of Amenities on Office Values and Suburban Economic Development in Portland, OR
Bibliographic record
Abstract
This dissertation examines the spatial and economic implications of urban amenities in the formation of suburban office markets in the post-pandemic period. The research applies principles from agglomeration theory, spatial econometrics, and urban planning to analyze the statistical relationships between amenity distribution, rent formation, and location behavior across central and non-central submarkets. The research consists of three integrated papers focused on the Portland–Vancouver–Hillsboro, OR–WA Metropolitan Statistical Area (MSA). The first paper employs spatial econometric modeling, specifically a Spatial Autoregressive Model with Autoregressive Disturbances (SARAR), to examine the relationship between proximity to cafés and office rent levels in Portland’s central business district (CBD) and non-CBD areas. The findings indicate strong spatial dependence in office rent data and a clear divergence between urban and suburban markets. Proximity to cafés is associated with higher office rents in non-CBD areas, whereas in the CBD the effect is comparatively weaker, reflecting amenity saturation and congestion externalities that may diminish marginal returns from additional amenity concentration in dense urban cores. The second paper extends this analysis by incorporating a broader set of amenities categorized under the Accommodation and Food Services (NAICS 72) sector and applies Spatial Durbin Models (SDM) across four counties within the metropolitan region. The results demonstrate that amenity-driven rent premiums are more prominent in emerging suburban markets, particularly in Clackamas County, where localized amenity clustering contributes to higher office values, whereas the CBD area and other suburban counties exhibit weaker or statistically insignificant relationships. The third paper employs qualitative methods to explore how planners, brokers, and tenants interpret and mobilize amenity value within location strategies. Focusing on two submarkets in Lake Oswego, Oregon, it shows that amenity value is not fixed but interpreted through actor-specific logics: planners use it as a livability tool, brokers as marketable assets, and tenants as a workforce attraction strategy. Together, this dissertation argues that amenities are not peripheral to economic geography, but constitute symbolic, spatial, and strategic infrastructure. Their presence signals quality, mediates spatial desirability, and supports place-making strategies in uneven office geographies. This dissertation contributes to the literature by offering an integrated, stakeholder-centered account of amenity logic, and by extending agglomeration theory into the interpretive domain of suburban development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".