A Value Planning Framework for Predicting and Recapturing the Value of Rapid Transit Infrastructure
Why this work is in the frame
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Bibliographic record
Abstract
Land value capture (LVC) has been used to capitalize on the symbiotic relationship between rapid transit and its potential land value uplift (LVU) benefits for more than a century. For the public sector in particular, the rationale to engage in LVC to recapture the ‘unearned increment’ is strong. While interest in LVC has wavered over this time, planners and policymakers in Ontario and around the world are increasingly looking to value capture as a potential solution for raising more revenue to fund the construction and operation of rapid transit projects. However, significant theoretical, conceptual, and practical gaps remain in our knowledge of LVU and LVC that prevent the wider adoption of value capture as a strategy. First, a fundamental flaw in applications of LVC is that the value increment caused by rapid transit must to some degree be known a priori to set benchmark levels and ensure LVC tools capture the actual changes in land values caused by the project. Yet despite a rich history of research into the LVU benefits of rapid transit in cities around the world, a method for arriving at more empirical predictions of future LVU beyond simple approximation remains elusive. This leads to a second issue. Previous research into the LVU effects of rapid transit has produced a body of work that exhibits significant heterogeneity in results. Such diversity in research outcomes is due to a singular focus on expectations of LVU from rapid transit accessibility, which has led previous research to ignore the potential for additional land value impacts from sorting into different bundles of transit-oriented development (TOD) based on individual preferences. As such, the results of previous studies consider the value placed on a bundle of transit and TOD characteristics. This context-dependency makes them unsuitable for extensions to estimate the potential for LVC in future transit corridors. To overcome these issues, the present dissertation develops a value planning framework for rapid transit. This is accomplished through five objectives. First, Chapter 2 establishes a theoretical framework for understanding the LVU effects of rapid transit accessibility and TOD. Second, Chapter 3 develops a typology of station area TOD to reduce the complexity of station area heterogeneity and control for such contextual factors in further research. Third, Chapter 4 applies the TOD typology to unbundle the LVU effects of existing rapid transit in the City of Toronto. Fourth, Chapter 5 develops the value planning framework to better conceptualize the drivers of LVU benefits and capturable revenues, the policy interventions to maximize them, and the beginnings of a model to utilize unbundled estimates of LVU in other study areas to derive context-sensitive predictions of LVU in future transit station areas. Finally, Chapter 6 conducts a theoretical application of the value planning framework to the case of a light rail transit line in Hamilton, Ontario, to demonstrate a rationale for engaging in value planning to promote value capture. In accomplishing these objectives, the present dissertation makes a number of contributions to research and practice. However, it also raises a number of questions for future research. Nevertheless, this work presents a significant first step towards realizing research on rapid transit’s LVU effects that is more theoretically comprehensive and practical for better informing LVC planning and policy around the world.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 it