Bibliographic record
Abstract
The dissertation examines the linkages among real estate liquidity risks, price risks, and the thickness of a market, as they are observed in cities. It attempts to explain why large metropolitan property markets can be attractive to investors and continue to thrive in terms of liquidity. The dissertation uses real estate markets as the context, and adopts the perspective of an asset vendor to examine the decision processes leading to spatial agglomeration in light of liquidity risks. Property prices are uncertain in the form of price risks, and the asset vendor is risk-averse. The dissertation examines how a thicker market can reduce liquidity risk and explains why thicker markets can be attractive to investors in terms of lower liquidity risks. The dissertation is comprised of three papers, and each paper deals with a specific question that attempts to forge the linkages among real estate liquidity risks, price risks, and the thickness of a market. The first paper precisely defines the cost of liquidity as a discount in price (or utility) that an investor would be willing to tradeoff for an immediate transaction. The second paper uses the definition of liquidity cost established in the first paper to examine how and to what extent the thickness of a market may affect the cost of liquidity. Thickness is defined as the number of trading partners in the market. The third paper adopts a different stance to analyze how an agent copes with uncertainty in general, in light of the indivisible nature of home ownership. It uses home ownership as an example to study the relationship among price risk, risk aversion, and the investor's propensity to invest. The dissertation highlights the role of uncertainty in shaping a risk-averse investor's location choice. It provides a different lens to explore the processes leading to spatial agglomeration. It emphasizes the agent's behavior in understanding economic activities and spatial outcomes. It broadens our understanding of cities' role as a thick market, which helps investors to cope with uncertainty.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".