Topics in Hedonic Valuation
Bibliographic record
Abstract
<p>Environmental goods such as clean air and water are integral to human quality of life. However, because these amenities are not priced, their monetary values are not directly apparent. As a result, Hedonic methods have been employed as a tool to recover household Marginal Willingness To Pay (MWTP) for these goods to inform policy-making given constrained public resources. This thesis consists of three chapters tied to the Hedonic valuation of a particular environmental `bad,' a brownfield site. Brownfield properties are lands that cannot be used due to the presence of a low-risk, hazardous substance. </p><p>The first chapter uses property value hedonics to reveal household willingness to pay for brownfield cleanup (joint work with Kevin Haninger and Christopher Timmins). We exploit variation in space and time to deal with the potential bias in estimating MWTP due to unobservable variables that are correlated with both housing prices and site cleanup. Furthermore, there has been recent work showing that if equilibrium price functions change over time, the capitalization of changes in neighborhood amenities into property values over time (e.g. brownfield cleanup) may neither represent the preferences of those living in the neighborhood before changes occurred or after. To address this, we provide a way to estimate cleanup without assuming that the hedonic price function is stable over time, an assumption that would likely be violated if site cleanup brought about significant changes to the community populations around the sites.</p><p>The second chapter considers two sources of distortions in the valuation of non-marketed goods - an expectations bias and a learning bias. If consumers suspect that cleanup of a brownfield is likely before it is cleaned (expectation) or gain new information about the severity of the brownfield contamination (information), then baseline period prices need to be adjusted to account for these potential distortions to the MWTP estimate. To address this, I collect a new data set on brownfield contamination information over time from Massachusetts, and recover hedonic values from a dynamic neighborhood choice framework that allows agents to learn about brownfield hazards in a Bayesian fashion. I find a MWTP estimate of \\$888.38 per unit of site contamination when accounting for learning and forward-looking behavior, which is more than double the simple hedonic estimate. Furthermore, parameters from my model can be used to calculate the average value of information provided by a site assessment.</p><p>The final chapter, joint work with Gabrielle Inder, examines whether different types of information about brownfield contamination capitalize into property values differently. More specifically, we estimate a property value hedonic model to test if housing prices are impacted differently if information about nearby contamination is released as a continuous measure as opposed to a binary measure (i.e. exceeding a threshold value or not). We do this by exploiting variation in contaminant thresholds used, holding constant the contaminant level, due to regulatory requirements for brownfield investigations in the State of Massachusetts. As the variation in threshold levels are tied to the level of human exposure of the areas in which these contaminated sites exist, threshold exceedance is potentially endogenous to unobserved neighborhood characteristics that also impact housing values. To deal with this, we take an Instrumental Variables approach using variation in threshold exceedance due to the location of underground water sources. After instrumenting for threshold exceedance with the presence of an aquifer underground, our estimates indicate a 10\\% decrease in housing values from exceeding contaminant thresholds, but that continuous toxicity levels have a negative but insignificant effect. These findings suggest that polices aimed to improve public awareness about pollution should be cognizant of how information is conveyed, as it may allow for better design of information provision programs aimed to improve environmental quality.</p>
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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) | 0.000 | 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) | 0.000 | 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".