Topics in Hedonic Valuation
Notice bibliographique
Résumé
<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>
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».