Essays on Climate Risks, Policy Shocks, and Housing Markets
Bibliographic record
Abstract
This dissertation investigates how external shocks, both environmental and regulatory, influence residential real estate markets. By examining climate-related temperature exposures in the United States and foreign buyer tax policies across multiple countries, this research provides critical insights into how housing markets price climate risks and respond to policy interventions. Together, these essays reveal how market participants rapidly capitalize new information about risks and regulatory changes into property values. This research contributes to our understanding of housing market dynamics in an era of increasing climate uncertainty and global capital flows, offering important implications for homeowners, investors, and policymakers navigating these evolving challenges. Chapter 1 High Temperature, Climate Change, and Real Estate Prices Combining granular daily data on temperatures across the continental United States with comprehensive listing-level data for residential properties, we study the impact of temperature shocks on real estate prices. We show that temperature exposures are associated with higher climate change concerns. We find that temperature exposures result in a significant decrease in house prices. This impact is more substantial in areas with greater awareness of global warming, during periods of heightened public attention to climate change, and in locations more vulnerable to sea-level rise. An instrumental-variables strategy based on ENSO teleconnections mitigates endogeneity concerns and yields similar conclusions. While temperature exposures influence property sale prices, we find no detectable effect on rental rates, suggesting that the observed price discount from temperature exposures is driven by concerns about long-term climate risks. The discount also persists after accounting for insurance costs. Our results highlight the importance of climate uncertainty in affecting real estate prices. Chapter 2 Do Foreign Buyer Taxes Affect House Prices? This paper studies the impact of foreign buyer taxes on house prices using recent policy changes in Canada, Australia, and New Zealand. We combine machine learning–based prediction techniques with inference methods from the synthetic control method literature to estimate counterfactual house prices for treated locations. In general, we find that these taxes had large, negative, and persistent effects on house price growth, with stronger effects in areas with higher tax rates and larger immigrant shares. Alternative outcome variables, including population growth, GDP growth, and unemployment rates, were either unaffected or only slightly affected in ways that do not confound our results.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".