Is the dream still alive? Tracking homeownership amid \nchanging economic and demographic conditions
Bibliographic record
Abstract
The United States (U.S.) is undergoing three major trends, which are converging and changing the housing market. The first trend is housing inventory is constrained in much of the U.S. As a result, home prices have increased to an inflation‐adjusted 49 percent from 2012 to 2017 (National Association of Realtors 2018b) and has become out of reach for many Americans as incomes have risen 14 percent in the same timeframe (U.S. Census Bureau 2018a). The second trend is the homeownership rate for those under the age of 35, Black/African American, and Hispanic/Latino adults has not rebounded since the Great Recession in the United States (U.S.). The third trend is the amount of student loan debt in the U.S. has increased about 70 percent from 2007 to 2017 (Chakrabarti et al. 2017) and is concentrated among those under the age of 35, Black/African Americans, and Hispanic/Latinos. This thesis explores the intersection of these trends through the application of quantitative and qualitative analysis. Through a Two‐Stage Least Squares econometric approach, those with student debt, Black/African American, and Hispanic/Latino buyers purchase a lower priced home, even while controlling household income and home size purchased. These three populations are most at risk to be impacted by the reduction in housing inventory, increased home prices, and the increase in student loan debt. As these three populations face limited affordable housing inventory and student debt increases, the homeownership rate has declined. To understand how local economic and demographic factors play a role, the days on market, unemployment rate, the share of those over the age of 65, and share of those with Bachelor’s degrees within the Metropolitan Statistical Area (MSA) are added into the model. Results from the econometrics are triangulated through focus groups conducted in cities across the U.S. Focus groups explored themes that were not able to be understood through econometrics, such as the idea that individuals may prefer to rent. The thesis contains policy recommendations based on the findings from the econometrics and focus groups.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".