Government regulation and changes in the affordable housing stock
Bibliographic record
Abstract
This paper identifies the relationship between government controls on housing supply and changes in the stock of market housing affordable to low-income households. In studies of housing, policy makers and academics have focused considerable attention on both the shortage of good-quality affordable rental housing for low-Income households and the effect of land use regulations on house prices and the supply on new residential units without linking the two topics. This is the first effort we know of to test how government controls on the construction of new units affects the supply of affordable rental accommodations. We conduct these tests using a filtering model, where units move between quality sub-markets depending on demand and the maintenance, renovation, and repair decisions of landlords. Here, we take advantage of the panel nature of the AHS metropolitan surveys to identify the relationship between restrictions on new construction and rent control policies and the movement of individual housing units in and out of the stock of units affordable to low-income households. First, we find that the greater the supply elasticity for new construction, the less likely are affordable rental units to filter up and out of the affordable stock. Second, our empirical results suggest that restrictions on new construction are likely to reduce the affordable stock as it increases the probability that an affordable unit becomes unaffordable. Third, the relationship between affordable uncontrolled units and the presence of rent control is more confusing. We find the surprising result that as the percentage of rent controlled units in an area rises, the remaining affordable non-rent controlled units actually have a lower probability of filtering up relative to staying affordable. We suspect that this is an artifact of a selection bias in the identification of market-rate units that are affordable in the presence of rent control. This process might result in uncontrolled units that remain affordable when rent control is more pervasive having much lower unobserved quality or the presence of negative externality from poorly maintained rent control stock. Both of these factors would reduce the probability that these units filter up.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".