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Effectiveness of Rent Controls: Evidence from Spain

Luis Perez Garcia

TL;DR

The paper evaluates the 2024 Catalan rent-control policy under the national Ley por el Derecho a la Vivienda using municipality-level administrative data and multiple identification strategies (DiD, event studies, Propensity Score Matching DiD, Synthetic DiD). It finds initial evidence of slowed rent growth and fewer tenancy agreements, but results are fragile due to pre-treatment trend violations and data limitations; when robustness methods are applied, the rent-growth effect largely disappears while the drop in rental activity persists, suggesting a potential negative impact on rental supply. The analysis underscores the importance of robust causal inference in evaluating price controls and highlights data limitations that constrain inference, including unadjusted price measures and missing control observations. The study thus provides a cautious, early look at a newly implemented policy and points to avenues for future research, such as incorporating the second wave of treated areas and exploring shifts to tourist rentals or ownership. The findings have implications for policy design in housing affordability, indicating that price caps may reduce rental supply even if effects on rents are uncertain in the long run.

Abstract

Growing concerns about housing affordability have prompted the adoption of rent control policies and renewed debates over their effectiveness. This paper provides the first empirical evaluation of the 2024 rent control policy implemented in Catalonia under Spain's new national housing law. To identify the causal effect of the policy on the rental market, I use municipality-level administrative data and implement several difference-in-differences strategies and event study designs. The results point to a reduction in tenancy agreements and a less robust decrease in rental price growth. While the findings highlight important short-term consequences of rent control, they also underscore the need for caution due to data limitations and limited robustness in some estimates.

Effectiveness of Rent Controls: Evidence from Spain

TL;DR

The paper evaluates the 2024 Catalan rent-control policy under the national Ley por el Derecho a la Vivienda using municipality-level administrative data and multiple identification strategies (DiD, event studies, Propensity Score Matching DiD, Synthetic DiD). It finds initial evidence of slowed rent growth and fewer tenancy agreements, but results are fragile due to pre-treatment trend violations and data limitations; when robustness methods are applied, the rent-growth effect largely disappears while the drop in rental activity persists, suggesting a potential negative impact on rental supply. The analysis underscores the importance of robust causal inference in evaluating price controls and highlights data limitations that constrain inference, including unadjusted price measures and missing control observations. The study thus provides a cautious, early look at a newly implemented policy and points to avenues for future research, such as incorporating the second wave of treated areas and exploring shifts to tourist rentals or ownership. The findings have implications for policy design in housing affordability, indicating that price caps may reduce rental supply even if effects on rents are uncertain in the long run.

Abstract

Growing concerns about housing affordability have prompted the adoption of rent control policies and renewed debates over their effectiveness. This paper provides the first empirical evaluation of the 2024 rent control policy implemented in Catalonia under Spain's new national housing law. To identify the causal effect of the policy on the rental market, I use municipality-level administrative data and implement several difference-in-differences strategies and event study designs. The results point to a reduction in tenancy agreements and a less robust decrease in rental price growth. While the findings highlight important short-term consequences of rent control, they also underscore the need for caution due to data limitations and limited robustness in some estimates.
Paper Structure (9 sections, 2 equations, 1 figure, 4 tables)

This paper contains 9 sections, 2 equations, 1 figure, 4 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Data
  4. Empirical Strategy
  5. Results
  6. Baseline Results
  7. Propensity Score Matching DiD
  8. Synthetic DiD
  9. Conclusion

Figures (1)

  • Figure : (a) Rent Growth