Design a quasi-experiment for FHFA policy
Causal Impact of FHFA's Multifamily Underwriting Tightening
Context
On April 15, 2024, the Federal Housing Finance Agency (FHFA) implemented tightened underwriting standards for multifamily loans purchased or guaranteed by Government-Sponsored Enterprises (GSEs). You are tasked with designing a quasi-experimental study to estimate the causal impact of this policy on two key outcomes:
- Monthly originations (measured in both counts and dollar volumes)
- 12-month delinquency rates on newly originated loans
Assume access to comprehensive loan-level GSE data, which includes application, lock, and close dates, various underwriting metrics (such as Loan-to-Value (LTV), Debt Service Coverage Ratio (DSCR), and debt yield), detailed property characteristics, geographical information (specifically, Metropolitan Statistical Area (MSA)), lender identifiers, and historical loan performance data.
Tasks
- Design Choices and Identification:
- Clearly define the treatment group, control group, unit of analysis (e.g., MSA or a more granular property_type × MSA combination), and the precise timing of the intervention.
- Justify the selection of a staggered Difference-in-Differences (DiD) approach utilizing Sun–Abraham event-study estimators, providing a rationale against or in comparison to alternative methods like synthetic control.
- Diagnostics and Robustness:
- Describe the methodology for testing and visualizing pre-trends to validate the parallel trends assumption.
- Specify various falsification tests (e.g., using placebo policy dates, examining treatment leads) and robustness checks (e.g., employing alternative time windows, varying event-study binning strategies).
- Spillovers, Composition, and Interference:
- Address potential confounding factors such as capital reallocation spillovers across different MSAs, shifts in the composition of borrowers or deals, and interference effects propagated through lender networks.
- Propose methods like instrumental variables (IV) or reweighting techniques if deemed necessary to mitigate these issues.
- Measurement and Inference:
- Precisely define the outcome variables.
- Specify the appropriate clustering level for standard errors to ensure valid inference.
- Outline how uncertainty will be communicated to executive stakeholders (e.g., using prediction intervals versus standard errors or confidence intervals).
- Data and Compliance:
- Detail the specific data requirements beyond what is assumed.
- Explain how to handle challenges related to policy anticipation (e.g., a “pull-forward” effect of originations) and partial compliance (e.g., grandfathered loan pipelines, policy waivers).
AI interview prep
Core dilemma
The central engineering conflict is establishing a credible causal link between a non-randomized policy intervention and its outcomes in a complex, dynamic financial market. This requires balancing the rigor of econometric identification strategies with the practical limitations of observational data, while effectively accounting for confounding factors, spillovers, and potential policy anticipation.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Design a quasi-experiment for FHFA policy
Causal Impact of FHFA's Multifamily Underwriting Tightening
Context
On April 15, 2024, the Federal Housing Finance Agency (FHFA) implemented tightened underwriting standards for multifamily loans purchased or guaranteed by Government-Sponsored Enterprises (GSEs). You are tasked with designing a quasi-experimental study to estimate the causal impact of this policy on two key outcomes:
- Monthly originations (measured in both counts and dollar volumes)
- 12-month delinquency rates on newly originated loans
Assume access to comprehensive loan-level GSE data, which includes application, lock, and close dates, various underwriting metrics (such as Loan-to-Value (LTV), Debt Service Coverage Ratio (DSCR), and debt yield), detailed property characteristics, geographical information (specifically, Metropolitan Statistical Area (MSA)), lender identifiers, and historical loan performance data.
Tasks
- Design Choices and Identification:
- Clearly define the treatment group, control group, unit of analysis (e.g., MSA or a more granular property_type × MSA combination), and the precise timing of the intervention.
- Justify the selection of a staggered Difference-in-Differences (DiD) approach utilizing Sun–Abraham event-study estimators, providing a rationale against or in comparison to alternative methods like synthetic control.
- Diagnostics and Robustness:
- Describe the methodology for testing and visualizing pre-trends to validate the parallel trends assumption.
- Specify various falsification tests (e.g., using placebo policy dates, examining treatment leads) and robustness checks (e.g., employing alternative time windows, varying event-study binning strategies).
- Spillovers, Composition, and Interference:
- Address potential confounding factors such as capital reallocation spillovers across different MSAs, shifts in the composition of borrowers or deals, and interference effects propagated through lender networks.
- Propose methods like instrumental variables (IV) or reweighting techniques if deemed necessary to mitigate these issues.
- Measurement and Inference:
- Precisely define the outcome variables.
- Specify the appropriate clustering level for standard errors to ensure valid inference.
- Outline how uncertainty will be communicated to executive stakeholders (e.g., using prediction intervals versus standard errors or confidence intervals).
- Data and Compliance:
- Detail the specific data requirements beyond what is assumed.
- Explain how to handle challenges related to policy anticipation (e.g., a “pull-forward” effect of originations) and partial compliance (e.g., grandfathered loan pipelines, policy waivers).