📈 economics

Comparative Study of the Regulatory Effects of Dual-Pillar Policies on Housing Prices: A DSGE-Based Policy Simulation Analysis

This study utilizes a DSGE model to demonstrate that while monetary policy is the primary driver of housing price fluctuations, macroprudential policies offer more targeted and less costly regulation of real estate risks, suggesting that the two pillars function best as complementary rather than substitutive tools for macroeconomic stability.

Original authors: Feilong Zhao, Mengkai Chen, Xianzhu Wang

Published 2026-07-14

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Feilong Zhao, Mengkai Chen, Xianzhu Wang

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine the housing market as a giant, bouncy trampoline. On one side, you have families trying to jump on it (buying homes for living), and on the other, investors and developers trying to bounce as high as possible (buying homes as investments). The problem is, if you bounce too hard, the whole trampoline could snap, taking the whole neighborhood down with it.

This study asks a big question: Who should be the referee? Should it be the "Interest Rate Captain" (Monetary Policy) or the "Loan Limit Coach" (Macroprudential Policy)?

Here is what the researchers found by building a super-complex computer simulation (a digital twin of the economy) to test these two referees.

The Two Referees

1. The Interest Rate Captain (Monetary Policy)
This referee controls the cost of borrowing money. When they lower the "ticket price" (interest rates), everyone rushes to the trampoline.

How it works: It's like turning up the volume on the whole stadium. It makes borrowing cheaper for everyone, not just homebuyers.
The Catch: The study found this referee is a bit of a "sledgehammer." When they try to fix the housing market, they accidentally shake the entire economy.
- In the simulation, 65.951% of the wobbles in housing prices were caused by this referee's moves.
- Even crazier, 99.060% of the changes in how much families spend on food, clothes, and fun were caused by this referee!
- The Cost: Trying to fix housing prices with interest rates is expensive. The "pain score" (policy loss) for using this tool was 6.4543. That's a huge price to pay just to calm down the housing market.

2. The Loan Limit Coach (Macroprudential Policy)
This referee controls the "down payment" rules (called the Loan-to-Value or LTV ratio). They can say, "You need to put more of your own money down before you can borrow."

How it works: This is like a targeted laser beam. They only tell the real estate players to slow down. They don't mess with the rest of the economy as much.
The Effect: This coach is great at stopping real estate firms from hoarding too many empty houses or taking on too much debt.
The Cost: It's much cheaper to use. The "pain score" for this tool was only 0.1379. It's like using a gentle nudge instead of a sledgehammer. However, it doesn't explain as many of the big price swings on its own (only about 5.441% of the housing price wobbles).

The Big Reveal: They Are Not Rivals; They Are a Team

The study explicitly rules out the idea that these two referees are interchangeable substitutes. You can't just swap one for the other and expect the same result.

What the paper argues against: It argues against relying solely on the Interest Rate Captain to fix housing. The simulation shows that if you try to use interest rates to control housing prices, you end up causing massive, unnecessary chaos in people's daily spending and the overall economy.
What the paper suggests: The best strategy is a Dual-Pillar approach.
- Let the Interest Rate Captain handle the big picture: keeping inflation low and the whole economy growing steadily.
- Let the Loan Limit Coach handle the specific trouble: stopping real estate bubbles and keeping banks safe from risky loans.

The "Loss" Scoreboard

To prove this, the researchers ran a "Policy Loss Frontier" simulation. Think of this as a video game where you try to minimize the "Game Over" screen.

When they tried to fix everything with just interest rates, the game got messy fast.
When they used the Loan Limit Coach, the game stayed stable.
The "sweet spot" (the best coordination) happened when the Interest Rate Captain focused on inflation (with a response coefficient around 2.10) and the Loan Limit Coach focused on housing prices (with a response coefficient around 0.65).

How Sure Are They?

The authors didn't just guess; they ran thousands of numbers through their model.

They tested if their results would break if they tweaked the rules slightly (a "sensitivity test"). The results held up, showing the findings are robust.
They simulated different scenarios, like a sudden change in how much people want to buy houses. Even then, the conclusion remained the same: Interest rates are too blunt for housing; loan limits are the precise tool.

The Takeaway for the Future

The study suggests that if we want to keep the housing market from bouncing too wildly, we need to stop expecting the Interest Rate Captain to do everything. Instead, we need a clear division of labor:

Interest Rates = Keep the whole economy calm and growing.
Loan Limits (LTV) = Keep the real estate sector from getting too greedy or risky.

If we mix them up or rely too much on just one, the simulation shows we risk high costs for everyone. The "Dual-Pillar" team is the only way to win the game without crashing the trampoline.

Technical Summary: Comparative Study of the Regulatory Effects of Dual-Pillar Policies on Housing Prices

Problem Statement
The real estate market possesses dual attributes as both a consumption good and an investment asset, acting as a critical transmission channel for macroeconomic cycles through household wealth effects, financing constraints, and bank balance-sheet mechanisms. While traditional monetary policy (interest rate adjustments) effectively manages aggregate demand, it suffers from significant limitations in housing regulation: it is an economy-wide tool that may induce overheating in other sectors, lags in supply response can lead to price overshooting, and prolonged low-rate environments may distort risk appetite and increase leverage. Conversely, macroprudential policy (specifically Loan-to-Value or LTV adjustments) offers sectoral targeting and countercyclical capabilities but lacks direct control over aggregate demand and relies heavily on implementation effectiveness. Existing literature often analyzes these policies in isolation or lacks a systematic comparison of their transmission mechanisms, economic costs, and coordination boundaries. This study addresses the need for a scientific, coordinated policy framework to balance growth stabilization and risk prevention in the real estate sector.

Methodology
The study employs a Dynamic Stochastic General Equilibrium (DSGE) model featuring three distinct sectors: households, real estate firms, and commercial banks. The model integrates two policy rules:

Monetary Policy: An interest-rate-based rule responding to inflation and output gaps.
Macroprudential Policy: A dynamic LTV adjustment rule responding to housing prices and real estate output.

The model captures interactions through deposit/loan markets, labor supply, housing demand/supply, and collateral constraints. The analysis utilizes:

Bayesian Estimation: Parameters are calibrated based on literature and estimated using data from 2004–2024 (central bank rates, National Bureau of Statistics data) via Dynare 5.5 in Matlab.
Impulse Response Analysis: To trace the dynamic effects of monetary and macroprudential shocks on key variables.
Variance Decomposition: To identify the contribution of different shocks (monetary, macroprudential, inflation, etc.) to the fluctuations of housing prices and other variables.
Policy Loss Function & Frontier Analysis: To quantify regulatory costs (housing price, output, consumption, and credit fluctuations) and determine the optimal coordination boundary between the two policy pillars.
Counterfactual Simulations: To compare policy combinations (baseline, strengthened monetary, strengthened macroprudential, and dual-pillar coordination) under housing preference shocks.

Key Contributions
The paper claims three primary marginal contributions:

Unified Framework: It embeds both interest-rate-based monetary policy and dynamic LTV-based macroprudential policy within a single DSGE framework, enabling a direct comparative analysis of their housing price regulatory effects.
Mechanism Clarification: It elucidates the differentiated transmission paths of the dual pillars. It distinguishes the aggregate impact of monetary policy (via interest rates and asset prices) from the structural, targeted regulation of macroprudential policy (via credit constraints and collateral values).
Multi-Dimensional Evaluation: By combining variance decomposition, loss functions, loss frontier analysis, and counterfactual simulations, the study evaluates policies from the perspectives of fluctuation sources, regulatory costs, and coordination boundaries, moving beyond static empirical comparisons.

Key Results

Shock Sources: Monetary policy shocks are the dominant driver of housing price fluctuations (contributing 65.951%) and household consumption fluctuations (99.060%). In contrast, macroprudential policy shocks contribute only 5.441% to housing price variance, indicating they are less effective as a primary driver of price levels compared to interest rates.
Transmission Dynamics:
- Monetary Policy: Shocks cause significant, sustained cumulative effects on housing prices and household consumption. They alter household intertemporal asset allocation (increasing deposits) and contract enterprise financing, leading to long-term price adjustments but with high volatility costs.
- Macroprudential Policy: Shocks have a more direct but smaller impact on prices. They primarily affect real estate enterprise inventory and credit behavior through LTV constraints. The cumulative effect on prices can shift from negative to positive as the market adjusts, but the spillover to general household consumption is minimal.
Policy Loss Comparison: The comprehensive policy loss associated with monetary policy shocks (6.4543) far exceeds that of macroprudential policy shocks (0.1379). Monetary policy incurs high costs in terms of output and consumption volatility. Macroprudential policy exhibits lower spillover costs and is more efficient for sector-specific risk containment.
Coordination Boundaries: The policy loss frontier analysis suggests that dual-pillar coordination is not a simple additive strengthening of both tools. Excessively strong LTV responses to housing prices can increase comprehensive loss, particularly when price fluctuations are driven by inflation or macro-demand factors rather than pure credit leverage. Optimal coordination requires balancing the inflation response coefficient ( $\phi_\pi$ ) and the housing price response coefficient ( $\phi_q$ ).
Robustness: Sensitivity tests confirm that the main conclusions hold across variations in policy response parameters, though the model is most sensitive to the monetary policy response to output ( $\phi_y$ ).

Significance and Claims
The paper asserts that the dual-pillar framework should not view monetary and macroprudential policies as substitutes but as complementary components of a coordinated architecture.

Functional Division: Monetary policy is best suited for stabilizing aggregate demand and managing inflation, while macroprudential policy is superior for containing procyclical real estate credit risks and managing leverage.
Cost Efficiency: Relying solely on monetary policy for housing regulation entails substantial volatility costs for the broader economy. Macroprudential tools offer a more targeted approach with lower spillover effects.
Policy Design: The study advocates for a state-dependent coordination mechanism. When fluctuations stem from macro-demand or inflation, monetary policy should lead; when risks arise from credit expansion and collateral overvaluation, macroprudential tools should take the lead. The goal is to minimize comprehensive loss rather than focusing on the short-term stability of a single variable.

The authors conclude that effective regulation requires improving risk monitoring systems and enhancing policy transparency to manage market expectations, ensuring that the dual-pillar framework achieves both price stability and financial stability.