How RBI’s macroprudential policies shape housing loan decisions #IndiaFinance

The recent surge in housing finance in India calls for a robust regulatory framework to ensure financial stability and avert overheating of the housing market. Using data on individual mortgage loans from a nationally representative public sector bank for the period 2010-2021, this article examines the behavioural responses of borrowers to macroprudential policies employed by the RBI to modulate the housing finance market.

The housing finance market in India has seen remarkable growth in recent times (see Figure 1) primarily driven by rapid economic growth and increasing purchasing power. This surge in housing finance has necessitated a robust regulatory framework to ensure financial stability and avert overheating of the housing market.

Figure 1. Outstanding housing loans of scheduled commercial banks in India  (in Rs. crore)

The Reserve Bank of India (RBI) has employed various macroprudential policies (MPPs) to manage this growth effectively. It has been deploying several MPP tools to modulate the housing finance market, by preparing banks to manage potential downside risks rather than curbing genuine credit growth. MPPs are non-interest rate tools that are standard financial stability tools, especially in the mortgage market. The three key objectives of MPPs, as identified by the Financial Stability Board (FSB), Bank for International Settlement (BIS), and International Monetary Fund (IMF), are: (i) increasing the resilience of the financial system to aggregate shocks through the creation and release of capital buffers, (ii) constraining financial booms, (iii) and reducing structural vulnerabilities in the financial system.

The RBI uses many such tools, either targeted at the borrower to modulate their eligibility vis à vis the size of mortgage loans or targeted at banks to modulate their ability to lend to the mortgage segment. Examples of the former include time-varying Loan-to-Value (LTV) ratios¹ and debt service-to-income ratios (DSTI)². The latter category may comprise varying capital requirements by adjusting risk weights³and periodic changes in provisioning requirements⁴. These tools are periodically updated through master circulars issued by the RBI to all scheduled commercial banks.

Research on India’s macroprudential policies

Literature suggests a significant impact of MPPs on the economy, especially the mortgage market (Kinghan et al. 2019; Acharya et al. 2022). The RBI has been actively pursuing financial stability as a part of its policy objectives since 2005. Yet only a limited body of research explores the role of MPPs in the Indian context, primarily focusing on aggregate credit growth and house prices. For instance, Verma (2018) uses banks’ annual reports to examines the impact of RBI’s MPPs on credit growth, and finds a negative effect of policy tightening on GDP (gross domestic product) and credit growth. Similarly, Singh (2022) shows that the RBI’s MPPs have a strong influence on the dynamics of house prices in the country; there is also a significant countercyclical effect⁵albeit more pronounced for larger mortgages. Kumar et al. (2022) also explores the effect of MPPs on the growth of bank credit overall, credit for housing, and appreciation of house prices and find that policy tightening contains the growth of house prices across Indian cities.

We deviate from these studies by examining the behavioural responses of individual borrowers of mortgage loans from a public sector bank to the RBI’s macroprudential tools related to the housing sector (Saha, Rooj and Sengupta 2023).

Creating a Macroprudential Policy Index

To analyse the effectiveness of these tools, we first create a Macroprudential Policy Index (MPPIND), capturing the periodic changes in LTV limits, risk weights, and provisioning requirements. This index provides a comprehensive measure of the overall stringency of MPPs in the housing finance market⁶. Figure 2 plots the overall index and its components. It shows that in recent times, RBI has primarily resorted to relaxing MPP norms to facilitate the growth of India’s housing sector.

Figure 2. Macroprudential Policy Index and its components, 2011-2021

Note: MPPIND refers to the aggregate Macroprudential Policy Index, whereas LTVLM, RISKWT & PROVR are the loan-to-value limits as per the size of the loan, the associated risk weights, and provisioning requirements, respectively.

Source: Authors’ calculations, based on information provided in the master circulars of the Reserve Bank of India (RBI) relating to housing finance.

Impact of macroprudential policies on borrower behaviour

We utilise the MPPIND to examine the role of macroprudential tools related to mortgage finance on the behaviour of mortgage loan borrowers in India. We use a unique dataset of individual mortgage loans from a nationally representative public sector bank covering the period between 2010 and 2021. In all 50,210 individual mortgage loans, accounts with information on borrower demographics (gender, age, and credit score), loan contract, and the location of loan origination enumerated by the concerned bank. This dataset allowed for an in-depth analysis of the heterogeneous impacts of MPP regulations on individual banks’ LTV decisions based on socioeconomic characteristics and creditworthiness of borrowers.

The baseline findings indicated that for an average borrower with an LTV of approximately 69%, one unit tightening of MPP would imply a reduction of LTV by over 2.5%. This impact is quite substantial when compared to the findings of other studies such as Kinghan et al. (2019).

Heterogenous impact of macroprudential policies

The findings of the study reveal several insights on how MPPs impact leverage decisions across different borrower segments:

Income groups: Macroprudential measures have a greater impact on individuals belonging to high-income groups. They typically have higher leverage ratios, and hence, are more affected by the tightening of MPPs, leading to a reduction in their borrowing capacity.

Credit scores: Borrowers with higher credit scores are found to be less sensitive to changes in MPPs. These borrowers typically have better access to credit and can negotiate better loan terms. On the other hand, borrowers with lower credit scores are more impacted by changes in LTV limits and risk weights, as these measures directly affected their borrowing capacity.

Loan amounts: The impact of MPP changes is also observed to vary based on the loan size. Larger loans, which are subject to stricter LTV limits and higher risk weights, see a more significant reduction in leverage following MPP tightening. Smaller loans are less affected, as the changes in policy measures have a relatively lower impact on their overall cost.

Transmission mechanisms and lagged effects

Understanding the transmission mechanisms of MPPs is crucial for designing effective policies. We identify several channels through which MPPs influence borrower behaviour, including the downpayment and housing cost channels. The downpayment channel operates through changes in LTV limits, which directly affect the amount of upfront payment required from borrowers. On the other hand, the housing cost channel influences the overall cost of housing finance through adjustments in interest rates and loan terms.

We also examine the lagged effects of MPP changes on mortgage loan decisions. The impact of MPP tightening on borrower leverage decisions tends to materialise gradually, with significant effects observed over several months. This suggests that timely and forward-looking interventions are essential to ensure orderly growth in the housing market and maintain financial stability.

Policy implications

These findings have significant implications for policymakers and financial regulators. The evidence suggests that MPPs, mainly changes in risk weights, effectively manage borrowing risk and ensure stability in the mortgage market. The study also highlights the importance of timely interventions by the RBI to address emerging risks in the housing finance sector.

The heterogeneous effects of MPPs across different borrower segments underscore the need for a nuanced approach to macroprudential regulation. Policymakers should consider the diverse impacts on different borrower groups and design measures that promote equitable access to credit while ensuring financial stability. This may involve targeted interventions for specific borrower segments, such as first-time homebuyers or low-income households. A robust framework for the design of balanced monetary, macroprudential, and fiscal policy formulation, including the effective communication of policy decisions, should continue to evolve for the healthy and sustainable growth of the mortgage finance market in India.

Notes:

1. LTV ratio is the policy option used by individual lending institutions while deciding the maximum exposure that the institution is ready to take on an individual based on the book value of the mortgaged property. The RBI modulates the LTV limits for banks as an MPP tool.
2. DSTI is ratio of debt service payments (that is, equated monthly instalment or EMIs in housing loans) to the total disposable income of the housing loan borrower.
3. Risk weights are the multiplication factor used by lending institutions to compute the regulatory capital requirement. Risk weights vary according to the creditworthiness of a borrower.
4. Provisioning is the amount statutorily set aside by lending institutions from their yearly revenue to provide for expected future losses from the exposure in case of non-payment.
5. Countercyclical effect of MPP is the intervention made by RBI to contain the possible build-up of a housing bubble during an economic boom by modulating the LTV ratio, risk weights, and provisioning requirements – thereby constraining the flow of credit to the housing market.
6. For a detailed description on the construction of the index, please refer to Saha, Rooj and Sengupta (2023).

Further Reading

• Acharya, Viral V, Katharina Bergant, Matteo Crosignani, Tim Eisert and Fergal McCann (2022), “The Anatomy of the Transmission of Macroprudential Policies”, The Journal of Finance77(5): 2533-2575.
• Kinghan, Christina, Yvonne McCarthy and Conor O’Toole (2019), “How do macroprudential loan-to-value restrictions impact first-time home buyers? A quasi-experimental approach”, Journal of Banking & Finance138: 105678.
• Kumar, Sanjiv, K. P. Prabheesh and Omar Bashar (2022), “Examining the effectiveness of macroprudential policy in India”, Economic Analysis and Policy75: 91-113.
• Saha, Asish, Debasis Rooj and Reshmi Sengupta (2023), “Macroprudential Policy and mortgage leverage decisions—Evidence from micro data”, Economic Analysis and Policy, 80: 1430-1444.
• Verma, R (2018), ‘Effectiveness of macroprudential policies in India’, Macroprudential Policies in SEACEN Economies, The SEACEN Centre.