Authors: Dr Ranjith Kumar Billa, Dr Dulari
Abstract: Monetary policy has a pronounced impact on India’s mortgage lending patterns: tightening central bank rates curb housing loan growth while easing measures stimulate credit (outstanding mortgage loans rose from about 8.0% of GDP in 2014–15 to 11.2% by 2024–25; total housing credit jumped from ₹9.97 lakh crore to ₹37.14 lakh crore over 2000–2025). This study examines how RBI policy tools (repo rate, CRR, SLR) affect mortgage volumes, loan pricing and access across borrower segments and regions. Objectives include quantifying transmission channels, institutional differences (public/private banks vs. HFCs), and the dynamic effects of policy changes. The analysis employs a longitudinal approach (2000–2025, quarterly data) using secondary sources: RBI databases (policy rates, banking credit, liquidity ratios), NHB reports (housing loan disbursements, outstanding loans, RESIDEX prices), CMIE and macroeconomic indicators. Key variables include the policy rate, repo, CRR/SLR, housing loan volumes and interest spreads, lender type (HFC vs bank) credit flows, and NPA ratios. Empirical methods combine time-series and panel econometrics: vector autoregressions, VECMs with impulse-response and variance-decomposition analysis, panel regressions and difference-in-differences with fixed effects, supplemented by robustness checks (alternative lags, sub-samples, threshold models). Results indicate that contractionary policy significantly dampens mortgage growth and loan approvals, raising lending rates and spreads. For example, a 1% repo rate hike lowers credit growth by roughly 0.8% in the short run (with error-correction adjustment). Banks’ lending is generally more interest-sensitive than HFCs, reflecting HFCs’ diversified funding. HFCs continue to supply ~45–50% of formal housing credit, but both lenders slow lending under tight policy. Borrowers in lower-income segments and tier-II/III regions face disproportionately higher credit constraints. These effects work with lags, and interact with housing price cycles and macroprudential measures. Policy implications: Regulators should calibrate monetary and macroprudential tools jointly to support housing demand (e.g. adjust LTV or provisioning norms when rates rise) and safeguard affordable housing credit. Lenders should adapt loan pricing and origination to minimize exclusion of vulnerable borrowers. The findings underscore the need for better data and coordination between monetary and housing policy.
