---
ticker: "KRHHCREDITYOY"
title: "South Korea Household Credit Growth (YoY)"
unit: "%"
frequency: "Quarterly"
source: "Schularick and Taylor (2012); Jordà, Schularick, and Taylor (2017)"
release: "Updated Quarterly"
category: "Credit & Financial Cycle"
country: "KR"
language: "en"
canonical: "https://kred.dev/en/series/KRHHCREDITYOY"
license: "https://creativecommons.org/licenses/by-nc-nd/4.0/"
latest_value: 3.58
latest_date: "2026-04-01"
first_date: "2003-10-01"
observations_total: 91
observations_shown: 91
---

# South Korea Household Credit Growth (YoY)

## Overview

Tracks how fast household debt is expanding, a leading gauge of the financial cycle and systemic risk.

## Key Figures

|  | Value | Date |
|---|---|---|
| Latest | 3.58 | 2026-04-01 |
| Change from previous | 0.00 | 2026-01-01 |
| Change over one year | +0.55 | 2025-04-01 |
| Highest on record | 11.84 | 2006-10-01 |
| Lowest on record | -0.56 | 2023-01-01 |
| Period covered | 2003-10-01 – 2026-04-01 |  |
| Observations | 91 |  |

## Recent observations

| Date | Value | Change |
|---|---|---|
| 2003-10-01 | 1.58 |  |
| 2004-01-01 | 1.94 | +0.37 |
| 2004-04-01 | 2.74 | +0.80 |
| 2004-07-01 | 4.10 | +1.36 |
| 2004-10-01 | 4.69 | +0.59 |
| 2005-01-01 | 5.22 | +0.53 |
| 2005-04-01 | 8.11 | +2.89 |
| 2005-07-01 | 9.23 | +1.12 |
| 2005-10-01 | 9.85 | +0.62 |
| 2006-01-01 | 10.73 | +0.88 |
| 2006-04-01 | 10.34 | -0.39 |
| 2006-07-01 | 10.50 | +0.17 |
| 2006-10-01 | 11.84 | +1.33 |
| 2007-01-01 | 11.28 | -0.56 |
| 2007-04-01 | 10.57 | -0.70 |
| 2007-07-01 | 10.23 | -0.35 |
| 2007-10-01 | 9.60 | -0.63 |
| 2008-01-01 | 10.58 | +0.99 |
| 2008-04-01 | 10.90 | +0.32 |
| 2008-07-01 | 11.10 | +0.20 |
| 2008-10-01 | 8.74 | -2.36 |
| 2009-01-01 | 6.38 | -2.36 |
| 2009-04-01 | 5.46 | -0.92 |
| 2009-07-01 | 5.73 | +0.27 |
| 2009-10-01 | 7.25 | +1.52 |
| 2010-01-01 | 8.74 | +1.49 |
| 2010-04-01 | 8.66 | -0.08 |
| 2010-07-01 | 8.19 | -0.47 |
| 2010-10-01 | 8.66 | +0.47 |
| 2011-01-01 | 9.22 | +0.56 |
| 2011-04-01 | 9.63 | +0.41 |
| 2011-07-01 | 9.23 | -0.40 |
| 2011-10-01 | 8.65 | -0.57 |
| 2012-01-01 | 7.13 | -1.53 |
| 2012-04-01 | 5.86 | -1.27 |
| 2012-07-01 | 5.55 | -0.31 |
| 2012-10-01 | 5.20 | -0.35 |
| 2013-01-01 | 5.06 | -0.14 |
| 2013-04-01 | 5.50 | +0.44 |
| 2013-07-01 | 5.62 | +0.12 |
| 2013-10-01 | 5.73 | +0.11 |
| 2014-01-01 | 6.19 | +0.45 |
| 2014-04-01 | 5.74 | -0.45 |
| 2014-07-01 | 6.32 | +0.58 |
| 2014-10-01 | 6.50 | +0.18 |
| 2015-01-01 | 7.42 | +0.92 |
| 2015-04-01 | 9.23 | +1.81 |
| 2015-07-01 | 10.27 | +1.03 |
| 2015-10-01 | 10.86 | +0.59 |
| 2016-01-01 | 11.41 | +0.55 |
| 2016-04-01 | 11.14 | -0.27 |
| 2016-07-01 | 11.29 | +0.15 |
| 2016-10-01 | 11.59 | +0.30 |
| 2017-01-01 | 11.06 | -0.53 |
| 2017-04-01 | 10.35 | -0.71 |
| 2017-07-01 | 9.46 | -0.89 |
| 2017-10-01 | 8.05 | -1.41 |
| 2018-01-01 | 8.03 | -0.02 |
| 2018-04-01 | 7.52 | -0.51 |
| 2018-07-01 | 6.66 | -0.86 |
| 2018-10-01 | 5.92 | -0.74 |
| 2019-01-01 | 4.89 | -1.02 |
| 2019-04-01 | 4.33 | -0.57 |
| 2019-07-01 | 3.88 | -0.44 |
| 2019-10-01 | 4.14 | +0.26 |
| 2020-01-01 | 4.63 | +0.48 |
| 2020-04-01 | 5.18 | +0.56 |
| 2020-07-01 | 6.99 | +1.81 |
| 2020-10-01 | 8.02 | +1.03 |
| 2021-01-01 | 9.59 | +1.56 |
| 2021-04-01 | 10.50 | +0.91 |
| 2021-07-01 | 9.61 | -0.89 |
| 2021-10-01 | 7.68 | -1.93 |
| 2022-01-01 | 5.41 | -2.27 |
| 2022-04-01 | 3.17 | -2.25 |
| 2022-07-01 | 1.36 | -1.81 |
| 2022-10-01 | 0.22 | -1.14 |
| 2023-01-01 | -0.56 | -0.78 |
| 2023-04-01 | -0.41 | +0.15 |
| 2023-07-01 | 0.36 | +0.77 |
| 2023-10-01 | 0.92 | +0.57 |
| 2024-01-01 | 1.55 | +0.62 |
| 2024-04-01 | 1.82 | +0.27 |
| 2024-07-01 | 1.90 | +0.08 |
| 2024-10-01 | 2.11 | +0.21 |
| 2025-01-01 | 2.43 | +0.32 |
| 2025-04-01 | 3.03 | +0.60 |
| 2025-07-01 | 2.79 | -0.23 |
| 2025-10-01 | 2.95 | +0.16 |
| 2026-01-01 | 3.58 | +0.63 |
| 2026-04-01 | 3.58 | 0.00 |

## Definition

The group collects year-over-year growth rates derived from a credit-stock level series. Each rate expresses the outstanding balance at quarter $t$ as a percent change from its value four quarters earlier, so the level of the index and its base cancel and what remains is a pure rate of expansion.

$$g_t = \left( \frac{C_t}{C_{t-4}} - 1 \right) \times 100$$

Here $C_t$ is the end-of-quarter outstanding household-credit stock and $g_t$ is the four-quarter growth rate in percent. Because the same units enter numerator and denominator, the transform is scale-free and the resulting rate is comparable across regimes in which the level series was rebased or re-benchmarked. A growth rate constructed this way isolates the flow of new net borrowing relative to the existing stock, which is the quantity the financial-cycle literature treats as the leading margin of systemic risk (Schularick and Taylor 2012). The year-over-year window differences out the within-year seasonal pattern of the stock, so the rate is read directly without further seasonal adjustment (Jordà, Schularick, and Taylor 2017).

## Methodology

The rate is the four-quarter percent change of the underlying credit-stock level. For a quarterly level series $C_t$ the growth rate is

$$g_t = \left( \frac{C_t}{C_{t-4}} - 1 \right) \times 100$$

with a lag of four quarters at quarterly frequency, the analogue of the twelve-month window used at monthly frequency. No deflation, seasonal adjustment, or smoothing is applied beyond the year-over-year differencing itself, so the first four quarters of the level sample have no defined growth rate and are dropped. The construction follows the standard treatment of credit aggregates as growth rates in the macrofinancial-history literature (Schularick and Taylor 2012; Jordà, Schularick, and Taylor 2017), where the ratio form is preferred over the log difference so that the reported number matches the conventional year-over-year percentage that policy institutions publish (Drehmann, Borio, and Tsatsaronis 2012).

## Applications in Economics

The year-over-year growth of household credit is one of the most informative single indicators for macroprudential surveillance, because rapid credit expansion systematically precedes financial instability. Schularick and Taylor (2012) establish on long-run cross-country data that credit growth is the strongest single predictor of financial crises, reframing leverage as a leading cause of crisis rather than a passive by-product of growth. Jordà, Schularick, and Taylor (2017) refine this finding with the observation that mortgage-led household borrowing tracks the financial cycle and deepens subsequent downturns.

The growth rate carries information about the real economy that the level of the stock does not. Mian, Sufi, and Verner (2017) show across thirty countries that a rise in household credit growth is followed by slower output growth two to three years later, mediated by the consumption contraction that a debt overhang forces once income disappoints. Mian and Sufi (2018) formalize this credit-driven household-demand channel, in which the pace of borrowing rather than its accumulated level governs the boom-and-bust dynamic. Drehmann, Borio, and Tsatsaronis (2012) place credit growth at the center of their empirical characterization of the financial cycle, whose medium-term swings are longer and larger than the business cycle.

For Korea the household-credit growth rate sits at the center of the macroprudential debate. The rate accelerated through the mid-2000s and again through 2020 and 2021 before decelerating sharply as policy tightened through 2022, and each turning point maps onto a phase of the domestic credit cycle. Borio (2014) argues that such growth rates are the operational core of a financial-cycle view of macroeconomics, and Aikman, Haldane, and Nelson (2015) show that credit-growth-based rules would have flagged the build-up of vulnerabilities ahead of past crises. Read against income, the rate measures the intertemporal reallocation of household spending toward the present that a rising debt stock represents.

## Applications in Financial Markets

For market participants the household-credit growth rate is a forward-looking gauge of balance-sheet risk in the household sector. Because credit growth leads crises, an accelerating rate raises the probability of a subsequent disorderly deleveraging and feeds directly into the credit spreads that desks charge on bank and consumer-finance exposures (Schularick and Taylor 2012). Jordà, Schularick, and Taylor (2016) show that mortgage-driven credit expansions are the most dangerous variety, which lets fixed-income desks calibrate tail scenarios for mortgage and unsecured household portfolios from the growth rate directly.

The rate is a canonical input to macroprudential-policy timing. Drehmann and Juselius (2014) rank credit-based indicators as the most powerful early-warning signals across horizons, and Borio and Lowe (2002) embed the credit-growth signal in the framework that the countercyclical capital buffer operationalizes. For investors anticipating domestic macroprudential tightening, an accelerating household-credit growth rate is among the clearest leading indicators of when borrower-level loan limits and buffer requirements will be raised.

The rate also anchors the bank credit-loss cycle. Lombardi, Mohanty, and Shim (2017) show that fast household-credit growth predicts subsequent declines in consumption, which for banks translates into a rising credit-loss cycle at a one-to-two-year lead. For positions in domestic bank debt and equity, in consumer-finance receivables, and in mortgage-backed exposures, the growth rate is the leading indicator of the loss cycle that drives earnings and capital adequacy. Cecchetti and Kharroubi (2012) add that beyond a threshold, faster credit growth becomes a drag on real growth, which conditions the policy-rate path that discounts every domestic fixed-income position. Read alongside the credit-to-GDP gap and the debt-service ratio, the growth rate completes the layered read on credit-cycle stress that no single series delivers alone (Drehmann, Borio, and Tsatsaronis 2012).

## Statistical Tests

KRHHCREDITYOY is the year-over-year growth of the household-credit stock, a four-quarter percent change of the outstanding balance, tested over 90 quarterly observations from 2003-10-01 to 2026-01-01 under a constant-plus-trend specification. The augmented Dickey and Fuller (1979) test with the lag augmentation of Said and Dickey (1984) fails to reject a unit root at p = 0.766, the Phillips and Perron (1988) test concurs and fails to reject at p = 0.176 without the over-rejection toward spurious stationarity that afflicts it under near-unit moving-average errors (Ng and Perron 2001), and the KPSS test rejects level stationarity at p = 0.049 (Kwiatkowski et al. 1992). The two opposite-null families therefore agree, so the growth rate carries a clean I(1) reading.

The Ljung and Box (1978) portmanteau on the level, the candidate stationary rate, rejects the no-autocorrelation null at p = 0.000 at both the lag-4 and lag-8 rungs. Because the rate is the candidate stationary object, a level break search is the appropriate mean-shift diagnostic (Perron 1989), and the Bai and Perron (1998) procedure computed by the algorithm of Bai and Perron (2003) locates four mean shifts, at 2012-01-01, 2015-04-01, 2018-07-01, and 2022-04-01.

Because the year-over-year construction is itself the seasonal-differencing filter that removes the seasonal unit roots the HEGY class interrogates, the HEGY and Canova-Hansen seasonal-unit-root tests are deliberately not run, since testing seasonal unit roots that the transform has already annihilated would be circular (Hylleberg et al. 1990; Canova and Hansen 1995; Ghysels and Osborn 2001).

## Frequently Asked Questions

### What does household credit growth represent?

The four-quarter change in household credit outstanding, which is the pace at which household debt is accumulating.

### Why read household credit growth rather than the level?

The stock rises almost mechanically with nominal income, so the level alone identifies little. What reveals acceleration and deceleration in the credit cycle is the growth rate.

### Is household credit growth a raw series?

No. It is computed by KRED from the published stock, which is why it is grouped apart from the credit level series.