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Can government stabilize the housing market? The evidence from South Korea
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Can government stabilize the housing market? The evidence from South Korea ∗
Hanwool Jang a , Yena Song b , , Kwangwon Ahn c ,
∗
a
Graduate School of Future Strategy, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141 Republic of Korea b Department of Geography, Chonnam National University, Gwangju 61186, Republic of Korea c Department of Industrial Engineering, Yonsei University, Seoul 03722, Republic of Korea
article
info
Article history: Received 6 August 2019 Received in revised form 5 December 2019 Available online xxxx Keywords: Housing policy Bubble Long-run equilibrium Market efficiency
a b s t r a c t This study aims to examine the effectiveness of government policies in stabilizing the housing market in South Korea. Analysis of condominium prices between 2012 and 2019 in conjunction with housing policy and regime changes reveals that: (1) housing policies effectively restrained the rapid increase of housing prices; (2) market efficiency was also improved; and (3) finally, housing prices approached close to long-run equilibrium. Our findings strongly indicate that government policies can be effective tools to stabilize housing prices, as well as improve market conditions. © 2019 Published by Elsevier B.V.
1. Introduction In the wake of the global financial crisis in the previous decade, house prices plummeted in many countries, with a downturn in the US housing market. The impact was systemwide, because firms and households owned real estate, and construction was one of the important sectors of the real economy [1]. In order to recover the real economy and to stabilize the housing market, each government developed and imposed real estate policies that suited its own contexts. The housing market failed, so the government intervened. Then a question naturally followed, whether the government policies were effective in correcting and stabilizing the market or not. Traditionally, government tries to stabilize the housing market by employing fiscal and/or regulatory approaches. Fiscal policy can have significant impact on the housing market by controlling household disposable income. The most notable fiscal approaches are taxation and subsidies. Many countries adopt tax systems that involve favorable elements for debtfinanced home ownership, such as tax deductions for mortgage interest payments [2,3]. This effectively lowers the burden of debt leading to higher home ownership. By contrast, transaction and property taxes greater than the rate of house price appreciation can restrict speculative motivation [2,4]. When the house market is overheated, imposition of higher property tax rates can control the housing market [2]. However, such tax treatment on the housing market does not have the same effects in all countries. During the recent real estate market boom, housing prices significantly increased in both Sweden and France, and the former had a highly favorable treatment of debt-financed home ownership, whilst the latter employed relatively unfavorable tax rules [2]. Further, we can find examples of restricted house price appreciation in Portugal and Japan which respectively adopted favorable and unfavorable fiscal policies towards home ownership. Regulatory policies, such as loan-to-values (LTVs), have been implemented to control the real estate booms and busts. These policies, such as maximum LTV, appeared to be effective in curbing house price [2,5]. Similarly, lower debtto-income (DTI) limits can reduce the upward pressure on real estate prices [2,6]. These measures were found to be ∗ Corresponding authors. E-mail addresses: [email protected] (Y. Song), [email protected] (K. Ahn). https://doi.org/10.1016/j.physa.2019.124114 0378-4371/© 2019 Published by Elsevier B.V.
Please cite this article as: H. Jang, Y. Song and K. Ahn, Can government stabilize the housing market? The evidence from South Korea, Physica A (2020) 124114, https://doi.org/10.1016/j.physa.2019.124114.
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H. Jang, Y. Song and K. Ahn / Physica A xxx (xxxx) xxx Table 1 Summary statistics. Tight regulations were enacted on 3 Nov. 2016.
Obs. Mean Std. Max Min Skewness Kurtosis
Whole period (May 2012 – April 2019)
Pre-regulation (May 2012 – Oct. 2016)
363 0.010 0.041 0.117 −0.059 0.051 2.533
234 0.018 0.043 0.117 −0.059 −0.178 2.422
Post-regulation (Nov. 2016 – April 2019) 128
−0.003 0.031 0.053 −0.053 0.015 2.785
consistently effective in many Asian countries. With the empirical study in Hong Kong, Wong et al. [7] argued that proper LTV cap adjustments could effectively reduce the systemic risk caused by the boom–bust cycles in the property market. Igan and Kang [8] and Kim [9] found that tight LTV and DTI caps were significantly associated with lower growth rate of house prices, mortgage lending, and house brokerage activity in South Korea. Their results showed that tight regulations on the Korean real estate market led to lower housing prices, less transactions, and low expectations of a rise in housing prices. Further, such situations made home owners more pessimistic on the growth of house prices compared to those who did not own a home, as such regulatory policies could be effective at suppressing speculative investment. This study aims to empirically explore whether a mixture of government policies could prevent the bursting of housing price bubbles with the case of the Korean housing market. After the recent financial crisis, in order to revitalize the depressed housing market, the Korean government implemented the easy money policy, lowered taxation on the acquisition and transfer income, and revoked the revocation of speculative zones in several districts. Furthermore, government-led housing supply schemes were implemented to stimulate the construction industry, and stabilize housing demand. After a series of deregulations and fiscal expansions, the housing market became buoyant; however, the excessive housing supply and price appreciation started to form a market bubble that might cause collapse of the market. Thus, the government changed its view on the housing market, and executed regulations to cool down the market from late 2016. However, there is limited discussion about the effectiveness of fiscal policy and regulation in stabilizing the markets. This study aims to answer the following two questions: (1) Did the tight policy measures suppress the housing bubble? (2) How were the market efficiency and long-term equilibrium affected by the series of policy interventions? The next section delineates the data used for empirical analysis, and explains key methodologies. Section 3 discusses the results with emphasis on housing bubbles, market efficiency, and long-term equilibrium. Section 4 concludes the paper. 2. Data and methodology 2.1. Data The weekly price index of condominiums provided by the Korea Appraisal Board is used for the analysis, which enabled us to track the countrywide changes of house prices. Our sample period spans from the launch of the index, 7 May 2012, to 29 April 2019, and this period ranges over three consecutive administrations, namely the Lee Myungbak administration (25 Feb. 2008 – 24 Feb. 2013), the Park Geunhye administration (25 Feb. 2013 – 9 May 2017), and the Moon Jaein administration (10 May 2017 – present). The data is then rearranged into three sets: the entire period, and before and after the initiation of tight regulations (see the Appendix). Table 1 presents the summary statistics of our data. All data are annualized log returns. The maximum value of our data appeared on 7 Oct. 2013, which corresponds with the policy implementation intended to promote homeownership, such as permanently lowering the acquisition tax. Mean and standard deviation decreased during the post-regulation period, indicating that the price growth turned downwards, and the volatility of condominium price was suppressed. Skewness and kurtosis imply that the log returns became closer to a normal distribution during the post-regulation period: the market behaved more like geometric Brownian Motion, in other words, more efficiently [10]. The summary statistics imply a possible association between policy implementations and housing markets. 2.2. Log-periodic power law The log-periodic power law (LPPL) is one of the tools adopted to assess the bubbles in market [11–13]. It was first introduced in statistical physics, and subsequently claimed our attention for bubble diagnosis and prediction [14–16]. In this model, traders’ actions, e.g., buy or sell, depend on the decisions of others: agents form groups with self-similar behavior through interactions [17,18]. For instance, when some traders make an investment in assets with overconfidence, others in the same network ape one another in a boom cycle. The resultant increase in the asset value leads to speculative reinvestment, and this loop repeats over time [14]. Such positive feedback continues up to a certain point, often called the critical time; and the LPPL model can predict the crash date of bubbles [19–21]. Please cite this article as: H. Jang, Y. Song and K. Ahn, Can government stabilize the housing market? The evidence from South Korea, Physica A (2020) 124114, https://doi.org/10.1016/j.physa.2019.124114.
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The critical time can be detected by examining the signature of a faster-than-exponential growth, and its decoration by log-periodic oscillations [22–29]. Mathematically, the concepts can be explained as follows: Yt = A + B(tc − t)β {1 + C cos[ω ln(tc − t) − φ]} for t < tc ,
(1)
where Yt > 0 is the log of the asset price at time t; tc > 0 is the critical time; A > 0 is the log price at the critical time tc ; B < 0 is the size of increase in Yt over the time before the crash when C is close to 0; C ∈ [−1, 1] controls the magnitude of the oscillations around the exponential trend; β ∈ [0, 1] is the exponent of the power law growth; ω > 0 is the frequency of the fluctuations during the bubble; and φ ∈ [0, 2π ] is a phase parameter. In order to estimate the parameters, we simulate the initial values for the parameters with a price gyration method, and then estimate these parameters using a nonlinear optimization algorithm, the so-called genetic algorithm, following [14]. 2.3. Hurst exponent To obtain the aggregated series x(m) (k) for k ≥ 1, we divide the series x = {xi for i ≥ 1} into blocks of size m, as follows [30,31]: x(m) (k) =
km ∑
1 m
x(i),
i=(k−1)m+1
where, k is a block label, and m is a successive value. Let V (x(m) ) be the sample variance of x(m) (k) within each block k of size m. Then, the Hurst exponent (HE) θ is given by: V (x(m) ) ∼ m2θ −2 . The variable θ can measure the weak form market efficiency, i.e., long-range memory of the time series [32]. A value in the range 0 < θ < 0.5 indicates a time series with mean reverting property, i.e., an anti-persistent series, while a value in the range 0.5 < θ < 1 reveals positive correlation, i.e., a persistent series with long-term memory. A value of θ = 0.5 indicates a completely uncorrelated series, which supports the weak form efficient-market hypothesis (EMH).1 We follow the estimation procedure of θ employing the methodology suggested by Block [32]. 2.4. Entropy Shannon [36] first proposed entropy, which can measure the dispersion of the probability allocation assigned to each state. It has been widely adopted to examine the degree of randomness and uncertainty of financial time series, due to its robustness of extreme value [37–39]. High entropy characterizes randomness of the system, while low entropy is related to a highly deterministic change of asset returns [37]. The entropy of the discrete random variable Z is given by: H(Z ) = −
M ∑
p(zj ) ln p(zj ),
j=1
where, M is the number of possible outcomes of random variable Z , and p(zj ) is the probability assigned to state zj . We use the symbolic time series analysis (STSA) to capture time varying pattern in the log return series by transforming the real values into a restricted number of symbols. STSA is widely used in physics, information theory, and finance [37, 40,41]. Each sequence consists of the consecutive returns of an asset S, which can be denoted as taking two values: 0 for a negative return, and 1 otherwise. Afterwards, we convert the 0–1 sequence from a binary system to a decimal number Z S . The Shannon entropy of Z S is then calculated following [37]: H(Z S ) = −
M ∑
p(zjs ) log2 p(zjs ),
j=1
where, M = 2S . Then, the Shannon entropy is normalized as follows: h(Z S ) =
1 S
H(Z S ).
1 The EMH is that new information received by investors is almost instantly reflected in the asset prices [33,34]. Since new information cannot be predicted, the future price of the assets cannot be predicted [32]. In particular, the weak form of EMH can be examined when the price fluctuation follows a random walk [35]. Please cite this article as: H. Jang, Y. Song and K. Ahn, Can government stabilize the housing market? The evidence from South Korea, Physica A (2020) 124114, https://doi.org/10.1016/j.physa.2019.124114.
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Fig. 1. Logarithm of condominium prices and corresponding alarm. The shaded area is Park’s presidency. Table 2 The best fit of LPPL parameters. A
B
tc
β
C
ω
ϕ
4.760
−0.045
222.701
0.332
0.055
5
0.001
3. Results and discussion As previously noted, the tight regulations appeared effective at reducing the volatility in condominium markets. In order to examine the efficacy of the government policies on the housing markets, we further estimate the critical time of housing price bubbles by applying the LPPL model. The red solid line in Fig. 1 is an in-sample estimate from 18 March 2013 to 31 Oct. 2016 (pre-regulation). Root mean square error, which shows the difference between the log price and its estimated value, is minimized at 0.01, and therefore our model fits the data fairly well [14]. Table 2 reports the parameter estimates. It satisfies the two conditions B < 0 and 0.1 ≤ β ≤ 0.9, ensuring a faster than exponential acceleration of the log price [42]. The value of ω is 5, which corroborates existing studies, such as Johansen [43], who documented that ω ≈ (6.36 ± 1.56) for 30 crashes in major financial markets. We then conduct out-of-sample forecasting during the period of post-regulation, i.e., after 7 Nov. 2016. In the case of observations (black line), there are no significant increases around the critical time (19 June 2017). Though the LPPL model indicates the critical time in the second half of 2017, market prices are slowly increasing without the collapse of bubbles. This implies that the government policies enacted to curb a boom of the housing market were properly timed. Housing policies can have effects not only on the price appreciation but also at the volume of transactions. For further examination, the trading volumes of condominium are presented in Fig. 2. During the period of consecutive loose policies, between the early 2012 and the end of 2016, the trading volume sharply increased. However, after enacting tight regulation at the end of 2016, the volume of transactions began to decreased: 13% from peak to peak and 26% from valley to valley. It indicates that the government policies effectively cooled down the excessive demand which in turn stabilizes the housing price. We examine the impact of the government policy on the housing market in terms of weak-form EMH by estimating the HE. Table 3 shows that the housing market has long-term memory (H = 0.907); however, after the regulations were tightened, it drops by 6.284% (to 0.850). Along with the increasing frequency of similar price change patterns, the HE has a higher value, H > 0.5 [44]. With this, it can be concluded that the policies worked on the market as intended. Further, the entropy is calculated to analyze the status for long-run equilibrium of housing price dynamics.2 In particular, the spread of the probability assigned to each time varying pattern is measured by STSA. We applied S = 4, which denotes 1 month in the time horizon. Table 3 shows the increment in entropy during the post-regulation period, implying the dynamic pattern of the returns closer to the long-run equilibrium. Then, the robustness of our results were tested with S = 3 and 5, and the results are not significantly different from the case of S = 4. In sum, the regulations indeed influenced the housing market; more specifically, the regulations improved the market conditions in terms of efficiency and long-run equilibrium. 2 The entropy can be applied to find a distribution of a non-degenerated probability of the target systems that accounts for the central tendency and the fluctuations around [45]. In this paper, the long-run equilibrium indicates stable status in which all competing influences are balanced. The equilibrium price, thus, can be derived by maximizing the entropy of system which is the likely state of the system in the form of a probability distribution, namely ‘statistical equilibrium’ [46,47]. Please cite this article as: H. Jang, Y. Song and K. Ahn, Can government stabilize the housing market? The evidence from South Korea, Physica A (2020) 124114, https://doi.org/10.1016/j.physa.2019.124114.
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Fig. 2. Trading volume of condominium. The shaded area is Park’s presidency. The trading volume is the total number of condominiums traded during a month (Source: Korean Statistical Information Service). Table 3 Characteristics of the housing market. Hurst exponent calculated by the aggregated variance method [31]. Whole Period (7 May 2012 – 29 April 2019)
Pre-Regulation (7 May 2012 – 31 Oct. 2016)
Post-Regulation (7 Nov. 2016 – 29 April 2019)
0.907 0.462
0.939 0.403
0.850 0.560
Hurst exponent Entropy
Our results corroborate the existing literature, and confirm that both the regulatory and fiscal policies can be effective tools in stabilizing the housing market. In terms of the regulatory policy, it is found that lowering the loan ceiling led to the decrease of purchasing power, and, finally to the suppression of housing prices: LTV and DTI limits could effectively rein the upward pressure on house prices [2]. Further, the government tried to control housing price appreciation by deploying increased transfer income tax from Aug. 2017. This fiscal contraction strategy appeared to have suppressed speculative investment and rapid increase of housing prices [2,4]. On the other hand, fiscal expansion began to be deployed from Nov. 2017, which specifically targeted the economically vulnerable groups, i.e., new employees, college graduates, newlyweds, senior citizens, and low income earners, so was considered as welfare policy, rather than general housing policy. Fig. 1 shows how such targeted policies worked in the housing market: there were no significant changes in the housing price movement trend. This indicates that the targeted fiscal expansion does not have significant impacts on the market in general. 4. Conclusion This study aims to examine the effectiveness of the government policies in stabilizing the housing price and market conditions. We find that the regulations effectively restrained the upward trend of condominium prices in South Korea. Implemented policies contribute not only to improvement of the efficiency of housing market, but also to decrease of the discrepancy between the distribution of condominium prices and their long-run equilibrium. Our findings suggest that timely implementation of the regulatory policies is an appropriate and effective means of stabilizing the market, as well as improving the market conditions. At the same time, the market might be excessively cooled once strong policies were implemented, because housing price can be characterized as having long-term memory, regardless of the implementation of regulations. In this regard, in establishing policies, it is necessary to take the long-run movements of the housing market into account. It should be noted that our findings may not be applicable to other countries and regions. Each country has its own contexts determining housing prices along with universal variables such as housing characteristics, local characteristics and environmental characteristics. Although the results may not be universally applicable, our methodologies and measures can be used to assess the effectiveness of housing policies in other countries and regions. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Please cite this article as: H. Jang, Y. Song and K. Ahn, Can government stabilize the housing market? The evidence from South Korea, Physica A (2020) 124114, https://doi.org/10.1016/j.physa.2019.124114.
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Acknowledgments This research was supported by the Future-leading Research Initiative at Yonsei University (Grant Number: 2019-220200; K. Ahn) and by National Research Foundation of Korea Grant funded by the Korean Government (NRF-2019-Global Ph.D. Fellowship Program: 2019H1A2A1074420; H. Jang). Appendix See Table 4.
Table 4 Housing market policies in South Korea. The policies were retrieved from the Ministry of Land, Infrastructure and Transport, and the National Archives of Korea. Type (Date) President Lee
President Park
Policy
Deregulation, fiscal expansion (May 2012)
May 10 Policy: Lifting three Gangnam districts from the speculative zone list; increasing LTV and DTI from 40% to 50%; relaxing the period for restricted resale of condominium purchase right; expanding eligibility to and maximum amount of bogeumjari loans; relaxing the heavy transfer tax imposed on short-term possessions.
Deregulation, fiscal expansion (Apr. 2013)
Apr. 1 Policy (encouragement of house transactions): Exempting transfer tax for 5 years for new and unsold houses worth KRW 900 million or lower; temporarily exempting acquisition tax for first-time house purchasers; repealing heavy transfer income tax, and relaxing tax imposed on short-term possessions; permitting vertically expanded remodeling.
Fiscal expansion (Aug. 2013)
Aug. 28 Policy (stabilization of key money deposit and monthly rent; inducing key money deposit-based tenants to purchase houses): Permanently lowering acquisition tax, and introducing shared mortgages.
Fiscal expansion (Dec. 2013)
Dec. 3 Policy: Various supportive policies, including expanding shared mortgages and REIT-based leases; integrating policy mortgages; vitalizing ‘‘Happy House’’.
Deregulation (Jul. 2014)
July 24 Policy: Relaxing regulations and unilaterally applying 70% of LTV and DTI; incorporating housing subscription accounts (relaxing the requirements for first priority).
Deregulation (Sep. 2014)
Sep. 1 Policy: Relaxing regulations on the minimum period to eligible for reconstruction; relaxing the requirements for first priority in housing subscription (1 year shortened for minimum monthly deposits).
Deregulation (Oct. 2014)
Oct. 30 Policy: Measures to mitigate ordinary citizens’ bearing of housing expenses (encouraging transactions through deregulation).
Deregulation, fiscal expansion (Dec. 2014)
Dec. 23 Policy: Applying flexible limitation on the maximum condominium price of private housing site; extending the grace period for 3 years for the redemption of excessive earnings from reconstruction.
Fiscal expansion (Jan. 2015)
Jan. 13 Policy: Measures to innovate middle-class housing by fostering enterprise-type housing rental business.
Fiscal expansion (Apr. 2016) Strengthened regulation (Nov. 2016)
Apr. 28 Policy: Increasing the supply for ‘‘Happy House’’ and ‘‘New Stay’’.
Strengthened regulation (Dec. 2016) Strengthened regulation (Jan. 2017)
Dec. 24 Policy: Strengthening the restrictions on loans for balance payment.
Strengthened regulation (Jun. 2017)
June 19 Policy (selective and tailored responses to ensure stable management of the housing market): Designating additional controlled areas; strengthening lending regulations with respect to housing subscription and LTV/DTI within the controlled areas.
Strengthened regulation, fiscal contraction (Aug. 2017)
Aug. 2 Policy (measures to stabilize the housing market through protection of real customers and containment of short-term speculative demand): Designating speculative areas and speculation-prone areas; modifying the regulations on reconstruction and redevelopment; strengthening transfer income tax; strengthening LTV/DTI-related financial regulations; mandating the report of financing plans.
Nov. 3 Policy (control of speculative demand): Prohibiting the resale of condominium purchase right in four Gangnam districts and other areas in the speculative zone list; strengthening the requirements for first priority in housing subscription.
Announcing the measures to introduce the debt service ratio (DSR), which is a more tightened lending criterion than DTI.
(continued on next page)
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Table 4 (continued). Type (Date) President Moon
Policy
Strengthened regulation (Sep. 2017)
Designating Bundang-gu (Seongnam) and Sujeong-gu (Daegu) as additional speculative areas; facilitating the improvement of requirements applicable to restrictions on maximum parcel price of private housing sites.
Strengthened regulation (Oct. 2017)
Oct. 24 Policy (comprehensive measures for household debt): Applying the new DTI in 2018; applying the debt service ratio (DSR) early; maintaining the increase rate of household debt under 8%; strengthening the regulations on real estate lease businesses; providing tailored support for vulnerable households, small business owners, and vulnerable borrowers.
Fiscal expansion (Nov. 2017)
Nov. 29 Policy (housing welfare roadmap): Developing measures to provide housing support to households at diverse life stages, such as young citizens, newlyweds, senior citizens, and low income earners, based on lifecycle and class; formulating plans to supply 1 million publicly supported houses within the next 5 years.
Fiscal expansion (Dec. 2017)
Dec. 13 Policy (measures to encourage rental house registration to benefit both landlords and tenants): Encouraging rental house registration through tax benefits for transfer income tax and gross real estate tax; strengthening the protection of tenants’ rights through, for example, guarantee on the return of key money deposit.
Fiscal expansion (Jul. 2018)
July 05 Policy (measures to provide housing support for newlyweds and young citizens for happy marriage and child-rearing): Expanding and specifying the support programs for newlyweds and young citizens in the housing roadmap, such as supplying 100,000 houses in towns for new couples, and youth-priority housing subscription accounts.
Strengthened regulation, fiscal expansion (Aug. 2018)
Aug. 27 Policy: Developing approximately 30 additional public housing sites that can supply at least 300 thousand houses within Greater Seoul; designating additional speculative areas and speculation-prone areas in Seoul, and in some areas within Gyeonggi-do.
Fiscal contraction (Sep. 2018)
Sep. 13 Policy: Imposing up to 3.2% of heavy gross real estate tax for those who own two or more houses in a controlled area; increasing the upper limit of tax burden from 150% to 300%; adding a new tax bracket, between KRW 300 and 600 millions; increasing the tax rate by 0.2% p.
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Can government stabilize the housing market? The evidence from South Korea ∗
Hanwool Jang a , Yena Song b , , Kwangwon Ahn c ,
∗
a
Graduate School of Future Strategy, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141 Republic of Korea b Department of Geography, Chonnam National University, Gwangju 61186, Republic of Korea c Department of Industrial Engineering, Yonsei University, Seoul 03722, Republic of Korea
article
info
Article history: Received 6 August 2019 Received in revised form 5 December 2019 Available online xxxx Keywords: Housing policy Bubble Long-run equilibrium Market efficiency
a b s t r a c t This study aims to examine the effectiveness of government policies in stabilizing the housing market in South Korea. Analysis of condominium prices between 2012 and 2019 in conjunction with housing policy and regime changes reveals that: (1) housing policies effectively restrained the rapid increase of housing prices; (2) market efficiency was also improved; and (3) finally, housing prices approached close to long-run equilibrium. Our findings strongly indicate that government policies can be effective tools to stabilize housing prices, as well as improve market conditions. © 2019 Published by Elsevier B.V.
1. Introduction In the wake of the global financial crisis in the previous decade, house prices plummeted in many countries, with a downturn in the US housing market. The impact was systemwide, because firms and households owned real estate, and construction was one of the important sectors of the real economy [1]. In order to recover the real economy and to stabilize the housing market, each government developed and imposed real estate policies that suited its own contexts. The housing market failed, so the government intervened. Then a question naturally followed, whether the government policies were effective in correcting and stabilizing the market or not. Traditionally, government tries to stabilize the housing market by employing fiscal and/or regulatory approaches. Fiscal policy can have significant impact on the housing market by controlling household disposable income. The most notable fiscal approaches are taxation and subsidies. Many countries adopt tax systems that involve favorable elements for debtfinanced home ownership, such as tax deductions for mortgage interest payments [2,3]. This effectively lowers the burden of debt leading to higher home ownership. By contrast, transaction and property taxes greater than the rate of house price appreciation can restrict speculative motivation [2,4]. When the house market is overheated, imposition of higher property tax rates can control the housing market [2]. However, such tax treatment on the housing market does not have the same effects in all countries. During the recent real estate market boom, housing prices significantly increased in both Sweden and France, and the former had a highly favorable treatment of debt-financed home ownership, whilst the latter employed relatively unfavorable tax rules [2]. Further, we can find examples of restricted house price appreciation in Portugal and Japan which respectively adopted favorable and unfavorable fiscal policies towards home ownership. Regulatory policies, such as loan-to-values (LTVs), have been implemented to control the real estate booms and busts. These policies, such as maximum LTV, appeared to be effective in curbing house price [2,5]. Similarly, lower debtto-income (DTI) limits can reduce the upward pressure on real estate prices [2,6]. These measures were found to be ∗ Corresponding authors. E-mail addresses: [email protected] (Y. Song), [email protected] (K. Ahn). https://doi.org/10.1016/j.physa.2019.124114 0378-4371/© 2019 Published by Elsevier B.V.
Please cite this article as: H. Jang, Y. Song and K. Ahn, Can government stabilize the housing market? The evidence from South Korea, Physica A (2020) 124114, https://doi.org/10.1016/j.physa.2019.124114.
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H. Jang, Y. Song and K. Ahn / Physica A xxx (xxxx) xxx Table 1 Summary statistics. Tight regulations were enacted on 3 Nov. 2016.
Obs. Mean Std. Max Min Skewness Kurtosis
Whole period (May 2012 – April 2019)
Pre-regulation (May 2012 – Oct. 2016)
363 0.010 0.041 0.117 −0.059 0.051 2.533
234 0.018 0.043 0.117 −0.059 −0.178 2.422
Post-regulation (Nov. 2016 – April 2019) 128
−0.003 0.031 0.053 −0.053 0.015 2.785
consistently effective in many Asian countries. With the empirical study in Hong Kong, Wong et al. [7] argued that proper LTV cap adjustments could effectively reduce the systemic risk caused by the boom–bust cycles in the property market. Igan and Kang [8] and Kim [9] found that tight LTV and DTI caps were significantly associated with lower growth rate of house prices, mortgage lending, and house brokerage activity in South Korea. Their results showed that tight regulations on the Korean real estate market led to lower housing prices, less transactions, and low expectations of a rise in housing prices. Further, such situations made home owners more pessimistic on the growth of house prices compared to those who did not own a home, as such regulatory policies could be effective at suppressing speculative investment. This study aims to empirically explore whether a mixture of government policies could prevent the bursting of housing price bubbles with the case of the Korean housing market. After the recent financial crisis, in order to revitalize the depressed housing market, the Korean government implemented the easy money policy, lowered taxation on the acquisition and transfer income, and revoked the revocation of speculative zones in several districts. Furthermore, government-led housing supply schemes were implemented to stimulate the construction industry, and stabilize housing demand. After a series of deregulations and fiscal expansions, the housing market became buoyant; however, the excessive housing supply and price appreciation started to form a market bubble that might cause collapse of the market. Thus, the government changed its view on the housing market, and executed regulations to cool down the market from late 2016. However, there is limited discussion about the effectiveness of fiscal policy and regulation in stabilizing the markets. This study aims to answer the following two questions: (1) Did the tight policy measures suppress the housing bubble? (2) How were the market efficiency and long-term equilibrium affected by the series of policy interventions? The next section delineates the data used for empirical analysis, and explains key methodologies. Section 3 discusses the results with emphasis on housing bubbles, market efficiency, and long-term equilibrium. Section 4 concludes the paper. 2. Data and methodology 2.1. Data The weekly price index of condominiums provided by the Korea Appraisal Board is used for the analysis, which enabled us to track the countrywide changes of house prices. Our sample period spans from the launch of the index, 7 May 2012, to 29 April 2019, and this period ranges over three consecutive administrations, namely the Lee Myungbak administration (25 Feb. 2008 – 24 Feb. 2013), the Park Geunhye administration (25 Feb. 2013 – 9 May 2017), and the Moon Jaein administration (10 May 2017 – present). The data is then rearranged into three sets: the entire period, and before and after the initiation of tight regulations (see the Appendix). Table 1 presents the summary statistics of our data. All data are annualized log returns. The maximum value of our data appeared on 7 Oct. 2013, which corresponds with the policy implementation intended to promote homeownership, such as permanently lowering the acquisition tax. Mean and standard deviation decreased during the post-regulation period, indicating that the price growth turned downwards, and the volatility of condominium price was suppressed. Skewness and kurtosis imply that the log returns became closer to a normal distribution during the post-regulation period: the market behaved more like geometric Brownian Motion, in other words, more efficiently [10]. The summary statistics imply a possible association between policy implementations and housing markets. 2.2. Log-periodic power law The log-periodic power law (LPPL) is one of the tools adopted to assess the bubbles in market [11–13]. It was first introduced in statistical physics, and subsequently claimed our attention for bubble diagnosis and prediction [14–16]. In this model, traders’ actions, e.g., buy or sell, depend on the decisions of others: agents form groups with self-similar behavior through interactions [17,18]. For instance, when some traders make an investment in assets with overconfidence, others in the same network ape one another in a boom cycle. The resultant increase in the asset value leads to speculative reinvestment, and this loop repeats over time [14]. Such positive feedback continues up to a certain point, often called the critical time; and the LPPL model can predict the crash date of bubbles [19–21]. Please cite this article as: H. Jang, Y. Song and K. Ahn, Can government stabilize the housing market? The evidence from South Korea, Physica A (2020) 124114, https://doi.org/10.1016/j.physa.2019.124114.
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The critical time can be detected by examining the signature of a faster-than-exponential growth, and its decoration by log-periodic oscillations [22–29]. Mathematically, the concepts can be explained as follows: Yt = A + B(tc − t)β {1 + C cos[ω ln(tc − t) − φ]} for t < tc ,
(1)
where Yt > 0 is the log of the asset price at time t; tc > 0 is the critical time; A > 0 is the log price at the critical time tc ; B < 0 is the size of increase in Yt over the time before the crash when C is close to 0; C ∈ [−1, 1] controls the magnitude of the oscillations around the exponential trend; β ∈ [0, 1] is the exponent of the power law growth; ω > 0 is the frequency of the fluctuations during the bubble; and φ ∈ [0, 2π ] is a phase parameter. In order to estimate the parameters, we simulate the initial values for the parameters with a price gyration method, and then estimate these parameters using a nonlinear optimization algorithm, the so-called genetic algorithm, following [14]. 2.3. Hurst exponent To obtain the aggregated series x(m) (k) for k ≥ 1, we divide the series x = {xi for i ≥ 1} into blocks of size m, as follows [30,31]: x(m) (k) =
km ∑
1 m
x(i),
i=(k−1)m+1
where, k is a block label, and m is a successive value. Let V (x(m) ) be the sample variance of x(m) (k) within each block k of size m. Then, the Hurst exponent (HE) θ is given by: V (x(m) ) ∼ m2θ −2 . The variable θ can measure the weak form market efficiency, i.e., long-range memory of the time series [32]. A value in the range 0 < θ < 0.5 indicates a time series with mean reverting property, i.e., an anti-persistent series, while a value in the range 0.5 < θ < 1 reveals positive correlation, i.e., a persistent series with long-term memory. A value of θ = 0.5 indicates a completely uncorrelated series, which supports the weak form efficient-market hypothesis (EMH).1 We follow the estimation procedure of θ employing the methodology suggested by Block [32]. 2.4. Entropy Shannon [36] first proposed entropy, which can measure the dispersion of the probability allocation assigned to each state. It has been widely adopted to examine the degree of randomness and uncertainty of financial time series, due to its robustness of extreme value [37–39]. High entropy characterizes randomness of the system, while low entropy is related to a highly deterministic change of asset returns [37]. The entropy of the discrete random variable Z is given by: H(Z ) = −
M ∑
p(zj ) ln p(zj ),
j=1
where, M is the number of possible outcomes of random variable Z , and p(zj ) is the probability assigned to state zj . We use the symbolic time series analysis (STSA) to capture time varying pattern in the log return series by transforming the real values into a restricted number of symbols. STSA is widely used in physics, information theory, and finance [37, 40,41]. Each sequence consists of the consecutive returns of an asset S, which can be denoted as taking two values: 0 for a negative return, and 1 otherwise. Afterwards, we convert the 0–1 sequence from a binary system to a decimal number Z S . The Shannon entropy of Z S is then calculated following [37]: H(Z S ) = −
M ∑
p(zjs ) log2 p(zjs ),
j=1
where, M = 2S . Then, the Shannon entropy is normalized as follows: h(Z S ) =
1 S
H(Z S ).
1 The EMH is that new information received by investors is almost instantly reflected in the asset prices [33,34]. Since new information cannot be predicted, the future price of the assets cannot be predicted [32]. In particular, the weak form of EMH can be examined when the price fluctuation follows a random walk [35]. Please cite this article as: H. Jang, Y. Song and K. Ahn, Can government stabilize the housing market? The evidence from South Korea, Physica A (2020) 124114, https://doi.org/10.1016/j.physa.2019.124114.
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Fig. 1. Logarithm of condominium prices and corresponding alarm. The shaded area is Park’s presidency. Table 2 The best fit of LPPL parameters. A
B
tc
β
C
ω
ϕ
4.760
−0.045
222.701
0.332
0.055
5
0.001
3. Results and discussion As previously noted, the tight regulations appeared effective at reducing the volatility in condominium markets. In order to examine the efficacy of the government policies on the housing markets, we further estimate the critical time of housing price bubbles by applying the LPPL model. The red solid line in Fig. 1 is an in-sample estimate from 18 March 2013 to 31 Oct. 2016 (pre-regulation). Root mean square error, which shows the difference between the log price and its estimated value, is minimized at 0.01, and therefore our model fits the data fairly well [14]. Table 2 reports the parameter estimates. It satisfies the two conditions B < 0 and 0.1 ≤ β ≤ 0.9, ensuring a faster than exponential acceleration of the log price [42]. The value of ω is 5, which corroborates existing studies, such as Johansen [43], who documented that ω ≈ (6.36 ± 1.56) for 30 crashes in major financial markets. We then conduct out-of-sample forecasting during the period of post-regulation, i.e., after 7 Nov. 2016. In the case of observations (black line), there are no significant increases around the critical time (19 June 2017). Though the LPPL model indicates the critical time in the second half of 2017, market prices are slowly increasing without the collapse of bubbles. This implies that the government policies enacted to curb a boom of the housing market were properly timed. Housing policies can have effects not only on the price appreciation but also at the volume of transactions. For further examination, the trading volumes of condominium are presented in Fig. 2. During the period of consecutive loose policies, between the early 2012 and the end of 2016, the trading volume sharply increased. However, after enacting tight regulation at the end of 2016, the volume of transactions began to decreased: 13% from peak to peak and 26% from valley to valley. It indicates that the government policies effectively cooled down the excessive demand which in turn stabilizes the housing price. We examine the impact of the government policy on the housing market in terms of weak-form EMH by estimating the HE. Table 3 shows that the housing market has long-term memory (H = 0.907); however, after the regulations were tightened, it drops by 6.284% (to 0.850). Along with the increasing frequency of similar price change patterns, the HE has a higher value, H > 0.5 [44]. With this, it can be concluded that the policies worked on the market as intended. Further, the entropy is calculated to analyze the status for long-run equilibrium of housing price dynamics.2 In particular, the spread of the probability assigned to each time varying pattern is measured by STSA. We applied S = 4, which denotes 1 month in the time horizon. Table 3 shows the increment in entropy during the post-regulation period, implying the dynamic pattern of the returns closer to the long-run equilibrium. Then, the robustness of our results were tested with S = 3 and 5, and the results are not significantly different from the case of S = 4. In sum, the regulations indeed influenced the housing market; more specifically, the regulations improved the market conditions in terms of efficiency and long-run equilibrium. 2 The entropy can be applied to find a distribution of a non-degenerated probability of the target systems that accounts for the central tendency and the fluctuations around [45]. In this paper, the long-run equilibrium indicates stable status in which all competing influences are balanced. The equilibrium price, thus, can be derived by maximizing the entropy of system which is the likely state of the system in the form of a probability distribution, namely ‘statistical equilibrium’ [46,47]. Please cite this article as: H. Jang, Y. Song and K. Ahn, Can government stabilize the housing market? The evidence from South Korea, Physica A (2020) 124114, https://doi.org/10.1016/j.physa.2019.124114.
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Fig. 2. Trading volume of condominium. The shaded area is Park’s presidency. The trading volume is the total number of condominiums traded during a month (Source: Korean Statistical Information Service). Table 3 Characteristics of the housing market. Hurst exponent calculated by the aggregated variance method [31]. Whole Period (7 May 2012 – 29 April 2019)
Pre-Regulation (7 May 2012 – 31 Oct. 2016)
Post-Regulation (7 Nov. 2016 – 29 April 2019)
0.907 0.462
0.939 0.403
0.850 0.560
Hurst exponent Entropy
Our results corroborate the existing literature, and confirm that both the regulatory and fiscal policies can be effective tools in stabilizing the housing market. In terms of the regulatory policy, it is found that lowering the loan ceiling led to the decrease of purchasing power, and, finally to the suppression of housing prices: LTV and DTI limits could effectively rein the upward pressure on house prices [2]. Further, the government tried to control housing price appreciation by deploying increased transfer income tax from Aug. 2017. This fiscal contraction strategy appeared to have suppressed speculative investment and rapid increase of housing prices [2,4]. On the other hand, fiscal expansion began to be deployed from Nov. 2017, which specifically targeted the economically vulnerable groups, i.e., new employees, college graduates, newlyweds, senior citizens, and low income earners, so was considered as welfare policy, rather than general housing policy. Fig. 1 shows how such targeted policies worked in the housing market: there were no significant changes in the housing price movement trend. This indicates that the targeted fiscal expansion does not have significant impacts on the market in general. 4. Conclusion This study aims to examine the effectiveness of the government policies in stabilizing the housing price and market conditions. We find that the regulations effectively restrained the upward trend of condominium prices in South Korea. Implemented policies contribute not only to improvement of the efficiency of housing market, but also to decrease of the discrepancy between the distribution of condominium prices and their long-run equilibrium. Our findings suggest that timely implementation of the regulatory policies is an appropriate and effective means of stabilizing the market, as well as improving the market conditions. At the same time, the market might be excessively cooled once strong policies were implemented, because housing price can be characterized as having long-term memory, regardless of the implementation of regulations. In this regard, in establishing policies, it is necessary to take the long-run movements of the housing market into account. It should be noted that our findings may not be applicable to other countries and regions. Each country has its own contexts determining housing prices along with universal variables such as housing characteristics, local characteristics and environmental characteristics. Although the results may not be universally applicable, our methodologies and measures can be used to assess the effectiveness of housing policies in other countries and regions. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Please cite this article as: H. Jang, Y. Song and K. Ahn, Can government stabilize the housing market? The evidence from South Korea, Physica A (2020) 124114, https://doi.org/10.1016/j.physa.2019.124114.
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Acknowledgments This research was supported by the Future-leading Research Initiative at Yonsei University (Grant Number: 2019-220200; K. Ahn) and by National Research Foundation of Korea Grant funded by the Korean Government (NRF-2019-Global Ph.D. Fellowship Program: 2019H1A2A1074420; H. Jang). Appendix See Table 4.
Table 4 Housing market policies in South Korea. The policies were retrieved from the Ministry of Land, Infrastructure and Transport, and the National Archives of Korea. Type (Date) President Lee
President Park
Policy
Deregulation, fiscal expansion (May 2012)
May 10 Policy: Lifting three Gangnam districts from the speculative zone list; increasing LTV and DTI from 40% to 50%; relaxing the period for restricted resale of condominium purchase right; expanding eligibility to and maximum amount of bogeumjari loans; relaxing the heavy transfer tax imposed on short-term possessions.
Deregulation, fiscal expansion (Apr. 2013)
Apr. 1 Policy (encouragement of house transactions): Exempting transfer tax for 5 years for new and unsold houses worth KRW 900 million or lower; temporarily exempting acquisition tax for first-time house purchasers; repealing heavy transfer income tax, and relaxing tax imposed on short-term possessions; permitting vertically expanded remodeling.
Fiscal expansion (Aug. 2013)
Aug. 28 Policy (stabilization of key money deposit and monthly rent; inducing key money deposit-based tenants to purchase houses): Permanently lowering acquisition tax, and introducing shared mortgages.
Fiscal expansion (Dec. 2013)
Dec. 3 Policy: Various supportive policies, including expanding shared mortgages and REIT-based leases; integrating policy mortgages; vitalizing ‘‘Happy House’’.
Deregulation (Jul. 2014)
July 24 Policy: Relaxing regulations and unilaterally applying 70% of LTV and DTI; incorporating housing subscription accounts (relaxing the requirements for first priority).
Deregulation (Sep. 2014)
Sep. 1 Policy: Relaxing regulations on the minimum period to eligible for reconstruction; relaxing the requirements for first priority in housing subscription (1 year shortened for minimum monthly deposits).
Deregulation (Oct. 2014)
Oct. 30 Policy: Measures to mitigate ordinary citizens’ bearing of housing expenses (encouraging transactions through deregulation).
Deregulation, fiscal expansion (Dec. 2014)
Dec. 23 Policy: Applying flexible limitation on the maximum condominium price of private housing site; extending the grace period for 3 years for the redemption of excessive earnings from reconstruction.
Fiscal expansion (Jan. 2015)
Jan. 13 Policy: Measures to innovate middle-class housing by fostering enterprise-type housing rental business.
Fiscal expansion (Apr. 2016) Strengthened regulation (Nov. 2016)
Apr. 28 Policy: Increasing the supply for ‘‘Happy House’’ and ‘‘New Stay’’.
Strengthened regulation (Dec. 2016) Strengthened regulation (Jan. 2017)
Dec. 24 Policy: Strengthening the restrictions on loans for balance payment.
Strengthened regulation (Jun. 2017)
June 19 Policy (selective and tailored responses to ensure stable management of the housing market): Designating additional controlled areas; strengthening lending regulations with respect to housing subscription and LTV/DTI within the controlled areas.
Strengthened regulation, fiscal contraction (Aug. 2017)
Aug. 2 Policy (measures to stabilize the housing market through protection of real customers and containment of short-term speculative demand): Designating speculative areas and speculation-prone areas; modifying the regulations on reconstruction and redevelopment; strengthening transfer income tax; strengthening LTV/DTI-related financial regulations; mandating the report of financing plans.
Nov. 3 Policy (control of speculative demand): Prohibiting the resale of condominium purchase right in four Gangnam districts and other areas in the speculative zone list; strengthening the requirements for first priority in housing subscription.
Announcing the measures to introduce the debt service ratio (DSR), which is a more tightened lending criterion than DTI.
(continued on next page)
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Table 4 (continued). Type (Date) President Moon
Policy
Strengthened regulation (Sep. 2017)
Designating Bundang-gu (Seongnam) and Sujeong-gu (Daegu) as additional speculative areas; facilitating the improvement of requirements applicable to restrictions on maximum parcel price of private housing sites.
Strengthened regulation (Oct. 2017)
Oct. 24 Policy (comprehensive measures for household debt): Applying the new DTI in 2018; applying the debt service ratio (DSR) early; maintaining the increase rate of household debt under 8%; strengthening the regulations on real estate lease businesses; providing tailored support for vulnerable households, small business owners, and vulnerable borrowers.
Fiscal expansion (Nov. 2017)
Nov. 29 Policy (housing welfare roadmap): Developing measures to provide housing support to households at diverse life stages, such as young citizens, newlyweds, senior citizens, and low income earners, based on lifecycle and class; formulating plans to supply 1 million publicly supported houses within the next 5 years.
Fiscal expansion (Dec. 2017)
Dec. 13 Policy (measures to encourage rental house registration to benefit both landlords and tenants): Encouraging rental house registration through tax benefits for transfer income tax and gross real estate tax; strengthening the protection of tenants’ rights through, for example, guarantee on the return of key money deposit.
Fiscal expansion (Jul. 2018)
July 05 Policy (measures to provide housing support for newlyweds and young citizens for happy marriage and child-rearing): Expanding and specifying the support programs for newlyweds and young citizens in the housing roadmap, such as supplying 100,000 houses in towns for new couples, and youth-priority housing subscription accounts.
Strengthened regulation, fiscal expansion (Aug. 2018)
Aug. 27 Policy: Developing approximately 30 additional public housing sites that can supply at least 300 thousand houses within Greater Seoul; designating additional speculative areas and speculation-prone areas in Seoul, and in some areas within Gyeonggi-do.
Fiscal contraction (Sep. 2018)
Sep. 13 Policy: Imposing up to 3.2% of heavy gross real estate tax for those who own two or more houses in a controlled area; increasing the upper limit of tax burden from 150% to 300%; adding a new tax bracket, between KRW 300 and 600 millions; increasing the tax rate by 0.2% p.
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Please cite this article as: H. Jang, Y. Song and K. Ahn, Can government stabilize the housing market? The evidence from South Korea, Physica A (2020) 124114, https://doi.org/10.1016/j.physa.2019.124114.
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Please cite this article as: H. Jang, Y. Song and K. Ahn, Can government stabilize the housing market? The evidence from South Korea, Physica A (2020) 124114, https://doi.org/10.1016/j.physa.2019.124114.