Vol. 5 Real Estate Market Report

2016 Real Estate Market Trends and 2017 Outlook

Real Estate Research Institute of KAB KAB Real Estate Market Report

Greetings

This is the fifth volume of the Korea Appraisal Board (KAB) Real Estate Market Report (2016 Real Estate Market Trends and 2017 Outlook). With the enactment of the Korea Appraisal Board Act in September 2016, KAB has undergone a transformation from an organization specializing in real estate appraisal to an organization dedicated to real estate market surveys and management as well as making announcements of official land value and related statistics. Maintaining order and stability in the Korean real estate market is an especially important task undertaken by KAB. The KAB Real Estate Market Report published semiannually in the first and second halves of the year provides reliable information on the real estate market including the future outlook, based on comprehensive and in-depth analyses of the current trends, with the aim of ensuring order in the real estate market. The real estate market in Korea in 2016 became more stable compared to the previous year due to the influence of the economic slowdown and the new loan restriction policy concerning tighter loan review regulations. Notable trends in the year included the overheated pre-construction parceling-out market for the Gangnam reconstruction projects and the rise in housing prices in the Capital Area; however, following the announcement of the real estate policy on November 3, 2016 speculative investments declined, and the market became more stabilized centering on actual demand. Also, in some of the regions outside the Seoul Capital Area, real estate prices began to be on a downturn as a result of an industrial recession and increased housing supply. As for the real estate market outlook for 2017, the intent to purchase homes is expected to fall due to the high probability of a hike in the interest rate in Korea, as a result of the U.S. Federal Reserve’s announcement of its plans to raise the interest rate over the course of the year. Also, the continued economic uncertainty at home and abroad, the rationalization of the mortgage policy, and higher housing supply are expected to become factors contributing to the shift in the sales market toward a downward stabilization, while the Jeonse market is expected to show stability. For this report, the major issues in the real estate market in addition to the current trends and outlook were analyzed in depth for a better understanding of the real estate market. First, in order to make an accurate diagnosis of the increasing economic risk factors in Korea and abroad and the rise in household debts, the level and structure of KAB Real Estate Market Report

household debts in Korea as well as the characteristics of the households with debts were analyzed. In addition, amid the growing voices of concern regarding the demographic cliff and a sharp drop in housing demand resulting from low fertility and population aging as well as long-term stagnation in the housing market, data on the actual residential property transactions were used in a regression analysis of apartment purchases by age group, the results of which showed that the aging population contributed to the demand in the housing market. Moreover, the apartment subscription market in 2016 was diagnosed based on a comparison of the regional apartment subscription competition ratios through analyses of subscription competition ratios and time-space hot spots, as well as analyses of the determinants of the subscription competition ratios and similarities of the apartment subscription markets across the regions. Lastly, in order to analyze the factors influencing the predictions of housing prices, an empirical analysis was performed in regard to the correlations and causal relationships between the macroeconomic variables and housing prices nationwide. It is our hope that the market analysis data provided in this report serve as a basis for making judgments from a more balanced perspective. KAB will be committed to its role in performing multi-faceted, in-depth analyses and providing fair and comprehensive real estate market information through the Real Estate Market Report as a means to ensure stable housing for the citizens of Korea and support the policy implemented to advance the real estate market.

Thank you.

Real Estate Research Institute of KAB

Director Chae Mie Oak

CONTENTS

PART 1 | Market Trends 1

Macro Economy and Real Estate Market 2 Housing Market 12 Land Market 42 Commercial Real Estate Market 61

PART 2 | 2017 Housing Market Outlook 85

PART 3 | In-Depth Analysis 91

Analysis ① | Risk‌ Diagnosis of Domestic Household Debts and Response Measures 92 Analysis ② | The‌ Population Aging and Housing Transactions : Evidence From the Real Estate Trade Management System Data 104 Analysis ③ | Diagnosis‌ of the 2016 Housing Subscription Market 116 Analysis ④ | Analysis‌ of Patterns in the Determinants of Housing Prices 132

PART 4 | Issue Analysis 143

Shift‌ Toward Rentals and Rent Burdens 144 Supply,‌ Transaction Volume and Sales Price Index of Row Houses and Multi-housing 154 Burden‌ of Jeonse and Rental Costs on Single-member Householdsg 165 Market‌ Trends and Outlook on Aggregate Retail Shop Market 173 KAB Real Estate Market Report Korea Real Estate Market Report

P A R T 1 Market Trends

Macro Economy and Real Estate Market Housing Market Land Market Commercial Real Estate Market Macro Economy and Real Estate Market

Min Chulhong, Park Jinbaek

Domestic Economic Trends

In the case of the domestic economic trends, exports began to show an upward trend in November 2016, but market anxiety caused a slowdown in the domestic economy resulting in slower recovery.

○ Economic Growth Rate The economic growth rate in 2016 3Qwas recorded at 2.6%, compared to the same period in the previous year, while it fell from 3.3% in 2016 2Q.

○ Private Consumption The rate of increase in private consumption was 2.7% in 2016 3Q, which was a 0.6%p decrease from the previous quarter, indicating a slowdown in the private consumption growth rate.

Figure 1-1 Real GDP and growth rate

400 9

300 6

200 3

100 0

0 -3 `02.3Q `03.3Q `04.3Q `05.3Q `06.3Q `07.3Q `08.3Q `09.3Q `10.3Q `11.3Q `12.3Q `13.3Q `14.3Q `15.3Q `16.3Q GDP (Real, original series, YOY comparison %)(right) GDP (market price, trillion KRW)(left) Source: Bank of Korea (BOK)

2 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 1 Market Trends

Figure 1-2 Rate of change in private Figure 1-3 Rate of change in equipment consumption investment (Unit: %) (Unit: %)

8% 30%

20% 6%

10% 4% 0%

2% -10%

0% -20% `10.3Q `11.3Q `12.3Q `13.3Q `14.3Q `15.3Q `16.3Q `10.3Q `11.3Q `12.3Q `13.3Q `14.3Q `15.3Q `16.3Q Rate of change in private consumption (real, YOY comparison) Rate of change in equipment investment (real, YOY comparison)

Source: Statistics Korea Source: Statistics Korea

○ Equipment Investment The equipment investment growth rate decreased to 4.2% in 2016 3Q due to the plunging demand for transportation equipment following the termination of a discount policy on the individual consumption tax, and the equipment investment index is projected to be low for some time, as the global economic slowdown is expected to continue.

Construction Industry Trends

Construction investment has shown a growth trend with a high value of construction completed centering on the building sector. Amidst this trend, an improvement was observed in construction contract wins, which are a leading indicator, in 3Q.

○ Construction Investment Investments into residential buildings increased by 21.27%, which drew strength from the favorable conditions in residential construction investment, while investments into building construction and non-residential buildings increased by 15.58% and 10.60%, respectively. The total construction investments recorded an 11.27% increase in 2016 3Q.

3 Table 1-1 Rate of change in construction investment, value of construction completed and contract value by sector

(YOY comparison, Unit: %) 2015 2016 1Q 2Q 3Q 4Q 1Q 2Q 3Q Construction investment 0.72 1.37 5.50 8.29 9.59 11.04 11.27 Building construction 3.15 4.44 7.95 13.92 15.16 15.86 15.58 Residential buildings 6.26 10.66 15.56 24.60 23.06 24.42 21.27 Non-residential buildings 0.63 -0.45 2.07 5.58 8.39 8.37 10.60 Civil engineering construction -3.38 -4.19 0.64 -2.84 -0.54 1.45 2.06 Value of construction completed -1.72 -3.80 6.16 8.00 14.78 19.20 17.16 Architecture -0.20 -3.53 10.76 16.31 22.11 26.54 22.93 Civil engineering -4.27 -4.27 -1.91 -4.52 1.99 6.41 5.71 Contract value 57.44 49.06 50.35 40.09 13.81 -6.30 2.71 Architecture 89.47 49.33 42.53 37.81 13.13 2.46 4.24 Civil engineering 1.67 48.26 83.57 45.86 16.00 -32.81 -2.34 Note: Data on the YOY changes in real (construction investment for gross capital formation), fixed (value of construction completed) and current (contract value) accounts (%) Source: Statistics Korea

○ Value of Construction Completed Although the rates of increase in both the architecture and civil engineering sectors decreased slightly, they still showed favorable trends, with the value of construction completed in the architecture and civil engineering sectors increased by 22.93% and 5.71%, respectively, and the total value of construction completed in 2016 3Q increased by 17.16%.

○ Contract Value The contract value in the architecture sector increased by 4.24%, whereas the rate of decrease in the contract value in the civil engineering sector contracted to 2.3% compared to 2Q. Accordingly, the rate of change in the contract value of construction projects in 3Q shifted to an upward trend. However, the level of increase is expected to become low in the offing due to the base effect and a weakened rate of increase compared to the year 2015.

○ The 1.7%p increase in GDP in 2016 3Q was attributable to the growth in construction investments, and the ratio of construction investment growth to GDP growth was approximately 65%. This indicates favorable trends in construction investments, which are shown to be leading the economic growth. It should be noted, however, that the economic growth was heavily reliant on civil engineering and housing construction, rather than an increase in private consumption and equipment investment.

4 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 1 Market Trends

Figure 1-4 GDP growth attributable to construction investments (YOY comparison, Unit: %p, %)

10

8

6

4 2.6

2 1.7 0

-2 1Q 2Q 3Q 4Q 1Q 2Q 3Q 4Q 1Q 2Q 3Q 4Q 1Q 2Q 3Q 4Q 1Q 2Q 3Q 4Q 1Q 2Q 3Q 4Q 1Q 2Q 3Q 2010 2011 2012 2013 2014 2015 2016 GDP growth _ Excl. construction investments (%p) GDP growth attributable to construction investments(%p) GDP growth (%) Source: Bank of Korea (BOK)

While the number of unsold apartment units from pre-construction parceling-out sales has been stabilized, there has been a slowdown in the rate of increase in the number of construction permits and approvals given and construction projects that have commenced. However, the cumulative number of construction permits and approvals given and construction projects that commenced prior to the period under examination is resulting in an increase in the completion of construction projects. Thus, there is a need to examine the number of unsold apartments periodically in the future.

○ The number of unsold apartment units from pre-construction parceling-out sales (unclaimed supply) was 58,000 as of November 2016, which was a 0.2% decrease from the previous month. The number of unsold apartment units has gradually been decreasing since August 2016 in a relatively stable manner.

○ The number of housing construction projects that have been initiated were 574,000 as of November 2016, which was a 9.5% YOY decrease. The number of commencing housing construction projects showed a YOY decrease every month in 2016, indicating that the new housing supply is being adjusted in accordance with the concerns of excess housing supply.

○ Housing construction permits and approvals have been given for 637,000 housing units as of November 2016, which was a 4.5% YOY decrease. While this is still a high number compared to in the past, it has been exhibiting a decline.

5 ○ The number of housing construction completions in 2016 recorded a 14.5% YOY increase, with the construction 450,000 units completed as of November 2016. This reflects the increase in the construction permits and approvals that had been given and the construction projects that were initiated in 2014 and 2015.

Figure 1-5 Unclaimed supply trends Figure 1-6 Housing construction (Unit: thousand units) commencements (Unit: thousand units)

80 140 150% 70 120 120% 60 100 90% 50 80 60% 40 60 30% 30 40 0% 20 20 -30% 10 0 -60% 0 `13.2M `13.11M `14.8M `15.5M `16.2M `16.11M `13.2M `13.11M `14.8M `15.5M `16.2M `16.11M YOY rate of change in housing construction commencements (Right) Number of housing construction commencements

Source: Ministry of Land, Infrastructure and Transport Source: Ministry of Land, Infrastructure and Transport

Figure 1-7 Housing construction permits and Figure 1-8 Housing construction completions approvals (multi-housing by individual building)

(Unit: thousand units) (Unit: thousand units)

140 80 120% 120 60% 90% 60 100 60% 80 40 30% 60 10% 0% 40 20 20 -30% 0 -40% 0 -60% `13.2M `13.11M `14.8M `15.5M `16.2M `16.11M `13.2M `13.11M `14.8M `15.5M `16.2M `16.11M YOY rate of change in housing construction permits and approvals (Right) YOY rate of change in housing construction completions (Right) Number of housing construction permits and approvals given (Left) Number of housing construction completions (Left), thousand (individual building)

Source: Ministry of Land, Infrastructure and Transport Source: Ministry of Land, Infrastructure and Transport

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Loan Market Trends

Household loans recorded KRW 1,228 trillion in 2016 3Q, which was a 3.0% YOY increase and a 0.1%p increase compared to the 2.9% increase in 2016 2Q.

○ Household loans, which were KRW 806 trillion in 2011 4Q, increased by KRW 422 trillion over the course of 6 years to KRW 1,228 trillion in 2016 3Q.

○ The increase in household loans is mainly attributable to the increase in mortgages in recent years. Based on the prediction that there will be a growing demand for loans to pay for day-to-day living expenses resulting from the prolonged economic slowdown and declining disposable income, it is expected that real estate-secured loans for day-to-day living expenses will be taken out and this will cause the household loans to continue to grow.

○ The market interest rate will increase due to the concerns of inflation in the U.S.A., which means that a hike in the lending interest rate by the domestic banks will be inevitable. Accordingly, there will be a need to manage the credit risks of small business owners and low income households, who have taken out loans to fund their businesses and to pay for their living expenses, respectively.

○ It is necessary to continue to the efforts to qualitatively improve the structure of household debts by encouraging borrowers to make a principal repayment by an installment plan or change to a fixed interest rate, as a means to prepare for the shock arising from a hike in the interest rate in the mid and long term. This is also a time point at which an employment promotion plan and a reinforced financial support system should be pursued to assist small business owners and low income households, who are at a relatively greater risk of defaulting on their loans.

7 Figure 1-9 Trends in household loans and Figure 1-10 Rate of change in household mortgages loans and mortgages

1,500 1,228 5% 1,158 1,200 1,101 999 1,039 4% 939 965 886 909 840 863 900 771 806 3% 2% 600 1%

300 544 445 470 480 509 0% 354 369 383 393 399 401 409 422 -1% 0 `10.3Q `11.3Q `12.3Q `13.3Q `14.3Q `15.3Q `16.3Q `10.3Q `11.3Q `12.3Q `13.3Q `14.3Q `15.3Q `16.3Q Mortgages from banks (trillion KRW) Rate of change in household loans (YOY) Household loans (trillion KRW) Rate of change in mortgages from banks (YOY) Household loans Source: Statistics Korea Source: Statistics Korea

Interest Rate

In the case of the U.S., the continuous drop in the unemployment rate, economic recovery, and projections of stable economic growth are factors that are raising the probability of an inflation. The U.S. Federal Open Market Committee (FOMC) raised the benchmark interest rate in December 2016, with the aim of maintaining the inflation rate at around 2%, and this consequently has increased the pressure to make an interest rate hike in Korea.

○ As for the factors influencing the domestic interest rate, the economic growth rate was 2.6% in 2016 3Q, but it should be noted that the 1.7% growth was attributable to construction investments and only 0.9% to other sectors. The unemployment rate has been on a continuous rise from 3.1% in 2013 to 3.9% in 2016 3Q. These factors are putting a downward pressure on the economy.

○ The annual inflation rate in 2016 was 1.0%, which was lower than the price stability target, and considering that it has been dropping continually from 4.0% in 2011, a monetary policy to stimulate the economy is deemed necessary. However, due to the interest rate hike by the U.S. FOMC in December 2016 and three additional hikes announced for the year 2017, there is a pressure to raise the interest rate in Korea.

8 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 1 Market Trends

○ A hike in the benchmark interest rate may potentially cause the inflation rate to decline further from 1.0% (2016) and impose a heavy burden on borrowers to pay back the principals of their household loans, thereby resulting in a sluggish asset market. Thus, there is a need to pay careful attention to the monetary policy announced by the Financial Services Commission (FSC). It is projected that the interest rate hike in Korea will occur in the latter half of 2017 when the U.S. benchmark interest rate surpasses that of Korea.

Table 1-2 Current and projected benchmark interest rates in Korea and the U.S.A.

(Unit: %) `10 `11 `12 `13 `14 `15 `16 `17(e) Korea 2.5 3.25 2.75 2.5 2 1.5 1.25 - U.S.A. 0.13 0.13 0.13 0.13 0.13 0.38 0.63 1.38 Source: BOK, IMF, FOMC

Forecasts on External Conditions

It appears that none of the advanced countries will be able to boost economic growth in the near future. Amidst this situation, countries around the world have been implementing fiscal and financial policies to the point where they no longer have the capacity to further execute additional policies. This is expected to cause the difficult economic circumstances to continue, and in turn result in weakened solidarity among the nations and a wider implementation of protective trade policies. Consequently, this will ultimately lead to anti- globalization tendencies.

○ In the case of the U.S., there has been a boost in consumption, centering on durable goods, and the unemployment rate has dropped below 5%, which is a near-full employment rate. There are expectations of an increased probability of mid- and long-term economic growth and a higher inflation rate based on President Trump’s pro-business and large-scale fiscal policies, and accordingly, the benchmark interest rate will continue to be raised to attain the target price stability level of 2%.

9 ○ As for Japan, Abenomics, which has been implemented for years, is deemed to have failed in boosting exports, and it did not result in a sufficient rise in wages. Thus, a virtuous cycle for economic recovery could not be formed. The value of the Japanese yen surged every time anxiety was high in the global financial markets, due to the preference for safe assets, and the burden on the fiscal soundness has made aggressive spending difficult as well.

○ In , sluggish growth has continued on, and the global economic downturn is expected to prolong the slump in exports, thereby causing a contraction in corporate investments. However, there is a possibility that a large-scale economic stimulus plan may be implemented prior to the upcoming National Congress of the Communist Party of China, where the new leadership of the Communist Party of China will be elected.

○ In the case of Europe, the economy was stimulated by an increase in bank loans resulting from the monetary easing policy implemented by the European Central Bank (ECB); however, the low interest rate has deteriorated the soundness of the banks and it is deemed difficult to implement an additional monetary easing policy due to the risks involved. Also, due to the prolonged economic recession, refugee issues, and risk of terrorism, there have been a growing number of supporters for the conservative party in each country, with intensified nationalistic tendencies.

○ Shale oil production in the U.S. has declined as it has become less lucrative due to low oil prices, and the excess supply of crude oil has contracted. Accordingly, the international oil prices have risen quickly from around USD 20 in 2016 to about USD 50 more recently. Also, with the technical advancement of shale oil extraction, the crude oil prices are expected to stabilize in 2017.

Table 1-3 Economic growth rates and forecasts announced by IMF

(Unit: %)

Advanced Emerging Global U.S.A. China EU Japan Korea nations nations

2015 3.2 2.1 4.0 2.6 6.9 2.3 0.5 2.6 2016 (e) 3.1 1.6 4.2 1.6 6.6 1.9 0.5 2.7 2017 (e) 3.4 1.8 4.6 2.2 6.2 1.7 0.6 3.0 2018 (e) 3.6 1.8 4.8 2.1 6.0 1.8 0.5 3.1 Source: IMF

10 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 1 Market Trends

The real estate markets in major countries are showing varying trends depending on the extent of their domestic economic recovery.

○ There was a sharp increase in real estate property prices in the U.S. and U.K. in 2016, whereas the rate of increase slowed down in Australia.

○ In Japan, the rate of increase in housing prices slowed down once in 2014, but the prices have been on the rise since then. As for China, there was a shift to an upward trend in 2016.

○ Continuous monitoring of the real estate market in each country is deemed necessary due to the presence of various risks pertaining to the political and economic issues in Europe, possibility of interest rate hikes in the U.S., and sluggish growth in China among other factors.

Table 1-4 Rate of change in housing prices

(YOY rate of change, Unit: %) U.S.A. U.K. Australia China Japan EU 2012 8.0 1.0 3.0 -0.5 -0.4 -2.1 2013 10.3 4.4 10.0 9.1 2.8 -1.4 2014 5.0 8.5 6.8 -3.4 0.6 0.8 2015 5.3 6.5 8.7 -0.3 2.0 2.2 2016 5.7 8.7 4.1 5.2 3.0 3.0 Source: BIS

11 Housing Market

Lee Junyong, Min Chulhong

Trends in Housing Prices

1) Housing Price Trends in 2016

House Sale Price The rate of increase in the house and apartment sales prices in 2016 was 0.7% and 0.8%, respectively, which were substantially lower that of 2015 (3.6% for house and 4.8% for apartments).

Table 1-5 Rate of change in the sales prices of residential properties in 2016 by region

(Unit: %) Rate of change in sales prices of house Rate of change in sales prices of apartments Region 2014 2015 2016 2016 1H 2016 2H 2014 2015 2016 2016 1H 2016 2H Nationwide 1.7 3.6 0.7 0.1 0.6 2.7 4.8 0.8 0.1 0.7 Seoul 1.1 4.6 2.1 0.6 1.6 2.0 6.7 3.2 0.7 2.5 1.1 3.5 3.1 0.6 2.5 1.7 4.8 4.2 0.8 3.4 6.3 8.0 -1.8 -1.3 -0.6 7.8 9.0 -3.1 -1.9 -1.2 1.4 3.4 0.8 0.1 0.7 3.2 5.0 1.4 0.4 1.0 1.5 5.8 0.4 0.3 0.1 2.2 7.2 0.3 0.2 0.1 0.6 0.4 0.3 -0.1 0.4 0.6 0.2 0.1 -0.3 0.4 3.1 3.2 0.4 0.6 -0.2 3.7 4.2 0.6 0.8 -0.2 Sejong -0.2 -0.1 0.8 0.2 0.6 -1.8 -1.0 0.5 0.2 0.3 Gyeonggi 1.8 4.5 0.8 0.2 0.6 2.8 6.1 1.1 0.2 0.9 Gangwon 0.3 2.2 1.4 0.6 0.8 0.5 3.4 2.1 0.8 1.3 Chungbuk 2.4 1.1 -0.7 -0.3 -0.4 3.8 2.1 -1.5 -0.7 -0.8 Chungnam 2.3 0.8 -1.5 -0.9 -0.6 4.3 0.7 -3.1 -1.6 -1.5 Jeonbuk -0.5 0.4 0.1 -0.1 0.2 -0.8 0.6 -0.2 -0.3 0.1 Jeonnam -0.8 0.9 1.1 0.4 0.7 -1.8 0.8 1.6 0.7 0.9 Gyeongbuk 3.6 2.6 -1.7 -0.8 -0.9 6.6 3.7 -4.4 -2.0 -2.4 Gyeongnam 2.2 1.6 -0.6 0.0 -0.6 2.7 2.1 -1.3 -0.3 -1.0 Jeju 1.5 8.1 4.6 3.8 0.8 3.3 13.7 7.2 5.2 1.9

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○ The rate of increase in the residential property sales prices in Seoul and Busan dropped significantly in the first half of 2016; however, the rate of increase recovered to the level observed in the previously year, and was steady at around 2~3% in the second half.

○ In contrast, the residential property sales prices fell in the first or second half of the year in Daegu, Daejeon, Ulsan, Chungbuk, Chungnam, Jeonbuk, Gyeongbuk, and Gyeongnan, indicating a downward trend in the metropolitan cities and provinces outside the Seoul National Capital Area.

House Jeonse Prices The rate of increase in the jeonse (referring to residential property leases with large deposits without monthly rent payments) prices of house and apartments nationwide in 2016 was 1.3% and 1.9%, respectively, and this is substantially lower than the rate of increase (4.9% and 7.0%, respectively) reported for 2015.

○ Of particular note, the jeonse prices skyrocketed in the Seoul National Capital Area in 2015, but it stabilized in 2016. As for the changes in the rate of increase in the apartment jeonse prices between 2015 and 2016 by region, it decreased considerably from 10.8% to 2.8% in Seoul, 8.6% to 3.4% in Incheon, and 10.0% to 2.9% in Gyeonggi-do Province.

○ As for Daegu and Chungnam (Chungcheongnam-do Province), where there has been an increased supply of housing as of late, both the sales prices and jeonse prices showed a downward trend. In Daegu, in particular, the level of decrease in prices in 2016 was around one-third to one-fourth of the level of the annual average increase rate between 2014 and 2015, and considering this, it is difficult to say that the prices have plummeted.

○ In Seoul, Gyeonggi and Jeju, where the rate of increase in the jeonse prices of apartments in 2015 was over 10%, the price increases slowed down to a rate of 1/4, with the jeonse market becoming largely stabilized.

13 Table 1-6 Rate of change in the jeonse prices in 2016 by region

(Unit: %) Rate of change in jeonse prices of house Rate of change in jeonse prices of apartments Region 2014 2015 2016 2016 1H 2016 2H 2014 2015 2016 2016 1H 2016 2H Nationwide 3.4 4.9 1.3 0.7 0.6 5.1 7.0 1.9 1.1 0.9 Seoul 3.5 7.2 1.9 1.0 1.0 5.3 10.8 2.8 1.3 1.4 Busan 1.7 3.2 2.9 1.1 1.8 2.5 4.7 4.4 1.8 2.6 Daegu 6.1 6.9 -1.6 -0.9 -0.7 7.8 8.4 -2.3 -1.4 -1.0 Incheon 4.8 6.0 2.2 1.2 1.1 7.6 8.6 3.4 1.7 1.6 Gwangju 1.8 6.3 0.9 0.8 0.1 2.7 8.0 1.0 0.8 0.2 Daejeon 1.5 1.4 1.4 0.8 0.6 1.7 1.9 2.0 1.1 0.9 Ulsan 1.8 2.0 0.4 0.7 -0.3 2.2 2.5 0.6 0.9 -0.3 Sejong -6.4 0.1 4.0 2.0 2.0 -13.6 -0.2 5.7 2.9 2.7 Gyeonggi 5.7 7.3 2.0 1.2 0.9 8.0 10.0 2.9 1.6 1.2 Gangwon 0.8 2.4 1.4 0.6 0.8 1.7 3.7 2.2 0.9 1.3 Chungbuk 2.4 2.3 1.7 0.9 0.8 4.0 3.8 2.8 1.6 1.2 Chungnam 3.3 2.1 -1.0 -0.5 -0.5 6.3 3.1 -1.8 -0.9 -0.9 Jeonbuk 0.2 0.7 0.9 0.5 0.4 0.6 1.3 1.2 0.6 0.6 Jeonnam -0.3 1.4 1.2 0.5 0.7 -0.4 1.7 1.5 0.6 0.9 Gyeongbuk 2.9 2.2 -1.1 -0.5 -0.6 5.4 3.6 -2.5 -1.1 -1.4 Gyeongnam 2.5 1.6 0.6 0.3 0.3 3.0 2.0 0.8 0.5 0.3 Jeju 2.0 5.3 1.8 1.6 0.3 5.0 10.2 2.8 1.9 0.8

Summary The region where the housing market was booming in 2016 was Jeju, where an upward trend in housing prices has been continuing since 2015. The Seoul National Capital Area and Busan showed a steady increase in residential property prices, while the prices remained steady or showed a steady downward trend in other regions.

○ With the increase in jeonse prices in the Happy City regions, there was a marked increase in the jeonse prices in Sejong. This could be attributed to the temporary increase caused by expiration of jeonse contracts as well as the base effect from the drop in jeonse prices a couple of years ago (-13.6% in 2014 and -0.2% in 2015) resulting from an increased housing supply.

○ In Jeju, where the apartment sales prices surged (13.7%) in 2015, the rate of increase dropped to 7.2% in 2016. More specifically, the rate of increase fell to 5.2% in the first half of 2016 and 1.9% in the second half. - The rate of increase in the jeonse prices of apartments in 2016 (2.8%) dropped

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significantly compared to the previous year (10.2%). Considering the noticeable downward trend in the apartment jeonse prices in the second half of the year, it is deemed that the price stabilization is also occurring in Jeju.

○ Meanwhile, in the case of Ulsan, the rate of increase remained steady in districts other than Dong-gu, where workers in the shipbuilding industry and related industries mostly resided, in the first half of 2016. However, in the second half of the year, the sales and jeonse prices of residential properties showed a downtrend across the entire city of Ulsan, based on which it can be inferred that the stagnancy of the housing market caused by the decline of the shipbuilding industry is becoming more widespread.

Table 1-7 Summary of the changes in the residential property prices in 2016

2015 2016 Price change House Apartments House Apartments Increase in jeonse Seoul, Incheon, Seoul, Incheon prices Gwangju, Gwangju, Sejong > Increase in sales Gyeonggi, Gyeonggi

crease prices In Incrase in jeonse or Daegu, Jeju Daegu, Jeju Jeju sales prices Busan, Ulsan, Seoul, Busan, Gangwon, Busan, Ulsan, Busan, Sejong, Incheon, Marginal increase Chungbuk, Gyeongbuk Jeju Gyeonggi, Chungnam, Gyeo- Chungbuk ngbuk Sales domi- nance Seoul, Daegu, Sejong, Gangwon, Gwangju, Sejong, Jeonbuk, Daegu, Gwangju, abilization Jeonbuk, Ulsan, Gangwon, St Sales=jeonse Jeonnam, Ulsan, Gangwon, Jeonnam, Chungnam, Jeon- Gyeongnam Jeonnam Gyeongnam buk, Jeonnam, Gyeongbuk Incheon, Daejeon, Daejeon, Chun- teadiness or decrease Daejeon, Jeonse domi- Gyeonggi, gnam, Jeonbuk, Chungbuk, Daejeon nance Chungbuk, Gyeo- Gyeongbuk, Chungnam ngnam Gyeongnam

Note: An “increase” is defined as an increase of 5% or more in the annual price increase rate, and “marginal increase” as an increase of 2.5% or more and less than 5%, while all other cases fall into the category of “steadiness or decrease.” If the deviation between the price increase rates is under 1%, the rate is considered similar. The rate of price increase was adjusted based on the standards set for the aforementioned annual period.

15 2) Trends in Apartment Prices by Period1)

Short- and Long-term Changes in the Apartment Sales Prices The rate of change in the real apartment sales prices nationwide in the past year was -0.5%, which was lower than the short- and long-term annual average rates of change.

○ The short- and long-term annual average rates of real price change (1.1% for both) increased, but the rate of real price change in the past year was in the minus, indicating a decrease in the real prices, without taking inflation into consideration.

○ The regions where the rate of change in the real apartment sales prices in the past year surpassed the short- and long-term annual average rates of change were Seoul, Busan and Jeju. Jeju, in particular, recorded the highest rate of real price change (5.8%) among these regions.

○ The regions where the rate of change in the real apartment sales prices changed markedly in the past compared to the past were Daegu, Chungbuk, Chungnam and Gyeongbuk. It was significantly lower than the short- and long-term annual average rates of change, indicating a need to continually monitor the supply-and-demand situation of the housing market in these regions.

Figure 1-11 A comparison of the short- and long-term rates of change in the real apartment sales prices by region

Metropolitan cities Provinces

10% 10%

5.8% 5% 5% 2.4% 2.8% 1.2% 0.0% 0.7% 0.2% 0% 0% -0.5% -0.3% -1.0% -1.2% -0.7% -1.5% -4.4% -2.8% -4.4% -2.6% -5% -5% Gang Gang Incheon Busan Daegu Gwang Dae Ulsan Gyeon Gang Chun Chung Jeon Jeon Gyeong Gyeong Jeju -buk -nam -ju -jeon -ggi -won -gbuk -nam -buk -nam -buk - nam Nation Seoul 6 metropolitan cities 9 provinces -wide Long-term (Dec. 2013~Dec. 2016) Short-term (Dec. 2012~Dec. 2016) Past year (Dec. 2015~Dec. 2016)

1) The comparison of the average housing prices by time period (long-term, short-term, past year) was performed by converting the nominal housing price index into the real housing price index (excl. inflation), and the annual average rate of change in the housing prices was used.

16 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 1 Market Trends

Short- and Long-term Changes in the Jeonse Sales Prices The rate of change in the real apartment jeonse prices nationwide in the past year (Dec. 2015 to Dec. 2016) was 0.6%, with a noticeable downtrend in the Seoul National Capital Area and Daegu, where the jeonse prices had soured in the past.

○ The short-term annual average rate of change in the real apartment jeonse prices in the aforementioned regions was approx. 6% (Seoul 5.8%, Incheon 5.9%, Gyeonggi 6.0%), but the rate of change dropped significantly in the past year to around 2%.

○ Of particular note, the short-term annual average rate of change in the apartment jeonse prices in Daegu was 5.7%, which was relatively high among the 17 cities and provinces under analysis; however, the rate of change in the real apartment jeonse prices showed a downtrend at -3.6% in the past year.

○ Provinces where the real apartment jeonse prices have dropped in the past year were Chungnam, Gyeongbuk and Gyeongnam regions, and this contrasts with the stabilization of jeonse prices in other regions.

○ Considering the rate of change in the real apartment jeonse prices in the past year has fallen below the short- and long-term rates of change in the real apartment jeonse prices in other regions, the rate of change in the jeonse prices has become very stable compared to the past.

Figure 1-12 A comparison of the short- and long-term rates of change in the real apartment jeonse prices by region

Metropolitan cities Provinces

10% 10%

5% 3.0% 5% 1.9% 2.0% 1.5% 1.1% 0.8% 1.4% 1.4% 0.6% 0.6% 0.2% 0% 0% -0.4% -0.7% -0.1% -0.5% -3.1% -5% -3.6% -5% -3.8% Gang Gang Incheon Busan Daegu Gwang Dae Ulsan Gyeon Gang Chun Chung Jeon Jeon Gyeong Gyeong Jeju -buk -nam -ju -jeon -ggi -won -gbuk -nam -buk -nam -buk - nam Nation Seoul 6 metropolitan cities 9 provinces -wide Long-term (Dec. 2013~Dec. 2016) Short-term (Dec. 2012~Dec. 2016) Past year (Dec. 2015~Dec. 2016)

17 3) Trends in the Apartment Prices in the Seoul National Capital Area

Apartment Sales Prices in Seoul by Apartment Size The apartment sales prices in Seoul in 2015 increased among the small- and mid-size apartments. Similar patterns were observed in 2016, but the overall level of increase in the sales prices was significantly lower.

○ The rate of change in the sales prices in Gangbuk, Seoul in 2016 was lower than that of Gangnam for all apartment sizes. It has dropped to half the level of increase recorded in 2015. - The rate of change in the sales prices for the small, small-to-medium, medium, medium- to-large and large apartments was 3.7%, 2.1%, 2.3%, 1.2%, and 0.9%, respectively, for the Gangbuk region, and 4.8%, 3.6%, 5.5%, 3.1%, and 2.1%, respectively, for the Gangnam region. The level of increase dropped to half the level of increase reported in 2015.

○ The rate of change in the sales prices in Gangbuk and Gangnam in 2016 was higher than the rate of change reported in 2014. A steady uptrend was observed in Gangbuk compared to a couple of years ago, while an uptrend continued in Gangnam.

Figure 1-13 Changes in the apartment sales prices in Seoul by apartment size (nominal)

Gangbuk, Seoul Gangnam, Seoul

10% 10% 10% 9.11%0% 9.1% 7.7% 7.7% 7.6% 7.6% 5.5% 55.6.5%% 5.6% 5.75%.5% 55.6.7%5%.5% 5.6% 4.8% 4.8% 4.1% 5% 35.7%% 3.7% 3.5% 5% 5% 3.6% 4.1% 3.5% 3.6% 3.1% 3.1% 2.3% 2.1% 22.3.1%% 2.1% 2.1% 1.5%1.2% 1.5% 1.2% 0.9% 0.9% 0% 0% 0% 0%

-5% -5% -5% -5% Small SmallSmall MediumSmall MediumMedium LargeMedium Large Small SmallSmall MediumSmall MediumMedium LargeMedium Large -medium -medium -large -large -medium -medium -large -large 2014 22001154 22001165 2016 2014 22001154 22001165 2016

18 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 1 Market Trends

Figure 1-14 Changes in the apartment jeonse prices in Seoul by apartment size (nominal)

Gangbuk, Seoul Gangnam, Seoul

15% 15% 15% 15% 12.6% 12.6% 11.4% 11.4% 11.6% 11.6% 12% 12% 12% 111.20% 11.0% 10.1% 10.1% 10.5% 10.5% 9.3% 9.3% 8.6% 8.6% 9% 9% 8.3% 8.3% 9% 9%

6% 6% 6% 6% 3.8% 3.8% 3.1% 3.1% 3.5% 3.5% 2.5% 22.5.8%% 2.8% 2.8% 2.8% 2.7% 22..79%% 2.9% 3% 3% 2.5% 32%.5% 31%.9% 1.9% 1.1% 1.1%

0% 0% 0% 0% Small SmallSmall MediumSmall Medium MediumLarge Large Small SmallSmall MediumSmall Medium MediumLarge Large -medium -medium -large -large -medium -medium -large -large 2014 22001145 22001156 2016 2014 22001145 22001156 2016

Apartment Jeonse Prices in Seoul by Apartment Size In the case of the apartment jeonse prices in Seoul in the first half of 2016, the level of increase dropped regardless of apartment size, similar to the sales prices, to a very stable level.

○ The rate of change in the apartment jeonse prices in Gangbuk showed similar patterns as in 2015, when the jeonse prices rose mainly among the small apartments, but the level of increase contracted considerably. In the case of Gangnam, the rate of increase in the apartment jeonse prices was similar between the small and small-to-medium apartments, and the medium-to-large and large apartments.

Summary of the Trends in Seoul The number of “up areas,”2) where the rate of change in the apartment sales prices in 2016 surpassed the long-term annual average rate of change, gradually decreased, and this was also the case in the apartment jeonse market. As such, the price changes in the sales and jeonse markets across the districts occurred in a similar manner.

○ The areas where the YOY rate of change in the sales prices was high at the end of 2016 were Gangnam-gu (6.0%), Gangseo-gu (4.8%) and Gwanak-gu (4.1%), but the level of increase has

2) “Down area” is defined as a si, gun or gu (city/town, county or district) where the YOY rate of change is less than 0; “Stable area” is defined as a si, gun or gu (city/town, county or district) where the YOY rate of change is greater than 0 and less than or equal to the long-term annual average rate of change for the city or province in question (Dec. 2003~Dec. 2016); “Down area” is defined as a si, gun or gu (city/ town, county or district) where the YOY rate of change is greater than the long-term annual average rate of change for the city or province in question (Dec. 2003~Dec. 2016). The same criteria apply hereunder.

19 been gradually dropping. - Seongdong-gu, Gwangjin-gu, Dongdaemun-gu, Jungnang-gu, Seongbuk-gu, Gangbuk- gu, Dobong-gu, Nowon-gu, Guro-gu and Geumcheon-gu, which had been categorized as an “up area,” entered the category of “stable area” in the second half of 2016. As such, the apartment sales market in Seoul showed a stable uptrend overall.

○ In the case of the four districts of Gangnam, which had been towing the uptrend in the apartment jeonse prices, the level of increase in the jeonse prices dropped significantly in the second half of 2016. Meanwhile, Seongdong-gu, Gwangjin-gu, Dongdaemun-gu, Jungnang-gu, Seongbuk-gu, Gangbuk-gu, Dobong-gu, Nowon-gu, and Eunpyeong-gu, which had belonged to the category of “up area,” entered the category of “stable area” in the second half. - The areas that were an “up area” based on the YOY rate of change in the jeonse prices at the end of 2016 were Gwanak-gu (6.4%), Yangcheon-gu (6.1%), Mapo-gu (6.1%), Seodaemun-gu (5.7%) and Guro-gu (5.4%).

- On‌ the other hand, the area where the YOY rate of change in the jeonse prices was in the minus was Gangdong-gu, where the YOY rate of change was recorded at -0.9%. However, considering that the YOY rate of change in the jeonse prices at the end of 2015 was 18.1%, it is deemed that the jeonse market in this particular district became very stable after the move-in process for the apartments built through the reconstruction projects was completed.

Figure 1-15 Patterns in the apartment price trends in Seoul by month

Number of districts by type of apartment sales price changes Number of districts by type of apartment jeonse price changes

25 25 25 1 1 1 11 11 11 1 1 1 25 1 1 1 1 1 1 1 1 1 3 2 2 23 2 2 2 3 2 2 2 22 22 2 2 2 2 2 22 2 2 1 1 2 1 2 4 3 4 4 3 4 3 3 4 4 3 3 3 3 33 3 3 3 5 5 5 5 4 4 8 8 7 7 87 87 8 8 7 7 7 7 7 7 7 7 9 9 9 9 9 9 8 8 9 1010 111010 11 9 12 12 11 11 12 12 11 11 16 16 16 16 16 161 7 17 16 16 14 14 19 19 17 17 202 0 202 0 20 20 18 18 17 17 23 23 23 22332 3 232 3 22 22 19 19 212 4 242 244 242 4 232323 21 22 222223232322 242424 242424 24 24 21 21 2021 21 23232322332233232323 23 232233 2323 20 212122 22 2222222222222222 1818 11781178 20 20 2121 141 5 1164 1 5 16 17 17 16 161717 15 1145 17 18 18 181818 181818 14 13 13 16 1716 14 14 14 14 9 9 9 13 13 9 9 8 9 8 6 10 10 5 5 5 5 5 6 5 8 9 9 7 8 7 7 7 1 1 1 1 1 1 2 2 0 0 1 1 1 1 1 1 3 4 4 1 1 1 0 1 2 0 1 2 0 1 2 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 3 . . . 1 1 1 0 1 2 0 1 2 0 1 2 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 1 1 1 1 1 1 1 1 1 . . . 4 5 6 1 1 1 1 1 1 1 1 1 0 4 5 6 0 1 1 1 1 1 1 0 1 2 0 1 2 0 1 2 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 ` ` ` 1 1 1 . . . 1 1 1 0 1 2 0 1 2 0 1 2 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 ` ` ` 1 1 1 1 1 1 1 1 1 . . . 4 5 6 1 1 1 1 1 1 1 1 1 4 5 6 1 1 1 ` ` ` 1 1 Up area Stable area Down area 1 ` ` Up area Stable area Down area ` Up areaUp areaStable Stablearea areaDown areaDown area

20 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 1 Market Trends

Figure 1-16 Apartment sales prices in Incheon·Gyeonggi by apartment size Incheon Gyeonggi

10% 10% 10% 10% 8.1% 8.1%

5.7% 5.6% 5.16% 5.1% 5.7% 4.3% 4.3% 5% 5% 4.0% 34.80% 3.8% 5% 5% 3.6% 3.6% 2.0% 2.0% 2.3% 2.3% 2.0% 2.0% 2.2% 2.2% 1.4% 1.4% 0.9% 0.9% 0.0% 00..30% 0.3% 0.0% 0.10% 0.1% 0% 0% 0% 0% -0.4% -0.4% -0.2% -0.2%

-5% -5% Small-5 % Small MediumSmall MediumMedium MediumLarge Large-5% Small Small MediumSmall MediumMedium MediumLarge Large -medium -medium -large -large -medium -medium -large -large 2014 22001154 20165 2016 2014 22001154 22001165 2016

Apartment Sales Prices in Incheon·Gyeonggi by Apartment Size In 2016, the apartment sales prices in the Incheon and Gyeonggi area increased slightly mainly among the small and small-to-medium apartments, while the changes in the prices of medium and medium- to-large apartments were negligible.

○ In the case of the , the rate of change in the sales prices generally saw a significant drop. Of particular note, the rate of change in the sales prices of small apartments decreased greatly to 2.0% in 2016 compared to the previous year (8.1%). - However, in the Gyeongui (, and ) and Gyeongwon (, , , ) areas of Gyeonggi-do Province, the sales prices of small apartments increased by 3.5% and 3.4%, respectively, indicating that the uptrend was continuing compared to other regions.

Apartment Jeonse Prices in Incheon-Gyeonggi by Apartment Size Compared to 2015 when the jeonse prices increased by approx. 8~10% in the Incheon and Gyeonggi area, the rate of increase fell to around 2~3% in 2016 for most apartment sizes.

○ The jeonse prices of small apartments in Gyeonggi in 2015 increased by over 10% in most of the regions, but the rate of increase has been around 2~3% for most regions in 2016.

○ The jeonse prices have increased at a slower rate, similar to the trends observed in the sales prices. In the Gyeongui and Gyeongwon areas of Gyeonggi-do Province, where the sales prices showed an uptrend, the jeonse prices increased by 4.6% and 5.0%, respectively.

21 Similar levels of increase were observed for small, small-to-medium and medium-to-large apartments.

Figure 1-17 Apartment jeonse prices in Incheon·Gyeonggi by apartment size

Incheon Gyeonggi

15% 15% 15% 15%

12% 12% 12% 101.72% 10.7% 10.3% 10.3% 10.1% 10.1% 8.9% 8.9% 8.6% 8..63% 8..35% 88.5.5%% 8.5% 8.8% 8.8% 9% 9% 9% 9% 7.5% 7.5%

6% 6% 6% 6% 4.2% 4.2% 3.5% 3..56% 3.6% 3.3% 3.3% 2.4% 2.4% 2.8% 2..86% 22.6.5%% 2.5% 3% 3% 2.0% 2.0% 3% 3% 2.1% 2.1%

0% 0% 0% 0% Small Small MediumSmall Medium MediumLarge Large Small Small MediumSmall Medium MediumLarge Large -medium -medium -large -large -medium -medium -large -large 2014 20154 20165 2016 2014 20154 20165 2016

Summary of the Trends in Incheon·Gyeonggi The number of “up areas” in relation to apartment sales prices in Incheon and Gyeonggi decreased continually, similar to Seoul. Similar patterns were observed in the case of apartment jeonse prices.

○ All of the areas in Incheon were considered a “stable area” in terms of the apartment sales prices, and most were considered an “stable area” in terms of apartment jeonse prices. The only “up area” based on the jeonse prices was Bupyeong-gu. The YOY rate of change in the apartment jeonse prices determined in Dec. 2016 was 6.0%.

○ In Gyeonggi, most areas fell into the category of “stable area” based on the YOY rate of change in the apartment sales prices reported for Dec. 2016. The “down areas” in terms of apartment sales prices were -si (-0.3%) and -si (-0.3%). The “up area” and “down area” in terms of apartment jeonse prices were Yangju-si (5.8%) and -si (-1.0%), respectively. - The main reason behind the uptrend in prices in these regions is the improved transportation environment in the northern part of Gyeonggi-do Province. The results of an analysis showed that the uptrend was caused by the impact from the construction

22 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 1 Market Trends

of -Pocheon Expressway, set to be opened next year, and the Outer Ring Express II, set to be opened in 2020, as well as the possibility of an extension of Subway Line 7 in the northern part of Gyeonggi-do Province.

Figure 1-18 Patterns in the apartment price trends in Incheon by month

Number of districts by type of apartment sales price changes Number of districts by type of apartment jeonse price changes

8 8 8 8 1 1 1 1 1 1 1 1 11 1 11 11 1 1 1 1 1 2 2 2 2 2 22 22 2 2 2 2 2 3 3 3 33 3 3 3 3 3 3 3 3 3 4 44 2 4 2 4 4 4 44 44 4 44 4 4 4 4 4 4 3 3 5 5 6 6 6 6 6 6 7 7 7 77 77 77 7 7 7 7 7 7 7 7 7 8 8 8 88 8 8 8 8 8 8 8 8 88 88 88 88 88 88 88 88 88 88 8 8 8 8 8 8 88 88 88 88 8 8 8 5 5 7 7 7 7 77 7 77 77 7 7 7 7 7 6 6 6 66 66 6 6 6 6 6 3 3 5 5 5 55 55 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 4 44 44 4 44 4 4 4 4 4 4 3 3 2 2 2 2 2 2 1 1 1 1 1 1 1 11 11 11 1 1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 1 1 1 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 ...... 1 1 1 1 1 1 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ...... 4 5 6 4 5 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 4 5 6 4 5 6 1 1 1 1 1 1 ` ` ` ` ` ` 1 1 1 1 1 1 ` ` ` ` ` ` Up areaUp areaStable Stablearea areaDown areaDown area Up areaUp areaStable Stablearea areaDown areaDown area

1-19 Patterns in the apartment price trends in Gyeonggi by month

Number of districts by type of Number of districts by type of apartment jeonse price changes

28 28 28 28 2 2 2 2222 2 2 2 1 2 1 2 2 2 1 1 121 2 2 22 2 2 2 2 2 1 1 1 1 4 3 3 4 3 3 4 4 44 4 44 4 4 4 3 4 4 43 44 4 4 4 3 3 3 3 4 4 6 5 5 56 555 5 5 5 5 5 5 6 5 6 5 6 6 5 5 7 7 7 7 7 7 7 8 7 8 9 10 9 10 9 9 9 109 9 9 91 0 9 9 10 9 10 11 11 12 12 12 12 13 13 13 13 121 2 121 2 16 16 15 15 11 11 18 18 16 16 20 20 14 151 174 151 7 212 1 212 1 131 4 131 4 191 91 9 191 91 9 23 23 16 16 252 5 252 5 17 17 26 26 252 6 252 6 17 171 5 15 26 26 26 26 2626 2622662626 262626 24 242244242244242424 25242422452244242424 2525 252524 24 23 23 23 23 2223 2223 22 22 23 23 21 21 21 21 2120 2120 15 15 1918 1918 191919118919191198 19 1918 1918 16 16 16 16 14 14 15 15 15 15 1515 1515 12 12 13 13 10 9 10 910 10 91010 10 9 10 10 10 7 7 7 7 7 7 7 7 8 8 7 7 7 7 5 656 6 6 5 5 2 2 3 3 3 3 2 2 0 0 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 ...... 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 4 4 5 5 6 6 4 4 5 5 6 6 1 1 1 1 1 1 1 1 1 1 1 1 ` ` ` ` ` ` ` ` ` ` ` ` Up areaUp areaStable areaStable areaDown areaDown area Up areaUp areaStable areaStable areaDown areaDown area

23 Trends in Housing Transaction Volume

1) Housing Sales Transaction Volume

Summary The cumulative housing sales transaction volume as of the end of November in 2016 was 964,000 which was lower compared to that of the same period in the previous year (1,106,000), but higher than that of the same period in 2014 (914,000).

○ The cumulative sales transaction volume reported for the Seoul National Capital Area as of the end of November in 2016 was 520,000, which was the highest volume ever to be reported following 2006 (610,000) and 2015 (570,000); however, in the case of the five metropolitan cities and rural areas, the transaction volume has decreased slightly compared to other time periods.

○ The sales transaction volumes reported for the five metropolitan cities and rural areas are similar to the level in 2013, prior to the recovery of the housing market in 2014 and 2015; however, considering that the total housing supply available for transaction was higher in 2016 than in 2013, with the supply of new housing over the course of 3 years, it is deemed that the sales transaction volumes in these regions were someone low.

○ In summary, it is deemed that the overall decrease in the housing sales transactions observed recently was mainly attributable to the decrease in housing sales transactions in

Figure 1-20 Trends in housing sales transaction volume by region

(Unit: ten thousand transactions) Annual housing sales transaction volume Housing sales transaction volume from Jan. to Nov

141040 121020 11101.60.6 11191.49.4 10180.28.2 949.54.5 969.46.4 121020 101000 878.57.5 919.41.4 989.18.1 10100.50.5 969.46.4 858.45.4 292 9 89.4 323 2 797.59.5 797.59.5 101000 212 18 68.86.889.4 878.07.0 858.25.2 191 9 757.95.9 252 5 808.00.0 8080 707.10.1 272 7 737.53.5 303 0 252 5 262 6 313 16 26.72.7 8080 171 7 353 5 151 5 212 1 242 4 252 5 232 3 272 7 272 7 282 8 272 7 252 5 191 9 292 9 6060 262 6 6060 272 7 242 4 191 9 141 4 161 6 232 3 222 2 161 6 171 7 181 8 232 3 202 0 262 6 212 1 4040 191 9 4040 222 2 191 9 202 0 161 6 707 0 616 1 616 1 575 7 525 2 484 8 525 2 444 4 444 4 424 2 2020 454 5 404 0 37 464 6 2020 373 7 333 3 323 2 282 8 37 272 7 363 6 252 5 232 3 0 0 0 0 `06`06`07`07`08`08`09`09`10`10`11`11`12`12`13`13`14`14`15`15`16`16 `06`06`07`07`08`08`09`09`10`10`11`11`12`12`13`13`14`14`15`15`16`16 CapitalCapital area area 5 metropolitan5 metropolitan cities cities RuralRural areas areas CapitalCapital area area 5 metropolitan5 metropolitan cities cities RuralRural areas areas

24 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 1 Market Trends

the metropolitan cities outside the capital area. This is due to the drop in sales transactions in the second half of the year as a result of reinforced loan restrictions and increased housing supply in the rural areas.

By Housing Type In 2016 (as of the end of November), the ratio of apartment transactions to total residential property transactions increased, while the ratio of detached homes, multi- housing and row house transactions to total residential property transactions decreased. This was opposite the trends observed in 2015.

○ The ratio of apartment sales transactions to total residential property transactions increased in the capital area, whereas the ratio of detached home sales transactions to total residential property transactions was higher than that of multi-housing and row houses, and the ratio of apartment sales transactions to total residential property transactions increased in the regions outside the capital area. As such, the ratios of transactions by housing type varied across the regions.

○ Even when the data were subdivided by region, the ratio of apartment sales transactions was shown to have increased in 2016. This was due to the increase in the inventory apartments available for transactions resulting from a greater supply of new apartments, and this is why the trends observed in 2016 contrast with that of 2015.

Figure 1-21 Ratio of sales transactions by housing type and region

Nationwide Capital area Non-capital area

1100010%0%0% 100100%% 1100010%0%0% 100% 66.1.61%.%1% 7.76.%6% 9.98.%8% 9.92.%2% 1111.16.16%.%6%1122.19.29%.%9% 151.50.%0%1133.16.36%.%6% 7.6% 9.8% 9.2% 161.63.%3% 17.6% 15.0% 16.3% 17.167%.6%2200.24.04%.%4%1188.19.89%.%9% 2020.7.%7% 808%0% 1155.15.5%.%5% 151.57.%7% 8080%% 20.7% 2121.217.7%.%7% 808%0% 80% 15.7% 19.7% 1188.18.8%.%8%80% 2626.7.%7%2525.257.7%.%7%80% 1111.11.1%.%1%1100.14.04%.%4% 101.04.%4% 19.179%.7% 26.7% 1122.13.23%.%3%10.4%

6600%6%0% 6060%60%% 6600%6%0%

4400%4%0% 4040%40%% 4400%4%0% 7722.79.29%.%9%7711.74.14%.%4% 676.76.%6% 7373.732.2%.%2%7070.707.7%.%7% 7722.76.26%.%6%7722.70.20%.%0% 7700.77.07%.%7% 6655.64.54%.%4%67.6% 6363.635.5%.%5%6565.651.1%.%1% 6677.63.73%.%3% 2200%2%0% 2020%20%% 2200%2%0%

00%%0% 00%%0% 00%%0% 22QQ2Q44QQ4Q22QQ2Q44QQ4Q22QQ2Q44QQ4Q22QQ2Q44QQ4Q 22QQ2Q44QQ4Q22QQ2Q44QQ4Q22QQ2Q44QQ4Q22QQ2Q44QQ4Q 22QQ2Q44QQ4Q22QQ2Q44QQ4Q22QQ2Q44QQ4Q22QQ2Q44QQ4Q 220012130313 220012140414 220012150515 220012160616 220012130313 220012140414 220012150515 220012160616 220012130313 220012140414 220012150515 220012160616 ApartmentsApartmentsApartments Multi-housingMulti-housingMulti-housing & & row row& row houses houses houses ApartmentsApartmentsApartments Multi-housingMulti-housingMulti-housing & & row row& row houses houses houses ApartmentsApartmentsApartments Multi-housingMulti-housingMulti-housing & & row row& row houses houses houses DetachedDetachedDetached homes homes homes DetachedDetachedDetached homes homes homes DetachedDetachedDetached homes homes homes Note: The volume reported for 2016 4Q is the cumulative transaction volume from Oct. to Nov. 2016 (same applies hereunder) Source: RTMS

25 By Sales Price Range Generally speaking, there was a distinct pattern in which the ratio of transactions involving residential properties in the low (under KRW 200 million) and low- to-mid (over KRW 200 million and under KRW 400 million) price ranges decreased, while the ratio of transactions involving residential properties in the mid-to-high (over KRW 400 million and under KRW 600 million) and high (over KRW 600 million) price ranges increased. There was a marked increase in the ratio of transactions involving residential properties in the mid-to-high price range in the second half of 2016.

○ The ratio of nationwide transactions involving residential properties in the mid-to-high price range was 8~9% in 2015 (8.4% in 1Q, 9.2% in 2Q, 9.8% in 3Q, and 9.8% in 4Q), and it increased to the 11% range in 2016 (8.5% in 1Q, 9.4% in 2Q, 11.3% in 3Q and 11.9% in 4Q).

○ In the capital area, the transaction volume concerning low-priced, mid-to-high-priced and high-priced homes increased in 2016, whereas the ratio of transactions involving low-to- mid range homes decreased. - The ratio of transactions involving low-priced homes to total residential property transactions generally increased between 2015 (41.2% in 1Q, 39.% in 2Q, 38.9% in 3Q, and 39.0% in 4Q) and 2016 (36.6% in 1Q, 37.1% in 2Q, 38.5% in 3Q and 39.3% in 4Q).

○ As for the areas outside the capital area, the ratio of transactions involving low-to-mid range residential properties remained high, and the ratio of transactions involving low- priced homes increased, similar to the capital area, whereas there were no significant

Figure 1-22 Ratio of sales transactions by price range and region

Nationwide Capital area Non-capital area

110010%0%0% 110010%0%0% 110010%0%0% 55.6.65%%.6% 77.5.57%%.5% 99.6.69%%.6% 33.1.13%%.1% 44.1.14%%.1% 5.1% 66.8.86%%.8% 88.0.08%%.0% 111.16.61%%.6% 5.15%.1% 55.9.95%%.9% 99.8.89%%.8%11.9% 111.12.21%%.2%12.5% 111.91%.9% 121.52%.5%1144.3.3%% 8800%8%0% 8800%8%0% 14.3%1166.18.86%%.8%8800%8%0%

4422.4.4%% 6600%6%0% 5588.58.8%%.8%555.58.85%%.8%51.9% 6600%6%0% 42.4%4400.41.10%%.1% 6600%6%0% 7722.78.82%%.8%6699.64.49%%.4%666.63.36%%.3%6622.6.62%%.6% 515.91%.9%4466.40.06%%.0% 3388.31.18%%.1%3322.3.32%%.3%

4400%4%0% 4400%4%0% 4400%4%0%

20% 20% 40.7% 40.0% 39.3% 20% 202%0% 31.7% 31.9% 34.3% 202%0% 404.70%.7% 404.0%.0%3388.30.08%%.0%393.39%.3% 202%0% 3311.30.01%%.0% 313.71%.7% 313.91%.9%343.34%.3% 24.5% 25.6% 28.3% 222.27.72%%.7% 242.54%.5% 252.65%.6%282.38%.3% 00%%0% 00%%0% 00%%0% 2Q 4Q 2Q 4Q 2Q 4Q 2Q 4Q 2Q 4Q 2Q 4Q 2Q 4Q 2Q 4Q 2Q 4Q 2Q 4Q 2Q 4Q 2Q 4Q 2Q2Q4Q4Q2Q2Q4Q4Q2Q2Q4Q4Q2Q2Q4Q4Q 2Q2Q4Q4Q2Q2Q4Q4Q2Q2Q4Q4Q2Q2Q4Q4Q 2Q2Q4Q4Q2Q2Q4Q4Q2Q2Q4Q4Q2Q2Q4Q4Q 2013 2014 2015 2016 2013 2014 2015 2016 2013 2014 2015 2016 20210313 20210414 20210515 20210616 20210313 20210414 20210515 20210616 20210313 20210414 20210515 20210616 Under KRW 200M KRW 200M~400M Under KRW 200M KRW 200M~400M Under KRW 200M KRW 200M~400M UnderUnder KRW KRW 200M 200M KRWKRW 200M~400M 200M~400M UnderUnder KRW KRW 200M 200M KRWKRW 200M~400M 200M~400M UnderUnder KRW KRW 200M 200M KRWKRW 200M~400M 200M~400M KRW 400M~600M Over KRW 600 M KRW 400M~600M Over KRW 600 M KRW 400M~600M Over KRW 600 M KRWKRW 400M~600M 400M~600M OverOver KRW KRW 600 600 M M KRWKRW 400M~600M 400M~600M OverOver KRW KRW 600 600 M M KRWKRW 400M~600M 400M~600M OverOver KRW KRW 600 600 M M Source: RTMS

26 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 1 Market Trends

changes in the ratio of transactions of homes in other price ranges. - As such, the increase in the ratio of transactions involving low-priced homes was attributable to the accumulated fatigue among renters resulting from the rising jeonse prices, and the increase in transactions of row houses and multi-housing, units, which are relatively cheaper than apartments.

By Age Group The residential property transaction volume by age group in 2016 showed similar patterns as the previous year. The No. 1 buyers by age group were buyers in their 40s and 50s, who accounted for the 50% of the residential property sales transactions, followed by buyers in their 30s or younger and buyers over the age of 60.

○ In the case of the capital area, the sales transactions made by buyers in their 30s or younger and buyers in their 40s and 50s recovered dramatically in the second and third quarters, but the number plummeted again in the fourth quarter. - Buyers in their 30s or younger accounted for 29,000 transactions in 1Q, 42,000 transactions in 2Q, 49,000 transactions in 3Q and 35,000 transactions (as of the end of November), while buyers in their 40s and 50s accounted for 50,000 transactions in 1Q, 75,000 transactions in 2Q, 84,000 transactions in 3Q, and 60,000 transactions in 4Q (as of the end of November).

○ In the regions outside the capital area, the sales transaction volume increased in the second

Figure 1-23 Ratio of sales transactions by age group of buyer and region

Nationwide Capital area Non-capital area

10101%00%0% 10101%00%0% 10101%00%0% 10.8% 12.7% 101.014.0%4.%4% 12.5% 15.7% 11.1% 12.8% 10.180%.8%12.172%.7%141.419.4%9.%9%151.515.5%5.%5% 12.152%.5%151.51.5%1.%1%15.175%.7% 11.11%.1%12.182%.8%141.416.4%6.%6%151.514.5%4.%4% 808%08%0% 808%08%0% 808%08%0%

48.5% 606%06%0% 484.841.8%1.%1% 51.2% 606%06%0% 48.458%.5% 51.4% 606%06%0% 474.747.7%7.%7% 51.0% 51.521%.2%505.059.0%9.%9%515.154.1%4.%4% 51.541%.4%515.152.1%2.%2%515.158.1%8.%8% 51.501%.0%505.056.0%6.%6%515.150.1%0.%0%

404%04%0% 404%04%0% 404%04%0%

202%02%0% 373.73.7%3.%3% 202%02%0% 373.735.7%5.%5% 202%02%0% 373.731.7%1.%1% 323.234.2%4.%4%303.03.0%3.%3%292.929.9%9.%9% 323.235.2%5.%5%303.037.0%7.%7%303.030.0%0.%0% 323.234.2%4.%4%292.928.9%8.%8%292.927.9%7.%7%

0%0%0% 0%0%0% 0%0%0% 2Q2Q2Q4Q4Q4Q2Q2Q2Q4Q4Q4Q2Q2Q2Q4Q4Q4Q2Q2Q2Q4Q4Q4Q 2Q2Q2Q4Q4Q4Q2Q2Q2Q4Q4Q4Q2Q2Q2Q4Q4Q4Q2Q2Q2Q4Q4Q4Q 2Q2Q2Q4Q4Q4Q2Q2Q2Q4Q4Q4Q2Q2Q2Q4Q4Q4Q2Q2Q2Q4Q4Q4Q 202102310313 202102410414 202102510515 202102610616 202102310313 202102410414 202102510515 202102610616 202102310313 202102410414 202102510515 202102610616 30s30s 30sand and andunder under under 40s~50s40s~50s40s~50s OverOverOver 60 60 60 OtherOtherOther 30s30s 30sand and andunder under under 40s~50s40s~50s40s~50s OverOverOver 60 60 60 OtherOtherOther 30s30s 30sand and andunder under under 40s~50s40s~50s40s~50s OverOverOver 60 60 60 OtherOtherOther Source: RTMS

27 and third quarters compared to the first quarter, similar to the capital area, but the level of increase was not very large. The sales transaction volume showed a noticeable downtrend.

- ‌Buyers in their 30s or younger accounted for 31,000 transactions in 1Q, 34,000 transactions in 2Q, 35,000 transactions in 3Q and 28,000 transactions (as of the end of November), while buyers in their 40s and 50s accounted for 53,000 transactions in 1Q, 63,000 transactions in 2Q, 61,000 transactions in 3Q, and 49,000 transactions in 4Q (as of the end of November).

2) Jeonse and Monthly Rent Transaction Volumes

Summary Although the jeonse and monthly rent transaction volumes continually increased throughout the year, the total transaction volume as of the end of November 2016 was reported to be 1,341,000, which was slightly lower than the previous year (1,354,000).

○ The ratio of jeonse and monthly rent transaction volumes to total home lease transaction volumes were 55.8% and 44.2%, respectively, in 2015, and 54.7% and 45.3%, respectively, in 2016 (as of the end of November). As such, the portion of jeonse transactions decreased, while the portion of monthly rent transactions increased (1.1%p). Compared to the decrease in 2015 (-3.2%p) compared to 2014 (59.0% jeonse and 41.0% montly rent), the rate of decrease in jeonse transactions and the rate of increase in the monthly rent transactions has slowed down.

Figure 1-24 Trends in jeonse and monthly rent transactions by region

(Unit: ten thousand transactions) Nationwide Capital area Non-capital area

160160160 1461.476 1.1744 716.42.77 .124 7.2 120120120 60 60 60 1371.37 .133 7.3 1341.314 .113 4.1 50.500 .05 0.0 1401401134201.312 1.1133 212.34.12 .143 2.4 97.987 .899 77.92.87 .29 7.2 48.488 .84 8.8 100100100 91.951 .59 1.5 50 50 50 45.495 .94 5.9 46.416 .14 6.1 88.828 .288 88.8.28 .88 8.8 88.808 .08 8.0 43.493 .944 33.4.693 .64 3.6 120120120 60 60 60 44 44 44 54 54 54 65 65 65 45 45 45 80 80 80 37 37 37 40 40 40 100100100 61 61 61 27 27 2277 2 7 3237 3 3 33 41 41 41 23 23 2243 2 4 24 39 39 39 17 17 1187 1 8 2118 2 1 21 22 22 22 80 80 80 60 60 60 30 30 30

60 60 60 40 40 40 20 20 20 89 89 8879 8 7 87 87 87 87 40 40 40 83 83 83 82 82 82 62 62 6612 6 1 5681 5 8 6508 6 0 5660 5 6 56 73 73 73 49 49 49 27 27 2267 2 6 2256 2 5 2265 2 6 2266 2 6 2246 2 4 24 20 20 20 10 10 10 20 20 20

0 0 0 0 0 0 0 0 0 201210121200121201122200121302123200121403124200121504125200121605162016 201210121200121201122200121302123200121403124200121504125200121605162016 201210121200121201122200121302123200121403124200121504125200121605162016 JeonseJeonseJeonseMonthlyMonthly rentMonthly rent rent JeonseJeonseJeonseMonthlyMonthly rentMonthly rent rent JeonseJeonseJeonseMonthlyMonthly rentMonthly rent rent Source: RTMS

28 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 1 Market Trends

○ In the case of the capital area, the ratio of jeonse transactions to total home lease transactions was higher than the national average, with the ratio reported to be 56.1% as of the end of Nvoember in 2016. Meanwhile, the ratio of monthly rent transactions to total home lease transactions was 43.9%. As such, the ratio of jeonse transactions was higher than the national average, while the ratio of monthly rent transactions was lower than the national average. - As for the changes in the ratio of jeonse transactions to total home lease transactions, it decreased by 4.0%p from 2014 to 2015, and by 1.6%p from 2015 to 2016, indicating that the rate of decrease in jeonse transactions has slowed down.

○ As for the areas outside the capital area, the volume and ratio of jeonse and monthly rent transactions recorded as of the end of November in 2016 were fairly similar compared to the previous year, with no marked changes.

By Type of Monthly Rent The ratio of semi-jeonse transactions to total home lease transactions has continually increased from 16.3% in 2014 to 18.5% in 2015 to 20.2% in 2016 (as of the end of November), whereas the ratio of semi-monthly rent and monthly rent transactions to total home lease transactions has steadily decreased.

Figure 1-25 Trends in monthly rent transactions

(Unit: ten thousand transactions)

Nationwide Capital area Non-capital area

7070 70 65.615 .16 5.1 4545 45 41.411 .14 1.1 3030 30 60.670 .76 0.7 60.610 .16 0.1 37.347 .43 7.4 38.368 .63 8.6 7.97 .9 7.9 4040 40 4.3 24.204 .02 4.0 6060 60 54.504 .05 4.0 4.3 4.3 25 22.6 7.57 .5 7.5 7.27 .2 7.2 33.343 .43 3.4 4.04 .0 4.02 5 25 22.62 2.6 22.212 .12 2.1 3535 35 4.14 .1 4.1 20.7 20.72 0.7 3.63 .6 3.6 5050 4530.6 45.405 .046 5.1.06 .1 6.1 3.33 .3 3.3 43.64 3.6 26.6 27.257 .52 7.5 3.43 .4 3.4 3.23 .2 3.2 3030 3026.62 6.6 2020 1270.107 .01 7..1507 .512 7.7.52 .7 2.7 4.54 .5 4.5 4040 4.014 .1 4.1 2.3 2.62 .6 2.6 2525 252.3 2.3 1.81 .8 21..082 .0 2.0 45.1 1515 15 45.14 15..4211 .24 1.2 29.229 .22 69..2426 .42 6.4 3030 30 42.482 .84 2.8 2020 20 27.287 .82 7.8 39.369 .63 9.6 15.0 16.106 .01 6.0 25.265 .62 5.6 14.104 .01 4.105 .01 5.0 14.184 .81 4.8 32.362 .63 32..3263 .23 3.2 1515 15 21.4 1010 1110.8 11.181 .81 1.8 2020 20 20.290 .92 0.291 .42 1.4 11.81 1.8 1010 10 5 1010 10 5 5 5 5 5 8.2 8.4 9.89 .81 92..810 2 .01 2..1302 .31 2.3 5.5 7.67 .6 7.68 .2 8.2 3.5 3.8 3.9 4.34 .3 4..434 .4 4..144 .1 4.1 6.96 .9 76..397 .3 7.38 .4 8.4 3.43 .4 3..543 .5 43..454 .4 4.45 .5 5.5 3.5 3.53 .8 3.83 .9 3.9 0 0 0 0 0 0 0 0 0 20121012101221012012320123012430124012540125012650162016 20121012101221012012320123012430124012540125012650162016 20121012101221012012320123012430124012540125012650162016 semi-jeonsesemi-jeonsesemi-jeonsesemisemi-monthly-monthlysemi-monthly rent rent rent semi-jeonsesemi-jeonsesemi-jeonsesemisemi-monthly-monthlysemi-monthly rent rent rent semi-jeonsesemi-jeonsesemi-jeonsesemisemi-monthly-monthlysemi-monthly rent rent rent MonthlyMonthly rentMonthly rent rent MonthlyMonthly rentMonthly rent rent MonthlyMonthly rentMonthly rent rent Source: RTMS

29 ○ The semi-jeonse transaction volume in the capital area increased by 11,000 in 2014 and by 21,000 in 2015 compared to the previous year. The number of semi-monthly rent transactions made in 2016 as of the end of November was 82,000, which was higher than the transaction volume reported for the previous year (76,000), indicating a continuous increase in the volume of semi-monthly rent transactions in the capital area.

○ Also, in the case of the semi-monthly rent transaction volume, it appears to be increasing with the overall increase in the total monthly rent transaction volume; however, the ratio of semi-monthly rent transactions to total monthly rent transactions has actually been declining from 74.2% in 2014 to 70.9% in 2015 to 68.5% in 2016.

○ As for the areas outside the capital area, there were no significant changes in the semi- jeonse transaction volume, with the ratio of semi-jeonse transaction reported at 18.9% in 2014, 18.5% in 2015 and 18.5% in 2016. Also, the ratios of semi-monthly rent and monthly rent transactions remain fairly similar to the ratios reported for the previous year.

Jeonse by Price Range With the continuous increase in the jeonse prices, the number of jeonse transactions in the mid-to-high price range (KRW 200 million to KRW 400 million) and in the high price range (over KRW 400 million) has increased, whereas the number of jeonse transactions in the low price range (under KRW 100 million) and low-to-mid price range (KRW 100 million to KRW 200 million) has fallen.

○ This trend is noticeable in the Seoul National Capital Area, where the jeonse prices are high. The jeonse transactions under KRW 100 million accounted for 35.5% of the total jeonse transactions in 2016 as of the end of November, and this was a 4.7%p decrease from 2015 (40.2%). The jeonse transactions in the KRW 100 to 200 million range dropped 0.9%p from 29.7% in 2015 to 28.7% in 2016 (end of Nov.).

○ In contrast, the ratio of jeonse transactions in the mid-to-high and high ranges to the total jeonse transactions increased slightly. The ratio of jeonse transactions in the KRW 200 to 400 million range increased to 26.8% as of the end of November in 2016, which was a 3.5%p increase from the previous year, while the ratio of jeonse transactions over KRW 400 million to the total jeonse transactions reached 9.0%, which was a 2.1%p increase from the previous year.

30 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 1 Market Trends

○ In the case of regions outside the capital area, there were almost no jeonse transactions in the high price range. But similar to the capital area, the jeonse transactions under KRW 100 million decreased, while the ratios of jeonse transactions in the low-to-mid and mid-to- high price ranges to the total jeonse transactions increased markedly. - As for the changes from 2015 to 2016, the ratio of jeonse transactions in the low price range to total jeonse transactions decreased from 61.5% to 56.1%, while the ratio of jeonse transactions in the low-to-mid price range increased from 28.3% to 31.2% and the mid- to-high price from 9.7% to 12.0%. The ratio of jeonse transactions in the high price range increased slightly from 0.5% to 0.7%.

Figure 1-26 Jeonse transaction volume by price range and region

(Unit: ten thousand transactions)

Nationwide Capital area Non-capital area

100100100 80 80 80 40 40 40 1.51.51.561.61.6 2.0 3.03.03.0 2.02.0 4.0 4.0 9.59.59.5 4.0 1.51.51.5 80 80 80 11.121.121.2 1.61.61.6 2.92.92.9 11.191.1914.9164.164.6 4.64.6640.660 60 2.02.02.0 3.93.93.9 0.001. 0010. .00101 . 001. 01 0.004. 0040. .01402 . 102. 12 15.165.165.6 8.68.6180.6.120.120.2 0.002. 002. 02 25.235.235.3 0.80.80.8 0.107. 107. 17 26.276.7 10.150.15102..5182.182.8 4.54.54.5 1.01.011..031.31.81.812..852.52.5 60 60 60 26.7 16.16.16.1 5.9 1.3 24.274.2745.7255.255.5 13.13.13.1 5.9 5.96.6 2.92.92.9 19.149.149.4 6.6 6.66.57.27.27.2 24.204.204.0 40 40 40 20.210.210.1 13.220 20 20 6.5 6.5 7.47.47.4 18.128.128.218.3 13.2 13.2 21.271.271.7 18.3 18.3 7.57.57.5 40 40 40 16.166.166.6 14.124.124.2 52.2 52.2 5427.2497.497.944.7 20 20 20 20.210.210.118.4 20 20 44.7 443.7453.453.538.6 32.312.1 18.4 1187..4137.13177..317.1176..1106.0 20 38.6 38.6 3229..1259.25297..5247.4 26.4 1163..0143.4 30.390.390.9 2276..44 2262..4252.252.5 13.4 17.157.157.5 0 0 0 0 0 0 0 0 0 201210121012120122012230123012340124012450125012560126016 201210122100112120122200112230122300112340122400112450122500112560126016 201210122100112120122200112230122300112340122400112450122500112560126016 UnderUnder KRWUnder KRW 100M KRW 100M 100MKRWKRW 100~200MKRW 100~200M 100~200M UnderUnder KRWUnder KRW 100M KRW 100M 100MKRWKRW 100~200MKRW 100~200M 100~200M UnderUnder KRWUnder KRW 100M KRW 100M 100MKRWKRW 100~200MKRW 100~200M 100~200M KRWKRW 200~400MKRW 200~400M 200~400MOverOver KRWOver KRW 400M KRW 400M 400M KRWKRW 200~400MKRW 200~400M 200~400MOverOver KRWOver KRW 400M KRW 400M 400M KRWKRW 200~400MKRW 200~400M 200~400MOverOver KRWOver KRW 400M KRW 400M 400M Source: RTMS

31 Trends in the Housing Supply Market

1) Trends in New Housing Supply

Summary Amid the concerns of excess housing supply, construction permits and approvals were given for 765,000 housing units in 2015, which was an all-time high. In 2016, construction firms pushed for pre-construction parceling-out sales for one last time. Construction permits and approvals were given for 637,000 housing units in 2016 as of the end of November, which was slightly lowered compared to the same period in the previous year (667,000); however, due to the burden from the increase in housing supply, construction firms tended to adjust their housing construction project plans.

○ Permits and approvals The number of housing construction permits and approvals as of the end of 2016 (637,000) was lower than that of the same period in the previous year (667,000). The number of permits and approvals declined in the capital area compared to the previous year, while it increased by 33,000 in the non-capital area. Thus, if all of the housing construction projects, for which permits and approvals have been granted, commence and are completed, then the housing supply is expected to increase at a greater rate in the long term in the non-capital area than the capital area.

○ Pre-construction sales and construction commencement Meanwhile, the number of housing construction projects that commenced was similar to the number of permits and approvals given in the capital area. In contrast, the number of housing construction projects that commenced was substantially lower than the number of permits and approvals given in the non-capital area. It is expected that there will be multiple projects where construction will not commence despite having obtained the necessary permit and approval.

○ Construction completions As of the end of November in 2016, 450,000 housing units were built in the year. The number of completed housing constructions has been on the rise, due to the increase in the number of permits and approvals granted and the number of housing construction projects that commenced between 2014 and 2016.

32 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 1 Market Trends

Figure 1- 27 Trends in housing supply nationwide (cumulative, from Jan. to Nov.) (Unit: ten thousand (buildings) units)

80 66.7 63.7 63.4 57.4 60 49.4 44.8 44.6 44.7 45.0 31.2 42.1 29.4 42.3 34.3 39.9 39.3 35.6 36.7 28.5 40 33.1 33.1 34.2 28.8 29.1 23.2 24.5 24.5 26.8 20.2 22.7 23.7 22.4 21.7 20.3 20.7 14.5 18.1 16.3 20.7 20 35.5 34.0 18.7 29.4 28.9 26.2 7.5 6.1 6.2 5.8 20.3 22.1 22.4 4.0 15.9 20.1 18.4 16.1 17.9 18.7 16.1 17.6 17.6 10.1 12.8 12.4 4.2 2.8 3.1 3.9 - 2.1 `12 `13 `14 `15 `16 `12 `13 `14 `15 `16 `12 `13 `14 `15 `16 `12 `13 `14 `15 `16 `12 `13 `14 `15 `16 Permits & licenses Construction commencement Pre-construction sales Construction completion Unclaimed supply Capital area Non-capital area

Note: The data are based on the cumulative value determined as of the end of November (excl. unclaimed supply) Source: MOLIT

○ Unclaimed supply The unclaimed supply (unsold housing units in pre-construction parceling-out sales) in 2015 was 50,000, which increased from 2014 by 10,000. However, the level of increase has dropped slightly in 2016, along with a slower rate of increase in the supply of housing units made available through pre-construction parceling-out sales.

Permits and Approvals Construction permits and approvals were given for 637,000 housing units nationwide as of November 2016, which was a 4.5% YOY decrease (667,000 as of November 2015).

○ The number of permits and approvals given as of November 2016 decreased by 17.1% in the capital area (294,000), and increased by 20.2% in the five metropolitan cities (98,00) and 198.9% in provinces outside the capital area (245,000). Based on this, it can be inferred that the decrease in the number of permits and approvals granted was mainly attributable to the trends observed in the capital area. More specifically, the number of permits and approvals granted decreased in Seoul (66,000), Incheon (19,000) and Gyeonggi (209,000) by 26.3%, 26.5% and 12.7%.

○ By supplier As of November 2016, the number of permits and approvals granted decreased by 4.3% for the private sector (597,000) and by 7.9% for the public sector (40,000).

33 ○ By size While the number of permits and approvals granted decrease for most home sizes in the capital area, it increased by 12.8% for homes that were 135㎡ in size or bigger. In the five metropolitan cities and provinces, the number of permits and approvals granted increased for most home sizes.

Pre-construction Sales The number of housing units made available through pre- construction parceling-out sales in 2016, as of November, was 423,000, which was a 14.3% YOY decrease (494,000 in 2015 as of November).

○ Pre-construction sales decreased nationwide by 15.9% in the capital area (221,000), 11.8% in the five metropolitan cities (57,000) and 12.9% in provinces outside the capital area (146,000). - While the housing supply made available through pre-construction sales declined in most regions, it increased in Seoul (9.4%), Gwangju (59.1%), Gangwon (49.8%), and Gyeongnam (25.3%).

Construction Commencement In 2016, as of November, construction projects commenced for 574,000 housing units, which was a 9.5% YOY decrease (634,000 in 2015 as of November).

○ Commencement of housing construction projects declined by 15.2% in the capital area (289,000) and by 5.7% in provinces outside the capital area (206,000), while it increased by 4.5% in the five metropolitan cities (79,000). - While project commencement decreased in most regions, it increased in Gwangju (15,000) and Busan (30,000) by 73.9% and 4.8%, respectively.

○ By supplier In 2016, as of November, the number of housing construction projects initiated by the private sector (536,000) decreased by 16.4% compared to the same period in the previous year, and by 20.7% for the public sector (38,000).

○ By size In the capital area, five metropolitan cities, and provinces outside the capital area, the construction projects for homes smaller than 60㎡ that were started dropped the greatest in number among all the different home sizes. In particular, there was a 27.9% decrease in the projects that were commenced for the construction of homes that were

34 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 1 Market Trends

smaller than 40㎡ in the provinces outside the capital area.

Construction Completions In 2016, as of November, the number of housing units, of which the construction was completed, was 450,000, which was a 14.5% YOY increase (393,000 units in 2015 as of November).

○ Completion of housing construction projects increased in the capital area (224,000), five metropolitan cities (69,000), and provinces outside the capital area (157,000) by 27.0%, 10.3% and 2.1%, respectively. - The rate of increase in Seoul (72,000) and Gyeonggi (138,000) was 22.7% and 35.2%, respectively, and contributed to the increase in total construction completions nationwide.

○ The completion of housing construction projects by the private sector (369,000) increased by 11.5%, while it increased by 30.9% in the case of the public sector (82,000).

○ By size Completion of housing construction projects for homes smaller than 40㎡ increased nationwide by 51.0% in total, while it increased by the largest amount in the capital area at 61.2%

35 Table 1-8 Trends in housing supply by region (Unit: thousand units) 2015 2016 Ratio 4/4 against Cumu Category Cumulative total, long- 1/4 2/4 3/4 lative 계 1/4 2/4 3/4 as of Nov term total, as average of Nov. Nation- 118.8 181.3 240.1 225.2 667.2 765.3 163.0 192.3 164.2 636.8 (-4.5) 1.19 wide Capital 61.3 99.3 133.9 114.3 354.7 408.8 79.2 84.9 71.5 293.9 (-17.1) 1.13 area Five metro- 13.3 18.3 27.3 36.9 81.8 95.8 21.2 36.2 23.0 98.3 (20.2) 1.26 politan

rmits and approvals and rmits cities

Pe Provinc- 44.1 63.8 78.9 74.0 81.8 260.8 62.6 71.1 69.7 244.6 (198.9) 1.22 es Nation- 58.3 160.5 117.6 189.6 493.9 525.9 81.9 127.9 115.0 423.0 (-14.3) 1.22 wide Capital 24.5 80.4 65.9 101.5 262.4 272.3 37.8 61.4 68.5 220.7 (-15.9) 1.41 area Five metro- 7.8 24.6 14.5 22.2 64.2 69.0 11.0 19.0 13.5 56.6 (-11.8) 0.85 politan cities e-constructionsales

Pr Provinc- 26.1 55.5 37.2 65.9 167.4 184.6 33.0 47.5 33.0 145.8 (-12.9) 1.17 es Nation- 110.5 178.3 173.4 254.5 634.3 716.8 117.7 181.8 150.9 573.8 (-9.5) 1.11 wide Capital 53.2 93.0 97.8 139.8 340.2 383.9 54.8 90.2 84.1 288.5 (-15.2) 1.21 area Five metro- 14.9 21.8 19.0 27.7 75.9 83.3 21.1 23.5 15.4 79.4 (4.5) 0.94 politan cities

nstruction commencement nstructioncommencement Provinc- 42.4 63.5 56.6 87.1 218.3 249.6 41.8 68.1 51.5 205.8 (-5.7) 1.04

Co es Nation- 91.1 98.4 123.1 147.5 393.0 460.2 100.4 142.3 122.8 450.1 (14.5) 1.12 wide Capital 35.3 49.5 61.8 57.1 176.1 203.7 46.7 74.6 61.6 223.6 (27.0) 1.13 area Five metro- 19.7 16.6 13.8 25.0 62.6 75.1 14.2 26.1 18.5 69.1 (10.3) 1.02 politan cities nstruction completion nstructioncompletion

Co Provinc- 36.2 32.4 47.4 65.4 154.2 181.3 39.5 41.6 42.8 157.4 (2.1) 1.16 es Note: “Long-term‌ average” refers to the cumulative total at the end of November each year from 2011 to 2016, and the number in brackets is the rate of YOY change Source: MOLIT

36 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 1 Market Trends

Table 1-9 Trends in housing supply by capital area and major cities

(Unit: thousand units) Permits and approvals Pre-construction sales 2015 2016 Ratio 2015 2016 Ratio Region gainst gainst 4/4, long- 4/4, long- 1/4 2/4 3/4 Total 1/4 2/4 3/4 Nov. 1/4 2/4 3/4 Total 1/4 2/4 3/4 Nov. Nov. term Nov. term average average 29.7 65.6 23.2 47.5 Seoul 16.1 20.2 35.2 101.2 15.8 20.7 19.6 0.9 4.3 10.6 7.9 46.0 10.5 12.6 14.1 1.3 88.9 (-26.3) 43.4 (33.4) 6.2 19.2 10.9 13.4 Incheon 3.9 5.6 14.9 30.6 3.0 3.7 8.1 0.9 2.1 7.3 7.0 27.2 1.1 2.5 6.6 0.9 26.1 (-26.5) 27.0 (-45.9) 78.4 209.2 67.4 159.8 Gyeonggi 41.2 73.5 83.8 276.9 60.4 60.5 43.8 1.5 18.0 62.5 51.1 199.0 26.2 46.3 47.8 1.5 239.7 (-12.7) 192.0 (-10.2) 12.2 30.9 6.8 16.7 Busan 5.4 6.2 9.7 33.5 8.9 12.3 6.6 1.1 1.6 8.9 6.8 24.2 3.2 6.0 3.9 0.7 30.2 (31.5) 22.2 (-25.2) 14.0 20.9 4.0 13.2 Daegu 2.4 5.5 27.1 27.1 2.5 10.4 5.0 1.5 1.8 5.7 4.5 16.0 4.9 3.4 2.8 0.8 19.5 (25.8) 14.6 (1.7) 4.6 20.0 2.0 11.5 Gwangju 1.2 2.7 14.7 14.7 3.1 7.2 4.3 1.6 1.8 2.7 1.9 8.4 2.1 4.3 3.8 1.1 12.4 (81.3) 7.2 (51.4) 2.0 11.3 2.8 8.0 Daejeon 3.1 1.3 1.7 8.0 1.4 3.5 1.9 1.3 0.4 5.5 0.8 9.4 0.7 1.9 2.8 1.0 7.8 (7.1) 9.4 (-24.3) 4.1 15.3 6.7 7.2 Ulsan 1.3 2.6 4.5 12.5 5.4 2.8 5.3 1.5 2.1 1.8 0.4 11.1 0.2 3.4 0.2 0.8 11.9 (43.1) 10.7 (-5.5) Region Construction commencement Construction completion 35.0 72.8 16.2 72.2 Seoul 13.2 19.6 28.9 96.8 16.7 21.8 18.5 1.1 12.6 16.8 19.1 67.8 19.3 20.8 22.1 1.2 84.7 (-14.0) 58.9 (22.7) 12.1 14.2 3.2 13.3 Incheon 4.3 6.3 8.3 31.0 2.6 2.7 5.4 0.8 2.6 7.1 3.8 19.7 1.8 6.8 2.7 0.7 26.1 (-45.5) 15.1 (-11.4) 92.8 201.5 37.8 138.1 Gyeonggi 35.6 67.2 60.6 256.1 35.5 65.6 60.2 1.5 20.1 25.6 38.9 122.3 25.6 47.0 36.7 1.4 229.4 (-12.2) 102.1 (35.2) 5.9 30.2 9.7 17.0 Busan 5.5 10.4 8.5 30.3 8.7 9.8 5.5 1.1 5.8 6.3 3.1 25.0 2.9 3.8 7.3 0.7 28.8 (4.8) 21.3 (-20.1) 7.3 13.6 8.7 27.2 Daegu 3.2 3.5 4.6 18.6 5.0 3.4 3.6 1.0 5.3 2.6 4.8 21.4 6.7 10.4 5.0 2.2 15.5 (-12.5) 15.8 (72.1) 3.3 14.8 1.3 11.3 Gwangju 1.6 1.9 3.4 10.2 3.1 5.6 3.3 1.1 3.8 0.6 2.9 8.6 2.5 5.4 2.9 1.2 8.5 (73.9) 7.8 (44.1) 4.1 7.9 2.2 7.8 Daejeon 2.1 3.2 0.8 10.2 3.1 0.8 1.2 0.9 1.1 3.4 0.7 7.4 1.6 3.2 2.2 0.9 9.4 (-15.4) 7.2 (8.0) 7.0 12.9 3.0 5.8 Ulsan 2.5 2.8 1.7 14.0 1.3 4.0 1.7 1.3 3.6 3.6 2.4 12.7 0.6 3.4 1.1 0.8 13.7 (-6.0) 10.5 (-44.7) Note: 1) Indicated under “November” is the cumulative total from Jan. to Nov. 2) ‌Note: “Long-term average” refers to the cumulative total at the end of November each year from 2011 to 2016, and the number in brackets is the rate of YOY change Source: MOLIT

37 2) Trends in Unclaimed Supply

Unclaimed Supply The number of unsold homes from the pre-construction parceling- out sales (unclaimed supply) in 2016, as of November, was 58,00, which was a 15.8% YOY increase (50,000 units as of Nov. 2015).

○ It decreased by 31.4% in the capital are (18,000), while it increased in the five metropolitans and the provinces outside the capital area by 33.4% and 72.6%. - As for the capital area, the unclaimed supply increased in Seoul, whereas a 34.0% YOY decrease was observed in the Gyeonggi area (14,000), which ultimately contributed to the decline in the unclaimed supply in the capital area.

○ By size The unclaimed supply of homes that are smaller than 60㎡ increased in the capital area, five metropolitan cities and provinces outside the capital area. Of particular note, the highest increase was observed in the five metropolitan cities, which recorded a 193.3% YOY increase.

Table 1-10 Trends in unclaimed supply

(Unit: thousand units) 2015 2016 Region 4/4 1/4 2/4 3/4 1/4 2/4 3/4 Nov. Nov. Nationwide 28.9 34.1 32.5 61.5 49.7 53.8 60.0 60.7 57.6 (15.8) Capital area 14.2 16.1 14.5 30.6 26.6 23.3 23.3 19.0 18.2 (-31.4) Seoul 1.1 0.6 0.3 0.5 0.2 0.8 0.4 0.3 0.3 (11.2) Incheon 2.8 2.5 2.8 4.2 2.8 3.5 3.2 2.4 3.6 (27.4) Gyeonggi 10.3 12.9 11.5 25.9 21.8 19.0 19.7 16.3 14.4 (-34.0) Five metropolitan cities 2.6 2.1 2.5 6.1 2.9 5.2 5.7 5.1 3.9 (33.4) Busan 1.3 1.0 1.3 1.3 1.1 1.3 1.6 1.3 1.1 (3.4) Daegu 0.5 0.0 0.1 2.4 0.1 1.8 1.2 1.4 1.0 (757.9) Gwangju 0.1 0.2 0.3 0.7 0.3 0.8 1.1 1.1 0.7 (131.9) Daejeon 0.5 0.8 0.8 1.2 1.1 0.8 0.8 0.7 0.6 (-43.2) Ulsan 0.3 0.1 0.1 0.4 0.4 0.6 1.0 0.6 0.5 (39.5) Provinces 12.1 15.9 15.5 24.8 20.2 25.4 31.0 36.6 34.9 (72.6)

Note: The number in brackets is the YOY rate of change Source: MOLIT

38 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 1 Market Trends

- ‌In the provinces outside the capital area, there was a 175.8% YOY increase in the unclaimed supply of homes that are larger than 85㎡.

Unclaimed Supply After Construction Completion In 2016, as of November, the number of homes that remained unsold even after the construction projects were complete was 10,2000, which was a 2.9% YOY decrease (10,500 units in 2015 as of November).

○ It decreased in the five metropolitan cities (700, -36.5%) and the capital area (5,100, -22.5%), and increased in the provinces outside the capital area (4,400, 55.3%). - In the capital area, the unclaimed supply decreased in Seoul, Incheon and Gyeonggi by 30.3%, 65.3% and 20.4%, respectively. - Among the five metropolitan cities, the unclaimed supply dropped most significantly in Ulsan, with only 14 unclaimed homes after construction completion. In Daegu, there has been no unclaimed supply after construction completion since Sept. 2015.

Table 1-11 Trends in unclaimed supply after construction completion

(Unit: thousand units) 2015 2016 Region 4/4 1/4 2/4 3/4 1/4 2/4 3/4 Nov. Nov. Nationwide 13.5 12.6 11.5 10.5 10.5 10.5 10.8 10.7 10.2 (-2.9) Capital area 8.7 15.7 7.5 6.6 6.6 7.0 6.5 5.9 5.1 (-22.5) Seoul 0.1 0.2 0.1 0.2 0.2 0.2 0.1 0.1 0.1 (-30.3) Incheon 2.1 4.3 2.2 2.0 4.3 2.0 1.9 1.5 1.5 (-65.3) Gyeonggi 6.4 11.1 5.3 4.4 4.4 4.9 4.5 4.3 3.5 (-20.4) Five metropolitan cities 1.3 2.1 0.9 0.9 1.0 0.6 0.6 0.7 0.7 (-36.5) Busan 0.6 0.8 0.3 0.5 0.6 0.2 0.2 0.2 0.3 (-54.6) Daegu 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 - Gwangju 0.1 0.4 0.2 0.2 0.2 0.2 0.2 0.2 0.2 (-7.3) Daejeon 0.4 0.7 0.3 0.2 0.2 0.1 0.2 0.2 0.2 (-3.2) Ulsan 0.1 0.1 0.1 0.1 0.1 0.1 0.0 0.0 0.0 (-76.7) Provinces 3.6 6.9 3.1 3.0 2.8 2.9 3.6 4.1 4.4 (55.3)

Note: The number in brackets is the YOY rate of change Source: MOLIT

39 Comparative Analysis of Move-in Ready Homes (Construction Set to Be Completed)

Calculation Method There are two ways to calculate the number of move-in ready homes: 1) Estimation of the number of homes expected to be built based on the number of homes, for which the construction projects have begun, using a simple equation; and 2) Estimation of the number of homes expected to be built by examining the expected completion dates for housing construction projects.

○ Simple estimation In the case of non-apartments such as detached homes, multi-housing and row houses, it typically takes about a year for the construction work to finish, whereas it takes about 3 years for large apartment complexes to be built. Thus, an estimation can be made up to 3 years.

○ Research-based estimation The estimation method involving an examination of the expected completion date is highly reliable in the short term, but it is accompanied by high variability resulting from external environmental factors in the short term. Thus, there is no significant difference compared to the aforementioned method. In principle, there is a high probability that the estimated number of move-in ready homes is lower than the actual number of move-in ready homes because the former excludes the housing supply assigned to the reconstruction association members.3)

A Comparison with the Number of Move-in Ready Apartments The result of the simple estimation (345,000 in 2017 and 497,000 in 2018) is 59,000 higher than the result of the research-based estimation (362,000 in 2017 and 421,000 in 2018), which is about a 30,000- unit difference in a year.

3) Real Estate 114 examines the number of move-in ready apartments, based on the number of housing units and scheduled construction completion date indicated on the pre-construction parceling-out sales notice posted on Apt2you (subscription application site) run by the Korea Financial Telecommunications and Clearings Institute (KFTC). The number of homes for which the construction is set to be completed is aggregated based on the expected construction completion date, provided that the actual construction completion of the apartment complexes that are set to be built in the offing (within 4 months of the investigation) by the scheduled date is examined in order to make the necessary corrections. However, only the homes sold through the ordinary pre-construction parceling-sales are included on Apt2you (excludes the number of homes assigned to the reconstruction association members) and this accounts for only part of the total number of newly supplied homes.

40 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 1 Market Trends

- Although there are no significant differences in the results between the estimation methods, in the case of Gyeonggi, there is a marked difference in the results by year and estimation method, with the total move-in ready homes estimated for 2017 and 2018 to be 6,800 units higher using the simple estimation method than the research-based method.

Figure 1-28 A comparison of move-in ready homes

of move-in ready homes in 2017 of move-in ready homes in 2018

(ten thousand units)(ten thousand units) (ten thousand units)(ten thousand units) 14 14 25 25 12 12 20 20 10 10 8 8 15 15 6 6 10 10 4 4 5 5 2 2 0 0 0 0 Jeju Jeju Jeju Jeju Ulsan Ulsan Seoul Seoul Ulsan Ulsan Seoul Seoul Busan Busan Busan Busan Daegu Daegu Daegu Daegu Sejong Sejong Sejong Sejong Incheon Incheon Incheon Incheon Daejeon Daejeon Daejeon Daejeon Jeonbuk Jeonbuk Jeonbuk Jeonbuk Gwangju Gwangju Gwangju Gwangju Gyeonggi Gyeonggi Jeonnam Jeonnam Gyeonggi Gyeonggi Jeonnam Jeonnam Gangwon Gangwon Gangwon Gangwon Chungbuk Chungbuk Chungbuk Chungbuk Chungnam Chungnam Chungnam Chungnam Gyeongbuk Gyeongbuk Gyeongbuk Gyeongbuk Gyeongnam Gyeongnam Gyeongnam Gyeongnam Research-based estimationResearch-basedSimple estimation estimationSimple estimation Research-based estimationResearch-basedSimple estimation estimationSimple estimation

41 Land Market

Lee Seokhee

Long-term National Trends

1) Trends in Land Value Index

Trends in Land Value index has been rising overall. More specifically, the real land value index had decreased until 2013 and began showing an uptrend since 2014.

○ The land value index recorded 95.19 in Jan. 2011, and showed steadiness* between 2012 and 2013; however, the land value has been rising steadily to 105.11 by Nov. 2016, which is a 6.9% increase from Jan. 2014. * ‌The monthly average rate of change in land value was 0.090% between 2011 and 2013 and 0.177% between 2014 and 2015, while it recorded 0.049% between Aug. 2012 and Jan. 2013, characterized by a steady trend, and 0.032% between July and Sept. 2013.

Figure 1-29 Land value index and rate of Figure 1-30 Land value index and real land change in land value value index

(%) 110 105 0.3 105 100 0.2

95 0.1 100

90 0.0 95 (Land value index 2014.11=100) (Land value index : 2014.11=100) (Real land value index: 2014.11=100) 85 -0.1 90 2011 2012 2013 2014 2015 2016 2011 2012 2013 2014 2015 2016 Rate of change in land value Land value index Real land value index Land value index Source: MOLIT Source: MOLIT

42 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 1 Market Trends

○ The real land value, which takes into account the consumer price index, has been on a continuous decline since Jan. 2011, and recorded the lowest point in Aug. 2011 at 99.34. Since 2014, however, it has been rising steadily (Nov. 2014=100).

○ It reached 102.73 in Nov. 2016, which was the highest point in 5 years (101.37 in Jan. 2011), and this was a 1.48% increase from Jan. 2014.

Real Land Value Index by Zoning Following the financial crisis, the real land value index of commercial zones exhibited the sharpest decline, and has been recovering slowly since then, whereas the land value of residential zones has been rising the most quickly.

○ The real land value index of residential zones has been rising most rapidly since 2014*, and this is deemed to be due to the relatively surge in housing prices resulting from the lowered benchmark interest rate and the fact that houses are increasingly perceived as real estate property that yields a return. * ‌The real land value index of residential zones plunged from 101.37 in Jan. 2011, and has since recovered most quickly out of all other zones. It recorded 103.45 in Nov. 2016, which was the highest real land value index compared to other zones.

○ The real land value index of commercial zones has dipped most markedly, and has been recovering at a slow rate*, and this is deemed to be due to the prolonged economic recession. * ‌It dropped most significantly from 102.17 in Jan. 2011 to 100 in Nov. 2014, and has been making a sluggish recovery to 102.12 as of Nov. 2016.

○ The level of increase in the real land value index is the lowest for industrial zones (101.50 in Nov. 2011), and this is attributed to the slowdown in the manufacturing industry caused by the economic recession.

○ The real land value index of the planned management zone remained steady from Jan. 2011 (99.71) until 2014; however, it rapidly increased compared to commercial, industrial and green zones since 2015 and recorded 102.83 in Nov. 2016.

43 Figure 1-31 Real land value indices of major zoning types

110044 110044 110033 110033 110022 110022 110011 110011 110000 110000 9999 9999 9988 (Real(Real landland valuevalue indexindex :: 2014.11=100)2014.11=100) 9988 (Real(Real landland valuevalue indexindex :: 2014.11=100)2014.11=100) 9977 9977 22001111 22001122 22001133 22001144 22001155 22001166 22001111 22001122 22001133 22001144 22001155 22001166 ResidentialResidential zonezone CommercialCommercial zonezone IndustrialIndustrial zonezone PlannedPlanned managementmanagement zonezone GreenGreen zonezone

Source: MOLIT

2) Trends in Land Transaction Volume

The land transaction volume (based on total number of lots) has generally been on the rise since 2011, with an increase in transactions centering on the cities outside the capital area after 2011 and on the capital after 2013.

○ The land transaction volume (lot-based) has increased steadily since 2013; however, it remained steady after reaching its peak in April 2015 (292,000 transactions), and recorded 274,000 in Nov. 2016. The land transaction volume calculated based on the total land area subject to the transactions has remained steady.

○ The land-only transaction volume calculated based on total number of lots and total land area has remained steady for 5 years. In terms of the total land area, there have been transactions of around 150k㎡ of land in total, on average, a month.

○ The ratio of land transactions (lot-based) in provinces outside the capital area was around 40~50% between 2011 and 2015, but it became to decline in the second half of 2016 and dropped to 38.6% in Nov. 2016, which is lower than that of the capital area. * ‌In the capital area, the ratio of land transactions has continually risen since 2013 and has been around 40% from 2015 onwards.

○ As for the ratio of land-only transaction s by region, it has remained steady without no significant changes.

44 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 1 Market Trends

Figure 1-32 Land transaction volume (3MA) Figure 1-33 Land-only transaction volume (3MA)

300,000 200,000

200,000 100,000 100,000

0 0 2011 2012 2013 2014 2015 2016 2011 2012 2013 2014 2015 2016 Land transaction volume (number of lots) Land transaction volume (number of lots) Land transaction volume (area: 1,000㎡) Land transaction volume (area: 1,000㎡) Source: MOLIT Source: MOLIT

Figure 1-34 Ratio of land transaction volume Figure 1-35 Ratio of land-only transaction by region (lot-based) volume by region (lot-based)

60% 100%

80% 40% 60%

40% 20% 20%

0% 0% 2011 2012 2013 2014 2015 2016 2011 2012 2013 2014 2015 Capital area Other metropolitan cities Capital area Other metropolitan cities Other provinces Other provinces Source: MOLIT Source: MOLIT

The land transactions (lot-based) by foreigners have generally been on the rise, and have doubled from 2010 to 2014. On the other hand, the land transactions involving foreigners in Jeju-do have been declining since 2015.

○ While the land transactions (lot-based) involving foreigners have been increasingly steadily since 2010, and the transaction volume surged in all regions starting in 2014*. * ‌The monthly average land transactions involving foreigners from 2010 to 2013 increased steadily from 732 to 865, but it skyrocketed to 1,286 in 2014, 1,645 in 2015 and 1,747 in 2016.

○ In Jeju, the land transactions involving foreigners have been continually rising since 2010 after the introduction of the real estate investment-based immigration scheme; however, the monthly average transaction volume has been declining after reaching its peak in 2014, and recorded 107 in 2016.

45 * ‌The monthly average transaction volume in Jeju was 21 in 2010, but it rose to 63 in 2012 and 155 in 2014, and dropped to 142 in 2015 and 107 in 2016.

Figure 1-36 Land transactions involving Figure 1-37 Land transactions involving foreigners (lot-based) foreigners (area-based)

2,500 2,000

2,000 1,500 1,500 1,000 1,000 500 500 0 0 01040710010407100104071001040710010407100104071001040710 01040710010407100104071001040710010407100104071001040710 2010 2011 2012 2013 2014 2015 2016 2010 2011 2012 2013 2014 2015 2016 Seoul IncheonㆍGyeonggi Seoul IncheonㆍGyeonggi Other metropolitan cities Other provinces Other metropolitan cities Other provinces Note: 3-month moving average (3MA) Note: 3-month moving average (3MA) Source: MOLIT Source: MOLIT

Figure 1-38 Land transactions involving Figure 1-39 Land transactions involving foreigners (lot-based) in Jeju foreigners (area-based) in Jeju

400 600 500 300 400 200 300 200 100 100 0 0 01 04 07 10 01 04 07 10 01 04 07 10 01 04 07 10 01 04 07 10 01 04 07 10 01 04 07 10 01 04 07 10 01 04 07 10 01 04 07 10 01 04 07 10 01 04 07 10 01 04 07 10 01 04 07 10 2010 2011 2012 2013 2014 2015 2016 2010 2011 2012 2013 2014 2015 2016 Land only Complex real estate property Land only Complex real estate property Note: 3-month moving average (3MA) Note: 3-month moving average (3MA) Source: MOLIT Source: MOLIT

46 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 1 Market Trends

Trends in Land Market by Region

1) Trends in Land Value

Trends in Land Value rose in 2014, 2015 and 2016, at a growing rate of increase. Of particular note, the rate of increase in land value has been the highest in Jeju.

○ In 2016, the rate of increase in land value was higher in non-capital areas than the capital area and in the metropolitan cities outside the capital area than the provincial area. However, the rate of increase in land value in metropolitan cities outside the capital area was slightly lower compared to the previous years. * ‌In the case of the capital area, the rate of increase was around 1.6% (Incheon)~2.7% (Seoul), whereas it was around 1.8% (Chungnam)~ 7.9% (Jeju) in the case of the provincial areas, and 2.2% (Ulsan)~3.8% (Busan) in the case of metropolitan cities (excl. Sejong). * ‌In Daegu, Incheon, Gwangju, and Ulsan, the rate of increase in land value decreased from 2015 to 2016.

Figure 1- 40 Rate of change in land value by city and province % % % % % 5% % % . 8 . 7 . 3 . 0 . 3 . 6 % % % % % % % 3 3 4 4 4 % %

5% % 3 . 2 % % %

4% % % % . 8 . 7 . 0 . 3 . 3 . 9 3 . 6 . 7 . 7 % . 7 % % % . 6 % 3 3 4 4 4 % . 5 % 2 % 3 . 5 . 5 . 2 . 4 2 2 % 2 % % % %

4% % % % 2 . 2 % 2 . 2 . 1 % 2 2 . 9 3 2

3% . 0 . 7 . 7 % . 7 % % . 6 % 2 . 5 . 8 2 . 8 2 2 . 5 . 5 % . 4 . 7 2 2 2 . 6 % 2 % % . 6 % 2 . 2 % 2 1 1 . 2 . 1 % 2 2 2 1

3% . 0 1 1 . 2 2 . 1 . 8 2 . 8 2

2% % . 7 2 . 6 % . 6 1 1 1 1 1 1 1 . 2 2% . 1 1

1% 1 1% 0% 0% Nationwide Seoul Busan Daegu Incheon Gwangju Daejeon Ulsan Jeju Nationwide Seoul Busan Daegu Incheon Gwangju Daejeon Ulsan Jeju 2014 (Dec. 2013~Nov. 2014) 2015 (Dec. 2014~Nov. 2015) 2016 (Dec. 2015~Nov. 2016) 2014 (Dec. 2013~Nov. 2014) 2015 (Dec. 2014~Nov. 2015) 2016 (Dec. 2015~Nov. 2016) 7.9% 7.9% 5.4% 5.4% 3.3% 3.3% % % % % % % % 3% % % % % % % % % % % % % % . 4 . 7 . 5 . 3 . 3 . 3 . 2 3% . 2 % % % % % % % . 1 . 0 % . 0 2 2 2 2 2 2 2 . 4 . 5 . 3 . 7 . 3 . 3 . 0 2 % . 2 . 2 % % % % % % 2 % 2 . 1 . 0 2 . 8 % . 8 . 0 . 8 2 2 2 2 2 2 . 7 2 2 . 0 2 % % % % . 6 % 2 2 % 1 . 6 . 6 2 1 . 8 1 % . 8 1 . 5 . 8 . 7 2 % . 4 %

2% 1 . 6 % 1 1 1 . 6 . 6 . 3 1 1 1 1 . 5 . 3 % 1 . 4

2% 1 . 1 1 1 1 . 3 1 1 . 3 1 1 . 1 1 1 1% 1 1%

0% 0% GyeonggiGyeonggi GangwonGangwon ChungbukChungbuk ChungnamChungnam JeonbukJeonbuk JeonnamJeonnam GyeongbukGyeongbuk GyeongnamGyeongnam JejuJeju 20142014 (Dec.(Dec. 2013~Nov.2013~Nov. 2014)2014) 20152015 (Dec.(Dec. 2014~Nov.2014~Nov. 2015)2015) 20162016 (Dec.(Dec. 2015~Nov.2015~Nov. 2016)2016) Source: MOLIT

47 ○ Sejong and Jeju recorded the highest rate of increase in land value in the nation; Sejong has been recording 4% for 3 consecutive years, while it has been rising persistently in Jeju. * The rate of increase in land value in Sejong was 4.3% in 2014, 4.0% in 2015, and 4.3% in 2016. * The rate of increase was the highest in Jeju in 2016 at 7.9%, which was triple the national average.

2) Zoning-based4) Trends5) in Rate of Change

Nationwide Trends The rate of increase in land value by zoning in the past 3 years has generally been rising nationwide, but the rate of increase in the land value of industrial zones was at its lowest in 3 years in 2016.

○ The rate of increase in the land value of residential zones was the highest among the zoning types, and it reached its peak (2.84%) in 2016.

○ The land value of commercial zones increased by 2.25%, which was largely attributable to the increase observed in the non-capital area (3.09%) rather than the capital area (1.85%).

○ The land value of industrial zones rose by 1.57%; it increased by 1.40% in the non-capital area, which was the lowest rate of increase.

○ The land value of planned management zones increased by 2.54%, with the lowest rate of increase observed in the capital area.

4) Land use zonings are divided into urban zone, management zone, agricultural zone and natural reserve zone, and the urban zone is further subdivided into residential zone, commercial zone and industrial zone, while the management zone is subdivided into planned management zone, production green zone, and natural reserve zone. 5) In this case, the trends in the land value index are analyzed centering on the urban zone (residential, commercial industrial, and green zones) and the planned management zone, which is managed under similar regulations as the urban zone.

48 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 1 Market Trends

Figure 1- 41 Rate of change in land value by reigon and zoning

4.0% Nationwide Metropolitan cities Capital area Rural areas

3.0%

2.0%

1.0%

0.0% Residential zone Commercial zone Industrial zone Green zone Planned management zone

Table 1-12 Rate of change in land value by region and zoning

(Unit: %) Planned Residential zone Commercial zone Industrial zone Green zone Region management zone 2014 2015 2016 2014 2015 2016 2014 2015 2016 2014 2015 2016 2014 2015 2016 Nationwide 2.21 2.49 2.84 1.34 1.77 2.25 1.74 1.91 1.57 1.24 1.83 2.12 1.86 2.04 2.54 Metropolitan 2.52 2.75 3.04 1.67 1.98 2.55 1.91 2.09 1.74 1.51 2.12 2.06 2.49 1.98 2.45 cities Capital area 2.20 2.35 2.66 1.37 1.41 1.85 1.71 2.01 1.74 0.87 1.34 1.68 1.47 1.70 2.28 Rural areas 2.27 2.92 3.35 1.27 2.54 3.09 1.77 1.81 1.40 1.75 2.43 2.68 2.21 2.33 2.75

Note: The data are based on the YOY rate of change determined at the end of November Source: MOLIT

The rate of increase in land value in 2016 was very high in Jeju, Sejong, and Busan.

○ In the case of residential zones, the rate of increase in land value was the highest in Jeju (7.86%), followed by Sejong (4.28%), Busan (4.16%), Daegu (4.07%), Daejeon (3.94%), and Gangwon (3.55%), and it was the lowest in Incheon (2.01%) and Chungbuk (2.19%).

○ As for commercial zones, the rate of increase in land value was the highest in Jeju (5.98%), followed by Sejong (5.16%), Busan (5.13%), Daegu (3.21%), Daejeon (2.49%), and Gyeongnam (2.43%), and it was the lowest in Chungbuk (1.02%) and Incheon (1.24%).

○ As for industrial zones, the rate of increase in land value was the highest in Jeju (3.37%), Sejong (3.15%), Gwangju (2.58%), Seoul (2.05%), and Chungbuk (1.94%), and it was the lowest in Jeonbuk (0.19%), Ulsan (0.63%), Gyeongbuk (0.97%), and Gyeongnam (1.27%).

49 ○ In the case of green zone, the rate of increase in land value was the highest in Jeju (7.74%), followed by Sejong (3.88%), Daegu (3.31%), and Jeonnam (3.06%).

○ The rate of increase in land value of planned management zones was the highest in Jeju (9.56%), followed by Sejong (4.54%), Ulsan (2.77%), and Gangwon (2.72%).

Table 1-13 Rate of change in land value by region and zoning

(Unit: %) Planned Residential zone Commercial zone Industrial zone Green zone Region management zone 2014 2015 2016 2014 2015 2016 2014 2015 2016 2014 2015 2016 2014 2015 2016 Seoul 2.76 2.72 2.93 1.84 1.60 2.20 1.61 1.98 2.05 0.90 1.22 1.70 - - - Busan 2.21 2.82 4.16 1.91 3.29 5.13 1.94 1.82 1.68 1.67 2.79 2.81 - - - Daegu 3.09 3.98 4.07 1.62 2.83 3.21 4.18 3.64 1.93 2.68 3.50 3.31 - - - Incheon 1.17 2.19 2.01 1.16 1.45 1.24 1.99 2.14 1.55 0.75 1.21 0.92 1.12 0.93 1.26 Gwangju 1.20 2.70 2.45 0.24 1.73 2.25 1.49 2.35 2.58 1.36 2.95 2.97 0.25 2.14 2.37 Daejeon 1.68 2.98 3.94 0.85 1.90 2.49 1.82 2.51 1.91 2.21 2.73 1.93 -0.16 1.82 2.53 Ulsan 2.57 2.74 2.86 0.83 2.11 2.23 0.89 1.33 0.63 1.12 1.87 1.99 0.68 2.44 2.77 Sejong 2.90 2.08 4.28 0.91 10.07 5.16 0.51 1.30 3.15 7.31 3.97 3.88 5.75 3.87 4.54 Gyeonggi 1.47 1.77 2.33 0.43 0.96 1.26 1.62 1.95 1.61 0.87 1.37 1.78 1.49 1.75 2.34 Gangwon 1.86 3.12 3.55 0.63 1.85 2.23 0.51 1.27 1.31 2.02 2.56 2.71 2.30 2.47 2.72 Chungbuk 1.99 2.30 2.19 0.34 1.21 1.02 1.36 2.07 1.94 1.13 1.52 1.48 2.43 2.01 2.28 Chungnam 2.57 2.04 2.30 0.29 0.60 1.85 1.25 1.18 1.49 0.58 1.20 1.56 1.33 1.36 1.79 Jeonbuk 1.77 2.66 2.41 0.58 2.08 2.00 0.79 1.25 0.19 1.71 2.06 1.97 1.65 1.77 2.02 Jeonnam 1.99 3.58 2.84 1.23 2.91 2.04 1.37 0.96 1.33 1.66 3.13 3.06 1.82 2.24 2.51 Gyeong- 2.80 2.82 2.89 1.53 1.85 1.99 2.19 2.07 0.97 1.49 2.11 2.37 2.64 2.54 2.58 buk Gyeo- 2.11 2.36 2.71 1.83 1.99 2.43 1.58 1.69 1.27 1.48 1.72 2.10 2.07 2.24 2.13 ngnam Jeju 3.45 5.78 7.86 1.27 3.99 5.98 1.47 2.09 3.37 3.57 5.25 7.74 4.39 6.53 9.56

Note: The data are based on the YOY rate of change determined at the end of November Source: MOLIT

50 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 1 Market Trends

Busan The rate of increase in the land value of residential and commercial zones in Busan was the highest among the metropolitan cities, with a sharp increase observed every year.

○ The rate of increase in the land value of residential zones was 4.16% (2016), with the level of increase on a steady rise from 2014 (2.21%) and 2015 (2.82%).

○ The rate of increase in the land value of commercial zones was 5.13% (2016), with the level of increase on a steady rise from 2014 (1.91%) and 2015 (3.29%).

○ The rate of increase in the land value of industrial zones was 1.68% (2016), with the level of increase continually contracting over the years.

Daegu In the case of Daegu, which recorded the highest level of increase in land value in 2015, the rate of increase land value of residential and commercial zones rose slightly, but the rate of increase in the land value of industrial zones contracted.

○ The rate of increase in the land value of residential zones and commercial zones were high at 4.07% and 3.21%, respectively.

○ The rate of increase in the land value of industrial zones was 1.93%, which was lower compared to 2014 (4.18%) and 2015 (3.64%).

Ulsan The rate of increase in land value in Ulsan has been on a steady rise overall in Ulsan, but due to the downturn of the local industry, the rate of increase in the land value of industrial zones fell sharply.

○ The rate of increase in land value by zoning in 2016 was around the national average at 2.86% (9th in the country) for residential zones, 2.23% (8th) for commercial zones, 1.99% (10th) for green zones, and 2.77% (3rd) for planned management zones.

○ In contrast, the rate of increase in the land value of industrial zones was recorded at 0.63%, which was very low. This was caused by the recession in the local economy* caused by the slowdown in the shipbuilding and marine industries, which are the primary industries of Ulsan.

51 * ‌Land value has been continually declining in the past 3 years in Dong-gu, Ulsan, where Hyundai Heavy Industries is based, and the rate of decrease has been on the rise (△0.05% in 2014, △0.08% in 2015, △1.13% in 2016).

Sejong The rate of increase in land value in Sejong was very high, and it was the second highest in the country for all types of zoning, following Jeju.

○ The rate of increase in land value was 4.28% for residential zones, 3.15% for industrial zones, 3.88% for green zones and 4.54% for planned management zones.

○ The rate of increase in the land value of commercial zones was 5.16%, which is fairly high, but this was half the rate of increase reported for the previous year (2015), which was 10.07%. * It appears that the rate of increase dropped due to the mass supply of commercial shops in Sejong.

Jeju The rate of increase in land value in Jeju was the highest in the country for all types of zoning in 2016.

○ The rate of increase in the land value of residential zones has been the highest in the country since last year, and it rose even higher in 2016 to 7.86%.

○ The rate of increase in the land value of commercial and industrial zones rose steadily and reached 5.98% and 3.37%, respectively.

○ The rate of increase in the land value of planned management zones was 9.56%, which was around double that of Sejong (4.54%), which came in second place.

○ Overall, the rate of increase in land value rose continually in Jeju due to the growing pressure for land development for all zoning types. * ‌Persistent pressure for development arising from a growing number of foreign tourists visiting and investing in Jeju, investment in and move-ins in the English Education Village and Inno-City, and construction of a secondary airport.

52 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 1 Market Trends

Geoje Due to the recession in the shipbuilding and marine industries, which are the primary industries of Geoje, the land value of all zoning types falling under the “urban zone” has declined.

○ The rate of increase in land value has shifted toward a downtrend with the rate of increase in the land value being only △0.59% for residential zones, △1.05% for commercial zones, and △0.02% for green zones.

○ Of particular note, the land value of industrial zones, where the infrastructures of the manufacturing industry including the shipbuilding and marine industries are located, dropped sharply by 3.32%.

○ While the land value of green zones increased by 0.29%, this was the lowest increase in the country.

53 Figure 1-42 Rate of change in land vale by zoning type and region

Residential zone Residential zone Seoul 4% Busan 4%

2% 2% Planned Commercial Planned Commercial management zone management zone zone 0% zone 0%

Green zone Industrial zone Green zone Industrial zone

Residential zone Residential zone 4% 4% Daegu Incheon

2% 2% Planned Commercial Planned Commercial management zone management zone zone 0% zone 0%

Green zone Green zone Industrial zone Industrial zone

Residential zone Residential zone 4% 4% Gwangju Daejeon 2% 2% Planned Commercial Planned Commercial management zone management zone zone 0% zone 0%

Green zone Industrial zone Green zone Industrial zone

Residential zone Residential zone 4% 4% Ulsan Sejong Planned 2% 2% Commercial Planned management Commercial zone management zone zone 0% zone 0%

Green zone Industrial zone Green zone Industrial zone

Residential zone 10% 4% Residential zone 8% Jeju Geoje 2% 6% 0% Planned 4% Commercial Planned Commercial management - 2% zone management 2% zone zone zone - 0% 4%

Green zone Industrial zone Green zone Industrial zone

Source: MOLIT

2014 2015 2016 (Dec. 2013~Nov. 2014) (Dec. 2014~Nov. 2015) (Dec. 2015~Nov. 2016)

54 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 1 Market Trends

3) Trends6) in Land Transaction Volume by Region

Land Transaction Volume7) The land transaction volume reached its peak in 2015, and has generally been on the rise, albeit it has staggered slightly afterwards.

○ The land transaction volume has been rising centering on the capital area, and declining centering on the rural areas.

○ The land transaction volume in the capital area has steadily risen, with transactions of 1.15 million lots in 2016. The transaction volume has been on the rise in Seoul and Gyeonggi-do.

○ In the case of the rural areas, there were transactions of 1.56 million lots in 2016, which was slightly lower compared to the previous year. The number of land transactions declined in both the metropolitan cities (540,000 lots) and provinces (1.13 million lots) outside the capital area.

○ In particular, the land transaction volume also dropped in Sejong, while it showed a steady trend in Jeju for the first time in 5 years.

Figure 1-43 Cumulative land transaction volume from Jan. to Nov. by region (Unit: thousand lots) 2,000 1,000 1,713 1,569 1,343 1,500 1,393 678.0 1,100 1,150 500 420.0 496.4 1,000 696 334.3 663 239.4 175.7 500

0 0 Capital area Rural areas Seoul Gyeonggi

1,183 1,200 1,134 1,000 1,017 957 65.0 65.0 800 619 543 476 457 500 43.5 40.3 29.8 400 30.8 14.9 - - 0 Other metropolitan cities Other provinces Sejong Jeju 2011 2012 2013 2014 2015 2016 Note: Cumulative land transaction volume from Jan. to Nov. of the year concerned Source: MOLIT

6) In this chapter, the transaction volume is analyzed based on the number of lots sold because the number of lots sold better reflect the real life and real estate market situations. 7) The land transaction volume is determined based on the data on the land-only transactions and transactions involving both land and attached properties.

55 Land Only8) The land-only transaction volume reached its peak in 2014, and has been steady from 2015 to 2016.

○ The land-only transaction volume in Seoul National Capital Area has been on a steady rise since 2013, whereas it has been declining outside the capital area.

○ The land-only transaction volume has been continually rising in the capital area, with 250,000 transactions in total attributable to the sales of 17,000 lots in Seoul and 210,000 lots in Gyeonggi in 2016.

○ The land-only transaction volume declined in the non-capital area in both the metropolitan cities and provinces in 2016 compared to the previous year, with transactions of 89,000 lots in the metropolitan cities and 670,000 lots in the provinces for a total of 750,000 transactions.

○ The land-only transaction volume in Sejong has been on a steady decline since 2013, but there was a surge in 2016, with transactions of around 7,300 lots.

○ The land-only transaction volume in Jeju has been on a continuous rise, but it was somewhat stagnant in 2016, with transactions of around 41,000 lots.

Figure 1-44 Cumulative land-only transaction volume from Jan. to Nov. by region (Unit: thousand lots)

300 250 1,000 20 17.0 300 224 786 15.1 685 719 754 213.5 175 182 200 200 144.4 500 10 100 100

0 0 0 0 Capital area Rural areas Seoul Gyeonggi

150 1,000 10 60 7.0 7.3 43.3 97.8 625 701 677 41.4 89.2 594 5.7 100 74.9 40 70.8 24.7 500 5 19.4 50 2.2 20 - - - 0 0 Other metropolitan cities Other provinces Sejong Jeju

2011 2012 2013 2014 2015 2016

Note: Cumulative transaction volume from Jan. to Nov. in the year concerned Source: MOLIT

8) Analyzed solely based on land-only transactions among all types of land transactions.

56 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 1 Market Trends

Trends in Land Value Related to Major Policy Projects

1) KTX Station Area

Major KTX Station Areas The rate of increase in the land value of the KTX station areas in 2016 was high in regions, where there was aggressive development of areas adjacent to subway and train stations, and around the newly established Line.

○ In the case of (3.2%), Gimcheon Gumi Station (3.5%), (4.5%), and Singyeongju Station (4.1%), the level of increase in land value was higher compared to their respective mother cities, due to the development of the station area and the adjacent areas. * ‌Compared to the mother cities of other KTX station areas, the mother cities of the KTX station areas of Daegu and Gyeongbuk saw high levels of increase in land value, but the level of increase in the land value of the station areas was higher. ** ‌The increase in land value in the station areas was deemed attributable to the development of the station area for Osong Station, the impact of the development of Gyeongbuk Inno-City for Gimcheon Gumi Station, the construction of Shinsegae Department Store and transfer center for Dongdaegu Station, and the plans to build a new access road for Singyeongju Station.

○ In the case of the newly established Gwangju Songjeong Station, the level of increase in the land value of the station area was higher than its mother city due to the opening of the station. * ‌KTX Honam Line was opened in April 2015, and it is deemed that the developmental potential and psychological effect (anticipation) have continued on ever since.

57 Figure 1-45 The land value change of major KTX station influence areas and mother cities rate in 2016

8% % % % % . 1 % % % . 5 % % 7 % . 1 % 6% . 6 % . 8 4 % % % % . 5 % . 7 4 . 2 . 2 3 3 . 0 % % % % % % . 7 3 % . 1 2 . 5 . 5 % 3 3 % . 0 . 7 3 . 2 2

4% 2 . 9 . 8 2 2 . 8 . 0 . 0 . 7 2 1 . 5 2 . 3 . 2 1 1 1 2 2 1 1 1 2% 1 0%

Iksan Station Osong Station Jeongeup Station Dongdaegu Station Singyeongju Station Station Station Gimcheon Gumi Station

Capital area Chungcheong area area Honam area Gwangju Songjeong Station

KTX station area Mother city Note: 1) Calculated the rate of change in land value based on the sample lands within the KTX station area (1,000m radius) 2) Excluded station areas with a small number of samples 3) Cheonan Asan Station’s mother city was condered Asan, and Gimcheon Gumi Station’s mother city was considered Gimcheon. 4) Pioerd: Dec. 2015 ~ Nov. 2016 Source: KAB

2) Inno-City

In the case of the inno-cities in all regions, except for Daegu, Gimcheon and Jeju, the rate of change in land value in 2016 was slightly higher than that of their respective mother cities.

○ The rate of increase in the land value of the Daegu Inno-City was 2.3%, which was lower than that of Daegu. This is deemed to have been due to the relatively limited room for increase, as all of the enterprises have moved in by 2015.

○ Although the Jeju Inno-City recorded a very high rate of increase of land value at 7.0%, its mother city, Seogwipo, recorded an even higher rate of increase, thanks to the favorable conditions presented by the plans to build another airport in Jeju. This is deemed to have resulted in a relatively lower rate of increase in the land value of the Inno-City.

○ As for the inno-cities of all the small- and medium-sized cities, the rate of increase in land value was higher than that of their respective mother cities. Of particular note, land value in the Gangwon Inno-City (Wonju), Jeonbuk Inno-City (Jeonju and Wanju), Jeonnam Inno- City (Naju) and Gyeongnam Inno-City increased by 4.0%, 3.6%, 5.4% and 5.3%, respectively, which were the highest among all of the inno-cities nationwide.

58 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 1 Market Trends

* ‌It is deemed that the increase in land value resulting from these inno-cities was perceived high because the local economy of their respective mother cities were not very large.

Figure 1-46 Rate of change in the land value of inno-cities in 2016 % . 3 % 8

8% . 0 7 % % . 4 . 3 % 5

6% % 5 % . 8 . 4 % % 3 % 4 . 0 % % . 7 . 6 4 . 6 % % 3 % 3 3 % . 6 . 1 4% % . 9 % . 8 2 3 . 7 % . 6 % 2 . 5 2 % 2 2 . 3 % 2 . 2 . 0 2 . 8 2 . 7 2 1 2% 1

0% Busan Daegu Ulsan Gangwon Chungbuk Jeonbuk Jeonnam Gyeongbuk Gyeongnam Jeju (Wonju) (Jincheon, (Jeonju, (Naju) (Gimcheon) (Jinju) (Seogwipo) Eumseong) Wanju)

Inno-City Mother city 1 Mother city 2 (Chungbuk: Eumseong-gun Jeonbuk: Wanju-gun) Note: 1) Subsituted the inno-city name with the name of the region 2) Period: Dec. 2015~Nov. 2016 Source: KAB

3) Industrial Complex

Generally speaking, the rate of increase in the land value of industrial complexes was very low, and only the industrial complexes in Chungbuk recorded a very high rate of increase in the land value.

○ The rate of increase in the land value of the industrial complexes in Ulsan was the lowest in the country at 0.0%, as a result of the slump in the primary local industries including the shipbuilding and heavy industries, and it was considerably lower than the rate of increase in land value in the mother city (2.2%).

○ In the case of other metropolitan cities such as Busan, Daegu and Gwangju, the rate of increase in the land value of the industrial complexes in Busan, Daegu and Gwangju increased by 0.9%, 1.5%, and 2.4%, but the level of increase was lower compared to that of their respective mother cities.

59 ○ In the case of the industrial complexed in Chungbuk, the rate of increase in land price was very high at 9.3%, which was 5 times higher than that of its mother city. * ‌The rate of increase in land value was very high due to the construction of the Chungju Megapolis Industrial Complex and the designation as a foreign investment zone.

Figure 1-47 Rate of change in the land value of the regional industrial complexes an their respective mother cities % . 3 9 8%

6% % % . 8 . 6 3 % 3 % % % 4% % % % % . 7 % % . 5 % . 4 % . 3 . 3 2 % % . 2 . 2 . 2 2 % . 1 2 . 0 2 2 % % 2 . 8 2 . 8 2 2 . 6 % . 6 2 % . 5 1 1 . 3 % . 2 1 1 1 . 0 . 9

2% 1 1 % . 6 1 0 0 . 0 0 0% Busan Daegu Gwangju Ulsan Gyeonggi Gangwon Chungbuk Chungnam Chungnam Jeonnam Gyeongbuk Gyeongnam

Industrial complex their respective mother cities

Note: 1) “Industrial complexes” include national, general, urban high-tech, and agricultural-industrial complexes. 2) Regions without industrial complexes or a small sample size were excluded. 3) Period: Dec. 2015~Nov. 2016 Source: KAB

60 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 1 Market Trends

Commercial Real Estate Market

Woo Namkyo

Trends in Office Rent Market

1) Vacancy Rate and Rent

In the office building rental market, the rent and the rent price index in 2016 showed a continuous downtrend, due to the influence of the delayed recovery of business economy resulting from the slump in exports and the manufacturing sector. Vacancy climbed up slightly in the first half of the year, but it declined by a small margin in the second half.

○ The Rent was high in the capital area including Seoul and Gyeonggi, where there was high demand for rent by enterprises, while the rent price index dropped in Ulsan and Gyeongnam. As of the end of September in 2016, the rent of nationwide was KRW 14,700/㎡,

Table 1-14 Trends in office rentals by rent market (YOY rate of change, as of Sept. 2016) ju oul egu san san angju ejeon ngwon onbuk eonggi Je cheon ungbuk onnam ungnam tionwide Ul eongbuk Se Bu eongnam Da In Da Je Gw Je Gy Ga Ch Ch Na Gy Gy Vacancy 13.0 9.4 17.2 18.4 19.3 17.2 24.5 22.8 5.3 6.7 17.7 26.9 10.1 23.1 18.2 17.2 12.6 rate (%) Rent (thousand 14.7 20.4 7.9 7.4 9.3 6.1 5.0 7.9 11.2 6.7 4.5 7.3 4.2 5.3 7.3 6.5 4.7 KRW/㎡) Rate of change in -0.2 -0.2 0.2 -0.1 -0.2 -1.0 -0.2 -1.6 -0.3 -0.2 -0.2 0.0 0.8 -0.2 -0.5 -0.8 0.0 rent price index (%)

61 which was a 0.4% YOY decrease, and the rent price index fell by 0.2% compared to the same period in the previous year.

○ The vacancy rate showed an uptrend due to the unfavorable conditions at home and abroad including Brexit and the restructuring of the shipbuilding industry in the first half of 2016; however, it declined to a slight extent to 13.0%, as of the end of September in 2016.

○ In Seoul, the rent has been on a downtrend overall, due to the continuous supply of new office spaces, and the vacancy rate has been maintained at a relatively low level compared to the national average.

○ In the case of Ulsan and Gyeongnam, the rent has generally declined due to the impact of the local economic recession resulting from the restructuring of the shipbuilding and shipping industries, and the shrinking demand for office rentals has increased vacancy.

Figure 1- 48 The rent and rate of change in the rent price index of office building by region (compared to the end of the previous year)

20.4

14.7

11.2 9.3 7.9 7.4 7.9 7.3 7.3 6.1 6.7 6.5 5.0 5.3 4.5 4.2 4.7

0.8 0.2 0.0 0.0

-0.2 -0.2 -0.1 -0.2 -0.2 -0.3 -0.2 -0.2 -0.2 -0.5 -0.8 -1.0 -1.6 Nation Seoul Busan Daegu Incheon Gwangju Daejeon Ulsan Gyeonggi Gang Chungbuk Chung Jeonbuk Jeon Gyeong Gyeong Jeju -wide -won -nam -nam -buk -nam

Rent (thousand KRW/㎡) Rate of change in the rent price index(%)

62 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 1 Market Trends

Table 1-15 Trends in office rent price index and rate of change compared to the end of the previous year by region

Rate of Region `15.1Q `15.2Q `15.3Q `15.4Q `16.1Q `16.2Q `16.3Q change Nationwide 100.3 100.2 100.0 100.0 99.9 99.9 99.8 -0.2 Seoul 100.3 100.2 99.9 100.0 100.0 99.9 99.8 -0.2 Busan 100.1 100.0 100.0 100.0 100.0 100.0 100.2 0.2 Daegu 101.0 100.1 100.0 100.0 99.9 99.9 99.9 -0.1 Incheon 100.0 100.0 100.0 100.0 100.0 99.8 99.8 -0.2 Gwangju 100.9 100.7 100.6 100.0 99.1 98.8 99.0 -1.0 Daejeon 99.9 99.9 99.9 100.0 100.0 100.4 99.8 -0.2 Ulsan 100.3 100.1 100.0 100.0 99.8 99.3 98.4 -1.6 Gyeonggi 100.4 100.3 100.0 100.0 99.7 99.7 99.7 -0.3 Gangwon 100.1 100.1 102.9 100.0 100.0 99.8 99.8 -0.2 Chungbuk 100.7 100.5 100.0 100.0 100.0 99.8 99.8 -0.2 Chungnam 101.8 101.7 100.9 100.0 100.0 100.0 100.0 0.0 Jeonbuk 100.3 100.3 99.8 100.0 100.0 100.0 100.8 0.8 Jeonnam 100.7 100.4 100.3 100.0 100.0 99.9 99.8 -0.2 Gyeongbuk 100.3 100.0 100.0 100.0 99.6 99.6 99.5 -0.5 Gyeongnam 100.3 100.2 100.2 100.0 100.2 99.6 99.2 -0.8 Jeju 100.0 100.0 100.0 100.0 100.0 100.0 100.0 0.0

Note: 1) The index was calculated based on 2015, 4Q (=100), and negative changes are indicated in red 2) The quarterly index was calculated based on the last date of the last month of the quarter.

Seoul The vacancy rate is lower and the rent is higher than the national average. Vacancies became filled, with the drop in rent in the Dongdaemun and Chungmuro commercial districts, and in the commercial districts with a high vacancy rate such as the Gangnam- daero area of Gangnam. This contributed to the overall decline in the vacancy rate.

○ Central The vacancy rate in the central business district, centering on the Dongdaemun, Seoul Station and Chungmuro commercial districts, steadily declined to 10.3%, which was a 0.5%p drop compared to the end of last year. Most of the commercial districts in this particular area, except for Dongdaemun and Chungmuro, show a higher level of rent compared to the average rent in Seoul. The rent price index fell by 0.3% since the end of last year, with rent being around KRW 24,300/㎡.

○ Gangnam Vacancies along Gangnam-daero and in Seocho were reduced, and the overall vacancy rate in Gangnam fell to 9.0% (a 2.5%p decrease since the end of last year). While rent in the Gangnam-daero and Teheran-ro commercial districts is higher than the average

63 rent in Seoul, rent and the rent price index have remained on a downtrend. Rent has decreased by 0.9% since the end of last year to KRW 21,100/㎡, while the rent price index has dropped by 0.1% since the end of last year.

○ Yeouido and Mapo Vacancies in the Yeongdeungpo commercial sphere have climbed up, but vacancies have filled up in the Gongdeok Station and Yeouido commercial districts, resulting in an overall vacancy rate of 9.1% in the Yeouido and Mapo area. Rent increased by 0.3% since the end of last year to KRW 18,500/㎡, which is around the average in Seoul, while the rent price index has remained steady since the end of last year.

○ Others Although vacancies have been filled to some extent in the Hwagok commercial district, vacancies climbed up in Mok-dong and Sadang, causing the vacancy rate in the “other areas” of Seoul to be maintained at 9.5% after an increase at the beginning of 2016 (1.2%p increase since the end of last year). The rent level in most of the commercial districts, excluding Sadang, is below the average in Seoul. Amidst this trend, rent in the Jamsil and Hwagok commercial districts fell somewhat, and thus the rent price index in other areas dropped by 0.1% since the end of last year.

Table 1-16  Vacancy rate, rent and rent price index of office buildings in Seoul

Vacancy rate (%) Rent (thousand KRW/㎡) Rent price index Small-to- Small-to- Compared to the Large size medium Total Large size medium Total Index end of the size size previous year (%) Seoul average 9.5 9.3 9.4 24.7 17.7 20.4 99.8 -0.2 Central 7.8 12.9 10.3 29.2 18.9 24.3 99.7 -0.3 Gangnam 4.1 10.3 9.0 24.3 20.2 21.1 99.9 -0.1 Yeouido-Mapo 13.7 5.9 9.1 23.1 15.0 18.5 100.0 0.0 Others 23.3 6.6 9.5 13.5 14.2 14.1 99.9 -0.1

Note: 1) Reference point for the rent price index: 2015 4Q (=100) 2) Size standard: Large size = Gross floor area of over 33,058㎡ / Small-to-medium size = under 33,058㎡

64 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 1 Market Trends

2) Return on Investment

With the benchmark interest rate cut once again in June 2016 (from 1.50% to 1.25%) and the strengthened low interest rate trend, there has been continued interest in investing in commercial real estate. Accordingly, the asset value of commercial real estate has remained on an uptrend, with the return on investment (ROI) in the past year being 6.02% as of the end of Sept. 2016.

○ Seoul (6.76%), Busan (6.47%), and Gyeonggi (7.21%) recorded a relatively high ROI, whereas Chungbuk (1.54%), Gwangju (3.27%), and Daejeon (3.38%) recorded a low ROI. In particular, Jeju recorded the highest ROI of 9.51%, thanks to the thriving commercial real estate market and high anticipation for development.

○ In Seoul and Gyeonggi, the major trends were stable rental income and investment in small- to-medium-sized buildings due to low interest rates, and this resulted in a relatively high ROI. On the other hand, in Chungbuk, there was generally a lack of demand for rentals, which led to a high vacancy rate (26.9%) and the lowest ROI in the country.

Table 1-17 Annual ROI for office buildings by region

(Unit: %) ju oul egu san san angju ejeon ngwon onbuk eonggi Je cheon ungbuk onnam ungnam tionwide Ul eongbuk Se Bu eongnam Da In Da Je Gw Je Gy Ga Ch Ch Na Gy Gy Income 4.56 5.13 4.22 3.93 4.39 2.70 2.56 4.30 6.29 3.98 1.87 2.83 2.72 4.03 3.77 3.26 2.62 rate ROE 1.41 1.57 2.18 1.40 0.78 0.56 0.80 0.42 0.88 0.77 -0.32 1.63 1.35 0.58 1.01 1.82 6.76 ROI 6.02 6.76 6.47 5.37 5.20 3.27 3.38 4.73 7.21 4.77 1.54 4.50 4.09 4.63 4.80 5.12 9.51 Note: Rate of return was calculated based on the rate of return in the past year, as of the end of Sept. 2016

65 Figure 1- 49 ROI for office buildings by broad-area commercial spheres in Seoul

9.51

7.21 6.76 6.47 6.02 5.37 5.20 5.12 4.73 4.77 4.50 4.63 4.80 4.09 3.27 3.38

1.54

Nation Seoul Busan Daegu Incheon Gwan Daejeon Ulsan Gyeon Gang Chung Chung Jeon Jeon Gyeong Gyeong Jeju -wide -gju -ggi -won -buk -nam -buk -nam -buk -nam

Income rate (%) ROE (%) ROI (%)

Seoul The rate of return in Seoul is higher than the national average. The annual ROI, as of the end of Sept. 2016, was 6.76%, due to the increased interest in and investment into commercial real estate.

○ Central The rate of increase in asset value has declined relatively to other spheres, but the income rate was maintained at a high level, which resulted in the highest ROI of 7.10% among the four major areas in Seoul.

○ Gangnam Although the rent level has dropped slightly, the annual rate of return was 6.59%, due to the decline in vacancies and high rate of increase in asset value.

○ Yeouido-Mapo The steady demand for rent resulted in a stable rental income, and an annual ROI of 7.05% was observed.

○ Other areas Other areas recorded the lowest ROI in Seoul at 6.03%. There was high deviation among the lower markets, and ROI in these areas was similar to the national average.

Table 1-18 Annual ROI for office buildings in Seoul

(Unit: %) Nationwide Seoul Central Gangnam Yeouido-Mapo Other areas Income rate 4.56 5.13 6.20 4.32 5.61 4.46 ROE 1.41 1.57 0.86 2.20 1.38 1.52 ROI 6.02 6.76 7.10 6.59 7.05 6.03

Note: Rate of return was calculated based on the rate of return in the past year, as of the end of Sept. 2016

66 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 1 Market Trends

Figure 1- 50 ROI for office buildings in Seoul

7.10 7.05 6.76 6.59 6.02 6.03

Nationwide Seoul Central Gangnam Yeouido-Mapo Others Income rate (%) ROE (%) ROI (%)

Long-term Trends The annual ROI was maintained fairly high at 6.02% for office buildings nationwide, as of the end of Sept. 2016. The average ROI has been 5.93% in the past 3 years.

○ The annual rate of return for commercial real estate has been on a steady uptrend, due to the continued interest and increase in investor demand.

○ However, in the Ulsan and Gyeongnam area, the local economic slowdown resulting from the slump in the shipbuilding and manufacturing industries has led the annual ROI to continue on a downtrend (annual ROI fell to 1.48% in 2015 compared to 2014).

○ In the recent 3 years, the average annual ROI was the highest in Seoul (6.74%), followed by Busan (6.44%) and Gyeonggi (6.29%), which were all higher than the national average. Meanwhile Chungbuk recorded the lowest ROI at 1.81%.

○ In the four commercial spheres of Seoul, the annual ROI declined slightly, yet it is still fairly high (CBD - 7.28%, Gangnam - 6.39%, Yeouido-Mapo - 7.02%, Other areas - 6.20%). In particular, the average ROI was the highest in CBD at 7.28% in the past 3 years.

67 Fig. 1- 51 Trends in annual ROI (%) for office buildings by region

9.51

7.21 6.76 6.47 6.02 5.37 5.20 5.12 4.73 4.77 4.50 4.63 4.80 4.09 3.27 3.38

1.54

Nation Seoul Busan Daegu Incheon Gwan Daejeon Ulsan Gyeong Gang Chung Chung Jeon Jeon Gyeong Gyeong Jeju -wide -gju -gi -won -buk -nam -buk -nam -buk -nam

2013 2014 2015 2016 3Q (end of Sept.)

Table 1-19 Trends in annual ROI by region

(Unit: %) Region Trends in the rate of change in the annual ROI (%) for office buildings 3-year 2013년 2013 2014 2015 2016.9월 기준 average* Nationwide 5.29 5.91 5.93 6.02 5.93 Seoul 6.25 6.71 6.83 6.76 6.74 Central 6.84 7.30 7.48 7.10 7.28 Gangnam 5.92 6.28 6.29 6.59 6.39 Yeouido-Mapo 7.11 6.86 7.37 7.05 7.02 Others 5.14 6.52 6.17 6.03 6.20 Busan 6.01 6.56 6.28 6.47 6.44 Daegu 3.62 5.71 5.53 5.37 5.53 Incheon 3.54 3.78 4.16 5.20 4.44 Gwangju 2.37 2.43 3.10 3.27 2.97 Daejeon 2.02 3.68 2.69 3.38 3.05 Ulsan 6.62 6.22 4.74 4.73 5.46 Gyeonggi 5.31 5.73 6.45 7.21 6.29 Gangwon 4.17 3.38 4.60 4.77 4.01 Chungbuk 2.56 2.81 0.80 1.54 1.81 Chungnam 2.76 4.36 2.97 4.50 3.56 Jeonbuk 3.96 4.49 4.41 4.09 4.38 Jeonnam 4.57 5.48 5.47 4.63 5.14 Gyeongbuk 4.92 6.06 5.42 4.80 5.55 Gyeongnam 4.18 4.53 5.23 5.12 4.92 Jeju 3.83 4.57 6.30 9.51 5.74

Note: Annual ROI= , where is the ROI in quarter, and the 3-year average ROI (2013 4Q to 2016 3Q) is the geometrical mean of the annual ROI.

68 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 1 Market Trends

Trends in Retail Rent Market

1) Vacancy Rate and Rent

Vacancies in retail stores among commercial real estate properties in 2016 occurred in some of the commercial districts that were stagnant, with an increase in the vacancy rate of small and medium-to-large retail stores. As private consumption made a steady recovery, the rent price index of medium-to-large retail stores and rental fees of small retail stores were on a continuous uptrend, whereas the rent price index of aggregate retail stores dropped by a small margin.

Medium-to-large retail stores Vacancy rate decreased in Busan (9.5%), Gwangju (10.1%), Daejeon (11.6%), Gyeonggi (6.7%), Jeju (9.7%) compared to the end of the previous year. Vacancy rate was the highest in Jeonbuk at 19.8%. Rent was high in the capital area and Busan, where the land value and floating population were high. While the rent price index climbed 0.3% since the end of last year, it declined in some of the regions (Gwangju, Daejeon, Ulsan, Gyeongnam) due to the slump in the local commercial districts.

○ In Seoul, the vacancy rate fell as a result of the continued drop in rental feels in the Myeong- dong commercial district. In the Hongdae-Hapjeong commercial district, there was a slightly increase in vacancies, but rent still climbed due to the expansion of the commercial district resulting in a higher demand for retail stores outside the existing area. Rent was reduced in areas with a relatively high vacancy rate such as Cheongdam and Mok-dong; thus, rent in Seoul declined somewhat from the level recorded in the beginning of the year.

○ Due to the impact of the opening of the Incheon Line 2 (light rail system), the long-term vacancy issue in the Juan commercial district was alleviated, and rent increased due to the growing floating population. In the case of Daejeon, vacancy rate dropped as the vacancies became filled in the commercial districts with a high vacancy rate such as the commercial district around the bus-train terminal.

69 Small retail stores The vacancy rate, which increased to some extent in the first half 2012, recorded 5.2% in the second half due to the growing demand for small retail spaces on the first or second floor as a result of a growing number of self-employed individuals. Due to the relatively conditions such as the building size, rent was around KRW 16,500/㎡, which was lower than that of medium-to-large retail stores, but it was a 0.4% increase since the end of the previous year.

○ In Seoul, vacancy declined as the demand for new rentals remained steady around the vigorous commercial districts such as the Sungshin Women’s University area. In the case of the Chungmuro and Jukjeon (Daegu) commercial districts, new lease contracts were signed based on a higher rent than the conventional rental fees, and this led to an increase in rent.

○ In the case of Gyeongbuk, vacancies occurred in retail stores that had poor accessibility from the Gumi Industrial Complex, and this increased the vacancy rate. As for Gangwon, vacancies were reduced with new lease agreements signed in the Gangneung commercial district.

○ In the case of the original Central of Daejeon and the Wonju commercial district, demand for rentals shifted to the new commercial districts, causing these commercial districts to shrink and rent to decline.

Aggregate Retail Stores The market rent declined slightly in Ulsan and Sejong, and thus the national rent price index fell by 0.1% from the end of last year. Rent was high in metropolitan cities such as Seoul and Busan, and rent increased by a small margin in Daejeon, while rent was adjusted downward in Sejong, as the initial pre-construction parceling-out sales prices were too high.

○ In Seoul, the rent is generally stable, but it was reduced by a small margin in Mok-dong and Yeongdeungpo due to the slight stagnation in the commercial districts.

○ In Sejong, the initial pre-construction parceling-out sales prices were high compared to the actual demand for lease; despite this, however, new aggregate retail stores continued to be supplied, which in turn pulled down the rental fees.

○ In Jeju, the rent and the rent price index increased by a small margin due to the continued inflow of tourists.

70 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 1 Market Trends

Table 1-20 Vacancy rate of retail stores by region (%)

(as of the end of Sept. 2016) ju oul egu san jong san angju ejeon ngwon onbuk eonggi Je cheon ungbuk onnam ungnam tionwide Ul eongbuk Se Bu eongnam Da Se In Da Je Gw Je Gy Ga Ch Ch Na Gy Gy Medium 10.7 7.7 9.5 12.5 9.7 10.1 11.6 12.5 - 6.7 15.7 16.2 13.8 19.8 13.3 17.0 9.3 9.7 -to-large

Small 5.2 2.6 8.8 4.0 4.1 5.0 12.1 0.6 5.9 6.2 3.5 3.2 5.5 7.7 3.7 7.8 5.8 3.2

Note: In the case of aggregate retail stores, the statistical data on the vacancy rate was not calculated as only some of the units of the buildings were examined.

Table 1-21 Rent for retail stores (A, thousand KRW/㎡) and rate of change in the rent price index (B, %) by region

(As of the end of Sept. 2016, rate of change calculated in comparison to the end of the previous year) ju oul egu san jong san angju ejeon ngwon onbuk eonggi Je cheon ungbuk onnam ungnam tionwide Ul eongbuk Se Bu eongnam Da Se In Da Je Gw Je Gy Ga Ch Ch Na Gy Gy

Medium- A 31.1 58.1 30.2 23.9 31.8 22.2 18.5 18.6 - 32.3 20.5 20.8 13.5 16.0 11.2 14.9 16.9 12.5 to-large B 0.3 0.1 0.9 0.6 0.7 -0.2 -0.1 -0.4 - 0.8 -0.1 -0.3 0.1 0.5 0.7 -0.3 -0.7 0.9 Small A 16.5 46.8 26.3 21.2 15.2 14.3 12.3 13.7 15.0 22.1 13.0 11.2 11.9 9.3 9.8 12.3 13.1 12.2 Aggre- A 28.7 50.2 36.5 26.3 27.6 23.3 24.2 22.5 32.6 30.3 20.3 18.4 17.1 24.1 15.8 17.1 22.5 10.0 gated B -0.1 0.0 -0.3 0.1 -0.1 -0.6 0.4 -1.7 -4.3 0.1 0.1 -0.2 0.2 0.5 -0.2 -0.3 -0.5 1.0

Note: 1) The rent price index for small retail stores was not calculated as the time series data necessary for generating statistical data could not be obtained. 2) Reference point for the calculation of the rent price index: 2015 4Q (=100)

71 Figure 1- 52 Rent for retail stores by region (thousand KRW/㎡)

60.0 58.1

50.2 50.0 46.8

40.0 36.5 31.8 32.6 32.3 31.1 30.2 30.3 28.7 30.0 26.3 26.3 27.6 23.9 24.2 24.1 22.22 3.3 22.5 22.1 22.5 21.2 20.5 20.8 18.5 18.6 18.4 20.0 16.5 20.3 17.1 17.1 16.9 15.2 15.0 16.0 15.8 14.9 14.3 13.7 13.5 12.3 13.0 12.3 13.1 12.5 12.2 11.2 11.9 11.2 9.3 10.0 10.0 9.8

0.0 0.0 Nation Seoul Busan Daegu Incheon Gwan Daejeon Ulsan Sejong Gyeong Gang Chung Chung Jeon Jeon Gyeong Gyeong Jeju -wide -gju -gi -won -buk -nam -buk -nam -buk -nam

Medium-to-large retail stores Small retail stores Aggregated retail stores

Table 1-22 Trends in the rent price index of medium-to-large retail stores and rate of change compared to the end of the previous year by region

Rate of Region `15.1Q `15.2Q `15.3Q `15.4Q `16.1Q `16.2Q `16.3Q change Nationwide 99.9 99.9 100.1 100.0 100.2 100.3 100.3 0.3 Seoul 99.8 99.9 100.0 100.0 100.1 100.2 100.1 0.1 Central 100.1 100.0 100.0 100.0 100.2 100.2 100.2 0.2 Gangnam 99.8 99.9 100.0 100.0 100.0 100.0 100.0 0.0 Sinchon-Mapo 99.8 100.1 100.1 100.0 100.0 100.1 100.5 0.5 Others 99.7 99.8 99.9 100.0 100.1 100.2 100.1 0.1 Busan 99.1 99.5 99.8 100.0 100.3 100.8 100.9 0.9 Daegu 100.2 99.7 100.0 100.0 100.3 100.4 100.6 0.6 Incheon 99.5 99.4 100.4 100.0 100.0 100.2 100.7 0.7 Gwangju 99.7 100.2 101.0 100.0 100.0 100.1 99.8 -0.2 Daejeon 100.1 100.0 100.0 100.0 100.4 100.4 99.9 -0.1 Ulsan 99.8 99.9 100.0 100.0 99.8 99.8 99.6 -0.4 Gyeonggi 100.2 100.0 100.1 100.0 100.3 100.6 100.8 0.8 Gangwon 99.3 100.0 100.1 100.0 100.0 99.8 99.9 -0.1 Chungbuk 100.1 100.1 100.0 100.0 100.0 100.0 99.7 -0.3 Chungnam 101.3 101.4 100.7 100.0 100.2 100.2 100.1 0.1 Jeonbuk 98.8 99.4 99.7 100.0 100.3 100.3 100.5 0.5 Jeonnam 99.6 99.7 99.9 100.0 100.2 100.6 100.7 0.7 Gyeongbuk 100.1 100.0 100.0 100.0 99.8 99.8 99.7 -0.3 Gyeongnam 100.2 100.2 100.2 100.0 100.6 99.7 99.3 -0.7 Jeju 99.4 99.5 99.5 100.0 100.2 100.3 100.9 0.9 Note: 1) The index was calculated based on 2015 4Q (=100), and negative changes are indicated in red. 2) The quarterly index was calculated based on the last date of the last month of the quarter.

72 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 1 Market Trends

Table 1-23 Trends in the rent price index of aggregates retail stores and rate of change compared to the end of the previous year by region

Rate of Region `15.1Q `15.2Q `15.3Q `15.4Q `16.1Q `16.2Q `16.3Q change Nationwide 100.0 100.0 100.0 100.0 100.0 100.0 99.9 -0.1 Seoul 99.8 99.9 100.0 100.0 100.0 100.0 100.0 0.0 Central 99.9 99.9 100.0 100.0 99.8 99.8 99.8 -0.2 Gangnam 99.9 99.9 100.0 100.0 100.0 100.0 100.0 0.0 Sinchon-Mapo 100.0 100.0 100.0 100.0 100.0 100.0 100.0 0.0 Others 99.9 99.9 100.0 100.0 100.1 100.1 100.0 0.0 Busan 100.1 100.2 100.3 100.0 99.9 99.8 99.7 -0.3 Daegu 100.1 99.9 100.0 100.0 100.1 100.1 100.1 0.1 Incheon 100.0 100.0 100.0 100.0 99.9 99.9 99.9 -0.1 Gwangju 99.9 99.8 99.8 100.0 100.0 100.0 99.4 -0.6 Daejeon 99.9 99.8 100.0 100.0 100.0 100.1 100.4 0.4 Ulsan 100.5 100.1 100.1 100.0 99.5 98.6 98.3 -1.7 Sejong 114.9 110.5 108.2 100.0 98.5 97.9 95.7 -4.3 Gyeonggi 99.7 99.8 99.9 100.0 100.0 100.0 100.1 0.1 Gangwon 100.3 100.1 99.8 100.0 100.1 100.1 100.1 0.1 Chungbuk 100.1 100.0 100.0 100.0 100.0 99.9 99.8 -0.2 Chungnam 100.8 100.7 100.3 100.0 100.1 100.2 100.2 0.2 Jeonbuk 100.0 99.9 99.8 100.0 100.8 100.8 100.5 0.5 Jeonnam 100.5 100.4 100.4 100.0 100.0 100.0 99.8 -0.2 Gyeongbuk 99.9 99.9 100.0 100.0 99.9 99.9 99.7 -0.3 Gyeongnam 100.0 100.0 100.0 100.0 99.9 99.7 99.5 -0.5 Jeju 99.4 99.4 99.7 100.0 100.8 101.0 101.0 1.0 Note: 1) The index was calculated based on 2015 4Q (=100), and negative changes are indicated in red. 2) The quarterly index was calculated based on the last date of the last month of the quarter.

Seoul The vacancy rate was relatively low in Seoul compared to the national average, with the vacancy rate being 7.7% for medium-to-large retail stores and 2.6% for small retail stores. In the case of rent, it was around 1.7 (aggregated retail stores) to 2.8 (small retail stores) times higher than the national average.

○ In the case of small retail stores in Seoul, there was consistent demand for new rentals centering on the vigorous commercial districts, which in turn helped the vacancy rate drop by 0.7%p compared to the end of last year to 2.6% and rent to increase by 0.6% from the end of last year.

○ Central In some parts of the commercial sphere, vacancies among small and medium-to- large retail stores filled up, leading to a drop in the average vacancy rate. Rent was higher

73 in most of the commercial districts, except for Dongdaemun and Chungmuro, than the average in Seoul. While the monthly rent for medium-to-large retail stores in the CBD declined slightly compared to the end of last year, the rent price index increased by 0.2%. The rent for small retail stores climbed up from the end of last year, whereas both the rent and rent price index for aggregated retail stores declined.

○ Gangnam The vacancy rate of medium-to-large retail stores in Gangnam dropped by 0.2%p compared to the end of last year with the level of vacancy being alleviated in some of the commercial districts. Meanwhile, the highest rental fees were observed along Gangnam- daero (KRW 136,000/㎡) for medium-to-large retail stores and along Dosan-daero (KRW 79,400/㎡) for aggregated retail stores. Rent generally remained steady, while the rent price index declined by a small margin.

○ Sinchon-Mapo Different trends were observed among retail stores depending on the size: there was a slight increase in vacancies among medium-to-large retail stores and a decrease in vacancies among small retail stores. The rent for medium-to-large retail stores and aggregated retail stores was the highest in the Sinchon commercial district at KRW 58,400/㎡ and KRW 74,300/㎡, respectively, whereas the rent for small retail stores was the highest in the Hongdae-Hapjeong commercial district at KRW 68,000/㎡.

○ Other areas The rent for retail stores in most of the commercial districts was lower than the average in Seoul, and the rent price index dropped marginally.

Table 1-24 Vacancy rate, rent and rent price index of retail stores and rate of change compared to the end of last year in seoul

Seoul CBD Gangnam Sinchon-Mapo Other areas Vacancy rate (%) 7.7 7.6 7.3 8.2 7.5 Medium Rent (thousand KRW/㎡) 58.1 93.5 78.0 51.1 43.1 -to-large Rate of change in rent 0.1 0.2 0.0 0.5 0.1 price index (%) Vacancy rate (%) 2.6 2.4 - 2.0 3.9 Small Rent (thousand KRW/㎡) 46.8 52.7 - 55.1 34.5 Aggre- Vacancy rate (%) 50.2 86.1 59.4 38.9 43.1 gated Rent (thousand KRW/㎡) 0.0 -0.2 0.0 0.0 0.0

74 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 1 Market Trends

2) Return on Investment

As of the end of September 2016, the annual return on investment (ROI) was 6.51% for medium-to-large retail stores and 6.13% small retail stores. Such high annual ROI was achieved due to the growing interest in investing in commercial buildings based on the continued trend of low interest rates. Of particular note, aggregated retail stores, which are accessible to private investors, recorded the highest ROI of 7.28%.

○ The return on equity (ROE), indicating the changes in asset value, showed an uptrend due to the continued interest in commercial real estate properties resulting from the trend of low interest rates.

Medium-to-large retail stores Busan (8.45%), Daegu (8.01%), and Jeju (10.26%) recorded high ROIs, while Daejeon (4.92%), Jeonnam (4.90%), and Chungbuk (4.98%) recorded low ROIs.

○ While Seoul (Apgujeong, Gongdeok Station and Hongdae-Hapjeong), Busan (Haeundae), and Incheon (Guwol, Ganseok) recorded high ROIs centering on the vigorous commercial districts with a large floating population, the original CBD of Daejeon and the Geoje- Gyeongnam commercial districts recorded low ROIs due to decrepit commercial facilities and stagnation of the local economy, respectively.

Table 1-25 ROI for medium-to-large retail stores by region

(Unit: %) ju oul egu san san angju ejeon ngwon onbuk eonggi Je cheon ungbuk onnam ungnam tionwide Ul eongbuk Se Bu eongnam Da In Da Je Gw Je Gy Ga Ch Ch Na Gy Gy Income 4.64 4.20 5.07 4.90 5.98 5.65 3.89 5.03 4.99 5.35 5.41 4.34 4.29 3.97 4.53 4.03 5.01 rate ROE 1.81 2.30 3.26 3.00 1.55 0.80 1.00 0.95 1.15 0.35 -0.42 1.51 1.07 0.90 0.97 1.69 5.06 ROI 6.51 6.58 8.45 8.01 7.59 6.49 4.92 6.01 6.18 5.71 4.98 5.89 5.39 4.90 5.53 5.77 10.26

75 Small Retail Stores Busan (7.67%), Daegu (7.31%), and Jeju (11.20%) recorded high ROIs, whereas Daejeon (4.89%) and Sejong (5.54%) recorded low ROIs due to vacancies and lower rent.

○ High ROIs were achieved in the Hongdae-Hapjeong commercial district of Seoul, Haeundae-Seomyeon commercial district of Busan, and Dongseong-ro commercial district of Daegu, based on an abundant floating population and demand for profitable real estate properties.

○ The original CBD of Daejeon continued to see signs of stagnation in the commercial district such as an increase in vacancies, while Sejong recorded a low ROI due to the continuing trend of excess supply of retail stores.

Table 1-26 ROI for small retail stores by region

(Unit: %) ju oul egu san jong san angju ejeon ngwon onbuk eonggi Je cheon ungbuk onnam ungnam tionwide Ul eongbuk Se Bu eongnam Da Se In Da Je Gw Je Gy Ga Ch Ch Na Gy Gy Income 4.13 3.47 3.94 3.45 4.04 5.33 3.25 3.40 3.80 4.52 4.40 4.70 4.60 3.80 4.64 3.98 3.54 5.24 rate ROE 1.95 2.24 3.62 3.76 1.50 1.55 1.60 2.24 1.69 1.88 1.16 0.31 1.24 1.24 1.29 1.88 2.19 5.75 ROI 6.13 5.77 7.67 7.31 5.58 6.94 4.89 5.70 5.54 6.46 5.60 5.02 5.88 5.07 5.97 5.92 5.79 11.20

Aggregated Retail Stores Busan (8.42%), Gyeongnam (8.29%), and Jeju (8.74%) recorded high ROIs, whereas low ROIs were observed in Sejong (3.01%), Daejeon (5.73%), and Chungbuk (5.16%).

○ Due to the growing demand for investment with little capital by private investors along Dosan-daero of Seoul, and in Haeundae of Busan and Siji District of Daegu, satisfactory ROIs were achieved.

○ The Jeonbuk and Jeonju commercial districts contracted due to the demand for rentals and the floating population moving to the commercial districts of new urban areas nearby. It also caused a drop in rent and resulted in low ROIs.

76 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 1 Market Trends

Table 1-27 ROI for aggregated retail stores by region

(Unit: %) ju oul egu san jong san angju ejeon ngwon onbuk eonggi Je cheon ungbuk onnam ungnam tionwide Ul eongbuk Se Bu eongnam Da Se In Da Je Gw Je Gy Ga Ch Ch Na Gy Gy Income 5.33 5.41 5.18 4.77 6.00 5.44 4.34 6.18 2.47 5.48 5.27 4.68 4.49 4.74 4.75 5.07 6.81 4.77 rate ROE 1.87 2.07 3.12 3.35 1.10 1.50 1.35 1.15 0.53 1.30 0.88 0.46 1.21 1.06 1.00 1.36 1.41 4.80 ROI 7.28 7.56 8.42 8.23 7.15 7.00 5.73 7.38 3.01 6.84 6.18 5.16 5.74 5.84 5.78 6.49 8.29 9.74

Figure 1-53 Annual ROI for retail stores by region (Unit: %)

11.20 10.26 9.74 8.45 8.42 8.01 8.23 8.29 7.56 7.59 7.38 7.28 7.15 7.00 6.51 6.58 6.49 6.84 6.49 7.67 6.01 6.18 6.18 7.31 5.73 5.71 5.89 5.74 5.84 5.785 .53 5.77 6.94 5.16 5.39 4.92 5.54 6.46 4.98 4.90 6.13 5.97 5.77 5.58 5.70 5.60 5.88 5.92 5.79 5.07 4.89 3.01 5.02

Nation Seoul Busan Daegu Incheon Gwan Daejeon Ulsan Sejong Gyeong Gang Chung Chung Jeon Jeon Gyeong Gyeong Jeju -wide -gju -gi -won -buk -nam -buk -nam -buk -nam

Medium-to-large retail stores Small retail stores Aggregated retail stores

Seoul The vacancy rates are relatively low at 7.7% for medium-to-large retail stores and 2.6% for small retail stores, while the rental income is stable at above the national average. ROI is higher than the national average thanks to the vivacity of the commercial districts, with a large floating population.

○ Central Although ROI decreased slightly compared to the previous quarter, it was high for medium-to-large retail stores in the Jongno, Myeong-dong and Seoul Station commercial districts, for small retail stores in Chungmuro and Dongdaemun, and for aggregated retail stores in Jongno and Dongdaemun, in comparison with the CBD average.

○ Gangnam The highest ROI was recorded in the Apgujeong commercial district by medium- to-large retail stores and the Dosan-daero commercial district by aggregated retail stores, due to the increase in asset value.

77 Table 1-28 ROI for retail stores in Seoul

(Unit: %) Nationwide Seoul CBD Gangnam Sinchon·Mapo Other areas Medium Income rate 4.64 4.20 5.18 3.55 4.74 4.16 -to-large ROE 1.81 2.30 1.61 3.04 2.63 2.13 retail stores ROI 6.51 6.58 6.86 6.67 7.46 6.35 Income rate 4.13 3.47 3.37 - 4.42 2.83 Small retail ROE 1.95 2.24 1.80 - 3.02 2.42 stores ROI 6.13 5.77 5.21 - 7.54 5.31 Income rate 5.33 5.41 5.70 3.92 5.65 4.84 Aggregated ROE 1.87 2.07 2.66 1.83 1.97 1.72 retail stores ROI 7.28 7.56 8.48 5.81 7.70 6.62

○ Sinchon-Mapo Among the four broad-area commercial spheres of Seoul, the Sinchon- Mapo area showed the highest ROI for all of the commercial sizes. In Hongdae-Hapjeong, the annual ROI was clearly high at 8.36% for medium-to-large retail stores, 8.39% for small retail stores and 7.70% for aggregated retail stores.

○ Other areas Other areas recorded the lowest ROI among the four major commercial spheres of Seoul. ROI was 6.35 for medium-to-large retail stores, 5.31% for small retail stores, and 6.62% for aggregated retail stores, which were all lower than the average in Seoul.

Long-term Trends The ongoing interest in commercial real estate properties has been boosting the annual ROI. The ROI is the highest for aggregate retail stores.

○ In Jeju, one of the most popular tourist destinations in Korea, the annual ROI has been surging every year as a result of a growing number of tourists and the anticipation for local development heightened by the construction of a second airport in the region (3.27%p↑ in 2015 and 5.67%p↑ in 2016 compared to 2013 for medium-to-large retail stores).

○ In Sejong, the annual ROI for aggregated retail stores has been on a downtrend due to the high initial pre-construction parceling-out sales prices and the continuous supply of retail stores, and it reached the lowest point in Sept. 2016 (1.45%p↓ from 2014, and 0.57%p↓ from 2015).

78 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 1 Market Trends

Table 1-29 National annual ROI by retail store type

(Unit: %) 2013 2014 2015 2016.9월 Medium-to-large 4.69 6.16 6.24 6.51 retail stores Small retail - - 5.85 6.13 stores Aggregated retail - 6.39 7.32 7.28 stores

Figure 1- 54 Trends in annual ROI by retail store type (Unit: %) 7.32 7.28 6.51 6.39 6.16 6.24 6.13 5.85

4.69

Medium-to-large retail stores Small retail stores Aggregated retail stores

2013 2014 2016 Oct. 2015~Sept. 2016

C. Trends in Transactions and Supply of Commercial Real Estate Properties

1) Office Building Sales Transactions9)

Nationwide Office building sales transactions rose in the year 2015, but due to the uncertainty of the real estate market arising from the hike in the benchmark interest rate in the U.S. on top of the stagnant economy, the sales volume of office buildings declined to 187 (2016 3Q), which was a 24.1% decrease since the end of last year.

9) This is based on the data on the actual transactions (sales transactions) of office facilities of general buildings, for which the sales contracts were concluded by the end of Sept. 2016, that have been aggregated as of December 2016. The actual value is subject to change depending on the transactions that are registered at a later date.

79 ○ The area with the highest office building sales transaction volume was Seoul (52 transactions), which saw a 20.9% increase since the end of last year. The office sales transaction volume increased by 50.0% in Incheon (9 transactions),but it decreased by 46.9% in Gyeonggi (43). Thus, the total transaction volume in the capital area, which accounts for 55% of the national office building sales transaction volume, decreased by 20.0% compared to the end of last year.

○ In all five metropolitan cities, the transaction volume sharply declined by 42.9% compared to the end of last year, whereas there was an 18.6% decrease in the provinces outside the capital area.

Figure 1- 55 Trends in office building sales transaction volume

249 250 218 208 202 189 200 178 172 161 152 149 137 150 116 123 103 110 100

50

- 1Q 2Q 3Q 4Q 1Q 2Q 3Q 4Q 1Q 2Q 3Q 4Q 1Q 2Q 3Q 2013 2014 2015 2016 Capital area Metropolitan cities Provinces Nationwide

2) Trends in Office Building Supply10)

Nationwide The inventory of office buildings as of November 2016 was 20,528. The area of the office buildings, the construction of which was completed in 2016 3Q, was 314,000㎡ (81 buildings), which was a 42.5% (-38.2%) decrease compared to the end of last year.

10) Based on the General Building Register, as of Nov. 2016 (excl. public office facilities and office-tels)

80 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 1 Market Trends

Figure 1-56 Trends in the construction of office buildings

Number of buildings Area (㎡)

131 131 722,974 722,974 752,850 752,850 120 120 117 117 115 115 620,109 620,109 101 98 101 98 563,631 563,631 92 92 93 93 535,575 53553,567,552 9 536,529 545,788 545,788 85 85 88 89 88 89 79 79 81 81 459,767 459,767 77 74 77 74 439,548 439,548 384,415 38349,431,158 2 393,182 332,018 33321,041,080 0 314,000 280,713 280,7132 67,945 267,945

1Q 2Q1Q3Q2Q4Q3Q1Q4Q2Q1Q3Q2Q4Q3Q1Q4Q2Q1Q3Q2Q4Q3Q1Q4Q2Q1Q3Q2Q 31QQ 2Q1Q3Q2Q4Q3Q1Q4Q2Q1Q3Q2Q4Q3Q1Q4Q2Q1Q3Q2Q4Q3Q1Q4Q2Q1Q3Q2Q 3Q 2013 2013 2014 2014 2015 2015 2016 2016 2013 2013 2014 2014 2015 2015 2016 2016 Capital areaCapital area MetropolitanMetropolitan cities cities Capital areaCapital area MetropolitanMetropolitan cities cities ProvincesProvinces NationwideNationwide ProvincesProvinces NationwideNationwide

○ The number of office buildings that were built by the end of last year was 131 (545,788㎡), which was the highest during the time period under examination. The supply of new office buildings has been generally high every quarter in the provinces outside the capital area rather than the capital area, but there are more office buildings with a large area in the capital area.

○ With the heightened interest in commercial buildings as of late, the number of office buildings completed has been on the rise, with a new supply of office buildings with a total area of 188,923㎡ (30 buildings) in the capital area, 38,288㎡ (17 buildings) in the metropolitan cities, and 86,789㎡ (34buildings) in the provinces outside the capital area.

○ In terms of the supplied buildings (number of buildings), the supply of completed office buildings remained steady in the capital area compared to the previous year, whereas it increased by 21.4% in the five metropolitan cities and decreased by 55.3% in the rural provinces.

81 3) Retail Store Sales Transactions11)

Nationwide In the first half of last year (April to June 2015), there were 9,070 retail store sales transactions, which was the highest during the time period under examination. Since then, it has been on a continuous decline until the first half of 2016, when it showed a recovery. However, the transaction volume has been generally stagnant in the second half of the year.

○ Approx. 43% of the retail stores sold between July and Sept. 2016 were in Seoul, Incheon and Gyeonggi-do, indicating a concentration of transactions in the capital area (3,607 transactions), and this was a 9.8% increase from the end of last year.

○ In contrast, the sales transaction volume dropped overall in the non-capital area, with a 2.5% decrease in the five metropolitan cities and 6.2% decrease in the rural provinces since the end of last year.

○ In Sejong, the transaction volume increased due to steady supply. There were 92 sales transactions between June and Sept. 2016, which was the highest increase (76.9%) in the country since the end of last year.

Figure 1-57 Trends in retail store (building) sales transaction volume

10,000 9,070 8,781 8,468 9,000 8,172 8,166 8,281 8,361 7,722 7,360 7,506 7,293 8,000 6,850 7,150 7,000 5,855 5,935 6,000 5,000 4,000 3,000 2,000 1,000 - 1Q 2Q 3Q 4Q 1Q 2Q 3Q 4Q 1Q 2Q 3Q 4Q 1Q 2Q 3Q 2013 2014 2015 2016 Capital area Metropolitan cities Provinces Nationwide

11) This is based on the data on the actual transactions (sales transactions) of retail stores of general buildings (Class I and II Community Living Facilities, Sales Facilities, Recreational Facilites, etc. prescribed in the Building Act) for which the sales contracts were concluded by the end of Sept. 2016, that have been aggregated as of December 2016. The actual value is subject to change depending on the transactions that are registered at a later date.

82 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 1 Market Trends

4) Trends in Retail Store Supply12)

Nationwide The total inventory of retail stores was found to be 1,070,281, as of Nov. 2016, and the area of the retail stores that were built by 2016 3Q was 2,771,393㎡ (7,884 buildings), which was a 5.8% decrease from the end of last year.

○ The number of retail buildings built in the second half of 2015 (Oct.~Dec.) was the highest at 8,368 (2,640,452㎡). The supply of retail buildings, which was quite stagnant in the early 2016, showed a steady increase and recorded 1,224,712 ㎡ (2,554 buildings) in the capital area, 351,491㎡ (1,061 buildings) in the metropolitan cities, and 1,195,191㎡ (4,269 buildings) in the rural provinces in 2016 3Q.

○ The number of retail building built and supplied, compared to the end of last year, decreased by 4.1% in the capital area and 6.6% in the metropolitan cities. While the supply of retail buildings, based on the supplied area, increased by 30.3% in the capital area, it decreased by 16.3% in the metropolitan cities outside the capital area.

○ Supply decreased in all of the rural provinces, excluding Sejong (64 buildings, 28.0%↑) and Jeju (241 buildings, 1.7%↑), and thus the total supply in the rural provinces decreased by 6.5%

compared to the end of last year.

Figure 1-58 Trends in the completion of retail store construction

Number of buildings Area(㎡)

8,368 8,368 2,739,534 2,739,5342 ,664,291 2,664,291 2,771,393 2,771,393 7,882 7,882 7,731 7,731 7,884 7,884 2,483,890 2,483,890 2,538,063 2,5328,6,04603,4 52 2,640,452 7,538 7,538 7,264 7,264 2,373,102 2,373,102 2,341,998 2,341,998 6,724 6,724 6,605 6,605 6,934 6,934 2,228,450 2,228,450 6,112 6,112 6,189 6,189 6,448 6,448 1,937,544 1,937,544 2,176,291 2,176,291 2,129,795 2,129,795 5,930 5,930 5,983 5,983 1,942,342 1,942,342 5,178 5,178 1,862,029 1,862,029 1,598,595 1,598,595

1Q 2Q1Q3Q2Q4Q3Q1Q4Q2Q1Q3Q2Q4Q3Q1Q4Q2Q1Q3Q2Q4Q3Q1Q4Q2Q1Q3Q2Q 31Q 2Q1Q3Q2Q4Q3Q1Q4Q2Q1Q3Q2Q4Q3Q1Q4Q2Q1Q3Q2Q4Q3Q1Q4Q2Q1Q3Q2Q 3Q 2013 2013 2014 2014 2015 2015 2016 2016 2013 2013 2014 2014 2015 2015 2016 2016 Capital areaCapital area MetropolitanMetropolitan cities cities Capital areaCapital area MetropolitanMetropolitan cities cities ProvincesProvinces NationwideNationwide ProvincesProvinces NationwideNationwide

12) Based on the General Building Register, as of Nov. 2016

83 KAB Real Estate Market Report Korea Real Estate Market Report

P A R T 2 2017 Housing Market Outlook

Trend Category 2017 forecast 2015 2016 Changes in the residential property sales 3.51% 0.71% -0.2% prices (nationwide) (4.37%/2.73%) (1.32%/0.17%) (-0.2%/-0.4%) (capital area/non- capital area) Changes in the residential property jeonse prices 4.85% 1.32% 0.3% (nationwide) (7.14%/2.79%) (2.04%/0.67%) (0.4%/0.0%) (capital area/non- capital area) Changes in the residential 19.0% -11.6% -7.1% property sales transaction 119.4ten thousand 105.5ten thousand 98.0ten thousand volume (nationwide) units units units 2017 Housing Market Outlook

Lee Jiyeon, Shin Ranhee

In 2017, the housing market is expected to be reorganized centering on the actual end- users, while there will be a decline in speculative investment due to the possibility of interest rate hikes in Korea in line with the interest rate hikes in the U.S., the government measures taken for the rationalization of mortgages through household debt management, loan regulations and subscription market adjustments, and the boom of the pre-construction parceling-out sales market resulting from an increase in housing supply.

While the domestic economic growth rate in 2017 is projected to be around 2.5~3.0%, it is expected that the domestic and international economic uncertainties caused by the domestic economic slump resulting from the slowdown of the industrial economy and unstable domestic political-economic situation will be risk factors in the housing market.

As for the move-in ready housing supply, there has been an increase in the supply of pre- construction parceling-out sales of apartments since 2014, and it is expected that the number of move-in ready homes will rise this year and next year. In some of the regions, it is possible that the housing prices will undergo an adjustment due to the supply-and-demand imbalance resulting from the growing volume of unclaimed new residential properties. However, there has been consistently new demand from the growing number of single- member and two-member households as well as potential demand arising from housing demolitions and replacement of decrepit homes; thus, it is expected that the supply-and- demand imbalance will occur in only some of the regions.

86 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 2 Real Estate Market Outlook in the Second Half of 2016

Residential Property Sales Market The U.S. Federal Reserve interest rate was hiked (0.25%) in December 2016, and an announcement was made in regard to the plans to raise the interest rate three times a year until 2019 for a total of 9 times. Accordingly, there is a high probability that the benchmark interest rate will be raised in Korea, which is expected to cause cooling of the housing market. Amidst this situation, it appears that the persistent domestic and international economic uncertainties as well as the recent real estate restriction policies such as household debt management, loan restrictions and subscription system adjustments will be as risk factors that cause a downtrend in the residential property sales market.

○ In the capital area, the pre-construction parceling-out sales market will continue to cool down centering on the regions directly impacted by the real estate regulation measures announced on Nov. 3. Even in the case of existing housing, the market is expected to shift toward a downward stabilization trend due to the economic situation at home and abroad. While there will be housing demand by the end-users in some of the more in-demand areas, a downward stabilization trend is expected to be observed in the Gyeonggi-do area in the outskirts of Seoul.

○ As for the non-capital area, residential property prices are expected to rise in a localized fashion in the areas with favorable conditions for long-term development such as Jeju (construction of a second airport), Busan (redevelopment of the North Port), and Gangwon (hosting of the Winter Olympic Games). In contrast, the sales prices are expected to fall in areas with a supply-and-demand imbalance resulting from the new housing supply such as in Gyeonggi, Chungnam and Chungbuk.

Residential Property Jeonse Market This year, there will be areas with a large number of homes on the market for jeonse due to the localized increase in the number of move-in ready homes. Depending on the supply-and-demand situation, there will be a noticeable polarization phenomenon occurring among the regions. It is projected that the jeonse market will continue to show a stabilization trend without any significant rises in prices, as the downward stabilization trend continues in the sales market and new move-in ready homes are put out on the market.

87 Residential Property Sales Transaction Market Mortgage interest rates have been climbing since late last year due to the probability of a hike in the domestic benchmark interest rate rising as a result of a hike in the U.S. interest rate as well as the announcement of the household debt management and loan restriction policies. This will increase the financial burden to purchase and maintain homes. Also, the growing economic uncertainties at home and abroad arising from the economic slowdown, there is a possibility that potential buyers will take on a wait-and-see stance when it comes to buying a home. Thus, the housing transaction volume is expected to be lower compared to last year. The recovery period for the residential property sales transaction market will be determined based on the time point and level of the interest rate hike and the changes in the domestic economic situation.

As for the 2017 housing market outlook, a metric method, through which an econometrics model was reviewed at multiple angles, was used, and a presumption predicated on the forecasts on the real estate policy and domestic and international economic situations was included in the model.

○ More specifically, predictive values were considered based on a presumption of an endogenous relationship between the housing market and the macroeconomic variables, and a conservative approach was made, taking into account the possibility that the domestic and international conditions will worsen (interest rate hikes, persistence of domestic and international economic uncertainties, etc.)

88 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 2 Real Estate Market Outlook in the Second Half of 2016

Table 2-1 Summary of the changes in the housing market in 2015~2016 and forecasts for the housing market in 2017

Trends Category 2017 forecast 2015 2016 Changes in the residential property sales prices (nation- 3.51% 0.71% -0.2% wide) (4.37%/2.73%) (1.32%/0.17%) (-0.2%/-0.4%) (capital area/non-capital area) Changes in the residential property jeonse prices (nation- 4.85% 1.32% 0.3% wide) (7.14%/2.79%) (2.04%/0.67%) (0.4%/0.0%) capital area/non-capital area) Changes in the residential 19.0% -11.6% -7.1% property sales transaction vol- 119.4ten thousand units 105.5ten thousand units 98.0ten thousand units ume (nationwide)

* The residential property sales transactions volume for the month of December (2016) was not confirmed; thus, an estimated volume was used instead

Based on the estimation results for the 2017 outlook, the nationwide residential property sales prices are projected to drop by 0.2%, while the residential property sales prices are expected to fall by 0.2% in the capital area and 0.4% in the non-capital area. Meanwhile, the jeonse prices will rise by 0.3% nationwide and 0.4% in the capital area, whereas it will remain steady in the non-capital area (0.0%).

The total residential property sales transaction volume in 2017 is expected to be around 980,000, which is a 7.1% (75,000 transactions) decrease compared to last year (approx. 1.055 million).

89 KAB Real Estate Market Report Korea Real Estate Market Report

P A R T 3 In-Depth Analysis

Analysis ① | Risk‌ Diagnosis of Domestic Household Debts and Response Measures Analysis ② | The‌ Population Aging and Housing Transactions :Evidence From the Real Estate Trade Management System Data Analysis ③ | Diagnosis‌ of the 2016 Housing Subscription Market Analysis ④ | Analysis‌ of Patterns in the Determinants of Housing Prices 1 Risk Diagnosis of Domestic Household

Analysis Debts and Response Measures

Lee Junyong

1. Introduction

The domestic household debt, which had amounted to approximately KRW 180 trillion during the 1997 Asian financial crisis, grew to around KRW 630 trillion 10 years later in late 2007, and it is now expected to exceed KRW 1.3 quadrillion by the end of 2017. The current level of domestic household debt may not be comparable to the past levels, considering that loan regulations were in place, and the loan market had not grown back in the day. However, it should still be noted that the loan and financial markets have undergone dramatic growth in the past two decades. Under the current circumstances, the economy is highly sensitive to external shocks, unlike during the financial crisis around 20 years ago, and the loan market has grown substantially in size. For these reasons, it is essential that an accurate diagnosis of the size and structure of debt be made. Of particular note, tension has intensified even further due to the external uncertainties arising from the U.S. Federal Reserve’s plans to issue series of interest rate hikes in addition to the one last year. In this section, the external economic risk factors that have been on the rise will be noted in addition to diagnosing the risks associated with the domestic household debt and establishing the corresponding response measures. The basic diagnostic framework will include an international comparison, an analysis of the level and structure of household debts in Korea, and determination of the characteristics of households with debts. Through this process, we will be able to test our fitness to counter the external economic risk factors and diagnose our

92 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 3 In-Depth Analysis

weaknesses, and it will be possible to use the data and knowledge produced from this process as basis for establishing various response measures.

2. ‌Why is the domestic household debt of Korea considered to be at an international risk level?

1) Korea’s domestic household debt is growing at the fastest speed in the world

One of the most widely accepted and commonly used indices worldwide is the “disposable income-to-debt ratio.”

shows a scatter plot on the ratios of the household debt- to-net disposable income and GDP of major countries by time period. The y-axis in represents the household debt-to-net disposable income ratio. Countries with a higher household debt-to-net disposable income ratio than Korea includes the Netherlands, Denmark, Australia, Switzerland, Norway, Sweden and Canada. The net disposable income, in particular, is the income after taxes; that is why it is low in countries with advanced welfare systems that typically have high tax rates. Thus, when the advanced welfare states in Northern Europe are excluded from the picture, it is difficult to say that the household debt level of Korea should not cause any concern. In , the x-axis represents the household debt-to-GDP ratios of the countries under comparison. It clearly shows that there is a positive (+) correlation between the household debt-to-GDP ratio and the household debt-to-net disposable income ratio. In other words, countries with a high household debt-to-net disposable income ratio had a high household debt-to-GDP ratio. It could be argued that the household debt was overestimated in the advanced welfare states, considering their disposable income is relatively lower than other countries; however, taking into the fact that the household debt is also high against the economic scale, it is difficult to say that the household debt was overestimated. Nevertheless, a difficult conclusion can be reached when a comparison is made between the levels reported in the past and present. For this purpose, 2008, the year of the global financial crisis, was designated as the reference point. When a scatter plot is prepared based on the two indices reported in 2008, it looks like the scatter plot shown in , and the countries where the two indices increased between 2008 and 2014 can be clearly

93 Figure 3-1-1 Household debt-to-net disposable income and GDP ratios by time period

a. 2014

350

) Denmark

( % 300 350 Netherlands m e o c

) Denmark n I

( % 250

e 300 l Norway Netherlands b m e a

o Australia s c o n p I

s 220500 i e

l NSowrwedaeyn b Switzerland t D a Canada Australia e s Korea o N p 150 Japan

s 200 i t o Spain SwUenditeend Kingdom t France

b Switzerland t D

e Canada e Korea Portugal Italy D N 110500 GJraepeacne d l t o Czech Republic Spain

o United Kingdom t

h France

b United States e

e Germany s Portugal

u Italy D 50 Poland Greece

o 100 d l

H Czech Republic o

h United States e Germany s

u 500 Poland o

H 0 20 40 60 80 100 120 140 Household Debt to GDP(%) 0 0 20 40 60 80 100 120 140 Household Debt to GDP(%) b. 2008 350 Denmark )

( % 300 350 Netherlands Denmark m e o c ) n I

( % 250

e 300 l Netherlands b m e a

o Norway s c

o Portugal n Australia p I

s 220500 i e l

b Sweden

t D Switzerland a Norway e s Canada United Kingdom o Japan Portugal

N Australia p 150

s 200 i t o

t Sweden b t D Greece Korea Spain Switzerland e e France Japan Canada UniUtendit Sedta Kteinsgdom D N 110500 d Italy l t o o t

h Czech Republic b

e Greece GerKmoraenay Spain e

s France United States u

D 50

o 100

d Italy l H o

h Czech RepubPloicland

e Germany s

u 500 o

H 0 20 40 60 80 100 120 140 Poland Household Debt to GDP(%) 0 0 20 40 60 80 100 120 140 Household Debt to GDP(%)

Source: OECD, BIS

discerned. The countries where the household debt-to-GDP ratio surged include Switzerland (18.1%p), Norway (17.2%p), Sweden (14.6%p), Australia (12.6%p), Canada (11.9%p), and Greece (8.0%p), while the countries that saw a surge in the household debt-to-disposable income ratio include Greece (27.9%p), Norway (17.1%p), Switzerland (17.3%p), Australia (16.7%p),

94 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 3 In-Depth Analysis

Canada(16.4%p), and Sweden (14.0%p). In Korea, in particular, the household debt-to-GDP ratio rose by 10.1%p , and the household debt-to-disposable income ratio by 20.9%p. On the other hand, the countries that saw a significant drop in the household debt-to-GDP ratio in the same period were the United States (-15.2%p), Spain (-9.5%p), and Portugal (-7.5%p), and the countries where the household debt-to-disposable income declined were Denmark (-34.5%p), Spain (-22.9%p), the United States (-21.8%p), and the United Kingdom (-14.8%p). In summary, the household debt of Korea increased at one of the fastest rates in the world in the period above, and it has continually risen thereafter. Thus, it is deemed that the size of the domestic household debt and the speed at which it has been increasing are at dangerous levels. Also, mortgages in Korea are typically taken out as short-term loans with a grace period and variable interest rates, and the risk associated with this loan structure may act as another risk factor.

2) Low capacity to manage household debt compared to income

The debt service ratio (DSR) level of Korea is the lowest among the countries with low capacity to manage debts, and high among the countries with high capacity in relation to income. As for the long-term trends, the DSL level was 8.7% in 2000, indicating a very good loan soundness, but it increased to 11.2% in 2007, and underwent a steady uptrend and

Figure 3-1-2 DSR trends in major countries

a. Countries with low income capacity compared to debt size b. Countries with high income capacity compared to debt size

252.50.0 252.50.0

202.0.0 202.0.0

151.50.0 151.50.0 101.0.0 101.0.0 5.50.0 5.50.0 0.0.0 `0`000 `0`202 `0`404 `0`606 `0`808 `1`010 `1`212 `1`414 `1`616 0.0.0 AuAsutsratrliaalia CaCnaandaada JaJpaapnan `0`000 `0`202 `0`404 `0`606 `0`808 `1`010 `1`212 `1`414 `1`616 KoKroeraea NeNtehtehrelarnladnsds ItaItlyaly GeGremrmanayny SpSapinain SwSewdeednen

Note: The‌ Bank for International Settlements (BIS) publishes a report on DSR every quarter. DSR for the household sector includes data on the general households and non-profit organizations. The annual numbers are based on the data from the end of the first quarter. Source: BIS

95 downtrend from thereon until it returned to the pre-global financial crisis level of around 11.1% at the end of the first quarter of 2016. Generally speaking, the debt soundness of Korea is not very good, compared to the advanced nations, and considering the continuous increase in the size of the domestic household debt, it is deemed necessary to put forth an effort to improve the soundness of debts.

3. ‌What are the causes of the increase in the domestic household debt?

1) Increase in mortgages caused by the easing of loan regulations

At the end of the second quarter of 2008, prior to the subprime mortgage crisis and the bankruptcy of Lehman Brothers, the domestic household debt was KRW 660.8 trillion, and mortgages amounted to KRW 229.7 in total, with KRW 248.7 trillion loaned from deposit banks and KRW 51.0 from non-deposit banks. Of the total household debt, mortgages account from approximately 45.4% (KRW 229.7 trillion/KRW 660.8 trillion). The domestic household debt increased by 1.8-fold to KRW 1,191.3 trillion by the second quarter of 2016, and KRW 527.2 trillion was in mortgages. As such, household debts nearly doubled within 8 years.

Table 3-1-1 Balance by household debt type

`08(A) `09 `10 `11 `12 `13 `14 `15 `16(A) (B)/(A)

Total household debt 660.8 700.1 756.9 826.9 875.0 926.3 978.4 1,072.0 1,191.3 1.8

De- Mortgages 248.7 266.5 280.6 299.2 312.4 321.2 338.3 372.2 420.1 1.7 posit banks Other loans 128.2 133.8 138.3 145.1 145.5 149.4 151.3 155.0 166.6 1.3

Non- Mortgages 51.0 58.0 68.5 77.5 85.0 85.8 93.7 94.6 107.1 2.1 deposit banks Other loans 67.0 69.3 79.3 92.8 102.9 110.0 121.9 138.1 159.5 2.4 Other financial insti- 165.9 172.4 190.1 212.3 229.2 259.9 273.2 312.1 338.0 2.0 tutions Note: 1) Balance at the end of 2Q every year 2) Non-deposit banks include mutual savings banks, credit unions, mutual finance service institutions, MG KFCC, Korea Post, etc. Source: Bank of Korea

96 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 3 In-Depth Analysis

Figure 3-1-3 Annual trends and distribution of the amount of increase in the loan balance by type of household debt

2016 47.9 11.6 12.5 21.4 25.9 119.1

2015 33.9 3.7 16.2 38.9 93.6

2014 17.1 7.9 12.0 13.3 52.1

2013 8.8 4.0 7.0 30.7 51.3

2012 13.2 7.5 10.1 16.9 48.1

2011 18.7 6.7 9.0 13.5 22.1 70.0

2010 14.1 4.5 10.5 10.0 17.7 56.8

2009 17.7 5.6 7.0 6.6 39.3

0 20 40 60 80 100 120 trillion KRW

ortgages from deposit banks Other loans from deposit banks Mortgages from deposit banks Mortgages from non-deposit banks Other financial institutions Note: The amount of increase in the loan balance was calculated based on the YOY increase at the end of 2Q each year. Source: Bank of Korea

As for the loan accounts with a loan balance from the highest to lowest in the same period, it is “other loans” and “mortgages” from non-deposit banks, and “mortgages” and “other loans” from other financial institutions including insurance agencies and deposit banks. For a comparison of the level of increase in household debts, the trends in the annual increase in the loan balance by household debt type are shown in . The period during which household debts increased the most within the 8-year period was 2014 3Q~2015 2Q (KRW 93.6 trillion) and 2015 3Q~2016 2Q (KRW 119 trillion). Until the second quarter of 2014, the level of increase in household debts was not very large; thus, it could be said that the countless press reports on the household debts exceeding KRW 1 quadrillion in the third quarter of 2014 were made from an alarmist view. The reason for the sharp increase in household debts after the second quarter of 2014 was the economic stimulus policy that was based on the deregulation of loan restrictions in accordance with the measures announced on September 1. As if to attest this, the amount of increase in the mortgages from the deposit bank sector was just around KRW 15 trillion from 2009 to 2014, whereas a YOY rate of increase of KRW 33.9 trillion was reported in the second quarter of 2015. In the second quarter of 2016, the YOY increase was KRW 47.9 trillion, while the mortgages issued by non-deposit banks also increased by KRW 12.5 trillion. The

97 results of an analysis revealed that the latter was caused by a balloon effect resulting from the implementation of far more stringent loan reviews in the banking sector. In addition, household loans from other financial institutions saw a YOY increase of KRW 38.9 trillion in the second quarter of 2015 and KRW 25.9 trillion in the second quarter of 2016, and this is deemed to have resulted from the increase in policy-based mortgage loans from the Korea Housing Finance Corporation. The amount of increase in the loan balance of other financial brokerage firms was KRW 29.2 trillion and KRW 11.0 trillion in the same periods.

2) An increase in mortgages primarily in the capital area and metropolitan cities

The mortgage balance in the second quarter of 2016 was KRW 527.2 trillion in total, with KRW 328.3 trillion in the capital area, KRW 101.7 trillion in the five metropolitan cities, and KRW 97.2 in the provinces outside the capital area. As such, more than half is concentrated in the capital area, and it could be said that most of the mortgages (81.6%) were taken out in large cities including the five metropolitan cities. However, considering that the capital area and the five metropolitan cities accounted for 86.0% of the total mortgage balance in 2008, it is deemed that the ratio of the mortgage balance of large cities to the total mortgage balance is decreasing, while the ratio of the mortgage balance of provinces to the total mortgage balance is on the rise.

Figure 3-1-4 Trends in the balance and amount of increase in mortgages by region and year

a. Trends in the mortgage balance by year b. Trends in the amount of increase in mortgages by year

600 45.0 42.5 42.5 600 527.2527.2 45.0 40.6 40.6 501.2501.2 460.6460.6 500 500 11.3 11.3 418.1418.1 97.2 97.2 9.5 9.5 392.0349024..02404.2 92.7 92.7 29.2 29.2 362.8362.8 83.2 83.2 27.3 27.3 400 400 338.5338.5 30.0 30.0 26.0 26.0 64.7 6741..79 71.9 101.7101.7 24.3 24.3 311.2311.2 57.8 57.8 96.7 96.7 9.5 9.5 50.5 50.5 87.2 87.2 2.6 2.6 7.3 7.3 9.8 9.8 46.1 46.1 64.9 6741..90 7717..04 77.4 2.0 2.0 4.6 4.6 trillion KRW trillion KRW trillion KRW 300 30403.5 43.5 56.5 56.5 trillion KRW 4.3 4.3 51.6 51.6 5.0 5.0 49.6 49.6 4.9 48.9.4 8.4 13.9 13.9 200 200 15.0 15.0 12.2 12.2 311.8331218..83328.3 22.7 22.7 255.9225659..94226698..45226688..58226980..82290.2 67.9.2 72.12. 3 2211.3.7 21.7 218.0221480..08240.8 6.9 16.5 16.5 100 100 15.1 1153.1.5 13.5 6.1 66.1.4 6.4 0 0 0.0 0.0 0.3 0.3 `08 ``0089 ``0190 ``1101 ``1112 ``1123 ``1134 ``1145 ``1165.6`16.6 `09 `0`190 `1`101 `1`12 `1`123 `1`134 `1`145 `156.6`16.6 CapitalCapital area area5 metropolitan5 metropolitan cities citiesRural areasRural areas CapitalCapital area area5 metropolitan5 metropolitan cities citiesRural areasRural areas Note: Mortgage balance determined at the end of the year Source: Bank of Korea

98 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 3 In-Depth Analysis

shows the amount of increase in mortgages by region, and it confirms that the amount of mortgages taken out in provinces outside the capital area is indeed rising. As mentioned above, the mortgage balance decreased until 2013 and began to surge in 2014. The time point at which the mortgage balance rose clearly differs between the capital area and the non-capital area. Between 2012 and 2013, the amount of increase in the mortgages in the capital area was marginal, but it continually increased in the five metropolitan cities and rural provinces. On the other hand, it surged in the capital area in 2014, and this was due to the deregulation of the mortgage regulations in 2014, as mentioned earlier.

3) An increase in loans to pay for everyday living costs

shows the purpose of taking out secured loans by income class. A closer examination reveals a distinct difference across the age groups: those under the age of 40 primarily used secured loans to purchase homes and other investment properties, whereas 38~40% of those over the age of 50 took out secured loans to pay for their everyday living costs. In the case of credit loans, a large number of young adults took out credit loans to put down the deposit for jeonse or rentals, while the older population mainly obtained credit loans to pay for the everyday living expenses.

Table 3-1-2 Uses of loans by income class (based on 2015 data)

To purchase a home or investment property (%) To pay for living expenses (%) To T pay for To pay for To To Other Category purchase an the jeonse To start a everyday Total purchase reimburse uses, investment or rental business living a home debt etc. property deposit expenses Under 30 42.3 4.7 40.4 2.8 90.2 0.6 4.1 5.1 9.8 100 30~40 y.o. 60.2 10.2 15.5 0.8 86.7 8.6 2.5 2.3 13.4 100 Secured 40~50 y.o. 49.3 14.5 7.2 2.4 73.4 20.0 3.3 3.3 26.6 100 loans 50~60 y.o. 34.7 19.9 3.8 2.7 61.1 30.3 3.4 5.1 38.8 100 Over 60 26.7 24.1 3.3 4.9 59.0 27.7 5.5 7.9 41.1 100 Under 30 14.7 - 41.2 1.0 56.9 4.8 15.2 23.1 43.1 100 30~40 y.o. 11.2 5.0 21.4 3.8 41.4 20.8 23.0 14.8 58.6 100 Credit 40~50 y.o. 12.6 5.2 9.1 5.5 32.4 32.9 25.3 9.3 67.5 100 loans 50~60 y.o. 6.1 9.2 4.2 12.2 31.7 33.1 19.5 15.7 68.3 100 Over 60 7.0 7.3 2.6 5.5 22.4 40.9 18.7 18.0 77.6 100 Source: Statistics Korea

99 Table 3-1-3 Uses of loans by job status (based on 2015 data)

To purchase a home or investment property (%) To pay for living expenses (%) To To pay for To pay for To To Other Category purchase an the jeonse To start a everyday Total purchase reimburse uses, investment or rental business living a home debt etc. property deposit expenses Full-time 53.6 17.7 9.8 2.5 83.6 9.2 3.0 4.1 16.3 100 workers Secured Temporary 47.1 10.5 11.1 6.4 75.1 6.9 11.0 6.9 24.8 100 loans workers Self-employed 28.8 16.9 3.4 2.6 51.7 42.2 2.3 3.8 48.3 100 Unemployed 34.8 22.0 4.9 2.5 64.2 15.3 9.7 10.8 35.8 100 Full-time 14.4 7.4 15.2 7.3 44.3 10.6 26.2 18.9 55.7 100 workers Temporary Credit 3.3 3.1 7.5 7.5 21.4 23.5 31.3 23.9 78.7 100 workers loans Self-employed 4.1 7.0 3.1 7.2 21.4 60.3 13.2 5.1 78.6 100 Temporary 12.2 3.2 3.4 7.6 26.4 22.9 35.8 15.0 73.7 100 workers Source: Bank of Korea

shows the uses of loans by job status. Full-time workers with a fixed income primarily used loans to purchase homes and investment properties, whereas 48.3% of self- employed individuals obtained loans as a means to pay for living expenses. On the other hand, in the case of credit loans, self-employed individuals used the loan to start a business, while workers used the loan toward living expenses.

4. What should we prepare for?

From the aspect of household debt management, it seems that the measures to reinforce the management of group loans are being put forward with an incorrect perception of the group loan system as a system for the common class or for first-time home buyers. group loans are actually stable loan products from the perspective of lending institutions because they are guaranteed by the Korea Housing & Urban Guarantee Corporation or the Korea Housing Finance Corporation in addition to the joint guarantee provided by the developer. However, if

100 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 3 In-Depth Analysis

group loans benefit a third party, rather than sound investors or the end-users, then there is a need to re-examine the policies from the starting point. This is because the primary party benefiting from group loans are the developers or construction firms concerned, as they can finance their construction projects via the home buyers at low interest rates. If there were no such group loan system, the project would have to be financed using the funds of a small number of home buyers with the financial capacity. However, the group loan system allows even the buyers without any income or assets to become the source of financing, and this facilitates the process of obtaining sufficient funds. In other words, developers and construction firms indirectly benefit from the group loan system, which creates a lot of buyers with high financial capacity. Undoubtedly, the proper function of group loans is to assist potential home buyers with low financing capacity to obtain the necessary funds to purchase a home, but the actual end-users make plans to secure the lump-sum money for the interim payments and final payment until the construction project is scheduled to be completed based on their financial capacity. Thus, group loans enable speculative investors, with low income or capital, to purchase a property, and this consequently raises the pre-construction home sales prices. In other hands, the ultimate consumers (end-users and lessees) must pay high costs to purchase or live in the homes in question, and this process ultimately increases profits for speculative investors and developers. Therefore, theere is a need to realize that those who ultimately benefit from group loans are the speculative investors, who sell the property prior to its construction, and developers, rather than the end-users and home investors with sound financial capacity. Second, there is a need to establish preventive measures in relation to self-employed individuals and the baby boomers, who take out loans to pay for their day-to-day living expenses. As shown above, the increase in household debts among these individuals is clearly discernable compared to other classes, and their debt-to-asset ratio is rising quickly. Of particular note, they use secured loans to pay for their living expenses, meaning that in case of an external economic shock, they will not be guaranteed to maintain their businesses or homes. Their grim future can be observed among the owners of shops, restaurants and one-room buildings around shipyards, which have been hit hard by the restructuring of the shipbuilding industry. The number of self-employed individuals is on the rise, with a large number of baby boomers starting their own businesses after retirement, and this will cause

101 even more uncertainties. In the future, the scale and possibility of insolvency among these individuals resulting from internal or external shocks will continually rise. Thus, in relation to the loans to pay for their living expenses, there is an urgent need for the government to prepare a guideline and a wide range of protective devices such as postponement of the loan payment deadline, switching to low-interest rate loans, and setting the standards for non- performing loans involving households that have taken out loans to pay for living expenses. Third, there is a need to create an environment that promotes the formation of a sound residential property investment market. In order to promote the government slogan for its debt management measures, “Borrow within your financial capacity, and pay back the principal in installments from the start,” with consistency, there is a need to examine the loan applicants’ ability to pay back their loans based on their income. Homes are costly, and one needs to have a certain amount of income in order to possess and manage a home. In the case of Korea, the debt-to-income (DTI) ratio is high, and it needs to be lowered. According to a report on the household debts in Korea issues by IMF (2016 ARTICLE 4)1), the DTI of Korea is around 60%, which is higher compared to the surrounding countries, and it is advisable to gradually lower the ratio to around 30~50%. Also, it was suggested that the DTI cap should also be applied to group loans, explaining that such planned monitoring will induce a decrease in household debts, thereby reducing financial risks and exerting positive effects on consumption and economic growth. Unlike in the past, when Korea recovered quickly from an economic crisis, there is an increasing chance that it will fall into a long-term recession in case of an external shock. The increase in the domestic household debt in the past two to three years has deteriorated financial soundness and raised the risks in the domestic housing market, and the voices warning that we have reached an alarming situation are growing louder. Fortunately, the government has recognized the severity of the problem, and put forth various measures and policies, which have helped to improve the household debt structure. However, temporary measures such as limiting the total loan amount may be easy to apply as short-term measures,

1) The IMF Article 4 Consultation refers to the process through which IMF consults with its member states on a regular basis, in accordance with Article 4 of the IMF Articles of Agreement, and it typically takes place once every year. During a consultation process, the IMF team visits the member state in question, and exchange opinions regarding a wide range of topics including economic trends, outlook and policies, with the government departments, central bank, and research institutes. IMF engaged in discussions and surveyed the Ministry of Strategy and Finance, Financial Services Commission and Bank of Korea from May 26 to June 8, 2016, and published a comprehensive report based on the data and information they gathered (Aug. 2016).

102 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 3 In-Depth Analysis

but it can actually cause the housing market to shrink too early, and this can cause a domino effect across the related industries. Instead of applying the same measures as in the past such as applying restrictions or pursuing deregulation, it is necessary to concentrate on continually improving the actual constitution of loan structures, similar to Northern European countries, and creating an environment that allows the home prices to be set at appropriate levels.

103 The Population Aging and Housing 2 Transactions: Evidence From the Real

Analysis Estate Trade Management System Data

Park Jinbaek, Min Chulhong

1. Introduction

The population structure of Korea has been rapidly changing in recent years as a result of low fertility and population aging. According to an estimation of the future population presented by Statistics Korea, the population of Korea is expected to reach its peak in 2030 due to the impact of low fertility, and the working age population began to decline since 2016. As the changes in the population structure can impact housing demand, the impact of such changes can have an important consequence for the housing market. In relation to this issue, there have been concerns pertaining to a decrease in the working age population, who are the main consumers in the housing market, an increase in the senior population, who are unlikely consumers in housing transactions, and a decrease in housing demand due to the birth rate being persistently low. As such, there has been a common outlook that the changes in the population structure will have a negative impact on the housing market. However, a counterargument has been presented by Chae (2016), based on the need for an empirical analysis of the impact of the population structure changes on the housing market has been noted. In order to understand the impact of the changes in the population structure on the

104 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 3 In-Depth Analysis

housing market, it is necessary to examine the differential impact of the various age groups on the actual market. In this study, real apartment sales, jeonse and rental transaction data were used to identify the trends in the real apartment transaction volume by age group as a means to examine the differences in the transaction patterns by age group. Because this study is based on the real transaction data, unlike any other preceding studies, it is expected that the findings will provide more clarity in relation to the market response by age group. While there are limitations in discussing the exact changes in the population structure, due to the use of a short time series, the results of the analyses of each age group will be useful in understanding the differential residential property sales structure. More specifically, in this study, attention was paid to the fact that the individual inclination to purchase a residential property tended to increase when the return on investment properties was higher than the market interest rate. To explain, purchasing an apartment becomes more attractive when the return on the investment through leasing of the property is higher than making a fixed-term deposit of a fairly large amount of lump-sum money. The level of attractiveness of investing in a real estate property may vary depending on the individual economic situation and financial capacity, and in this study, it was assumed that such differences would exist among various age groups when performing the following analyses. This paper is comprised of the following content. First, we will review the literature on the impact of the changes in the population structure on the housing market, and examine the trends in the apartment sales transactions by age group from 2006, which was when the real transaction data for the housing market began to be generated , until 2015. Then, the trends in the rate of purchasing investment properties by age group will be examined to determine the inclination to invest in real estate. Afterwards, using real transaction data, the method of estimating return on investment from leasing a real estate property and the estimation results will be presented, and the results of a regression analysis will be examined. Lastly, a summary of the major findings and implications thereof will be presented in addition to the limitations of this study.

105 2. Trends in apartment sales transactions by age group

In this section, the trends in apartment sales tractions by age group from the aspect of buyers will be examined. Generally, apartment purchases began to increase across all age groups after the policy interest rate was reduced and the market interest rate shifted to a low interest rate structure in 2012. Without considering the population size of each age group, it was people aged 35 to 44, with the highest housing demand, who accounted for the largest part of the total apartment sales transaction volume, and it should also be noted that the apartment sales transaction volume increased among people over the age of 60. During the four years before 2015, there had generally been an increase in apartment sales transactions across all age groups, yet it is difficult to exactly determine the amount of increase in the transaction volume for people aged 29 and under and people aged 30 to 34. However, considering that there is a heavy population outflow and inflow in each age group, due to the continuous changes in the population structure caused by low fertility and population

Table 3-2-1 Trends in apartment sales transactions by age group (Unit: ten thousand properties) Age 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 group 29 and 5.88 4.71 4.30 3.64 3.23 3.72 2.48 2.46 2.59 3.10 under (0.30) (0.24) (0.22) (0.19) (0.17) (0.20) (0.14) (0.14) (0.15) (0.18) 12.53 9.22 8.97 9.02 8.67 10.92 7.57 8.91 8.59 9.06 30-34 y.o. (2.93) (2.23) (2.25) (2.32) (2.23) (2.76) (1.89) (2.20) (2.16) (2.39) 14.74 10.57 10.97 11.79 10.41 12.79 8.72 10.91 12.29 13.90 35-39 y.o. (3.27) (2.31) (2.40) (2.60) (2.35) (2.99) (2.12) (2.74) (3.17) (3.58) 10.57 7.83 8.75 10.31 9.70 12.15 8.67 10.74 12.27 13.31 40-44 y.o. (2.49) (1.88) (2.07) (2.38) (2.20) (2.70) (1.90) (2.35) (2.72) (3.02) 8.34 6.16 6.89 7.75 7.00 8.63 6.40 7.97 10.06 11.80 45-49 y.o. (2.00) (1.42) (1.57) (1.77) (1.62) (2.05) (1.54) (1.90) (2.34) (2.70) 5.35 4.29 5.22 6.32 6.03 7.45 5.35 6.51 8.11 9.02 50-54 y.o, (1.73) (1.30) (1.49) (1.69) (1.53) (1.80) (1.25) (1.50) (1.88) (2.11) 3.38 2.68 3.10 3.91 3.85 4.99 3.66 4.69 6.37 7.89 55-59 y.o. (1.45) (1.12) (1.25) (1.51) (1.38) (1.64) (1.13) (1.36) (1.73) (2.03) 2.47 1.99 2.20 2.52 2.36 3.01 2.16 2.70 3.84 4.99 60-64 y.o. (1.25) (1.01) (1.10) (1.21) (1.08) (1.33) (0.92) (1.11) (1.52) (1.84) 65 and 4.76 3.59 3.48 3.38 3.13 4.11 3.03 3.77 4.91 6.21 over (1.07) (0.76) (0.70) (0.65) (0.59) (0.74) (0.53) (0.63) (0.78) (0.95) The numbers in parentheses indicate the apartment sales transaction volume per 100 people (Unit: properties/100 people) Source : RTMS

106 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 3 In-Depth Analysis

aging, there is a need to standardize the sales transaction volume based on the population size of each age group for analysis. In

, the numbers inside parentheses represent the results of calculating the sales transaction volume per 100 people in the population after standardizing the sales transaction volume based on the population size of each age group. The results of the analysis showed that the 35~39 y.o. age group had the highest transaction volume, with 3.58 transactions per 100 people in 2015, while in the case of the 60~64 y.o. age group, the sales transaction volume increased at the fastest rate between 2012 and 2015, with 0.92 transactions per 100 people in 2012 and 1.84 transactions per 100 people in 2015. In the senior population over the age of 65, which has been growing at a rapid rate with a strong population inflow, there was an increase in sales transactions at the absolute level, and the transaction volume per 100 people was 0.95 in 2015, which indicates that there has been an increase in residental property purchases by the senior population, albeit not at a rapid rate.

3. Real ROI by age group

There may be significant differences in the financial structure such as income and assets among the various age groups, and this can lead to substantial differences in terms of investment inclination.

shows the estimations of the “percentage of the population purchasing investment properties” made for each group using the data provided by the Korean Labor & Income Panel Study (KLIPS). Based on the results, the characteristic difference among the age groups is that in the case of people aged 55 and older, there was an increase in the percentage of the population purchasing investment properties, while such percentage was low for people aged under 54. In the case of seniors aged 65 and older, 1.17% invested in real estate properties other than their own homes in 2006, while 2.43% purchased investment properties in 2014, which was a 1.26%p increase. An increase in the percentage of the population purchasing investment properties was also noticeable in the 60~64 y.o. age group over time, with an increase from 0.90% in 2006 to 1.94% in 2014. The numbers in brackets represent the percentage of households with investment properties within the age group. It has decreased slightly or remained steady for most age groups; however, in the case of people aged 60 and older, it increased from 10.90% in 2006 to 19.69% in 2014 for the 60~64 y.o. age group, and 6.26% in 2006 to 10.14% in 2014 for the 65 and over age group, indicating

107 Table 3-2-2 Percentage of the population purchasing investment properties

Age 2006 2007 2008 2009 2010 2011 2012 2013 2014 group 29 and 0.34 0.06 0.08 0.11 0.12 0.05 0.05 0.07 0.05 under (8.05) (1.46) (1.73) (2.12) (2.24) (1.06) (1.05) (1.50) (1.07) 0.52 0.19 0.54 0.55 0.54 0.26 0.30 0.20 0.35 30-34 y.o. (7.05) (2.49) (7.33) (7.37) (7.72) (3.97) (4.81) (3.43) (5.98) 0.78 0.78 0.78 0.78 0.68 0.80 0.59 0.65 0.53 35-39 y.o. (7.47) (8.12) (9.06) (9.67) (8.35) (9.31) (7.25) (8.08) (7.21) 1.50 1.30 1.07 1.06 0.87 1.00 0.85 0.54 0.97 40-44 y.o. (11.90) (10.64) (8.94) (9.40) (8.15) (9.83) (9.46) (6.69) (12.44) 1.56 1.57 1.55 1.62 1.47 1.57 1.37 1.50 1.19 45-49 y.o. (10.72) (10.82) (10.97) (11.66) (11.11) (12.63) (11.56) (12.57) (9.81) 1.74 1.86 1.66 0.99 1.43 1.45 1.45 1.85 1.64 50-54 y.o, (12.66) (14.27) (11.85) (7.27) (10.52) (10.22) (9.70) (12.56) (11.10) 1.26 1.40 1.11 1.27 1.31 1.34 1.74 1.65 1.36 55-59 y.o. (12.49) (12.79) (10.87) (11.86) (11.58) (11.19) (14.83) (12.32) (10.17) 0.90 0.83 1.27 0.77 1.01 0.81 1.01 1.05 1.94 60-64 y.o. (10.90) (10.32) (14.60) (8.74) (10.45) (8.98) (10.06) (11.12) (19.69) 65 and 1.17 1.44 1.61 1.59 1.70 2.01 1.64 2.36 2.43 over (6.26) (7.29) (7.81) (7.63) (8.02) (8.91) (6.97) (9.81) (10.14) Total 9.77 9.42 9.66 8.75 9.13 9.29 9.01 9.86 10.47

The numbers in brackets parentheses the percentage of the population (of the age group) with investment properties Source: Authors' estimation based on the data from KLIPS

an increase in the percentage of the population purchasing investment properties over time. In summary, while the percentage of the population purchasing investment properties other than their own homes remained similar across time from 2006 to 2014 for all of the age groups, but relative to other age groups, there was an increase in the investment ratio among those aged 60 and older.

4. Empirical analysis

A. Empirical Strategy

The topic of interest in this study is whether an increase in investment based on age causes an increase in apartment sales transactions. For the purpose of analysis, there is a need to estimate the return on investment (ROI) for apartment properties. ROI can be defined as

108 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 3 In-Depth Analysis

the investment value that is realizable through the real amount of investment made into the asset, and as the sum of the return on equity (ROE), which is realized as the value of the asset changes over time, and the income rate, which is the income made from leasing the property through jeonse or rental. Then, the total ROI can be calculated by deducting the related costs such as the acquisition tax, holding tax, transfer tax, building maintenance and repair costs, and depreciation. In this paper, ROI will be estimated based on the real transaction data on apartment jeonse and rentals which is called as the real estate trade management (RTMS) provided by KAB. However, there are limitations in the information available to estimate ROE and all of the factors listed above. Thus, in this paper, ROI through monthly income will be calculated to determine the simplest form of ROE, so as to examine the impact of ROI on the purchases of apartment properties by each age group.

(1)

where in ROI, is the total monthly income for a year, is the amount of principal to be repaid in a year, is the rate of changes in the sales price expected a year later, is the sales price, and is the amount of the deposit. Of these variables, the data that can be obtained from the real transaction data on apartment jeonse and rentals are the data on the deposit money, , and the total monthly income in a year, . Accordingly, the jeonse deposit-monthly rent conversion rate ( ) was used to convert the amount of monthly rent into the amount of jeonse deposit, and the average sales price-to-jeonse deposit ratio ( ) was used to estimate the final sales price of the property in question.

(2)

In this study, in order to take only monthly rent into consideration in relation to ROI, the expected rate of increase in the sale price was set to zero ( ), under the assumption that there would be no ROE. Also, because the amount of loan and the amount of the principal to be repaid in a year cannot be determined, an assumption that was made.

shows the results of estimating ROI using Eq. (1) and Eq. (2). It shows that ROI, which was 4.99%

109 in 2012, decreased to 4.48% in 2016, and considering that the standard deviation in 2016 is lower, there were more lessers and ROI was lower in 2016 compared to 2012. As such, the cause behind this phenomenon where there is a higher concentration of lessors and lower ROI is deemed to be the low interest rate policy that has been maintained during this period. Since 2012, the base in Korea has been reduced 8 times from 3.25% in 2012 to 1.25% in 2016, as a means to stimulate the economy.2) During this period, the market interest expected

Table 3-2-3 Estimations of nominal ROI by year

Category 2012 2013 2014 2015 2016 4.99 5.06 4.92 4.68 4.48 ROI (1.22) (1.10) (1.02) (0.94) (0.95)

The numbers in parentheses the standard deviation. The data for 2016 are the average of the values estimated for the months up to October. Source: Authors' estimation based on RTMS

Figure 3-2-1 A comparison of the nominal ROI and market interest rates

5.50

5.00

4.50

4.00

3.50

3.00

2.50

2.00

1.50

1.00 `12.6 `12.12 `13.6 `13.12 `14.6 `14.12 `15.6 `15.12 `16.6 ROI Treasury bonds (3-year maturity) Corporate bonds (3-year maturity) Fixed-term deposit Source: ROI (Authors' by estimation based on RTMS), market interest rates (Bank of Korea)

2) The bese rate was lowered on July 12, 2012 (3.00%), Oct. 11, 2012 (2.75%), May 9, 2012 (2.50%), Aug. 14, 2014 (2.25%), Oct. 15, 2014 (2.00%), March 12, 2015 (1.75%), June 11, 2015 (1.50%), and June 9, 2016 (1.25%).

110 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 3 In-Depth Analysis

from investing in the market has continually declined. As shown in

, the market interest rate plummeted in comparison with the ROI on leasing apartment properties. The gap between the interest rate for corporate bonds, which have the highest market interest rate, and the ROI on leasing apartment properties widened from 0.75%p in January 2012 to 2.71%p in October 2016. The gap between the interest rate for fixed-term deposits and the ROI on leasing apartment properties also widened from 1.22%p in January 2012 to 3.10%p in October 2016. Also, the gap between the interest rate for treasury bonds and the ROI on leasing apartment properties increased from 1.61%p in January 2012 to 3.13%p in October 2016. This shows that the ROI on leasing apartment properties is much higher than the ROI on any other investments in the market. Thus, the standard deviation was lower despite the decrease in the ROI on leasing apartment properties compared to the past, and this could be interpreted as a result of investors aiming for a relatively higher ROI among what is available. Based on this, it can be inferred that the ROI that can be expected by lessors is maximized when the gap between the ROI on leasing apartment properties and the market interest rate, which is the opportunity cost of investing in other places, is large. Accordingly, in the study, the real ROI for lessors was set as the amount minus the market interest rate, which is the opportunity cost. This is because investors are likely to be inclined to take out loans to purchase a residential property to increase the expected income when the return on bank deposits is lower than the return on an investment property purchased with a loan. There may be a limited number of people with the finally capacity to purchase an apartment property for leasing purposes using solely their own assets, and the relatively low interest rate on loans makes it attractive to take out personal loans to make an investment. Accordingly, in this study, the real ROI ( ) was estimated based on the nominal ROI ( ), estimated using Eq. (1) and Eq. (2), and loan interest rate ( ).

(3)

In order to examine the impact of ROI on the increase in apartment sales transactions, an econometric analysis was attempted. The dependent variable was the log of the apartment sales transaction volume, and the explanatory variable was set as the real ROI esimated using Eq. (3). The control vector, , was the column vector determined taking into account

111 the inflation rate and population size. The heat vector, , includes the coefficient value, with the coefficient value for the inflation rate set as , and the coefficient value for the population size set as .

(4)

where subscripts represent the following: is provinces and cities, is the floor area, and is time. As for the provinces and cities, 16 broad-area local governments excluding Sejong were taken into consideration. In the case of the floor area, apartment properties were divided into five categories based on the floor area for exclusive use by the home owner: under 65 , 65- 84 , 85-101 , and 102-134 , and 135 and over. Time was divided on a monthly basis, and due to the limitations of the data available, the data from January 2012 to October 2016 were used for the analysis. As for the residual analysis, the fixed effects by province or city, , fixed effects by floor area, , and fixed effects by time, , were controlled to correct the differential sales transaction volume caused by the different characteristics of the provinces and cities, area and time so that the coefficient value could be a consistent estimator. Also, the age groups taken into consideration were 30~34, 35~39, 40~44, 45~49, 50~54, 55~59, 60~64, and 65 and older, based on the fact that it is at the age of 30 that people begin to become the primary consumers of residential properties. In order to identify the differential decisions made by the different age groups, each age group was categorized for the regression analysis.

B. Results of the regression analysis

shows the results of the regression analysis. First, the results of the Hausman test in relation to the panel regression analysis model setup, the null hypothesis was rejected in all of the models from Column (1) to Column (9), indicating that the fixed effects model was appropriate. Accordingly, only the results of the fixed effects model are shown in
. As for the notable findings, the coefficient value of the real ROI expected from leasing an apartment property was analyzed to be a positive value at a statistically significant level in all of the models, which confirms that an increase in the real ROI is correlated to an increase in apartment sales transactions. Based on the results shown in Column (1), an increase of 1%p in the real ROI resulted in an 3.3% increase in apartment sales transactions, on average, for all

112 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 3 In-Depth Analysis

age groups. As for the results shown in columns (2) through (9) for each age group, the impact of the real ROI on apartment sales transactions varied across the age groups. First, people in their 30s, which is the typical age to give birth to and raise a child, showed a weak response to the real ROI. This provides evidence that having a child and acquiring an asset such as a residential property have a competitive relationship, as noted by Park and Lee (2016). Column (4) shows the analysis results in regard to the 40~44 y.o. age group. The results of the regression analysis showed that it is this age group that showed the strongest response to the real ROI, and that made the most aggressive investments. This is why the 40-44 y.o. age group accounts for the highest portion of the population structure of Korea as of 2016. As for columns (5) through (7), showing the results of the regression analysis on people aged 45 to 59, whose children are at the age to leave home, the impact of the real ROI was relatively weakened. Also, in this age group, the sales transaction volume actually decreased despite an increase in the population, and this phenomenon could be explained by the fact that this age group had difficulties in building their assets, as they experienced the 1997 Asian financial crisis and the 2008 global financial crisis during their prime years of economic activity (Lee

Table 3-2-4 The effect of real ROI on apartment sales transactions by age group

(9) (1) (2) (3) (4) (5) (6) (7) (8) 65 and All ages 30~34 y.o. 35~39 y.o. 40~44 y.o. 45~49 y.o. 50~54 y.o. 55~59 y.o. 60~64 y.o. over 0.033** 0.018+ 0.029** 0.055** 0.031** 0.027** 0.027** 0.034** 0.045** Real ROI (0.003) (0.010) (0.009) (0.009) (0.009) (0.009) (0.009) (0.010) (0.010) -0.021 -0.006 0.002 -0.055 0.006 0.009 0.017 0.030 -0.140* Inflation rate (0.022) (0.064) (0.058) (0.055) (0.060) (0.060) (0.064) (0.069) (0.067) Population 1.485** 0.562 2.448** 1.874** -0.489 -1.839** -0.834** 1.748** 5.126** (log) (0.046) (0.584) (0.434) (0.541) (0.501) (0.328) (0.302) (0.267) (0.466) -15.53** -4.29 -27.05** -20.07** 8.73 25.43** 12.53** -18.47** -62.68** Constant (0.567) (7.113) (5.302) (6.658) (6.140) (4.044) (3.637) (3.147) (5.910) Observation 28,680 3,726 4,037 4,118 3,942 3,661 3,339 2,852 3,005 point Number of 16 16 16 16 16 16 16 16 16 regions R-squared 0.422 0.341 0.424 0.430 0.435 0.416 0.496 0.534 0.512 Hausman’s 208.3** 72.04** 84.58** 53.20** 114.4** 336.2** 255.2** 353.6** 344.8** The numbers in parenthesis represent the standard error. In all of the analyses, the fixed effects of the region, apartment size and time are included, but not reported in the regressions. ** p<0.01, * p<0.05, + p<0.1

113 and Kim, 2013). Also, their income transferred to their children increased as they began to leave home. As such, these factors caused their investment capacity to decrease, relatively speaking (Beom and Moon, 1992). As for the columns (8) and (9) on people over the age of 60, when the real ROI increased by 1%p, the apartment sales transaction volume increased by 3.4% among the 60~64 y.o. age group, and by 4.5% among people aged 65 and older. This indicated that seniors showed a strong response to the real ROI. These analysis results are considerably different from the conventional theories such as the life-cycle theory proposed by Modigliani and Brumberg (1954), suggesting that people purchase assets using the income they have generated in their younger years and sell their assets after retirement for post-retirement consumption purposes, and the such sales of assets act as a factor that reduce the asset price. The results are also different from the findings of the empirical studies where it was diagnosed that the increase in the senior population would reduce housing demand and cause a decline in residential property prices (Mankiw and Weil, 1989; Takáts, 2010; Kim, 1999; Park and Kim, 2014)

5. Conclusion and future tasks

In this study, the impact of the real return on investment (ROI) on apartment sales transactions across various age groups was empirically analyzed. The analysis results substantiated that in all age groups, an increase in the real ROI could lead to an increase in apartment sales. This reflects the fact that due to the recent low interest rate trend, the ROI expected from purchasing and leasing an apartment property is relatively higher than the ROI expected from deposit the money in a bank or investing in corporate or treasury bonds. Of particular note, a relatively high real ROI from leasing an apartment property due to low interest rates increased the inclination to purchase an apartment property among seniors, who were not considered major buyers in apartment sales transactions, and this was a major finding in this study. However, considering that the ROI expected could change depending on the market interest rates, there are limitations in generalizing these research results. There needs to be more in-depth research by collecting and using time series data for a longer time period in the future.

114 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 3 In-Depth Analysis

References

•Chae, Mie Oak (2016), "The Korean Elders, Elders' Emergence as New Housing Buyers", 2016 Korean Real Estate Policy Seminar, Chosunilbo. •Kim, Gyeong-hwan (1999), “Changes in the Age Structure of the Population and Housing Demand and Prices”, The Journal of the Korea Real Estate Society, 17:69-84 •Park, Heon-su and Min-jeong Kim (2014), "Analysis of the Impact of Population Structure Changes on Residential Property Prices: Centering on Seoul and Six Metropolitan Cities", Real Estate Research, pp. 24-2, pp. 23-32 •Park, Jinbaek and Jaehee Lee (2016), “Housing Price and Birth Rate under Economic Fluctuation : Evidence From 19 OECD Countries”, Childcare Policy Research, Vol. 10 Issue No. 3, pp. 51~69 •Beom, Su-in and Suk-jae Moon (1992), “A Study of Household Investment Plans Based on Family Life Cycle”, The Journal of the Korean Home Economic Association, Vol. 30 Issue No. 1, pp. 199-217 •Lee, Su-uk (2010), “Changes in Income for Baby Boomer Households and the Housing Market”, Seminar package on joint policies by the Korean Association for Housing Policy Studies and the Korea Real Estate Analysis Association, pp. 1~20 •Jeong, Ui-cheol and Seong-jin Jo (2005), “A Study of the Long-term Housing Demand Outlook Based on the Changes in Population Structure”, Land Development Plan, 40:37-46 •Lee, Chang-moo and Mi-kyoung Kim (2013), “Modelling the Demand for Housing. Considering the Effect of Householder’s Birth Cohort”, Real Estate Science Research, 19(3): pp. 5-25 •Hamilton, B. W.(1991), "The Baby Boom, The Baby Bust and the Housing Market: A Second Look", Regional Science and Urban Economics, Vol. 21, pp. 547-552 •Mankiw, N. Gregory and David N. Weil(1989), "The Baby Boom, The Baby Bust and the Housing Market", Regional Science and Urban Economics, Vol. 19, pp. 235-258 •Modigliani, F. and R. H. Brumberg(1954), "Utility analysis and the consumption function: an interpretation of cross-section data", Post Keynesian Economics, Rutgers University Press, pp. 388-436. •Takáts, Előd(2010), “Ageing and asset prices”, BIS Working Papers No 318, Bank for International Settlements

115 3 Diagnosis of the 2016 Housing

Analysis Subscription Market

Joo Seungmin, Kim Minsup

1. Introduction

Since its introduction, the housing subscription system has undergone ceaseless development. It cooled down the overheated housing market in in a time when there was an absolute lack of housing supply, in accordance with the rule on preferential supply of housing to the public adopted in 1977, and was used as an economic regulation means to reinvigorate the apartment subscription market that became stagnant due to financial crises among other factors. The real estate boom that began in late 2014 resulted in successful subscriptions and pre-construction parceling-out sales in the housing market, but the atmosphere in the subscription market in 2016 varied across the country. While deregulation occurred between 2014 and 2015, the government policies on loan regulations, guarantee restrictions on loans for interim payments, and restrictions on subscription and resale of the property rights were reinforced in 2016. A residential property has dual characteristics in that it is a consumable and an asset, and the characteristics unique to apartment properties that are parceled out exert a close impact on new housing demand. Thus, the indices associated with the subscription and pre-construction parceling-out sales markets are barometers with which one can read the general atmosphere of the housing market. In this paper, we will analyze the regional differences in the competition ratio for subscribing to a parceling-out sale and the parceling-out sales market, determine the the patterns of changes in the temporal and spatial

116 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 3 In-Depth Analysis

hot spots and the factors of impact, and derive the relationships among the variables related to the competition ratios for subscribing to a parceling-out sale for the purpose of diagnosing the apartment subscription market in Korea.

2. Trends in the Apartment Subscription Market

A. Analysis of the Apartment Subscription Market

The total number of apartment properties made available for parceling-out sales nationwide began to rise in 2010, reaching its peak in 2015 before undergoing a downtrend in 2016. As of November, 423,000 apartment properties were made available for parceling- out sales in the year 2016, which was a 70,000 decrease from 493,000 in the same period in the previous year. There were seasonal patterns of an increase in supply between April and June and September and October. Considering the number of apartment properties set to be parceled out in December, the total supply made available in 2016 is expected to exceed 458,000 (Figur 3-3-1).

Figure 3-3-1 Number of apartment properties made available for parceling-out sales nationwide

600,000 525,945 458,453 500,000

400,000 348,315 Expected number in December 302,832 286,363 293,769 [35,423 ] 300,000

200,000 178,523

100,000

- 2010 2011 2012 2013 2014 2015 2016 Note: The number for December 2016 is a projected value Source: MOLIT Housing Information System (HIS)

117 Figure 3-3-2 Percentage of the parceled-out apartment properties in the capital area and non-capital area

Capital area Non-capital area 100.0%

48.2% 48.9% 64.1%

51.8% 51.1% 35.9%

0.0% 2014 2015 2016 Source: MOLIT Housing Information System (HIS)

As shown in , 64.1% of the apartment properties made available for parceling- out sales were located in the non-capital area, while the remaining 35.9% were located in the capital area in 2014. However, in 2015 and 2016, the situation was overturned with capital area accounting for 51% of the apartment properties made available for parceling-out sales. This change is deemed to have resulted from the strong demand in the pre-construction parceling- out sales market centering on the capital area, indicating a higher chance of successfully selling all of the housing units for construction firms, and the interest of the construction firms to complete the parceling-out sales by the end of the year, due to the movement to tighten the loan regulations.

B. Competition Ratio Among Apartment Parceling-out Sales Subscribers

As for the YOY change in the average competition ratio by region in 2016, it was on an uptrend in the capital area (Seoul and Gyeonggi-do Province), while it declined in some parts of the non-capital area. In the case of Ulsan and Gwangju, where the subscription competition ratio declined substantially compared to the previous year, the competition ratio reached its peak in 2015 and declined thereafter. On the other hand, a high subscription competition ratio was observed in Sejong, Jeju, Daejeon and the capital area. In Busan, the subscription rate was the highest in the country in both 2015 and 2016.

118 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 3 In-Depth Analysis

Table 3-3-1 Trends in the subscription competition ratio by region

Region `14 `15 `16 `16-`15 Seoul 4.8 13.6 22.5 9.0 Gyeonggi-do 4.1 4.9 9.0 4.1 Busan 21.0 79.6 99.3 19.7 Daegu 14.5 56.1 36.9 -19.2 Incheon 1.9 1.9 2.4 0.6 Gwangju 14.3 28.3 20.4 -7.9 Daejeon 3.1 5.4 12.0 6.6 Ulsan 14.1 45.1 14.0 -31.1 Gangwon 0.9 2.3 3.8 1.6 Gyeongsangnam-do 6.6 10.7 13.0 2.3 Gyeongsangbuk-do 4.5 7.6 2.4 -5.2 Jeollanam-do 1.7 2.8 3.2 0.4 Jeollabuk-do 6.2 13.9 3.9 -10.0 Chungcheongnam-do 6.5 2.9 1.0 -1.9 Chungcheongbuk-do 2.0 4.6 4.1 -0.5 Jeju-do 5.8 3.2 68.8 65.7 Sejong 6.5 17.6 49.1 31.5 Source: Korea Appraisal Board (KAB) and Korea Financial Telecommunications and Clearings Institute (KFTC)

shows the subscription competition ratio by month. The subscription competition ratio decreased in December to a national average of 7.5:1. In all regions, except for Busan, Daegu and Jeju, the competition ratio is in a digital digit. Of particular note, the competition ratio in Seoul is only 7.4:1, and based on this, it is deemed that the apartment subscription market has cooled down due to the policy announced on November 3, and the market is being reorganized centering on the end-users.

119 Table 3-3-2 Monthly trends in the apartment subscription competition ratio by region in 2016

Region `16.1 2 3 4 5 6 7 8 9 10 11 12 Nationwide 9.6 6.1 7.5 23.3 12.4 11.1 13.6 17.2 23.4 20.7 18.4 7.5 Seoul 22.7 0.5 17.6 3.8 7.3 16.6 67.7 23.6 12.8 33.6 23.7 7.4 Gyeonggi 2.3 2.4 2.6 6.6 7.0 14.1 16.5 2.2 12.9 14.5 3.6 Busan 3.1 13.7 41.7 168.8 135.1 49.1 27.5 221.0 392.4 188.1 205.9 33.7 Daegu 132.2 22.4 20.5 83.4 48.5 7.2 19.7 89.4 Incheon 1.7 0.1 2.2 1.9 1.6 3.6 3.9 1.7 1.3 Gwangju 12.2 4.3 2.0 24.0 14.4 3.0 40.8 4.2 36.1 4.3 Daejeon 1.6 1.8 21.3 2.5 Ulsan 2.2 10.6 3.4 1.8 1.0 3.0 47.5 10.8 15.7 Gangwon 3.0 1.9 2.7 1.9 2.2 2.1 1.8 6.8 6.8 1.3 7.5 Gyeongnam 1.5 9.5 8.7 34.4 0.3 11.2 17.8 1.5 16.4 27.0 3.2 4.4 Gyeongbuk 1.0 3.9 0.6 1.3 0.7 3.0 1.5 0.8 40.3 2.0 0.2 Jeonnam 0.2 0.9 2.1 0.0 1.0 8.4 0.0 3.0 3.2 1.0 Jeonbuk 1.2 4.4 1.3 2.9 0.0 1.5 4.5 8.4 2.4 2.8 Chungnam 1.8 0.9 0.9 1.3 1.0 0.0 0.6 0.3 0.0 1.0 Chungbuk 0.0 2.9 4.3 1.4 3.5 2.4 11.8 Jeju 100.4 3.7 0.1 0.3 104.7 2.5 Sejong 10.5 106.2 1.6 14.4 126.4 138.8 31.4 Source: Korea Appraisal Board (KAB) and Korea Financial Telecommunications and Clearings Institute (KFTC)

3. Analysis of the Apartment Subscription Market in 2016

A. Analysis of Competition Hot Spots by Region

The hot spots3) in each si, gun, and gu (city, county and district) determined based on the subscription competition ratio in the past 3 years are shown in . The purpose of analyzing the hot spots was to determine the clustering characteristics of the adjacent areas of the hot spots, and not simply the areas with a high competition ratio. Hot spots with concentrated subscriptions were observed in some regions from 2014 to 2016. In 2014, hot spot clusters were

3) The hot spot analysis involves calculating GI* of the low and high values within the accepted range, centering on the target point, through the Getis-Ord GI* analysis, and hot spots with high values and cold spots with low values, at statistically significant levels, are derived (Ord·Getis, 1995)(Eq. 2). For this process, Arc GIS 10.3, a GIS analysis program, was used.

120 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 3 In-Depth Analysis

observed in the outskirts of Ulsan, Suseong-gu in Daegu, -si in Gyeonggi-do Province, and Gwangju. Hot spot clusters were observed in Dong-gu and Suseong-gu in Daegu and Seongnam-si in Gyeonggi-do Province in 2015, and in Sejong, Jeju, Hanam-si in Gyeonggi, Suseong-gu in Daegu and some parts of Busan in 2016. In order to analyze the spatial patterns of the apartment subscription competition hot spots, the location data on the subscription competition ratio were used for a hot spot cluster analysis. The Space Time Pattern Mining and Emerging Hot Spot4) analysis function of the Geographic Information System (GIS) was used to derive the type of hot spot and analyze the clusters by type. The results of analyzing the time-space patterns of hot spots in relation to the subscription competition ratio from 2014 to 2016 are shown in . The results of the hot spot analysis presented in shows the scope of the clusters of subscription competition in the time period concerned, but they could not be used to determine the

Figure 3-3-3 Apartment subscription competition hot spots by si, gun and gu

2014 2015 2016

4) “Time-space cluster” means high spatial density of a certain phenomenon (e.g. increase or decrease in the apartment subscription competition ratio) and a cluster of the individual cases (physical distance between apartment complexes accepting subscriptions) that are in relatively proximity of each other. When time is measured based on distance (temporal distance) similar to space and the closeness and farness are compared, one can differentiate the type of hot spots where both the temporal and spatial distances are very close among the individual cases and the type of hot spots where such is not the case, and this can be referred to as a tie-space cluster analysis. In this study, the data on the apartment subscription competition ratio for a 3-year period from 2014 to 2016 were divided on a quarterly basis to create a time-space data set for analysis.

121 patterns through which the clusters formed. However, based on the three types of clusters derived from the time-space hot spot cluster analysis, the prior time-space signs of emerging hot spots can be explained. The results of the analysis showed that there were two hot spots with a consistently high subscription competition ratio (“Consecutive Hot Spots”), three hot spots where the competition ratio fluctuated (“Oscillating Hot Spots”), and three hot spots where there was a recent increase in the competition ratio (“New Hot Spots”). In the case of the Consecutive Hot Spots, a relatively high competition ratio was maintained compared to other regions for more than 90% of the period under analysis, and they were Suseong-gu in Daegu and Hanam. In oscillating hot spots, the subscription competition ratio was maintained high, but it also increased or decrease considerably in some regions, with a marked drop in the competition ratio recently in the Gangnam area of Seoul, Changwon and Geoje. As for the New Hot Spots, where there was a recent increase in the competition ratio, a dramatic increase was observed in Yongsan, Yeongdeungpo and Mapo in Seoul as well as Busan, Sejong and Jeju City from 2014, which was the starting point of the time-space analysis, until 2016.

Figure 3-3-4 Time-space subscription competition hot spots

Consecutive Hot Spots Oscillating Hot Spots New Hot Spots

Source: MOLIT Housing Information System (HIS)

122 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 3 In-Depth Analysis

Table 3-3-3 Results of the time-space subscription competition hot spots

Symbol Description Regions Hot spots with consistently Suseong-gu in Daegu, Consecutive Hot Spots high competition and Hanam Three districts of Gangnam in Seoul, Hot spots with a fluctuating Oscillating Hot Spots and some of the area of competition ratio Changwon and Geoje Yongsan, Yeongdeungpo, and Mapo in Seoul, Yeonje-gu, Hot spots with a recent increase New Hot Spots Haeundae-gu, and in the subscription competition ratio Dongnae-gu in Busan, Sejong, and Jeju

In summary, as the hot spots became distributed in the non-capital area and the capital area with the implementation of the 2016 Loan Guidelines nationwide in 2015, a balloon effect contributed to the increase in the apartment subscription competition ratio in some of the areas in Seoul as well as in Busan and Sejong, with the non-capital area taking a superior position in the subscription market. The widely-accepted theory, based on the phenomenon observed in the subscription market thus far, is that macro and policy factors affect the subscription market. It is believed that the uncertain macro factors and government policies in 2017 will make the subscription market react more sensitively to changes and shocks.

B. Impact Factors in the Subscription Market

The apartment subscription competition ratio is determined by not only the end-user demand, but also the anticipation among potential investors for the home values to rise in the future. In addition to the demand for newly built apartments, if existing apartments are expected to rise in prices or there exist favorable conditions for the prices to rise such as a reconstruction project, then the anticipation for the property values to rise will be heightened, and this will cause investors to flock to the subscription market. To provide an example, Yeonje-gu and Haeundae-gu in Busan, which recorded the highest subscription competition ratio in 2016, were areas with favorable investment conditions resulting from the reconstruction and redevelopment projects. In this study, variables that influence the subscription competition ratio by affecting the anticipation for a price increase in parceled-out apartments were selected, based on

123 preceding studies. Of particular note, taking into consideration of the characteristic variables related to the favorable conditions created by development projects, data sets were created for each si, gun and gu, based on the rate of changes in apartment sales prices, the rate of changes in apartment jeonse prices, the rate of changes in transactions, the number of areas in which improvement projects are taking place, the number of implementation committees, the number of approvals granted to establish a reconstruction/redevelopment union, the rate of changes in the supply of apartment properties available for parceling-out sales, the number of unsold apartment properties through parceling-out sales (unclaimed supply) and premium on the parceling-out sales prices, in order to perform an empirical analysis of the factors that affected the subscription competition ratio in 2016. Of the variables, the rate of changes in apartment sales prices, the rate of changes in apartment jeonse prices, and the rate of changes in transactions are characteristic variables related to the market for existing housing, while the rate of changes in the supply of apartment properties available for parceling-out sales and the number of unsold apartment properties through parceling-out sales (unclaimed supply) are characteristic variables related to the pre-construction parceling-out sales market. On the other hand, the number of areas in which improvement projects are taking place, the number of implementation committees and the number of approvals granted to establish a reconstruction/redevelopment union are characteristic variables related to the favorable conditions created by development projects. In order to satisfy the assumptions made in the regression model, the apartment subscription competition ratio was converted into ln(subscription competition ratio) through Box-cox conversion, and a stepwise selection method was applied to select the variables for analysis. As shown in

, the rate of changes in apartment sales prices, the number of implementation committees, and the number of unsold apartment properties through parceling-out sales (unclaimed supply) (unit: hundred) were selected as statistically significant variables. The model was found have a goodness of fit at a significance level of 5%, and had explanatory power of 36.5%. According to the results of the analysis, the characteristics related to the existing housing market, parceling-out sales market, and favorable conditions created by development projects were all factors that influenced the subscription competition ratio. Based on the regression coefficient, it was found that high competition ratio was observed in regions with a high rate

124 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 3 In-Depth Analysis

of increase in sales prices, a large number of implementation committees and low unclaimed supply. Low unclaimed supply indicates an abundant demand in the parceling-out sales market, and a large number of implementation committees suggests that there is high anticipation for the prices to increase due to the favorable conditions created by development projects. A high rate of increase in sales prices means that the sales prices of parceled-out apartment properties will be lowered relatively at a certain time point. This is why these variables cause the subscription competition ratio to rise.

Table 3-3-4 Results of the analysis of the impact factors in the subscription market

Estimate Std. Error t value Pr(>|t|)

(Intercept) 1.943799 0.239818 8.105 1.41e-11*** Rate of changes in 0.137330 0.067107 2.046 0.04458* sales prices Number of implementation 0.300569 0.091296 3.292 0.00158** committees Unclaimed supply -0.010105 0.003897 -2.593 0.01165*

Dependent variable: ln(subscription competition ratio) *, **, and *** indicate that they are significant at significant levels of 0.05, 0.01, and 0.001, respectively. Multiple R-squared: 0.3647, Adjusted R-squared: 0.3367 F-statistic: 13.01, p-value: 8.222e-07 Durbin-Watson D 1.791, Pr < DW 0.1662, Pr > DW 0.8338 Shapiro-Wilk W 0.986952 Pr < W 0.6655 , Kolmogorov-Smirnov D 0.067329 Pr > D >0.15003) Source: KAB, MOLIT, Real Estate 114

C. Relationship Between Apartment Subscription Competition Ratio and the Rate of Increase in Existing Residential Property Sales Prices

In the analysis explained above, it was found that a causal relationship could not be established for certain time periods. Thus, the data from 2013 to 2016 were divided into the capital area and the non-capital area, which was further sub-divided into five metropolitan cities and eight provinces, in order to analyze the relationship between the apartment subscription competition ratio and the rate of increase in sales prices. In the

125 analysis, statistically significant results were observed for the non-capital area and the five metropolitan cities, and it was found that the subscription competition ratio affected the rate of increase in sales prices a month later in the non-capital area and two months later in the five metropolitan cities. It should be noted that rate of increase in the existing residential property sales prices was high in the capital area, while the subscription competition ratio was high in the non-capital area. As such, due to these contrasting trends in the capital area and the non-capital area, a causal relationship was not apparent between the two variables under examination. Unlike in the past, in the non-capital area, a high subscription competition ratio had a strong impact on the rate of increase in the sales prices.

D. Analysis of Similarities Among Regions Using the Subscription Competition Ratio and the Rate of Changes in Sales Prices

As shown by the analysis results above, the subscription competition ratio and the rate of changes in sales prices were found to be correlated. The degree of activity in the housing market can be determined based on the subscription competition ratio and the rate of changes in sales prices, and the results of analyzing the similarities of the regional real estate markets using the multidimensional scaling (MDS) method are visualized in

.

Table 3-3-5 ‌ Granger causality analysis of the subscription competition ratio and the rate of increase in the sales prices

Null hypothesis Time difference P value A high subscription competition ratio in the non-capital area does not Grang- 1 month 0.049 ** er-cause an increase in the sales prices in the non-capital area An increase in the sales prices in the non-capital area does not Grang- 1 month 0.1158 er-cause a high subscription competition ratio in the non-capital area A high subscription competition ratio in the five metropolitan cities does not 2 months 0.002 *** Granger-cause an increase in the sales prices in the five metropolitan cities An increase in the sales prices in the five metropolitan cities does not Grang- 2 months 0.993 er-cause a high subscription competition ratio in the five metropolitan cities

Note: * The null hypothesis is rejected at a significance level of 0.1. ** The null hypothesis is rejected at a significance level of 0.05. *** The null hypothesis is rejected at a significance level of 0.01.

126 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 3 In-Depth Analysis

The MDS results show that the stress value was near zero, indicating that it would fit a two- dimensional planar diagram. On the 2D planar diagram, the closer the points are, the more similarities they share. When the similarities of the real estate markets are examined, Busan and Jeju, plotted on the right side of the diagram, had a significantly high subscription ratio and rate of increase in sales prices, with a booming housing market, and they were plotted far from other cities and provinces. In the case of Gyeongbuk and Chungnam, found on the left end of the diagram, they are regions, where the subscription competition ratios were low and the real estate property sales prices were declining. Also, Gangwon, Jeonnam, Incheon and Gyeonggi, which are at the bottom, could be classified as regions with a subscription competition ratio and a low rate of increase in sales price, and they could be grouped together as similar subscription markets as they are near one another on the 2D planar diagram. 4)5) shows the results of MDS that have been subdivided into si, gun and gu. Even the stress value for si, gun and gu was near zero, meaning that it would fit a two-dimensional planar diagram. While most of si, gun and gu showed similarities, Yeonje-gu, Busan was located on the rightmost side away from other groups. Yeonje-gu has been recording a high subscription competition ratio and a high premium on the resale, due to the impact of the local improvement project, and it can be regarded as an area with the most booming subscription market in the country in 2016. Dongnae-gu and Haeundae-gu, Busan, which are plotted closely to Yeonje-gu, could also be classified as an area with a booming subscription market, similar to Yeonje-gu. These areas share something in common, and it’s the fact that they are under the influence of the local improvement project. Geoje, Gyeongnam, plotted on the leftmost side has a low subscription competition ratio of 1.13:1 and suffered the worst decline in sales prices. The regions that are close to Geoje on the 2D planar diagram are Gyeongsan in Gyeongbuk and Dong-gu in Ulsan, which also recorded low subscription competition ratios and a decline in the sales prices.

5) Stress value indicates the goodness of fit; there is a negative correlation between the stress value and the goodness of fit of the MDS results in the dimension concerned. As for the stress value indicating the goodness of fit, a value of 0.0 indicates a “perfect fit,” a value of up to 0.025 indicates an “excellent fit,” a value of up to 0.05 indicates a “good fit,” a value of up to 0.10 indicates an “average fit,” and a value of up to 20 indicates a “bad fit.”

127 Figure 3-3-5 Results of MDS by city and province

Busan

1 Gyeongbuk Jeju Jeonbuk Seoul Chungbuk Gangwon Gyeonggi Incheon Dimension 2 0

Daegu Chungnam Ulsan Gwangju Daejeon -1 Gyeongnam Jeonam

-1 0 1 2 Dimension 1 Stress value: 2.083E-124) Source: KAB, KFTC

Figure 3-3-6 Results of MDS by city and province

Yeonje-gu, Busan 3 Gyeongsan, Gyeongbuk

2 Jung-gu, Daegu Dongnae-gu, Busan Geoje, Gyeongnam Suseong-gu, Daegu Nam-gu, Daegu Yongsan-gu, Seoul Haeundae-gu, Busan 1 Dong-gu, Daegu Gangseo-gu, Busan

Dimension 2 Sejong Nam-gu, Busan Jeju-si, Jeju

0

-1 Gangnam-gu, Seoul

-2 0 2 4 Dimension 1 Stress value: 1.236E-11 Source: KAB, KFTC

128 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 3 In-Depth Analysis

4. Recent Government Policies and Subscription Market Outlook

The major government policies that affected the real estate market in 2016 were the house- hold debt management measures announced on Aug. 25, Nov. 3, and Nov. 24. When the do- mestic household debt exceeded KRW 1,257 trillion at the end of the second quarter in 2016, the government announced the Aug. 25 Household Debt Management Measures for the pur- pose of suppressing the increase in household debts. The most important framework included the contraction of the land supply for public housing and reinforcing the pre-construction parceling-out sales guarantee reviews, with the aim of effectively tackling the household debt problem. However, potential buyers and investors focused on the intent to reduce the housing supply, and this caused the housing market to become overheated, centering on the three dis- tricts of Gangnam, which have been traditionally popular. In response, the government an- nounced the Nov. 3 Household Debt Management Measures, which targeted the subscription market. The measures included putting restrictions on resale of parceled-out properties in the areas with an overheated housing market such as the three districts of Gangnam, , Hwaseong and Dongtan, reinforcing the requirement to fulfill the No.1 subscriber status and limiting multiple wins by subscribers. In the case of the Nov. 3 measures, the countermea- sures to cool down the market were focused strictly on the subscription market. Also, with the use of the expression, “localized overheating,” it was clarified that the overheating of the housing market was not a nationwide phenomenon. In other words, the key strategy was to select the areas with signs of overheating and apply the necessary regulations accordingly. The measures to “manage and stabilize the housing market by forming a market centering on end-users” are expected to lead to a reorganization of the housing market in the capital area, especially in the three districts of Gangnam in Seoul and Gwancheon in Gyeonggi-do, centering on the actual end-users. In fact, in December 2016, the national subscription com- petition ratio was found to be 7.5:1, and of particular note, the competition ratio has dropped to 7.4:1 in Seoul and to 3.6:1 in Gyeonggi. In the case of the subscription market in the major parts of the capital area, the barrier to entry is expected to become slightly higher. This will in turn enhance the probability of the end-users successfully subscribing to an apartment property, and the quasi-demand, with an aim to profit from a quick resale, will decline to a certain extent. However, in some other

129 regions such as Suyeong-gu, Busan, a high subscription competition ratio has been observed despite the implementation of the Nov. 3 measures. This is because they were excluded from the scope of application for an “adjustment of the resale restriction period,” and they are high in demand from the actual end-users and investors, due to the favorable conditions created by redevelopment and reconstruction projects. Under the more stringent rules that will be applied to loan reviews in 2017, it will be more difficult to take out a loan because DSR will be applied if the total amount of the loan with grace period and fixed interest rate is large. This will cause the end-users to take a wait- and-see stance, and it will in turn suppress the overheating of the subscription market. Accordingly, it is unlikely that a nationwide overheating of the subscription market will occur, but a boom of the subscription market will continue in the in-demand regions, where are an abundant number of end-users and where reconstruction and redevelopment projects are being pursued and implemented.

5. Conclusion

The results of this study could be summarized as follows: The prior signs of the hot spots in the subscription market from 2014 to 2016 were identified, based on which the hot spots could be categorized into three different types: Consecutive Hot Spots with a consistently high subscription competition ratio (Suseong-gu in Daegu and Hanam in Gyeonggi-do Province), Oscillating Hot Spots where the competition ratio fluctuated (3 districts of Gangnam, Changwon and Geoje), and New Hot Spots where there was a recent increase in the competition ratio (parts of Seoul, Busan, Sejong and Jeju). The results of the analysis of the impact factors in the subscription market showed that the subscription competition ratio was high when the rate of increase in sales prices was high, when there was a large number of improvement project implementation committees, and when there was a low supply of unclaimed parceled-out apartments. The results of analyzing the causal relationship between the subscription competition ratio and the increase in sales prices showed that recently, the subscription competition ratio affected the rate of increase in sales prices a month later in the non-capital area and two months later in the five metropolitan cities. Then, the space-time pattern mining and

130 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 3 In-Depth Analysis

emerging hot spot analysis method and a multidimensional scaling method were applied to analyze the spatial characteristics and similarities of each region to present visual data. To date, the government has been reinforcing and deregulating restrictions adequately in order to keep the real estate market stable. Considering that the housing market is comprised of subordinate markets, there is a need to implement the policies flexibly depending on the region such as easing regulations in regions where the subscription market has cooled down or there are concerns of a stagnant housing market, and reinforcing regulations in regions that may potentially attract speculative investors. In addition, it is necessary to alleviate the impact of the current domestic political and economic instability and economic slowdown on the housing market and minimizing the side effects of real estate policies in order to ensure stability in the housing market. It is deemed that in-depth research to identify the spatial characteristics of the subordinate components by analyzing the characteristic variables such as the subscription competition ratio, premium on resale prices, and unclaimed supply for each apartment complex will be useful in establishing a more detailed and specific real estate policies in the future.

References

•Kim Nam-joo, 2011, A Model for Application Rate to Large Scale Housing Development Project Area, The Journal of the Korea Planning Association, 46(4):121-130. •Son Jae-young, 2005, A Model for Apartment Pre-sales and its Application, Land Development Research, 47:201-214. •Lee Min-seok, 2012, Analysis of Determinants of Apartment Subscription Rate in the Capital Area. •Kim Hak-yeon, 2016, An Empirical Study of the Factors Influencing the First Priority Apartment Subscription Competition Ratio. •Kang Myeong-gu, 2014, A Study on the Relationship Between Apartment Pre-sales Competition Rate and Housing Price Change. •Ju Seung-min and Choi Jin-ho, 2015, Spatial Pattern of Transactions and Trading Volume of Apartment, 14(2):1-15. •Assuncao, R. M., & Reis, E. A., 1999, A new proposal to adjust Moran’s I for population density. Statistics in medicine, 18(16):2147-2162. •Mankiw, N. and D. Weil, 1989, The Baby Boom the Baby Bust and the Housing Market, Regional Science and Urban Economics 19: 362-384. • Ord, J. K., & Getis, A., 1995, Local spatial autocorrelation statistics: distributional issues and an application. Geographical analysis, 27(4):286-306.

131 4 Analysis of Patterns in the

Analysis Analysis Determinants of Housing Prices

Lee Jiyeon

1. Introduction

At present, the real estate market plays a major role in the national economy of Korea, and homes account for a large part of the household assets. Thus, stabilization of residential property prices has been perceived as an important policy goal for the purpose of not only protecting the habitation of ordinary people, but also ensuring social and economic stability. The government of Korea has been strengthening regulations when home prices surged, and implementing deregulation when real estate transactions remained stagnant, as a means to mitigate the issues that arose in the housing market. However, there have been cases in which policies were announced after the market had already moved, and the effects of the policies began to show after a considerable time had passed since their announcement. It should be noted that in order to ensure effective results from the measures applied to either stimulate residential property transactions or stabilize residential property prices, it is of the utmost importance to analyze the factors influencing housing prices and determine the correlations and causal relationships among these factors. Accordingly, in this study, the subordinate markets were subdivided by “nationwide,” the “capital area” and the “non-capital area” in order to identify the determinants of housing prices in the regional markets. This will in turn enhance the efficiency of establishing various policies for stabilization of the housing market, and be helpful in forecasting the housing market in the future.

132 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 3 In-Depth Analysis

2. Housing Prices and Determinants Thereof

A. Determinants of Housing Prices

Real estate prices are influenced by internal factors of the real estate market as well as a wide range of macroeconomic variables such as income and consumer prices. Also, fluctuations in the real estate prices can have a serious impact on other aspects of the economy including consumption and investment. Generally, housing prices are determined by a combination of macroeconomic, social, psychological, policy and institutional factors through complex correlational and causal relationships. However, in the case of social, psychological, policy and institutional factors, it is difficult to estimate their impacts due to the limitations in quantification and the uncertainties related to policy and institutional changes. This is the reason previous studies have focused on theoretical discussions of macroeconomic factors influencing housing prices. However, studies on the causal relationships and predictions of housing prices and macroeconomic variables have presented contrasting results, depending on the period under analysis, analysis method, and selected variables, and it has been difficult to maintain consistency. Accordingly, for the analysis in this study, variables that could explain the fluctuations in the general economy and currency market were selected, under the presumption that the general economy and the real estate market economy are closely related. Of particular note, since this study aims to identify the factors influencing the changes in housing prices, preceding studies (Son Jae-young (1991), Yun Seong-hun (2002), Kim Jung-young (2006), Kim Yeon-hyeong and Jeong Young-suk (2006)) were reviewed. After the review, the determinants of housing prices were subdivided into economy-, interest rate- and housing supply-related factors and other internal factors of the real estate market, and the representative variables were selected to verify the determinants of housing prices using a statistical model.

B. Economy-related Factors and Housing Prices

Real estate prices are generally known to be affected by the changes in the overall economy. According to Kim Gyeong-hwan and Lee Han-sik (2004), when real state prices rise, there is an increase in consumption through an “asset effect,” which in turn boosts the total demand and consumer prices. In contrast, a dip in real estate prices not only lowers consumption,

133 but there is an increase in non-performing loans of financial institutions and this can lead to a decrease in loans issued by financial institutions. In turn, corporate investment will shrink and the total demand will fall. The contracted demand leads to an additional decline in real estate prices. As such, real estate prices is generally perceived to react sensitively to the changes in the overall economy. Thus, in this study, the economy-related factors will be analyzed taking into consideration the representative indices including the Consumer Price Index(CPI), Producer Price Index(PPI), Composite Indexes of business indicators(CI), Korea Composite Stock Price Index(KOSPI), and exchange rate.

C. Interest Rates and Housing Prices

Generally, a low interest rate trend and excessive liquidity caused by it are known to increase housing prices. According to previous studies, there are three channels through which low interest rates affect real estate prices that can be explained as follows (Choi Hee- gap (2002) and Kim Gyeong-hwan and Lee Han-sik (2004)): First, low interest rate increases jeonse prices and this in turn raises the housing prices; Second, at low interests, more people are willing to take out loans to purchase a home, and this increases the demand to purchase a home, thereby causing the housing prices to rise; Lastly, when the return on financial assets shrinks due to low interest rates, money in the market is invested into the housing market, and this boosts housing prices. As such, interest rate is a major factor that influences housing prices. In this study, the impact of interest rates was analyzed using interest Certificate of Deposite(CD) as the market interest rate, the fixed-term deposit interest rate as the deposit interest rate, and the mortgage interest rate as the loan interest rate.

D. Housing Supply and Housing Prices

Economy-related indices and interest rates, described above, are factors associated with the demand aspect of the housing market. There is also a need to consider the supply aspect for a more systematic analysis of the currents in the real estate market. In the case of Korea, studies have reported that the changes in the policies related to housing supply have a huge impact on housing prices. According to Kim Gyeong-hwan and Lee Han-sik (2004), housing prices were stabilized with a consistent supply of more than 600,000 residential properties

134 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 3 In-Depth Analysis

annually since the 90s; however, the annual supply of new housing plummeted to just 300,000 immediately after the 1997 Asian financial crisis, causing a supply-and-demand imbalance, which in turn resulted in the housing prices to surge in the early 2000s. For the purpose of analysis, the number of construction permits and approvals granted, the number of housing construction completions, the value of construction completed, and the number of unsold properties through parceling-out sales (unclaimed supply) as a means to take into account the supply effect.

E. Factors Related to the Real Estate Market and Housing Prices

The relationship between the housing sales prices and transaction volume have been mentioned in multiple other studies that have been conducted previously. As it can be seen in a diagram on a long-term time series of housing prices and transaction volume, these two factors fluctuate in a very similar pattern, with one preceding or following the other. While contrasting findings have been reported on whether it is the sales prices that lead the transaction volume or the opposite is true, the common opinion is that these two factors have a significant causal relationship. The rate of change in jeonse prices and land value are also indices with a close relationship with sales prices, according to multiple researchers. Thus, in this analysis, the transaction volume, jeonse prices and land value will be used as real estate indices that have a close relationship with housing prices, and their impact on the determination of housing prices will be analyzed. Moreover, the real estate market is known to be heavily influenced by the sentiments of its participants. In order to take this into consideration, the Real Estate Consumer Sentiment Index (RECSI) will be used. RECSI is a quantification of the consumer sentiments in regard to the real estate market by the Korea Research Institute for Human Settlements, based on the data obtained through the monthly real estate market trends and outlook surveys (questionnaire-based). The sentiments among the market participants are expressed as a value between 0 and 200. When the index is above 100, it indicates an increase in prices or transaction volume compared to the previous month, and a more favorable perception of the market atmosphere by the market participants compared to the previous month. Considering this, RECSI could be described as predictive index, where the market participants forecast the real estate market atmosphere in the future.

135 3. Empirical Analysis

A. Data

In order to analyze the factors that influence the housing prices by region, a total of 168 monthly data sets from December 2003 to December 2016 were used. As for the variable representing housing prices, the Sales Price Index for the composite housing type in the Housing Price Trends Survey by KAB, and the influencing factors that were taken into consideration were economy-related variables (KOSPI, CPI, PPI, and CI), current (exchange rate, money supply (M2), and liquidity aggregate of financial institutions (Lf)), iinterest rate on CD, fixed-term deposit interest rate, and mortgage interest rate), housing supply (unclaimed supply, number of construction permits/approvals granted, and number of housing construction completions), and real estate market-related variables (jeonse prices, land value, transaction Volume, and RECSI).

B. Analysis Method

As for the method of estimating the factors that change the housing prices, the cross correlation test, Granger causality test and VAR model were used in this study. The cross correlation test is the method of analyzing the similarity and precedence relationship between two variables. When the variables, and , at time are and , the cross correlation at time lag represents the correlation between and ; thus, when the correlation between and is high, it means that precedes as much as the time period, . Thus, a cross correlation diagram showing the diverse time lags, , between the housing prices and factor variables can provide information on the precedence relationship between them. Say that the factor variable at housing sales price, , and at time point, , is . When the two variables accompany each other, correlation is the highest when , but when one follows the other, correlation is the highest when . The Granger causality test is a method of statistically testing whether one variable helps predict another variable. The statement, ‘ Granger causes ,’ means that helps predict the variable, . During analysis, careful attention should be paid to the fact that Granger causality is not the same as the common concept of a causal relationship, which could be described

136 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 3 In-Depth Analysis

as ‘ is the cause, and is the result.’ The Granger causality test, instead, is performed to determine whether two variables have precedence relationship, and whether one variable can be used to predict the other variable. In this study, the Granger causality between housing prices and factor variables will be tested to check which variables help predict housing prices.

C. Causality Relationship Between Housing Prices and Factor Variables

According to the results of the Granger causality between housing sales prices and the factor variables, the transaction volume and RECSI had a bilateral casual relationship with the nationwide housing prices. This means that the transaction volume and RECSI had an impact on the prediction of housing prices, and vice versa. On the other hand, KOSPI, CPI, and land value were found to have an impact on housing prices, but housing prices did not have an effect on these variables. In other words, the factor variables were useful in predicting the movement of housing prices, but housing prices were not useful in predicting these variables. For the nationwide housing prices, RECSI was the most significant determinant of housing prices. This is because RECSI acts as a type of leading indicator for the real estate market, and it also signifies that the national real estate market is heavily impacted by the overall market sentiments than any other factors. Also, the rise or fall of housing prices arising from market sentiments has a significant impact on the sentiments toward the real estate market, and this in turn gets reflected in housing prices. As such, it could be said that these two have a bilateral Granger causality relationship.

137 Table 3-4-1 Results of analysis of the Granger causality test by region

Region Housing prices↔Factor variable Housing prices←Factor variable Housing prices→Factor variable Transaction volume interest rate on CD Transaction volume Composite Stock Price Index Nation- Fixed-term deposit interest rate Real Estate Consumer Consumer Price Index wide Mortgage interest rate Sentiment Index⁺ Land Value Index Liquidity aggregate of financial institutions (Lf) Unclaimed supply Transaction volume⁺ Land Value Index Number of housing construction interest rate on CD Capital completions Fixed-term deposit interest rate area Real Estate Consumer Senti- Mortgage interest rate ment Index⁺ Liquidity aggregate of financial Composite Stock Price Index institutions (Lf) Transaction volume Number of housing construction completions Non- Unclaimed supply⁺ Number of housing construction Mortgages capital Consumer Price Index⁺ completions⁺ Real Estate Consumer Senti- area Producer Price Index ment Index interest rate on CD Fixed-term deposit interest rate Note: The‌ results of the Granger causality test: the Granger causality of housing prices and variables is significant at a significance level of 10%, and those indicated

with ⁺ are significant at a significance level of 1%.

The determinants of housing prices in the capital area were found to be the transaction volume, number of housing construction completions, RECSI, and KOSPI. In the non-capital area, a bilateral causal relationship existed between the number of housing construction completions and housing prices, while unclaimed supply, CPI and PPI were determined to be significant determinants of housing prices. In the case of the capital area, the changes in the housing transaction volume caused the most significant movement in housing prices. This could be explained by how the capital area is a region with relatively high investment value, and the price increases occur sensitively. Plus, demand exceeds supply in the capital area, and this is why the transaction volume determines the price. Accordingly, in the case of housing policies established for the capital area, it would be more efficient to regulate the housing market with a focus on the housing transaction volume. Meanwhile, in the case of the non- capital area, it was the number of housing construction completions and unclaimed supply that had the most significant impact on the changes in housing prices. This is because in the

138 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 3 In-Depth Analysis

non-capital area, supply exceeds demand, and the market does not react flexibly to excess supply. Thus, it is the supply factors, rather than the demand factors, that become important determinants of housing prices. What is notable about these results is that the indices related to interest rate were not determinants that help predict the housing prices, based on the Granger causality results. In fact, it was found that it is the changes in housing prices that help predict the interest rate- related indices. This result seems to contradict with the common theory that low interest rate trends tend to increase housing prices; however, as mentioned earlier, the Granger causality test results merely explain the causal relationship in relation to the prediction power, and not the cause-and-result relationship. In regard to this issue, the analysis results showed that the low interest rate trends did not have any direct impact on how well housing prices could be predicted, but it could be inferred that there was a causal route through which interest rates have an impact on the transaction volume, which in turn has an impact on housing prices. Based on the findings of preceding studies and the results of additional analyses in this study, it was confirmed that there was a Granger causal relationship between interest rates and transaction volume. Thus, interest rates have an impact on the prediction of the transaction volume, and since the transaction volume Granger causes housing prices, it is through this relationship that interest rates exert an impact on housing prices. Accordingly, when predicting housing prices, it would be more helpful to use information obtained from the transaction volume, which reflects the information on the changes in interest rates, rather than using information on the interest rates itself.

D. Cross Correlation Between Housing Prices and Factor Variables

The results of the cross correlation test on housing prices and the factor variables, based on the significant Granger causal relationship tested above, are shown in

. In the case of nationwide housing prices, there were no factor variables that significantly preceded the changes in housing prices; instead, it was the changes in housing prices that preceded the factor variables. In the case of the capital area, transaction volume preceded the changes in housing prices by a month, and these two had a significant positive correlation (0.2449). This means that when the transaction volume rises, the rate of changes in the housing prices will start to

139 increase a month later, whereas when the transaction volume declines, the rate of changes in the housing prices still start to drop a month later. In addition, RECSI was found to precede housing prices by two months, and these two have a significant negative correlation (-0.3395). This means that when there is an increase in the changes in RECSI, the rate of changes in the housing prices will decrease two months later. In the case of the non-capital area, the number of housing construction completions preceded price changes by four months, with a significant positive correlation (0.4110). In other words, when there is an increase in the number of housing construction completions, there will an increase in the rate of changes in the housing prices four months later. Unclaimed supply and CPI also preceded price changes by a month, with a significant negative correlation (-0.1886) and positive correlation (0.1699), respectively. This signifies that when the unclaimed supply expands, the rate of changes in the housing prices will decrease a month later, and when CPI increases, the rate of changes in the housing prices will increase a month later.

Table 3-4-2 Results of the cross correlation test by region

Precedes/ Region Factor variable Time difference Correlation Follows Transaction volume Follows 2 months -0.2730 KOSPI Follows 1 months -0.1224 Nationwide CPI Follows 3 months 0.2249 Land value Index Follows 5 months -0.2010 Transaction volume Precedes 1 months 0.2435 Number of housing construction Capital area Follows 6 months 0.4968 completions RECSI Precedes 2 months -0.3395 Number of housing construction Precedes 4 months 0.4110 completions Non-capital area Unclaimed supply Precedes 1 months -0.1886 CPI Precedes 1 months 0.1699 Note: Correlation is significant at a significance level of 5% for all variables.

140 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 3 In-Depth Analysis

4. Conclusion and Implications

In this study, an empirical analysis was performed to determine the determinants of housing prices and the regional differences in relation to the determinants. The results showed that there were significant differences in terms of causal relationships of the selected factor variables and the national housing prices, and housing prices in the capital area and non-capital area, and the determinants of such housing prices. In the case of the national housing prices, the variable that had the most significant impact on the changes in housing prices was the Real Estate Consumer Sentiment Index (RECSI), and the two had a significant bilateral causal relationship. From this, it can be inferred that the national housing prices are most heavily influenced by the overall sentiments and atmosphere of the housing market. Also, in the case of the capital area, it was found that the housing transaction volume had the most significant impact on the changes in housing prices, whereas it was the number of housing construction completions and unclaimed supply in the non-capital area. In the capital area, it was the demand factors including the transaction volume to which housing prices reacted sensitively. In contrast, in the non-capital area, it was the supply factors that had a huge influence on housing prices. This suggests that the housing markets in the capital area and the non-capital area may respond and move differently to the same policy or external shock. Accordingly, in order to accurately predict the movement of the housing market and make a preemptive response, it is important to approach each housing market differently in recognition of the different characteristics of the regional housing markets. In order for housing policies to be effective, it is deemed advisable to place the focus on the housing transaction volume in the case of the capital area in order to stabilize the housing prices, where for the non-capital area, the supply aspect should be regulated effectively to invigorate the housing market.

141 References

•Kim Gyeong-hwan and Lee Han-sik, 「Predictive Modeling of the Housing Economy」, Korea Housing Institute, 2004 •Kim Yeon-hyeong and Jeong Young-suk “Time Series Analysis of Housing Prices and Macroeconomic Variables”, Journal of the Korean Data & Information Science Society, Vol. 8 Issue No. 6, 2006 •Kim Jung-young, “An Empirical Study on the Interrelation between Land Price and Macroeconomic Variables in the Arbitrage Pricing Model”, Appraisal Research, Vol. 16 Issue No. 1, 2006 •Son Jae-young, “Causality between Land Price Increase and Macroeconomic Variables ”, Korea Development Research, Vol. 13 Issue No. 3, 1991 •Yun Seong-hun, 「Effects of Rapid Changes in Asset Value on Consumption」, Finance and Economics Research, Vol. 131, Bank of Korea, 2002 •Choi Hee-gap, 「Recent Trends in Asset Value and Possibility of an Asset Bubble」, Samsung Economic Research Institute, 2002

142 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) Korea Real Estate Market Report

P A R T 4 Issue Analysis

Shift Toward Rentals and Rent Burdens Supply, Transaction Volume and Sales Price Index of Row Houses and Multi-housing Burden of Jeonse and Rental Costs on Single-member Householdsg Market Trends and Outlook on Aggregate Retail Shop Market Shift Toward Rentals and Rent Burdens

Lee Junyong

Increase in Rental Transaction Volume

Increase in Rental Transactions The data on the “definite date” of move-in show a clear trend of a decrease in the ratio of jeonse transactions to total lease transactions and an increase in the ratio of rental transactions to total lease transactions.

○ In the case of jeonse and monthly rentals with a deposit down, the lessees report the definite date as the deposit is a large amount of money. However, in the case of some rentals requiring only a small deposit or those who renew their jeonse contract with the increase in the deposit converted into monthly rent, a significant number of these lessees do not register the definite date.

○ Despite the fact that such cases with unregistered definite date, which are mostly rentals, are not taken into consideration, it is still apparent that there has been a continuous increase in rental transactions, while the jeonse transactions have been on a decline.

○ In the non-capital area, there appears to be no significant change, as rental transactions had surpassed jeonse transactions in the past, but in the capital area, the increase in the rental-to-jeonse ratio is markedly apparent.

Indirect Measurement of Conversion into Rentals The residential properties for which the definite date of move-in have been registered can be tracked to analyze the important issues of the current home lease market such as the increase in conversions to rentals and changes in the leasing fees.

144 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 4 Issue Analysis

Figure 4-1 Ratios of jeonse and rental transactions by region

Nationwide Capital area Non-capital area

1100%00% 1100%00% 1100%00%

441.0%1.0% 338.3%8.3% 446.4%6.4% 880%0% 333.0%3.0% 339.4%9.4% 880%0% 330.1%0.1% 336.5%6.5% 880%0% 338.8%8.8% 445.1%5.1% 334.0%4.0% 444.2%4.2% 330.9%0.9% 442.3%2.3% 440.2%0.2% 447.9%7.9% 446.0%6.0% 444.9%4.9% 447.8%7.8% 660%0% 660%0% 660%0%

440%0% 666.0%6.0% 559.0%9.0% 440%0% 669.1%9.1% 661.7%1.7% 440%0% 559.8%9.8% 553.6%3.6% 667.0%7.0% 554.0%4.0% 669.9%9.9% 555.1%5.1% 661.2%1.2% 552.2%2.2% 660.6%0.6% 555.8%5.8% 663.5%3.5% 557.7%7.7% 554.9%4.9% 552.1%2.1% 220%0% 220%0% 220%0%

00%% 00%% 00%% 220110112201201222013013220140142201501522016016 220110112201201222013013220140142201501522016016 220110112201201222013013220140142201501522016016 JeonseJeonse RentalRental JeonseJeonse RentalRental JeonseJeonse RentalRental

○ There is the panel survey method through which the lessors or lessees are tracked down for a survey, but this can be highly costly and the group reflecting the changes in the lease market cannot be distinguished; thus, it is impossible to design a sample for lessors and lessees whose form of lease or form of occupation continues to change.

Size of Residential Properties with Two or More Definite Date Registrations

Matching Individual Residential Properties For apartments, with a clearly differentiation of the housing units, when the definite date registration data1) are aggregated, one can distinguish apartments for which two or more definite dates have been registered. When these registered data are paired together, then they can be divided into four different types depending on whether there has been a change in the form of occupation.

○ For example, the lessee registers the definite date for his jeonse deposit around the time of his move-in after concluding a jeonse contract. When a new jeonse contract is signed or the previous jeonse contract is renewed for the property after some time passes and a new

1) Trends in the number of definite date registrations for apartment jeonse and rentals

145 definite date is registered, then these two dates can be distinguished and the change or no change in the form of occupancy (in this case, jeonse → jeonse) can be determined.

○ As such, there are two types of “no change in the form of occupancy,” which are “jeonse→jeonse,” and “rental→rental,” and two types of “change in the form of occupancy,” which are “jeonse→rental” and “rental→jeonse.”

An Increased Shift to Rentals The definite date registration data from 2011 show that the “jeonse→jeonse” has decreased, while there has been a continuous increase in a shift to rentals (“jeonse→rental”).

○ Residential property lease contract periods are commonly one to two years, and for a definite date to be registered for the same property two or more times, it typically takes three to four years. As such, data pairing (the first set of data being from 2011) could be done at an increased rate for the 2013 data than the 2012 data.

○ 8% of the residential properties were converted from jeonse to rental in 2013, but this figure increased to 9.4% in 2014, 13.1% in 2015 and 13.6% in 2016, indicating an increased shift to rentals.

Table 4-1 Number of definite date registrations for apartment jeonse and rentals

(Unit: ten thousand) 2011 2012 2013 2014 2015 2016 Total Jeonse 43.8 44.1 39.4 39.6 34.8 17.1 218.9 Rental 15.2 15.7 18.3 18.9 21.3 10.7 100.2 Total 59.0 59.9 57.7 58.6 56.1 27.8 319.1

146 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 4 Issue Analysis

Table 4-2 Size and ratio of data pairs by type of change in occupation form

Number of matching data pairs Ratio of matching data pairs Jeonse Jeonse Rental Rental Jeonse Jeonse Rental Rental Total →Jeonse →Rental →Jeonse →Rental →Jeonse →Rental →Jeonse →Rental 2011 13,504 924 995 4,326 19,749 68.4% 4.7% 5.0% 21.9% 2012 51,031 3,845 4,787 20,766 80,429 63.4% 4.8% 6.0% 25.8% 2013 163,015 18,861 10,053 45,196 237,125 68.7% 8.0% 4.2% 19.1% 2014 193,588 26,925 13,061 53,925 287,499 67.3% 9.4% 4.5% 18.8% 2015 188,323 41,020 15,777 67,567 312,687 60.2% 13.1% 5.0% 21.6% 2016 97,516 22,410 8,623 35,900 164,449 59.3% 13.6% 5.2% 21.8%

Regions with Increased Shifts to Rentals

Regions with Increased Shifts to Rentals The regions where there is a high ratio of residential properties that have been converted from jeonse to rental, i.e. an increased shift to rentals at a high rate, is the capital area. The regions where such shift has been occurring rapidly in the past couple of years are Ulsan and Sejong.

Figure 4-2 Ratio of Jeonse-to-Rental Conversions by Region (Unit: %) Capital area and metropolitan cities Rural provinces

220%0% 220%0%

115%5% 115%5%

110%0% 110%0%

55%% 55%%

00%% 00%% SeoulSeoul GyeonGyeon InIn BusanBusan DaeguDaeguGongjuGongju DaeDae UlsanUlsan SejongSejong GangGang ChungChung ChungChung JeonJeon JeonJeon GyeongGyeong GyeongGyeong JejuJeju -ggi-ggi -cheon-cheon -jeon-jeon -won-won -buk-buk -nam-nam -buk-buk -nam-nam -buk-buk -nam-nam

22013013 22014014 22015015 22016016 22013013 22014014 22015015 22016016

147 Figure 4-3 Number of Jeonse-to-Rental Conversions by Region (Unit: properties)

Capital area and metropolitan cities Rural provinces

1180008000 11600600

1150005000 11200200 1120002000

99000000 880000

66000000 440000 33000000

00 00 SeoulSeoul GyeonGyeon InIn BusanBusanDaeguDaeguGongjuGongju DaeDae UlsanUlsan SejongSejong GangGang ChungChung ChungChung JeonJeon JeonJeon GyeongGyeongGyeongGyeong JejuJeju -ggi-ggi -cheon-cheon -jeon-jeon -won-won -buk-buk -nam-nam -buk-buk -nam-nam -buk-buk -nam-nam

22013013 22014014 22015015 22016016 22013013 22014014 22015015 22016016

○ In Seoul, where there is a large apartment inventory, there are a large number of residential properties that have been converted into a rental property. In 2014, 8,800 apartment properties have been converted from jeonse to rental, and the jeonse-to-rental conversion nearly doubled to around 16,000 in 2015.

Number of Jeonse-to-Rental Conversions in Seoul The districts of Seoul where there have been a large number of apartment properties that were converted into rental properties include the four districts of Gangnam, Nowon-gu, Yangcheon-gu, Dongjak-gu and Gangseo-gu.

○ In 2015, Gangnam-gu recorded the largest jeonse-to-rental conversions (2,101 properties), followed by Songpa-gu at 1,810, Seocho-gu at 1,305, Nowon-gu at 1,056, Gangdong-gu at 1,015, Yangcheon-gu at 893, Dongjak-gu at 773, and Gangseo-gu at 722.

○ However, because the number of apartment properties vary by district, the districts with higher ratios of jeonse-to-rental conversions compared to others are Songpa-gu, Gangnam- gu, Seocho-gu, Dongjak-gu and Yangcheon-gu.

○ In other words, the areas where there is a rapid conversion into rentals in the apartment market of Seoul are the three districts of Gangnam, Yangcheon-gu and Dongjak-gu. These areas are high in demand due to education among other factors, and it is the lessors, rather than lessees, who take a dominant position in the market. This is the reason for the rapid conversion from jeonse to rental, the latter of which is currently favored by lessors.

148 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 4 Issue Analysis

Figure 4-4 Number and ratio of jeonse-to-rental data pairs in Seoul

Number of jeonse-to-rental data pairs Ratio of jeonse-to-rental data pairs

2500 Gangnam2500 Gangnam Dongjak Dongjak Jongno Jongno Songpa Songpa JungnangJungnang0.25 0.25 Gwangjin Gwangjin GeumcheonGeumcheon Seocho Seocho EunpyeongEunpyeong Yongsan Yongsan 2000 2000 0.2 0.2 Gangbuk Gangbuk Nowon Nowon Gangseo Gangseo Jung-gu Jung-gu 1500 1500 0.15 0.15 Jung-gu Jung-gu GangdongGangdong Nowon Nowon YangcheonYangcheon 1000 1000 0.1 0.1 JungnangJungnang YangcheonYangcheonGangbuk Gangbuk Songpa Songpa 500 500 0.05 0.05 Eunpyeong DongjakGangdongGangdong Jongno Jongno Eunpyeong 0 0 Dongjak 0 0

SeodaemunSeodaemun Gangseo Gangseo Guro Guro GangnamGangnam

Gwangjin Gwangjin YeongdeungpoYeongdeungpoDongdaemunDongdaemun Seocho Seocho

Yongsan Yongsan SeongdongSeongdong SeodaemunSeodaemun SeongdongSeongdong Dobong Dobong Mapo Mapo Dobong Dobong YeongdeungpoYeongdeungpo DongdaemunDongdaemun SeongbukSeongbuk GeumcheonGeumcheon SeongbukSeongbuk Gwanak GwanakGuro Guro Mapo GMapowanak Gwanak

2013 20132014 20142015 20152016 2016 2013 20132014 20142015 20152016 2016

Changes in Rent Burden Caused by Jeonse-to-Rental Conversion

Measurement of Changes in Rent Costs When a jeonse property is converted into a rental property, there is an increase in rent burden. The changes in the cost burden can be measured using the above data.

○ When a jeonse property is converted into a rental property, the increase in deposit is converted into rent or the deposit is decreased in order to increase rent. The common notion in Korean society is that the cost burden is higher for rentals than jeonse.

○ Thus, when a property is converted into a rental property, it places more burden on the lessee. The difference between the cost of leasing a jeonse property and a rental property can be estimated from the cost perspective.

○ However, the deposit for jeonse or rental in Korea is calculated based on the sales price of the property or the jeonse deposit. There is a need to convert this into “cost” from the opportunity cost perspective. Thus, the cost of leasing jeonse and rental properties was calculated, under the assumption that by putting down a deposit for the lease period, the lessee is essentially giving up the potential income from bank deposit interest. 2)

2) A 2-year cumulative moving average of fixed-term deposits provided by the Bank of Korea was applied to the jeonse or rental

149 Method of Calculating the Burden Cost of Leasing Rental Properties When a jeonse property is converted into a rental property, there is an additional burden on the lessee. The change in burden can be calculated based on the difference in the cost burden of jeonse and rental at a specific time point.

○ Cost difference between jeonse and rental = Cost of leasing a rental property ÷ Cost of leasing a jeonse property in the same apartment building/complex

○ The calculations were performed on solely the apartment properties that have been converted into rental properties. The cost of leasing a rental property was calculated by “deposit money x 2-year cumulative deposit interest + monthly rent.” The cost of leasing a jeonse property was calculated based on the data on the jeonse transactions that occurred for an apartment unit of the same size, within the same apartment complex.

Results of Calculating the Cost Burden of Leasing Rental Properties The cost of leasing a rental property compared to a jeonse property has been increasing steadily by 1.43-fold in 2013, 1.46-fold in 2014, 1.49-fold in 2015, and 1.53-fold in 2016.3)

○ The reason behind the continuous rise in the cost of leasing a rental property is that the opportunity cost of a jeonse property, which requires a larger deposit, has been declining due to the drop in interest rates and the number of rental properties has been on the rise.

○ Generally, the burden of leasing a rental property has been on the rise across cities and provinces, but it is lower for the capital area and higher for the rural provinces. This is deemed to be because in the rural regions, the rate of converting jeonse properties into rental properties has been high, and the rent converted in the market is relatively high.

deposit. This is the way of converting the deposit money in terms of opportunity cost, based on the nature of lease deposits and the way lessees secure such funds. For more details, refer to “Demonstrative Generation and Implications of Housing Cost Burden Level” (KAB Real Estate Market Report Vol. 4, 2016). 3) Aside from the jeonse→rental apartment properties, it is also possible to estimate the difference in the cost of leasing a rental property compared to a jeonse property for apartment properties that are still leased as a jeonse property. In this case, the difference in cost is calculated by the “cost of leasing a rental property in the same apartment building/complex ÷ The cost of leasing the jeonse property. The estimations show that it has increased by 1.55-fold in 2013, 1.60-fold in 2014, 1.65-fold in 2015, and 1.71-fold in 2016

150 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 4 Issue Analysis

Table 4-3 Ratio of the cost of leasing a rental property to the cost of leasing a jeonse property

Number of Type of data Contract year observed Average S.D. Min. value Max. value values 2013 5,610 1.43 0.35 0.46 2.63 Jeonse→Rental 2014 7,793 1.46 0.38 0.50 2.63 apartment 2015 9,929 1.49 0.39 0.38 2.63 2016 4,576 1.53 0.40 0.71 2.63

Figure 4-5 Ratio of the cost of leasing a rental property to the cost of leasing a jeonse property by region

Capital area and metropolitan cities Rural provinces

33 33 22.5.5 22.5.5 2.02 1.75 11.99.99 1.88 2.02 11.98.98 1.72 22 1.75 1.72 1.75 1.76 1.66 22 1.88 11.79.79 1.72 1.57 1.75 11.65.65 1.72 1.76 1.66 11.66.66 11.63.63 11.67.67 11.39.39 1.57 11.5.5 11.5.5 11 11 00.5.5 00.5.5 00 00

서울 경기 인천 부산 대구 광주 대전 울산 세종 강원 충북 충남 전북 전남 경북 경남 제주

○ The districts with a relatively large ratio of rental cost and jeonse cost in Seoul in the past two to three years were Geumcheon-gu, Nowon-gu, and Dobong-gu, which recorded higher ratios than the national average (1.53).

○ The districts with a large number of properties that have been converted into rental properties and a large ratio of jeonse→rental properties to the number of matching data pairs were 높은 Gangnam-gu, Dongjak-gu, Seocho-gu, Songpa-gu, and Yangcheon-gu. These areas were found to have the smallest difference between the cost of leasing a rental property and jeonse property.

○ In summary, the three districts of Gangnam, Dongjak-gu, and Yangcheon-gu have a lessor-dominant lease market. Although jeonse properties are being converted into rental properties at a rapid rate, the level of increase in the rent burden is not very large. On the other hand, in Geumcheon-gu, Nowon-gu, and Dobong-gu, the rate at which jeonse properties are being converted into rental properties is not as fast as in the other regions, but it is still leading to a large increase in the cost burden of leasing a rental property.

151 Table 4-4 ‌Ratio of the cost of leasing a rental property to the cost of leasing a jeonse property by district in Seoul

2016 2015 2014 2013 District Average Ranking Average Ranking Average Ranking Average Ranking Gangnam-gu 1.43 21 1.40 19 1.38 16 1.35 19 Gangdong-gu 1.44 16 1.42 16 1.41 10 1.37 17 Gangbuk-gu 1.52 10 1.47 13 1.39 14 1.38 15 Gangseo-gu 1.52 9 1.49 6 1.47 4 1.40 10 Gwanak-gu 1.55 7 1.51 5 1.41 11 1.46 4 Gwangjin-gu 1.38 22 1.33 23 1.30 24 1.30 24 Guro-gu 1.50 13 1.49 7 1.47 5 1.32 22 Geumcheon-gu 1.68 2 1.53 4 1.59 3 1.42 6 Nowon-gu 1.71 1 1.68 1 1.62 2 1.56 1 Dobong-gu 1.67 4 1.65 2 1.65 1 1.52 2 Dongdaemun-gu 1.52 8 1.49 9 1.46 7 1.41 8 Dongjak-gu 1.38 23 1.34 22 1.31 23 1.31 23 Mapo-gu 1.44 17 1.41 18 1.39 15 1.37 16 Seodaemun-gu 1.50 11 1.49 8 1.36 19 1.44 5 Seocho-gu 1.43 20 1.38 21 1.37 18 1.33 21 Seongdong-gu 1.43 19 1.39 20 1.36 20 1.38 14 Seongbuk-gu 1.46 15 1.41 17 1.37 17 1.41 9 Songpa-gu 1.37 24 1.31 25 1.28 25 1.27 25 Yangcheon-gu 1.32 25 1.31 24 1.34 22 1.33 20 Yeongdeung- 1.44 18 1.47 14 1.43 9 1.35 18 po-gu Yongsan-gu 1.50 14 1.48 10 1.40 13 1.39 12 Eunpyeong-gu 1.50 12 1.48 11 1.36 21 1.39 13 Jongno-gu 1.63 5 1.47 12 1.40 12 1.40 11 Jung-gu 1.68 3 1.43 15 1.44 8 1.41 7 Jungnang-gu 1.57 6 1.56 3 1.47 6 1.50 3

○ The reason behind the large difference between the cost of leasing a rental property and a jeonse property in Geumcheon-gu, Nowon-gu, and Dobong-gu is that the “jeonse→rental conversion rate” is higher than other regions. The districts that recorded a high jeonse→rental conversion rate in the past year were Jung-gu, Jungnang-gu, Dobong-gu, Nowon-gu, Eunpyeong-gu, Geumcheon-gu, and Gangdong-gu, and in most of these regions, there has been a large increase in burden resulting from the shift to rental properties.

152 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 4 Issue Analysis

Implications

Determination of Jeonse→Rental Areas and Increase in Cost Burden The analysis performed in this section has a significance in that it determined the districts of Seoul in which there has been a continuous conversion of jeonse properties into rental properties, and that it measured the level of burden felt by lessees resulting from this conversion.

○ While the jeonse→rental conversion has been occurring at an accelerated rate, it was impossible to confirm this by data due to the lack of related statistical data. However, the movements in the market such as the areas with an increase in rental properties and the cost burden of rentals were determined using the definite date registration data.

○ Of particular note, the areas in Seoul, with higher ratios or rate of jeonse→rental conversions compared to others are Songpa-gu, Gangnam-gu, Seocho-gu, Dongjak-gu and Yangcheon-gu, but the cost burden resulting from this phenomenon was actually lower compared to other districts.

Selection of Targets for Housing Support The areas with the highest housing cost burden resulting from the jeonse→rental conversions did not include Gangnam, where such conversions were occurring at a rapid rate, but actually, they were Geumcheon-gu, Nowon- gu, and Dobong-gu.

○ These regions are populated mostly with households with an income that is less than that of the middle class. There is a need to prepare measures to delay the speed of the jeonse→rental conversions of residential properties that are tailored to the socioeconomic class of the residents of these areas.

○ It is possible to reduce the cost burden of leasing a rental property by providing tax exemptions to the lessors leasing residential properties that fall under the category of the “public housing size” as jeonse properties, or widening the scope of tax deductions for rentals for the lessees leasing such properties.

153 Supply, Transaction Volume and Sales Price Index of Row Houses and Multi-housing

Joo Seungmin, Kim Minsup

▶ In 2016 (as of November), the number of permits and approvals granted for construction of row houses and multi-housing properties and the number of construction commencements decreased by 5.5% and 13.8%, respectively, while the number of construction completions increased by 21.2% compared to the previous year.

▶ In 2016 (as of November), the transaction volume of row houses and multi-housing properties decreased by 3.6% nationwide compared to the previous year, while it increased by 2.8% in the capital area and decreased by 17.8% in the non-capital area.

▶ In 2016 (as of November), the prices of row houses and multi-housing properties increased by 4.6% nationwide, 5.3% in the capital area and 2.8% in the non-capital area, but the largest price increase was observed in the capital area due to the rising apartment prices and redevelopment projects.

Changes in the Supply and Transaction Volume of Row Houses and Multi-housing Properties

The number of permits and approvals granted for construction of row houses and multi- housing properties as of November 2016 decreased nationwide by 5.5%, in the capital by 6.4% and in the non-capital area by 1.7%, compared to the previous year.

○ This contrasts with the 11.4% increase in the number of permits and approvals granted for new apartment construction projects.

154 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 4 Issue Analysis

○ Compared to the 3-year average, the number of permits and approvals granted for new row house and multi-housing construction projects increased by 19.2% nationwide, by 24.9% in the capital area and 1.7% in the non-capital area.

Table 4-5 ‌Rate of change in the number of permits and approvals granted for apartment, row house and multi-housing construction projects by year (YOY change, Unit: %) 2016 (compared to cumulative total in Nov.) Category 2013 2014 2015 Compared to the YOY change 3-year average Apartments -22.8 39.1 47.5 11.3 54.3 Nationwide Row houses & multi-housing -33.3 -5.9 51.3 -5.5 19.2 Apartments -24.4 40.8 91.8 -9.2 45.7 Capital area Row houses & multi-housing -29.4 9.5 54.9 -6.4 24.9 Non- Apartments -21.8 38.0 18.6 33.0 61.2 capital area Row houses & multi-housing -40.2 -37.8 38.2 -1.7 1.7

Source: MOLIT

In relation to the regional differences, the number of permits and approvals granted for construction of row houses and multi-housing properties in Jeju increased at the highest level in the country at 42.9%.

○ Jeju was followed by Ulsan at 39.5%, whereas the number of permits and approvals granted for construction of row houses, and multi-housing properties decreased in Daegu by 29.9% from the previous year, which was the largest decrease in the country.

Table 4-6 ‌Rate of change in the number of permits and approvals granted for row house and multi- housing construction projects by city and province (YOY change, Unit: %) Seoul Incheon Gyeonggi Busan Daegu Gwangju Daejeon Ulsan -11.6 -1.0 -1.3 -3.7 -29.9 -13.8 -0.8 39.5 Gangwon Chungbuk Chungnam Jeonbuk Jeonnam Gyeongbuk Gyeongnam Jeju 2.3 -18.9 -26.4 -23.3 16.5 -31.0 -13.5 42.9 Source: MOLIT

155 As of November 2016, the number of row house and multi-housing construction completions decreased by 13.8% nationwide, 15.4% in the capital area, and 6.6% in the non-capital area compared to the previous year.

○ The number of apartment, row house and multi-housing construction commencements all decreased nationwide and in the capital area and non-capital area in 2016 compared to the previous year.

○ Compared to the 3-year average, the number of apartment, row house and multi-housing construction commencements increased nationwide by 10.0% and in the capital area by 15.3%, but it decreased by 7.5% in the non-capital area.

Table 4-7 ‌Rate of change in the number of apartment, row house and multi-housing construction commencements by year (YOY change, Unit: %) 2016 (compared to cumulative total in Nov.) Category 2013 2014 2015 Compared to the YOY change 3-year average Apartments -4.6 25.5 44.3 -14.0 17.2 Nationwide Row houses & multi-housing -29.6 5.0 45.7 -13.8 10.0 Apartments 6.6 2.5 109.4 -19.5 30.5 Capital area Row houses & multi-housing -26.7 18.7 54.0 -15.4 15.3 Non- Apartments -11.5 42.7 9.4 -8.4 7.4 capital area Row houses & multi-housing -35.3 -24.7 17.4 -6.6 -7.5

Source: MOLIT

In relation to the regional differences, the number of new row house and multi-housing construction commencements in Jeju increased at the highest level in the country at 42.5%.

○ Gwangju recorded a 81.5% decreased compared to last year, which was the biggest drop in the country, followed by Daegu at -48.6% and Daejeon at -47.3%.

156 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 4 Issue Analysis

Table 4-8 ‌Rate of change in the number of apartment, row house and multi-housing construction commencements by city and province (YOY change, Unit: %) Seoul Incheon Gyeonggi Busan Daegu Gwangju Daejeon Ulsan -19.8 -16.6 -10.3 2.7 -48.6 -81.5 -47.3 -22.5 Gangwon Chungbuk Chungnam Jeonbuk Jeonnam Gyeongbuk Gyeongnam Jeju -29.6 -37.3 -30.3 -18.4 18.1 -38.8 14.0 42.5

Source: MOLIT

As of November 2016, the number of row house and multi-housing construction completions increased by 21.2% nationwide, 25.7% in the capital area and 4.7% in the non- capital area.

○ The number of apartment, row house and multi-housing construction completions all increased nationwide and in the capital area and non-capital area in 2016 compared to the previous year.

○ Compared to the 3-year average, the number of apartment, row house and multi-housing construction completions increased nationwide by 26.4% and in the capital area by 40.8%, but it decreased by 13.0% in the non-capital area.

Table 4-9 ‌Rate of change in the number of apartment, row house and multi-housing construction completions by year (YOY change, Unit: %) 2016 (compared to cumulative total in Nov.) Category 2013 2014 2015 Compared to the YOY change 3-year average Apartments 21.5 18.0 2.6 15.1 17.5 Nationwide Row houses & multi-housing -7.3 -12.6 16.4 21.2 26.4 Apartments -12.8 7.9 0.0 30.5 23.7 Capital area Row houses & multi-housing -8.9 -0.8 21.9 25.7 40.8 Non- Apartments 66.8 25.0 4.1 6.1 13.4 capital area Row houses & multi-housing -3.9 -36.1 -0.6 4.7 -13.0

Source: MOLIT

157 In relation to the regional differences, the number of new row house and multi-housing construction completions in Ulsan increased at the highest level in the country at 115.1%.

○ Jeju came in second at 51.8%, whereas the number of number of new row house and multi- housing construction completions decreased the most in Gyeongnam (21.7%), followed by Daegu (17.4%).

Table 4-10 ‌Rate of change in the number of apartment, row house and multi-housing construction completions by city and province (YOY change, Unit: %) Seoul Incheon Gyeonggi Busan Daegu Gwangju Daejeon Ulsan 33.2 2.3 21.2 -15.8 -17.4 -22.2 19.5 115.1 Gangwon Chungbuk Chungnam Jeonbuk Jeonnam Gyeongbuk Gyeongnam Jeju 50.3 -7.6 16.3 42.7 -2.4 -14.5 -21.7 51.8 Source: MOLIT

As of November 2016, the number of row house and multi-housing transaction volume decreased by 3.6% nationwide compared to the previous year. It increased by 2.8% in the capital area, and decreased by 17.8% in the non-capital area compared to the previous year.

○ Based on the cumulative annual transaction volume recorded in November, the transaction volume increased by 22.0% nationwide compared to the 3-year average. While it increased by 39.8% in the capital area, it decreased by 9.9% in the non-capital area.

○ Of particular note, in the capital area, it is deemed that the high prices of apartments contributed to the increase in the transaction volume of row house and multi-housing properties, which are relatively more affordable.

158 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 4 Issue Analysis

Table 4-11 ‌Rate of change in apartment, row house and multi-housing sales transaction volumes by year (YOY change, Unit: %) 2016 (compared to cumulative total in Nov.) Category 2013 2014 2015 Compared to the YOY change 3-year average Apartments 20.0 17.3 14.0 -16.2 -2.1 Nationwide Row houses & multi-housing 7.3 17.1 33.5 -3.6 22.0 Capital Apartments 47.8 26.6 25.3 -12.9 8.8 area Row houses & multi-housing 8.5 26.0 46.3 2.8 39.8 Non- Apartments 5.4 10.5 4.5 -19.3 -12.0 capital area Row houses & multi-housing 5.6 4.5 11.9 -17.8 -9.9

Source: MOLIT

In relation to the regional differences, the number of new row house and multi-housing transaction volume in Incheon increased at the highest level in the country at 12.7%, followed by Jeju at 2.6% and Seoul at 2.1%.

○ In contrast, it decreased by 44.4% in Daegu, which recorded the biggest drop in transaction volume compared to the previous year.

Table 4-12 ‌Rate of change in apartment, row house and multi-housing sales transaction volumes by city and province (YOY change, Unit: %) Seoul Incheon Gyeonggi Busan Daegu Gwangju Daejeon Ulsan 2.1 12.7 -0.1 -16.9 -44.4 -26.4 -14.5 -25.7 Gangwon Chungbuk Chungnam Jeonbuk Jeonnam Gyeongbuk Gyeongnam Jeju -4.9 -19.0 -11.2 -5.6 1.2 -21.6 -19.3 2.6

Source: MOLIT

In 2016, the inventory volume of row house and multi-housing properties increased by 4.2% nationwide, 4.8% in the capital area and 2.9% in the non-capital area.

○ The inventory of row house and multi-housing properties in 2016 was 2.39 million nationwide, 1.69 million in the capital area, and 700,000 in the non-capital area. As such, the inventory in the capital area accounts for approx. 71% of the total inventory.

159 ○ The YOY rate of change in the inventory volume in 2016 was recorded at +4.2%, which was higher than the rate of increase for apartments (3.0%). In the capital area, in particular, the inventory volume of row house and multi-housing properties increased by 4.8%, which is more than double the rate of increase for apartments (2.2%).

○ From 2007 to 2011, the annual rate of increase in the apartment inventory volume was higher, but since 2012, the rate of increase in the inventory volume of row house and multi- housing properties has exceeded that of apartments.

Table 4-13 Inventory volume and rate of change by property type in the past 5 years

(YOY properties, Unit: %) 2016 (compared to cumulative total in Nov.) Category 2013 2014 2015 Compared to the YOY change 3-year average Apartments 2.2 2.6 3.1 3.0 6.0 Nationwide Row houses & multi-housing 5.2 4.7 3.9 4.2 8.5 Apartments 2.8 2.1 2.4 2.2 4.5 Capital area Row houses & multi-housing 5.0 4.3 4.3 4.8 9.2 Non- Apartments 1.6 3.1 3.8 3.7 7.4 capital area Row houses & multi-housing 5.8 5.7 3.0 2.9 6.8 Note: ??????? Source: KAB, Official Public Housing Supply

Figure 4-6 Rate of change in the national inventory volume by property type and year

(YOY change, Unit: %) 6.0 Apartments Row houses and multi-housing 5.0

4.0

3.0

2.0

1.0

6.0 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Source: KAB, Official Public Housing Supply

160 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 4 Issue Analysis

Trends in Real Transaction Index Prices for Row Houses and Multi-housing Properties

Due to the recent rise in the apartment sales prices, there has been a growing demand for row houses and multi-housing properties, which are lower in cost, and this has resulted in climbing prices. The prices increased by 4.6% in 2016 3Q, which was a higher rate of increase compared to that of apartments (3.0%).

○ Until 2008, the rate of increase in the prices of row houses and multi-housing properties had significantly exceeded that of apartments, due to the impact of urban redevelopment projects including the New Town projects. However, starting in 2009, the rate of increase in the prices of apartments surpassed that of row houses and multi-housing properties.

○ Starting in 2013, the prices of row houses and multi-housing properties rebounded, and the rate of increase in the prices of row houses and multi-housing properties have been surpassing that of apartments until 2016 3Q.

Table 4-14 Rate of change by year and property type (Unit: %) Category 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016.3Q Row houses and multi-housing 28.4 5.1 7.3 -3.0 -0.9 -4.2 0.3 2.2 5.5 4.6 properties Apartments 4.9 -4.1 9.6 1.7 6.4 -2.6 4.5 5.2 6.6 3.0

Note: There‌ is a time difference in the termination of the real transaction index as the index can only be determined after the registration of a transaction has been completed, which typically takes 60 days. Source: KAB, Real Transaction Index of Row Houses-Multi-housing Properties

161 In 2014 and 2015, the rate of increase in the prices of row houses and multi-housing properties was higher in the capital area than in the non-capital area. In 2016, the rate of increase in the prices of row houses and multi-housing properties was 5.3% in the capital area and 2.8% in the non-capital area.

○ The prices of row houses and multi-housing properties in the capital area rebounded in 2014 and began rising along with the non-capital area. In 2016, prices increased by 5.3% in the capital area, as of 3Q, and this surpassed the rate of price increase in the non- capital area (2.8%). It is deemed that this resulted from the apartment price increases and improvement projects in the capital area.

Table 4-15 Rate of change in the real transaction index by region and year (Unit: %)

Region 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016.3Q

Capital area 33.1 5.4 8.3 -4.2 -2.8 -5.9 -1.4 1.2 4.8 5.3 Non-capital 2.9 4.5 5.0 11.1 11.2 3.2 6.1 5.4 7.7 2.8 area

Source: KAB, Real Transaction Index of Row Houses-Multi-housing Properties

In relation to property size, the prices of row houses and multi-housing properties of under 60㎡in floor size began increasing in 2014, and its rate of increase surpassed the rate of increase in the prices of row houses and multi-housing properties of over 60㎡ in floor size by 2016 3Q.

○ Until 2009, the rate of increase in the prices of row houses and multi-housing properties of under 60㎡in floor size surpassed that of properties of over 60㎡in floor size, but the rate of increase in the prices of row houses and multi-housing properties of over 60㎡in floor size has been exceeding its counterpart since 2010.

○ The rate of increase in prices for rate of properties of under 60㎡in floor size has been rising starting in 2014. It has increased by 5.1%, as of 2016 3Q, and surpassed the rate of increase in the prices of row houses and multi-housing properties of over 60㎡ in floor size.

162 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 4 Issue Analysis

Summary

In 2016, the number of permits and approvals granted for row house and multi-housing construction and the number of construction commencements declined compared to the previous year. However, the transaction volume increased by 2.8% in the capital area from last year, and the rate of increase in the prices of row houses and multi-housing properties was recorded at 4.6%, which was higher than that of apartments.

In relation to the regional differences, prices of row houses and multi-housing properties increase by 5.3% in the capital area and 2.8% in the non-capital area, and the rate of increase was higher for the properties of under 60㎡ in floor size than those of over 60㎡ in floor size.

Considering that around 71% of all row houses and multi-housing properties in the nation are concentrated in the capital area, the rise in prices and transaction volume could be attributed to the increase in apartment sales and jeonse prices and the localized improvement projects in the capital area.

Also, the growing demand among the end-users and investors for small residential properties, as a result of an increasing number of single- and two-member households, has

Figure 4-7 A comparison of the rate of increase in sales prices of apartments, row houses and multi-housing properties (Unit: %)

35.0 31.5 30.0 Apartments Row houses and multi-housing

25.0

20.0 15.8 15.0

10.0 9.5 6.1 4.8 5.1 5.5 3.7 5.0 3.6 3.4 3.0 2.5 1.5 1.5 1.6 0.0 -0.1 -1.1 -5.0 -1.9 -2.9 -4.1 -10.0 2007 2008 2009 2010 2011 2012 2013 2014 200715 2016.3Q

163 impacted the rate of increase in the prices of row houses and multi-housing properties of under 60㎡ in floor size.

The prices of row houses and multi-housing properties are sensitive to the changes in the apartment prices. Due to the risks associated with the foreseeable uncertainties such as the U.S. federal interest rate hikes and the domestic loan restrictions, it is expected that the end-users and investors will take a wait-and-see stance, which in turn will push down the transaction volume and prices in 2017.

164 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 4 Issue Analysis

Burden of Jeonse and Rental Costs on Single-member Householdsg

Won Youho

▶ The changes in the industrial structure and the low-growth trends observed worldwide are quickly transforming the common household structure into single-member households in Korea. According to the 2015 Population and Housing Census, there was a shift in the household structure primarily to single-member households, and there was an increased housing cost burden among those living alone, indicating an urgent need to formulate a policy to stabilize housing supply and costs. The cost incurred to the users of jeonse and rental properties among young and middle-aged single- member households was analyzed based on the Household Financial Welfare Survey and Housing Conditions Survey. The results showed that the gap in cost among the tenants was relatively low, but the gap was significant for renters depending on the age and income bracket. In order to address this issue, there is a need to continually increase housing supply for jeonse and rental property tenants among young adults and middle-aged adults through local development and construction projects in addition to the increased construction of rental housing for single-member households by the private sector. Furthermore, it is necessary to predict the housing demand among the subdivided young and middle-aged single-member households by region as a means to supply adequate housing, with an urgent need to introduce specific financial assistance measures that can ease the burden of housing costs.

165 Increase in Single-member Households in Korea

The changes in the industrial structure and the low-growth trends observed worldwide are contributing to the rapid transformation of the common household structure into single- member households.

○ According to the 2015 Census, the average number of members in a household was 2.53, which was a 0.15 decrease from 2.68 reported in 2010.

○ The primary type of households until 2005 was “four-member household,” but it was “two-member household (24.6%)” in 2010 and “single-member household (27.2%)” in 2015, indicating a gradual contraction of the household structure.

○ In 2015, 27.2% of all households were single-member households (5.2 million households), which was a 3.3%p increase from 23.9% (4.22 million households) in 2010.

The increase in the single-member households has had direct and indirect ripple effects on the housing lease market.

○ A review of the national jeonse and rental transaction volumes nationwide shows a growth of the housing lease market, with an increase in the ratio of rentals to jeonse.

Figure 4-8 Changes in the percentage of single- Figure 4-9 4-9 Percentage of households type member households nationwide based on number of members by year

Nationwide Capital area (Unit: %)

1인 2인 3인 4인 5인이상

27.2

23.9 20.0

15.5 12.7 9.0 6.9

1985 1990 1995 2000 2005 2010 2015

Source: Statistics Korea,2015 Population and Housing Census

166 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 4 Issue Analysis

○ Single-member households can be divided into diverse income brackets and ages. It is deemed that the housing cost burden is relatively higher for single-member households with low income levels who are younger.

Figure 4-10 Ratio of housing occupation form (Unit: %) 100

18.2 18.6 21.8

21.7 21.5 19.6

54.2 53.8 53.8

0 2010 2012 2014

Own home Jeonse Rental Source: MOLIT

Figure 4-11 Ratio of jeonse to rental transaction volume by region (Unit: ten thousand)

Nationwide Capital area Non-capital area

160160 146.7146.7 120120 6060 132.1132.1132.4132.4137.3137.3 135.4135.4 140140 97.897.8 49.949.9 100100 88.288.2 88.888.891.591.5 89.689.6 5050 43.943.9 43.643.645.945.9 45.845.8 120120 6060 4444 4545 5454 3737 6060 8080 2727 2727 3333 4040 2323 100100 3333 1717 1818 2121 2222 8080 6060 3030 6060 4040 2020 8989 8787 8383 8787 6262 6161 5959 6060 4040 7676 5252 2727 2626 2525 2525 2424 2020 1010 2020 0 0 0 0 0 0 2011201120122012201320132014201420152015 2011201120122012201320132014201420152015 2011201120122012201320132014201420152015 JeonseJeonse RentalRental JeonseJeonse RentalRental JeonseJeonse RentalRental

Source: RTMS and KAB Market Report

167 Increase in Housing Cost for Young and Elderly Single-member Households

The results of an analysis of the housing cost for leasing households, based on the 2012~2015 Household Financial Welfare Survey, showed that the housing cost burden was high for the third quintile income class and the 20~30 y.o. age group, and that the burden was increasing gradually.

○ An analysis of the monthly average housing costs for jeonse and rental tenants (ten thousand KRW/3.3㎡) among the single-member households, based on their income bracket, showed that there were relatively little changes in the housing cost for the first- and second quintile income class.

○ On the other hand, for the third quintile income class, housing costs increased by KRW 8,000/3.3㎡ from 2012 to 2015. The 2015 data showed that housing costs increased relative to the income class from the first quintile to third quintile.

○ Next, an analysis of the monthly average housing costs (ten thousand KRW/3.3㎡) based on age showed that the biggest increase was recorded by people in their 20s, with an increase of KRW 8,000/3.3㎡ from 2012 to 2015. People in their 30s recorded an increase of KRW 4,000/3.3㎡, and people in their 40s KRW 3,000/3.3㎡, indicating an intensifying housing

Figure 4-12 Changes in housing costs for Figure 4-13 Changes in housing costs for lessees among singe-member households by lessees among singe-member households income level by age (Unit: ten thousand KRW/3.3㎡) (Unit: ten thousand KRW/3.3㎡) 4.0 4.0 3.5 3.5 3.0 3.0 2.5 2.5 2.0 2.0 1.5 1.5 1.0 1.0 0.5 0.5 0.0 0.0 2012 2013 2014 2015 2012 2013 2014 2015 20s 2.6 2.6 3.4 3.4 1st quintile 1.9 2.1 2.0 1.8 30s 2.7 3.0 3.0 3.1 2nd quintile 2.8 3.0 2.8 2.8 40s 2.5 2.4 2.5 2.8 3rd quintile 2.6 2.3 2.8 3.4 50s 2.4 2.4 2.2 2.2 4th quintile 2.4 2.7 2.6 2.6 60sand over 1.7 2.0 1.7 1.7 5th quintile 1.9 3.2 3.4 3.1

Source: 2012 Household Financial Welfare Survey - 2015 Source: 2012 Household Financial Welfare Survey - 2015

168 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 4 Issue Analysis

cost burden on the young and middle-aged people compared to seniors.

○ It is deemed that there would be many college students and first-time workers among people in their 20s and 30s, who experience relatively large changes in housing costs. There are concerns that the housing cost burden will be prolonged by a delay in graduation and difficulty in finding employment.

Costs Incurred to Jeonse and Rental Tenants Among Young and Middle-aged Single-member

Costs calculated by dividing the jeonse and rental tenants among single-member households described in the 2014 Housing Conditions Survey into jeonse, rental with deposit, and rental without deposit

= Housing costs for jeonse tenants = Housing cost for deposit-based rental tenants = Housing cost for rental tenants (no deposit) = Jeonse rental cost (Jeonse deposit), = Monthly rent = Market interest rates, = Loan, = Mortgage interest rates

Additionally, an analysis of the monthly average housing cost (ten thousand KRW/3.3㎡), including the burden of repayment, for jeonse and rental tenants showed that the housing costs for rental tenants in their 20s and 30s were higher than other age groups. It was shown that the younger the household was, the higher the rental housing cost burden was.

○ A comparison of the monthly average costs for jeonse and rental tenants (ten thousand KRW/3.3㎡) showed that the costs for renters were 19,000~51,000 KRW/3.3㎡, which was a considerable gap compared to the jeonse tenants.

169 Figure 4-14 A comparison of housing costs for jeonse and rental tenants among single-member households by age (Unit: ten thousand KRW/3.3㎡)

5.5 4.3 20s 30s 40s 50s 60s 70s and over 4.5

4.0 3.7 3.5 3.2 3.0 3.0 2.8 2.9

2.5 2.3 2.3 2.2 2.1 2.1 2.1 2.0 2.0 1.9 1.8 2.0 1.4 1.7 1.3 1.4 1.4 1.5 1.3 1.3 1.1 1.3 1.1 1.1 0.9 0.9 1.2 1.1 0.8 1.0 1.0 0.8 0.7

0.5

0.0 Rental Jeonse Rental Jeonse Rental Jeonse 1-member household 2-member household 3-member household 20s 5.1 1.1 3.7 1.4 2.9 1.1 30s 4.3 1.4 3.0 1.4 2.2 1.3 40s 3.2 1.3 2.3 1.1 2.1 1.3 50s 2.8 0.8 2.1 0.8 2.0 1.2

60s 2.1 0.9 2.3 0.9 1.8 1.3 70s and 1.9 0.7 2.0 1.0 1.7 1.1 over Source: 2014 Housing Conditions Survey

○ Also, when the costs for rental tenants (ten thousand KRW/3.3㎡) are compared based on age, it showed that the costs were 51,000 KRW/3.3㎡ for people in their 20s and 30,000 KRW/3.3㎡for people in their 30s, which indicated that the housing cost burden was higher for younger people.

A comparative analysis was performed on the monthly average housing costs (ten thousand KRW/3.3㎡) for jeonse and rental tenants among single-member households based on their income class. The results showed that the costs were higher for rental tenants than jeonse tenants, and that the gap increased gradually from the first to third quintile income class, but it began to decrease in the fifth quintile income class.

○ The gap in the housing costs for single-member households based on income class tended to increase at higher income classes; however, it increased until the fourth quintile income

170 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 4 Issue Analysis

Figure 4-15 A comparison of housing costs for jeonse and rental tenants among single-member households by income class (Unit: ten thousand KRW/3.3㎡) 5.0 5.0

4.4

4.0 4.0

3.8

3.0

2.6

2.0 2.0

1.4 1.2 1.0 0.9 0.7

5.0 1st quintile 2nd quintile 3rd quintile 4th quintile 5th quintile

Rental 2.6 3.8 4.0 5.0 4.4 Jeonse 0.7 0.9 1.2 1.4 2.0

Source: 2014 Housing Conditions Survey

class (50,000 KRW/3.3㎡) and began decreasing in the fifth quintile (44,000 KRW/3.3㎡)

○ It is expected that there will be an increase in the number of people in the low income class among single-member households to switch from jeonse to rental due to the small gap between the housing costs incurred from jeonse and rentals. Of particular note, young-to- middle-aged people tend to have a lower income compared to the middle-aged-to-senior people, and thus it is more likely for the former class to choose rentals over jeonse as a form of housing occupation.

There are signs that the employment difficulties for the young and middle-aged people will be prolonged, meaning that there is a possibility that the burden of housing costs will increase even further for young and middle-aged people among single-member households. Accordingly, this is a time when a policy must be considered to stabilize housing costs and provide assistance to the vulnerable classes and young adults.

171 ○ In this study, the housing costs and costs incurred to jeonse and rental tenants among single-member households by age and income class were analyzed. The results showed that the gap between the costs incurred to jeonse tenants was relatively small, but in the case of renters, the gap was bigger for young adults and as one moves up the income class.

○ To address this issue, there is a need for the government to supply housing and to promote the construction of rental housing for single-member households by the private sector. To this end, there is a need to predict the housing demand among the subdivided young and middle-aged population by region and aggressively review continuous housing supply measures for renters within this population.

172 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 4 Issue Analysis

Market Trends and Outlook on Aggregate Retail Shop Market

Woo Namkyo

▶ With the ongoing low interest rate trend, there has been increased interest in commercial real estate, especially aggregate retail shops that even individual investors can access easily with small capital.

Aggregate Retail Shops

○ Nationwide As of November 2016, there were 34,681 aggregate buildings (total gross floor area: 92.237㎡) nationwide, with 1,376,955 retail shops within the buildings, based on the Building Register.

○ In Seoul, there were 5,602 aggregate buildings (250,406 retail shops), accounting for 16.2% of the national total, and the total gross floor area was 16.351 million㎡. In Gyeonggi, where there were the greatest number of aggregate buildings than any other regions in the country, there were 8,406 aggregate buildings (373,260 retail shops), accounting for 24.2% of the national total, and the total gross floor area was 30.956 million㎡.

173 Figure 4-16 Aggregate buildings by region

Buildings (%) Shops (%) Gross floor area

3095.6 ㎡

1635.1 ㎡

580.9 ㎡ 745.8 ㎡ 771.7 ㎡ 332.5 ㎡ 291.8 ㎡ 194.8 ㎡ 265.9 ㎡ 117.8 ㎡ 97.6㎡ 27.1% 204.7 ㎡ 188.9 ㎡ 235.7 ㎡ 209.3 ㎡ 229.9 ㎡ 25.7 ㎡ 24.2%

18.2% 16.2%

9.4% 8.3% 8.1% 6.8% 7.4% 6.9% 5.2% 5.9% 4.8% 4.2% 3.0% 3.7% 2.9% 3.0% 3.9% 2.5% 2.6% 2.4% 3.0% 2.1% 2.1% 3.3% 3.0% 3.0% 0.7% 2.2% 2.3% 0.6% 0.6% 0.4%

Seoul Busan Daegu In Gwan Dae Ulsan Sejong Gyeong Gang Chung Chung Jeon Jeon Gyeon Gyeong Jeju -cheon -gju -jeon -gi -won -buk -nam -buk -nam -gbuk -nam

Table 4-16 Aggregate retail shops Figure 4-17 nationwide distribution map (dong standards)

Aggregate retail Aggregate buildings shops Region Perce Perce Buil Total gross -ntage floor area Units -ntage -dings (%) (10,000㎡) (%) Nationwide 34,681 100.0 9,223.7 1,376,955 100.0 Seoul 5,602 16.2 1,635.1 250,406 18.2 Busan 2,368 6.8 580.9 114,352 8.3 Daegu 1,665 4.8 332.5 71,825 5.2 Incheon 2,552 7.4 745.8 95,478 6.9 Gwangju 865 2.5 194.8 36,269 2.6 Daejeon 825 2.4 265.9 40,723 3.0 Ulsan 725 2.1 117.8 29,302 2.1 Sejong 249 0.7 97.6 7,842 0.6 Gyeonggi 8,406 24.2 3,095.6 373,260 27.1 Gangwon 1,151 3.3 204.7 30,255 2.2 Chungbuk 1,031 3.0 188.9 41,348 3.0 Chungnam 1,268 3.7 291.8 41,894 3.0 Jeonbuk 1,444 4.2 235.7 31,693 2.3 Jeonnam 1,008 2.9 209.3 41,886 3.0 Gyeongbuk 2,041 5.9 229.9 53,568 3.9 Gyeongnam 3,261 9.4 771.7 111,513 8.1 Jeju 220 0.6 25.7 5,341 0.4

174 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 4 Issue Analysis

Trends in the Supply and Sales Transactions of Aggregate Retail Shops

○ Supply Trends In 2016, a total of 1,408 aggregate buildings (65,043 retail shops) were supplied nationwide until November, with the highest supply in Gyeonggi with 335 aggregate buildings (20,585 retail shops). It was followed by Gyeongnam (137 aggregate buildings), Daegu (126 aggregate buildings), Jeonnam (90 aggregate buildings), and Seoul (85 aggregate buildings). In other regions, there was relatively higher supply of aggregate buildings in the rural provinces compared to metropolitan cities.

Figure 4-18 Trends in supply of aggregate buildings by region in 2016

New supply (buildings) Total supplied area (10,000㎡)

144.6 53.7 28.3 44.3 28.0 32.6 48.5 24.0 6.9 9.5 10.9 17.4 11.8 15.0 15.1 10.6 1.1

335

126 137 85 77 78 90 70 57 56 65 53 36 53 44 30 16

Seoul Busan Daegu In Gwan Dae Ulsan Sejong Gyeong Gang Chung Chung Jeon Jeon Gyeon Gyeong Jeju -cheon -gju -jeon -gi -won -buk -nam -buk -nam -gbuk -nam Source: Building Register (as of Nov. 2016), Seumteo

○ A review of the supply of aggregate buildings by year from 2006 to date showed that there was a gradual increase until 2008 due to the impact of the global financial crisis. It has steadily rebounded since then, with consistent supply of new aggregate buildings each year.

175 Figure 4-19 Trends in supply of aggregate buildings nationwide by year

New supply (buildings) Total supplied area (10,000㎡)

499.6 502.4 423.8 391.0 418.5 283.6 273.7 264.8 258.6 245.5 266.5

1,423 1,408 1,335 1,223 1,156 1,039 956 952 881 853 813

'06 '07 '08 '09 '10 '11 '12 '13 '14 '15 '16 Source: Building Register (as of Nov. 2016), Seumteo

○ The largest aggregate building in the country is Garden Five Life (in Munjeong-dong, Songpa-gu), the construction of which was completed in 2008. It is comprised of 5,368 individual retail shops, with a total gross floor area of 426,636㎡ (11 above-ground floors and 5 underground floors).

- ‌The top 5 aggregate buildings in terms of gross floor area are all retail facilities, and four of the five buildings are located in Seoul. The largest aggregate building in the rural provinces is Yojin Y-City in Asan, which has a gross floor area of 310,811㎡ (4 above- ground floors / 1 underground floors).

Table 4-17 연면적 규모 상위 5개 집합건물 현황

Usage approval Gross floor Location Building Floor count Units date area (㎡) Seoul Songpa-gu Garden Five Life 2008.12.19. 426,636 11F/B5 5,369 Seoul Yeongdeungpo-gu Time Square 1994.08.20. 340,914 20F/B5 - Chungnam Asan-si Yojin Y-City 2011.06.03. 310,811 4F/B1 - Seoul Guro-gu Sindorim Techno Mart 2007.12.28. 305,934 40F/B7 4,402 Hyundai IPARK Mall Seoul Songpa-gu 2005.04.26. 278,105 10F/B3 - (Main Branch) Source: Building Register (as of Nov. 2016), Seumteo

176 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 4 Issue Analysis

○ In 2016, W Tower (gross floor area: 25,069㎡) and Open Plaza (20,440㎡) were newly built in Magok-dong, Seoul, and Woosung Tram Tower (40,587㎡) and A Plus Tower (31,250㎡) were built in Seongnam-si, Gyeonggi. In Najo Inno-City in Jeonnam, Yegaram Tower (203,429㎡) was newly built, and it was the largest aggregate building to be supplied in the year 2016.

Table 4-18 Supply of major aggregate buildings in 2016 (Unit:%) Usage approval Gross floor Location Building Floor count Units date area (㎡) Seoul Magok-dong W Tower 2016.04.14 25,069 11F/B3 146 Seoul Magok-dong Open Plaza 2016.01.25 20,440 11F/B5 66 Gyeonggi Goyang-si Yojin Y-City 2016.09.30 42,598 3F/B1 2,695 Sujeong-gu, Gyeonggi Woosung Tram 2016.06.16 40,587 11F/B3 223 Seongnam Sujeong-gu, Gyeonggi A Plus Tower 2016.10.13 31,250 11F/B5 122 Seongnam Gyeonggi Hwaseong-si Woosung KTX Tower 2016.10.13 25,680 11F/B3 117 Gyeonggi Dongan-gu, Anyang W Ace Tower 2016.01.15 22,634 7F/B2 140 Gyeonggi Dongan-gu, Anyang Daehan Smart Tower 2016.03.07 21,843 9F/B3 89 Gyeonggi Gimpo-si Asta Plaza 2016.06.27 20,678 9F/B2 92 Gyeonggi Gimpo-si Herium Town 2016.03.22 19,578 7F/B2 106 Gyeonggi Uijeonbu-si Mega Tower 2016.04.08 16,494 9F/B3 - Busan Gijang-gun Fantasy O Square 2016.05.25 25,944 4F/B1 205 Technopolis M Daegu Dalseong-gun 2016.04.14 29,973 4F/B1 95 Square+ Jeonnam Naju-si Yegaram Tower 2016.07.22 203,429 5F/B1 110 Jeonnam Naju-si Rich Tower 2016.01.22 31,650 9F/B1 149 Naju Inno-city Star Jeonnam Naju-si 2016.07.22 27,930 9F/B1 167 Tower

Source: Building Register (as of Nov. 2016), Seumteo

○ Sales Transaction Trends There have been considerable sales transactions of aggregate retail shops due to the recent interest shown by individual investors. The number of sales transactions had been on the rise since the second half of 2013, but it faltered in the second half of 2015. However, it has been gradually increasing in 2016, with 15,778 transactions (1.168 million㎡) made by the third quarter, based on the sales contract date.

177 Figure 4-20 Trends in aggregate retail shop sales transactions

Number of sales Area (10,000㎡)

145.8 129.1 124.1 116.8 105.5 98.6 98.8 84.7 92.3 92.8 88.1 93.3 78.4 76.3 72.7 15,837 15,778 14,882

12,358 12,272 12,036 12,209 11,658 10,918 9,763 9,504 9,976 8,906 8,241 8,297

`13.1Q `13.2Q `13.3Q `13.4Q `14.1Q `14.2Q `14.3Q `14.4Q `15.1Q `15.2Q `15.3Q `15.4Q `16.1Q `16.2Q `16.3Q Source: RTMS (based on contract conclusion date)

- ‌The biggest aggregate building (excl. office facilities) ever to be sold was Halla High Hill (gross floor area: 98,996㎡), which was sold by Halla Corporation to KTB Asset Management for KRW 330 billion. The operation of High Hill Outlet was commissioned to , which became the first to start an “urban outlet mall” business.

- A‌ review of the distribution of transactions by sales price in 2016 showed that most were under KRW 300 million and located in Seoul (73.9%) and Gyeonggi (78.6%). Meanwhile, in Gangwon, most of the sales transactions (83.5%) were under KRW 100 million.

- ‌The areas where large transactions of KRW 500 million to 2 billion occurred the most in 2016 were Seoul (11.4%), Daegu (17.0%), and Sejong (20.3%). Of particular note, these transactions in Seoul involved aggregate retail shops located near subway stations, in specialized alleys (e.g. Garosu-gil, eatery alley in Nonhyeon-dong), and in other geographically favorable areas.

178 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 4 Issue Analysis

Table 4-19 Distribution of aggregate retail shop sales transactions by sales price

(Unit:%) KRW 100 M KRW KRW KRW KRW KRW KRW KRW Over and under 100~200M 200~300M 300~400M 400~500M 500M~1B 1~2B 2~3B KRW 3B Nationwide 35.0 30.1 14.5 7.5 4.6 6.6 1.2 0.2 0.2 Seoul 18.9 35.5 19.5 9.1 5.1 8.5 2.4 0.5 0.6 Busan 43.2 29.2 12.5 4.4 3.0 5.9 1.2 0.5 0.1 Daegu 20.4 26.4 16.4 11.5 7.2 15.0 1.6 0.3 1.0 Incheon 34.4 33.2 12.4 6.8 6.3 5.9 0.8 0.1 0.1 Gwangju 48.8 22.8 12.2 7.1 3.0 4.3 1.3 0.2 0.3 Daejeon 45.1 22.1 13.5 8.7 4.1 5.5 0.8 0.1 0.1 Ulsan 26.9 31.3 17.9 8.1 5.6 5.6 1.3 1.7 1.7 Sejong 11.2 14.1 13.7 24.2 16.6 19.5 0.8 0.0 0.0 Gyeonggi 27.7 34.7 16.2 8.3 5.1 6.9 0.9 0.1 0.1 Gangwon 83.5 9.0 3.2 1.5 1.0 1.4 0.3 0.1 0.0 Chungbuk 48.3 26.3 12.3 6.6 3.0 3.0 0.4 0.1 0.1 Chungnam 49.5 20.9 10.8 9.7 4.1 4.5 0.4 0.0 0.0 Jeonbuk 37.7 26.7 14.4 8.0 5.1 6.8 1.2 0.0 0.2 Jeonnam 42.5 23.0 13.3 8.4 6.5 5.4 0.8 0.1 0.0 Gyeongbuk 49.1 30.2 10.4 5.1 2.5 2.2 0.3 0.2 0.0 Gyeo- 43.3 25.4 11.8 7.1 4.3 7.1 0.9 0.1 0.1 ngnam Jeju 42.4 30.8 10.3 4.8 4.4 6.5 0.7 0.1 0.0 Source: Real Estate Real Transaction Data (based on contract conclusion date)

Figure 4-21 Distribution of aggregate retail shop sales transactions by sales price and region

100M and under 200~300M 100~200M 300~40M 400~500M (%) 100.0 3.2 90.0 9.0 10.4 12.3 80.0 12.5 12.2 10.3 12.4 13.5 10.8 11.8 14.5 16.2 14.4 13.3 70.0 17.9 19.5 30.2 26.3 60.0 29.2 22.8 20.9 30.8 16.4 22.1 23.0 25.4 50.0 30.1 33.2 26.7 34.7 40.0 31.3 83.5 35.5 26.4 30.0 13.7 48.8 48.3 49.5 49.1 43.2 45.1 42.5 43.3 42.4 20.0 35.0 34.4 14.1 37.7 26.9 27.7 10.0 18.9 20.4 11.2 0.0 Nation Seoul Busan Daegu In Gwang Dae Ulsan Sejong Gyeong Gang Chung Chung Jeon Jeon GyeongGyeong Jeju -wide -cheon -ju -jeon -gi -won -buk -nam -buk -nam -buk -nam

179 Outlook on the Aggregate Retail Shop Market

The ongoing low interest rate trend has drawn interest among investors to retail shop investments that would yield profits. There has been a boom of investment into aggregate retail shops, which require a relatively small capital.

○ The benchmark interest rate in Korea has been lowered and frozen over the years, and it has reached 1.25% as of December 2016. The current 1-year deposit interest rate is currently 1.61%, which will continue to investors to search for better investment products* and return on investment, thereby increasing demand for real estate properties that can yield a return. * ‌Treasury bonds (3-year maturity): 1.609%; Corporate bonds (3-year maturity, AA-): 2.021% Fixed-term deposit interest rate: 1.49%; CD interest rate (91 days) 1.420% (Bank of Korea, as of Nov. 2016)

○ The annual return on investment for mediu-to-large retail shops in general buildings nationwide was 6.51%, as of the end of September 2016, and 6.13% for small retail shops. On the other hand, the return on investment is higher for aggregate retail shops at 7.28%. Due to the relatively high return on investment, investors prefer aggregate retail shops over other types of retail shops, and for this reason, it is expected that there will continue to be new supply of and demand for aggregate retail shops.

○ It is expected that the demand for and interest in aggregate retail shops, which can be purchased by individual investors with a relatively small capital, will continue to grow. However, the commercial real estate investment market may shrink in case the economic slowdown continues and the domestic real estate market is affected by the U.S. federal interest rate hike, which will result in market uncertainties.

180 Korea Appraisal Board Korea Real Estate Market Report(2016 Real Estate Market Trends and 2017 Outlook) P A R T 4 Issue Analysis

Korea Real Estate Market Report

Date of Issue January 2017

Publisher Seo Jongdae Real Estate Research Institute of KAB 291, Innovalley-ro, Dong-gu, Deagu 41068, Korea Tel. +82-1644-2828 www.kab.co.kr

Editing Min Chulhong ([email protected])

Design/Print Design CREPAS

ISSN 2508-3260

This report and all contents generated by authors are protected by copyright. Any reproduction, use, and revive without consent are prohibited.

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