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Disclaimer gr e y sh a d i n g t h e a v e r a g e i n o u r6 3 - c i t y s a m pl e )L a n d b a n k e x p o s u r eK e y C h i n e s e d e v elo p e r s g r o s s f l o o r a re a( G F A ) e xp o s u r e i n t h e s e 1 6 c i t ie s :K e y f a c t s i n C h i n a s d eb t - f u n d e d ho u s i n g b o o mC h in a h o u se h o l d l o a n - t o - d e p o s i tr a t i o h a s i n c r e a s e d f r o m 3 7 % i nJ a n ua r y 2 0 1 2 t o 6 3 % i n 1 H 1 86 3 % 8 2 3 %T h e p e r c e n t a g e g r o w t h i n o u t s t an d i n gm o r t g a g e s s in c e 2 0 0 7 ; i n S e p t em b e r2 0 1 8 t h e t o t a l h a d r e a c h e d RM B 2 4 . 9 t r n45 9 %T o t a l bo r r o w i n g f o r r ea l e s t a t ed e v e lo p m en t o n a n a ti o na l l e v e lin c r e a s e d f r o m R M B 1 . 8 t r n i n 2 0 0 7 t oRM B 1 0 . 1 tr n i n S e p t em b e r 2 0 1 8C h an g e i n ho u s eh o l d l o a n - t o - d e p o s i t - r a t i o( r e l a t i v e ba s i s , c ha n g e b e t w e e n 2 0 1 5 a n d 2 0 1 7 )2 7 p p t 1 5 p p tC i t y - l e v e l r ea l e s t a t e i nv e s t m en t a s ap e r c en t a g e o f G D P ( 2 0 1 7 )2 1 % 1 6 %C i t y - l e v e l g o v e r n m en t d eb t a s ap e r c en t a g e o f G D P ( 2 0 1 7 )4 4 % 3 8 %H o u s eh o l d l o a n - t o - d e p o s i t r a t i o( a b s o l u t e b a s i s , 2 0 1 7 )9 4 % 6 9 %1 8 8 %1 3 3 % 1 0 5 %9 7 %79%6 7 %5 9%4 9%Z hu h a iH a r b i nB a o t o uJ i a n g m e nH u a ia nS ha n gh a iH u z h o uB e i j i n gH e f e iX i a m e nH u iz h o uS u z h o uZ hen gz h o uS a n yaW e nz h o uD a l i a nZ he n j i a n gY a n gz h o uA r e a in d e t a ilL o g a nS Z I n v e s t m en tC GAg i l eC R L a n dS h i m a oY an lo r dC I F IG Z R The scale of local government leverage at the city level; The 16 cities we think are most vulnerable to a slowdown in the property market; The developers whose land banks have the most exposure to these cities; and A scenario analysis on different mortgage scenarios The rising tide of debt We believe the boom in Chinas property market is largely attributable to loose monetary conditions that have prevailed since 2016. Home buyers have had easy access to cheap mortgages and developers were able to tap the onshore and offshore bond markets at historically low interest rates. The accommodative credit environment led to asset price inflation across all levels of the residential property market, from giant tier-1cities like Beijing and Shanghai to much smaller lower tier cities in less prosperous provinces. In extreme cases, prices in some cities have risen by more than 70% in the last three years. Times have changed, and concerns about the rising tide of debt have resulted in policymakers trying to place a cap on leverage. Time for a debt check We have written a series of reports in the past two years identifying investment opportunities in Chinas sprawling property market. As the real estate market starts to cool, we shift the focus to assessing which cities face the highest risk in the event of a correction in house prices. As the government tries to put a cap on leverage, we look at the level of household debt and how dependent city economies are on the property sector for raising revenue. We identify the 16 cities we think are most vulnerable to a slowdown in the housing market and also highlight the developers whose land banks have the highest level of exposure to these cities. From finding the hot cities to a risk assessment 7 EQUITIES REAL ESTATE January 2019 Scenario analysis Deleveraging in the form of more restricted mortgage lending to home buyers will have an impact on the market. This report quantifies the potential impact on housing market based on three different mortgage scenarios. In the base case we assume the annual new mortgage-to-property sales ratio remains unchanged, at c35%, with 2019e residential housing sales of 1,231m sqm, which implies a 10% decline in national sales volume in 2019e. We calculate that annual new mortgages totalling RMB3,530bn will flow into the residential property market. This implies a loan-to-deposit ratio of 52.1% in 2019, slightly higher than 49.2% in 2018e. In the bear case, we demonstrate the effect of extreme tightness in mortgage conditions similar to 2012. Our calculation shows a decline in the loan-to-deposit ratio, to 48.6%, which signifies efforts at deleveraging. The outcome implies a further 20% downside to national residential sales GFA from our base case of 1,231m sqm to our bear case of 985m sqm, implying a 28% y-o-y decline in national sales volume in 2019e. In the bull case mortgage lending remains accommodative and the loan-to-deposit ratio grows to 54.7%, we project a 15% upside to national property sales to our base case scenario. Please see pages 22-25 and Figure 25 for more details. Key facts and stats about Chinas debt-funded housing boom Mortgages and leverage Outstanding mortgages have grown by 823% since 2007 to RMB24.9trn (USD3.61trn) in September 2018, according to data released by the Peoples Bank of China (PBOC). Outstanding mortgage loans as a percentage of GDP stood at 26% in 2017, up from 10% in 2007. Outstanding mortgages in China exceeded the GDP of both the UK (USD2.62trn) and France (USD2.58trn) in 2017, which are the second and third largest economies in Europe. China household leverage is increasing rapidly the household loan-to-deposit ratio has risen from 37% in January 2012 to 63% in 1H18. Over RMB300bn of short-term consumption loans were channelled into the property market between March and August 2017, according to industry estimates. Real estate developers have relied heavily on debt to fund growth. Total borrowing for real estate development increased from RMB1.8trn in 2007 to RMB6.6trn in 2015 and RMB10.1trn in September 2018, cumulative growth of 459%. Home price growth Shenzhen (131%) and Xiamen (113%) recorded the highest rate of price increases between 2011 and October 2018. These two cities also had close to the highest household loan-to-deposit ratios in 1H18 157% and 178% respectively. 14 cities in China recorded increases in home prices of more than 80% between 2011 and October 2018. Of the 99 cities we track only three registered a decline in home prices during this period Sanya (-10%), Jilin (-5%) and Yingkou (-1%). EQUITIES REAL ESTATE January 2019 8 Screening for cities with high risk profiles A three-pronged approach First, we calculate a risk index for each city to gauge their respective risk profiles. The index is a weighted average value calculated based on four factors: (1) 2017 household loan-to-deposit ratio (35% weighting), (2) change in household loan-to-deposit ratio during 2015-17 (35% weighting), (3) city-level real estate investment as a percentage of GDP (15% weighting), and (4) city-level government debt as a percentage of GDP (15% weighting). Cities that have leveraged up aggressively are seen to be more vulnerable to potential shocks from a deflating housing market. Second, we compare our assessment of city-level risk to macro indicators such as strong economic growth and population inflows in order to understand the potential offsetting factors that help reduce a citys risk profile. The logic here is that macro strength increases the ability to service higher levels of debt which, in turn, means more manageable risk levels. This analysis is based on our macro growth index. Third, we take city-level property supply (using inventory levels and inventory months) into consideration as cities with tight supply are likely to be better shielded from a housing market correction. This process eliminates 11 cities from our preliminary list of cities with potentially higher risk profiles. Based on this three-pronged screening methodology, we identify 16 cities Zhuhai, Xiamen, Huaian, Sanya, Huizhou, Hefei, Zhengzhou, Suzhou, Zhenjiang, Huzhou, Yangzhou, Baotou, Wenzhou, Harbin, Jiangmen and Dalian as cities with potentially higher risk profiles. Figure 3: HSBCs list of 16 cities with potentially higher risk profiles _ Loan-to-deposit ratio _ REI/GDP Gov debt/GDP _ ASP change _ Cities 2017 Change in 2015-2017 (ppt) 2017 2017 2016-1H18 9M18 Zhuhai 139% 61 25% 64% 53.9% -0.4% Xiamen 181% 48 20% 44% 32.8% 1.1% Huaian 188% 30 9% 74% 19.6% 3.3% Sanya 49% 26 104% 34% 25.0% 5.8% Huizhou 104% 44 23% 19% 41.7% -0.6% Hefei 133% 37 22% 25% 46.8% 10.0% Zhengzhou 97% 35 37% 38% 22.7% 3.3% Suzhou 105% 32 13% 19% 22.3% 1.1% Zhenjiang 59% 33 8% 76% 32.7% 9.8% Huzhou 67% 14 12% 104% 20.8% 3.7% Yangzhou 79% 16 9% 38% 36.2% 9.4% Baotou 67% 17 4% 30% 8.5% 5.4% Wenzhou 86% 5 19% 32% 20.6% 3.3% Harbin 56% 11 8% 36% 27.4% 7.8% Jiangmen 43% 13 17% 21% 28.7% 3.3% Dalian 49% 6 8% 45% 19.3% 7.1% Average 94% 27 21% 44% 28.7% 4.7% Sample average 69% 15 16% 38% 29.2% 6.1% Note: Based on the 63 cities in our sample. Ranked by our own risk index with the highest on the top Source: CEIC, CREIS, local governments websites, PBOC, HSBC estimates Three steps: risk, macroeconomics and housing supply 9 EQUITIES REAL ESTATE January 2019 Figure 4: The 16 cities, by geography: the highest presence is in south and the east of the country Source: HSBC HSB Cs c it ies w it h th e h ig h e s t risk p rofi lesJiangsu P r ovinceS uzhouX ia me nZhu ha iHuizhouS a ny aHuai a nHefe iZhengzhouZhenj ia ngHuzh ouY a ngzho uW e nzhouJ ia ngme nHar binDal ia nHSB Cs Hou s e o f T e c hHSB Cs p h o e n ix c it iesBaotou
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