China s Debt Map. + Follow the Cash. + Jiangsu and Zhejiang. + Holding Pattern. + Smear Effect. China Property. January 25, 2013

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1 China Property January 25, 2013 Anne Stevenson-Yang Susannah Kroeber China s Debt Map + Follow the Cash Our first look at China s heat map of debt suggests that the regional economies that now seem to be in trouble are the very economies that have had superior access to credit by virtue of high earnings or powerful political connections. Those areas include the coal-rich county of Ordos in Inner Mongolia, wealthy cities of the Yangtze River Delta, Tianjin, with its gradiose plan to become a financial center, and the cities of Hefei in Anhui, and Guiyang in Guizhou, both political bases of former President Hu Jintao. + Jiangsu and Zhejiang We have found the most debt distress in the prosperous cities of the Yangtze River Delta. In addition to Wenzhou, we are watching especially Chaozhou, Taizhou, Wuxi, Nanjing, and Ningbo. + Holding Pattern Our very incomplete data suggests that cities can hover for a long time without resolving their debt issues. But we believe from our primary research that the areas in crisis are seeing a reduction in spending and increases in unemployment. + Smear Effect Anecdotally, we believe that the good money left in debt-stricken regions like Wenzhou and Ordos is moving into as-yet unbroken bubbles in nearby regions. Ultimately, this is likely to make the problem far worse than it might otherwise have been. Disclaimers: Last page 1

2 Table of Contents WHERE IS THE NEXT ORDOS? 3 METHODS AND CRITERIA 4 DEFAULTS 5 GRAY MARKET FINANCIAL INSTITUTIONS CLOSE 5 HALTED CONSTRUCTION 6 FALLING PRICES 6 RISING HOUSING INVENTORY 6 GOOD MONEY AFTER BAD 6 THE CASE OF TIANJIN 6 THE CHINESE CONCESSION 7 SANYA, HAINAN 9 BEIJING 9 THE DETAILS 10 DISCLAIMER 13 2

3 Where Is the Next Ordos? Looking across China to evaluate the likelihood of insolvencies by geographic region, we find that cities in Zhejiang and Jiangsu appear to be most imperiled. Chongqing and Inner Mongolia are tied for the next riskiest provinces. Tianjin rates very high on our risk scale, but Tianjin is also a small, compact area with relatively high income. We suspect that cities like Kunming, with empty, overbuilt suburbs, may actually be closer to the brink. Zhengzhou raises every red flag, from empty buildings to a rash of Ponzi schemes gone bust. The debt crises of Ordos and Wenzhou have been widely publicized. We believe that the same crisis will inevitably strike the nation as a whole. To gain insight into which regions are closer to insolvency, we have been collecting data on 140 cities. We have pieced the map together city by city, and many individual cities rank as high on the danger scale as Tianjin but do not have enough weight in their provinces to merit a red or orange flag for the whole area. The city that scores highest across our parameters is Zhengzhou in Henan. Other cities equal to Tianjin in debt and property risk include: Tangshan, Hebei Suzhou, Jiangsu Wenzhou, Zhejiang Hefei, Anhui Fuzhou and Xiamen in Fujian Ordos, the Inner Mongolian city that made itself the poster child of Chinese property excess by building the ghost city of Kangbashi, does not even merit an orange light on our heat map. But this is largely because of data we regard as incorrect. Ordos, according to national statistics, showed its first negative growth in property prices historically in September 2012, with less than a 1% drop YoY. Price decreases then accelerated, with the county showing a 4% drop in residential property prices in December, according to Wind Info. However, our visits to the county and inquiries at the same developments in 2011 and 2012 revealed that asking prices had 3

4 fallen by as much as 50% by September last year and that actual transaction prices were likely even lower. Likewise, NBS numbers show that, although real estate investment fell by more than 65% in Q4 2012, floor space under construction grew steadily, if at a low rate. We are skeptical, as construction appears to be almost totally halted in the city. In 2011, the city stopped publishing its statistic on the sale of commercial buildings. The number had dropped by 10% in 2011 over Our heat map of property risk. Bright green represents the lowest risk, then pale green, yellow, pale orange, dark orange, and red. Source: J Capital Research Methods and Criteria The data universe in China is very frustrating: many numbers are available but they are often not useful. Probably the most important criteria for assessing a debt crisis are property prices, employment trends, and consumer spending. But China s information on property prices is highly unreliable, largely because local governments hide the data in various ways. Unemployment data is wholly unavailable, except for the tiny percentage of urban residents who are eligible and have registered for unemployment 4

5 benefits. Consumer spending numbers include government spending and are notoriously unreliable. We also looked at levels of city debt to GDP. However, the city-bycity data on debt is very spotty, and the sources incomplete. We therefore found it useful to review some cities as benchmarks but did not include debt-to-gdp as a criterion for our heat map. Therefore, we devised our own, admittedly idiosyncratic criteria. They were: The frequency of reported defaults over 100 million RMB Wide-scale shuttering of gray-market finance companies A cluster of construction stoppages A week-to-week drop in prices for the same properties in a given city, accessed on Soufun.com High levels of housing inventory and/or rising inventory with declining sales. We have included this criterion on only some of the 140 cities. Defaults Zhejiang and Jiangsu have had the highest number of commercial defaults reported. We found 13 billion RMB in defaults reported in Zhejiang and 12 billion in Jiangsu over the last year, counting only those over 100 million and looking only at defaults reported in the press. In Jiangsu, the defaults have been principally real estate developers and finance firms, especially small Ponzi schemes. There are also steel and solar industry defaults. In Zhejiang, the defaults are more in high-tech industries, although there are also finance companies and property developers. In Inner Mongolia, we found 3.7 billion in defaults, in private lending schemes and property. Fujian also has many financing schemes gone bad. Gray Market Financial Institutions Close Tianjin rated high among the areas with gray-market shutdowns. One article reported that 30 of the city s private equity funds had been closed, leaving behind unpaid debts of 3.35 billion. Four lending schemes were reported as having closed when they were no longer able to pay out their investors the 5-8% monthly returns they had guaranteed. 5

6 Halted Construction In construction halts, Shijiazhuang in Hebei Province ranks high, with the projects of five developers halted in summer Nearby Tangshan and Baoding also reported many halted projects. The cities of Hefei and Wuhu in Anhui also showed a lot of halts. Several cities in Hainan reported widespread stoppages as well. Falling Prices Property prices did not show much movement. We found some falling prices in Nanjing, Huizhou in Guangdong, and Wuhan, and the steepest price drops in Luoyang in Henan Province. We noted not averages but single properties being advertised for significantly lower prices week to week in these areas. Rising Housing Inventory The inventory of housing appears especially high in Inner Mongolia and several cities in Jiangsu and Zhejiang, plus the outskirts of Shanghai. As for new construction growing faster than sales, we noted Wuxi and Changzhou in Jiangsu, Yingkou in Liaoning, and Taizhou in Zhejiang. Good Money After Bad We have noted a trend of investment capital seeking targets in regions farther west. A finance company in Guizhou Province commented this week that Guizhou is attracting a good deal of speculative capital from Zhejiang. This is because the bust in Wenzhou made local investors cautious about local opportunities, but many still have money to spend. We found the same comments in Baotou, a city about 150 kilometers from the famously bust Ordos in Inner Mongolia. In many cases of near defaults, new debt has been issued in a new province in order to cover the defaulting fund; this happened, for instance, in the Henan Province default that was behind the wealth management product distributed by a branch of Huaxia Bank in Shanghai. We believe that the suppression of information about the debt crises unfolding around the country is making the problem far worse by attracting new capital to cover the bad. The Case of Tianjin Tianjin is among the cities that ticked all of our risk criteria. Based on bond prospectuses issued by the larger LGFVs in the city, as at 6

7 the end of 2011, the city s net debt was about 63% of its 2011 GDP. The prospectuses list total bank lending at the end of 2011 at billion RMB and total outstanding liabilities of billion. But these numbers do not tell the whole story; the reported debt in Q was well over 1 trillion RMB, with many new issues planned, and undisclosed debt is likely to be significant. Some omissions from the bond prospectuses: Trust loans: These are not included in the bond disclosures but can be very substantial. In 2012, trust lending to local government financing platforms was estimated at between 200 and 300 billion RMB. LGFV debt: Many local government financing platforms do are treated as normal corporations, and their debt is not reported as part of the city s debt. In March 2012, the 21st century Economic Herald reported that Tianjin LGFVs had about 300 billion in outstanding debt. Bank acceptances: These bank checks, originally used for shortterm finance needs, have become a new form of medium-term loan, easily rolled over beyond the standard six-month term. According to a Tianjin finance company specialized in acceptances, issues in Tianjin in 2012 were between 800 billion and 1 trillion. The Chinese Concession Fairly typical of Tianjin s appetite for debt are two new districts, both of them sub-districts of the grandiose Binhai New Area region, which is pitching itself as China s upcoming financial hub. One, Dongjiang, is built on landfill, because land is so valuable in China that making more of it is also valuable, said a district official, plus landfill saves the cost of carrying dredged silt out to the deep water to dump. Dongjang is a container port with facilities for oil storage, but local officials have also planned a resort, with a sandy beach, a five-star hotel, golf course, and three beautiful terminals housing berths for six cruise ships that somehow will be making the journey to Tianjin, perhaps from Vladivostock or North Korea. Once notoriously a foreign-concession port, Tianjin is now proudly Chinese except that this port area does not technically belong to the Chinese people. Instead, it belongs to an SOE with Shanghaiand Shenzhen-listed arms, the Tianjin Port Group. In return for financing the development, TPG has the right at least until 2020, 7

8 to collect 100% of the tax revenues of the Dongjiang area. The Dongjiang District Management Committee reports that TPG has transferred the best assets from the development into its listed arm. Although the Tianjin municipal government lurks as the background owner of TPG, in 2009, a majority of the company s equity was shifted into an offshore holding company. TPG s listed arms have credit of about 40 billion from banks and about 5 billion RMB in outstanding bonds. A 20-minute drive away is the Yujiapu District, China s Little Manhattan. The district has recently added to its own Lincoln Center and Rockefeller Center a Mall of America and a Sydney Opera House, a yacht club and marina, an ecological park, and an aquarium. Some 39 local government financing platforms are involved in the construction, with at least 200 billion raised so far, according to local authorities, and a planned billion required for each building being constructed by each of the LGFVs. When all is said and done, this administrative zone, one of 14 sub-zones of one of the city s 15 districts, will end up costing about half of the whole city s GDP. The 5 million people to fill up the zone, the officials say, will move to Yujiapu from Hong Kong and Korea. The Little Manhattan plan Source: Tianjin Yujiapu District, Photo by J Capital 8

9 Construction now going on. Photo by J Capital, January 17, 2013 Sanya, Hainan China s beach resort of Sanya in Hainan Province has attracted heady price growth. According to the NBS, average housing prices in Sanya increased 43% year on year in Hainan s capital, Haikou, saw prices grow 36% that year. Now, transactions have been slowing. Our calculations indicate that commercial housing inventory will take at least 20 months to sell based on average sales per month in the first three quarters of In the last two months of 2012, developers discounted to shed inventory. According to our Soufun checks, prices decreased at various projects between 5-26% compared with the previous June. Given the rise in land prices in Hainan, margins for developers have thinned very considerably. Beijing Beijing s numbers are weak but have improved somewhat in The over-investment in property is apparent: Inventory available for sale rose 13% in 2012, while 9

10 average prices for residential property fell 4%. Property investment was 49% of total fixed-asset investment, down a point from 2011 but still very high. Total new investment in property in 2012 was 18% of the city s GDP, down from 19% in 2011 and 21% in These are nosebleed levels: anything above 10% is generally considered to be a sign of a property bubble. On the other hand, the square meters of residential property sold rose 59% YoY, and new starts fell by 44%. That means that some of the inventory is clearing. One should approach questions of inventory clearing with great caution, however: in many cases, purchases of distressed properties or assumptions of loans are booked as property sales, because property that in reality is being used to collateralize lending changes hands when the loan is purchased by a third party. The Details In this first iteration of our property heat map, we used four criteria to create a 0-4 ranking system, with each of 140 cities ranked on each criteria on a yes/no basis (1=yes, 0=no). The 140 cities were selected simply because data on them is available from the real estate listing website Soufun.com, enabling us to collect property data on a granular level. To aggregate the data into provinces, we created an average of those rankings for every city for which we had data in the province (1-13 cities per province), giving double weight to cities that fall into the top 10 cities in terms of population in that province. Municipalities (Beijing, Tianjin, Shanghai and Chongqing) were not weighted. These are the four initial criteria and methods we used for gathering data on a city wide level: Property prices: we tracked property prices at 10 villa or highend apartment complexes in each of the 140 cities listed on Soufun. If no price movement was recorded, or there were two or fewer properties that registered only small movement (within - 10% to 10% range), a score of 0 was recorded. If there was a large price increase or decrease at one or more properties (greater than +/-20%), or moderate to great price movement in multiple properties (greater than +/-10%), a score of 1 was recorded. 10

11 Construction halts: We scanned a variety of online news and blog sources for information about halts of construction at commercial properties. A score of 1 was recorded if a) there was a documented case since October 1, 2012 in the city, or b) if there were at least two cases reported within two months of each other (defined as a cluster) reported since the beginning of No reports or isolated incidents before Oct. 1, 2012 were recorded as 0. Gray market shutdowns: We scanned a variety of online news and blog sources for information about grey market activity and shutdowns on a city-wide level. A score of 1 was recorded if there was a full shut down of the grey market at any point in the last two years or if there were smaller shutdowns reported since the start of 2H Other activity or no activity was recorded as 0. Defaults: We scanned a variety of online news and blog sources for information about large-scale defaults with a value of over 100 million RMB. A score of 1 was recorded if we could locate a record of such a default since January 2012, and a score of 0 was recorded if defaults occurred before 2012 or if no reports could be located. Inventory: We later added a fifth factor to our heat map, inventory of commercial housing, which we could collect only on a provincial and municipal level. We calculated it by collecting data on new construction starts of commercial housing by sq.m and new property sold by sq.m, factoring in a two year lag between starting construction and selling of property, aggregated the data over a 5 year period (new starts from 2006 to 2010, and sq.m sold from ), and calculated the difference which we interpreted as a calculation of inventory on the market. We then averaged the sq.m sold for each province over the five years to come up with an estimate for average sq.m sold per year, and calculated the inventory as a percentage of that number. The result is an approximation of how many years of inventory exists in each province. All provinces that had more than two years of inventory were considered hot, and received an adjustment to their 0-4 ranking, other provinces/municipalities did not have their score altered. The result was a ranking on a 0-3 scale, as represented on the map in gradations from dark green (0-0.4) to red (2.5-3), due to the fact that no province received a score greater than 3. Weighting was done on a two-tier system: top-10 cities by population in the province were weighted double, i.e. counted 11

12 twice, others were unweighted/counted once. Differentiating to a lower degree of specificity wound up creating noise in the data, and smaller cities became underweighted. We will publish an updated heat map next week including more detail on inventory overhang and city debt. 12

13 Disclaimer This publication is issued by J Capital Research ( J Capital ) This publication has been prepared for the general use of the clients of J Capital and unauthorized copying or distribution is prohibited. Nothing in this document shall be construed as a solicitation to buy or sell any security or product. In preparing this document, J Capital did not take into account the investment objectives, financial situation and particular needs of the reader. Before making an investment decision, the reader needs to consider, with or without the assistance of an adviser, whether the advice is appropriate in light of their particular investment needs, objectives and financial circumstances. J Capital accepts no liability whatsoever for any direct, indirect, consequential or other loss arising from any use of this publication and/or further communication in relation to this document. 13