摩根大通：2020年中國家庭槓桿率達到61％，趕上新加坡日本等國家?2018年中國消費品行業三大關鍵趨勢分析

【小編按：摩根大通的這份報告從消費貸款的角度看中國消費的未來，還是有一定的新鮮感，但是。。。但是。。。。小編還是有些不同的看法，首先，中國的家庭槓桿和其他國家相比確實確實是低了，但是中國人社保體系、中國人的存錢的習慣雖然90後有變化但也沒變這麼多吧，第二，中國很多消費貸款其實都進入房地產了，小編的一個哥們抵押房地產融資然後發現單子上寫的都是消費貸款呢。

不過，這份報告的結論，就是假如你要投資消費品行業還是可以參考的，反正速食麵已經被外賣打敗了，不知道啤酒是被什麼打敗的！

在21個子行業中，我們認為「最佳集群」（電子商務，運動裝，家居應用和酒類）享受估值溢價和持股，而「落後集群」（牛奶，小吃，麵條，啤酒 ，和超市）將上升，並在2018年產生Beta的回報。

這個報告很長，小編試圖翻譯了一些，不一定精確，供大家參考！發完睡覺，祝大家周末快樂！】

China Consumer Sector 2018 outlook - the three critical trends that will drive growth and impact

我們對中國消費者行業有積極的看法。 2018年，根據市場預期，中國消費行業銷售額/自由現金流將同比增長10.9％/ 13.5％，OP利潤率為12.4％，ROE為13.8％。 除了千禧一代和年輕中產階層的支出需求，人口老齡化，稅收改革，進口放鬆管制，自動化，大數據等等，我們認為以下三個關鍵趨勢將推動消費增長並影響行業動態。人工智慧技術，環境保護和教育/醫療體系改革。

We assume coverage of the China consumer sector with a positive view. In 2018, China consumer sector sales/FCF will grow by 10.9% /13.5% yoy, with a 12.4% OP margin and a 13.8% ROE, based on consensus estimates. We believe the following three critical trends will drive consumption growth and impact industry dynamics, on top of other key themes, including the spending appetite of millennials and the young middle class, an ageing population, tax reform, import deregulation, automation, big data, AI technology, environmental protection, and education/medical system reform.

? Premiumization高檔化——消費升級. Chinese consumers now pay more but buy less. They are focusing more on quality, design, and wellness rather than price. The companies that are able to satisfy the demand for premiumization will enjoy accelerating sales growth and margin expansion, in our view. We quantify the innovation capabilities of consumer stocks with a balanced scorecard. Among H shares, ZHY, Dali, and XBXB stand out from peers.

? 新的零售生態系統中的一個甜蜜點A sweet spot in the new retail ecosystem. We argue that the online low price advantage will decline in the next five years, following the government』s crackdown on counterfeit, end of the tax holiday, and rising costs of last-mile delivery. We expect consumers to move back to bricksand-mortar stores (Sun Art, YUMC, XBXB and ZHY), and strong brands (Mengniu, and Dali) to thrive in the new ecosystem.

? 大數據，小額貸款，加速Big data, small loans, gearing up. 「Save for a rainy day」 is not a tradition among the younger generation. China』s household leverage (debt/GDP) increased from 29% in 2011 to 47% in 2016, and is expected to reach 61% in 2020, catching up with Singapore』s and Hong Kong』s. We believe the fast-growing consumer credit (mainly credit cards and online lending) will supplement spending power and fuel consumption growth. CTF is a good proxy to play the theme, in our view.

Among the 21 sub-sectors, we believe the 「Best-in-class Cluster」 (ecommerce, sportswear, home app and liquor) deserves a valuation premium and core holding, while the 「Laggard Cluster」 (milk, snacks, noodles, beer, and supermarkets) will rise and generate Beta return in 2018.

在21個子行業中，我們認為「最佳集群」（電子商務，運動裝，家居應用和酒類）享受估值溢價和持股，而「落後集群」（牛奶，小吃，麵條，啤酒 ，和超市）將上升，並在2018年產生Beta的回報。

We prefer the powerhouses with cutting-edge innovation capabilities, solid execution, robust growth, visible improvement, strong cash flow, and decent equity return, although their valuation might not necessarily look low. Our top picks are China Mengniu and Dali Foods. Our top avoids are Luk Fook and Hengan.

我們更傾向於具有尖端創新能力，穩健執行力，強勁增長，明顯改善，強勁現金流和良好股權回報的強大企業，儘管其估值可能不一定看起來很低。 我們的首選是中國蒙牛和達利食品。 我們認為最應該退避三舍的是六福和恆安。

Risks to our view include: worse-than-expected demand (economy, income, unemployment, property and equity market price), competition (oversupply, price war and inventory), input cost, policy, FX, weather, and food safety.

China household leverage to reach 61% in 2020, catching up with peers

2020年中國家庭槓桿率達到61％，趕上新加坡日本等國家

中國家庭槓桿率從2007年的18％上升到17年第三季度的48％。我們的銀行團隊預計到2020年，這一比例將進一步上升至61％。這與新加坡61％，日本63％，中國香港68％，泰國70％，馬來西亞70％的水平相當，但明顯低於韓國93％，美國80％，英國88％（所有數據為2016年）。根據我們的銀行團隊估計，家庭槓桿的增加將主要由信用卡快速增長（2017-2013年複合年增長28％）和在線P2P貸款（複合年增長率22％）所推動。

China』s household leverage went up from 18% in 2007 to 48% in 3Q17. Our banking team expects it to further go up to 61% in 2020. This would be at a similar level to Singapore』s 61%, Japan』s 63%, Hong Kong』s 68%, Thailand』s 70%, and Malaysia』s 70%, but significantly lower than Korea』s 93%, the US』s 80%, and the UK』s 88% (all data for 2016). The increase in household leverage will be mainly driven by rapid growth in credit card (up 28% CAGR over 2017-20E) and online P2P loans (up 22% CAGR), according to our banking team estimates.

Why has household debt increased rapidly in China?

為什麼中國家庭債務快速增長？

? Chinese customers have been under-served for years. China』s total household leverage was 47% as of end-2016, lower than the international average of 55%.

中國客戶多年來一直服務不足。 截至2016年底，中國的家庭總體槓桿率為47％，低於國際平均水平的55％。

? Consumer credit demand fueled by rising property prices and resilient consumption demand. The property price hike since 2015 has contributed to the household credit growth in mortgages, as well as the wealth effect which favors overall retail consumption.

消費信貸需求受到房價上漲和消費需求彈性的推動。 自二零一五年以來的樓價上漲，促使住戶按揭貸款增長，以及有利整體零售消費的財富效應。

? Banks are more willing to make consumer loans, given lower leverage in the household sector (household leverage of 47% vs corporate leverage of 164% in 2016) and better asset quality in retail loans (non-performing loan ratio of 0.92%, lower than the corporate loan ratio of 2.42% for banks under J.P. Morgan』s banking team』s coverage, a weighted average).

由於家庭部門槓桿率較低（家庭槓桿率為47％，2016年企業槓桿率為164％），銀行更願意提供消費貸款，零售貸款的資產質量較好（不良貸款率為0.92％，低於摩根大通銀行覆蓋銀行的公司貸款比率為2.42％，加權平均）。

? Technology has made online credit more accessible, while the convenience of online shopping boosts demand for both consumption and credits. What』s more important, historical online shopping data create a consistent and reliable credit profile which traditional banks are unable to capture. As a result, online P2P lending saw exponential growth of 181% CAGR over 2014-2016.

技術使得網上信用更容易獲得，而網上購物的便利增加了對消費和信用的需求。 更重要的是，歷史的在線購物數據可以創造出傳統銀行無法捕捉的一致可靠的信用狀況。因此，在線P2P借貸在2014-2016年的年均複合增長率為181％。

? Investment alternatives stir people』s desire for products with high yield. Yields for wealth management products, P2P and micro-lending are much higher than banks』 deposit rates. This has led to exponential growth of P2P lending and WMP products, supported by increasing investment demand for yield-seekers with higher risk appetite.

投資選擇激起人們對高收益產品的渴望。 理財產品，P2P和小額貸款的收益率遠高於銀行的存款利率。 由於對風險偏好較高的高收益者的投資需求增加，P2P貸款和WMP產品呈現指數式增長。

Online lending - big data, small loan, tightening regulation

網上貸款 - 大數據，小額貸款，監管緊縮

Online lending businesses target 20-to-40-year-old consumers who typically are more receptive to internet financial services, have no/limited credit record, and urgently need small loans for discretionary spending (such as a smart phone, clothes and bags, education, travel and home decoration).

網上貸款業務的目標對象是20至40歲的消費者，他們通常更願意接受互聯網金融服務，沒有/有限的信用記錄，急需小額貸款用於自由支配（如智能手機，衣服， 教育，旅遊和家庭裝飾）。

Most of these consumers are in the low-income group, and are unserved or underserved by traditional banks. They don』t carry a credit card (China』s credit cards per capita amounted to only 0.34 in 2016, lagging far behind Singapore』s 1.6, Taiwan』s 1.73, and Korea』s 1.88).

這些消費者大部分是在低收入群體，而且沒有得到傳統銀行的服務或得不到服務。他們沒有信用卡（2016年中國人均信用卡只有0.34，遠遠落後於新加坡1.6，中國台灣1.73和韓國1.88）。

However, most of them are very active in online shopping, which creates a consistent track record of payroll changes, cash flow movements, shopping habits, and risk appetite. The big-data-backed scoring system accurately captures portraits of the targeted consumers (which most traditional banks fail to achieve), and serves as a meaningful foundation for consumer credit assessment and risk management.

Leveraging the powerful big data analysis and China』s well-established real-time fund transfer, online lending companies are able to approve and transfer the loan within just a minute.

According to iResearch, the outstanding balance of China』s online consumer finance market will increase from Rmb327bn (US$48.2bn) in 2016 to Rmb3.8tn (US$0.6tn) in 2020, representing a CAGR of 84%.

The rapid growth of household leverage in the past 10 years raises concerns of a potential household debt crisis down the road. This concern was intensified with the rising propensity of the younger generation borrowing from non-bank lenders to fund their consumption of discretionary goods.

According to local media (Yicai & Tencent News), regulators are drafting regulations on web-based lenders. Measures mentioned were to halt issuing new internet lending licenses, restrict ownership of such licenses to SOEs & internet companies, and suspend securitization of assets by these micro lenders. Reports noted that the detailed regulation will be announced in the near future.

We expect the online lending business to be subject to closer scrutiny by regulators. We believe tighter regulations on risk management will increase entry barriers, accelerate industry consolidation, and support healthy development for the industry in the long run.

我們預計在線貸款業務將受到監管機構的嚴格審查。 我們認為，加強風險管理的規定將會加大進入壁壘，加速行業整合，長期支持行業健康發展。

In sum, our China banking analyst Katherine Lei believes that the household debt issue will be contained in the medium term. This is mainly supported by: 1) a manageable household debt servicing ratio (17% in 2016, vs Denmark』s 16%), 2) lower leverage levels relative to global peers (47% in 2016, vs the international average of 55%), and 3) a high household savings rate (31% in 2016, vs Taiwan』s 22%, Korea』s 8%,, the US』s 5%, and UK』s -1%).

總而言之，我們的中國銀行業分析師凱瑟琳·雷認為，中期來看，家庭債務問題將沒有問題。 這主要得益於：1）可管理的家庭債務償還率（2016年為17％，丹麥為16％），2）與全球同業相比槓桿率較低（2016年為47％，而國際平均水平為55％）， 3）居民儲蓄率高（2016年為31％，中國台灣為22％，韓國為8％，美國為5％，英國為-1％）。

Correlation analysis of consumption and household leverage

消費與家庭槓桿的相關分析

Empirical study suggests that the major determinants of consumer spending include: (1) disposable income; (2) consumer confidence (inflation, macro economy, and job security); (3) consumer credit (an immediate supplement to household income; interest rate change); (4) wealth effect (property/stock/bond price; education/medical/retirement benefits); and (5) tax rate change.

We have run a regression analysis of consumption and household leverage for the US, UK, Japan and Korea. The regression results demonstrate the tight co-movement trend between 1) household consumption, 2) disposable income, and 3) household leverage. Further regression analysis substantiates the argument. Please refer to more technical details in the appendix.

我們對美國，英國，日本和韓國的消費和家庭槓桿進行了回歸分析。 回歸結果顯示1）家庭消費，2）可支配收入，3）家庭槓桿之間的緊密合作趨勢。 進一步的回歸分析證實了這一論點。 請參閱附錄中的更多技術細節。

Among the four scenarios presented below, Japan demonstrates the strongest regression relationship, with R-squared reaching 0.928, followed by Korea』s 0.895, the UK』s 0.878, and the US』s 0.787. R-squares is an indicator of how close the dependent variables (i.e., disposable income and household leverage) are to the calculated mean of household consumption. The threshold is 0.72.

We believe that as China household leverage and disposable income increase steadily, household consumption will rise at a similar pace.

Implications for China"s domestic consumption

對中國國內消費的分析

We believe the fast-growing consumer credit (mainly credit cards and online lending) will supplement spending power and fuel consumption growth. We expect outstanding consumer credit (ex-mortgages & auto loans) to account for 58% of China』s overall retail sales (ex-auto sales) in 2020, up from 40% in 2016 and catching up with the UK』s 49% and the US』s 67% in 2016.

? In order to get a sense of how much of an impact consumer credit might have on future retail sales ex cars, we have adopted the metric 『outstanding consumer credit/total retail sales (ex-auto sales)』, where outstanding consumer credit is the residual after mortgages and car loans are deducted from total household debt, and total retail sales ex cars is retail excluding auto sales.

? This measure does not capture how much retail is generated from consumer credit, but to provide a point of angle to picture the sizing landscape, i.e., how large is the outstanding consumer credit compared with the retail sales.

? China』s outstanding consumer credit (ex-mortgages & auto loans; including credit card loans and online lending) accounted for 40% of China』s overall retail sales (ex-auto sales) in 2016, a sharp increase from 17% in 2011.

? This is lower than the UK』s 49% and the US』s 67%.

? Our banking team forecasts China』s household leverage (family debt/GDP) to increase from 48% in 3Q17 to 61% in 2020, catching up with Singapore』s 61%, Japan』s 63%, Hong Kong』s 68%, Thailand』s 70%, and Malaysia』s 70%, but significantly lower than Korea』s 93%, the US』s 80%, and the UK』s 88% (all data for 2016).

? The increase in household leverage will be mainly driven by rapid growth in credit cards (up 28% CAGR over 2017-20E) and online P2P loans (up 22% CAGR), in our view.

? We estimate China』s consumer credit to account for 58% of total retail sales in 2020, catching up with the UK』s 49% and the US』s 67% in 2016.

Riding on such a trend, most consumer brands and services – especially the higherticket-sized segments (such as home appliance or luxury goods) – will be the major beneficiaries, in our view. We think QD Haier (rising exposure to premium products including Casarte and GEA) and CTF are good proxies to this theme.

How will financial metrics evolve in 2018?

2018年財務指標將如何發展？

In 2018, the China consumer sector』s sales/FCF growth will accelerate to 10.9% /13.5% yoy, with operating margin expansion to 12.4% and ROE improvement to 13.8%, based on consensus earnings forecasts.

Among the 21 sub-sectors, we think the 「Best-in-class Cluster」 (including ecommerce, sportswear, home appliance and liquor) deserves a valuation premium and the core holding position in China portfolios. We think this is justified by the cluster』s favorable sector dynamics, proven business model, and resilient financial performance. We estimate the cluster will deliver 16% yoy sales growth, 22% yoy FCF growth, 18% operating margin, 20% ROE, and a 38% net cash position.

The 「Laggard Cluster」, including milk (ex-IMF), snacks, noodles, beer, and supermarkets, has lagged behind in the past two years. However, this cluster looks set to improve margins and ROE in 2018, through initiatives on streamlining execution, innovation, reforming organization, disposing of idle assets, increasing free cash flow, and lifting dividend payout ratios. We see EPS accretion and multiple re-rating potential for the market leaders. We expect them to generate Beta return in 2018.

China』s consumer sector sales growth will accelerate to 10.9% yoy (2018) from 5% yoy (2016), the operating margin will expand to 12.4% (2018) from 10.4% (2016), and ROE will expand to 13.8% (2018) from 11.1% (2016), based on Bloomberg consensus estimates. Sector net gearing ratio will rise to 12.4% (2018) from 8.2% (2016). FCF yoy growth will remain stable at 13.5% in 2018, at a similar level to 2016.

From the perspective of operating profitability and equity return, we group the 20 subsectors into three clusters: best-in-class, stay-in-between, and bottom-of-list.

? Best-in-class: seasonings, meat processing, liquor, e-commerce, catering, home appliance, sportswear, and textile will deliver 16.4% sales yoy growth and 21.5% FCF yoy growth, with a 20% ROE, 18% op margin and 38% net cash position in 2018. We think their solid and resilient fundamental justifies premium valuation multiple, and deserve a core holding position in China consumer space.

? Stay-in-between: personal care, snacks, brands, milk (ex-IMF), beverage, travel, and gold & jewelry will achieve 9.8% sales yoy growth and 14.8% FCF yoy growth, with a 13.5% ROE, 12.5% op margin and 14.5% net cash position in 2018, by our estimates. Some sectors, such as milk, snack, gold & jewelry are improving financial metrics through organization reform, efficiency improvement, and innovation. They have potential for both EPS enhancement and multiple re-rating, and are likely to generate beta return in 2018.

? Bottom-of-list: noodles, department stores, dairy farms, domestic IMF, beer, and supermarkets currently sit at the bottom of the list, with 6.4% sales growth, 4.4% FCF growth, 7.8% ROE, 7% op margin, and 15.2% net gearing. We think the structural challenges they are dealing with (such as e-commerce, capped penetration, and cost disadvantage) are the major reasons for the disappointing margins and ROE. However, we estimate that all of them are set to improve ROE with margins largely flattish in 2018. We think this is because while it is not easy to drive sales growth, most of them are putting more effort into streamlining execution, disposing of idle assets, increasing free cash flow, and lifting dividend payout ratios. They are not attractive sectors, in our view, but there are attractive turnaround names.

Both China staples and discretionary trade close to 1x standard deviation above the five-year mean, which we believe is a reasonable valuation level with potential for multiple re-rating.

? China staples trade at 22.9x 12-month forward P/E, slightly below 23.3x of 1x standard deviation above the five-year mean. In the past five years, the forward P/E has ranged from 15.2x to 26.4x, with a mean value of 21.2x.

? China discretionary trades at 18.2x 12-month forward P/E, slightly above 17.4x of 1x standard deviation above the five-year mean. In the past five years, the forward P/E has ranged from 12x to 20.5x, with a mean value of 15.7x.

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