Chief Economist & Political Strategist, CrossASEAN Research

Prasenjit K. Basu

Chief Economist & Political Strategist

CrossASEAN Research

At CrossASEAN Research we strive to produce unbiased and differentiated on the ground insights on companies across South East Asia. Our Research is produced for the most part after face to face meetings and conversations with company management to dig deep into the long term vision and strategy of the companies we cover. We seek to deliver investable and actionable themes on sectors and markets across SE Asia, often in collaboration with other Insight Providers across the region, pooling the vast experience of the analysts within the Smartkarma Ecosystem. We also offer bespoke work and tailor made trips for our clients. 

Insights across South East Asia

Cross Asset Strategy

13 months after its introduction, China's "Three Red Lines" (TRL) policy initiative (to reduce leverage, improve debt coverage and increase liquidity) for property companies is beginning to have a serious impact. A key contributory factor is that TSF (total social financing) has increased by a more moderate RMB21.68 trillion in Jan-Aug 2021 than last year (RMB25.97 trillion in Jan-Aug 2020), which still implies 10.3% YoY growth in the stock of TSF (to RMB306.83 trillion, or US$47.5 trillion), which is near the slowest-ever pace since TSF began to be published. The stock of M2, bank credit and TSF in China are at historic highs never reached by any other country, and the growth of all these aggregates continues, albeit at a marginally slower pace.Despite the abundance of liquidity (and the cut in RRR in mid-July), real GDP is growing barely over 5% YoY in 3Q 2021, with retail sales decelerating to 2.5% YoY growth and industrial production to 5.3% YoY growth in August 2021. The latter was largely export-driven (helped by a surge in global trade), as FAI (fixed asset investment) declined 2.1% YoY in May-August 2021. Such an economic deceleration in the year before an important leadership transition (i.e., rubber-stamping of continuity for Xi, change for others around him) will be met by further monetary easing in October 2021. That half of the 10 largest residential-property developers are Guangdong-based (a province traditionally known for both entrepreneurship and rebellion) makes them particularly vulnerable to the political crackdown on capitalists. The focus increasingly will be on cutting the costs of property, healthcare and education, not just tech titans.  However, moral hazard will be tightly managed. Onshore (local currency) bond holders will bear relatively few losses on high-profile names like Evergrande (although there will be credit events for bank loans), while offshore foreign-currency bond holders are likely to feel considerable pain (i.e., the partial end of the "moral hazard play"). The political crackdown on sundry capitalists -- tech titans, others seeking overseas listings, property developers, gaming companies -- will persist, adding more to the China Premium. We would recommend staying Underweight China equities amid the uncertainty generated by this political instability, which will result in a new equilibrium discount on Chinese valuations emerging by early 2022.

13 months after its introduction, China's "Three Red Lines" (TRL) policy initiative (to reduce leverage, improve debt coverage and increase liquidity) for property companies is beginning to have a serious impact. A key contributory factor is that TSF (total social financing) has increased by a more moderate RMB21.68 trillion in Jan-Aug 2021 than last year (RMB25.97 trillion in Jan-Aug 2020), which still implies 10.3% YoY growth in the stock of TSF (to RMB306.83 trillion, or US$47.5 trillion), which is near the slowest-ever pace since TSF began to be published. The stock of M2, bank credit and TSF in China are at historic highs never reached by any other country, and the growth of all these aggregates continues, albeit at a marginally slower pace.Despite the abundance of liquidity (and the cut in RRR in mid-July), real GDP is growing barely over 5% YoY in 3Q 2021, with retail sales decelerating to 2.5% YoY growth and industrial production to 5.3% YoY growth in August 2021. The latter was largely export-driven (helped by a surge in global trade), as FAI (fixed asset investment) declined 2.1% YoY in May-August 2021. Such an economic deceleration in the year before an important leadership transition (i.e., rubber-stamping of continuity for Xi, change for others around him) will be met by further monetary easing in October 2021. That half of the 10 largest residential-property developers are Guangdong-based (a province traditionally known for both entrepreneurship and rebellion) makes them particularly vulnerable to the political crackdown on capitalists. The focus increasingly will be on cutting the costs of property, healthcare and education, not just tech titans.  However, moral hazard will be tightly managed. Onshore (local currency) bond holders will bear relatively few losses on high-profile names like Evergrande (although there will be credit events for bank loans), while offshore foreign-currency bond holders are likely to feel considerable pain (i.e., the partial end of the "moral hazard play"). The political crackdown on sundry capitalists -- tech titans, others seeking overseas listings, property developers, gaming companies -- will persist, adding more to the China Premium. We would recommend staying Underweight China equities amid the uncertainty generated by this political instability, which will result in a new equilibrium discount on Chinese valuations emerging by early 2022.  

Global Emerging Market Equities, Propitious Research

Wium Malan, CFA

Global Emerging Market Equities

Independent, Fundamental Quantitative Research

Propitious Research

Fully independent, fundamental and quantitative equity research, focused on companies in the Telecommunication, Media, Technology, Internet and Healthcare sectors in the Global Emerging Markets. Idea generation focused on Quality stocks, trading below their intrinsic Value, with sound Corporate Governance.

Quantitative Analysis

This insight provides the output, and an overview of our, machine learning-driven, proprietary GEM screening model, which takes a three-pronged approach to find the best medium-term long ideas within the top100 largest stocks in the MSCI GEM Index. The model is based on 1) a typical quantitative multifactor screening model, enhanced by two Artificial Intelligence Technology-based approaches using 2) a Deep Neural Network model, and 3) a Recurrent Neural Network model. This model, which has been developed and tweaked over the past 24 months (and implemented in its current state for &gt;12 months), has a solid real-world performance track record, which I set out in the detail below. Notable new entrants to the top decile this month include a host of energy plays (<a href="https://www.smartkarma.com/entities/gazprom-pjsc" target="_blank" data-smartkarma-citation="entities:23749:1" rel="noopener">Gazprom PJSC (GAZP RX)</a>, <a href="https://www.smartkarma.com/entities/lukoil-pjsc-2bfe0130-461e-4ff3-af12-93bec12e6325" target="_blank" data-smartkarma-citation="entities:27148:1" rel="noopener">LUKOIL (LKOH RM)</a>, <a href="https://www.smartkarma.com/entities/ptt-pcl" target="_blank" data-smartkarma-citation="entities:197:1" rel="noopener">PTT PCL (PTT TB)</a>,  and <a href="https://www.smartkarma.com/entities/cnooc-ltd" target="_blank" data-smartkarma-citation="entities:175:1" rel="noopener">CNOOC Ltd (883 HK)</a>) as well as a return for South African banking group <a href="https://www.smartkarma.com/entities/firstrand-ltd" target="_blank" data-smartkarma-citation="entities:24971:1" rel="noopener">FirstRand Ltd (FSR SJ)</a>, and a recovery call for Chinese online gaming powerhouse <a href="https://www.smartkarma.com/entities/netease" target="_blank" data-smartkarma-citation="entities:29030:1" rel="noopener">NetEase (9999 HK)</a>.<table class="table table-striped">
<colgroup>
<col>
<col>
<col>
<col>
<col>
</colgroup>
<tbody>
<tr>
<td style="text-align: center; width: 48pt;">rank</td>
<td style="width: 169pt;">Name</td>
<td style="width: 119pt;">Sector</td>
<td style="width: 95pt;">Location</td>
<td style="text-align: center; width: 48pt;">prev.</td>
</tr>
<tr>
<td style="text-align: center;">1</td>
<td>
<a href="https://www.smartkarma.com/entities/gazprom-pjsc-6a7231a9-d5e4-4da4-bf21-aa1b9d50fbf8" target="_blank" data-smartkarma-citation="entities:27578:1" rel="noopener">Gazprom PJSC (GAZP RM)</a> </td>
<td>Energy</td>
<td>Russian Federation</td>
<td style="text-align: center;">12</td>
</tr>
<tr>
<td style="text-align: center;">2</td>
<td>
<a href="https://www.smartkarma.com/entities/lukoil-pjsc-2bfe0130-461e-4ff3-af12-93bec12e6325" target="_blank" data-smartkarma-citation="entities:27148:1" rel="noopener">LUKOIL (LKOH RM)</a> </td>
<td>Energy</td>
<td>Russian Federation</td>
<td style="text-align: center;">28</td>
</tr>
<tr>
<td style="text-align: center;">3</td>
<td>
<a href="https://www.smartkarma.com/entities/ptt-pcl" target="_blank" data-smartkarma-citation="entities:197:1" rel="noopener">PTT PCL (PTT TB)</a> </td>
<td>Energy</td>
<td>Thailand</td>
<td style="text-align: center;">30</td>
</tr>
<tr>
<td style="text-align: center;">4</td>
<td>
<a href="https://www.smartkarma.com/entities/china-resources-land-ltd" target="_blank" data-smartkarma-citation="entities:418:1" rel="noopener">China Resources Land (1109 HK)</a> </td>
<td>Real Estate</td>
<td>China</td>
<td style="text-align: center;">5</td>
</tr>
<tr>
<td style="text-align: center;">5</td>
<td>
<a href="https://www.smartkarma.com/entities/netease" target="_blank" data-smartkarma-citation="entities:29030:1" rel="noopener">NetEase (9999 HK)</a> </td>
<td>Communication</td>
<td>China</td>
<td style="text-align: center;">47</td>
</tr>
<tr>
<td style="text-align: center;">6</td>
<td>
<a href="https://www.smartkarma.com/entities/firstrand-ltd" target="_blank" data-smartkarma-citation="entities:24971:1" rel="noopener">FirstRand Ltd (FSR SJ)</a> </td>
<td>Financials</td>
<td>South Africa</td>
<td style="text-align: center;">68</td>
</tr>
<tr>
<td style="text-align: center;">7</td>
<td>
<a href="https://www.smartkarma.com/entities/cnooc-ltd" target="_blank" data-smartkarma-citation="entities:175:1" rel="noopener">CNOOC Ltd (883 HK)</a> </td>
<td>Energy</td>
<td>China</td>
<td style="text-align: center;">25</td>
</tr>
<tr>
<td style="text-align: center;">8</td>
<td>
<a href="https://www.smartkarma.com/entities/bank-of-china-ltd-h" target="_blank" data-smartkarma-citation="entities:656:1" rel="noopener">Bank Of China Ltd (H) (3988 HK)</a> </td>
<td>Financials</td>
<td>China</td>
<td style="text-align: center;">51</td>
</tr>
<tr>
<td style="text-align: center;">9</td>
<td>
<a href="https://www.smartkarma.com/entities/mmc-norilsk-nickel-pjsc-dcb09449-eb24-4747-b1c9-77fbf35e8398" target="_blank" data-smartkarma-citation="entities:33242:1" rel="noopener">MMC Norilsk Nickel PJSC (GMKN RX)</a> </td>
<td>Materials</td>
<td>Russian Federation</td>
<td style="text-align: center;">22</td>
</tr>
<tr>
<td style="text-align: center;">10</td>
<td>
<a href="https://www.smartkarma.com/entities/new-oriental-education-technology-group" target="_blank" data-smartkarma-citation="entities:32210:1" rel="noopener">New Oriental Education &amp; Technology Group (9901 HK)</a> </td>
<td>Consumer Discretionary</td>
<td>China</td>
<td style="text-align: center;">3</td>
</tr>
<tr><td colspan="5" style="text-align: left;">Source: FactSet financial data and analytics, my estimates and calculations, Tensorflow, Keras, Google Colaboratory, pandas</td></tr>
</tbody>
</table>As usual, please feel free to reach out for the full output table, any questions, clarifications needed or suggestions.

This insight provides the output, and an overview of our, machine learning-driven, proprietary GEM screening model, which takes a three-pronged approach to find the best medium-term long ideas within the top100 largest stocks in the MSCI GEM Index. The model is based on 1) a typical quantitative multifactor screening model, enhanced by two Artificial Intelligence Technology-based approaches using 2) a Deep Neural Network model, and 3) a Recurrent Neural Network model. This model, which has been developed and tweaked over the past 24 months (and implemented in its current state for &gt;12 months), has a solid real-world performance track record, which I set out in the detail below. Notable new entrants to the top decile this month include a host of energy plays (Gazprom PJSC (GAZP RX), LUKOIL (LKOH RM), PTT PCL (PTT TB),  and CNOOC Ltd (883 HK)) as well as a return for South African banking group FirstRand Ltd (FSR SJ), and a recovery call for Chinese online gaming powerhouse NetEase (9999 HK).









rank
Name
Sector
Location
prev.


1

Gazprom PJSC (GAZP RM) 
Energy
Russian Federation
12


2

LUKOIL (LKOH RM) 
Energy
Russian Federation
28


3

PTT PCL (PTT TB) 
Energy
Thailand
30


4

China Resources Land (1109 HK) 
Real Estate
China
5


5

NetEase (9999 HK) 
Communication
China
47


6

FirstRand Ltd (FSR SJ) 
Financials
South Africa
68


7

CNOOC Ltd (883 HK) 
Energy
China
25


8

Bank Of China Ltd (H) (3988 HK) 
Financials
China
51


9

MMC Norilsk Nickel PJSC (GMKN RX) 
Materials
Russian Federation
22


10

New Oriental Education &amp; Technology Group (9901 HK) 
Consumer Discretionary
China
3

Source: FactSet financial data and analytics, my estimates and calculations, Tensorflow, Keras, Google Colaboratory, pandas

As usual, please feel free to reach out for the full output table, any questions, clarifications needed or suggestions. 

Pan-Asia Index Rebalances/ Events/ Delta One, Periscope Analytics

Brian Freitas

Pan-Asia Index Rebalances/ Events/ Delta One

The property sector in China has been in the spotlight over the last few years. Regulators have made various attempts to temper price growth but the silver bullet has been elusive so far.The powers that be realise that the lack of affordable housing is one of the reasons for China's birth rate dropping and there is now a renewed effort to ensure that property prices do not continue to run up. As part of the measures that started with the 'Three Red Lines' almost a year ago, Bloomberg reports that China has halted PE funds from raising money to invest in residential property developments.We take a look at some of the regulatory changes that have been implemented over the last year and see how the companies in the sector are faring. We then look for stocks with similar characteristics but different stock returns over the last year to find trade opportunities.

The property sector in China has been in the spotlight over the last few years. Regulators have made various attempts to temper price growth but the silver bullet has been elusive so far.The powers that be realise that the lack of affordable housing is one of the reasons for China's birth rate dropping and there is now a renewed effort to ensure that property prices do not continue to run up. As part of the measures that started with the 'Three Red Lines' almost a year ago, Bloomberg reports that China has halted PE funds from raising money to invest in residential property developments.We take a look at some of the regulatory changes that have been implemented over the last year and see how the companies in the sector are faring. We then look for stocks with similar characteristics but different stock returns over the last year to find trade opportunities.

This insight provides the output, and an overview of our, machine learning-driven, proprietary GEM screening model, which takes a three-pronged approach to find the best medium-term long ideas within the top100 largest stocks in the MSCI GEM Index. The model is based on 1) a typical quantitative multifactor screening model, enhanced by two Artificial Intelligence Technology-based approaches using 2) a Deep Neural Network model, and 3) a Recurrent Neural Network model. This model, which has been developed and tweaked over the past 24 months (and implemented in its current state for &gt;12 months), has a solid real-world performance track record, which I set out in the detail below. Notable new entrants to the top decile this month include bottom-fishing in Chinese education through <a href="https://www.smartkarma.com/entities/new-oriental-education-tech-grp-adr" target="_blank" data-smartkarma-citation="entities:531:1" rel="noopener">New Oriental Education (EDU US)</a>, Russian internet play <a href="https://www.smartkarma.com/entities/yandex-nv-a" target="_blank" data-smartkarma-citation="entities:414:1" rel="noopener">Yandex (YNDX US)</a>, Brazilian beverages giant <a href="https://www.smartkarma.com/entities/ambev-sa" target="_blank" data-smartkarma-citation="entities:24454:1" rel="noopener">Ambev SA (ABEV3 BZ)</a>, and a rebound call on <a href="https://www.smartkarma.com/entities/tencent-holdings-ltd" target="_blank" data-smartkarma-citation="entities:26:1" rel="noopener">Tencent (700 HK)</a>.<table class="table table-striped">
<colgroup>
<col>
<col>
<col>
<col>
<col>
</colgroup>
<tbody>
<tr>
<td style="text-align: center; width: 74.9062px;">rank</td>
<td style="width: 262.578px;">Name</td>
<td style="width: 185.766px;">Sector</td>
<td style="width: 147.797px;">Location</td>
<td style="text-align: center; width: 74.9531px;">prev.</td>
</tr>
<tr>
<td style="text-align: center; width: 74.9062px;">1</td>
<td style="width: 262.578px;">
<a href="https://www.smartkarma.com/entities/tata-consultancy-svcs-ltd" target="_blank" data-smartkarma-citation="entities:18708:1" rel="noopener">Tata Consultancy Svcs (TCS IN)</a> </td>
<td style="width: 185.766px;">Information Technology</td>
<td style="width: 147.797px;">India</td>
<td style="text-align: center; width: 74.9531px;">19</td>
</tr>
<tr>
<td style="text-align: center; width: 74.9062px;">2</td>
<td style="width: 262.578px;">
<a href="https://www.smartkarma.com/entities/shenzhou-intl-group-holdings-ltd" target="_blank" data-smartkarma-citation="entities:10415:1" rel="noopener">Shenzhou Intl Group Holdings (2313 HK)</a> </td>
<td style="width: 185.766px;">Consumer Discretionary</td>
<td style="width: 147.797px;">China</td>
<td style="text-align: center; width: 74.9531px;">12</td>
</tr>
<tr>
<td style="text-align: center; width: 74.9062px;">3</td>
<td style="width: 262.578px;">
<a href="https://www.smartkarma.com/entities/new-oriental-education-technology-group" target="_blank" data-smartkarma-citation="entities:32210:1" rel="noopener">New Oriental Education &amp; Technology Group (9901 HK)</a> </td>
<td style="width: 185.766px;">Consumer Discretionary</td>
<td style="width: 147.797px;">China</td>
<td style="text-align: center; width: 74.9531px;">92</td>
</tr>
<tr>
<td style="text-align: center; width: 74.9062px;">4</td>
<td style="width: 262.578px;">
<a href="https://www.smartkarma.com/entities/yandex-nv-a" target="_blank" data-smartkarma-citation="entities:414:1" rel="noopener">Yandex (YNDX US)</a> </td>
<td style="width: 185.766px;">Communication</td>
<td style="width: 147.797px;">Russian Federation</td>
<td style="text-align: center; width: 74.9531px;">88</td>
</tr>
<tr>
<td style="text-align: center; width: 74.9062px;">5</td>
<td style="width: 262.578px;">
<a href="https://www.smartkarma.com/entities/china-resources-land-ltd" target="_blank" data-smartkarma-citation="entities:418:1" rel="noopener">China Resources Land (1109 HK)</a> </td>
<td style="width: 185.766px;">Real Estate</td>
<td style="width: 147.797px;">China</td>
<td style="text-align: center; width: 74.9531px;">33</td>
</tr>
<tr>
<td style="text-align: center; width: 74.9062px;">6</td>
<td style="width: 262.578px;">
<a href="https://www.smartkarma.com/entities/ambev-sa" target="_blank" data-smartkarma-citation="entities:24454:1" rel="noopener">Ambev SA (ABEV3 BZ)</a> </td>
<td style="width: 185.766px;">Consumer Staples</td>
<td style="width: 147.797px;">Brazil</td>
<td style="text-align: center; width: 74.9531px;">56</td>
</tr>
<tr>
<td style="text-align: center; width: 74.9062px;">7</td>
<td style="width: 262.578px;">
<a href="https://www.smartkarma.com/entities/ncsoft-corp" target="_blank" data-smartkarma-citation="entities:5169:1" rel="noopener">NCSOFT Corp (036570 KS)</a> </td>
<td style="width: 185.766px;">Communication</td>
<td style="width: 147.797px;">Korea (South)</td>
<td style="text-align: center; width: 74.9531px;">18</td>
</tr>
<tr>
<td style="text-align: center; width: 74.9062px;">8</td>
<td style="width: 262.578px;">
<a href="https://www.smartkarma.com/entities/tencent-holdings-ltd" target="_blank" data-smartkarma-citation="entities:26:1" rel="noopener">Tencent (700 HK)</a> </td>
<td style="width: 185.766px;">Communication</td>
<td style="width: 147.797px;">China</td>
<td style="text-align: center; width: 74.9531px;">61</td>
</tr>
<tr>
<td style="text-align: center; width: 74.9062px;">9</td>
<td style="width: 262.578px;">
<a href="https://www.smartkarma.com/entities/hindustan-unilever-ltd" target="_blank" data-smartkarma-citation="entities:13828:1" rel="noopener">Hindustan Unilever (HUVR IN)</a> </td>
<td style="width: 185.766px;">Consumer Staples</td>
<td style="width: 147.797px;">India</td>
<td style="text-align: center; width: 74.9531px;">70</td>
</tr>
<tr>
<td style="text-align: center; width: 74.9062px;">10</td>
<td style="width: 262.578px;">
<a href="https://www.smartkarma.com/entities/maruti-suzuki-india-ltd" target="_blank" data-smartkarma-citation="entities:733:1" rel="noopener">Maruti Suzuki India (MSIL IN)</a> </td>
<td style="width: 185.766px;">Consumer Discretionary</td>
<td style="width: 147.797px;">India</td>
<td style="text-align: center; width: 74.9531px;">89</td>
</tr>
<tr><td colspan="5" style="text-align: left; width: 746px;">Source: FactSet financial data and analytics, my estimates and calculations, Tensorflow, Keras, Google Colaboratory, pandas</td></tr>
</tbody>
</table>As usual, please feel free to reach out for the full output table, any questions, clarifications needed or suggestions.

This insight provides the output, and an overview of our, machine learning-driven, proprietary GEM screening model, which takes a three-pronged approach to find the best medium-term long ideas within the top100 largest stocks in the MSCI GEM Index. The model is based on 1) a typical quantitative multifactor screening model, enhanced by two Artificial Intelligence Technology-based approaches using 2) a Deep Neural Network model, and 3) a Recurrent Neural Network model. This model, which has been developed and tweaked over the past 24 months (and implemented in its current state for &gt;12 months), has a solid real-world performance track record, which I set out in the detail below. Notable new entrants to the top decile this month include bottom-fishing in Chinese education through New Oriental Education (EDU US), Russian internet play Yandex (YNDX US), Brazilian beverages giant Ambev SA (ABEV3 BZ), and a rebound call on Tencent (700 HK).









rank
Name
Sector
Location
prev.


1

Tata Consultancy Svcs (TCS IN) 
Information Technology
India
19


2

Shenzhou Intl Group Holdings (2313 HK) 
Consumer Discretionary
China
12


3

New Oriental Education &amp; Technology Group (9901 HK) 
Consumer Discretionary
China
92


4

Yandex (YNDX US) 
Communication
Russian Federation
88


5

China Resources Land (1109 HK) 
Real Estate
China
33


6

Ambev SA (ABEV3 BZ) 
Consumer Staples
Brazil
56


7

NCSOFT Corp (036570 KS) 
Communication
Korea (South)
18


8

Tencent (700 HK) 
Communication
China
61


9

Hindustan Unilever (HUVR IN) 
Consumer Staples
India
70


10

Maruti Suzuki India (MSIL IN) 
Consumer Discretionary
India
89

Source: FactSet financial data and analytics, my estimates and calculations, Tensorflow, Keras, Google Colaboratory, pandas

As usual, please feel free to reach out for the full output table, any questions, clarifications needed or suggestions.

Founder of Antler Capital

Stanley Tsai, CFA

Thematic (Sector/Industry)

For all the roller coaster rides the sector has taken investors on over the years, the Chinese property developers have been unsettlingly consistent in one regard. The CNY1tn fixed asset investment (FAI) homebuilders coughed up in May continued a two-decades long secular trend that may make or break China's still fragile recovery (Exhibit 1).
The Debt-Fueled Supercycle. The sector has seen its share of excesses and corrections, exemplified by the post-GFC FAI expansion and subsequent retrenchment. Covid might have driven up construction costs, but the enthusiasm of would-be homeowners has shown no signs of abating. Just as the developers are working overtime to deleverage their balance sheets, household debt is piling up and may approach alarming levels over the next five years if mortgage growth doesn't decelerate quickly enough.
The Inventory Drawdown. Behind the headline FAI number is a more nuanced picture of inventory reductions and landbank replenishment. Housing starts slowed in May and dipped below project completions on a 12-month rolling basis. This affords cash-strapped property firms an opportunity to rebuild their cashflows, while deploying cash to projects in hand.
The Insatiable Demand. The 0.5% m/m or 5% y/y rise in home prices last month may appear benign in other settings. In the context of China's already exorbitant base, recent data points are a cause for concern. Among the largest cities across the country, those in the Greater Bay Area stand out as outliers, with valuations skyrocketing in spite of the latest round of lockdowns.
The Aggressive Developers. The increasingly bullish undertones of sell-side notes that have landed on our desks are centered around the inventory drawdown / cashflow rebuild story of the perceived higher-quality names in the space. We are not buyers of that story just yet and our work suggests that many developers have been acquiring land at a more aggressive rate than they had planned at the beginning of the year. Regulators are already keeping a close eye on recent transactions and we think we should too.
<table class="table table-striped">
<tbody>
<tr>
<td style="width: 746px;">Exhibit 1: Monthly Housing FAI | 3M Moving Avg (CNY bn)</td>
</tr>
<tr>
<td style="width: 746px;">
<figure class="editor-figure mceNonEditable"><img src="https://static-prod.smartkarma.com/images/media/000/215/144/original/image_45072834061624549646155.png?1624549646" class="editor-image img-responsive" data-smartkarma-image-id="215144"></figure>
Source: National Bureau of Statistics, Our analysis
</td>
</tr>
</tbody>
</table>
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For all the roller coaster rides the sector has taken investors on over the years, the Chinese property developers have been unsettlingly consistent in one regard. The CNY1tn fixed asset investment (FAI) homebuilders coughed up in May continued a two-decades long secular trend that may make or break China's still fragile recovery (Exhibit 1).
The Debt-Fueled Supercycle. The sector has seen its share of excesses and corrections, exemplified by the post-GFC FAI expansion and subsequent retrenchment. Covid might have driven up construction costs, but the enthusiasm of would-be homeowners has shown no signs of abating. Just as the developers are working overtime to deleverage their balance sheets, household debt is piling up and may approach alarming levels over the next five years if mortgage growth doesn't decelerate quickly enough.
The Inventory Drawdown. Behind the headline FAI number is a more nuanced picture of inventory reductions and landbank replenishment. Housing starts slowed in May and dipped below project completions on a 12-month rolling basis. This affords cash-strapped property firms an opportunity to rebuild their cashflows, while deploying cash to projects in hand.
The Insatiable Demand. The 0.5% m/m or 5% y/y rise in home prices last month may appear benign in other settings. In the context of China's already exorbitant base, recent data points are a cause for concern. Among the largest cities across the country, those in the Greater Bay Area stand out as outliers, with valuations skyrocketing in spite of the latest round of lockdowns.
The Aggressive Developers. The increasingly bullish undertones of sell-side notes that have landed on our desks are centered around the inventory drawdown / cashflow rebuild story of the perceived higher-quality names in the space. We are not buyers of that story just yet and our work suggests that many developers have been acquiring land at a more aggressive rate than they had planned at the beginning of the year. Regulators are already keeping a close eye on recent transactions and we think we should too.



Exhibit 1: Monthly Housing FAI | 3M Moving Avg (CNY bn)




Source: National Bureau of Statistics, Our analysis




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China Macro: PPI Peaks, while CPI Eases Tightening Fears 

China Macro: Exports Disappoint, but It's Still Early in the Game 


China Properties: Like Clockwork, Real Estate Investments Haven't Skipped a Beat

Hong Kong

China Resources Land Limited, through its subsidiaries, develops and invests in properties.  The Company also provides corporate financing and electrical engineering services.

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China Resources Land (1109 HK)

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