Tag: Economy

Nikkei 250

After a 20-year [1982 to 2002] journey, Nikkei 250 index was back to the starting point.

And it hasn’t yet reached the previous high 34 years ago (1989 level) as of 2023.

What happened?

A lot of things to unpack.

GDP

I am looking at GDP (in local currency terms) first – equity market should be a ratio of GDP.

Japan already enjoyed a robust growth (1972-1982) with GDP almost tripped in 10 years (!), which translates to 11.4% cagr. 

The miracle continued for another decade.

1991 GDP also grew 6.4% yoy vs. 1990; however, GDP growth dropped to 2.5% in 1992 and to 0.0% in 1993.

During the second phase of which ended on 1991, Japan’s GDP still compounded at ~6% cagr (1981 – 1991), although not as high as the last decade. And Nikkei index climbed during this period as well.

What’s wrong then?

The “10-year GDP cagr” would drop continuously from 1991’s 6% to below 1% in 2002. 

Remember, Nikkei index peaked in 1989 (red mark).

While in 1990 and 1991 Japan’s GDP still enjoyed 7.6% and 6.4% growth, 1992 would be 2.5% and 1993 would be 0%.

It was the mid-term / 5-year projection that’s worrisome. And indeed, the 10-year GDP cagr would start to decay, with no reversal in sight.

Nikkei index bottomed in 2003, when the dot-com bubble also came to an end. S&P 500 dropped ~24% in 2002 (after double-digit drop in 2000 and 2001), but grew 26% in 2003.

The index bottomed as the 10-year GDP growth would be bottoming and things won’t go much worse from here.

 

Nikkei index is now (2023) ~4x the 2003 bottom though, what happened?

Nikkei index climbed 4 consecutive years (2003 – 2006), before the Global Financial Crisis hit.

Japan’s 10-year GDP cagr would still be ~0% in 2007, but from 2004 to 2007 it experienced a 4 consecutive year of GDP growth.

Things would look better in 2012, when Japan GDP would be re-entering a growth mode. 10-year GDP cagr would bottom in 2011 at -0.7% and recovered to 0.1% in 2015 and to 1.2% in 2019 before covid.

To make it a full graph.

As mentioned above, although 10-year GDP cagr still has pressure from 2003 onward, actually yearly GDP growth is positive from 2004-2007. Therefore the 3-5 year outlook would actually be reversing in 2003.

US new home sales resilient

Looks like the monthly sales is still health. Month to clear inventory is steady and up a bit to ~7.8 months in Oct 2023.

Currently monthly sales pace is better than 2018 and 2022, despite record high interest rate in recent years.

New homes for sales has gone up more. So the number month to clear new home inventory has gone up to 7-8 months recently vs. an average of 6.2 months in 2018. And is much better than the 2020-21 average of 5.1 months.

Better availability should be good for inflation and soft-landing scenario.

New residential sales Oct 2023

See the other post for China new home sales – the inventory stood at over 20 months the last time I checked.

US car vs. home loans

Read the q3 quarterly report on US household debt and credit (here). One interest takeaway is how divergently different loans perform, vs. the GFC era.

When the GFC hit, both all loans perform badly. Transition into delinquency (30+ days) for credit cards, mortgages, and auto loans reached over 10%. Mortgages delinquency were picking up faster and looks worse than auto loans.

This time around, mortgages looks fine (as of 23q3), and delinquency is going up not only slowly but at the level even lower than 2005-06, while auto and credit cards are deteriorating at a faster rate.

By age groups, for 18-29 and 30-39, the percentage of auto loan balance falling 3-month behind is reaching about the similar level of GFC era. (Another theme: younger generation is under more pressure than the older for the past 2 years)

To reflect back, there was a shortage of cars during the pandemic and used car prices were shooting up. It could cost some money if someone bought a car back then and sold it this year, as 2nd-hand car price has been on a downward path.

The selling and downward pricing trend could be a self-reenforcing process.

Meanwhile, house is a more resilient asset class and current macro is still ok. After 10 years, houses are very likely to worth more but cars very likely won’t.

As shown with the FHFA house price index, which is keeping up.

…which is very different from the GFC era when HPI declined and under pressure for years.

China new home sales 2023 Sep vs. 2020 Sep

Facts for China new home sales

2020 2023
Sep Sep
‘0000 sqm 万，平方米
Supply 供应面积           7,529           2,990
Demand 成交面积           5,228           2,262
Inventory 库存         50,739         51,221
Sales pace (month)             9.71           22.64

Inventory actually didn’t increase much, flat after 3 three years.

But the willingness to purchase (new homes) has decreased, area sold in Sep 2023 is less than half (43%) of 2020 Sep level.

Therefore, the resulting month-to-clear-for-sale-homes is more than doubled from ~10 month to almost 23 month – it will need almost 2 years to clear new house inventory at current sales pace.

The above figure is for 100 cities in China.

To look at the bottom 10 cities: back in Sep 2020, the worst 10 cities needed 21.5 – 35.3 month to clear inventory whereas in Sep 2023, the worst 10 cities will need 57.6 – 93.9 month to clear inventory.

Source:

http://m.fangchan.com/news/320/2023-10-25/7122778841134993672.html

http://news.dichan.sina.com.cn/2020/10/27/1274844.html

 

 

 

China’s housing market (2) [WIP]

Asset allocation in China

Very different from the US, where financial assets represent a large portion of families’ wealth, for most Chinese families, real estate assets (residential) are the top choice and the most important part for asset allocation. According to a survey conducted in 2019, among urban families’ total assets, 59.1% is residential homes while only 20.4% is in financial assets[1],[2]. In a previous analysis on another series of data, housing asset represents 74.1%, 73.8%, and 72.7% of urban households’ total assets in 2010, 2012 and 2014 respectively[3]. Comparatively, in 2016, US households’ mean value of primary residence is only 24.4% of their mean value of total assets, while financial assets are around 42.5%[4].

For one thing, China’s A-share market doesn’t provide meaningful return over the last decade, compared with S&P 500. The chart below is from 2010 to 2019: the black line is CSI 300 Index, which consists of the 300 largest and most liquid A-share stocks, while the blue line is S&P 500.

Exhibit 2. CSI 300 and S&P 500 performance over from 2010 to 2019

In fact, among the 20.4% financial assets, the report said only around 10% (or 2% of total asset) is equity exposure, including stocks and mutual funds.

Meanwhile, housing prices in China is climbing steadily. Buying a home in top tier cities in China is like a leveraged long position in S&P 500 in the US, with lower risks and higher returns. Indeed, it would be comparable to a leveraged buyout deal – and millions of people are actively participating. According to some 2010 info, prices under RMB 30k per sqm[5] were common in Nanshan, Shenzhen; after 10 years, in the same district, new homes are priced at least at RMB 70k per sqm. Indeed, for the same community, prices can easily triple up.

The last decade’s rise is already on the back of a steady growth in the previous decade. In another study, Shenzhen’s housing price index rose from 1 to 3.65 between 2003 and 2013[6]. The same is true for other first tier cities – index increased from 1 to 4.43 for Shanghai, from 1 to 5 for Guangzhou, and from 1 to 7.6 for Beijing. Tier two and Tier three cities generally experience the same trajectory, with average growth rate a few points lower.

With a common 10x return and the ability to take leverage, residential real estate has become the most obvious, safest asset allocation choice with the best return profile for most people in China.

It is also worth noting that many high-quality companies in China, mostly in the internet industry, are not listed on the A-share market. Thus, they remain inaccessible to many people, further limiting their asset allocation choices.

[1] https://www.reuters.com/article/china-household-assets-0424-fri-idCNKCS2260VC (Chinese)

[2] http://pdf.dfcfw.com/pdf/H3_AP202004271378696212_1.pdf (Chinese)

[3] https://www.mdpi.com/2071-1050/12/7/2946/htm

[4] https://www.bostonfed.org/publications/research-department-working-paper/2019/trends-in-household-portfolio-composition.aspx

[5] https://sz.leju.com/news/2020-09-27/06456715630843769180881.shtml (Chinese)

[6] journals.uchicago.edu/doi/full/10.1086/685953

China’s housing market (1) [WIP]

Urbanization and population

According to the World Bank, China’s urban population percentage increased from 36% in 2000 to 60% in 2019, or around 843 million[1]. Two largest cities in China, Shanghai and Beijing, have 24.3 million and 21.5 million residents respectively in 2019 (Exhibit 1).

in millions 2000 2005 2010 2015 2019
Beijing 13.64 15.38 19.62 21.71 21.54
Shanghai 16.09 18.90 23.03 24.15 24.28

Exhibit 1. Beijing and Shanghai residents growth over the past two decades

While the definition for a “resident” is to live for more than 6 month of a given year, two other metrics are “Hukou” and “actual population served / managed” – Hukou, the most conservative one, is a concept dated back to ancient China while the latter is newly disclosed. In 2019, measured by actual population served / managed, Guangzhou has over 22 million, Shenzhen has over 22 million[2], Chengdu has over 21 million[3]. Meanwhile, their official residents in 2019 are 15.3, 13.4 and 16.6 million respectively.

While Hukou population for Shenzhen is only around 5 million, the actual population is 4 times more. The gap seems to be a good proof for Shenzhen’s fast growth over the past few decades, and also implies an outsized demand for future home buying. As further discussed below, Hukou (or certain years of social security tax) has become the prerequisite to buy homes in certain large cities such as Shenzhen. Therefore, fundamentally, the long-term increase in buying pressure is from 1) among the actual population, those who would like to stay in Shenzhen, obtain Shenzhen Hukou and buy homes, 2) the constant increase in actual population managed / served, as Shenzhen continues to attract businesses and provides high-quality jobs.

[1] https://data.worldbank.org/indicator/SP.URB.TOTL?end=2019&locations=CN&start=2000

[2] https://www.thepaper.cn/newsDetail_forward_7843440 (Chinese)

[3] https://m.bjnews.com.cn/detail/159193229415528.html (Chinese)

End of Decade Thoughts (1): An Increasingly Divided United States

This is a series about what we have seen in the past decade.

An Increasingly Divided United States

Three aspects:

1. The 2008 financial crisis provided a great opportunity for those who had equity while made many others in debt work years to recover. And the tax reform exacerbated the process.

– When we entered the past decade, prices were cheap for a lot of equities, but only for those who can buy.

– Differences were then created when the economy recovered – those who held equities enjoyed it.

– On the other hand, those who can’t buy didn’t share the growth (in any bull markets like stock, housing, etc.)

– Thus, more wealth inequalities were created. Supporting evidences could be found for a graph

2. The Republican and Democratic parties are more divided than ever – in fundamental values and action plans.

– The voters were divided before and in the 2016 election.

– It’s a result from dissatisfaction caused by the inequalities mentioned above and also from clashes over values [which is fueled by a multi-year accumulation of “opinions” mentioned below in 3].

– “Like the American public, Congress is also deeply divided. Lack of trust in the other party as well as a lack of bonds between representatives have fueled greater partisanship.” [Harvard Politics Review]

Democrats and Republicans More Ideologically Divided than in the Past
Source: PEW Research

– They are also unable to agree on what issues they should prioritize for policymaking.”

Republicans and Democrats differ over key priorities for the president and Congress in 2019
Source: PEW Research

3. Social medias fueled bias

– “Fake news” is a popular phrase. And misinformation is wide-spread. Meanwhile, social medias have become the primary sources of news.

– Machine-learning enabled “feeds” fulfills the confirmation bias among others.

– Personalization feeds “the most engaging and relevant” content for each individual user, which could easily compromise objectivity and expose human’s weakness.

– When people connect directly with their peers, the social biases that guide their selection of friends come to influence the information they see. [phys.org]

– Social medias made the discovery of “similar” peers, influencers and public accounts much easier, which again made the sources of information biased.

Summary: The econ pressure and social medias “cultivated” the public, leading further disconnections between parties, who made policies that most won’t see as “uniting” forces.

The dividing problems affect the policies again other nations, which are usually used when there is chaos inside.

The fight with the tech industry is also inevitable as political power is diminishing in driving/organizing the society. But tech is needed for overall growth and jobs – making them look more like monopolies is a good way to tackle/regulate.