另一點有趣的是作者是在另有本職的情況下辦到的,在另一本社畜的財務自由計畫的作者也是如此。兩個作者在韓國股市都是用 Swing Trade。不確定韓國股市有多 efficient,有鑑於台灣產業跟韓國產業組成沒有差太多的情況下 (週期強的半導體產業組成為重),台股應該也辦得到吧。mytropicfish 分享的也是在他有本職的情況下做到的,在他文章中也說到其實在有本職,特別是管理職的情況下,對於生意本職有敏感度反而對於產業的趨勢有把握,能夠做得不錯。變成全職之後雖然自由但也有各種不方便 (像是不易貸款,要擔心生活的現金流,失去產業敏感度,失去社交圈)。對於做美股這麼有效率的市場是否能辦到我就不確定了。不過有本職且操作不錯的這種生活型態很接近我理想中的狀態,有了工作的現金流對於心態上也會比較穩定。希望我有朝一日也能辦到。
書的後段著墨於心理面,特別強調當低潮的時候如何改善,還有當不順的時候可以對股票失去信心但不能對於自己失去信心。這其實跟我這幾年的摸索很像。而且這也不僅僅是股票市場而已。其實人生各方面都是如此。文字寫得精煉但也許只有走過一遭的人才能體會箇中深意,如果沒有經驗的話可能並不會對於他的文字有所共鳴。我特別喜歡一個段落就是把目標設定過高而躁進反而讓自己操作亂掉的經驗,當失望而沒有想太多去操作的時候,反而一切開始改善。這也跟快樂操盤人講得慢慢來比較快一樣。當有了貪念而心態亂掉的時候,反而讓自己的步調亂掉。把眼前的事情做好不要想太多,慢慢累積並且當時機來臨的時候自然會水到渠成。書本的這段章節看起來是對於內心是特別療愈就是。
]]>I spent a few days to read the paper and read the reference implementation done by jasonge27 in C++. The C++ implementation only implemented the fixed size approach in the paper but lack the generalized approach to support the online streaming update. I read the paper carefully and implement the generalized algorithm and then we have zw-fast-quantile crate.
The naive benchmark with criterion shows that the update operation is 2.6x faster than GK01 implementation in postmates/quantiles
zw unbound quantile update
time: [60.780 us 60.855 us 60.936 us]
change: [-1.4032% -0.9510% -0.5005%] (p = 0.00 < 0.05)
Change within noise threshold.
Found 8 outliers among 100 measurements (8.00%)
2 (2.00%) high mild
6 (6.00%) high severe
gk quantile update time: [156.84 us 157.02 us 157.24 us]
change: [-0.1907% -0.0503% +0.0969%] (p = 0.50 > 0.05)
No change in performance detected.
Found 11 outliers among 100 measurements (11.00%)
6 (6.00%) high mild
5 (5.00%) high severe
and query operation is 1.5x faster than GK01
zw unbound quantile query
time: [229.62 ns 230.16 ns 230.77 ns]
change: [+1.3422% +1.8105% +2.2504%] (p = 0.00 < 0.05)
Performance has regressed.
Found 11 outliers among 100 measurements (11.00%)
3 (3.00%) high mild
8 (8.00%) high severe
gk quantile query time: [350.21 ns 350.48 ns 350.76 ns]
change: [-0.4638% -0.3109% -0.1670%] (p = 0.00 < 0.05)
Change within noise threshold.
Found 8 outliers among 100 measurements (8.00%)
1 (1.00%) low severe
2 (2.00%) high mild
5 (5.00%) high severe
It matches with the emprical study from the paper that it is able to achieve 200-300x speedup over GK01.
To benchmark the memory usage, by storing complete 100M values in the Vec
and calculating the quantiles is using up 7812k
of heap, and with the error rate set to 0.01
the zw-fast-quantile
is only using up 1947k
of heap. It looks like it fulfills the goal of approximate quantile by saving the storage size without sacrificing the precision too much.
Implementing zw-fast-quantile
is a lot of fun and I learned a lot. By reading the review paper now I understand we can categorize the approximate quantile algorithm by different use cases. They could be
zw-fast-quantile
is falling on the category 3, and it is good to know that it influenced the implementation in Tensorflow’s boosted trees. It could be another handy algorithm put in your toolbox and use it when the use case is right.
對於投資跟投機的定義,市場上對於不同人一直有不同的定義。許多人會依照自身經驗,把比較不熟悉跟新生的事物,或是基本面比較模糊的東西就直接定義成投機。對照自己學習的來說,與其因為符號的關係而把事物認定成二元是本末倒置,事實上實際的運作更有可能的是一個光譜。這有點像是李小龍開創的 Mixed Martial Art,把原本各個流派有用的理論結合成現代武術,或是按照實證醫學的講法可能就是結合運動科學跟實驗精神去打造實證武術。我自己的路程由於工程師背景最早其實接觸的是 Quant,但意識到 Quant 基本上是拼機器跟基礎建設的軍備競賽而轉向基本面,對於傳統技術面來說大概已經比不太過經過實證統計的 Quant,大概只有在對手比較弱或散戶組成比較多的市場還顯著有效,記得有看過索羅斯的前交易員說傳統技術面在美股已經不太有效,但也許像是陸股或台股之類散戶組成比較多的地方大概還是有效,只是弱化而已。正由於手法如此多種而自己都多少接觸過,先來說說我自己覺得不同面向的投資定義方式
這幾種定義反應的是對於這世界理解,自身的限制,能力以及與哲學上的態度。由於你的交易總是會有不如預期的時候,如果你沒有一套自己的哲學跟邏輯論述,在交易不如預期的時候會陷入進退失據的狀態。在最開始的時候雖然我能接受純技術面或 Quant 能夠賺錢,但自己操作的時候是無法接受純技術面去操作的,主要是心理面無法克服。隨著越讀越多,還有實際看到其他人找到市場中暫時 displacaement 的部分去博弈,漸漸反思自己的整體作法而逐漸改變。
能夠調整主要是意識到,其實不只在投資中,我們在生活中經常依賴所謂的技術面來外包我們的判斷給其他人,就算是那些堅稱自己做價值投資的人,我是很難想像他們在生活中不曾因為某些餐廳大排長龍而,或是在新聞中看到比較多次關鍵詞而被影響去吃一些餐廳。現代社會是複雜的而個人時間有限,對於工程師來說就好像是一個複雜的分散式系統一樣,而我們不可能讀完所有的原始碼,而我們也並沒有一個 dashboard 來追蹤經濟這個機器的每一個面向。所以這時候難免會需要依賴於一些看似巫術的指標去逼近,只要逼近得足夠好不妨礙到我們生活就沒必要改變,除非是指標不夠準而需要精進。反應回交易,這就是必須承認我們實在玩 partial information 的遊戲,如何去權衡在自己的主場中玩 full information 的遊戲或是依賴於一些指標去逼近,就依賴於自己的哲學。而我自己是比較傾向於索羅斯定義的反身性以及他老師 Carl Popper 的科學哲學,就算你相信自己在玩 full information 的遊戲,很多事情是只能被証偽的,相信自己在玩 full information 跟使用巫術來選股,哲學上在被証偽之前可能都是被金融市場視為非無效,這該怎麼取捨還是取決於你自己的貝氏機率的 prior,而這個 prior 很可能決定於你出身的時代,你經歷過的事,當這些成為市場參與者的心理共識時,又會進一步改變市場結構。
說到共識,投資是一個尋求共識的過程,而有些方法就是比較容易說服別人。而所謂的別人,就是市場中資金的參與者,這會根據散戶的比率而造成某些現象在不同市場的差異,因為所需說服的人不同。有些研究說歷史上或有價值跟成長投資的風格切換週期,通常成長股表現比較好是在 Big Debt Cycle 裡面的經濟成長週期,而價值表現比較好通常是經濟走出低谷剛進入復甦的階段。我覺得就是市場在不同的階段,市場上的參與者具有說服力的說詞是不同的,在成長週期中,業績成長就是一番兩瞪眼,不需要多聰明都可以明白。而困境反轉是比較有難度的,畢竟原本的氣氛可能是十分悲觀,但只要業績沒有那麼慘,就可以用數據說服市場的參與者。那究竟能不能用一些指標來知道何時會風格切換呢?目前研究應該是覺得沒辦法,所以反應在自己的作法就是類似艾謝克那樣,用總經資訊像是零售,美元強弱,PMI 等等判斷持股水位,跟風格的倉位。總經方面的資訊很難用到實際的交易,但我覺得用來決定配置的資訊還是有些顯著性的,這就好像是玩大亂鬥之中有一些場地的亂入攻擊你必須要避開,這會來決定你整體策略為何。
手法方面,自己對於每種方法適用的情境,還有自己對於不同產業的估值模型也比較純熟,甚至對於 position sizing 從原本的無所是從,也漸漸有自己的因情境改變的手法。
例如這陣子很流行的瘋狗流,雖然我目前還沒看到有嚴謹的定義,就我的理解,比較像是利用基本面或籌碼面或其他經濟的另類指標,抓到起漲點然後去衝浪,並且加大資金 turn-over 來有效幫助資金成長。這看似不是價值投資但其實我覺得也是非常吃操盤人的經驗跟基本面分析主觀判斷的,但差別點在於他更多的是看待投資為期望值打法,承認自己有些許資訊優勢,但在整個波段可能不是資訊最強的,所以會外包很多判斷給籌碼跟資金流向。然後用停損來控制期望值是正的。對於操盤人本身的心理素質要求也是蠻高的。我個人認為適用的主場是像是台股這樣沒有個人資本利得稅,而且半導體,電子股還有傳產是或多或少算是週期較短的地方,畢竟週期股波峰跟底可能會相差到十倍。在美股的話,如果交易人是美國資本利得稅務身分,而美股市場又是期權市場比較發達的地方,改用 Options 的策略來做區間或是波段會是更適合的作法,但風險不對稱還有下檔保護風險的打法我覺得精神上都是類似的,就是建造一個期望值為正的系統,並且讓你心理可以承受的住。
由於知道瘋狗流的作法,對於一般人理解傳統價值投資的一些盲點也有了更深的體會。我覺得傳統價值投資麻煩的一點就是你不知道會不會是價值陷阱,而多數的書裡面對於 position sizing 以及下檔保護等等的著墨並不多,多數的價值投資人都是說並不會停損,好一點的會說會依照新的資訊如果對於原本進場的 thesis 造成實質的改變就會賣掉。而實際去檢視其實說這些話的人多半都是股東會有實際話語權甚至是決策權的,因為實質的權力可以保護下檔。就算沒有控制權,他們可能也是在挑對手比較弱的主場,譬如說是 micro-cap space,對手沒有其他法人的話就可以優先接觸到管理層,因而有資訊優勢,當然壞處就是 micro-cap 的流動性比較不好,在系統性風險發生的時候不是很好逃跑。對於部位不夠大而價值投資做的好的人,我觀察到的幾乎其實都是在做 Value Trading,其實他們賭的並不見得是三年以上的維度,而是幾季甚至是一年為 timeline 的短暫 value displacement,因而要跑的判斷可能比較容易定義。如果是要完全不停損的話,另一種方式就是要等安全邊際夠大的時期左側交易,大概只能等那種十年一次的系統性風險,那就是變成十年一次壓身家。太過分散的話超額報酬又不高,畢竟超額報酬是博弈而來的。 但這又會牽扯到 position sizing 的問題,萬一你錯了怎麼辦?按照 Joel Greenblatt 的訪談時說法,他的 position sizing 原則是他不是看風險報酬比,而是他覺得會輸錢的絕對數值不大的時候他才會放大部位。如果一個 binary event 上檔是十倍但下檔是歸零的時候是不符合壓大的定義的,但這其實跟期望值打法是異曲同工之妙,只是是利用基本面的計算來控制下檔風險,畢竟下檔控制不外乎幾種方法。
從書裡描寫的價值投資直接照搬一般個人情況,在艱難時期或是看錯的時候,常常就是賠大錢的時候。
零零總總打了一堆,算是紀錄一些想法的改變。
]]>I explored and came out my own methodology, I have tried it out on my friends seeking my advice and the experiments so far look positive. It’s not for every one, but if you are from similar background as mine, you might find the methodology helpful. My background is:
I noticed that my methodology is very software operational influenced, or could be logically structured like this:
The first step of mitigating the problem is to make sure you are not sick (that could be measured by modern medicine). Not to jump into the topic of Materialism v.s. Spiritualism, we definitely agree that your body would play a role affecting how you think, or if you’re still able to think rationally. Ruling out the possibility of depression etc would be the first step.
After making sure you’re not sick, you could use body hacking to make yourself better.
Once you finished the mitigation step, we would need to find out the root cause of the problems. And usually you would be stucked at these steps and you have no idea how to make a breakthrough.
Before jumping into the detail, I’d like to address what’s the foundation / framework on tackling these problem. It’s the logic system borrowed from Philosophy and Mathematics. Like dealing with any problem in life you must admit a foundation where you could build solid ground on. To me it is the logic rules and axiom, even the modern theology is built on logic and axiom. I don’t see any better foundation you could rely upon at this moment. The difference and the key part is to know yourself well, since that would play a key role to know what your axiom are. For example: Your personality is like Alex Honnold in the documentary film: Free Solo where you have a big heart for adventure. You can’t tell why since that’s a part of yourself. It is what it is and you can’t ask further, that’s your axiom and it would be the starting point for reasoning in your logic system. It would be another story if your personality is conservative on risk. When facing on the same type of scenario, the same logic rules with different axiom could lead to very different reasonable action items. They are true respectively in their own logic system. Understanding this fact would aslo help you whether to take advice from others. Their advice could be very helpful in their own background but may not apply that well in your system.
Apart from the logic system mentioned above, I also borrowed probabilistic thinking from the master poker players and investing guru. If you read enough you’d notice that the probablistic thinking they are not only applied well in those domains. Your daily life are challenged by a lot of uncertainty and unsure, where they are subject to certain known or unknown distribution. But people rarely see it that way, they would see they did something wrong when the outcome was not right. It might be true, but you have to take a grain of sale on that where you need to guess what’s the oracle distribution behind the world you’re facing and you still might face bad outcome with certain probability even you are doing the right thing. It might just you’re just unlucky for the time being. And the reasonable action is to keep doing it until it reverses to the mean.
Beyond that, sometimes the feedback loop are unnoticeable in incremental amount. The typical tasks like these are language learning and gym training. You have to sort of “believe” that eventually it would work out and stick with it for a time window until you make it. You have to be able to identify the difficulties you are facing is just a part of that kind.
With the above said, now we could tackle the three scenarios mentioned above to find out the root cause.
Usually you would know the action items once you identify the root cause. Sometimes it is not that easy. However, the world is big, I would like to lay my belief not on deity but the fact that the world is big and believing that the difficulty you face today might have been resolved by somebody else in the world before. Read more blog posts and books to learn from others’ expereience. Especially the brighter ones. I like Bill Gates’ method to reset, where he would bring tens of books and lock himself into an isolated woodhouse in the forest and focus himself on the topic. I also tried this method by myself. I read the tens of books from business people, or the topics that I wouldn’t usually touch to explore the inspiration from others. And they might share the misearble situation they faced before and when realizing those, you would feel you are in a much better position. It would do good on seal healing the wound. This method create positive optionality. Traveling to challenging place would sort of play the same type of roles, but mostly distraction since those wouldn’t bring knowledge from reading a book.
Again, these are the framework I adopted, and I assume the methodology would only apply to certain group of audience. I kind of abstract the process how people self cure the mental problems over the years and come up with this framework. I don’t know how far it could apply but it works well for myself and a few samples around me. Hope that is helpful.
]]>I’ve learned to like Audiobooks and I would say it really help a lot on your reading efficiency since it would make you effectively use your time on the road or while you are jogging. This easily adds another hour or two on each of the day. It could also let your eyes to rest, especially for a software engineer where you already use your eyes a lot. I can simply borrow the Audiobook from Singapore library and finish it in one week or two (The deadline of borrowing a book also creates urgency so that you feel like finishing it).
最後、2020年で日本語が上手になりたいです。練習のため、このブログでもとも日本語の文を書きます。真剣に日本語を勉強したい動機は日本の株式の情報収集です。決算報告の日本語分かったら、それが私の競争上の優位性です。後、勉強の仕方を教えます。
]]>Brendan Gregg has listed the command line tools that are useful for analyzing a Linux instance if he has to ssh into the instance. They could be preliminary analysis to quickly get a feel on what’s going on on a specific instance.
To dig deeper into the detail, these eBPF-based tooling would be helpful.
When the information you are trying to get are not there, and strace
is not enough. The following tools could be helpful to peek into the critical paths in the kernel modules.
A nice book to read is a new book authored by Brendan.
While studying for the SRE materials, I also found Cloudflare has many good blog posts that I could learn from.
A blog post on how to optimize for http2 stack gives really good insight on how the Linux network stack works.
net.core.default_qdisc = fq
net.ipv4.tcp_congestion_control = bbr
net.ipv4.tcp_notsent_lowat = 16384
Their interview questions also makes you start to question yourself if you really know the modern TCP/IP stack.
Archaeology
What is the lowest TCP port number?
The TCP frame has an URG pointer field, when is it used?
Can the RST packet have a payload?
When is the "flow" field in IPv6 used?
What does the IP_FREEBIND socket option do?
Forgotten Quirks
What does the PSH flag actually do?
The TCP timestamp is implicated in SYN cookies. How?
Can a "UDP" packet have a checksum field set to zero?
How does TCP simultaneous open work? Does it actually work?
Fragmentation and Congestion
What is a stupid window syndrome?
What are the CWE and ECE flags in TCP header?
What is the IP ID field and what does it have to do with DF bit? Why do some packets have a non-zero IP ID and a DF set?
Fresh Ideas
Can a SYN packet have a payload? (hint: new RFC proposals)
Can a SYN+ACK packet have a payload?
ICMP Path MTU
ICMP packet-too-big messages are returned by routers and contain a part of the original packet in the payload. What is the minimal length of this payload that is accepted by Linux?
When an ICMP packet-too-big message is returned by an intermediate router it will have the source IP of that router. In practice though, we often see a source IP of the ICMP message to be identical to the destination IP of the original packet. Why could that happen?
Linux Configuration
Linux has a "tcp_no_metrics_save" sysctl setting. What does it save and for how long?
Linux uses two queues to handle incoming TCP connections: the SYN queue and the accept queue. What is the length of the SYN queue?
What happens if the SYN queue grows too large and overflows?
Touching the router
What are BGP bogons, and why are they less of a problem now?
TCP has an extension which adds MD5 checksums to packets. When is it useful?
And finally:
What are the differences in checksumming algorithms in IPv4 and IPv6?
]]>Roughly speaking the fund’s strategy is to value the countries or regions by Shiller 10 year CAPE ratio and pick the bottom 25% as the universe, and then select the top 30 stocks by the market cap and filter them further by traditional P/E, P/B, P/FCF, EV/EBITDA etc. The final picks are about 100 stocks and rebalance the portfolio every year. For the detail, you could check the prospectus
And the pie chart for the regions and countries like this.
Since fund’s introduction mentioned Graham and Dodds. I couldn’t help to link that with Graham’s net-net strategy, to use a bag of extremely low value to bet it statistically would “somehow” reverse to the mean. I know Graham in general encourage to hold it for 3 years, if nothing happen then sell it. However, seeing the fund’s underperformance in the last two years, I couldn’t help to list a few of the reasons that may not happen.
I tried to make another list by the data compiled by Damodaran. I removed the countries that has too few stocks listed, and sorted the list by from high to low by ROIC, then ROE. and low to high by EV/EBIT. The reason to make this sorting is that I think marginal investment return would be a stronger reason to attract the money to flow in, but not simply low value. The list is like the following:
country |
---|
Thailand |
Finland |
China |
Singapore |
Denmark |
Japan |
Hong Kong |
South Africa |
Taiwan |
Bangladesh |
Vietnam |
United Arab Emirates |
Argentina |
Italy |
Nigeria |
Jordan |
Netherlands |
Russia |
Mexico |
Switzerland |
Romania |
Turkey |
Sri Lanka |
Egypt |
Oman |
For the top 20 there are some overlaps, but the rankings that was in the low Shiller 10 year CAPE are ranked lower. This ranking somehow matches with some of my private findings of good value company globally. I would argue this ranking should be a relative good pool to fish. But not the simply low multiple ranking. With that said, I didn’t do thorough research to do back-testing etc. It’s simply my hunch though.
This is just a rundown of my thoughts, not a rigorous research.
]]>I can barely recall when was the first time I heard about the name of Renaissance Technologies. It is probably back in 2010 when I got intrigued by the world of Quant and I was reading Derman’s book: My life as a Quant. I googled a little bit of the development of the financial modeling and tried to know the landscape of the hedge funds that are quantitative funds. It didn’t take too long to run into the name of Jim Simons. As a STEM student growing up in Asia, his associate with Shiing-shen Chern and Chern–Simons theory immediately brought my attention. By further digging up the techniques adopted by Renaissance Technologies. The names of Peter Brown and Robert Mercer came up, they are the students of Frederick Jelinek, a figure you must know if you were in my topic of research: Speech Recognition. The rumor that they were using Hidden Markov Model at the time of early 90s excited me, where HMM was the model you could not skip in my field. Most of the research was trying to build adds-on to the core HMM infrastructure.
This book isn’t about to tell you about the skills adopted by Renaissance Technmologies, but a biography of Jim Simons by Gregory Zuckerman. Renaissance Technologies has stayed low-key for a long time and their secretive style makes few people know the history of the fund. Gregory did a good job to piece together the history, the challenges and the crisis where Jim has faced, and key figures helped establish the foundation of the company. It shed more humanity colors to the stereotype that a group of mathematicians and scientists should be run the company rationally and pursue the path of philosopher king as described by Plato. However, it is far from what was happening in the fund. Drama and crisis was no different from other companies.
The book is so much fun to read and I would like to put down a few notes and thoughts in bullet points.
首先我定義我的 Cap Rate After Tax 如下
(Net Operating Income - Tax) / Current Market Value
我把稅務還有維護費用成本加進來,但沒有算進折舊,就股票來比較的話比較像是 NOPAT 吧。Top Line 的部分假設沒有任何一個閒置的月份,是最理想的情況。
挑的三個樣本有
他們大概都是位在離市中心搭地鐵 30-40 分鐘的地方,在新加坡東北邊,屬於比較早期發展的區域。周邊生活機能還算不錯,有大的 shopping mall 也有 hawker Center。硬是要類比台北的話,可能有點像新店吧。這邊都用公開能夠找到的資料,Property Guru 有點像是信義房屋或是 Redfin。
Item | Price |
---|---|
Rent Income | 33600 |
Property Tax | (3423) |
Cando Fee + Utility | (3600) |
Maintenance | (1000) |
Home Insurance | (200) |
Calculation | Rate |
---|---|
Rental Yield | 1.816% |
Cap Rate | 1.371% |
網站上寫了 Rental Yield Estimated: 2.37% - 2.71%,但自己計算是遠低於那個。我猜網站是照 2016 年的交易價格算的。那時候的成交價是 1.3M SGD。
1.8% 的 Rental Yield 有夠爛。US 10 year Treasury 都有 1.7% 左右,而且幾乎無風險。當然房子的好處是會隨通膨而漲價。估計如果自己改裝 layout 成出租隔間的話,總租金可以收到 4000 SGD。那 Cap Rate 可以到 2.15%,這樣會比較好一些,不過自己管理的時間成本也高了很多。
Item | Price |
---|---|
Rent Income | 43200 |
Property Tax. | (4584) |
Cando Fee + Utility | (3600) |
Maintenance | (1000) |
Home Insurance | (200) |
Calculation | Rate |
---|---|
Rental Yield | 2.618% |
Cap Rate | 2.05% |
網站上的 Rental Yield Estimated 是 2.5% - 3.17%,還是高估不過比較接近了。
這一棟比較小戶型,比較類似 Studio
Item | Price |
---|---|
Rent Income | 30600 |
Property Tax. | (3072) |
Cando Fee + Utility | (2400) |
Maintenance | (1000) |
Home Insurance | (200) |
Calculation | Rate |
---|---|
Rental Yield | 2.49% |
Cap Rate | 1.945% |
所以稍微小的戶型也並沒有比較好。
可以參考這兩篇
而 Tax 的部分比較複雜
而 Condo 的話有區分是 Free-hold,租期 99 年或 999 年,細節可以看這兩篇
政府每一季會發報告。根據政府編制的房地產指數,過去 Private properties 的走勢如這裡,從前面的低點又回來一些。不過要注意一個細節,他的所謂 private properties 包含了 shop, office, user factory and warehouse。我沒有找到只包含住宅的指數。如果是 HDB 的話,走勢可以從這裡看到,可以看出來是不斷走低的。
另外 HDB也有公布租金價格中位數跟價格中位數。我擷取了其中一個地區。簡單計算一下 Rental Yield 高了很多,可以到 7.5%,HDB 的話還不用付 Condo Fee,所以粗估 Cap Rate 可能有 7%
Time | Location | Type | Price |
---|---|---|---|
2019-Q2 | ANG MO KIO | 3RM | 1700 |
2019-Q2 | ANG MO KIO | 4RM | 2100 |
2019-Q2 | ANG MO KIO | 5RM | 2300 |
Time | Location | Type | Price |
---|---|---|---|
2019-Q2 | ANG MO KIO | 3RM | 272000 |
2019-Q2 | ANG MO KIO | 4RM | 412500 |
2019-Q2 | ANG MO KIO | 5RM | 680000 |
不過 HDB 並不是一個自由的市場,要出租的規定也很多。一手的只有公民可以買。要買二手的話至少也要 PR,對於沒有結婚的 PR 還只能買其中一種比較貴的,結婚的話才能買其他種。對於收入超過一定的也沒辦法買 HDB,所以新加坡年輕人結婚的話也早,因為要買租屋的話等到收入高了就只能買 Condo。
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