Bank Negara Malaysia is having none of it. They are tired of people blaming them (too much) for the generally weak residential property market in the country.
In its 1Q17 Quarterly Statistical Bulletin, BNM wrote housing loan approval rate over the past few years had not fallen, citing statistics that 74.2% of all applications were approved in the first quarter, and this number almost matches the 2012-2016 average. This is in contrast to the 40% approval rate often cited in the media, which originates from developers and other players in the private sector. With this as a proof, the central bank calls the 40% rate a myth.[1]
Except, BNM may have been too hasty in passing a conclusion and they may have overlooked an alternative method to calculate the approval rate.
The central bank calculates the ratio by taking the number of housing loan applications approved by all banks in Malaysia to the number of housing loan applications received by the banks during the same period.
But the 40% rate is calculated based on total value of all housing loans approved, to the total value of application in the banking system. Some analysts calculate it differently by lagging the value of approved loans by a month in an attempt to capture the fact that banks take several weeks to process and deliberate on any application. The lagging would change the number, but the overall trend would be pretty much the same.
You can see the rates under the value-approach here:
The 40%, in fact, comes from a database maintained by BNM. Specifically, you can get the 40% rate by taking the value of residential property found file 1.10 and divide it by the corresponding value found in file 1.12.[2]
So, the 40% it is not a myth. That particular rate has not been picked out of thin air. It is just that BNM may have overlooked the fact that there is a different way to calculate the rate. Instead of volume-approach, there is a value-approach alternative.
Which method is more appropriate, now, that is a different and a much more interesting discussion altogether.
[1] The overall housing loan approval rate remains high at 74.2% (average 2012-2016: 74.1%). The approval rate is the ratio of the number of housing loan applications approved by all banks in Malaysia to the number of housing loan applications received by the banks during the same period. In 1Q 2017, banks approved a total of RM22.3 billion of house fi nancing to 90,137 borrowers. Of these, more than half was for buyers of affordable housing units priced below RM500,000. [Lim Le Sze. Debunking the Myth: Property Measures Have Led to Higher Loan Rejection Rates. BNM Quarterly Bulletin. Bank Negara Malaysia. Accessed May 26 2017]
[2] See the BNM Monthly Statistical Bulletin.
Some time back in 2015, I walked down to the train station after having a late meal in Bangsar. It was almost an hour to midnight and I did not want to miss the last train home. It usually takes about 15 to 20 minutes to get to the Bangsar train station from Jalan Telawi by foot, and the train stops running close to midnight.
I remember jaywalking across Jalan Maarof and then strolling down to near the bottom of the street, before turning into the slip road into a smaller and less busy Lorong Maarof.
It was there where I saw two officers, one standing and another sitting on his motorcycle, interrogating what seemed to be a migrant worker by the road. The worker was of South Asian origin, I think.
It was somewhat dark in the area. The road was lit with orange light, making the night felt quiet and lonely. There is something about orange light emitted from fluorescent lamp. It makes a street feels mysterious, if not sinister. The sound of cars zooming by, speeding beyond the speed limits, made the atmosphere all the more isolating. Nobody on the main road would care or notice anything if something happened.
I walked closer toward the three men while wishing I was already in my bed. I had to walk pass them to get to the train station. It was the shortest distance available to me.
I was tired, but my eyes were fixed on the three. But the fact that there were two police officers and a migrant worker there did not quite register in my mind. I observed with my senses, but my mind saw nothing. My mind was that absent security officer snoozing at night in front of the countless screens in the central CCTV room. The cameras were recording, but nobody was watching.
The officers did not notice me, until I was right next to them. They were startled by the sound of my footsteps. There was almost fear in their eyes, in contrast to the South Asian person’s blank expression.
I did not comprehend what was going on but I noticed an officer was forcing his hand into the Bangladeshi’s pants pocket, taking out a wallet. I had a sense that that was inappropriate.
I understand everything now in retrospection. If you asked me what was the expression on the Bangladeshi’s face, I now would be tempted to claim that it was an expression of resignation. It was an expression that said, tonight, I was being unlucky.
I looked at the officers, but I walked on as my mind slipped further into some of kind delirium. I was a zombie, for all I care. I was both aware and unaware at the same time.
That was until the bright white light at the train station jolted my mind out of its slumbering state. Already used to the low energy orange light, the eyes screamed in pain while adjusting to the high-frequency, high-energy LED white light. The announcement blaring through the PA system made a good alarm clock, even as apart of me felt that I wanted to disobey its instruction.
By the time I boarded the train and was zooming across Kuala Lumpur, I was fully awake. White light filled the largely empty two-car carriage. I wish they had dimmed the lights.
The train runs on a viaduct from Bangsar to just after the Central Market in downtown Kuala Lumpur. Then it dives into a long dark tunnel, making repetitive whooshing sound as the train compresses the air against the concrete wall of the tube. It was at that moment, as train rushed underneath the city that I began to consider things that I saw.
It did not take long for me to suspect that the two officers were extorting the migrant workers for money. I have no proof of it happening, except for my memory and suspicion.
I am telling this story today because yesterday, the Deputy Prime Minister, who is also the Home Minister in charge of the police, said that “Amalan mengutip ‘duit pau’ untuk perlindungan dalam kalangan agensi penguat kuasa sudah lama berlalu.” In Malay, in short, the police does not do extortion, anymore.[1]
Anymore, he said. Should I believe him?
Back in the train, I was filled with regrets, asking myself what if I had stopped and asked questions to the officers. I asked, if I had realized it earlier there and then, what would I do? Would I do what was right? I remember wondering, the degree of corruption in our society.
The train emerged from the tunnel just before the Ampang Park station. The swooshing sound was replaced with a cool humming.
It was dark outside. It was midnight, and I was leaving the last train.
[4] Amalan mengutip ‘duit pau’ atau habuan untuk perlindungan dalam kalangan agensi penguat kuasa sudah lama berlalu, kata Datuk Seri Dr Ahmad Zahid Hamidi. Bagaimanapun, Timbalan Perdana Menteri berkata, gejala rasuah itu masih menular kononnya menanggung perbelanjaan ‘meraikan’ pegawai atasan yang turun melawat anggota bawahan, khususnya di peringkat daerah. “Ini alasan tidak cerdik kerana bercanggah dengan pendirian anggota penguat kuasa yang diberi amanah untuk memikul tanggungjawab melindungi negara dan masyarakat. “Tindakan pegawai atasan membuli anggota bawahan juga perlu dihentikan kerana amalan ini menjurus kepada perlakuan menghalalkan rasuah dalam kalangan unit beruniform. [Mohd Iskandar Ibrahim. Farah Mashita Abdul Patah. Luqman Arif Abdul Karim. Ahmad Suhael Adnan. Era polis ‘pau’ duit perlindungan sudah berlalu – Zahid. Berita Harian. May 21 2017]
The first quarter 2017 Malaysian GDP figures will be out on May 19. So…
Industrial production in 1Q17 did not grow as strongly as it did in the previous quarter. Nevertheless, manufacturing had swell of a time. Trade figures were very good, with both goods exports and imports grew double digits, which indicates both the external and the domestic demands are somewhat healthy. But in terms of net exports, I do not think it would contribute much to the GDP growth since import growth was stronger than export expansion.
Talking about the domestic market, the unemployment rate seems to have finally responded to the better economic environment. Eyeballing, the seasonally-adjusted UE for the quarter is about 0.2 percentage points lower than what seems to be the average for 2016. Core inflation is slightly up, also showing domestic demand is recovering, assuming this core inflation calculation by the Department of Statistics completely isolates cost-push inflation.
By the way, the 4Q16 GDP grew 4.5% YoY.
How fast do you think did the Malaysian economy expand in 1Q17 from a year ago?
Total Voters: 7
Loading ...
In the past week or so, there were several news reports stating that Malaysia was regressing backward relative to the high-income country GNI benchmark of $15,000 per person by 2020. The Economic Planning Unit showed the figure fell to $9,291 in 2015 and to $8,821 in 2016, from $10,677 in 2014.[1]
From the figures alone, it is plain to see that the gap between current level and the $15,000 per capita has increased. The 2020 target was set by the government as the benchmark Malaysia needed to hit to in order to declare the country as a “high-income nation.”[2] Pemandu’s whole reason for existence is predicated on this.
But such conclusion (and the target itself) is superficial and largely a non-issue as far as economic growth is concerned. What is more important in terms of development is the levels of welfare, which is better represented by the purchasing power parity calculation, instead of the Atlas method used to calculate the GNI per capita figures in the US dollar.
There are three reasons why I claim the conclusion is superficial.
First, the $15,000 GNI per capita by 2020 target is susceptible to foreign exchange rate fluctuation. This is despite the Atlas method is designed to minimize the same fluctuation. The ringgit depreciation relative to the US dollar in 2015 and 2016 was just too big for the method to handle. Its inability to control for the fluctuation makes its output less reliable that it normally is. You can see why it does a bad job within the context of 2015 and 2016: the Atlas method controls the forex rate variation by averaging the latest three years of the relevant rates (the method does include inflation differential between countries but it is not nearly as good as the PPP). But even under normal circumstance, the Atlas method is inferior to the purchasing power parity just because the former does not adjust for domestic living costs properly. The PPP may have its own failings but its failings are considerably less serious than the Atlas method.
To understand this point further, we have to realize that for most Malaysians, earning and spending are carried out in the local currency, the ringgit. Only a small minority earn in ringgit but spend in foreign currency, among them the US dollar. So for most Malaysians, it is unclear why the size of the economy translated through the Atlas method into the US dollar is meaningful in determining the state of a country’s development level or its population welfare, apart from the fact that the World Bank uses it and that Pemandu was just following suit.
The World Bank states it is using the Atlas method for operational purposes,[3] which makes sense because the organization lends money to national governments mostly in major currencies and that the repayments are susceptible to the forex fluctuation due to currency mismatch. They need to take the forex fluctuation into account.
Meanwhile, Pemandu and Malaysia use it as a target… because the World Bank uses it for its global lending purposes done largely in US dollar. You can see the problem here. The World Bank’s and Malaysia’s purposes for using the Atlas method are vastly different. It fits the World Bank’s goal better than Malaysia’s. PPP, on the underhand, fits Malaysia’s purpose better than the World Bank’s. It is a case of using the wrong tool.
Second, even if we accept the target and the Atlas method wholly, the actual benchmark for high-income is likely lower than the $15,000 barrier. The $15,000 benchmark itself did not come from the World Bank but projected into the future by Pemandu based on figures from the international body. The latest 2017 high-income benchmark actually used by the World Bank is $12,476.[4] Pemandu had projected the figure from 2010 (if I am not mistaken), assuming the 2010-2020 growth rate of high-income countries to average 2.0% yearly. The reality is that the 2010-2017 average is only 0.2% yearly so far. At the current actual growth rate, the benchmark will be $12,563 per capita by 2020 assuming everything else remains the same. It is still a widening gap, but not as bad as when the $15,000 per capita is the target.
Third, the implications of the conclusion are outrageous, if the Atlas method completely addresses concerns over forex fluctuation: either Malaysia had run into a two-year long recession, or we had an extraordinary population boom during the same period.
But we did not have a recession. We did have a growth slowdown however. The Malaysian economy grew by 5.0% in 2015 and 4.2% in 2016. But no recession, which is a contraction of the GDP by two consecutive quarters.
And we did not have a population boom either during the two years. The size of the Malaysian population in 2015 and 2016 grew 1.4%-1.6% yearly, lower than in the previous years.
Since both did not happen (with inflation was not big enough to matter: Malaysia’s 2%-3% and in the US, 0%-1%), we must question the validity of the Atlas method in measuring the well-being of Malaysians. And by extension, it questions the ability of the Atlas method to determine the status of Malaysia as a high-income nation. The one factor that changed was the forex rate.
But ultimately, the term high-income nation itself is fluffy. There are attempts to give it concrete meaning but would crossing the not too distance line suddenly transform Malaysia into a rich country? It is never as clear as that. While Malaysia has done well compared to a lot of countries in the world, entrance to “first world” is actually harder then merely cross the line defined by the World Bank or Pemandu. Just cross over to Singapore, or visit Japan, or Australia or any of the generally recognized high-income countries. Would Malaysia crossing the GNI per capita $12,475 line suddenly make the us like those countries? Maybe someday, but the barrier will be way above the World Bank’s line.
You know a high-income country when you see one: some classifications are looser than others and many of them are arbitrary. This is the limits of mathematics and economics. So, be careful of turning a soft arbitrary line in the sand as your true north. Managing a country’s development is not like running a business.
But coming back to the original point, no, we have not regressed in terms of economic development. We have regressed in other aspects, like our institutions, but the economy has grown, contrary to what the imperfect Atlas method tells us. If you really want to make an international comparison, the purchasing power parity model is far superior than the Atlas method, especially at a time when forex fluctuation is great.
[1] — Page 5.The Malaysian Economy in Figures 2016. Economic Planning Unit
[2] — Page 59. Economic Transformation Programme: A Roadmap for Malaysia. October 26 2010
[3] — The income groupings use GNI per capita (in U.S. dollars, converted from local currency using the Atlas method) since they follow the same methodology used by the World Bank when determining its operational lending policy. While it is understood that GNI per capita does not completely summarize a country’s level of development or measure welfare, it has proved to be a useful and easily available indicator that is closely correlated with other, nonmonetary measures of the quality of life, such as life expectancy at birth, mortality rates of children, and enrollment rates in school. [Why use GNI per capita to classify economies into income groupings? The World Bank. Accessed March 23 2017]
[4] — For the current 2017 fiscal year, low-income economies are defined as those with a GNI per capita, calculated using the World Bank Atlas method, of $1,025 or less in 2015; lower middle-income economies are those with a GNI per capita between $1,026 and $4,035; upper middle-income economies are those with a GNI per capita between $4,036 and $12,475; high-income economies are those with a GNI per capita of $12,476 or more. [World Bank Country and Lending Groups The World Bank. Accessed March 24 2017]