ISSN (Print) 0473-453X

Discussion Paper No. 1081

ISSN (Online) 2435-0982

WHY ISTHE HONG KONG HOUSING MARKETUNAFFORDABLE?
SOME STYLIZED FACTS
AND ESTIMATIONSCharles Ka Yui Leung
Joe Cho Yiu Ng
Edward Chi Ho Tang

March 2020

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

Why is the Hong Kong housing market unaffordable? Somestylized facts and estimationsΛ
Charles Ka Yui LEUNG, City University of Hong Kong
Joe Cho Yiu NG, City University of Hong Kong
Edward Chi Ho TANG, Hong Kong Shue Yan University ℜ

This version: March 2020
Abstract
The house price in Hong Kong is well-known to be "unaffordable." This paper argues that the commonlyused house price-to-income ratio may be misleading in an economy with almost half of the population
living in either public rental housing or subsidized ownership. Moreover, we re-focus on the relationships
between economic fundamentals and the housing market of Hong Kong. While the aggregate GDP,
population, longevity continues to grow, the real wage and household income fall behind. The trend
component of the real GDP growth suffers a permanent downward shift after the first quarter of 1989 (a“political scar”). The trend component of real wage growth is close to zero, and the counterpart of realconsumption and real investment decline steadily. Meanwhile, the trend component of the real housing rent
and price display patterns that decouple from the macroeconomic variables. We also discuss the directions
for future research.

Keywords: housing demand, migration, structural break, time series decomposition, wage index andhousehold income
JEL Classification Numbers: E20, J01, R00

Λ

Acknowledgment: We are grateful to Alain Bertaud, Nan-Kuang Chen, Bob Edelstein, Eric Hanushek, Fred Kwan,
Tommy Leung, Steve Malpezzi, Shane Su, Byron Tsang, Isabel Yan, Matthew Yiu, anonymous referees, seminar
participants of the National Taiwan University for many helpful discussion, and City University of Hong Kong
(through the Internal Grant No. 7005001) and Hong Kong Shue Yan University for financial support. Part of theresearch is conducted when Leung visits the Hoover Institution; Ng visits the Virginia Tech through a Junior Fulbright
Scholarship. The hospitality of these institutions is gratefully acknowledged. Part of the work described in this paper
was supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China(Project No.: UGC/FDS15/B01/18). The usual disclaimer applies.

ℜ

Correspondence: Leung and Ng, Department of Economics and Finance, City University of Hong Kong, Kowloon
Tong, Hong Kong, kycleung@cityu.edu.hk, joecyng-c@my.cityu.edu.hk; Tang, Department of Economics andFinance, Hong Kong Shue Yan University, North Point, Hong Kong, chtang@hksyu.edu.

1. Introduction
Housing affordability has become a global concern. According to McKinsey(2017), “One feeling unites billions of people in cities around the world: a sense of
sticker shock whenever they attempt to find a new home. From London to Lagos,
housing costs are creating financial stress for a large share of the world’s urban
residents.” Some authors claim that Hong Kong performs worst in providing
affordable housing among the global cities. For instance, according to Demographia(2019), “The 29 severely unaffordable major housing markets are shown in Table 7.
Hong Kong has a Median Multiple of 20.9, the least affordable Median Multiple yet
recorded. For the ninth year in a row, Hong Kong has the worst housing affordability
in the Demographia International Housing Affordability Survey...”Motivated by these observations, this paper addresses the following questions:
Is Hong Kong housing that unaffordable? Why is it so prohibitive? What can otherplaces learn from the Hong Kong experience? To make this paper as accessible aspossible, we first present some stylized facts graphically. We provide explanations
and interpretations of those graphs. We will also provide some statistical analysis
when necessary.
This paper builds on several strands of the literature. First, there is an effort tocompare and contrast the institutions and performance across different housing
markets.1 Second, many works of literature have studied how the housing market isrelated to the macroeconomy.2 Third, there is relatively small literature that focuses on
the Hong Kong real estate markets.3 Fourth, since most residents in Hong Kong are
ethnic Chinese and at the same time being exposed to the global culture, this paper isalso related to the literature on the Chinese housing market.4 Fifth, the trade conflict1

It is beyond the scope of this paper to review the literature. Among others, see Bardhan et al. (2012),
Ben-Shafar et al. (2008), Bertaud (2018), Hårsman and Quigley (1991), Mera and Renaud (2000),
Renaud et al. (2016).
2
It is beyond the scope of this paper to review the literature. Among others, see Case and Quigley(2008), Chang et al. (2012, 2013, 2016), Chen and Leung (2008), Favilukis et al. (2017), Garriga et al.
(2017), Jin et al. (2012), Kan et al. (2004), Kiyotaki and Moore (1997), Leung (1999, 2001, 2003),
Leung and Chen (2006), Leung and Teo (2011), Leung and Tse (2017), Lin et al. (2004), Malpezzi(2017), Tse and Leung (2002). For a review of the literature, see Davis and Van Nieuwerburgh (2014),
Leung (2004, 2017), Leung and Chen (2017), Leung and Ng (2019), Piazzesi and Schneider (2016).
3
It is beyond the scope of this paper to review the literature. Among others, see Cheung et al. (2017),
Chong and Yiu (2019), Chow et al. (2002), Funke and Paetz (2018), Huang et al. (2018), Kwan et al.
(2015), Leung and So (2004), Leung and Sung (2006), Leung and Tang (2012, 2015a, b), Leung andWong (2004), Leung et al. (2013, 2014, 2015, 2019), Oxfam (2018), Sung et al. (2015), Tang (2017,2019), Wong (1998), Wong et al. (2018), Wu et al. (2017).
4
It is beyond the scope of this paper to review the literature. Among others, see Chen and Wen (2016),

between China and the United States has non-trivial consequences to other countries,
as some authors have found preliminary evidence on that.5 Hong Kong, being a part ofChina and intermediates many goods trade as well as financial transactions for China,
is affected. It also sheds light on the “spillover effects” of the China-USA trade conflicts.
And while the Hong Kong housing market has been discussed previously (Leung andTang, 2012, 2015a, b, and the reference therein), this paper provides some updated
analysis on the housing affordability and conduct a time-series decomposition of the
key macro variables. This paper intends to be accessible to a broad audience in Asianand other countries, as economic lessons are typically general. Therefore, we present
our results graphically.
The structure of this paper is straightforward. We present some stylized facts ofthe Hong Kong housing market and the macroeconomy. We then decompose the timeseries of the main variables of the housing market and the macroeconomy. The evidence
suggests that the "unaffordability" of the housing market may have its roots in somelong term trend. We provide some simple thoughts in the concluding section.

2. Hong Kong Housing Market and the Macroeconomy

As an international financial center, Hong Kong is mentioned in global media
from time to time. With a fixed boundary and a fixed exchange rate with the UnitedStates, Hong Kong has been able to attract much non-local investment in its real estate
sector.6 Therefore, the housing unaffordability concerns by Demographia and otherorganizations deserve serious attention. We begin our discussion with the house price.
Unless specified otherwise, our data comes from the Hong Kong government official
website.7 Figure 1a shows that the real house price in 2018, i.e., the house price after
removing the inflation factor, is 400% of the 1979 level. It also shows that the house

Chen et al. (2017), Deng et al. (2017), Huang et al. (2015), Leung and Wang (2007), Leung et al.
(2011), Wu et al. (2012, 2016).
5
The literature related to this recent trade war is growing. Among others, see Abiad et al. (2018), Amiti
et al. (2019), Guo et al. (2018), Mandelman and Waddle (2019), Tham et al. (2019).
6
Notice that the United States follows (approximately) a version of the Taylor rule, which is an interest
rate rule (Koenig et al., 2012). Since Hong Kong maintains a fixed exchange rate with the U.S., Hong
Kong nominal interest rate follows the U.S. counterpart. See Chang et al. (2012, 2013, 2016) for morediscussion.
7
The government official website for data is https://www.censtatd.gov.hk/home.html.

price and stock price, while both have an upward trend, do not always move together.8
Also, some authors suggest that, since the Hong Kong dollar exchange rate is linked
with the U.S., the U.S. monetary policy will dictate the Hong Kong house price. Figure
1b shows that while the U.S. interest rate influences the Hong Kong real housing pricegrowth rate, it is not the only factor.
Moreover, Figure 1c suggests that the house price increases faster than the wage,
as the house price-to-wage ratio increases from 100 (1982 level) to about 270 (2018level). To facilitate the comparison, we also plot the same ratio for the United States.
The U.S. counterpart increases from 100 to 160 during the same period. These figures
confirm the Demographia assertion that the housing affordability in Hong Kong has
indeed deteriorated.
(Figure 1 about here)
However, those figures overlook an essential feature of the Hong Kong
housing market; namely, a significant number of households live in need-based public
rental housing, and hence they are somehow "protected" from the appreciation of thehouse price.9 To illustrate the point that the size of the public rental housing matters,
let us consider an economy with only three agents, A, B, and C. The agents arelabeled in such a way that the income of agent A is lower than agent B, and theincome of agent B is, in turn, more moderate than agent C. In this artificial economy,
the median agent is B. Hence, agent B's house price-to-income ratio is the economy's
median house price-to-income ratio. Now assume that agent A instead lives in public
housing, whose rent is subsidized by the government. The private housing market
consists of only two agents, B, and C. The median house price-to-income ratio of theeconomy becomes the average of the house price-to-income ratio of agent B and C. In
other words, the existence of public housing changes the median house price-toincome ratio in the private housing market.
Let us consider another economy with five agents, A, B, C, D, and E. As in theprevious example, the agents are arranged so that the income of agent A is lower thanthat of agent B, and so on. Agent E has the highest income. In a scenario with nogovernment intervention, the median agent is agent C. The house price-to-income

8

For the co-movement between the stock price and housing price, see Leung (2007), Leung and Teo(2011) for theoretical analysis, Chen and Shen (2005) for the case of Taiwan, and Chang et al. (2013),
Kwan et al. (2015) for the case of Hong Kong.
9
For more details, see https://www.housingauthority.gov.hk/en/flat-application

ratio of agent C is the economy's median house price-to-income ratio. Now thegovernment provides not only public rental housing to agent A but also subsidized
ownership to agent B. In this new scenario, only agents C, D, and E face the private
housing market, and the (new) median agent in the private housing market is agent D.
And if we want to measure the house price-to-income ratio in the private housing
market, we should measure the ratio of agent D, not agent C. In other words, in aworld without any government intervention, the median agent in the income
distribution and the private housing market would be the same person. However,when the government intervenes through public rental housing or subsidized
ownership, the median agent in the income distribution will no longer coincide withthe median agent in the private housing market. Hence, if the government
participation in the housing market is sizable, we expect the distortion of using themedian house price-to-income ratio would be sizable as well.
In Hong Kong, government involvement in the housing market is indeed
massive. The public rental housing project began in 1953 and since then grows with theeconomy (Smart, 2006; Yeung and Wong, 2003). 10 Figure 2a shows the number ofhouseholds lives in different types of housing: public rental, subsidized ownership, andprivate. Figure 2b shows the number of housing units for each kind of shelter. While
the growth rate of public rental housing and occupants seem to be lower than the private
housing in recent years, the public rental sector still accounts for roughly one-third of
the housing market. The subsidized ownership approximately accounts for another oneseventh of the housing market. Together, it means that the private housing market(rental plus ownership) accounts for roughly half of the population.
Furthermore, Figure 2c shows that public housing rent is significantly below themarket counterpart. 11 Since the rent in public housing is significantly below the market

10

Among others, see Gong and Leung (2019), Leung et al. (2012), Leung and Tang (2015) for morediscussion on the public housing allocation.
11
Since the public rental housing is highly subsidized, there is rationing. To be eligible for applying for
public housing, the family should satisfy the income and asset test prescribed by the Housing
Authority. It typically takes several years for a household to receive a public housing unit.
The official account of the rent policy in those public rental units can be downloaded from thegovernment website: https://www.housingauthority.gov.hk/en/common/pdf/about-us/policyfocus/policies-on-public-housing/B01/B01.pdf
In October 2017, the Housing Authority tightened “Well-off Tenants Policies.” Households whose
income exceeds five times the current income limits (PRHILS) or whose total household net asset
value exceeds 100 times PRHILs should vacate the flats. For an earlier discussion, see Wong (1998)
and the reference therein.

and adjusts slower than the market, it appears to be safe to conclude that up to a thirdof the population is shielded from the housing market fluctuations, and they tend to be
the lower-income households. Since public housing shelter in such a scale is absent inmost countries, it may suggest that merely comparing the Hong Kong house price-toincome ratio, or similar metrics, with other countries, may not be as informative as
previously thought.
(Figure 2 about here)

Many authors compare the housing market of Hong Kong with Singapore.
While the two economies have many similarities, we caution a direct comparison of thetwo housing markets. For instance, while nearly 90% of the population in Singapore
live in “public housing,” most of those public housing units are “subsidized ownership.”Those Singaporean owner-occupiers would need to bear the capital risk should theyneed to re-sell the housing units in the market. The portion of the population living inpure public rental housing is less than 10% of the whole population (Deng et al., 2014).
It is very different from the case of Hong Kong.

Besides, a higher level of house price-to-income ratio may be driven by a house
price level which is too high, or an income level which is too low, or both. We present
a series of graphs that may give us some hints. Figure 3a shows that the real GDP per
capita does increase over time. However, the wage in Hong Kong does not grow as fast.
To facilitate the comparison, we plot the ratio of the official wage index relative to thereal GDP per capita and rebase that as unity in 1982. Leung and Tang (2015) show thatif (1) the aggregate production is in a Cobb-Douglas form, as many macroeconomics
textbooks suggest, and (2) both the product markets and input markets are perfectlycompetitive, then the ratio of wage relative to GDP per capita should be a constant. This
result holds even when the aggregate productivity fluctuates. This analytical result
gives us a handy benchmark.
Figure 3b shows that this is not the case. The ratio drops from unity to around
0.6 over the years, which significantly and persistently departs from the theoreticalbenchmark. There are different possible interpretations. For instance, some suggest thatalternative contractual arrangements are now available in Hong Kong, and hence thewage index has ceased to be an informative indicator. OECD (2016) recognizes that inEurope, at least half of the employees benefit from various kinds of flexible working

arrangements, including working part-time, working at home, etc. The presence of thisarrangement could affect the information contents of the wage index. Also, the wage
index concerns an individual, while in practice, there are many multi-member
households. There is evidence that in multi-member families, some members may
specialize in market production while other members may specialize in householdproduction (Becker, 1994; Erosa et al. 2017). In that case, household income-based
measures may be more relevant than the individual-based wage index.
Figure 3c presents both the ratio of median household income relative to the
GDP per capita and the ratio of wage index relative to the GDP per capita. Again, we
normalize both ratios as unity at the initial period that we can access the relevant data,
which is 1999. Several patterns are apparent. First, relative to the GDP per capita, boththe median household income and the wage index roughly declines at the same speed
as the wage index between 1999 to 2006. In other words, the median household income
series and the wage index follow the same downward trend during the period 1999 to
2006, and hence our previous result is robust in this sense. After the global financial
crisis (GFC) began, the median household income almost stays constant, while thewage index continues to decline from roughly 85% to 75% of the per capita GDP. The
difference between the individual-based wage index and the household income isconsistent with the "income insurance" view of marriage. When a husband, for instance,
loses his job in a recession, the labor supply of his wife tends to increase and hence
stabilizes the household income (e.g., Bredtmann et al., 2018; Bryan and Longhi, 2018,
Greenwood et al., 2017, and the reference therein).
There are different possible explanations of the decline of wages relative to theper capita GDP. Recall that the ratio is supposed to be a constant under perfectcompetition. Hence, the observed departure suggests a violation of the perfectlycompetitive assumption, which is consistent with some earlier studies. For instance,
Lubik (2012) proposes a search-and-match model to account for the Hong Kong labormarket dynamics, and he structurally estimates his model with the Bayesian method.
He finds that the bargaining power of the employees is minimal.
Alternatively, it is also consistent with emerging literature on the declining labor
share of GDP (Dao et al., 2017; Gutierrez, 2017; Karabarbounis and Neiman, 2013).
Autor et al. (2017) suggest that it is possible that while the labor share of output among
most firms is high, some "superstar firms," which are large and growing, could bringthe aggregate labor share to decline over time. Notice that if the labor share of GDP

shrinks over time, the wage may grow slower than the per capita GDP. Since a fullaccount of this issue will demand another paper, we will return to the housing market
of Hong Kong.
(Figure 3 about here)

Taking the high price of housing as given, we now ask what are the driving
forces behind. Again, we address this question through a series of graphs. We havealready shown that the real GDP per capita of Hong Kong grows over time (Figure3a). Also, Figure 4a shows that both the population and life expectancy of Hong Kong
increase over time. While the fertility of Hong Kong is below the replacement level,
there is a continuous flow of immigrants coming to Hong Kong, especially from
Mainland China. With more people, and on average longer lives and higher income
per person, it is not surprising that the housing demand increase over time. Besides,
Figure 4b shows the composition of Hong Kong households in terms of the size of thefamily. The proportion of one-person and two-person households has increased overtime. It means that even if the total population is constant, there is a higher demandfor housing units. Thus, with growing income, growing population, lengthening thelongevity of life, shrinking the household size, the aggregate housing demand of Hong
Kong increases over time.
(Figure 4 about here)

The housing supply of Hong Kong is affected by different factors and has been
discussed recently by Leung et al. (2020), we provide only a simple overview here.
Figure 4c compares the population densities across countries and regions anddemonstrates that the population density of Hong Kong may indeed be high. If we take
into considerations that Hong Kong is hilly and a substantial amount of land are notsuitable for high-rise building, the population density is even higher. It is notuncommon to see residential buildings with more than 30 floors. Thus, it is not
surprising that the house price of Hong Kong would be high. Yet the housing supply
does not increase enough to meet the growing demand. Figure 4d shows that the new
housing supply of Hong Kong, adjusted for the existing stock, decreases over time andis below 2% since 2005, while the real house price displays a clear upward trend. Noticethat the official data provides only the gross new supply, not the net new supply. Itmeans that while there may be, say, 20,000 new units being supplied in the market in aparticular year, there may be 5,000 units being demolished in the same year for theurban re-development purposes. Hence, the net new supply could be far less than the
gross counterpart.
Why did not the housing supply increase as the house price increases? A popularand convincing reason is that the housing supply is artificially constrained by regulation
under different justifications. Many studies have confirmed that as a nearly universal
reason for limited housing supply. 12 In Hong Kong, the density of residential
development is guided by the plot ratio. It compares the gross floor area of a building
and the site's area. 13 While the institution of the plot ratio may enhance theenvironmentally friendly dimension of the city, it restricts the gross floor area to becompleted in a project. Hence, it may reduce the incentives for developers to redevelop
some areas, which indirectly limits the new housing supply. Such distortion in housing
supply can lead to severe economic consequences.14
The institutions of Hong Kong may contribute to this housing supply shortage
problem. Unlike many countries with similar income levels per capita, land ownershipin Hong Kong is public. Strictly speaking, the "land sale" in Hong Kong is the sale of
the right of usage of a particular piece of land during a specific period. Thus, the officialname of the "land sale revenue" is "land premium." An alternative way for thedevelopers to obtain land is to apply for changes in land use. Usually, it involves a shiftof agricultural land to residential land, and the developer needs to pay a mutually agreed
price to the government. The Government can also invoke the Lands Resumption
Ordinance to resume three types of private land for developing public housing andStarter Homes.15 Hence, the Hong Kong government almost control the land supply of
Hong Kong and influence the land price. Leung and Tang (2015b), among others,
promote the view that the Hong Kong government effectively operates as a durable
goods monopoly with limited supply, dynamically tradeoffs the quantity of land andrevenue that can be generated from the land sale. Figure 5a demonstrates the increasing
tendency for the Hong Kong government to depend on real-estate related tax. Here we
only include the revenue from land sale ("land premium") and property tax ("rate").16

12

Among others, see Glaeser and Gyourko (2018), Green et al. (2005), Saiz (2010), and the reference
therein.
13
For details, see https://www.pland.gov.hk/pland_en/tech_doc/hkpsg/full/pdf/ch2.pdf
14
Among others, see Herkenhoff et al. (2017) for a structural estimation for the U.S. case.
15
16

For details, please visit https://www.policyaddress.gov.hk/2019/eng/highlights.html
The Hong Kong government also collects "stamp duties," which are transaction taxes in the stockBetween 1990 and 1996, the proportion of real estate-related tax constitutes no morethan 15% of the total revenue. The ratio experiences ups and downs since the 1997
handover to China and reaches a historical high of 30% in 2018. Figure 5b provides
another illustration of the Hong Kong government's real estate-dependency. It showsthat both the fiscal-GDP and hypothetical fiscal surplus-GDP ratio of Hong Kong overtime, where the hypothetical fiscal surplus is the official budgetary surplus minus theland sale revenue. It is clear that if the land sale revenue were zero, the Hong Kong
government has been in deficit since 2008, when the Global Financial Crisis (GFC)
broke out.
Figure 5c shows that, while the Hong Kong government is increasingly
dependent on land sale and other real estate-related revenue on the income side, its
expenditure on housing, include the provision of public housing, has decreased overtime. The share of housing-related spending is about 14% of the total government
expenditure in 1989 and falls since then. It rebounded to its peak around 15% near
2000 and touched down to around 7% since 2004. In light of these graphs, it may not
be surprising that the queue for public housing has extended, and the shelter in Hong
Kong becomes increasingly "unaffordable" in recent years.

(Figure 5 about here)

We should stress that the dependence of real estate tax revenue needs not to beundesirable. For instance, Henry George argues that the government should use landtax to replace other forms of taxation. According to George (1879, chapter 22), as“population grows and technology advances, land values rise. This steady increase
leads to speculation, as future increases are anticipated. Land values are carried beyond
the point at which labor and capital would receive their customary returns. Production,
therefore, begins to stop.”
Furthermore, George (1879, chapter 32) argues that “to formally confiscate allland would involve a needless shock, and would require a needless extension of
government. Both can be avoided… It is not necessary to confiscate land—only toconfiscate rent…. Land values increase as population grows and progress advances….

market and the real estate market. However, we are unable to identify an official breakdown of the twomarkets. Hence, our measure of the "real estate-related tax revenue" is indeed a lower bound.

Taxation of rent would increase as we abolish other taxes…. To abolish all taxes—
except on land values.” While this idea seems radical, it has been widely discussed byeconomists (e.g., Behrens et al., 2015; Kawano, 2014; Newton, 1971; Stigler, 1974).
On the other hand, we are not aware of any severe calibration work to examine theoptimality or practicality of the Henry George theorem. We recommend future researchto take up this challenge, which may enhance our discussion of the policy design forHong Kong or economies in similar conditions.
Another factor that may contribute to the apparent "housing shortage" in Hong
Kong is the change in immigration. Interestingly, the Hong Kong government does notcollect the immigrant data, or they have not made it publicly accessible. We have accessto the data for the so-called "one-way permit," which keeps track of the number of
immigrants from Mainland China to Hong Kong. Also, there is the Admission Scheme
for Mainland Talents and Professionals (ASMTP), which allows Mainland Chinesewith unique talents to migrate to Hong Kong.17 We have no information about how
many people from other places move to Hong Kong, or how many moves from Hong
Kong to other countries.
Several remarks regarding this one-way permit system are in order. First, theone-way permits are issued by the Mainland China government, and hence the Hong
Kong government cannot select who would be granted access to Hong Kong. Second,since we do not have access to the out-migrant data, we are unable to distinguish those
Mainland Chinese immigrants permanently stay in Hong Kong and those who
ultimately migrate to other countries through Hong Kong. The daily quota of the oneway permit has also changed over time. It increases from 75 to 105 in 1994 and further
to 150 in 1995. The age composition of Chinese immigrants to Hong Kong is displayedin Figure 6. There exhibits a clear trend that the proportion of young Mainland
immigrants decreases while the percentage of senior adult increases, especially inrecent years.
These changes may have significant consequences. First, since the Hong Kong
government cannot select the immigrants, and hence skill-mismatch is likely, meaningthat those who immigrate to Hong Kong might not have the skill set that Hong Kong's
economy demands. Since it is easier to train young people than senior adults, the17
For details, see https://www.immd.gov.hk/eng/faq/admission-scheme-mainland-talentsprofessionals.html

composition change may have implications for the quality of the workforce in HongKong. Second, the literature suggests that other things being equal, immigrant
workers are paid less in the labor market (Chiswick and Miller, 2008, 2009; Ferrer
and Riddell, 2008; Nanos and Schluter, 2014). The early study of Hong Kong is also
consistent with the literature (Lam and Liu, 2002). Hence, although the immigrants toHong Kong might have a little overall effect on the general wage (Suen, 2000), thecoming of immigrants contribute to the increase in income inequality in Hong Kong
(Lam and Liu, 1998). While the immigrants affect the labor market in different
dimensions, their very presence would imply a further tightening of the housing
market, because every human being demands a shelter. An increase in the population
imposes additional pressure on the apparent housing shortage. Some social scientists
report that there is a deterioration of attitude against Mainland Chinese immigrants inHong Kong (Lee et al., 2016; Lee and Chou, 2018). Some authors seek to investigate
the effect of Mainland Chinese immigrants on the housing price and rent in HongKong. We conjecture that the immigrant issue compounds with the Hong Kong
identity problem, and exerts pressure on the housing market policies in Hong Kong.
We leave the empirical validation to future research.

(Figure 6 about here)

3. More Time Series Analysis
The previous section provides an informal overview of the Hong Kong housing
market and the macroeconomy. In this section, we attempt to provide more stylized
facts with the minimum statistical model. In particular, we show how the time series of
the aggregate data in Hong Kong can be decomposed into different components and theimplications of such a diagnosis. To our knowledge, this is the first time to apply theunobserved component approach to decompose time series of aggregate data in HongKong. In section 3.1, we explain the justifications and the setup of our econometricmodel. Readers who are not interested in such technical issues can go directly to section
3.2, which presents our decomposition results of the Hong Kong aggregate data.

3.1. Econometric Model
We have several considerations. First, for Hong Kong and Mainland China,

the aggregate data is non-stationary. There are some "upward trend" or "growthcomponents" in the data. Hence, statistical moments are not well defined in the raw
data, and we need to appropriately "de-trend" the data. Second, it is well known thatthe growth of GDP and other aggregate variables are stochastic (Chu et al., 2012;
Durlauf and Quah, 1999; King et al., 1988; Leung and Quah, 1996; Leung et al.,2006; Tang et al., 2008). It means that the traditional method of removing a linear
trend would not be appropriate. Third, we try to maintain a "reduced form" approachand do not impose much "structure" in the statistical model. A merit of this approachis to establish some stylized facts that would inform the future development oftheoretical modeling (Cooley, 1995). Fourth, the model should help us to visualize thedifferences between changes that happen in the "trend" component (i.e., changes thatwould have lasting effects) from structural breaks (i.e., dramatic changes that occur
once and for all). Fifth, if possible, we would like to take advantage of the relatively
new development in time series econometrics, which may help us to uncoveroverlooked "stylized facts."
Based on these considerations, we consider the following unobserved
component model to be appropriate (Commandeur and Koopman, 2007). It allows us
to decompose a time series into a trend component, a seasonal component, a cyclicalcomponent, an irregular component, and the possible structural breaks. Both thenumber and timing of structural breaks are endogenous in the sense that if there is no
structural break in the data, our econometric model would truthfully report there is no
break. On the other hand, if there are multiple structural breaks, the model would alsofaithfully reflect those breaks. Formally, our econometric model for the aggregate
quarterly data is as follows,18
,

,

Where

is the raw time series data,

component,

18

~

0,

is the trend component,is the cyclical component,,,

1, … , ,is the seasonal

are the level breaks, andis theData for Hong Kong is collected from Census and Statistic Department and Rating and Valuation
Department. Data for China is collected from CEIC. Real variables are obtained directly from the official
sources, except for real housing prices and rents of Hong Kong and the real GDP of China which is
calculated by dividing the nominal variables by CPI. Real GDP per capita of Hong Kong is calculatedby dividing Real GDP by population (interpolated from semi-annual data).

irregular component. Following the literature, the trend component is specified as,,is the slope or gradient of the trend.

where

,

~

0,

,

~

0,

, and

are mutually uncorrelated.

Thus, our formulation allows for a change in the “growth rate,” if any. Some previous
research argues that the seasonal component is important and may bias our estimation
if we neglect it (Beaulieu and Miron, 1992; Brendstrup et al., 2004). We do not ex-ante
conduct seasonal adjustment on the series. Rather, we remove the seasonal component
through the unobserved component model. The time-varying seasonal can beestablished in a time-varying dummy seasonal form (see, among others, Koopman andOom (2011)):
⋯

,

,

~

is serially independent and mutually independent of all
1, . . . ,other disturbance series, for
γ,

4 for quarterly data),

where is the number of seasonal frequencies in a period (i.e.,
and the disturbance series

0,

and

2, . . . ,1. The initial variables, … , γ are treated as unknown coefficients that need to be estimated together withthe other coefficients in the model. If the disturbance varianceis estimated to bezero, this stochastic formulation of seasonal component reduces to a deterministic form.
Our specification of the cyclical component is standard,∗∗

where 0∗1 is a damping factor, 0∗ ,π is the frequency, in radians.

andare two mutually uncorrelated NID disturbances with zero mean and common

variance. A merit of this specification is to include both regular and irregular cycles.

The structural break terms, , which are also known as “intervention variables,”take the following forms:
01
01

for outliers,
for level breaks

For detecting level breaks and outliers, a t-test for auxiliary residuals would be
carried out. Table 1 shows different variations of the unobserved component model.
For growth rates, we employ model 1-2 and 5-8 (without a cycle); for level data, weuse 3-4 and 9-12 (with a cycle). Our model selection procedures are as follows. (1) We
estimate a candidate model without any level breaks. (2) We conduct a t-test for theauxiliary residuals to detect level breaks. (3) We include level breaks detected into thecandidate model and repeat (2). To guard against the possibility of “over-fitting,” we,therefore, do not add interventions for every level break detected. (4) We use both AIC
and BIC for model selection. Table 2 shows the selected models.
(Table 1 and 2 about here)

Before we present our results of the decomposition, we first justify ouradoption of the unobserved component approach. A merit of using the unobserved
component approach via Kalman filter is that one can control for outliers and breaks(if any), which may otherwise bias our estimation. To illustrate this, we compare theKalman filter approach with the Christiano-Fitzgerald (bandpass) filter and the
Hodrick-Prescott (HP) filter. The latter two do not control for outliers and breaks. We
use the real export of Hong Kong (level data) as an example. First, we follow theusual practice to adjust for the seasonal pattern by using the Census Bureau's X-13
procedure.19 Then, we employ different filters to decompose the seasonally adjusted
series. Figure 7a shows that the estimated trends of different filters are very similar.
Now, we add an artificial level break to the series by multiplying the data by 1.05
starting from 1996Q1. We then use the three filters to decompose the artificial series.
If the Kalman filter approach works well in controlling for the break, the trend andnon-trend components will look similar before and after adding the fake break.
Indeed, Figure 7b shows that it is the case. For the cases of the Christiano-Fitzgerald
filter and HP filter, a higher trend results from 1996 onwards since the break is notadequately taken into account. The unobserved component approach via the Kalman

19

Previously, we estimate the seasonal components by formulating in our model. However, the usual
practice before applying the HP filter and the Christiano-Fitzgerald is to conduct seasonal adjustmentson the series since they do not consider seasonal components within their frameworks. Therefore, to beconsistent, we drop in our model for this comparison part and adjust for the seasonal pattern by using
the Census Bureau's X-13 procedure before applying any filters.

filter is more robust when facing breaks and outliers.
(Figure 7 about here)

3.2.Decomposition of Hong Kong aggregate data
Now, we present our decomposition results. A few remarks are in order.
Constrained by data availability, some variables may have a more extended timeseries than the others. We focus on real variables (i.e., the inflation factor has been
removed). To facilitate the comparison, we present the decomposition of different
variables in the same format. The first panel shows the raw data (solid black line) andthe trend with structural break components (red dotted line). The second panel shows
the seasonal part. The third panel shows the irregular part, which we can interpret as"noises." Notice that the scales of the three panels may vary.
Figure 8a shows the real GDP growth of Hong Kong over the years. It
captures the economic downturn due to the stock market crash in 1973 and thesubsequent rebound. It also captures the "short recession" in 1985. Interestingly, itshows that the trend of real GDP growth slows down, starting from the first quarter of
1989. After the Joint Anglo-Chinese Declaration in 1983, some people Hong Kong
consider the possibility of migration. The 4th June event occurred in Beijing 1989,
and the subsequent trade restrictions imposed by the U.S. on China accelerate themigration.20 According to Skeldon (1990, p. 502), the average emigration from Hong
Kong is around 20,000 people between 1980 and 1986. Starting 1987, the annual
emigration from 1987 to 1991 are 30,000, 45,800, 42,000, 62,000, and 58,000
respectively. Furthermore, Skeldon (1990, p.505-6) reports that “… For example, theapplications for certificates of no criminal conviction, which are required by mostcountries in the course of visa applications, averaged around 3,200 a month over the
first half of 1989 but jumped to an average of 6,200 a month for the second half of theyear (there were 7,225 applications in August 1989 alone). The number of visa
petitions to the United States consulate in August 1989 was 85 percent higher than forthe same month in the previous year while the number of petitions for September was

20
Due to the lobby of the U.S. business sector, those trade restrictions were removed soon (Dietrich,
1999; Skidmore and Gates, 1997). For more discussion of the 4th June event, see Zhao (2001), among
others.

up by 233 percent.” Since emigrants tend to be well-educated, the sudden increase inemigration gives pressure to the supply of human capital, and the Hong Kong
government is forced to respond. Wong (1992, p.13) reports that “… Hong Kong
government announced in 1989 an accelerated plan to increase the number of firstyear, first degree places from about 7,000 at that time to 15,000 in 1995. This
meant making tertiary places available to 18 per cent of the relevant age group in
1995 as compared to the 7 per cent in 1989. The dramatic move was made after theshock of the Tiananmen incident in China. The main aims, according to thegovernment, were to cope with the exodus of talents, to upgrade skills for the future,and to satisfy parental demands so as to restore public confidence in the territory.”(Figure 8 about here)

Recent empirical research confirms that human capital is crucial to economic
growth (Hanushek et al., 2017; Hanushek and Woessmann, 2015, 2016). Thus, suchmassive emigration would dampen the economic growth in Hong Kong. Our
decomposition confirms such intuition. We identify a structural break in the trend
component of the economic growth rate in 1989Q1 (Figure 8a). While the drop may
appear to be "small," its importance should not be underestimated. We have donesome "back of the envelope" calculation. First, we normalize the real GDP of Hong
Kong in 1988Q4 (i.e., the fourth quarter of 1988) to be 100. Before the structural
break occurs (before 1989Q1), the trend component of the real GDP grows at 2.17%
per quarter. After the structural break (starting from 1989Q1), the average quarterly
growth rate of the trend component becomes 1.03%. The real GDP in 2019Q2 is349.1. If the structural break never happened, the real GDP in 2019Q2 would be1372.32, which is almost four times the observed data.
Since this "political scar" in the trend component of the real GDP growth ratein Hong Kong is new, some "robustness checks" are necessary. The first objection isthat China was also hurt by the trade restrictions imposed by the U.S. at that time. We
should check whether such a political scar is also found in the Chinese GDP data.
Unfortunately, we are unable to access quarterly real GDP data of China with acomparable sampling period. Figure 8b displays what we can find so far. Both the raw

data and the trend component of the real GDP growth of China behave differently
from Hong Kong. The second possible objection is that the changing population ofHong Kong over the sampling period, and hence the growth rate of the aggregate
GDP may be affected by the growth rate of the people. Unlike many metropolitans in
the world, Hong Kong has a fixed boundary, and hence the population factor may besignificant. Therefore, we repeat the exercise with per capita real GDP growth andreport the results in Figure 8c. The same permanent downward shift is also observedin the first quarter of 1989. Hence, the diminished growth trend is not about thequantity of the population, but rather the quality of the people.
Figure 8d shows the decomposition results of the unemployment rate of HongKong. Interestingly, the trend component of the unemployment rate increases around
1989, which is the same year the permanent downward shift in the real GDP growthrate occurs. Notice that the unemployment rate is often a lagging indicator (NBER,
2020), and hence the structural break occurs in 1989Q1 would affect the
unemployment rate in subsequent quarters. The unemployment rate experiences afurther upward change around the Asian Financial Crisis. It hits a peak around 2003
and then started to decline. It stabilizes at a relatively low level after 2011. Yet thetrend component of the unemployment rate never restores the low level in the late1980s. Again, to facilitate comparison, Figure 8e displays the decomposition results
for the counterpart of China. Adjusting for the fact that the sampling period of China
is shorter, the China unemployment rate exhibits a very different pattern. It is not
surprising since China is a vast country and has a more complicated economic
environment.
Since some authors argue that international trade tends to lead the housing
market in Hong Kong (Ho and Wong, 2008; Leung et al., 2006), Figure 8f shows thegrowth rate of the real trade volume, which is the sum of import and export (including
re-exports). The trend component displays a downward trend. It means that the tradevolume grows at a decreasing rate over the sampling period. Figures 8g and 8h show
the decomposition of the growth rates of export and import, respectively. They are
qualitatively the same. These graphs are consistent with the observation that Hong
Kong becomes increasingly dependent on the financial sector, and the weight oninternational trade diminishes over time.

Some authors debate whether we should model the joint dynamics of nondurable consumption and housing (Davis and Martin, 2009; Kwan et al., 2015; Leung,2007, 2014; Leung et al., 2011; Piazzesi, 2007). Therefore, we also conduct a
decomposition of the aggregate consumption of Hong Kong and present the results in
Figure 8i. The trend component is downward sloping, suggesting that the aggregate
real consumption of Hong Kong is growing at a decreasing rate. Such a downward
trend may go unnoticed because the seasonal fluctuations of consumption are large.
One possible interpretation is as follows. In contrast to many theoretical models,
housing in practice is not infinitely divisible. Facing continually increasing housing
prices, potential, as well as constrained home-buyers, need to reduce non-durable
consumption to pay for the home mortgage.
We also decompose the aggregate time-series of investment, as some authors
highlight the joint dynamics of residential and commercial investment (Greenwood
and Hercowitz, 1991; Iacoviello and Neri, 2010; Jin et al., 2012; Jin and Zeng, 2004).
Unfortunately, there is only one aggregate investment time series for Hong Kong, andwe are unable to separate residential investment from commercial investment. Figure
8j shows that the trend component of the aggregate real investment in Hong Kong
also displays a downward trend. Again, covered by the volatile seasonal cycles, anaverage citizen might not detect such a downward trend. At the same time, as theaggregate investment is growing at a decreasing rate, and the population is increasingat the same time, the economy may not be able to generate enough "good jobs."
Figure 8k shows the decomposition results of the real wage in Hong Kong. While theraw data of the wage growth rate is volatile, the trend component is close to zero. It isconsistent with our previous result that, relative to the per capita GDP, the wage index
and the household income suffers a deterioration over time.
Fiscal Policies impact the housing market (Alpanda and Zubairy, 2017; Jinand Zeng, 2007). Economists have developed different tools to assess the impact ofvarious fiscal policies, such as generational accounting (Auerbach et al., 1994).
Unfortunately, we have no access to the needed data. Therefore, we only decompose
the growth rate of real government spending. Figure 8l shows that except for a few"unusual quarters," the trend component of the real government spending is almost a
constant. Throughout the sampling period, there are many structural reforms in thegovernment, including the privatization of some shopping malls and parking lots

located in public rental housing. However, the data we can access is simply anaggregate sum, and we can only leave a more detailed analysis of future research.
Finally, Figures 8m and 8n present the decomposition results of the real house
price and rent. Several observations are in order. While the trend components of thereal GDP growth, real wage, real trade volume, real consumption, real investment,
etc. are like straight lines, the counterparts of the real house price and rent behave
very differently. In other words, it may not be easy to predict the "trend" of the house
price and rent with merely the trend components of the real variables. Figures 8m and
8n also show that, relative to the trend components, the irregular components of bothhouse prices and rent are small. The seasonal factors are significant, but by ourconstruction, they are predictable.

4. Concluding Remarks
Why is the housing in Hong Kong so "unaffordable"? We propose a variety ofreasons. First, there is a measurement issue. Hong Kong has an unusually large public
rental sector, and about one-third of the population is insulated from the market
fluctuations. Another one-seventh live in the housing of subsidized ownership. The
private housing market accounts for roughly half of the people. The usual house priceto-income ratio may not be an appropriate metric for the housing affordability in HongKong. Second, there seem to be some long term reasons, including persistent GDP
growth, persistent population growth, extended longevity, etc., which tend to drive up
the housing demand.
On the other hand, relative to the GDP per capita, we show that the wage index
and the household income are unable to catch up with the house price. The "competitive
labor market paradigm" is apparently at odds with the data. Besides, we provide timeseries decomposition of many time-series data of Hong Kong. We find that since early
1989, the trend component of the real GDP growth suffers a significant drop, which welabel as a "political scar." The trend component of the real wage growth is almost zero,and the counterpart of real consumption growth and real investment growth declines
steadily. Thus, the unaffordability of Hong Kong housing may reflect the loss of growth
momentum in Hong Kong. At the same time, the trend component of both real house
prices and rent are volatile and do not seem to be in line with the counterparts of thereal variables we have decomposed. In Leung et al. (2020) and other ongoing research,
we point to the possibility of restricted housing supply. This issue alone may not explain
the whole puzzle, and more research efforts are needed in the future.

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Figures and Tables
Table 1. Model specifications
Without a slope ( )
Model
1
2
3
4

*
*
*
*

Model
5
*
6
*
7
*
8
*
9
*
10
*
11
*
12
*
* indicates any positive values.

0*0*With a slope ( )
0
*
*
0
0
*
*
0

Cycle
No
No
Yes
Yes

0
0
*
*
0
0
*
*

Cycle
No
No
No
No
Yes
Yes
Yes
Yes

Table 2. Selected models
EconomyHong Kong

Variables
Real GDP growth rate

Sample period
1973Q2-2019Q2

Model
1

Seasonal component
Stochastic

R-squared
0.85

Real GDP per capita growth rate

1973Q2-2019Q2

1

Stochastic

0.86

Unemployment rate

1981Q4-2019Q2

11

Stochastic

0.96

Real trade volume growth rate

1973Q2-2019Q2

5

Stochastic

0.82

Real export growth rate

1973Q2-2019Q2

5

Stochastic

0.85

Real import growth rate

1973Q2-2019Q2

5

Stochastic

0.81

Real consumption expenditure growth rate

1973Q2-2019Q2

5

Stochastic

0.80

Real investment growth rate
Real government spending growth rate

1973Q2-2019Q2
1973Q2-2019Q2

5
5Stochastic
Stochastic

0.46
0.87

Real wage growth rate
Real housing prices growth rate
Real housing rents growth rate

2004Q2-2019Q2
1980Q1-2019Q2
1980Q4-2019Q2

5
2
2

Stochastic
Stochastic
Stochastic

0.82
0.33
0.65

Breaks and outliers
1974Q4 O: (+ve); 1976Q1 O: (-ve);
1985Q2 O: (-ve); 1989Q1 L: (-ve);
1974Q4 O: (-ve); 1976Q1 O: (+ve);
1978Q3 O: (-ve); 1985Q2 O: (-ve);
1989Q1 L: (-ve)
1983Q1 O: (+ve); 1985Q1 L: (-ve);
2003Q2 L: (+ve); 2009Q1 L: (+ve)
1974Q3 O: (-ve); 1974Q4 O: (-ve);
1976Q1 O: (+ve); 2009Q1 O: (-ve);
2009Q2 O: (+ve)
1974Q3 O: (-ve); 1974Q4 O: (-ve);
1975Q1 O: (+ve); 1976Q1 O: (+ve);
2009Q1 O: (-ve);
1974Q1 O:(-ve); 1974Q3 O:(-ve);
1974Q4 O:(-ve); 1975Q3 O:(+ve);
1998Q3 O:(-ve); 2009Q1 O:(-ve);
2009Q1 O:(+ve);
1974Q1 O: (-ve); 1974Q4 O: (-ve);
1976Q3 O: (-ve); 1976Q4 O: (+ve);
2003Q3 L: (+ve)
1998Q3 O: (-ve)
1981Q1 O: +(ve); 1981Q2 O: -(ve);1974Q3 O: +(ve); 1979Q1 O: +(ve);1981Q4 O: +(ve); 1992Q1 O: +(ve);1997Q3 O: -(ve)2014Q3 O: (-ve)
1994Q1 O: (+ve); 1998Q4 L: (+ve)
1989Q3 O: (-ve); 1994Q2 O: (+ve);
1998Q1 O: (-ve); 1998Q3 O: (-ve);
2008Q4 O: (-ve); 2009Q1 O: (-ve)

ChinaReal GDP growth rate

1993Q2-2019Q2

1

Stochastic

0.99

2003Q3 L: (-ve); 2008Q4 O: (+ve);
2011Q2 L: (-ve)
Unemployment rate
1999Q4-2019Q2
12
Deterministic
0.98
2008Q4 L: (+ve); 2009Q1 L: (+ve)
All the breaks and outliers are significant at 1% level. L and O denote level break and outlier, respectively. -ve and +ve denote negative and positive value,
respectively.

Figure 1a: Real Housing Price (RHP) and Real Stock Price (RSP) in Hong Kong (1979~2018)

900
800

Real index

700
600
500
400
300
200
100

RHP

Dec‐19Dec‐17

Dec‐15

Dec‐13

Dec‐11

Dec‐09Dec‐07

Dec‐05

Dec‐03

Dec‐01

Dec‐99Dec‐97

Dec‐95

Dec‐93

Dec‐91

Dec‐89Dec‐87

Dec‐85

Dec‐83

Dec‐81

Dec‐790

RSP

Figure 1b: U.S. Federal Fund rate and Hong Kong Real Housing Price growth rate (1982~2018)
25
20
15

%

10
5
0
‐5
‐10
‐15
‐20
3‐month treasury bill rates

HP growth rate

Figure 1c: House price-to-wage ratio in Hong Kong and U.S. (1982~2018)

Figure 2a: Distribution of household by types of housing (in thousand)Figure 2b: Distribution of housing stock by type (in thousand)Figure 2c: The rent in public housing is below the private sector counterpart in Hong Kong
(1993~2019)

Key: To make the two rental indices comparable, we use the New Territories rental indices.
For the public housing rent, we use the one with size 43.3 square meters. For the private
housing rent, we use the class B rental index.

Figure 3a: Hong Kong per capita GDP (real terms) increases over time

Figure 3b: Hong Kong wage index fails to catch up with the per capita GDP (real terms)

Figure 3c: Hong Kong Median household income (MHI) and wage index fail to catch up withthe per capita GDP after Return to China
105
100
95
90
85
80
75
70
65
60
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
MHI/GDP per capita

wage/GDP per capita

Figure 4a: Hong Kong Population and Life Expectancy increases over time

Figure 4b: Composition of Hong Kong Households (by number of members within thehousehold)

100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

1

2

3

4

5

6+

Figure 4c: Population densities across countries and regions

Source: CIA

Figure 4d: New housing supply and real house price in Hong Kong

Figure 5a: Proportion of real estate‐related tax as a percentage of total government revenue
in Hong Kong

Figure 5b: Fiscal surplus‐GDP and hypothetical fiscal surplus‐GDP ratios of Hong Kong

Note: The hypothetical fiscal surplus is the official fiscal surplus minus the land sale revenue

Figure 5c: The composition of the Hong Kong government expenditure

100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

Housing

Community and external affairs

Economic

Education

Environment and food

HealthInfrastructure

SecuritySocial welfare

SupportFigure 6 Age Composition of the Immigrants from Mainland China
70,00060,00050,00040,00030,00020,00010,000

0‐4

5‐14

15‐24

25‐34

35‐44

45‐54

55‐64

65 or above

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

0

14.0

13.6

1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019

Figure 7a. Trend component of real export of Hong Kong estimated by different filters

14.4

Kalman filter
Christiano-Fitzgerald filter
HP filter

13.2

12.8

12.4

12.0

11.6

11.2

10.8

10.4

Original.2
Trend - with breakNon- tr end component - with br eak

1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019

1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019

Real export (logged, seasonally adjusted)

13
.2

12
.1

10

Christiano-Fitzgerald filter

Trend - originalNon- tr end component - orig inal

12

11

.4

.2

.0
.0

-.2
-.2

-.4
-.4
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019

.4

1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019

Figure 7b. Comparison between different filters: an example

15

Kalman filter

With break

15

14
14

13

Trend - original

Trend - with break (adjusted)

Non-trend component - originalNon-trend component - with break (adjusted)

13
Trend - with breakNon-tr end component - with br eak

10

12

11

10

11
.0

-.1

-.2

15

Hodrick-Prescott filter
15

14
14

Trend - original

13

Non-tr end component - or ig inal

12

11

10

Figure 8a. Decomposition of Hong Kong real GDP growth rate

Figure 8b. Decomposition of China real GDP growth rate

Figure 8c. Decomposition of Hong Kong real GDP per capita growth rate

Figure 8d. Decomposition of Hong Kong unemployment rate

Figure 8e. Decomposition of China unemployment rate

Figure 8f. Decomposition of Hong Kong real trade growth rate

Figure 8g. Decomposition of Hong Kong real export growth rate

Figure 8h. Decomposition of Hong Kong real import growth rate

Figure 8i. Decomposition of Hong Kong real consumption expenditure growth rate

Figure 8j. Decomposition of Hong Kong real investment growth rate

Figure 8k. Decomposition of Hong Kong real wage growth rate

Figure 8l. Decomposition of Hong Kong real government spending growth rate

Figure 8m. Decomposition of Hong Kong real housing price growth rate

Figure 8n. Decomposition of Hong Kong real housing rent growth rate