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Forecasting the Manpower Demand for Quantity
Surveyors in Hong Kong
Paul H K Ho (City University of Hong Kong, Hong Kong)
Abstract
Recently, there has been a massive infrastructure development and an increasing demand
for public and private housing, resulting in a shortage of qualified quantity surveyors. This
study aims to forecast the demand for qualified quantity surveyors in Hong Kong from 2013
to 2015. Literature review indicates that the demand for quantity surveyors is a function of
the gross values of building, civil engineering and maintenance works. The proposed
forecasting method consists of two steps. The first step is to estimate the gross values of
building, civil engineering and maintenance works by time series methods and the second
step is to forecast the manpower demand for quantity surveyors by causal methods. The
data for quantity surveyors and construction outputs are based on the ‘manpower survey
reports of the building and civil engineering industry’ and the ‘gross value of construction
works performed by main contractors’ respectively. The forecasted manpower demand for
quantity surveyors in 2013, 2014 and 2015 are 2,480, 2,632 and 2,804 respectively. Due to
the low passing rate of the assessment of professional competence (APC) and the
increasing number of retired qualified members, there will be a serious shortage of qualified
quantity surveyors in the coming three years.
Keywords: Hong Kong, Manpower forecast, Quantity surveyors
Introduction
Since the early 2000s, Hong Kong has suffered a great economic recession which affects
nearly all economic sectors, including the construction industry. Construction related firms
and professions have actively looked for business and job opportunities outside Hong Kong.
The massive building and infrastructure developments in Macau, Mainland China and some
overseas countries provide a good opportunity for quantity surveying firms to expend their
business. Following a long hibernation period and the global financial crisis in 2008, the
Hong Kong Special Administrative Region (HKSAR) Government has decided to revitalise
the economy by promoting infrastructure development, sustaining employment and
improving living standard. To fulfil this policy objective, the HKSAR Government has
significantly increased its expenditure in capital works from $20.5 billion in 2007/08 to $62.3
billion in 2012-13 and will further increase over $70 billion in the next few years. In addition
to the large-scale infrastructure projects, the government also provides public rental housing
for low-income families, targeting to complete 75 000 units in the next five years from 2011-
12. Besides, the HKSAR Government has also resumed the new Home Ownership Scheme,
targeting to supply more than 17 000 flats over four years from 2016-17 (HKSAR
Government, 2012). Furthermore, the demand for private residential properties has also
increased significantly due to the modest economic growth, better living standard
requirements and increasing number of households, thus attracting more developers to
invest in private residential properties. As a result of the massive infrastructure development
and the increasing demand for public and private housing, many consultancy firms,
developers and contractors have experienced difficulties in recruiting quantity surveying
staff.
In Hong Kong, the University of Hong Kong, the City University of Hong Kong and the Hong
Kong Polytechnic University offer full-time surveying degree programmes which admit totally
Australasian Journal of Construction Economics and Building
Ho, P H K (2013) ‘Forecasting the manpower demand for quantity surveyors in Hong Kong’, Australasian Journal of
Construction Economics and Building, 13 (3) 1-12
2
about 200 students per annum. All surveying programmes offered by these universities is a
generic programme combining the building surveying, quantity surveying and general
practice surveying disciplines. Some graduates may eventually opt for building surveying
and general practice surveying disciplines. Thus, the number of graduates who finally ends
up in the quantity surveying discipline is much smaller than the admission quota.
The Hong Kong Institute of Surveyors (HKIS) is the only professional organisation
representing the surveying profession in Hong Kong. In order to become a qualified member
of the HKIS, all surveying graduates are required to undergo at least two years professional
training under the supervision of an experienced qualified surveyor before attending the
assessment of professional competence (APC). If a probationer can pass the APC, he/she
will become a professional member of the HKIS. Currently, there are about 1 180
probationers undertaking the professional training in the quantity surveying discipline.
However, the average number of candidates passing the APC is only about 89 each year.
The low APC passing rate creates a bottleneck for the supply of qualified members. In
addition, some qualified members also retire after reaching the normal retirement age of 65.
The net increase in the number of qualified members is nominal.
At present, there is a strong demand for the qualified quantity surveyors due to the
significant increase in the construction workload. However, there is limited supply of qualified
quantity surveyors due to the low APC passing rate and the retiring of qualified members. As
a result, there has been an unhealthy competition of human resources in the quantity
surveying field. It is necessary to understand the demand for, and supply, of quantity
surveyors in the near future so that the relevant parties may take the appropriate action to
resolve the human shortage problem. Therefore, the aim of this study is to forecast the
manpower demand for professionally qualified quantity surveyors in Hong Kong in the
coming 3 years. While this study primarily deals with a practical human shortage issue, it will
also propose a theoretical forecasting model that can overcome the problem of insufficient
input data, while providing a reasonably accurate forecast.
Literature Review on Forecasting Manpower Demand
Manpower forecasting models in construction can be divided into four levels: the national
aggregate manpower level, occupational manpower level, regional manpower level and
regional manpower by the occupation level (Briscoe and Wilson, 1993). There are four
commonly used statistical approaches to forecast the aggregate manpower based on time
series data: (1) single-equation regression model, (2) simultaneous-equation regression
model, (3) autoregressive integrated moving average (ARIMA) model and (4) vector
autoregression (VAR) model (Gujarati and Porter, 2009).
Over the past five decades, economists have developed a wide range of theoretical models
to forecast the employment level (Wilson, 1980). Early versions of short-run employment
functions were derived from neoclassical economics theories. The most seminal short-run
employment model was proposed by Ball and St. Cyr (1966), who postulated that the
employment level was determined by output via a production function, while the time trend
reflected the productivity growth generated by the capital accumulation and technical
progress. This autoregressive model included one lagged dependent variable among its
explanatory variables as follows:
(1)
where E = the employment level, Q = the product output and t = the time trend as a proxy for
the capital input term. Ball and St. Cyr’s model was considered as a short-term
approximation since in the long-term the capital labour ratio would be influenced by factor
price ratios.
Australasian Journal of Construction Economics and Building
Ho, P H K (2013) ‘Forecasting the manpower demand for quantity surveyors in Hong Kong’, Australasian Journal of
Construction Economics and Building, 13 (3) 1-12
3
Dhrymes (1969) argued that a profit-maximizing employer would have a desired level of
labour input such that the marginal product of additional labour was related to the cost of that
labour relative to the price of output. Dhrymes incorporated wages as one of the
determinants of the desired employment level as follows:
(2)
where E = the employment level, Q = the product output, W = the labour cost and P = the
output price. In examining the forecasting ability of these two employment functions, Evans
and Roberts (1975) concluded that both the Ball and St. Cyr’s and Dhrymes’ models seemed
to have under-predicted the level of employment.
Early derived models are based on the assumption that employment is demand determined.
However, traditional economics theory asserts that the labour market is determined by both
the supply side and demand side which are eventually brought into balance through wage
adjustments. On this basis, some economists derived the labour supply and demand
functions by the simultaneous-equation regression approach. For instance, Beenstock and
Warburton (1982) proposed a dynamic relationship between the labour supply and demand
as follows:
(3)
(4)
where Q = the product output, w = the real wage rate and P = the population of working age.
The equilibrium condition occurs when labour supply and demand is in balance (i.e.
On substitution from equations (3) and (4), this implies the following equilibrium
relationships for real wage rate and employment for the given level of economic activity and
population of working age:
(5)
(6)
where
,
,
,
.
By identifying the appropriate reduced-form equations, the relevant coefficients were
estimated by the ordinary least squares. However, the parameters estimated from the
simultaneous-equation regression model were unstable over time.
The autoregressive integrated moving average (ARIMA) approach focuses on analysing the
probabilistic properties of time series data itself. The dependent variable is explained by its
lagged values and stochastic error terms. It comprises five basic steps: (1) differencing the
series so as to achieve stationarity, (2) identification of a tentative model, (3) estimation of
the model, (4) diagnostic checking and (5) using the model for forecasting (Maddala, 2001).
It is fairly accurate and reliable for short-term forecasts. However, there are two major
weaknesses. First, ARIMA is not derived from any economic theory and cannot provide
insight into underlying factors that cause changes in the time series. Second, because
ARIMA is based on extrapolation of the past trend into the future, there will be a large error if
a sudden change occurs in the future trend. Wong et al. (2005) applied the univariate ARIMA
model to forecast five construction manpower time series in Hong Kong: labour productivity,
employment level, wage level, unemployment rate and underemployment rate. Their study
indicated that except for the employment level, the univariate ARIMA model could produce
Australasian Journal of Construction Economics and Building
Ho, P H K (2013) ‘Forecasting the manpower demand for quantity surveyors in Hong Kong’, Australasian Journal of
Construction Economics and Building, 13 (3) 1-12
4
reasonably good forecasts for other variables. However, they also concluded that univariate
projection is not appropriate for forecasting construction employment and that multivariate
forecasting analysis would produce more accurate estimates.
The vector autoregression (VAR) approach resembles the simultaneous-equation regression
model where several endogenous variables are considered together, but each is explained
by its lagged values and the lagged values of all other endogenous variables in the model.
Since all variables in a VAR model are usually derived from economic theory, the forecasts
obtained can be better than those obtained from the simultaneous equation model (Gujarati
and Porter, 2009). Ball and Wood (1995) argued that the UK construction employment was a
function of the construction output, different types of output, cost of capital, construction
labour wages and materials prices. By combining these variables, they proposed four
general models relating the employment growth as follows:
(7)
(8)
(9)
(10)
where L = the total employment, E = number of employees, SE = the self-employment, YH =
the output of house building, YN = the output of non-residential construction, YR = the output
of repair and maintenance, Y = the total construction output, CC = the cost of capital, PW =
the real product wage and W = the deflated construction wage. All variables are in natural
logarithms. Contrary to the theoretical prediction, Ball and Wood’s study indicated a weak
link between the total output and employment because of poor data quality, arising
particularly from the self-employed workers. They concluded that more accurate construction
data would be necessary; if not available, site-based estimate of labour requirements would
be required.
When compared the accuracy among the univariate ARIMA, multiple regression and VEC
approaches in predicting the construction manpower demand, Wong et al. (2010) derived
the following manpower demand functions:
Regression model:
(11)
VEC model: (12)
where for the regression model: MD = the manpower demand, Q = the construction output,
LP = the labour productivity and v
t
is an autoregressive parameter; and for the VEC model: .
d = the manpower demand, q = the construction output, rw = the real wage, mp = the
material price, br = the interest rate, lp = the labour productivity and all variables are in
natural logarithms. Their study found that the multiple regression model was the most
accurate method (MAPE = 2.93%), followed by the VEC model (MAPE = 4.38%) and then
ARIMA model (MAPE = 11.92%).
The statistical approaches reviewed above are appropriate for forecasting the aggregate
manpower demand. However, for a specific occupational manpower, one commonly used
forecasting method is the multiplier approach, which is based on the premise that each type
of project will demand the same level of human resources per unit of construction output
because there should be no significant change in their productivity in the short run
Australasian Journal of Construction Economics and Building
Ho, P H K (2013) ‘Forecasting the manpower demand for quantity surveyors in Hong Kong’, Australasian Journal of
Construction Economics and Building, 13 (3) 1-12
5
(Uwakweh and Maloney, 1991). The manpower requirement is formulated by first deriving its
multiplier.
(13)
where M
i
= the multiplier based on historic data, D
i
= the total manpower employed in
different types of projects, E
i
= the total expenditure of different types of projects, and i =
different types of projects such as building works, civil engineering works and maintenance
works. Once the multiplier is derived from historic data, it can be ulitised to forecast the
manpower requirement based on the projected expenditure for different types of projects. Ng
et al. (2011) adopted this approach to forecast the manpower demands of construction
related professionals in Hong Kong.
As the multiplier approach is based on the proposition that a direct relationship exists
between the total expenditure of projects and the total manpower demand, it is very
essential that the future expenditure of each category of projects can be accurately
estimated in order to produce a reliable forecast. If there is any change in the construction
method, construction price level, productivity, wage and working hour, the multiplier must be
adjusted in order to produce an accurate forecast.
Proposed Forecasting Model
As revealed from the above literature, there are some sophisticated statistical methods for
forecasting the manpower demand. However, all of these methods require correspondingly
sophisticated, comprehensive and reliable data in order to produce an accurate forecast.
Given these constraints, it is necessary to decide whether to formulate a stringent theoretical
model first and make appropriate adjustments to cater for the non-availability of data, or to
look closely at existing data and develop a theoretical model which can make the best use of
the available data. While both of these approaches have its own merits, this study adopts the
latter approach because of the non-availability of reliable data.
The above literature review indicates that one key variable commonly used by all
researchers is the product/construction output. Other variables such as the labour wage,
labour productivity, output price, material price and cost of capital have a certain effect on
the overall labour demand. However, these variables may not be significant for a specific
occupational manpower. For instance, the manpower demand for quantity surveyors does
not much depend on the construction cost, material price and cost of capital. Indeed, based
on the classical economics theory, these variables can be assumed to be fixed in the short
term. Therefore, the short-term demand for quantity surveyors is a function of the value of
various construction outputs as follows:
MD
t
= f (OB
t
, OC
t
, OM
t
) (14)
where t = the time trend, MD
t
= the manpower demand for quantity surveyors, OB
t
= output
value of building work, OC
t
= output value of civil engineering work and OM
t
= output value
of maintenance work. This model specification is in line with Ball and St. Cyr’s (1966) and
Uwakweh and Maloney’s (1991) models which have been used for forecasting aggregate
and disaggregate labour demands, respectively.
The proposed forecasting model involves two steps. The first step is to estimate the future
values of building, civil engineering and maintenance works. This can be done by univariate
forecasting (or time series) methods because it only uses the past, internal patterns in data
to forecast the future. Univariate methods include exponential smoothing models (such as
the simple, Brown’s linear trend, Holt’s linear trend, simple seasonal, damped trend, and
Winters’ additive and multiplicative models) and univariate ARIMA models. Since different
Australasian Journal of Construction Economics and Building
Ho, P H K (2013) ‘Forecasting the manpower demand for quantity surveyors in Hong Kong’, Australasian Journal of
Construction Economics and Building, 13 (3) 1-12
6
models may produce different forecasts, a search process is undertaken to find out the best-
fitting model that produces the most accurate result.
The second step is to forecast the manpower demand for quantity surveyors based on the
forecasted values of building, civil engineering and maintenance works. This can be done by
multivariate forecasting (or causal) methods which make projections of the future by
modelling the relationship between the dependent variable, MD
t
, and the independent
variables, OB
t
, OC
t
and OM
t
. Multivariate methods include multiple regression, econometric,
multivariate time series and few advanced techniques. However, most of these models
require a large amount of statistical data in order to produce a reliable result. Due to this
data limitation, it is found that the multiple regression model is the only feasible approach.
Data Sources
This study requires two types of time series data: the manpower demand for quantity
surveyors and the gross value of various construction outputs. The data for quantity
surveyors was based on the “Manpower Survey Reports of the Building and Civil
Engineering Industry” published by the Vocational Training Council (VTC). The demand
figures were compiled by summing the series of “number employed” and “vacancy”. Since
the VTC’s manpower survey produced only biennial statistics, the manpower figure laid
between two survey years was based on the simple average of two consecutive surveys.
Figure 1 shows the manpower demand of professional quantity surveyors from 2000 to 2012.
Figure 1 Manpower Demand for Quantity Surveyors
The construction output data was based on the “Gross Value of Construction Works at
constant (2000) market prices performed by Main Contractors” published by the Census and
Statistics Department of the HKSAR Government. The construction sector is divided into
three main subsectors: building subsector, civil engineering subsector and maintenance
subsector. It can be noted from Figure 2 that the construction output in both the building and
civil engineering subsectors were declined substantially from its peak in 1997 to 2008 due to
the Asian financial crisis in 1998 and the outbreak of SARS epidemic in 2003, but have
significantly increased because the HKSAR Government has implemented a number of
mega-infrastructural and housing projects to promote economic growth since 2009. On the
other hand, the construction output in the maintenance subsector was gradually increased
up to 2008, but has then declined in recent years.
It is revealed from previous studies such as Ball and St. Cyr (1966), Dhrymes (1969),
Beenstock and Warburton (1982), Uwakweh and Maloney (1991), Ball and Wood (1995) and
0
500
1,000
1,500
2,000
2,500
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
No. of Quantity Surveyors Required
Year
Manpower Demand for Quantity Surveyors
Number of Quantity
Surveyors Required
Australasian Journal of Construction Economics and Building
Ho, P H K (2013) ‘Forecasting the manpower demand for quantity surveyors in Hong Kong’, Australasian Journal of
Construction Economics and Building, 13 (3) 1-12
7
Wong et al. (2010) that there is a direct relationship between the level of employment and
the product output. As observed from Figures 1 and 2, both the manpower demand and total
construction output curves are in downward and upward trends between 2000-2009 and
2010-2012, respectively, indicating a close relationship between the level of employment
and construction output. Nevertheless, there are also few mismatches in some years
between these two curves due to various reasons; for instance, the level of employment is
only adjusted slowly over time rather than instantaneously. Based on this observation, it is
found that these two sets of statistical data generally reflect the theoretical proposition, while
there are also few abnormalities.
Figure 2 Annual Gross Value of Construction Output at Constant (2000) Market Prices
Findings and Discussions
The manpower forecast for quantity surveyors involves two steps. The first step is to
estimate the gross values of various types of construction outputs by univariate time series
methods, whereas the second step is to forecast the manpower demand for quantity
surveyors by multivariate causal methods.
Forecast of Future Construction Output
First, the time series data in both normal and natural logarithm values are tested by
univariate forecasting models. It is found that the data based on natural logarithm values
produces a lower mean absolute percentage error (MAPE) and should be adopted. Second,
since there are a number of univariate time series models, it is found that after testing all
models, the Brown’s double exponential smoothing is the best-fitting model for the building
and civil engineering works time series, whereas the ARIMA (1,1,0) is the best-fitting model
for the maintenance work time series. For both the building and civil engineering works time
series, there is a linear trend but no seasonality. For the maintenance work time series, there
are one order of autoregression, one order of differencing and zero order of moving average
in the ARIMA model. Table 1 shows the model parameters of these three time series.
Model
Estimate
SE
t
Sig.
Log
e
Building Work
No
Transformation
Alpha (Level
and Trend)
0.787
0.141
5.597
0.000
Log
e
Civil Engineering
Work
No
Transformation
Alpha (Level
and Trend)
0.951
0.123
7.724
0.000
Log
e
Maintenance
Work
No
Transformation
AR Lag 1
Difference
0.603
1
0.231
2.614
0.024
Table 1 Model Parameters for Forecasted Construction Output
0
50,000
100,000
150,000
2000200120022003200420052006200720082009201020112012
Construction Output (in HK$
Million)
Year
Gross Value of Construction Output
Building Work
Civil Eng. Work
Maintenance Work
Total Output
Australasian Journal of Construction Economics and Building
Ho, P H K (2013) ‘Forecasting the manpower demand for quantity surveyors in Hong Kong’, Australasian Journal of
Construction Economics and Building, 13 (3) 1-12
8
Table 2 shows the forecasted values of building, civil engineering and maintenance works (in
natural logarithm) from 2013 to 2015. These values will be used for forecasting the
manpower demand for quantity surveyors.
Model
2013
2014
2015
Log
e
Building Work
24.648
24.763
24.878
Log
e
Civil Engineering Work
24.413
24.639
24.865
Log
e
Maintenance Work
24.479
24.474
24.471
Table 2 Forecasted Construction Outputs
Forecast of Future Demand for Quantity Surveyors
First, it is observed from Figure 2 that the time series data of the building, civil engineering
and maintenance works are not linear. Thus, it is necessary to transform these data by
natural logarithm. Second, the manpower demand function can take the following two forms:
log
e
MD
t
= f (log
e
OB
t
, log
e
OC
t
, log
e
OM
t
) (15)
or
log
e
MD
t
= f (log
e
MD
t-1
, log
e
OB
t
, log
e
OC
t
, log
e
OM
t
) (16)
Equation (15) implies that the employers would hire and dismiss their employees
instantaneously in response to the current construction workload. On the other hand,
equation (16) implies that the employers would adjust the level of employment slowly over
time in response to the current and future construction workload. After testing both
manpower demand functions, it is found that equation (16) produces a R
2
higher than
equation (15) and should be adopted.
With log
e
MD
t
as the dependent variable and log
e
MD
t-1
, log
e
OB
t
, log
e
OC
t
and log
e
OM
t
as
independent variables, regression analysis is used to estimate the various coefficients. By
using backward elimination method which evaluates all variables in the model and remove
the one that results in the smallest change in R
2
, and stops when there are no more
variables that meet the criterion for removal, the model is summarised in Table 3. It is found
that model 1 consists of log
e
MD
t-1
, log
e
OB
t
, log
e
OC
t
and log
e
OM
t
as independent variables,
whereas model 2 consists of log
e
MD
t-1
, log
e
OC
t
and log
e
OM
t
as independent variables. Both
models 1 and 2 have the same R
2
, but the adjusted R
2
of model 2 is higher than model 1. In
model 1, the observed significance level is relatively small (p=0.092) so that the null
hypothesis that the population values of all of the regression coefficients are 0 can be
rejected. On the other hand, the null hypothesis in model 2 cannot be rejected because the
observed significance level is high. Therefore, model 1 should be adopted.
Model
R
R
Square
Adjusted
R Square
Std. Error
of the
Estimate
Change Statistics
R Square
Change
F
Change
df1
df2
Sig. F
Change
1
.799
.639
.433
.053
.639
3.097
4
7
.092
2
.799
.639
.504
.049
.000
.000
1
7
.985
3
.753
.567
.470
.051
-.072
1.605
1
8
.241
Table 3 Model Summary
Australasian Journal of Construction Economics and Building
Ho, P H K (2013) ‘Forecasting the manpower demand for quantity surveyors in Hong Kong’, Australasian Journal of
Construction Economics and Building, 13 (3) 1-12
9
Model
Unstandardized
Coefficients
t
Sig.
B
Std. Error
1
(Constant)
-3.672
15.712
-.234
.822
Log
e
Building Work
.006
.336
.019
.985
Log
e
Civil Engineering Work
.143
.053
2.680
.032
Log
e
Maintenance Work
.157
.337
.467
.655
Log
e
Manpower Demand
(t-1)
.516
.295
1.751
.123
Table 4 Coefficients
Based on the coefficients of model 1, the derived demand function for quantity surveyors is
as follows:
log
e
MD
t
= -3.6723 + 0.006log
e
OB
t
+ 0.143log
e
OC
t
+ 0.157log
e
OM
t
+ 0.516log
e
MD
(t-1)
(17)
By applying the above derived equation and based on the forecasted values of building, civil
engineering and maintenance works, the forecasted manpower demand for quantity
surveyors in the coming three years is shown in Table 5. The forecasted demand for
qualified quantity surveyors in 2013, 2014 and 2015 are 2 480, 2 632 and 2 804
respectively, representing an average annual increase of 5.7%.
Year
Forecasted log
e
MD
t
Forecasted MD
t
Additional No. of
QS
Percentage
Increase
2013
7.815
2 480
128
5.2%
2014
7.875
2 632
152
5.8%
2015
7.938
2 804
172
6.1%
Table 5 Forecasted Manpower Demand for Quantity Surveyors
Supply of Quantity Surveyors
In theory, the supply of quantity surveyors is determined by (1) the number of students
currently studying the surveying programme (i.e. potential members), (2) the number of
probationers currently undertaking the professional training under the supervision of qualified
quantity surveyors, (3) the number of existing qualified quantity surveyors (i.e. active
members), and (4) the number of retired quantity surveyors (i.e. inactive members).
Currently, three local universities admit about 200 full-time surveying students each year.
About 100-150 surveying graduates will join the quantity surveying discipline, while the
remaining graduates will join the general practice or building surveying disciplines. In order
to become a qualified member of the HKIS, a surveying graduate needs to apply to be a
probationer and then undertake at least two years professional training. If a probationer
passes the HKIS’s assessment of professional competence (APC), he/she will become a
qualified quantity surveyor. Therefore, there is a gradual increase in the number of qualified
members each year.
According to the HKIS’s membership statistics, the numbers of available qualified quantity
surveyors, probationers and newly qualified members as at 1 January 2013 are shown in
Figure 3. At present, there are 2 207 and 1 185 qualified quantity surveyors and probationers
respectively. While there are a large number of probationers, only a small number of these
Australasian Journal of Construction Economics and Building
Ho, P H K (2013) ‘Forecasting the manpower demand for quantity surveyors in Hong Kong’, Australasian Journal of
Construction Economics and Building, 13 (3) 1-12
10
probationary members can pass the APC each year. This reflects the relatively low passing
rate of the HKIS’s APC which has created a bottleneck for the supply of qualified members.
Figure 3 Supply of Quantity Surveyors
Currently, probationers are the main source of supply for qualified members. Based on the
past three years’ figures, the average number of probationers becoming qualified members
(i.e. newly qualified members) is about 90. The past trend is fairly stable as shown in Figure
3. Assuming that the APC passing rate will remain unchanged, there will be a supply of 90
qualified members in coming three years. Based on the current number of qualified
members and an increase of 90 members each year, the forecasted supply of qualified
quantity surveyors in 2013, 2014 and 2015 are 2 297, 2 387 and 2 477 respectively.
Some of the qualified members would retire at the ages typically at 60-65, depending on
individual’s situation. Assuming the retirement age of 65 and based on the HKIS’s
membership profiles, the number of qualified members aged 66 and above will be 52, 59
and 71 in 2013, 2014 and 2015 respectively. After the adjustment of retired members, the
number of qualified quantity surveyors will be 2 245, 2 328 and 2 406 in 2013, 2014 and
2015 respectively. The forecasted supply and demand of qualified quantity surveyors are
summarised in Table 6.
Year
Forecasted Supply
Forecasted Demand
Shortage
2013
2 245
2 480
235
2014
2 328
2 632
304
2015
2 406
2 804
398
Table 6 Forecasted Supply and Demand of Quantity Surveyors
As shown in Table 6, there will be more and more serious shortage of qualified quantity
surveyors in the coming three years because the increasing demand for quantity surveyors,
the limited supply of newly qualified members and the retirement of more experienced
members.
Conclusions
In view of the increasing workload available in the construction market, this study has
investigated a practical issue of the shortage of qualified quantity surveyors in the short term.
While there are many sophisticated models for forecasting the supply and demand of
manpower, the availability of reliable data has created a great limitation on the choice of
0
500
1,000
1,500
2,000
2,500
1 2 3 4 5 6 7 8 9 10 11 12 13
No. of Quantity Surveyors
Year
Supply of Quantity Surveyors
No. of QS Avaiable
No. of Probationers
No. of Newly Qualified QS
Australasian Journal of Construction Economics and Building
Ho, P H K (2013) ‘Forecasting the manpower demand for quantity surveyors in Hong Kong’, Australasian Journal of
Construction Economics and Building, 13 (3) 1-12
11
these models. As a result, this study has adopted a practical approach to develop a model
by making the most efficient use of the available data. If some advanced modelling methods
are to be used for improving the accuracy of forecasts, the data quality should be improved
first; otherwise, the “garage-in and garage-out” situation would still exit.
As at 1
st
January 2013, there has been already a shortage of about 145 members. This
study indicates that the annual demand for additional quantity surveyors would be 128, 152
and 172 members in 2013, 2014 and 2015 respectively, while the net annual supply would
only be 38, 31 and 19 members in 2013, 2014 and 2015 respectively after deducting the
retired members. It is clear that the gap between the supply and demand would become
wider and wider in coming years as the construction workload will be kept on increasing.
In order to mitigate the manpower shortage, the universities should increase the admission
quota for the surveying programme to ensure that there would be adequate supply of fresh
graduates to cater for the industrial need. Since education and professional training would
take totally at least six years, the increase in surveying students would only be useful in the
medium term. In the short term, the HKIS should find ways to help their probationers pass
the APC so that there would be more supply of qualified members. In this regard, the HKIS
should organise some specific APC training courses (including the mock APC) in addition to
the normal pre-qualification structured learning (PQSL) and continued professional
development (CPD) seminars. In addition, the employers should also strengthen the on-job
professional training for their young surveyors to ensure that they would undergo an
adequate training to cover all required competency areas before attending the APC. It is
noted that if half of the current probationers could pass the APC, it would be adequate
qualified members for serving the construction industry. However, if the above measures still
could not resolve the manpower shortage, it would be necessary to import some foreign
quantity surveyors. Finally, the manpower supply and demand should be continually
monitored so that an appropriate action would be taken to remedy the situation.
Due to the data limitation, this study is based on an assumption that local quantity surveyors
would primarily serve the local construction industry. Nowadays, many local quantity
surveyors are actually working in projects located in the Mainland China, Macau, Middle
East and other countries. In the short term, this could be considered as a constant in the
statistical estimate. However, as the globalization trend would increase, more and more
quantity surveyors would work on overseas projects. This additional manpower demand
should not be neglected and would be a good area for future study, subject to the availability
of reliable data.
Traditionally, researchers spend a considerable effort to develop the best all-purpose
forecasting model (i.e. one that can forecast all situations accurately). However, in order to
overcome the data limitation problem, this study has adopted a different approach where
various theoretical models are reviewed at the beginning, and the model that produces the
most accurate result is chosen for carrying out the forecast. This study has demonstrated
that the traditional goal for the development of all-purpose forecasting model may not be
achievable and that some approaches are better than others in particular circumstances. A
simple forecasting model may perform better than an advanced model. In addition, most
statistical models are only appropriate for forecasting the aggregate manpower demand of
individual industry. There are also few previous studies based on a specific occupational
manpower. Therefore, this study has contributed to a better understanding of the theory and
practice in forecasting specific occupational manpower in the construction industry.
Acknowledgement
I would like to thank the HKIS for providing the relevant membership statistics for this study.
Australasian Journal of Construction Economics and Building
Ho, P H K (2013) ‘Forecasting the manpower demand for quantity surveyors in Hong Kong’, Australasian Journal of
Construction Economics and Building, 13 (3) 1-12
12
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