ELABORACION DE LA PROPUESTA DIRECTRIZ DE GESTION DE

It is obvious from the results o f the statistical selection that despite the use o f a less restrictive

variable selection procedure, it did not select all o f the theoretical indicators in any case. This is to

be expected because the use of significance levels ensures that only those variables that have met the

specified selection criteria are included. Now, there is a need to re-examine those theoretical

indicators that have not been statistically selected to decide if they should be omitted totally.

Otherwise, they can still be included solely on good theoretical grounds.

For residential construction demand, the indicators that have not been statistically selected are "GDP

deflators", "Interest rate" and "Household formation". Firstly, GDP deflators are indicators of

overall national price changes, covering all sectors namely, consumer, government, investment, and

international components. Although they are considered as the broadest and best indicators of

economy-wide inflation, they may become too general when related specifically to demand in the

residential construction sector. As housing has always been viewed as the single most expensive,

hence important, consumer good of an average household, the Consumer Price Index may be a more

precise indicator o f this type o f demand. This may help to explain why the Consumer Price Index,

and not the GDP deflators, has been selected as one of the significant variables. Secondly, according

to economic theory, interest rate is expected to have a relationship with demand for residential

construction since it is the main determinant o f the cost o f borrowing which affects mortgage loans.

However, this departure from theory may be justifiable in the case o f Singapore as the public sector

has the largest share in the country's residential construction. The ongoing public housing scheme

implemented by the government has to date housed 90 per cent o f the total population. Demand for

government-subsidised housing would not be greatly influenced by the rate o f interest because, in

any case, the substantial savings in purchasing these low-cost housing would have far compensated

formation has often been considered, in theory, as a crucial factor that influences demand for housing

construction. However, quantitative studies (Tang et a l , 1990; and Goh, 1996) have shown the contrary. In this study, the statistical insignificance o f household formation may be attributable to

the use of the national level statistical series "Number o f marriages registered". Again, this may be

a unique case for Singapore as young married couples are encouraged to live with their parents to

form a multi-generation family unit. Hence, for the purpose o f studying housing construction

demand, the number o f marriages registered may not accurately depict household formation in

Singapore. However, no alternative series was available to represent this indicator.

The only indicator that was found to be insignificant for industrial construction demand is "GDP per

head". In general, this indicator reflects national output per person, which acts as a good guide to

a country's general hving standards. Although generally useful, it may bear little direct relations with

the industrial sector. On the other hand, it should have more direct and immediate influence on the

residential and commercial sectors since the general living standards o f a nation usually imply the

population's spending power on items such as housing and retail goods. This line o f reasoning is

supported by GDP per head having been found to be a significant indicator o f both residential and

commercial construction demand.

In the case of demand for commercial building construction, three theoretical indicators were found to be statistically insignificant and they are, "GDP deflators", "Fixed investments" and "Population".

Firstly, GDP deflators may, again be too wide or general in terms o f measuring the level o f inflation

as it covers many other sectors besides the consumer. Since the commercial sector deals mainly with

the retail trade, a narrower indicator of the level o f price change, such as the Consumer Price Index,

may have more direct effects on this consumer-based sector and, hence, the level o f construction

demand. Secondly, the indicator "Fixed investments" has been represented by the national level

statistics "Total cost of commercial developments", which may have measured and reflected only the

cost o f construction. In land-scarce Singapore, the cost o f construction constitutes only a small

proportion o f total development cost which also includes the cost o f land. Hence, decisions to invest

in commercial developments are chiefly influenced by the prevailing price o f commerical land. This

point is substantiated by "Commercial land price" being one o f the significant indicators. Thirdly,

although population size was not found to be significant by itself, this does not mean that it has no

influence on the commercial sector at all. This indicator is often used as a yardstick for minimum

GDP growth because of its use in GDP per head. When interpreting GDP per head, real GDP must

grow faster than the size of population if living standards are to improve. As explained earlier,

and, therefore, buildings to contain such increased activities. From this, it may be suggested that

although the size of population does not relate directly to demand for commercial buildings, it does

have its influence through GDP per head.

Justification can be found for all the unselected indicators, indicating that the statistical approach

has successfully achieved its objective o f sieving out the less suitable variables. However, there are

some general shortcomings o f such statistical procedures which need to be addressed here. Firstly,

it has been emphasized that automated variable selection procedures are not the panacea many users

have been led to expect; prior information about the model can be useful to the selection of a more

suitable set o f modelling variables (Freund and Littell, 1986). From a statistical viewpoint. Draper

and Smith (1981) attributed the shortcomings o f statistical procedures to the lack o f knowledge of

the magnitude of the true random variance o f the observations for any single well-defined problem,

making the choice of a best regression equation impossible. Hence, it becomes necessary to rely on

personal judgement in any o f the selection methods used. Secondly, a major limitation of any

statistical analysis is that the outcome is largely dependent on the quality and quantity o f data used.

For quahty, this is particularly true when dealing with archival data, as opposed to empirical, since

there is less control over the recording and updating o f data; the competence o f the personnel maintaining the data banks has to be relied upon. With regard to quantity, those indicators that were

found to be insignificant may perhaps become significant when more data become available.