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Chapter 5

ROLE OF THE HOUSING BANK IN THE DEVELOPMENT OF JORDAN'S ECONOMY

The • • SOC10- BconomlC changes brought Qut by the devel opment plans

supported by the banking system created rapid economi c growth . In

the Kingdom of Jordan . Jordan got its place in the list of

developing countries from its earl ie r posi tion a6 an

underdeveloped country. Our hypothesis i s that the Housing Bank

promoted construction activity by giving l ong term housi ng loans

and reduced the acute s hor tage o f housi ng problem i n Jordan and

t he Bank 's othe r devel opmental programmes he lped . In

• lncreasing

national income. In this chapter we have t ri ed to give emp l rlca l

verification of the hypothes is with the help of available

i nf ormat ion.

Before any statistical analysis can be done, one needs a

mathematical f ormulat ion of t he r elevan t economic theory. But

the theory is rarely i nformative about functional forms. We have

to use statistical methods t o choose the fun ctiona l form as wel l.

The statistical analys is gives reliable results when we use the

appr opriate variables whi ch support economlc theory.

Availability o f sufficient informati on on such variables IS the

major problem faced by any researcher . To see t he r ole of the

Hous ing Bank in the J ordan economy we have to have information on

t he developmental process related to construction activity.

Growth of construction activity in t he Kingdom brings out

simultaneous growth in other related sec t ors , like • Increase • In

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employment opportunities, growth in industrial production • " ; building materials like iron and steel, cement, etc. We C()uJ rl

not analyse information in these sectors due to vari ous r~aSOTt ::

The gross national product (GNP), the contribut ion of ~rt'~

construction activity t o the graBS domestic produc t, and lo~n ~(, rl

credit facilities given by the Housing Bank and other bart ,' i fI·'f

system are the var iables used in t he anal ysia . So with 1 im; ~ v~

resources we are making an attempt to analyse the data t/.J S<=I::>;

whether it supports the hypothes i s. We have used the reg!!!,;;:: I'~ '

analysis t o analyse the data and instead of the simple [egr~::.:: .. ', '.

analysis , we used a modified ver Sl on , the

iteration procedure.

Cochrane. - 0r':' . ':.~-

The reason behind using General Linear Regression is tha: -- --... -

unknown error term u is included in the regressi on equat i on ~:: : ;

with known values of y and x because of the following reaSCG3

(l) Unpredictable element of randomness

(2) Effect of large number of omitted vari ables

( 3 ) Measurement error in y

To estimate the unknown parameter we have t o make

assumptions about the error term u, The assumpti ons are :

( 1 ) Zero mean

(2) Common variance

(3 ) Independence of ui and Uj for all i not equal to )

( 4 ) Independence of ui and Xi

(5) Normality: ui ' follows t.he norma.!. distribution.

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The last assumption is important in the c ontext of testing and

making inferences be c ause inferences are made on the basis of t he

normal dis tribution and the t and F dist ribut i ons .

Us ua lly the method of leas t squares i s adopted i n estimating the

values of a and b. The correlation coef f icien t R is a measure of

t he st rength o f the relationsh i p a nd its squar e (R2 ), known as

the coe ffic i ent of determinati on , is the measure of goodness of

fit. It i s the propor tion of the total varia nce explained by the

regress i on equation and l ies betwe en a and 1. The F- stat i st i c

is us ed fo r t esting the s ignificance of R2. That i s whe t her t he

estimated mode l can be considered as a proper re l a tio nsh ip In

e xplai ning the var i a tions. The addition of exp l ana to ry variab l es

lncr e ases the value of R2 . But this always doe s not me an that

the regression has been i mproved . Because with l ncrease • In

the degree of freedom also changes and leads t o a n lncrease I n

the standard error. So, when more explanatory variab l es are

present , R2 IS adjusted fo r t he degrees of f r eedom and the

adjusted R2 IS used for interpretation. For two or three

explanatory variables with sufficient number of observa tions R2

itself • IS as good as adjusted R2. In the same way t he t -

statistics whic h is a function of the mean and standard dev ia t ion

of the regression coefficients determines its signif icance. Tha t

IS to what ext ent the es t imated values of a and b can be taken as

a me asure i n explaining the movements of the var i ables .

There ar e t wo situations under wh ich third assumpti on about

independence of ui and Uj are violat ed. I n cross-section da ta

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the error terms can be correlated among contiguous units such as

households or states. The error term picks up the effect of the

omitted variables and this type of correlation is called spatial

correlation. Some of the factors that produce this type of

correlation among the terms can be taken care o f by dummy

variables.

The correlation between the error terms arising in time .

serles

data lS called autocorre lation or seria l correlation . The

occurrence of such correlation is possible for those variab l es

whose present value is influenced by the previous year's values.

Most of the economic variables like GNP , • prlC8, per capita

• • lncome, population, etc. are influenced by the prevlOus year 's

figures. The Durbin Watson (DW) statist ic is us ed to test the

presence of autocorrelation . If DW = 2 there is no correlation

between the error terms or the residuals. The lowest and highest

values of DW are 0 and 4. So if the calculated va lue i s nearer

to 0 or 4 the residuals are highly correlated and if the value is

nearer to 2 there is no significant relationship. This • IS the

general conclusion for large sample data. for small samples the

significant test should be based on the lower and upper limit

values for various degrees of freedom given in the tables.

If OW < dl (the lower limit), we reject the null hypothesis of no

autocorrelation.

If OW > du (the upper limit), we do not reject the nul l

hypothesis

If dl < OW < du, the test is inconclusive.

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•

• •

So the limitations of this test are as follows :

(1) it tests only for first order serial correlation

(2) The test is inconclusive if the computed value lies between

dl and du

(3) the test cannot be applied to models with lagged dependent

variables.

In spite of these limitations the DW statistic is commo nly used

for testing the presence of serial correlation. In the pr es ence

of first degree autocorrelat ion the estimat i on pr oc edure can be

changed to estimate the coeff i cient. The Coch r ane - Or cutt

iterative procedure is one among them. In t hi s p r oc edur e t he

regression equation is written as

Yt ~ a + b x t t ut

u t = r ut _l + at where at is serially uncor re lated and r i s the

correlation between ut and U t - t , This equa tion i s known as

autoregression because it is the usual regressi on mode l with u t

regressed on u t _i' In actual practice the va l ue o f r not • 18

known.

The procedure is to estimate equation first by the ord i nary least

square to get an estimate f or r. Using this estima t ed va lue the

variables are transformed into ,

Yt ~ Yt ~ r Yt~l ,

xt ~ Xt ~ r Xt ~ l ~

and the estimation procedure for this transformed var i ab l es IS

repeated. The interactive process • IS repeated unt il t he

parameters get converged . I.e. until t wo the paramete rs of

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consecutive interactions are s ame , The estimated coefficients of

this converged results are considered f or the analysis.

Tables 5.1 , 5,2 and diagram 5.1 give t he original data used • In

the analysis.

Table 5.1 GNP, Construction and Gross Fix ed Capital Formation

( In Mil l i on J D) ---- - - -- - ---------------- -- - - - - - - -- ----- -- ------- - ---------------

Year

1

GNP at Marke t Pri ce

2

Construc ti on Gross Fixed Capital Forma tio n

3 4 - - - -------- - ----------------------------------------- - ------- - ---

1964 196 5 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 19 77 1978 19 79 1980 198 1 1982 1983 1984 1985 1986 1987 1988 1989 1990

160.6 180.5 185. 7 142.5 166.4 197.4 187 .0 199.4 221.0 241. 5 279.3 376.0 562.4 660.1 781. 0 921. 3

1190.1 1482 .7 1673.4 1770 . 3 1853. 6 1875 . 2 2022. 2 2038.3 2112.8 2348.4 2257.3

5 .5 7.9 9 . 3 6 .1 9. 7

10 . 7 7 .7 7. 4 9 .2

15 . 2 16 . 8 19.2 26 . 6 36. 8 51. 6 70.5 97 . 5

110.6 121. 9 126.8 127 . 0 144 . 4 131. 4 124 . 3 126. 8 129.1 136.7

18 . 8 23.9 27. 7 24 .0 27 . 0 35.8 25 . 2 30 . 7 36. 3 47 . 2 63 . 2 87.9

138. 0 197. 0 229 .1 294 .5 39 7 . 8 564. 8 59 7 .3 502.8 485.6 471. 4 484. 3 486. 7 503 . 9 560 .1 54 1 , 7*

- -- ----------- - ------------- - --- -- --- - ---------- ------- - - -- -- ----

SOUrce: COlumn 2 & 3 : Central Bank of Jordan, Yearly statistical Series (1964- 1989 )

October 1989 , Special Issue, p.58. Column 4: Central Bank of Jordan, Yearly Statistical Series (1964- 1989 )

October 1989, Special Issue, p,59. 'I< Estimated Note: C'.ollJlTlJ. 3 :Contr~tion of Construction Activity to Gross Domestic

Product.

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Table 5.2

Loans Granted for Construction (In Million JD ) ------------------------

Year

1 -------

1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990

---------

-----------------------------------------Amount granted loans by housing bank

2

Mortgage Loans*

3 ----------------- ----------------------- ---------

1. 80 8.60

33.00 15.23 22.67 20.01 27.64 34.21 63.95 90.72 80.00 97.50 81.00 91.60

111.90 129.00 118.00

1. 80 8.6 0

33 . 00 15.2 3 22. 70 20 . 01 27 . 64 34. 21 39 . 53 42.92 43.8 0 33.10 4 3. 90 37. 30 49.2 0 50 .6 0 58. 70

------------------------------------------------------

* Including loans to the Housing Corpor ati on fI nanced by advan ces from the Central Bank of Jordan.

column 2 Amount A B C

of Granted loans by Housing Mortgage loans Development loans Credit facilities di re ct ed other development purpose s

Bank loclud<:!s:

for housing and

Source: Annual Report of the Hous i ng Bank , va rious years.

The f i gures show a steadily increasing trend i n the early pe riods

except for the years 1967 and 1970 when both GNP and construction

declined from the previous year's figures. Co nstruction activ ity

started declining in 1986 but picked up i n 1989. Lo ans from both

the Housing Bank and commercial banks show an ove r all . lncreaslng

trend except for t wo - three years.

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~

N (/1

Amount of Loan Granted for Construction and Mortgage Loan By The Housing Bank

(In Million JD) Diagram 5.1

160 -<1 I

140

120

100

80

60

40

20

o

64 =i'I

91 -( 80

98 =i'I

81 :::::;'

92 11

112 (j

129 118

1982 1983 1984 1985 1986 1987 1988 1989 1990

'----'i Loans

1 (138)

The yearly percentage growth rates are us ed in the • regre ss lon

analys i s. Tables 5.3 , 5 . 4 , diagrams 5 . 2 and 5 .3 gIve t he yea rl y

perce ntage growt h rates of t hese variables. Natura l l y t he

pe rcentages are f luctuat ing f rom year t o year with a noticeably

de creasi ng magnitude in t he 80s.

Table 5.3

Growth Rate s o f GNP , Const ruc tion and Gros s Fi xed Capital Formation

--- - -- - - - -- --------- --- - -- - - -------------------- - - ---------------Year

1

Growth Ra te of GNP at Market Pri ce

2

Growth Rate Growth Ra te o f of Const ructi on Gross Fi xed

Capita l f ormation 3 4

-- - - - - - - - ------ - - - - - ----- - - ------------ - ----------- - - - -------- - - -

1965 1966 1967 1968 1969 1970 1971 1972 197 3 1974 1975 19 76 19 77 1978 1979 1980 1981 1982 1983 1984 198 5 1986 198 7 1988 1989 199 0

12. 40 2 . 90

-23.30 16.80 18 . 60 -5 . 30

6.60 10 .80

9 . 30 15.65 34.60 49.6 0 17 . 40 18.30 17. 96 29.2 0 24.6 0 12 . 90

5. 79 4 .71 1.1 6 7.27 O. 79 3.65

11.15 -3.87

43.60 17 . 70

- 34 . 40 59 . 01 10 . 31

- 28. 03 - 3.90 24 .30 65.20 10.50 14 .29 38.50 38.30 38.60 38 . 24 38. 30 13.44 10.20

4 .02 O. 16

13 .70 -9.00 - 5 .40

2. 01 1. 81 5.88

27.13 15 .90 1 3.36 12. 50 32. 60 - 2 . 96 21. 80 18 . 24 30 . 03 33 . 90 39.1 0 56.99 42.75 16 . 29 28. 55 35 . 10 49 . 98

5. 75 - 15 . 80 -3. 40 - 2.92

2 . 74 0 . 49 3 . 53

11. 15 - 3 .2 8

--------- - - - -- - ---- - --- - ------------------- - ------ - - ------- ---- - -

Sourc e : Based on Table 5.1

12 7

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Table 5.4

Growth Rates of Loans Granted by Housing Bank

------------------------------- - - -- -- - ----------- - --------------'fear

1

Growth Rate of Loans

2

Growth Rate of Mortgage Loans

3 ------------ --- - -- -------------- ---- - ---------------- - - - - --- - ---

1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990

377.80 283.70 -53.80 48.85

-11.70 38.10 23.80 86.90 41. 86

-11.80 21.9 0

-16.90 13.10 22.10 15.28 - 8.53

377.80 283. 70 - 53.80

48 .85 - 11. 70

38 .10 23 . 80 15.55

8 .58 2 .0 5

- 24.4 3 32.63

- 15. 03 31. 90

2. 85 16. 01

- ---- - - - - -------------- ------ - - --- - ---- -- - ---------- -- ----------

Source : Based on Table 5.2

The regress ion analysis is done separately f or t wo time periods

( i) f or the period 1964 - 1973 (before the establi s hmen t of t he

Housing Bank and (ii) f or t he period 19 74- 1990 ( aft e r t he

establishment o f the Housing Bank .

The firs t regress ion equation is

y = a + b x + u

where y - the yearly growth of GNP. .

x - the contribution of the cons truction sector t o GOP

u is the error tern • ,

128 i ,,-

'(::-'~l~l,\; ,;'

•

1 (140)

Growth Rate of GNP,Constructlon &. Grol. Fixed Clpltal (1~64-1973)

80 ----------------------------------,

60

40

20

-20 + • * -40 ----'---- ___ ...i-.-__ --'----__ -'---__ .L-__ L-__ L-_

1964 68 66 67 66 69 ro 71 72 73

I GNP * Conltructlon o GFC

•

Growth Rate of GNP,Construction &. Gro .. Flxad Capital (1974-1990)

Diagram 5.3 60.----------- ----------,

o

40

20

o~------------_+~

o _20 L-~-L-~-L--L~L-·~1--~1--~1--~1 --~1--~1 --L-_L __ L__

1974 78 76 77 76 79 80 81 82 83 84 86 86 87 88 89 90

-+- GNP -..... Conatructlcn 8 GFC

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1 (141)

For the first period the estimated results are:

The

GR, (GNP) - 0,750 + (0,22)

0,273 GR, ( 2,96 )

( Con , )

R2 - 0,556, DW = 1,40 , F (0 ,5:2 ,8 ) - 8 , 8

figures , 1n the bracket s are the t - value s . The t- val ue

associated with constructi on activity i s posi tive and gre ate r

than 1.96 showing the sign i f icance o f the regr eSS I on coe f f 1c ient

at 5 per cent confidence level. That in TNith co nfiden ce we c an

say tha t if the percentage gr owth o f const r uctIon actI V1t y

changes by 1 per cent upward , t he GNP gr owt h · ... 111 cha nge by 0.273

percentage. TheR2 value is 0.556 whi c h a l s o is sign 1f Icant at 5

per cent level because the tabular value of P- statlst1 c 13 4 4~

and the calculated value is great e r t han that. By t hIS 'Ne mean

that about 56 per cent of the var i a ti on i n t he GII P 1S ezp lal nod

by the changes in construction ac t i vi ty . Th e r emalning 44 pl'!C

cent unexplained varia tion is due to the Inf lu ence of changes 1n

other factors. As explained ear lie r t he estIma tI on pr oc~dur8

i tself had taken care of the possib l e pre s ence of autoco r relatl o~

and t he OW value (1 . 4 ) here shows the absence of autoco r relatIon.

For the second period the estimated re sult r are

Gr, (GNP ) = 5,157 + ( 1. 2 )

0 ,491 GR, (2 , 85)

( Con , )

R2 = 0,384, DW = 2,06 F (0,5 :2,15 ) - 8 ,1 0 (Table value 3 ,68 )

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1 (142)

A look at the result shows that after the establishment of the

Housing Bank a 1 per cent change in construc tion will bring about

0. 491 per cent change in the GNP whereas it was only 0 .273 per

cent before the establishment of the Housi ng Bank. The per cen-• tage Increase in GNP due to change in co nstrucUon has almost

doubled. This shows that the facilities provided by the Housing

Bank in construction activity have brought out an increase in t he

contribut ion of other related sectors towards GNP.

Although the R2 value is significant the relati onship could cat ch

up only 39 per cent of the Variation. It indicates that afte r

the establishment of the bank there is an increase lr. the

influence of additional factors on the growth o f Gnp. As the

co rrect ions are done here also, the OW shows absence of

autocorrelation.

The above two regreSSion results suppor t the hypothesis t hat the

activities of the Housing Bank . 10

. . Impr OVing constructIon

activity in Jordan will improve the country's gr oss national

product as well. In other words, the Housing Bank p l ays a

positive role in improving the national income of Jo rdan.

The second set of regression is f or the period 19 74 to 1990 .

The relationship of GNP with t he Housing Bank loans and the gross

fixed capita l f ormation is analysed. Th6 gl' owth rate of GNP ,. the dependent variable and the growth of HB l oan and growth of

.

gross fixed capital formati on are taken 3S axplanatory var iables.

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1 (143)

The estimated equation is • •

GR (GNP) - 4.029 t 0.082 GR. (LHB ) t 0. 46 5 GR. (GFC) -( 2.79) (5 . 15 ) (8.06)

R2 - 0.914 , OW ; 2.12 , F (0. 5:3,1 5 ) - 63.96 - -(Table Value 3 . 29)

where GNP is gross nat ional pr oduct

LHB is loan of t he Housi ng Bank for construction activit y

GFC is gross fixed capi tal formati on

The re s ult shows • that both explanatory variabl es are important In

explaining the var i a tion . GNP is positively influenced by a

change • In t he gr owt h rate of loans of the Housi ng Bank and the

gr owth r a t e of gross fixed capital f ormati on. A 1 per cent

incr ease in t he growth of the HB loan, keepi ng the growth i n GFC

constant, increases t he GNP growth by 0 . 082 per ce n t . Although

the magnitude of va r iation is small it is h igh l y signif icant In

influencing the variation. The magni tude of variati on is sma ll

because t he variation in the HB loans is i nc reas ing at a very

high rate compared t o the upward movement of GNP. The t value

associated with GFC also is hi gh l y significant and 1 per cent

growth In GFC, keeping t he gr owt h in HB l oans constant, will

bri ng out a 0. 47 per ce nt change In the growth of GNP . • lS

highly signi f icant, both variables (GFC and LHB ) together

exp laining about 91 per cent variation in GNP growth. The above

relationship i s sat i sfactor y in predicting the growth in GNP.

The next re l ati onship tried is that of the dependence of

cqnstruction act i vi ty and t he allotment of l oan amount by the

Hous ing Bank fo r construc t ion purposes.

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•

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The estimated regression equation is

Con. = 37. 28 + (3.54)

0.938 LHB (6.50)

R2 = 0.74, OW = 0 .4 3 F (0.5 , 2 , 15 ) - 42.27

It is interestin9 to note that the t value corresponding to LHB

is highly significant and a 1 per cen t i ncre ase i n loan f ac i l it y

will increase construction activity by 0 ,94 per cent 1, e , almost

by the same amount. a one - t o- one • 16 We can say there

relationship between the two variables, both moving in the s ame

direc tion in almost the same magnitude . In other words,

construction activity 1n J ordan i s highly depe ndent on loans f r om

the Housing Bank, The DW va l ue is nearer t o zer o s howing t he

presence of significant serial corre l at i on.

Another observation of the relationsh i p of t he s ame t ype, . ~ . e.

the dependence of construction activ ity on the amount of loan

given by t he Housing Bank f or construction of new units , gives

CON = 10 .33 + (0 .69 )

2. 258 HLHB (6. 18 )

R2 = 0 . 72 , DW = 1.21 , F (0.5 , 2 , 15) = 38.2 4

where HLHB is mortgage loans of the Hous ing Bank .

The t value i s highly s i gnificant as expected. Not onl y t hat , a

1 per cent increase in mortgage loan wi ll increase construction

ac tivity by 2.26 per cent. This impl i es t hat demand f o r new

houses gives rise t o a large s cale demand f or const r tlct ion

materials and related things. It i s natural tha t l oan s for

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developing existing houses , renovation , or extension will not

create that much demand for construction ma t e rial s as new houses

can.

The table values of all the F statistics ar e less than the

c alculated values. This shows that R2 • significant for all is

• regress10n relationships. Exc ept f or the relationsh i p o f

construc tion with the Housing Bank l oans , t he DW • not is

significant indicating the absence o f autocorrelation. This 1S

mainly due to the modified estimation te c hnique, wher e t he

presence of autocorrel ati on is correc ted in es t imating t he

cae! f i c ient. This suggests that all the es timated results are

reliable e st imates and our hypo t tles is i s in co nfo rmit y with

empirical observations .

•

134