Hedonic Prices and the Demand for Housing Attributes in a Third ...

Urban Studies, Vol. 29, No . 5, 1992 639-651

Hedonic Prices and the Demand for Housing Attributes in a Third World City : The Case of Ibadan, Nigeria Ben C. Arimah (Paper first received, August 1990 ; in final form, October 1991]

Summary . This paper estimates the demand functions for a set of housing attributes for the city of Ibadan, Nigeria, using Rosen's two-step estimation procedure . The empirical results reveal that the most important determinants of the demand for housing attributes are: income, price of the attribute in question, household size and the occupational status of the head of household . The paper further indicates that the demand for housing attributes is inelastic as all estimated elasticities are below unity .

Introduction

To date, housing-demand studies princi- policy-makers, as well as the inability to pally sponsored by the World Bank have distinguish between housing needs (based proliferated in cities of the relatively `more on ad hoc assumptions) and effective dedeveloped' Latin American and Asian mand, have partly been responsible for the countries (Follain et al., 1980, 1982 ; failure that characterises housing projects Strassman, 1980; Ingram, 1981 ; Quigley, in many Third World countries. 1982 ; Follain and Jimenez, 1984, 1985a ; This state of affairs could be attributed Lim et al., 1984 ; Malpezzi and Mayo, to : the difficulty associated with obtaining 1987) . Apart from Megbolugbe's (1983a, property values in a developing country ; 1986, 1989) study of Jos, Nigeria, there the apparent belief that housing markets exists a dearth of studies on the demand are inhibited by socio-cultural and politifor housing attributes in sub-Saharan cal institutions, thereby raising doubts as Africa . Consequently, very little is known to the applicability of micro-economic about the behaviour of housing markets in models to such markets ; and the fact that this region. Yet, the findings of such data from such markets are unreliable studies are crucial to the successful formu- (Dalton, 1962 ; Megbolugbe, 1986). Nonelation and implementation of housing pro- theless, it is still possible to undertake an grammes in developing countries . This analysis of the demand for housing in a apparent lack of understanding of the developing country . This is because public operations of the housing market by policy in terms of government intervenBen C. Arimah is at the Centre for Urban and Regional Planning, University oflbadan, Ibadan, Nigeria . This paper is a revised version of some portions of the author's doctoral thesis submitted to the Department of Geography, University of Ibadan (1990) . The author wishes to express his sincere appreciation to members of his thesis committee-Professors Sylvester Abumere, Olusegun Areola and 'Bola Ayeni-and his supervisor, Dr Stanley Okafor, for numerous helpful comments . He is also indebted to Isaac F. Megbolugbe of the National Association of Home Builders, Washington, DC, for the assistance rendered in the course of the study. The comments and constructive criticisms of the anonymous referees on an earlier draft of the paper are highly appreciated.

639

640

BEN C. ARIMAH

tion, which is the reason often cited for housing-market imperfections in the form of public-housing programmes and rent control, is quite ineffective in preventing substantial market transactions (Koenigsberger, 1986), while public-sector housing accounts for less than 5 per cent of urban housing in Nigeria (Megbolugbe, 1986) . The purpose of this paper is to present an empirical analysis of the determinants of the demand for housing attributes in a developing country, using the city of Ibadan, Nigeria, as an empirical focus . In this respect, this paper can be seen as extending the spatial scope of housing-demand studies in developing countries . Housing is conceptualised as a multi-dimensional package of goods and services extending beyond the shelter itself . Consequently, environmental amenities such as waste disposal, water supply, neighbourhood roads and locational services implied by the spatial links between necessary economic and social infrastructure such as education, health and recreation are all part of the package of services designated housing (Harvey, 1972) . In the remainder of the paper, the methodology used in estimating the demand parameters is outlined, and the sampling procedure, data base and variables utilised in the estimation are described . The empirical results are then presented and discussed, and finally the conclusions and some of the policy implications of the paper are summarised . Theoretical Framework The model used in estimating the demand for housing attributes in this paper is an adaptation of Rosen's (1974) two-step model which provides a framework for estimating the demand for a single commodity with many characteristics . Since the theoretical issues have been highlighted by Rosen (1974) and previous housing analysts, the model is presented in its fundamental form . Households are assumed to consume a

bundle of housing attributes Z (Z,, Z2, . . . Z„) and other commodities X. The consumption decision is characterised by a certain level of well-being known as the utility function . Formally, U = U(X, Z 1 , Z2, . . . Z„) (1) which is subject to a budget constraint, namely: Y = X + P(Z) (2) where U is the household's utility function ; X is a vector of other goods consumed by the household ; Z is a row vector of housing attributes; Y is a measure of income ; and P(Z) is a non-linear hedonic function . The first stage of the model entails specifying the hedonic housing-value function P(Z) . This involves regressing a measure of housing price on all housing attributes using the best-fitting functional form . From the resulting estimates, a set of marginal implicit prices is obtained. These are estimates of the household's willingness to pay for marginal increases in the individual housing attributes, and are calculated by taking the partial derivative of the hedonic function with respect to the housing attribute in question . The relationship between housing values and the bundle of attributes is more often than not non-linear . Rosen (1974) and Harrison and Rubenfeld (1978) have suggested that non-linearity of the hedonic function is to be expected because, unlike the attributes of less durable commodities, housing attributes cannot always be untied and repackaged to produce an arbitrary set of attributes by households in order to allow them to purchase and consume a particular set at any desired location . If this were the case, the implicit price of the various housing attributes would be constant for all households and independent of the quantity being consumed . A nonlinear function implies that the implicit price of a particular attribute will depend on the quantity consumed and those of other attributes. Other reasons that might necessitate a non-linear hedonic function include the absence of a long-run equilib-

HOUSING DEMAND IN IBADAN, NIGERIA

rium in the housing market and the assumption of diminishing returns in both production and consumption (Harrison and Rubenfeld, 1978 ; Witte et al., 1979) . The second step involves using the marginal implicit prices and quantity of the various housing attributes as endogenous price and quantity vectors in the estimation of the demand functions . Consequently, the demand for housing attributes can then be specified in line with traditional demand theory, and of the standard form :

DZ, = Z, = f(Y, P,, P, T)

(3)

where DZ; is the demand for the ith attribute ; Z; is the quantity of the ith attribute consumed ; P, is the price of the ith attribute ; P is a price vector of substitutes and complements ; Y is a measure of household income ; and T is a row vector of taste-determining variables. This type of specification has been used by Linneman (1981, 1982), Blomquist and Worley (1981, 1982) and Megbolugbe (1983a) in estimating the demand for various attributes . Such specification is based on the assumption that the supply of housing with respect to these attributes is perfectly elastic . Recently, an increasing number of researchers (Blomquist and Worley, 1982 ; Brown and Rosen, 1982 ; Diamond and Smith, 1985 ; Follain and Jimenez, 1985b ; Ohsfeldt and Smith, 1988) have identified some of the practical problems associated with the application of Rosen's two-step model. Chief amongst these is the issue of simultaneity bias inherent in the model . The first type of simultaneity, described as traditional, is that in which the error terms are correlated with the independent variables in the demand equation . As observed by Linneman (1982) and Follain and Jimenez (1985b), this type of simultaneity bias becomes problematic in studies with units of observation sufficiently large enoughsuch as neighbourhoods or census tracts-to ensure market clearance . In studies employing micro-data, this does not pose a serious problem .

641

The second type of simultaneity bias which occurs irrespective of the nature of the data, arises due to the non-linearity of the hedonic function . The non-linearity of the hedonic function implies that the marginal price paid for a unit of housing attribute depends on the quantity of attributes consumed, as well as those of other attributes. In other words, though the household faces an exogenous price function, it simultaneously chooses the quantity of housing attributes consumed as well as the price paid for these attributes . Hence the simultaneity bias . This means that ordinary least squares (OLS) will yield biased and inconsistent parameter estimates . Therefore, a correction for the simultaneity bias (whereby some form of exogenous variation independent of quantity is introduced into the marginal prices) is needed to identify the structural parameters of demand . Housing analysts have employed various econometric procedures in solving this problem . One approach as utilised by Linneman (1981), Quigley (1982) and Follain and Jimenez (1985a) is to replace the marginal price with an instrumental variable obtained by a regression of the implicit price on a group of variables not correlated with the error term . On the other hand, Blomquist and Worley (1982) investigated the existence of simultaneity bias using Hausman's test, while Ohsfeldt and Smith (1988) proposed a methodology using the three-stage least-squares estimates, whereby the sensitivity of the estimated parameters to changes in normalisation can be used as an ad hoc indicator of accuracy . While these different econometric procedures yield further insights into the identification problem, the snag, however, as pointed out by Follain and Jimenez (1985a), is that there is no guarantee that the instruments used are the good/valid ones . Furthermore, Megbolugbe (1983a) notes that the results of some of the various tests to determine the severity of the simultaneity bias are not always conclusive .

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BEN C . ARIMAH

Table 1 . Definition and summary statistics of hedonic housing variables for Ibadan housing market Variable

Definition

ANRENT ROOMS

Annual housing rent (naira)a Number of rooms occupied by household Average room size (m 2 ) Prorated plot size (m 2 ) = plot size/number of households in housing unit Number of floors in housing unit Equals 1 if lavatory is water-operatedb Equals 1 if facilities are not sharedb Equals I if source of water is pipe-borneb Equals 1 if source of power supply is electricityb Equals 1 if wall is concreteb Equals 1 if roofing material is asbestosb Equals 1 if roof is looking oldb Equals 1 if neighbourhood school quality is highb Equals 1 if neighbourhood crime level is highb Equals 1 if recreational facilities are present in neighbourhoodb Distance to the CBD (km) Distance to work-place of head of household (km) Average distance to children's school (km) Distance to nearest major hospital (km) Average distance to other patronised hospitals (km)

AVRMS PPLOTSZ

NOF LAVH20 NSHARE WATER POWER WALL ROOF ROOFOLD SCHQLTY POLLUT RECREATE DISTCBD DISTWK DISTSCH DISTMHOSP DISTOHOSP

Mean

Standard deviation

912 .66

1041 .16

2 .46 28 .22

1 .38 20 .14

133 .27 1 .74

324 .56 0 .55

0 .49

0 .36

0 .24

0 .43

0 .75

0 .43

0 .99 0 .80

0 .11 0 .40

0 .25 0 .35

0 .43 0 .50

0 .45

0 .50

0 .55

0 .50

0 .30 3 .23

0 .46 1 .91

2 .53

2 .10

1 .37

1 .52

2 .30

1 .24

1 .54

1 .73

a The annual housing rent is in Nigerian currency (naira), where US$1 equals 11 .35 naira, as at September 1991 . b Otherwise equals zero .

The Sample Data The data used in this paper were obtained from a questionnaire survey for a larger study of the Ibadan housing market, undertaken between November 1987 and September 1988 (Arimah, 1990) . The survey was designed to obtain information on house prices, housing attributes and socioeconomic characteristics for both the renter and owner-occupier housing sub-

markets . The sampling frame utilised was the total number of housing units in Ibadan, broken down into the city's 47 census enumeration wards . This was compiled by the Estate and Valuation Department of the Ibadan Municipal Government based on the number of houses assessed for the payment of tenement rates for 1982 . There was a total of 67 951 assessed houses, out of which 1262 were

64 3


sampled in proportion to the number of houses in each ward. The choice of the dwelling unit to be sampled was randomly systematic, as it entailed the random choice of streets but a systematically random choice of housing units along these streets. It is important to point out that the dwelling units in the sample are basically privately owned or rented units . Publicly provided low-cost housing units, and houses serving as residential quarters for parastatals or private institutions, are excluded from the sample . This is because rents paid on such institutional housing do not reflect their prevailing market value . Furthermore, the houses in the sample cannot be said to be under rent control, as the provision of the rent edict promulgated in 1977 remains a dead letter. This is because of the wide deviations that occur between edict-stipulated rents and the actual rents paid in the city of Ibadan. The effect of all these, as observed by Megbolugbe (1986, 1989), is to eliminate or at least minimise the possible public-policy bias . The analysis reported in this paper pertains to the renter housing submarket. The definition and summary statistics of the variables used in the empirical analysis are listed in Tables 1 and 2 . Table 1 presents the variables used in estimating the hedonic function, while Table 2 presents those utilised in estimating the demand functions . The housing attributes in Table 1 are to some extent similar to those employed by Follain and Jimenez (1985a) in that they are representative of the dwelling-unit size, quality, neighbourhood and location . Our measure of house price is the annual housing rent. This is chosen because it gives an observable and unambiguous measure of the housing value for renters . The annual rent refers to the net rather than the gross term lease. This is because in Nigeria, landlords are only concerned with the provision of the housing unit itself. Tenants have to make their own arrangements for the provision of furni-

ture and payment for utilities such as water and electricity . The variables used in estimating the demand functions in Table 2 are comparable to those employed in previous studies . It is important to point out that our measure of income relates to current rather than permanent income . While the use of current income in the estimation of income elasticities has been shown to be downward biased (Follain et al., 1980 ; Jimenez and Keare, 1984 ; Shefer, 1990), the difficulty and possible bias involved in using some of the methods of measuring permanent income in a developing country might far outweigh those of using current income . However, Malpezzi and Mayo (1987), while acknowledging that permanent income does generally yield higher estimates, note that such differences are "comparatively modest" . In this respect, our use of current income can be viewed as exploratory .

Empirical Estimation and Discussion of Results The first stage in implementing our housing-demand model requires the estimation of the hedonic price function. The estimating equation is of the double-log form in which all the variables with the exception of those measured on a binary scale are in logarithms . Namely: n

In (P) = bo + > b ; In Z;

(4)

r=i

A combination of factors was crucial in the final choice of the double-log model over the linear and semi-log models. These include : interpretation of the implied relationship; level of explanatory power (R 2); the significance and stability of the hedonic coefficients ; and the use to which the implicit prices will be put . Following our utilisation of the double-log functional form, the resultant marginal implicit prices of the housing attributes used in the second

644

BEN C . ARIMAH

Table 2 . Definition and summary statistics of housing-demand variables for Ibadan housing market

Variable

Definition

ROOMS

Number of rooms occupied by household Average room size (m2) Prorated plot size (m2 ) Average distance to children's school (km) Average distance to other patronised hospitals (km) Annual income of head of household (naira)a Price per room (naira) Price per m 2 of average room size (naira) Price per m2 of prorated plot size (naira) Price per km of average distance to children's school (naira) Price per km of average distance to other patronised hospitals (naira) Number of persons in household Number of children at school Equals 1 if head of household is white-collar workerb Number of years of schooling completed by head of household Equals 1 if head of household is marriedb Equals 1 if head of household is male b Age of head of household Average age of household

AVRMS PPLOTSZ DISTSCH DISTOHOSP ANINC PROOMS PAVRMS PPLOTSZ PDISTSCH

PDISTOHOSP

HOUSIZE SCHCHRN

occur EDUC

MARITAL SEXH AGEH AVEAGE

Mean

Standard deviation

2 .46 28 .22 133 .27

1 .38 20 .14 324 .56

1 .37

1 .52

1 .54

1 .73

3359 .92 151 .53

3425 .74 71 .57

3 .98

2 .53

0 .08

0 .04

9 .89

14 .37

11 .01

16 .11

5 .63

4 .00

1 .91

1 .64

0 .50

0 .50

9 .76

5 .26

0 .86

0 .32

0 .88 38 .42 19 .84

0 .34 7 .82 5 .72

a See footnote to Table 1 . b Otherwise equals zero .

stage of the analysis were calculated for each individual household using:

PZ, = aP,/aZ, = b,(P;IZ,)

(5)

The mean implicit prices in Table 3 were evaluated at the mean values of each attribute for the entire city . We have opted to utilise the marginal implicit price as our price vectors in spite of its endogeneity with the quantity of attribute consumed . Our contention is that

the paucity of previous studies on the demand for housing attributes in subSaharan African housing markets does not necessarily call for the use of sophisticated and expensive econometric correction procedures, as the benefits of these can best be appreciated after the basic features of the determinants of the demand for housing attributes have been uncovered . Table 3 contains the estimates of the hedonic price function . Quite a number of

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Table 3 . Hedonic regression for Ibadan housing market

Variable

Regression coefficient

ROOMS AVRMS PPLOTSZ NOF LAVH2O NSHARE WATER POWER WALL ROOF ROOFOLD POLLUT SCHQLTY RECREATE DISTCBD DISTWK DISTSCH DISTMHOSP DISTOHOSP CONSTANT

0.4849 (11 .56)* 0.1412 (2 .75)* 0.0066 (0 .12) -0.0432 (1 .41) 0.0467 (1 .49) 0.0838 (2 .00)** 0 .0077 (0 .29) 0 .1188 (5 .07)* 0 .1166 (3 .70)* 0 .0057 (1 .62) -0 .0327 (1 .17) -0 .0333 (1 .20) 0 .0807 (2 .90)* 0 .0143 (0 .59) -0 .0739 (2 .85)* - 0 .0014 (0.05) 0 .0127 (0.43) - 0 .0030 (0.51) 0 .0172 (0.70) 2 .5138 (7 .55)*

R2 Adjusted R 2 F-ratio N

0 .7446 0 .7353 80.246 544

Mean implicit price (naira) 176.90 4.57 0.05 -22 .61 86.98 318.67 9.37 109.52 133.02 20.80 -90.44 -69 .05 133 .91 43 .50 -20.88 -0.51 8 .46 -0.52 10 .19

* Significant at the 0 .01 level and above (one-tail test) . ** Significant at the 0.05 level (one-tail test) . Note: Absolute t-values are in parentheses .

variables are significant and have the correct signs . The R2 is generally high, as the housing attributes account for 74 .5 per cent of the variation in annual rental values . The table also reveals that spacerelated attributes such as the number of rooms occupied (Rooms) and average room size (AvRMs), utilities such as the presence of electricity (PowER), construction materials such as concrete wall coverings (WALL), and neighbourhood and locational attributes which include school quality (SCHQLTY) and distance to the CBD (DISTCBD), are the important determinants of annual housing rent in the city of Ibadan . A common critique of hedonic coefficients is that they are not generally robust with respect to changes in specification ; hence the estimated predicted rents are

reliable, but the interpretation of the hedonic coefficients as the price of characteristics can be questionable (Ozanne and Malpezzi, 1985). Butler (1982), however, has shown that the coefficient bias arising with respect to changes in specification is negligible . Our estimated hedonics can be seen as robust in that they compare favourably with those of other sub-Saharan African housing markets as reported by Megbolugbe (1983a, 1986, 1989), Ndulo (1985) and Willis et al. (1990) . The second stage of the analysis estimates parameters of the demand function using equation (3). The demand equations were estimated using a series of singleequation OLS . The analysis of the residuals did not indicate any cross-equation correlation . All variables with the exception of those calibrated on a binary scale

646

BEN C . ARIMAH

are in logarithms . This was done to enable us interpret the coefficients directly as elasticities . The housing attributes whose demand functions are estimated in Table 4 are representative of the structural and locational attributes of the dwelling unit . These are the number of rooms occupied by the household (ROOMS), average room size (AVRMs) and prorated plotsize (PPLOTSZ) . The locational attributes whose demand functions are estimated are the average distance to children's school (DISTSCH) and average distance to other patronised hospitals (DISToHosP) . These locational attributes can be viewed as demand for access to these facilities .

Income Elasticities The estimates of the demand function for the various housing attributes reveal that the annual income of the head of household (ANINC) is an important determinant of the amount of housing attributes consumed. The income elasticity estimates show that the demand for ROOMS, AVRMS, PPLOTSZ, DISTSCH and DISTOHOSP increases by 0 .60 per cent, 0 .76 per cent, 0 .32 per cent, 0.46 per cent and 0 .49 per cent respectively, given a 1 per cent increase in the annual income of the head of household . Such inelastic income demand implies that the demand for housing attributes increases with an increase in income, but that such increases are less than proportional . The values of the income elasticity estimates are consistent with those obtained by Follain et al. (1980) and Lim et al. (1984) for Korea, Jimenez and Keare (1984) for El Salvador, Ingram (1981) and Strassman (1980) for Colombia, Ndulo (1986) for Zambia and Shefer (1990) for Indonesia, in that they are all less than unity . A further inspection of the absolute values of the income coefficient shows that the income elasticities of the demand for AvRMS and Rooms, which are space-related

attributes, are greater than those for DISTSCH and DISTOHOSP, which are locational attributes. What this means is that the demand for space-related attributes is more responsive to changes in income . This in effect implies that households are more likely, following an increase in income, to increase their demand for spacerelated attributes than for other attributes of the house . Price Elasticities The price elasticities of various housing attributes yield results that conform to a priori expectation. The only exception is the own-price elasticity for ROOMS, which is insignificant. The estimates of the price elasticities indicate that the demand for AVRMS, PPLOTSZ, DISTSCH and DISTOHOSP decreases by 0 .27 per cent, 0.55 per cent, 0.42 per cent and 0 .50 per cent respectively, given a 1 per cent increase in their unit prices. That these price elasticity estimates are all less than 1 indicates that the demand for housing attributes is priceinelastic. This implies that as the price of such attributes increases, the demand for the attributes decreases, but at a less than proportional rate . A further examination of the various elasticity estimates shows that with the exception of the own-price elasticity for PPLOTSZ, the values of the price coefficients of the demand for access to children's schools and other patronised hospitals are greater than that of the demand for average room size, which in turn is greater than that of the demand for number of rooms . What this implies is that an increase in the price of the various housing attributes has a greater effect on the demand for access variables, while the number of rooms occupied and average room size are least sensitive to price changes . This finding is quite reasonable in that increases in housing rent would not necessarily imply that households in the city of Ibadan would seek to rent units with a smaller number of rooms and a smaller average room size

647


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than hitherto consumed-except, probably, if such increase were astronomical . The cross-price effects were estimated by employing the price per square metre of prorated plot size in all but the demand function for PPLOTSZ, in which the price per room is utilised . Attempts at incorporating the price of other attributes did not yield plausible results because of the occurrence of multi-collinearity among the price vectors. The cross-price effects are significant in the demand equations for AVRMS, PPLOTSZ and DISTSCH . For AVRMS, the crossprice effect of PPLOTSZ is negative, implying that average room size and prorated plot size are complementary goods. The cross-price elasticity indicates that the demand for AvRMS decreases by 7 .1 per cent given a doubling in the price per square metre of plot size . In the case of demand for plot size, the cross-price effect is positive, indicating that the number of rooms occupied and plot size are substitutes . The cross-price elasticity reveals that the demand for plot size increases by 0 .43 per cent given a 1 per cent increase in the price of rooms . With respect to DISTSCH, the coefficient of the cross-price effect indicates that a 1 per cent increase in the price per square metre of plot size will increase households' demand for DISTSCH by 0 .05 . This implies that both attributes are substitutes . Household Size Elasticities The results of the effect of household size (HOUSIZE) on the demand for housing are also consistent in that they are positive and significant . The HOUSIZE elasticity estimates are 0 .36, 0 .18, 0 .21 and 0.30 for ROOMS, AVRMS, PPLOTSZ and DISTOHOSP . The number of school-attending children in the household (SCHCHRN) rather than HOUSIZE is used in estimating the demand for DISTSCH ; and this gives an estimate of 0.11 . Our HOUSIZE coefficients appear plausible in that they fall within the range of those obtained by Follain et al. (1980) and Lim et al. (1984) . The implication of a less-

than-unity household-size elasticity for space-related attributes such as ROOMS, AVRMS and PPLOTSZ is that a greater percentage of households would be associated with crowded living conditions within the city of Ibadan . This is because the demand for these space-related attributes is less than proportional to increases in household size. The HOUSIZE elasticity estimates for the housing attributes in Table 4 also indicate that the demand for ROOMS is more responsive to changes in the household size than for the other housing attributes . This is plausible and it confirms findings obtained by Lim et al. (1984) for the Korean housing market. This is because, of all housing attributes, it is the space-related attributes that are most likely to display the immediate effects of an increase in household size . Attribute Elasticities: Socio-economic Characteristics The effect of social status as measured by the occupation of the head of household (occup) is positive and significant, thereby indicating that households in which the head is employed in a white-collar job are perhaps likely to consume greater quantities of housing attributes . The elasticity estimates reveal that the demand for ROOMS, AVRMS, PPLOTSZ, DISTSCH and DISTOHOSP is greater for white- than bluecollar households by 0 .11 per cent, 0 .12 per cent, 0 .06 per cent, 0 .1 per cent and 0 .06 per cent respectively . These findings are consistent with those obtained by Blomquist and Worley (1981, 1982) and Witte et al. (1979), in which occupation was found to be positively related to the number of rooms, dwelling-unit quality and two locational attributes . The impact of the educational attainment of the head of household (EDUC), which also measures the socio-economic status, is less pronounced . Here EDUC is only significant in the demand for AVRMS, DISTSCH and DISTOHOSP, indicating that, all other things being equal, the demand for


average room size increases by 0 .14 per cent, 0 .04 per cent and 0 .07 per cent respectively, given a 1 per cent increase in the years of completed schooling of the head of household . Although the elasticity estimates for EDUC are well below unity, the fact that only the demand for AVRMS among the space-related attributes displays a significant coefficient implies that within the city of Ibadan, highly educated households are more likely to live in more spacious dwelling units . The impact of marital status (MARITAL) tells a mixed story . For AVRMS, the coefficient indicates that households in which the heads are married are more likely to consume rooms with smaller dimensions . For DISTSCH, the impact of MARITAL is positive and significant . That marital status is significant for this locational attribute is of spatial importance, as it reveals that households in which the head of household is married are more likely to demand more of this attribute. This finding is plausible when seen from the perspective that married households are likely to demand more of this locational attribute as a result of their greater needs in terms of interaction than households in which the heads are single. However, the consistency of this finding cannot be ascertained, as studies (Blomquist and Worley, 1981, 1982 ; Linneman, 1981) which specified the demand functions for locational attributes did not include the marital status of the head of household as one of the explanatory variables . The elasticity measuring the impact of the sex of the head of household (sExH) on room consumption and prorated plot size reveals that for households with male heads, the consumption of these attributes is about 0 .04 per cent and 0 .02 per cent greater than for households with female heads. For AvRMS and DISTOHOSP, the coefficients indicate that households with female heads are more likely to consume a greater proportion than households with male heads .

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Attribute Elasticities: Age and Family Life-cycle The last set of elasticities are those that relate to the stage in family life-cycle . The first of these is the coefficient that measures the impact of the age of the head of household (AGEH) . The AGEH elasticity is positive and significant only in the demand for Rooms and PPLOTSZ . The average age of the household (AVEAGE) provides a better indicator of the stage in family life-cycle in that the coefficients are positive and significant in all but the demand function of PPLOTSZ. The coefficients for AvEAGE are 0.04, 0 .06, 0 .20 and 0 .10, indicating that the demand for ROOMS, AVRMS, DISTSCH and DISTOHOSP increases by 4 per cent, 6 per cent, 20 per cent and 10 per cent respectively, given a 100 per cent increase in the stage of family life-cycle . That the demand for locational attributes is more responsive to changes in the stage of family life-cycle than space-related attributes could be an indication that, all other things being equal, as households move up the stages of family life-cycle, there is a greater emphasis on favourable location with respect to distance of the locational attributes consumed . Summary and Implications In this paper, we have estimated the parameters of the demand functions for a set of housing attributes using data drawn from a Third World housing market . In so doing, the results have shown that the most important determinants of the demand for the various housing attributes are income, price of the said attribute, the household size and the occupation of the head of household . Our empirical analysis also indicates that the demand for housing attributes is inelastic since all estimated coefficients are less than unity . The result of an inelastic income demand has implications for the affordability of housing programmes in Third World countries . Public-housing programmes in

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developing countries have in part emphasised the direct construction of housing units and, quite recently, sites and services projects. If public planning agencies are to continue with these programmes, they must be affordable to the intended beneficiaries (mainly the low-income group) . This issue is quite relevant in the Nigerian context, because the low effective demand for the original beneficiaries of housing projects has resulted in their re-targeting to the middle- and low-income groups . Even when the original groups benefit, this is done with high subsidies on the part of government such that no more than 5-6 per cent of the urban low-income group benefit from such housing programmes (Megbolugbe, 1983b) . This in turn has serious implications for cost recovery and the replicability of such housing projects on a large scale . The analysis of the cross-price effects, which reveals that the number of rooms and plot size are substitutes, has implications for the federal government's lowcost-housing programme in Nigeria . This programme, because of its preponderance of one-bedroom units, has been the subject of much criticism . Our suggestion here is that if such units are constructed on larger dimensions of plot size, it is then likely that they will be more acceptable to the intended beneficiaries . This is because such housing units can be expanded to include more rooms, since these are substitutes for plot size .

References ARIMAH, B .C . (1990) An analysis of urban housing markets in a developing country : a case study of Ibadan, Nigeria. Unpublished PhD thesis, University of Ibadan . BLOMQUIST, G . and WORLEY, L. (1981) Hedonic prices, demand for urban housing amenities, and benefit estimates, Journal of Urban Economics, 9, pp. 212-221 . BLOMQUIST, G . and WORLEY, L. (1982) Specifying the demand for housing characteristics : the exogeneity issue, in : D . B . DIAMOND and G. S . TOLLEY (Eds) The Economics of Urban

Amenities, pp . 89-102 . New York : Academic Press . BROWN, J.N. and RosEN H .S . (1982) On the estimation of structural hedonic price models, Econometrica, 50, pp . 765-768 . BUTLER, R.V . (1982) The specification of hedonic indexes for urban housing, Land Economics, 58, pp . 96-108 . DALTON, G . (1962) Traditional production in primitive African economies, Quarterly Journal of Economics, 76, pp . 360-369 . DIAMOND, B .D . and SMITH, B .A. (1985) Simultaneity in the market for housing characteristics, Journal of Urban Economics, 17, pp . 280-292 . FOLLAIN, J .R. and JIMENEZ, E . (1984) The demand for housing characteristics in three developing countries: Colombia, Korea and the Philippines . World Bank Staff Working Paper, The World Bank, Washington, DC. FoLLAIN, J .R . and JIMENEZ, E . (1985a) The demand for housing characteristics in developing countries, Urban Studies, 22, pp. 421-432 . FoLLAIN, J.R . and JIMENEZ, E . (1985b) Estimating the demand for housing characteristics : a survey and critique, Regional Science and Urban Economics, 15, pp. 77-107 . FoLLAIN, J., Lim, G .C . and RENAUD, B . (1980) The demand for housing in developing countries : the case of Korea, Journal of Urban Economics, 7, pp . 315-336 . FOLLAIN, J., LIM, G.C . and RENAUD B . (1982) Housing crowding in developing countries and willingness to pay for additional space: the case of Korea, Journal of Development Economics, 11, pp . 249-272. HARRISON, D . and RUBENFELD, D.L . (1978) Hedonic housing prices and the demand for clean air, Journal of Environmental Economics and Management, 5, pp . 81-108 . HARvEY, D .W. (1972) Society, the city and the space economy of urbanization . Commission of College Geography Resource Paper No . 18, Association of American Geographers, Washington, DC . INGRAM, G. (1981) Housing demand in the developing metropolis: estimates from Bogota and Cali, Colombia . World Bank Urban and Regional Report No . 81, The World Bank, Washington, DC . JIMENEZ, E . and KEARE, D .H . (1984) Housing consumption and permanent income in developing countries : estimates from panel data in El Salvador, Journal of Urban Economics, 15, pp. 172-194 . KOENIGSBERGER, O . (1986) Third World housing policies since the 1950s, Habitat International, 10, pp . 27-32 .


Lim, G .C ., FoLLAIN, J . and RENAUD, B . (1984) Economics of residential crowding in developing countries, Journal of Urban Economics, 9, pp. 126-148 . LINNEMAN, P. (1981) The demand for residence site characteristics, Journal of Urban Economics, 9, pp . 129-148 . LINNEMAN, P. (1982) Hedonic prices and prices and residential location, in : D . B. DIAMOND and G. S . ToLLEY (Eds) The Economics of Urban Amenities, pp . 69-88 . New York : Academic Press . MALPEzzI, S. and MAYO, S .K . (1987) The demand for housing in developing countries : empirical estimates from household data, Economic Development and Cultural Change, 35, pp . 687-721 . MEGBOLUGBE, I .F. (1983a) A hedonic index based model of housing demand for Third World cities . Unpublished PhD thesis, University of Pennsylvania . MEGBOLUGBE, I .F. (1983b) The hopes and failures of public housing in Nigeria : a case study of Kulende and Adewole housing estates, Ilorin, Third World Planning Review, 5, pp . 350-369 . MEGBOLUGBE, I .F . (1986) Econometric analysis of housing trait prices in a Third World city, Journal ofRegional Science, 26, pp . 533-547 . MEGBOLUGBE, I.F. (1989) A hedonic index model : the housing market of Jos, Nigeria, Urban Studies, 26, pp . 486-494 . NDULO, M . (1985) The determinants of urban housing value : the evidence from Zambia, Malaysian Journal of Tropical Geography, 12, pp . 31-36. NDULO, M . (1986) Patterns of housing demand

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in a low income economy : the case of Zambia, Eastern Africa Economic Review, 2, pp . 68-76 . OHSFELDT, R.L . and SMrrH, B .A. (1988) Assessing the accuracy of structural parameter estimates in analyses of implicit markets, Land Economics, 64, pp . 135-146. OzANNE, L . and MALPEZZI, S . (1985) The efficacy of hedonic estimation with the annual housing survey: evidence from the demand experiment, Journal of Economic and Social Measurement, 13, pp . 153-172 . QUIGLEY, J .M . (1982) Non-linear budget constraints and consumer demand : an application to public programs for residential housing, Journal of Urban Economics, 12, pp . 177-201 . RosEN, S . (1974) Hedonic prices and implicit markets : product differentiation in pure competition, Journal of Political Economy, 32, pp . 34-55 . SHEFER, D . (1990) The demand for housing, and permanent income, in Indonesia, Urban Studies, 27, pp . 259-272 . STRASSMAN, P.W. (1980) Housing improvement in an opportune setting: Cartagena, Colombia, Land Economics, 56, 156-168. Wnms, K.G ., MALPEZZI, S . and TIPPLE, A .G . (1990) An econometric and cultural analysis of rent control in Kumasi, Ghana, Urban Studies, 27, pp . 241-258 . WITTE, A .D ., SuMKA, H .J . and EREKSON, H. (1979) An estimate of a structural hedonic price model of the housing market : an application of Rosen's theory of implicit markets, Econometrica, 47, pp . 1151-1173 .