The demand for housing in developing countries: The case of Korea

JOURNAL

OP UFLBAN

ECONOhUCS

7, 315-336

(1980)

The Demand for Housing in Developing The Case of Korea’ JAMES The

Countries:

FOLLAIN

Urban

Institute

GILL-CHIN

LIM

Northwestern

Universi@ AND

BERTRAND The World Received

April

RENAIJD Bank

21, 1978; revised

September

20, 1978

This paper presents the results of an analysis of urban housing demand for Korea taking into account the most recent findings of housing demand analysis concerning specification and aggregation biases. In order to obtain correctly specified demand functions, a procedure. based on a model of the housing market originally proposed by Muth is used. Drawing on the detailed laud information available in Korea, this procedure permits the calculation of an individual price per unit of housing services for each household The results show conclusively that both the income and price elasticity of the demand for housing services in Korea are comparable to those found in the United States: the income elasticity is smaller than one and the price elasticity is negative and smaller than one in absolute value. Given the number of countries found within the per capita income range between Korea ($700) and the United States ($7800), the finding that these two countries have comparable demand elasticities is of major significaucc: in the absence of god national estimates, the order of magnitudes found here would be used for other c4xmtry analyses. I. INTRODUCTION

The need for accurate estimates of the response of housing expenditures to changes in income and prices is even greater in developing countries ‘The research reported in this paper is based on a larger study of the Korean housing market organized by the World Bank under the overall responsibility of Bertrand Renaud. However, msponsibilities have varied for each section of the analysis and the names of the authors are listed accordingly in the resulting papers. Primary credit is due to James Follain and Gill-Chin Lint in this paper. Helpful comments were received from several staff members of the World Bank and the Urban Institute, in particular, Gregory lngram and Larry 0zanne. The authors are also grateful for the comments received from Tong-Hun Lee. The project has also benefited from Raymond Struyk’s contributions to the design and the initial analyses of the survey. This study may not be quoted as representing the views of the World Bank and its affiliated organizations or the views of the Urban Institute. 315 0094-1190/80/030315-22SO2.00/0 CopyriBht 0 1980 by Academic Fess, Inc. AII lighl.s of reproduction in any farm reesrvd.

316

FOLLAIN,

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RENAUD

than in richer countries because housing problems are more severe and resources very scarce. Reliable elasticity estimates are needed for better forecasts of housing demand and the sound formulation of urban policies. They are central to the analysis of the incidence of property taxes on which SO many local governments are or could be relying extensively. Further, in land-scarce countries such as Korea, it is important to develop better perspectives on patterns of city expansion or on the magnitude of suburbanization under the compounded impact of high rates of income and population growth. The results presented here are of interest for two additional reasons. First, the analysis takes into account the most recent findings on demand analysis which have shown that some of the previous uncertainty on the value of the income elasticity of the demand for housing services can be explained by a series of specification and aggregation problems (see Polinsky [17], Campbell and Smith [I, 231, and Lee and Kong [S])? In order to obtain correctly specified demand functions this paper uses a procedure based on a model of the housing market originally proposed by Muth. Drawing on the extremely detailed land information available in Korea, this procedure permits the calculation of an individual price per unit of housing services for each household. Alternative specifications of the demand equations allow the testing of the directions of the biases created by aggregation and specifications errors as recently discussed in the U.S. literature. Second, and more importantly, this analysis provides for the first time a consistent basis for comparison of the characteristics of demand under sharply different economic conditions: the per capita GNP in the United States was $7867 and it was only $710 in Korea in 1976 when the survey data for this study were collected. The results fully confirm the findings presented in recent U.S. articles concerning the direction of various biases which can affect the estimates of the demand parameters. They also show conclusively that both the income and the price elasticity of the demand for housing services in Korea are comparable to those found in the United States: the income elasticity of demand for housing is smaller than one and the price elasticity is negative and smaller than one in absolute value. The presentation of the results is organized as follows: First, Section II briefly describes the data base used for the survey and the characteristics of the housing market in Korea. Section III reviews briefly the state of research on the estimation of housing demand in the United States, and describes the method chosen for the specification of housing demand. % order papers for housing.

to avoid

repetition

of previous work the reader is referred to these four recent the state of the art in the aoaIysis of the demand for

a comprehensivereviewof

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317

Section IV explains how the theoretical model has been implemented and describes in turn the specification of the price per unit of housing services, household income, transportation costs, the sociodemographic determinants of housing demand, and the alternative forms of the estimating equation. Section V presents the results of the analysis. Section VI draws the conclusions of the study. II. THE KOREAN

CONTEXT

Korean housing standards are still extremely low in spite of the very high rate of increase in income over the last 15 years when the average growth rate of GNP has been around 10% per year. At the time of the 1975 census the ratio of urban households to housing units was 1.278, indicating that in cities 27.8% of the households cannot afford to rent separate dwelling units and can only find rooms to rent. This situation is due to the very high rate of urban growth3 as well as the destruction of a significant proportion of the housing stock during the Korean War. Between 1955 and 1975 the population living in cities over 50,000 grew from 5.3 to 16.8 million. The analysis reported in this paper is based on the Family Income and Expenditure Survey regularly conducted by the Bureau of Statistics of the Economic Planning Board of the Korean government, which has been combined with a Special Housing Survey conducted in July 1976.4 The sample size is 1293 households with a reported monthly average income of 75,500 ($156) and an average of 4.9 household members. Forty-eight percent of these households own their dwellings. The sample average size of a dwelling is 17.24pyeong (613.5 square feet). These orders of magnitude were found consistent with the results of the Korean housing censuses. The sample survey shows that the median Korean household devotes about 15% of its income before tax to housing exclusive of such items as utilities, water charges, and home furnishings. The rent-to-income ratio by income group declines as income increases, as shown in Table 1. This decline is very marked and suggests that rental expenditures are not very responsive to income changes. For example, the ratio of the percentage change in rent between the 30,000- to 39,000-won income class and the 80,000- to 90,000-won class is only 0.18. Yet it is impossible to conclude that demand is very inelastic with respect to income in the absence of @ver the period 1950-1970 South Korea experienced the highest rate of urbanization in the world for countries with more than 15 million people in 1950. See Renaud [19]. tie original questionnaire design was prepared by Raymond Struyk at the time of a review of the Korean housing policy for the fourth 5-year plan, 1976-1981. A full description of the regular housing and expenditures surveys based on monthly diaries and of the special housing survey can be found in Renaud et a/. [20].

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FOLLAIN,

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TABLE 1 Rent and Rent/Income Ratios by Income Class for Entire Sampld Income class (Wonp Zero or not reported l-29,999 3o,ooo-39,999 4o,ooo-49,999 50,oaL59,999 60,000-69,999 7o,ooo-79,999 80,000~99,999 loO,OOO-129,999 + 130,000 Average I (entire sample) Average II (excluding incomes less than 30,000)

WY

R

N

1.79 0.24 0.19 0.16 0.14 0.12 0.12 0.13 0.08

8021 7944 8527 8737 8638 10119 14400 14616

161 91 101 125 161 134 109 180 115 111

0.24

10126

1293

0.14

11856

1041

0 8898

aN = number of sample households in the income class; R = monthly rental expenditure (zero or inputed); Y = monthly current income. “The 1976 foreign exchange rate was 484 Wons to one U.S. dollar.

information on price elasticity and without controlling for other factors affecting the demand of a given household.5 In most developed countries households can be classified into owneroccupants and renter-occupants with no further breakdown. In Korea, however, seven types of tenure exist: In addition to homeownership, the rental arrangements are in order of frequency: chonsei, security deposit with monthly payment, decZining chomei, pure rental, free housing, and government housing. These various rental arrangements are distinguished from one another by their payment schemes, terms of contract, and sometimes eligibility criteria.6 In the two cases of declining chonsei and rent with security deposit, a household payment agreement has been converted % would not be correct to conclude from such a simple calculation that the demand for housing is extremely inelastic because other factors influencing the demand of a given household are not being controlled. For instance, the rent-to-income ratio could decline as it does in Table 1 even if the true income elasticity were greater than one in a case where the price elasticity is less than one and prices vary in such a way that high-income households face a lower price per unit of housing services than low-income households. 6A full analysis of the determinan ts of home ownership and reliance on chonsei arrangements is presented in a separate paper by Lim et al. [9]. Home ownership in Korea is not widespread (48.3% in our sample) because of the limited availability and terms of mortgage

HOUSING

Rent and Rent/Income Tenure Owner Free housing Government housing Chonsei Security deposit Declining Chonsei Rent

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IN KOREA

TABLE 2 Ratios by Tenure for Entire Sample R/Y 0.19 0.21 0.13 (0.13OY 0.25 0.11 0.11

R

N

12,060 11,038 13,222 8,164 11,459 6,714 6,297

624 13

&

114 70 %

“The rent/income ratio for Chonsei is seriously distorted by the figure for the income group l-29,999, where the value of the ratio is 5.24! Elimination of observations relating to that group only yields a mean ratio for the rest of the population of 0.130.

into an imputed monthly rental charge.7 In the other five cases no additional computation was required since households were asked to convert their rental arrangements into an imputed monthly rental charge. It may seem strange that owners would know precisely such a number. In Korea, however, it is quite believable because owners frequently sublet part of their homes so that they are quite aware of the rental values of their homes. The estimates of imputed rent by occupants of government housing and free housing are more suspect. Therefore, we report results obtained with and without this relatively small part of the sample. Table 2 lists the seven tenure arrangements encountered in the sample as well as the average rent and the average rent to income ratio for each tenure arrangement. These imputed rental charges-either calculated or reported-are used as the measure of housing expenditures in the housing demand equations. markets is not possible because the rates are very high (between 3 and 5% month) and the terms of maturity very short (typically a maximum of 20 months). The chonsei is based on this capital scarcity: instead of a monthly rent the households give a deposit varying generally between 250,000 and 600,000 wons to the landowner, who places the funds on the money markets or uses them; the total deposit is returned in whole (in the case of a pure chonrei) or in part at the end of the lease. The pure rent, Western style, is used only by the poorest households. n ‘For a declining chonsei the imputed rent is estimated as R = c [(A - D. t)i + D]/r, where A = amount of original chomei, D = monthly deduction fro:, chonsei, n = period, i = curb market interest rate. For security deposits with monthly rent, R is equal to the monthly rent to which is added the deposit multiplied by the monthly curb rate. The unregulated money market monthly rate for the sample households is estimated by taking the ratio of imputed rent to chonsei deposit in the sample chonrei data. The average value of i = O.O326/month is very consistent with the regular monetary market surveys conducted by the Bank in Korea.

FOLLAIN,

III. THE

LIM,

CHOICE

AND

RENAUD

OF METHOD

The model of demand estimated in this paper is one of a general family of models of housing markets in which the central units of measurement are units of “housing services” and “housing stock.” Given some minimum amount of operating inputs, each unit of housing stock produces one unit of housing services per period. Housing services refer to the sum of all services, inclusive of neighborhood attributes, provided by a housing unit during some period of time such as space, privacy, availability and dependability of utilities, and other features of the unit which provide comfort and pleasure. Given the price per unit of housing services (p), the rent of a dwelling unit measures the units of housing services (4) produced by a dwelling since rent equals p . 4. Similarly the value of a dwelling unit equals the price per unit of housing stock (P) times the units of housing stock embodied in the dwelling unit (Q). The principal developers of this family of models are Mills [ 111 and Muth [14]. The most specific model which underlies the estimates reported is that developed by Muth [ 15, 161. The details of the model are discussed below; here, an important reason for employing the model is noted. One of the most salient features of the model is that the price per unit of housing stock (and subsequently, housing services) can be constructed from the price per unit of land, the prices per unit of structure, and the shares of housing expenditures attributable to land and structure inputs. Since such data are available in Korea, a price term can be included in the demand for housing equations we estimate. As long as a unit of existing housing stock and an identical unit of new housing stock produce the same quantity of housing services, this measure serves as a measure of the price of both new and existing housing services. We feel this is a reasonable assumption which is commonly employed in studies of housing markets. Including the price term not only allows inferences to be made about the price elasticity of demand but also ensures that the estimates of the income elasticity are not biased because of the omission of a relevant variable (Polinsky [ 171, Strassheim [24]). An added advantage is that the procedure yields individual estimates of the price per unit of services for each individual household when past studies have frequently employed citywide indices. Polinsky [17] has shown that if demand is price inelastic, and prices decline with distance from the center of a city, then use of a citywide index rather than a more precise measure of the price faced by each household yields biased estimates of the income elasticity. The model employed has both a supply side and a demand side. The supply side is crucial to the construction of a price per unit of housing services. The key relation on the supply side is the production function for housing services: 4 = q*(Q, 0) = q*[ Q(L

W, 01,

(1)

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321

where C.Jmeasures the units of housing services per period; Q measures the units of housing stock; L measures the units of land inputs; N measures the units of structure inputs; 0 measures the units of operating inputs. We assume operating inputs are proportional to the units of housing stock, so that q is directly related to L and N. This new production function, q = q(L, N), is assumed to be homogenous of degree 1. Profit maximization behavior by suppliers of housing in a competitive market will satisfy the conditions PJP

=

PN/P

= qw

ClLY

4 = 4(L, N)

(2)

(3) (4)

where qL and qN are the marginal products of land and nonland, respectively. Muth 1151 shows that the logarithmic differentials (e.g., q* = d log q) of (2), (3) and (4) can be arranged as - k,L*

+ k,N*

+ op* = op;,

(5)

kLL* - kLN* + up* = up&

(6)

q* - kLL* - kNN* = 0,

(7)

where u is the elasticity of substitution of land for structures in the production of housing. On the demand side, consumer responses to changes in p and y are derived from the standard utility maximization problem. Max U(q, X) subject topq + X = Y,

(8)

where U(q, X) is the household utility function and X is the quantity of all goods other than housing (the price of X is the numeraire and equals 1); Y is household income. Differentiating the first-order conditions for this problem yields the fourth and final condition for our system, q* = $p*

+ eyy*,

(9)

where er and c,. are elasticities of demand with respect to price and income, respectively.

322

FOLLAIN,

LIM,

AND

RJZNAUD

Because the survey data provide rental expenditures we can solve (5), (6), (7), and (9) for (pq)*, which yields the two key results:

(P4P = (1 + +LP:. p* = k,p;

+ kNP3 + cyY*9

+ k,p;.

(10) (11)

Equation (10) expresses changes in the demand for housing as a function of changes in the prices of land and nonland inputs and changes in income. Equation (11) simply says that any percentage change in the price per unit of housing services resulting from changes in the price of land or nonland inputs is a weighted average of the percentage changes in pL and PN.

The expression in the second set of parentheses in (10) equalsp* by (11) and suggests the following two-step procedure for estimating ep and l Y: First, calculate p* for each household using an equation based upon (11) from the survey data; second, substitute the calculated pricep into Eq. (10) and estimate it using ordinary least squares (OLS). The problems associated with implementing this procedure are discussed in the next section. IV. IMPLEMENTATION OF THE MODEL Equations (10) and (11) raise five problems regarding the implementation and estimation of the demand for housing: 1. Choosing the appropriate approximation of (11) which permits the computation of the unobservable price per unit of housing services from PL, PN, k,, and k,. 2. Choosing the appropriate income concept and examining the sensitivity of the results to a variety of measures. 3. Adjusting for short-term variations in housing prices due to market disequilibria. 4. Adjusting income for transportation costs. 5. Identifying the principal sociodemographic determinants of the demand for housing. ZV.Z. Calculation

of the Price per Unit of Housing

Services

Equation (11) shows precisely how a given percentage change in the price per unit of land/or nonland inputs produces (in a static equilibrium sense) a percentage change in the price of housing. Unfortunately, Eq. (11) does not allow the calculation of the price of housing services because it is expressed in terms of percentage changes (for which we do not have data) and not of actual prices (for which we do have data). The absolute level of P from PL, PN, k,, and k, can be approximated with an equation based on

HOUSING

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323

Eq. (11). The obvious first choice which is eventually used is lnp = k,lnp,

+ k, lnp,.

(12)

This formulation is exact if the elasticity of substitution is unity, the value associated with the Cobb-Douglas production function. Although the Cobb-Douglas function is a frequently employed function, uncertainty concerning the value of (I does exist. If anything, it is likely to be less than one (Muth [16]). If it is less than one, our estimates of u are probably upward biased. This bias results because our measure of p would understate the true change in p given some change in the price of land. This suggests caution in the estimates of (I if they are large in absolute value. As seen below, the estimates are significantly below unity, so the bias does not alter our ultimate conclusion that demand is price inelastic. Although we do not have the exactp, orp, faced by each household, an excellent estimate is available because the Korean government keeps systematic records of land prices and publishes every year three land prices for each neighborhood or D0ng.s The chosen measure of pL faced by a household is the average of the high, middle, and low prices of land per pyeong in the Dong in which the household resides. The housing construction cost per pyeong is derived from the construction cost records of the Korea National Housing Corporation. The data are based upon the nonland costs of constructing a particular type of apartment unit in eleven cities. 9 For the five cities in the sample not represented in these unpublished data, we assigned the cost figures provided for the nearest city. Because of the close geographic proximity of these cities this is probably not a serious error. The theoretical justification for this approach is that competitive forces eventually eliminate significant differences in construction costs between two nearby areas as long as transportation costs and localized market powers are not too great. This reasoning is confirmed in the analyses where estimates computed with and without these five cities are very similar. The factor shares, k, and k,, are calculated as k, = ip,L/R;

kN = 1 - k,,

(13)

8A Dong is an administrative subdivision of Korean cities. In Seoul, the number of households in one Dong varied between loo0 and 8000 households in 1975; a typical figure would be 3000 for Seoul. The official Korea Appraisal Board has been maintaining records of land price3 by Dong since at least 1%2 in most cities. This rather remarkable monitoring system is used in various ways in the implementation of land policies. See Doebele and Hwang [5] for an upto-date review of Korean urban land policies, and Mills and Song [12] for an analysis of these data. these cities are Seoul, Busan, Incheon, Chuncheon, Daejon, Jeonju, Daegu, Masan, Chungju, Weonju, and Suwon.

324

FOLLAIN,

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RENAUD

where i is the interest rate used to convert the land and nonland prices from prices per unit of stock to rentals per unit of stock per period. i is set equal to O.O028/month. Selection of this value is based upon two considerations. One, the rental values of the apartments built by the Korean Construction Company are between 0.0025 and 0.0035 of the total cost of construction per unit. This should be adjusted downward since the construction data do not include land costs. Two, k, typically equals about 0 .33 for Korean homes.” From this value, the rental value of land can also be calculated since the definition of k, implies i = k,R/Lp,. Inserting the averages forp,, L, k,, and R of 63729, 12.9, 0.32, and 8251, respectively, into the equation yields a value for i slightly over 0.003. This number may appear low in comparison to the 3.26% per month interest rate which can be earned in Korea’s curb market. However, a rent-to-value ratio equal to 0.0028 does not mean landlords earn only 0.28% per month; it is perfectly consistent with the fact that a large part of Korean landlords’ profits comes in the form of capital gains on the real estate which they own. Consider the following demonstration of this point. The rate of return can be expressed as NI -= V

R+ra-X v

’

(14)

where NZ V R ra X

= = = = =

net income per month, value of assets, rent per month, rate of appreciation of V per month, operating costs, property taxes and depreciation.

This is equal to NZ -=V

R-X V

+T=i+CG,

where CG is the rate of return attributable to capital gains. If i = 0.003, then the landlord needs capital gains of about 2% per month in order to earn a return competitive with that in the curb market. (One would probably expect the return earned outside of the curb market to be less ‘qn a survey of recent housing units the share of land in total cost was found to vary between 40% for single-unit houses and 24% for apartments. See “Survey Report on Financing Status of Private Housing Funds,” Korea Industrial Development Research Institute (April 1976).

HOUSING

IN KOREA

325

than the curb rate since the curb market is likely to be more risky.) It is not uncommon for land values to increase by 2% per month; for instance, land values averaged about 3.2% increase per month in Suwon during the period 1968 to 1975. ZV.2. Measurement of Income

The choice of the appropriate measure of household income has been a source of considerable difficulty in practically all housing demand estimations. Many argue that for the estimation of the long-run income elasticity the appropriate measure is probably not the current rate of income which is easily measured, but rather de long-run expected income (the permanent income) which is not directly measurable. Choosing the appropriate variable is difficult but important because estimates of the income elasticity obtained using current period incomes are downward biased if the permanent income hypothesis is true (see Theil [25]). Given the nature of the survey data, two alternatives for measurement of the permanent income are used in addition to the monthly income reported in the survey. First, total household consumption expenditures can be substituted for the measure of household disposable current income. The reasoning is that the level of consumption is likely to be a good proxy for permanent income if, according to the permanent income theory, consumption is proportional to permanent income [6]. We use the log-linear specification so the coefficient of consumption is the estimate of zY. An alternative which has often been proposed is to estimate the parameters of the demand equation using grouped household data as the unit of observation, e.g., SMSA. We choose to aggregate by Dong. This aggregation procedure is used under the assumption that the nonpermanent or transitory components of current income will cancel out when households are grouped by factors unrelated to transitory income. This aggregation imparts an upward bias to the income elasticity estimates if rental expenditures are tightly grouped and the distribution of rental expenditures within Dongs is significantly tighter than their distribution throughout the sample. We have little information about the severity of this problem except that Dongs are thought to be significantly more heterogeneous than their counterparts (e.g., census tracts) in the United States. If the grouping produces estimates of the income elasticity significantly below unity, then knowledge of the upward bias will only strengthen the finding that demand is inelastic with respect to income. ZV.3. Adjustment for

Short-run Excess Demand

Variations in the current market price of housing services are linked not only to variations in its long-run determinants but also to short-run fluctuations caused by excess demand. Since our goal is to estimate the

326

FOLLAIN,

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elasticity of demand with respect to the long-run price of housing, we introduce an additional variable into the demand equation which controls for short-run variations in the price of housing associated with excess demand. The variable (S) is a measure of the housing shortage in a particular city and equals the ratio of the number of households minus the number of housing units to the number of housing units in a particular city based on the 1975 census. We expect the sign of S to be positive, when statistically significant. In addition, if the price elasticity l p is estimated with and without S, the estimates of e,, when S is included are expected to be algebraically smaller. The reason would be that failure to control for short-run variations causes variation in the long-run price of housing to be overstated, and consequently, estimates of ep to be biased downward. This procedure is analogous to efforts which include the vacancy rate as an explanatory variable in equations explaining the supply of housing (see de Leeuw and Ekanem [4]). Estimates are computed with and without S. The differences in the estimates of ep and ey with and without S are small but consistent with our interpretation. W.4. Adjustment

of Income for

Transportation

Expenditures

Muth, Mills, and others argue that the appropriate income concept in a demand equation for housing is income minus transportation costs whenever the marginal costs of transportation with respect to distance are positive-an eminently reasonable assumption (see Muth [ 131). Precise measurements of the costs of transportation expenses (e.g., gasoline, bus fare, etc.) and the opportunity costs of travel time are required. Since we were unable to find a satisfactory Korean study of the value of travel time, we adopted the simplified but probably reasonable assumption that transportation costs are proportional to travel time from the central city for which data were collected. IV.5

Sociodemographic

Determinants

of the Demand for Housing

Some variables which were considered for inclusion in the demand equation included average age of household members, number and ages of children in the household, number of subfamilies in the household, sex and occupation of the household head, and number of people in the household. Household size was finally selected for inclusion in the complete demand equation because it performs most consistently. Since household size is often correlated with the other determinants considered, we suggest that household size be interpreted as a proxy for some of the sociodemographic variables which undoubtedly influence the demand for housing but whose effects are difficult to sort out precisely. Past research shows that the introduction of sociodemographic variables often leads to somewhat smaller income elasticities [8, 181.

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EMPIRICAL

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KOREA

RESULTS

Essentially two types of equations are estimated. The first one uses the household as the unit of observation and includes either one of the two income measures. The equation is: In R = a,, + a, ln Yi + a2 In P + a,S + a4 In HS + a5 In T (i = 1, 2), (15) where R = imputed or explicit monthly rental expenditures, Y, = current disposable monthly income, consumption expenditures, y2 = total monthly S = the housing shortage variable (see above), HS = the number of people in a household, T = travel time to central city. The second type of equation uses averages of the variables by Dong as the unit of observation:

where bars above the variables indicate that they have been averaged over all sample households in a particular Dong. For example, one observation would be

where L is the number of households in the sample residing in the ith Dong, and Rij is the rental expenditure of the jth household in the ith Dong. The household income and expenditures survey on which the housing survey is based covered households located in 17 cities so that various groupings of observations could be made on the basis of city size as well as more common characteristics such as homeowners versus renters or highincome versus low-income households. A large number of equations were estimated but only the most important results are reported here. Two types of nationwide samples are reported as groups 1 and 2. Group 1 consists of all households in the original sample except (a) those in Jeju, an island off the Southern Coast; (b) those with reported zero income; and (c) those with a calculated land share k, not between zero and one.” Only demand “The city of Jeju has been deleted because it is on an island 100 km off the Southern Coast of Korea for which transportation costs may be substantial in the case of building materials. Unfortunately, this is one of the cities for which local building costs data were unavailable. In the case of households with reported income of zero and/or calculated value of the land share outside the interval zero-one, deletion was necessary because of measurement errors.

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FOLLAIN,

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equations based on individual observations are reported for group 1. The second type of sample, labeled group 2, is a sample slightly reduced in size by withdrawing from the group 1 sample the households presumed to be benefiting from government housing subsidies as well as households residing in cities without construction cost data. The basic results for the two national samples are reported in Tables 3, 4, and 5. In addition demand estimations based on group 2 were estimated on interesting subsamples such as owners versus renters, households residing in Seoul (population, 7.5 million), households residing in Busan and Taegu (two other cities of over 1 million people), and households residing in cities other than the three largest. By estimating demand functions for such subsamples we can test for statistically significant differences in housing demand over groups which are of major significance to policy formulation. The results for these subsamples are presented in Tables 6, 7 and 8. Their discussion follows that of the analyses based on the two national samples (groups 1 and 2). V.I. Demand Results Based on the Nationwide The discussion of the four parts deal with the to (a) income; (b) price, tures. They are followed findings.

national results is divided into five parts. The first estimates of the elasticity of demand with respect (c) household size, and (d) transportation expendiby an overall assessment of the demand equation TABLE

Estimates Dependent variable

R

constant

-2.30 -3.14

Samples (Groups 1 and 2)

Y,

of Demand

Y,

P

0.61 0.03 0.58 0.03

0.79 0.05 0.80 0.05 0.72 0.04 0.72 0.04

0.18 0.02 0.16 0.02

-6.23 -6.23

Note. N - 1055. Standard

3 I” for Group

S

HS

I*, ’

T

0.005 0.35 -0.04 0.009 0.04 0.02 0.005 0.14 0.004 0.04

-0.004 0.02

R2

cp

0.26

-0.21

0.31

-0.20

0.48

-0.28

0.48

-0.28

errors are below the estimates. “Demand equation I has the household as the unit of observation. *Group 1 includes all observations in the sample except those with zero reported incomes, calculated k,s not between zero and one, as well as households residing in Jeju. ‘Y, = current disposable income; Y, = consumption expenditures; P = price per unit of housing services; S = housing shortage = ratio of number of households minus number of dwellings to number of dwellings; HS = household equal to regression size; T = travel time to center of city; ep = price elasticity estimate minus one.

HOUSING

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TABLE 4 Estimates of Demand P for Group 2” ’ Dependent variable

constant

Y,

1

-4.00

2

-4.93

0.20 0.02 0.17 0.02

3

- 7.94

4

-8.04

Y,

P

0.62 0.03 0.58 0.03

0.91 0.07 0.95 0.07 0.85 0.06 0.87 0.06

s

T

HS

R=

0.19 0.009 0.006

0.36 -0.06 0.05 0.03 0.14 0.04

-0.02 0.02

-0.09

0.25 -0.05 0.43

0.002 0.005

cp

-0.15

0.44 -0.13

Note. N = 896. Standard errors are below the estimates. ‘Demand equation I has the household as the unit of observation. bGroup 2 includes all observation in group 1 minus those in cities without construction data and those residing in government housing. ‘See footnote c, Table 3, for definitions of parameters.

TABLE 5 Estimates of Demand Equation II” for Group lb’ ’ Dependent variable

Constant

1

- 10.14

2

- 10.14

T 0.57 (0.17) 0.57 (0.17)

p

3

HS

1.10 (0.16) 1.08 0.001 0.07 (0.17) (0.07) (0.37)

r

R2

c+,

0.55 0.10 -0.06 0.56 0.08 (0.07)

Note. Standard errors are below the estimates. “Demand equation II uses averages by Dong as the unit of observation. bGroup 1 includes all observations in the sample except those with zero reported incomes, calculated k, values not between zero and one, and those residing in Jeju. ‘See footnote c, Table 3, for definitions of parameters.

(a) Income elasticity estimates (e,). The estimates of the income elasticity equal the estimates of the coefficients of the income term since all variables are in logarithmic form. The estimates of er obtained using national data are given in Tables 3, 4, and 5. Three major aspects of the results should be emphasized. First, the estimates of l y are less than one for the three measures of income used: the estimates of e,, using household data and Y,, the reported income in the month of July 1976, range from 0.16 to 0.20 (lines 3 and 4 of Tables 3 and 4). The estimates of zy obtained using yt (grouped disposable income) both equal 0.57. Second, the estimates are all highly significant in the sense that the ratio of the estimate of its standard error is always greater than 8 with household data and about 3.4 with grouped data. Third, the estimates vary with the income measure

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TABLE 6 Estimates of Demand Equation I” for Special Groups: Owners vs Renters* Dependent variable

Constant

Y,

-6.4

0.2 1 (0.W

Y,

P

s

-

HS

T

R2

cp

R

1. Owners (4

- 8.85

0) 2. Renters (a)

09

- 5.22

0.12 (0.03)

- 7.32

1.07 0.02 (0.1) (0.01) 0.95 0.001 (0.08) (0.008)

0.14 (0.08) - 0.02 wJ7)

-0.12 0.28 ww -0.05 0.48 (0.04)

1.03 0.001 (0.09) (0.007) 0.42 0.94 0.001 (0.04) (0.09) (0.007)

0.25 @oa) 0.14 (0.W

0.03 0.25 (0.03) 0.03 0.36 (0.03)

0.62 (0.04)

0.07 -0.05 0.03 -0.06

Note. N = 415 (owners); N = 481 (renters). Standard errors are below the estimates. ‘Wemand equation I has the household as the unit of observation. %kc footnote c, Table 3, for definitions of parameters.

TABLE 7 Estimates of Demand Equation Ia for Special Groups: Low vs High Incorn& c Dependent variable

Constant

Y,

Y,

P

S

HS

T

R2

cp

R

1. Low income (a) 0-9 2. High income (a) 09

- 1.98

0.02 0.03

0.81 0.05 0.59 0.73 0.04 0.05

0.013 0.006 0.006 0.005

0.32 -0.01 0.32 -0.19 0.06 0.03 0.13 0.016 0.49 -0.27 0.05 0.02

0.47 0.07

0.77 0.08 0.54 0.73 0.05 0.08

0.010 0.008 0.004 0.007

0.28 0.07 0.15 0.07

-6.61 -6.09 - 5.85

-0.06 0.27 0.036 -0.04 0.36 0.03

-0.23 -0.27

Note. N - 640 (low income); N = 415 (high income). Standard errors axe below the estimates. ‘Demand equation I has the household as the unit of observation. *Low-income households are those below the mean. High-income households are those above the mean. %x footnote c, Table 3, for definitions of parameters.

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KOREA 8

Estimates of Demand Equation I’ for Special Groups: Seoul; Busan and Daegu; All Other Cities in the Samples* Dependent constant

variable

Y,

Y,

P

S

0.51 (0.04)

0.88 (0.11) 0.83 (0.10)

0.68 (0.05)

0.83 (0.18) 0.43 (0.07)

-0.10 (0.02) - 0.07 (0.02)

0.37 (0.06)

0.39 (0.12) 0.40 (0.12)

0.04 (0.01) 0.05 (0.01)

T

HS

R=

cp

R 1. Seoul - 3.67

(a)

0.23 Pw

-6.13

@) 2. Busan Daegu

0.34 (0.06) 0.19 (0.06)

-0.23 (0.05) -0.17 (0.05)

0.34

-0.12

0.45

-0.17

0.33 (0.09) - 0.002 (0.07)

-0.21 (0.05) 0.01 (0.05)

0.27

-0.17

0.60

-0.67

0.27

-0.61

0.27

-0.60

and

(a)

3.46

0.08 (0.W

OJ)

0.05

3. All cities except Seoul, Busan, and Daegu (a)

- 1.63

@)

- 2.61

0.33 (0.05)

Note. N = 455 (Seoul); N = 264 (Busan “Demand equation I has the household “See footnote c, Table 3, for definitions

0.22 (0.09) 0.2 1 (0.09)

and Daegu); N = 2 16 (all as the unit of observation. of parameters.

other

0.007 (0.07) 0.05 (0.07) cities

except

above).

used. The estimates of E,, using Y, and r,, our two proxies for permanent income, are about three times larger than the estimates of ey using Y,. The findings of this analysis of Korean housing demand are remarkably similar to those of several recent studies of the income elasticity of the demand for housing in the United States (Carliner [2], Roistacher [21], Mayo [IO], Polinsky [17], Smith and Campbell [23], and Lee and Kong [8]. The behavior of the alternative income elasticity estimates obtained when the three different income variables are used is quite consistent with the theoretical expectation that estimates of zy using proxies for permanent income are larger than those using current income.” ‘*In fact the ratio of one to three found for Korea is much larger than the U.S. and U.K. findings, where “on the average, the permanent income elasticity estimates of these studies are 50% higher than the current income elasticity estimates,” as summed by Polinsky [17]. One likely reason for the wider gap is that the Korean current income figure which had to be used is the income figure for the month of July 1976 reported in the diaries collected in the regular income and expenditures survey. A monthly income averaged over a 12-month period would have been much better but could not be estimated. The low elasticity baaed on current income may be attributed to this problem.

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(b) Estimates for theprice elasticity (er). The estimates obtained indicate that the Korean housing demand for housing is price inelastic. For the nationwide samples (Tables 3, 4, and 5) the estimates range from -0.28 to + 0.10.13 The two cases of positive elasticity estimates arise when grouped data are used, and in these two cases the standard errors are large enough to find the two estimates not significantly greater than zero. In his 1977 paper Polinsky [ 17, p. 51 also finds that the use of grouped prices will bias the price elasticity toward zero. It must also be noted that, in the rest of the equations estimated, the price elasticities have larger absolute values when the better proxy for permanent income is used. On the basis of the estimates obtained using nationwide samples of individual households, the price elasticity of the Korean housing demand is within the range - 0.20 to - 0.30. This is consistent with the sketchy evidence already presented on the value of E,, for Korean households [12], and the fact that the estimates are in agreement with the most precise study to date of the value of c,-the Experimental Housing Allowance Program [lo]. (c) Estimates of household size elasticity (cHS). The coefficient of HS is the elasticity of rental expenditures with respect to household size. This number, l HS, is estimated to be 0.36 and 0.14 when the income measures are Y, and Y,, respectively, and the household is the unit of observation. The estimates are at least three times the value of their standard error. When the average by Dong is the unit of measurement, cHS is not significantly different from zero. This is probably due to the fact that average household size does not vary much by Dong. The estimates of the coefficient of HS are consistent with the hypothesis that the demand for housing is relatively insensitive to the size of the household. This suggests that the urban demand for housing will not be much affected by future changes in average family size. (d) Elastic@ with respect to transportation expenditures (CT). Estimates of the coefficient of T, the travel time to the center of the city, indicate that the elasticity of the demand for housing with respect to T is quite small: eT is less than 0.06. The precision of the estimates is very difficult to assess. On the one hand, most of them have the correct sign. On the other hand one would expect their magnitude to be closer in absolute value to the coefficient of income. The fact that the estimates of ey are typically four or more times larger than the estimates of eT suggests that the proxy for transportation expenditure is picking up only part of the households’ transportation expenditures, that is, the out-of-pocket travel expenses which are directly a function of travel time. The other component of transportation expenditures, the opportunity cost of travel time, is a function of income, not ‘%hen reading the regression results price variablep is equal to 1 + cP.

one must keep

in mind

that the coefficient

of the

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distance. So, if more precise estimates of eT are to be had, Korean estimates of the value of travel time must be made first. (e) Assessment of the overall equation. The demand equations using the national groups are very good. This assertion is supported by three kinds of evidence. First, the signs of the estimated coefficients are as expected, and the estimates are almost always significant. Second, the explanatory power of the estimated equations is reasonable for equations estimated using a cross section of households as a data base. The values of R ’ for the full equation range from 0.25 to 0.48 with the household as the unit of observation. R2 reaches a high of 0.56 when the average by Dong is the unit of observation and F2 is the income measure. Third, the explanatory power and overall pattern of results are fully consistent with and quite comparable to those obtained in other studies of the demand for housing which used household data. V.2. Analysis of Special Subsamples Additional demand equations have been estimated for groups of households which may be of interest to policymakers. The comparison of the results across groups also sheds light on the assumption that the parameters are constant across groups as implicitly assumed in the national group estimates. (a) Owners us renters. Table 6 presents estimates of the household demand for owners and renters. The results indicate that the income elasticity is larger for owners than for renters: ey is estimated to be 0.62 (0.21) for owners when Y, (Y,) is the income measure while ey is estimated to be only 0.42 (0.12) for renters. The differences in the estimates are statistically significant, and are consistent with those reported by DeLeeuw for owners vs renters in the United States [4]. A comparison of the estimates of the other coefficients suggests that the behavior of the two groups is similar in some respects but not in others. The price elasticity estimates are quite similar for both groups. The renters are slightly more responsive to household size, but less responsive to transportation expenditures. Finally, consider the estimates of zy for renters and owners with the national estimates of group 2. Comparable national estimates, of ey are 0.17 and 0.58. Both are about midway between the estimates of e,, for owners and renters. What is more important to note is that the results obtained using these subgroups are quite consistent with the contention that E,, is less than unity. (b) Low us high-income householdv (Table 7). National estimates of ey and zP are important for some government policy considerations, but at other times policy development is aimed at the lower-income households. By separating group 2 by income at the mean sample income more precise

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estimates of 4 and ep for low-income households may be obtained. The estimates indicate that low-income households are much less responsive to changes in current income than high-income households (Y,), but not significantly different in their response to changes in consumption expenditures, the proxy for permanent income (Y,). Since the estimates of ey are biased downward when current income is used as an income measure, the estimate of 0.02 for zy obtained with Y, is probably much too low. The alternative estimates of ey with consumption expenditures as the permanent income proxy are probably a better guide to the responses of low- and high-income households. The conclusion is that Korean low-income households behave essentially in the same way as the higher-income households. Price elasticity estimates are also quite similar. In fact, the estimates of er, with Y, as the income measure are identical (-0.27). The similarity continues upon comparison of the estimates of eHS and ET. On the whole then, the fact that the estimates for the two subgroups are not significantly different from each other and are not much different from the national estimates suggests that little is lost by pooling households with different incomes. (c) Grouping by size of cities. Three subgroups of group 2 have also been analyzed separately. They are Seoul by itself, Busan and Daegu, and the other remaining cities in the sample. Seoul is the largest city in Korea, (over 7 million residents), Busan and Daegu are the next two largest (over 1 million), and the third group of cities range from about 100,000 to slightly over a million. Estimating separate demand equations for these three subgroups provides some insight into the potential significance of city size for the demand for housing. Statistically significant differences arise among the estimates presented in Table 8. The income elasticity estimates (with Y, as the income measure) is largest in Busan-Daegu (0.68) and smallest in the group of smaller cities (0.37). The differences do not conform, however, to a simple positive relationship with city size, nor do they reverse the earlier claim that demand is income inelastic. The most significant differences concern the estimates of cr. The estimates for Seoul (-0.12 and -0.17) are quite consistent with the national estimates. Estimates of ep for Busan-Daegu and the smaller cities are, however, three to four times smaller (algebraically). VI. CONCLUSIONS Given the significant number of countries found within the income range between Korea’s income at the time of the survey ($710 in 1976) and the U.S. figure ($7870) the finding that these two countries have strikingly comparable demand elasticities is of major importance. Lacking good local

HOUSING

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estimates of the price and income elasticities of the demand for housing policy analyses in other countries, one should rely on income elasticity estimates smaller than one and on price elasticity estimates also smaller than unity in absolute value. The best point estimate is 0.6 given that the estimates obtained with the two proxies for permanent income are the most precise. While further refinements of the findings presented in this paper are desirable, these first results give reason for optimism in that available econometric models developed for the analysis of cities in the United States could perform well in Korea and other countries. REFERENCES 1. J. Campbell and B. Smith, The demand for housing: A new look at urban empiricism, Paper presented at the Western Economic Association Meeting (1976). 2. G. Carliner, Income elasticity of housing demand, Reu. Econ. Statist., 55,528-532 (1973). 3. F. DeLeeuw, The demand for housing: A review of cross-section evidence, Amer. Bon. Reu. (February 1971). 4. F. DeLeeuw and N. F. Ekanem, The supply of rental housing, Amer. Econ. Rev., 61, 814-826 (1971). 5. W. Doebele and M. C. Hwang, Land policies in Korea: With special reference to land development, mimeo (1977). 6. M. Friedman, “A Theory of the Consumption Function,” Princeton Univ. Press, Princeton N.J. (1957). 7. T. H. Lee, Housing and permanent income: Tests based on a three-year reinterview survey, Rev. Econ. Statist., 480-490 (November 1968). 8. T. H. Lee and C. M. Kong, Elasticities of housing demand Southern Econ. J., 44, 298-305 (1977). 9. G. Lim, J. Follain, and B. Renaud, The determinants of home ownership and rental arrangements in Korea, manuscript. 10. S. Mayo, “Housing Allowance Demand Experiment, Housing Expenditures and Quality,” Part 1, “Report on Housing Expenditures under a Percent of Rent Allowance,” Chap. 4, Abt Assoc., Boston (January 1977). 11. E. S. Mills, An aggregative model of resource allocation in a metropolitan area, Amer. Econ. Rev., 197-210 (May 1967). 12. E. S. Mills and B. N. Song, “Korea’s Urbanization and Urban Problems 1945-1975,” K.D.I. Working Paper No. 7701, Korea Development Institute, Seoul (1977). 13. R. Muth, “Cities and Housing,” Univ. of Chicago Press, Chicago (1%9). 14. R. Muth, Demand for non-farm housing, in ‘“The Demand for Durable Goods” (A. Harberger, Ed.), pp. 29-99, University of Chicago Press, Chicago (1960). 15. R. Muth, The derived demand for a productive factor and the industry supply curve, Oxford Econ. Pq. (July 1964). 16. R. Muth, Derived demand for urban residential land, Urixm Studies, 8, 243-254 (1971). 17. A. M. Polinsky, Demand for housing: A study in specification and grouping, Econometrica, 45, 447-461 (1977). 18. A. M. Polinsky and D. T. Ellwood, “An Empirical Reconciliation of Micro and Grouped Estimates of the Demand for Housing.” Discussion Paper 567, Harvard Institute of Economic Research (August 1977). 19. B. Renaud, “Economic Fluctuations and Speed of Urbanization: A Case Study of Korea, 1955-1975,” World Bank Staff Working Paper No. 270 (November 1977). 20. B. Renaud, G. Lim, and J. Follain, “Housing in Korea,” to appear. 21. A. Roistacher, Short-run housing responses to changes in income, Amer. Econ. Reo. Pq.

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Proc., 381-387 (February 1977). 22. P. Rydeil, Measuring the supply response to housing allowance, Rand Pq. Ser. (January 1976). 23. B. Smith and J. M. Campbell, Jr., Aggregation bias and the demand for housing, Paper presented at the Econometric Society (December 1976). 24. M. Strasheim, Estimation of the demand for urban housing services from household interview data, Rev. Econ. Statist., 55, l-8 (1973). 25. H. ‘Ileil, “Princigks of Econometrics” Wiley, New York (1971).