CROSS SECTION ANALYSIS OF DEMAND FDR HOUSING IN VENEZUELA Thesis for the Degree of Ph. D. ‘ " MICHEGAN STATE UNIVERSITY - * EMEL om; HERBOLLHEIMER’ 1972 I 7- ‘zi " L1 ()1‘. [651" if t buitqhgdn State 115 uvcrsi ty 6 " i '. N~*-"--~--~ - -.»~ -".m{ I. t - ~~4III|1III3IIIIIIIIIII 1007 ANALYST‘: LE “fl" '\3 ‘.L’"~ 2’.“ "it. Lin . A} ‘. 'Wnn 11 I-o-V-I r, _. a: ‘0‘ "' .mly’.‘ use 1973" ‘ . doacr-,nw_r i A “I III p79 ;cr?,‘- I: v accounsa SA nflfl“r”w :u ~1'ar Been-1.53; 1W - (my macaw-.13 gram: - ~~ r :.tx_y,:-;, “a; ”a; i -,.poh1an. ”nt'v‘an'fi’: Islxa; : has rt: “nursing my . I _‘ tljbieh 3“.:.a.‘..1;,', {Aye-I1 ’n‘r 3hr: Last, :32; “,3 W “Mulw <6 “m housfins. market an m: “ ‘ I.” oar-1: laws. 2M 77mins pram-en. sucr- as tam-9W by A I . IAIN-mat. mantr- nmI Minty. at new Imwlodgc ”.mw‘w may ‘ . .g . , ~ ‘V. -_':‘ '~" -—~‘- : »:-.-‘..~_w. ”‘3'Wflflwmpmm this My 1: he mama; to mm I ABSTRACT CROSS-SECTION ANALYSIS OF DEMAND FOR HOUSING IN VENEZUELA BY Emil Otto Herbolzheimer Despite the proliferation of literature on housing in Iatin America in recent years, very little work has been done at the analytical level. Host of the writing has consisted of either descriptions of housing policies and programs or estimates and projections of housing needs and deficits. This course is understandable. Partly for political and partly for economic reasons, most Latin American govern- ments have only recently shown a genuine interest in solving the housing problem. Moreover. reliable data on housing was simply not available. Finally, even for the most developed nations, rigorous theories of the housing market did not appear until the early 19608. Ambitious housing programs. such as those envisioned by the Venezuelan government, require extensive knowledge of the housing market. This knowledge can only be obtained by building a comprehensive housing model which is a very com- plex task. The purpose of this study is to contribute to the demand side of the model. The major part of the thesis tests three Emil Otto Herbolzheimer hypotheses that relate a string of socio-economic variables to the demand for housing. Statistically, which are the most significant variables and what are the values of their para— meters? Of particular interest is whether housing is a normal, a luxury or an inferior good, i.e., whether the income elasticity of the demand for housing is equal to, more than, or less than one. There are several by-products in the study which may prove to be of more practical help for the policy-maker. In Chapter IV I determined total and direct (on site) employment created by the construction of a housing unit according to type, structural area and location of the housing unit. Chap- ter II is devoted to a review of the present stock of housing, the nature and growth of the mortgage market, the foreseeable bottlenecks in the construction industry and a critique of land use. Finally, some of the tastes and preferences ex- pressed by heads of households with respect to type, location and expenditure on housing are tabulated. The econometric model is based on multiple regression equations in four different functional forms. The data is cross-sectional and was drawn from four different sources. Three of these were household surveys taken during 1967 and 1970 in urban areas. The main portion of the analysis is based on one survey which covered nearly 90,000 households in 86 cities throughout Venezuela. The fourth source of data consisted of information collected from the application forms '3. o.» ‘ - | '0“ . I Emil Otto Herbolzheimer of 3,290 applicants for mortgage loans in 22 savings and loan association offices during 1970. As expected, the results show that income is by far the most important determinant of the demand for housing. The value of the income elasticity varies drastically, however, depending on the method used to measure income and the income range under consideration. The elasticity is consistently higher for the middle income ranges, when attempts are made to approximate permanent, as compared with current income, or when income is adjusted to reflect downpayments. Holding income constant, the only other consistently significant variable is the level of education. Age of head of house- , hold is sometimes important. The differences among cities are minor as compared with differences among income groups and urban sectors. One fac- tor which becomes apparent is the extent to which the housing market is segmented. Any serious housing analyst should avoid lumping public housing or squatter settlements together with conventional housing. Institutional practices, in par- ticular as they refer to credit terms, also play an important role in the housing market. Some regressions with low coefficients of determination may serve as a reminder of the complexity of social phenomena and of the inadequacy of data. CROSS-SECTION ANALYSIS OF DEMAND FOR HOUSING IN VENEZUELA By Emil Otto Herbolzheimer A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Economics 1972 He ACKNOWLEDGEMENTS This study was undertaken under the constant guidance and advice of Dr. Paul Strassmann, to whom I am most deeply - V indebted. Without his help, the completion of this study would have been most difficult. .'v The collection of data would not have been possible without the financial assistance of the Institute for Inter- national Business and Economic Development Studies at Michigan State University, the Latin American Teaching Fellowship Pro- ngram of Fletcher School of Law and Diplomacy and Union Carbide . I ‘of Venezuela. ' 7 My most sincere appreciation goes to the Venezuelan ranthorities as well as private individuals who made their data accessible to me, in particular, Messrs. Tulio Pinedo ,. and Angel Buenafio of the National Savings and Loan Associa- ; r ‘7‘,“ V ' 4. :0 ’itions Bank, Mrs. Johanna G. de Iopez of the Corporacion ~1 Vin6201ana de Guayana and Mr. Luis Alberto Suarez of the 'afiflhtro de Planificacion at the University of Carabobo. " in SurVival through the enormous amount of computation ’Caaee possible by Mr. Alan Filipski at Michigan State iguity and by Mr. Jorge Ormefio at the Central University i C 211813 e 11 The encouragement received from my wife, Kathy, in times of progress and occasional despair and the time and effort she contributed towards the completion of this study are immeasurable. 111 TABLE OF CONTENTS Page ACKNOWLEDGEMENTS . . . . . . . . . . . . . . . . . . . 11 LIST OF TABLES . . . . . . . . . . . . . . . . . . . . V11 LIST OF FIGURES.-. . . . . . . . . . . . . . . . . . . xii Chapter I INTRODUCTION . . . . . . . . BEOkground I I I I I I I Purpose of the Study . . . Statistical Sources. . . . I. MERCAVI (study of rea ema d for h0u81ng) I I I I I I I I II. National Savings and Ioan.Asso- ciation Bank (B. N.A. P. ). . . . . . . III. University of Carabobo (U. C. ). . . . . IV. Corporacion Venezolana de Guayana (C.V.G.) I I I I I I I I I I I 10 Data and Chapter Arrangement . . . . . . . . 11 Review of Literature on Housing in Vbnezuela I I I I I I I I I I I I I 15 l n I QI II I References to Chapter I. . . . . . . . . . . 19 II HOUSING AND RELATED FACTORS IN VENEZUELA s e e 21 General Overview I I I I I I I I I I I I I I 21 urbanization I I I I I I I I I I I I I I I I 22 Housing conditions I I I I I I I I I I I 23 Financial Mortgage Markets . . . . . . . . . 25 Institutions Geared to Housing . . . . . . . 30 I. Banco Obrero (public). I I I I I I I I 31 II. Savings and Loan Associations (mixed). I I I I I I I I I 3“ III. Mortgage Banks (private) . . . . . . s 37 The Construction Industry. . . . . . . . . . #0 Land value I I I I I I I I I I I I I I I I I “7 Summary. . . . . . . . . . . . . . #9 References to Chapter II I I I I I I I I I I 51 III THE MODEL. I I I I I I I I I I I I I I I I 55 Meth0d0108yI I I I I I I I I I I I I I I I 55 Description of variables and Their Functional RelationShips I I I I I I I I I 56 iv Chapter Page summBTYI I I I I I I I I I I I I 66 References to Chapter. IIII I I I I I I I I I 70 IV DEMAND FOR HOUSING: ANALYSIS OF NEW no MGAGO as U O O I I O C I C O O O o G I 7 2 Sample Characteristics . . . . . . . . . . . 72 Data Arrangement . . . . . . . . . 74 Housing Demand with Respect to: 1; Income (Y). I I I I I I I I I I I I I I 75 A86 (A )0 I I I I I I I I I 82 )HousehOId 8126 .(N). I I I I I I I I I I 8“ 2; Sex (3) I I I I I I I I I I 86 Credit Terms. (D): (P). (I). I I I I I I 88 6; occupation (0)I I I I I I I I I I I I I 92 Other variables I I I I I I I I I I 92 The Employment Multiplier (E). . . . . . . . 97 ry. I I I I I I I I I I I 100 References to .Chapter. IV I I I I I I I I I I 104 V DEMAND FOB HOUSING! ANALYSIS OF RENTERS AND MOBTGAGED OWNERS I I I I I I I I I I I I 105 Data Composition I I I I I I I I I 107 Housing Demand With Respect to: 1) Income (I). I I I I I I I I I I I I I I 111 2) Education (E) I I I I I I I I I I I I I 122 B) HousehOId 3128 (N). I I I I I I I I I I 123 ) Age Of Head (A) I I I I I I I I I I I I 125 5) Sex (3) I I I I I I I I I I I I I I 129 6) HOHSOhOld Type (H). I I I I I I I I I I 130 7) Income Earners (E!) I I I I I I I I I I 132 8) Rooms (R) I I I I I I I I I I I I I I I 133 Preferences. I I I I I I I I I I I I I I I I 133 Summary. I I I I I I I I I I I I I I I I I I 1 9 References to Chapter V. . . . . . . . . . . 1 3 v1 SUMMARY AND CONCLUSIONS. . . . . . . . . . . . 11m IntrOduOtion I I I I I I I I I I I I I I I I 1h“ The MOdOlI I I I I I I I I I I I I I I I I I 1u6 The BesultBI I I I I I I I I I I I I I I I I 1&8 } Hypothesis I I I I I I I I I I I I I I I Ins Hypotheses II and IIII I I I I I I I I I I I 150 concluSIOn I I I I I I I I I I I I I I I I I 152 APPENDICES Appendix A On Squatter Settlements. . . . . 15h Appendi! B Imputed Weights of Construction Materials Used and Available Public Services for I Each Housing Unit. I I I I I I I I I I I 158 v Page Additional Regression Results. . . 160 Questionnaire Forms Used in Surveys. . 171 1 HBRCAVI. . . . 2 national Savings and Loan °Aseo- ciation 311k I I I I I I I I University of Carabobo . . Corporeoicn venezclena de Guayena. _v1 Table 11-1 11-2 II-3 11-h II-5 II-6 11-7 11-8 II-9 II-lO IV-l LIST OF TABLES Page Mortgage Loans for the Purchase of Structures by Financial Institutions . . . . . 28 Percentage Distribution of Urban Mortgages According to Amortization Periods Between 1959 and 1968I I I I I I I I I I I I I 29 Average Aggregate Mortgage loan Interest Rates I I I I I I I I I I I I I I I I 29 Nominal and Effective Interest Rates by Mortgage Lending Institutions in 1967 . . . 30 Housing Units Built and Mortgage Credits Granted by the Banco 0brero Since 1928 . . . . 31 Total Savings Accumulated and Loans Given by the Savings and Loan Associ- ations Since 1962. I I I I I I I I I I I I I I 3” Financial Composition of the Savings and Loan System in Percentages by Origin of Funds from 1963 to 1970 I I I I I I I I I I 35 Total Amount of Bonds Issued and Mortgage loans Given by Mortgage Banks Since 1959 . . . 39 Housing Units Built By the Private Sector Between 1965 and 1968 . . . . . . . . . #2 Apparent Consumption of Construction Materials Between 1961 and 196?. . . . . . . . #6 Purpose of the Mortgage Loan and Tenure at the Time of Application of Mortgagor . . . . . 7“ Table of Averages of Apartment Mortgages: Credit Terms and other Characteristics by Income Group and City I I I I I I I I I I I 76 vii Table IV-3 IV-6 IV-7 IV-10 IV-ll IV-12 IV-13 IV-14 Table of Averages of Home Mortgages: Credit Terms and Other Characteristics by Income Group and City. I I I I I I I I I I I I I I I Means of Share of Income Spent on Housing and Ratios of Downpayment to Total Value by Type of Dwelling, Income Group and City. . . . . . Table of Regression Results With (M) and (V) as Dependent variables and (Y) as Independent variables for Total Sample, Income Groups and Cities by Type Of Dwelling. I I I I I I I I I Table of Averages of Income, House value and Household Size According to Age Groups by Type Of Dwellinge I I I I I I I I I I I I I I Table of Regression Results with (v) as Depen- dent variable and (Y), (N), (S), (A) and (o) as Independent variables by Type of Dwelling for Caracas I I I I I I I I I I I I I I I I I Table of Regression Results with (M) as De endent variable and (Y), (L/V), (P), (N? and (S) as Independent variables by Type of Dwelling. I I I I I I I I I I I I I I Distribution in Percentages of Mortgage Applicants by Sex According to Age and Incomeo I I I I I I I I I I I I I I I I I Origin of Downpayment Fund Resources According to Size of Downpayment. . . . . . . Table of Regression Results with (v) as Dependent variable and (Y) and Terms of Credit as Independent variables for Caracas and Other cities (Combined) . . . . . . . . . Table of Regression Results with (S?) as Dependent variable and (V) and (Y ) as Independent variables for Homes by Income Group and Citye I I I I I I I I I I I I I I I Ratios of Means of Housing Characteristics by Income Groups, Cities and Type of Dwelling. . Table of Regression Results with (a) as Depen- dent variable and (N) as Independent variable for Different Home values by Type of Dwelling viii Page 77 78 80 83 85 87 88 89 91 93 95 96 Table Iv-15 IV-16 V-l v-6 V-7 v-16 V-17 V-ZO Total and Direct Employment Creation in the Construction of Housing Units by Type Of Dwelling I I I I I I I I I I I I I I I I I Employment Multipliers (E' per 38.10“) for Type of Housing by Income Group and Cities. . Percentage of Adequate Housing by Urban Zone, City and Tenure I I I I I I I I I I I I I I I Percentage of Slum Dwellings (Zone 1) With Inadequate Services According to Type of Service and City. I I I I I I I I I I I I I I Average Housing Expenditure (X) and Household Income (Y) by City for 19 Cities. I I I I I I Table of Regression Results With (X) as Depen- dent variable and (Y) as Independent variable for Average values of Cities by Income Level and zone C C C C O U I O C O O C O I O I O I C Table of Averages on Percentage of Income Spent on Housing and Household Size for U.C. Sample by Income Range I I I I I I I I I I I I I I I Table of Regression Results with (X) as Depen- dent variable and (Y) as Independent variable in Double Logarithmic and Quadratic Form for Four Cities I I I I I I I I I I I I I I I Table of Regression Results with (X) as Dependent variable and (Y) or (TEXP) as Independent variable for Ciudad Guayana and Valencia by Dwelling Type I I I I I I I I I I Table of Averages of (X), (Y), (T EXP) and (X/Y,T EXP) with Increases in (N) . . . . . . Table of Regression Results with (X) as Dependent variable and (N) as Independent variable in Three Functional Forms for Four Cities by Tenure I I I I I I I I I I I I Table of Averages of (X), (Y), (T EXP), (X/Y,T EXP) and (N) for Different Age Groups by CitYI I I I I I I I I I I I I I I I ix Page 99 101 109 111 114 116 117 119 121 12h 126 127 Table V-21 V-22 V—23 V-25 V—26 V-27 V-28 v—8 VEIO V-11 Table of Regression Results with (X) as Dependent variable and (A) as Independent variable in Three Functional Forms for Four Cities and Constant (Y) and (N) . . . . . . . . Percentage of Female Household Heads by UrbanZoneandC1ty............o. A) Households According to Number of Principal and Additional Families Living Together in Percentage by City. . . . . . . . . B) Distribution of Additional Families According to Income Ranges in Percentage for Caracas. . . Regression Table with (R) as Dependent variable and (Y) as Independent variable in Two Functional Forms for Three Cities by Zone with Constant Household Size (“-6). . . . . . . Opinion of Household Head by City (UrbanZonOSZ-S)..c...o...ooug. Opinion of Household Head by City (Urban Zones 2-5) and Income Range I I I I I I I I I I Opinion of Household Head by City and TypeOfDWellinSIIIIIIIIIIIIIIII Table of Regression Results with (X) as De endent variable and (Y), (E), (S), (N), (A) and (H) as Independent variables for Caracas by Zone and Tenure. . . . . . . . . . . Table of Regression Results with (X) as De endent variable and (Y), (E), (S), (N), (A) and (H) as Independent variables for Valencia by Zone and Tenure I I I I I I I I I I Table of Regression Results with (X) as De endent variable and (Y), (E), (S), (N), (A3 and (H) as Independent Variables for Barquisimeto by Zone and Tenure . . . . . . . . Table of Regression Results with (X) as De endent variable and (Y), (E), (S), (N), (A) and (H) as Independent variables for Ciudad Guayana by Zone and Tenure . . . . . . . x Page 128 129 131 131 13h 135 136 137 160 161 162 163 Table v-12 V-13 V—lh V-15 v—18 v-19 v-l9a v—zh Table of Regression Results with (X) as De endent Variable and (Y), (E), (S) and (H) as Independent variables for Caracas by Zone and Tenure. . . . . . . . . . . . . . Table of Regression Results with (X) as De endent Variable and (Y), (E), (S) and (a) as Independent Variables for Valencia byZoneandTenure....o......... Table of Regression Results with (X) as ‘ ndent Variable and (Y), (E), (S) and (H as Independent variables for Barquisimeto byZoneandTenure....I....o.... Table of Regression Results with (X) as Do endent variable and (Y), (E), (S) and (H) as Independent variables for Ciudad Guayana by Zone and Tenure. . . . . . . . . . Table of Regression Results with (X) as Dependent variable and (Y), (A) and (N) as Independent variables by Tenure and Type of Dwelling with U.C. Data . . . . . . . Table of Regression Results with (X) as Dependent variable and (N) as Independent variable by Type of Dwelling and in Three Functional Forms With CIVIGI mta I I I I I I Table of Regression Results with (X) as Dependent variable and (A) as Independent variable in Three Functional Forms with Constant (N) and (I) With CIVIGI mta I I I I Table of Averages(with Constant Income Bs.1500-2000) of Share of Income Spent on Housing According to Number of Income Contributors by Tenure for Caracas and CdIGuayanaIIIIIIIIIIOIOIIII x1 Page 16k 165 166 167 168 169 169 170 LIST OF FIGURES Page . H Product versus Gross Construction "‘}'."mau°tIIIIIIIIIIIIIIIIIIIO#3 . . 2%? \\ I. - ,_ v ‘P "- ' v ' L 4?!a_:J ,; 7‘. . ., I .. ‘. . ‘, fawfigyva _ . .‘r , 3" 1 1. '2 ' v k ‘\ - I“ . 'I 9’ 111 CHAPTER I INTRODUCTION Bac round Most countries face housing shortages. This problem is shared equally by capitalist and socialist, developed and developing nations, although it differs widely in its intensity and causes. In most developed countries the housing deficit is basically a physical problem caused by market imperfections, mainly on the supply side. The solution is relatively easy through better financing, an increase in the capacity of the construction sector and cheaper construction methods. The problem faced by most capitalistic developing nations, and in particular, Latin America, however, is of a different nature. The lack of housing is directly related to the lack of development or lack of balanced growth. The housing problem is only a physical expression of all other ills. The massive and accelerated construction of new housing units will not solve the problem unless it is coupled with the implementation of programs that are direc- ted towards changes in the structure of the economy itself. Torrealba lists four structural factors which are related to the housing situation common to most of Latin America: [1] a a) ”incompatibility between income levels and cost of housing b) structural limitations in the financing and construction of large number of housing units 0) the phenomenon of accelerated urbanization d) sooio-cultural and administrative-political limitations” Even if considered strictly from the point of view of a quantitative deficit, as is conventionally done, the problem is staggering. The United Nations estimated the housing needs for 1970 in Latin America, including replace- ment, to be approximately 52 million units, "based on the assumption that no percent of the urban population and 50 percent of the rural population now live in bad housing."[2] Despite the rough approximation of this and similar estimates, they nevertheless point at the dramatic situation. To simply keep the deficit stationary under present rates of urban- ization and population growth, a group of experts estimated that Latin America would need to build 10 units per 1,000 inhabitants yearly, which is far above the level of the actual construction capacity and would require 10 percent of the GNP.[3] aEach of these points is discussed in Chapter II as they relate to venezuela. Until recently the interest of the government and private institutions in housing was very limited. Central mortgage banks had been formed in a few countries mainly to pacify, by their sporadic action, social and political pres- sures. It was only after the mid-19508 that housing began to receive the attention it deserved through the creation of national housing institutes and savings and loan asso- ciations. Some countries went so far as to create ministries of housing. More recently, housing construction has been spurred by foreign credit mainly from AID. These loans usually included clauses stipulating matching funds from local sources. The main approach used by national govern- ments in increasing the supply of housing has been through direct construction and the provision of incentives to the private sector in the form of +ax exemptions and guarantees. Despite all of these efforts, the housing deficit has increased and many programs have failed. Part of the problem stems from the "low capacity resulting from shortage of domestic capital and the characteristic bureaucratization and political interference by which the institutions are often trammeled."[#]. In addition, all too often the primary reason for establishing housing programs was political or social rather than economic. It is not surprising that one of President Caldera's main points in his 1968 campaign plat— form was "100,000 housing units per year," which only mater- ialized in 40,000 the ensuing yenr.[5] Or that among the A various reasons given for building the famous high-rise low-cost apartments in the 19508 a highly plausible one was that "the image-conscious dictator thought that their ranches spoiled the new look of the capital...so he bull- dozed them off their sites."[6] Purpose of the Study A better knowledge of the housing market was needed for the new programs and policies. Thus, research on housing began in some countries. A vast amount of literature covering many aspects of housing in Latin America has emerged. These writings are basically descriptive. They review and define housing policies and projects. Most of the statistical work limits itself to cross-tabulation analysis from survey infor- mation on housing stock or potential demand for housing.[7] A comprehensive study of the housing market, however, entails analyzing all the economic, demographic and physical factors that determine the supply and demand of housing units. If one examines present stock only, all he derives is deficit estimates. A dynamic analysis requires making projections on the shifts of the demand and supply curves. This is a very complex task. The purpose of this study is to partially contribute to this task by studying the demand for housing in Venezuela. The question I attempt to answer is: What determines the amount that households are willing to spend on housing and what are the characteristics of this demand? The response A will be obtained through means of an econometric model that will determine and weigh the main socio-economic variables that affect housing consumption. The study will be directed towards testing the following hypotheses:b a) Housing is a normal good, i.e., the elasticity of income with respect to expenditure on housing is close to one: b) Urban size and sector, age, sex and downpayment are the most important determinants of housing demand, other than income; and c) All other socio-economic variables are not statis- tically significant. The large sample of data and the diversity of informa- tion available would have allowed for a more comprehensive study of housing demand, such as potential demand estimates and stratified demand projections. Part of the analysis will touch on these points, in particular, in relation to expressed housing preferences. The remainder will be left for further work. With the results obtained in Chapter Iv on new mortgagors, I will determine total and direct (on site) employment created by the construction of a housing unit according to type, structural area and location of the house. bThe rationale for these hypotheses is given in Chapter III. me A Statistical Sources Four sources of data were used in the analysis. Except for the savings and loan associations' data, all the rest stem from household surveys undertaken in Venezuelan urban areas between 1967 and 1970. Since some of the information overlaps I can test with different data to compare the results and test their consistency. Surprisingly, the abundance of data on housing has not prompted other researchers to utilize it more extensively. The sources are: I. MERCAVI (study of real demand for housing) In an effort to quantify and qualify the real and poten— tial demand for housing in Venezuela, the National Housing Committee undertook a housing survey during 1970, unprece- dented in scope and size. The need to obtain reliable infor- mation became imperative because of the proliferation of qualitative and quantitative, but inconsistent housing defi- cit estimates used at public and private policy levels. Most of these estimates were based on either projections or small sample surveys. The sample of MERCAVI's survey covered all cities (86 total) in the country above 10,000 inhabitants. Included were 65 percent of the total population. Nearly 90,000 households were interviewed, stratified by city and type of residential area. The questionnaire consisted of four parts (see Appendix D—1)c. 1) The physical and economic characteristics of the housing unit. \ 2) Family composition and income, 3) Opinion expressed by head of household on tastes and preferences, 4) Migration history of head of household. The information used in the analysis is drawn basically from parts two and three, after a series of transformations. One noteworthy element in the questionnaire is the distinc— tion made between principal and additional households. This distinction reflects the prevalence of the extended family over the nuclear family in Venezuela. Furthermore, house- holds often include persons who are not members of the exten- ded family. A principal household is classified as a group of people, related or not, who lead a common life under the same roof. If within this group there are some members who would prefer to live separately and express a desire to move as soon as the impediments disappear, this group will be con- sidered an additional household. Thus, there may be one principal and three additional households in the same dwelling. Apart from the first section of the questionnaire that relates to the physical aspects of the house, each household is treated separately in the survey. This distinction is par- ticularly important in establishing potential demand, and a also because a household with several members who feel their stay is only temporary may have different expenditure patterns. The survey was satisfactory in general, although some questions could have been improved had the analytical outline A been developed before the questionnaire. 8 II. NATIONAL SAVINGS AND LOAN ASSOCIATION BANK (B.N.A.P.) Established in 1961, the savings and loan system in venezuela has experienced a spectacular growth. By 1970 there were 22 branches in the country and membership had risen to 55,000. These branches are spread across the nation and are under the jurisdiction of the National Savings and Loan Bank which guarantees the loans and savings. In 1970 the National Bank began to build a data bank for its own research purposes. The punched cards that were stored con- tained information related to the financial terms of the loan, physical characteristics of the house and family characteristics of the applicants. Since I felt that the analysis would be enriched by obtaining more information available in the application forms, I collected additional data on: sex, age, and profession of the family head, as well as tenure and expenditure on the dwelling occupied at the time of application (see Appendix D-2). Only accepted applications were included in the sample of 3,290 corres- ponding to 1970. This set of data is without doubt the most reliable of that used in the study, as the information provided in the forms is based on documentation required by the institutions for the loan agreements. III. UNIVERSITY OF CARABOBO (U.c.) In 1969 the University of Carabobo in valencia. in cooperation with the Central Bank, took a survey in valencia and vicinity for the elaboration of a cost of living index. The family budget was divided into: a) food, b) clothing, 0) housing, utilities and household goods, and d) others. In addition, information was gathered on income and family characteristics. Only the data related to housing (c) were used in the analysis (see Appendix D-3). An initial sample was drawn which consisted of 1,500 households. Information was gathered about the families' expenditures on a daily basis during one month. From this sample #23 were chosen. on a stratified basis and on the willingness to cooperate, for the final sample. These selected families were interviewed again for another month. In my study I use the data on the final sample. Some doubts arise as to the accuracy of reported housing expenditure and income. Even though the consistency of the replies was checked by posing the same question in both sur- veys. it is only with respect to actual rent paid that con- sistent answers were obtained. In case of ownership, if the mortgage, if any, had been paid up, the head of the family was asked to assess what would be the amount for which he could rent the dwelling. His estimate was used as the imputed rent value and added to the family income. Most likely these imputed rents will suffer from inflation. When the head was unable to reply, the interviewer imputed the rent directly by applying 1 percent per month of the house value. 10 IV. COBPORACION VENEZOLANA DE GUAYANA (C.V.G.) The Corporacion venezolana de Guayana is a governmental agency responsible for the urban and industrial planification of Ciudad Guayana. Planning is difficult without up-tc-date data on the economic and demographic characteristics of the population living in the city. In order to collect this data, C.V.G. began taking a series of surveys in 1967. These surveys are being taken continuously, every four months, at the household level. In addition to information on household characteristics and employment, the survey also collects data on income and budget expenditure as well as on housing (see Appendix D—h). Similar to the survey by the University of Carabobo, the households selected are interviewed on a daily basis about their daily expenses during one month. The main difference is that in the C.V.G. survey one of the household groups, a control unit, is interviewed every four months to allow for seasonal fluctuations. The initial sample, drawn on a 1/20 scale of all housing units in Ciudad Guayana, consisted of 940 households. These were stratified into four sectors: 1) residential, 2) downtown (old town), 3) transitional (that sector which is in a stage of progressive improvement either by public or private initiative), h) squatter settlements. 11 The final sample used in my analysis, randomly chosen, consisted of 319 households. About one fourth of the sample consists of control units. The information on income and budget expenditures of these control households is based on the average of three interviews (in one year). Unfortunately, all the repeated attempts at obtaining data from private mortgage banks were futile. These banks cater basically to high-income groups. It is interesting to note that some of the questions in these surveys were careful in reflecting the peculiari- ties of Venezuela, such as: "Is the land owned or was it taken by force?". "Are you married or kept7", "Was the dwelling purchased, built by others or by yourself?” Data and Chapter Arrangement The study is limited to urban areas greater than 10,000 inhabitants. The urban areas merit special attention because of the rapid urbanization of Venezuela during the past #0 years. During the last decade alone, while total population grew at an average rate of 3.5 percent per year, that of urban areas was double this rate. This rapid transition ) has caused the rural-urban ratio to be reversed in the period 1920-1969. [8] Since this trend is not expected to change in the near future, where the housing problem will remain ) critical is in the cities. In addition, no survey similar to those available of cities had been taken of rural areas. , I . 9 ‘I - O -—._‘ , 3, 12 Rapid urbanization was not restricted to the capital but has affected cities of all sizes throughout the country. Given the difference in economic activities, politico- administrative position, migration trends and topography, the impact of such a rapid growth in terms of housing, dif- fered widely between cities. Furthermore, the values and attitudes of families towards housing are influenced by their urban experience, how permanently they view their residence, by land accessibility, income levels and others. To test for these differences, four cities were chosen as case studies all of which have experienced rapid growth: 1. Caracas - 2 million inhabitants, narrow valley, EESIEEI of the nation, modern, main activity in services, large concentration of high income, intense sporadic immigration. N I Valencia - 300,000 inhabitants, wide valley, 015 coIonial city, traditional, main center of industrial growth when import-substitution (light industries) impulse began in late '508, gradual absorption of immigrants. 3. Bar uisimetc - 350,000 inhabitants, no space Iimitations, rural outlook, regional center of cattle and farming country, little industry, basically services, heavy steady immigration. 9. Ciudad Gua ana - 150,000 inhabitants, no space Iimitations, new city formed in 1960 as growth pole, heavy industry and mining, no politico- administrative center, modern, largest immigra- tion rates in the nation. The Venezuelan government has focused much attention on Ciudad Guayana as an experiment in economic decentrali- zation. Housing has, however, been one of the major pro- blems in that city. Since other Latin American countries 13 may try to follow the example if it succeeds, it was felt that this case deserves special attention. The character of housing problems differs sharply be- tween social groups. Any housing program needs to recognize these differences and in turn apply alternative approaches. Income seems to be the best social group index since it incor- porates many other non-quantifiable variables. In the analysis the sample is divided frequently into high, middle and low income groups or urban sectors. Unlike some authors who believe that the need is for a policy that would "stimulate low-cost housing directed to the popular groups, and depress luxury housing,” [9] I feel that one should not exclude the other. If the capacity of the construction industry suffices, both should be stimu- lated since both have important economic effects. For this reason I am concerned with all income groups in this study. The housing needs of the high income class have been regularly satisfied, as traditionally the market funds that went into housing tended to be associated with luxury con- struction for the upper classes. The high-middle and the middle income groups have been slowly incorporated into the housing market as well. Their savings potential began to be tapped by the new mortgage banks and savings and loan institutions which offer easier credit terms on mortgage loans. With regard to the urban low-income groups, however, the problem is basically one of economic, social and political A l4 “marginalization."c The housing provided to them has come from direct government intervention or self-construction. Their housing problem demands non-conventional and inno- vative solutions. Furthermore, the family, employment and expenditure characteristics of low-income groups (in partic— ular, expenditure on housing) seem erratic and diversified. Previous surveys have found it difficult to obtain honest and consistent responses, particularly on income.[10] For these reasons, I pay special attention in this study to the households living in squatter settlements. In Appen- dix A of Chapter V, I describe how squatter settlements are formed and their significance in the urbanizing process of cities. The division of the survey samples by certain sec- tors enables us to separate this group. By comparing the results of this group with that of the others, the differences will be ascertained and tested. Not all low-income people live in such squatter settlements nor do all the people living in those areas have low income. Yet, there is gen- erally a direct correlation between low income and living in squatter settlements. There are some problems in defining households and measuring incomes and expenditures in Venezuela. The surveys have,in general, tried to take this into consideration, yet °Marginalization as applied here, and in most of Latin American literature, refers to that economic process whereby a segment of the population (marginal population) is kept outside the realm of organized society. r" 15 there are still measurement errors. This is one of the reasons I am performing the same regressions for two cities with different data sources. Significant differences in the results will cast some doubt on the reliability of the data. Some further data adjustments will be discussed in Chapter III when the model is developed. Chapter II briefly describes the political, social, and economic situation in Venezuela and reviews the housing situ- ation and related aspects. The choice of variables, the application of the model and the interpretation of the results require a full preliminary understanding of the country, and its housing problems. The model presented in Chapter III is tested with data on mortgagors of new housing in Chapter IV, and renters and all mortgagors in Chapter v. The results are discussed in the last chapter (conclusion and summary). Review of Literature on Housing in Venezuela As mentioned above, during the past decade much liter- ature on housing has appeared in Latin America, including Venezuela. Most of this literature stems from papers pre- sented in conventions and seminars. [They relate to housing policies [11] and needs [12], housing financing [13] and mobilization of savings [14], judicial structure of housing [15] or squatter settlements [16]. A large variety of statis- tics have also been obtained from censuses, publications of construction magazines and surveys such as those of the Dance A Obrero and Banco Central. The most important survey was that prepared by a committee appointed by presidential decree in 1964. The committee's recommendations were applied, in part. in the 1965-1968 National Housing Plan.[17] Only a few of all these writings, however, have added insight into an analysis of the housing market. In a study of housing in Caracas, Carlos Acedo Mendoza [18] reviews the major factors which have caused a deterior- ation of housing conditions. These include the price of land and speculation, mal-distribution of income, intensive immi- gration and inadequate supply of funds for housing. He proceeds to study the circumstances which have led to a relative increase of the population living in squatter set- tlements, and arrives at the same conclusion which has been postulated above: the housing problem is twofold. He made a clear distinction between marginal and non-marginal popu- lation, and stressed the need for different solutions to the two groups' housing problems. An evaluation of living conditions in the government- built high-rise apartments indicated how the project failed because it did not recognize the complexity of the problem. [19] Eradicating squatter settlers and placing them in vertical slums is not the solution. Reverting the attention to high-income groups, a sociol- Ogy student, in 1968, wrote a thesis on the characteristics of the mortgage loans of the largest mortgage bank in Caracas.[20] A 1? Using a sample of 510 successful applications, she looks into the purpose of the loan, the residential choice, mobil- ity patterns of the applicants and their socio-economic char- acteristics. Furthermore, she makes a cross-tabulation analysis on the relation between income and loan, income and monthly payments and distribution by age and number of family members. Since our study does not include data on mortgage banks, these results will prove helpful in completing the analysis of housing demand. A much broader study of the mortgage market in Venezuela was that undertaken by the Central Bank in cooperation with AID. [21] Based on two surveys taken in the fourth quarter of 1962 and during 1957, it tries to establish a measure of the mortgage market characteristics. It determines certain aspects of the mortgages processed, the volume of the market and, finally, its distribution. The findings are basic in understanding the present housing situation. The summary states, "Up until 1962, the mortgage market in Venezuela was characterized by a predominant number of short- term (1 to 3 years), high interest (12 percent annual) loans which did not provide for the amor- tization of the principal in fixed periodical payments. The principal suppliers of capital for the mortgage market have been private investors." A more recent study, URVEN, [22] showed that despite the increase in mortgage funds between 1962 and 1965, non- institutionalized mortgage loans, made by private investors, still represented 70 percent of the total. URVEN is the most comprehensive analysis of urbaniza- tion done in venezuela. Volume V is directly concerned with 18 housing. This study provides not only the best summary of such housing aspects as financing and housing services, but it also makes the first attempt at calculating what are the specific credit terms the financial institutions should pro- vide to the different income groups according to living areas. The living areas were divided into urban, intermediate and rural. Using data from the Commission on Urban Development and Housing, they correlated the income distribution of the total population with housing expenditure and derived the following functions: a) urban Ya95 (l-e ) xamonthly -0.01073X housing b) intermediate Y—96 (l—e ) payment -0.0159X 0) rural Ya98 (l-e ) Yapercent accumulated of population according to income Applying these coefficients, the paying capacity for housing of each income group can be determined. The percentage of income spent on housing varies between income groups and areas from 11.25 percent and 25 percent. Although interesting in its appraoch, the study stops short of what it could have accomplished. REFERENCES T0 CHAPTER I Rafael Torrealba, "Diagnostico de las Condiciones Habita— cionales en VeneZuela," Banco Nacional de Ahorro y Prestamos, Caracas, 1969, p. 3 (mimeographed). United Nations, Economic and Social Council, Committee on Housing, Building and Planning, 2nd Session, World Housi Conditions and Estimated Housin Re uirements: Pa er Pre red b the Secretariat (E7C.E7I§), December 4, 19E5, p: gEI United Nations. Economic and Social Council, Economic Commission for Latin America, 10th Session, Provisional Re rt of the Latin American Seminar on Housin Statistics and Programmes (E7CN.127647), FeBruary 1963, p. 27. Ruben D. Utria, "The Housing Problem in Latin America in Relation to Structural Development Factors," Economic Bulletin for Latin America, XI (October 1966), p. 159. "Promesas Incompletas," El Universal, October 13, 1970, p. 2. Talton Ray, The Politics of the Barrios of Venezuela (Berkeley: University of California Press, 1969), . 32. See: Facultad de Ciencias Economicas, Estudio de la Demands de Vivienda en la Ciudad de MedeIIIn (fiedeIIin, CoIomBia: Universidad de Antioquia, 1969): also, Insti- tute de Fomento de Hipotecas Aseguradas, Estudio de la Demands Efectiva de Vivienda en la Ciudad e Gua ema a (GuatemaIa: Instituto de Romento de Hipotecas Asegura- das, 1969). In 1920, 26 percent of the population lived in urban areas (above 5,000 inhabitants): by 1969 it was esti- mated that this percentage was 75 percent. See Minis- terio de Fomento, Venezuela: Indicadores Socio-Economicos 1 68 (Caracas: Ministerio de Fomen o, Direccion Genera e stadisticas y Censos Nacionales, 1968). Ruben D. Utria, E1 Problems de la Vivienda e1 Desarollo de America Latina (Caracas: Rondo EditoriaI Comun, 1969), p. 98. 19 A ‘1 II I Q). AV/ ‘ C ‘v 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 20 CENDES-CIS, Estudio de anflictos y Consensos Serie 12: Habitantes de la Zona de Ranches (Caracas: Universidad Central de Venezuela, 1967), p. 33. Banco Obrero, Politics de Vivienda on Venezuela (Caracas: Banco Obrero, oficina de Programacion y Presupuesto, 1969). Ministerio de Fomento , Estimacion de Necesidades de Vivienda en Venezuela durante el eriodo 9 -19 l (Caracas: Ministerio de Fomento, Departamento General de Estadistioa, 1963). Juan P. Bombino, E1 Financiamiento de la Vivienda Pro is en Venezuela (Cumana, VenezueIa: EditoriaI Universidad de Oriente, 1966). Agency for International Development," Mobilization of National Savings to Increase Home Construction in Vene- zuela" (paper presented at the II Interamerican Congress on Housing, Caracas, February 1969). Tomas Polanco, ”El Regimen Juridico de la Vivienda on Venezuela," ibid. Michael Hamburger, ”The Barrios of Venezuela," Inter- national Affairs, XLIV (December 1968). Ministerio de Obras Publicas, Informs de la Comisicn are e1 Desarollo Urbano 1a Vivienda (Caracas: fiInisEerio de OBras PuBIicas, 1965). Carlos Acedo Mendoza, La Vivienda en el Area Metro 01- itana de Caracas (Caracas: Rondo EHitorIaI Comun, 1969). Eric Carlson, Pro ecto de Evaluacion de los Su erblo ues (Caracas: Banco Obrero, 1939). Maria Garcia velutini, El Financiamiento a le Vivienda 1a Estratificacion Social (Caracas: EditoriaI Arte, 1975). Banco Central de Venezuela, Mercados Financiero de venezuela: Mercado Hi otecario (Caracas: Banco Central de Venezuela, Departamento de Cuentas Nacionales, 1963). CENDES and United Nations, URVEN: Fenomeno de Urbani- zacicn on Venezuela, vol. v, Part IV Caracas: Univer- sIdaH CEntraI de Venezuela, 1968), p. 13. \‘I III“ C II. v‘.‘ CHAPTER II HOUSING AND RELATED FACTORS IN VENEZUELA General Overview Few countries have changed as much as Venezuela has over the past 45 years. Change began with the discovery of oil. 011 soon displaced cocoa and coffee as the country's main export, and led the country into an almost uninterrupted process of economic growth. By the 19603 Venezuela had be- come the main oil exporter in the world. Rapid growth affected society at all levels. Economically, Venezuela has risen from one of the most backward positions in Latin America to the leading one in terms of per capita income and monetary stability. Politi- cally, the traditional pattern of civil power subordinated to military force and prestige has been finally reversed. Since 1959, the country has enjoyed the first three consec- utive presidential terms through peaceful elections. Change was not limited to a few social groups. From colonial times, Venezuelan society lacked the rigid social stratification so prevalent in other Spanish colonies. Eco- nomic growth and good communication networks have further facilitated social mobility. 21 22 "It is evident that in all groups horizontal mobility has been widespread. Yet despite the fact that the majority of the population has moved upward, it did in such a way that their relative ranking has changed very little." [1] The blessings of rapid growth were mixed. Serious malad- Justments deve10ped which were enhanced by the dualistic nature of the production sector.8 This dualism was reflected in the distribution of income and, in particular, in rapid urbani- zation, perhaps the most important phenomenon of the past #5 years. Urbanization Until the 19208, Venezuelan population remained relatively stable. Since then, coinciding with rapid economic growth, it has quadrupled. The population concentrated in urban areas. By 1970, 66.5 percent of the total population was living in areas above 5,000 inhabitants. [3] Rural population remained almost steady. [h] Caracas, the capital, was affected, but so were cities of all sizes. It is interesting to note that migration occurs in stages, so that rural emigrants do not go directly to the largest cities, but pass through the neighboring urban centers first. Caracas receives practically no direct migrants. [5] Despite this rate of urbanization, urban planning was passive and with the exception of those areas where government 8During the period of fastest economic growth, 19h8-58, traditional agriculture, while employing 31 percent of the nation's active population, contributed only 3 percent of the GNP. Petroleum employed only 2 percent of the working force but contributed 29 percent of the GNP. [2] 23 invested directly, such as the Banco Obrero or Centro Simon Bolivar, the cities' growth has been anarchic. [6] The situ— ation has been particularly critical in the area of housing. The few regulatory plans developed were not implemented, either because the municipalities lacked the funds and admin- istrative capacity, or because they did not agree with the centralized planning agency. [7] An exception has been the urban planning agency of the Federal District set up in l96h. This agency unfortunately has Jurisdiction over only half of the city of Caracas. Since urbanization has preceeded sufficient industrial- ization, rates of unemployment and underemployment have been high. Large segments of the population are forced to live outside the realm of the market society, including the housing market 0 Housing Conditions Several groups have tried to assess the housing condi- tions by determining absolute housing ”needs” or "deficits". b All of these esti- They commonly showed enormous deficits. mates suffer from the pitfalls of subjective normative stan- dards as to what constitutes an acceptable housing unit or over- crowding. Most of the standards are adopted from advanced bThe most widely used estimate by politicians, newspapers and professionals is 800,000 units by the late sixties. This figure probably stems from a study by the Ministerio de Fomento which showed a deficit of 69#,000 by 1961 [8] In that year there were l,h62,000 housing units in the country of which 511,000 were slum squatters. 2“ countries and often have little relevance with the economic, social or cultural patterns of Venezuela. The most important bias in the derived estimates is the persistent failure to include some of the squatter settle- ments as part of the housing stock. This becomes apparent in the study made by the Banco Obrero of the change in housing stock between the National Census of 1961 and its survey of 1967. Using standards more in line with Venezuela's needs, the study shows that of all new "acceptable” housing additions, in a sample of nine cities, 67 percent consisted of either new or improved "ranches”. [9] Of all the housing deficit estimates the most reliable and relevant to this study are those of MERCAVI. The defi— nition of total deficit used is the weighted sum of the quali- tative, quantitative, technical and hygenic deficits. The results indicate that 23 percent of all families in cities above 10,000 inhabitants either live in inadequate housing or lack housing. More relevant than the numbers themselves is the distribution of this deficit. Of those families who needed housing, 4h.7 percent had incomes below 38.500 per month and 81.5 percent below Bs.lOOO per month. Only 6.1 percent were in the income brackets above Bs.l,500 per month [10](4.5 Bolivares = $1.00). These results are quite consis- tent with those of URVEN in 1965, which estimated that only 11 percent of those families with income above Bs.l,300 needed housing. [11L As has been shown, the housing problem is to a large extent one of income, but other structural factors 25 make it difficult for large segments of the population, who could otherwise afford housing to satisfy their needs. These factors are: the structure of the financial mortgage markets, the capacity and productivity of the construction industry and the allocation of land. They are analyzed in the rest of the chapter. Financial Mortgage Markets According to a study by the Interamerican world Bank, writing in 1968, "In venezuela, contrary to other Latin American countries, there exists a vigorous and growing mortgage market, basically due to the nation's monetary sta- bility.” [12] This statement refers to recent developments. Mortgage loans have traditionally been the favored form of investment by private investors. The contact between lender and borrower was direct (non-institutional), and this was the mortgage market operation par excellence. Commercial banks also channeled a considerable amount of funds into the mortgage market. By law, they operate on a short-term basis. Thus, until the end of the decade of the 19508, long term mortgage loans were rare, with the exception of those made by insurance companies. They were the first lenders to intro- duce loans on a monthly amortization basis and for maturity periods between 5 to 10 years. [13] Moreover, only a small portion of these mortgage loans was actually used for the purchase or construction of buildings, the reason being that the mortgage market served as a substitute ‘xl he». I“ Mr. 26 for the stock exchange. The few large local companies were usually closed and used internal resources for investment. There was also a general lack of confidence among investors in the stock market. Its legal provisions were limited, in particular as it relates to the rights of small investors. A study by the Banco Central of the mortgages registered by 1957 summarizes the market characteristics [14]: a) 76 percent of all loans had maturity periods of less than three years b) 82 percent of the documents do not contemplate perio- dic amortization payments c) nominal interest rates ranged mainly from 11 to 12 percent d) only 19 percent of the loans were specified as being for the purchase or construction of buildings. Given these financial terms, mortgage loans were out of reach for lower income and most of the middle income groups who wanted to buy a house. The government tried to solve, in part, the problem by creating the Banco Obrero in 1928, which built homes and provided loans to the working class. Yet, the government soon realized the bank's financial limi- tations. In 1957, in an effort to form a more organized and specialized mortgage market directed towards housing, it passed a law creating the mortgage banks. It was not until the mid-19603, however, that the housing mortgage market began to gain momentum. There are two main reasons for this development: [15] a) The substantial change undergone by the Venezuelan economy: 27 When the external sector no longer provided the necessary stimulus for economic develop- ment, it was replaced by the import substitu- tion industry as the leading sector. The growth of such industries required an effec- tive mobilization of internal resources. These new demands for funds led to a remarkable devel- opment of the country's financial structure. The existing financial institutions have adapted to the new demands and new ones have been created. b) The monetary and financial, as well as political stability the country has experienced during the 19608! Private financial institutions flourish only in an atmosphere of confidence. This confidence was provided by the general stability of Venezuela during the last 13 years. Table II-l indicates the growth (net flow) of the housing mortgage market since 1962. [16] Some important trends are shown in Table II-l. Insti- tutional lenders have steadily increased their share in the mortgage market from.33 percent in 1962 to #9 percent in 1970. The Table also shows the decline in the relative importance of the insurance companies and commercial banks which were so important before. These trends also hold true for the nonhousing mortgage market. [171 The share of these mort- gages placed in Caracas has not changed and still constitutes two-thirds of the total of the nation. [18] With respect to amortization, there has been a steady lengthening of the periods, as shown in Table II-2. [l9] Longer amortization periods have been coupled with in- creases in the money interest rates. This is consistent with monetary theory (see Table II-3). [20] It should be stressed, 28 n2. .H 3m So a con om own 3. 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O.“ 88 The main problems that face the construction industry are financial.f There are in turn three aspects to the financial sides a) obtaining the initial capital to form a construction firm (direct financing), b) financing the production itself (working capital - medium or short term), c) financing the purchase of the production by the customers (indirect - long term). Most direct financing capital of construction corpora- tions originates either from the companies' own reinvestment or from stocks or bonds. In the case of small construction entities the capital stems from personal loans. The above two forms of capital supplies have been relatively adequate, but the industry has often suffered from the complementary medium term loans it needs from the banking system as working capital. [88] As we have seen, the mortgage banks and savings and loan associations have intensified their efforts to fill this gap. Yet, as the constructors themselves recognize, it is the indirect financing which has caused the most serious bottle— necks in the industry. The uncertainty as to the marketa- bility of its product has forced them to build high income housing. Since this market reaches a point of saturation quickly, they were unwilling to expand their capacity unduly, fPart of the financial problem is due to the large amounts of government receivables held by construction firms. The government debt amounted to Bs.750 million at the end of 1970. 85 which in turn made cost reduction difficult. Prefabrication methods have had limited application thus far. The minor reductions in cost experienced in other countries has not encouraged their use. Governmental decrees, mentioned above, are directed towards stabilizing the industry, and providing the incen- tives to build homes for the middle and lower middle groups. Following are some of the main decrees: [85] a) Income tax exemption i) 10 years exemption for rental housing units which rent does not exceed Bs.900/month in Caracas or Bs.750/month in the rest of the country. 11) up to 13 years exemption, depending on the finan- cial terms, for housing units whose sale price does not exceed Bs.60,000 without land or B8.80,000 to 100,000 with land. b) Guarantee of investment 1) if a construction company building housing units below Bs.85,000 has not sold a unit after two years, the Banco Obrero will purchase it at 95 percent of its value. ii) if a buyer fails to comply with its payments for six months, the Banco Obrero will pay 95 percent of the remaining payments. 111) if a buyer cannot pay the initial down payment, the Banco Obrero will lend him 15 percent of the house value for this purpose under easy credit terms. It is not likely that the induced increase in housing construction will create serious bottlenecks in the supply of materials. In the case of cement, which is the main material used in home building, Venezuela has been a net exporter and is proud to claim the high investment produc- tivity in this industry. The plumbing industry also has an excess of capacity. The newly expanded steel industry has 86 covered almost all the needs of the construction sector. Wood is imported, by and large. However, it is a minor com- ponent in housing construction itself. Furthermore, the bolivar is freely exchangeable and because of Venezuela's ample supply of foreign exchange, the duties on products not produced nationally are very low, including machinery. The following table indicates the steady increase of local production versus imports in the construction materials industry. [86] TABLE II-10 Apparent Consumption of Construction Materials Between 1961 and 1967 (Millions of Be.) 1961 1962 1963 1968 1965 1966 1967 O Production 8 292 289 353 886 529 589 650 Imports 211 288 258 270 329 289 281 This relative abundance of materials is reflected in the low rates of inflation of construction material costs despite the growth of housing construction. Using 1963 as base year, the index in 1970 was 123. [87] It is the supply of skilled construction labor which may prove to be the main bottleneck to an ambitious housing program. "venezuela has a managerial organization, drawn to a large extent from foreign immigrants, with sufficient technical capacity to undertake exten- sive new programs. It is also an undisputed fact that the industry has a potential capacity to build 87 100,000 houses per year. If there is one area, however, which could cause problems, that is in the supply of skilled construction labor.” [88] The National vocational School (INCE) has made some efforts to remedy the situation with little success for a variety of reasons. Meanwhile, skilled workers continue to receive wages far above those stipulated in the union contracts. Employment creation is a very crucial issue, particularly for venezuela. If the construction sector continues employing an average of two to three man-years per housing unit, a boom in the housing industry could have important economic impli- cations. Land value Land value increases in those areas of rapid urbaniza- tion. The problem becomes particularly acute when there are physical limits to a city's expansion, as in Caracas. There, the average value per square meter has risen from Bs.76/m2 in 1951 to Bs.l8l/m2 in 1959 and up to Bs.250/m2 by 1965. [89] The reason for this increase in land value is complex, but it arises basically from the peculiar characteristics of land as a capital asset, and from the private appropriation of a good whose actual value is determined by the growth of the community. Land is the safest form of investment in an economy where investment opportunities have been limited. Further- more, it is also the most common form of speculation. This speculation has been facililited by the lack of municipal 88 city planning, which did not ensure for the control of the land required by the city's expansion. It is not uncommon for a government's low cost housing project to be frustrated because of the price of land. The response is to build in poorly located areas, such as the new satellite city Caricuao, in Caracas. This new government housing development of 150,000 inhabitants had to be built at a considerable distance from the economic center of activity of its inhabitants. Increases in land value follow a pattern of stages as urbanization progresses. These are: [50] l) Zonigg - the incorporation into the city limits of an area which was "virgin" or used for agriculture 2) Services and Infrastructure - when either through private initiative or public investment an area has been provided with the necessary services and roads for urban development. 3) Rezoning - the change in zoning codes which allow increases in the density of population or permit the use for commercial purposes of a residential areas These stages proceed as the demand for land increases. Yet the fact that the supply is fixed and is mainly in the hands of institutionalized real estate oligopolies distorts the usual laws of supply and demand. These oligopolists, who control the land supply, are able to pursue their unilateral interest by fixing prices and leaving extensive areas of land unused in expectance of future price increases. By 1965 the value of land in Caracas had reached such levels that no matter how far the cost of construction fell, the low middle 89 and low income groups would not have had the opportunity of acquiring housing except in land owned by official agencies. During that year, in areas with a density of 1,000 inhabitants/ hectar, the cost of land was on the average the same or slightly higher than that of the housing structure. In high residential areas in the city with lower densities (200 inha- bitants/hectar) the cost of land surpassed by far that of the structure. [51] To remedy the situation the government has considered taking several steps, already in use in other countries. These are not likely to be implemented, however, because of vested interests in the power structure. a) To modify the "law of public expropriation" and give the official agencies more power to expropriate, with adequate compensation, land which may be used for the public welfare. b) To apply the "surplus value right" whereby the increases in the value of an area of private land due to public investment in the community is appropriated by the government. c) To introduce "progressive land taxation". This tax reduces speculation by penalizing land which is kept idle on a progressive tax basis (according to number of years left unused). Summary Despite steady high rates of growth for 85 years, the Venezuelan economy has not been able to provide adequate housing to a large segment of the population. Rapid urbanization and one of the highest population growth rates in the world have been partially responsible 50 for the housing shortage. The main causes, however, have been the uneven distribution of income and the lack of an insti- tutionalized mortgage market that would provide loans at adequate credit terms. Land speculation has made matters worse. Recent trends have opened the gates towards an improve- ment, although not a solution, of the housing problem. Mort- gage banks and savings and loan associations have been created which have spurred the private housing market. In addition, the public housing agencies have introduced new programs that may prove to be more successful than previous ones. Foreign private capital will complement local funds directed to housing. The capacity of the construction industry in Venezuela is presently adequate for a progressive expansion of housing demand. The only bottleneck could arise in the supply of skilled labor. 1. 2. 8. 9. 10. 11. 12. 13. 18. REFERENCES T0 CHAPTER II Silva Michelena, The Illusion of Democracy in Dependent Nations (Cambridge, Mass.: M.I.T. Press, 1971), pp. 90, Antonio Cordova and Manuel Gardicochea, Inversiones Extranjerasiy Desarollo Econogico: Primers arte (Caracas: Universidad Central de venezuela, Instituto de Investigaciones de la Facultad de Economia, 1966), pp. 88-85- Banco Obrero, ”La Vivienda Como Factor de Desarollo Urbano“ (paper presented at the II Interamerican Con- gress on Reusing, Caracas, February 1969), p. 3. Ibid., p. 6. Chi-Yi-Chen, Movimientos Migratorios en Venezuela (Caracas: Universidad Catolica Andres Belle, Instituto de Investigaciones Economicas, 1968), p. 15?. Ministerio de Obras Publicas, pp, cit. Banco Obrero, "Toma Especial de Informacion: Vene- zuela" (paper presented at the II Interamerican Congress on Housing, Caracas, February 1969), p. 17. Ministerio de Fomento, gp, cit. Banco Obrero, Tema Especial, p. 80. Tulio Pinedo, ”El Problems Habitacional en Venezuela," 'VTvienda 708, No. 3 (May 1971), p. 18. CENIES and United Nations, gp. cit., p. 62. Banco Interamericano de Desarollo, El Mercado de Ca itales en venezuela (Mexico, D.F.: DeItec Pana- merica, l9 , p. . Ibidep p. 600 Banco Central de Venezuela, Mercado Hipotecario, p. 5. 51 52 15. Antonini Bach and Milic Kybal, Capital Markets in Latin America: A General Surve of Six Countries Studies (New York: Praeger Special Studies, Praeger PuElisher, 1970), pp. 187-189. 16. Banco Central de Venezuela, Informe Economico, 1969, Anexo Estadistico, Tabla A-III:3, and 19'0"? ,—Anexo Esta- distico,Tabla A-III-38. 17. Ibid., 1970, Anexo Estadistico, Tabla A-III-8. 18. Ibid., 1970, Anexo Estadistico, Tabla A-III-6. 19. Camera venezolana de la Construccion, Memoria y Cuenta, 2nd semester 1966. P. 6. 20. Banco Central de venezuela, Informe, 1970, Anexo Esta- distico, Tabla A-III-7. 21. Banco Interamericano de Desarollo, pp, cit., p. 81. 22. Banco Obrero, 1928-1968: 80 Afios del Banco Obrero -——-__ (Caracas: Banco Obrero, 1968), p. 182. 23. Jese Matos Mar (ed.), Urbanizacion y Barriadas en Amer- ica del Sur (Lima: Instituto de Estudios Peruanos, : p0 590 28. Pinedo, Problems Hgbitacional, p. 18. 25. Banco Obrero, Politica Vivienda, pp. 3, 7-8. 26. Banco Nacional de Ahorro y Prestamo, Memoria_y_Cuenta, 19709 P. 32. 27. Tulio Pinedo, "Funcion del Banco Nacional de Ahorro y Prestamo en la Asistencia Tecnica a las Entidades de Ahorro y Prestamo,“ (paper presented at the II National Convention of Savings and Loan Associations, Porlamar, venezuela, October 1970), p. 18. 28. Banco Nacional de Ahorro y Prestamo, Memoria, 1970, p. 88. 29. Banco Nacional de Ahorro y Prestamo, "Ley y Normas de Operacion del Sistema Nacional de Ahorro y Prestamo," Caracas, 1966 (pamphlet). 30. Pinedo, Funcion del Banco, p. 18. 31. ”Hacia un Mercado Secundario de Hipotecas de Ahorro y Prestamo,” Construccion (Feb. 1971), p. 92. 53 32. Banco Interamericano de Desarollo, pp, cit., p. 101. 33. Ewart Goodwin and Ralph Grutsch, Infogme Sobre el Mercado de Hipotecas en Venezuela (Caracas: Departamentb de AsesorniSosre la Vivienda, 1965), p. 10. 38. Oscar Garcia velutini, Anotaciones Sobre Empresas Bancarias (Caracas: Banco CentraI Hipotecario,II97l), FITS??— 35. Polanco, pp, gi§,, p. 9. 36. Maria Garcia, gp. gi§., pp. 69, 73. 37. Banco Obrero, Politics Vivienda, p. 86. 38. Banco Central de venezuela, Informe, 1969, Apendice Estadistico, Tabla A-VII-59. 39. Ibid., 1970, Apendice Estadistico, Tabla A-VII-59. 80. Camara venezolana de la Construccion, Memoria, 2nd Semester 1969, Apendice Estadistico. 81. Banco Central de venezuela, "Metodologia Utilizada en la Encuesta sobre el valor de la Construccion Privsda Efectivamente Realizada," Caracas, 1970 (mimeographed). 82. "E1 Sector de la Construccion,” Orientacion Economics, No. 32 (October 1969), p. 16. 83. Jeaquin Sanchez Covisa, "El Desarollo de las Actividades Constructoras" (paper presented at the XVI Annual Assembly of Fedecamaras, Maracay, Venezuela, May 1970), p. 8. 88. Camara Venezolans de la Construccion, ”E1 Financiamiento de la Industria de la Construccion," Memoria, 2nd Semester 1968, p. 5. 85. Oficina del Ministro do Estado para la Vivienda, ”Incen- tives para las Inversiones en Viviendas de Interes Social," Caracas, 1970 (pamphlet). 86. Camara Venezolans de la Construccion, Memoria, lst Semester 1969, p. 28. 87. Organization of American States, America en Cifrss (1970). Situacion Economics: 2 - Industria de la Construccion, (Washington, D.C., 1970). 88. Rodolfo Moleiro, Venezuela 1968 - La Vivienda (Caracas: Accion en venezuela, 1968), p. 85. 89. 50. 51. 58 Acedo Mendoza, 9p. cit., p. 17. CENDES and United Nations, 92. cit., p. 112. Ibid., p. 126. CHAPTER III THE MODEL Methodology The model consists of a set of multiple regressions that test the hypotheses regarding the influence on demand for housing of a set of socio-economic variables. Demand is expressed mainly in terms of monthly housing expenditure. The variables included in the model and the functional rela- tionships were chosen on the basis of economic theory, empir- ical studies in other countries, and on examination of past Venezuelan data. The basic regression forms used are four: 1) normal linear - the coefficients are marginal pro- pensity values and are additive: 2) double-logarithmic - the coefficients give directly constant elasticity values: 3) semi-logarithmic - similar to the double-logarithmic form, but the elasticity values are non-constant and inversely proportional to the level of the dependent variable: 8) quadratic - the relationship between the explanatory and the dependent variable is parabolic. 55 56 Functional forms b and c are not linear, but they can be transformed and made linear in the parameters. After the transformation, ordinary least square analysis can be applied. Dummy variables are used extensively in case of qualitative variables. They are also applied when a quantitative coef- ficient is suspected of being non-linear. “By partitioning the scale of a conventionally measured variable into intervals and defining a set of dummy variables on them, we obtain unbiased estimates since the regression coefficients of the dummy variables conform to any curvature that is present." [1] The analysis is based on cross-section data. There are frequently problems of heteroskedasticity and multicolline- arity in cross-sectional analysis. Pooling time-series with cross-section data lessens these statistical problems. Unfor- tunately, there are no housing time-series data available in venezuels. The large number of observations collected have facilitated the analysis. Such a large sample allowed the groupings of data at all levels and the exclusion of odd cases (which would have distorted the results) without reducing the degrees of freedoms significantly. Description of variables and Their Functional Relationshipg Housing is traded both in an asset and a service market. In the asset market, the purchase of a home reflects a demand for a stock of accommodation services as well as an invest- ment. It is a demand for a stock of accommodation services because the home provides a flow of services beyond the period 57 in which it was purchased. It is an investment because a home can be resold at a gain or a loss from its original a In the service market, the demand for housing re- value. flects a demand for accommodation services at one point in time (flow). Given the dual nature of demand for housing (X), it is essential to separate owners from renters. In the case of renters, housing demand is expressed in terms of monthly contract rent. For owners, I use monthly payments, adjusted or unadjusted for the downpayment, as a measure of housing consumption. Paid-up owners have been excluded from the analysis. I found the information on house values too inaccurate to impute rents that would be meaningful. Fur- thermore, even if updated house values were available, there has been no research done on how to accurately impute rents. Only in the case where imputed rents were reported in the surveys, such as data from the University of Carabobo and Corporacion venezolana de Guayana, were these applied in the analysis, mainly for comparisons. Rents more closely reflect desired levels of housing consumption than monthly payments, adjusted or unadjusted. 8All too frequently economists underestimate the impor- tance of homeownership as a means of accumulating capital. In the U.S.A. "homeownership is clearly the most important method of wealth accumulation used by low and middle income families in the post-war period. Equities in single-family, owner-occupied structures account for nearly one-half of all the wealth of the lowest income group...snd one-third of the wealth of all U.S. households earning between $10-15,000 in 1962 [2]. Thus, to view housing demand merely as a demand for a stock (owners) or a flow (renters) of accommodation services would be an oversimplification of the nature of housing. 58 Renters can move and adjust their housing consumption needs more easily. Since stock adjustments are infrequent because the purchase of a house entails a large cash outlay, monthly payments often reflect past or expected housing consumption needs. Comparing renters with owners has the additional problem of relating all the services which are included in the cash outlays. While contract rents sometimes include utilities or furniture, monthly mortgage payments usually fail to in- clude property taxes, insurance or maintenance costs. For- tunately, property taxes are low in Venezuela and it is unusual for contract rents to include furniture or utilities, with the exception of water. Lastly, since "housing is fixed in location, consumers buy not merely a quantum of housing, but also a package of environmental and governmental services which often have little to do with the shelter as such". [3] In order to reduce the influence of these exogenous factors I have per- formed regressions for different cities and sectors within the cities. Of all the explanatory variables in the model, the most significant, according to theory, is income (Y). The problem arises as to how accurately current income reflects household behavior with respect to a durable good like housing. All studies have consistently shown that if there is one type of expenditure which fits the permanent income hypothesis, it 59 is housing. Even though high discrepancies as to the exact value of the income elasticity of demand for housing have characterized the work in this area, they all agree that the elasticity is larger with respect to permanent or normal income than it is for current income [8]. If consumer units are completely alike With reSpect to normal income, then all the difference in current income represents transitory income. Several techniques are used throughout the analysis to separate this transitory income. The main approach used is grouping data into homogenous sub- sets in order to keep constant some of the ”nuisance varia- bles" that exist because of large family differences. The percentage of income spent on housing depends on the origin of the income. Given the same income, a smaller share of the income will be used for housing consumption the larger the number of earners. [5] The additional income from earners other than the main earner (EY) is viewed as more transitory and thus not used for housing consumption. This situation arises particularly in those households that are formed by principal and additional families.b Not only are the earnings of additional earners viewed as more tempor- ary, but their expressed desire to leave when conditions permit affects the housing space considered necessary. I test for the influence of Eggitional families (H) in the household by performing a regression with (H) as a dummy bThe definition of an additional family was given in Chapter I. 60 variable, with value zero if no additional family is present, and one, if otherwise. There are two components that determine the value of the dwelling purchased: quality and quantity (space) desired. Inasmuch as household size (N) affects directly the quantity of housing needed, there should be a positive correlation between housing expenditure and family size. However, pre- vious studies have shown that although the correlation is first positive, it becomes negative after (N) reaches a certain peak. [6] The reason given for this fall is that the largest families are usually those in the lower economic strata. This is not likely to be the case in venezuela. Sur- vey information has shown that household sizes are quite con- stant irrespective of income level. The influence of income and substitution effects provides a better explanation for the fall in expenditure after a cer- tain size of (N) has been attained. The income effect refers to the drop in the standard of living of the household on a per capita basis with an increase in (N). The substitution effect, in turn, is caused by the shift of expenditure from housing to other needs. In addition, as in food consumption, there are economies of scale in the consumption of housing services which further reduce the likelihood of linearity. From the above, I assume the functional form of (N) with respect to expenditure on housing to be non-linear. I will test for this assumption with dummy variables, semi-log and quadratic functions. 61 Age of the household head (A) is an instrumental variable which reflects the life cycle stage of the family. Not only does the number of children vary with (A), but so do income, future expectations and financial assets, all of which have an effect on housing demand. Adjustments between desired and actual expenditure on housing are notorious for their lag. In the case of owners, part of the disequilibrium is due to the large investment required for the purchase of a home. The disequilibrium is most noticeable for young household heads. Over the life cycle, family size and income fluctuations do not tend to be synchronized. During the early family stages, family size increases, which creates a need for more housing space. Yet, at this stage, income growth usually lags be- hind family growth. It is when the family stops growing that incomes tend to reach their peak, enabling the family to close the gap between desired and actual stock of housing. This adjustment is said to be lagged because housing need preceeds paying capacity. In the case of old household heads, the disequilibrium is frequently reversed, so that available housing exceeds desired housing.c Several studies have found (A) to be an important and statistically significant variable in demand for housing. [8] °Atkinson found that "the value of new houses purchased by households with male heads increased directly with age in the younger age groups, reached a maximum in the intermediate age groups, and declined for the oldest age groups.” [7] 62 I hypothesize again a non-linear relationship between (A) and (X) or (M) and use the same functional forms applied in the analysis of (N). Education (E) is closely related to income and may pre- sent problems of multicollinearity. However, for constant income, I assume a positive and linear correlation between preference for housing consumption and educational level. As Morgan states, "While formal education is clearly important in determining not only income, but consumption of housing relative to income, the explanation of the fact is probably not because of differential long- run or life-time incomes, past or expected, but because of more immediate direct effects of edu- cation, such short-run income security and sta- bility, the capacity to plan ahead, and the re- sulting willingness to make major contractual commitments." [9] Education was measured according to years of schooling. Education has been preferred to occupation (O) as an explanatory variable because of the difficulty in measuring occupation. Whenever I introduce occupation as a variable, it is in the form of dummy variables. Other than income, it is availability of credit with long enough amortization periods (P), low égterogt rates (I) and downpaypents (D) that has traditionally been the most limiting factor to demand for housing, particularly for the middle class. [10] The "multiple-term hypothesis” states that the three-credit conditions have a direct association. The implication is that decreases in the interest rates tend to be associated with extensions of amortization 63 periods and lower downpayments, and vice-verse. One of the explanations given for this hypothesis is given by Muth. ”A fall in the pure rate of interest...mean8 that the cost of some low downpayment and long-maturity loans considered too expensive at the higher rate falls enough to induce borrowers to make loans of this kind." [11] A8 I indicated in Chapter II, the ”multiple-term hypothesis" is historically valid in Venezuela. However, it is difficult to assess a priori which of the three credit terms has indi- vidually had the most influence on housing demand. Gelfand found that the most sensitive factor in mortgage credit is the downpayment requirement and the least sensitive, the maturity period. [12] I hypothesize that demand for housing is most responsive to changes in the downpayment. While interest rates have fallen to some extent, and amortization periods increased considerably in Venezuela, it is the reduction in downpay- ments as well as the increased availability of funds (in particular for the middle income groups) which was most instrumental in broadening the borrower base. I test for the significance of (D) and the sign of its coefficient in the regression analysis using the variable loan/value ratio. Downpayment was also used to adjust for monthly pay- ments reported. Two owner households with identical homes will report entirely different cash expenditures depending on the downpayment they make. In order to compare the two, 68 some adjustment is needed. Downpayments are funds with an opportunity cost which come either from savings, which could have been invested elsewhere, or from a second mortgage, which was borrowed and interest paid on. The sample from B.N.A.P. indicates that in most cases the funds come from both sources. Monthly payments and income should thus be adjusted up- ward by a rate which expresses either the interest rate paid, or the return foregone on the downpayment. Given the present interest rate structure in Venezuela and applying tax-free bond yields as a yardstick, I imputed an interest rate of .8 percent per month.d Economists disagree as to the proper way of measuring the cost of the mortgage and the way in which the borrower weighs the different financing alternatives open to him. Lee argues that households consider both the mortgage rates and the contract length jointly as the burden of mortgage cost. [13] Thus, even though demand will increase with lower interest rates and with a smaller downpayment, the same is not true for amortization periods, because maturity periods increase the total interest cost. He uses a measure of the interaction (1 x P). I apply this variable to my analysis dTo be more correct (P) of the second mortgage should have been considered. If it is shorter than in the first mortgage, the principal will have to be repaid at a faster rate which would increase the monthly payments proportionately. Given that households allocate a certain percentage of their income to housing, this will force them to buy a lower-priced home. 65 but I drop (I) because it is constant in my sample from B.N.A.P. One problem with Lee's view is that if (P) in- creases the total interest cost, so does a larger mortgage because this would also increase the total amount of interest the borrower pays the lender. ”This would mean that the larger the debt and the longer the maturities the more demand would fall." [18] Sex of the household head (S) has not been considered a major explanatory variable in the studies of demand for housing in deve1Oped countries. I consider (S) to be crucial in the case of venezuela because of the large number of house- holds with a female head. Female heads are most common in the lower-income groups. The abandonment of the family by the male creates an atmosphere of instability which is conducive to a set of priorities and expectations different from those of a normal household. It is difficult to know a priori how housing demand is affected by (S). Hewever, the fact that employment oppor- tunities and job security differ widely between men and women suggests that female heads will tend to avoid large debts or high monthly cash outlays on housing. Using dummy variables, I test for the difference between female and male heads with respect to (X). There are other explanatory variables which are used in the analysis to which I will refer as they are successively applied in the next chapters. These relate in particular to 66 the physical characteristics of the housing unit. I will study their correlations with (Y), (N), (X) and (M) and the trade off effects between the quality and quantity components of housing as demand rises. Summary In this chapter I have explained the analytical approach to the model. the economic reasoning for the choice of vari- ables and the functional forms assumed. I have stressed the statistical problems involved in measuring demand for housipg (X) and income (Y). Measuring (X) is difficult because of the differences in housing with regard to quality, location and tenure. I have attempted to minimize the measurement problems through means of instrumental variables and sample stratifi— cation, which reduce the differences between consumer units and the product purchased. Usually in cross-section analysis, there are problems of multicollinearity and heteroskedasticity. Multicollin- earity refers to the interrelation between the explanatory variables. Increases in income are accompanied by a rise in education, number of earners and age. Most other variables are also interrelated to some degree. A high degree of multi- collinearity is harmful in the sense that the estimates of the regression coefficients are imprecise. A rule of thumb used in econometrics is that: "multicollinearity is not necessarily a problem unless an explanatory variables' multiple correlation 67 with other members of the independent set is greater than the dependent variables' multiple correlation with the entire set." [15] The violation of the homoskedasticity assumption leads to unbiased and consistent but inefficient estimates of the regression coefficients. The (t) ratios are affected due to the bias in the estimates of the standard errors of the coef- ficients. In the following chapter I discuss how I correct for heteroskedasticity. 2. 10. 11. 12. 13. 18. 15. 16. INDEX OF VARIABLES Expenditure on Housing Rent or Imputed Rent Monthly Payment Adjusted Monthly Payment Income Current Income Adjusted Income Age of Head Members in Household Education of Head Occupation of Head Sex of Head Number of Esrners Down Payment Amortization Period Interest Rate Total value loan Rooms Structural Cost Structural Area 68 3 Y0 COOWZ> EY me sous Haws omonu one .nuo>HHoo 8H oommouaxo one mosHs> assesses o>mn uonu coHpsHus> HHe use: ecu was nouasnu many usonwsouna "muoz MH.8 ONH oom.wm oes.MH man man cmNm OMHm on nonuo HH< .o o~.~ «NH ooo.~n oo~.~H Hon coo owns case n «scheme .vo .n «8.8 eHH oo~.~o oom.n one are ean coon a oooaHoHeouon .e H~.m oeH coo.nw ooo.oH mos ago oaHe omen mm oesoocoez .m ne.n ha coo.mm oo~.mH wmn mum omHm Omen NNHH meomusu .N m.m om.m ONH ooe.oOH ooe.on oqa moo oeeo oo~o me o>oomn~ooe N.n oo.m HHH oom.ma oow.m~ «mm wee Oman ommn mNN oooelHoon o.n mm.m so ooo.mw ooe.mH ema gem oHAN oemN Ame ooomuHOON m.~ mm.n em oom.so oom.~H nwn awe OHaH onH mmN ooo~:HomH H.N NH.m Nu oom.wn ooe.~H com Noe oweH owMH ea oomHno esoocH unasmm Hench .H m.~ ne.n om oom.em ooo.aH ens owm onmm coon womH «Hascm . Hosea mason . swam mou¢ o=Hw> .shsm ueoshmm.= smashed osouaH.z osooaH swam vaonocaom .uuw Hsuoa area vouesfio< anuco: voumsnod mHnuaoz 0Hasmm ashes uuoouo NI>H me< no canon 7”? o.e nan eon.mH 85H oom..o oo~.no coo.eH an» own noo.~ ~ee.~ on" torso Hae .a 8." New ooe.eH ens oom.Ho oom.He ooe.eH own «an eHo.~ Hna.~ NN .ee»eeo.oo .o «.8 nae ooa.oH sea oea.na ooe.No ooe.- mas «on ooe.n oan.n he once :«nwsvasn .n ~.e coo oom.o~ «AH ooH.eh eon.ooH ooe.en moo coo e-.n eHo.n ee neocoHo> .e o.e en. oe~.sH oeH ooo.oo oo~.aa eon.AH one Hen coa.m nno.m no connects: .m n.e one ooe.ne «oH oe~.oo eee.meH oo~.eo ~s~._ one Hmo.e aoH.e oe~ ooootoo .N 8.8 m.e Han oo~.me oNN ooa.eoe oom.qu ooo.mo men.H one nmm.o new.m mo~ o>ooc-Hooe m.e «.8 men coo.Hn on oom.om oo~.-H ooH.He omo.H ewe mow.m smm.m NAH oooe-Hoom H.e 8.8 see ooo.- sea oos.oa 060.88 ooo.m~ oHn ooe Hoa.~ Hon.~ emu coon-Hoo~ e.m H.. was oes.mH HeH coo.em 60H.oA oo~.AH “on ooe mmo.a nmm.H on ooo~-aona m.m o.n own ooa.~H mHH ooe.an .ooo.on ooH.nH nae own oom.H ~o~.H no oomH-o useuaH . uHassm Hence .H n.e m.e new ooH.n~ NeH oom.ea eon.oOH oes.em sea nno om~.n «ne.m Hos oHoaem Hosea macaw swam sou< osHe> mead ~8Hs> ~8He> accessm uawshmm.x ueoshsm secuaH.: vacuum swam vHozoescm puma vesq .uosuum .uusuum Hooch area vounsfiv< aqucox vouesnv< thusoz «Haesm auwo one cacao osoocu an coHuoHuouusueao nocuo was renew quouu "mommwuuoz use: we soweuo>< Ho oHomu MI>H mHm\a mm. mm. mm. mm. mm. me. He. em. mm. mm. mm. em. mosom NN. mH. ma. 8H. mm. mH. NN. am. Hm. em. mm. senescence LC .2 mm. mm. mm. mm. Hm. mm. Hm. mm. mm. om. mm. mm. mosom wH. 8H. 0H. NH. aH. HH. ma. mm. 5N. om. mH. musesuuma< :2 om. Hm. wH. mm. wH. aH. 8H. Hm. em. mm. mm. 6H. mosom amnuo mammmso cumsHm mac oonu moo o>onm oooe ooom ooom oomH mHaEMm HH< seesaw :Hecuem leoHe> Imam: Imueu :Hooe :Hoom :Hoom :HomH :o Hmuow Muao mam aeouo oaoucH .maHHHosn bemsmmaszon mo moHumm one waHmsom so me came an m=Hm> Hmuoe cu acoam esooeH we macaw me name: ¢I>H @493. 79 high income groups, which fits Schwabe's Law of Rent. This law stipulates that the higher the income of a family, the lower the proportion of income reserved for housing. The average ratio for the total sample does not differ between homes and apartments, but the difference between the low and the high income levels is more pronounced for apartments. The cause for this may be that the range in the value of apartments is not as wide as for homes. Reusing consumption preference, measured by(M/YL,is quite uniform in all the cities. The income elasticity with respect to monthly payments (bMY) is low: .83 for homes and .35 for apartments. The elasticity, however, is not constant for all income levels. It rises at figgt, reaches a peak at the middle income group, and drops drastically at high in68;;_18;e18 (see Table IV-5). Also at high income levels, the elasticity is not signifi- cant.b It is understandable that (bMY) should fall since there is an upper limit of the mortgage loan. Schwabe's law may thus be institutionally determined. Testing for income elasticity with respect to house value (bVY) instead of monthly payments, gave almost iden- tical results for all the cities other than Caracas and Ciudad Guayana. For Caracas, the elasticity dropped from .53 to .31 (see Table IV-l). The coefficient is found in PY)when (I/P-L/V) is deleted. bNot significant as applied in this analysis, refers to a ”t" value of the coefficient which does not fall with- in the 95 percent confidence intervals. 8C) .oeoNo .oemom on." a on.. a no.. a He.» a sec use N N N N HOH.V Hon.o Hoo.o AoH.o HoN.v Hno.o HamoHoNuzooH sN. ON. No. 8N. oN. on. Nan oN .Ns oo uNN no .Nm on..Ns 8N .Na om uNm HNH.V HON.o Hom.o HoN.o HoN.o Hmo.o H.»Noaomu.swoN nN. oe.H oo.H oN.N OHH. oo. .w.so No ooH no oo oo onN coco oNNsso mucosuuea< so .Nm as .N« no .Ns om .Na om..Nm Nm.uNm om uNa om -Ns No..NN mo.uNa so..Nm Hso.o HNo.o aNo.o HHo.o HNo.o HNo.o HNo.o aNo.o HNo.o Hmo.o HHo. H.xooHoN.>ooN so.H so.H as. no. No. we. om. oo.H no. No.H so. .zeo on.. N No.» a No.. a Ho.. a so.: a .oewso .oeseo oN.u a .thno oN.u a os.n a m N N N N eoe cos N coo N N HNH.V Hoo.o Hoo.o Hmo.o Hmo.o Hoo.o Hom.o HoN.o Hmm.o HNN.V Hoo.o Hemoaocuzooa NN. so. on. no. no. oN. NH. oH.N on. me. no. 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H80.v 80. oN.n 0NN senescence Ho.zon.<0oH.NNoHoc.>NoH .o .e .e .0 on No on Na on Na N0 Ns on H0H.o H80.o H80.o Ho0.o oH. o0.- NH. 08. HH.N oNN moses nN. H0o.o H.o N.08H.0o 0NN coconutooo o o o o HN0.H0.0<.N<.H<.o.z.»om.> 8N Na mN Nu 0N Na N0 N8 nN. HN8HNo HONmoo H8o0No HoN.Ho NN0.NH ooo.0H H888 o.NH 0.0oH.HN oNN moses a e m n< N< z a o coHo eoHooooNoa N o o o o o o oHoaoo esocusu new wsHHHoso we waxy hp coHnsHus> unopaoaopaH em HOV pas A uauesuaoa as A>v :uHa ouHsoom aOHoeuuwom mo oHosH ~I>H NdnosouHs cH mucosHHaH cuH oueHc oHoeHue> muoumeeHaxo so no ecosHueH uoe cH A>\Hv .ucoshmaasOp saw you payments can Hwy pas Hz. Ho cosHm> ecu «use: mceHmmoawou one 8H "ouoz .ouasuHustHc He Ha>oH usouuoa OH on tho useUHHstHm 8 8 H00.. 8. mm. m. H OMN mucosuusa< Hm.zw0H p .mNOH..>onva.zweH 0N Ne HHN.V H80.o NN. cmm.: 88. N.H oNN o no: 0d Id on Na 0H Nm HQH.V Ammo.v H8. 0N.: mm. N.N omN mucosuuea< Hm.zon.mon v v .>\Hw0H.wonvmux NOH 08 Na 8N Nm Hmo.v H8o.v on. cm.: 08. N.N 0NN modem Hmoo.o NN. oN0. 0.N8mH 0MN oesoeuoooe o Hm.z.o..»oa..x HHo. m8. Hoo. 8.m8mN 0NN mono: .o. .v .0 NH Nd mH Na 80 Nm HHN.0 Ao.ose H800.o mH. 0N8.: n.NNH: wHo. o.noo cmN mesmauumn< 0 e o Hm.z.N.>\H.>omuz 88 NM Nm Nm mm NM . HOHN.o Ho.Hoo Ho00.o N8. NNN.: m.omm: one. n.08HH CNN ecsom a a Nx o >\Ho ”w No a owmwwm soHccoumom osHHHoso oo cosy so .oHoeHees osossoooosH so Hoe es. Hzo .Hdo .H>\Ho .Hyv van oHosHus> uauvaoaoo as sz :uHs ouHsmux conaouwcm mo oHomh mI>H mHm3.m\H _33.m\ d an e «Mam a H assaoaeev an as sew mummmlm mes .wmeavm . ewes Avmawpaouv sewage nonuo use avocado you moanswus> unmeaonmecu no agrouu we canon use Rwy use oanuuud> usevsuaan ed A>v sua3_uuascom oowmmoumom we wanna HHI>H Nani—e. 92 is highly significant and in two regressions it increases the value of (32) by more than (Y). 6) Occupation (0) This variable is not at all significant when applied as a dummy variable at three levels in the regression with respect to (V) (see Table IV-7). 7) cher variables The quality of a house is determined by the value of the structure (St) and of land (T). Quantity of housing, in turn, is measured by the area of the structure (a) and of land (Ar)' And finally, the number of rooms per house- hold member (R/N) determines the level of crowding. The following analysis studies the correlation between these housing components and (M) and (V). The first consideration is whether spending on structure rises faster than spending on land. In the computations, (bSV) is less than one for almost all cases (see Table IV-12). This is an indication that as house values rise, a larger share of housing consumption is directed towards land. In M I, ma.*—. the regression of (St) with adjusted income, we see again that the elasticity for the total sample is below one, al- though not so for different income groups (see Table IV-12). Notice that the(BZ)for (bStY) is much lower than that for (batV) . The Table of Ratios also indicates that the share of land (T/V) increases with income. Caracas and valencia, which face the greatest shortage of land, have the highest 93 Sims we “mm em "Na on "NM 3 «mm NH nus a "Na mm "Na an "Na 3 "mm .3 "Na bamodmnewmfi ANH.V Ano.v Awo.v A¢O.v Amo.v ANH.V Aom.v AmN.v ANM.V AQH.V Aoo.v Wum mm. #0. mm. Ho. am. no. mm.H ON.H mv.H MN.H Om. . n .H a" ." n .H .N .u e“ e" s .H .H u“ mm NM cm NM Hm NM .0 NM wm NM me NM Nu NM No NV mm NM om NM ow NM A>w0vanumwoH Aoo.v Amo.v Amo.v Aao.v Aoo.v Amo.v Amo.v Ano.v ANO.V Avo.v Amo.v >um HO.H NO.H 00. mm. ow. mu. ow. mm. Nw. 0w. Hm. A Om Mn Hm OMH Om «OH am On 05 mm Om mufim wHQEMW “cmtmmao OUUd—Hm NfiU ODHNU mmu 0>ODN OOOQ 000m OOON oomH NHQENW .UU Ifisdhmm IGmHm> Imhmz lmHmU IHOOQ IHOOM IHOON IHOmH IO HMuOH huuu was nacho TacosH an «mac: now nuanmwum> uaovssauusH mm A.>v was A>V use mHanum> uumvumawa mm Aumv an“: muaamem scammouwmm we wanna oaaamm HmuOH NHI>H mam<9 98 land prices (T/Ar). The difference in the price of land is so marked, that home owners in Caracas and Valencia are forced to buy smaller land areas despite their larger absolute (T/V) and relative (T/Ar) expenditure on land.f Mortgagors in Caracas and valencia also pay more for a structure of given size (St/a) (see Table IVel3). This could be attributed to the difference in costs which have been consistently higher in these cities. Comparisons between apartments and homes with respect to structural cost are difficult, since the value of land is included in the price of apartments. This difference alone, however, cannot account for the large discrepancy in (St/a) between homes and apartments. In no case is there overcrowding but it is surprising, that for homes,the number of rooms per member (B/N) falls as incomes rise. Yet, (A) and (a) increase with income for constant (N). The assumption is that increases in housing consumption take the form of larger rooms per member. I perform a simple regression of (a) with respect to (N) to test for this assumption (see Table IV-l8). Three regres- sion forms are used: normal, double-log and semi-log, for total sample, homes and apartments. As expected, (N) is only significant for homes. The low values of the coeffi- cients show that changes in the area of the structure are fThe fact that land price, for the same city, increases with income is an indication that rich people do not move to the suburbs, where land prices are lower, but rather to higher income residential areas within the city. 95 om. mu. ma. em. NN. Hm. mm. @N. mm. mm. «N. on. mmaom >\H mm mm a 8 mm 63 2 ee 3 em 2 mm essem as: man Hmo mom own cum mam com mam mom mam «mm mucmauuma< u m\ m 0mm non 0mm fine mos Hom mes awe was Nmm men Hme meson 3 . em . mm. M. S . S . easesaaeas z\m mm. oo.a no.a «H.H wH.H mo.H meson umsuo mammmsu oumawm mac onamo moo m>onm oooq ooom ooo~ coma manamm HH< .vo awsvumm Icoam> Imam: Imumo IHooe IHoom Iaoom naoma no Hmuoa wcfiHHosa mo mama use mmauwu .maaouo oaouaH an mofiumfiuouomumno weamsom mo memo: mo moHumm MHI>H mgmmH unmuuma OH msu um unmoawficwwm mos mum mucmwowmwmoo amuse « . once was Aao.v as No -ooo.NNN meo a Nm N eON oNo 60 m N oeo m e e NON mNN "est> a Ae.ee .aNo.V .Ae.av -mww.mwa NNo N ON m NNN NNo No N N mac m N e 0NN NNN "est> muamauumm< .m upon— an sam.Nae same.e .Ae.ev -eee.maa eNo. N.Nm N eeN Nmo No. N.N mNo N.N N.NNN oNN "est> a tam.we «aNe.e .Ae.NV -mww.mma Noe m m N omN coo Noe N N Hoe m N N Nma eON "esNe> mmfiom .N «Ae.ev «ANo.V «Am.Nv eNassm Noe. 6N.NN N.NeN moo. No. N.N ONO. m.m N.NeN NNN Nessa .N m z m m z m M z m mswm N a N a N a eNassm AzmeNvaue AzweNVanemeN szane mafiaaosn mo 09%H an mosam> mac: ucmuwwmwn pow mapmwum> unmvamamvsH mm sz use canmflum> ucovnoamn as Amy nuaa muasmem scammmuwom mo manna «HI>H manna 97 only slightly determined by changes in the household size. Although the fit of the regression is low, it is better for the sample with high home values than for low ones. The Emplozpent Multiplier (E) Venezuelan policy makers are faced with the task of reducing the level of unemployment. The question they pose is: What type of investment and at what level will it create most Jobs? To help answer this question, I measure the employ- ment generation of investment in housing by income group and city. Employment may be raised by making construction tech- niques more labor-intensive or by increasing the volume of construction. This study centers on the latter. More invest- ment in housing can be stimulated by: a) making mortgage funds more accessible and cheaper, through better credit terms, or b) increasing the payment capacity of mortgagors, through tax cuts or direct subsidies. Both policies can be interpreted by the mortgagors as an increase in income. If this income is treated as normal income, the effect it will have on employment creation from the increase in housing expenditure is determined by the multiplier. The analytical framework used to determine (E) is the same as that developed by Strassmann. “The criterion for choosing an income group as most employment generating in housing should be a relation of the labor content of the house to income (L/Y).” [3] Based on this assumption the employment multiplier, related to a change in income, is: 98 (l) E = bLY(L/Y) where (2) be.= bMY 0 bVM - b8v ° bLs and (3) L/Y = M/Y - V/M - s/v - Us This chain of products shows that the ultimate change in employment cannot be determined by one parameter alone but only through the combined interaction of several parameters. To note the types of homes bought by different income groups, and then encourage construction of the type which has the highest labor content per bolivar of structural cost (L/St) is not sufficient. The final effect on (E) will be modified by the share of income spent on housing (M/Y), the preference for expenditure on the structure as compared to land (L/V), and the length of the maturity period chosen which affects (V/M).8 (See Equation 3.) Similarly, the percentage change in employment is related to the percentage change in income through four elasticities (see Equation 2). If the ratios, or relative changes, are the same for all income groups, the employment multiplier is determined only by the elasticity chain (bLY). Since the ratios most likely differ, the employment multiplier is the product of Equations 2 and 3. Given that there is no available information in Venezuela on some of the parameters, the values for (L/St) and (bLSt) 8Differences in downpayment to value ratios have already been considered by using adjusted monthly payments (M') and adjusted incomes (Y') in the calculations. 99 are taken from statistical calculations made in Mexico for several representative types of housing. [8] The types are: normal, average, average-good, good, and luxurious. By com- paring the income ranges of the purchases of each of these house types in Mexico, with the income groups of the mortga- gors of the savings and loan associations, I distribute the labor content per thousand square meters of structure as indicated in Table IV-15. Normal and average types .are ex- cluded because they are inferior to those purchased by the venezuelan savings and loan mortgagors. Total employment refers to employment on-site and in the construction materials industry, while direct employment only refers to on-site labor. No distinction is made between skilled and unskilled labor. My concern is only with total number of workers. TABLE IV-15 Total and Direct Employment Creation in the Construction of Reusing Units by Type of Dwelling Hous ing Type ( $110;ng ) ( L/D 1:88:12 ) 333:8 Average-Good 20.85 12.97 0-1500 Good 23.51 18.58 1501-8000 Luxury 29.88 16.16 8001 + bLst = .81 for total employment bLst = .57 for direct employment I assume these elasticity values to be the same for all three dwelling types. 100 The employment multiplier calculations and results are presented in Table IV-l6. The number of man-years per dwel- ling was determined by applying the figures of (L/lOOOmZ) in Table IV-l5 to a weighted average of the structural areas of each income group. The results indicate that policy makers should give priority and incentives to the construction of housing for low-income groups (Bs.0-1500) and to middle-income groups (Bs.2001-3000) outside Caracas. Notice that the labor con- tent per Bs.10,000 of structural investment is greatest for the type of housing built by the highest income group. Yet, the low-income elasticity with respect to housing of this group, and their higher preference for land,more than off- h These results are only tenta- sets the high (L/St) ratio. tive until values of (L/St) and (bLSt) are calculated for Venezuela. Summary Income, as expected, is the most important determinant of demand for housing. Reusing with respect to current in- come appears to be an inferior good (bhca . .se:. me sasruxwaaeammeam No.» Nm.m NN.N NN.e em.e mN.m em.N NN.» NN.ON eo.NN No.N eeeaao m.. aw : »\a me.mN ee.NH em.NN em.NN ee.oa Hm.oN No.NN NN.eN ee.eN Nn.NN NN.NN Nessa a m . ee.NN Ne.NN oo.mN Ne.NN eN.NN Ne.eN eN.NN NN.mN mo.NN ON.eN mm.oN m.. hm...¢m u .w\m . NN. em. em. mm. om“ mm. mm. mm. mm. mm. em. ueeaaa e:ON x m\a me. no. mm. Ne. on. es. en. en. Ne. oe. mm. Nessa cameo eases NN.N me.N mm.N oN.N mm.N me.n mm.N ee.N mo.N em.N oN.N aueaao Ase chNNmse mm.m me.e NN.e me.m NN.e ee.e me.e Ne.m Nn.m mm.N mm.e Nessa easea:sex sseaesc oceans see erase see eases oooe oo0m OOON oomNuo Nessa seesau :aseaem :ssta :sasx neaeu :Hooe :Noom :NooN :HOmN mmwuwo was cacao oaoocH hp wsflmsom mo maze now Aeoa.wm you .mv mamwaafiuasz ucmshoflasm oHI>H mum assume we ammusmeuom : < w.nn "coco muomumo oooH on Home: mascocH paocomsom cu mvcoammuuou umnu wcwmsom mumsvovwaH mo cumuseuuam m.mh N.NN o.mq .OOH .ooH m.m H.0m N.¢¢ q.nm .00H «.05 q.w o.mm o.mN m.oo w. He N. aH N. we mcmhmsu.vo o.qo n.Hw m.em .OOH .OOH m.aH .OOH H.wa w.eN o.wo n.nm N.qn e. an c. an H. en maumoam> m.Nm <.wn o.nn w.oo .ooH c.NN .ooH .ooa o.HN .OOH .ooa m.o a.No H.mm ¢.mH w. an N. on H. no mmumuwo o n u n < o m < o m < u n < o . N o econ m ecom w econ n 0:0N N maON H econ cheese one Nuao .ecoN menu: an wcfimsom mumsvwv< mo owmusuouom HI> mgnaaon.vo 0mm means: mum assume moa oven mmomumo «em auscumauu.m on mmcaumm eaoa eewas>.o mma msommm.> cam maocoam> «mm oaamnmu.oum mum xmumumx mNe mcmssu ema assesses «mm anemone pmesau aoaa onamomamz Nmoa cuosamascumm mom ouoo wma ousa< men mmsapco swam muau maaamm moauao ma MON huau an va osooca vaonomsom one Axv casuapcmaxm wcamsom mmmuo>< nl> mam¢9 115 average of measured housing variables would over- state permanent housing consumption at a high level and understate it at a low level of permanent housing consumption." [5] I correct this averaging problem by performing the regres- sion for averages of each of the zones instead of using the original data of the total sample. Since permanent housing consumption and income levels tend to differ between zones, stratifying the data by zone avoids lumping high and low levels of housing consumption together. The results in Table V-8 are consistent with those ob- tained in Chapter IV. Income elasticity increases up to the middle-income group (1.71) and drops drastically for the well-to-do (.76). The elasticity for Zone 3 is low, as expected, because of institutional constraints. The high proportion of the variation of (X) around its mean explained by (Y) is remarkable, particularly for Zones 3 and 5. The average of the direct and reverse regression coefficients gives a value of (bXY) of around one for all zones. I performed one further test and grouped the cities into low and high average incomes to reduce some of the differences in the levels of employment and economic activity between cities. Although values of (R2) are substantially lower than those obtained from the aggregate sample, the elasticities of income are similar (see Table V-8). B) Table V-5 shows that, as in Chapter IV, ”Schwabe's Law” also holds true for the sample of the University of Carabobo. The same gradual fall in the share of income spent 116 .ucmu pmusaaa uo mucmakma hasuaoa .ucmu cu summon ua umsumn3 mo m>auooammuua casuavamaxo wcawscs now aopsam ecu ma ANV umuamco manu ca “muoz um>aaom amnoumauo.w meaamz mmcaumm eczema moa msummm.> moomumo oaamnmo.oum mmmum>.o mcmnbo maocmam> wuaasomH zmomumx cumaamaseumm mcmhmso ouoo enamomumx mu=a< mmaanmo "mmauao Tacoma swam mmaanmo ”moauao maooaa 30a we. ea. 3. om. mm. .3. 8. a Qmedm u $2 3 Amqo.v ano.v Ammo.v Anno.v Aoea.v Amma.v Aqwo.v xw om.a mm.a mm. mm. mN. am. we. a we. em. 8. ea. mm. .3. 8. m Smodm u xmea “N 38. 83. A28 $88 News :3; N.N; as on. we. am.a em.a ON.a ma.a wm.a 9 mm. mm. NN. em. No. as. mm. Nm vam n x Aa Anao. Amao.v aNmo.V nem.v Ammo.v aNqo.V AoNo.v w ea. mmo. aN. aN. mN. oa. ON. a ma ma ma ma oa 0a ma mcauao mo umnssz m oEON m m:0N N mcom a snow maaEMm maafimm maaamw oaouca maouca aa< :95 33 meow was ao>ma mfiooca an mmauau mo monam> mwmwm>< new manmaum> unmocmampaa Me AVV was manmaum> ucevamnen mm ANV nua3 nuanced :oammouwmm wo manna ¢I> mam amumumo moauao heck you such uauuuueso one oaanuauewoa cannon ca caneaum> uncucoaovca we va use oaamaue> unsecuaun me Axv sea: muasmem aoanmoummx mo sassy 0I> mansec OO. NON NN.Ne On. a On. a. DOS Aaa.v : mm N N weacsom ON. NO NNO.V NN0.0 NNN.O aeseaaseasec r ea. OO. NN. nNN . m Amusoshmm me N sarcasm so ON.Nm Oa.Na . amom .veusaaav Nwm.v ONa ANN.O Ne0.0 NON.O erases masses Nessa ee.- NN. Om. NO as. a ON. a On. a N musmahmm N N . NNH.O assess: NNN.O Ne0.0 NeN.O seem O a NO. NO ON. OO. OO. Ona z z N m Ne.Nm mm.Ne NN.Nm easeaasa m > same Nasesez O z NO0.0 Oeasesa NNN.O NmO.v NNN.O as uses O O ON. NON Oeusasa OO. NO. NO. NO Oeasasa m o a seam maasem .axma .axma .axma seam a oaasmm N A n a oaaamm Nameavmuxmea NN.mxma .axmavaax N.Nxmameavmnxmea AUDV maocmam> Au>ov mcmxmso pmvaau oaxa wsaaamsa an maoceao> use usehwsu vmvsao you magmaum> usovcoaovaa mm Amxmav uo va was oanmaum> acouceaon mo Axv :ua3 muasmmm scammouwmm mo magma nl> mam ousuaoaoaxm amuoa no Tacoma A< Ne mamas cal> man ON. N..- NN.: NN. en. NO. NO. e.N- N.ON NOO. N.ON NOO. eO. NOO. NON < O NON NNN . .N.MN NO. O o o o 0 .Hg 0 AnN.O AaN.O Noe.O aNo.O Aoo.O “no O No «O Na anO Noe O noucmuucz 0 ON. NN.: aN.: NO. me. On. NO. eN.- O.e NO. N.NO: NO. NN.- eO. an. O NON . NNN NNN Ne. ON.WN NON NNa Nee u . . C . . . . 'u0ugg Ann.O aNn.O Ann O Aaa O aNo O Raw O as aaO as say aoo O m NO. ON. NN.: NO.- NN. es. OOH- NN.- N.N NOO. N.N NOO. OO. nOO. N.N N O NON Nan NNN Nee NN.ms NON NNe NO O O C O O C I . "Hog “ Nen.O NNN.O NON O NON O NNO O NN NO NO NNO NO NOO NON O eouemmaez < NO.- NN.: NN. ON. ON. an. en. O.N- N.ON NO. N.On NOO. aN.- NO. 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ON.: NO. eON O NO N Naa sz a eN N: a N N: » eNa Nu e sz zm Na 2a Nu 2a Na ammuwm «Nessa Naao NNz .Nz .NNeN N2 .rmeN N2 .NNeNOa:NNeN NNz.an.N NzNONOauN ANNONON NOON . cusses ha eeauau seen you usuom acceauocsu cough ea oaa-auob acousuacesa as any new oaaeaas> uaeueoaon cc ANO saw: nuances ceacnoumeu we canny Nau> Man<fi 1127 o.m 5.0 5.0 o.e w.e .NOOO.OO .NO> VHonmmsom m.m m.o N.o o.m m.m mH. «H. MH. NH. OH. .NOOO.OO .NO> ONO Oo ONOOO huHo hp «macho uw< uauquMHa now sz van Aux» H.V\NO .Amxu HO .va .ANO mo mowwuo>< mo «Hams Om. om. om. 0H. NH. OHH ONN mcH cN~ OOH omN mMH owH om OmH .NOOO.OO .NO> «Haquaumxm wchaom o~l> mHn ousquaomxm ku09 no oaooaH + H9 colo¢ melon mmteu mule mwaux ow< 1j28 .oHpOu «nu cH vuvaHuaN No: mun Hu>oH auaouHucoo uauouoa on o>onu mmsHO> :u: saw: nuaouuummoou N=0Huoouwuu oHnHuHaa osu aH Nouoz muwmh + on I n¢ ONOON OOuNO a N< muwuh eulo I H4 «Nam» oqlmu I 04 "aux NO-OO ONNO ONOOOOOOO NN NONOOOO NNO NON OONNuOONN .OO OO. OOOONOO NON OOONuOOON .OOO oaOuON NN OOOOOOOOO NON NON NNN NNO NOO N “cm g 23.3 NOONO NOOO N.O.NO NO.OONO NOOOOO NONO OOOONW . OO.O NN.N- ON.N OO. O.N- .N.N- O.OO ON. N.OON- OO. OO.- OOO. OO NNN NON NO NNN NNO OOOaNO NN0.0 NN0.0 NON.O N0.00 NN.NNNO NON.O uNOONOO OO. OO. OO. NN. ON.- O.NN OO. N.ONN NO. NN. OOO. NO NNN NNN NON NNN NNO NOO NN.NO NNN.O NOO.O NNO.O NO.OO NO.OONO NNN.O ONOOONOO O.N- ON.N NO. ON. N.N OO.- N.O OOO. O.NN NOO. NO. NOO. NO NON NON NON NNN NNO.O NNO.O NNN.O NO.NNO NO.NONO NON.O OOOONOO OO.- ON.N OO. O.N NN.- N.NN NO. N.ONN- NO. NN.- OOO. NON w w a N unovnumua nu ANO auwa ouHaouu OOHmuuuwux no «Hana HNID mandfi 129~ The fit of the regression is higher for (A) as an inde- pendent variable than it is for (N), but still very low. The levels of significance (which indicate the probability of the results being simply random) are also too high to accept the coefficients as statistically meaningful. Since the coefficients of determination are highest and the levels of significance lowest applying the quadratic and semi-log form, the evidence points to a non-linear relationship between (A) and (x). Tests with U.C. and C.V.G. data show similar results (see Tables 18 and 19 in Appendix C). 5) Sex is! The following table gives the percentage of female household heads by urban zone. TABEE V-22 Percentage of Female Heusehold Heads by Urban Zone and City Zones 1 2 3 h 5 Total Caracas 19.1 18.8 25.2 16.9 10.3 18.9 valencia 20.7 27.9 16.6 5.9 17.7 Ciudad Guayana 17.0 16.“ 8.0 16.3 7.8 lh.5 The lowest percentage of female heads corresponds to Zone 5 in all three cities. The departure of the male mem- ber from the household is most common among poor people. It is not surprising to find a high percentage of female heads in Zone 3 in Caracas. The Banco Obrero has followed a policy 130 of priority to female heads in its allocation of housing units. Ciudad Guayana has a lower percentage of females than the other cities probably due to the larger number of males migrating to the new city. The coefficient of (S) is highly insignificant in almost every test of regressions (l) and (2). The sign of the coef- ficient. however, is negative in the more significant cases, both with respect to shifts of the regression intercept and changes in the slope of (logY). Hence, households with female heads seem to spend less on housing and have a lower elasti- city of income (bxy). This confirms the hypothesis that employment insecurity and social pressures make women more cautious with respect to the future. Their willingness to make large investments or commitments on monthly cash out- lays is thus reduced. 6) Hbusehold T e H In the description of MERCAVI's data in Chapter I, I mentioned the distinction made between principal and addi- tional households (families). A household living under the same roof, formed by separate families (though usually rela- ted) may have different housing consumption patterns than one with the same (N) but composed only of the principal family. The members of additional families and their income might be viewed as being only temporary. They are called "additional" because they have expressed the desire to move as soon as conditions allow them to. 131 Table V-23 shows that most households consist only of principal families. This does not preclude extended families. It only implies that in more than 90 percent of the cases. all members of a household have expressed desire to stay. There are a higher percentage of additional families in Caracas. probably because of the relatively greater need for doubling up given the higher cost of housing in this city in relation to income. TABLE V-23 A) Households According to Number of Principal and Additional Families Living Together in Percentage by City Principal One Two Three HOuseholds Addi- Addi- and More Only tional tional Additional Caracas 92.1 605 1.1 03 valencia 95.2 “.3 .h .1 Cd.Guayana 95.9 3.7 .3 .1 B) Distribution of Additional Families According to Income Ranges in Percentage for Caracas Income Range 0-500 501-1000 1001-1500 1501-2000 2001-3000 3000 + 32.6 “1.0 19.0 5.1 “.8 2.5 Looking at the distribution of additional families according to income ranges strengthens the view of the impor- tance of income in satisfying housing needs. 73.6 percent of additional families live in households whose total income is below Bs.lOOO/month. 132 In the regression analysis consumer units with addi- tional families do not differ statistically in their housing consumption from those composed only of principal families. As with sex of household head. however, the sign of the coefficients of (H) are negative for the coefficients modi- fying the intercept and (bXY). Thus, it seems that house- holds with additional families not only tend to spend less on housing, but divert a smaller share of increases in income to housing consumption. The reason seems to be the tempor- ariness of the income and the members of the additional families as it is viewed by the household head. no (she) is probably the person that makes the decisions on the budget share that will be spent on housing. and may prefer to crowd. given that it is only temporary. 7) Income Earners (E!) The effect of the number of contributors to the house- hold income on (x) is similar to that of additional families. It is assumed that. for a given income, the transitory compo- nent of income increases with (EY). Decisions on housing consumption are based mainly on the income of the main or more permanent earners. The income of additional earners tends to be spent on non-housing expenditures. The results in Table V-Zh (Appendix C) confirm this assumption. As (BY) increases from one to four for renters. the percentage of income spent on housing declines from .22 to .10 in Caracas and from .lh to .6 1n Ciudad Guayana. It 133 is similar for mortgaged owners. This decline in (X/Y) can- not be explained by differences in the size of the household because the analysis showed that (N) is not a significant variable. 8) Rooms (R) The last chapter showed that as house values rise. a larger share of housing consumption is directed towards land. The quantity of space consumed, in terms of rooms per capita (R/N), remained almost constant as incomes r as, yet the elasticity (bstY') was .50 for the total sample. Using MEHCAVI's data I want to test again for changes in quantity of housing by measuring the effect of (Y) on (H) holding (N) constant (see Table V-25). The results indicate that (R) varies significantly with changes in (Y) although not proportionally (the highest value of (ha!) is .36). The fit of the regression for the barrios zone is low compared to that of residential areas. Neverthe- less, it is clear that ranchos do not consist simply of single- room structures. but are improved into larger units with more rooms when incomes rise. Preferences Finally. I look briefly at the voiced preferences on housing of household heads. as they appeared in the ques- tionnaires. Housing programs can fail unless they take into consideration the tastes and preferences of the people who 13a .uamcHMstHu one oaoHooonoN ego mo mosHo>um ecu HH< "ouoz NO uNO OO uNO ON.uNN OO..NO NO0.0 NO0.0 NN0.0 NO0.0 NO.N NN. NON NO.N NN. ONN ooosNONsONOO NN .NO NN .NO NO .NO OO INN NO0.0 NN0.0 NON.O NO0.0 NO.N ON. ON ON.N NN. NO ONOOONO> ON..NN ON .NN NN INN NO .NO NNN.O NN0.0 NON.O NO0.0 ON.N ON. NON NO.N ON. 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NONN NONN NON O.NO N m\< Ummm NAHHEmh udwamm mUHOSOWDOE m\< v83 fiHOfimeOm Hawamm mUHOLU mDOm MHOHDDHHHGOU MO mSOUGH waouGH .HO ufimm MO O\O MO wEOUGH GBOUGH HO ucmm MO N wsoufiH ONOOO NOO NOO ONOOO NOO NOO OO OOOOOz mammwnu .wo mmumpmu ONI> mum< mo manwe APPENDIX D QUESTIONNAIRE FORMS USED IN SURVEYS 171 oxNH a eaoaoaNN .uuwcuum .UNmnox n6. - waH O onus .mOuuunm4N; rowan .pmmoua no oHNHnL ; one on: Boon m gjfluj 9:93- .NON % N 0>fl£ . Q G I a o face 0. .350 On «vacuum annumnwx 5 a mo § fl van: «Boom nHMounHow. . 11 naoom O. .m OOOOONO. OO>OOOO3NO. OONOOO NO NOONO .uo>NO nu. OONNO OOOOOO Oz Nu . .Tom mumvNun scum ouw>uoa canon Amchx unoNump may «Naa; nu. ONNONOO mumem nu . nu . “NONONNOOONNNO . MOOOONnnu . uOOO NO ONOONO . OOOO NO OMOMN nu. OOOO ON OOONOO.nu . mu . . . 9:30“ an o no .oNa 00H cmnu muoa uo N m nu. uanu u cmooa ~ oumlun «magnum “oownuww owU . .36 00H :23 mama uomam U . . Duowuunw N.NN W0 Ufifififlau . >uflou O waflfl 0ww3flmfiu . 0M8; Ou ”CHHOQfiQ HOOVCH D . XHNNJUWfim UCUHQ>ND¢ uNuuumNmO. «pl! ON ON ON ON nONummNasHHH owmmumuIIuL. owmsum uwumz mo muNNNnmNNm>< mmw> mo coOuNucoo . .1...‘ m00a>hmm in umfio U . . .350 S . . 90500 . ocaaaou 3 O 5.3m D N 3393 «235 Oman—own. . wagon Nam 35.. Nuauumnaoug . nauuomnN. anuuo .aNOn .ucuunu. Omma>ounaa no :onucmmznu . amuum .anm .oca0h_. nannu. wagon wnwuonnwaoc mauumnan . .uonnaa caumnu. OOOOOD . xuwun .xuoan .ucoamunh. ON muoon muoou uoaumuxm «Nada uoaumuxw .umm no mane: mcwumxmnu mason cN .ua4nu OONONNOO ON .238 awn: adaamu uchNmNu 0. -~' ammwwvuumaoo osu cw maawuuuma NOOQx . N .madu 0 muaumaumuuwuunu N canon no wawvwaaa uo madz nogasc ucoauuua< :oNuonqucou aHnu . 335333 2:50 . Bunuoru h5g0 ho vuucomU c OOpaonO. vmusmmU . acmump OONOOOOOD . o. ONNNNOmuNnmm muuw>umm ON OONOO OO O Nuuua .omam mo O unsouu< ouaom o O NOONONOOO oaao>< No uoouum h. C. n. O. HHHHmuNc: mo «NONN «and m nouoom 2 _ «can OONNOO OONOOOOON.NOO< "pupauumnam «0 O E coNuuouNa : i huwauoou oumuO m aowwom fill a u wflouuaoo onha . N OOOOO .' -.v|‘|.lu .O.ll I. 05 _> aw wcwoaom «cu uoxumx Hmom «nu mo annum 172 OO.... 7...! A... . .ufl’lo. .II!v No 2 ,w a: flnJJw meHHEmw quwHU uDOSuHB Do ~ [I HOCONOHOO< mum>wum nu. .. m. .. SE . O.Oas scmwfimwuwmw m“ 1. Hmaaucfium 4 xcmp mmmwuuoz nu. unmehowaw.mo wfiou ONNEmm On mdowuaufiumaw owannn umsuo HO. O 44.1ng fliméwzg ONES O.wauoz DO 1|: 3 cofiumwuommm cmoH O mwcfi>mm nu. uNcsL “mp umucmcwm mm on we mucmufinwncw Naa: Now maaucoa .muamam>oumeN no .cowuonuumcou muowum>ummno unocmeuma mo O .vwma .uam mumuwucH .cowuwmfincum mcu New uwvmuu ms“ mucmasuuo. N .maa hanger: N . Lhfico mpmgop wcLNIucchmr ON 1%n1mw O. mcofiumu.hh.nc NO vcmH ONMNOoB mam mucmfi .Hocw mmmsuuaa: I. am>ouaafi mo mofium mo mofium uwnuo nu. cowumaufim .O OO nMW ON Omvm>cH nu . munmca nu. muwoNOcH. human oum>fiua ou mawucmm nu. Omucmm nu. wcwmmmumamm D . wcfimmalmamm D N Oz 8 . mm» 3 . : ONOOuONOO D . ONOO-ONOO D. .Omuunuumcoolmamm ON mmsoz mfifiu . ON ON vcmq . mufiqfl:wcamaox humuso macoV nowuusuumaou .O N «magma .OO Oaaamm I wcwmsom mo QNAOGOHOOHOM N N mwma u.cou .H>NNnmmm ~ . . . . ONouucou >u mchcumm vcm wing 05 mo cowuwmoaaou 5." 53 m mwmn O.OOO .NOOOOOx aocx u.coa O J . . . . 174 Ownum.nm>wca wumaofioucw O nmauo O u.coao monum .nm>Ncs mnmaafioo O Bocx n.:ooo Haflxm no nonuoO w Noosom .moo> muwNanosH . snow nmsuo. ..mmmonm nooO cw Ochmoo on ooh chcNmuno mo anm. . Hoonom .moo> mumNafiou N muNc: chnmsm. nNc: .chupo ca OuaaoNMMHQ.mrcmumfimmm Hmwocmcfim nmuumm. . Hoonom anc mnmaofio aH O .onocmn: omncmmN A.onumemov mmmamo Hmnaumz. mo nommo muwnocou. m Hoonom xmw: vmumamfiou . :osocmn: noose. OOOOHQBMCQO mcommmn OHHSOM nmcfiu. m mmvmnw anolnuq O .umm wmucmm. mmNuNHNomm ucmfiofimmamcHonwnu OHHEOM no mummwnm o>mm. . mmnmnm onmnuma . .nom vmczo. wmcflcnwm nmzqu Naa: chcNmuno cw OONHNomm . m mwuwnz O mommn NNco N meow vmucmmN OHoonom ou OONHNnmmmmuumcHN mNoocow ou Oufiawnmmmmou4 . oumnmuNHHH . meon wmc30.mficoHnM\mm>Humamn mo Hm>Nnn<. uNO.>mna mo mmocmumanonwo . , -ummwmmomo -mu: mufim mo aoMuomNmm oowumoaom wo,momnu Nana mo mucmcmw.w mamB . . mcfi>oa.nmw-maommmm -ti nmsuo. w . ufimvdum. mcomnma . HmLuOo UMszmDOEN O\m m0>HumHmH Hmfiuo o oNuOon0uofi nmcuo momofiaam no xnoao. cmnoHNsoOcmnu N no mNomon. no ucm>nmm. . cmumnmmom O cmnnawso wwfinnms m.om:oam O mom. omumONomamocHO AOOOOHQBMIMHOO n& mucmsonmzo vmono>fin O amnoaflao wmfinnmz . nmo ofianm. nmxmfiwfiom. nononwao ouSoONz O cmnoawno mamcwm O unmvsum. mnONNHHx. nmox . mmoomm . nmo no nmo caou ommoaoamasu wcfisommH. vmfinnmz N OHNEOM .nwovm mo ommm . uOOu no. vmmoaofim. machNmmmmona o\m mnmwmcmz. mamcfim . OHNemw HmofioCNnu mo woo: . F. .unomwnmnu mo ovofimnumum cowumoso00r moammo no defimmmmonm OOOOOO NOONOOO ONOOOONOmmwO NNNOOO Amawowwcoammnnoo onwmccoanwmsv «Han mo >H can mmnou HH manna 6N movoo «moan mmbv O ammo .u.coo .H>‘I J .4 ’6 77‘ /' 9‘ ' L3 Habitate 1t ‘ llUse it occasionally to How did you find out of its program? 1' ' Rent it 2 ("Use 1 commerciall News- r L: .n t Y ,. n .n Rad to O QCinema 0&8 er a ;C- Selidlil: k Ii h ’ ‘ On TV on] Friends refer. or that >/ 0“. 3:“ I e :0 T? t ‘ 3 one. 2' Do you know of some office of this inst? \ OWN}: .00"! [J on .0 O[_] Yea I ['JNO-CO to part IV ' 1: R5 3 l' Sign your name What would you do with this unit? --. Or . 1.3 Rent it I f‘ SP.” It IL] Other j .. "R’p‘éé ii ié-a‘II;—n— L r~ . UHortgage bank O ”Other 0 D No no Do you plan to save'in the future? O (1 Yes " g O NO 2 Are you saving to buy a home? 0 [j YQB - L'} No .How much could you pay as a downpayment? --—l C) Nothing O C] Less than 2000 1001-2000 O 010:001- OD 201-500 O C] 2001-5000 O D;§,§§$- .0 501-1000 . c1 ”0140,0000; ; 5, l4 Do you uwn any hmncs (other than that which you inhabit)? ' DYes a ONO Os Will you sell it to buy other property? I Uch s DNO' «Do you own any land? a DYQB . 0N0 "What use do you plan to give the land? I DCons.on iqusell itOD Save w/o cons. «Do you have savings? DYes - Where? . DCommercial bank O [:1 Savings 5 loan association O DWorker's bank 22 Within how much time? more ' (1 Less than 1 year“ 02 years at) 1 year ‘ OMore than 2 yrs. =3What is the max. monthly pmt. you . (1 Nothing could make? '4 I) Less than 50 L) 151-250 O 0751-1000 so 51-100 . D 251-500 . 01503‘ OD 101-150 7 D 501-750 0 01501... “What institution are you acquainted with that deals with housing problems? .U None-Co to part IV: Migration 8:] Public institutions O ClLocal found. Om Worker's bank O Diortg. bank .0 Saving 8 loan assoc. , DOther 176 APPENDIX D-Z National Savings and Loan Bank MODALITIES 'Number of application . . . . . . . . . . . . . . . . . . . . Number of association . . . . . . . . . . . . . . . . . . . . Identification card number. . . . . . . . . . . . . . . . . . Purpose of loan . . . . . . . . . . . . . . . . . . . . . . . Purchase of Homes (1) Construction of Homes (2) Purchase of Apartments (3) Construction of Apartments (4) Number of rooms . . . . . . . . . . . . . . . . . . . . . . Number of baths . . . . . . . . . . . . . . . . . . . . . . . Location of structure . . . . . . . .'. . . . . . . . . . . . 'State (0), Municipality (00), "Barrio" or urbanization (000). Part paid of land . . . . . . . . . . . . . . . . . . . . . . Part paid of the construction . . . . ... . . . . . . . . . . Savings in the association. . . . . . . . . . . . . . . . . . Advanced payment. . . . . . . . . . . . . . . . . . . . . . . Second financing. . . . . . . . . . . . . . . . . . . . . . . Applicant's earnings. . . . . . . . . . . . . . . . . . . . . Amortization period . . . . . . . . . . . . . . . . . . . . . mount Of loan. 0 I O O O O O O O I O I O O O O O I O O O I 0 Date of acceptance. I O I O O O O I O I O O O O O O O O O O 0 Month: (00), Year (0) F.- o.— '--‘- —-___.——-———---__- ,Ffl-__.-__-- --—-—- —— ._....- -_O.—- -.. -7- L.___..- -.-_.» 4 ...—.—.—- ~— Area of construction . Value of construction. Value of outside labor Area of the land . . . Value of the land. . . Price of the land. . . 0 Application fees . . . Monthly payment. . . . Insurance (fire and life). Family group . O . . . Number of program. . . Sex’of family head . . Age of family head . . Occupation of family head. Tenure of previous housing 177 APPENDIX D-Z con't. Monthly expenditure of previous housing. . . . . woow>uooam . . .uo%o>usm "maowuovnoono .mmw. . woumuoaoou D. beam» 23 SE 0mm 3 7. E uaoammm manusoz no scum D aaumfiuam 2 . D mosh an . :oaoummén season“ . D ucoaunmafia D nausea ? _ F .63 saws.“ m: mammaom mo menswea- mcfimaom mo omNH as O msfimsom mo mowumauouuaumnu 178 moon , mmonvv< ilwopsaz.haaamm xooam uosua Houoom hawamm.mo coax mo mama ‘- ~~nausea; mo uuwuumao one no muwamafioacnx one «o mocou noon: muowvam zaafimm mo >v=um ecu now ho>wsm oponuuno mo zufimuo>fls= maoanoao> mo xcom Hmuucoo nonmomom usfioh mun xHQmem< J mu...“ ' O .m . S 8 M I 888 JUnd 1.0.0 8 S. S u 0 u n S 0 S S .J U OVA "3.... . Unu [ 3 3 u .h D 0 S m [.5 D D.“ .rrq 4. a. J S O m 0 M 1.. O H. 1 0 NO If n. S O 0J3 Km... 0...: D. . [u 0 3 9 I 3.6 9938 3u.u.u 31.08 .dIv-lo 890 I. (Jun .5 3 1i 0 HUJUJ O.O.” fiJUd J SUT11. 0 _ .U l\ a 0 J H 1w a 0 J J v A S .l 0 n all J U0 0 31¢ 3 m0 8 “I o u013n1n31u3 awoaul Ktqiuou dmoo 0 Ainauamat saoqmow Attmed Jo awoaul uo Jesus 179 ll q I use: " queues: 3:30“. 53.10 =50 use: Hausa luau. snags..." . shun-l: snack Do: U our 53: . . . c ,. . sounds—vor— “Jameson . slows—H anouuom uo nun-52 . _J_ ._.__J 5.9 nuns—Eu. _ _ _ _ _ _ _ _ j _ O _ x _ _ . _ . a _ . _ _ _ _ _ _ .1 . . mung-how an . . . . . D . .uuoauuauoum dunno-5a noun 333 No . a "nuance“. Iowa 2305. Us . . O 1 5 m3“. monsoon 1.3: La»: 353m 3333 noun-assoc 1:. 3 $4.3m Vanessa: >3!!— . .550 . ., I o m o . #303353 30:2...qu we sedan» ununuum sad-z u so": so: vacuum , Inez saga-«M30336 balm i 1' use». 1 coco: :2 ash—am Du ampupa— «and...» « aoauouou n.30o3 .;— 1. uoauumHQO4 . _ .. OO . J _ 1 . . manganese coca some 2 . . o..3m..n... . e ounce naumumswom a. 1. has mascaaam.a_ , woconm t 08o: mo uopgz .6 . Ozfl todaoaou a so.» o...mm . 1 . * xooan no neuumm :s . 1 mmounna . Hamuaousoom nu «=osucmm:nu . ocou a. . . muasmuoa a, oaa: mnamsom vomo>uam mo Houunoo new sowumuoa 1 4 18f) . Amway sumo coauamumamum unannom. so>u=m masmaom . HWIII.z MHHZD axe A .1 Hmmlmwn flhOh l nausea Hmcoausz was mofiumauaum mo cowuonumwcwavd Housman unannoao>oo we manage“: mamanoao> mo swansoom O .ovoo Hence one Have: nonowsae >H¢>aumuumaawaum on Haas suaaaauaovwmoou mo msowuuunmsw any «In NHQZNhhfi 181 "moonum>nomno Ozan sona— .moneuooz mo usaosm onu o>no sensuous H can» mama on node a so on: nsom no muse-nudge mcomcm moon :2 OzOu mono. .3. On nowa— hnnaaom . xoonmo>na .mnnnsmao man o>ww «Amhoxnau Omnoxunnov Huom on coco ones o>nn no xoonmo>wa o>mn mono: mnnu on sconce moon..a. nosoono man we was: one mpnu modom uwsoona no oaonnnm no on Oomso: mnnu cm :5. snow man an hun>nuo< unaccoom_.u {gadomov snow nonno D. Ahwnooowv.ocanflou D 2308 o .mmv condom D , nflfloommv out? nacho D now waning can pesto D « :onocnm: D a now 3mm aa.—”nuon man page D — . mac: Hens“ D n . swoon mannonnwno: a wannmsm mHHH w. ................... snow; mo anacoato— unmanned no 095: m wannsnm D n OO.O...OO....O.O.O.OO.... mamonvon H3093, . . wanvanan a.“ unoannoom D « OOO.OOOOOOOOOOOOOOOOOOOOOOOO.OOO.. .wBoOn Manon—TO: . uncomfm — maoom mo nongz J oaom we was“. :2 1 ‘1‘ one: monsoon mo ensues was manomnfioo Janna. . m 182 A.Uuu .unmn> >0: unansv “onenus>nunno “unannouoc ma mo :m:o«nu>numno: :« vananuouv no cucn an on w“ uo>o Onenmmow Ooh“ anon nae» o>nu .cwnmnOw nouV use: uzu no conunmoosou szu :« oweszu .on vonoflo asou ummn Ho>oH u>no canuausum .Od NQHGEU any i i a: 1‘ O2 .2 a! C3 A2 a: anmww 0H csnn novao auctn anu>n0usn as manna“: new connom nuuma> «0 :onnanumwusm an. vouno>nv one; . ounnnma ou< nononfiar oawcnm nun: u>wuv Imsnmnw oumnaunnm onosomsoz Owen mo unease: on< umnk mcomnom new sane on» O... O.» a unfi .mm» mu “unuummaon m". an ummanucn umaosm oz: omflo mnmru mu new naumnr .OS we soon 2E .convmnxu on” we HHm umnu .unon nomaos mnnn mo unnn a one znamnnon 0M3 mnemnmn omoxn .nmumn smug .omwun on“ we on»: on“ nuns unuum .socOnuNHom mcnmnom me soon ~nn. neat-noun”— 183 I unconus>nonno 2: AC So 23 A3 M: A3 E At SV SV 3v 3; 3 AC oonm no m «cm snooze uaaoou< son>num zununam unsouo< shadow : “Hmwwu Munch nuzuo «use: nunom Inaaozom cannon nonuo aonm =50 unnmuaon .mmn«>nm monsoon e . anonnu> :30 on nnonanum osoonH new anon nous: enoz Hon» amounm Ame : snuuceonu-usc sannun 184 4 . 1‘1 1.- fli . noOnuOn sac: no “.3533 unom . . OnNOn mount-now new dunmsom. — Hun mxom ME. mo mwfifiugg :«ONNON A . . zoom wswcuonu faunggu nonuo 2.3.N zoom _ snoop .3353 .396 "N.NNON. . . zoom momuonv soon “—3an «NOnuON O Luau . surnames ONONNON .38 «sown—onus a anonmoam was...“ 1 53 3% TAONNON . meow nnmoon oozm T.2.« can... as Fawn comm unsung TdONNON 50mm unwanwam “OM”; 11 zoom Confined—nan: wONNON zoom .4 anon—sun “.3.“ .l zoom 4 snouaonu. —m«O««ON o comm enema 03mm; mnONNOu . nomm 333m $0.3." noon. 3.13m WOONNOM , comm A sconce. Eco 31> ~58: Ammucgmefladmmaiwlwlféfi 43 .32 .HS .22 .3.” . $9. 3.. .. . 3n n. m n35. mmvmocooa xwvmosa >mvcof. «0 ~33. O éflumuwlvcomxwlrd 2505 3.5.. 1E35 o . 55 co.nN.n snow no.n~.n a comm No.n~.n . 53. Ho.n~.n n~.n oo.c~.n wo.e~.n :Omn no.ou.n munou co.qq.n noun; no.o~.n . nounm .mco eo.v~.n O 38 8.: .n r 8.: .n 1.538 9”” Ho.v~.n q~.n 1> oo.nu.n 8.2 .n O vo.n~.n no.n~.n No.nN.n .~o> .ua< fiaa> ..na< .~a> .un<. .Hs>.1;un< an: .nfim .Hm> .na< ..Hm> .ufid wmflnnm usOu oa~n> nnaoa< wmmmsm xmvnsnsm zmvnnm meomnsnr waommcmou_ amwmoau . >socox u“ «no» monn>nuon on monannuconxm uo unsou< . on D. mommonunux ano>oo macauuh mwsm mmmnnmnnnam one mnunnoHu Ouonnoa «anode; nonuo monvuau noonmnnm ooomonom Aoonuunx newv mongoose use nonounx .Axanuaonv Annownuuoau . duce a oases ozn mo noon>nuu nonno . Aomso: mov oncouu>onnu« was unnamed osonnoaon now nooshma mannuox ouspnsu new undated manage: nous: now unease“ mflsneoz unsunvcuaum mo «pox nonno no moaonnn< BIBLIOGRAPHY BIBLIOGRAPHY Asociacion Colombians de Facultades de Medicine. Urbani- zacion Mar inalidad. Bogota: Asociacion CSIom- Elana de Facultades de Medicine, 1968. Each, A. and Kybal, M. 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