NBS BUILDING SCIENCE SERIES 100

Building To Resist The Effect Of Wind

VOLUME 4. Forecasting the Economics of Housing

TA^ * Needs: A Methodological Guide

.U58

NO. 100-^

1 y ^ TMENT OF COMMERCE • NATIONAL BUREAU OF STANDARDS

' 19Z7

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;vo . voo-

^> In five volumes {\ 66 J^5.Ji;jv.i:.i. ..i)>M. "MJV^ ,

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NBS BUILDING SCIENCE SERIES 100-4

Building To Resist

The Effect Of Wind

VOLUME 4: Forecasting the Economics

of Housing Needs:

A Methodological Guide

Joseph G. Kowalski

Center for Building Technology

Institute for Applied Technology

National Bureau of Standards

Washington, D.C. 20234

Sponsored by: The Office of Science and Technology

Agency for International Development

Department of State Washington, D.C. 20523

U.S. DEPARTMENT OF COMMERCE, Juanita M. Kreps, Secretary

NATIONAL BUREAU OF STANDARDS, Ernest Ambler, Acting Director

Issued May 1977
Library of Congress Catalog Card Number: 77-600013

National Bureau of Standards Building Science Series 100-4

Nat. Bur. Stand. (U.S.), Bldg. Sci. Ser. 100-4, 36 pages (May 1977)

CODEN: BSSNBV

U.S. GOVERNMENT PRINTING OFFICE

WASHINGTON: 1977

For sale by the Superintendent of Documents, U.S. Government Printing Office, Washington, D.C. 20402

(Order by SD Catalog No. Cl3.29/2:100-4). Stock No. 003-003- Price $i.50 (Add 25 percent additional for other than U.S. mailing).
ABSTRACT

The Agency for International Development sponsored with the National Bureau of Standards, a three and a half year research project to develop improved

design criteria for low-rise buildings to better resist the effects of extreme winds. This report contains in- formation gathered from this project.

Project results are presented in five volumes. Volume

1 gives a background of the research activities, ac- complishments, results, and recommendations.

Volume 2 presents a methodology to estimate design

wind speeds and a guide to determine wind forces. In Volume 3, a guide for improved use of masonry

fasteners and timber connectors is discussed. Socio- economic and architectural considerations for Philippines, Jamaica, and Bangladesh are presented in Volume 5.

Housing is probably the single most important con- sumer good in most economies. Measuring the size of

a region's unmet housing need is a first step to planning and implementing improvements in housing

conditions. This report analyzes the concept of housing needs in an economic framework. A methodology

for estimating and projecting housing needs at the

regional level is developed. The methodology at- tempts to make explicit the income redistribution in- tent which is the core meaning behind the concept of housing needs.

Key words: Development; forecasts; growtli; liousing needs;

projections.

Hi

Cover: An overcrowded district in the Philippines. In locales such as this, physical damage from high winds may

be the same as in rural sites, but human losses are greater.

CONTENTS

1. INTRODUCTION I

1.1 The Normative Basis of Needs Estimates 2

1.2 Projecting Future Housing Needs 2

2. A NEEDS ESTIMATION AND PROJECTION METHODOLOGY 3

2.1 Estimation of Current Needs 4 2.1.1 Description of the Housing Inventory 5 2.1.2 Determining the Housing Standard 6

2.1.3 Determining the Needs or Shortfall Matrix 7 2.1.4 Calculating the Cost of Meeting Housing Needs 8 2.2 Projecting Future Housing Needs 10

2.2.1 Projecting the Housing Characteristics Matrix 10

2.2.2 Projecting the Population Matrix 12 2.2.3 Combining the Projected Matrix 12

3. AN ILLUSTRATION OF THE NEEDS ESTIMATION METHODOLOGY 13

4. THE IMPACT OF A CHANGE IN BUILDING TECHNOLOGY ON HOUSING NEEDS 29

5. SUMMARY AND RECOMMENDATIONS FOR FURTHER RESEARCH 21

5.1 Summary 11 5.2 Recommendations for Fiarther Research 22 APPENDIX A An Economic Interpretation of Housing Needs 23 APPENDIX B Derivation of the Housing Standard 29 ACKNOWLEDGMENTS 30 Figures

Fig. 1 The Demand and Supply of Housing 23

Fig. 2 The Demand for Housing Stock and the Demand for Additions to the Stock 24

Fig. 3 The Housing Market in Equilibrium 24

Fig. 4 The Adjustment of Housing Market to an Increase in Demand 25

Fig. 5 Consumers' Surplus 26

Fig. 6 Increase in Consumers' Surplus due to a Fall in Market Price 26

Fig. 7 An Example of the Gains in Consumers' Surplus Being Greater than Cost—Resulting from

a Program Designed to Meet Housing Needs 27

Fig. 8 An Example of the Benefits of Fulfilling Housing Needs Being Less than the Costs of Meeting those Needs 28

Fig. 9 Determining the Resource and Subsidy Costs of Meeting Housing Needs 29

Fig. 10 Measuring the Redistributive Impact of a Housing Needs Program 29 Tables

Table 1 Basic Research Steps in the Calculation of Current Housing Needs 4 Table 2 The Array of Housing Characteristics Required as Input to a Regional Needs Study .... 5 Table 3 Housing Characteristics Matrix 6 Table 4 The Cost Effectiveness of Meeting Housing Needs as a Means of Redistributing Income

(Sample Format) 20 Table 5 An Illustrative Housing Characteristics Matrix for a Hypothetical Urban Region

Renter Occupied Housing 14
Table 6 An Illustrative Population Characteristics Matrix for a Hypothetical Urban Region

Renter Occupied Housing 14 Table 7 An Illustrative Housing Quantity Per Capita Matrix for a Hypothetical Urban Region

Renter Occupied Housing 14 Table 8 An Illustrative Matrix of Housing Prices for a Hypothetical Urban Region—Renter Occupied Housing 15 Table 9 An Illustrative Matrix of Relative Housing Prices for a Hypothetical Urban Region

Renter Occupied Housing 15 Table 10 An Illustrative Matrix of the Shortfall in Housing Stock Per Capita Adjusted by Relative

Price when Compared to the Designated Standard or Target 15 Table 1 1 An Illustrative Matrix of Housing Needs for a Hypothetical Urban Region—Renter Occupied Housing 15 Table 1 2 The Real Income Gains of the Occupants of Renter Occupied Housing Arising from Fill- ing Housing Needs (Redistribution Effects) 16 Table 1 3 Annualized Net Resource Cost of Implementing Redistribution Effects Associated with

Fullfilling Housing Needs 17 Table 14 Cell-by-Cell Ratio of Redistribution Level to Net Resource Costs of Implementation .... 17 Table 15 Matrix of Housing Needs—Second Iteration 17 Table 1 6 The Real Income Gains of the Occupants of Renter Occupied Housing Arising from Fill- ing Housing Needs—Second Iteration 17 Table 17 Annualized Net Resource Costs of Implementing Redistribution Effects—Second Itera- tion 17 Table 18 Cell-by-Cell Ratio of Redistribution Level to Net Resource Costs of Implementation

Second Iteration 17

vi

Facing Page: Squatter settlements are the most visible manifestation of housing need as shown in this over- crowded district in the Philippines.
1. INTRODUCTION

Determining tiie size of a region's unmet housing

needs is an important first step to planning and enact- ing public policies and programs which will improve

the condition, size and quality of a country's housing

inventory. The assessment of current and future housing needs is particularly important in the developing

regions of the world where the twin pressures of population growth and urbanization especially aggravate the housing problem.

This report develops a basic methodology for estimat- ing and projecting housing needs. Procedures are developed to compare the costs with the potential income redistribution effects of meeting the housing

need shortfall. The condition, location, size, and qualit\- of the housing inventory are important elements in the set of fac- tors which describes a country's standard of li\ ing.

This is irrespective of the stage of economic development or the organizational basis of a ctiuntr\ 's economic system. Housing is probably the single most important consumer good in most economies. Housing expense is often the single largest component of a household's monthly budget. The public attention given housing often centers around the univ ersal

1
complaint that housing costs too much. Other

problems, slums and squatter settlements, congestion,

crime, and overcrowding become intertwined with

the heightened concerns over the adequacy of an

area's housing stock. Miserable housing conditions are

the most visible manifestation of an area's poverty

problem.

Such perceptions, if they are to be translated into

realistic and effective public intervention, require ac- curate assessment and definition of the nature and

size of the problems that exist in the housing area. Providing such measurement is the general purpose of studies of a country's or region's housing needs or re- quirements.

The twin pressures of population growth and ur- banization are forcing the developing nations of the world to make careful measure of their present and

future housing needs. Such assessments are an imperative input to the efficient allocation of scarce public resources. Public housing programs must compete for the public dollar with other vital social programs. The purpose of this report is to clarify some of the

issues that are involved in the measurement of housing need and to provide an implementable housing

needs estimation and projection methodology. First we shall discuss the economic issues involved in housing needs estimation. Next we shall present in some

detail a step-by-step description of the procedures for estimating the current level, and projecting the future

level of housing needs. A detailed example will il- lustrate the methodology. Finally we shall discuss the modifications to the methodology that are implied by

the development of a bu i Iding technology for lowrise/low-cost buildings to better resist extreme winds.

1.1 THE NORMATIVE BASIS OF NEEDS

ESTIMATES

At any particular point in time, a country's or region's

housing need is measured by the number or value of additional units that would be required to bring the current inventory up to some pre-defined standard of

acceptablility. Given this definition, the subjective or normative basis of housing "needs" or "require- ments" is clear. What is judged as acceptable housing

conditions will vary between countries and between

regions of a country. Differences in the minimally ac- cepted standard will be influenced by income,

methods of construction, climate, custom, and expectations. Only if the standard of acceptability is clearly

defined and the underlying normative basis for it is widely agreed upon, will the meaning of the needs

estimate be unambiguous and useful. Often in needs

studies the housing standard against which need is measured is not explicitly stated. Housing needs

studies usually imply their standard through a process

of elimination. For example, often what is done is to examine current housing statistics to identify the number of housing units which are substandard by some physical criteria, and then to count or estimate

the number of families that are living in doubled up

arrangements. The sum of these components is taken

as the magnitude of unfilled housing needs. The notion of housing need can be clarified and its subjectivity modified by examining the economic

meaning behind it. By definition an individual is in need if he or she does not have at his or her disposal an adequate amount of housing stock or housing

capital. In order for the policy maker or reader to judge if the need level is reasonable an explicit defini- tion of the housing standard or target level must be

stated. A housing need study which only defines

situations in which housing need exists and does not

specify the minimum socially acceptable level that each consumer should have (i.e., how much should

exist) has only fulfilled part of its function.

1.2 PROJECTING FUTURE HOUSING NEEDS

A housing needs study is concerned with more than

the assessment of the size of the housing gap at present. It also is concerned with projecting the future position of that gap given certain assumptions about

the path of housing production. Furthermore, a housing needs study must identify those sectors in the economy which are to receive the subsidies that are implied in meeting housing needs. These are difficult research tasks. To make a projection we must perform three tasks which are at the conceptual core of a housing needs

study. We need to ( 1 ) assess the future standard, which

will be influenced by conditions of supply and de- mand in the future, (2) to determine the gap between

the actual per capita quantity and the target per capita

quantity, and (3) to project the future size of the population.

In actual practice, it is very difficult, if not impossible,

to obtain accurate projections or forecasts of precisely these constructs, given the nature of the existing data.

In practice, an analyst will have to rely upon the in- formation that is available. Nevertheless, the concepts

of benefits and costs and the use of an economic model

are useful in defining the nature of the research task and in guiding the specification of a projection

methodology. In the next section a methodology for estimating and projecting housing needs is presented.

The economic conceptual model which guided the development of the methodology is discussed in Appendix A.

Facing Page: When land is at a premium, population densities reach up rather than out; as here in Hong Kong.

2
2. A NEEDS ESTIMATION

AND PROJECTION

METHODOLOGY

Two general researcli strategies can be used to deter- mine current and future levels of housing needs. The

first may be termed the national approach. In this ap- proach the current and future inadequacies of the housing inventory are analyzed from a national

perspective. A standard or target is determined and a country's housing stock is measured against this national standard. Usually a governmental housing or planning agency located in the central government is responsible for this type of research.

The second basic strategy is a decentralized approach

to housing needs research. This approach requires

that regional housing or planning agencies complete

housing needs studies for their respectix e jurisdictions

and that the local current and projected le\ els of housing needs be summed to obtain the national need.

Let us examine some of the factors supporting the

regional or summation research strategy for the deter- mination of housing needs. Many of the same argu3
merits used to indicate the inappropriateness of ex- pecting that some iiousing standard would be applicable across countries support the view that a single national standard would be inappropriate to apply

across the diverse regions within a particular country.

Of course, the problems of a single standard have considerably less relevance in a country with a high

degree of regional homogeneity. Examples of such

countries are rare, however.

The existence of wide regional divergences and variations in culture, weather conditions, levels of income

and development, income distribution, methods and

costs of construction, degree of urbanization, and ex- pected population growth indicates that a de- centralized approach to housing needs determination

is appropriate. The regional approach enables these

regional divergences to be reflected in the separate

regional determination of the housing standard or

target. Tliis approach is also consistent with the economic reality that housing markets are not national markets, but local markets. Regional

differences would most easily be reflected in housing

standards if the needs projections are the products or determinations of those most familiar with their local housing market conditions.

If a coordinated regional or summation approach is to be feasible several requirements must be met. First, the regional housing needs studies must follow a consistent needs estimation and projection methodology.

Also, it is necessary that overall coordination and

planning remain in some centralized housing or planning agency since the reconcilation and summation of the regional or local needs studies can only be ac- complished at one point. Finally, the separate needs

studies should share common factual assumptions

about future population projections. Again this re- quires a centrally located research agency to supply

the regional researchers with consistent population

and migration projections.

The purpose of this section is to provide one input to the regional or summation housing needs research

strategy, i.e., a basic methodology of estimating and

forecasting housing needs. The methodology is deisgned to be flexible. It will accommodate data scarcities and can be implemented by decision makers

with minimal background and/or training in social sciences.

2.1 ESTIMATION OF CURRENT NEEDS

Housing needs estimation involves the assessment of the size and nature of current deficiencies in the housing inventory and the projection of the future inadequacies in the inventory. Dichotomizing the research

task in this way allows us to keep separate two major

issues: (a) how does one calculate the level of housing

need at a particular point in time, and (b) what role

does projecting population growth and housing

growth play in housing needs estimated for future

years. We shall first concentrate on the question of the determination of current needs, and look later to the methodological issues involved in the projection of housing needs in the future. The calculation of current housing needs involves 11 basic research steps. These tasks can be apportioned

into 4 major areas of investigation or analysis, (see ta- ble 1). The first major area of analysis involves the ac- curate and relevant description of the housing inventory in detailed statistical terms.

TABLE 1. BASIC RESEARCH STEPS IN THE

CALCULATION OF CURRENT

HOUSING NEEDS

I. Description of the Housing Inventory

Task 1. Construction of a Housing Characteristics Matrix

Task 2. Construction of a Population Matrix

Task 3. Construction of a Per Capita (per family)

Housing Characteristics Matrix

II. Determining the Housing Standard

Task 4 Selection of Target Cell from the Per Capita Housing Characteristic Matrix

III. Determining the Needs or Shortfall Matrix

Task 5. Construction of a Matrix of Relative

Housing Values and Rents

Task 6. Construction of a Quality Deflator Matrix

Task 7. Cell by Cell Calculation of Housing

Needs

Task 8. Distribution of Housing Needs by Construction Type and by Income Class

IV. Calculating the Cost of Meeting Housing Needs

Task 9. Definition of Low-Income Housing

Task 10. Construction Cost of a New Standard

House: Total Annual Public Cost Deter- mination

Task 11. Cost Evaluation of the Redistributive Impact

The availability and accuracy of the data set will determine the overall accuracy and detail that is possible in the final needs calculations. The second

area of analysis requires the specification of the housing standard or target. This will be based on inspection of the data matrix arising out of the first major

task area. The third major research area involves the conversion of the housing matrix into housing stan- dard equivalents, and the calculation of cell by cell shortfalls of the housing inventory matrix. Finally, the housing need level has to be costed, and this total

4
cost can then be subjected to an analysis of its cost- effectiveness of redistributing income. If the total in- come redistribution gains of filling housing needs is less (more) than the implied resource costs, then a new, lower (higher) standard can be chosen and the housing needs estimates be reiterated.

2.1.1 Description of the Housing Inventory

Task 1. Construction of a housing characteristics matrix:The goal of this task is the construction of a data matrix which has the number of housing units arranged by locational/tenurial/physical characteristics and cross classified by the income class of the occupants' households. This matrix in effect will divide the housing inventory into a set of mutually

exclusive submarkets.

The characteristics of the housing inventory that are

of interest will include the location of the housing

units (rural or urban), the tenure class of the housing

units (owner occupied, renter occupied, or squatter),

and the physical attributes of the units (construction

type or the soundness of the condition, or the age). Table 2 illustrates a possible array and organization of the housing inventory. The degree of detailed

subclassification will be determined, for the most part, by the adequacy and detail of the available census

data. It would be desirable if data were available to

classify the housing inventory in even greater detail

that a national census will have the regional dwelling

unit data already so classified. If not, a method of dis- tribution is required.' Ideally, the data in table 2 would be extended into a matrix as seen in table 3. Each cell in this matrix will contain a measure of the number of dwelling units or the total number of rooms.

Task 2. Construction of a population matrix:The

goal of this task is to construct a population matrix

which in combination with the housing characteristics matrix of task 1 will enable a cell by cell computation of housing units per capita. The column and row headings of this matrix will be identical to that of

table 3. However, instead of housing unit data comprising the cellular entries, population data is re- quired.

The construction of the population matrix also may

require an estimation or data development procedure.

For example, data on the population size of urban

renters may be available but not be distributed by in- come class. In this case it would be reasonable to assume that the total or urban renters could be dis- tributed by income classes according to the national

distribution of the urban population among income

classes. The population of rental income class must

then be distributed according to their division among

building construction types. Information fordeterTABLE 2. THE ARRAY OF HOUSING CHARACTERISTICS REQUIRED AS INPUT TO A

REGIONAL NEEDS STUDY

The Number of Dwelling Units or Total Number of Rooms

LOCATION URBAN RURAL

TENURE Owner

Occupied

Renter Occupied Squatter Owner

Occupied

Renter Occupied Squatter CONSTRUCTION

TYPE^

S M L S M L S M L S M L S M L S M L

^ S: Strong M: Mixed

L: Light

than indicated by table 2. For example, the subcategory of age could be made a subset of the construction

type category. Another subset of interest, if data were

available, would be by condition of unit, i.e., sound

dilapidating, or deteriorating.

Once the cell totals" for an array have been deter- mined, the next step is to distribute the dwelling unit

totals in each cell among income classes. It may be

'Let us assume that the analyst only has available an income

distribution by families or households which is cross

classified by urban or rural and not by any other iiousing

characteristic. Two alternatives exist in a situation like this. Either the analyst requests and is able to get an unpublished

cross-classification from the census bureau, or he or she employs some method of interpolating the totals that are

available, among the construction types. Once again there may exist some description in a related area which will allow the construction of reasonable distribution procedures.

5
mining this distribution by construction types may be

available from tangential sources such as reports, sur- veys, or documents containing estimates of family

size by income class. Also data may exist on the extent

of "doubling up", two or more families occupying one

dwelling unit, which in turn is related to the physical

characteristics of dwelling units. These data could be

combined using specific assumptions in such a way

that the analyst would be able to distribute the population of an income/tenure class among the construction material categories. No set rules for data development are available because each country's

data contingencies will dictate the kinds of adjust- ments which may be required.

Task 3. Construction of a per capita (or per family)

housing characteristics matrix:This task is a straightforward division of the elements in the housing characteristics matrix by the elements determined

in task 2. The result will be a cell by cell or submarket

by submarket) portrayal of housing quantity per

capita, h j^/popj j. It permits comparisons between

sentially one of judgment. The analyst must designate

a target which represents the minimal socially acceptable level of housing consumption per capita. This

target, or standard, will be relative to the existing conditions in the current inventory. Thus, initially the analyst can designate one cell in the per capita housing characteristics matrix as depicting or representing

the highest standard of housing which should be at- tained by all household units. Separate standards can

be designated for the urban population and rural population.

In effect, the housing needs analyst makes the initial judgment that the housing quality and per capita

quantity represented by one cell of the per capita

housing characteristics matrix is what should be

available for all. It could be, for example, the kind of urban (and rural) owner occupied housing of mixed

construction consumed by the middle income class. This particular cell(s) of the per capita housing characteristics matrix is (are) designated as the standard or

target cell(s). It becomes the point from which the adeTABLE 3. HOUSING CHARACTERISTICS MATRIX

The Number of Dwelling Units or Total Number of Rooms

Housing

V Charac- \ teris- \ tics

In- \

come \

Class of \

Occupying\

Households \

LOCATION URBAN RURAL

TENURE

Owner

Occupied

Renter Occupied Squatter Owner

Occupied

Renter Occupied Squatter Subtotals CONSTRUCTION

TYPE'i s M L S M L s M L s M L s M L s M L

0 - ^^,IHII)

^ - 5,000

5 - 7,000

7 - 10,000

10,000 + TOTAL

a S: Strong M: Mixed

L: Light

cells of the amount of housing stock or housing service consumed by each income/housing type submarket of the housing stock.

2.1.2 Determining the Housing Standard

Task 4. Selection of a target celKs) from the per

capita housing characteristics matrix:This task is esquacy or inadequacy of housing conditions in the

region, as portrayed in the other cells of the matrix, is measured.

The advantage of this procedure lies in the clear meaning of the standard. Designating, say the housing

type and per capita quantity of a particular income

class as the standard is unambiguous and allows the

•6
reasonableness or unreasonableness of the standard to be perceived by tlie audience of policy makers. The

standard represents an implicit judgment by the housing analysts about the inadequacy of the income dis- tribution. The remaining tasks of a needs study can be

viewed as translating such an implicit judgment into

explicit terms.

2.1.3 Determining the Needs or Shortfall

Matrix

Task 5. Construction of a matrix of relative housing values and rents:The entries in the housing

characteristics matrix represent housing units which

vary widely in quality. Housing is a very complex

good with many factors determining the quality of one unit relative to another. Thus, the units in the

different cells of matrix 1 are in a real sense non-comparable. The purpose of this task and task 6 is to arrive

at a means by which the quantity units of matrix 1 can be adjusted for quality and thus be susceptible to direct comparison.

The simplest and most sensible measure of variation

in quality is recorded in judgments of the market

place in its determination of relative price. This means that if quality adjustments are to be made, a matrix of average housing prices is required. This means that a matrix comparable to that described in

table 3 must be constructed with housing value (if owner occupied), and rental values (if renter or squatter occupied) as the cell entries.

This is a difficult task and will require expertise and

knowledge on the part of the local needs analyst. Because there is wide regional variation in the struc- ture of housing prices, the completion of this task is only feasible at the regional level. Evidence suggests that such data are difficult to locate. Thus, the housing needs analyst will probably find it necessary to canvas local expert opinion in order to ascertain the actual structure of housing market

prices. Of course sample survey techniques are more

accurate, but the cost of a special census of current housing prices at the regional level are prohibitive.

Task 6. Construction of a quality deflator matrix:The housing price and rental data matrix of

task 5 can now be converted into a matrix of relative values—relative to the value or rent of the price of housing in the standard or target cell. This will result

in a cell by cell comparison of the price or rent in any

particular cell to that of the price of standard housing.

In other words, this task requires that each cell of this matrix of housing prices and rents be divided by the

price or rent of the standard or target unit.

In order that the value cells in the owner-occupied

columns be comparable to those in the renter and

squatter-occupied columns, the housing values in the owner-occupied columns must be converted into ser- vice values or prices. Thus, if a owner-occupied unit has a stock value of'$I 5,000, it would be necessary to convert this into a gross rental value of $1,500. This can be done by assuming a gross rent/price ratio of a given magnitude based on the following equation:

R/P = 4- V,, + (1)

where

R/P = the annual gross rental receipts divided by owner occupied value,

Vd = the rate of depreciation, maintenance, and

repair expenditures,

Vj^ = the tax rate on housing services, and

Vy = the rate of return on housing services.

The rate of return on housing services will be at least as large as the mortgage rate currently prevailing in the housing finance market.

7
Task 7. Cell by cell calculation of housing needs:

Housing needs now can be computed for each cell. This will result in a new matrix, the housing needs

matrix. The cell-by-cei! computation is relatively

straightforward. For each cell we wish to calculate need level, Njj. It will be the product of two terms: the per capita shortfall from the designated standard in housing quantity per capita times the population in that cell. Equation 2 specifies this procedure. The first term on the right-hand side of equation 2 represent

the per capita shortfall in cell ij.

N ij=(S-hij/popij Dij)x popij (2)

where

S

hjj/popi

>]

Dij

POPij

= per capita quantity observed in the standard cell. = per capita quantity in cell ij,

= the deflator value in cell ij,

= the population of cell ij.

For example, let us assume that the income row i represents the $3,000 to $5,000 income class and the housing characteristic column j is urban rental housing of medium construction. Assume that the standard

cell has an observed per capita quantity ratio of 1/5 while that for cell ij is 1/8. Also assume that the rental value deflator D; j is 0.70 (calculated in task 6) and that the cell contains a population of 5,000 (calculated in task 2). This data results in a need estimate of 562.5

units (= 10.20 -(0.125 x 0.7)J 5,000). This means that 562.5 units comparable to standard units would have

to be added to the housing stock of cell ij, either by new construction or in the form of rehabilitation of existing units, in order to bring housing in that portion of the housing inventory up to standard. Thus in

this cell the 562.5 units of standard quality plus the

437.5 existing units of standard quality (437.5 = 0.7 X

625) add up to a final total of 1,000 standard quality

dwelling units. Thus the goal of 0.2 standard units per

capita has been attained.

Task 8. Distribution of housing needs by construction type and by income class:The matrix approach

has the advantage that the distributions of housing

needs by income class or by urban/rural and building

characteristic will automatically result from the summation of the row entries and column entries. Some

cells, particularly those in the higher income rows,

will probably contain negative entries in the needs

matrix. This means that housing in those submarkets

surpasses that of the standard. 2 Thus, when summing

the rows and columns of the need matrix the negative

entries should be set equal to zero, indicating that no

housing need exists in those submarkets because the housing quantity per capita exceeds that of the housing standard specified in task 4.

2.1.4 Calculating the Cost of Meeting

Housing Needs

Task 9. Definition of low-income housing:

Defining a low income unit is equivalent to defining

how much the low income part of the population is willing and able to pay for the standard house or dwelling unit which has been defined in task 4. Thus,

this task is essentially one of determining that price or

rent level which can be afforded by the poorer part of

the population.

The income-class, price data of task 5 can be used to accomplish the analysis of the income and housing

price relationship. The data of task 5 can be inspected

to determine the current average price of housing

which isbeing paid by each income group. If facilities are available, a simple regression of housing prices(or

rents) can be estnnated as a function of income using

the data from task 5. Thus, the estimated relationship.

(3)

"A negative entry will arise in equation 2 when S< hjj/popjjXDj;

where P represents housing prices or rent, Y is the measure of income, and e is the residual, can be used

to determine what housing price can be afforded with

a given particular family income, Y. Once the low-in- come level of income is defined according to the

region's definition of relative poverty, the maximum

price of a low income dwelling unit can be deter- mined by substituting the low-income cutoff in the estimated regression and solving for the price or rent

level. Any dwelling unit renting for a higher price or whose market value implies a higher inputed rent can

not be considered as a low-income house. The

differential between the maximum price of housing

that the poor can afford and the price of the standard

dwelling unit describes the subsidy per dwelling unit

that will be necessary to fill the low-income populations' housing needs. Task 10. Construction cost of a new standard

house; total annual public cost determination: The

result of task 8 is a detailed estimate of the number of new standard housing units required to bring the housing stock up to the socially desirable standard. These estimates are categorized two ways: by income

class and by housing type. In order to determine if these housing needs are worth meeting, or, to deter- mine if the housing standard or target has been set too high, an estimate of the annualized public costs of fill- ing these needs is required.

A housing needs study does not require an elaborate and detailed cost estimation. Only an order of mag-
nitude estimate of cost is necessary. The cost concept

involved is tiie annual public subsidy required to supply standard housing to those who can not afford the

cost of standard housing (see task 9). Tiie additional

standard/needed units will not be supplied by the building community unless the subsidy is granted

since those for whom the housing is intended cannot

afford the standard house at its going market price.

The main capital costs that require estimation are the average unit costs of land acquisition and development and the average cost of construction of a new

unit which is comparable to the standard or target

unit. These total investment costs can then be an- nualized using an appropriate length of life and an

appropriate interest rate. The difference between

these annualized costs and the average yearly rentals

that the poor can afford represents the annual net resource costs which must be borne by the public sector. The total size of the annual governmental housing

subsidy, the public cost of meeting housing needs, can now be computed. The total public cost can be computed either on a cell-by-cell basis and summed or computed only for each income class and then summed. Within each cell or for each income class, the

total cost will be the product of the subsidy per unit times the number of needed units which require subsidization.

Task 11. Cost evaluation of the redistributive impact:The costs discussed above in task 10 have the primary purpose of attempting to raise the real income

levels of that portion of the population residing in housing which is less than standard. Augmenting the inventory in those housing submarkets where the need is the greatest will generate real income in- creases in those submarkets. The real income increases

will accrue to those who will occupy the subsidized

housing. Also of importance will be the impact on real incomes which will arise because of the price effect that will occur because of the significant increase in the size of the housing stock. A fall in the price of housing services should occur because of the increase

in supply. This will raise real income levels by reducing rent levels. Thus the primary effects of flilfilling housing needs are to be found in the changes in real income of those at the lower end of the income dis- tribution. The income increases discussed above are in effect transfer payments. This is the case since the lower housing prices, while of benefit to those not owning the stock (the renters), are net losses of rental receipts to the holders of the stock. The redistribution from the landlord to the tenant (in owner-occupied

units the landlord and tenant are one and the same) is not done costlessly. The costs discussed in task 10 represent the public sectors' administrative or income

redistribution implementation costs. In evaluating the reasonableness of statements about housing needs the policy makers of the public sector should be appraised

of the cost effectiveness of these implementation costs since alternative methods of achieving the same level of redistribution may be available. Thus the last task

of the needs estimation methodology is to assess the

size of the potential income redistribution and to identify the gainers and losers in each cell of the housing characteristic matrix. The amount of the income increase (redistribution)

within any particular submarket (or cell in the housing characteristic matrix) will be measured by the gain

in consumers' surplus^ which will arise because of the

fall in the price of housing services due to the increase

in the size of the inventory needed to bring that sub- market up to standard. Thus if the need level in a particular submarket cell has been computed to be x percent of the existing inFor an explanation of consumers' suplus, see Appendix A.

9
ventory, one must ask: What effect will an x percent

increase in the size of the inventory have on housing

prices?. Answering such a question presumes

knowledge of the price elasticity of demand'' for housing in that particular submarket. For the moment,

assume reliable information about price elasticities is available. The gain in consumers' surplus can be ap- proximated by the change in price, Ap,^ times the existing number of standard units, h, plus one-half times

Ap times the additions to the inventory(l/2Ap • Ah).

The cell-by-cell calculations of consumers' surplus

gains will, when summed, yield the income

redistribution change that would be associated with

attainment of the housing standard and the elimination of housing needs. This change in the income dis- tribution can then be compared to the resource costs or implementation costs which have been calculated

in step 10. It may be that the cost effectiveness of redistribution via the fullfillment of housing needs is competitive with other programatic approaches. If the housing needs levels and the income redistribution gains are not cost effective it may be that the housing standard has been set too high. A reiteration of the needs estimate may be called for where a lower

standard or target has been specified. The implementation costs will be lower and it may be that cost effec- tiveness will be increased. A table like table 4 below

can be computed as a means of explicitly presenting

these considerations to the policy maker.

TABLE 4. THE COST EFFECTIVENESS OF

MEETING HOUSING NEEDS AS A

MEANS OF REDISTRIBUTING

INCOME

(SAMPLE FORMAT)

(1) (2) (3) (4) (5)

STANDARD

use

Nl-.F.D FSTI- MATF

IMPLH- MFNTATION

COSTS

INCOME

REDISTRI- BUTION

AMOUNT

COSTEF- FECTIVE- NESS

(S)=(4)/(3)

A

BC

D

iPrice elasticity of demand is discussed in more detail in Appendix B.

5 The pr ice change will be equal to the product of the percentage change in quantity, Aq/q, times the price per unit in that submarket divided by the price elasticity of demand.

2.2 PROJECTING FUTURE HOUSING NEEDS

In the context of the needs methodology discussed above, the projection of future housing needs involves

applying tasks 1 through 11 to data which represent values that are expected to exist in the future projection year. Thus, the housing need analyst will be re- quired to modify the current needs matrices so they

contain projected values. This section discusses ways

in which the projections can be feasibly ac- complished.

2.2.1 Projecting the Housing Characteristics

Matrix

The problem involved in projecting the housing

characteristics matrix is in finding a set of factors which when multiplied by the elements in the housing characteristic matrix of the base or current period

yields reasonable projections of the corresponding

future values. If we let the current housing characteristics matrix be denoted by HC-, the set of projection factors be denoted by matrix F, and the projected

future housing characteristics matrix by HC^, then the conversion of HC^ to HC^, can be viewed symbolically as the operation of (in matrix notation):

HCC ^ F ^ HCf

(ixj) (jxj) (ixj)

Thus, the housing needs analyst must find a reasonable approximation of F. To further simplify the problem let us assume that F can be a diagonal matrix,

i.e., one in which the non-diagonal elements take on

zero values. If this is the case, then an element of F along the main diagonal will be equal to

fj =hcfij/hcCjj (4)

that is, the ratio of any element i from column j of the

future housing characteristics matrix divided by the corresponding i^h element from column j of the cur- rent housing characteristics matrix. This is equivalent

to assuming that all the elements a given column of the current matrix will grow at the same rate. In other words we are assuming that urban housing of a given

tenure and building characteristic type will increase

at the same rate across all income classes. This is not as

restrictive an assumption as it may first appear since

it is likely that a particular kind of housing, say owner-occupied of strong construction, or squatteroccupied of light construction, will be concentrated in a few income classes. This assumption will considerably ease the problem of projection. Without such an

assumption, the housing needs analysts would be re- quired to make separate projection of each cell of the current housing characteristics matrix. The j*^'^ diagonal element of the growth factor matrix

20
will be based on the net rate of growth:

Fi=(l +gj-dj)'\ (5)

where

gj = the projected annual average rate of gross additions to the housing stock of housing

type j' dj = the projected annual average rate of depreciation from the housing stock of housing type j, and

n = the length of the period in years over which the projection is to be made.

Thus, the projection problem is essentially one of determining for each housing type (for each of the col- umns of the housing characteristics matrix) reasonable

estimates of the net growth rate. The net growth rate

is the sum of two rates as seen above. The rate of gross additions of housing type j can be based on past ex- perience. Annual data on new construction by build- ing type, location, and tenure class can serve as the

basis for calculating gj. If a trend is observable in a time series of gj, the value of gj can be modified to

reflect projections of that trend. Annual data on losses from the inventory by housing type may also be

available; if not, alternative assumptions about the

deterioration rate can be substituted. For example,

different lengths of life can be assumed which are based on the kind of construction material used in the dwelling unit. Straight-line depreciation can be

assumed and the depreciation rate can be calculated as one over the average length of life of units of a given

construction type. Alternatively, an estimate of the projected rate of net additions (naj = gj ~ dj) can be in- cluded. This can be based on comparisons of the size of the inventory of housing of type j as revealed in the decennial census for 1970 and the census for 1960.

2.2.2 Projecting the Population Matrix

As was the case in the discussion above, the problem

is again one of finding a set of factors by which the base period population matrix can be multiplied to yield estimates of future population. A workable ap- proach to determining such factors even where data is extremely limited is to multiply all the cell entries in the base period population matrix by a factor, G,

G =(1 -I- p -I- nmr)" (6)

where

p = the rate of natural increase in the population nmr = the net migration rate of the region, and

n = the length of the period in years over which

the projection is to be, made.

Most central governments develop estimates of the projected rate of natural increase by region. Similarly,

they may project nmr by region.

The population projections can be refined. As was the

case in the housing matrix discussed above, a diagonal

population growth factor matrix, PG, can be constructed. For example, the population growth factor matrix could assume that all cellular entries will grow

at the same rate of natural increase but that the migration rate within a region will be positive in the urban

sectors and negative in the rural sectors. Further sub- divisions are possible. For example, within the urban

sector, the migration rate can be adjusted to reflect differences by tenure class. Specifically, a higher rate can be assumed for squatter occupied housing than for owner occupied or renter occupied housing. These

rates of migration can be based on recent historical ex- perience and adjusted according to the conditions that are expected to prevail over the projection period.

11
Special studies may exist which can allow the analyst

to justify the rates of growth that are entered in the diagonal matrix. The employment of a diagonal

growth factor projection matrix implicitly assumes

that the distribution of income will remain

unchanged between the current and future periods.

2.2.3 Combining the Projected Matrices

The above two projection tasks can now be combined

and a per capita housing characteristics matrix can be

constructed for a future period. This step brings us through the analogous task 3 as outlined above. From

this point, there are only slight deviations required

from the remaining calculations outlined for current

needs. The procedure for selecting a target cell is identical to task 4. Even the matrix of relative housing

values and rents is best left unchanged from that calculated for current housing needs. This does not mean that no price changes are expected to occur. In- flation may in fact exert a large influence on all housing prices. Nevertheless, since our interest is in real relative prices, the assumption that the matrix of rela- tive housing prices and rents of task 5 applies to the

future period avoids the construction and estimation

of simultaneous housing price determination models.

The housing analyst can then proceed with tasks 6 thru ! 1 , and obtain estimates of housing needs for a future period.

12

Facing Page: Housing projects, like this one in Kingston,

Jamaica, are required to meet housing needs.
3. AN ILLUSTRATION OF THE

NEEDS ESTIMATION

METHODOLOGY

The purpose of this section is to illustrate the basic

logic of the needs estimation methodology developed

in section 2. A numerical example—strictly hypothetical and highly simplified—has been developed

so as to illustrate the iterative nature of the needs

estimation methodology. This section can not be used

as a guide for the data development and forecasting

problems that the needs analyst will encounter. However, if the analyst follows this example through,

he or she will come to grips with the overall strategy

of the recommended methodology and will be in a better position to develop a data acquisition and

forecasting strategy consistent with the goals of this methodology.

Table 5 categorizes the renter occupied housing stock

of a hypothetical urban area into ISsubmarketsbased on structural characteristics and on the income class

13
of the dwelling units' household occupants. Since

housing needs will presumably be the most pressing

in the lower half of the income distribution, the matrix has a highly aggregated income class 5

wherein 50 percent of the renter occupied housing is located. As can be seen in this illustrative matrix, 20 percent of the housing stock is of light construction w ith 86 percent of these building units falling into in- come classes 1 through 4.

TABLE 5. AN ILLUSTRATIVE HOUSING

CHARACTERISTICS MATRIX

FOR A HYPOTHETICAL URBAN

REGION—RENTER OCCUPIED

HOUSING

Dwelling Units Per Housing

Category

Income Strong Mixed Light Total

Class (1,000's) (1,000's) (1,000's) (1,000's)

Class 1 9 4 21 34

Class 2 23 12 1? 30

Class 3 21 11 27 59

Class 4 38 20 9 67 Class 5 131 67 12 210

Total 222 1 14 84 420

Table 6 contains hypothetical data on the number of people living in housing of a corresponding construction type and income class. Income class 5, which contains 38 percent of the population, consumes 50 per- cent of the housing services produced by the housing

stock. The entries in table 5 are then divided by the

entries in table 6 to obtain estimates of housing stock per capita for each of the housing inventory sub- categories (see table 7). The inverses of the cellular entries in this matrix reveal the population per housing

unit within each subcategory of the inventory. For ex- ample, housing of light construction which is oc- cupied by households in income class 1 contains an

average of 9.1 persons per unit (1/0.1 1 = 9.1) whereas

housing of strong construction occupied by persons in households which fall in income class 5 contains 4.

1

persons per unit (1/0.244 = 4. 1 ). Thus there is considerable variation among the different sectors of the housing stock in degree of crowding.

After the construction of the per capita housing

matrix, the analyst can either arbitrarily or judgementally designate one of the cells as the target or standard cell. The analyst asserts that, say, for example, the kind of housing and degree of crowding

typified by those units which are occupied by households in income class 4 and which is constructed of

materials of mixed qualities should be the standard

level to which all the population should have access. This judgment may or may not be supported by the

rest of the analysis. (At this point the analyst is setting

a standard as a hypothesis.)

TABLE 6. AN ILLUSTRATIVE POPULATION CHARACTERISTICS

MATRIX FOR A HYPOTHETICAL

URBAN REGION—RENTER

OCCUPIED HOUSING

Population Per Housing Category

Income Strong Mixed Light Total Percent Class (lOOO's) (lOOO's) (lOOO's) (lOOO's)

Class 1 78.94 35.71 190.91 305.56 12.5 Class 2 179.69 97.36 125.00 402.25 16.4 Class 3 131.07 84.62 203.01 438.70 17.1 Class 4 193.88 119.76 57.69 371.33 15.1 Class 5 336.89 335.00 62.50 939.11 38.1 Total 1140.47 672.65 639.11 2452.23 Percent 46.3 27.4 26.1

TABLE 7. AN ILLUSTRATIVE HOUSING

QUANTITY PER CAPITA

MATRIX FOR A HYPOTHETICAL

URBAN REGION—RENTER

OCCUPIED HOUSING

Housing Stock Per Capita

Income

Class Strong Mixed Light

Class 1 .114 .112 .110 Class 2 .128 .123 .120 Class 3 .139 .130 .133 Class 4 .196 .167 .156 Class 5 .244 .200 .192

Table 8 contains hypothetical value data in dollars for the mean yearly rental value of housing in each of the

subcategories. The cell in row 4 column 2 is the designated standard or target cell. Dividing all the entries

in table 8 by the rent per unit of the standard cell yields table 9—the matrix of relative housing prices.

The matrix of relative prices represents a set of deflators to apply to the stock per capita quantities contained in table 6. Using relative prices as deflators in

effect is assuming that the amount of housing services rendered by a unit is measured by the number of

target or standard equivalents contained in that unit. For example, if the standard unit has an annual rental value of $1,000, and if another unit is valued at $500,

then the second unit is equivalent to 0.5 standard

24
units. Thus multiplying the units per capita in each

cell by the corresponding relative value yields a stan- dardized stock per capita figure. These figures are then subtracted from the standard or target stock per

capita which is given by row 4 column 2 of table 7 in order to determine the cell by cell shortfall of stock per capita (see eq. 2). Table 10 contains such shortfall estimates in per capita terms. To convert the stock per capita shortfall or housing

needs estimates into absolute numbers, the positive

entries of table 10 are multiplied by the corresponding

population estimates of table 6. Thus table 1 1 contains

the first iteration of a cell-by-cell calculation of needs

estimates.

This is the critical point of the analysis. The needs

estimates must now be evaluated as to whether or not they are worth meeting. If not we must lower the standard or target.

information on the cost of constructing a standard

equivalent is required. Here we shall assume that the standard-or target unit can be produced for $10,000.

(The analyst will require a realistic construction cost estimate and can not rely on assumption as is done in this example). This cost can then be converted to an

annualized cost of $1075.^

TABLE 8. AN ILLUSTRATIVE MATRIX OF

HOUSING PRICES FOR A HYPOTHETICAL URBAN REGIONRENTER OCCUPIED HOUSING

Matrix of Annual Rental Values

Income Strong Mixed Light

Class

Class 1 830 510 350

Class 2 960 830 500

Class 3 1,120 970 680

Class 4 1,240 1,000 900

Class 5 1,630 1,180 920

A cell-by-cell calculation of redistribution effects must now ensue. This will require that the analyst make

some assumptions about the impact that adding new

units to each subcategory will have on the market

price within each subcategory. Recall that the main

category of impacts to be considered are consumer

surplus benefits—as units are added to the inventory

prices will fall on the already existing housing and

those who do not experience a housing subsidy

This calculation assumes a 25 year life, a 10% interest rate and a scrap value of $3,000.

TABLE 9. AN ILLUSTRATIVE MATRIX OF

RELATIVE HOUSING PRICES

FOR A HYPOTHETICAL URBAN

REGION—RENTER OCCUPIED

HOUSING

Matrix of Relative Prices

Income

Class Strong Mixed Light

Class 1 0.83 0.51 0,35 Class 2 0.96 0,83 0.50 Class 3 1.12 0,97 0.68 Class 4 1.24 1,00 0.90 Class 5 1.63 1,18 0,92

TABLE 10. AN ILLUSTRATIVE MATRIX OF

THE SHORTFALL IN HOUSING

STOCK PER CAPITA ADJUSTED

BY RELATIVE PRICE WHEN

COMPARED TO THE DESIGNATED STANDARD FOR

TARGET

Shortfall in Housing Stock Per Capita

Income Strong Mixed Light Class

Class 1 0.072 0.110 0,128 Class 2 0.044 0,065 0,107 Class 3 0,011 0.041 0,077 Class 4 0.027 Class 5

TABLE 11. AN ILLUSTRATIVE MATRIX OF

HOUSING NEEDS FOR A HYPOTHETICAL URBAN

REGION—RENTER OCCUPIED

HOUSING

Matrix if Mousing Needs

Income Strong Mixed Light Total Percent of Class ( lOlld sl IKIUds) 1 IHOHs) 1 1110(1 si Fxisling Units

Class 1 5.7 3.9 24.4 34.0 100.0 Class 2 7.9 6.3 13.3 27.5 55.0 Class 3 1.7 3.5 15,6 20,8 35.3 Class 4 1,6 1,6 2.4 Class 5 Total 15.3 13.7 54,9 83,9 20.0

Percent of Existing Units 6,9 12.0 63,4

15
directly are made better off because less of their in- come need be spent on housing. (As price falls more,

housing can be consumed within each cell, and housing stock per capita is increasing).

Returning to the problem at hand, let us see what will happen in terms of real income effects going to renters

in the row 1 column 3 cell. The need estimate in this

cell calls for an extra 2,440 units. Currently this cell contains 2,100 units. The current market price of housing in this cell is $350. If the price elasticity of de- mand is -1.0 (an x percent change in price will lead to an X percent change in quantity) then increasing

quantity by 116 percent (2,440/2,100 = 1.16)would

lead to a price decline to zero.'' Thus consumer surplus

gains would equal 2,100 x $350 -1- 2,440 x $350 = $1.16

million. These benefits would arise if 2,440 units are produced at an annualized cost of $1,075 and given

away to the residents of this housing submarket. The

annualized cost of implementing the redistribution of $1.16 million will be $2.58 million (2,400 x $1,075). Table 12 contains the cell-by-cell computation of redistribution impacts based on a price elasticity of

-1.0.

The results in table 12 show that the aggregated

redistribution effect of meeting the designated target or standard. Table 13 contains the estimates, cell-bycell, of the annualized cost of administration or implementation. Table 14 computes the cell-by-cell ratio of redistribution level to administration costs.

For a second iteration we set the standard or target as those housing conditions obtaining in row 3, column 3 (income class 3 of light construction). Table 15 recomputes the matrix of housing needs based on this new

and lower standard or target. Table 16 recomputes the

Zero is assumed because negative prices are not meaningful.

TABLE 12. THE REAL INCOME GAINS OF

THE OCCUPANTS OF RENTER

OCCUPIED HOUSING ARISING

FROM FILLING HOUSING

NEEDS (REDISTRIBUTION

EFFECTS)

Income Strong Mixed Light Total

Class $ X 10" $ X 10" $x 10" $ X 10"

Class 1 0.623 0.296 1.16 2.079 Class 2 0.887 0.6S9 0.960 2.506 Class 3 0.198 0.393 1.366 1 .957 Class 4 0.157 0.157 Class s Total 170H 1.91S 3.643 7.266

matrix of cell-by-cell redistribution effects and table 17 recomputes the matrix of net resource costs. Let us assume that the housing industry can reproduce this lower quality unit for $6,000 because of special scale advantages that are associated with producing such

units in large numbers. We shall assume that these

units will have a 20 year life, zero scrap value and

that the interest rate is 10 percent. Thus the gross an- nualized cost will be $705 per unit, prior to any rental collections from those occupying the new units. Table

17 is based on the net annualized costs per unit. Again we have assumed in tables 15 through 18 a price

elasticity of -1.0.

This second iteration produces a considerably lower

need level and an annulized net resource cost approximately seven times lower then the first case. Also the

real income gains accruing to the row 1, column 3 cell are only 40 percent lower than those computed under

the first set of assumptions. In that cell the ratio of redistribution gains to implementation costs has

nearly doubled.

26
TABLE 13. ANNUALIZED NET RESOURCE

COST OF IMPLEMENTING

REDISTRIBUTION EFFECTS

ASSOCIATED WITH FULLFILLING HOUSING NEEDS

TABLE 16. THE REAL INCOME GAINS OF

THE OCCUPANTS OF RENTER

OCCUPIED HOUSING ARISING

FROM FILLING HOUSING

NEEDS—SECOND ITERATION

Income Strong Medium Light Total Income Strong Medium Light Total

Class $ X 10" $ X 10" $x 10" $ X 10" Class $x 10" $X 10" $ X 10" S X 10"

Class 1 .433 .414 2.623 3.470 Class 0.113 0.695 0,808 Class 2 .3S1 .422 1 .334 2.127 Class 2 0,325 0.325

Class 3 .007 .144 1.228 1 .379 Class 3 Class 4 0.038 0.038 Class 4

Class 5 Class 5 Total 0.741 0.980 5.243 7.014 Total .1 13 i.nio 1.123

TABLE 14. CELL-BY-CELL RATIO OF

REDISTRIBUTION LEVEL TO

NET RESOURCE COSTS OF

IMPLEMENTATION

TABLE 17. ANNUALIZED NET RESOURCE

COSTS OF IMPLEMENTING

REDISTRIBUTION EFFECTSSECOND ITERATION

" Income

Class

1 Strong Medium Light Total Income

Class Strong

$X 10" Medium

$ X 10" Light$ X 10" TotalS X 10" Class 1 .076 .875 .951 Class 1 1.44 0.71 0.44 0.60 Class 2 .210 .210 Class 2 2.53 1.56 0.71 1,18 Class 3 Class 3 28.29 2.73 1.11 1,42 Class 4 Class 4 4.13 4,13 Class 5 Class 5 Total 0.076 1 .085 1.161 Total 2.16 0.51 0.69 1.04

TABLE 18. CELL-BY-CELL RATIO OF

REDISTRIBUTION LEVEL TO

TABLE 15. MATRIX OF HOUSING NEEDS— NET RESOURCE COSTS OF IMSECOND ITERATION PLEMENTATION—SECOND

Income Medium Light Total ITERATION

Strong

Class (lOOO's) (lOOO's) (lOOO's) (lOOO's) Income Strong Medium Light Total

Class 1 1.8 14,7 16.5 Class

Class 2 5,5 5,5 Class 1 1.48 0.79 0.85 Class 3 Class 2 1 .55 1.55 Class 4 Class 3 Class 5 Class 4 Class 5 Total 0 1,8 20,2 22,0 Total 0.93 0.97

27
Alternative assumptions about demand elasticities could be employed in further iterations. For example

it may be reasonable to assume greater inelasticity in the lower income classes. This will improve the

redistribution to resource cost ratios because smaller

additions to the stock in those cells will have a greater

impact on market price. Thus a lower standard

would, with a lower associated annualized cost of implementation, generate larger income gains. As alter- native need estimates are generated under different assumptions a table like that of table 4 can be generated. This example illustrates that selecting a standard and

computing a level of need will not be an easy matter. Hopefully, even though this example is incomplete it should serve to give the methodology outlined in sec- tion 2 a degree of concreteness which will highlight the key problems and issues that are involved in needs estimation.

Facing Page: A pressed earth-block house, in Bangladesh. Construction costs can he reduced by the use of low-cost

materials. Such a house can he built by low-skilled workers,

usually by the very families who will live in them.

18
4. THE IMPACT OF A CHANGE

IN BUILDING TECHNOLOGY

ON HOUSING NEEDS

In this section we shall explore the effect that improvements in building technology will have on the

level of housing needs in the future and we shall dis- cuss ways in which anticipated changes in housing

technology can be incorporated into the housing

needs estimation methodology.

A good example of anticipated technology change in- volves building technology research which is related

to the impact of high winds on low rise buildings." Ex- treme winds are a primary contribution to building

damage and destruction in some developing counAny building technology research that leads to buildings more resistant to natural disasters could be used in this ex- ample.

19
tries. For example, in the Philippines between 1948 and 1971 there were 482 typhoons. Similar ex- periences with high winds are found in Jamaica and

Bangladesh.

If building technology develops methods of construction which are relatively low-cost and which result in substantial increases in the ability of low-rise buildings to withstand extreme winds there will be two

major economic impacts which could be incorporated

into a housing needs estimate.

First, the units which embody the new technology

will have longer expected lifetimes than those that do

not. This will lower the overall depreciation rate for the existing stock and will result in larger actual stocks at some future date. Coupled with this tech- nological factor is a demand factor. Demand for new

construction will also increase. This will be so because

the lowering of the depreciation rate will cause a basic

shift in the relationship between rental income and

housing value. Longer expected lifetimes results in greater rates of return to a housing investment. This

will result in greater demand for housing than would

have been the case if no technological change had oc- curred. This means that more units will be purchased

over time and that the actual stock of housing antici- pated to exist at the future projection date will be

larger. The gap between the desired level of housing

stock and the actual level will be lower. These effects will be offset in part if the new technology causes the

price of new housing to increase significantly.

The growth factor matrix must be judgmentally

modified if the new technology is to be anticipated in the needs projections. The annual average rate of gross additions will be higher because of the demand

affect discussed above and the annual average rate of depreciation will be lower because of the increases in expected building lifetimes. The actual percentage

modifications that need to be made to these two ele- ments will be determined by conditions which differ country by country. The needs analyst could provide

alternative forecasts based on high, low, or zero build- ing technology change assumptions.

Facing Page: Workers erecting a single family house where

the neiv tenants build interior walls and finishings. The

house offers precast concrete walls and a timherlcorrugated

iron roof, a good combination to meet both fragile

economics and high winds.

20
5. SUMMARY AND

RECOMMENDATIONS FOR

FURTHER RESEARCH

5.1 SUMMARY

Determining the size of a region's unmet housing

needs is an important first step to planning and enacting public policies and programs which will improve

the condition, size, and quality of a country's housing

inventory. The assessment of current and future housing needs is particularly important in the developing

regions of the world where the twin pressures of

population growth and urbanization especially aggra- vate the housing problem.

This study has developed a basic methodology of estimating and projecting housing needs. Procedures

have been developed to compare the costs with the

potential redistributive impacts of meeting the housing need shortfall. This methodology has been

designed to be flexible and adaptable to situations in

21
which housing and demographic data maybe sparse. The methodology is consistent with the economic

constructs which make the concept of housing needs

meaningful.

Finally, this study has shown that in countries where

natural disasters such as high winds or earthquakes

occur relatively frequently, the development and

adoption of new wind or earthquake-resistant build- ing technology will have a mitigating effect on a region's future housing need.

5.2 RECOMMENDATIONS FOR FURTHER

RESEARCH

One of the inputs to this paper's housing needs

methodology is demand for housing information.

Housing needs studies could be considerably improved in terms of accuracy and reliability, if reliable estimates of price elasticities of demand and supply

existed at the regional level. Thus, much work could

be done on developing and estimating regional

models of housing demand and supply. This is particularly critical in those regions of countries where

housing problems are the most pressing.

Further work is needed on conceptual and empirical

understanding of the market process in squatter set- tlements. Both tasks would necessitate special modeling of the squatter settlements as specialized sub- markets of the housing inventory. Parallel empirical

investigations of the economics of squatter settlements would deepen our understanding of the specific

problem areas and work to feed back a sense of

priorities into the conceptual work.

Specific empirical research on the housing market impact of the development of new construction

methodologies and design criteria could also be undertaken. Although we have some idea as to the

direction of the effects that extreme wind research

will have, more detailed information on the probable

impacts of this technology on both the stock demand

for housing and the supply of new housing units would enable those countries affected by high winds

to be more fully appraised of the benefits of developing such a new technology.

22
APPENDIX A AN ECONOMIC

INTERPRETATION

OF HOUSING

NEEDS

The needs estimation and projection methodology of section 2 has been constructed around a basic conceptual frameworl<;. This appendix analyses the economic

elements which underlie the notion of housing "need."

In Figure 1, the demand curve for housing is labelled

as DD. Assume that DD reflects the demand for housing services by a typical family unit. The total amount

of housing stock per family in existence, the fixed supply of housing, is represented by the vertical line SS. Assume that each unit of stock renders one unit of housing service. The intersection of the demand for housing services curve and the fixed supply deter- mines the current price per unit of housing (Pp in fig. 1).

S

1

1

1

l\

1 ^

1

1

1

1

$

1

1 hg h*

QUANTITY OF HOUSING SERVICES (STOCK)

PER FAMILY

FIGURE 1. THE DEMAND AND SUPPLY OF

HOUSING.

Let us assume that quantity of housing per family is measured in square feet of living space. The

equilibrium quantity of housing services (stock) per

family, hp, may be less than that which is deemed to be the minimally socially acceptable amount of housing space per family.

Assume for a moment that h* represents the judgement of that amount of housing services per family

which is minimally acceptable. The total housing

need in this instance will then be (h*-hj.) x N (number

of families). Of course h* may have been defined to be

either larger or smaller than shown on figure I . What

information could be developed which would help

guide the determination of where the standard ought

to be?

In order to arrive at some conceptual guidelines for determining the standard from which housing need is to be measured, it is necessary to discuss the economics of the housing market. The housing market

is composed of two basic markets whose interaction determines housing prices and the amount and kind

of new construction. Economists generally describe housing as a stock-flow market.

First there is the stock aspect of the housing market.

Consumers can be described as having demand for a quantity of housing services which are associated with a particular size of the housing stock. Such de- mand will be dependent upon the market price, the

level of income, the market rate of interest, and a number of other factors. Figure 1 portrays the interac- tion of demand with the fixed stock supply. In Figure

2, flow demand (dd), the demand for new construction, is derived from the demand for housing stock. At

the price p^ in figure 2, given the existing stock of size hg , there exists zero demand for additona! units, (bee

panel B.) The actual size of the housing stock is in agreement with consumers' preferences. However, at price Pi, consumers would be willing to purchase hi units of housing service (stock). Thus, we see in panel B that the demand for new construction at price P^

will be hj minus h^ units. Similarly, if price were P^, consumers would be faced with too large an inventory of housing stock. At that high a price the flow de- mand would be negative, hz minus h^ and holders of the stock would depreciate the stock by undermaintaining the existing inventory.

The housing construction industry responds to the flow-demand for housing. Since housing construction

approximates a competitive market, little reality is sacrificed if we add to the flow side of the market an upward sloping supply curve which describes the number of new housing units that housing producers

would be willing to build at each particular service

price (see curve ss in fig. 3).

23
FIGURE 2. THE DEMAND FOR HOUSING STOCK AND THE DEMAND FOR ADDITIONS

TO THE STOCK.

Figure 3 portrays the stock-flow housing market in equilibrium. The actual size of the housing stock, h^, is such that at the equilibrium price, P^, the desired

stock is equal to the actual stock and the level of new

construction will be zero.

If we introduce into this balanced equilibrium world a change in some underlying factor, be it an increase in

income, a fall in the rate of interest, or a change in consumer tastes for more housing, which causes de- mand to increase the service demand function and

flow demand function for housing will both shift up. Graphically, in figure 4, this would be illustrated by a

shift up in demand from D^D^ to D^D^ and in flow de- mand from djdj to d^d ^. was the previous

equilibrium price. It will not be maintained in the face

FIGURE 3. THE HOUSING MARKET IN EQUILIBRIUM.

24
of the increased demand. Price will immediately rise to P^. The price of housing will fall by the end of the

first period to Pp, the housing industry will be pro- ducing A h units per year. As these units are added to the existing inventory the flow demand schedule will

fall because the housing stock of panel A will be growing. The size of the stock after the end of the first period will be (hp -I- Ah) and the flow demand will be

shifting down. Eventually, as the stock continues to grow and flow demand continues to fall the housing

market will attain the long-run equilibrium price of Pi

.

The following discussion provides an approach for determining the resource costs and income redistribu- tion gains associated with reaching a target level of the housing stock. Before the redistributive nature of housing needs can be clearly seen we must consider

the notion of the "benefits" associated with any in- vestment, e.g., housing, which has as its object the reduction of the cost of a product or service. (Primary

to the notion that a housing "problem" exists is the impression that housing is too expensive.) In these in- stances the basic measure of benefits can be thought of

•E (hg+Ah)

UNITS OF STOCK

(A)

O Ah

NEW CONSTRUCTION PER PERIOD

(B)

FIGURE 4. THE ADJUSTMENT OF THE HOUSING MARKET TO AN INCREASE IN

DEMAND

The meaning of "housing needs" can now be put in the perspective of a stock-flow housing market

analysis. Using such an analysis can shed some light on the issues that are involved in determining the minimally acceptable standard which is set as the

target in a housing needs study. The obvious principle

behind the establishment of a housing standard is equivalent to the specification of an amount of income

redistribution that ought to take place. Accomplishing

a given size of income redistribution will entail real resource costs. Explicitly identifying the size and

beneficiaries of the income redistribution which is implied by a specific housing standard and asking how

much it will cost in terms of resources will clarify the

tradeoffs that are involved when discussing the concept of housing needs.

as a measure of "consumers' surplus." In a market

situation (see fig. 5), consumers purchase xi units of goods at price P^. The dollar costs to consumers for these units is given by the area OCBA. The total dollar value to consumers of the OC units can be measured

by the area under the demand curve, OCBD. Thus, consumers realize a surplus of the amount ABD in their valuation of the x, units over their costs. This

amount, ABD, is known as consumers' surplus. The

benefits to consumers of a fall in the price of a product

due to increased supply can be illustrated in a like manner. In figure 6, if price falls from Pi to P2 con- sumers will realize an addition to their consumers'

surplus of the amount DCBA.

Programs which fulfill unmet hosing needs are of

25
QUANTITY

FIGURE 5. CONSUMERS' SURPLUS

direct benefit to those which occupy the new, stan- dard, subsidized housing which has been built to satisfy the housing need. In addition to these direct

beneficiaries, other groups will reap benefits which

arise because of increases in consumers' surplus which

will occur because of the impact of the needs fulfill- ment programs on housing prices.^

Returning to the stock-flow framework, the fulfill- ment of housing needs will result in addition to the stock of housing which will lead to lower housing

prices for the rest of the inventory and corresponding

gains ill consumers' surplus. Figure 7 illustrates this process. If we start off in an equilibrium position at price Pj; in figure 7, but assert that the housing stock is not up to the desired standard of h*, in order to secure

h* prices must fall to P^. Those occupying the existing

units will benefit by an amount of EDBA because of the fall in price from P^; to P^. This dollar amount of gain can be considered to come at the expense of the

existing owners of the stock since they will ex- perience an equivalent loss in rental income. Thus one

effect of realizing h* will be to redistribute income

In addition to the redistribution effects which arise because

of changes in consumers' surplus, housing programs may

also generate other benefits. Examples of these are increases

in sanitation and subsequent reductions in ill-health, reduction of density and subsequent lowering of crime rates, and

increases in worker productivity because of the better lious- ing conditions. These by-products of iiousing programs may

or may not exist. In fact, a new housing program could ag- gravate crime rates, worsen the sanitation problem, and in- crease densities. Evaluating these other non-market benefits can only be done in the context of a specific housing program. They are not generalizable like the consumer's

surplus effects. Thus, the non-market benefits can not be

dealt with in this paper.

QUANTITY

FIGURE 6. INCREASE IN CONSUMERS'

SURPLUS DUE TO A FALL IN

MARKET PRICE

from one group (landlords) to another group (tenants). Additionally, area DCB will also be confered to those who enter the housing market in response to the fall in price from Pp to P^.'"

In figure 7 the shaded area in panel A can be thought

of as the amount of income redistribution that is ac- complished by attaining h*. But in order to provide

the extra (h*-h) units the housing construction industry will require that the housing price be P^. The total incremental resource cost of the (h*-h) units will be P^ X (h'^-h). Since consumers will pay P^, but not P^ the resource costs which must be met by the public sector

will be (Pj, — Pj) X (h*-h) (see the shaded area in panel B of fig. 7). This subsidy cost per unit times the extra number of units represents the resource costs borne by

the public sector of achieving the income redistribu- tion effect of ECBA.

The income redistribution will arise as these extra

units are added to the inventory and a fall in price is dispersed across the rest of the occupied inventory.

Price will fall to P^, given the subsidy, and the users of the existing stock, will realize a consumers' surplus

gain from the lower prices.

It will not always be the case that the amount of redistribution will be larger than the public cost of achieving that level of redistribution. Consider figure

8, where the subsidy required, P^HTP^ is greater than

the gain in consumers' surplus, DCBH. In this case, meeting the current housing need of (h*-h) confers

Society need not impose the cost of income distribution only upon the landlords. General tax revenues can be used

to compensate landlords for their losses.

26
much less in the way of redistribution change per dollar of resource cost than depicted in figure 7.

Within the context of this stock-flow model, three fac- tors interact to determine how expensive the income

redistribution associated with a particular mimimal

standard or target (specification of h*) will be. They

are: (a) the price elasticity" of the stock demand for housing, (b) the price elasticity of the supply of new

construction, and (c) the relative size (as a percent of the size of the existing inventory) or the housing

needs gap (see appendix B to this paper for a more

detailed discussion of these conditions). These factors can be manipulated to describe one upper limit of the housing standard. The size of the housing gap (h*-h) can be expressed as a fraction of the original size of

the housing stock, i.e., as /3 x h where ^ is a constant

multiplier equal to (h*-h)/h. This gap parameter can be

used to describe where a housing standard, h* max.

implies net resource costs equal to the amount of in- come redistributed. In equation (7), h* max. is the size of the stock of housing such that any additions to the

The elasticity of demand is the ratio of the percentage

change in quantity demanded divided by the percentage

change in prices. The elasticity of supply is the ratio of the percentage change in quantity supplied divided by the per- centage change in price.

stock past h* max will result in a housing needs construction program whose public sector resource costs are greater than the dollar value of its redistribution impact.

h%ax= 11+/^''^ (7)

In this model /3* depends on the ratio of the stock de- mand price elasticity of housing to the price elasticity of new construction supply. If the standard set in a needs study implies a standard where jS > /3*, then

the housing standard specifies that achieving $1 of in- come redistribution entails more than a $1 net resource cost. If /3 < fi*, fulfilling housing needs will

result in housing programs where the redistribution gains are greater than the net resource costs which

must be borne by the public sector.

Although it is the case that housing needs studies are bound to involve normative elements, the discussion above implies that there are economic factors relating

to the real resource costs of redistribution which can

guide the setting of the housing standards. These fac- tors are rooted in the housing market characteristics of a particular economy. The first economic characteristic of impact is the price elasticity of demand. It will depend upon the underlying tastes and

preferences of the consumers in a particular area. It will be influenced by an area's level of income. Of

equal importance is the elasticity of supply. It also

27
will depend upon many institutional factors which

vary across economies. The degree of competitiveness

in the housing industry, the level of production tech- nology employed within a particular country, and the responsiveness of the housing industry to innovation

are three underlying factors. Nevertheless the desire

for greater equity '^and a willingness to use the housing market as a tool to increase equity, fundamentally

potential Pareto'^ improvements. Thus care should be

taken not to interpret cost-effectiveness computations

as ranking redistribution schemes on the basis of effi- ciency grounds. Whether or not the equity or

redistributive effects of such programs are justifiable must be decided, not on economic grounds, but on the views of the policy makers towards the desirability of attaining a more equitiable distribution of income at some cost to real income.

FIGURE 8. AN EXAMPLE OF THE BENEFITS OF FULFILLING HOUSING NEEDS BEING

LESS THAN THE COSTS OF MEETING THOSE NEEDS

lies in the hands of the public policy decision makers.

A housing needs study can be used to specify the real resource costs of achieving greater equity. However,

other methods are available to the public sector for in- come redistirbution, e.g., negative income taxes, food

subsidy programs, and the non-enforcement of laws

against theft. The cost effectiveness in redistributing

income via housing programs must be compared to these other alternatives. The income redistribution goals of housing or other

welfare-type programs generally are not examples of

Greater equity is concerned with the redistribution of in- come from higher income groups in the population to those

at the lower end of the distribution.

A potential Pareto improvement is one in which an economic rearrangement can result in the gainers being

able to more than compensate the losers. In the case of fill ing housing needs the gainers (the tenant classes) will not be able to compensate the losers (the landlord classes) and

still remain better off.

28
APPENDIX B Derivation of the

Housing

Standard

The resource costs borne by the puboic sector of a housing program designed to meet the gap in housing

needs will be distance AB in figure 9 times the size of the gap, which will be /3 + h. Distance AB will be the

y

FIGURE 9.

NEW CONSTRUCTION

DETERMINING THE RESOURCE

AND SUBSIDY COSTS OF

MEETING HOUSING

DEMANDS.

sum of the change in price, CB, that moves along the demand curve, dd, ( APd) plus the change in price

resulting from moving along supply curve, SS, dis- tance C A, ( APg). Thus, the subsidy cost, SC will be

SC = (APd + APs)/3h (8)

The redistributive effect can be measured as the incre- ment in consumers surplus under the stock demand

function of Figure 10.

It will be the change in price APd times h plus (utiliz- ing straight line demand curves) 1/2 APd = /3h. Thus

the benefits, B, will equal.

B = APh + 1/2 APd pli (9)

Setting redistribution effects equal to implementation

costs, we can proceed to solve for the value of (3 which

can be used to determine the housing standard at

h h*

UNITS OF STOCK

FIGURE 10. MEASURING THE

REDISTRIBUTIVE IMPACT OF

A HOUSING NEEDS

PROGRAM.

which implementation costs are matched by income

redistribution effects.

(APd -I- APs)^h = APdh + 1/2 APh/3h (10)

Rewriting(lO),

2 APh/3h -I- 2APs j8h = 2 APdh + APd/3h, ( 1 1)

Subtracting A Pd/3 h from both sides and dividing by h x APd yields

iS + 2/3 A Ps/ = 2, (12)

since APd = jSh x P x 1 . ^ ^,

Es xh

(where Ed is the price elasticity of demand and Es is price elasticity of supply) then Equation (12) can be

rewritten

1/2 -hEd = 1//3 Es (13)

which can be rewritten to solve for /3

/3 = 1/2 Ed - 1 Es (14) Interpreting equation 14 yields the following conclusions: • The greater the size of the gap between the stan- dard and the actual size of the inventory, the less likely it will be that the housing needs are worth fill- ing.

• Knowledge of the degree of price elasticity of stock demand and flow supply is an important input

in the determination of housing needs.

29
• A combination of very elastic demand and very

inelastic supply will combine to make housing needs

not worth filling. • A combination of inelastic demand and elastic supply combine to make the filling of housing needs

socially profitable.

ACKNOWLEDGMENTS

This report was conducted by the Building Economics

Section to demonstrate the application of economic

analysis to the determination of housing needs. Dr. Harold Marshall and Ms. Rosalie Ruegg of the Build- ing Economics Section and Dr. Carl Muehlhause of the Institute for Applied Technology provided constructive reviews of this paper. Mr. Noel Raufaste Jr., project coordinator of the Center for Building Technology's project to develop design criteria and

methodologies for low-rise/low-cost buildings to better resist extreme winds, provided valuable editorial assistance. Those errors which remain are, of course, my responsibility.

30
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