The SAPOA Economic Impact report by SAPOA

THE ECONOMIC IMPACT of the COMMERCIAL REAL ESTATE SECTOR on the SOUTH AFRICAN ECONOMY

THE ECONOMIC IMPACT of the COMMERCIAL REAL ESTATE SECTOR on the

SOUTH AFRICAN ECONOMY

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THE ECONOMIC IMPACT of the COMMERCIAL REAL ESTATE SECTOR on the SOUTH AFRICAN ECONOMY

Foreword from SAPOA’s CEO

stablished in 1966, the South African Property Owners Association (SAPOA) is a unique, member driven organisation that aims to represent, protect and advance members’ commercial and industrial property interests within the property industry in terms of ownership, management

and development. Its objectives are based on the principles of the free enterprise system, as the only workable economic system and the inalienability of property ownership, not only for its members but also for the future of South Africa, and its competitiveness in the world arena. There were an estimated 35 million square

The property industry is not like the mining

meters of office, industrial and retail building

or banking sectors. They are by no means as

space in South Africa in 2013 (IPD) with this

proliferated as the property industry is with its

inventory growing by an average of 6.8

many layers and plethora of companies across

percent annually.

the country. The reality is, SAPOA needs to be

As of December 2013, SAPOA’s membership

able to represent our industry and to do that we

included 1,600 commercial office, institutional

need to understand who we are, what we are

and public buildings, constituting over 24

and what we contribute.

million square meters’ of rentable office space or

As important as the final figures will be, it’s

70% of all the commercial meterage available

important to find out why we’re doing this study

for rent in South Africa.

and what we hope to achieve. To us it may be

Today, SAPOA’s membership buildings’

important to know the sector comprises 6% to

support close to 200,000 formal and informal

8% of GDP, for example, or how many direct

jobs in addition to tenant employees.

and indirect employees translate into a certain

As evidenced by this report, SAPOA’s

number of jobs per square metre.

member buildings are a barometer of a city’s

The reality is that to the extent the economy

economic health and a major contributor to

grows, the real estate sector has been very

the economic lifeblood of the entire region –

efficient in raising capital, and investing that

from employment to tax generation to direct

capital on a fixed basis.

spending and beyond.

Over the past 12 years we’ve seen the

While the actual figures of South African

property’s

contribution

SAPOA CEO, Neil Gopal

emergence of a listed sector which has created

gross

a more efficient way of deploying capital into

domestic product and its size are important

the economy and it has enabled that market –

considerations, it’s vital to understand the

which is quite entrepreneurial – to be efficient in

rationale behind this study.

terms of developing and growing the real estate

In order for to engage government on a fair

sector in the country. Over the next 20 years we

and rational basis, we need to understand who

are going to see continued growth in this sector

and what we are in terms of the economy. And

because it’s transparent, efficient, liquid, and has

unless we know how many people we employ

an improving quality of management.

directly and indirectly, as well as our contribution to the tax base or economy, it will be very difficult to have these kinds of discussions.

APRIL 2014

SAPOA - the voice of commercial property

THE ECONOMIC IMPACT of the COMMERCIAL REAL ESTATE SECTOR on the SOUTH AFRICAN ECONOMY

Published by SAPOA, Paddock View, Hunt’s End Office Park, 36 Wierda Road West, Wierda Valley, Sandton PO Box 78544, Sandton 2146 t: +27 (0)11 883 0679 f: +27 (0)11 883 0684 e: sales@sapoa.org.za

SAPOA - the voice of commercial property

APRIL 2014

THE ECONOMIC IMPACT of the COMMERCIAL REAL ESTATE SECTOR on the SOUTH AFRICAN ECONOMY

CONTENTS Forewords from our sponsors

Executive Summary

Theoretical Framework Summary

Space And Capital Markets

Input-Output Matrix

Economic Activity Within The Real Estate Sub-Sector

General Economic Activity

Industry Specific Economic Activity

Contribution To The South African Economy

Economic Activity

Gross Domestic Product

Employment Created

Taxes Generated

Short-Term Future Forecasting

Influence On Economic Activity

Influence On Gross Domestic Product

Employment To Be Created

Net Taxes To Be Generated

Summary

References

SAPOA publications are intended to provide current and accurate information, and are designed to assist readers in becoming more familiar with the subject matter covered. SAPOA published this document for a general audience in accordance with all applicable laws. Such publications are distributed with the understanding that SAPOA does not render any legal, accounting, or professional advice. Use of this publication is voluntary and relianceon this document should be undertaken based on an independent review by the user. Information provided in this document is "as is" without warranty of any kind, either express or implied, including but not limited to the implied warranties of merchantability, fitness for a particular purpose, or freedom from infringement. SAPOA hereby disclaims all liability for any claims, losses, or damages in connection with use or application of this document. This document is the sole and exclusive property of SAPOA. Reproduction or redistribution in whole or in part without the express written consent of SAPOA is prohibited.

APRIL 2014

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THE ECONOMIC IMPACT of the COMMERCIAL REAL ESTATE SECTOR on the SOUTH AFRICAN ECONOMY

FOREWORDS FROM OUR SPONSORS Broll is pleased to share greater insight into South Africa’s property sector by sponsoring the SAPOA GDP Report: The Economic Impact of the Property Sector in South Africa.

outh Africa is expected to show a gradual

With infrastructure development critical

increase in growth over the next two years,

for the economy, and industrial and economic

following several years of disappointing

development high on the agenda, opportunities

growth. It is not alone in producing lacklustre

for the property sector abound. Highlighting

growth. Several countries, including South

these opportunities, the Broll Report 2013/2014

Africa’s main trading partners, have been slow

is an annual assessment of property trends, both

to recover since the global economic recession

historically and projected, in South Africa and

began in 2008. This has impacted South Africa’s

other key African markets. It presents significant

average growth rate of a muted 2.8% since

market trends and sector intelligence and we

2010. During 2013, real GDP growth was below

invite you to download this valuable tool at

market expectations at 1.9%.

broll.co.za.

The South African Reserve Bank estimates growth to reach 2.6% in 2014 and 3.1% in 2015. Despite these slightly positive projections, the country still faces several challenges. It has a 24.1% unemployment rate. Labour unrest and strikes continue, especially in the mining and platinum sectors. Electricity supply constraints and other limits are also choking growth. There are positive signs. Government has set up or proposed various frameworks and plans to improve infrastructure, promote growth, reduce unemployment and unlock economic the

opportunities.

National

Development

These Plan,

include several

Strategic Infrastructure Plans (SIPs), Industrial Development Zones and Special Economic Zones (SEZs). New power plant developments are also underway.

With our sponsorship of this important SAPOA GDP Report, Broll remains committed to undertake, encourage, support and share valuable research that shines a light on the South Africa property sector.

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APRIL 2014

THE ECONOMIC IMPACT of the COMMERCIAL REAL ESTATE SECTOR on the SOUTH AFRICAN ECONOMY

To Download The Broll Report 2013/2014 visit broll.com.

Reliable research is the key to property success Broll provides professional property services in South Africa and throughout sub-Saharan Africa. Our association with international property partner CBRE merges our African insight with global market knowledge. Foremost among the ingredients for property success are key professional relationships and reliable research. We believe the SACSC Research Conference creates a platform for both. It also continues our commitment to support shopping centres and retailers with research and relationships that ensure a competitive edge. Broll’s Research Division provides the following property research services: • Feasibility studies • Industry benchmarking • Nodal reports and analyses • Country reports and analyses • General and market research

Proud sponsors of the SAPOA GDP Report 2014

Visit broll.com or call our team on 08610broll

APRIL 2014

SAPOA - the voice of commercial property

THE ECONOMIC IMPACT of the COMMERCIAL REAL ESTATE SECTOR on the SOUTH AFRICAN ECONOMY

FOREWORDS FROM OUR SPONSORS STANLIB is a leading asset management company in South Africa and manages assets in excess of R540 billion for over 400 000 retail and institutional clients across the African continent. We have a physical presence in seven African countries.

he STANLIB Direct Property Investment Franchise

(SDPI)

committed

delivering long term inflation beating

returns to investors through a quality real estate portfolio. Our investment purpose is making real estate accessible sustainably. Our three main focus areas include the Liberty Property Portfolio, the Pan Africa Portfolio and Third Party Mandates. STANLIBâ&#x20AC;&#x2122;s Listed Property Franchise has a good track record as one of the leading Listed Property Managers in the country with a unique offering across all property markets in the world. The size of our local Property Book strengthens our influence when it comes to voting, private placements and liquidity. Our ties to SDPI is a competitive advantage, while our relationship with Standard Bank Properties further enhances our knowledge of the industry. In addition to doing our own analysis and research, the property team leverages off the greater STANLIB Asset Management team. As an active property asset manager, we conduct regular research on existing and planned investments. We make use of internal and external research to provide a comprehensive macro and micro study for each investment and the surrounding environment. Research data is compiled into knowledge which is then intelligently applied to support and steer strategy and therefore enhance profitability of investment returns. STANLIB is one of the only Asset Managers in Africa with such a broad range in Property Asset Management, namely Listed Property, Physical Property, Passive (ETF) Property solutions and Multi Management.

STANLIB recognises the impact of the property industry on the GDP at large and welcomes the opportunity to support a study where we can understand and analyse this very important contribution accurately.

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THE ECONOMIC IMPACT of the COMMERCIAL REAL ESTATE SECTOR on the SOUTH AFRICAN ECONOMY

Some choose not to follow their passion. Our track record is proof that we do.

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THE ECONOMIC IMPACT of the COMMERCIAL REAL ESTATE SECTOR on the SOUTH AFRICAN ECONOMY

FOREWORDS FROM OUR SPONSORS Standard Bank has the largest dedicated real estate finance platform of any financier in subSaharan Africa.

he established track record of our real estate finance activities in South Africa has been extended to key countries on

the continent, including Ghana, Nigeria, Uganda, Kenya, Angola and Mozambique. We provide financing in all areas of the real estate sector, ranging from vanilla funding to complex structures. With real estate specialists based in Johannesburg, Durban, Port Elizabeth, Cape Town, Lagos, Accra and Kampala, Standard Bank is uniquely positioned to tailor-make solutions to suit various organizations, industries and countries. Our ability to tap into the vast resources and expertise within Standard Bank Group further enables us to provide seamless access to other specialist financing areas to deliver flexible and comprehensive financing solutions for our clients positioning them at the forefront of their real estate-related initiatives.

Standard Bank is proud to be the sponsor of the SAPOA GDP 2014 report, a research report which highlights the importance of the real estate industry through acknowledging and demonstrating the crucial role the sector plays in the South African economy. We would like to take this opportunity to once again thank SAPOA for their longstanding commitment to the real estate sector. SAPOA - the voice of commercial property

APRIL 2014

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THE ECONOMIC IMPACT of the COMMERCIAL REAL ESTATE SECTOR on the SOUTH AFRICAN ECONOMY

Strong relationShipS are built on Solid foundationS We know the importance of relationships. Working together allows us to understand your needs so we can offer the best real estate solutions for you. With over 150 years of banking experience, this is how weâ&#x20AC;&#x2122;re moving real estate forward.

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Authorised financial services and registered credit provider (NCRCP15). The Standard Bank of South Africa Limited (Reg. No. 1962/000738/06). Moving Forward is a trademark of The Standard Bank of South Africa Limited. SBSA 179914-04/14

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THE ECONOMIC IMPACT of the COMMERCIAL REAL ESTATE SECTOR on the SOUTH AFRICAN ECONOMY

FOREWORDS FROM OUR SPONSORS Backed by a Level 1 black economic empowerment (BEE) certificate, Crane Construction Consultants is a leading quantity surveying and project management consultancy

pearheaded by Group CEO, Siva Veeran, the company is now in existence for 15 years, delivering exceptional projects, with

the highest standard and quality, within budget and on time. The company recently diversified its offering to include project management and now offers a full spectrum of feasibility studies, cost management, programme management and reporting. The sectors in which the company operates include Retail, Infrastructure, Education, Healthcare, Leisure, Residential and Workplace. Responding

growing

demand

for

business to be sustainable, Crane Construction Consultants is certified by the Green Building Council of South Africa. Crane

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successfully

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Consultants projects

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African countries and believes that innovation and cutting edge solutions, position it as the company of choice. The company offers a holistic approach, ensuring that clients receive optimal value, project by project.

Crane endorses the SAPOA GDP report as it plays an important role in providing insights and statistics into the property industry, a crucial sector in our business.

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THE ECONOMIC IMPACT of the COMMERCIAL REAL ESTATE SECTOR on the SOUTH AFRICAN ECONOMY

Crane Construction Consultants Crane Construction Consultants is a leading quantity surveying and project management consultancy. Established in 1999, the company offers a full spectrum of feasibility studies, cost management, programme management and reporting across seven sectors including Retail, Infrastructure, Education, Healthcare, Leisure, Residential and Workplace. Based in Johannesburg, Crane has offices in most major cities in South Africa and four associate offices in Africa. The company has a Level 1 (BEE) rating and Green Building Council South Africa certifications. Some of the projects successfully completed include: Sandton City expansion and refurbishment, Greenacres Shopping Centre refurbishment, Nelson Mandela Bay Stadium, Silverstar Casino and Hotel, Gautrain, Cresta Shopping Centre refurbishment and Mbabane Office Park (Swaziland).

People Talent People at the heart of the company and Crane encourage innovation and cutting edge solutions, to provide clients with a holistic approach. To build a talent pipeline for the engineering industry, Crane Construction Consultants sponsor previously disadvantaged students with bursaries, mentorship programmes and vacation work.

Giving Back As part of its commitment to making a difference in the communities in which it operates, Crane Construction Consultants supports a number of organisations, including City Parks Johannesburgâ&#x20AC;&#x2122; George Lea Park, Kids Haven and Villa of Hope.

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Tel : +27 11 783 8220 | Fax : +27 11 783 9205 | www.craneqs.co.za SAPOA - the voice of commercial property 143781

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THE ECONOMIC IMPACT of the COMMERCIAL REAL ESTATE SECTOR on the SOUTH AFRICAN ECONOMY

EXECUTIVE SUMMARY

eal Estate is considered globally as a very important contributor to economic activity. Not only does it provide in the space needs for virtually all other economic activities to take place, including manufacturing, retail activities, business services, etc. but in itself also contributes

directly and indirectly to economic activity and job creation. Altogether the real estate sub-sector is reported by the South African Reserve Bank within the Financial intermediation, insurance, real estate and business services sector. During 2013 the sector contributed a total of R1 320 billion to the fixed capital stock of the country, while the gross fixed capital formation for the sector added R97 856 million to this figure for the same period, which represents 20.9% and 14.95% respectively of the whole economy. Of the mentioned capital formation, R69 697 million, or 71.2% of the sector, is attributable to non-residential buildings. Apart from the capital investment of the sector in the economy, non-residential real estate also contributed R81 billion to the gross domestic product of South Africa during 2013, before the effects of taxes and subsidies are taken into consideration, which resulted in approximately 212 000 jobs, 1.5% of all jobs in the economy. With the above in mind, the South African

are applied in the rest of the document;

Property Owners Association has approached

◆ Section 3 provides the statistical facts in

Business Enterprises @ University of Pretoria to

terms of activity that leads to the economic

conduct a detailed analysis of the contribution

activity within the real estate sub-sector;

of Commercial Real Estate, which only forms

◆ Section 4 will show the analysis in terms of

part of the mentioned sector, to the South

the total contribution of the sub-sector to

African Economy.

the economy; and ◆ Section 5 will attempt to provide some

The report will be structured as follows:

forecasting of short term future activities.

◆ Section 2 will provide a theoretical framework of the research conducted and give an overview of the principles and methods that

Key findings of this study include the following. A. Non-residential real estate contributed approximately R174 billion to economic activity, or 5.15% of all economic activity in South Africa during 2013 B. The contribution to gross domestic product (GDP) in 2013 by non-residential real estate is approximately R81 billion, or 2.4% of the total GDP in South Africa. C. Non-residential real estate supports approximately 212 000 permanent jobs in South Africa, or 1.5% of all employment in the country as at the end of 2013. D. There was a total of approximately R6.5 billion in taxes generated by non-residential real estate during 2013.

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APRIL 2014

THE ECONOMIC IMPACT of the COMMERCIAL REAL ESTATE SECTOR on the SOUTH AFRICAN ECONOMY

THEORETICAL FRAMEWORK SUMMARY Although the South African National Income and Production Accounts (NIPA) provides a good overview of the South African economic system, there are various models and literature that explains the relationship within the property sector. These are collectively referred to as space and capital markets. Part a) of this section is an overview of the theory of space and capital markets will be provided. Boshoff (2013) performed a case study on the applicability of these models on the South African Economy. Future steps could include incorporating the relationship between economic activity with space and capital market activity in order to assess the total economic impact, but at this stage it is important to understand the principles of this to show specifically the interaction between the real estate and the construction sectors. Part b) provides a discussion of the theory of I-O modelling, which explains the relationship of all sectors in the economy in order to assess the impact of the different sectors on each other, but also on the economy as a whole. This technique will then be applied in the latter parts of this report to show the impact of the real estate sector on the economy.

a) Space and Capital Markets DiPasquale and Wheaton (1992) and Fisher,

1. Background

Hudson-Wilson and Wurtzebach (1993) further

The unique characteristics of real estate create,

refined this model, which is referred to as the

on the one hand, many opportunities for

diagrammatic model by Viezer (1999: 504)). The

real estate investors, and, on the other, many

model was officialized in a textbook on property

difficulties. The different factors influencing the

economics by DiPasquale and Wheaton (1992)

behaviour of real estate should therefore be

as the FDW-model, the most detailed treatment

investigated carefully.

found in a seminal textbook.

DiPasquale and Wheaton (1992: 181) stated that

Du Toit (2002) carried out research on the

analysing the market for real estate presents

FDW-model and describes the principles of

challenges because of the inter-relation of

the model with an accompanying practical

space- and asset markets.

example of office space in Pretoria. The FDWmodel conceptualizes the interrelationships

The earliest recording of work that distinguishes

between the market for space, asset valuation,

between

construction sector and stock adjustment.

use

decisions

and

investment

decisions with respect to real estate was probably Weimer (1966), but Hendershott and

Viezer (1998) developed a completely new

Ling (1984) were the first to integrate space-

model that similarly describes the space and

and capital markets into real estate. According

asset markets in the property sector, but

to Viezer (1999:504), Hendershott and Lingâ&#x20AC;&#x2122;s

this model is of an econometric rather than

model evaluated investment value responses to

diagrammatic nature. Viezer refers to it as

tax code alterations in a dynamic programming

the Real Estate Econometric Forecast Model

algorithm that used a traditional discounted

(REEFM), and uses statistical principles to

cash-flow equation with assumed parameters.

explain the property market, in contrast with the diagrammatical FDW-model.

Corcoran (1987) graphed the space market and capital market of real estate separately,

2. The FDW model

but interdependently, explicitly distinguishing

2.1 The FDW-model defined

between the short- and long-run supply of

Archour-Fischer (1999:33) states that the

space. A similar model was published by Fisher

Fischer-DiPasquale-Wheaton

(1992: 167). Fisher shows the equilibrium

elegant metaphor that integrates the different

existing between the short- and long-run

markets in the built environment, with specific

situations of the space and capital markets.

reference to the property market, the capital

APRIL 2014

model

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THE ECONOMIC IMPACT of the COMMERCIAL REAL ESTATE SECTOR on the SOUTH AFRICAN ECONOMY

market and construction activity. Du Toit (2002:

Quadrant 1 indicates the demand function

10) describes the FDW model as being a static

on the market for space demanded by users,

quadrant model that has the ability to trace the

represented in this study by the occupiers of

relationships between real estate market and

office space. With a static supply, the price of

asset market variables. Archour-Fischer also

space or rent level will increase when demand

suggests that it is a dynamic model (Archour-

increases, and conversely. In equilibrium, the

Fischer, 1999: 40-42), in which the parameters

supply of property should be equal to the

of the model can be changed to determine the

demand at various price levels.

influence in the different markets represented by the model, although Viezer (1998) criticizes

In Quadrant 2 the rent level applicable to the

the application of the model (see section 2.3).

equilibrium level of demand is discounted at the capitalisation rate, which is illustrated in Figure

Taking into consideration the flow of real

1 as the slope of the asset valuation curve, to

estate as discussed by DiPasquale and

arrive at the asset value, represented by the

Wheaton, it is evident that the depreciation of

function P = R/i .

real estate and the subsequent replacement of such depreciation is an output of the model,

Quadrant 3 represents the construction activity,

as it is a reduction in the stock level seen in

which is a function of the asset value. When the

quadrant four of the model. The reduction and

asset value is higher than construction costs

replacement cause a shift in the supply and

(represented in Figure 1 as the intersection of

demand patterns so that the market reacts

the construction curve and the x-axis), new

to it. It thus acts as an input to the rest of the

F construction will be triggered, otherwise

model. The model reacts to the changes and

construction will come to a halt. Thus, P = f(C).

further depreciation takes place, resulting in a change in the then present stock level.

The level of construction activity is carried over to Quadrant 4, the adjustment of supply, and

A discussion of the theory relating to the model

is given by the function S = C /d, or ∆S = C – dS.

will be presented in the following section. It should, however, be emphasized that Du Toit

2.3 Remarks on the FDW model

has already extensively discussed the principles

The FDW model seems to offer an acceptable

of the model, and it is not the intention of this

interpretation of the property market using a

study to reproduce his work. However, it is

diagrammatic model, which is mathematically

necessary to include a detailed discussion of

explained by the developers of the model.

the model in order to explain different concepts

However, Viezer (1998) points out that the FDW

later in the article.

model is of little value as an investment tool, and he develops a Real Estate Econometric

Figure 1 shows a graphical illustration of the

Forecast Model (REEFM) that is able to forecast

model, which consists of four quadrants, as

implicit market returns. The REEFM seems to be

shown in Table 1, and represents the following

of much more value as an investment tool, as

(Archour-Fischer, 1999: 34 – 37):

it can be used for calculating historical returns and forecasted returns.

Quadrant 1 – Demand function on the market for space;

According to Archer and Ling (1997), a multifactor asset pricing model should be used to

Quadrant 2 – The valuation function;

determine the discount rate, which in turn would determine both the market value and

Quadrant 3 – The construction function;

the cap rate, rather than assuming that the cap rate is exogenously determined. Viezer

Quadrant 4 – The adjustment supply.

SAPOA - the voice of commercial property

(1998) developed an econometric model for

APRIL 2014

THE ECONOMIC IMPACT of the COMMERCIAL REAL ESTATE SECTOR on the SOUTH AFRICAN ECONOMY

Figure 1 Diagrammatic FDW-model

R = f1(S)

Quadrant 1: Demand for space

S = E(b – a . R) P = f2(R)

Quadrant 2: Determination of value

P = R/i C = f3(P)

Quadrant 3: Construction function

C = (P – β)/α

Quadrant 4: Stock adjustment function

S = f4(C) S = C/d

Where:

R = rent per unit S = supply E = the number of office workers a and b = demand parameters P = price or value per unit i = the capitalisation rate C = construction α and β = construction parameters d = a depreciation rate

the integration of real estate’s space and capital

The FDW model is interpreted by Viezer (1998)

markets – the Real Estate Econometric Forecast

to suggest that equilibrium is a natural state

Model (REEFM). In his research, he answers the

where all values are determined simultaneously,

above comment by Archer and Ling by including

but in reality there are lags in the adjustment

a stochastic equation for, inter alia, the cap rate.

process. Viezer (1999: 507) also modifies the

Viezer’s equation contains five predetermined

DiPasquale-Wheaton (1992) model by positing

variables four of which are taken, with some

that real construction costs are a function of

modifications, from the pre-specified Arbitrage

the lagged net change in stock, rather than a

Pricing Theory (APT) model by Chen, Roll and

current-period new construction.

Table 1 FDW-model functions (Archour-Fischer, 1999: 38-39)

Ross (1986).

APRIL 2014

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THE ECONOMIC IMPACT of the COMMERCIAL REAL ESTATE SECTOR on the SOUTH AFRICAN ECONOMY

Viezer further points out that the FDW model

which is the function of a straight line

is of little value in terms of practical advice,

relationship between x and y.

and is limited to forecasting the changes in the

Each of the stochastic equations is a variation of

direction of real estate markets and general

the above equation, allowing for the different

levels of return. He maintains that the model

variables that influence the Yi - factor. In all six

should be estimated statistically in individual

equations the variable:

markets if it is to be useful to the practitioner.

Σt=

1T-1

δtYRDUMt

is also added, which is missing data indicators The REEFM integrates real estate’s space and

to be used in estimating the unbalanced panel

capital markets econometrically rather than

(Viezer, 1998: 115).

diagrammatically. The model also links the short- and long-run markets, and calculates

The deterministic equations are in different

implicit market returns for property markets

formats, calculating a specific result in each case

(Viezer, 1998: 143). The model can therefore

by combining the results from the stochastic

be used as an effective investment or forecast

equations.

tool. The only forecast inputs needed are the local economic variables, and national financial

In both the stochastic and deterministic

variables (Viezer, 1998: 144).

equations, the variables are given in the format Vp.m.t., which in this case would indicate a

3. The REEFM model

variable (V) for property type p, in metro area m,

3.1 Principles of REEFM

at time period t.

The conceptual framework of REEFM is illustrated in Figure 2 (Viezer, 1998: 107). REEFM

The different equations will be discussed in the

is a recursive model, containing six stochastic

text to follow, and the similarities and differences

equations (occupancy, real rents, capitalisation

relating to the FDW model will be explained.

rate, market value per unit, change in stock, and real construction costs) and seven deterministic

3.1.1Short-run asset market T-

equations (a net operating income proxy,

OCCt = αt + β1RNT$t-1 + γ1ECONt + Σt=1

market value per unit, stock of space, vacancy

rate, implicit appreciation market return, implicit

1 (Viezer, 1998: 115)

δtYRDUMt + εt

income market return and implicit total market return) (Viezer, 1998: 134-5).

T-1

RNT$t=αt + β1VACt-1 + Σt=1 δtYRDUMt + εt 2 (Viezer, 1998: 115)

The six stochastic equations given by Viezer are all in the format:

Viezer explains occupancy (OCCp.m.t.) as a

Yi = αi + β1Xi + εi

function of lagged real rent, (RNT$p.m.t, nominal

where:

rent deflated by the Consumer Price Index) and

αi = Y intercept for the population;

an economic variable. The economic variable

β1 = slope for the population;

in the case of office space would be office

εi = random error in Y for observation i.

employment. Real rents, in turn, respond with a lag to vacancies in the market (Viezer, 1998: 114-

This relationship is confirmed by two sources,

15). On the contrary, the FDW model only takes

with different formats for the same equation:

the demand as equal to the supply of office space, using only the equation S = E(b – a.R)

SAPOA - the voice of commercial property

Yi = β0 + β1Xi + εi

to calculate this (see section 2.2.2). Rent levels

(Levine, Berenson & Stephan, 1998: 538)

are taken as a given, and are not calculated

and;

as above. This means that the variables are

μy = α + βx

calculated according to a much less scientific

(Steyn, Smit & Du Toit, 1989: 378),

method, limiting the capabilities of the model

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Figure 2 REEFM’s conceptual framework (Viezer, 1998: 107)

for the historical explanation of the market, as

the first year’s income by the capitalisation rate,

well as possibilities for forecasting, which would

which is the discount rate minus CPI. As the

be much more useful.

discount rate is not determined, the Cap rate cannot be determined from the discount rate.

The first deterministic equation is a proxy for the net operating income (NOIp.m.t). The net

However, Viezer determines the Cap rate with

operating income is determined by multiplying

the equation:

the occupancy by the rental levels. However, the rental levels are calculated for real rent in terms

CAPp.m.t = αp.m.t + β1RISKt + Φ1TERMt + γ1INFLt

of equation 2 and should therefore be inflated

+ η1%ΔECONp.m.t + ξRNTp.m.t-1/MSFp.m.t-1

by the Consumer Price Index (CPI). The equation

+ Σt=1

for the net operating income is therefore:

4 (Viezer, 1998: 123)

T-1

δtYRDUMt + εp.m.t

NOIp.m.t =OCCp.m.t x RNT$p.m.t x (CPI/100)

The variable RNTp.m.t-1/MSFp.m.t-1 considers the

3 (Viezer, 1998: 117)

backward-looking comparisons of appraisers and therefore takes into consideration historical

3.1.2 Short-run capital market

data. %ΔECONp.m.t is the percentage change in

The capital market attempts to translate the

the economic variable as used in the equation

results of the short-run space market into asset

for occupancy. INFLt is the current inflation rate,

prices. “The reasonably calculated expected

while RISKt and TERMt are risk variables used by

future net income flow of an investment

Viezer as the difference between the corporate

property discounted to its present value, when

Baa bond rate and the 10-year Treasury bond

capitalised at the prevailing rate sought by

rate, and the difference between the 10-year

prudent investors, represents the estimated

Treasury bond rate and the 3-month Treasury

capitalised value of the property at that time”

bill rates respectively (Viezer, 1998: 123, 124).

(SAIV, 1999: 6-4). When considering the future income stream, it increases approximately in line

With the Cap rate established, it is possible to

with inflation, or the country’s CPI. The income

calculate the market value per unit for the metro

stream can therefore be capitalized by dividing

property stock with the equation:

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MSFEp.m.t = NOIp.m.t x (1 + (PASSp.m.t x INFLt)) ÷ STKp.m.t

STKp.m.t = STKp.m.t-1 + NEWp.m.t – RMVp.m.t 8 (Viezer, 1998: 132)

CAPp.m.t 5 (Viezer, 1998: 124)

Capital market: which the inflation rate is passed through to

CST$p.m.t = αp.m.t + β1NEWp.m.t-1 – RMVp.m.t-1 + Σt=1T-1δtYRDUMt + εp.m.t

the property appreciation. The MSFE variable

9 (Viezer, 1998: 132)

The PASS variable indicates the extent to

is then regressed to determine the actual In the above, the real construction costs are

property value per unit:

indicated as being a function of the lagged net T-

MSFp.m.t = αp.m.t + β1MSFEp.m.t-1 + Σt=1 1

change in stock (Viezer, 1998: 132).

δtYRDUMt + εp.m.t

6 (Viezer, 1998: 124)

The last equation to close the loop for the model is the vacancy rate:

While the above equations are used to determine

VACp.m.t = 1 – (OCCp.m.t / STKp.m.t)

the per unit market value of property, the FDW

10 (Viezer, 1998: 133)

model divides the demand, multiplied by the rate per unit, by the cap rate to get to the market

4. Conclusion

value. While REEFM takes into consideration

The two mentioned models investigate the

different risk factors as well as economic

space and capital markets in real estate. The

variables for calculating the cap rate, the FDW

value of this is in the possibility of applying the

model does not indicate how this is calculated

model in the South African context in order to

(Du Toit, 2002: 31). From this it is also taken that

monitor property behaviour more closely. As

REEFM calculates the market value of property

such it could be used successfully to explain

in a much more scientific way, which creates an

specific property economics with regards

opportunity for explaining the current market

to geographical areas or different types of

as well as forecasting future trends.

property, or even to valuate property in general.

3.1.3 Long-run space market The long-run space market is the addition of new stock or construction and the removals of stock or depreciation. These construction and removals are a function of the difference in quantities (STK – OCC) and real prices (MSF$ – CST$) (Viezer, 1998: 132). The asset market is expressed in quantities and the capital market is expressed in prices with the following equations: Asset market: NEWp.m.t – RMVp.m.t = αp.m.t + β1(STKp.m.t-L – OCCp.m.t-L) + γ1(MSF$p.m.t-L – CST$p.m.t-L) + Σt=1T 1

δtYRDUMt + εp.m.t

7 (Viezer, 1998: 132) The stock of space in the current period takes into consideration the stock in the previous period, plus the current period’s construction, minus the current period’s removals:

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b) Input-Output matrix

final demand for a product of an industry or sector (Surugiu 2009).

As mentioned, the South African NIPA accounts

A standard I-O table is shown in Figure 3. Input

provide an overview of the general economic

flows are recorded in the columns of the table,

system and how the property sector fits into this

and outputs are recorded in the rows (Sporriet

system. The more specific property activities

al 2007). Intermediate demand (Z) represents

could then be explained by theories on space

the inter-industry transactions table, a matrix

and capital markets and then these variables

of transactions between the producing sectors.

and the interrelated impact could measured by

Final demand (y) consists of the household,

way of I-O analysis.

government and rest of the world sectors. Value added to the producing sector consists of Capital

An Input-Output matrix (I-O) is a representation

and Labour, and receives interest and wages.

of national or regional economic accounting that records the way industries trade with

An I-O analysis is typically used to calculate

one another and produce (in other words the

the

flows of goods and services). Those flows are

exogenous changes in y, for example the

registered in a matrix, simultaneously by origin

economic impact (in terms of industry output,

and by destination (OECD 2006). The Input-

employment and income) of a new harbour

Output analysis is the standard method for

development, in both the short-and the long-

Figure 3

measuring the spread effects of changes in the

run on a specific economy.

An illustrative I-O table

economic

impacts

resulting

from

(Sporriet al 2007)

production side (input) (Index j )

Processing and manufacturing industry

Power, water utilities

Construction

Trade, motor vehicles, consumer goods

Hotel and restaurant industry

Transport and communications Financial indusrty, (excl. social security)

10 11

Zij intermediate demand Z

9 10

Real estate and services

Public enterprises

+ value added

(wages, interest, profit, taxes)

total output

Mining

exports

Fishery

government final demand

household final demand

production side (input) (Index j )

1 Agriculture and forestry

ric ult ur e an er d y M fo ini re str ng y Pr oc es s Po i we ng a n Co r, wa d m ns t an tru er u uf a ti cti Tr on lities ctur ad ing e, ind Ho mot us or te try la ve n h Tr d i c an les re s s ,c ta ur Fi port an onsu na t in nc and m er i du Re al in com go st m al du od un ry es sr s ica ty, Pu ta te tio (e bli xc c e an ns l. s d nt s oc er pr ervi ial ce ise se s s cu rit y)

Input-Output Table

+ imports = total input (= total output)

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If x represents the vector of industry outputs, y

Where:

the vector of final demand and Z the matrix of

vis the ratio of employment to output for each

inter-industry transactions, then the relationship

industry.

between these is (Sporriet al 2007): Lastly, the income effects show the impact

1 Equation 1

on income from employment throughout the economy arising from a unit increase in final

demand for industry j’s output.

x=y . +y .

(Incomeeffects)j = ΣiviLij

Equation 5

The following assumptions underlie any I-O A matrix of technical coefficients (A) is then

analysis:

derived by dividing inter-industry transactions

◆ The production functions of industries do not

by output:

change. ◆ The economy can be described with linear

Equation 2

aij =

production functions.

zij xj

◆ The region is large enough to make imports by individuals insignificant.

The elements of A describe the direct, first round

It is important to note that I-O tables assume

direct impact of any change in final demand. In

linear relations between inputs and outputs

other words, how much input from sector i is

from different sectors as well as linear

used per monetary output of sector j. When this

relations between outputs and final demand

is solved for production as a function of final -1 demand, the Leontief inverse matrix ( L=(I-A) )

(D’Hernoncourt, Cordier and Hadley 2011).

is calculated.

Employment impact The Leontief inverse matrix together with

The Leontief inverse matrix can then be used

employment data can be used to calculate the

to calculate the output multiplier, the income

employment multiplier and employment effects

multiplier and income effects (D’Hernoncourt,

(D’Hernoncourt, Cordier and Hadley 2011).

Cordier and Hadley 2011).

The employment multiplier shows the total increases in employment throughout the

The output multiplier for a particular industry

economy resulting from an increase in final

can be defined as the total of all outputs from

demand.

each domestic industry required in order to

wiLij (employmentmultiplier)j = Σi wj

produce one additional unit of output. Equation 3

Equation 4

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(Outputmultiplier)j = ΣiLij

Equation 8

The income multiplier indicates the increase in

Where:

income from employment as result of a change

w is equal to one full-time job per Rand of total

of R1 of income from employment in each

output for each industry.

industry.

Employment effects calculate the impact on

(Incomemultiplier)j = Σi

viLij vj

employment throughout the economy arising from a change in final demand for industry j’s output of one unit.

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GDP (Economic Growth) Direct Impact

Indirect Impact

◆ The direct economic impact is the change in economic activity directly related to the scenario simulated.

◆ The indirect economic impact seeks to capture the knock-on benefits to the host economy (e.g. the additional money spent in the local area by say a reduction in property taxes in the long-run). ◆ Indirect impact, also known as the multiplier effect, includes the re-spending within the local economy.

Employment (by skill level) Direct Impact

Indirect Impact

◆ Total employment created / destroyed

◆ Indirect employment is the total jobs

directly related to the scenario simulated.

created/destroyed as a result of specific scenarios simulated. Local companies that provide goods and services to the property sector increase/decrease their number of employees as property investment changes, thus creating an employment multiplier.

Table 2 Definitions of Direct and Indirect Impact

(employmenteffects)j = ΣjwiLij Equation 9

Depth of impact estimation The analysis will estimate direct, indirect and induced impacts. Table 2 provides definitions of the direct and indirect impacts. It is distinguished between GDP (economic growth) and employment (by skill level). The induced impact measures the next round of impacts which is the results from a change in household spending in the general economy.

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ECONOMIC ACTIVITY WITHIN THE REAL ESTATE SUB-SECTOR This section will provide some statistics on the activities within the real estate sector. It is important to note this in order to understand the extent of the sector in the economy, as well as indicate the trends of activity. It is important to note in this regard the relationship between the real estate sector within the wider economy, as well as the activity in the construction sector as explained in the theoretical discussion under the FDW and REEFM models.

a) General economic activity 1. Economic activity

2. Gross Domestic Product The nominal Gross Domestic Product (GDP) over the past 10 years also appeared to be very

The economic activity for South Africa over the

healthy, with a 10.28% average year on year

past ten years, up to the end of 2013 is displayed

growth as indicated by Figure 5. Although the

in Figure 4. It is evident that there was a gradual

y-o-y growth slowed down to 6.72% in 2009, it

increase with a stable growth in activity, with

increased again to 11.92% in 2010, after which

the actual figures in R million is indicated by

it reduced again to 9.69%, 7.03% and 7.85% for

the bars and the year to year growth in figures

the three subsequent years respectively.

next to each bar displayed. The period between 2004 and 2008 was characterised by large

The real GDP (at constant 2005 prices) does

increases with economic prosperity which was

show a slightly different picture, with a 10 year

felt throughout the economy, with growth of up

average growth of 3.40%. It is also indicated in

to 14.4% per year growth during the 2007/2008

figure 6 that during the 2009 recessionary period

period. During the 2009 recessionary period,

a negative growth -1.53% was experienced and

economic activity slowed down to just above

although increasing again to 3.14% and 3.60% in

7% and remained below 8% ever since, with the

2010 and 2011 respectively, the growth slowed

economic activity growing to just below R10

down to 2.47% and 1.89% during 2012 and 2013

trillion by the end of 2013.

respectively. This caused the average growth for the past 5 years, or the period since the start of

Although the economic activity showed stable

the 2009 recession, to reduce to 1.9%.

increases, it is considered to be a weak indicator of the real situation of the economic growth in

The GDP should, however, be considered also

the country. It is nevertheless important to note

taking into consideration the number of people

this as it indicates the sum of all activity in the

that contributes to this. In this regard the Per

different sectors of the economy.

Capita GDP, or GDP divided by the population

Figure 4 Actual Economic Activity at current prices Source: South African Reserve Bank

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Figure 5 Actual Gross Domestic Product at current prices Source: South African Reserve Bank

Figure 6 Gross Domestic Product at constant 2005 prices Source: South African Reserve Bank

of the country, provides another insight to

or at least for the period that data is available.

the economic situation. Still remaining on the

This, however, slowed down significantly in

real GDP, the y-o-y per capita GDP at constant

the subsequent years and, if continuing on the

2005 prices are indicated by Figure 6. From this

same trend, might turn negative soon.

it is evident that the growth started to slow down since 2008, turning negative in 2009

3. Employment

and although positive again in 2010 and 2011,

It was mentioned in the previous section that

slowed down again in 2012 and 2013. If a 5

the population number should be taken into

year moving average growth is considered, as

consideration when looking at the GDP figures.

indicated by figure 8 it could also be seen that the

Apart from that, the official employment

period immediately prior to the 2009 recession,

and unemployment figures should also be

was particularly prosperous years, with 5 year

considered in order to comprehend the influence

average growth for three consecutive years

of real estate on the economy. According to

being the highest in the history of South Africa,

the 2011 census, the total population of South

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Figure 7 Y-o-y growth of per capita GDP at constant 2005 prices Source: South African Reserve Bank

Figure 8 5

Africa is approximately 51,77 million people. For

that year, indicating a total workforce of

Year mov. ave. growth of per capita

the past 20 years the average annual population

approximately

GDP at constant 2005 prices

growth was approximately 1.7%, slowing down

distribution of employment vs. unemployment

Source: South African Reserve Bank

to an average of 1.34% for the past 10 years

is displayed in Figure 9.

18,70

million

people. The

and 1.18% for the past 5 years. This gives an estimated current population of approximately

Of the total employed workforce, approximately

53,01 million people.

72.4% is formally employed and 27.6% informally employed as indicated by Figure

The

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total

employed

workforce

was

10, while the former is divided as 17.4% highly

approximately 14,07 million people in 2011,

skilled, 42.2% skilled and 40.4% semi-skilled and

with a total unemployment of 24.8% during

unskilled, shown in Figure 11.

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Figure 9 % of workforce employed Source: Adapted from Statistics South Africa

Figure 10 Distribution of formal employment vs. informal employment Source: Adapted from Statistics South Africa

Figure 11 Source: Adapted from Statistics South Africa

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4. Tax generated Total taxes generated in the economy could be

approximately 11.45% and 11.61% per annum

viewed from the total taxes on products and

respectively over the past 20 years.

production in order to compare the amount of taxes on products and production generated by

Another two sets of taxes that are recorded by

the real estate sector to that of the rest of the

the SARB is the national government tax revenue

economy. This total is indicated in Figure 12.

from property and the local government cash receipts from taxes. These are displayed in

The total tax generated is approximately R439

Figure 13 and provide an indication of property

billion for taxes on products and R374 billion

specific taxes. It is not the same as taxes in

in taxes on production and imports as at the

products and production in the property sector,

end of 2013. This amounts to a total increase of

but should be viewed separately.

Figure 12 Source: Adapted from Statistics South Africa

Figure 13 Source: Adapted from Statistics South Africa

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INDUSTRY SPECIFIC ECONOMIC ACTIVITY In this section, the past economic activity within the construction sector and real estate sub-sector will be provided. The link between construction and real estate needs to be re-affirmed, as the real estate sub-sector creates the opportunities for the construction sector, while the latter again provides additional stock to the former, which changes the general equilibrium levels, as indicated in section 2a of this report. The emphasis of this section will be to indicate the activity specifically with regards to building plans passed and buildings completed. This enables the possibility to distinguish between the relative size of the economic activity within different geographical areas as well as different types of buildings. Lastly, by considering the amount spent on buildings completed in relation to the total square meter size of buildings completed, it is possible to provide an indication of the cost per square meter spent on these buildings, which does provide an indication of cost changes over time, but more importantly, provides an indication of the quality of buildings being built over time, in different geographical areas and for different purposes.

1. Building plans passed

It furthermore also suggests that residential

This total figure includes all property, the

property is less sensitive to economic activity,

contribution of non-residential property as a

but rather to affordability (refer Boshoff, 2010).

percentage of all property could be seen in

The contribution by each province in terms of

Figure 15. From this it is evident that commercial

plans approved during 2013 is shown as per

property

approximately

Figure 16 for all property and for non-residential

15% and 30% of total property, with a gradual

remains

between

property in Figure 17. It is evident from this that

increase since early 2004 up to 2010 and after

Gauteng is the dominant contributor for both

a short steep decline, it increased again since

all property and non-residential property at

early 2011. Although not empirically tested, this

41% and 43% of the total respectively, followed

seems to have a similar trend than the economic

by the Western Cape at 21% and then KwaZulu-

activity, suggesting a relationship between

Natal at 16% for all property, while these figures

economic activity and real estate demand.

are the opposite for the two provinces for non-

This is in line with the a priori expectation due

residential property. Figure 18 displays the

to property being required for all commercial

building plans passed for different types of

activity, i.e. offices for business services, shopping

buildings during 2013. The figures are displayed

centres for retail and industrial buildings for

as the national total for each building type.

manufacturing and other industrial activity.

Figure 14 Total national building plans passed per month Source: Statistics South Africa

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Figure 15 Non-residential buildings plans passed as % of all property nationally Source: Adapted from Statistics South Africa

Figures 19 to 23 provide the annual value

the 2000 decade, planning started to decline for

of building plans passed for different types

all areas, mostly from 2009, Gauteng had a slightly

of properties since 2000, distinguished per

longer lag, declining from 2010. Of importance is

province. These figures are important in order to

the level of decline in planning activity experienced

understand the planning activity per province

and the revival 2011/2012 onwards.

and per property type. It enables the possibility to do forecasting as to the future activity and

Figure 20, showing the planning activity in all

also gives insight into the effectiveness of the

provinces for office and banking space, again

different provinces for different property types

indicates Gauteng to be dominant, followed

with regards to the lag between planning and

by KwaZulu-Natal and Western Cape. Although

completion, as well as the actual percentage of

seemed to have passed the Western Cape in

buildings that reaches final completion.

terms of planning activity since 2008 due to the Western Cape starting with declined planning

In Figure 19, the planning for all types of

activity already in 2008, the recessionary decrease

property is dominated by the Western Cape,

seems to have prolonged longer for KwaZulu-

Gauteng and KwaZulu-Natal. It is evident that, after

Natal, with 2013 being the first year of increased

the increased planning activity in the latter half of

planning activity. This caused the Western Cape

Figure 16 Distribution of all property building plans passed per province Source: Adapted from Statistics South Africa

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Figure 17 Distribution of non-residential building plans passed per province Source: Adapted from Statistics South Africa

Figure 18 Distribution of national building plans passed per property type Source: Adapted from Statistics South Africa

Figure 19 Building plans passed per province All property Source: Adapted from Statistics South Africa

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to again take second position for planning

In Figure 23, other types of non-residential

activity in this property type since 2012, although

property could be seen to also contribute with

the 2013 figure for the province is slightly lower

little fluctuation through the recession, but the

than 2012. Although the Eastern Cape Province

total value of planned space is substantially

cannot compare to the other three provinces

lower than the previously mentioned types of

mentioned so far in terms of overall value, it

non-residential property.

nevertheless shows good prospects when

In addition to the building plans passed, it is also

compared to its own history. Eastern Cape only

interesting to note the relationship between

had a decline in activity during 2009 and again

new buildings and additions and alterations.

in 2012, but the activity for the 2010, 2011 and again in 2013 indicates healthy growth figures

Figure 24 provides the relationship between

planned for this property type in the province.

non-residential additions and alterations vs. new

Mpumalanga appeared to have promising

buildings, while Figure 25 provides information

prospects, but only 2008 had a planned figure

on additions and alterations for non-residential

in excess of R200,000,000, then declining to

property as percentage of the total additions

R116,000,000 in 2009 and thereafter remaining

and alterations planned for all property.

below R100,000,000 worth of office and banking space planned per year. The other provinces

2. Buildings completed

are all showing fairly low levels of planned

The statistics for buildings completed provides

contribution and rarely exceeds R50,000,000 of

a view on the addition of stock to the property

new planned space per year.

market and is useful in determining the economic activity between different types and

of buildings and in different geographical

warehouse space, although less represented

locations. Furthermore, by considering the

in the less economic active provinces, are

economic cycle in both building plans passed

more evenly distributed in the three most

and buildings completed, it is possible to see

active provinces, Gauteng, Western Cape and

how long it takes for the value of buildings

KwaZulu-Natal. It furthermore appears to be

completed to react to a change in building

less sensitive for economic changes, with a fairly

plans passed. This provides an economic lag

constant planning for new space, even through

which could then be used to forecast the value

the recessionary period.

of buildings to be undertaken, by considering

Figure

indicates

that

industrial

Figure 20 Building plans passed per province Offices and banking Source: Adapted from Statistics South Africa

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Figure 21 Building plans passed per province Shopping space Source: Adapted from Statistics South Africa

the current level of building plans being passed

completed for the different types of properties

and also to evaluate the percentage of buildings

per province in Figures 32, 34, 36, 38 and 40 as

that are actually built after the plans are passed.

the square meters completed and in Figures 33,

In Figure 26 the total annual value of buildings

35, 37, 39 and 41 as the total value completed.

completed nationally is shown alongside the value of non-residential buildings completed.

In Figure 28, it can be seen that Gauteng also

Figure 27 then shows the total value of non-

dominated the total buildings completed, which

residential buildings as a percentage of the total.

is expected, in line with the total building plans

The provincial total buildings completed

passed. Following is Western Cape and KwaZulu-

annually are indicated in figure 28 while the

Natal. The buildings completed in Eastern Cape

figures for total non-residential buildings

and Mpumalanga makes up only approximately

completed per province are displayed in

10 to 15% of the level of activity of Gauteng,

Figure 29. The total square meters of buildings

while it is approximately 20% of Western Cape

completed nationally for each type of property

and 30% of that of KwaZulu-Natal. It could

is then shown in Figure 30 and in value in Figure

therefore be seen that the main activity took

31. This is then also indicated for total buildings

place in Gauteng, Western Cape and KwaZuluFigure 22 Building plans passed per province Industrial and warehouse space Source: Adapted from Statistics South Africa

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Figure 23 Building plans passed per province Other property Source: Adapted from Statistics South Africa

Natal and that the activity in the other provinces

population and the subsequent requirement

are then substantially less, contributing in total

for property to operate from, in line with the

but a fraction of the total property activity.

principles of the FDW and REEFM models as discussed in section 2 (refer Figure 1 and Table

This is very similar for non-residential buildings

1). It is possible to see a substantial increase

completed as per Figure 29, although there are

from 2000 to 2008/09, but the long term growth

some individual years for some of the provinces

trend is obscured by the recessionary decline

that proved to be substantially more active, such

that took place after 2009. Offices and Banking

as the buildings completed in 2009 in the Eastern

property completed reached during 2013 for the

Cape, as well as in 2013, when Eastern Cape had a

first time volumes that are higher than the 2000

particular good increase in buildings completed.

figure, which are indicative of such a gradual increase since the 2008/09 recessionary period.

Figure 30 shows the total square meters of

But, cognisance should be taken of the business

buildings completed per property type. It is

cycle, which might cause future increases just

expected that there should be a gradual increase

to be the catch-up with too little development

in these volumes due to a growing GDP and

during the downturns. For shopping space the

Figure 24 Non-residential additions and alterations vs. new buildings Source: Adapted from Statistics South Africa

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Figure 25 Additions and alterations â&#x20AC;&#x201C; nonresidential vs. total Source: Adapted from Statistics South Africa

Figure 26 Non-residential buildings completed vs. Total buildings completed Source: Adapted from Statistics South Africa

Figure 27 Non-residential buildings completed as % of Total buildings Source: Adapted from Statistics South Africa

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Figure 28 Total value of buildings completed per province Source: Adapted from Statistics South Africa

Figure 29 Value of non-residential buildings completed per province Source: Adapted from Statistics South Africa

total square meters of buildings completed are

meters, results in fairly high fluctuations in total

still below the 2001 figure and are currently still

value of buildings completed. The total value

in line with the volumes achieved during the

of buildings completed, alternates between

early 2000â&#x20AC;&#x2122;s. Industrial and warehouse space

Offices and banking space and shopping space

is the only of the main property types where a

to be the most actively supplied, followed by

gradual increase is clearly visible, although the

industrial and warehouse space, but additions

recessionary influence did also show. In Figure

and alterations also making a meaningful

31, the gradual increase in volumes are more

contribution.

evident, which is partly due to the increasing cost of construction (refer also Figures 54 to 59).

In Figure 32 to 41, the distribution of different types of property in the different provinces

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The changing cost in construction, as mentioned

could be followed and compared with reference

earlier, is influenced by both inflationary

to the total square meters added as well as

pressure as well as the level of specification that

the total value of space added. This provides a

changes from time to time. The combination of

view on the relationship between the different

the change in cost and volume in terms of square

property types, the different provinces, as well

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as the difference in square meters added vs.

it changed over the past 5 years. These figures

value added.

could then also be seen as a percentage of total property in order to evaluate the contribution in

Figures 42 to 47 then provides the distribution

relation to all real estate activity in the country

of buildings completed as percentage of

and are shown in Figures 48 to 53. These last

national between the different provinces and

mentioned figures are those that are use within

different property types over the last 5 years as

the I-O analysis to determine the influence of

well as for 2013 only. This gives an indication of

each property type or geographical area on the

how the market is differentiated, but also how

national economy.

Figure 30 Square meters of non-residential buildings completed per type Source: Adapted from Statistics South Africa

Figure 31 Value of non-residential buildings completed per type Source: Adapted from Statistics South Africa

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Figure 32 Office buildings completed – total square meter Source: Adapted from Statistics South Africa

Figure 33 Office buildings completed – total value Source: Adapted from Statistics South Africa

Figure 34 Shopping space completed – total square meter Source: Adapted from Statistics South Africa

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Figure 35 Shopping space completed – total value Source: Adapted from Statistics South Africa

Figure 36 Industrial and warehouse space completed – total square meter Source: Adapted from Statistics South Africa

Figure 37 Industrial and warehouse space completed – total value Source: Adapted from Statistics South Africa

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Figure 38 Other non-residential buildings completed – total square meter Source: Adapted from Statistics South Africa

Figure 39 Other non-residential buildings completed – total value Source: Adapted from Statistics South Africa

Figure 40 Non-residential additions and alterations completed – total m2 Source: Adapted from Statistics South Africa

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Figure 41 Non-residential additions and alterations completed â&#x20AC;&#x201C; total value Source: Adapted from Statistics South Africa

Figure 42 All buildings per province completed as % of national Source: Adapted from Statistics South Africa

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Figure 43 Non-residential buildings completed per province as % of national Source: Adapted from Statistics South Africa

Figure 44 Office and banking space completed per province as % of national Source: Adapted from Statistics South Africa

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Figure 45 Shopping space completed as % of national Source: Adapted from Statistics South Africa

Figure 46 Industrial and warehouse space completed as % of national Source: Adapted from Statistics South Africa

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Figure 47 Other non-residential buildings completed as % of national Source: Adapted from Statistics South Africa

Figure 48 Shopping space completed as % of national Source: Adapted from Statistics South Africa

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Figure 49 Non-residential buildings completed as % of total property Source: Adapted from Statistics South Africa

Figure 50 Office and banking space completed as % of total property Source: Adapted from Statistics South Africa

Figure 51 Shopping space completed as % of total property Source: Adapted from Statistics South Africa

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Figure 52 Shopping space completed as % of total property Source: Adapted from Statistics South Africa

Figure 53 Other non-residential buildings completed as % of total property Source: Adapted from Statistics South Africa

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3. Cost per m2

and banking space are lately being provided

As mentioned earlier, Figures 54 to 59 provides

at the highest cost of construction, although

the information with regards to the cost of

there are a number of periods where shopping

construction of different geographical building

space are very similar. This is then followed by

types as well as the different areas. This not

other non-residential buildings and additions

only gives an indication of changing cost over

and alterations while industrial and warehouse

time due to inflationary pressures, but also

space are being added at the lowest cost of

provides a view on the change in cost due to

construction. Of importance in these figures are

specification level changes. Apart from previous

also the fluctuations in cost over time, caused

comments made which also referred to some

by specification levels and in comparison to

of these figures, it could be seen that office

different property types and geographical areas.

Figure 54 National cost per square meter Source: Adapted from Statistics South Africa

Figure 55 Office and banking space cost per square meter per province Source: Adapted from Statistics South Africa

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Figure 56 Shopping space cost per square meter per province Source: Adapted from Statistics South Africa

Figure 57 Industrial and warehouse space cost per square meter per province Source: Adapted from Statistics South Africa

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Figure 58 Other non-residential space cost per square meter per province Source: Adapted from Statistics South Africa

Figure 59 Additions and alterations cost per square meter per province Source: Adapted from Statistics South Africa

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CONTRIBUTION TO THE SOUTH AFRICAN ECONOMY This section will specifically consider the results of the I-O analysis and provides the influence that different property types and in different geographical areas have on the South African economy. The first set of results provides the economic activity that is created within the real estate sub-sector, followed by the influence on the GDP, employment opportunities created and lastly tax generated. It should be noted that the figures shown in the Figures to follow are based on the economic relationships between real estate and other sectors only. It excludes the physical aspect of property being required to have any economic activity and the influence of that on the economy.

Figure 60 Economic activity contribution All property per province Source: Authorâ&#x20AC;&#x2122;s calculations

Figure 61 Economic activity contribution - Nonresidential property per province Source: Authorâ&#x20AC;&#x2122;s calculations

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a) Economic activity

direct impact, R113,604,000,000 indirect impact

As mentioned in section 3, the total economic

and R173,973,000,000 induced impact. This

activity

impact on economic activity by non-residential

R9,316,400,000,000. Of this, the contribution of

South

Africa

amounted

real estate as attributed to the different

non-residential real-estate is calculated by the

provinces are provided as per figures 60 and

I-O analysis to be approximately 0.66% through

61 to different property types as per figures 62

direct impact, 1.22% through indirect impact

to 67. It is evident that the economic activity

and 1.87% by including induced impact. This

is mostly created in the Gauteng, followed by

totals to a total contribution of R61,416,000,000

Western Cape and then KwaZulu-Natal. Figure 62 Economic activity contribution - Non-residential property per property type Source: Authorâ&#x20AC;&#x2122;s calculations

Figure 63 Economic activity contribution Office and banking space Source: Authorâ&#x20AC;&#x2122;s calculations

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Figure 64 Economic activity contribution Shopping space Source: Author’s calculations

Figure 65 Economic activity contribution Industrial and warehouse space Source: Author’s calculations

Figure 66 Economic activity contribution Other non-residential buildings Source: Author’s calculations

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Figure 67 Economic activity contribution Additions and alterations Source: Authorâ&#x20AC;&#x2122;s calculations

b) Gross domestic product

through induced impact and is distributed in

The total GDP for South Africa in 2013 was

the different provinces as per figure 68.

approximately R3,385,369,000,000. Of this, 1.41% is created by non-residential real estate

The contribution of non-residential real estate

through direct activity, while the indirect

to the GDP in the different provinces is shown

impact was 1.55% and the induced impact

in figure 69, while the contribution by different

2.39%. This amounts to a total contribution

property types nationally is shown in figure 70.

of approximately R47,817,000,000 added to

Figures 71 to 75 then provides the detailed

the GDP by the real estate subsector directly,

contribution of each property type per province

R52,357,000,000 indirectly and R81,051,000,000

to the total GDP. Figure 68 Contribution to GDP - All property per province Source: Authorâ&#x20AC;&#x2122;s calculations

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Figure 69 Contribution to GDP All property per province Source: Author’s calculations

Figure 70 Contribution to GDP Non-residential property per property type Source: Author’s calculations

Figure 71 Contribution to GDP Office and banking space Source: Author’s calculations

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Figure 72 Contribution to GDP Shopping space Source: Author’s calculations

Figure 73 Contribution to GDP Industrial and warehouse space Source: Author’s calculations

Figure 74 Contribution to GDP Other non-residential buildings Source: Author’s calculations

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Figure 75 Contribution to GDP Additions and alterations Source: Author’s calculations

c) Employment created

the distribution between the different provinces

Within the total numbers, the distribution of

for total non-residential real estate jobs and

jobs between the different types of property

the different types of property are displayed in

nationally is estimated as per Figure 76, while

Figures 77 to 83.

Figure 76 Employment created Non-residential Source: Author’s calculations

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Figure 77 Employment created Total non-residential employment per property type Source: Authorâ&#x20AC;&#x2122;s calculations

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Figure 78 Employment created Total non-residential employment per province Source: Authorâ&#x20AC;&#x2122;s calculations

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Figure 79 Employment created Total employment office and banking Source: Authorâ&#x20AC;&#x2122;s calculations

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Figure 80 Employment created Total employment shopping space Source: Author’s calculations

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Figure 81 Employment created Total employment industrial and warehouse space Source: Authorâ&#x20AC;&#x2122;s calculations

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Figure 82 Employment created Total employment other nonresidential buildings Source: Authorâ&#x20AC;&#x2122;s calculations

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Figure 83 Employment created Total employment additions and alterations Source: Authorâ&#x20AC;&#x2122;s calculations

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d) Taxes generated

residential property to be lower than indicated

The estimation of taxes generated within the

here, while net taxes for non-residential real

real estate sub-sector is limited to net taxes on

estate would actually be higher. It would,

products as well as net taxes on production

however, require a further, more in-depth study

and other taxes. Net taxes include all taxes paid,

to obtain details of this.

less subsidies received and are therefore an indication of the level of taxes generated, which

The estimated net taxes generated by all property

could be applied towards other functions in the

activity is shown in Figure 84 while the estimated

economy. It, however, excludes an indication

taxes generated by non-residential property is

of taxes and subsidies that causes structural

shown in Figure 85, both shown per province.

changes within the sector, i.e. subsidies received

Figure 86 provides the national net taxes as

by households for residential property and

estimated per property type. Figures 87 to 91

which are funded by taxes from non-residential

then provides the results for net taxes generated

real estate would cause the net taxes for

per property type, shown per province.

Figure 84 Taxes generated All property per province Source: Authorâ&#x20AC;&#x2122;s calculations

Figure 85 Taxes generated Non-residential property per province Source: Authorâ&#x20AC;&#x2122;s calculations

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Figure 86 Taxes generated Non-residential property per property type Source: Author’s calculations

Figure 87 Taxes generated Office and banking space Source: Author’s calculations

Figure 88 Taxes generated - Shopping space Source: Author’s calculations

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Figure 89 Taxes generated Industrial and warehouse space Source: Author’s calculations

Figure 90 Taxes generated Other non-residential buildings Source: Author’s calculations

Figure 91 Taxes generated Additions and alterations Source: Author’s calculations

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SHORT-TERM FUTURE FORECASTING

In order to do some short-term forecasting, a view is taken on the average percentage of plans that are approved, are actually completed, as well as the lag between plan approval and completion. This provides the opportunity to forecast the total value of buildings activity, which would add stock to the real estate sector and provide a view of the expansion of the sector and the associated addition to economic activity, contribution to GDP, jobs created and taxes generated. These figures do, however, become fairly erratic and thus it is not attempted to do the forecasting per property type in each province, but only for each property type nationally and each province across all property types. As indicated in Figure 92, it is estimated that the

in place. In addition to this, the direct impact

non-residential real estate sector will contribute

will also cause indirect impacts and induced

an additional approximately R2,750,000,000 in

impacts, causing these to increase economic

direct economic activity in South Africa during

activity created by the sector to R5,093,000,000

2014, over and above the activity that is already

and R7,809,000,000 respectively. In line with this, Figure 92 Non-residential real estate economic activity and GDP growth Source: Authorâ&#x20AC;&#x2122;s calculations

Figure 93 Non-residential real estate new jobs to be created Source: Authorâ&#x20AC;&#x2122;s calculations

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Figure 94 Taxes generated Additions and alterations Source: Author’s calculations

the contribution to GDP will be R2,161,000,000

non-residential real estate sector, this is then

through direct activity, R2,367,000,000 through

shown nationally per property type in Figure 96

indirect activity and R3,670,000,000 by induced

and for all non-residential property per province

activity. This will also result in a total of 13,065

in Figure 97.

new jobs being created, which are made up by 10,421 formal jobs and 2,644 informal jobs

It is clear that Gauteng will dominate the

being created as per Figure 93. The sector

expected economic growth in all real estate,

will also contribute an additionally estimated

while KwaZulu-Natal is estimated to contribute

R20,800,000 in net taxes, made up of R1.400,000

the second highest level of growth in economic

net taxes on products and R19,300,000 net taxes

activity, followed by Western Cape. In terms

on production as displayed in Figure 94.

of non-residential real estate, KwaZulu-Natal

a) Influence on economic activity

is expected to fall slightly behind Western Cape, indicating that the expected activity

The total growth in economic activity to be

in KwaZulu-Natal is expected to be focussed

generated by the real estate sector as estimated

more on the residential side, while Western

per province is indicated in Figure 95. For the

Cape’s figure for non-residential real estate is

Figure 95 All real estate economic activity growth Source: Author’s calculations

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Figure 96 Non-residential real estate economic activity growth per property type Source: Author’s calculations

suggesting a decline in residential real estate

b) Influence on gross domestic product

activity in Western Cape. Free State Province has

The growth in GDP is directly related to the growth

the opposite, with a negative growth in the non-

in economic activity, although the scale differs.

higher than total real estate growth expected,

residential real estate sector, while the total is positive, indicating a possible larger expansion

The result is, however, a different contribution to

in the residential real estate sector.

the GDP of the country. In line with the economic activity, the GDP growth for which the real estate

In terms of property types, it can be seen in

sector will be responsible, as estimated per

Figure 96 that growth will be largely dominated

province, is indicated in Figure 98.

by shopping centre development, followed by industrial and warehouse properties, then office

The non-residential sector’s growth is then showed

and banking properties.

per property type nationally in Figure 99 and for all property per province in Figure 100.

Figure 97 Non-residential real estate economic activity growth per province Source: Author’s calculations

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Figure 98 All real estate GDP growth Source: Author’s calculations

Figure 99 Non-residential real estate GDP growth per property type Source: Author’s calculations

Figure 100 Non-residential real estate GDP growth per province Source: Author’s calculations

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c) Employment to be created The growth in economic activity and resultant

The total real estate jobs to be created are

GDP growth also will create new jobs within

estimated as per Figure 101, shown per province.

the sector directly as well as indirectly. The total new jobs to be created in the non-residential

The new jobs to be created due to non-residential

real estate sector due to planned activity are

real estate activity is indicated in Figure 102, as

estimated to be in excess of 13,000 as shown at

distributed per property type, while Figure 103

the beginning of this section.

shows the results for the different provinces. Figure 101 All real estate new jobs to be created Source: Authorâ&#x20AC;&#x2122;s calculations

Figure 102 Non-residential real estate new jobs to be created per property type Source: Authorâ&#x20AC;&#x2122;s calculations

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Figure 103 Non-residential real estate new jobs to be created per province Source: Author’s calculations

d) Net taxes to be generated Lastly are the net taxes to be generated by

non-residential real estate net taxes are shown in

the sector. Figure 104 shows net taxes to be

Figure 105 per property type and in Figure 106

generated throughout the sector, while the

per province.

Figure 104 All real estate net taxes to be generated Source: Author’s calculations

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Figure 105 Non-residential real estate net taxes to be generated per property type Source: Author’s calculations

Figure 106 Non-residential real estate net taxes to be generated per province Source: Author’s calculations

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SUMMARY In this study it was shown that the real estate sector has a markedly influence on the economy in South Africa. The theoretical principles as explained in section 2 have been taken into consideration, but there is still a vast amount of research that could be performed in empirically explaining the different relationships as mentioned at any given point in time, over different time periods, or in different economic climates. The essence of this study was to provide a view of the total activity that are taking place in the sector, as explained in section 3 and how this then feeds into the total economy and contributes to economic activity and job creation, as explained in section 4. Lastly the principles as explained in section 3 and 4 are used to estimate the short term future activity that will take place within the sector and the subsequent impact that it would have on the total economy of South Africa, as was shown in section 5. For any clarification on the study or for further research on this topic, the authors’ details are provided below: Dr. Douw Boshoff

Dr. Reyno Seymore

Department of Construction Economics

Department of Economics

University of Pretoria

douw.boshoff@up.ac.za

reyno.seymore@up.ac.za

REFERENCES Archour-Fischer, D 1999. An integrated property market model: A pedagogical tool, Journal of Real Estate Practice and Education, 2 (1). Archer, W R & Ling, D C 1997. The three dimensions of real estate markets: Linking space, capital, and property markets, Real Estate Finance, 14:7-14. Boshoff, D.G.B. 2010. The impact of affordability on house price dynamics in South Africa, Acta Structillia, 17(2):126-148 Boshoff, D.G.B. 2013. Empirical Analysis of Space and Capital Markets in South Africa - a review of the REEFM- and FDW models, South African Journal of Economics and Management Sciences, 16(4) Chen, N, Roll, R & Ross, S A 1986. Economic forces and the stock market, Journal of Business, 59:383-403. Corcoran, P J 1987. Explaining the commercial real estate market, Journal of Portfolio Management, 13:15-21. D’Hernoncourt J., Cordier M. and Hadley D. 2011. Input-Output Multipliers– Specification Sheet and Supporting Material. Spicosa Project Report. Universite Libre de Bruxelles. CEESE, Brussels. Dipasquale, D & Wheaton, W C 1992. The markets for real estate assets & space: A conceptual framework, Journal of the American Real Estate and Urban Economics Association, 20 (1): 187-97. Du Toit, H 2002. Appraisal of the Fischer-DiPasquale-Wheaton (FDW) real estate model and development of an integrated asset market model (IPAMM). Unpublished treatise submitted in part fulfilment of the requirements for the MSc (Real Estate), University of Pretoria. Fisher, J D 1992. Integrating research on markets for space and capital, Journal of Real Estate and Urban Economics Association, 20(1): 161-80. Fisher, J D, Hudson-Wilson, S & Wurtzebach, C H 1993. Equilibrium in commercial real estate markets: Linking space and capital markets, Journal of Portfolio Management, 19:101-107.

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Hendershott, P H & Ling, D C, 1984. Prospective changes in tax law and the value of depreciable real estate, Journal of the American Real Estate and Urban Economics Association, 12:297-317. Levine, D M, Berenson, M L & Stephan, D 1998. Statistics for managers. New Jersey: Prentice Hall. Miller R. E. 1998. Regional and Interregional Input-Output Analysis. Methods of Interregional and Regional Analysis. Ashgate, Aldershot 41–133. Organisation for Economic Co-operation and Development (OECD). 2006. Input-Output Analysis in an Increasingly Globalised World: Applications of OECD’s Harmonized International Tables. STI/Working Paper 2006/7. OECD. Rode & Associates 1997 to 2002. Rode’s Report on the SA property market. 1990:1 - 2008:3. South African Institute Of Valuers (SAIV) 1999. The Valuers’ Manual (Seventh ed.). Durban: Butterworths. Sporri C., Morsuk M., Peters I. and Reichert P. 2007. The Economic Impacts of River Rehabilitation: A Regional Input-Output Analysis. Ecological Economics. 62(2007) 341–351. Steyn, A G W, Smit, C F & Du Toit, S H C 1989. Moderne statistiek vir die praktyk (Fourth ed). Pretoria: J L van Schaik. Surugiu G. 2009. The Economic Impact of Tourism. An Input-Output Analysis. Romanian Journal of Economics. Institute of National Economy. Vol. 29 2(30) 142-16. Viezer, T W 1998. Statistical strategies for real estate portfolio diversification. Doctoral Dissertation, Ohio State University, Columbus, OH. Viezer, T W 1999. Econometric integration of real estate’s space and capital markets, Journal of Real Estate Research, 18(3): 503-19. Weimer, A M 1966. Real estate decisions are different, Harvard Business Review, 44:105-112.

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