Jbari Wafaa Thesis LinkedIn

1 Master Thesis - Cass | Wafaa Jbari

Dissertation Research Project

An Econometric Analysis of the Office Rental Cycle in London Area (City and Westend) and the Impact on

Vacancy Rate By: Wafaa Jbari 27 September 2012

Table of Contents

Dissertation Research Project ........................................................... 1

Abstract ................................................................................... 3

Chapter 1 ................................................................................. 3 Introduction .................................................................................... 3 Literature review ............................................................................. 5

Chapter 2 ................................................................................. 9 Methodology ................................................................................... 9

The model Specification ................................................................................. 9 Data analysis ................................................................................ 16

Chapter 3 ................................................................................ 20 Empirical Result ........................................................................... 20 Multi - equation model for office rent ................................................ 20

Rentals ......................................................................................................... 24 Rental Growth Theory .................................................................................. 25

Single equation model for office rent ................................................ 26 The city ........................................................................................................ 27 The Westend ................................................................................................ 28

Recommendation/Conclusion ......................................................... 28

Chapter 4 ................................................................................ 30 Acknowledgement ......................................................................... 30 Bibliography ................................................................................. 31

Research Data Sources ................................................................................. 31 References: .................................................................................. 31 Appendices: .................................................................................. 34


Abstract This paper represents the integration of real estate office market and rental cycle

in London and the impact on the vacancy rate. The dissertation will explore

different rent models of commercial office property market in the U.K. over the

past 30 year’s period to provide an econometric analysis of the office rental cycle

in London’s two main CBD’s the City and the Westend area. The paper will

attempt to use a multi-equation method and a single equation method to examine

the economic demand indicators based on the analysis of the demand side

variables (GDP, Service Sector Employment, and inflation) and supply side

variables (take up, absorption, availability, development, new supply and office

floor space) for office buildings by adopting supply and demand framework. The

mix of quantitative and qualitative information are laid to analyse the rental

influences despite some data limitations, and aimed to identifying the macro-

economic drivers of office rental values.

Chapter 1

Introduction

Forecasting rent is fundamental input in commercial property valuation and the

construction of a portfolio. The reliability and availability of over 30 years of data

in the U.K. and the development of econometric software has contributed

enormously to the expansion of the modelling of commercial property markets

(Ball et al., 1998). It goes without saying that the U.K. and U.S. have dominated the

published work on modelling dealing primarily with the office market.

This paper mentions the different models adopted by the U.K. and U.S within a

single framework where both consider the adjustment towards equilibrium in the

American literature, but will primarily focus on the use of multi-equation models

and single equation (reduced form) models common in the U.K. literature. In the

former, dependent variables in one equation may appear as explanatory

variables in other equations as these may be determined endogenously. However,

in the latter there is one dependent variable and all explanatory variables are

exogenous (Ball et al., 1998).

The main U.S. models including Multi-equation, user market and rent adjustment

models are an embodiment of the theoretical notion of equilibrium, thus if the

vacancy rate diverge from the equilibrium the rent tend to diverge as well. The

U.K. model, applied to the City of London where vacancy data is available,

characterised with the reduced form model based on demand and supply

equations to estimate the real rent as dependent variable, the service sector (FBS)

as demand driver and the supply of new stock or new construction order as

explanatory variable.

Previous studies have attempted to use demand and supply variables to

demonstrate the office rental movement. However, two issues have been

identified as an obstacle in this study. The demand and the supply variables are

proxy variables since there are no direct measured variables within the property

markets. Also the fundamental part of the research is the reliability and the

availability of data required (Gardiner et al. 1988).

This paper examines the short-run office rental cycle in London over the thirty-

year period 1982 – 2012 as the most researched real estate market, due to its

importance as the third largest financial centre in the world. The main objective of

this study is primarily to extend existing econometric research on London office

markets and further assess swings in supply within London two main Centre

Business District’s. In addition, this study aims to test the significance of previous

theoretical frameworks and the application of the multi equation and the single

equation models common in the U.K. as an econometric analysis using Ordinary

Least Square principle (OLS) of office rental changes for London main CBD’s the

City and Westend.

This research used longer data set for thirty years period 1982 – 2012 then earlier

modelling work based on two decades time series (ending 1996) dominated by

less than a full real estate cycle (Hendershott, Lizieri and Matysiak, 1999;

Hendershott, MacGregor and Tse, 2002; Wheaton, Torto and Evans, 1997). The

longer time series data set used to capture the impact of service sector

employment, GDP and other demand side variables on rent. And vacancy rate,


office new supply and other supply side variables depending on the stage of the

real estate cycle. The data used annually due to the lack of availability of

quarterly data from 1980’s, as property data was only collected annually in the

early 90’s. The gaps in the data have been removed by simple smoothing.

This paper is arranged in the following order. Chapter one covers an introduction

and literature review, chapter two outlines the specification of the model and

discusses the data used to estimate it. The result of the estimated model is set out

in chapter three with a concluding result from the quantitative research on office

markets. Chapter four is mainly for the bibliography and references used to

complete this research.

Figure 1: London City & Westend Rental Values (source: Collier maps)

Literature review Several studies have been conducted in the past using different econometric

models to explain and forecast office rental cycles (Brooks et al. 2010). Most of the

research has been carried out for the U.S. office markets and includes, Rosen

(1984) study on San Francisco office market, Hekman (1985) research adopted a

panel approach for 14 cities with 4 years period data estimating the equations for

rent and development, but excluded the occupied space. Wheaton (1987) study is

based on the U.S. national market and Pollakowski et al. (1992) used panel

approach for twenty-one U.S. metropolitan areas paying particular importance to

market size in the rent adjustment model. These methodologies are used in most

multi-equation models and tend to follow the common structure of the three

behavioural equations (demand for space, supply and rent) linking exogenous

variables to the property market. These studies have been applied to the U.K.

office market by Tsolacos et al. (1998) and McGough and Tsolacos (1994) on the

national level, adopting the multi-equation model instead of a rental adjustment

model and using vacancy rate where the model links the rental change to the

lagged changes in GDP, financial service sector employment and the volume of

new stock. Gardiner and Henneberry (1988, 1991) conducted a study at the

regional level, and Wheaton et al. (1997) and Hendershott et al. (1999) for the

London office market using a single equation model reduced form common in the

British literature. Other European countries remain under-researched due to lack

of data availability with the exception of some examples of European markets by

Giussani et al. (1993) who used a cross section model of ten European cities office

markets ‘A comparative analysis of the major determinants of office rental values in

Europe’. D’Arcy et al. (1997) further extended this study to twenty-tow European

cities using a panel approach. The cities were classified according to their size

and the rate of service sector employment growth where the change in rent is

estimated as function of change in GDP and the level of lagged short-term interest

rate. Both studies proved to be significant. Additional models not discussed in this

paper include ARIMA model adopted by McGough and Tsolacos (1995) taking

different approach to those mentioned above for one period rent forecasts and

found that office rents are linked to demand and supply shocks. ECM also adopts

different approach not emphasised in the property literature but used formally by

the RICS (1999) to model real property returns in the U.K. and explain London

office demand by Hendershott et al. (1999). The general rental adjustment model

is close to the ECM approach.

The majority of these models attempted to forecast the rental cycle (Wheaton et

al., 1997; McGough and Tsolacos, 1994 and others) but not many successfully

managed to evaluate the forecast performance of the specification. However,

McGough and Tsolacos (1994) compared their dynamic forecast with actual

values of office rent. Additional methodologies took more qualitative approach by

Kelly (1983) for the U.S. markets and Morisson (1997) for the U.K. markets, which


have been used to forecast the office market on the basis of demand and supply

indicators of office stock on the property market trend and local economic factors.

The single equation model of office rent determination used two economic

variables to capture the demand for office space, which is gross domestic product

(GDP) and service sector employment (SSE). These two variables exert a

proportionate influence on the demand for office space, as it is a derived demand

reflecting the demand for office base activities. The changes in real GDP plays an

important role in determining real office rents and has been supported by several

studies of European office markets by (Giussani et al., 1993; D'Arcy et al., 1997).

U.S. studies consisted of a range of service sector employment measures such as

employment in finance, insurance and real estate (FIRE), and U.K. studies

consisted of employment in banking, finance and insurance, both studies have

been supported by (Wheaton et al., 1997; Tsolacos et al., 1998). Giussani et al.,

and Tsolacos (1993) are the first to attempt researching office rent determination

across European cities by examining the relationship between rental value and

economic activity using cross section and time series analysis (OLS method). In

spite of the research been based on the demand and supply framework, it

ignored the supply side variable due to the lack of data across Europe. The result

indicate that GDP and unemployment rate are an important factors in determining

office rent thus demand side variables are sufficient in explaining office rental

market across Europe. However, the inclusion of supply side variables would

have been more theoretically robust. The two variables (GDP and SSE) are used

in this study as demand indicator for London office market determined by the

availability of the data. (Econometric analysis and forecast of office rental cycle in

Dublin area)

Variables Matrix

Authors Year

Multi-equation OFS

Vacancy

Absorption

Construction Rental Yields GDP

FBS Interest

Inflation

Single

Equation

Cost Value (SSE) Rate

Kelly 1983 M * * *

*

Rosen 1984 M * *

* *

*

*

Hekman 1985 M

*

* *

* *

Shilling 1987 M

*

*

*

wheaton 1987 M *

* * *

Gardiner & Henneberry 1988 S *

* *

Wheaton & Torto 1988 M

*

*

McClure 1991 M * *

*

* *

Gardiner & Henneberry 1991 S

*

Pollakowski 1992 M * *

* *

* Giussani, Hsia &

Tsolacos 1993 S

* * * D'Arcy, McGough &

Tsolacos 1994 S

* * * *

McGough & Tsolacos 1994 S *

* *

McGough & Tsolacos 1995 S

* Hendershott, Lizieri &

Matysiak 1996 M * * * * *

* * D'Arcy, McGough &

Tsolacos 1997 S *

*

*

Wheaton, Torto & Evans 1997 M * * * * *

* * Keogh, McGough &

Tsolacos 1998 S *

* * * * D'Arcy, McGough &

Tsolacos 1998 S *

* *

McGough & Tsolacos 1998 S *

* *


Chapter 2

Methodology

The model Specification

Multi-Equation Models of the London Office Market

The multi-equation model common in the U.S. literature has been developed by

Wheaton et al. (1997) and applied to London office market using annual data for

the period 1974-94. Hendershott et al. (1997) produced an alternative model

considering only the city of London main financial centres rather than the wider

areas for the period of 1977-96, which is the main topic of this research. The

distinguishing feature of this model from earlier models is the direct link to the

capital markets through a time varying equilibrium rent derived from the

conventional yield. Wheaton el al. model consisted of three behavioural

equations (for development, absorption and rental adjustment) whereas now

there are additional four identities (for the capital stock, the vacancy rate,

occupied space and equilibrium rent). The total of seven equations, three

behavioural and four identities, links two exogenous variables (employment and

real interest rate) with six endogenous variables (absorption, real rent,

completed development, vacancy and total occupied floor space). The variables

are the same as in Wheaton el al. (1997) model except for the exclusion of the

construction costs. The three identities (for the capital stock, the vacancy rate and

occupied space) have the following specification:

Kt = Kt-1 (1- δ) + CDt (1.1) VRt = (Kt – OSt)/Kt (1.2) OSt = OSt-1 + At (1.3)

Where Kt is total stock, CDt is total completed development, VRt is the vacancy

rate, OSt is the occupied space, At is the absorption and δ is the rate of

depreciation of the existing stock.

Equations (1.2 and 1.3) are the same as Wheaton et al. but (1.1) is different in two

ways. Firstly, it uses completed developments rather than lagged new

construction, as it is more logical and gives accurate measure of total stock. And

secondly, the depreciation rate is applied to the total stock rather than the new

developments.

The fourth identity, which provides a link to the capital markets by Hendershott et

al. has the following specification:

RRn

t = (RCt + δ + OEt) CRt (1.4)

Where RRnt is the time varying equilibrium rent, RCt is the real gross redemption

yield on 20 years government stocks (gilts), δ is the depreciation rate taking as

constant, OEt is the operation expenses ratio taken as constant, and CRt is the

replacement cost.

The three behavioural equations for rental adjustment, development and

absorption has the following specification:

Δ RRt/RRt-1 = λ (VRn – VRt-1) + β (RRnt – RRt-1) (1.5) Where Δ RRt is the change in real rent, RRt-1 is the real effective rent, VRn is the

natural vacancy rate, VRt-1 is the vacancy rate at t-1, λ and β are adjustment

factors, and RRnt is the equilibrium rent.

This analysis used effective rent taking into account rent free period that was

available to new tenant in the 1990’s. The estimated model has correct signs on

the coefficient and an adjusted R2 of 0.69, which implies a natural vacancy rate of

7 per cent. This plausible figure is the same as Rosen’s.

The development equation following Grenadier (1995) links the development to

the ratio of value to replacement costs plus the value of the option to wait for an

improvement in profitability. However, there are some data problems that arise in

estimating this model. Since data on property values are from valuations, thus it

could be smoothed estimate of true value. In addition, there is no data for the land

component in replacement value.

The equation for the proposed general form of development has the following

specification:


CDt = f(VRn – VRt-n, RRn – RRt-n) (1.6)

Where CDt is the completed developments, VRn is the natural vacancy rate, VRt-n

is the real rent with an appropriate lag. This analysis assumes rent were in

equilibrium when real effective rent changes, hence the real equilibrium rent is

taken as the average for the period 1977 to 1985.

The equation for net absorption has the following specification:

At/OSt-1 = g (%ΔEt-n, RRt-n) (1.7)

Where At is absorption; OSt-1 is the occupied space; % ΔEt-1 is the percentage

change in financial services sector with appropriate lags; and RRt is the real

effective rent.

This model has been tested dynamically using two main exogenous variables,

change in employment rate and the real interest rate in considering the three

equations. The estimation of this model has been for the period beginning in 1968,

where the process involved using the result of the exogenous variables

generated by the model in the previous period.

The purpose of this model is to generate forecasts of office rent in three different

scenarios. The first scenario is the constant employment growth and real interest

rate where rent and vacancy rate converge on equilibrium levels. The second

scenario is the employment boom and fall in real interest rate, where there is a

sharp decrease in the vacancy rate and a rise in rents. Both scenarios are

dampened over time by increased construction, but supply lags increase the

adjustment period. The third scenario has less dramatic effect where rents fall and

demand increases and vacancy falls to its natural rate (Ball et al., 1998).

One of the main advantages of this model over that of Wheaton et al. (1997) model,

such as the direct link to the capital market using data for the same geographical

area (city of London). However, it also has drawbacks such as not allowing the

interaction of submarkets, for example the Docklands area which grew rapidly in

the 1980’s attracting a large number of city users due to the rental differences,

and most importantly the mid-town and Westend long established markets. Thus

this model can be seen as partial equilibrium model, as this is general problem

when modelling local property markets (Ibid).

The multi-equation modelling framework is more theorised than single equation

model. It deals with the office market but there is no consistent forms in this area,

thus if certain function of the market is not described properly by equations, the

result can be spurious (D’Arcy et al. 1998, Wong 2002). The main features of this

model are:

§ The model has a basic system that comprise of three behavioural equations

(demand, development and rental change) and four identities linking

endogenous variables (absorption, rent, completed development, vacancy

rate and floor space) to exogenous variables (employment rate, interest

rate, inflation rate and construction cost).

§ Rental change is always modelled using the vacancy rate with some gaps,

and the variable always proved to be significant.

§ Apart from the interest rate, all other variables are normally in real terms.

§ The development (supply side) model is mainly driven by either the

profitability formulation or the vacancy rates. Thus the model is always

significant when vacancy rate is used.

§ Land prices are never included due to the lack of data, thus no model

includes land market equation.

§ American literature (Wheaton et al. 1983, Kelly 1983) suggest that desired

demand is taken as a function of the level of economic activity measured

by employment rather than output, thus this is not all equally effective in

explaining office rental values (Giussani et al., 1993a), but service sector

employment variable proved to be more significant.

The result of these studies indicates that the vacancy rate is always significant.

However the majority of the existing literature uses the average vacancy rate as

the natural vacancy rate, thus the rational behind this theory is that property

developers and owners are aware of the binding nature and the length of office

leases. “Withhold vacancy office space in inventory to capitalise on opportunities to

supply at higher rental during periods of increased demand” Shilling et al. (1987).

Deviation away from the natural vacancy rate commonly used as determinant of


rents and measure of the short-term demand and supply conditions in the office

market. Thus if demand for space rises and the level of office actual vacancy fall

below the natural vacancy rate, an upwards pressure will be place in rent.

However, if the actual vacancy rate is higher than the natural vacancy rate then

downward pressure will be place on rent. Hence, rent moves rapidly the further

away the actual vacancy rate moves form the natural vacancy rate in either

direction (Rosen 1984, Hekman 1985, shilling et al. 1987). In the UK, Hendershott

et al. (1996) used this idea to examine London office market (Ball et al., 1998).

Single-Equation Models of the London Office Market

The single equation, reduced form, models generally tries to capture the

relationship between economic activity and office rental markets, the theory is

based on the interaction of demand and supply framework. The majority of

previous empirical studies on rent determination used equations that includes

demand and supply proxy variables to predict the office market performance.

The office rental values modelled using this theoretical framework has been

successfully employed by Giussani et al., (1993a, 1993b); Gardiner et al., (1988,

1991); Tsolacos et al., (1993, 1998); d'Arcy et al., (1997, 1998) and others, although

the availability and reliability of data used might not have been robust. Thus, if

certain function of the market has not been described properly by the equation

the results can be spurious. However, as mentioned above this model can be

more difficult to interpert but it is easier to formulate and requires fewer variables

to estimate. Hence it has been described as the best practical modelling strategy

(Ball et al., 1998).

The U.K. office rent model uses two variables to proxy the supply side, which are

total floor space and the flow of new supply. Gardiner and Henneberry (1988,

1991) tested the changes in total floor space for U.K. office rent but no significant

result has been reported. They have conducted a research on rent determination

on regional areas using spatial disaggregated annual data for the period 1977 –

1984 and found that regional GDP and stock of floor space are the main variables

influencing office rents in expanding regions and previous rental levels and GDP

influence current rental levels in declining areas.

Tsolacos et al. (1998) included the volume of new office construction supply but

found that it had more influence in the demand side variables than rents. In

addition, RICS (1994) examined both variables; the stock of office floor space and

changes in this stock (proxies by new construction orders) and their result was

statistically significant. This study involved an estimated equation contained

lagged office rents, which requires several diagnostic tests to be performed

before confirming the validity of the result. Unlike Rosen and Hekman’s (1985)

approach was based on a panel approach for 14 U.S. cities using the actual

vacancy rate as measure to capture the supply side effect of office rents. Thus

although the results are significant, its impact on the supply side is marginal i.e.

small size coefficient. This study used office new supply (OFNS)1 to measure the

variation in the supply of office space, which is considered to have negative

impact on the growth of rents. The use of alternative variable is considered to

account for the overall supply of office building depending on the availability of

data. Commonly European office markets have the problem of the lack of data

series to measure the supply of office space. Wheaton and Torto (1988) estimated

the traditional rent adjustment model using U.S. office market to link the

proportional change in rent to the difference between the actual and natural

vacancy rate, a variable that is commonly used in the U.S. office studies (the

vacancy rate or the gap between the actual and natural vacancy rate2), and found

a significant relationship (Ball M, 2007). Hendershott (1995) made further

development and come up with more general adjustment model and suggested

two additional adjustment based on the deviation from the equilibrium vacancy

rate and from the equilibrium real rent. Hendershott proposition refer to three

problems identified in the standard approach, which is based only on the vacancy

rate. However, there are drawbacks to this model as it assumes a constant natural

vacancy rate, a constant risk premium and constant lease length and terms. These

assumptions are unlikely to be accurate. The rent adjustment model is largely

driven by the US preoccupation based on data availability rather than theoretical

urgency (Ball M., 2007). Different authors adopted different methodologies to

measure this variable, thus the result can only be exclusive to the measurement

used. The vacancy variable plays a major role in dealing with econometric issues,

it can be considered as endogenous variable in rent equation determined by

other variables, and consequently the result can be spurious. This enforces the

1 OFNS (office new supply) data provided by DTZ covers the period of 1983 till 2012 and account for total stock of office new supply for the City and Westend. 2 The natural vacancy rate is defined as the optimal vacancy in the market. The vacant space becomes an inventory, which is either withheld or released to the market depending on the perception of landlords as to the appropriate gap between the actual vacancy rate and the natural vacancy rate (Clapp, 1993).


carful interpretation of the estimated coefficients obtained supported by

appropriate misspecification test. The vacancy rate variable is giving a significant

importance in many studies regardless of its drawback, thus it is useful to include

it and test its significance in different market types.

Keogh, McGough and Tsolacos (1998) are the first to examine the three sub-

markets in the U.K. office markets. They used econometric models to estimate

rents in the development market (new volume of new office space), investment

(the financial asset market), and rents (the user market). Their research paper is

the first empirical investigation of the demand and supply framework in all three

submarkets. The empirical results shows that the investment market model was

not significant but the user shows better result as per the study of RICS (1994).

A single equation model of office rents (Rent) have two main drivers as demand

side determinant for office space, which is gross domestic product (GDP) and

Service sector employment (SSE), and the supply side variables is measured

using the stock of office floor space (OFS). These two variables allows for the

interaction of both demand and supply variables.

The equation for Rent has the following specification:

Rent = f (GDP, SSE, OFS) (2.1)

If equation (2.1) is estimated in levels of the variables, then the model can be

subject to misspecification problems in particular serial correlation (this proved

to be the case in previous studies). This problem occurs in the presence of long-

term trends in the data series. In order to overcome this problem the equation

need to be reformulated in changes of the variables and perform diagnostic tests

to ensure that trends do not have an impact on the final result. A possible

drawback to estimating this model in changes is the loss of data when using long-

run information about movement in rents, which only exist if the long-run

variables used cointegrate. Therefore, changes in office rents are specified as

function to changes in GDP and SSE, and the volume of OFNS as proxy for

changes in the gross of office stock in London. The study conducted by Tony

McGough using single equation model used OFNC3 as proxy for changes gross of

office stock in Dublin. However, due to the availability of data for London city and

Westend area, this study used OFNS as proxy for the changes in the gross of office

3 OFNC: office new construction

stock in London areas.

The equation for Rent and these variables has the following specification:

Δ lnRRt = α 0 + Σ α1i Δ lnGDPt-i + Σ α 2i Δ lnSSEt-i + Σ α3i lnOFNSt-i + et

For I = 0,1, … I (2.2) Where Δ is the first difference operator, ln is the natural logarithm, RRt is the time

varying equilibrium rent, Δ ln RRt is the changes of the logged series real rents, Δ

ln GDP and Δ ln SSE are the changes of the logged series of GDP and SSE

respectively, ln OFNS is the logarithm of OFNS, t-i denote lags, I is the maximum

lag length, and α taken as constant.

Similar studies addressed the same assumption that the impact of past changes in

the demand and supply side variables could induce rent changes in several

periods. The lags on the demand side demonstrate the time it takes to recognise

the magnitude, direction and persistence of economic activity that in return

interprets demand for office spaces, which have a huge impact on rental values

(An Econometric Analysis and Forecast of the Office Rental Cycle in Dublin Area).

Data analysis

The city of London is very well documented and well-measured market, thus it

proved to be an ideal case study for modelling the office rental cycle presented in

this study. The availability of consistent data series measuring the key indicators

of the City and Westend office behaviour is back to 1982 in all cases. Canary Warf

(Docklands) is the third main CBD in London, however it has been excluded from

this study due to the lack of data set compare to the City and Westend, as the first

buildings were completed in 1991.

The Property data used allow estimation of the model equations for occupier

demand, vacancy change, rental adjustment, development supply and building

new supply. The aim of the analysis presented here is to estimate the demand and

supply framework for London two main CBD’s to determine the rental movement.

The variables in the estimated equations are expressed either as ratios with

respect to the level of occupied stock in the City and Westend (Availability, New


supply, Total Stock, Vacancy, Take up and absorption), or as rates of change

(Rents, Development). Estimation is by Ordinary Least Square principle with the

estimation period ending 2012. The t-statistic and adjusted R-square for each

coefficient are quoted accordingly. The office property data used in this research

is primarily provided by DTZ. In the past property data was collected annually

until early 1990’s where the data was collected quarterly, annual data is used as

this study covers the past 3 decades, and any gaps in the data have been

removed by simple smoothing.

The data for the City and Westend office rental values is used as an index

(2000=100) of rents re-valued in constant prices using the annualized consumer

price index. The figures of new supply of office buildings are calculated as the

annual total amount of office space completed. Again the series is index

(2000=100). The variables cover the 30 years period 1982 – 2012.

The non-property variables used, real GDP, SSE, Inflation and the consumer price

index were supplied by the U.K. government Statistics databank (ONS) and are

indexed (2000=100) national series (Barras, 2009).

A problem with using economic variables is that many move together over time in

line with general economic growth, even when they are not directly causally

related. If variables have a trend, the estimation of the effect of a particular

explanatory may be bias. In order to remove spurious correlation, the variables

used in a regression have to be co-integrated with each other (Ball el al., 1998).

Economic and demand side variables used to model the office rent determination:

1) GDP

Barras (1983) described the real GDP as the most appropriate broad indictor of

office activity widely used as demand side measure at an aggregate level. It

normally represents economic conditions including manufacturing and service

sector of the economy. This assumption has been widely supported by other

empirical studies (Gardiner et al. 1988; Giussani et al. 1993, RICS 1994; D’Arcy et

al. 1994; Tsolacos et al. 1998; McGough et al. 1998).

2) Service sector employment (FBS)

According to previous studies, service sector employment has close link to office

renal values as the majority of service sector employment takes place in an office

environment (D’Arcy et al. 1994; Keogh et al. 1998). Since demand for office

space is derived demand, demand for office space is closely associated with

employment level changes. Thus, when the service sector employment rises,

demand for office space increases which lead to a rise on the rental values.

3) Inflation

Inflation is generally known as the rate at which prices of goods and services rise

and consequently leads to a decrease in consumption. The effect of inflation on

real estate can be seen in a rise in prices of labour, building material, insurance

as well as rent. The advantage of this can be seen in the increase value of the

building while the size of the mortgage stays the same.

4) Take up

Take up is defined as the amount of space taken up, but it doesn’t necessarily

mean new demand. Hence an occupier moving from an existing building to a new

building is recorded as take up (Brooks et al 2010). The business cycle has a

major impact on London’s economy as well as the demand shocks in the office

market (the stock market crash of 2001/02) thus the rate of take up of office space

is comprised of net absorption and the level of occupiers within an existing stock

(Barras, 2009).

5) Net Absorption

Net absorption is defined as the change in the occupied stock, unlike take up, it

represent new demand (Brooks et al 2010). The net absorption has no direct

statistical measure thus it can only be derived from the estimated changes in

occupied stock. During the late 1980’s the net absorption rate for the City was

highly volatile and barely showed a relationship to London’s GDP or the rate of

take up. This clearly shows the problem with using net absorption as demand

indicator (Barras, 2009).


Supply side variables used to model the office rent determination:

1) Stock (Office Floor space)

The existing literature used a number of supply side variables to examine the

influences on office rents. These studies mainly focused on office floor space and

the relevant issues to changes in total sock of office space (Gardiner et al. 1988),

the volume of new construction (Keogh et al. 1998, D’Arcy et al. 1998), the level of

new orders of office space (Giussani and Tsolacos et al. 1993a), the completion of

office space (Tsolacos et al. 1998) and changes in the volume of office buildings

output (McGough et al. 1994). The use of other supply side variables has been

restricted by the lack of availability of data, thus some measurement are not

adequately presented. Previous empirical studies found that office floor space

have a significant effect in determining office rents, however, it has less impact

compared to the demand side variables (Hekam 1985, Gardiner et al. 1988, RICS

1994, Keogh et al. 1998, D’Arcy et al. 1998)

2) Vacancy

Vacancy is either measured in physical terms (the amount of space vacant in the

market) or expressed as percentage of total stock (Brooks et al. 2010). Vacancy

has a close relationship with the cycle of office building completions in the market.

Thus when the rate of take up falls and the rate of new supply rises, it will typically

mean high vacancy rate in the market. This long-run relationship captures the

determinant of vacancy change, as change in vacancy rate can be negatively

related to take up and positively related to office new supply (Barras, 2009).

3) Development

The development supply to the market connects the real rental changes to the

building starts, thus high rental growth leads to high profit achieved in new

developments and therefore higher rate of building starts. This relationship has

been observed in the historic movement of peak period of real rental growth in

the City (1972, 1987, 1997, 2000 and 2006), which generated following a peak in

development starts with one-year lag. Developers see this relationship as

indicator to predict future rent levels based on the current rental growth (Ibid).

Chapter 3

Empirical Result

Multi - equation model for office rent The starting point of this analysis is to measure the size of the two areas to

estimate the vacancy rate equation (3.1). Table 1 shows some of the descriptive

statistics of the most important variables for the time period 1982-2012. The

descriptive statistics show that both the City and the Westend are large areas, as

can be seen in their large office spaces.

Table 1 – Descriptive statistics of variables from 1982-2012

The first identity for the vacancy has the following specification:

VRt = (Kt – OSt)/Kt (3.1) Where VRt is the vacancy rate at time t, Kt is total stock at time t, OSt is the

occupied space at time t.

The vacancy rates for the City and the West End are calculated from 1986 to 2012

because of the unavailability of stock data from 1982 to 1985. The mean and

standard deviation of vacancy rates of the City are 9.02% and 4.28% respectively.

The mean and standard deviation of vacancy rates of the Westend are 7.08% and

3.25% respectively. The mean values of vacancy rates show that vacancy rates

were lower in the West End during the period of this study. This was in spite of the

significantly higher average office rent in the Westend as can be seen in table 1.

Mean St. Deviation Mean St. DeviationFBS (2000 = 100) 86.63 9.76 84.20 14.04Space take up, sq m 475,887.27 145,024.61 327,566.59 117,633.26New supply, sq m 225,727.05 135,441.49 107,049.40 47,329.47Space, sq m 6,945,708.73 788,035.37 5,117,886.58 253,847.92Availability, sq m 585,348.39 302,878.82 354,497.08 152,575.07Office rent, £/ sq m 45.74 10.40 57.56 24.46

The City West End


One of the main reasons behind lower average vacancy rate in the Westend was

the lower availability of office space. Office space was, on average, 35% higher in

the City than in the Westend. The higher standard deviation of vacancy rates in

the City implies that the demand for the office space was more volatile than in the

Westend.

Chart 1 shows the annual vacancy rates. It can be seen that vacancy rates closely

follow economic cycle in both the City and the Westend. Vacancy rates in both

areas were high during recession and low during economic booms. Also, vacancy

rates in the City were higher than in the Westend in all years except 2000. It can

be seen from chart 1 that the demand for the office space in the City was linked

higher to economic growth that in the Westend.

Chart 1 – Annual vacancy rates

Vacancy rates are determined by both demand and supply of space. New supply

of office space is cyclical and more volatile than demand because supply is often

determined by the availability of financing than by the market need (Mueller,

1999, p. 132). The sharp movements in vacancy rates confirm observations in

other studies that real estate markets rarely move smoothly, but instead move in

“fits and starts” (Mueller, 1999, p. 132).

The second identity given by Wheaton et al. has the following specification:

0.00%2.00%4.00%6.00%8.00%10.00%12.00%14.00%16.00%18.00%20.00%

London (City) West End (WE)

OSt = OSt-1 + At (3.2)

Equation (3.2) is the same as Wheaton et al., however, this equation has been

reformulated to capture the absorption rate and has the following specification:

At = OSt - OSt-1 (3.3)

Where, At is absorption in year t and OS is occupied space. Absorption for both

the City and the Westend can be calculated from occupied space figures. Chart 2

shows annual office space absorption figures in square metres.

Chart 2 – Annual absorption figures, sq m

It can be seen from chart 2 that absorption turns negative when vacancy rates

start to increase. Overall absorption was more in the City than in the Westend.

This is also confirmed by change in the occupied space data. Occupied space in

the City increased by 2.3 million sq m as compared to an increase of 0.75 sq m in

the Westend from 1986 to 2012. The three times increase in occupied space in the

City was because of higher demand as average rates were significantly low in the

City than in the Westend.

The equation for net absorption has the following specification:

At/OSt-1 = g (%ΔEt-n, RRt-n) (3.4)

-400,000

-300,000

-200,000

-100,000

0

100,000

200,000

300,000

400,000

500,000

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012



Where At is absorption; OSt-1 is the occupied space; % ΔEt-1 is the percentage

change in financial and business services sector with appropriate lags; and RRt is

the real rent. The real rent is calculated by deflating the normal rent by the

Consumer Price Index.

Appendix I shows regression analysis results for net absorption rate in the City.

The summary results of regression analysis of the City are shown in table 2. The

value of R-square was highest for one-year lag. The R-square value of 0.28 in case

of one-year lag implies that 28% of variations in the net absorption rate were due

to changes in the independent variables. Also, values of Significance F shows that

results are significant for one-year lag only. Amongst the two independent

variables, the t-stat values show that only real rent in case of one-year lag is

statistically significant in determining the value of net absorption rate. This

implies that absorption rate is statistically dependent upon real rent in the City.

The negative coefficients of real rent imply that absorption rate is inversely

related to real rent, that is, an increase in real rent results in a decline in

absorption rates.

Table 2 – Summary results of regression analysis of the City net absorption rate

Appendix II shows regression analysis results for net absorption rate in the

Westend. The summary results of regression analysis of the Westend are shown in

table 3. The value of R-square was highest for no lag. 35.6% of variations in values

of the net absorption rate were due to changes in the independent variables. It

implies that the net absorption rate model for the Westend was a better predictor

of observed values than the City model. This is different from the City where R-

square value was highest in case of one-year lag. The values of Significance F

No lag Lag (-1) Lag (-2)R square 0.1312 0.2811 0.2114Significance F 0.2129 0.0313 0.0930Intercept value 0.0557 0.0699 0.0488Intercept t-stat 2.4063 3.1954 2.0579% change in FBS coefficient 0.0470 -0.1356 -0.2065% change in FBS t-stat 0.4057 -1.2550 -1.7737RIRENT coefficient -0.0008 -0.0010 -0.0006RIRENT t-stat -1.7990 -2.5001 -1.3554

show that results are significant for no lag, one-year lag and two-year lags. The

value of Significance F increased as lag period increased and was just below 0.05

in case of the two-year lag regression. Among the two independent variables, the

t-stat values show that the per cent change in financial and business services

employment was statistically significant in case of one-year lag and real rent was

statistically significant in case of one-year lag in determining the value of net

absorption rate. This implies that absorption rate is statistically dependent upon

both changes in employment and real rent in case of the Westend. The coefficient

of per cent change in employment varied across lag periods. It was positive when

there was no lag, which implies that the relationship between absorption and

employment is directly proportional. This is expected as higher growth in

employment would result in higher demand for office space and hence, an

increase in absorption. The negative coefficients of real rent imply that absorption

rate is inversely related to real rent in the Westend and is similar to the results for

the City. Table 3 – Summary results of regression analysis of the Westend net

absorption rate.

Rentals Chart 4 shows annual rents per sq metre in the City and the Westend. A number

of phases can be observed on the basis of rents in two areas. In 1980s, rents in the

City were higher than in the West End. This resulted in a sharp increase in new

supply in the City. The annual new space added in the City was about twice that in

the West End in mid 1980s but increased to more than three times by 1991. This is

reflected in change in rent patterns in 1990s. The rent in the City declined in the

first half of 1990s due to oversupply and recession. However, rents remain stable

in the Westend and were more than in the City in the first half of 1990s.

No lag Lag (-1) Lag (-2)R square 0.3567 0.3204 0.2718Significance F 0.0078 0.0173 0.0419Intercept value 0.0384 0.0689 0.0464Intercept t-stat 1.9473 3.3230 2.1098% change in FBS coefficient 0.2656 -0.0405 -0.1850% change in FBS t-stat 3.1142 -0.4530 -1.9392RIRENT coefficient -0.0006 -0.0010 -0.0006RIRENT t-stat -1.8479 -3.0540 -1.7440


Chart 4 – Annual rents £ per sq m

The rents in the City and the Westend were very similar in the second half of

1990s. The supply of new office space in two areas was also similar during this

period. However, the convergence was short-lived as rents diverged

substantially after 2000. In the last decade, rents in the Westend were

substantially higher than in the City. This is because of the sharp increase in new

office space supply in the City. From 2001 to 2012, the new supply in the City was

2.3 million sq m as opposed to 1.4 million sq m in the Westend. Higher supply in

the City resulted in lower rents as owners were willing to offer tenants lower rents

as opposed to keeping their offices vacant.

The divergence in rents was highest in the second half of the last decade. The City

of London has higher proportion of financial services tenants whereas the

Westend has a mix of both financial and other tenants including retailers. The

demand for office space by financial services firms declined sharply in the

second half of the last decade as they reduced the number of employees during

and after the financial crisis and recession. On the other hand, space is a premium

in the Westend as all major retailers want some presence there. The lack of new

supply in the Westend in the second half of the last decade also exerted an

upward pressure on rents.

Rental Growth Theory According to the rental growth theory, rental growth will be below inflation when

market space occupancies are below their long-term average values and vice-

versa (Mueller, 1999). It implies that rental growth will be higher than inflation

0

20

40

60

80

100

120

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012


when vacancies are low and vice-versa. The theory appeals intuitively as lower

vacancy implies higher bargaining power of owners, which translates into higher

growth in rents. Similarly, high vacancy implies that the bargaining power of

owners is less and hence, rental growth would be low.

Chart 5 shows annual rental growth rates in the City and the Westend. It also

shows annual inflation rates. The growth rates in office rentals were negative or

lower than inflation in the first half of 1990s. This was the period of high vacancy

rates as seen previously. During the second half of 1990s, vacancy rates declined

below their average over the 30-year period. The annual increase in rents in the

City and the Westend were more than inflation rates during this period. Similar

observations are made around 2005-2007. These empirical observations support

the rental growth theory.

Chart 5 – Annual rental growth rates in the City and the West End

Single equation model for office rent A single equation model of office rents has two main drivers – demand and supply

side determinants. The demand side determinants for office space, which are

gross domestic product (GDP) and Service sector employment (SSE), and the

supply side determinants is measured using the stock of office floor space (OFS).

These two variables allow for the interaction of both demand and supply variables.

The equation for Rent has the following specification:

-400,000

-300,000

-200,000

-100,000

0

100,000

200,000

300,000

400,000

500,000

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012



Rent = f (GDP, SSE, OFS) (4.1)

The equation for Rent and these variables has the following specification:

Δ lnRRt = α 0 + Σ α1i Δ lnGDPt-i + Σ α 2i Δ lnSSEt-i + Σ α3i lnOFNSt-i + et

For I = 0,1, … I (4.2) Where Δ is the first difference operator, ln is the natural logarithm, RRt is the time

varying real rent, Δ ln RRt are the changes of the logged series real rents, Δ ln

GDP and Δ ln SSE are the changes of the logged series of GDP and SSE

respectively, ln OFNS is the logarithm of OFNS, t-i denote lags, I is the maximum

lag length, and α taken as constant.

The city Appendix III shows the results of the regression analysis of real rent single

equation for the City. Four regression analyses are performed – no lag, one-year

lag, two-year lag and three-year lags. The highest value of R-square was 0.5854 in

case of three-year lag regression analysis. It implies that 58.5% of changes in the

natural log of real rents were explained by the independent variables when the

lag period was three years. The value of the Significance F was negligible, which

implies that the results were statistically significant. The three-year lag could be

due to the fact a substantial amount of new space was added in the last two

decades and tenants were tied in long-term leases. This delays their decision to

move to other economical offices in the City. In terms of the independent

variables, GDP and new office space independent variables were statistically

significant. The t-stat values of employment independent variable was low and

hence, not statistically significant in determining the value of real rent. The value

of R-square in case of no-lag period was 0.54.

The GDP variable was lagged by one-year period only in case of three-year lag

also as previous studies have shown that one-year lag in GDP as one of the most

important determinant of real rent (D’Arcy et al., 1999, p. 309). One-year lag in

GDP is also expected as landlords tend to be reluctant to reduce rents at the onset

of a recession (Fuerst, 2006). They tend to wait for some time to figure out whether

the recession will have long lasting effects or will it soon be over and hence, there

may be an increase in demand soon. In case of a short recession or low growth

period, it would be better to wait for some time and then agree a long-term lease

at a higher rent. This would result in higher cumulative rent inflows. Also,

vacancies are partially the result of landlords desire to obtain market information

(Frew and Jud, 1988). Landlords would prefer to wait for some time to obtain

better market information so as to increase their long-term rental yields.

The Westend Appendix IV shows the results of the regression analysis of real rent single

equation for the Westend. Four regression analyses are performed – no lag, one-

year lag, two-year lag and three-year lags. The highest value of R-square was

0.4164 in case of no-lag regression analysis. It implies that 41% of changes in the

natural log of real rents were explained by the independent variables. The value

of the Significance F (0.001) implies that the results were statistically significant. In

terms of the independent variables, only the first order difference operator of

employment was statistically significant. The t-stat values of other independent

variables, GDP and new office space, were low and hence, they were not

statistically significant in determining the value of real rent.

The value of Significant F in case of three-year lag regression analysis was higher

than the critical value and hence, the results were not statistically significant. It

implies that only lags up to two years were significant in determining the value of

real rents in the Westend. This result is different from the one obtained by D’Arcy

et al. (1999) when they analysed office rents in Dublin. D’Arcy et al. (1999, p. 309)

found that changes in real GDP lagged one period and changes in stock supply

lagged three periods were most important determinants of changes in real rent.

Recommendation/Conclusion This research analysed the rents and vacancy rates over a period of three

decades in the City and the Westend. Average annual space take-up and new

supply of offices were significantly higher in the City than in the Westend. As a

consequence of lower supply, average rents were higher in the Westend.

Vacancy rates showed high volatility and synchronisation with economic growth

cycles. Higher supply of office space in the City also resulted in higher vacancy

rates. However, cumulative absorption was higher in the City over the 30 year


period from 1982 to 2012. The regression analysis of the net absorption rates as

determined by real rents and employment showed that significant differences

between the City and the Westend. In case of the City, highest R-square value was

obtained with no-lag but similar results were obtained with one-year lag for the

Westend. The coefficient of real rents index was negative in both areas.

There are substantial differences in rental growth rates in the two areas in the last

three decades. In 1980s, rents were higher in the City. However, higher increases

in supply in the City has resulted in rates increasing in the Westend at a higher

rate in the last decade and are now substantially higher than those in the City. The

empirical results also support the rental growth theory as growth in rents was

higher than inflation when vacancies were low and vice-versa.

The regression analysis of single-equation model for rent showed significant

differences between the City and the Westend. The statistically significant highest

value of R-square was obtained in case of three-year lag when real rent index

values were estimated by using GDP, employment and new office space in the

City. In case of the Westend, highest values were obtained with no-lag regression

analysis. Also, GDP and new office space independent variables were statistically

significant in case of the City but employment was the statistically significant

determinant of real rents in the West End.

The results of this study are useful in understanding the impact of various demand

and supply variables. The study also covered a period of 30 years which included

multiple economic cycles and hence, useful in understanding the long-term

impact of the demand and supply variables. However, there are some limitations

of this study. Firstly, only annual data is used in empirical analysis. Using

quarterly data can enhance the usefulness of similar studies in the future, as it is

likely that rents reflect changes in variables such as GDP and employment

quicker than one-year lag. Secondly, additional data is required to do real rent

equilibrium and vacancy rate equilibrium. Analysis of these two parameters is

beneficial in understanding long-term characteristics of the office market in the

City and the Westend.

Word Count: 8200

Chapter 4

Acknowledgement The Author would like to thank professor Tony Key for his guidance and help in

collecting much of the economic data employed in this study. Tony McGough

(Head of Forecasting and Strategy Research) and Charlie Browne (Forecasting

and Strategy Research) for their provision of the main office data used in

completing this project. Dr Mark Andrew for his guidance in the quantitative

techniques. Sotiris Tsolacos for offering to share his extensive experience of

modelling office rental market, and finally my family for their patience during this

research.


Bibliography

Research Plan & Sources

Research Data Sources The primary research involved a direct contact with Charlie Brownie and Tony

McGough from DTZ to obtain data not available in secondary sources.

1. DTZ: Office vacancy, development, take up, new supply, availability, stock

and real rent for the City and Westend.

Sotiris Tsolacos was able to provide hard copies of journals in the field of

commercial real estate market not available at the university databank.

The list of secondary data sources used in the study of this research is for 30 years

going back to 1982 and collected from varies sources including:

2. IPD (Investment Property Bank: Rental Growth

3. U.K. Government Statistics (ONS): Consumer Price Index, financial and

business services employment, and inflation.

4. DataStream: Long Term Interest Rate

5. Academic Journals & Books

6. Jones Lang LaSalle

7. CB Richard Ellis

8. RICS

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DC.

D’Arcy E. (1998), Lecture Note: International Real Estate Markets, The University

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D’Arcy E., McGough T. and Tsolacos S. (1997), National Economic Trends, Market

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D’Arcy, E., McGough, T., and Tsolacos, S. (1999). An econometric analysis and

forecasts of the office rental cycle in the Dublin area. Journal of Property Research,

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Frew, J., and Jud, G.D. (1988). The vacancy rate and rent levels in the commercial

office market. The Journal of Real Estate Research, Vol. 3, No. 1, pp. 1-8.

Fuerst, F. (2006). Predictable or not? Forecasting office markets with a simultaneous

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Gardiner, C. and Henneberry, J. (1988) The development of a simple regional

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Hendershott, P.H., Lizieri, C.M. and Matysiak, G.A. (1999) The workings of the

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Kelly H. (1983), ‘Forecasting Office Space Demand in Urban Areas’, Real Estate

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Keogh, G., McGough, T. and Tsolacos, S. (1998) ‘Modelling use, Investment and

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McGough, T. and Tsolacos, S. (1995) Forecasting Commercial Rental Values in the

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Mueller, G.R. (1999). Real estate rental growth rates at different points in the

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Pollakowski, H., Wachter, S. and Lynford, L. (1992) Did Office Market Size Matter

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Market, AREUEA Journal, 20(2), 303-24.

Richard Barras, 2009. Building Cycles: Growth and Instability (Real Estate Issues). Edition. Wiley-Blackwell. RICS (1994) Understanding the Property Cycle, Main Report, Economic cycles and Property Cycles, The Royal Institution of chartered ssurveyors, London. RICS (1999) The UK Property Cycle – a History from 1921 to 1997, Royal Institution of Chartered Surveyors, London. Rosen, K. (1984) Toward a Model of the Office Building Sector, AREUEA Journal, 12(3), 261-69. Shilling, J., Sirmans, C. and Corgel, J. (1987), ‘Price Adjustment Process for Rental Office Space’, Journal of Urban Economics, 22(2), 90-100. Wheaton, W. (1987) The cyclic behaviour of the national office market, AREUEA Journal, 15(4), 281-99. Wheaton, W., Torto, R. and Evans, P. (1997) The cyclical behaviour of the Greater London office market, Journal of Real Estate Finance and Economics, 15(1), 77-92.

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Appendices: Appendix I – Regression analysis of the net absorption rate in the City

No lag

One-year lag

Regression StatisticsMultiple R 0.36219R Square 0.13118Adjusted R Square 0.05220Standard Error 0.02767Observations 25

ANOVAdf SS MS F Significance F

Regression 2 0.002543 0.001271 1.660871 0.212922Residual 22 0.016839 0.000765Total 24 0.019382

Coefficients Standard Error t Stat P-value Lower 95% Upper 95%Intercept 0.05569 0.02314 2.40631 0.02495 0.00769 0.10369

% change in FBS0.04703 0.11593 0.40572 0.68887 -0.19339 0.28746

RIRENT -0.00080 0.00045 -1.79904 0.08575 -0.00172 0.00012





% change in FBS (-1) -0.13558 0.10803 -1.25503 0.22326 -0.36023 0.08908RIRENT (-1) -0.00104 0.00042 -2.50006 0.02078 -0.00191 -0.00018


Two-year lag

Appendix II – Regression analysis of the net absorption rate in the West End

No lag




Coefficients Standard Error t Stat P-value Lower 95% Upper 95%Intercept 0.04881 0.02372 2.05787 0.05287 -0.00067 0.09828

% change in FBS (-2)-0.20651 0.11643 -1.77371 0.09134 -0.44937 0.03635

RIRENT (-2) -0.00061 0.00045 -1.35538 0.19041 -0.00155 0.00033




Coefficients Standard Error t Stat P-value Lower 95% Upper 95%Intercept 0.03842 0.01973 1.94734 0.06437 -0.00250 0.07934% change in FBS 0.26558 0.08528 3.11420 0.00505 0.08872 0.44245RIRENT -0.00056 0.00030 -1.84788 0.07811 -0.00119 0.00007

One-year lag

Two-year lag




Coefficients Standard Error t Stat P-value Lower 95% Upper 95%Intercept 0.06887 0.02073 3.32297 0.00323 0.02577 0.11197% change in FBS (-1) -0.04050 0.08940 -0.45300 0.65519 -0.22642 0.14542RIRENT (-1) -0.00098 0.00032 -3.05400 0.00603 -0.00165 -0.00031





% change in FBS (-2) -0.18502 0.09541 -1.93917 0.06672 -0.38404 0.01401RIRENT (-2) -0.00060 0.00034 -1.74399 0.09651 -0.00131 0.00012


Appendix III – Regression analysis of single-equation rental growth in the

City

No- lag

One-year lag




Coefficients Standard Error t Stat P-value Lower 95% Upper 95%Intercept 0.13593 0.13277 1.02379 0.31502 -0.13650 0.40836DLRIGDP 0.51462 0.51276 1.00363 0.32447 -0.53747 1.56671LIOFNS -0.03689 0.03223 -1.14460 0.26242 -0.10301 0.02924DLIFBS 1.70404 0.52623 3.23823 0.00318 0.62431 2.78377




Coefficients Standard Error t Stat P-value Lower 95% Upper 95%Intercept 0.0628 0.1798 0.3493 0.7297 -0.3068 0.4324DLRIGDP (-1) -0.2017 0.6953 -0.2901 0.7740 -1.6310 1.2275LIOFNS (-1) -0.0204 0.0437 -0.4677 0.6439 -0.1102 0.0693DLIFBS (-1) 1.3675 0.7126 1.9191 0.0660 -0.0972 2.8322

Two-year lag

Three-year lag




Coefficients Standard Error t Stat P-value Lower 95% Upper 95%Intercept 3.9060 0.2557 15.2752 0.0000 3.3793 4.4326DLRIGDP (-1) 0.7731 1.0859 0.7120 0.4831 -1.4633 3.0095LIOFNS (-2) -0.0014 0.0628 -0.0226 0.9822 -0.1308 0.1280DLIFBS (-2) 2.2799 0.8781 2.5963 0.0156 0.4713 4.0885




Coefficients Standard Error t Stat P-value Lower 95% Upper 95%Intercept 3.44354 0.18241 18.87794 0.00000 3.06707 3.82002DLRIGDP (-1) -2.17924 0.76747 -2.83951 0.00906 -3.76323 -0.59526LIOFNS (-3) 0.12218 0.04454 2.74308 0.01133 0.03025 0.21411DLIFBS (-3) -1.09633 0.63478 -1.72711 0.09700 -2.40644 0.21378


Appendix IV – Regression analysis of single-equation rental growth in the

West End

No- lag

One-year lag




Coefficients Standard Error t Stat P-value Lower 95% Upper 95%Intercept 0.05580 0.28302 0.19716 0.84518 -0.52491 0.63651DLRIGDP 0.32896 0.71782 0.45827 0.65043 -1.14389 1.80180LIOFNS -0.01291 0.05954 -0.21689 0.82993 -0.13509 0.10926DLIFBS 1.94456 0.67682 2.87308 0.00782 0.55584 3.33328




Coefficients Standard Error t Stat P-value Lower 95% Upper 95%Intercept -0.2722 0.3492 -0.7795 0.4427 -0.9901 0.4456DLRIGDP (-1) 0.5336 0.8491 0.6284 0.5352 -1.2118 2.2789LIOFNS (-1) 0.0589 0.0732 0.8047 0.4283 -0.0916 0.2094DLIFBS (-1) 1.3210 0.7999 1.6516 0.1106 -0.3231 2.9652

Two-year lag

Three-year lag

This project submitted as part of the requirements for the award of an MSc in Real

Estate, Cass Business School 2012

I hereby certify that this report is my own unaided work




Coefficients Standard Error t Stat P-value Lower 95% Upper 95%Intercept 3.4828 0.5404 6.4444 0.0000 2.3697 4.5958DLRIGDP (-1) 3.0006 1.2679 2.3666 0.0260 0.3894 5.6119LIOFNS (-2) 0.1246 0.1146 1.0876 0.2872 -0.1113 0.3605DLIFBS (-2) 1.3389 0.9803 1.3658 0.1842 -0.6800 3.3577




Coefficients Standard Error t Stat P-value Lower 95% Upper 95%Intercept 3.5256 0.5880 5.9955 0.0000 2.3119 4.7392DLRIGDP (-1) -1.7448 1.3497 -1.2927 0.2084 -4.5305 1.0409LIOFNS (-3) 0.1028 0.1234 0.8336 0.4127 -0.1518 0.3574DLIFBS (-3) -1.7262 1.0959 -1.5751 0.1283 -3.9881 0.5357

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