Addressing the Affordability of Housing in England Using a Residual Income Approach

Thomas Markovitch

Student ID: 1204381

RAE Tutor: Dr. Dean Garratt

Word Count: 4978 words

ABSTRACT

The UK is in a housing crisis, with both declining homeownership and housing affordability since 2002. The conventional measure of housing affordability is an earnings to house price ratio, measured at the lower quartile or median. This study builds upon previous research by developing a new measure; Real Residual Income (RRI), and estimates the causes of declining housing affordability in England, from 1996 to 2012. The primary finding is that a commitment to substantial year on year housing construction is necessary to mitigate housing affordability decline. The paper also encourages future use of RRI measurement and an exploration into the effects of tenure type.

I take this opportunity to thank Dr. Dean Garratt for his guidance during the academic year, insight into the topic, and invaluable reassurance throughout the challenges of the project.

Contents

1 Introduction………………………………………………………………………………… 1

2 Literature Review………………………………………………………………………… 1

3 Data 3.1 Defining Entities……………………………………………………………………………… 3 3.2 Real Residual Income………………………………………………………………………… 4 3.3 Supply-Side Variables………………………………………………………………………… 6 3.4 Demand-Side Variables……………………………………………………………………… 7

4 Methodology 4.1 Deriving Real Residual Income………………………………………………………………. 9 4.2 Hypotheses…………………………………………………………………………………… 10 4.3 Model………………………………………………………………………………………… 10

5 Results…………………………………………………………………………………………. 11

6 Discussion 6.1 Limitations and Extensions…………………………………………………………………… 14 6.2 Conclusion……………………………………………………………………………………. 14

7 References……………………………………………………………………………………. 15

8 Appendix 8.1 Appendix A: Help to Buy …………………………………………………………………… 19 8.2 Appendix B: NUTS 1 Regional Grouping …………………………………………………… 19 8.3 Appendix C: FRS Group Sizes……………………………………………………………… 20 8.4 Appendix D: Shelter Poverty Affordability Scale…………………………………………… 20 8.5 Appendix E: Additional RRI Findings……………………………………………………… 22 8.6 Appendix F: Variation of Mean Adults per Household ……………………………………… 22 8.7 Appendix G: Additional Population Variables……………………………………………… 23 8.8 Appendix H: Additional Variables…………………………………………………………… 24 8.9 Appendix I: RRI Composition……………………………………………………………….. 27 8.10 Appendix J: Data Mining Process…………………………………………………………… 28

1 Introduction

In 1914, 10% of Britons were homeowners with 89% privately renting and less than 1% 1

publicly renting (Mullins and Murie, 2006). Increasing homeownership spanned nine decades , 2

peaking at 69.52% in 2002, before falling to 63.85% by 2012, (DCLG, 2014). However, housing 3

affordability was in decline for some time prior to 2002. Barker (2004, p. 123) discovered that “In 2002, only 37 per cent of new households could afford to buy a property, compared to 46 per cent in the late 1980s”. Poon and Garratt (2012) illustrate this decline with house price, earnings and inflation data from 1969-2012; while real household income increased by a factor of 2.75, real house prices increased by a factor of 3.92. From a European prospective, this was due to soaring house prices, rather than slow earnings growth. During 1971-2001, the UK’s real average house price increased at 2.4% per annum (pa.); 1.3 percentage points (pp.) higher than the European average (Mean, 2011).

It is clear that potential first time buyers, or ‘outsiders’ (Meen, 2013), are losing access to the housing market. However, the fall in affordability, rather than plateau, shows that existing owners, or ‘insiders’, are also exiting . Thus, housing affordability is the dual problem of tougher attainability, 4

and tougher sustainability. This is of particular concern in England, where half of owner occupiers are mortgage holders (ONS, 2013c). In 2013-14, the balance tipped, with outright owners exceeding the number of mortgage holders for the first time in three decades (DCLG, 2015). To investigate the causes behind the decline in housing affordability in England, this paper proposes and develops an alternative measure; Real Residual Income (RRI), and estimates the model over 1996-2012.

2 Literature Review

Declining affordability has widened the economic gap between insiders, (especially those with outright ownership), and outsiders, with a bias in favour of older generations. In 2012, only 17% of 18-24 year olds were homeowners (Pannell, 2012). In 2013, George Osborne, Chancellor of the Exchequer, proposed the ‘Help to Buy’ scheme, (Appendix A), to counteract this issue (BBC, 2013). However, it was a housing demand solution to an inherently housing supply problem.

In 2003, the British Government commissioned the ‘Review of Housing Supply’, to analyse the “issues underlying the lack of supply and responsiveness of housing in the UK” (Barker, 2004, p. 3). In 2004, Economist Kate Barker, member of the Monetary Policy Committee, outlined the following motivations for leading the review: (1) weak housing supply hinders economic growth, causes macroeconomic instability, and reduces flexibility within the labour market; (2) housing security is necessary for households to financially plan for their futures, and access key services nationally, and within their local communities; (3) lastly, and most poignantly, Barker explains:

Includes housing associations.1

Brief exception to the trend during and caused by the Second World War.2

Calculated by owner occupied dwelling stock over total dwelling stock.3

Even if population growth contributed only to a non-ownership group, it still wouldn’t account for the 5.67pp. 4

decline.

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Barker’s final report recommended the Government establish a “market affordability goal”, and that each region “set its own target to improve market affordability”, to be “consistent with the Government target” (Barker, 2004, p. 131). This made the discussion of housing affordability particularly prominent throughout the late 2000s. In response to Barker’s recommendations, the Department of Communities and Local Government (DCLG) commissioned the construction of the ‘Affordability Model’, developed between 2005 and 2010 (Mean, 2011). The model has since been used as a basis for English housing affordability research and policy analysis. Like most previous affordability research, it adopts a house price to earnings ratio as its measure of housing affordability.

Sophisticated measures of housing affordability began to emerge in the UK during the early 1990s (Stone, 2006). However, in the US, “poverty and urban problems” initiated the discussion of appropriate housing affordability measurement from the late 1960s (Stone, 2006, p. 457). One of the earliest measures was the ratio of median house prices to median earnings. This method was soon identified as flawed by inadequately representing lower income households, and disregarding the effects of interest rates and mortgage repayments (Jones et al., 2010). The ratio fails for lower income households because an ‘acceptable’ ratio results in a level of non-housing income that is significantly less than required to sustain an acceptable standard of living (Grigsby and Rosenberg, 1975).

The ratio’s usefulness also diminishes the more heterogeneous the income of the population. Studies of the 2000s refine the approach, addressing such problems, for example, constructing the ratio at 25% quartiles. Wilcox and Bramley (2010) criticise this solution, affirming that 25% quartiles are arbitrary and familiarised among literature with little justification . Dolbeare (1966) offered one of 5

the first compelling arguments against the ratio approach by proposing the use of residual income. Residual income is defined: “the amount of money left after housing costs have been met that is crucial in determining whether the costs of housing are really affordable” (Brownill et. al, 1990, p.49).

Residual income is more logical in construction, but is nonetheless faced with significant resistance in adoption. Firstly, the ratio approach is widely recycled in housing affordability research and considered the conventional method. Secondly, residual income is difficult to operationalise; its generation requires comprehensive household surveys with individual specific information, rather than macroeconomic time series. Furthermore, survey based methods face the criticism that a result “is not universal; it is socially grounded in space and time” (Stone, 2006, p.459).

However, cross-sectional housing affordability research is not uncommon. For example, Bourassa (1996) explores the household specific factors effecting affordability in Australian cities. Stone (2006), an advocate for residual income, derives the variable by creating a ‘market basket’ of all non-housing necessities, to determine the amount a household can spend on housing, once the necessity market basket is paid for. To benefit from both the residual income approach, and time series variables, this paper aggregates multiple household surveys, to form real residual income over time.

Wilcox and Bramley prefer the midpoint between the 10% decile and 25% quartile.5

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For many people, housing has become increasingly unaffordable over time. The aspiration for homeownership is as strong as ever, yet the reality is that for many this aspiration will remain unfulfilled unless the trend in real house prices is reduced. This brings potential for an ever widening social and economic divide between those able to access market housing and those kept out. (Barker, 2004, p. 1)

3 Data

3.1 Defining Entities

A prerequisite in forming the residual income model is to define entities, such as region and tenure, and to determine the time period over which the model can be estimated. Regional effects of housing affordability follow a similar pattern through time, but with different magnitudes and volatilities, as shown by Figure 1 (Nationwide, 2014). By the fourth quarter of 2014, London’s ratio exceeded all other regions by a factor of 1.46 to 2.65, with additional volatility of 9.94% to 36.80% 6

over the period. Further support of specifying regional effects is the heterogeneous housing policy between regions, and historical factors influencing regional differences, such as economic sector proportions, wealth distribution and demography.

Previous affordability models, including the Affordability Model and Long-run Model of Housing Affordability (Meen, 2011), divide England into nine regions. This study uses the same approach. Appendix B contains a thorough justification, methodology and map outlining the regional boundaries. Figure 2 plots the housing affordability ratio, measured at lower quartiles, by these nine regions, during 1997-2011, which draw much the same conclusions as Figure 1 (Parliament, 2012). Dividing by region determines the time horizon of the model, due to annual regional data available from 1996-2012. To reduce repetition, this paper adopts abbreviations for regions by the bracketed letters in Figure 2. Furthermore, all variables and diagrams after Figure 2 are measured annually, from 1996-2012.

Only two data services measure variables by region and tenure; the Family Resources Survey (FRS) and English Housing Survey (EHS). The FRS was selected due to its age, containing seventeen years of data, rather than six. The FRS is an annual UK-wide cross-sectional survey, containing 25

Volatility is measured by the coefficient of variation (throughout this study) to account for magnitude effects.6

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1

2

3

4

5

6

7

8

9

10

83 85 87 89 91 93 95 97 99 01 03 05 07 09 11 13

Rat

io (m

ean)

Year

Figure 1: Quarterly Regional Housing Affordability Ratios, 1983 - 2014

Northern

Yorkshire and the Humber

North West

East Midlands

West Midlands

East Anglia

Outer South East

Outer Met

London

South West

datasets with more than 2,000 variables. During 1996-2012, the FRS’s distinguish between six types of tenure ; rent from the council, housing association or privately (furnished and unfurnished) and 7

owner occupied, with or without a mortgage.

A potential problem of grouping by region and tenure is generating a small sample size per group. However, during 1996-2012, the smallest annual survey contained 20,196 UK households , 8

consisting of 11,213 children and 35,207 adults. Once privately furnished and unfurnished renters were grouped together , the median group size contained 316 households. This is assumed sufficiently 9

large to be representative of the population, with the tolerance of error discussed in Section 3.2. Appendix C contains further group size statistics. Henceforth, renter tenure types are abbreviated to ‘Council’, ‘HA’ and ‘Private’, and homeowners to ‘Outright’ and ‘Mortgage’.

3.2 Real Residual Income (RRI)

RRI is derived in Section 4.1. Once computed by region and tenure, median (with a 2.5% upper confidence interval ) and mean household nominal residual income (NRI) are compared in 10

Figure 3 . The mean values often exceed the upper confidence interval of the median calculations. 11

Thus, similarly to the ratio approach, percentiles are preferred in the calculation of residual income because of the upward skewness caused by outliers (very high income households). To illustrate the two-way tolerance of error, and real transformation in comparison to Figure 3, Figure 4 plots median household RRI with a 5% confidence interval.

‘Part own, part rent’ was an additional category in 1996, containing 64 observations (0.291% of the 1996 7

sample). By including 1996 data, the overall dataset increased by 6.25%. It is assumed that removing the 64 observations was an insignificant random loss, not systematically related to any regressors.

Consisting of 14,365 English households, once removing Wales, Scotland and Northern Ireland.8

As the furnished category alone had a small sample size. In the 2012/13 survey, it only contained 11 - 40 9

observations per region, except for London with 75 observations.

Using a conservative binomial exact confidence interval (used throughout the paper) which makes no 10

assumptions about the underlying distribution of household residual income.

North East was chosen as it was coded region ‘1’ in dataset, but all regions show similar results.11

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2

3

4

5

6

7

8

9

10

97 98 99 00 01 02 03 04 05 06 07 08 09 10 11

Rat

io (l

ower

qua

rtile

)

Year

Figure 2: Regional Housing Affordability Ratios, 1997 - 2011

North East (NE)

North West (NW)

Yorkshire and the Humber (YH)

East Midlands (EM)

West Midlands (WM)

East of England (EE)

London (LO)

South East (SE)

South West (SW)

England

Unexpectedly, Figure 4 shows that measuring housing affordability by RRI, doesn’t produce a declining trend. However, lower quartile RRI results expose that a substantial proportion of renters, across all regions, have a standard of living below an ‘acceptable level’. This discrepancy is calculated by applying Stone’s (2006) ‘Shelter Poverty Affordability Scale’, discussed in detail in Appendix D. This finding supports that increasing RRI should remain a priority to policy makers.

A second unanticipated result is that mortgage holders have significantly higher RRI than outright owners. This is likely explained by the higher proportion of retirement aged people owning their homes outright, relative to younger people. For example, in the 2012 FRS, 66.95% of outright owner households contained at least one person of retirement age, compared to just 7.31% of mortgage holder households. As people of retirement age tend to work less, outright owners’ weekly median net income is lower, (£240.72 less in the North East, during 1996-2012), which doesn’t fully compensate for their housing cost savings (£49.85).

Further RRI findings are discussed in Appendix E.

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100

200

300

400

500

600

700

96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12

NR

I (£

/wee

k)

Year

Figure 3: NE NRI by Tenure

Council, mean Council, median Council, upper CI HA, mean HA, median HA, upper CI Private, mean Private, median Private, upper CI Outright, mean Outright, median Outright, upper CI Mortgage, mean Mortgage, median Mortgage, upper CI

100

200

300

400

500

600

700

96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12

RR

I (£/

wee

k)

Year

Figure 4: NE RRI by Tenure, (2012 Prices)

Council, lower CI Council, median Council, upper CI HA, lower CI HA, median HA, upper CI Private, lower CI Private, median Private, upper CI Outright, lower CI Outright, median Outright, upper CI Mortgage, lower CI Mortgage, median Mortgage, upper CI

3.3 Supply-Side Variables

Two supply-side variables are included in the final model; homeownership rate and housing stock, and both are unavailable by tenure (DCLG, 2012 and 2014). Since 2012, homeownership and housing stock were no longer collected at the regional level, so the 2012 observations are estimated from the change in the English rate . This approximation seems appropriate as all regions follow a 12

similar trend (and thereby to England), as shown in Figures 5 and 6. Aggregately, the estimation is correct because the combined weighted changes equal the English change.

Figure 5 shows that London’s homeownership is significantly lower than in other regions. This is mostly explained by its constantly higher house price increases and population growth (by both natural increase and net migration). The housing stock trends of Figure 6 appear linear, but are more revealing when scaled by their annual regional populations, as presented in Figure 7. Except for London, the housing stock per 1,000 residents, increased up to and past the 2002 homeownership percentage peak. The trend has only recently appeared to revert into decline, importantly exposing

For example, as English housing stock increased by 0.588%, regions were assigned a 0.588% increase.12

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48

52

56

60

64

68

72

76

80

96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12

Hom

eow

enrs

hip

Rat

e (%

)

Year

Figure 5: Regional and National Homeownership Rate

NE

NW

YH

EM

WM

EE

LO

SE

SW

England

1,000

1,500

2,000

2,500

3,000

3,500

4,000

96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12

Stoc

k (0

00s)

Year

Figure 6: Regional Housing Stock

NE

NW

YH

EM

WM

EE

LO

SE

SW

that housing stock is not only determinant driving homeownership decline. London’s housing stock is a completely separate case; during 1996-2012, homes became increasingly competitive at the mean rate of 1.86 fewer stock per 1,000 residents pa..

3.4 Demand-Side Variables

The demand-side variables of the model include the mean number of adults per household (FRS, 1995-2012); the unemployment rate (ONS, 2015); and a variety of population statistics (ONS, 1998a/b-2014a/b and 2014c). The mean number of adults is measured by region and tenure. The unemployment rate and population variables are only measured by region. Variation within the mean number of adults is primarily due to regional differences (59.5%) which are explored in Appendix F. 13

Figure 8 plots the unemployment rate which varies in magnitude from region to region, but has changed much the same in all regions, (a positive parabola between the early 1990s recession and the 2007-08 financial crisis).

Time and tenure account for 25.6% and 14.9% respectively. 13

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400

405

410

415

420

425

430

435

440

445

450

455

96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12

Stoc

k pe

r 1,0

00 R

esid

ents

Year

Figure 7: Regional Housing Stock per 1,000 Residents

NE

NW

YH

EM

WM

EE

LO

SE

SW

3

4

5

6

7

8

9

10

11

12

96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12

Une

mpl

oym

ent R

ate

(%)

Year

Figure 8: Regional Unemployment Rate

NE

NW

YH

EM

WM

EE

LO

SE

SW

The model includes three population statistics; births, deaths and net migration, with subsets and supersets discussed in Appendix G. Emigration and immigration aren’t included separately because of their high correlation (0.9527), which would induce multicollinearity in estimation.

Regional patterns are easily identified after scaling by annual population. Figure 9 shows that natural increase (births minus deaths) ranges between ≈-1 to ≈4 people per 1,000 residents, per region, except for London. The capital’s differences relate to its age profile. In 2012, it proportionately had 36% less retirement age inhabitants, and a median age (34) six years younger than the UK average (ONS, 2013d). Young migrants play a significant contributing factor with Figure 10 displaying regional migration per 1,000 residents pa.. London’s immigration was so high during 1996-2005, that its net migration rate exceeded the average immigration rate of the other regions.

Appendix H discusses some additional variables commonly used in housing affordability research and the reasons for their exclusion in this study’s model.

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-2

0

2

4

6

8

10

12

96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12

Nat

ural

Incr

ease

per

1,0

00 R

esid

ents

Year

Figure 9: Regional Natural Increase per 1,000 Residents

NE

NW

YH

EM

WM

EE

LO

SE

SW

-2

0

2

4

6

8

10

12

14

16

96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12

Mig

rant

s per

100

0 R

esdi

ents

Year

Figure 10: Regional Migration per 1,000 Residents

NE Net

NW Net

YH Net

EM Net

WM Net

EE Net

SE Net

SW Net

Regions exc. London Mean Immigration

Regions exc. London Mean Emigration

LO Net

4 Methodology

4.1 Deriving Real Residual Income (RRI)

There is no widely accepted mathematical derivation of residual income. Stone (2006) uses weekly disposable household income minus weekly shelter cost . Disposable income is used to best 14

represent the amount of money households have to spend on goods and services, outside of housing. This paper uses the same approach, but different terminology (net income and housing cost), to be consistent with the FRS. Appendix I contains a comprehensive FRS definition of these variables.

The desired unit of measurement was at the household level, as this reduces irrelevant net income fluctuation from households containing working and non-working adults. It also prevents otherwise necessary systematic division of housing costs amongst household members. The FRS 15

does not measure net income at the household level, but provides identification numbers (IDs) to all children, adults and households in three datasets. Thus, it was possible to construct residual income per household, by region and tenure for each survey year, by Equation 1.

(1)

The equation aggregates children and adult net income into their respective households and subtracts the housing cost of each household. The household index was then removed by finding the mean (Equation 2) or median (Equation 3). Finally, substantial data mining was conducted to obtain 16

real data from Equations 2 & 3, as outlined in Appendix J.

(2)

(3)

Housing benefit and council tax are only included in shelter cost (to not be counted twice).14

The process would need to take into account several factors such as relative net income in the household and 15

likelihood of being the payer of the housing cost.

Lower quartiles were also found by adjusting the median equation’s ‘(n+1)/2’ to ‘(n+1)/4’.16

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RRIhirt ≡ NIahirta=1

p

∑ + NIchirtc=1

q

∑ − HChirt , h = 1,...,n, a = 1,..., p & c = 1,...,q

where: RRI = real residual income NI = net income HC = housing costi = tenure r = region t = yeara = adult c = child h = householdp = total adults q = total children n = total households

RRIirtmean = 1

nNIahirt + HIchirt

c=1

q

∑a=1

p

∑⎛⎝⎜⎞⎠⎟− HChirt

⎡

⎣⎢

⎤

⎦⎥

h=1

n

∑

RRIirtmedian = n +1

2 term from: NIahirt + HIchirt

c=1

q

∑a=1

p

∑ − HChirt

4.2 Hypotheses Regional effects and housing supply are widely confirmed determinants of housing affordability. Thus, hypothesis 1 & 2 are partial validity tests of RRI measurement. Hypothesis 3 tests the less discussed demand-side impacts, and hypothesis 4 assesses London effects. Finally, hypothesis 5 tests the strength of the commonly reported primary determinant of housing affordability.

Hypothesis 1: H0: Regional effects have no effect on RRI. Hypothesis 2: H0: Supply-side variables have no effect on RRI. Hypothesis 3: H0: Demand-side variables have no effect on RRI. Hypothesis 4: H0: No supply-side or demand-side variables have an additional effect for London on RRI, relative to other regions. Hypothesis 5: H0: RRI is inelastic with respect to a change in the housing stock.

4.3 Model

Although Section 3 provides evidence of tenure differentiation, too few variables are available by tenure to include tenure effects, as the coefficients on the tenure dummy variables would be highly biased. The bias is caused by the omission of tenure effects from the variables not measured by tenure, but which do vary by tenure, and effect RRI, such as housing stock, unemployment and demography. Thus, all FRS series were re-calculated without tenure. The implications are discussed in Section 6.1. Equations (1) - (3) remain the same, except for the omission of the tenure (i) index.

If time invariant regional effects (unobserved heterogeneity) impact independent variables, they must be removed to prevent biased estimates. There are several examples of this problem, such as London's status as a global financial services centre, influencing employment and population variables, or the South East’s better weather, attracting a disproportionate number of retirement aged people, impacting the birth and death rate. Pooled OLS estimation contains heterogeneity bias by failing to remove the unobserved heterogeneity, ar. Random Effects is invalid by construction (requiring zero correlation between ar and RRI regressors, x1rt,…, xkrt).

Both fixed effects (FE) and first differencing (FD) remove unobserved heterogeneity. However, a problem with both methods that they cannot include time invariant variables, or variables which do not vary by entity. While the coefficient estimates remain unbiased, the impact of such variables (such as credit restrictions) cannot be estimated. Wooldridge (2013) states that under usual panel data assumptions, the decision between FE and FD, ultimately depends on the relative efficiencies of the estimators. FD is preferred when the observed factors which change over time are serially correlated. As serial correlation is detected in the FD idiosyncratic errors, Δεrt (p = 0.0004), the FD model is not appropriate. Wooldridge (2013) explains it is difficult to test for serial correlation for FE, so insignificant serial correlation in the time demeaned idiosyncratic errors is assumed. After including London interaction terms for variables which are noticeably different in London to other regions, the FE model is derived from pooled OLS (Regression 4), giving Regression 7. 17

Technically, the unobserved heterogeneity includes an intercept and Stata estimates FE by assuming: 17

but the outcome is the same. For interpretation purposes, net migration is measured in 000s and the homeownership and unemployment rate level variables are measured from 0 to 100 (e.g. 65 refers to 65%).

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arr=1

9

∑ = 0

(4)

(5)

(6)

(7)

5 Results

Hypothesis 1 is test by the dummy regression model given by Regression 8: 18

(8)

Hypothesis 1:

Result: Reject H0 at the 1% significance level , thus rejecting that regional effects have no effect on 19

RRI. All other hypotheses are test by Regression 7 with estimation results given in Table 1.

Produces identical coefficients to usual FE, but includes regional dummy variables for testing.18

Independent of the robustness decision of the standard errors.19

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ln RRIrt( ) = β1x1rt + ...+ β7x7rt + δ tdtt=2

17

∑ + Lr bjx jrtj=1

5

∑ + ar + ε rt

ln RRIr( ) = β1x1r + ...+ β7 x7r +1

17δ t

t=2

17

∑ + Lr bj x jrj=1

5

∑ + ar + ε r

⇒ ln RRIrt( )− ln RRIr( ) = β1 x1rt − x1r( )+ ...+ β7 x7rt − x7r( )+ δ t dt −1

17⎛⎝⎜

⎞⎠⎟t=2

17

∑

+ Lr bj x jrt − x jr( )j=1

5

∑ + ar − arremoval of the unobserved heterogeneity

!"# + ε rt − ε r

= ln R!!RIrt( ) = β1!!x1rt + ...+ β7!!x7rt + δ t!!dt

t=2

17

∑ + Lr bj !!x jrtj=1

5

∑ + !!ε rt

where : r = region t = yearx1 = ln housing stock( ) x2 = net migrationx3 = ln births( ) x4 = ln deaths( )x5 = homeownership rate x6 = ln adults per household( )x7 = unemployment rate L = London dummy variable

dtt=2

17

∑ = set of time annual dummy variables ar = unobserved heterogeneity,

Lt x jrtj=1

6

∑ = set of London interaction variables ε rt = idiosyncratic error

− = meaned variables ..= demeaned variables

ln RRIrt( ) = β1x1rt + ...+ β7x7rt + δ tdtt=2

17

∑ + Lr bjx jrtj=1

5

∑ + γ rλrr=2

9

∑ + ε rt

H0 :γ 2 = ...= γ 9 = 0H1 :γ 2 = ...= γ 9 ≠ 0

Table 1: Fixed Effects Output

* significant at p < 0.01; ** at p < 0.05; and *** at p < 0.01 Hypothesis 2 and 3 can be written as follows:

Hypothesis 2:

Hypothesis 3:

Result: Reject H0 from hypothesis 2 (3) at the 1% (5%) significance level, thus rejecting that 20

supply-side (demand-side) variables have no effect on RRI. The highly significant results from hypothesis 1 & 2 are consistent with the ratio approach, providing evidence that RRI is an appropriate measure of housing affordability.

Moreover, Table 1 shows that four variables are statistically and economically significant. Succinctly, a 1% increase in the housing stock, mean adults per household, births and deaths increase RRI by 1.304%, 1.114%, -0.536% and -0.474% respectively. While the directions for the housing stock and mean adults per household variables are obvious, the birth and death rate effects are less so. The negative birth rate effect is likely due to a parent(s) reducing employment, (and thereby reducing household net income), to look after the newborn. The negative death rate effect essentially works in the direct opposite manner to the mean adults per household variable; a death causes an immediate loss to household net income, while housing costs remain unchanged.

Any single variable test rejecting the null hypotheses is sufficient, for instance H0: β4 = 0, H1: β4 ≠ 0 for 20

hypothesis 3, or a supply or demand multivariable test, as the hypotheses were not variable/combination specific.

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Variable Coefficient Standard Error T-statistic

Housing stock 1.304*** 0.444 2.94

Net migration 0.000 0.000 1.10

Births -0.536** 0.221 -2.42

Deaths -0.474** 0.218 -2.18

Homeownership 0.011*** 0.004 2.70

Adults per household 1.144*** 0.158 7.24

Unemployment -0.012** 0.005 -2.48

London*Housing stock 1.613 1.981 0.81

London*Net migration 0.000 0.001 -0.35

London*Births -0.451 0.358 -1.26

London*Deaths -0.099 0.452 -0.22

London*Homeownership 0.005 0.010 0.53

H0 :β1 = 0H1 :β1 ≠ 0

H0 :β3 = 0H1 :β3 ≠ 0

The homeownership and unemployment variables are statistically significant and in the expected directions, but not economically significant, (a substantial 10pp. increase only increases RRI by 0.11% and -0.12% respectively). This is possibly due to the variables low year to year fluctuation, relative to the other variables. A more surprising result is that net migration is insignificant. Alternatively to the homeownership and unemployment variables, it is possible that the tracing of a relationship over the relatively short period was a too demanding task for the estimation, because of the very high fluctuation in the net migration variable, relative to RRI. Hypothesis 4:

Result: Do not reject H0 at the 10% significance level (p = 0.223 ), thus providing no evidence for 21

additional demand-side or supply-side effects on RRI, for London, relative to other regions. This 22

was a surprising result, as London often appeared to be an anomaly across the variables shown in Section 3. However, the insignificance may be a data problem, as London only contains seventeen observations per variable, and hence the differences may not have been fully picked up by the FE estimation. For this reason, the interaction variables were not removed from the final model.

Hypothesis 5:

Result: Do not reject H0 at the 10% significance level (p = 0.495), thus providing no evidence to reject that RRI is inelastic with respect to a change in the housing stock. However, housing stock is the only real supply-side driver of RRI so hypothesis 5 is modified below for evaluation.

Hypothesis 5 modified:

Result: When c = 0.731, 0.567, 0.257, H0 is rejected at the 10%, 5% and 1% level respectively. This result means, for instance, that one can state with 95% confidence, that a 1% change in the housing stock, increases RRI by at least 0.567%. Thus, housing stock is clearly a strong determinant of RRI, albeit not proven elastic. The reason for not finding evidence of an elastic relationship may be the lack of data (resulting in the reasonably large standard errors), rather than lack of truth.

Robust standard errors21

In other words, the distance between the London slope and the average slope of other regions is insignificant. 22

! /!13 28

H0 :b1 = ...= b5 = 0H1 :b1 = ...= b5 ≠ 0

H0 :β1 ≥1H1 :β1 <1

H0 :B1 ≥ cH1 :B1 < c

6 Discussion

6.1 Limitations and Extensions

Including a third tenure effect into the two-way effects model (region and time) is a likely improvement to the model. Omitting tenure effects is a common problem in the literature, because few relevant variables are measured by tenure. For example, the FRS and EHS do not include ‘by tenure’ data for the four economically and statistically significant variables of the model. Furthermore, the researchers which consider tenure, usually only define two or three groups. For example, Meen’s (2013) ‘insiders’ and ‘outsiders’ housing market model or the Affordability Model’s ‘Owner Occupiers’, ‘Private Renters’ and ‘Social Renters’ groups. While differentiating between a couple of groups is better than none, too few groups do not appropriately differentiate the factors effecting housing affordability across tenure types, resulting in biased coefficient estimates . For example, 23

within the ‘Owner Occupier’ group of the Affordability Model, a change in real interest rates, effects mortgage holders more so than outright owners. Similarly, the Conservative party’s recent pledge to renew the ‘Right to Buy’ scheme for housing associations (Economist, 2015) will effect housing association renters more so than council renters, within the ‘Social Renters’ group.

A second improvement would be to include a lag structure, which may make the model more complete. For example, housing starts have no contemporaneous impact on RRI, but adding lagged regressors may reveal delayed effects. However, creating a lagged structure invalidates the FE estimation and requires complicated econometric methods. A natural extension to the study would be to construct a model of this type and to compare results. Vector autoregression (VAR) models are not appropriate for this study because of the too few time periods . 24

Historically, most of the relevant variables have been measured at annual rates. Recently, more are available at quarterly or even monthly rates. Thus, a possible extension is to investigate the parameters over a shorter time period, but with a higher frequency of observations. This may also enable VAR modelling. However, the optimum extension would be to obtain more data by tenure, but this is easier said than done. Unless the FRS or EHS begin producing the relevant data, a researcher would need to collect his/her own random samples, necessarily requiring thousands of respondents to have a reasonable margin of error.

6.2 Conclusion

One draws two conclusions from the study. Firstly, as RRI works well as a measurement of housing affordability, and has a superior theoretical framework, it should replace or work alongside the ratio approach. Secondly, although the FE model has its criticisms, it still provides evidence relevant for policy makers. The government is unable to significantly effect the birth or death rate. Nor can, or should, the government develop policy aimed and getting more people to live together, as

The coefficients are a weighted average of the unbiased estimators.23

Isaac (2014) suggests a minimum of 40 and the model had 17.24

! /!14 28

this solution is trivial and will not improve homeownership. Thus, the solution , which has been 25

reiterated time and time again throughout the literature, is that England needs to build more homes.

Government policy can be assessed from the recent May 2015 General Election Manifesto releases. Fortunately, the parties are planning to deliver sensible housing policy. As a percentage of the 2012 UK housing stock, the Conservatives, Labour and (Liberal Democrats) seek to increase housing stock by an eventual annual 0.72% (1.08%), which will increase annual RRI by ≈0.94% (≈1.41%) respectively (BBC, 2015). Construction should also be targeted to meet required needs, 26

ensuring that houses become homes.

It is vital that the eventual government ensures that their plans materialise, as the aspiration to own a home is higher than ever before; 81% of British adults hope to own a home within 10 years, requiring a 24% increase in the current level (Pannell, 2012). Thus, if the housing affordability problem is not appropriately addressed, and homeownership continues to decline, the “very British sense of aspiration and self-reliance”, for many, will gradually only ever be an aspiration, rather than a reality (Brandon Lewis MP, Minister of State for Housing and Planning, 2015).

7 References

Bank of England (2015) Interactive Database: Search Results [ONLINE] Available at: www.bankofengland.co.uk/boeapps/iadb/FromShowColumns.asp?Travel=&SearchText=net+lending+individuals&point.x=0&point.y=0 [Accessed: 03 Apr 2015]

BBC (2013) Budget 2013: Chancellor Extends Home-Buying Schemes [ONLINE] Available at: www.bbc.co.uk/news/business-21849974 [Accessed: 24 Nov 2014]

BBC (2015) Cameron promises 200,000 Starter Homes If Tories Win Election [ONLINE] Available at: www.bbc.co.uk/news/uk-politics-31683974 [Accessed 10 Mar 2015]

Barker, K. (2004) Review of Housing Supply: Delivering Stability: Securing our Future Housing Needs: Final Report - Recommendations Norwich: Her Majesty's Stationery Office

Brownill, S., Sharp, C., Jones, C. and Merrett, S. (1990) Housing London York: Joseph Rowntree Foundation

Bourassa S.C. (1996) ‘Measuring the Affordability of Home-ownership’ Urban Studies 33 (10)

Department for Communities and Local Government (2012) Live Tables on Dwelling Stock (including vacants): Table 109: by tenure and region, from 1991 (final version) [ONLINE] Available at: www.gov.uk/government/statistical-data-sets/live-tables-on-dwelling-stock-including-vacants [Accessed: 24 Nov 2014]

Noting that variables unable to be estimated (such as credit availability) may also have policy implications.25

Conservatives (Labour) [Liberal Democrats] pledge to build 200,000 (200,000) [300,000] homes by 2017 26

(2020) [2020]. The estimates are slight overestimates, approximated by Table 1, by using the 2012 housing stock, and will diminish as housing stock increases.

! /!15 28

Department for Communities and Local Government (2014) Live Tables on Dwelling Stock (including vacants): Table 104: by tenure, England (historical series) [ONLINE] Available at: www.gov.uk/government/statistical-data-sets/live-tables-on-dwelling-stock-including-vacants [Accessed: 24 Nov 2014]

Department for Communities and Local Government (2015) English Housing Survey: Headline Report 2013-14 [ONLINE] Available at: https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/406740/English_Housing_Survey_Headline_Report_2013-14.pdf [Accessed: 29 Mar 2015]

Department for Work and Pensions (1998) National Centre for Social Research and Office for National Statistics: Social and Vital Statistics Division: Family Resources Survey 1996-1997 Colchester, Essex: UK Data Archive

………………………………………and all fifteen years between…………………………………………

Department for Work and Pensions (2014) National Centre for Social Research and Office for National Statistics: Social and Vital Statistics Division: Family Resources Survey 2012-2013 Colchester, Essex: UK Data Archive

Dolbeare, C. N. (1966) Housing Grants for the Very Poor Philadelphia: Philadelphia Housing Association

Economist (2015) The Right To Buy… Votes [ONLINE] Available at: www.economist.com/news/britain/21648714-conservative-party-returns-proven-poll-winner-right-buyvotes [Accessed: 18/04/2015]

Grigsby, G. and Rosenberg, L. (1975) Urban Housing Policy New York: APS Publications

GOV.UK (2014) Affordable Home Ownership Schemes [ONLINE] Available at: www.gov.uk/affordable-home-ownership-schemes/overview [Accessed 29 Dec 2014]

Isaac, A.K. (2014) Multivariate Time-Series Models University of Warwick: EC306: Lecture 6: p. 3

Jones, C., Watkins, D., Watkins, C and Dunse, N. (2010) Affordability and Housing Market Areas [ONLINE] Available at: http://www.ncl.ac.uk/curds/assets/documents/4b.pdf [Accessed: 02 Dec 2014]

Lewis, B. (2015) Brandon Lewis: Our Plan To Build Even More Homes [ONLINE] Available at: www.conservativehome.com/platform/2015/03/brandon-lewis-our-plan-to-build-even-more-homes.html [Accessed: 13 Mar 2015]

Meen, G. (2011) ‘A Long-Run Model of Housing Affordability’ Housing Studies 26 (7-8) pp. 1081-1103

Meen, G. (2013) ‘Homeownership for Future Generations in the UK’ Urban Studies 50 (4) pp. 637-656.

Mullins, D. and Murie, A (2006) Housing Policy in the UK Palgrave Macmillan.

Nationwide (2014) First Time Buyer House Price Earnings Ratios [ONLINE] Available at: www.nationwide.co.uk/about/house-price-index/download-data#xtab:affordability-benchmarks [Accessed: 01 Dec 2014]

Office for National Statistics (1998a) Key Population and Vital Statistics: Live Births 1996; and Conceptions 1995

…………………………………………and all four years between…………………………………………

Office for National Statistics (2003a) Key Population and Vital Statistics: Live Births 2001; and Conceptions 2000

! /!16 28

Office for National Statistics (1998b) Key Population and Vital Statistics: Stillbirths, Deaths, Infant and Perinatal Mortality during 1996

…………………………………………and all four years between…………………………………………

Office for National Statistics (2003b) Key Population and Vital Statistics: Stillbirths, Deaths, Infant and Perinatal Mortality during 2001

Office for National Statistics (2004a) Key Population and Vital Statistics: Deaths: Numbers and Standardised Mortality Ratios; and Perinatal and Infant Mortality: Numbers and Rates, 2002

…………………………………………and all four years between…………………………………………

Office for National Statistics (2009a) Key Population and Vital Statistics: Deaths: Numbers and Standardised Mortality Ratios; and Perinatal and Infant Mortality: Numbers and Rates, 2007

Office for National Statistics (2004b) Key Population and Vital Statistics: Live Births: Numbers, Rates, Percentages Outside Marriage, and with Low Birthweight; and Maternities: Numbers and Rates, 2002

…………………………………………and all four years between…………………………………………

Office for National Statistics (2009b) Key Population and Vital Statistics: Live Births: Numbers, Rates, Percentages Outside Marriage, and with Low Birthweight; and Maternities: Numbers and Rates, 2007

Office for National Statistics (2010a) Key Population and Vital Statistics: Deaths by Local Authority of Usual Residence, Numbers and Standardised Mortality Ratios (SMRs) by Sex, 2008 Registrations

Office for National Statistics (2010b) Key Population and Vital Statistics: Live Births by Local Authority of Usual Residence of Mother, Numbers, General Fertility Rates and Total Fertility Rates, 2008

Office for National Statistics (2011a) Key Population and Vital Statistics: Deaths by Local Authority of Usual Residence, Numbers and Standardised Mortality Ratios (SMRs) by Sex, 2009 Registrations

Office for National Statistics (2011b) Key Population and Vital Statistics: Live Births by Local Authority of Usual Residence of Mother, Numbers, General Fertility Rates and Total Fertility Rates, 2009

Office for National Statistics (2012a) Key Population and Vital Statistics: Deaths (numbers and rates) by Area of Usual Residence (administrative areas)‚ 2010 Registrations, United Kingdom and Constituent Countries

……………………………………………and the year between……………………………………………

Office for National Statistics (2014a) Key Population and Vital Statistics: Deaths (numbers and rates) by Area of Usual Residence (administrative areas), 2012 Registrations, United Kingdom and Constituent Countries

Office for National Statistics (2012b) Key Population and Vital Statistics: Summary: Live births (Numbers, Rates and Percentages): Administrative Area of Usual Residence, United Kingdom and Constituent Countries, 2010

……………………………………………and the year between……………………………………………

Office for National Statistics (2014b) Key Population and Vital Statistics: Summary: Live births (Numbers, Rates and Percentages): Administrative Area of Usual Residence, United Kingdom and Constituent Countries, 2012

! /!17 28

Office for National Statistics (2012c) Regions (Former GORs) [ONLINE] Available at: www.ons.gov.uk/ons/guide-method/geography/beginner-s-guide/administrative/england/government-office-regions/index.html [Accessed: Nov 20 2014]

Office for National Statistics (2012d) Household Debt by Tenure [ONLINE] Available at: www.ons.gov.uk/ons/about-ons/…/household-debt-by-tenure.xls [Accessed: 14 Apr 2015]

Office for National Statistics (2012e) Table 231 Housebuilding: Permanent Dwellings Started by Tenure and Region [ONLINE] Available at: www.gov.uk/government/statistical-data-sets/live-tables-on-house-building [Accessed: 14 Apr 2015]

Office for National Statistics (2013c) A Century of Home Ownership and Renting in England and Wales (full story) [ONLINE] Available at: www.ons.gov.uk/ons/rel/census/2011-census-analysis/a-century-of-home-ownership-and-renting-in-england-and-wales/short-story-on-housing.html [Accessed: 01 Dec 2014]

Office for National Statistics (2013d) London’s Population was Increasing the Fastest Amongst the Regions in 2012 [ONLINE] Available at: www.ons.gov.uk/ons/rel/regional-trends/region-and-country-profiles/region-and-country-profiles---key-statistics-and-profiles--october-2013/key-statistics-and-profiles---london--october-2013.html [Accessed: 17 Jan 2015]

Office for National Statistics (2013e) Introducing the New CPIH Measure of Consumer Price Inflation [ONLINE] Available at: www.ons.gov.uk/ons/rel/cpi/introducing-the-new-cpih-measure-of-consumer-price-inflation/2005-to-2012/index.html [Accessed: Dec 18 2014]

Office for National Statistics (2014c) Long-Term International Migration-2013 [ONLINE] Available at: http://www.ons.gov.uk/ons/rel/migration1/long-term-international-migration/index.html [Accessed: 30 Nov 2014]

Office for National Statistics (2015) Labour Market Statistics Dataset: LMS Labour Market Statistics-Integrated FR Colchester, Essex: UK Data Archive

Pannell, B. (2012) ‘Maturing Attitudes to Homeownership’ Council of Mortgage Lenders Housing Finance Issue 2

Parliament (2012) Regional House Prices: Affordability and Income Ratios House of Commons Library: Social and General Statistics Section

Poon, J. and Garratt, D. (2012) ‘Evaluating UK Housing Policies to Tackle Housing Affordability’ International Journal of Housing Markets and Analysis 5 (3) pp. 253-271

Stone, M. (2006) ‘A Housing Affordability Standard for the UK’ Housing Studies 21 (4) pp. 453-476.

UKdataservice.ac.uk (2014) Information on Derived Variables [ONLINE] http://discover.ukdataservic e.ac.uk/catalogue/?sn=7556&type=Data%20catalogue [Accessed: 06 Jan 2015]

Wilcox, S. and Bramley, G. (2010) Evaluating Requirements for Market and Affordable Housing [ONLINE] Available at: http://webarchive.nationalarchives.gov.uk/20120919132719/http://www.communities.gov.uk/documents/507390/pdf/1465577.pdf [Accessed: 28 Nov 2014]

Wooldridge, J. (2013) Introductory Econometrics: A Modern Approach Boston: Cengage Learning

! /!18 28

8 Appendix

8.1 Appendix A: Help to Buy

Since the 1st April 2013, first time buyers only required a 5% deposit with 20% of the property value loaned or guaranteed by the government. The ‘Help to Buy’ scheme applies to properties worth ≤£600,000 in England and ≤£300,000 in Wales. ‘Help to Buy: Equity loans’ are direct loans from the Government. ‘Help to Buy: Mortgage Guarantees’ are 20% Government guarantees to certain loaning banks (GOV.UK, 2014).

8.2 Appendix B: NUTS 1 Regional Grouping

Since March 2011, UK regions were classified under the EU’s Nomenclature of Territorial Units for Statistics (NUTS) (ONS, 2012c). NUTS contains three increasing levels of division. This study examines England divided at the first level (NUTS 1) which contains nine regions, as shown by the coloured regions of Figure B1 (not relating to the red lines). Further subdivision was not explored because several key variables were not measured at deeper levels, or across the full time period required. Data of annual NUTS 1 form can be manipulated back to 1996, after applying statistical adjustments to the previous Government Office Region (GOR) framework. These adjustments are outlined in Table B2 and relate to the red lines in Figure B1.

Figure B1: NUTS 1 and Areas of Change

Prior to 1996, the UK was classified under the Standard Statistical Regions (SSRs). SSR significant differences to NUTS 1 unfortunately make the tracing back of North East, North West and East of England statistics impossible. An unbalanced panel was not constructed because of missing pre-1996 regional net income data; necessary for constructing RRI. Fortunately, the data required for calculating RRI was collected from 1996 under GOR measurement. Hence, the RRI model was constructed using annual data from 1996-2012 due to the latest FRS (2012-2013) being published in June 2014. FRSs were coded by their initial year for comparison with other annual statistics. For example, 2012-2013 was coded as 2012.

The East Midlands, West Midlands and South West regions are omitted from Table B1 because they have no changes from SSR measurement to NUTS 1. Aggregated SSRs and NUTS 1 are equivalent at the English level, with national annual data available from 1971. Thus, for a ratio model, one can trade off the benefits from including regional effects for an increased time horizon.

! /!19 28

North East

Cumbria

Yorkshire and North the Humber Merseyside West Bedfordshire & Hertfordshire East Midlands West East of Midlands England

Essex London South South East West

Table B2: Regional Statistical Adjustments for Application of the FRS Survey

8.3 Appendix C: FRS Group Sizes

Table C1 contains statistics about the number of observations from all seventeen FRS surveys used in the study. The smallest group (starred) contains 32 observations, which is assumed large enough to apply central limit theorem. Ultimately, the final model omits tenure effects, and thus, the smallest group size (double starred) is 767 observations. The median group size of the final model (from 153 groups) is 2,120 observations.

Table C1: FRS Group Size Statistics

8.4 Appendix D: Shelter Poverty Affordability Scale

Stone (2006) estimated the minimum net income necessary to have an acceptable standard of living in the UK in 2004, for a given housing cost, for several household types. By applying Stone’s calculation of this

! /!20 28

SSRs (pre April

1996)

GORs 1 (Apr 1996 - Jul 1998)

GORs 2 (Aug 1998 - Dec 1998)

GORs 3 (Jan 1999 - Mar 2011)

NUTS 1 (Post Mar

2011)

North Name change to North East. No longer includes Cumbria

- - North East

North West Addition of Cumbria but no longer includes Merseyside

Addition of Merseyside

- North West

- Creation of Merseyside Abolished - -

Yorkshire and Humberside

Name changed to Yorkshire and The Humber

- - Yorkshire and The Humber

East Anglia Name change to Eastern. Addition of Essex and Bedfordshire and Hertfordshire

- Name change to East of England

East of England

- Creation of London - - London

South East No longer includes London, Essex and Bedfordshire and Hertfordshire

- - South East

Tenure Council Housing Association Private Outright Mortgage Regional National

Median 268 144 227 316 804 316 1,798

Mean 278 428 242 663 799 428 3203

Min 75 32* 63 241 216 32 348

Max 701 386 470 1,128 1,686 1,686 8,905

1st Percentile 90 46 78 254 279 63 369

5th Percentile 120 64 93 298 390 92 514

Region NE NW YH EM WM EE LO SE SW

Median 1,104 2,810 2,015 1,754 2,034 2,215 2,554 3,219 2,050

Min 767** 2,113 1,513 1,339 1,503 1,659 1,616 2,289 1,434

minimum income standard (MIS), the difference between the MIS and actual real net income was calculated for the years 2004 and 2012, at 2012 prices. This calculation is given by Equation D1. Note that NIirt and HCirt are measured at the lower quartiles, and are calculated in an almost identical way to RRIirt in Section 4.1. πt refers to CPIH (discussed in Appendix J). The differences are computed for a prototypical household type (containing two earning adults working 38.5 and 17 hours per week, with two children, aged four and ten years old). The 2012 results are given in Table D2 and are represented by Figure D3.

(D1)

Table D2: Difference between Actual Lower Quartile Net Income and MIS for a Prototypical Household, by Region and Tenure, 2012

Region and tenure combinations which have a lower quartile net income below Stone’s MIS are shown by the red cells in Table D2. The underlined cells indicate a real decline in the lower quartile net income relative to the MIS, during 2004-2012. Note that each cell contains a variety of household compositions, so precise inference by household type cannot be made without inspecting all household compositions. For example, albeit an extreme assumption, it could be that all lower quartile net income households are single occupiers, and thus, need less income than the prototypical household, resulting in fewer and less negative cells.

! /!21 28

Differenceirt = π tNIirtactual real net income!"# − π 2004MIS2004 +π tHCirt

MIS real net income! "### $###

⎛

⎝⎜

⎞

⎠⎟

where: i = tenure r = region t = time period (2004 or 2012)NI = net income HC = housing cost MIS = min income standardπ = real adjustments π 2004 = 1.252 and π 2012 = 1( )

-60

0

60

120

180

240

300

Council HA Private Outright Mortgage

Diff

erne

ce (£

/wee

k)

Tenure Type

Figure D3: Difference between Actual Lower Quartile Net Income and the MIS

NE

NW

YH

EM

WM

EE

LO

SE

SW

Region NE NW YH EM WM EE LO SE SW

Council £12 -£26 £4 -£6 -£25 £0 -£16 -£14 -£7

HA -£11 -£12 -£50 -£11 -£10 -£8 -£19 -£10 -£8

Private £14 -£1 £36 -£4 £10 £46 £10 £58 £46

Mortgage £85 £85 £77 £75 £67 £103 £86 £95 £93

Outright £209 £232 £211 £244 £252 £275 £281 £278 £266

The only inference which should be drawn from Table D2 and Figure D3 is that the prototypical household could not have an acceptable standard of living with a lower quartile net income in certain region and tenure combinations. This is true for all housing association renters, most council renters and private renters of the North West and East Midlands. One important consideration when observing the data is that the ‘market basket’, derived by Stone in 2006, could have significantly changed from 2004 to 2012. Thus, the above results should be used sparingly, giving an impression of the differences, rather than for precise inference.

8.5 Appendix E: Additional RRI Findings

The volatility of RRI varies by tenure. Measured at the lower quartile, council renter’s RRI volatility is not significantly different to housing association’s and outright owner’s volatilities (all with a coefficient of 0.14). However, it is significantly different to private renter’s (0.16 with p=0.028) and mortgage holder’s (0.077 with p=0.000) volatilities. Slightly higher RRI volatility amongst private renters is likely due to the higher rent setting flexibility of private landlords compared to centralised social housing planners. Mortgage holder’s lower volatility is likely explained by their relatively stable housing costs, primarily consisting of inflexible mortgage repayments.

Another observation from analysing the data is that the three rental groups have similar RRI, except in the East of England and South East, where private renter’s averaged £33.12 and £35.32 more respectively. This figure is measured relative to the mean lower quartile RRIs of council renters and housing association renters, over 1996-2012. The discrepancy is difficult to pinpoint. There could be increased heterogeneity between the rental groups in these two regions, such as household composition and employment type. Alternatively, relative to other regions, private rent increases could have been prevented by fiercer competition among private landlords, and/or an oversupply (or less undersupply) of social housing.

8.6 Appendix F: Variation of Mean Adults per Household

Considerably more variation is caused by regional differences (less within variation), rather than tenure differences (more within variation). Figures F1 and F2 plot the variable for the North East and council renters (which are representative of other regions and tenures). Identically scaled vertical axes are used to demonstrate the differing magnitude of variation. Table F3 provides the variable’s means and coefficients of variation.

! /!22 28

1.3

1.4

1.5

1.6

1.7

1.8

1.9

2

2.1

96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12

Adu

lts

Year

Figure F1: Mean Adults per NE Household

Council

HA

Private

Outright

Mortgage

Table F3: Mean and the Coefficient of Variation for Mean Adults per Household

8.7 Appendix G: Additional Population Variables

Figures G1 and G2 plot regional births and deaths per 1,000 residents respectively. They reveal a consistently declining death rate in all regions with a small drop in the birth rate during 1996-2001, returning back to the 1996 level by 2012. The graphs reveal that London has both a higher birth rate and lower death rate.

! /!23 28

1.3

1.4

1.5

1.6

1.7

1.8

1.9

2

2.1

96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12

Adu

lts

Year

Figure F2: Mean Adults per Council Renter Household

NE

NW

YH

EM

WM

EE

LO

SE

SW

9

10

11

12

13

14

15

16

17

96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12

Birt

hs p

er 1

,000

Res

iden

ts

Year

Figure G1: Regional Births per 1,000 Residents

NE

NW

YH

EM

WM

EE

LO

SE

SW

Tenure Council Housing Association Private Outright Mortgage

Mean 1.645 1.662 1.660 1.679 1.680C.o.F. 0.173 0.197 0.188 0.187 0.192Region NE NW YH EM WM EE LO SE SW

Mean 1.497 1.512 1.428 1.527 1.687 1.724 1.768 1.902 1.942C.o.F. 0.052 0.083 0.071 0.085 0.076 0.058 0.038 0.102 0.039

Figure G3 illustrates London’s substantially faster population growth, relative to other regions. However, the rate has been narrowing to the regional average; from 1996-2000, the rate was 9.04 times higher, falling to a factor of 5.26 during 2001-05, and again to 2.78, during 2006-12. Be that as it may, the narrowing is primarily due to the other region’s increasing growth rates (an average increase of ≈0.28 people per 1,000 residents pa.), rather than a decline in the London growth rate.

8.8 Appendix H: Additional Variables

All time constant variables or variables which have no variation by region are omitted from the FE estimation (as they are wiped out with the unobserved heterogeneity or cause perfectly collinearity (by entity) respectively). While these type of variables can’t have coefficient estimates, the model's other coefficient estimates remain unbiased, under the usual FE assumptions (Wooldridge, 2013). Examples of variables not varying by region are credit availability (or restrictions), real interest rate, government type and national policy. Interactions of these variables with other model variables could have been included, had there been a compelling reason to do this. Nevertheless, the impact of these variables can be discussed somewhat qualitatively.

Albeit somewhat intangible, credit availability can be estimated by means of a suitable proxy. The Bank of England publishes 681 different variations of net lending to individuals (Bank of England, 2015). The

! /!24 28

5

6

7

8

9

10

11

12

13

96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12

Dea

ths p

er 1

,000

Res

iden

ts

Year

Figure G2: Regional Deaths per 1,000 Residents

NE

NW

YH

EM

WM

EE

LO

SE

SW

-5

0

5

10

15

20

25

96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12

Popu

latio

n In

crea

se p

er 1

,000

Res

iden

ts

Year

Figure G3: Regional Population Increase per 1,000 Residents

NE

NW

YH

EM

WM

EE

LO

SE

SW

Regional average exc. London

measures vary dramatically, so selecting an appropriate measure requires careful consideration. For example, Figure H1 compares three commonly used measures; secured, unsecured and ‘consumer other’ versions of consumer net lending growth. Consumer other growth is approximately ten times higher than secured and unsecured growth (although highly correlated with unsecured at 0.918). While unsecured and secured growth are of similar magnitude, a decline in secured growth is somewhat associated with a increase in unsecured growth. As not all households have access to secured borrowing, a stock measure of unsecured lending to individuals seems like a good approach. Figure H2, provides such a measure, adjusted by inflation.

While the regional effects of credit availability are limited, tenure variation is not. No data exists that is separated by tenure, but it is possible to estimate differences using non-mortgage borrowing and household debt ratios with data from the ONS (2012d). Each tenure type has significant positive debt in informal loans and household arrears (mortgage debt for mortgage holders), which suggests on average, households have exhausted their formal lending options. This is because households would likely select formal lending as a first choice, for reasons such as accessibility, insurance and lower interest rates. Thus, ‘by tenure’ ratios of total formal lending can be used to estimate differing credit availability. By excluding mortgage debt and normalising council renters to 1 (which have access to £1,520 of formal lending), it can be found that the other tenure groups have higher credit availability by a factor of 1.43, 2.70, 3.68 and 4.28 for housing associations, private renters, mortgage holders and outright owners respectively.

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-8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 20

-0.8 -0.6 -0.4 -0.2

0 0.2 0.4 0.6 0.8

1 1.2 1.4 1.6 1.8

2

96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12

Other C

onsumer N

et Lending Grow

th (%)

Secu

red

and

Uns

ecur

ed G

row

th (%

)

Year

Figure H1: Monthly UK Net Credit Lending Growth to Individuals

Secured Unsecured Other consumer net lending

100

120

140

160

180

200

220

240

260

280

96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12

Rea

l Net

Len

ding

(£ b

illio

n)

Year

Figure H2: Total Real Unsecured Net Lending to Individuals, (2012 Prices)

Monthly

Annualised

Thus, changes in credit availability can impact different tenure groups disproportionally. The effect on RRI is hard to predict. An increase in credit availability can increase debt, decreasing net income (decreasing RRI), or increase RRI by causing a shift from renters to mortgage holders (assuming mortgage repayments are less than rents). There are of course many other effects at play as well. A similar variable intrinsically related to credit availability is the real interest rate. As the real interest rate is constant across tenure and region, most previous research adopts a credit availability (or restriction) variable. Although not identified in the literature, it may also be worthwhile to examine and test household debt by tenure and region in isolation.

The majority of the data analysed was during a Labour government (76.5%) with just one year of data under a Conservative government in 1996 and three years under the current Conservative-Liberal Democrat coalition. Thus, including government type isn’t appropriate for this study. Furthermore, annual time effects contain year to year national policy information, so a separate national policy variable can’t be included. A naive estimate for a particularly significant policy, is the t-statistic on the time dummy variable. However, this would include all changing information from that year (not explained by the variables of the model). Regional policy cannot be evaluated in FE estimation as the differences are cleared as part of the unobserved heterogeneity.

House prices and real GDP growth are omitted, as they are endogenous to the model’s parameters. House prices are also partially contained in RRI. Unfortunately, no appropriate instruments, measured by region, exist for the implementation of 2SLS estimation. Previous literature which utilises the ratio approach contains the house price variable within the dependent variable. The economists behind the Affordability model develop extremely sophisticated VAR models to include several endogenous variables. However, this type of approach is not suitable for this study because of relatively small number of time periods, invalidating VAR estimation.

Other studies have used both housing stock and houses completed pa.. However, they are extremely correlated so the houses completed variable was omitted to prevent multicollinearity. Furthermore, some researchers adopt houses started, but the lagged effect isn’t captured by a contemporaneous FE model. This was confirmed by a highly insignificant coefficient when including the variable in the model. However, the pattern of regional houses started does provide further evidence that London’s housing affordability problem is different to other regions. As illustrated by Figure H3, London has both a low build rate and small reaction to the 2007-2008 financial crisis, compared to the other regions (ONS, 2012e).

The mean number of bedrooms was a possible solution to control for differing homes sizes across regions and tenures. However, the volatility of the variable was in the opposite direction to the mean number of adults variable, with almost all variation between tenures, rather than regions. Thus, by excluding tenure effects, it was also removed from the model. These differences in variation across region and tenure are shown similarly as the mean adults variable, by plotting identically scaled vertical axes, given by Figures H4 and H5.

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2

3

4

5

6

7

8

9

10

11

96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12

Hou

ses S

tarte

d pe

r 1,0

00 S

tock

Year

Figure H3: Regional Houses Started per 1,000 Housing Stock

NE

NW

YH

EM

WM

EE

LO

SE

SW

Two variables commonly found in the literature are the rental rate and number of households. Both of these variables were omitted from the model because of equivalence. The homeownership rate equals one mins the rental rate, and the combined population variables are extremely correlated to the number of households (which was also missing the 2012 observation). A final variable worthy of consideration was planning permission. Unfortunately, the variable has only recently been recorded so is not available for application in the model. However, it is expected that an increase in granted planning permission would increase RRI indirectly, by exacerbating the increase in the housing stock.

8.9 Appendix I: RRI Composition

RRI is constructed from the addition of all the variables in Table I1 except for the subtraction of ‘Household - Total housing costs’ (UKdataservice.ac.uk, 2014)

Table I1

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1.6

1.8

2

2.2

2.4

2.6

2.8

3

3.2

96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12

Mea

n B

edro

oms

Year

Figure H4: Mean Bedrooms per NE Household

Council

Housing Assocation

Private

Outright

Mortgage

1.6

1.8

2

2.2

2.4

2.6

2.8

3

3.2

96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12

Mea

n B

edro

oms

Year

Figure H5: Mean Bedrooms per Council Renter Household

NE

NW

YH

EM

WM

EE

LO

SE

SW

Type of Income Variable Details

Adult - Net income from employment

Gross earnings are calculated from usual gross pay if it exists otherwise the last gross wage is used. Allowances such as for mileage, tax refunds and money from work accounts are deducted. Deductions for pensions/superannuations and union fees are added. Final adjustments are made for bonuses and deductions for SMP/SSP/SPP/SAP.

Table I1 continued

8.10 Appendix J: Data Mining Process

After merging a set of annual child, adult and household datasets into one dataset, many statistics were calculated. For example, a statistic was calculated for the median residual income for council renters in the North East in 1996. After generating all the required output in Stata, it was exported as raw data into an Excel spreadsheet. This process was repeated for all time periods, adjusting the Stata code for each FRS year to year variation (outlined in the do file). Once complete, all irrelevant information was removed from the spreadsheet, with relevant information ordered by macros into individual variables. In total, 12,138 statistics were ordered by region and tenure, and 2,142 statistics by region.

In addition to three residual income measures, the following series were also recorded; mean number of adults and bedrooms, housing costs and net income (both measured at the lower quartile and median), group observations and lower and upper confidence intervals for both lower quartile and median residual income.

Some variables then required further adjustment, such as scaling. As RRI takes logarithmic form in the estimated model, only adjustment for inflation (CPIH ) was necessary. CPIH includes an additional weight of ≈10% for housing costs (ONS, 2013e) which is integral to RRI’s calculation, containing housing cost by construction. CPIH is measured at 2005 prices, and was readjusted to 2012 prices.

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Type of Income Variable Details

Adult - Net income from self-employment

Based on profit or income.

Adult - Net investment income

Current accounts, NSB Ordinary or Investment accounts, savings or investments, government gilt edged stocks, unit/investment trusts, stocks or shares or bonds, PEPs, ISAs, member of share club, basic accounts and credit unions.

Adult - Retirement pension Plus IS/MIG/PC, pension credit, retirement pension, old person's pension, income support, DWP third party payments, IS/PC and social fund loan: repayment from IS/PC.

Adult - Pension income All other additional pension income.

Adult - Disability benefits DLAc, DLAm, war disablement pension, severe disability allowance, attendance allowance and industrial injury disablement benefit.

Adult - Other benefits Child benefit, widow's pension/bereavement allowance, widowed mothers/widowed parents allowance, war widow's/widower's pension, invalid care allowance, jobseeker's allowance, incapacity benefit, DWP third party payments - JSA, maternity allowance, NI or state benefit, guardians allowance, Rcpt last 6 months: in-work credit, return to work credit, maternity grant from social fund, funeral grant from social fund, community care grant from social fund, child maintenance bonus/premium, lone parent benefit run-on/job grant, widow's payment, winter fuel payments, social fund loan: repayment from JSA and extended HB and/or CTB, pension credit, income support, DWP third party payments - IS/PC and social fund loan: repayment from IS/PC. Amounts also added for SAP,SMP,SPP,SSP and housing/council tax benefit.

Adult - Total tax credits Working tax credit and child tax credit.

Adult - Net remaining income

Income from sub-tenants, oddjobs, school milk, school meals, school breakfasts, healthy start scheme private benefits, new deal/GTA, student/school grants, royalties, allowances from friends, relatives or an organisation, and allowance’s from local authorities/SS for foster and adopted children minus the amount of tax paid on the rent received from property.

Child - Income from Employment

Income from spare time job and employment training.

Child - Remaining income Income from trust funds, education grants, EMA, bursary fund and Christmas bonus benefit.

Household - Total housing costs

Total amount spent on water and sewerage rates, rent, mortgage interest, household rent, structural insurance (adjusted for combined cases to be consistent with HBAI) and service charges.