© University of Reading 2006. ukSeptember 19, 2015 The CLG Housing Affordability Model: Recent...

April 19, 2023 © University of Reading 2006 www.reading.ac.uk

The CLG Housing Affordability Model: Recent FindingsGeoffrey Meen

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Developments over the last year (originally constructed as part of government’s response to Barker 1)

(i) Tenure choice:

• Can we move towards higher levels of ownership?

(ii) Modelling demolitions and vacancies:

• Do increased levels of construction mean higher demolitions?

• Do they imply increases in vacant homes?

• Do they mean concreting over the Greenbelt?

• Do they mean hastening urban decline?

(iii) Adopting an additional “target” in terms of affordability fundamentally changes the nature of planning for housing.


Change of Approach/Perspective

• Many of us come to housing from a social perspective – meeting need (housing as a merit good).

• Adding affordability changes the emphasis – housing is dominated by the market and, given likely market outcomes, we need to treat the externalities that arise, i.e. the market will not guarantee decent homes for all. Extra “target” but no extra “instrument”?

• Therefore, the starting point is the market

outcome.


The Traditional Approach to Planning for Housing

• Based on detailed population projections and household representative rates (HRRs) by age, gender, marital status.

• HRRs are trend based. Implicitly, they assume the historical trend for affordability continues. This is not the same as housing need.

• However, the aim of affordability targets is to change the historical trend and, therefore, a trend based approach is not appropriate.

• Official projections do not capture the effects of improving or worsening affordability on the rate of household formation.

• Improvements to affordability through extra construction generate extra households (although not proportionately). Worsening affordability (cf. NHPAU projections) implies household formation will be lower than official figures.


2004-based Household Projections

• Only 20,000 out of 150,000 annual increase in one person households are under age of 35.

2004-Based Regional Household Projections All households 2004 2029 Change 2004-29 % change

England 21,062,000 26,497,000 5,435,000 25.80North East 1,095,000 1,275,000 180,000 16.44North West 2,895,000 3,507,000 612,000 21.14Yorks & Humber 2,122,000 2,703,000 581,000 27.38E Midlands 1,799,000 2,296,000 497,000 27.63W Midlands 2,206,000 2,657,000 451,000 20.44East 2,304,000 2,954,000 650,000 28.21London 3,112,000 4,078,000 966,000 31.04South East 3,368,000 4,211,000 843,000 25.03South West 2,160,000 2,817,000 657,000 30.42


Other Problems with the Traditional Approach (1)

• Revisions to Regional Spatial Strategies are slow. In recent years revisions to household projections are upwards, but changes to Spatial Strategies lag behind increases market pressures.

• The approach deals only in numbers of units – a house is a house – and does not distinguish between the quality of the different units or the quantity of housing services each dwelling contains. But the price effects differ.

• This implies that matching the number of units to the number of households is not sufficient to stabilise affordability. Typically, the quantity of housing services must rise faster than households in order to take into account the increased demands of existing households.


Housing Supply under RPG and draft RSS

• Chasing a moving target?

Table 4. Plans for Annual Net Additions to the Housing Stock (Nos) Finalised RPG

Plans Draft RSS Plans

North East 5,729 6,580 North West 12,790 22,844 Yorks & Humber 14,765 19,266 E Midlands 13,700 20,418 W Midlands 15,156 n.a. East 20,850 25,399 London 19,048 27,596 South East 28,050 28,900 South West 20,200 22,978


Other Problems with the Traditional Approach (2)

• The accuracy is questionable. Although household revisions are upwards, 1996-based projections for the last 10 years over-estimated the outturn – related to worsening affordability?

Vintages of Official Household Projections (000s) England 2004 based 2003 based** 1996 based* 2004 based-1996 based1996 19,727 19,727 20186 -4591997 19,816 19,816 20371 -5551998 19,924 19,924 20555 -6311999 20,052 20,052 20740 -6882000 20,222 20,222 20865 -6432001 20,523 20523 20990 -4672002 20,720 20714 21138 -4182003 20,904 20904 21286 -3822004 21,062 21098 21434 -372

2006 21,519 21485 21730 -2112011 22,646 22566 22520 1262016 23,837 23705 23310 5272021 24,973 24781 24000 9732026 25,975 257132029 26,497

* 1997, 1998, 2000, 2002-4 are interpolations

** 2002, 2004 are interpolations


Net Additions versus Gross New Completions

• RPGs are based on net additions, but the table below shows that net additions are a combination of new building, net conversions, net changes in use and demolitions. Hence an increase in net additions does not necessarily imply an increase in new construction (on Greenfields).

• One of the innovations of the new model is to determine conversions, change in use, demolitions with only gross completions as exogenous.

The Composition of Net Additions (England, Numbers) 2001/02 2002/03 2003/04 2004/05 2005/06

England

Housebuilding completions 129992 137977 143958 155893 163398Conversions (net) 3040 4009 5053 5624 7296Change of use (net) 15297 17829 16442 15631 19048Non-permanent dwelling additions (net) 6 490 623 0 0Demolitions 26256 23200 20155 20091.5 20424

Net additions 122079 137105 145921 157057 169318


Matching “Units”

• The following must hold at all points in time. But it does not necessarily imply that the increase in dwellings has to match household growth under a market system.

• It has been argued that vacancies, second homes, demolitions are historically small and are “fixed”. But in a market system, vacancies and demolitions may be higher. All items below have to be modelled.

DEMVACSECHSHH HH = number of households HS = number of housing units SEC = second homes VAC = vacancies CONV = conversions DEM = demolitions


An Alternative Approach

• This models:

(i) Household formation(ii) Tenure(iii) Interregional migration(iv) Demand for housing services(v) House prices(vi) Rents(vii) Vacancies, demolitions, second homes, conversions,

changes in use. (conversions/changes in use are simple equations in the model and are not discussed here.

Most of these change with variations in affordability. Therefore increases in net additions to the stock affect all the above.


An Alternative Approach

Population (t-1)+ (Births-Deaths)+ International Migration

Populationof type (i)

Inter-regionalMigration

Householdsof type (j)

Prob (individual of type (i) forms household type (j) )

Number of owning householdsNumber of private rentersNumber of social renters

Prob (household of type (j) is in each of the 3 tenures)

Demand for housing services by owners

Supply of owner-occupier housing services

House prices

AFFORDABILITY

Earnings

Rents

Vacancies, demolitions, second homes


The Demand for Housing Services

The key factor is:

(i) The income elasticity (responsiveness) of housing demand relative to the price elasticity (responsiveness) of demand.

(ii) If the former is greater than the latter, then as incomes grow over time, affordability will worsen unless the supply of housing services grow faster than the number of households (or interest rates rise).

(iii) In the model, the former is approx. 1.0 and the latter -0.5.

(iv) This is why NHPAU finds that house prices might be 10 times incomes in 2026. Current housing plans do not rise faster than 2004-based household projections.


Household Formation & Affordability

If, under current RSS, affordability is expected to worsen, then household formation is likely to be below official projections. Or, at least, a higher percentage of these households will require support in the social sector.

Probabilities of Household Formation (London, 2001)

Previously not separate household Probability (%) Female, 25-29, single, no children, income quartile 4

27.6

Male, 20-24, single, no children, income quartile 2

9.7


24.3

Male, 30-34, partner, children, income quartile 4

60.8


50.2

Previously separate household Female, 25-29, single, no children, income quartile 4

97.2


96.5


99.8


99.6


Ownership & Affordability (1)

• The percentage of households who are owners depend on (i) ownership housing costs, (ii) rents, (iii) ability to acquire the deposit, (iv) incomes.

• Broadly, household formation depends primarily on demographics, but tenure on economic factors.

• The model assumes that over the long run, rents rise in line with ownership costs. Otherwise there would be major (implausible) tenure shifts.


Ownership & Affordability (2)

Ownership Probabilities for Previous Renters and Previous Owners (South East, 2003) Female Head, Aged 30-34, Single, No Children Previous Owner Previous Renter Income Quartile 2 0.936 0.023 Income Quartile 4 0.961 0.040

Male Head, Aged 35-39, Partner, With Children Previous Owner Previous Renter Income Quartile 2 0.982 0.078 Income Quartile 4 0.991 0.120

Social Renting Probabilities for Previous Social Renters and Non-Previous Social Renters (South East, 2003) Female Head, Aged 30-34, Single, No Children Previous Social Renter Not Previous Social Renter Income Quartile 1 0.899 0.167 Income Quartile 4 0.473 0.010

Male Head, Aged 35-39, Partner, With Children Previous Social Renter Not Previous Social Renter Income Quartile 1 0.980 0.428 Income Quartile 4 0.763 0.064


Household Flows: Official & “Market” Projections(constant real housing costs)

Analysis of household Flows: Official Projections and Market Outcomes (000s) (constant real housing costs)

2004-2029

2004-2029

2004-2029

Official Market Difference GL 966 854 -112 SE 843 827 -16 E 650 631 -19 SW 657 606 -51 EM 497 470 -27 WM 451 423 -28 YH 581 579 -2 NW 612 532 -80 NE 180 173 -7 England 5437 5095 -342


Household Flows: Official & “Market” Projections(market clearing housing costs, rents rise with ownership costs)

The Stock of Households in 2029: Official Projections and Market Outcomes (000s) (market clearing housing costs)

Official Market Clearing Difference

NE 1275 1221 54 NW 3507 3298 209 YH 2703 2562 141 EM 2296 2224 72 WM 2657 2564 93 E 2954 2544 410 GL 4078 3102 976 SE 4211 3757 454 SW 2817 2419 398 England 26497 22470 4027


Achieving 75% Ownership

• Even with “constant real costs”, ownership is hard to increase substantially by 2015. Key problem remains inability to meet deposit requirements as house prices rise. Meeting 75% depends heavily on London, where the rate is currently low. But price problems are greatest there. “worsening affordability case” assumes official household projections are met, but those who cannot be housed in the market sector are housed in the subsidised social sector. There is little

change in the ownership share by 2015.

2001 2004 2015

England Constant Real Costs

Owners 70.5 70.0 71.9

Social Renters 19.9 19.9 18.7

Private Renters 9.7 9.7 11.3 Worsening Affordability

Owners 70.5 70.0 70.4

Social Renters 19.9 18.7 17.5

Private Renters 9.7 11.3 12.1


Why are housing demolitions so low?

• Diversity of property rights

• Land use controls and building regulations (limit filtering of the stock)

• Externalities – historical importance of buildings

• Housing shortages, which raise house prices and extend the average property life (obsolescence condition).

Overall, if we have a system that is more market orientated, we might expect demolitions to move a little closer to the commercial property sector (although not completely) and the average property life to fall . But historical data and experience will be a poor guide to the future.


Which properties are most likely to be demolished?

Properties do not depreciate linearly – hedonic analysis suggests the relationship is U-shaped. Surviving Victorian properties in many parts of the country are valued more highly than properties built in the post war period. This implies that if demolitions are to take place, they should be properties built between 1950 and 1970, not necessarily Victorian properties in urban areas. Decisions on demolitions need to look at the market.

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

pre1

919

age1

939

age4

060

age6

180

agep

ost8

0

newdw

el

Eastern

EM

London

NE

NW

SE

SW

WM

YH

Figure 3. The Relationship Between House Prices and Age


Higher Vacancies in the Future?

(i) Historically, vacancies may have been low because of shortages (opportunity cost of holding properties vacant is high).

(ii) A well-functioning housing market will always require a certain level of vacancies (for search). Therefore, vacancies are not necessarily “bad”.

(iii) We need to know what vacancies should be in equilibrium i.e. the rate towards which the housing system should converge.


Long-Term Vacancies: Influences (1)

Model distinguishes between LT (greater than 6 months) and ST vacancies.

• The size of the housing stock

• The level of local deprivation

• The difference between the number of households and the housing stock

Perhaps the novelty is the inclusion of the Index of Multiple Deprivation . The idea is that areas of high deprivation typically have higher vacancies (low demand areas) than LADs with low deprivation.

The following table shows the equilibrium (LT) vacancy rates. Note that housing shortages (London?) would reduce the values in the table.


Long-Term Vacancies: Influences (2)

• LT vacancies are approximately 50% of all vacancies.

• Therefore, if ST vacancies have a similar equilibrium, we would expect total vacancies to be around 3%-4% of the private housing stock and the model as a whole should converge to these values.Table 7. Estimated Equilibrium Regional Vacancy Rates

Region Estimated Vacancy Rate (%) North East 2.02 Yorks & Humber 1.77 E Midlands 1.37 East 1.17 London 1.78 South East 1.10 South West 1.25 W Midlands 1.62 North West 1.75


Second (holiday) homes (1)

• For this part of the model, we are not interested in second homes, which are held for investment purposes and are let out as private rentals – they do not “remove” properties from the market. But holiday homes do and are modelled explicitly.

• We model the probability that a household, who already owns a first home will also own a holiday home.

• SEH data are used. The probabilities depend on income, age, gender, marital status and location of second home.

• According to model estimates, well over 90% of holiday homes are owned by the over 40s.


Second (holiday) homes (2)

• Unsurprisingly, ownership of holiday homes is quite sensitive to incomes.

• London households are most likely to have a holiday home.

• NE households are the least likely.

Probabilities of Owning a Holiday Home (2002) Household Type Probability (%) London, male, children, married, income quartile 4, age 45

5.5

North East, male, children, married, income quartile 4, age 45

0.7

South East, male, children, married, income quartile 2, age 45

1.1

South East, male, children, married, income quartile 2, age 25

0.0

South East, female, no children, single, income quartile 4, age 25

0.3

South West, female, no children, single, income quartile 4, age 35

1.3


Model Properties: Increasing Housing Construction (1)

• In the model, the main policy instrument is now gross housing starts, rather than net additions to the stock. Demolitions, conversions etc. are determined within the model.

• Suppose we increase housing starts (relative to the base) by 100,000 units spread across the regions according to current RPG shares (and scaled for social housing). The increase is probably ridiculously high, but illustrates some points.

• A crucial finding arises:

In previous versions of the model, a one unit increase in starts implicitly led to a one unit increase in net additions. This now depends on the level of vacancies. For regions where vacancies are below equilibrium, the relationship is closer to one-to-one than in regions where vacancies are in equilibrium. In the latter, demolitions are proportionately higher.



• The table shows the distribution of the increase in housing and vacancy rates in the Base.

• Note Vacancies are negative in London. This appears to be an implication of current plans. Also vacancies in most regions are below equilibrium.

Housing Starts and Vacancies Additional

Gross Starts (000s)

Private Vacancy Rate in Base (%)

London 17.4 -4.33 South East 17.7 1.42 East 13.1 1.20 South West 12.7 2.62 E Midlands 8.6 1.73 W Midlands 9.5 3.71 Yorkshire & Humberside 9.3 0.37 North West 8.1 1.60 North East 3.6 2.39 England 100



• Tables concentrate on changes in affordability, net additions, vacancies, household formation in the market sector, and the private housing stock.

Key Variables in the Base and Simulation (2016) Base Simulation Difference Afford Net

Additions (000s)

Private Housing Stock (000s)

Afford Net Additions (000s)


Afford Net Additions (000s)


Net Additions /Gross Starts

London 10.39 29.49 2624 9.04 44.36 2768 -1.35 14.87 144 0.855 South East 10.13 32.64 3325 9.13 41.60 3420 -1.00 8.96 95 0.506 East 8.97 27.70 2258 7.79 31.76 2347 -1.18 4.06 89 0.310 South West 10.04 26.04 2197 9.19 31.54 2247 -0.85 5.50 50 0.433 E Midlands 6.86 19.58 1737 6.21 22.85 1777 -0.65 3.27 40 0.380 W Midlands

7.22 22.02 2019 6.60 21.47 2030 -0.62 -0.55 11 0

Yorkshire & Humberside

6.11 22.15 1970 5.59 28.06 2033 -0.52 5.91 63 0.635

North West 6.23 23.05 2620 5.69 30.48 2669 -0.54 7.43 49 0.917 North East 5.85 7.34 921 5.32 9.79 937 -0.53 2.45 16 0.680 England 8.01 7.20 -0.81 51.9



• Net additions are approximately half the increase in gross starts – the difference is mainly due to demolitions. But the increase is larger in London where vacancies were well below equilibrium in the Base.

• In the simulation, the vacancy rates are much closer to equilibrium.

• Affordability improves by 0.8 percentage points similar to earlier versions, but are not comparable, because of changes in quality (not discussed here).

Key Variables in the Base and Simulation (2016) Base Simulation Difference Private

Vacancy Rate

“Market” Households

Private Vacancy Rate


Private Vacancy Rate


London -4.33 3268 3.77 3368 8.10 100 South East 1.42 3747 4.84 3777 3.64 30 East 1.20 2543 5.86 2555 4.66 12 South West 2.62 2354 5.31 2364 2.69 10 E Midlands 1.73 2102 4.49 2109 2.76 7 W Midlands

3.71 2513 4.37 2520 0.66 7

Yorkshire & Humberside

0.37 2409 4.17 2416 3.80 7

North West 1.60 3190 3.86 3200 2.26 10 North East 2.39 1195 4.47 1199 2.08 4



• The table shows the induced increase in market households arising from the increase in the private housing stock. As before it is only about a third with a bigger effect in London.

• Therefore, it is still the case that an increase in construction is not matched by household formation.

Change in Households Relative to Private Housing (2016) Difference Change in “market”

households/ Change in private housing stock

London 0.69 South East 0.32 East 0.13 South West 0.20 E Midlands 0.18 W Midlands 0.64 Yorkshire & Humberside 0.11 North West 0.20 North East 0.25 England 0.34


Conclusions (1)

One of the big questions that arose in previous model versions was, “who will live in the extra homes?”:

(i) It remains the case that only about a third are taken up in the market sector by new household formation. Existing households take up rest, moving upmarket as affordability improves. Implies that filtering occurs and demolitions if there are excess properties.

(ii) But, for most regions, vacancies are likely to be below equilibrium on current housing projections (notably in London) if official household projections are to be achieved. Therefore, much of the increased construction (or rather filtered properties) restores vacancies to equilibrium. Unless additional construction is large, increases in demolitions are likely to be modest.

(iii) If only market demand (rather than official household projections) is to be met, then demolitions could be noticeably larger, leading to improvements in the quality of the housing stock.

(iv) But improving affordability requires the stock of housing services to rise faster than the number of households.


Conclusions (2)

(v) The quantity of housing services rather than the number of units is the important concept for affordability. We have to take into account the increasing demand for housing services by existing households as incomes rise.

(vi) Housing services can be increased by conversions, renovations, changes in use, demolitions as well as by new building. Therefore, it does not necessarily imply that affordability targets require more new building on Greenfield sites. This depends on where households wish to live (and needs sub-regional analysis).

(vii) The results do not imply the destruction of urban centres through demolitions. If demolitions are to occur (and these are likely to continue to be small), they are more likely to be in areas dominated by properties built between 1950-1970.

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