1. School of the Built Environment Economic and housing market influences on household formation: a review. Prof Glen Bramley (Heriot-Watt University, Edinburgh, UK Contact: g.bramley@hw.ac.uk; +44 (0)131 451 4605)g.bramley@hw.ac.uk 16 December 2013 BSPS Seminar: Household Formation

2. School of the Built Environment Background Migration & household formation are central to sub-national demographic forecasts and important for physical & service planning and especially for housing Traditional approach reliant on extrapolative projections remains popular There has been an economic critique of this, arguing that labour and housing markets influence these trends Speculate about reasons for reluctance to incorporate these in projections – unfamiliarity with econometrics – predicting the predictors – ‘need’ vs demand – taint of uncertainty Problems which can result – ‘circularity’ and underprovision - out of phase with cycles - persistent discrepancies households vs dwellings - lack of realism about adjustment mechanisms in market – inappropriate planning between related geographical areas

3. School of the Built Environment Objectives Review literature on economic influences on household formation Highlight particular findings from this literature Consider economic-based forecasts for (domestic) migration and household formation at sub-regional scale Introduce one such modelling framework Demonstrate application of this model in England with particular reference to relationships between supply and household growth Comment on recent household numbers and projections in the light of this Suggest some ways forward

4. School of the Built Environment Earlier Review Bramley, Munro & Lancaster (1997) reviewed economic influences on household formation for DOE Drew on range of earlier studies, esp US work Confirmed importance of demographic fundamentals (age, sex, mar/ptnr status) & demographic events Main arena for econ infl is younger non-family adults, altho’ marriage/partnership & fertility may be affected also Income elasticities ranged 0.05-0.40, but much higher for young non- family (0.3-1.8) Relatively inelastic with housing costs (-0.01 to -0.28); income & price offsetting Some evidence that social housing (rationed) has direct supply effect Higher educn; skills; ethnicity & culture; benefits?

5. School of the Built Environment DAE/DTLR Model & other work c.2000 Peterson et al (DETR, 1999) used aggregate GHS time series data to model household formation as part of wider economic ‘need’ model, updated in DTLR (2002) Found income effect of 0.33 (higher due to use of consumption), also –ve influence of unemployment Sensitivities quoted in 1999 DETR Household Projections Other studies involving micro-modelling of household transitions included Ermisch, J. (1999) Prices, Parents and Young People's Household Formation. Journal of Urban Economics 45, 47-71 Clark, W.A.V. and Mulder, C.H. (2000) Leaving Home and Entering the Housing Market. Environment and Planning A 32, 1657-1671.

6. School of the Built Environment DCLG ‘Affordability’ Model This model drew on work by Andrew & Meen (2003), and included household formation function in ‘Affordability Model’ (ODPM 2005, Meen et al 2007, Meen 2011) Micro-simulation based on probit model fitted to BHPS data (part of wider tenure choice model) Also found demographic variables most important Incomes, unemployment & housing cost played modest role - however, housing cost only tested at regional level - model only applied to under-35s Similar approach subsequently adopted in Leishman et al (2008) Scottish Affordability Model Meen & Nygaard (2008) & Nygaard 2011 looked at effects of different international migrant flows

7. School of the Built Environment Further studies and EHN Bramley, Champion & Fisher (2006a) explore household transitions and relationship of migration and mobility with household formation, finding effects (often indirect, via mobility) of range of economic variables Bramley et al (2006b) modelled LA level aggregate headship x age in Scotland, finding effects of rental tenure, income, class, house prices Bramley et al (2010) Estimating Housing Need study for DCLG modelled new household transitions (for under/over 40s) using logit in BHPS micro data with housing & labour market variables attached at SAR district level Found effects from recent migrancy, tenure, qualifications, working, area unemployment, house price, income, & social lettings Incorporated in regional simulations of housing need outcomes (with linked inputs from CLG-Reading ‘Affordability’ model) - although additional direct feedback from vacancy rates was needed

8. School of the Built Environment EHN Supply Scenarios

9. School of the Built Environment Other Recent Literature Several studies claiming clear evidence of cyclical recession effects (from incomes and labour market) on household formation (Lee & Painter 2013, Dyrda et al (2012), Paciorek (2013) Some of these also point to effect of housing costs (Paciorek 2013) or sub-prime crisis Studies focused on longer term decline of owner occupation, suggesting real situation compounded by declining young headship (Rosenbaum 2013) Studies comparing ownership rates x ethnic group misleading for same reason (Yu & Haan 2011, Nygaard 2011, Yu & Myers 2010)

10. School of the Built Environment ‘Gloucestershire’ Model Model arose out of feasibility study into sub-regional housing market models undertaken for former NHPAU 2009-10 Operationalised in study for Gloucestershire County & Districts in 2011, used to inform SHMA A medium-term model geared for policy simulations with particular focus on new build, household growth, affordability, housing needs Geographical framework of 102 Housing Market Areas (HMAs) based on LA Districts developed in parallel NHPAU research (Jones, Coombes et al) Econometric functions for key variables based mainly on aggregate panel data, but some based on micro-models Other exogenous or intervening variables projected in simpler mechanistic fashion Simulation model implemented in Excel workbook Similar model subsequently developed for New Zealand

11. School of the Built Environment Main Behavioural Components of Model Real house prices (mix-adj) New private build completions (mix-adj) Migration gross flows x 4 age groups Household formation (headship) x 3 age groups [micro – BHPS] Household income (proxy-based prediction) & low income Social housing lettings Private rents Housing needs incidence [micro – EHS/S E H]

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14. Examples of Migration Equations

15. School of the Built Environment Influences on Migration Structural effects – in-migration -> out-migration; size of area/popn; adjacent out-migrn -> in-migrn Geographical effects - sparsity & counter-urbanisation Demographic effects – singles vs couples; younger (like attracts like); ethnic effects Socio-economic effects - employment -> mobility and moving towards opportunity by younger groups; students Income –ve? but poverty more -ve Tenure - social renting -> less in-migrn Housing market – relative house price -> -ve for in-migrn Housing supply – strong +ve effects on in- & net migrn; but –ve diversion effect of adjacent supply Environmental effects, esp climate +ve

16. School of the Built Environment Household Formation (HRRs) - Elasticities

17. School of the Built Environment Influences on headship Range of expected age & household type background effects; also migrant (+0.10), student (+0.27), ethnic (-0.014) for younger group Income elasticities 0.24 / 0.22 /0.17; also high SEG. House price -0.174 / / -0.046 [ in retrospect, should have also modelled age 25-34 separately ] Unemployment marginal -ve Tenure (previous): priv rent +0.17 / 0.02 / 0.02 : soc rent +0.07 / 0.03 /0.05 Social lettings supply +0.14 /+0.04 / -0.06 Vacancy rates – no consistent/significant effects (but necessary to impose some feedback in simulation)

18. School of the Built Environment Household Growth Rates Previous 2008-based projections envisaged higher growth, due to higher int migrn; ‘Reality’ of shortage of supply has led to much lower growth up to 2011. Gloucs Model tracked actuality reasonably. Looking forward, new interim projections envisage resumption of similar growth, but GAM predicts a lower likely outturn, due to recession and very low new build output in early years.

19. School of the Built Environment Regional Household Growth New projection and GAM show lower growth for Y&H, SW and EE than 2008 projn New projection, but not GAM, show somewhat lower growth for NE, NW, EM New projection shows similar for SE (& WM) but GAM shows signif lower New projection shows much higher growth for London, but GAM shows much lower! London figures strain credibility

20. School of the Built Environment Young Adult Headship Source: Fitzpatrick et al (2013) Homelessness Monitor 2013 CRISIS, based on Labour Force Survey Comment: London & SE rates have fallen significantly since early 1990s; EM & NE fell a bit later; all regions blipped up in 2010 but dropped back in 2012

21. School of the Built Environment Tenure and New Household Flows Overall new household formation slumped in 2008-09, recovered in 2010 Biggest drop in owner occupation – only partially recovered by 2011 Generally lower level of access to social renting as well Situation ‘saved’ by rise of private rented lettings (BTL)

22. School of the Built Environment Identity Relationship There is an identity relationship between households and dwellings (sometimes called the ‘Holmans identity’) In change form, this states that ΔHH ≡ ΔDWG - ΔVAC - ΔSEC + ΔXSHR [the change in households is identically equal to the change in dwellings (‘net additions’) minus the change in vacancies minus the change in second homes plus the change in ‘excess sharing households’] This helps to explain recent events in household numbers game If the supply of dwellings is dramatically reduced, and vacancies cannot go much lower, and second homes don’t change very much, and sharing is pretty rare, then… household growth will inevitably fall, mainly through mechanism of new household formation, mainly affecting younger adults (age related dissolutions unaffected) This shows that household growth will be strongly influenced by dwelling supply, particularly in a ‘tight’ situation - in a looser market you may see more change in vacancies and demolitions

23. School of the Built Environment Model Simulations of Response

24. School of the Built Environment Implications of Simulation As expected (on basis of past research, theory, and ‘identity’), household formation responds to new build supply Response is lagged, takes time to build up; still quite low at yr 5 (20-30%) After 8-10 years, response level is high, 80-100% After 18-20 yrs, response level fades somewhat (70-85%) Local variation in response rates, also depending on contextual/adjacent supply changes – London particularly high Local responses strongly affected by migration Building more social/affordable housing would have earlier positive impact on household growth, but more moderate later peak (Note that examples are mainly pressured South; England level responses slower initially)

25. School of the Built Environment Household Formation vs Migration

26. School of the Built Environment Comments on Household Formation Share At national level, virtually all of difference between scenarios in household growth is attributable to net household formation, and none to migration (given fixed international migration & balanced internal migrn) At local level, this share is quite variable, and it also varies over time e.g. Gloucs relatively low, West of England rel high In long run, local household growth responses mainly dominated by migration London responses strongly dominated by migration

27. School of the Built Environment Migration & Growth Scenarios Higher international immigration would...raise household growth, esp in London, worsen affordability (& need), but reduce household formation - hhd increase % only 0.26-0.34 of popn increase % (vs 0.45 in New Zealand) (ie. elasticity of hhd wrt popn) Higher economic growth (+0.5% pa) would raise household growth by 7-13,000 pa (2-6%), ignoring any induced extra international migration - in 2031 household numbers would be 0.6% higher (vs GVA 10.5% higher) - note offsetting effects of higher prices (similar to NZ model) Combination of these would raise hhd growth by 14-26,000 (8-11%) - 2031 hhd numbers 1.5% higher (vs. 2.2% more popn) - affordability would be 1.4% worse in 2016 but +0.3% by 2031 - household formation would be suppressed by 15-25,000 pa

28. School of the Built Environment Other Scenarios Ending credit rationing completely could greatly increase new build (45-65%), affordability (20-25%) and household growth (3%-46%) [but treatment of this factor in model is crude, with lack of pre-2007 experience to calibrate it] More Buy to Let activity would worsen affordability to buy (-1 to -7%) but net effect on household growth/formation slight Relaxing planning controls over size mix would lead to a moderate increase in housebuilding numbers (1-9,000 pa), associated with a general reduction in dwelling size, and a modest increase in household growth (3-4,000 pa), with stronger effect in London Very high London supply (doubling plan numbers) would raise output a lot (10-20,000 pa) and would increase household growth (4-20,000 pa, 26-72%), almost entirely thru’ migration (i.e. little extra net household formation); affordability would be a bit better (1.3-2.2%) - but even this would not match the 2013 Household Projections!!

29. School of the Built Environment So What (is to be done)? Demographers, planners & economists need to talk We need to talk to Boris about his figures Traditional household projections are necessary but not sufficient Recent turbulence has exposed weaknesses in process, exacerbated by austerity cuts in analytical capacity in government Planning policy guidance (2013) rightly emphasizes a range of measures of (in) adequacy of housing numbers to be presented through SHMA, including household projections, affordability, price trends, housing needs (incl concealed hhds) and employment growth ‘Planning’ in full sense requires longer forward look and comparisons of options with outcome performance measures Such a forward look will be more meaningful if it is based on models which take account of economic feedback effects

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