School of the Built Environment Housing Mobility and Tenure Choice with varying constraints and rationing: a model for English regions built from micro household transition data Prof Glen Bramley (Heriot-Watt University, Edinburgh, UK Contact: +44 (0) ) with Prof Michael White (Nottingham Trent University) July ENHR Housing Economics Workshop

School of the Built Environment Overview of Paper Paper is about housing tenure choice and outcomes in England Reviews literature Describes general approach and data sources - micro estimation on BHPS transitions over 7 pairs of waves - macro regional simulation model built on S.E.H., LFS, etc. Findings on drivers of mobility, moves to buy and social rent Simulation model features Baseline and alternative scenarios Conclusions

School of the Built Environment Previous Literature Fundamentals of ‘choice’ to buy or rent Embedded within wider housing demand – also hhld formation, quantity/quality of housing services Affordability & income; permanent income; relative ‘user costs’ Transaction costs & mobility (length of stay) Econometric issues around identification Credit constraints – savings & wealth/age of purchase/ Demographics – age, family type, ethnicity Tax & inflation effects Subsidies e.g. Housing Allowances/Benefits Location – region, market area, labour market conditions - migration interactions

School of the Built Environment Our Approach While taking much from previous literature, we see some limitations, esp in British context Better to focus on flows – active decision making households - path dependence Hierarchical sequential approach (Form/move – Buy – Soc Rent) Mobility models generate flows & also affect ‘expected length of stay’ Household formation modelled on similar drivers Social rented lettings clearly rationed Market rents, prices, unemployment etc linked at subregional HMA level plus, in simulation stage, Recognition of physical limits to stock-> feedback Vacs->HHFm, PRS Recognition of (re-emergence of ) credit rationing post 2007

School of the Built Environment Hierarchical Choice Model

School of the Built Environment Mobility Rates

School of the Built Environment Household Formation Reviewed theory and past research, and highlighted trends from data since early 1990s Base period ‘profile’ of new households from S.E.H. & BHPS Modelled propensity to form new household using BHPS linked to local/subregional market variables Key demographic drivers include age (younger), migrancy, marriage, childbirth Key economic factors include prices (-0.27) incomes (0.31), unemp (-0.24) Social lettings supply (0.26) Smaller effects from prev tenure, ethnicity, qualifs, hsg type Model is consistent with previous research including Reading model but adds some extra elements

School of the Built Environment Mobility Models Logistic regression to predict 1-year moves by origin tenure Young are more mobile, esp in rental tenures Larger households less mobile, tho children & crowding may trigger moves Higher (current & perm) income increases mobility in private market; wealth mixed Unemployment mixed but mainly positive Rates in private renting 4-5x other tenures

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Mobility and Tenure Choice Logistic regression fitted to pooled BHPS data on 1-year transitions Movers Buying related to mobility (-, via user cost), age (young -), hhd size (-), children (+), working (+), students (-), unemployment (- ), income (+), wealth (+), house prices & int rates (-, via user cost), private rents (+ for PR), soc rents (mixed) Movers Social renting related to similar factors, but usually with reverse sign (except students; user cost omitted); migrants (-), young (-), sick/disabled (+); low income (+); crowding (+); supply of social lettings signif (+); priv rents (+ for PR); soc rents (?)

School of the Built Environment Forward Forecast Model now simulates system forward year-by-year from 2009 to 2021 Takes inputs from parallel run of Reading Affordability model Model is recursive – endogenous variables calculated sequentially with some use of lagged values Forecasts of household formation and tenure flows and stocks by household age-type and region Stock-household reconciliation, affecting vacancies, new household formation and concealed/sharing households Model to predict private rents – simple reduced form regression Social lettings rationing constraint, now formularised 9 categories of specific need incl homelessness forecast each period, based on models for each need

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Stock-Household Reconciliation Ex poste, system must satisfy identity relationship: Households=Stock-Vacancies-2 nd homes+Sharing Households-Shared Dwellings ‘Natural’ vacancy rate (3.5%) in private sector; if a region goes below this, adjustments are made to new household formation by (younger) singles in PRS (equiv to half the difference) With corresponding increase in multi-adult households, and also in concealed households and sharing (specific needs) Argue that concealed effects bigger than sharing under current conditions

School of the Built Environment Private Rents Equation In simulation model, vacancy relationship imposed, raising rents where vacancies below natural rate

School of the Built Environment Baseline Scenario Modified version of Reading model baseline Income growth curtailed in this period, leading to incomes nearly 10% lower than trend by 2014 New private build reduced sharply in , (95,000 comps in 2010) recovering to 150,000 by 2012 & 185,000 by 2015 New social output static at 17,000 pa. Altho prices and HPIR fall sharply in , we apply a ‘shadow price of credit rationing’ in model to represent ‘effective affordability’ (1.9 in 2009, tapering off to 1.10 by 2015). Populations are as per Sept 2009 Reading model, but household growth is endogenous

School of the Built Environment Baseline Forecast

School of the Built Environment Components of Change in Owner Occupation and User Cost

School of the Built Environment Comments on Baseline unusual for seeing decline in OO and large rise in PRS This will reverse, particularly up to 2016, but later growth of OO slows again as user cost rises Net growth quite sensitive to moderate changes in gross flows Some regional differences e.g. more persistent shift from OO to PR in poorer regions

School of the Built Environment Differing Supply Policy Scenarios

School of the Built Environment Differing Financial Contextual Scenarios

School of the Built Environment Backlog Need

School of the Built Environment Scenario Impacts Overall new build supply makes relatively little difference to tenure outcomes; somewhat counter-intuitive Priv renting mainly mirror image Main reason is effective quantity rationing in private sector, suppressing marginal household growth at expense of private rental sector Providing more subsidised housing does impact on tenure, e.g. more LCHO->more OO, but quite small impact even in medium term; more social renting would displace private renting. Contextual financial conditions, esp credit rationing, would have bigger effect – persistent CR would see OO stalled or falling Inequality – not big effect at level modelled Rents – effects shown may be too strong (?)

School of the Built Environment Concluding comments Findings of both stages of modelling support need take account of institutional & quantity rationing effects Nature and extent of recent changes go beyond conditions observed in base period ( ); hence estimated functions not sufficient to deal realistically with some of strains on system; need for additional feedback mechanisms in simulation General need to measure & model credit rationing & operation of private rented sector more effectively General supply policies have less impact on tenure than expected, although they do help to reduce need (gradually) Credit rationing has bigger effects on both tenure and need Maybe governments should be more concerned with need outcomes than with tenure per se