Regional differences in housing price dynamics: panel data evidence

1. Regional differences in housing price dynamics:panel data evidence European Real EstateSociety19th Annual Conference June 13-16, 2012, Edinburgh Elias Oikarinen & JanneEngblom

2. Background • Data on housing prices and fundamentals determining housing prices are typically complicated • Infrequent data • Relatively short sample periods • Panel data help to diminish the problems caused by the data availability • Conventionally used panel data models to study housing price dynamics do not allow for regional differences in the dynamics • The assumption of similar dynamics across regions may lead to suboptimal policy and investment decisions • The models should be more flexible?

3. Potential reasons forRegional differences in housing price dynamics • ”Land leverage” (supply restrictions) • Land value component is the volatile component of housing prices that reacts notably to demand changes (e.g. Davis and Heathcote, 2007; Bourassa et al., 2009) • Construction costs and supply restrictions • Higher construction costs and more inelastic supply are likely to cause stronger housing price momentum and slower adjustment of prices towards their ”sustainable” level (Capozza et al., 2004; Glaeser et al., 2008) • Informational factors • In larger and more densely populated areas, housing demand should more rapidly adjust to reflect new information (e.g. Clapp, 1995): shorter momentum and faster adjustment towards the fundamental price level

4. Which are more prone to bubbles, large densely populated cities or more sparsely populated smaller cities? • Depends on whether the dynamics are dominated by construction costs and supply restrictions or by informational factors: • Case 1: construction costs and supply restrictions dominate → stronger momentum and slower reversion in bigger cities • Case 2: cc and s dominate momentum, info factors reversion → stronger momentum but faster reversion in bigger cities • Case 3: cc and s dominate reversion, info factors momentum → weaker momentum but slower reversion in bigger cities • Case 4: informational factors dominate → weaker momentum and faster reversion in bigger cities • Also the long-run elasticities matter • Empirical evidence is needed to make conclusions

5. Previous literature • A large number of studies on housing price dynamics using panel data • Typically fixed-effects models in which only the intercept differs between the regions • Dynamics are likely to vary significantly across regions (e.g. Malpezzi, 1999; Capozza et al., 2004) • Malpezzi (1999, JHE) allows the adjustment speed towards the long-run price-income relationship to vary across US regions • Greater supply restrictions induce slower adjustment towards the fundamental level • Holly et al. (2010, JE) allow momentum and reversion dynamics to vary in the US regions (but not the long-run income elasticity)

6. Aim and contribution • Aims • To examine empirically the significance of regional differences in housing price dynamics • The long-run elasticity w.r.t. income and the short-run momentum and reversion dynamics are of particular interest • To test for the validity of the conventionally used fixed-effects models • Contributions • Regional variation in long-run elasticity w.r.t. income, in short-term momentum effect, and in the adjustment speed towards the long-run relation with income (to our knowledge, first paper combining all of these) • Comparison between the baseline model and the less restrictive models • More analysis on the relationship between the coefficients and the fundamentals and on the correlation structure between the coefficients in the future

7. Empirical methodology • Aim: a model that corresponds well to the actual city level housing price dynamics • Short-run momentum (persistence , ”bubble builder”) • Tendency to revert towards a fundamental price level in the long run (”bubble burster”) • Potential short-term effects of the fundamentals (given, e.g., by the life-cycle model) • The long-run ”fundamental” or ”sustainable” price level is determined by a cointegrating relation between housing prices and income pit* = bie yit • The coefficient on income is allowed to vary across regions

8. Empirical methodology cont’d Baseline model: conventional fixed-effects model (in an ECM form) that does not allow for differences in momentum and reversion speeds The baseline model is compared withmodels that allow for regional variation in the parameters (interaction models)

9. data • Annual data for fourteen Finnish cities for the period 1988-2009 • Hedonic housing price indices • Fundamentals: • income • construction costs • after-tax mortgage rate • Real variables in natural logs

10. Long-run relationship between housing prices and income Cointegrating relation in all the cities Income elasticity parameter varies between 1.7 (Helsinki) and 0.8 (much smaller cities) Strong positive correlation between city size (≈ land leverage) and the long-run income elasticity parameter

11. Evidence from panel models • The momentum effect and the reversion towards the long-run sustainable level dominate the dynamics • In general, regional variation in the short-term dynamics appears to be relatively small in terms of statistical significance • No significant differences in a panel of fourteen cities; the point estimates vary between .58 -. 88 (momentum) and (-.21) – (-.50) • Significantly slower reversion, -21%, in Helsinki than in the other cities, -35% (preferred model based on the adjusted R2) • Some evidence for weaker momentum in Helsinki, .58, than in the other cities, .71 • The findings suggest that • Supply restrictions are of greater importance w.r.t the adjustment towards the fundamental level • Informational factors play a greater role w.r.t. the momentum effect

12. Impulse responses indicate that it is not obvious whether Helsinki or the smaller cities are more prone to price overshots

13. Concluding remarks • The conventionally used fixed-effects models are likely to be too restrictive when studying housing price dynamics • Expectedly, the elasticity of housing prices w.r.t. income greatly varies across Finnish cities • We find evidence of slower adjustment towards the long-run sustainable level in Helsinki than in the smaller cities • Weaker evidence of smaller momentum effect in Helsinki • Different factors probably dominate the momentum dynamics than the reversion dynamics • The slower adjustment in Helsinki does not necessarily imply that the area is more prone to bubbles • The regional differences are likely to be more significant in geographically larger and culturally and economically less coherent countries

14. What next? • Some additional tests using panel data methods • Catering for the impacts of potential cross-sectional (spatial) dependence in the data • Comparison with the current models