Does Public Investment Spur the Land Market?: Evidence from Transport Improvement in Beijing

1. Does Public Investment Spur the Land Market?: Evidence from Transport Improvement in Beijing Wen-jie Wu Department of Geography and Environment, London School of Economics June 14, 2012

2. Content 1. Context 2. Model and Data 3. Results 4. Conclusion

3. Context • Background 1: Land market reform since the 1990s • ---Price signal become effective (Cheshire, 2007) • ---Local public goods captialisation effect (Zheng and Kahn, 2008) • Background 2: Heavy public investment in new rail transit construction • ---Public infrastructures are fully controlled by the city central government • ---Beijing: GBP14 billion, during 2003-2012 • Motivation: How would land prices respond to changes in the parcel-station distances as a result of transport improvement

4. Contribution: 1 • Extending the literature on valuing transport improvement • (Ahlfeldt, 2011; Kahn, 2007; Gibbons and Machin, 2005; McMillen and McDonald, 2004) • ----the changing nature of geographical links between parcels and stations • ----the opening and planning effect of new stations • A land parcel is assigned to a treatment group if: • ---(1) it experienced a fall in parcel-station distance with the building of new lines • ---(2) the outcome parcel-station distance is now less than 2km • ---Note. Will try to use different distance bands to explore the robustness of the results

5. Contribution: 2 • ---Commercial & residential land prices • (Debrezion et al., 2011; Cheshire and Hilber, 2008; Cervero and Duncan, 2001) • ---Valuing rail access: • ---in terms of its structural characteristics (direct effects) • ---how these characteristics interact with local socio-demographics (indirect effects) • (Cheshire and Sheppard, 2004; Bowes and Ihlanfeldt, 2001)

6. Contribution: 3 • From a policy perspective: • ------to show complementary effects between public investment (rail transit construction) and private investment (land development)

7. New rail transit development • The supply of new stations increased over time after 2003 • ---2 lines were opened at 2003 • ---4 lines were opened at 2008 • ---7 lines were planned to open after 2009 (planned to be completed before 2012) 2003 Plan 2008 Old

8. Vacant Land Parcel Data • 1999-2009 vacant land parcel data: parcels’ location, price, size • ----Total 1490 commercial land parcels • ----Total 2640 residential land parcels • ----The land supply is exogenous with the public transport planning • To meet with transport improvement, land data are grouped into 3-periods • Period 1: 1999<=Year<2003 • Period 2: 2003<=Year<2008 • Period 3: Year>=2008

9. “Treatment” groups • To examine the opening and planning effect of new stations, 3 nested treatment groups are created: • Treatment 1i: station opening after 2003 (station>=2003) • A land parcel is assigned to the treatment 1i if: • (1) it experienced a fall in parcel-station distance with the building of new stations after 2003 • (2) the outcome parcel-station distance is now less than 0.5km, 1km, 2km, 4km respectively • Treatment 2i: station opening after 2008 (station>=2008) • Treatment 3i: station opening after 2009 (station>=2009)

10. Model • Treatmentj refers to a specific treatment group; • Periodt is a set of “policy-on” time dummy variables; • show different treatment effects (Treatmentj * Periodt); • Xilk is a matrix of land structural and localised characteristics • fis the local fixed effect

12. Results • Step 1: balancing tests of “treated” and “control” characteristics • Step 2: main results • Step 3: robustness checks

13. Balancing test of treated and control places • Aim: • ---test if treated places are markedly different from control places in terms of the observable demographic characteristics • Method: • A set of OLS regressions: • ---Dependent variable: the log of pre-treatment observable demographic characteristics • ---Independent variables: the treatment groups • ---Fixed effects are included

14. Balancing test of treated and control places Residential • The aim is to see if treated places would be markedly different from control places in terms of the observable demographic characteristics • A set of OLS regressions are run based on the following Y and X variables: • Dependent variable is the log of initial observable demographic characteristics • The main independent variables are the treatment groups • Fixed effects are included

15. Balancing test of treated and control places Commercial • The aim is to see if treated places would be markedly different from control places in terms of the observable demographic characteristics • A set of OLS regressions are run based on the following Y and X variables: • Dependent variable is the log of initial observable demographic characteristics • The main independent variables are the treatment groups • Fixed effects are included

16. Main results • Implicit assumptions: • ------The measured new rail transit’s effect happened only when parcel-station distance changes result from the transport improvement • ---NOT from the mortgage risk; land supply constraints; economic climate changes • ------Land parcels located more than 4 km away from a new station might also benefit from the opening and planning of a new station • ---4 km is sufficient for defining the impact of rail access at station areas---not at remote places

17. Overview treatment effects’ estimates

18. Overview treatment effects’ estimates

19. Robustness Checks • 3 sensitivity tests • ----spatial selections in the parcel sample: central city VS suburb • ----spillover effect within and across treatment groups • ----interactions between treatment effect variables and local contextual factors • Headline findings: • Treatment effects (opening and planning effects) are quite robust • No significant spillover effects • Using the sample mean effect would over/under-estimate the amenity benefits

20. Limitations • Data limits the analysis to price changes happened within 3 years: • ---Underestimate the whole effect of rail access when the price adjustment is long before or after the opening of new lines • ---Overestimate the benefits if negative externalities at station areas evolve with the improved rail access • See McDonald and Osuji (1995), McMillen and McDonald (2004) for a detailed discussion

21. Conclusion • A short answer: • ----Public investment did spur the spatially targeted land market • An elaborate answer: • ----Positively significant: the opening and planning effect of new stations • ----The results vary with distance band selections and treatment scenarios

22. Thank You!

23. Robustness Checks

24. Robustness Checks: spillover effects • Questions to ask?: • Within-group spillover effects:----whether parcels in the subsequent treatment group affect the rail access effect on parcels in the prior treatment group • Cross-group spillover effects:----whether the new rail transit’s effect on residential land parcels is affected by adjacent commercial land parcels • Methods: Interaction the “distance” with treatment effect variables (Irwin and Bockstael, 2001) • Answers are yes: • Treatment effects (opening and planning effects of new stations) are robust

25. Robustness Check: interaction effects • Aim: to test the relationships between socio-demographics and rail access effect • Interactions: • treatment effect * educational attainment: • ----price premiums are greater for being a station at high education attainment place • treatment effect * employment accessibility (gravity model, see McMillen, 2001) • ----price premiums are greater in higher employment accessibility areas • treatment effect * crime rates: • ----price premiums are not significantly influenced by crime rates