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

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

ContextContext 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 Motivation: How would land prices respond to changes in the parcel-station distances as a result of transport improvement

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

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)

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

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

Vacant Land Parcel Data 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

“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)

ModelModel Treatment j refers to a specific treatment group; Period t is a set of “policy-on” time dummy variables; show different treatment effects (Treatment j * Period t ); X ilk is a matrix of land structural and localised characteristics f is the local fixed effect

ResultsResults Step 1: balancing tests of “treated” and “control” characteristics Step 2: main results Step 3: robustness checks

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

Balancing test of treated and control places 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 Residential

Balancing test of treated and control places 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 Commercial

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

Overview treatment effects’ estimates

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

LimitationsLimitations 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

ConclusionConclusion 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

Thank You!

Robustness Checks

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

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