Does Public Investment Spur the Land Market?:

Evidence from Transport Improvement in Beijing

Wen-jie WuDepartment of Geography and Environment, London School of Economics

June 14, 2012

1. Context 1. Context

2. Model and Data 2. Model and Data

Content

3. Results 3. Results

4. Conclusion 4. Conclusion

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

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 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

Plan2008

Old

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

“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)

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

f is the local fixed effect

Results

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

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

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

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

Thank You!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