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
Content
1. Context
2. Model and Data
3. Results
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
2003
2008

The supply of new stations increased over time after Plan
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
Old
before 2012)
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
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
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
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!
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