Project Case
Hedonic Price Regression
An econometric model estimating how the Jin-Yi-Dong rail line affects Yiwu housing prices.
Role
Sample collection / Variable design / Double-log regression / Result interpretation
Stage
Data collection, model refinement, and urban-investment interpretation
Key Outcome
Built a hedonic pricing model using 140 housing samples and identified metro distance as a weak but meaningful price factor.
Strategy Snapshot
BUSINESS QUESTION
How much does rail access really affect property value?
The case tests a common investment assumption with regression evidence, separating transport accessibility from property quality, CBD distance, and school factors.
REPORT EVIDENCE
140 samples, 12 predictors, marginal metro signal
The double-log model suggests a negative relationship between metro distance and price, but the significance level requires careful interpretation rather than overclaiming.
STRATEGY SIGNAL
Use rail access as one signal inside a wider valuation frame
For investment or planning, metro proximity should be combined with school quality, CBD access, property features, and local market maturity.
Background
The project evaluates whether a new intercity rail line creates measurable housing-price premiums in Yiwu's urban area. It uses hedonic price modeling to isolate transport effects while controlling for property and neighborhood attributes.
Problem
Rail transit investment is often assumed to raise property values, but the effect may be uneven or limited in mature urban districts. The key task was to separate metro accessibility from other value drivers such as school quality, CBD distance, balcony, elevator, property type, and decoration.
Approach
I helped collect 140 housing samples across seven districts, defined 12 variables, cleaned outliers, tested multicollinearity with VIF, compared the initial model with a log-transformed regression, and interpreted significant and non-significant predictors.
Key Evidence
SAMPLE SIZE
140 observations
The dataset covered seven urban districts with 20 randomly selected residential samples per district.
VARIABLES
12 predictors
The model included property attributes, school quality, CBD distance, railway distance, and nearest metro-station distance.
METRO EFFECT
-7.03%
A 1% increase in distance to the nearest metro station was associated with a 7.03% decrease in price per square meter, with marginal significance.
MODEL CHECK
VIF tested
Variance Inflation Factor was used to check multicollinearity before interpreting regression results.
Regression Interpretation
The output separates direct property features from urban-accessibility effects.
Positive
Balcony / elevator / school quality
Attributes associated with higher price per square meter.
Negative
CBD and metro distance
Greater distance generally weakens property value.
Caution
Metro effect is marginal
The rail signal exists, but should not be overclaimed.
Decision Logic
The rail line has positive but limited urban price impact
The result suggests some value premium near metro stations, but the effect is not strong enough to treat rail access as the only investment signal.
Infrastructure value should be read with local context
The line may create broader regional integration benefits even if immediate urban property-price uplift is limited.
Outcome
The model found that balcony, elevator, school quality, and some location variables were meaningful price predictors. Metro distance showed a negative relationship with price per square meter, suggesting a positive but relatively limited rail-transit value effect in Yiwu's urban market.
Reflection
This case is useful because it shows how to avoid overclaiming: the model identifies a signal, explains its limits, and converts statistical output into a realistic urban-investment judgment.
Original Deliverable
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