A novel hedonic price modelling approach for estimating the impact of transportation infrastructure on property prices
Новый подход к моделированию гедонических цен для оценки влияния транспортной инфраструктуры на цены недвижимости
2019-11-20
SCID: 54.1/sfa87m33
Discuss with AI
hedonic price modellingproperty pricesspatial autocorrelationspatial error modeltransportation infrastructure
Figures from the paper
Abstract (AI)
Hedonic estimations of the effect of transport infrastructure on property prices vary widely. This high variability demonstrates a deficit in our understanding of these relationships, limits the utility of econometrics for the valuation of urban property markets, and limits the development and implementation of effective and fair market-based policy tools. Several avenues may lead to improved consistency: re-consideration of accessibility, inclusion of urban design characteristics, assessment of spatial dependence and spatial heterogeneity, and consideration of geographic scale. This paper outlines the rationale and opportunities for inclusion of, and presents empirical tests for, these assertions using a case study in western Sydney, Australia. Results show a number of urban design characteristics to be significant determinants of residential property price. Street connectivity and higher density in areas surrounding residences negatively impact price, higher density close to train stations positively impacted price in one model. Park-and-ride stations led to decreases in property values. Smaller study area results indicate a nonlinear relationship between distance to train station and property price and a disamenity impact for residences within 400 m of train stations. Relative accessibility measured as frequency of peak hour trains is a significant and positive determinant of price in the larger study area. Incorporation of a price trend surface and estimation using a spatial error model reduce the extent to which spatial autocorrelation overstates the effect of a train station on prices. These conceptual and empirical improvements further develop our understanding of the effect of transport infrastructure on property values.
Key Findings
1
Greater street connectivity and surrounding-area density generally reduce property prices, while higher density near train stations increases prices in one model.
2
Including a price trend surface and spatial error model reduces spatial-autocorrelation bias in estimated train-station effects.
3
Park-and-ride train stations decrease nearby property values, and residences within 400 m of stations experience a train-station disamenity effect.
4
Relative accessibility, measured by peak-hour train frequency, positively and significantly affects property prices in the larger study area.
5
The relationship between distance to train stations and property prices is nonlinear in smaller study areas.
6
Urban design characteristics significantly influence residential property prices, with effects varying by spatial context and model specification.
Research Object
Residential properties and transportation infrastructure in western Sydney, Australia
Research Subject
The effects of transportation infrastructure, accessibility, urban design, spatial dependence, and geographic scale on residential property prices
Publication Details
Publication Date
2019-11-20
Journal
Publisher
ISSN
Cited by
77
Open access PDF
Access Type
Author Information
Download PDF
Subscribe to digest