Combined impacts of highways and light rail transit on residential property values: a spatial hedonic price model for Phoenix, Arizona

Seo, Kihwan; Golub, Aaron; Kuby, Michael

doi:10.1016/j.jtrangeo.2014.08.003

Cited by 128 publications

(106 citation statements)

References 24 publications

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“…Moreover, the housing prices in areas neighboring highways are significantly higher than those in areas near railway stations (H3). We conclude the key factors as transportation and accessibility (Seo et al, 2014) and confirm that for housing prices, the effect of door-to-door transport is greater than that of point-to-point transport. The housing prices in business districts are significantly higher than those in historic districts (H5),…”

Section: Conclusion and Discussionsupporting

confidence: 69%

“…Housing prices increase with high population growth. Compared with light rail stations, accessibility to highway access points has a stronger positive effect on housing prices (Seo et al, 2014) because generally, a location within a 2 km distance of a railway or light rail station results in higher housing prices, whereas a location within a 4-5 km distance of a highway access point (Debrezion et al, 2005) results in higher housing prices. The results of the present study show that housing prices are significantly higher in areas neighboring highways than in areas neighboring railway stations (t = 4.289, p = .0001);…”

Section: Research Datamentioning

confidence: 99%

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Housing Price Differences Under the Development of New Towns: Using Taiwan as an Example

Chen

2017

Asian Economic and Financial Review

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Article History JEL Classification G11,R10, R23, R58The purpose of this study is to examine the housing price differences between new towns and old urban areas. In addition, related issues such as spread options, the anchoring effect, and the ripple effect are discussed. By investigating the static spread, dynamic spread, and new town theory, we test seven hypotheses and verify most of these hypotheses. Furthermore, the key causes of the spread are identified: For example, spread options for different residence types depend on risk attitudes; and the ripple effect and anchoring effect of neighboring areas are determined by regional compactness and market transparency. The findings and relevant factors addressed in study can serve as a reference for investment decision making as well as for governments to implement projects of new town development or old urban area revitalization.Contribution/ Originality: The paper's primary contribution is finding that the key factors cause housing prices differences between new towns and old urban areas. It can serve as a reference for investment decision making as well as for governments to implement projects of new town development or old urban area revitalization.

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Section: Conclusion and Discussionsupporting

confidence: 69%

Section: Research Datamentioning

confidence: 99%

Housing Price Differences Under the Development of New Towns: Using Taiwan as an Example

Chen

2017

Asian Economic and Financial Review

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“…Other studies also find a concave effect, where the impact is either negative or insignificant and peaks some distance away (e.g., Billings, 2011;Seo, Golub, and Kuby 2014). For example, Billings (2011) for Charlotte, North Carolina, found that the effect of the LRT on the value of single family homes was not significant within 0.8 kilometer while it was positive in the range of 4 percent to 13 percent for all properties located within 1.6 kilometers.…”

Section: Comparison With Existing Evidencementioning

confidence: 98%

The effect of the Dubai Metro on the value of residential and commercial properties

Mohammad

Graham

Melo

2015

JTLU

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This paper analyzes the impact of the newly operated Dubai Metro on the sale transaction value of dwellings and commercial properties. The effect is estimated for properties within different catchment zones of a metro station using difference-in-differences and hedonic pricing methods on both repeated cross-sectional data and pseudo panel data. Our estimates show a positive effect of the metro on sale values of both residential and commercial properties, although the effect is stronger for commercial properties. The models also reveal that the effect of the metro on the value of dwellings and commercial properties is largest within 701 to 900 meters of a metro station and is about 13 percent and 76 percent, respectively. IntroductionA large number of researchers have examined the effect of rail systems on property values and the range of estimates varies substantially across studies (Mohammad et al. 2013). The majority of studies suggests that proximity to rail stations enhances property values (e.g., Laakso 1992;Pan and Zhang 2008;Voith 1991;Weinberger 2001), some indicate a negative impact at certain locations mainly due to negative environmental externalities (e.g., Bolling, Ihlanfeldt, and Bowes 1998;Cervero 2003;Du and Mulley 2006), and a few show no noticeable effect (Gatzlaff and Smith 1993).The empirical literature has generally adopted hedonic pricing (HP) methods to estimate the relationship between rail and property values. Although HP models can control for unobserved heterogeneity across properties, they cannot identify a causal relationship between rail access and property value, only indicative results. For example, stations may be found at high-valued areas like a commercial hub zone, and HP models typically do not account for the impact of these attributes on the reported values (Billings 2011;Gibbons and Machin 2005).As an improvement to the HP models, a number of studies have recently started using an innovation-based model, the difference-in-differences (DID) estimator (e.g., Agostini and Palmucci 2008; 1995;McMillen and McDonald 2004). Although the application of this method differs across studies, it generally relies on comparing prices before and after the treatment for the properties that experienced the treatment (treated) compared to those that did not (control). Some studies have also compared results obtained from DID to those from conventional HP models. In a study on the effect of new rail stations on dwelling prices in London, Gibbons and Machin (2005) found that HP models produced statistically larger effects than DID models. On the other hand, Agostini and Palmucci (2008) found that the DID models produced higher estimates of the effect of Santiago Metro on property values, compared to HP models. The difference in the magnitude of estimates between the DID and HP models in the two studies may be related to the data structure; while Agostini and Palmucci (2008) used repeated cross-sectional data, Gibbons and Machin (2005) used pseudo panel data.This paper contributes to...

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“…In the United States, Bowes and Ihlanfeldt [16] found both positive and negative effects of rail stations on the local house prices in Atlanta. Several other studies conducted in the USA showed a positive relationship between property values and the distance from light rail (LRT) stations, such as in Santa Clara (California) [17,18], Charlotte (North Carolina) [6], Buffalo (New York) [19], Dallas (Texas) [20], Portland (Oregon) [21] and Phoenix (Arizona) [22]. In Brisbane (Australia), Mulley et al [23] found that being close to a bus rapid transit (BRT) added a premium to the housing price of 0.14% for every hundred meters closer to the BRT station.…”

Section: Literature Reviewmentioning

confidence: 99%

Spatial Heterogeneity of Sustainable Transportation Offer Values: A Comparative Analysis of Nantes Urban and Periurban/Rural Areas (France)

Bulteau¹,

Feuillet

Boennec

2018

Urban Science

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Abstract:Innovative solutions have been implemented to promote sustainable mobility in urban areas. In the Nantes area (northwestern part of France), alternatives to single-occupant car use have increased in the past few years. In the urban area, there is an efficient public transport supply, including tramways and a "busway" (Bus Rapid Transit), as well as bike-sharing services. In periurban and rural areas, there are carpool areas, regional buses and the new "tram-train" lines. In this article, we focus on the impact on house prices of these "sustainable" transportation infrastructures and policies, in order to evaluate their values. The implicit price of these sustainable transport offers was estimated through hedonic price functions describing the Nantes urban and periurban/rural housing markets. Spatial regression models (SAR, SEM, SDM and GWR) were carried out to capture the effect of both spatial autocorrelation and spatial heterogeneity. The results show patterns of spatial heterogeneity of transportation offer implicit prices at two scales: (i) between urban and periurban/rural areas, as well as (ii) within each territory. In the urban area, the distance to such offers was significantly associated with house prices. These associations varied by type of transportation system (positive for tramway and railway stations and negative for bike-sharing stations). In periurban and rural areas, having a carpool area in a 1500-m buffer around the home was negatively associated with house prices, while having a regional bus station in a 500-m buffer was non-significant. Distance to the nearest railway station was negatively associated with house prices. These findings provide research avenues to help public policy-makers promote sustainable mobility and pave the way for more locally targeted interventions.

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Combined impacts of highways and light rail transit on residential property values: a spatial hedonic price model for Phoenix, Arizona

Cited by 128 publications

References 24 publications

Housing Price Differences Under the Development of New Towns: Using Taiwan as an Example

Housing Price Differences Under the Development of New Towns: Using Taiwan as an Example

The effect of the Dubai Metro on the value of residential and commercial properties

Spatial Heterogeneity of Sustainable Transportation Offer Values: A Comparative Analysis of Nantes Urban and Periurban/Rural Areas (France)

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