The impact of metro services on housing prices: a case study from Beijing

Li, Shengxiao; Chen, Luoye; Zhao, Pengjun

doi:10.1007/s11116-017-9834-7

Cited by 57 publications

(40 citation statements)

References 58 publications

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“…Table 4 Summary of externalities related to proximity to public services found in the literature Proximity to public services Author(s) Proximity to schools, citizen support services, public transport Cordera, Coppola, Dell'Olio and Ibeas (2019); Finnigan and Meagher (2019); Stotz (2019). Proximity to public transport Dai, Bai and Xu (2016); Li, Chen and Zhao (2019).…”

Section: Proximity To Public Servicesmentioning

confidence: 99%

The Profile of Portuguese People when Choosing an Apartment

Santos

Tavares

2021

Real Estate Management and Valuation

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It is important to study and to know the determining factors of the housing purchase business, as this is often the greatest business deal in a family’s life. The purpose of this study is to identify the profile of the Portuguese regarding the determining factors when searching for apartments to acquire. The methodology used is based on a questionnaire that analyzes the determining factors in the choice of apartments to acquire and the sociodemographic profile of Portuguese people looking for an apartment. The sample consisted of 646 individuals who were looking for an apartment in different regions of Continental Portugal. The determining factors in the choice of apartments have quite adequate levels of validity and reliability, and four dimensions are obtained: negative externalities, positive externalities, a business located on the ground floor and rational interest in proximity to public services. Cluster analysis results show the formation of five clusters, classifying individuals into a generic cluster, urban business cluster, urban services cluster, urban citizens cluster and elitist cluster. This study is expected to contribute to increasing scientific knowledge on the topic and to help real estate developers better match their clients’ preferences.

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Section: Proximity To Public Servicesmentioning

confidence: 99%

The Profile of Portuguese People when Choosing an Apartment

Santos

Tavares

2021

Real Estate Management and Valuation

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“…Например, рынок недвижимости характеризуется обратной представленной ранее тенденцией: развитие транспортной инфраструктуры способствует увеличению стоимости недвижимости. Этот тезис эмпирически подтверждается на примере Испании [9], Китая [10,11]. Можно предположить, что подобный рост цен на недвижимость по мере развития транспортной инфраструктуры 5 Staal S., Delgado C., Baltenweck I., Kruska R. Spatial aspects of producer milk price formation in Kenya: a joint household-GIS approach.…”

Section: обзор литературыunclassified

Relationship Between Transport Infrastructure Development and Price Formation

Сергеева¹

2020

JAER

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The paper suggests a bidirectional nexus between transport infrastructure and price formation. It is argued that the link between these two factors is not only through microeconomic effect that transport infrastructure development has on pricing, but also that it can be analyzed from the macroeconomic perspective. Hence, the article is aimed at investigating the bidirectional nexus between transport infrastructure and price formation. The main hypotheses of the research are the following: (1) Macro- and macroeconomic effects are different with respect to the sign of their influence; (2) Microeconomic effect is described by the negative effect of transport infrastructure development on price level; (3) Macroeconomic effect is perceived as a positive influence of price level increase on transport infrastructure development. Therefore, it is assumed the link between transport infrastructure and price formation is bidirectional: transport infrastructure has a negative effect on price formation, whereas price formation positively impacts transport infrastructure development. To test the hypothesis, analysis was conducted of 118 countries of different economic, social and political systems with the use of panel regression. The panel data for 2009-2018 years was used. The results indicate the presence of the said bidirectional link and prove the hypotheses set. Transport infrastructure development, represented by transport infrastructure score through the global competitiveness index, leads to reduction in price level, measured by the conversion factor of purchase power parity to exchange rates. In contrast, an increase in price level positively impacts transport infrastructure development. The nexus of transport infrastructure and price formation is explained through two directions: 1) from specific to general (including price fall by transport capacity increase – microeconomic effect); 2) from general to specific (as a social development level that determines the efficiency of management processes, in transport and logistics – macroeconomic effect).

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“…Centre is the log form of distance from the neighbourhood to the nearest city centre. Most studies have concluded that the proximity of housing to subway stations positively affected value [10]. The subway station list used for this study was obtained from Guangzhou Metro (http://www.gzmtr.com/).…”

Section: Hedonic Housing Pricementioning

confidence: 99%

“…Researchers use online housing price data to investigate the determinants of housing prices, relevant policy, and macroeconomic and social situations, such as tax policy, stamp duty [3], housing purchase restriction policy [4], institutional mediation [5], and disease [6]. Structural attributes, such as gross floor area, storey level [7], age of properties [8], and differentials between large-scale estates and single-block buildings [9] and location attributes, such as metro services [10], green space [11,12], neighbouring and environmental effects [13], and the effects of theme parks on local areas [14], were all investigated by using online housing price data. Moreover, online housing price data are employed to explain various 2 Complexity phenomena in the housing market, such as the spatiotemporal trends concerning housing price fluctuations [15], the spatial pattern of rent prices [16], the transmission of house price changes across quality tiers [17], the housing ladder effect [18], buyers' preferences for high-end residential property [19], and corruption in China's land market auctions [20].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Residential Housing Prices on the Internet: Data Pitfalls

Zhang

Chen

et al. 2019

Complexity

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Many studies have used housing prices on the Internet real estate information platforms as data sources, but platforms differ in the nature and quality of the data they release. However, few studies have analysed these differences or their effect on research. In this study, second-hand neighbourhood housing prices and information on five online real estate information platforms in Guangzhou, China, were comparatively analysed and the performance of neighbourhoods’ raw information from four for-profit online real estate information platforms was evaluated by applying the same housing price model. The comparison results show that the official second-hand residential housing prices at city and district level are generally lower than those issued on four for-profit real estate websites. The same second-hand neighbourhood housing prices are similar across each of the four for-profit real estate websites due to cross-referencing among real estate websites. The differences of housing prices in the central city area are significantly fewer than those in the periphery. The variation of each neighbourhood’s housing prices on each website decreases gradually from the city centre to the periphery, but the relative variation stays stable. The results of the four hedonic models have some inconsistencies with other studies’ findings, demonstrating that errors exist in raw information on neighbourhoods taken from Internet platforms. These results remind researchers to choose housing price data sources cautiously and that raw information on neighbourhoods from Internet platforms should be appropriately cleaned.

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The impact of metro services on housing prices: a case study from Beijing

Cited by 57 publications

References 58 publications

The Profile of Portuguese People when Choosing an Apartment

The Profile of Portuguese People when Choosing an Apartment

Relationship Between Transport Infrastructure Development and Price Formation

Evaluation of Residential Housing Prices on the Internet: Data Pitfalls

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