Using GIS and Hedonic in the modelling of spatial variation of housing price in Xiamen city

Li, Yuan; He, Lang; Xu, Wangtu; Wang, Hui; He, Zizhang

doi:10.14246/irspsd.1.4_29

Cited by 10 publications

(12 citation statements)

References 9 publications

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“…surprising but consistent with studies of Yang et al (2018), Li et al (2013) and Peng et al (2015). The possible explanation for this is provided by Yang et al (2018) who state that normally, young adults seldom require the hospital facility.…”

Section: Factors Affecting Rentalssupporting

confidence: 91%

“…So they may not consider proximity to hospitals as very critical, when making residential location decision. It is the elder ones may look to stay close to the hospitals (Li et al, 2013). The possibility of a positive effect of distance from hospital and rental value cannot be ruled out in Pune, India due to the immigration of young adults as Sethi (2017) notes that most of the immigrants in Pune are young professionals.…”

Section: Factors Affecting Rentalsmentioning

confidence: 99%

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Factors affecting rentals of residential apartments in Pune, India: an empirical investigation

Singla

Bendigiri

2019

IJHMA

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Purpose The purpose of this paper is to find out the factors affecting rentals of residential apartments in Pune, India. Design/methodology/approach Four regression models are developed, i.e. basic ordinary least square (OLS) regression model, OLS regression model with robust estimates, OLS regression model with clustered robust estimates and generalized least square (GLS) regression model with maximum likelihood (ML) robust estimates. Based on the Akaike information criterion and Bayesian information criterion criteria, OLS regression model with clustered robust estimates and GLS regression model with robust estimates are best fit. The data are tested for multicollinearity and the models are tested for heteroscedasticity. The study uses the expected rent value data collected from Web portals and the data on factors affecting the rental value of residential property are collected through the study of land use maps, Google earth software and field visits. Findings Total floor area and number of rooms are structure related factors that positively affect the rental value, i.e. more the area and number of rooms, higher the rental value. The distances from the nearest police station and fire station are security and safety factors. The results suggest that higher distance from these factors leads to lower rental values, as safety and security is the top priority of residents seeking residential property on rental basis. The distance from employment zones, distance from nearest school/college and the distance from the nearest public transport terminal are convenience related factors that negatively affect the rental value, as greater the distance, lesser the rental value and vice versa. The distance from Central Business District and hospitals has a positive effect on the rental values of a residential property implying that higher distances from these places command higher rental value. Research limitations/implications The study relies on rental data that owner is expecting for a particular property, it is not certain that the property would be actually rented for the same value. Second, researchers had to drop certain important drivers of rental value because of the issue of multicollinearity. Practical implications This is one of the rare studies conducted in Indian context, and the findings of the study are useful from the owner, tenants, urban bodies and developers’ point of view. Knowing that India is one of the fastest growing markets and need for housing is increasing day by day (including housing facility on rental basis), the stakeholders need to take care of the factors that affect the rental values of a residential property. Social implications The authors suggest the governments and the municipal bodies in India to come up with a public rental housing policy that separately caters to the needs of the lower income group, middle and upper income group in at least metros, tier I and tier II cities that are witnessing unprecedented growth in job seeking immigrants, who are seeking properties on rental basis. While developing a public rental policy, they must keep in mind the factors that are driving the rental values, such as proximity to employment zones, proximity to proper school and college, efficient public transport system as well as all safety and security measures. Creation of such a public rental policy is a win–win situation for immigrants, property owners and government/urban development bodies. Originality/value This paper is the first empirical study about the factors affecting rental values in Pune, India. The study will help property owners, immigrant and local tenants, government and urban development bodies to develop an understanding about the important factors affecting rental value and come up with their respective plans. Advanced econometric regression models are used based on the data that is collected through actual field visits, study of maps and secondary information rather than use of survey method or creation of dummy variables.

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Section: Factors Affecting Rentalssupporting

confidence: 91%

Section: Factors Affecting Rentalsmentioning

confidence: 99%

Factors affecting rentals of residential apartments in Pune, India: an empirical investigation

Singla

Bendigiri

2019

IJHMA

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“…This indicates the nuisance effect of being located near hospitals, which seems somewhat surprising, but consistent with the findings in Li et al (2013) and Peng, B. et al (2015). There are two possible explanations.…”

Section: Initial Hedonic Price Regressionsupporting

confidence: 88%

“…Normally, young adults seldom go to hospital, so they may not consider proximity to hospitals when making residential location decisions, even tend to live far away from hospitals. Notwithstanding, the elderly are in general more likely to go to hospital (Yang, 2016;Szeto et al, 2017;Wong et al, 2017) and may tend to live close to hospitals for better access due to higher visiting frequency (Li et al, 2013). In addition, it is crowded around highlevel comprehensive hospitals generally.…”

Section: Initial Hedonic Price Regressionmentioning

confidence: 99%

Willing to pay more for high-quality schools?

Yang

Wang

Zhang

et al. 2018

IRSPSD International

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Abstract:Principally, the enrolment of elementary school in China is solely based on residential location. Due to the scarcity of prestigious schools, the household registration (or hukou in Chinese) and the territorial-based school admission policy, a feasible approach for parents to provide the children with good education is to purchase the house in the attendance zone of a high-quality school (or xuequfang in Chinese). The supply-demand imbalance gives rise to the xuequfang phenomenon (much higher prices of xuequfang relative to nonxuequfang). Based on 1250 housing samples in 286 multi-or high-storey residential districts, this paper firstly develops two basic and four Box-Cox transformed hedonic price models to estimate the effect of high-quality schools on residential property prices. The consistency of six models greatly enhances the credibility of this study. Moreover, complementary, the propensity score matching technique is used to estimate the treatment effect. The two methods consistently suggest that residential property values are affected by top-tier schools. They reveal that xuequfang exhibit values that are between 9.3% and 12.1% higher than non-xuequfang, ceteris paribus. The negative influences of the xuequfang phenomenon and several countermeasures (gradually reforming household registration system, optimizing resource distribution to balance the quality of education, highlighting family education and children's all-round development) are discussed.

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“…This characteristic makes the location an influential factor in modelling the commercial property market. It is also the reason why location is considered as one of the most critical determinants of commercial property prices (Bhattacharya, Lamond, Proverbs, & Hammond, 2013;Li, He, Xu, Wang, & He, 2013;Droj & Droj, 2015;Hayunga & Pace, 2010;Özyurt, 2013, 2014. This, therefore, underscore the necessity for modelling the commercial property market such that the state of the market and performance of the property market is reflected in the pricing (Corgel, Liu, & White, 2015).…”

Section: Introductionmentioning

confidence: 99%

A Review of Spatial Econometrics in Explicit Location Modelling of Commercial Property Market

Usman

Lizam

Burhan

2021

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‘Location, location, location’ is a real property parlance mostly used to describe the influence of location in the property market. Location is mainly considered as the most significant influencer of commercial property prices. Location is modelled traditionally using hedonic pricing model by either proxy location dummies or distances relative to other neighbourhood features. This was shown to be inadequate due to spatial autocorrelation and heterogeneity inherent in spatial data, which jeopardises the estimates' consistency. Consequently, spatial econometrics is used to explicitly model location into property pricing by controlling spatial effects of autocorrelation and heterogeneity. Housing studies dominate the use of this approach with limited application in the commercial property market. This paper reviewed spatial econometrics and found that the commercial property market exhibits significant spatial dependence and heterogeneity. Accounting for such effects improves model accuracy significantly. It, therefore, recommends increase use of spatial econometrics in commercial property market modelling.

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Using GIS and Hedonic in the modelling of spatial variation of housing price in Xiamen city

Cited by 10 publications

References 9 publications

Factors affecting rentals of residential apartments in Pune, India: an empirical investigation

Factors affecting rentals of residential apartments in Pune, India: an empirical investigation

Willing to pay more for high-quality schools?

A Review of Spatial Econometrics in Explicit Location Modelling of Commercial Property Market

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