Residential Environment and Spatial Variation in House Prices in the Netherlands

Visser, Petra; Dam, Frank van; Hooimeijer, Pieter

doi:10.1111/j.1467-9663.2008.00472.x

Cited by 48 publications

(38 citation statements)

References 25 publications

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“…In spite of the long term interest in the impacts of race, ethnicity diversity and other social characteristics, it is probably correct to claim that this is currently an understudied area in housing market studies (Visser et al (2008); Herath and Maier (2010)), and the results differ substantially mainly due to data and endogeneity issues. Moreover, most papers are based on US data, where the degree of racial or ethnical transitions and level of segregation of neighbourhoods are more prevalent, have a far longer history and a different structure (Zabel, (2008); Moye (2014)).…”

Section: Preferences Sorting and Home Prices 21 Preferences Over Nementioning

confidence: 99%

Putting a price on your neighbour

Nordvik

Osland²

2016

J Hous and the Built Environ

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Neighbourhood population composition affects the willingness to pay for housing units. This paper utilises a large and rich data set and hedonic regression techniques to disentangle the effect that neighbourhood affluence and presence of inhabitants with an immigrant background have on home prices. Furthermore, we specify an empirical model in a way that also enable us to test for the effect of diversity, both in terms of income levels and of the composition of the immigrant population of a neighbourhood. The hedonic model can be viewed as a variety of an amenity interpretation of the population composition of a neighbourhood. Estimation of effects of population composition is not straightforward as there is good reason to believe that population composition is both endogenously determined together with house prices and that area level omitted variables could bias estimates. This is addressed by lagging the composition measures and by formulating two different models that address these difficulties in different ways. We estimated one random effects model that instruments within neighbourhood variation in population composition and one fixed effects model that control for omitted variables. We find that coefficient estimates are robust across these specifications.

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Section: Preferences Sorting and Home Prices 21 Preferences Over Nementioning

confidence: 99%

Putting a price on your neighbour

Nordvik

Osland²

2016

J Hous and the Built Environ

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“…Locational factors considered in property valuation studies to date are closely related to accessibility measures. Accessibility to major business districts, transportation infrastructure such as major roads, bus stops, and subway and railway stations, and various facilities including schools, shopping centers, and churches were examined [14][15][16]. The relationship between housing prices and location in a particular zone such as a commercial zone has also been explored [12].…”

Section: Introductionmentioning

confidence: 99%

“…Visser et al [15] considered some socio-demographic variables, including population density and the proportion of single-family dwellings in a neighborhood. It was found that they had negative impacts on house prices, which was possibly due to congestion and insecurity.…”

Section: Introductionmentioning

confidence: 99%

Investigating How the Rents of Small Urban Houses are Determined: Using Spatial Hedonic Modeling for Urban Residential Housing in Seoul

Won

Lee

2017

Sustainability

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Abstract:The Seoul metropolitan government has launched the Urban Residential Housing (URH) program to address the shortage of small urban houses, and enhance residential stability for the increasing number of one-and two-person households in Seoul, Korea. While studies have examined the price premium of locational and neighborhood environmental features for houses in general, little is known about how and to what extent these features influence the rents of small urban houses. We estimate and compare conventional and spatial hedonic price models (HPMs) to examine the effects of those features on the monthly rents of URH units, while using geographic information system techniques to measure the variables. We found that the spatial HPM outperforms the conventional HPM in terms of goodness of fit measures. All of the locational features, and most of the variables with respect to the neighborhood environment, had a significant impact on rents. Rent tended to be lower in areas adjacent to a university, suggesting that it is a good place for one and two-person households to enhance residential affordability. However, access to parks does not appear to be important for residents. We conclude that the people who live in small urban houses are inclined to place a premium on rents in different ways.

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“…Some (Sedgley, Williams et al, 2008) detected how public school quality influence real estate markets in Howard County, Maryland. Some (Visser, Frank et al, 2008) used Hedonic price modelling to derive different models of property price from which the contribution of the characteristics of the residential environment were estimated. It was demonstrated that an important factor was the accessibility to employment opportunities.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2013

IRSPSD International

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Abstract:Valuing the built environment with the support of GIS and housing price model is an interesting topic because it connects the spatial thinking with economic concerns. In this paper, we chose the ordinary commercial housing in Xiamen City as our research targets, and the technology of GIS and Hedonic price model were used. Xiamen city is a typical coastal city, and from the research result, we found that the distribution of higher housing price was kept consistent with the coastal areas of Xiamen city. Besides, the average housing price of Xiamen inner-island (the old urban island area) was far higher than the outer-island. To reveal the degree of different spatial elements affecting housing price, we chose 21 variables (three types: location characteristic, structure characteristic and neighbourhood characteristic) and measured them in GIS platform. The Hedonic model showed that the main elements that influencing the price of commodity housings in Xiamen included Property Management Fee, Distance to Commercial Center, Distance to Hospital, Distance to Primary School, Near Village or Not, Near Green space or Not, Near kindergarten or Not. At the end of the paper, we provided three suggestions to balance the spatial variation of housing price in Xiamen City.

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Residential Environment and Spatial Variation in House Prices in the Netherlands

Cited by 48 publications

References 25 publications

Putting a price on your neighbour

Putting a price on your neighbour

Investigating How the Rents of Small Urban Houses are Determined: Using Spatial Hedonic Modeling for Urban Residential Housing in Seoul

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

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