Impacts of Urban Environmental Attributes on Residential Housing Prices in Warsaw (Poland): Spatial Hedonic Analysis of City Districts

Ligus, Magdalena; Peternek, Piotr

doi:10.1007/978-3-319-54885-2_15

Cited by 11 publications

(9 citation statements)

References 17 publications

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“…In one study, the authors argue that the insignificant effect of NO x concentrations on housing prices is due to the fact that NO x does not tend to exceed the standard; on the contrary, SO 2 shows a significant and negative impact on housing prices in the same study, because SO 2 has exceeded the official air quality standard over a long period of time [16]. The other two studies believe that the insignificant results are caused by either an insufficient degree of efficiency [11] or that the change of air pollution concentration is more important than air pollution concentration itself [12]. In examining the literature, the results are suggestive that air pollution's effects tend to be insignificant when overall ambient air pollution concentrations are relatively low.…”

Section: Discussionmentioning

confidence: 86%

“…For example, positive spatial autocorrelation, which is a more common situation, means that sites that are located close together tend to have similar values. The most common spatial hedonic models are the spatial lag model (SLM) [11,12], spatial error model (SEM) [11,12], spatial Durbin model (SDM) [13], geographically weighted regression (GWR) [14,15], and quantile regression models (QRM) [15]. The results from the literature are inconclusive: some of the studies conclude that air pollution concentrations do not significantly influence housing prices [11,12,16], while others find that air pollution concentrations negatively and significantly influence housing prices [10,[17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

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How Does Air Pollution Influence Housing Prices in the Bay Area?

Tang

Niemeier

2021

IJERPH

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In this paper we examine the effects of localized air pollution measurements on the housing prices in Oakland, CA. With high-resolution air pollution measurements for NO, NO2, and BC, we can assess the ambient air quality on a parcel-by-parcel basis within the study domain. We combine a spatial lag model with an instrumental variable method to consider both the spatial autocorrelation and endogeneity effects between housing prices and air pollution concentrations. To the best of our knowledge, this is the first work in this field that combines both spatial autocorrelation and endogeneity effects in one model with accurate air pollution concentration measurements for each individual parcel. We found a positive spatial autocorrelation with housing prices using Moral’s I (value of 0.276) with the total sample number of 26,386. Somewhat surprisingly, we found a positive relationship between air pollution and housing prices. There are several possible explanations for this finding. Homeowners in high demand, low-stock housing areas, such as our study, may be insensitive to air pollution when the overall ambient air quality is relatively good. It is also possible that under clean air conditions, low variability in pollutant concentrations has little effect on property values. These hypotheses could be verified with more high-resolution air pollution measurements with a diversity of regions.

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Section: Discussionmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

How Does Air Pollution Influence Housing Prices in the Bay Area?

Tang

Niemeier

2021

IJERPH

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“…There are many aspects that contribute to environmental attributes, for instance air quality [30]. The author made an attempt to value air quality in Poland with the use of HPM [31][32][33]. However, the limitation in applicability of this method is the requirement of developed and effective residential property market.…”

Section: Methods For Valuing Environmental Costs and Benefitsmentioning

confidence: 99%

Measuring the Willingness to Pay for Improved Air Quality: A Contingent Valuation Survey

Ligus¹

2018

Pol. J. Environ. Stud.

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“…Some researchers use spatial econometric models and hedonic price models to understand the effect of air pollution on housing prices, considering their spatial autocorrelation. The most commonly-used spatial hedonic models are the spatial lag model (SLM) [33,34], spatial error model (SEM), spatial Durbin model (SDM) [35], geographically weighted regression (GWR) [36], quantile regression models (QRM) [37], bootstrap autoregressive distributed lag (BARDL) [38], and the fine-grained PM 2.5 data retrieval model that includes high-resolution satellite imagery data [39].…”

Section: Introductionmentioning

confidence: 99%

An Attempt to Determine the Relationship between Air Pollution and the Real Estate Market in 2010–2020 in Gdańsk Using GLM and GRM Statistical Models

et al. 2023

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World economic development, technological progress, and irrational exploitation of natural resources have intensified the negative effects of economic activity, which causes more attention to be paid to environmental pollution and the deterioration of the standard of living. Therefore, over the past few years, the concept of sustainable development has experienced a period of increased interest, accompanied by changes in the attitudes and expectations of all market participants. The article attempts to analyse the relationship between air pollution and selected parameters of the residential market in Gdansk in 2010–2020. The study considered the peculiarities of the region due to its geographical location. To identify the effects of air pollution, the GLM (generalised linear models) and GRM (generalised regression models) were used with a progressive stepwise regression method. Based on the results, it was found that the existing air pollution and climatic conditions in Gdańsk have an impact on the number of apartments sold and their prices. All models were identified for the first time for monthly data, and prices were taken from the real estate sale contracts database. These indicate the advantage and novelty of the study. In addition, this paper is the first in a series of publications examining the impact of air pollution on the real estate market in Poland’s largest agglomerations. From the main model following results were obtained: (1) A statistically significant (p ≤ 0.05) factor affecting the number of sales of premises above 80 m2 on the secondary market is PM2.5. With an increase in PM2.5 by 10 µg/m3, the number of sold apartments above 80 m2 decreases on average by slightly more than 20. (2) The interaction (p ≤ 0.05) of O3 (ozone) and PM2.5 on the number of transactions affects the secondary market of flats with an area of 60–80 m2. Simultaneous to the decrease in the concentrations of O3 and PM2.5 is an increase in the number of sales of apartments in the given size in Gdańsk. (3) Simultaneous to the decrease in PM10 and NO2 concentrations due to the increased urban traffic is an increase in the price of 1 m2 of residential premises with an area of 40 m2 on the secondary market in Gdańsk.

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Impacts of Urban Environmental Attributes on Residential Housing Prices in Warsaw (Poland): Spatial Hedonic Analysis of City Districts

Cited by 11 publications

References 17 publications

How Does Air Pollution Influence Housing Prices in the Bay Area?

How Does Air Pollution Influence Housing Prices in the Bay Area?

Measuring the Willingness to Pay for Improved Air Quality: A Contingent Valuation Survey

An Attempt to Determine the Relationship between Air Pollution and the Real Estate Market in 2010–2020 in Gdańsk Using GLM and GRM Statistical Models

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