Examining the spatial relationship between environmental health factors and house prices: NO2 problem?
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Citations
A Spatial Difference-in-Differences Estimator to Evaluate the Effect of Change in Public Mass Transit Systems on House Prices
Take a Look Around: Using Street View and Satellite Images to Estimate House Prices
Take a Look Around: Using Street View and Satellite Images to Estimate House Prices
Does environmental noise affect housing rental prices in developing countries? Evidence from Ecuador
Modelling Housing Rents Using Spatial Autoregressive Geographically Weighted Regression: A Case Study in Cracow, Poland
References
Geographically Weighted Regression: The Analysis of Spatially Varying Relationships
Model-based Geostatistics
The Interpretation of Dummy Variables in Semilogarithmic Equations
Hedonic housing prices and the demand for clean air
Geographically Weighted Regression
Related Papers (5)
Economic Evaluation of the Indoor Environmental Quality of Buildings: The Noise Pollution Effects on Housing Prices in the City of Bari (Italy)
Estimating environment impacts on housing prices
Frequently Asked Questions (13)
Q2. What are the future works in this paper?
In consolidating these findings future work will seek to utilise data which is envisaged to become available in terms of nuances in air quality data which may permit a difference in difference methodology to be adopted, to more robustly capture the change in pricing and air quality using a two-step hedonic framework. Future work will seek to address limitations implicit herein: Results are restricted to one urban area, therefore it would be beneficial to compare peri-urban areas to examine or reflect the changing density of housing market and urban form – extending the analysis. Also, a number of aspects of estimation were not taken into consideration and remain the subject of future work. The strong evidence of remaining heterogeneity and spatial correlation would suggest that perhaps a different scale of analysis might be more appropriate.
Q3. What is the effect of road noise on property value in Belfast?
low road noise has a detrimental impact on property value in the more built-up urban environment towards the CBD, with high road noise associated with increasing value.
Q4. What is the effect of the low air quality coefficient on the price of a detached property?
The Low air quality estimates show significant variation remaining negative until the 3rd quartile of the coefficient value, thereby inferring that low air quality (high levels of NO2) impact negatively upon prices, with the exception of well-established upmarket housing areas towards the South- South East of the city reflective of the utility trade-off between level of air pollution and desirable living locales.
Q5. What is the effect of a variation of particulate matter on the average value of houses?
According to their estimates, a variation of 1 g/m3 of particulate matter causes an increase of 0.20 percentage points in the average value of houses.
Q6. How much does the distance effect of the linear OLSmodel affect?
In terms of adjacency, properties located within 250 metres of arailway seemingly are negatively impacted in terms of their prices when scrutinising the linear OLSmodel, however only the distance bands up to 125m are significant at the 10% level.
Q7. What is the effect of the NO2 coefficient on property prices?
Turning to the air pollution variables of interest, examination of the NO2 coefficient (Model 3) reveals it to have a negative impact on property prices (β = -0.190, p<.001),inferring that the higher the NO2 level, the stronger the spatial autocorrelation of house prices, as indicated by the positive coefficient of the interaction term ( /01P* ∗ NO = 0.017).
Q8. What is the importance of including spatial variables in valuation and house price models?
Early studies by Ball (1973) and Berry and Bedarz (1975) presented arguments for the importance of including spatial variables in valuation and house price models – concluding that a traditional ordinary least squares (OLS) model that treats all locations equally is flawed; error terms will likely fluctuate across submarkets, and will also be correlated with similar, nearby properties, therefore violating the assumption of a constant error variance (in residuals) which may occur due to structural instability of parameters across space, modelled functional forms that are not spatially representative, or missing variables (Anselin, 1988).
Q9. What has led to the calls for sharper policy responses andsolutions?
This has once again led to calls for sharper policy responses andsolutions, with recommendations including the ‘phasing out’ of diesel vehicles, the creation of Ultra LowEmission Zones.
Q10. What is the effect of the noise coefficient on property prices?
The rail noise coefficient is negative illustrating it to comprise a statistically significanteffect on property prices (β = -0.908, p<.001), and indicating that the higher the noise level, the strongerthe spatial autocorrelation of house prices, as displayed by the positive coefficient of the interaction term/01a* ∗ j kl.
Q11. What is the effect of the GWR coefficients on the urban environment?
As a result, heteroscedasticity, orspatial heterogeneity inherent in the property price data may also represent differences in the urbanenvironment which need to be modelled more reliably by the spatially varying GWR coefficients.
Q12. What is the need for spatial consideration within hedonic pricing models?
The need for spatial consideration within hedonic pricing models has long been a concern within the valuation arena as both supply and demand of real estate will vary across a given location as tastes, preferences, willingness, and abilities to buy flucutate.
Q13. What is the main argument of Muller and Loomis (2008)?
This is also highlighted by Muller and Loomis (2008) who also caution that the gap between coefficients corrected and uncorrected for spatial dependence may not always be economically significant – inferring that the inefficiency attributable to spatial influences may not be large enough to cause critical errors in policy decisions.