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Escola Superior de Actividades Imobiliárias. Portugal. The traffic noise influence in the housing market A case study for Lisbon Sandra Vieira Gomes PhD in Civil Engineering. European Real Estate Society 21st Annual Conference 25th-28th June 2014.
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Escola Superior de Actividades Imobiliárias Portugal The traffic noise influence in the housing market A case study for Lisbon Sandra Vieira Gomes PhD in Civil Engineering European Real Estate Society 21st Annual Conference 25th-28th June 2014
The increase of motorization has brought many advantages for mobility, but has also decreased the quality of life of populations in terms of noise and air quality.
Some car, truck, and bus traffic on neighborhood streets is a necessity given our current life style. However, this does not mean that these streets must accommodate ever increasing amount of traffic. As traffic volumes increase the safety of our streets declines along with property value, air quality, and the quiet we enjoy in our homes.
Traffic noise Traffic noise can be considered as an environmental externality from road transport system. As so, it affects the value that people are willing to pay for their houses. Considering the current trend of expanding cities, increasing the volume of associated vehicular traffic and also the increased density of the urban areas, one can say that the problems associated with noise tend to aggravate.
Noise is defined as unwanted sound. Human ears are able to respond to sound over the frequency range of about 20 Hz to 20 kHz and over the audible range of 0 dB (the threshold of perception) to 140 dB (the threshold of pain)
Portuguese housingprofile Vacant housing units by purpose in Portugal (2011)
Finished housing units in new construction and rehabilitation 2001-2011
Excess of housing units facing the number of classic families - 2011
Data Statistical data on housing characteristics, from Census (National Statistical Institute) Traffic volumes from a traffic assignment model (Lisbon City Council) GIS Housing sales (Real Estate Agency LANE)
Spatial analysis between road traffic and housing in Lisbon 1) Identify the locations were housing units density was higher Housing units per square meter in Lisbon
2) Traffic assignment model This model has a partial coverage which enables a complete analysis of the Lisbon city. For the streets without traffic information, an estimate was performed, based on the hierarchic class of the streets and on the average peak hour traffic volume given by the traffic assignment model.
Conversion factors for Peak hour traffic (8:00 to 9:00) to AADT
This information was crossed with the housing areas locations, through the touching boundaries, allowing to identify the areas exposed to higher traffic
This information was crossed with the housing unit’s density, in order to find whether there were any dens areas with high levels of traffic. 75th percentil ofhousing units density crossed with the 75th percentil of traffic
The relation between traffic noise and the variation on the housing value in Lisbon This exercise was made although it was not possible to collect the data on all the houses for sale in Lisbon. This kind of data is only collected to the local council level and not to the specific location (street and number). The housing data used in this analysis was provided by the Real Estate Agency LANE and comprises 613 houses that were put into the housing market in 2013, which are about 10% of the total house market for Lisbon.
Location of the housing units for sale included in the dataset
Modelling the effect of traffic on housing prices A classic linear model was used to analyse the existence of an influence: The functional form tested was: Price = β0+ β1.Traffic.
The model did not present a good fitting with a multiple R-squared of 0.0012. The independent variable AADT cannot be considered significant, since the p-value is higher than 0.05. This means that no evidence was found that traffic is influencing housing prices. Mention must be made to the fact that the dataset was rather small (about 10%) when compare to the full house market. This fact may have some influence on the conclusions.
Final Notes The analysis presented in this study aims to contribute to the scientific knowledge of the effect of traffic in the housing market in Portugal In order to achieve these goals, a geocoded accident database was developed, which allowed a fast extraction of relevant information on a geographical basis. The exploration of this database allowed to identify areas of the city of Lisbon that are affected by higher road traffic.
Final Notes (cont.) • This information can be used to: • promote the quality of housing units if they are located outside the areas with critical exposition, • require the proper measures to minimize the effects of traffic noise when they are located inside the areas with critical exposition in terms of traffic.
Final Notes (cont.) In what concerns the modelling task, no evidence was found that traffic is influencing housing prices. Mention must be made to the fact that the dataset was rather small (about 10%) when compare to the full house market. This fact may have some influence on the conclusions. The importance of increasing the number of observations is crucial, in order to obtain more robust results. The inclusion of additional explanatory variables could also be explored.
