The traffic noise influence in the housing market A case study for Lisbon Sandra Vieira Gomes

The traffic noise influence in the housing marketA case study for Lisbon

Sandra Vieira GomesPhD in Civil Engineering

1

Escola Superior de Actividades ImobiliáriasPortugal

European Real Estate Society 21st Annual Conference 25th-28th June 2014

The traffic noise influence in the housing market. A case study for Lisbon

2

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.


3

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

4

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.


5

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 housing profile

6

Vacant housing units by purpose in Portugal (2011)


7

Finished housing units in new construction and rehabilitation 2001-2011


8

Excess of housing units facing the number of classic families - 2011


Case study: Lisbon, Portugal

9


Data

10

Statistical data on housing characteristics, from Census (National Statistical Institute)

Traffic volumes from a traffic assignment model

(Lisbon City Council)

Housing sales(Real Estate Agency LANE)

GIS


Spatial analysis between road traffic and housing in Lisbon

11

1) Identify the locations were housing units density was higher

Housing units per square meter in Lisbon


12

2) Traffic assignment model

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.

This model has a partial coverage which enables a complete analysis of the Lisbon city.

Class 1 Class 2 Class 3 Class 4 Class 5150 470 1400 1130 2750


13

ClassConversion

factors

Class 1 16,389

Class 2 19,036

Class 3 22,518

Class 4 25,999

Class 5 29,481

Conversion factors for Peak hour traffic (8:00 to 9:00) to AADT


14

This information was crossed with the housing areas locations, through the touching boundaries, allowing to identify the areas exposed to higher traffic


15

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 of housing units density crossed with the 75th percentil of traffic


The relation between traffic noise and the variation on the housing value in Lisbon

16

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.


17

Location of the housing units for sale included in the dataset


18

Apartments Villas

Average Minimum Maximum Average Minimum Maximum

Price (€) 617 797,47 47 800,00 3 995 000,00 1 909 456,79 280 000,00 9 500 000,00

Price/m2 3 006,45 1 260,16 9 296,48 3 190,83 700,00 7 539,68

Area (m2) 159,76 20,00 735,00 548,17 90,00 7 542,00

Nº of rooms 4,2 1,00 10,00 9,06 4,00 28,00

Nº of bedrooms 2,9 0,00 8,00 4,62 2,00 10,00

Nº of bathrooms 2,7 1,00 14,00 4,93 1,00 15,00

Summary statistics of the housing unit’s dataset


Modelling the effect of traffic on housing prices

19

A classic linear model was used to analyse the existence of an influence:

Coefficients Estimate Std. Error t value Pr(>|t|)

Ln(0) 856989.306 72962.700 11.746 <2e-16 ***

AADT(1) -5.377 6.047 -0.889 0.374

The functional form tested was: Price = β0+ β1.Traffic.


20

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

21

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.


22

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.)


23

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.

Final Notes (cont.)


Thank you

Sandra Vieira [email protected]

24

The traffic noise influence in the housing market A case study for Lisbon Sandra Vieira Gomes

Documents

Transcript of The traffic noise influence in the housing market A case study for Lisbon Sandra Vieira Gomes