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App to analyze noise levels, will Trulia beat them to the punch?

Measuring noise levels could soon be a reality for all listings, and is Trulia the company to make it happen?

New app to analyze noise

Scientists at the University of Granada in southern Spain are creating a new application1 to analyze noise pollution, and combines data like street type, road conditions, and average vehicle speed to predict the noise level on any given street at any given time. It is said the technology is 95 percent accurate.

The app is seen as immediately useful to people who are looking to rent or purchase a home, taking noise levels into consideration as they select their location. Researchers are currently trying to reduce the number of variables required to produce an accurate forecast of the noise levels in a given area.

To develop this new system, the researchers analyzed a set of noise data collected in Granada in 2007, although they are collecting further data in other cities “to validate the model.” The noise forecasting models employed to date have been based on traditional mathematical methods that predict noise levels using a specific set of data. “This is the first system to apply Soft Computing methods in urban noise assessment,” adds one of the researchers, “and there is scarce literature available on this method”.

According to GOOD Magazine2, “Noise pollution can contribute to some serious health problems—including stress-related illnesses, high blood pressure, hearing loss, and heart attacks. It’s also lousy for ecosystems, threatening coral reefs and killing whales. If this application helps us study, and mitigate, those effects, it could be useful for much more than just home shopping.”

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A different method, a different company

While researchers at the Spanish university devote time and money to this complicated algorithm, the private sector has already hinted that American real estate may someday have noise measurements included in listing data.

In late 2010, Trulia acquired, an insanely innovative real estate search company that used animated heat maps to explain things, including noise levels, particularly in San Francisco by using physical monitors of areas throughout the day and night.

Click the image below to watch the animation of a single neighborhood:

Imagine a visualization like this in every city in America. Because Trulia acquired Movity and who knows how many patents, along with their ridiculously intelligent talent, Trulia is the only real estate search company that could beat the University of Granada to the punch, without having to develop billions of dollars worth of algorithms.

What would be even more dramatic and innovative is if Trulia (or any real estate search company) worked with the University of Granada to adopt their algorithm, then implement the Movity-style heat maps. That would be genius.

1 University of Granada Report
2 GOOD Magazine

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Lani is the COO and News Director at The American Genius, has co-authored a book, co-founded BASHH, Austin Digital Jobs, Remote Digital Jobs, and is a seasoned business writer and editorialist with a penchant for the irreverent.



  1. Eric Wu

    June 18, 2012 at 6:32 pm

    Thanks Lani!  Noise pollution and it’s affects on health, location, home pricing, etc has definitely been an area of interest at Trulia.  We’ve discussed creating a noise model based on similar factors as the University of Granada, and also including building data to help accurately predict how noise travels around different types/sizes of buildings.  Vehicle traffic is one factor, with foot traffic another that’s difficult to model.  I agree that it’s a still an unsolved problem for movers and will discuss synergies with the University of Granada.  Thanks again! – Eric

  2. NoiseScore

    June 24, 2012 at 11:43 am

    There is another related noise project and app from the Sony Computer Lab in Paris called NoiseTube that may be worth considering in this equation. Also, I have a shameless plug for a side project I started a few years ago to turn San Francisco’s static noise map into searchable information to help renters and home buyers make more informed decisions about noise. It’s called  I created an algorithm based on traffic, population density, proximity to business areas, and other factors.  

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