Most city dwellers know their ideal location downtown is probably going to come with a little noise from the streets below. What they may not be expecting to deal with is extra noise from nearby train stations, police stations, or construction sites.
RentLingo, an apartment listing site, wants renters to know exactly what kind of noise they’ll be dealing with in each location. At the beginning of September, the company launched its Noise Index, which shows the decibels of noise in the top 100 cities across the country.
The measurement, which ranges from 40db to 70db, can be viewed on a Google map. Website users can type in a ZIP code and see a city overlaid with colored hexagons ranging from blue to orange to find the quiet and noisy areas, respectively. When users click on each hexagon, which covers roughly half a city block, a data box pops up with information such as the average decibel level and noise sources within 200 feet, 500 feet, and the vicinity. Users can also switch to a night view to see the difference in noise levels between daytime and nighttime. The average noise level, map, and possible noise sources also appear on each apartment listing located in a major U.S. city.
“I took a tour once, and it looked great. I didn’t realize a train was right on the other side of the wall and you didn’t hear it,” says Dan Laufer, RentLingo CEO.
After this experience, Laufer found other apartment hunters were also concerned with noise levels. He realized it was in a lot of the reviews on RentLingo already. “It’s one of the biggest pieces of information renters are looking for,” he says. “[Trains] come at different times, and if you’re not right there, you’re not looking or thinking about that."
The database was formed over the past several months after the
RentLingo team analyzed available data on noise levels and crowdsourced
information on potential noise sources, such as locations of construction
sites. They tested out the data by going to select locations with a sound meter
to see if what they found firsthand matched what the data say.