Location, location, location: As the old joke goes, those are the three keys to business success. Now, with big data analysis, corporations can be smarter than ever before about where to open up new offices or businesses.

But what if you run a mom-and-pop shop, or you’re dreaming of quitting your corporate job and opening a boutique? Even medium-size businesses do not have the money to spend on the sort of systems and analysis teams that corporate behemoths use to locate new businesses.

This is where ZoneSavvy, a new website created by software engineer Mike Wertheim, could help. The site is straightforward: You enter a business type, the ZIP code of the general area where you want to locate the business, and the distance from that ZIP code you are willing to consider. ZoneSavvy then gives you suggestions for which nearby neighborhoods would be the best locations for your business.

ZoneSavvy does this by sifting through and cross-referencing demographic, real estate, and economic information. It looks at the age and income of people living in your target area, the price of commercial real estate, and what types of businesses are located there. By comparing that information with data from other areas, it determines which types of businesses are popular in similar neighborhoods  and under-represented in the area you’re interested in.

For example, if you’re thinking of opening up a dance club in New York City within a 10-mile radius of midtown Manhattan, ZoneSavvy will look at neighborhoods with the same profile as your target area. It will then tell you which neighborhoods in the vicinity of your target ZIP code have no dance clubs, but are similar to areas where dance clubs are clustered. In this way, you can not only identify the types of neighborhoods where dance clubs prosper, but also which neighborhoods of that type currently offer no competition.

ZoneSavvy also lets commercial property owners and real estate agents do the reverse: enter an address of a property for which they are trying to find a tenant. The site will then suggest which types of businesses would most likely succeed in that neighborhood.

This would be especially useful to real estate agents who are having trouble finding tenants for a property, by giving them ideas for the type of tenants they should be marketing to and additional information they can use in pitching the property, said Wertheim.

The main thrust of the site, though, is helping people figure out where to locate new businesses.

“Big retailers, companies like Burger King and McDonald’s, spend a lot of time and money figuring out where to locate new businesses and franchises,” noted Ray Wang, founder of Constellation Research. “They have corporate real estate offices, facility management staff, planners and huge databases. Small businesses don’t have anything like that.”

Several real estate agents agreed that up to now, they haven’t seen anything on the market like ZoneSavvy. “The site sounds like it would really help narrow down the neighborhoods where you should be looking at for your business,” said Carlo Caparruva, managing director of the commercial practice at Keller Williams Mid-Town Direct Realty in Maplewood, New Jersey.

But business owners shouldn’t rely completely on ZoneSavvy, Caparruva said. “You’ll still need to do due diligence,” he stressed.

Just as real estate site Zillow may not indicate why a home may be priced well below the average sale price of houses in a particular neighborhood, ZoneSavvy may not give you a complete understanding of why a certain type of business is underrepresented in a given neighborhood. There may be negative factors that the system does not take into account.

ZoneSavvy includes government-produced data as well as information available online, Wertheim said.

Wertheim, who is also a senior software engineer at LinkedIn, wrote the app that the system uses in Java and is hosting the site on AWS. He plans to use customer-support contractors as the site attracts users. Terms of use include a flat rate of US$39.95 per month or $29.95 per month for multiple months of use.