Move over Realtor.com, Trulia and Zillow with your old-fashioned data search tools that rely primarily on bedroom, bath and price. Your days on top of the real estate search heap are numbered. Analytics searches that evaluates lifestyle, affordability and commute time is the new kid on the block that’s about to make finding the right home for your buyers a whole lot easier.
How many hours have you spent in the last year showing buyers house after house, but nothing seems to be quite right? This is especially difficult in large metropolitan areas where relocating buyers want to see everything or have their hearts set on a certain school district they can’t afford.
Now imagine what it would be like if you could pinpoint the top four or five houses in your entire market area that are the best possible fit for your buyers? Data analytics search promises to let you do exactly that.
Data Companies vs. Analytics Companies
Krishna Mayala, the founder and CEO of TLCEngine.com, described data search (which includes brokerage, MLS, Realtor.com, Trulia and Zillow) as being part of the dreaming stage of homeownership. The buyer sets up their initial search by bedroom, bath, price and location. In large cities, the search can generate hundreds of options. The question is, which option is the right option?
In the past, answering that question meant going to a Realtor and hoping that he or she could sort out the best choices. But even the most knowledgeable and talented Realtor cannot objectively sort out which combination of lifestyle, affordability and commute time will provide the buyer with the best possible lifestyle option. Making this decision requires more than just sorting the data – it requires analyzing and evaluating thousands of pieces of data.
Apps such as Inrix and Waze can help clients assess commute times as well as identifying the best possible routes based upon current traffic conditions.
Housefax.com can help your buyers determine whether that charming 1930s Spanish style bungalow is money pit in disguise by answering such questions as:
• Has the house had fire, flood, earthquake or a mold claim?
• Have there been any other insurance incidents on the property?
• What natural hazards exist in the area?
• Has the house (or neighboring house) ever been a meth lab?
• Approximately how much will the new owner pay in utilities?
Bernice Ross
While these tools are helpful, they are only pieces of the puzzle. Data analytics seeks to pull all these pieces together and then identify the four or five best possible choices for the buyer. The goal is for the buyer’s search to be able to answer such questions as:
“Is it more affordable to buy that smaller, but older, property in town, or is it better to commute and get a larger, newer home in the suburbs? What are the tradeoffs?”
“What’s the difference in cost for an all electric home versus one that has natural gas?”
“Is there another equally good school district that has the same type of neighborhood and lifestyle, but that I can also afford?”
Price vs. Affordability
Most purchase decisions focus almost exclusively on price without factoring in other costs associated with homeownership. For example, if you live in Los Angeles, you will pay much more for your home insurance if you live in a flood plain or in a hillside area that requires flood or California Fair Plan insurance.
In our case, when we built our last home we thought the subdivision had natural gas service, but it had piped-in propane instead. Propane is three times more expensive than natural gas; this translated into a $750 heating bill rather than $250.
Commute Time
While apps can help quantify commute time, they lack the ability to determine which locations would be best if, for example, one spouse wanted to commute no more than 30 minutes and the other no more than 20 minutes. Data analytics searches identify which properties meet these criteria as well as assessing the needs of both parties together.
It also can consider the cost of having one car rather than two if one of the options allows one party to walk, bike or take public transit to work. Consequently, a more centrally located, higher-priced home may actually be a more affordable choice than a more distant lower-priced home once the commuting data is factored in.
Lifestyle
Seven years ago Onboard Informatics launched Spatial Match, a lifestyle search engine. The tool was designed to help consumers search for neighborhoods or communities based upon demographics, education, schools, the right coffee shop, places of worship, etc.
HomeJunction.com is another lifestyle search engine that has evolved into a robust application that provides this information for agents and brokers, as well as supplying enterprise solutions. It also provides home values, local content and hyperlocal lifestyle data.
Ultimately, analytics search promises to reshape the buyer search experience as we know it. As Mayala observes, “You dream at the data search sites, but you will make an actionable decision based upon the information you acquire from an analytics search engine like TLCEngine.com.”
Bernice Ross, CEO of RealEstateCoach.com, is a national speaker, trainer and author. Email: Bernice@RealEstateCoach.com.
