Move over realtor.com, Zillow and Trulia — and take your old-fashioned data search tools with you. Your days on top of the real estate search heap are numbered, as are your listings that focus on bedroom, bathroom and price.
Analytics that evaluate lifestyle, affordability and commute time are the new kids on the block that are about to make finding the right home for your buyers a whole lot easier.
How many hours have you spent in the past 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 particular 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 versus 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 choice?
In the past, answering that question meant going to an agent and hoping that he or she could sort out the best choices. But even the most knowledgeable and talented agent 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 on 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 a fire, flood, earthquake or 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?
Although these tools are helpful, they are only pieces of the puzzle. Data analytics seek 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 in-town property, or is it better to commute and get a larger, newer home in the suburbs? What are the trade-offs?
- 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, which I can also afford?
According to Mayala, “Bedroom, bath and price are dead — today’s buyer wants to know about affordability, commute time and lifestyle.”
Price versus 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 as expensive as natural gas, this translated into a $750 heating bill rather than $250.
Although Inrix.com and Waze are apps that share commute time, they lack the ability to determine which locations would be best if the husband wanted to commute no more than 30 minutes and the wife no more than 20 minutes, for example.
Data analytics search identifies which properties meet these criteria and assesses the needs of both parties together.
It also can consider the cost of having a single vehicle rather than two cars, if one of the options is close enough to walk or bike to work.
Consequently, a more centrally located, higher-priced home might be a more affordable choice than a more distant lower-priced home once the commuting data is factored in.
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 on demographics, education, schools, the right coffee shop, places of worship, etc.
Today, HomeJunction.com has evolved into a robust application that provides this information for agents and brokers as well as 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, author and trainer with over 1,000 published articles and two best-selling real estate books. Learn about her training programs at www.RealEstateCoach.com/