• The focus for homebuyers has shifted from bedroom bath, and price to lifestyle, affordability and commute time.
  • Agents will use data analytics to augment their personal feature and lifestyle expertise in the future.
  • Companies are already working on creating algorithms that will calculate the true cost of overall homeownership including the age of the home's features.

Competitive market analyses (CMAs) have been around for decades. Although commercial real estate usually relies on price-per-square-foot evaluations, residential real estate has been slower to adopt this approach for a variety of reasons.

Regardless of whether you use price per square foot or some other system of evaluation, the single-family CMA of today is about to change forever.

In January, I wrote an article called, “The days of bedroom, bath and price are dead.” The article outlined how the data analytics revolution is reshaping the buyer decision-making process from being focused on bedroom, bath and price to a new focus on lifestyle, affordability and commute time.

As data analytics companies proliferate and their algorithms become more sophisticated, buyers will be able to search properties by the variables they rank as most important rather than being limited to a fixed format as they are today.

For example, a buyer might search using variables such as total monthly carrying costs (utilities, taxes, mortgage and insurance), school quality, proximity to his or her favorite hike and bike trail, percentages of live music venues or a host of other factors.

Bedroom, bath and location will still be relevant but might not be as critical when weighted against other lifestyle variables.

Feature expertise vs. lifestyle expertise

The best agents know the market so well that they price a property without looking at the comparable sales.

For example, they know that a home with an unobstructed view is worth $10,000 more than a home with a partial view and $20,000 more than a home with no view. This approach might be best classified as “feature expertise.”

Many of the most successful brokers also excel in lifestyle expertise, a primary requisite for working the luxury market. The broker can help his or her client find the local food truck with the best Belgian waffles, the right person to groom the dog or where to get the healthiest plants and flowers at the best price for the garden.

In the future, agents will use data analytics to augment their personal feature and lifestyle expertise. This will open the door to new pricing models incorporating a wealth of factors that the consumer can rank based upon their preferences.

Customizable algorithms

One of the most exciting innovations in data analytics is the ability to customize the search algorithm. MoveUp.com was an early innovator in this field.

Its automated home pricing model allows users to manipulate its algorithm by selecting comparable sales plus making adjustments for the level of market activity, interior and exterior condition, lot size, amenities and view, location and privacy, as well as the age of the property.

This feature provides a much more accurate picture of the property value, as opposed to a single number that you get from a Zestimate or other automated valuation models (AVM) that don’t allow adjustments to the algorithm.

How utility costs can play into future pricing models

Now imagine that you can layer pricing data with the utility costs as part of your CMA. To illustrate how this works, MyUtilityScore.com is a new player that not only predicts how much your utility costs will be, but it also lets you adjust the algorithm by how many occupants live in a given property and the summer and winter thermostat settings, as well as whether the house will be occupied during the day.

For example, the area where my brother lives has an annual average utility bill (electric, gas and water) of $2,904. Because we upgraded the appliances, the plumbing and the electrical, the amount MyUtilityScore predicted for his cost was is $1,792.

This amount is within a few dollars of what he actually pays on an annual basis, which translates into savings of approximately $93 per month as opposed to the average for the area.

Assuming a 30-year mortgage with a 4 percent interest rate, the $93 difference in the monthly payments would allow a buyer of our property to have the same payments as if he or she had purchased our property for $20,000 less.

On the flip side, the buyer could afford to buy a house that was priced $20,000 higher and still have the same carrying costs due to the difference in utility costs.

The true cost of ownership CMA

Companies are already working on creating algorithms that will calculate the true cost of overall homeownership. Here’s how this could work on a listing appointment.

You load your data into a CMA platform that compares a wide variety of features — the overall cost of ownership, the value of the view, the deduction for the airport noise, the premium for good schools, etc.

The algorithm can also be adjusted to fit local market conditions, property condition, location and other factors that you or your clients deem relevant. For first-time buyers, the algorithm could also factor in the amount of down payment assistance the buyer might obtain for this property.

Theoretically, the algorithm could also project the cost of repairs over a 10-year period including when the water heater, the roof and other major systems would most likely have to be replaced.

To illustrate how this would impact pricing, assume that a property has a 25-year-old tile roof that has a 50-year life expectancy. That property should have no roof repairs over the next 10 years.

Now compare that to the same size property with a 12-year-old roof that has a 20-year life expectancy. The second property will have to replace the composition roof for $24,000.

Consequently, the cost of ownership for the first property is $24,000 less than the cost of ownership for the second property.

Although it still might be several years before this type of technology is widely available, the foundation pieces already exist.

It’s just a matter of time until this data can be factored into current pricing models that represent the real cost of ownership rather than the simple comparable sales price approach the industry currently relies on.

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/AgentTraining and www.RealEstateCoach.com/newagent

Email Bernice Ross.

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