Competitive market analyses (CMAs) have been around for decades. While 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 foot or some other system of evaluation, the single-family CMA of today is about to change forever.
In January I wrote an article, “Bed, Bath And Price Is 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 being most important rather than being limited to a fixed format as they are today. Bedroom, bath and location will still be relevant, but may not be as important when weighted against other lifestyle variables.
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 may 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. 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 that incorporate a wealth of factors that the consumer can rank based upon their preferences.
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, it lets you adjust the algorithm by how many occupants live in a given property, 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 $1,792. This is within a few dollars of what he actually pays on an annual basis. This translates into a savings of approximately $93 per month compared 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 they had actually 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 the utility costs.
The True Cost Of Ownership
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 and/or your clients deem to be 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 hot 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 a cost of $24,000. Consequently, the cost of ownership for the first property is $24,000 less than the cost of ownership for the second property.
While it still may 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 true cost of ownership rather than the simple comparable sales price approach upon which the industry currently relies.
