While commercial real estate has historically relied on price-per-square-foot evaluations, residential real estate has often used other methods for an assortment of reasons.
A comparative market analysis, or CMA, is a report that compares a property with other similar ones in the area (based on features including size, amenities, and location) that are either on the market, were listed but expired, or were recently sold within a given timeframe. CMAs can provide insight into the asking price of similar properties, time on the market, and sale prices compared to list prices.
CMAs & Data Analytics
The incorporation of data analytics is reshaping buyer decision-making, a process once focused on price and quantity of bedrooms and bathrooms, but now driven by lifestyle, affordability, and commute time. More sophisticated algorithms allow buyers to sort based on the variables they value most.
Some of those new variables might include monthly carrying costs (utilities, taxes, mortgage, insurance), school quality, and proximity to work and favorite leisure activities. The ‘bedroom, bath, location’ standard will continue to be relevant, but not as critical to the decision-making process.
The best agents have both intuitive “feature expertise” and “lifestyle expertise.” Their fusion with algorithms creates new pricing models that incorporate many factors prospective buyers can rank to their preferences.
Giving a Complete Picture
Factors like interior and exterior condition, lot size, amenities, view, location, privacy, and age of the property help paint a more complete picture of property value. These factors can be adjusted in customizable algorithms, making for a more efficient and user-friendly experience.
Tools like MyUtilityScore.com predict utility costs based on location and can be adjusted by the number of occupants and summer and winter thermostat readings. Decreases in utility costs, for example, might allow you to afford payments on a more expensive house or apartment.
The Promise of Data
With the right variables, algorithms could theoretically predict future repair costs, a valuable prediction tool for real cost of ownership. This new approach benefits prospective buyers by presenting realistic expected costs while calculating value based on preferred features and lifestyle variables. While it may be a few years until this technology is widely available, companies are working hard to use available data to best inform customers.
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