The growing role of Automated Valuation Models (AVM) in real estate

Published by FirstAlign

Owning a house has an emotional connection, but that’s not all; it is still the best investment option that many people prefer. The market value of real estate appraisal in the United States is 7.57 billion US dollars in 2020. Surprisingly, real estate has been slow to adopting innovation, but they are starting to change.

Purchasing a property is a severe financial and emotional undertaking. Detailed explanations of property value is fundamental, and this is where Automated Valuation Models (AVM) come in.

Property valuation is essential as it not only determines the property’s value; it determines taxes, insurance, and more. It depends on many data points such as schools, public transit, and amenities like supermarkets, hospitals etc.

Traditional valuation methods include:

  • Sales comparison:  The value of a property depends on the sale price of similar properties.
  • Cost approach:  In this method, the building and land prices are calculated separately and then combined.
  • Income approach:  Mainly used for commercial buildings. The real estate valuation depends on the income the property generates.  It is obtained by dividing the net operating income by the capitalization rate.
  • Discounted cash flow ( DCF) approach: DCF determines the value of an investment based on its future cash flows. DCF is also used for commercial properties.

In Traditional methods, trained professionals perform the valuation. It is both time-consuming and costly. On the other hand, AVMs offer fast, affordable, and statistical estimation of a property.

Automated Valuation Model

Automated Valuation Model (AVM) uses a mathematical model for estimation. It uses machine learning to analyze large volumes of data to predict the property’s value. Typically, data is a combination of property records, age of the property, trends, neighborhood etc.

For residential property, AVM uses public records to estimate the value of a property. The machine learning algorithm also considers elements such as the number of rooms, amenities (air conditioning, swimming pool, etc.), and location before providing the final estimation. Zillow’s Zestimate is an excellent example of a residential valuation model.

Advantages and Disadvantages

The two main advantages of using AVM are speed and cost. With AVM, we can now maker faster decisions in lesser time.  Unlike traditional approaches that are prone to human error, AVM is more statistical and factual. Lenders, investors can employ AVM to get the most accurate value for their property.

Data is fuel to Machine Learning models. Hence AVM is more accurate when they have more data to analyze.  Consider the scenario where a property is in a rural area, where data is scarce; in such cases, AVM can be less accurate. In AVM, there is no physical visit to the property, and hence some crucial factors may be ignored, which might affect the property’s value.

Conclusion

Real estate is embracing digitization and automation, and we cannot ignore the growing role of AVM in valuation. The mathematical model of AVM provides an accurate estimation compared to traditional approaches. AVM is faster, reliable, and cost less. Advancements in Machine Learning will soon resolve the shortcomings of AVM. Thus, it is safe to say that AVM will be more developed and utilized in real estate.

References

Published by FirstAlign

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